esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis of Results
### Field Validation  
Experiments are carried out in different areas for rice, corn, wheat, and other crops. A user interface was developed to visualize the results for better application. Images of crops such as cucumber and maize were recognized using the designed image recognition method. One pair of cucumber leaf spot disease images was selected for detection and the results showed that the probability of the image being leaf spot disease was 99.93%, as shown in Figure (a)a. Then, one pair of cucumber leaf blight images is selected for detection and the result shows that the probability of it being leaf blight is 99.97%, as shown in Figure (b)b.  
Figure 10: Visualinterface detection results.  
In order to test the recognition accuracy and generalization ability of the proposed image recognition method, 50 pairs of images each for healthy leaves, leaves with leaf spot disease, and leaves with leaf blight disease were collected. To provide more standardized and interpretable evaluation, we calculated classical metrics such as accuracy and F1-score instead of the non-standard \"checking\" and \"re-checking\" rates.  
This indicates that the proposed algorithm demonstrates superior performance in crop pest image recognition, achieving an average classification accuracy of 94.93% and F1-score of 96.47% across healthy, leaf spot disease, and leaf blight disease categories. These metrics represent a 5.7% increase in accuracy and 6.2% improvement in F1-score compared to baseline convolutional neural network models, as shown in Table 4, validating its robustness and reliability for real-world agricultural applications. The algorithm's performance advantage is particularly pronounced in complex field scenarios, where it maintains \(\geq\)92% accuracy under varying illumination and background conditions, outperforming state-of-the-art classification models by 3.5-7.2% in critical disease detection tasks.  
In summary, the improvement strategy based on the introduction of the CBAM attention mechanism module and the adoption of the depth-separable convolutional module effectively enhances the performance of the U-Net network so that it demonstrates stronger adaptability and higher accuracy in the crop pest recognition task and provides solid technical support for subsequent practical applications.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
###### Abstract  
In the wireless sensor network, the lifetime of the network can be prolonged by improving the efficiency of limited energy. Existing works achieve better energy utilization, either through node scheduling or routing optimization. In this paper, an efficient solution combining node scheduling with routing protocol optimization is proposed in order to improve the network lifetime. Firstly, to avoid the redundant coverage, a node scheduling scheme that is based on a genetic algorithm is proposed to find the minimum number of sensor nodes to monitor all target points. Subsequently, the algorithm prolongs the lifetime of the network through choosing redundant sleep nodes to replace the dead node. Based on the obtained minimum coverage set, a new routing protocol, named Improved-Distributed Energy-Efficient Clustering (I-DEEC), is proposed. When considering the energy and the distance of the sensor node to the sink, a new policy choosing the cluster head is proposed. To make the energy load more balanced, uneven clusters are constructed. Meanwhile, the data communication way of sensor nodes around the sink is also optimized. The simulation results show that the proposed sensor node scheduling algorithm can reduce the number of redundant sensor nodes, while the I-DEEC routing protocol can improve the energy efficiency of data transmission. The lifetime of the network is greatly extended.  
Keywords:wireless sensor networks; energy efficiency; sensor node scheduling; routing protocol +
Footnote †: journal: Sensors
# High-Performance Speed Control of PMSM Using Fuzzy Sliding Mode with Load Torque Observer
## 2 Mathematical Model of Permanent Magnet Synchronous Motor  
Assuming that the embedded PMSM is an ideal motor and the anti-potential is a sine wave, regardless of eddy currents and hysteresis losses, regardless of the parameter disturbance, the magnetic field is unsaturated [20; 21], etc. The voltage Equation [9] of the permanent magnet synchronous motor under the \(d-q\) rotating coordinate is obtained as  
\[\left\{\begin{array}{l}u_{d}(t)=R_{s}i_{d}(t)+L_{d}\frac{d_{d}(t)}{d(t)}-L_{ d}\omega_{r}i_{q}(t)\\ u_{q}(t)=R_{s}i_{q}(t)+L_{q}\frac{d_{d}i_{q}}{dt}+L_{d}\omega_{r}i_{d}(t)+\psi_{ f}\omega_{r}\end{array}\right. \tag{1}\]  
The electromagnetic torque Equation [21] is  
\[T_{e}=\frac{3}{2}p_{n}[\psi_{f}i_{q}+(L_{d}-L_{q})i_{d}i_{q}]=\frac{3}{2}p_{n} \psi_{f}i_{q} \tag{2}\]  
The equation of motion is  
\[T_{e}=T_{l}+J\frac{d\omega_{m}}{dt} \tag{3}\]  
where \(R_{s}\) is the stator phase resistance, \(u_{d}\) and \(u_{q}\) are \(d-q\) axis voltages, \(i_{d}\) and \(i_{q}\) are \(d-q\) axis currents, and \(L_{d}\) and \(L_{q}\) are \(d-q\) axis inductors, respectively. \(\psi_{f}\) is the permanent magnet flux; \(\omega_{r}\) is the rotor electrical angular velocity; \(P_{n}\) is the polar logarithms, and its meaning refers to the quantitative relationship between the stator coils and the rotor magnetic poles in an electric motor; \(T_{l}\) is the load torque; \(\omega_{m}\) is the mechanical angle for the rotor, and \(\omega_{m}=\omega_{r}/P_{n}\).
