esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 2 Related Work
### Radar Signal Modulation-Recognition Methods Based on CNN  
Owing to the rapid development of CNN in recent decades, the recognition methods of radar signals based on CNN have been proposed [21; 22; 23]. In [24], a CNN-based radar signal modulation-recognition technique was proposed, which offered significant improvement over the recent radar-signal modulation recognition technology. In [25], a generic training strategy improved the prediction performance of radar-signal modulation recognition. In [26], a radar emitter signal classification algorithm based on CNN was presented, and the simulation results indicated superior recognition accuracy. In [27], a feature fusion algorithm for radar signal automatic modulation recognition using CNN was proposed, and the simulation results revealed superior accuracy. In [28], a cost-efficient CNN for radar-signal modulation recognition was proposed, and the results demonstrated excellent generalization ability. In [29], a CNN for low-complexity and robust modulation recognition was proposed, which achieved better recognition performance. In [30], a CNN-LSTM to address automatic modulation recognition was proposed, and experimental results demonstrated superior recognition performance. In [31], a multi-class learning framework based on CNN under the same SNR was proposed, and the results demonstrated superior performance over others. In [32], an efficient deep CNN with feature fusion at low SNR was presented, and recognition performance was up to 84.38% at \(-\)12 dB. Although the CNN-based network has demonstrated better robustness in the single-component radar signal, it cannot be applied completely to dual-component radar signals. Because of the existing difference in time-frequency images and features, the CNN-based network may demonstrate poor recognition performance for dual-component radar signals.
## 3 Proposed Methods
### Multi-Objective Normalization and Weighted Aggregation  
Since total cost, transport time, and carbon emissions differ in units and magnitudes, directly summing them may lead to biased optimization results. To ensure comparability and fairness in multi-objective scheduling, this study adopts a weighting approach. Each objective function is first normalized using a linear transformation to the range (0, 1). The multi-objective model is then converted into a single-objective formulation by assigning the weight coefficient \(\omega_{i}=1\) for one objective and \(\omega_{j}=0\) for others, minimizing total cost and transport time individually and determining the minimum carbon emissions by adjusting the corresponding weight. The maximum values of the remaining objectives are recorded as upper bounds for normalization. These reference values ensure consistent scaling of all objectives, preventing any single objective from dominating due to its larger magnitude. The objective function values are then transformed into dimensionless form using the max-min normalization method, as defined in Equation (21).  
\[\begin{cases}C^{\prime}=\frac{C_{i}-C_{i}^{min}}{C_{i}^{max}-C_{i}^{min}},\\ T^{\prime}=\frac{T_{i}-T_{i}^{min}}{T_{i}^{max}-T_{i}^{min}},\\ E^{\prime}=\frac{E_{i}-E_{i}^{min}}{E_{i}^{max}-E_{i}^{min}},\end{cases} \tag{21}\]  
where \(C_{i}\), \(T_{i}\), and \(E_{i}\) represent the original objective value. \(C_{i}^{min}\), \(T_{i}^{min}\), and \(E_{i}^{min}\), along with \(C_{i}^{max}\), \(T_{i}^{max}\), and \(E_{i}^{max}\), correspond to the minimum and maximum values obtained under single-objective optimization. The normalized dimensionless objective values, denoted as \(C^{\prime}\), \(T^{\prime}\), and \(E^{\prime}\), fall within the range of (0, 1), indicating the relative deviation of the current solution from the optimal one.  
A comprehensive evaluation of the objective function is performed by applying different weight combinations. The weighted objective function is constructed to calculate the overall evaluation value, as defined in Equation (22).  
\[Z^{\prime}=\omega_{1}C^{\prime}+\omega_{2}T^{\prime}+\omega_{3}E^{\prime}. \tag{22}\]  
Based on this, transport decisions are analyzed under various weighting schemes to assess the trade-offs between different objectives. By evaluating different weight combinations, the optimal scheduling solution is identified, considering factors such as cost efficiency, transport time, and carbon emissions. This analysis helps determine the most effective balance between these conflicting objectives, leading to an optimal low-carbon and cost-effective transport schedule.
