esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, it is still surjective matching, since all pixels of the query frame can refer to the reference frames. HMMN [23] addresses the problem by introducing a kernel-guided memory matching module. Unlike the one used in KMN, it imposes temporal smoothness constraints, which is an important cue for the VOS. Additionally, a top-k guided memory matching method is present to bootstrap the prediction of more accurate target masks. Unlike the above works, BMVOS [24] provides a bijective matching mechanism that prevents both reference frame pixels from being referenced, and, therefore, considers the best match of the reference frame pixels first. In other words, bijective matching considers both the reference frame and query frame to find the best match, and connects the pixels only when they are definite matches to each other. Thus, it can effectively eliminate background interference. Although bijective matching can solve the limitation of surjective matching, it can only replace surjective matching in the testing phase. The reason is that it is based on discrete functions (argmax or top \(K\) operation), which has an impact on stable network training. Moreover, the hyperparameters need to be carefully adjusted manually during testing. Inspired by the work mentioned above, we propose a segmentation tracking algorithm based on pixel-level equalized memory matching network (PEMMN) for intelligent inspection of substations. The main contributions of this work are as follows:1. An equalized memory matching network is designed, which stores historical information of the target through a memory bank and utilizes a pixel-level equalized matching method to ensure that the detailed information of the reference frames is efficiently delivered to the current frame, so that the segmentation tracker can focus on the most informative region in the current frame. 2. To avoid excessive consumption of memory resources and accumulation of erroneous segmentation templates, a memory storage and update strategy is designed to filter and store frames with high segmentation quality to ensure that the process of updating the memory bank is both accurate and reliable. 3. The synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. 4. Experimental results show that the method performs well on both real videos of substation inspection scenarios and commonly used benchmark datasets.
## Appendix B Bearing Fault Diagnosis  
```
1%=================PROBLEMDEFINITION=================
2 rawData=load('Simulation');%Dataload
3 sampRate=3e3;%Samplingrate(Hz)
4 rpm=60;%Shaftrotatingspeed bearFreq=[10]*rpm/60;%BPFO,BPFI,FTF,BSF
5 maxP=300;%MaximumorderofARmodel
7 windLeng=[2^42^52^62^7];%WindowlengthofSTFT
8%=================Discretesignalseparation(ARmodel)=================
9x=rawData.vib(:);N=length(x);
10 forp=1:maxP
11 ifrem(p,50)==0;disp(['p='num2 str(p)]);end
12a=aryule(x,p);%yarulereturnstheARmodelparameter,a(k)
13X=zeros(N,p);
14 fori=1:p;X(i+1:end,i)=x(1:N-i);end
15 xp=-X*a(2:end)';
16e=x-xp;
17 tempKurt(p,1)=kurtosis(e(p+1:end));
18 end
19 optP=find(tempKurt==max(tempKurt));%===Optimumsolution
20 optA=aryule(x,optP);
21 xp=filter([0-optA(2:end)],1,x);
22e=x(optP+1:end)-xp(optP+1:end);%residualsignal
23%=================Demodulationbandselection(STFT&SK)=================
24 Re=length(e);
25 numFreq=max(windLeng)+1;
26 fori=1:length(windLeng)
27 windFunc=hann(windLeng(i));%===ShortTimeFourierTransform
28 numOverlap=fix(windLeng(i)/2);
29 numWind=fix((Ne-numOverlap)/(windLeng(i)-numOverlap));
30n=1:windLeng(i);
31 STFT=zeros(numWind,numFreq);
32 fort=1:numWind
33 stft=fft(e(n).*windFunc,2*(numFreq-1));
34 stft=abs(stft(1:numFreq))/windLeng(i)/sqrt(mean(windFunc.^2))*2;
35 STFT(t,:)=stft';
36n=n+(windLeng(i)-numOverlap);
37 end
```* 38 forj=1:numFreq%====SpectralKurtosis
* 39 specKurt(i,j)=mean(abs(STFT(:,j)).^4)./mean(abs(STFT(:,j)).^2).^2-2;
* 40 end
* 41 lqd{i}=['windowsize=',num2 str(windLeng(i))];
* 42 end
* 43 figure(1)%====Results
