esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Self-Consumption: Spanish Legislative Framework
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{8}{c}{**Annual Variable Tax (Euro/kWh)**} \\ \cline{2-5}
**Access Cost** & \multicolumn{2}{c}{**Period 1**} & **Period 2** & **Period 3** & **Period 4** & **Period 5** & **Period 6** \\  & P1 & P2 & P3 & P4 & P5 & P6 \\ \hline
2.0 A (P \(\leq\) 10 kW) & 0.043187 & & & & & \\
2.0 DHA (P \(\leq\) 10 kW) & 0.057144 & 0.006148 & & & \\
2.0 DHS (P \(\leq\) 10 kW) & 0.057938 & 0.006430 & 0.006112 & & \\
2.1 A (10 \(\leq\) P \(\leq\) 15 kW) & 0.054883 & & & & \\
2.1 DHA (10 \(\leq\) P \(\leq\) 15 kW) & 0.068081 & 0.015450 & & & \\
2.1 DHS (10 \(\leq\) P \(\leq\) 15 kW) & 0.068875 & 0.018220 & 0.011370 & & \\
3.0 A (P \(>\) 15 kW) & 0.020568 & 0.013696 & 0.008951 & & \\
3.1 A (1 kV to 36 kV) & 0.015301 & 0.009998 & 0.012035 & & \\
6.1 A (1 kV to 30 kV) & 0.011775 & 0.011336 & 0.007602 & 0.009164 & 0.009986 & 0.006720 \\
6.1 B (30 kV to 36 kV) & 0.011775 & 0.008312 & 0.007322 & 0.008260 & 0.009403 & 0.006349 \\
6.2 (36 kV to 72.5 kV) & 0.012669 & 0.011554 & 0.007881 & 0.008377 & 0.008716 & 0.006245 \\
6.3 (72.5 kV to 145 kV) & 0.015106 & 0.012816 & 0.008530 & 0.008510 & 0.008673 & 0.006278 \\
6.4 (\(\geq\)145 kV) & 0.011775 & 0.008531 & 0.007322 & 0.007788 & 0.008257 & 0.006104 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spanish variable fees and taxes applied to the sale of surplus energy through self-consumption: requirements according to RD900/2015 [31]. type of facility are the following: P5 in May, P3 and P4 in June, P1 in the rest of June and July, and P6 in August. Irrigation is not usual in September for the considered crops, but it would be framed in periods P3 and P4. To clarify the Spanish electricity market, in terms of selling the excess energy to the grid at a price determined by such electricity market, Figure 9 shows an example for an 870 kWp PV installation, 1000 ha aggregated crop area, 40 m aquifer depth and 1500 m\({}^{3}\)/ha crop water need. Energy demanded by the crop, surplus of energy and estimated benefits--excluding and including Spanish taxes--are determined by the different months. Time periods to be applied according to the Spanish electricity market, see Figure 8, are also included.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Type of System** & **Characteristics of System** \\ \hline \hline \multirow{7}{*}{Sand component and quantity of deposits [12]} & & Formed as a result of sandstone deposition in non-channel or outer lobe areas. Produces sandstone bodies and lobes. \\  & Type 1: Sand dominant & Formed as a result of the platform or \\  & Type 2: Sand dominant and lack of mud & littoral zone contribution. Produces \\  & Type 3: Mud dominant & channel–lobes complex and \\  & & fan–supra lobes. \\  & & Formed from overflows of mud sediment \\  & & from platforms and slopes. Produces a \\  & & channel–levee complex. \\ \hline \multirow{7}{*}{Flow efficiency [13]} & & Rich in sand contributed by the shelf. \\  & & Forming lobes that are dominated by \\  & Type 1: Low efficiency & A mixture of sand and silt contributed by \\  & Type 2: Medium efficiency & fluvial and deltas. Forms aggradation \\  & Type 3: High efficiency & features with mixed lithology. \\  & & Rich in fluvial and delta mud. Forming a \\  & & dominant degradation of mud and sand \\  & & in the distal area. \\ \hline \multirow{7}{*}{Fed-system characteristics and sediment supply [14]} & & Sediment from toe-slope erosion. Does \\  & & not have a shelf or a very narrow shelf. \\  & & Sediments resulting from river transport, \\  & Type 1: Hinterland-fed system & coastal currents, and tidal currents. \\  & Type 2: Shelf-fed system & Dominant with sand. Experience the \\  & Type 3: Delta-fed system & original transport by waves, tides, and \\  & & ocean floor currents. \\  & & Sediment is contributed by rivers. Rich \\  & & in mud grains. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of characteristics and depositional system [12; 13; 14].