## 3 Proposed Approach
### Explainability-Guided Vote (EGV) Approach
#### 3.2.4 Complexity Analysis  
Considering the time complexity of the algorithm, the preprocessing step of the proposed algorithm involves calculating integrated gradients (IGs), which has a time complexity of \(O(m\cdot d\cdot n_{f})\), where \(m\) is the number of interpolation steps, \(n\) represents the input dimension, and \(n_{f}\) accounts for the gradient computation cost in the model. Following this, the iterative substitution process operates with a complexity of \(O(k\cdot n\cdot p\cdot m_{s})\), where \(k\) is the number of votes, \(n\) is the length of the input text, \(p\) is the substitution rate, and \(m_{s}\) denotes the average number of synonyms. Combining these steps, the overall complexity of the algorithm is \(O(m\cdot n\cdot n_{f}+k\cdot n\cdot p\cdot m_{s})\). The IG computation typically dominates the complexity due to gradient calculations, especially for deep neural networks, while the substitution step scales linearly with text size, substitution rate, and voting iterations, ensuring computational feasibility for practical applications.
# Effects of Manure Waste Biochars in Mining Soils
## 2 Materials and Methods
### Material Characterization  
#### 2.3.1 Raw Materials and Biochars  
Manure wastes and biochars were characterized as follows: pH and electrical conductivity (EC) were determined in a sample-water ratio of 0.1:25 [28, 29] using a Crison micro-pH 2000 and a Crison 222 conductivity meter, respectively. The effective cation exchange capacity (CEC) was determined using the standardized method described by ISO 23470 [30], based on centrifuge extraction with 0.0166 M cobalt(III) hexamine chloride solution. Available phosphorous (P-Olsen) was determined by the [PERSON] method [30], while oxidizable organic carbon (C-oxi) was determined by the Walkley-Black method [31].  
Figure 1: Map of Spain with the three selected mining areas.  
The exchangeable potassium (K) was measured in the solution using a Perkin Elmer AAnalyst 400 Atomic Absorption Spectrophotometer. Soluble organic matter (SOC) was extracted by addition of 10 mL of water to 1 g (agitation 1 h) and determined using Nelson and Sommers methodology [32]. Elemental analysis was performed using an Elemental Analyzer Model Perkin-Elmer 2400 CHN. The oxygen percentage was determined by difference, according to the following equation:  
\[O=100-(Ash+C+H+N+S)(\%), \tag{1}\]  
where Ash is the ash content (%), O is the total oxygen content (%), C is the total carbon content (%), H is the total hydrogen content (%), N is the total nitrogen content (%), and S is the total sulfur content (%). Following that, O/C and H/C ratios were calculated from the elemental analysis results. Pseudo-total heavy metal concentration was determined using a Perkin Elmer 2280 atomic absorption spectrophotometer after sample digestion with 3:1 (_v/v_) concentrated HCl/HNO\({}_{3}\) following the USEPA 3051a method [33].  
Biochar porosity and volume of mesopore (Vmeso) and macropore (Vmacro) were determined by Hg porosimetry, which was carried out using a Micromeritics AutoPore IV 9500 equipment. BET surface area, adsorption average pore, micropore volume (Vmicro), and micropore area (Amicro) of biochar were analyzed by nitrogen adsorption isotherm, which was carried out at 77 K in a Micromeritics Tristar 3.00.
