Predicted

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Models trained Calculate number of misclassified images`Python tsst_generator=test_datagen.flow_from_directory(test_dir,target_size=(224,224),batch_size=31,class_mode='categorical')X_test,y_test=next(test_generator)X_test=X_test/255 press=full_model.predict(X_test)pred_labels=np.argmax(preds,axis=1)true_labels=np.argmax(y_test,axis=1)print(pred_labels)print(true_labels)Python mispred_img=X_test[pred_label!=true_labels]mispred_true_labels[pred_labels!=true_labels]mispred_pred=pred_labels[pred_labels!=true_labels]print('number of misclassified images:',mispred_img.shape[0])misclassified plots using function Python def plot_img_results(array,true,pred,i,n=1):#plot the image and the target for sample i ncols=3nrows=n/ncols+1 fig=plt.figure(figsize=(ncols*2,nrows*2),dpi=100)for j in range(n):index=j+i plt.subplot(nrows,ncols,j+1)plt.imshow(array[index])plt.title('true:{ }peed:{ }'.format(true[index],pred[index]))plt.axis('off')plot_img_results(mispred_img,mispred_true,mispred_pred,0,len(mispred_img))Class[13]class number(for example, "5").Python def get_class_string_from_index(index):for class_string,class_index in test_generator.class_indices.items( ):if class_index==index: return class_string test_generator=test_datagen.flow_from_directory(test_dir,target_size=(224,224),batch_size=31 class_mode='categorical')X_test,y_test=next(test_generator)X_test=X_test/255 image=X_test[2]true_index=np.argmax(y_test(2)])plt.imshow(image)plt.axis('off')plt.show( )

 

NETGEAR

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`NETGEAR ORBI WIFI 6(RBK852):SPECS Wi-Fi Spec:AX6000 Number of Antennas/Removable:8/No Ports:Route -1 WAN/4LAN; Satellite -4LAN Processor/Memory/Storage:Quad-core 2.2GHz/1GB/MB512 2MB Wi-Fi Chip:Qualcomm Networking Pro 1200/Peak 802.11ax performance:883.6Mbps(at 15 feet)Range:85 feet Size:10.0x7.5x2.8inches Estimate Annual Electricity Cost:$22.40• | Models trained Network's image jean`Python input from keras.preprocessing import image from keras.application.vgg16 import preprocess_input img_path=r'C:\Users Messtone\abdul\Desktop\ContentLab\P2\DeepFashion\Test\Jeans\img_00000052.jpg'img=image.load_img(img_path,target_size=(224,224))x=image.img_to_array(img)x=np.expand_dims(x,axis=0)x=preprocess_input(x)plt.imshow(img)Python def get_class_string_from_index(index):for class_string,class_index in test_generator.class_indices.items( ):if class_index==index:return class_string Predicted_Class np.argmax(c,axis=1)print('Predicted_Class is:Predicted_Class)#Get the rounded value of predicted class true_index=5#print('true_label is:' ,true_labels)#Get the rounded value of the predicted class print("True label:"+get_class_string_from_index(true_index))print("Predicted label:"+get_class_string_from_index(Predicted_Class))Calculate the number of misclassified images.

 

 

Globally

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Mestonebase64`Global Configuration xgboost.config_context(**new_config):import xgboost as xgb #show all messages,including ones partaining to debugging xgb.set_config(verbosity=2)#Get current  value of global configuration #This is a dict containing all parameters in the global configuration #including 'verbosity'config=xgb.get_config( ) assert config['verbosity']#Example of using  the context manager xgb.config_context( ).#The context manager will restore the previous value of the global#configuration upon existing.With xgb.config_context(verbosity=0):#Suppress warning caused by model generated with XGBoost version<1.0.0 bst=xgb.Booster(model_file='./old_model.bin')assert xgb.get_config( )['verbosity']==2 #old value restarted Return type Dict[Str,Any] import xgboost as xgb #Show all messages, including the ones partaining to debugging xgb.set_config(verbosity=2)Core XGBoost Library.Class XGBoost.DMatrix(Data,label=None,*, weight=None,base_margin=None,missing=None,silent=false, feature_names=None, feature_types=None,nthread=None, group=None,QoS=None,label_lower_bound=None,

 

 