## 7 Numerical Examples
### Problem 2 (Mixture Model)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(x_{i}\) & **Ralston’s Scheme** & **RK3 Scheme** & **ARK3 Scheme** & **Heun’s Scheme** & **VRK3 Scheme** \\ \hline zero & zero & zero & zero & zero & zero \\
0.1 & \(3.7246\times 10^{-9}\) & \(4.1133\times 10^{-9}\) & \(3.8694\times 10^{-9}\) & \(3.3207\times 10^{-9}\) & \(3.1607\times 10^{-9}\) \\
0.2 & \(7.4366\times 10^{-9}\) & \(8.2127\times 10^{-9}\) & \(7.7257\times 10^{-9}\) & \(6.6303\times 10^{-9}\) & \(6.3109\times 10^{-9}\) \\
0.3 & \(1.1136\times 10^{-8}\) & \(1.2298\times 10^{-8}\) & \(1.1568\times 10^{-8}\) & \(9.9282\times 10^{-9}\) & \(9.4499\times 10^{-9}\) \\
0.4 & \(1.4821\times 10^{-8}\) & \(1.6367\times 10^{-8}\) & \(1.5397\times 10^{-8}\) & \(1.3214\times 10^{-8}\) & \(1.2577\times 10^{-8}\) \\
0.5 & \(1.8494\times 10^{-8}\) & \(2.0424\times 10^{-8}\) & \(1.9213\times 10^{-8}\) & \(1.6488\times 10^{-8}\) & \(1.5694\times 10^{-8}\) \\
0.6 & \(2.2154\times 10^{-8}\) & \(2.4466\times 10^{-8}\) & \(2.3015\times 10^{-8}\) & \(1.9752\times 10^{-8}\) & \(1.8800\times 10^{-8}\) \\
0.7 & \(2.5801\times 10^{-8}\) & \(2.8493\times 10^{-8}\) & \(2.6804\times 10^{-8}\) & \(2.3003\times 10^{-8}\) & \(2.1895\times 10^{-8}\) \\
0.8 & \(2.9435\times 10^{-8}\) & \(3.2507\times 10^{-8}\) & \(3.0579\times 10^{-8}\) & \(2.6243\times 10^{-8}\) & \(2.4979\times 10^{-8}\) \\
0.9 & \(3.3057\times 10^{-8}\) & \(3.6506\times 10^{-8}\) & \(3.4342\times 10^{-8}\) & \(2.9472\times 10^{-8}\) & \(2.8053\times 10^{-8}\) \\
1 & \(3.6665\times 10^{-8}\) & \(4.0491\times 10^{-8}\) & \(3.8090\times 10^{-8}\) & \(3.2689\times 10^{-8}\) & \(3.1114\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the absolute errors among third-order schemes in Problem 2, for \(h=10^{-1}\). Figure 5: The absolute errors for numerical results in Table 4. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Step Size**} & \multicolumn{5}{c}{**CPU Time**} \\ \cline{2-6}  & **Ralston’s Scheme** & **RK3 Scheme** & **ARK3 Scheme** & **Heun’s Scheme** & **VRK3 Scheme** \\ \hline \(h=0.1\) & 0.003325 & 0.003508 & 0.004631 & 0.005653 & 0.001558 \\ \hline \(h=0.05\) & 0.003407 & 0.004827 & 0.003017 & 0.005066 & 0.001027 \\ \hline \(h=0.025\) & 0.003655 & 0.003144 & 0.005021 & 0.004364 & 0.001273 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons of CPU time in Problem 1, for different step sizes \(h\). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Step Size**} & \multicolumn{5}{c}{**CPU Time**} \\ \cline{2-6}  & **Ralston’s Scheme** & **RK3 Scheme** & **ARK3 Scheme** & **Heun’s Scheme** & **VRK3 Scheme** \\ \hline \(h=0.1\) & 0.004160 & 0.003116 & 0.004698 & 0.004627 & 0.001358 \\ \hline \(h=0.05\) & 0.003328 & 0.004782 & 0.004879 & 0.003469 & 0.001003 \\ \hline \(h=0.025\) & 0.003506 & 0.004392 & 0.003230 & 0.004234 & 0.001296 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparisons of CPU time in Problem 2, for different step sizes \(h\). Figure 6: The absolute errors for numerical results in Table 5.  
