esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Probabilistic Forecasting in Demand Response
## 3 Results
### Application 2: Determining the Number of Loads Needed for DR Actions  
Another interesting application consists of determining the number of loads needed for DR actions for a given coverage probability. That is, we want to calculate how many  
Figure 8: Example of Application 1 for the date 7 February 2011 and the _QRF_ method: (**a**) results of steps 1 to 3 using the QRF48 model; (**b**) quantiles potentially covered by DR using the QRF48 model.  
Figure 7: Example of Application 1 for the date 7 February 2011 and the LQR method: (**a**) results of steps 1 to 3 using the LQR48 model; (**b**) quantiles potentially covered by DR using the LQR48 model.  
loads (of each type) we would need to cover with DR actions, with a given percentage of uncertainty with respect to the predicted baseline.  
The process can be summarized as follows:  
* Step 1: Select a day of the test period and obtain the predicted baseline of consumption for that day. In this step, the predicted baseline is given by the predicted \(\alpha\)-quantile for each of the 24 h of the selected day, with \(\alpha=0.5\) (the median) and a prediction horizon of 48 h. Also, select a coverage probability \(\beta\in(0,\,1)\).
* Step 2: For each of the 24 h of the selected day, obtain the centered predicted quantiles corresponding to the given coverage probability \(\beta\). Denote with \(\hat{Q}_{h,\,\frac{1-\beta}{2}}\) and \(\hat{Q}_{h,\,\frac{1+\beta}{2}}\) those predicted quantiles for each hour \(h\). Compute the energy to rise by \(\hat{Q}_{h,\,0.5}-\hat{Q}_{h,\,\frac{1-\beta}{2}}\) and the energy to fall by \(\hat{Q}_{h,\,\frac{1+\beta}{2}}-\hat{Q}_{h,\,0.5}\) for each hour \(h\).
* Step 3: Use the PBLM to simulate the number of loads needed to increase or decrease, with DR actions, the energy obtained in the previous step.  
For example, Figures 9 and 10 depict the results of Application 2 for three days in the test period, a given coverage probability of 70%, and the two forecasting methods LQR and \(QRF\). For the LQR method, the round number of loads/appliances needed during the testing period was 650 electric heaters and 900 Water Heaters. For the \(QRF\) method, the round number of appliances needed was 600 electric heaters and 750 Water Heaters. Obviously, each day has a different level of demand, and this evaluation makes it possible to consider 48 h ahead an estimated number of customers and loads that should be involved in DR. As an example, three different days have been selected to obtain the number of EHs and WHs involved. Table 7 shows the results.  
Figure 9: Example of Application 2 for three days in the test period. Coverage probability of 70% and method LQR: (**a**) purchased energy, and predicted quantiles 0.15 and 0.85 using the LQR48 model on 7 February 2011; (**b**) energy to rise and fall according to step 2 on 7 February 2011; (**c**) purchased energy, predicted quantiles 0.15 and 0.85 and DR efforts on 26 October 2010; (**d**) purchased energy, predicted quantiles 0.15 and 0.85 and DR efforts on 2 December 2010.
## 3 Multipath Multimedia Dynamic Source Routing (MMDSR)
### MMDSR Control Packets  
Decisions such as path selection or tuning of configuration parameters are operated from the source. MMDSR periodically discovers _AP_ available paths between source and destination by sending monitoring _Probe Message_ (PM) packets. After that, a _Probe Message Reply_ (PMR) packet is generated at destination to carry the collected information about the quality of the available paths. The reduced size of these packets (around 64 bytes, depending on the number of paths found out) and the low frequency of sending them (every 3-18 s depending on the network state, see Equation (4)) makes the incurred overhead almost negligible. Figure 3 shows PM and PMR packets which are periodically interchanged between source and destination.  
