esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 4 Experimental Analyses
### Performance Comparison with Other Models  
To validate the effectiveness of the proposed model, experiments were conducted and compared with six other models, including SVM, RF, DT, Wide & Deep CNN [18], LSTM & MLP [16], SOSTLink & Bi-GRU [24], Bi-RNN [36], CNN & LSTM [20], and LSTM & GRU [23] models. To comprehensively evaluate the performance of the models, the study utilized both PR and ROC curves as evaluation metrics. By using the same dataset and evaluation methods across all experiments, the PR and ROC curves were generated based on the probability values generated by each model. The resulting curves are illustrated in Figure 17 and serve as valuable tools for assessing the effectiveness of the models.  
Figure 17: PR and ROC curves compared with other models. (**a**) PR curves; (**b**) ROC curves.  
In Figure 17, it was evident from the PR-AUC of only 0.3743 and ROC-AUC value of only 0.6144, that the SVM model could not classify well when the sample of electricity consumption data in this paper was too large. The PR-AUC value of 0.4703 and ROC-AUC value of 0.7053 for the RF model indicated a poor result for processing our 1D electricity consumption data. The PR-AUC value was 0.5824 and ROC-AUC value was 0.7444 for the DT model, which indicated that it was prone to overfitting and had little room for improvement. The PR-AUC value was 0.5724 and the ROC-AUC value was 0.8123 for the Wide & Deep CNN model, which did not consider the temporal characteristics of electricity consumption data well, resulting in poor results. The PR-AUC value was 0.6719 and the ROC-AUC value was 0.8789 for the LSTM & MLP model, indicating that the network structure was too simple and did not extract the deep features of customers' electricity consumption data well. The PR-AUC value was 0.7795 and the ROC-AUC value was 0.9332 for the SOSTLink & BiGRU model, which did not extract user periodicity features well and had relatively singular features, which limited the performance improvement. The PR-AUC value of Bi-RNN was 0.7799 and the ROC-AUC value was 0.8488. The PR-AUC value of CNN & LSTM was 0.8146 and the ROC-AUC value was 0.8733. The PR-AUC value of LSTM & GRU was 0.6656 and the ROC-AUC value was 0.7328. Table 3 displays the detailed results.  
From Table 3, it was clear that the proposed model obtained the best results, with precision reaching 96.64%, recall reaching 96.87%, the FPR was as low as 3.77%, the PR-AUC value improved to 0.9469, and the ROC-AUC value improved to 0.9684. These results further illustrated that use of the hybrid multi-time scale neural network-based model effectively extracted user features, considering not only the temporal characteristics of user electricity data but also the periodic characteristics and deep features of user electricity consumption. Additionally, using the CatBoost classifier achieved user classification with a high accuracy rate and low false detection rate.
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
## 5 Experimental Results and Analyses  
### Experiments of the Proposed Multi-Frequency Waveform Control Method  
In this work, the effects of four parameters, \(N_{fTH}\), \(DC_{TH}\), \(T_{bL}\), and \(I_{pT}\), on the welding quality could be explored by means of experiments. Firstly, the maximum and minimum values of each parameter can be found. To complete this task, orthogonal experiments were employed. Orthogonal experiments are an important engineering experimental method which is commonly used to evaluate  
Figure 4: Integrated experiment platform used in this work.  
According to these experiments, most experiments were stable. Especially, when the \(N_{fTH}\) equaled to 3, the experiments could be successfully conducted with satisfactory welding quality under the condition of unchanged traveling rates and wires feed rates, and no failures of striking arc or arc interruption appear. It showed that the ranges in which parameters can be adjusted in this new method are very large, thus parameter matching is more convenient than that of others.  
On the other hand, the worst welding quality was obtained from experiment 1 according to Table 2, whose transient current waveforms are shown in Figure 5. It could be observed that the arc interruption occurred at 142 ms. At that time, the leading wire current cannot achieve the peak stage, while the base stage of the trailing wire current was relatively stable. In addition, some short circuit phenomena appeared during the process. All of these phenomena showed that this experiment was unstable.