* 44 freq=(0:numFreq-1)/(2*(numFreq-1))*sampRate;
* 45 plot(freq,specKurt);legend(lqd,'location','best')
* 46 xlabel('Frequency[Hz]'); ylabel('Spectralkurtosis'); xlim([0 sampRate/2]);
* 47[freqRang]=input('Rangeofbandpassfiltering,[freq1,freq2]=');
* 48[b,a]=butter(2,[freqRang(1)freqRang(2)]/(sampRate/2),'bandpass');
* 49X=filter(b,a,e);%band-passedsignal
* 50%=================================================================Envelopeanalysis=================================================================
* 51 aX=hlibert(X);%hlibert(x)returnsananalyticsignalofx
* 52 envel=abs(aX);
* 53 envel=envel-mean(envel);%envelopesignal
* 54 fftEnvel=abs(fft(envel))/Ne*2;
* 55 fftEnvel=fftEnvel(1:ceil(N/2));
* 56 figure(2)%====Resultplot
* 57 freq=(0:Ne-1)/Ne*sampRate;
* 58 freq=freq(1:ceil(N/2));
* 59 stem(freq,fftEnvel,'LineWidth',1.5);holdon;
* 51[xx,yy]=meshgrid(bearFreq,ylim);
* 52 plot(xx(:,1),yy(:,1),'*-')
* 53%,xx(:,2),yy(:,2),'x-',xx(:,3),yy(:,3),'d-',xx(:,4),yy(:,4),'-')
* 54 legend('Envelopespectrum','BPFO','BPFI','FTF','BSF');
* 55 xlabel('Frequency[Hz]');ylabel('Amplitude[g]');xlim([0 max(bearFreq)*1.8])
## 4 Results
### Comparison and Analysis of the Results
Figures 12-14 show the prediction results of the proposed model at different integration ratios, and Tables 12-14 show the prediction errors of the proposed model at different integration ratios. Through the experiment, we can draw the following conclusions. 1. Significant Reduction in Error Metrics: The introduction of more data from neighboring stations significantly reduced error metrics such as RMSE and MAE. By applying Bayesian optimization to determine the optimal fusion ratios of data from nearby stations based on similarity, RMSE decreased by 20.04%, 28.24%, and 30.94% under sunny, cloudy, and rainy conditions, respectively, and MAPE decreased by 30.30%, 18.83%, and 29.27%. Similarly, MAE also decreased by 23.07%, 17.58%, and 31.36% under these weather conditions. These reductions emphasize that the model's ability to predict PV power output is enhanced when supported with more extensive spatial data. 2. Variability in Prediction Accuracy Across Weather Conditions: The improvement in prediction accuracy varied across different weather conditions. Particularly during rainy conditions, because more data from surrounding areas were integrated, compensating for the lack of historical data at the target site, the reduction in prediction error was the greatest, reaching 31.36%. This shows that the model especially benefits from additional data where there is a deficiency, enhancing its accuracy. 3. Improvement in R\({}^{2}\) Value and the Trade-off with Time: As more data were integrated, the model's R\({}^{2}\) value improved, indicating a stronger correlation between predicted and actual values. However, this accuracy came at the cost of increased computational time, especially as the degree of data integration increased, leading to longer prediction times. Figure 12: Forecast result under sunny weather. Figure 14: Forecast result under rainy weather. Figure 13: Forecast result under cloudy weather. In predicting photovoltaic power, the Conv-LSTM-ATT model that integrates spatial data from surrounding stations exhibits excellent performance. This strategy effectively utilizes diverse data sources, enhancing the model's predictive accuracy across various weather conditions, and proving its practical application potential in real-world PV power forecasting scenarios.
## 3 OLSR-Based Protocols
### Ml-Olsr  
The Mobility and Load Aware OLSR (ML-OLSR) [27] was developed to mitigate the impact of high mobility and load imbalance on UAV network performance. The ML-OLSR protocol contains both mobility-aware and load-aware algorithms. Several simulated scenarios were used to evaluate the algorithms and modeling components of the ML-OLSR protocol [27]. Two mobility-related metrics comprise the mobility-aware algorithm:  
1. _Stability Degree of Node_ (SDN);
2. _Reachability Degree of Node_ (RDN).  