Then, a score is given to each one of the paths after analyzing the feedback information at the source node to classify them accordingly. That score is updated continuously after the reception of each PMR packet. Looking at the QoS parameters of the paths, the source selects three paths (if it is possible, or less if it is not) to compose the multipath scheme. The vector \(path-state_{k}^{j}\) contains all the quality parameters calculated for each one of the available paths:  
\[path-state_{k}^{j}\equiv\{BW,L,D,J,H,RM,MM\}_{k}^{j} \tag{2}\]  
where \(j\) is the iteration number of the algorithm and \(k\) refers to each one of the paths (with \(k\leq AP\)). The QoS parameters are: available bandwidth (\(BW_{k}^{j}\)), percentage of packet losses (\(L_{k}^{j}\)), delay (\(D_{k}^{j}\)), delay jitter (\(J_{k}^{j}\)), hop distance (\(H_{k}^{j}\)), reliability metric (\(RM_{k}^{j}\)) calculated from the SNR (Signal to Noise Ratio) of the links involved in each path, and mobility metric \(MM_{k}^{j}\) calculated from the relative mobility of the neighboring nodes within each path. The last two metrics were previously proposed in [3].  
This process is repeated periodically to refresh the paths since the topology of MANETs vary and might produce link breakages. The routing period depends on the network state, as it is explained in Section 3.5.  
Figure 3: PM and PMR packets.
## 2 Materials and Methods
### Simulated Battery Cycles  
Simulating battery cycles mimics the charging and discharging patterns a battery undergoes throughout its lifecycle. This process is critical for understanding energy management within the UAV system, especially in relation to battery sizing and its compatibility with the propulsion system. The developed model incorporates key factors, such as flight duration, energy consumption rates, and charge/discharge frequencies, to emulate real-world UAV operations.  
The simulation not only predicts potential capacity loss, changes in internal resistance, and overall battery degradation, but also assesses the suitability of the battery configuration for the UAV's propulsion demands. Specifically, the battery's capacity and voltage are carefully calculated to ensure that they are sufficient to power the motor under various operating conditions. The equations used in the model include:  
\[miv\text{\emph{voltage}}=3.25\cdot\text{\emph{seriescells}} \tag{28}\]  
\[max\text{\emph{voltage}}=4.2\cdot\text{\emph{seriescells}} \tag{29}\]  
\[max\text{\emph{capacity}}=\text{\emph{cellcapacity}}\cdot\text{\emph{ parallelcells}} \tag{30}\]  
By analyzing these simulated cycles, we can make informed decisions about the optimal battery size and capacity required for the UAV's propulsion system. This analysis could also be further improved on via integration of hybrid storage systems, such as combining batteries with supercapacitors, to enhance energy management. A hybrid storage system could provide the UAV with the capability to handle short bursts of high-power demand while maintaining overall energy efficiency during longer missions.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
A modular hybrid-full bridge dc converter is proposed in [22]. The full-bridge on the MVDC side is composed of modular-full-bridge (MFB). A dc-dc converter with active front end is presented in [23] to avoid dc capacitors  
Figure 1: PV MVDC grid-connected system. of power modules being directly exposed to the MVDC side. These converters avoid overcurrent during LVRT by introducing an additional circuit on the MVDC side, which increases the cost and reduces the efficiency of the converter. Isolated buck-boost topology is a suitable solution for a PV MVDC converter with LVRT capability. In order to increase the density and achieve higher voltage, developing a MV dc-dc module is important for a MVDC converter (output voltage above \(\pm\)10 kV). It is difficult for the aforementioned isolated buck-boost topologies to develop a MV dc-dc module because active semiconductors are applied on the high-voltage side. A large number of modules are required and the power density of the MVDC converter is decreased. Moreover, these existing solutions for achieving LVRT must involve additional semiconductors circuit, which leads to additional loss and restricts output voltage of the dc-dc modules. In order to solve this problem, this paper proposes a new solution for a PV MVDC grid-connected converter with LVRT capability. Compared to the existing work, this paper's contribution includes:  