# Routing Schemes in FANETs: A Survey
## 6 Open Issues  
FANETs possess unique characteristics (e.g., high mobility degree, high dynamicity of network topology, low energy constraint, as well as different node densities in different network scenarios) that require novel routing schemes, although there are limited routing schemes for FANETs in the literature, but this survey paper can motivate researchers by identifying more research gaps in the respective domains. For instance, the high mobility degree characteristic has brought about three challenges to routing:* high dynamicity (X.1), whereby nodes with high mobility can cause significant changes to network topology;
* high cost (X.2), whereby nodes with high mobility can cause link disconnections, resulting in high packet retransmission due to route failure, route discovery, and route maintenance;
* low residual energy (X.3), whereby higher number of message exchanges can cause high energy consumption and shorter route lifetime.  
Novel routing schemes can cater to the unique characteristics in order to achieve better network performance, such as higher QoS performance (P.1) (e.g., higher throughput, as well as lower packet loss rate and delay) and lower energy consumption (P.2). In addition, novel routing schemes must be designed to cater for next-generation applications, which have stringent QoS requirements, such as high throughput as well as low latency and packet loss rate in real-time surveillance with video, voice, and images. Table 5 depicts the summary of open issues. The rest of this section presents open issues that can be pursued in this research topic.
## 5 Further Discussion about Raising PLED by Convex Quadratic Optimization Commutation  
Although the decrease of boundary constraint \(\mathrm{I_{m}}\) of optimization commutation can raise PLED, this decrease is not boundless and PLED cannot be raised unlimitedly. Figure 15 shows the commutated currents of four forcers by optimization commutation with the boundary constraint \(\mathrm{I_{m}}=1.95\) A, which are expected to generate electromagnetic forces and torques \(\mathrm{W_{d}}=(11\) N, \(1\) N, \(11\) N, \(10\) N\(\cdot\)mm, \(10\) N\(\cdot\)mm\()^{\mathrm{T}}\). As observed from this figure, the commutated currents of many coils in position \(x=-6.896\) mm and \(x=3.713\) mm are zero, so they cannot produce any desired electromagnetic force or torque. Figure 16 shows the electromagnetic forces and torques generated from the optimization commutation currents with boundary constraint \(\mathrm{I_{m}}=1.95\) A.  
It can be seen from Figures 15 and 16 that the optimization cannot obtain a feasible solution when the boundary constraint \(\mathrm{I_{m}}\) is less than \(1.95\) A. This indicates that the boundary constraint \(\mathrm{I_{m}}\) should have the lowest bound, which can guarantee the generation of desired electromagnetic forces and torques. This lowest bound, of course, depends upon the desired electromagnetic forces and torques. As far as two examples in this paper are concerned, the lowest bound of boundary constraint is \(1.95\) A. It is about \(65\%\) of \(3.0\) A, which is the maximum amplitude current when the commutation is optimized without the boundary constraint.  
On one hand, the existence of the lowest bound limits further increase of the PLED. On the other hand, when the lowest bound of boundary constraint is determined, the capacity of coil power  
Figure 14: Results of 17 measured points of electromagnetic forces and torques when the desired electromagnetic forces/torques are \(W_{\mathrm{d}}=(F_{\mathrm{xd}},F_{\mathrm{yd}},F_{\mathrm{zd}},T_{\mathrm{xd}},T_{\mathrm{yd}},T_{\mathrm{zd}})^{\mathrm{T}}=(10\) N, \(0\), \(10\) N, \(0\), \(0\), \(0)^{\mathrm{T}}\).  
amplifiers can be consequently selected. In the two examples given in Section 3.2, the coil power amplifier capacity can be selected as 2.0 A. If not, it has to be 3 A at least, and this will greatly increase costs, because 20 coil power amplifiers of the same capacity are required.  
Figure 16: Electromagnetic forces and torques generated from optimization commutation currents with boundary constraint \(\mathrm{I_{m}}\) = 1.95 A.  
Figure 15: Coil currents of (**a**) Forcer I, (**b**) Forcer II, (**c**) Forcer III, and (**d**) Forcer IV by optimization commutation with boundary constraint \(\mathrm{I_{m}}\) = 1.95 A.