Figure 7: The absolute errors for numerical results in Table 6.
# Blockchain Applications in Agriculture: A Scoping Review
## 4 Discussion
### Summary of Evidence
#### 4.1.5 Reason for Using Blockchain  
In this scoping review, we also research the reason for using blockchain in the agriculture sector (RQ9). These reasons are mainly to solve the various cyber threats and food safety issues of the existing IT solutions in agriculture. As a result, most solutions use blockchain for its inherent characteristics, such as data transparency and integrity. This happens at 61% and 50%, respectively. Moreover, few papers (3%) [57; 72; 99] use another intrinsic feature of blockchain technology: its data availability. It should be mentioned that many solutions involve the use of blockchain over conventional databases due to the availability provided but do not clearly define it, so it has not been included in our respective count. In addition to the above reasons for using blockchain, a typical process is storing product information and tracking information. This immutable data logging is used in most research papers (72%), e.g., [95; 100; 101]. It should be noticed that although blockchain is used for logging and storing data, such as IoT data (solving the problem of counterfeiting), it should not be misused. Blockchain in general and especially public permissionless blockchains should not be used to store the overall data of an application. Such storage is costly and increases the size of the blockchain, making it non-functional. Instead, blockchain technology should be used as designed to store critical data to which the blockchain gives an immutable feature. Another common reason for using blockchain is traceability, which is found in 18% of the solutions, e.g., [57; 102]. As before, we need to be aware that some solutions misinterpret that traceability is an inherent feature of blockchain, which is not entirely accurate. Although the ledger itself provides traceability, this possibility cannot be easily and efficiently achieved without a specific architecture and without third-party frameworks [34].  
Blockchain has also been used to schedule various processes in the agricultural sector. Scheduling may involve hiring machinery from farmers for specific tasks [94; 103] or hiring seasonal workers for agricultural jobs [104]. It may also involve priority scheduling for defined tasks with robot coalitions [99] or fixing IoT devices using autonomous drones [67]. Based on our findings, blockchain technology has also been used to provide access control solutions [60; 61; 93; 105; 106; 107; 108]. These solutions store data in the blockchain for access control, such as the public keys and access policy, for security reasons. Most devices for which access control is used are IoT devices. Finally, the blockchain has been utilized as an incentive mechanism for the effective management of waste by farmers [109].
# Suitability of Active Noise Barriers for Construction Sites
## 2 Theory  
### Diffraction Model  
Numerous available methods estimate the diffracted sound field. An analytical model is preferable in the case of a repetitive calculation due to its low computational requirements. Among the various analytical models proposed for calculating the pressure of diffracted sound wave, the one developed by [PERSON] [40] is widely used for the barrier calculations, which we have also used throughout this study.  
Given one receiver, one sound source, and one semi-infinite barrier, three different acoustic fields are found depending on the relative locations of these elements [30]. These fields include the shadow zone of the barrier (Region I), Region II, and Region III as shown in Figure 1.  