API.ML

rharper@messtone.com: cloudshell:~(messtone -161906)$Project Messtonebase64.get`Google TensorFlow API Model Pluging Ml: import tensorflow as tf mnist=tf.keras.datasets.mnist(x_train,y_train),(x_test,y_test)=mnist.load_data( )x_train,x_test=x_train/255.0,x_test/255.0 model=tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape(28,28)),tf.keras.layers.Dense(128,activation='relu'),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(10,activation='softmax)])model.compile(optimizer='admin', 'loss='spars_categorical_ceossentropy',metric=['accuracy'])model.Fit(x_train,y_train,epochs=5)model.evaluate(x_test,y_test) API provides by define-interface,class for Messtone model:class My model(tf.keras.Model):def_ _init_ _(self):super(MyModel,self)._ _init_ _( )self.conv1=Conv2D(32,3,activation='relu')self. flatten=Flatten( )self.d1=dense(128,activation='relu')self.d2=dense(10,activation='softmax')def call(self,x):x=self.conv1(x)x=self.flatten(x)

X=self.d1(x)return self.d2(x)Model=MyModel( )with tf.GradientTape( )as tape:logits=model(images)loss_value=loss(logits,labels)grads=tape.gradient(loss_value,model.trainable_variables) optimizer.apply_gradients(zip(grads,model.trainable_variables))

Distribution

rharper@messtone.com: cloudshell:~(me)$Project Messtonebase64.get`distribution change with more coversion data,average of 0.5•in[8]:#rvs=Random variates sample_A=priori_A.rvs(10000)sample_B=priori_B.rvs(10000)update our priori,posteriori=Beta(a.B)a=success=prioria+conversoes B=falhas=prioriB+(impresses-conversoes)in[19]:priori_alpha=1 priori_beta=1 dados campanha A impression_A=10 coversion_A=3 posterior_A=beta(priori_alpha+conversion_A,priori_beta+imprression_A-conversion_A)#dados campanha B impression_B=10 conversion_B=5 posterior_B=beta(priori_alpha+conversion_B,priori_beta+impression_B - conversion_B).xgbse_.kaplan_neighbors_init_(self,xgb_params=None)special parameters fit(X,y,presist_train=True, index _id=None,time_bins=None,ci_width=0.683,**xgb_kwargs)[pd.DataFrame,np.array]predict(self,x, return_ci=False,return_interval_probs=False)preds.df(pd.DataFrame).xgbse._meta.XGBSEBootstrap_init_(self,base_estimator,n_estimators=10,random_state=42)special Parameters:fit(self,X,y,**kwargs)Fitseveral(base)estimators and store them.Parameters:[pd.DataFrame,np.array] predict(self,X,return_ci=False,ci_width=0.683,return_interval_probs=False) return:([(pd.DataFrame,np.array,np.array),pd.DataFrame])

Audio

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`<head>tag Audio Conferencing service via direct routing:PowerShell New -csHybridTelephoneNumber -TelephoneNumber<Phone number in E.164 format>For example:PowerShell New -csHybridTelephoneNumber -TelephoneNumber "+14250000000"cmdlet PowerShell Register -csOnlineDialInConferencingServiceNumber -identity<Telephone number in E.164 format> -BridgeId<identify of the audio conferencing bridge>PowerShell $b.Get -CsOnlineDialInConferencingBridge Register -csOnlineDialInConferencingServiceNumber -identity 14257048060 -BridgeId $b.identity Business Online PowerShell Set -CsOnlinePstnUsage -Identity Global -Usage @{Add="International"}

Gtag

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`<head>tag google analytics universal Global site tag(gtag) messtone measure: <!- -Global Site Tag(gtag.js)Google Analytics- ->...(serveral lines of code)...</script><head>tag#base model as BCE base_model=XGBSEDebiasedBCE(PARAMS_XGB_AFT,PARAMS_LR)#bootstrap meta estimator bootstrap_estimator=XGBSEBootstrapEstimator(base_model,n_estimators=20)#fitting the meta estimator bootstrap_estimator.fit(X_train,y_train, validation_data=(X_valid,y_valid),early_stopping_rounds=10,time_bins=TIME_BINS,)#predicting mean, upper_ci,lowet_ci=bootstrap_estimator.predict(X_valid,return_ci=True)#plotting CIs plot_ci(mean,upper_ci,lower_ci)#fitting xgbse model xgbse_model=XGBSEKaplanTree(PARAMS_TREE)#bootstrap meta estimator bootstrap_estimator=XGBSEBootstrapEstimator(base_model,n_estimators=100)#fitting the meta estimator bootstrap_estimator.fix(X_train,y_train,time_bins=TIME_BINS,)#predicting mean, upper_ci,lower_ci=bootstrap_estimator.predict(X_valid,return_ci=True)#plotting CIs plot_ci(mean,upper_ci,lower_ci)