## 3 Analysis of the Main Geomorphological Features of the Pit Rim Structure
### Extraction and Analysis of Valley Lines and Ridge Lines
#### 3.4.2 Extraction and Analysis of Ridge Lines  
The valley lines inside the pit rim structure were primarily distributed on the mountains in the northwest direction (Figure 10b), and the 18 main ridge lines (J1-J18) were evenly distributed outside (Figure 12a). The left side was mostly a single ridge line extending from the highest to the bottom (Figure 12b), whereas the ridge lines on the right were densely distributed and diverged from the end to form shorter ridge lines connected with the main ridge lines. In general, the elevation change in the upper and middle parts of the ridge lines was significant, and the elevation change trend in the end area tended to be flattened (Figure 12c).  
According to the statistics (Table 5), the mean length of the morphological parameters of the ridge lines reached 376.05 m, the mean maximum elevation difference was 116.77 m, and the mean dip angle was determined as 21.93\({}^{\circ}\). The length of the ridge lines exceeded 110 m, the trend was relatively gentle compared with the valley lines, and the vertical drop distance was higher. The longest ridge line length of J5 reached 820.24 m, the maximum elevation difference was determined as 131.93 m, the dip angle was obtained as 9.26\({}^{\circ}\), and the topography of the #4 ridge changed gently. The J3 ridge line was located on the #3 ridge, the maximum elevation difference was only 21.63 m, and the minimum dip angle was 6.51\({}^{\circ}\). The J9 and J12 ridge lines were located in the #7 ridge, with a dip angle greater than 40\({}^{\circ}\), and the #7 ridge was the steepest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Length (m)** & **Maximum Elevation Difference (m)** & **Dip (\({}^{\circ}\))** \\ \hline G1 & 123.68 & 62.23 & 30.21 \\ G2 & 68.09 & 46.20 & 42.73 \\ G3 & 289.11 & 127.68 & 26.21 \\ G4 & 166.50 & 72.32 & 25.74 \\ G5 & 233.60 & 115.88 & 29.74 \\ G6 & 206.02 & 107.02 & 31.30 \\ G7 & 168.87 & 67.74 & 23.65 \\ G8 & 141.88 & 68.54 & 28.89 \\ G9 & 107.00 & 42.84 & 23.60 \\ G10 & 213.52 & 87.91 & 24.31 \\ G11 & 62.75 & 18.60 & 17.24 \\ G12 & 140.29 & 86.09 & 37.85 \\ G13 & 97.28 & 59.90 & 38.01 \\ G14 & 254.97 & 107.97 & 25.05 \\ G15 & 72.00 & 52.49 & 46.81 \\ G16 & 302.87 & 134.61 & 26.39 \\ G17 & 154.60 & 95.23 & 38.02 \\ G18 & 172.33 & 96.72 & 34.14 \\ G19 & 89.01 & 49.58 & 33.85 \\ G20 & 208.90 & 48.09 & 13.31 \\ G21 & 398.41 & 187.54 & 28.08 \\ G22 & 404.75 & 191.77 & 28.28 \\ G23 & 581.88 & 219.17 & 22.13 \\ Mean & 202.53 & 93.31 & 29.37 \\ \hline \end{tabular}
\end{table}
Table 4: Measurement results of valley lines’ morphological parameters.
# Enhancement of PV Panel Power Production by Passive Cooling Using Heat Sinks with Perforated Fins
## 2 Related Research  
Experimental tests and numerical analyses on monocrystalline or polycrystalline photovoltaic panels show that the decrease in efficiency, as the operating temperature increases, and the necessity of cooling solutions are current concerns in the literature [11; 12; 13; 16; 30; 31].  
