esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 2 Materials and Methods
### Methodological Framework
#### 2.4.5 Soil Erodibility Factor
[75] evaluated the use of the Durigon algorithm [76] in deriving \(C\) factor values based on NDVI for tropical areas, while [PERSON] [47] recommended the use of LULC maps to assign \(C\) parameters in Kenya. Similar to other regional studies in other parts of the world focusing on future rainfall erosivity [3,24,50,77], the nationwide 2016 LULC map was used to obtain \(C\) coefficients to compute the present soil erosion status, as well as for the 2030s and 2080s periods. Table 3 shows \(C\) factor values used in this research, as sourced from past literature within the East Africa region. \begin{table}
\begin{tabular}{c c c} \hline
**LULC** & **C Factor Coefficient** & **Source** \\ \hline Forest & 0.01 & [16,78] \\ Shrubland & 0.08 & [44,79] \\ Grassland & 0.05 & [78] \\ Cropland & 0.15 & [16,78] \\ Bareland & 0.50 & [44] \\ Aquatic vegetation & 0.03 & [44] \\ Sparse vegetation & 0.03 & [44] \\ Urban areas & 0.01 & [16] \\ \hline \end{tabular}
\end{table} 
Table 3: \(C\) factor values for different land uses in Kenya compiled from published literature.
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 1 Introduction
### Phenomenon-Centric Explainability  
In the rapidly evolving field of machine learning, the concept of explainability has become increasingly critical, particularly as models grow in complexity and are applied in high-stakes domains. Techniques such as SHAP (SHapley Additive exPlanations) [20], LIME (Local Interpretable Model-agnostic Explanations) [21], and the frameworks proposed by [PERSON] [22] exemplify this approach by providing insights into how individual predictions are made. These methods aim to enhance user trust and enable validation of complex models by attributing importance scores to individual features, thereby making the model's decisions more transparent and comprehensible.  
However, recent work by [PERSON] et al. [23], in \"Unmasking Clever Hans predictors and assessing what machines really learn\", highlights the significant limitations of relying solely on traditional explainability techniques. Their study demonstrates that models can generate seemingly accurate predictions based on spurious correlations within the training data, reminiscent of the \"Clever Hans\" effect observed in psychology [24].  
Similarly, machine learning models may appear to perform well by leveraging incidental patterns in the data patterns that do not genuinely reflect the underlying phenomena. These issues become particularly evident in studies where models achieve high predictive accuracy by leveraging spurious correlations in the training data [25; 26]. Similarly, in computer vision, research has shown that models can misclassify images due to subtle perturbations that are imperceptible to the human eye [27]. Additionally, recent work by [PERSON] et al. [28] has demonstrated how dataset biases can influence the conclusions drawn from SHAP values. These examples underscore the necessity of ensuring that model predictions are grounded in genuine, relevant patterns that accurately capture the true underlying processes, rather than being influenced by misleading artifacts.  
In light of these findings, we propose a paradigm shift towards _phenomenon-centric explainability_, which emphasizes understanding the underlying phenomena that models are designed to capture. This approach is particularly critical in scientific research and in addressing complex challenges such as the \"all-relevant problem\". In these contexts, the primary objective is to gain deep insights into domain-specific processes, recognizing the importance of uncovering relationships--whether causal, dependent, or instrumental--rather than merely optimizing predictive accuracy. This perspective suggests that phenomenon-centric explainability represents a logical and necessary evolution, aligning with the traditional goals of scientific inquiry, such as exploring complex relationships and underlying mechanisms, without relying on interpretations that may be influenced by spurious correlations in the training data.
## 2 Materials and Methods
### Methods
#### 2.3.5 Accuracy Metrics  
The segmentation results were examined by visually interpreting the data after adjusting the boundaries of the Third Survey data as the true values of each type. The overall accuracy (OA) [29], Kappa coefficient [30], intersection over union (IoU) [31], mean intersection over union (MIoU) [31], F1 score [32], producer accuracy (PA) [29], and user accuracy (UA) [29] were used to evaluate the accuracy of each model.
# Peri-Implantitis Causal Therapy with and Without Doxycycline: Retrospective Cohort Clinical Study
###### Abstract
Systemic antibiotics fail to achieve adequate concentrations in peri-implant pockets due to the presence of bacterial biofilm, which acts as a protective barrier. In addition, the systemic use of antibiotics raises concerns about the increased risk of bacterial resistance development and systemic side effects, such as allergic reactions and gastrointestinal disorders [21]. For these reasons, the efficacy of systemic antibiotics in managing peri-implantitis is often temporary, with short-term clinical improvement not resulting in long-term prevention of bone loss. Local antibiotics represent a therapeutic option that has gained increasing relevance. The application of antibiotics directly into the peri-implant pocket allows for high concentrations of the drug at infected sites, improving clinical efficacy and minimizing systemic side effects. Recent studies have shown that the use of doxycycline or minocycline in the form of gels or microspheres applied locally in the peri-implant pocket, in combination with mechanical decontamination, leads to a significant reduction in probing depth and bleeding levels compared with mechanical decontamination alone [22]. [PERSON] et al., in a 2021 meta-analysis, confirmed that the use of local antibiotics led to a clinically significant reduction in bacterial load and clinical signs of inflammation [23]. [PERSON] et al. also reported improvements in clinical parameters with the use of topical antibiotics, suggesting that this treatment modality may optimize the results of nonsurgical therapy and prevent the need for surgery [24]. Such topical formulations, particularly doxycycline, are often delivered in the form of bioabsorbable gels or microencapsulated microspheres that provide sustained release of the active ingredient for a period generally ranging from 7 to 14 days. After application at the infected site, doxycycline reaches high local concentrations, exceeding the minimum inhibitory concentration (MIC) for the main periodontal and peri-implant pathogens. The mechanism of action is based on the inhibition of bacterial protein synthesis through binding to the 30S ribosomal subunit. In addition, doxycycline also possesses documented antiinflammatory properties, including the inhibition of matrix metalloproteinases (MMPs), which contribute to the reduction of soft and hard tissue destruction. This dual effect, antibacterial and modulating the host response, makes topical formulations a valuable adjunct to non-surgical therapy in cases of incipient mucositis or peri-implantitis [25,26]. The aim of this retrospective observational clinical study was to compare non-surgical causal peri-implant therapy with and without doxycycline according to variables smoking, systemic diseases, and a kind of implant-prosthetic rehabilitation (single, bridge, or full arch).