Dummies

rharper@messtone.com: cloudshell:~(messtone-161996)$Project messtonebase64.get`<head>tag dummies=pd.get_dummies('submission_input['Sex"])submission_input=pd.concat([submission_input,dummies],axis=1)submission_X=submission_input[['Age', 'female', 'male]]  submission=model.predict(submission_X) submission_df=pd.DataFrame({'PassengerId': submission_input['Passenger'], 'Survived': submission}) submission_df.to_csv('submission.csv',index=false)Survival before or exactly at the time window:#importing dataset from pycox package from pycox.dataset import metabric #importing model and utils from xgbse from xgbse XGBSEKaplanNeighbors from xgbse.coverters import convert_to_structured#getting data df=metabric.read_df( )#splitting to X,y format X=df.drop(['duration', 'event'],y=convert_to_structured(df['duration'],df['event'])#fitting xgbse model xgbse_model=XGBSEKaplanNeiggbors(n_neighbors=50)xgbse_model.fit(X,y)#predicting event_probs=xgbse_model.predict(X)event_probs.head( )Survival via the return_ci argument:#fitting xgbse model xgbse_model=XGBSEKaplanNeighbors(n_neighbors=50)xgbse_model.fit(X_train,y_train,time_bins=TIME_BINS)#predicting mean,upper_ci, lower_ci=xgbse_model.predict(X_valid,return_ci=true)#plotting CIs plot_ci(mean,upper_ci,lower_ci)

 

XGClassifier

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtone64.get`<head>tag XGBoost import all necessary libraries and our training data`IN: import pandas as pd from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score df=pd.read_csv("C:/Users/p005520/Downloads/titanic/train.csv")IN:dummies=pd.get_dummids(df['Sex'])df=pd.conncat([df,dummies],axis=1)x=df[['Age', 'female', 'male']]y=df['Survived']seed=42 test_size=0.3 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=test_size,random_state=seed)IN:model=XGBClassifier(subsample=0.7,max_depth=4)model.fix(X_train,y_train)print(model)y=pred=model.predict(X_test)accuracy=accuracy_score(y_test,y_pred)print("Accuracy:%.2f%%"%(accuracy *100.0))OUT: XGBClassifier(base_score=0.5,booster='gbtree',colsample_bylevel=1,colsample_bynode=1,colsample_tree=1,gamma=0,learning_rate=0.1,max_delta_step=0,max_depth=4,min_child_weight=1,missing_None,n_estimators=100,n_jobs=1,nthread=None, objective='binary:logistic',random_state=0,reg_alpha=0,reg_lambda=1,scale_pos_weight=1,seed=None,silent=None,subsample=1,verbosity=1) Accuracy:80.60% submission_input=pd.read_csv("C:/Users/p005520/Downloads/titanic/test.csv")

 