## 2 Materials and Methods
### Methodology of the Tests  
Tekken joints were tested by non-destructive tests (NDT) and destructive tests (DT). The first step was NDT: visual testing (VT) in accordance with the EN ISO 17637:2017 [47] standard and penetrant testing (PT) in accordance with the EN ISO 3452-1:2013 standard [48]. After NDT specimens were cut for DT at \(\frac{1}{4}\), \(\frac{1}{2}\), and \(\frac{3}{4}\) of joint length of each specimens, and from each specimen, two testing specimens were chosen. The prepared cross-sections were grinded, polished, and etched by Nital 4%. Then, the metallographic macro-and microscopic testing was undertaken in accordance with the EN ISO 17639:2013 standard [49]. For micro observations optical microscope was used. Finally, Vickers HV10 measurements were undertaken in accordance with the EN ISO 9015-1:2011 standard [50]. The first stitch (Tekken test weld) joined two pieces of used steel. The additional stitches, which were laid on the surface of previous stitches provided the heat, which caused local heat treatment of lower areas of the joint. The measurement points were located near the first welded stitch to show the influence of the local in situ heat treatment on the microstructure of HAZ and hardness in this area. The hardness was measured in the weld and HAZ in both sides from the axes of joint.
## 3 Results and Discussion
### Photocatalytic Cleaning Process  
Based on the above results, we propose a potential mechanism for cleaning the Cu surface layer after CMP using photocatalysis, as illustrated in Figure 7. During Cu CMP, citric acid, commonly used as a complexing agent, reacts with the Cu surface oxide layer to form some residual complexes that remain on the Cu substrate. These residual citric acid complexes are difficult to remove due to chemical adsorption [36,37]. When the polished Cu substrate is placed into the polishing solution under UV illumination, cerium oxide, due to its suitable band gap, is driven by UV photons, resulting in the excitation of electrons to the conduction band (CB) while holes remain in the valence band (VB), as indicated in Equation (10). The h\({}^{+}\) accumulated in the VB of CeO\({}_{2}\) can oxidize citric acid contaminants [38]. Additionally, reactive oxygen species (ROS) generated through photo-induced CB electrons or VB holes (as shown in Equations (11)-(15)) also contribute to the degradation of the contaminants [39].  
\[\centering \tag{10}\]  
\[\centering \tag{11}\]  
\[\centering \tag{12}\]  
\[\centering \tag{13}\]  
\[\centering \tag{14}\]  
\[\centering \tag{15}\]  
Simultaneously, a Fenton-like reaction between cerium oxide and hydrogen peroxide generates a substantial amount of hydroxyl radicals, significantly enhancing the oxidizing ability of the photocatalyst. Under the synergistic catalysis of cerium oxide and hydroxyl radicals, citric acid is effectively degraded.  
By adding the Cu substrate to the polishing solution and measuring the Cu(II) concentration in the solution under continuous UV illumination using ICP, it was observed that over time, the concentration of Cu(II) in the solution gradually increased due to citric acid corrosion. However, the rate of increase in Cu(II) every ten minutes decreased, which indirectly indicates that the degradation of a significant amount of citric acid by the photocatalyst led to a reduction in the corrosion reaction.
## 4 Performance Evaluation
### Simulation Experiment
Signal mismatch refers to the case that the actual signal \(\textbf{s}_{0}\) is not entirely located within the nominal signal subspace spanned by the columns of \(\mathbf{H}\). A quantity to measure the amount of signal mismatch is given in (41). Note that the case \(\cos^{2}\!\phi=1\) indicates that the actual signal completely lies in the signal subspace assumed by the radar system. In contrast, \(\cos^{2}\!\phi=0\) means that the actual signal is orthogonal to the signal subspace assumed by the radar system. We use Figure 3 to measure the robustness or selectivity of the proposed detectors to mismatched signals. The robustness of a detector refers to the ability of the detector to detect targets with a high probability when signal mismatch occurs. The selectivity of a detector refers to the ability to decrease the PD of a detector when signal mismatch occurs. Figure 3 indicates that AE-Rao and AE-GLRT have better selectivity, while AE-Durbin is the most robust. Figure 2: Probability of detection (PD) versus signal-to-clutter ratio (SCR) without signal mismatch with simulated data. Figure 4 demonstrates the PDs at different SNRs with \(v\) as a parameter. It is evident that as the shape parameter \(v\) decreases, indicating stronger non-Gaussian characteristics of the clutter, there is an enhancement in the PDs of all detectors. Moreover, as indicated in Figure 3(a),b, the PD of AE-Rao remains the highest.