The receivers in the incident zone (Region III) are in the direct, reflected, and diffracted field of the sound source. However, the barrier reflected wave fronts disappear in Region II, and only diffracted field remains in the Region I.  
Equation (1) represents the [PERSON]'s solution for computing the pressure of diffracted wave fronts along the barrier edge.  
\[P_{D}=\frac{k^{2}\rho\ c}{4\pi}q_{0}[sgn(\zeta_{1})\int_{|\zeta_{1}|}^{\infty} \frac{H_{1}^{(1)}\Big{(}kR_{1}+s^{2}\Big{)}}{\sqrt{s^{2}+2 kR_{1}}}\ ds+sgn( \zeta_{2})\int_{|\zeta_{2}|}^{\infty}\frac{H_{1}^{(1)}\Big{(}kR_{2}+s^{2} \Big{)}}{\sqrt{s^{2}+2 kR_{2}}}ds] \tag{1}\]  
where \(k\) is the wave number, \(q_{0}\) (m\({}^{3}\) s\({}^{-1}\)) is the source strength, and \(\rho\) and \(c\) are the air density and the sound speed, respectively. \(H_{1}^{(1)}(\ )\) is the Hankel function of the first kind, \(R_{1}\) and \(R_{2}\) are the distances from the source and its barrier image to the receiver, respectively, as illustrated in Figure 1. In this  
Figure 1: Schematic diagram of the diffraction wave [30].  
regard, \(s\) is the variable of the contour integral and the limits of the two contour integrals in Equation (1) are determined according to:  
\[\zeta_{1}=\text{sgn}(|\theta_{s}-\theta_{r}|-\pi)\sqrt{k(R^{\prime}-R_{1})} \tag{2}\]  
\[\zeta_{2}=\text{sgn}(\theta_{s}+\theta_{r}-\pi)\sqrt{k(R^{\prime}-R_{2})} \tag{3}\]  
where \(\text{sgn}()\) is the sign function, and \(\theta_{s}\) and \(\theta_{r}\) are the source and receiver angles, respectively. \(R^{\prime}\) is the shortest path from the source to the receiver through the edge. Keeping in mind that all the sources are considered as three-dimensional point sources, they create direct and reflection acoustic pressure as follows,  
\[P_{d}=\frac{-ik\rho\ c}{4\pi}q_{0}\frac{e^{ikR_{1}}}{R_{1}} \tag{4}\]  
\[P_{r}=\frac{-ik\rho\ c}{4\pi}q_{0}\frac{e^{ikR_{2}}}{R_{2}} \tag{5}\]
# Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms
## 4 Results  
Generally, an anomaly detection algorithm is trained based only on normal data in a situation where most instances belong to the normal class and only a few instances are from the abnormal class. Under this condition, it is impossible to set a cut-off value for detection as generally used by classification models. Hence, for daily activity-based model and e-mail contents-based model, the performances of anomaly detectors are evaluated as follows. First, the dataset is split to the training dataset, which contains 90% of randomly selected normal instances, and the test dataset, which contains the remaining 10% of normal instances and all abnormal instances. Second, an anomaly detection algorithm is trained based on the training dataset only. Third, the anomaly scores for the instances in the test dataset are computed and sorted in descending order. Finally, we compute the true detection rate using seven different cut-off values (1%, 5%, 10%, 15%, 20%, 25%, and 30%) based on Equation (10):  
\[\begin{array}{c}\text{ {True detection rate (in top X\%)}}\\ =\ \frac{\text{ {The number of malicious activities included in the top X\% anomaly scores}}}{\text{ Total number of malicious activities}}\end{array} \tag{10}\]  
In order to achieve statistically reasonable performances, we repeated the above process 30 times for each anomaly detection algorithm and used the average true detection rate in the top X% as the performance measure for insider-threat detection. Since the number of samples for e-mail network data is not as sufficient as those of daily activity data and e-main contents data, we used all normal instances for training, and anomaly scores are computed for all normal instances and abnormal instances for the e-mail network-based anomaly detection model.