(1) A novel MV isolated buck-boost topology and its modulation method is proposed. By applying only diodes on the high-voltage side, MV dc-dc module can be developed by this topology to reduce the number of modules in the converter and increase the power density. High-voltage gain will be realized in boost mode, and LVRT will be realized in buck mode. (2) The LC filter for MVDC converter is proposed to suppress the overcurrent caused by voltage dip, and the LC filter parameter design method is proposed. Compared with the method mentioned above, the converter efficiency is increased and the cost is decreased. In order to solve the unstability problem caused by the resonance spike of the LC filter, a control algorithm based on feedback capacitor current for the isolated buck-boost converter is proposed to ensure the stability. (3) Experiments of the converter for MVDC system in the normal and LVRT condition are presented. The rest of the paper is organized as follows. The proposed isolated buck-boost topology and its principle modulation method are described in Section 2. The LC filter for the MVDC converter is also discussed in this section. In Section 3, the unstability problem caused by the resonance spike of the LC filter is analyzed. A control algorithm based on feedback capacitor current is proposed. The complete control strategy for the converter is also proposed there. In Section 4, 5 kV/50 kW SiC-based dc-dc modules and \(\pm\)10 kV/200 kW PV MVDC converters are developed and laboratory tests result are analyzed. Section 5 concludes this paper.
## 2 Materials and Methods
### Features  
The _time domain features_ contain the amplitude range (AR), peak-to-peak amplitude (PPA), mean amplitude (MA), median stepping increment (MSI), signal integral (SignInt), two definitions of the VF waveform root mean square (RMS) value, RMS1 and RMS2 [23], mean and median slope (MS and MdS) [24], and a smoothed nonlinear energy operator (SNEO) [25; 33].  
The _spectral domain features_ were based on a 2048 point Fast Fourier Transform (FFT) of the Hamming windowed analysis interval and comprised the AMSA (the amplitude spectrum area), centroid frequency (CF), dominant or peak frequency (PF), energy (ENRG), spectral flatness measure (SFM), centroid power (CP), maximum power (MP) and power spectrum analysis (PSA) [24].  
The _complexity measures_ derived from nonlinear dynamics are the Hurst exponent (Hu) [26], the scaling exponent (ScE), the logarithm of the absolute correlations (LAC) [27],the median stepping increment (MSI) resulted from the Poincare plot analysis [28] and two kinds of entropy: wavelet entropy (WE) [29] and spectral entropy (SpeEnt) [30].
## 3 Algorithm of System
### Movement Recognition  
As a deep learning model, it is constructed based on an RNN model generally used in the field of signal processing. The RNN model is advantageous for processing one-dimensional data, and the CNN model can process one-dimensional and two-dimensional data but has a heavy disadvantage over the RNN model. The RNN model exhibits excellent performance in sequential and iterative data analysis. The proposed model was composed of the LSTM layer and the FC layer among the layers of the RNN model. In addition, two-way learning techniques were used to learn the reverse flow as well as the sequential flow of signals. The structure of the deep learning model is proposed in Figure 4.  
The rehabilitation training process before wearing a robot prosthetic leg was designed as a simple ankle movement repetition process in all rehabilitation simulations. The amputee selects one of the rehabilitation simulation methods. Raise your feet in a sitting position and exercise your ankles according to the number of evaluations.  
The signal processing process measures the EMG signal emitted by the amputee through the connected EMG sensor. The measured electromyogram signal is transmitted to the raspberry pie to determine whether it is a motion signal, and the obtained signal is classified into a static state (0) or a dynamic state (1). If there is the movement of leg muscles, signals are classified. Among the classified signals, a moving signal is transmitted  
Figure 4: Proposed Deep-Learning Model Structure.  