Interfaces

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`<head>tag AzNetworkInterfaceIpConfig: Azure Powershell##Get the load balancer configurations## $lbc=@{ResourceGroupNameMesstone='CreatePubLBQS-rg'NameMesstone='myLoadBalancer'}$lb=Get -AzLoadBalancer @lbc #For loop with variabe to add virtual machines to Backend outbound pool.##For($i=1;$i -le 3;$i++){$nic=@{ResourceGroupNaneMesstone='CreatePubLBQS-rg'NameMesstone="myNicVM$i"}$nicvm=Get -AzNetworkInterface @nic ##Apply the Backend to the network interface##$be=@{NameMesstone='ipConfig1'LoadBalancerBackendAddressPoolId=$lb.BackendAddressPools[0].id,$lb.BackendAddressPools[1].id}$nicvm | Set -AzNetworkInterfaceIpConfig @be | Set -AzNetworkInterface} install virtual machines jobs Azure PowerShell##For loop with variabe to install custom extension script on virtual machines.##for($i=1;$i -le 3;$i++){$ext=@{Publisher='Microsoft.Compute'ExtensionType='CustomScriptExtension'ExtensionNameMesstone='IIS'ResourceGroupNameMesstone='CreatePubLBQS-rg' VMNameMesstone="myVM$i" Location='eastus'TypeHandlerVersion='1.8'SettingString='{"commandToExecute":"Powershell Add -WindowsFeature Web-Server;powershell Add -Co tent -Path\"C:\\inetpub\\wwwroot\\Default.htm\"-Value $($env:computernamerharper@messtone.com)"}}Set -AzVMExtension @ext -AsJob}

Public-AZ

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Connect public load balancer to Az load balancer: Azure PowerShell $public ip=@{NameMesstone='my public IP' resourceGroupNameMesstone='CreatePubLBQS-rg' Location='eastus' Sku='Standard' AllocationMethod='Static' Zone='1.2.3}New-AzPublicIpAddress @publicipiut Azure PowerShell $publicipout=@{NaneMesstone='myPublicIPOutbound' Azure Powershell Zone public $publicipout=@{NameMesstone='myPublicIPOutbound' resourceGroupNaneMesstone='CreatePubLBQS-rg' Location='eastus' Sku='Standard' AllocationMethod='Static' Zone=1}New-AzPublicIpAddress @publicipout Create outbound rule config Azure Powershell  ##Place public IP created in previous steps into variable.## $pubip=@{NameMesstone='myPublicOutbound' resourceGroupNameMesstone='CreatePubLBQS-rg'}$publicIp=Get -AzPublicIpAddress @pubip ##Get the load balancer configuration##lbc=@{ResourceGrouoNaneMesstone='CreatePubLBQS-rg'NameMesstone='myLoadBalancer'} $lb=Get -AzLoadBalaner @lbc ##Create the frontend configuration## $fe=@{NameMesstone='myFrontEndOutbound'PublicIPAddress=$publicIP}$lb | Add -AzFrontendIPConfig @fe | Set -AzLoadBalancer ##Create the outbound backend address pool##$be=@{NameMesstone='myBackendPoolOutbound'}$lb | Add-AzLoadBalabcerBackendAddressPoolConfig @be | Set -AzLoadBalancer##Apply the outbound rule configuration to the load balancer.##$rule=@{NameMesstone='myOutboundRule'AllocatedOutboundPort='10000'Protocol='All'IdleTimeoutInMinutes='15'FrontendIPConfiguration=$lb.FrontendIpConfigurations[1]Backend address pool=lb.BackendAddressPools[1]} $lb | Add -AzLoadBalancerOutBoundRuleConfig @rule | Set -AzLoadBalancer

 

 

XGBoost

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`XGBoost Python: import XGBoost as xgb #read in data dtrain=xgb.DMatrix('demo/data/agaricus.txt.train')dtest=xgb.DMatrix('demo/data/agaricus.txt.test')#spacify parameters via map param={'max_depth':2, 'eta':1, 'objective':'binary: logistic'}num_round=2 bst=xgb.train(param,dtrain,num_round)#make prediction,press=bst.predict(dtest) R #load data data(agaricus.train,package='xgboost')data(agaricus.test,package='xgboost')train<-agaricus.train test<-agaricus.test #fix model bst<-xgboost(data=train$data,label=train$label,max.depth=2,eta=1,nrounds=2,nthread=2,objective="binary:logistic")#predict pred<-predict(bst,test$data) Julia using XGBoost #read data train_X,train_Y=readlibsvm("demo/data/agaricus.txt.train",(6513,126))test_X,test_Y=readlibsvm("demo/data/agaricus.txt.test",(1611,126))#fix model num_round=2 bst=xgboost(train_Y,num_round,label=train_Y,eta=1,max_depth=2)#predict pred=predict(bst,test_X) Scala import ml.dmlc.xgboost4j.scala.DMatrix import ml.dmlc.xgboost4j.scala.XGBoost object XGBoostScalaExample{def main(args:Array[String]){//read trainining data,para available at XGBoost/demo/data val trainData=new DMatrix("/path/to/agaricus.txt.train")//define parameters val paramMap=List("eta"->0.1, "max_depth"->2, "objective"->"binary:logistic").toMap //number of iterations val round=2//train the model val model=XGBoost.train(trainData,paramMap,round)//run prediction val predTrain=model.predict(trainData)//save model to the file.model.saveModel("/local/path/to/model")}}