## 4 Experiments and Results
### Ablation Experiments  
Table 5 presents the results of the ablation experiments conducted to evaluate the individual influence of each component on the proposed model's performance on the MSTAR dataset. The evaluation process involved starting from the local path (ConvNeXt branch); adding the Swin Transformer branch, SimAM attention module, and Bayesian optimization hyperparameters; and finally forming our model.  
Figure 8: Data distribution before and after model processing under EOCs. (**a**) Raw data distribution in EOC experiments; (**b**) data distribution after Bayes-MFN model processing in EOC experiments.  
Figure 7: Data distribution before and after model processing under SOCs. (**a**) Distribution of raw data in SOC experiments; (**b**) data distribution after Bayes-MFN model processing in SOC experiments.  
The accuracies of the ConvNeXt branch under SOCs and EOCs were 86.52% and 69.01%, respectively. The accuracy increased by 8.61% and 23.7% after the Swin Transformer branch was added, indicating the insufficiency of considering only local features. The fusion of local detail and global semantic features significantly improved the performance of the model, validating the proposed approach. After adding the SimAM attention module in the ConvNeXt branch, the accuracy was improved by 1.08% and 0.26% under SOCs and EOCs, respectively, demonstrating the attention mechanism's ability to enhance the model's feature extraction and presentation. Finally, using the Bayesian optimization algorithm to optimize the model's hyperparameters increased the accuracy in SOC and EOC experiments by 3.05% and 1.3%, respectively.  
In addition, this study compared the influence of the Bayesian hyperparameter optimization algorithm and empirical selection hyperparameters on the classification performance of the model. Under SOCs and EOCs, the results of the hyperparameter set optimized by the Bayesian algorithm and those selected by experience are shown in Table 6. Furthermore, the comparison results of the MFN model using the hyperparameter set selected by experience and those optimized by the Bayesian hyperparameter optimization algorithm are shown in Table 7.  
The results demonstrated that using the Bayesian optimization algorithm effectively improved the model's classification performance under SOC and EOC across all four evaluation indexes. Moreover, the Bayesian hyperparameter optimization algorithm significantly saved time and resources during the model optimization process.
# Arsenic (V) Removal by an Adsorbent Material Derived from Acid Mine Drainage Sludge
## 2 Materials and Methods
### Arsenic Adsorption Batch Test  
#### 2.3.1 Adsorption Kinetic  
The kinetics of arsenic adsorption was evaluated by a batch test with a 30 mg/L initial concentration of As(V). First, 100 mL of an aqueous solution containing 30 mg/L As(V) was poured into a beaker, and the pH was adjusted to 6.0 by adding 0.1 M HCI and NaOH. Then, 0.1 g of adsorbent material was added to this 100 mL solution and the solution was stirred continuously. Subsequently, 3 mL samples were collected at 1, 3, 6, 24, 48, 72, 120, and 168 h for analysis. Samples were filtered by using a polyethersulfone (PES) membrane filter (0.2 \(\upmu\)m, Hyundai micro Co. Ltd. (Seoul, Korea) and arsenic content was determined as described above.  
The adsorption capacity of arsenic was obtained by using Equations (1) and (2) below:  
\[q_{e}=(C_{0}-C_{e})\times V/M \tag{1}\]  
\[\%\ Removal=(C_{0}-C_{e})/C_{0}\times 100 \tag{2}\]  
where \(C_{0}\) is the initial concentration of arsenic (mg/L), \(C_{e}\) is the equilibrium concentration of arsenic (mg/L), \(m\) is the mass of adsorbent (g), and \(V\) is the volume of the solution (L).  
The adsorption kinetics were evaluated by applying the following kinetic equations, i.e., pseudo-first-order (Equation (3)) and pseudo-second-order (Equation (4)) [21] as follows:  
\[q_{t}=q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{3}\]  
\[q_{t}=\frac{k_{2}q_{e}^{2}t}{1+k_{2}q_{e}t} \tag{4}\]  
where \(q_{t}\) is the adsorbed amount of arsenic at time \(t\) (mg/g), \(q_{e}\) is the equilibrium concentration of arsenic (mg/g), \(k_{1}\) is the first-order rate constant (1/h), and \(k_{2}\) is the second-order rate constant (g/mg:h).