SDN evaluates the connectivity between two UAVs. A UAV utilizes its GPS data to develop a mobility-aware UAV node. The neighboring UAV gives position information to the UAV, allowing the UAV to determine the distance between the two UAVs. Using statistical information about the distance and the following SDN function, the connection stability for the link between two UAVs is computed using Equation (1):  
\[SDN_{AB}=\left(\sum_{i}\frac{d_{i}^{2}}{n}\right)-\left(\sum_{i}\frac{d_{i}}{n} \right)^{2} \tag{1}\]  
where \(d\) is the distance between UAV\({}_{A}\) and UAV\({}_{B}\), and \(n\) is the number of neighbor UAVs, \(i\) is the counter number for neighbor UAVs. Neighbor UAVs communicate with UAV\({}_{A}\) via messages that contain GPS data so that UAV\({}_{A}\) may determine the distance between UAV\({}_{A}\) and the neighbor UAVs. After receiving a message from neighbor UAV\({}_{Ni}\), the UAV\({}_{A}\) calculates \(SDN_{ANi}\), then selects the more stable neighbor UAV. The neighbor UAV with the lowest \(SDN_{ANi}\) is the most stable. When two UAVs have the same value of \(SDN_{ANi}\), the neighbor with the shortest message exchange distance is deemed to be more stable.  
RDN refers to the degree of accessibility only for stable UAV nodes. As mentioned previously, the reachability level of a UAV node represents the weight of the UAV nodes, and the stability level of a UAV \(SDN_{AB}\) represents the weight of the connection between  
Figure 3: Flowchart of the D-OLSR mechanism, where S—Source and D—Destination.  
UAV nodes A and B. When the MAC layer of IEEE 802.11 cannot deliver packets promptly, the packets are stored in the buffer. If more packets are waiting, the greater the load becomes. The quantity of nearby UAVs immediately affects the PDR and transmission delay. To increase the data communication efficiency of the UAV ad-hoc network, the load-aware algorithm of UAV nodes is essential.  
Equation (2) is used to determine the load of nodes:  
\[\textit{Load\_factor}=\textit{MAC\_factor}_{i}+\textit{Ne\_factor}_{i} \tag{2}\]  
where _Load_factor_ denotes the load on node \(i\); _MAC_factor_\({}_{i}\) is the node's own load; and _Nei_factor_\({}_{i}\) denotes the load on neighboring nodes.  
Equation (3) can be used to describe the load along the whole route:  
\[Path\_factor=\sum_{i\;\epsilon\;path}\textit{Load\_Factor}_{i} \tag{3}\]  
_Path_factor_ is the total load on the route from the source UAV to the destination UAV.
## 2 Data and Methods  
### Data Preprocessing Methods  
Before training or testing an ANN, the training and testing data must go through a series of preprocessing steps. These methods included removing any unnecessary information, interpolating any missing data points, and organizing the energy production data chronologically. Finally, the resulting data column was normalized using feature scaling before it was used as input to the LSTM network.  
All data preprocessing was done in Python 3.6 using the Spyder integrated development environment (IDE) through Anaconda. The Python script contained all the functions that were used to process the Cyprus PV data and build, train, and validate the single-layer LSTM network.  
The first step in data preprocessing was to load the data into Python and reformat it. The original data files were separated by month, and each column represented a day of PV generated DC energy. Once reformatted, all the data were loaded into a single data frame with a DateTime index.  
Once the data were formatted, the quality of the data was evaluated. A function was developed to search for and record the location of any nan, negative, and duplicate values. To reduce the number of zeros in the data frame, the nighttime values between 20:00 h and 5:15 h were removed. The times were specifically chosen so that the LSTM network trained on these data could be directly compared to the existing, previously trained LSTM network. After removing the nighttime data, the missing data were interpolated, using the time or linear interpolation method.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Modified Dynamics with Entropy Coupling
#### 3.8.1 Canonical Example: Harmonic Oscillator  
* **Hamiltonian**: \[H=\frac{p^{2}}{2m}+\frac{1}{2}kq^{2}\] (20)
* **Entropy function**: \[\overline{S}(q,p)=-\frac{\zeta_{q}}{2T}q^{2}-\frac{\zeta_{p}}{2 mT}p^{2}\] (21) where \(\zeta_{q}\) (kg/s) and \(\zeta_{p}\) (dimensionless) are coupling parameters.