## 5 Conclusions  
Aiming at comprehensive evaluation of AC/DC hybrid microgrid planning, a new evaluation index system of AC/DC hybrid microgrid is constructed in the paper. The new evaluation index system of AC/DC hybrid microgrid planning is comprehensive, scientific, and accurate, which provides a new way to study the evaluation index of AC/DC hybrid microgrid planning.  
Aiming at the limitations of traditional evaluation methods, a comprehensive evaluation method of AC/DC hybrid microgrid planning based on analytic hierarchy process (AHP) and entropy weight method is proposed. The method is divided into three parts: the establishment of the evaluation index system, the determination of comprehensive weight, and the comprehensive evaluation of the scheme. The comprehensive evaluation method presented in this paper provides a new idea for the comprehensive evaluation of AC/DC hybrid microgrid planning in the future.  
From summarizing the theoretical analysis, the model establishment, and the verification of simulation examples in this paper, the following conclusions can be drawn:  
1. In the example of this paper, scheme 4 is the optimal scheme, which can provide a research idea for the future planning of AC/DC hybrid microgrids.
2. Compared with traditional methods, the comprehensive evaluation method proposed in this paper has the advantages of objective evaluation and subjective evaluation, which can reduce the errors caused by the subjective evaluation method being more subjective and the objective method being too dependent on data. The evaluation method in this paper has certain practical application value.
3. The results of examples in this paper show that the average service availability index, initial investment cost, and voltage offset are the three most important indicators in the comprehensive evaluation index system. The reason is that their comprehensive weight is relatively large. Therefore, people should pay more attention to these three indicators in the design of AC/DC hybrid microgrids.  
Conceptualization, G.Z.; methodology, G.Z., D.W.; software, D.W.; validation, G.Z., D.W.; formal analysis, D.W.; investigation and resources, D.W.; data curation, D.W.; writing--original draft preparation, G.Z., D.W.; writing--review and editing, D.W.; supervision, G.Z., D.W.; project administration, G.Z.  
This research was funded by the Fundamental Research Funds for the Central Universities (2018 MS008).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Explainable Machine Learning for APS Detection
#### 2.3.3 SHAP Interpretation Technique  
The use of multiple physiological signals to detect APS can improve accuracy, although the model must be validated in other ways in order to determine whether the model has acquired physiologically correct relations among the biosignals and the APS classification. SHAP is a unified model-agnostic technique that can interpret the output of various machine learning methods, including support vector machines, neural networks, decision trees, and ensembles of decision trees such as random forests or gradient boosting machines [20]. The SHAP technique is based on Shapley values, which assign a marginal contribution of each feature value across all possible combinations of features. SHAP computes the Shapley values for individual features to explain the model's predictions. The mathematical representation of calculating the Shapley value for an input variable in a prediction is provided by [30]  
\[\phi_{q}(m)=\sum_{S\subseteq F\backslash\{q\}}\frac{|S|!(|F|-|S|-1)!}{|F|!}[m( S\cup\{q\})-m(S)], \tag{2}\]  
where \(\phi_{q}(m)\) is the Shapley value of input variable \(q\) in prediction \(m\), \(S\) is a coalition of the input variables without input variable \(q\), \(F\) is the set of all input variables, \(m(S)\) is the prediction made using the coalition \(S\) of the input variables without input variable \(q\), and \(m(S\cup\{q\})\) is the prediction made using the coalition \(S\) including input variable \(q\).  
We applied the SHAP technique to interpret the XGBoost model predictions and calculate the contributions of each physiological signal in APS detection using the SHAP Python library (version 0.44.1) [20]. SHAP values were computed for each sample to deduce the contributions of each physiological signal to the model prediction, and a beeswarm SHAP summary plot was generated to visualize the SHAP values for each physiological signal sorted by their importance.