Different technologies to improve the efficiency of photovoltaic panels by passive cooling, using air, are studied in the literature [8; 13; 32; 33; 34; 35; 36; 37; 38; 39]. Detailed studies on air cooling of photovoltaic panels are carried out in the following works [11; 40]. Air cooling with heat sinks and perforated fins is studied both numerically [11; 32] and experimentally [34]. The characteristic values for multiple photovoltaic panels cooled with aluminum and copper heat sinks were compared through experimental tests under different conditions [34]. The results of the simulation and from the experiment showed that the heat sink with perforated fins can significantly reduce the temperature of the panel and increase its performance; there was an increase in the heat extracted from the photovoltaic cell and a decrease in its temperature, with values between 7 \({}^{\circ}\)C and 10 \({}^{\circ}\)C [34].  
Also, numerical simulations on a particular case of backside ventilation [11; 16; 41] revealed that the temperature was reduced by 10 \({}^{\circ}\)C below the value obtained in the base case. This aspect was favorable for conversion efficiency, determining a generation of approximately 90% of the maximum power produced in STCs [14], while the average power production of PV panels was about 70-80% of \(P_{mp}\) in STCs [42].
## 5 Results
### Interpretability Analysis
#### 5.4.1 Clinical Decision Insight from MIMIC-IV  
Our evaluation on the MIMIC-IV thrombosis task aimed to reveal how the framework's disentangled representations can illuminate the basis of clinical predictions.  
We began by visualizing the latent spaces via t-SNE, which provides a window into the model's internal reasoning. As presented in Figure 6, each point represents a patient sample, colored by its ground truth label ('Thrombus' or 'No Thrombus'). While considerable overlap is expected given the complexity of clinical data, the visualization nonetheless reveals a critical insight. In the Event latent space, specifically designed to capture acute occurrences, 'Thrombus' cases form several dense clusters, distinct from the general 'No Thrombus' population.  
Figure 5: Sensitivity analysis of the KL divergence weights (\(\beta\)) on model performance.  
This non-random clustering is the cornerstone of the model's interpretability. It demonstrates that our model has autonomously learned a clinically relevant rule; specific patterns of acute events are key discriminators for thrombosis. In other words, the model does not merely provide a prediction, but also reveals that its judgment heavily relies on whether a patient exhibits certain \"high-risk\" event signatures. This data-driven reasoning aligns closely with clinical intuition--where physicians pay close attention to acute changes in a patient's condition--thereby enhancing the trustworthiness of the model's predictions.  
Then, to quantify the contribution of each disentangled component, we analyzed the feature importance derived from the downstream classifier. The results, shown in Figure 7, confirm our qualitative findings. Features from the Event and Trend spaces dominate the importance ranking, possessing the most significant predictive power. The prominence of features like Event_3 and Trend_15 indicates that the model's risk assessment is driven by a combination of acute, transient signals and sustained physiological trajectories. This synergy between event detection and trend analysis mirrors established clinical practice, making the model's decision-making process transparent and verifiable.  
Figure 6: Class-level latent space visualization. t-SNE visualization of the disentangled latent spaces for thrombosis prediction. Figure 6 presents t-SNE projections of the three disentangled latent spaces: Static, Trend, and Event. Each subplot represents a latent space, where blue points indicate ’No Thrombus’ cases and red points indicate ’Thrombus’ cases, visualizing the distribution of different class samples within their respective latent spaces.  
Figure 7: Feature importance analysis. Figure 7 shows the top-ranked features, where the names (e.g., Event_3) correspond to indices of specific event or trend features extracted during our preprocessing. Feature importance ranking for the thrombosis prediction task. The analysis shows that Event and Trend type features are the most influential, suggesting the model prioritizes acute events and dynamic physiological trends when assessing risk.