Figure 3: The architecture of the XR Rehabilitation System.  
to the Unity game engine through TCP communication. The usability evaluation data of rehabilitation training conducted with a mixed reality rehabilitation simulation system were classified through usability evaluation and _t_-test.  
For the training parameters, the optimizer was set to Adam, the epoch was set to 100, and the batch size was set to 200. The length of the data to be trained and inferred was set to 64. For the loss function, the L1 loss function is used, defined as Equation (1).  
\[L_{D}=\sum_{i=0}^{n}|D_{i}-f_{D}(x_{i})|. \tag{1}\]  
Equation (1) is the Mean Absolute Error (MAE), which calculates the absolute error between the prediction result and the correct response data. \(f_{D}\) is the detecting deep learning network, \(x_{i}\) is the input data, and \(D_{i}\) is the correct response data. This formula is used to calculate and learn the difference between the detected correct data and the detected deep learning prediction value.
## 4 Conclusions  
Aiming at the problem of floating object detection for the surface cleaning robot in the case of small sample acquisition and interferences factors from waves, reflections, and uneven illumination, an effective detection method is proposed. The method combines texture detection in spatial domain and saliency detection in frequency domain.  
In texture detection, a useful combination of hand-crafted texture features is implemented. Compared with 4 traditional texture detection methods, precision of our texture detection is 88.24%, recall is 96.43% (IoU \(>=\) 0.8), F\({}_{1}\)-Measure is 0.92, and average detection time is 0.333 seconds, which demonstrates that our texture feature (FHOG-GLCM) describes floating objects effectively and efficiently.  
In saliency detection, the proposed method combines frequency-domain processing and low-rank decomposition. Compared with six traditional saliency detection methods, the precision of our saliency detection is 94.29%, the recall is 94.29% (IoU \(>=\) 0.8), the F\({}_{1}\)-Measure is 0.89, and the average detection time is 0.415 seconds, which demonstrates that our saliency detection method performs better than other saliency detection methods in complex water scenes.  
Compared with 17 existing methods including traditional image segmentation, saliency detection, hand-crafted feature classification, and CNN-based object detection, our final method has the highest precision (97.12%) and the highest recall (96.43%) with little time consumption (0.504 seconds). The results show the effectiveness of combining spatial and frequency domains, and the reliability and rapidity of our method of floating object detection for surface cleaning robots.  
[PERSON] designed the algorithm, performed the experiments and wrote the paper. [PERSON], [PERSON], and [PERSON] modified the paper. [PERSON] supervised the research.  
This work was supported by National Natural Science Foundation of China (Grant No.11602292. 61701421, 61601381) and Postgraduate Innovation Fund Project by Southwest University of Science and Technology (Grant No.19 ycx0112).  
The authors are grateful for the experimental platform and resources provided by the Sichuan Province Key Laboratory of Special Environmental Robotics.  
The authors declare no conflicts of interest.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results
### Comparison of Different Fusion Strategies
To further validate the importance of each individual component integrated in our proposed multimodal threat detection framework, we conducted ablation experiments isolating or removing core modules, namely synthetic data generation, transfer learning, and decision-level fusion layer. This allows us to quantify their respective contributions to the overall system performance. To this end, four configurations were considered and evaluated using the same test dataset. Full Model: All components enabled (considered as the main experiment in the current study). 2. No GAN-based Augmentation: Real data only, synthetic samples removed. 3. No Transfer Learning: CNN trained from scratch on malware images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Accuracy** & **F1-Score** & **Precision** & **Recall** & **AUC (ROC)** \\ \hline Majority Voting & 0.955 & 0.954 & 0.957 & 0.951 & 0.976 \\ Rule-Based Method & 0.942 & 0.943 & 0.931 & 0.955 & 0.969 \\ Confidence-Weighted Avg. & 0.967 & 0.966 & 0.969 & 0.963 & 0.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summarization of Fusion Methods Comparison Results. Figure 10: Visualization of the Comparative Performance between the different Fusion Methods. No Decision-Level Fusion Layer: Only tabular branch used (ignoring image input). Table 18 summarizes the ablation experiment results for the selected configurations. The results confirm that each module provides meaningful performance gains. Removing GAN-based augmentation resulted in a notable F1-score drop of 3.9% and a 6.4% decrease in accuracy, highlighting the critical role of synthetic data in improving generalization and supporting rare threat detection. Eliminating transfer learning also reduced F1-score by 2.7% and accuracy by 4.4%, underscoring the advantage of leveraging pretrained feature extractors for image-based malware detection. Lastly, bypassing the decision-level fusion led to a 2.2% drop in F1-score and a reduction in overall precision, confirming the added value of combining multimodal predictions to improve robustness and reduce false positives. These findings reinforce the architectural design choices made in this study and validate the integrative contribution of each module within the proposed framework.
# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 3 Data Pre-Processing
### Missing Data Processing  
In the process of data transmission and storage, data may be lost due to network disconnection, database maintenance, or some unexpected malfunction. Different methods were used to pre-process lost historical and forecast data, which are described as follows:  
1. The pre-processing method of the historical data, including the data from the CWB and local weather station, involved directly deleting abnormal and missing data. The data for such days were erased when the historical data were \(-9999\), null, or intermittently lost. The number of processed data points is shown in Figure 6. The hollow bars show the amount of data that can be used for one-day-ahead solar photovoltaic forecasting training after processing by the above method. There are 170 days of hybrid data, 308 days of local weather station data, and 181 days of CWB data. The original data are from 1 August 2020 to 2 June 2021, a total of 324 days.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Unit** \\ \hline Temperature & \({}^{\circ}\)C \\ Relative Humidity & \% \\ Rainfall & mm \\ Average Wind Speed & m/s \\ UV Index & - \\ \hline \end{tabular}
\end{table}
Table 4: Final features selected from the CWB.  
Figure 5: The filtration result of solar photovoltaic power data. The green circle shows the change from the blue line to the red line.  
2. However, the pre-processing method used for lost weather forecast data differs from that used for historical data. There were two methods used in this study. The first was the interpolation of data that were missing for one or two hours of the day. If more than one-fourth of the forecast data for a day were lost, the following method was used. The historical data of the next day's forecast were searched on the CWB's database, and similar weather data were directly used as the forecast weather factor for the next day. For example, if the forecast for the next day was mostly clear from 6:00 to 18:00, the forecast data for the same weather were searched in the historical database and used as the next day's forecast data. The reason for adopting this method is that the unavailability of weather information for the next day will cause the predictive system to crash. As a result, it cannot be applied to actual cases, and it will be meaningless to introduce the predictive module into the energy management system.
# A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks
## 4 Network Size Analysis
### Scale Analysis  
In this section, we firstly analyze the worst charging situation, that is, when the moving distance of the MC reaches its maximum value at the same time as the energy consumption rate of each sensor. In this situation, we regard \(n_{low}\) as the lower network size limit. When the number of sensor nodes is less than \(n_{low}\), we can make the network run continuously with one MC.  
Then, we analyze the normal charging situation. That is, the moving distance of the MC has a value \(\mu_{y}\pm\sigma_{y}\), while the energy consumption rate of each sensor has the value \(\mu_{x}\pm\sigma_{x}\). In this situation, we regard \(n_{up}\) as the higher network size limit. According to the characteristics of the Gaussian  
Figure 1: Situation where the MC must move the longest distance.  
distribution, the probability of the higher limit is as high as 68.5%. To improve the accuracy of the probability, we can take the energy consumption rate and the distance of MC value as \(\mu\pm 2\sigma\). When the number of sensor nodes is greater than \(n_{up}\), we cannot prolong the network lifetime significantly using one MC. When the number of sensor nodes is between \(n_{low}\) and \(n_{up}\), our charging scheme can prolong the network lifetime greatly.