## 4 Simulation and Discussion
### Reconstruction Performance versus the Measurement Length  
In this subsection, we compared reconstruction performance of FRSVssMP with those of the competing algorithms versus the measurement length \(M\). In this simulation, for both synthetic and real sparse signals, signal length \(N\) and sparsity level \(K\) were both set to \(N=1024\) and \(K=20\), respectively. Other parameters were set in the same way as in the previous part.
## 3 Optimum Design of SOE
### KFTS II-Type SOE  
As shown in Figure 5a, the two basic design parameters of the KFTS are hieight H and inclination angle 0 of the walls. A parametric design process with various values of H and 0 were performed to determine the optimum KFTS-type SOE, designated as KFTS II.  
The optical efficiency with various values of H and 0 under normal incidence and under an incidence deviation of 1\({}^{\circ}\) are shown in Figure 15(a,b), respectively. A feasible value of inclination angle 0 was found in the range of 8\({}^{\circ}\) and 18\({}^{\circ}\), according to Figure 15a. In addition, Figure 15b revealed that the optimum value of inclination angle 0 was 10\({}^{\circ}\) to achieve a highest optical efficiency under an incidence deviation of 1\({}^{\circ}\). Moreover, when the inclination angle 0 is 10\({}^{\circ}\), the optical efficiencies were 81% at H = 28 mm, 81.2% at H = 29 mm, 81.1% at H = 30 mm, and 81% at H = 31 mm, respectively. Accordingly, the optimum values of H and 0 were selected as 29 mm and 10\({}^{\circ}\), based on the simulation results shown in Figure 15.  
Figure 14: Irradiance distribution on the receiver for the Fresnel lens with KOD II-type SOE (**a**) under normal incidence; (**b**) incidence angle = 1.7\({}^{\circ}\).  
Figure 15: Optical efficiency for the Fresnel lens with KFTS-type SOE with various design values of H and 0. (**a**) under normal incidence; (**b**) incidence angle = 1\({}^{\circ}\).  
Figure 16 shows and compares the optical efficiency of the CPV unit with KFTS and KFTS II SOEs under various incidence angles. The optimum KFTS II SOE extends the acceptance angle to 1.1\({}^{\circ}\) and the corresponding optical efficiency at the acceptance angle is 76.9%. The optical efficiency under normal incidence is 85%, and it decreases gradually to 0% at an incidence angle of 2\({}^{\circ}\). Figure 17(a,b) display the irradiance distribution on the solar cell when the incidence angle deviation is 0\({}^{\circ}\) and 1.1\({}^{\circ}\) (acceptance angle), respectively. A very uniform distribution was obvious in Figure 17a, which indicates that the KFTS II SOE exhibits good solar energy redistribution on the receiver under normal incidence.
## 1 Introduction  
### Research Background  
Improving rural habitat helps foster beneficial interactions between the living environment and the rural social, economic, and resource environments. It is a significant index of the resident's quality of life. Improving rural habitat has become a key initiative to realizing the rural revitalization strategy [1]. To analyze the current environmental status and lead the improvement measures, a scientific and reliable evaluation technique must be established. Most of the research that has already been conducted is based on the three dimensions of environment, society, and economy. These studies selected the appropriate index factors, built an indicator evaluation system [2] for the rural habitat environment, established a multi-criterion decision-making model [3, 4], and objectively assessed the quality of the sustainable development level and the rural habitat environment using the entropy weight method [3], hierarchical analysis [5], principal component analysis [6], and GIS technology platform [7]. Furthermore, studies on the perceptions of rural residents are steadily growing to investigate the satisfaction of the rural habitat environment, develop a subjective evaluation scale and evaluation system, and learn more about the villagers' sense of participation and participation behavior [8; 9].  
Index assessment methods, which are widely used as the predominant evaluation approach in contemporary research on rural human settlements, are not without their limitations. The comprehensive analysis of complex environmental phenomena may be limited in its ability to encompass all aspects, potentially overlooking certain factors that are challenging to quantify, such as cultural values, emotions, and subjective feelings. Additionally, the subjective and controversial nature of assigning importance and weight to different indexes may result in evaluation outcomes that lack sufficient accuracy. Furthermore, the subjective rating scale exhibits a deficiency in accuracy attributable to the overall limited cognitive abilities of the inhabitants and the inherent bias introduced by the structure of the questionnaire, hence leading to a dearth of precision in the obtained questionnaire outcomes.  