# Mobility, Citizens, Innovation and Technology in Digital and Smart Cities
## 3 Cities Transportation Trends
### Smart Parking
However, even considering the power of actual algorithms, no new organizational and CI system can be validated and implemented using only optimization techniques because many aspects are not considered, or, if included, they would lead to higher technical difficulties. For instance, we can highlight the following points to be considered: uncertainty on the congestion phenomenon; uncertainty on the customer plans (list of activities to perform); uncertainties on traveling times; and the effect of multiple similar demands on the traffic congestion. Consequently, studies on multi-agent simulation (as we introduced in Section 3.4) are now a standard technique [95] used to understand what may happen if a new organization intervenes in a city life (including traffic, and environmental impacts). Taking into account that an urban area is a complex system, counter-intuitive consequences may occur which cannot be viewed simply by solving some optimization problems. Reference [94] also dealt with a parking selection problem in an SC scenario, their work uses a multi-agent flexible negotiation mechanism to address the parking space by taking into account the car owner preferences in locations, parking vendors' preferences regarding car park occupancy and social city benefits. The insertion of renewables is also in connection with electric powered transportation systems, in which new challenges arise. On the other hand, electric based vehicles require a different moving paradigm, in which they should be smartly charged. In this sense, power dispatching systems [96] are incorporating Vehicle-to-Grid (V2G) in their daily schedules [97], creating a new wave of parking systems with electrical capabilities. There have been efforts of studies that tried to forecast and determine the optimal time for charging these vehicles [89; 98]. In addition, multi-objective models are also being developed for pondering the different cost-emissions that V2G technologies may be able to reduce and balance [99]. The reduction of waiting times in the queues of fast charging stations has also been proposed using MAS, serving as the control strategy of the network [100; 101].
# NDN Content Store and Caching Policies: Performance Evaluation
## 4 Related Work
Management mechanism through cache replacement (M2 CRP), a caching replacement policy based on multiple metrics (i.e., the content popularity; the freshness; and the distance between the location of two specific nodes) was proposed in [11]. When a replacement was required, the proposal used the aforementioned factors to calculate the candidacy score for each content in CS. The average weight of each of these parameters was computed, and the content with the lower weight was the candidate for replacement. The proposal was evaluated and compared with LRU and FIFO, in terms of CHR, interest satisfaction delay and interest satisfaction ratio. The authors concluded that the proposed solution presents better performance than others in terms of the chosen metrics. The immature used (IMU) replacement policy was proposed in [15]. Besides the popularity, the proposal resorts to the content maturity index, which is calculated by using the content arrival time and its frequency within a specific time frame. When CS is full, the least immature content is evicted. The performance evaluation based on CHR, path stretch, latency, and link load is performed and compared with FIFO, LRU, LFU and a combination of LRU and LFU (LFRU) [58]. An adaptive least recently frequently used (LRFU) [59] replacement policy was proposed by [14]. The proposed solution calculates the content distribution by its recency and frequency, and is capable of adjusting the heap list and linked list in the router CS based on the CS size. The proposed solution is evaluated in terms of network latency, delay distribution, CHR, and miss rate, and compared with LFU, LRU and LRFU. The authors concluded that the solution presents better performance than the others in terms of the chosen metrics. In order to reduce the cost of estimating content popularity, [16] categorized data for caching (CaDaCa) was proposed, which is based on data categorization (e.g., politics, sports, and science), to make the content names more informative. The proposal includes a caching decision strategy and a cache replacement policy. The proposal was evaluated (based on CHR, hop reduction ration, and server load ratio metrics) and the cache replacement policy was compared with LRU and FIFO. Table 1 shows and summarizes related work in terms of their specific configuration for simulation. In summary, the main differences, and thus the main contributions of the present work compared to the existing similar studies are as follows. The work by [5] only evaluated the performance of the LFU strategy, with the consumer request rate of 5 packets/s. The study did not consider the re-transmission and the hop-count metrics. As shown in the next
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
## 5 Conclusions  
This work analyzes the fault characteristics of distribution networks with PV and BESS. A protection principle for symmetrical and asymmetrical phase-to-phase faults is proposed, and the performance of the proposed principle is verified using simulation tests. The proposed protection principle operates correctly in selected typical fault scenarios. The main conclusions are as follows:  
1. During asymmetric faults, the fault direction can be detected by utilizing the dynamic characteristics of the PV and BESS negative sequence impedance through monitoring negative sequence impedance angle at the protection relay location.  