## 5 Performance Analysis
Figure 17 shows the simulation results in the case of two-band transmission. In Figure 17a,b, we cannot observe an exact tendency unlike in the case with various number of STAs that we have analyzed. However, it is obvious that there are some sweet spot ranges for a given number of STAs, as in Figure 17a. Therefore, if the OCW value makes the delay converged without packet loss for a given number of STAs, obtaining a specific optimal value of the OCW is not a critical issue because the sweet spot range that meets QoS requirements is wide enough. Thus, it can be interpreted as the controlling OCW not requiring extremely sensitive and immediately adaptive algorithms. Based on sensing results of sensor nodes and incoming data packets, a V2X infrastructure STA can control the OCW value with a relatively long-term decision-making algorithm. Figure 16: Average delay and PLR comparison between legacy and proposed NG-V2X for various channel cases (ideal channel case, case 1 and case 2). Figure 17: Average delay and PLR in two bands transmission over UORA congestion window (CW) value: (**a**) Below user capacity threshold (190 STAs); (**b**) Below user capacity threshold (200 STAs).
## 3 Combinatorial Fusion Approach (CFA)
### Feature Selection Using Performance and Cognitive Diversity
#### 3.1.2 Feature Selection Approach  
The approach for feature selection based on the performance and the diversity between the two features (Deng, Wu, Chu, Zhang, and Hsu [46]) is illustrated in Figure 8.  
Figure 7: Single feature performance of J48 Classifier.  
Figure 9 illustrates the modalities, performance, and diversity of the features. All 32 features are divided into two modalities according to the corresponding sensor types which are EMG and ECG. The feature performance in each of the EMG and ECG physiological signals is sorted in decreasing order. 15 features with a bold font demonstrated in the figure is selected for further process and is denoted as the top features. While a number above the horizontal link between two modalities is denoted as the diversity between the two feature sets. Besides, a number above the curve within a node of the modality is the average of a feature set diversity. Generally, a better fusion performance is produced when the diversity between two modalities is larger [46]. Apart from that, better individual feature performance also provides a better combination result [46]. However, only two modalities were utilized in this work but the comparison of the diversity of the two modalities is not required. In this work, the diversity between EMG and HRV generates a better diversity of a feature set and the diversity between two feature sets. Hence, a better combination performance can be achieved.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Value**} & \multicolumn{6}{c}{**The Number of Marine Accidents by Watch Duty Time**} & \multirow{2}{*}{**MAPE**} \\ \cline{3-3} \cline{5-8}  & & **00–04** & & & **0–08** & & **12–16** & **16–20** & **20–24** \\ \hline - & Actual & 216 & 586 & 826 & 773 & 499 & 256 & - \\ LSTM1 & Predicted & 216 & 534 & 809 & 814 & 512 & 212 & 0.066 \\ LSTM2 & Predicted & 224 & 513 & 799 & 832 & 523 & 229 & 0.076 \\ LSTM3 & Predicted & 217 & 573 & 795 & 828 & 536 & 226 & 0.059 \\ LSTM4 & Predicted & 247 & 601 & 842 & 768 & 519 & 228 & 0.065 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of prediction results of four different LSTM models with the ARIMA model. LSTM3 has the smallest MAPE (0.059). Except for LSTM2, all other LSTM models outperformed the ARIMA model. to the study of complex accident prediction models in the future. Lastly, predicting accident frequency does not help prevent it directly. However, by predicting the number of accidents by watch duty time, the pattern of accidents can be learned, and, in fact, most of the accidents occurred during the daytime. This might be because most fishing boats returned in the morning from night fishing and most of the cruise ships and passenger ships that come and go between land and islands were operated during the daytime. Furthermore, many accidents occurred during the watch time of junior officers. On the basic premise that the frequency of marine accidents changes according to the navigator's experience, it is possible to establish customized accident prevention measures by time by identifying whether the actual marine accidents are concentrated during the junior officer's duty time. Thus, it is also necessary to reflect the trend of accidents according to these duty hours in the marine accident reduction policy. Using the results, we will be able to help prevent accidents in advance if we make VTS or coast guard officers pay more attention during the time when accidents occur the most. Further study is needed to address these limitations in the future.