To this day, there has been a limited number of research that has explicitly assessed the rural habitat landscape in terms of its impact on human physiology. Electroencephalography (EEG) can serve as an objective measure of physiological responses to assess individuals' holistic experience of their immediate surroundings. Hence, this study focuses on the rural streetscape, a significant component of the rural habitat environment. The objective was to investigate the impact of rural streetscape on individual environmental perceptions and emotional responses using electroencephalogram (EEG) measurements within an immersive virtual reality (VR) setting. Additionally, the study aimed to evaluate people's satisfaction with the design of enhanced rural streetscapes, thereby offering novel insights and approaches for assessing the landscape of the rural habitat environment.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 4 Conclusions  
In this review, the two core elements of wideband mixed signal separation based on photonic signal processing, which are interference management and stealth communication, are summarized. We show principles, experimental results for interference management for photonic stealth communication systems. Additionally, the challenges and future perspectives for hybrid radio frequency and photonic communication systems are explored. The challenges and related work of interference management are proven. Using under-sampled signals to perform principal component analysis (PCA) and independent component analysis (ICA) is explained and theoretically proven. The experimental setup of the photonic interference circuit for stealth communication is demonstrated and the principle of using the physical properties of the wide bandwidth of optical carriers to hide the signal of interest is explained and experimentally demonstrated to transmit 500 Mbps-5 Gbps binary signals in optical noise with bandwidth up to 5000 GHz. The feasibility and future perspectives of the seamless integration of analog signal processing with photonic circuits and digitally processing the statistical patterns of the under-sampled signals is discussed.  
Conceptualization, B.W.; investigation, B.W.; writing--original draft preparation, B.W.; writing--review and editing, B.W., Y.Q., and T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Materials and Methods
### Neural Network-Based Methods  
#### 2.3.1 Convolutional Neural Networks (CNNs)  
The CNN operates by applying convolutional filters across the GHI data. Each filter captures unique local characteristics, like rapid shifts or daily trends, as it slides through the temporal sequence. Subsequent convolutional layers combine and aggregate these features to construct a hierarchical representation of the information, allowing the CNN to discern complex spatial and temporal patterns [35]. This approach enables CNN to effectively capture relationships within GHI data, facilitating precise predictions based on automatically identified patterns [16; 36]. Figure 1 graphically presents the use of the CNN for time series prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Lat. (\({}^{\circ}\))** & **Long. (\({}^{\circ}\))** & **Elev. (\({}^{\circ}\))** & **Clear Days** & **Partly Cloudy Days** & **Cloudy Days** \\ \hline OMZ & 22.779 & \(-\)102.565 & 2650 & 640 & 693 & 127 \\  & & & & 43.84\% & 47.47\% & 8.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of clear, partly cloudy, and cloudy days at OMZ station.
## 4 Formulation of an Operation and Maintenance Strategy for Equipment with a High Failure Rate
### Data Processing and Fault Prediction
#### 4.1.2 Operation and Maintenance Strategy Formulation
By reducing in-depth maintenance measures, the maintenance cost of video surveillance equipment is significantly saved, thus saving the cost of operation and maintenance of this kind of equipment and increasing the value of the equipment.
## 3 Vision, Key Features and Challenges of 6G
### Vision and Key Features of 6G
#### 3.1.5 Human-Centric  
It is believed that human-centric communication is a key feature of the 6G network. With the help of this technology, sharing and/or accessing different physical features can be possible by humans. To accelerate human-centric communication rather than technology/machine-centric communication, the principal means of human perception must be incorporated into the communication system module [195]. A human-centric communication framework needs two fundamental aspects--technology and user experience (UE). The latter includes human behavior as well as psychological and socioeconomic contexts and needs to be considered during the modeling and analysis of the communication system [183; 184; 195].  