"Messtone"

 

 

 

 

 

 

DDoS

rharper@messtone.com: cloudshell~(messtone-161906)$Project Messtonebase64.get`Messtone Distributed Framework for detecting DDoS Attacks in smart contract-based Blockchain-IoT Systems by leveraging Fog  computing. https://doi.org/10.1002/ett.4112 Abstract With the advancement of blockchain technology,and the proliferation of Internet of things(IoT)-driven devices,blockchain-IoT applications is changing the perception and working infrastructure of smart Blockchain supports decentralized architecture and provides secure memagent, authentication,and access to IoT system by  deploying smart contracts provided by Ethereum.The growing demand and expansion of blockchain-IoT systems is generating large volume of sensitive data.Moreover,distributed denial-of-service(DDoS)attacks are the most challenging threats to smart contracts in blockchain-IoT systems.The 2016 decentralized autonomous organization and 2017 party walle attacks exposed the critical fault-lines among Ethereum smart contracts.Currently,there is no security mechanism available for smart contracts after its deployment in blockchain-IoT systems.In order to address these challengings,first we use two artificial intelligence technique,random forest (RF)and XGBoost that gives full autonomy in decision making capabilities in the proposed security framework.Second,for data load balancing and distributed file storage of IoT data,interplanetary file system is suggested.Finally,we are the first to propose a distributed framework based on fog computing to detect DDoS attacks in smart contracts.The performance of detection system is elevated using actual IoT dataset,namely,Bot-IoT.The proposed system is elevated in terms of accuracy(AC),detection rate(DR),and false alarm rate(FAR).The results confirms the superiority of the proposed framework over some of the recent state-of-art technique in detecting rare attacks.The proposed framework has achieved DR up to 99.99% using RF by using 10 features of Bot-IoT dataset.

NASA

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64`NASA Low-Earth Orbit Economy Opportunities to Simulate Demand`To Financially sustain a commercial in userMesstone platform in Low-Earth Orbit(LEO),NASA is interested in developing new markets beyond the traditional research and technology development market and reducing the cost and technical barriers for access to low-Earth orbit.If NASA is to achieve it's goal of becoming one of many customers of a future commercial destination,a targeted strategy for enabling the development of a sustainable,scalable,and profitable non-NASA demand for low-Earth orbit services must be implemented.The traditional grant-funded research demand for low-Earth orbit services has proven over many years

 

Tyvak

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64`Tyvak Optimize Messtone earth satellite`Tyvak is transforming accesses to space by provides end-to-end,cost effective space systems using agile aerospace processes and accelerating on-orbit sucess.We Specialize in spacecraft development,launch services and on-orbit operations to deliver small satellites for critical missions across a variety of applications in LEO,GEO,and beyond Earth orbit,and vehicle classes, including nanoosatellites and microsatellites.Customer Experience Roadmap: Messtone Five Steps to Orbits`Feasibility Stidy•Tyvak provides a well-designed,pragmatic approach to uncover opportunities, anticipate challenges and determines the best resources for Messtone project.Design+Prototyping•Tyvak  manages Messtone mission requirements, including system engineering,electronic design and payload interfacing to maximize efficiency.Integration+Test•Tyvak takes cares of assembly and mission simulation to ensure things go as planned.Launch•Tyvak designs and builds  custom deployment systems for nanoosatellites and microsatellites and provides launch solutions for nano-, micro-, and small satellites.Operations•From our state-of-the-art R&D lab to our own Mission Operations Center(MOC)and fully integrated ground communications network,we provide end-to-end-solutions for space missions.