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 5 Numerical Result
### Simulation Settings  
We constructed a simulated scenario involving multiple UAVs performing coverage tasks. The map size was assumed to be \(10\times 10\), with each grid cell having a size of \(1\times 1\), amounting to a total of 100 grid cells. The simulation environment was set up using Python 3.6 and PyCharm, with the deep learning library implemented in PyTorch. The initial UAV positions were initialized randomly, and initial actions were randomly selected. The training loop was then executed using the hyperparameters listed in Table 2. At each iteration, experiences were sampled from memory to update the networks. The current policy was evaluated after completing each round of updates. The scenario simulation, algorithm implementation, initialization of parameters, and training procedure helped validate the effectiveness of the proposed MATD3 approach for multi-UAV coverage path planning in a systematic manner.
## 6 Performance Evaluation
### Control Traffic Overhead  
Bio-2 FA-IoD demonstrated a very low control traffic overhead of **150** packets per hour. This minimal overhead is crucial for preventing network congestion and efficiently utilizing the limited bandwidth available in IoD networks. The protocol's direct communication model and reliance on compact authentication messages reduce the need for extensive signaling or state synchronization, differentiating it from protocols that might involve more frequent control plane updates (e.g., Protocol A at 400 packets/h, Protocol B at 250 packets/h, [PERSON] et al. [5] at 300 packets/h). This efficient use of network resources ensures scalability and stability in dense IoD deployments. In summary, the simulation results strongly corroborate the theoretical analysis, affirming Bio-2 FA-IoD's position as a highly efficient and lightweight authentication solution for the Internet of Drones. Its superior performance across critical metrics like latency, PDR, and energy consumption, under realistic network conditions, makes it well suited for resource-constrained IoD components.
## 3 Public Datasets for Scene Understanding Methods
### Solutions to Handle Dataset Imbalance
However, the structure of multi-class datasets is not definite, and the relations among classes are not always obvious. For example, one class might be a majority compared to one other class, but a minority or well balanced for the rest of them [111]. Another way is to set the false negative prediction cost to a fixed value and vary only the false positive cost. The ideal cost matrix is identified using a validation set. This has the advantage of exploring a range of costs but can be expensive and even impractical if the dataset size or number of features is too large [90]. **Thresholding methods** adjust the decision threshold of a classifier to convert a predicted probability or scoring into a singular class label. In the context of a binary classification problem with class labels 0 and 1, a common approach is to utilize normalized predicted probabilities and apply a threshold of 0.5. Values below the threshold are attributed to class 0, while values equal to or above the threshold are assigned to class 1. The presence of imbalanced data or low acceptance of _false_ predictions poses a challenge, as the default threshold may not accurately reflect the optimal interpretation of predicted probabilities. Adjusting the threshold through hyperparameter tuning can enhance the classifier recall in a binary classification scenario. Nonetheless, multi-class classification, as typically encountered in point cloud classification, where each scan point can carry a single class label, cannot rely extensively on thresholding methods. Instead, class labels are \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Metric** & **Ref. Example** \\ \hline
3D Point Cloud Classification & OA, mAcc & [105,106] \\
3D Semantic Segmentation & OA, mAcc, mIoU & [107,108] \\
3D Instance Segmentation & mAP, mAP@25, mAP@50, mPrec, mRec & [109,110] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of frequently used evaluation metrics for 3D point cloud scene understanding tasks. Overall accuracy (OA), mean overall accuracy (mAcc), mean Intersection over Union (mIoU), mean average precision (mAP), average precision scores with IoU thresholds set to 25% and 50% (mAP@25 and mAP@50, respectively), mean precision (mPrec) and mean recall (mRec). Figure 9: Confusion matrix for binary testing. Note: Other references may use a different convention for axes. Declaration: real positive (RP), real negative (RN), predicted positive (PP), predicted negative (PN). typically assigned based on the highest predicted probability. In conclusion, it is noteworthy that the solutions discussed for addressing dataset imbalance are representative of a broad range of techniques in machine learning. [PERSON] and [PERSON] [90] suggest that the current research on the use of deep learning to tackle class imbalance in non-image data is limited and should be further explored. Since point cloud data are a novel research domain, they pose a unique challenge for deep learning methods, and therefore, there is still limited progress in developing effective solutions.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation