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 2: Self-Constructed Bearing Experimental Dataset
#### 4.3.5 Comparative Experiments and Analysis Under Small Samples Conditions  
Consistent with the experimental setup in Case 1, this experiment was conducted with three different dataset sizes (A, B, and C), where the numbers of training samples were 100, 200, and 300, respectively, and the number of test set samples was fixed at 700.  
The experimental results are presented in Table 9 and demonstrate that the proposed model achieves optimal diagnostic accuracy across different datasets. ResNet-18 performed excellently, with an average accuracy only 0.5% lower than that of the proposed model. EfficientNetV2-S and MPNet performed well on datasets B and C, but exhibited poor performance on dataset A, resulting in average accuracies 2.76% and 3.26% lower than those of the proposed model, respectively. RepViT, SqueezeNet, and MobileNetV3 exhibited poor performance overall, with average accuracies 5.96%, 8.95%, and 10.69% lower than those of the proposed model, respectively. As the number of training samples decreased, the accuracy of the proposed model decreased slightly by 2.59%, for a value 0.49% higher than that of ResNet18, the model with the smallest decrease. However, it remained more stable compared to the other five models, which showed decreases of 5.1%, 6.02%, 12.3%, 5.28%, and 13.78%. The results indicate that HFDF-EffNetV2 improves the ability to perceive the relationships between different channels in the input FM and that it continues to capture key information from important channels even with small sample sizes.  
Figure 17: Confusion matrix visualization for different models at SNR (4 dB) in Case 2.
## 3 Results
### Visual Analysis  
As shown in Figure 9, 4D_TT and 4D_TR exhibit good detection results but suffer from missed detections when target responses are low and false alarms in regions with high background responses. RCTV can detect most targets but generates numerous false alarms, especially in ommatidia that do not capture targets. RCTVW further improves the ability to distinguish targets from the background, yet it still faces false alarms in non-target ommatidia regions and missed detections in images with low target responses. MPCM and ADMD exhibit missed detections even for strong targets, indicating that the local features of small targets in compound eye images are not prominent. ASTTV_NTLA and NFTDGSTV produce fewer false alarms in images with smooth background grayscale transitions but struggle with detecting bright backgrounds as targets. ICEW and Top_hat show varying degrees of false alarms across different image sequences. The IPI algorithm can detect targets almost completely but still misidentifies background regions in ommatidia images without targets as targets.  
Traditional model-based small target detection methods typically produce intermediate or post-processed result maps, which are well suited for direct visual comparison and qualitative analysis. In contrast, deep learning-based methods often provide detection results in the form of bounding boxes or confidence maps, which differ in format and interpretation.  
Therefore, in the qualitative analysis section, we mainly focus on comparing traditional algorithms, as their visual outputs are more consistent and interpretable for analysis. Nonetheless, we have included quantitative comparisons with deep learning-based detection networks to ensure a comprehensive evaluation of all methods.  
\begin{table}
\begin{tabular}{c c} \hline
**Methods** & **Parameters** \\ \hline \multirow{2}{*}{4D\_TT} & Patch size \(N_{1}\times N_{2}:10\times 10\), temporal size: \\  & \(N_{3}=15,\lambda_{1}=\sum_{i=1}^{m-1}2/\sqrt{max\left(\prod_{i=1}^{k}N_{i}, \prod_{i=k+1}^{m-1}N_{i}\right)}\),  
Figure 9: Detection results obtained using different methods on five infrared image sequences (img-seq-1 to img-seq-5). The img-part subset highlights four ommatidia within the yellow dashed region for more detailed comparison. Each result image is annotated in the upper-right corner with the name of the corresponding method, including 11 existing approaches and our proposed method. The green elliptical regions in the image indicate false alarms, while the red square regions represent actual targets.