Figure 13: Forward fault: Fault at the midpoint of line AC (**a**) Positive sequence voltage at PR4. (**b**) Reactive current measured by PR4. (**c**) Amplitude of active current measured by PR4.  
2. During symmetric faults, the fault direction can be identified by utilizing the characteristics of both PV and BESS/synchronous sources injecting reactive current and PV and BESS active current output being significantly lower than grid-side active current. For the distribution network integrated with PV and BESS, this work proposes a fault detection principle applicable to symmetric faults and asymmetric phase-to-phase faults. However, other types of faults, such as single-phase short-circuit faults and two-phase-to-ground short-circuit faults, may also occur in distribution networks. In future work, protection principles suitable for single-phase short-circuit faults and two-phase-to-ground short-circuit faults in PV and BESS integrated distribution networks will be further investigated.  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author. This work is supported by the project funded by the Ningxia Natural Science Foundation (2023 AAC03838). Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company State Grid Ningxia Electric Power Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Related Works
### Biometrics Using EMG Signal  
[PERSON] [10] studied biometrics using EMG signals of the hand muscles. Individuals were classified using euclidean distance (ED), KNN, and support vector machine (SVM) after feature extraction from EMG signals using PCA, unsupervised discriminant projection (UDP), and class dependent feature analysis (CFA). [PERSON] [11] studied biometrics using two-channel EMG signals for three hand gestures. Noise was removed from the EMG signal by filtering with a 0.5 Hz high pass filter (HPF), a 200 Hz low pass filter (LPF), and a 60 Hz notch filter (NF). Subsequently, the time-frequency features were extracted to classify individuals through artificial neural networks (ANNs), SVMs, and KNNs. [PERSON] [12] studied biometrics using EMG signals for open hand gestures in twenty-one people. EMG signals were converted into CWT for each channel to classify individuals through CNN. [PERSON] [13] studied gesture recognition and biometrics using three gestures. Noise was removed from the signal using a 20 Hz HPF and a 60 Hz NF; feature extraction and classification were performed using CNN. [PERSON] [14] studied biometrics using muscles other than that of the hand. EMG signals of a subject while walking were used, and noise was removed with a 20-450 Hz BPF and a 60 Hz NF. Time-frequency features were extracted from EMG signals and individuals were classified using linear discriminant analysis (LDA). [PERSON] [15] studied biometrics using EMG signals in the mouth muscles. EMG signals were collected while a subject pronounced five Japanese vowels, and noise was removed with a 5-500 Hz BPF and a 60 Hz NF; feature extraction and classification were performed using CNN. [PERSON] [16] studied biometrics using EMG data from open hand gesture. After data conversion using discrete wavelet transform (DWT) and CWT, twenty-one people were classified as CNN-based siamese networks. [PERSON] [17] conducted biometrics studies using EMG signals when unlocking patterns. Noise in EMG signals was eliminated with a 5 Hz HPF and a 60 Hz NF, utilizing eleven time domain features and a one-class support vector machine (OCSVM) and local outlier factor (LOF). [PERSON] [18] studied biometrics using speech EMG. _[PERSON]_ used empirical mode decomposition (EMD) to eliminate noise in signals and set areas of interest and utilize time-frequency domain features.  
In most related works conducted on single biometrics, biometrics was performed using an EMG signal of only one gesture, indicating that the behavior was limited (except [11; 13; 15]). In addition, [PERSON] [11], [PERSON] [13], and [PERSON] [15] conducted biometrics experiments using up to eight people as subjects. Therefore, there is a need for biometrics experiments that classify more subjects.