### RSU Allocation  
In this section, we will evaluate our proposed solution with other RSU allocation strategies in the simulation environment. We consider the Top-K strategy (i.e., the RSUs are allocated in the most density areas.) and uniform distribution strategy (i.e., the RSUs are allocated along the road segment with uniform distance.) as the benchmark strategies for comparison.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 6 Evaluation
### CPS Hub with Heterogeneous Data Planes
#### 6.4.2 HTTP/CoAP as the Data Plane Protocol  
The testing arrangements for the data plane negotiation with HTTP/CoAP as the data plane protocol are depicted in the Figure 23. The system consists of two endpoints, the CoAP server and HTTP client, that are willing to communicate with each other. The CoAP server runs in the NS-3 simulator, whereas the HTTP client is located in company internet. They do not understand each other directly, and thus need the HTTP/CoAP proxy for interpreting purposes. The endpoints were embedded with data plane negotiation-capable XMPP clients. There are two XMPP servers working in federation to transfer XMPP traffic for data plane negotiation purposes.  
Like in the MQTT case, there are two virtual communication spaces involved in the system, belonging to Company-a and Company-b. Hence, the data plane protocol negotiation is required. In this test arrangement, the HTTP/CoAP proxy was left out of the data plane negotiation for simplicity.  
In this configuration, the DPP-client is deployed in the HTTP client, while the DPP-server locates in the CoAP server. When the HTTP client is ready to start reading data from the CoAP server, it first initiates data plane negotiation in order to get the required information for the HTTP connection. Company-a's trusted list contains Company-b, which means that a trust relationship can be established. As a result of the data plane negotiation, the HTTP client learns the IP address and other communication parameters of the CoAP server. The IP address of HTTP/CoAP proxy was hard-coded in the HTTP client. By combining these two IP addresses together with the port number, the HTTP client is able to constitute the HTTP URL to be used in the connection. The evaluation case indicated successful operation of the data plane negotiation when using HTTP/CoAP as the data plane (transport) protocol.
## 3 Numerical Results and Complexity Analysis
### BER Performance  
We evaluated the bit error rate (BER) performance for the number of decoding iterations against signal-to-noise ratio (SNR) as shown in Figures 4 and 5, respectively. The BER performance of this scheme was achieved after I = 7 iterations were performed.  
In Figures 6 and 7, we present the BER performance of the decoder with CRC error detection and effect of coding rates (R = 1/2 and R = 1/3), respectively. Figures 8 and 9 show the BER performance of our proposed receiver for \(\Delta f_{n}=15\) kHz and QPSK constellation using the proposed MMSE FDTE compared to other LMSE equalizer algorithms. At the first decoding iteration, the proposed FDTE yielded significant performance gains of about 3.2 dB and 3.5 dB, which tended to converge after four decoding iterations. Similarly, Figure 7 illustrates the BER performance of our proposed receiver for \(\Delta f_{n}=3.75\) using the QPSK constellation scheme. In both Figures 6 and 7, the proposed receiver has slight improvement when working with a lower order modulation scheme over the conventional FDTE algorithm for both \(\Delta f_{n}=15\) kHz and \(\Delta f_{n}=3.75\) kHzSCS, while yielding a signifi  
Figure 4: BER performance of Turbo decoding using \(\Delta f_{n}=15\) kHz.  
Figure 5: BER performance of Turbo decoding using \(\Delta f_{n}=3.75\) kHz.  
icant performance gain over the conventional LMSE FDTE in [29], which is considered enough for desired uplink single tone transmission.  
Figure 6: BER performance with CRC mechanism.  
Figure 7: BER performance of Turbo decoding with different code rates.