In 2016, Society 5.0 was initiated by the Japanese cabinet in its Fifth Science and Technology with a vision to build a \"Super Smart Society\" [196]. Later, the vision was revised and presented by the Keidanren Business Federation with the prime focus of delivering sustainable development goals (SDGs) through the creation of Society 5.0 [183; 184; 197]. Society 5.0 is designed to solve different social issues by taking advantage of technological advancements. Considering different aspects of economic growth, social and environmental conditions, 17 primary objectives and 167 goals are listed in the Agenda 2030 by the United Nations to address several global challenges [198; 199].
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 3 Proposed Methods
### Multi-Objective Tabu Search
#### 3.2.3 Cost Function  
The cost function is defined based on the objective to be optimized. The TS algorithm is executed three times, each targeting a different objective. Similar to the MOILP approach, the first execution of the TS aims to maximize the number of VMs hosted in the data center. Once this maximum is achieved, the algorithm is run again with the goal of minimizing resource wastage, while ensuring that the previously obtained maximum number of hosted VMs is maintained. Finally, the TS is executed for a third time, focusing on minimizing energy consumption, while keeping both the maximum number of hosted VMs and the reduced resource wastage from earlier iterations.
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 4 EEDCF Algorithm
### Cluster Head Competition Phase
\[\beta_{j}=\partial_{j}/\sum\limits_{j=1}^{m}\partial_{j} \tag{12}\]  
\[\hat{y} =\sum\limits_{j=1}^{m}\beta_{j}y^{i}(X) \tag{13}\] \[=\sum\limits_{j=1}^{m}\beta_{j}(p_{0}^{i}+p_{1}^{i}A_{1}(NE)+p_{2 }^{i}A_{2}(ND)+p_{3}^{i}A_{3}(NNE))\] \[=[\beta_{1},\ \beta_{1}x_{1},\ \cdots,\ \beta_{1}x_{n};\ \cdots;\ \beta_{m},\ \beta_{m}x_{1},\ \cdots,\ \beta_{m}x_{n}]\] \[\times[p_{10},\ p_{11},\ \cdots,\ p_{1n};\ \cdots;\ p_{m0},\ p_{m1},\ \cdots,\ p_{mn}]^{T}\]  
Combining the membership function of the three parameters (Node Energy (NE), Node Degree (ND) and Neighbor Node Energy (NNE)) and the previous experience examples in data base, we express the upper formula in a concise manner as shown in Equation (14). \[Y=XP \tag{14}\]  
Then, we can get the least square estimation for \(P\) and further confirm the consequence parameter in TSK fuzzy model. Then we can calculate the probability of the final crisp output value \(y_{TSK}(X)\) with Equation (15). \[y_{TSK}(X)\equiv\frac{\sum_{i=1}^{M}f^{i}(X)y^{i}(X)}{\sum_{i=1}^{M}\left(X \right)} \tag{15}\]  
where \(f^{i}(X)\equiv T_{K=1}^{P}\mu_{F_{k}^{i}}(x_{k})\), it means the rule's excitation intensity, and \(T\) means \(T\)-norm. After getting the crisp output, each node turns into compete CH state, sends Head_compete message including node's ID and its own output to all its neighbor nodes within the communication radius \"R\". Each node compares the output with its neighbors' output. The node with less output turns into the member node state, and waits for a suitable cluster to join in after CH election; the node with higher output turns into the elected CH state as well.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 3 Results
### Comparative Analysis: Precipitation vs Groundwater Storage (2003-2023)
#### 3.2.1 Northern Zone  
- _Precipitation_: Fluctuates between 740 and 800 mm/year.  
- _Interpretation_: Over 20 years, the estimated precipitation decreased by only 1.35 mm, which is practically stable. The near-zero R\({}^{2}\) value confirms that there is no significant representative trend; rainfall in the northern region is highly variable.  
\(\circ\) The trend line shows a slight increase in annual precipitation.  
\(\circ\) This suggests a gradual recovery of rainfall after several years with moderate to low values.  
- _GWS_: Continuous decline from \(\sim\)80% to \(\sim\)52%.  
- _Relation_: Despite relatively stable rainfall, GWS drops significantly, suggesting aquifer overexploitation driven by agro-export and urban expansion rather than reduced rainfall.