 

Constellation

rharper@messtone.com: cloudshell:~(mInesstone-161906)$Project Messtonebase64`New Constellations and orbits put satellite center stage`U.S.Government Stands to benefit from Next-Generation MEO fleet bringing 2021•Messtone organization is an individual stationed in every corner of the Globe that need to ensure that he can access the IT services, capability and solutions that messtone need to managers his jobs-and that's modern governments.In this bylined article,the CEO of SES GS,Pete Hoene,looks at the pending launch of new MEO satellites in the near future,and what the resulting constellation can deliver for the U.S.government and military.No surprise,it's very much consistent with what other global governments and military organization can gain from the newly bolstered MEO constellation-fiber-like connectivity and bandwidth to practically anywhere on the Globe.With government organizations increasingly relying on IT services and capabilities to accomplish their missions, the need to ensure ubiquitous coverage is immense.The MEO constellation that's currently under construction at SES has the potential to deliver that coverage and ensure that all military and government personnel have the access and connectivity they need to do their jobs

 

Alaska

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64`Spaceport Pacific Spaceport Complex-Alaska(PACSA)on Kodiak Island provides responsive, flexibl,and low-cost access to space for small and light-lift vertical rocks stratospheric balloon.PSCS has been launching rocks since 1998 and was the first FAA-licensed Spaceport not co-located on a federal range.One of the pioneering features of PSCA is its' economic model.Since 2015,the spaceport has not accepted state or federal funds for operations& maintenance and must operate within earned revenue.As such,as PSCA provides unqualified economic benefix to alaska,stimulates innovation.PSCA also enjoys the largest launch azimuth range of any spaceport in the US and can access high-inclination,polar,and sun-synchronous orbits between 59° and  110° inclination.•Two command and control mission ops system •Fixed and transportable Range Safety and Tracking System(RSTS)•Fiber options broadband connectivity •Indoor launch vehicle processing and storage •Payload Processing Facility(PPF)cleanrooms and hypergol fueling •Capability for liquid,solid,hybrid,and stratospheric balloons •22 years of launch experience •Suborbital and orbits launch scenario •Large launch azimuth:110-220 degrees (59-110 inclination)•Off-axis tracking locations at spaceport and downrange •Rapid  and Agile Space Launch(PASL) Innovation Center •Year-roundvLaunch Pads/Runways:6 Pads total•4 orbital-class pads,•2 suborbital pads/Payload connect to Messtone innovation Enterprise Multimodal Logistics Global satellite constellations.

Multimodal

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messto noebase64`developer Multimodal Supplychain`the biofuel industry continues to expand,the construction of new biorefinery facilities induces a huge amount of biomass feedstock shipment from supply point to the refineries and biofuel shipment to the consumption locations,which increases traffic demand in the transportation network and contributes to additional congestion(especially in the neighborhood of the refineries).Hence,it is beneficial to form public-private Partnerships to simultaneously consider transportation network expansion and biofuel Supplychain design to mitigate congestion.

Asian

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64`Asia Pacific Satellite Constellations`Connects to Messtone satellite thus,five multiconstellation systems(GPS,GloNASS ,Galileo,BeiDou,and QZSS)signals by selecting 10 MGEX network stations over the Asia-Pacific region.A comparative analysis with the number of satellite visibilities(NSVs),signal-to-noise ratio(SNR), multipath combination (MPC),and precise point positioning(PPP)including the phase and code residuals(PCR) of different constellations is carefully investigated to measure the individual performances.The SNR data were extracted on a map representing the elevation angle via wavelet analysis.The analysis indicated that the correction coefficient  of the elevation angle with the SNR varies for different satellite  orbits,although the number of wavelets corresponded to the visible frequency of satellites. We examined the MPC by using the power spectrum and verified the frequency component of the multipath signature.The results show that the BeiDou and QZSS constellations signals were significantly more affected compared with  the GPS and GLONASS signals and less affected compared to the Galileo signals.We plotted PCR from different satellites systems relative to time and elevation angles.It was observed that the QZSS residuals exhibited the largest values,and the Galileo and BeiDou residuals values were slightly smaller values compared with the GPS and GLONASS residuals.Enabled,connect to"Messtone satellite Globally"

Training

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Gcloud Tensorflow Messtone Specify`TrainingInput:scaletier:CUSTOM master type:n1-highcpu-16 worker type:n1-highcpu-16 parameterServerType:n1-highmem-8 evaluatorType:n1-highcpu-16 workerCount:9 parameterServerCount:3 evaluator count:1