## 8 Comparative Analysis
### Comparative Analysis Under Continuous Random Disturbance  
In Table 7, the performance of both controllers is evaluated under continuous random disturbance, where the system is subjected to prolonged and unpredictable forces. The key metrics assessed include ITAE and MSE.  
ITAE: The DL-PID controller achieves an ITAE value of 3.2347, which is an improvement over the DL controller's ITAE of 6.4588. This indicates that, even under continuous random disturbances, the DL-PID controller maintains better control by reducing the system's deviations from the setpoint more effectively.  
MSE: The MSE value of 0.7631 for the DL-PID controller is lower than the DL controller's 0.9690, signifying more accurate vibration suppression. The DL-PID controller  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Controller Type** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) & **Settling Time (s)** & **ITAE** & **MSE** \\ \hline DL Controller & - & - & - & 3.34 & 1.5818 & 5.9703 \(\times\) 10\({}^{-9}\) \\ \hline DL-PID Controller & 324.3 & \(-\)2887.8 & 182.6 & 1.15 & 0.7326 & 3.4370 \(\times\) 10\({}^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Key Performance Metrics for the Controllers under Impulse Disturbance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Controller Type** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) & **ITAE** & **MSE** \\ \hline DL Controller & - & - & - & 6.4588 & 0.9690 \\ \hline DL-PID Controller & 233.5 & \(-\)2877.9 & 113.5 & 3.2347 & 0.7631 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Key Performance Metrics for the Controllers under Continuous Random Disturbance.  
demonstrates better precision in managing the random fluctuations caused by continuous disturbances.  
Overall, the DL-PID controller exhibits superior performance compared to the DL controller under continuous random disturbance as well. While both controllers perform adequately, the added tuning of the PID parameters in the DL-PID controller ensures more effective disturbance rejection and vibration control.  
This analysis reveals that incorporating PID parameters into the deep learning architecture significantly improves the control performance of the system, particularly in terms of response speed, error minimization, and handling of both impulse and continuous random disturbances.
## 7 Bird Nest Deployment
### System Evaluation
#### 7.3.3 Network Evaluation  
As there were several times when the image application was stopped for maintenance or debugging purposes, our networking log files are discontinuous. We present the observations we have made for one week during 21 July\(\sim\)28 July. During this period, there were total of 9940 attempts to transfer an image from a total of 19 nodes. Among these attempts, 1489 were made by three nodes when they had low battery and, thus, were unable to turn on the camera. Out of 8453 actual image transfer attempts, 79 attempts resulted in incomplete transfers. Hence, 8372 image transfers, which corresponds to 99% of initiated transfers, were completed during this one-week period. For these complete images, the average data rate achieved by the network was 1.1 packets/s per node, and the average number of packets required to deliver one image was 833.2 (the size of each image is 40 kB, which means 1000 uncompressed packets, but the number of encoded packets differ, since the compression ratio differs for each image), which translates to an average compression ration of 16.7%. As a result, one image transfer took 12.6 min on average. This was achieved despite extremely poor link connectivity (e.g., Node 905 had an end-to-end packet delivery ratio (PRR) of less than 50%). Figure 15 shows the PRR along with their received signal strength (RSSI) readings to the nearest routing parent node for each node during the deployment period.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Hiding Module
#### 3.1.3 Adaptive LSB  
This block carries out the hiding process of the binary information derived from the scrambled secret signal within the binary information of the host signal. In contrast to the LSB classic method, a fixed number of the least significant bits from the host signal are not modified, but the total of modified bits depends directly on the sample range and the \(BH\) input parameter. The higher the sample value, the higher the number of bits that can be modified. In this case, \(BH\) bits numbered from the most significant bit \"1\" from the input data are preserved intact, and only the remaining less significant bits (LSB) are modified.  
Broadly speaking, this block carries out the next steps:  
1. Reading the number of bits to hold (\(BH\)).
2. Representing the samples of the host signal in 16-bit format. The resulting signal is called \(i\).
3. Determining the minimum quantity of necessary bits to represent the sample of the host signal. This value is assigned to the \(MSB\) (most significant bit) variable.
4. Converting to zero the less significant (\(MSB-BH\)) bits from \(H\) (Equation (4)). The resulting signal will be called \(\widetilde{H}\).  
\[\widetilde{H}=2^{MSB-BH}*\left\lfloor\frac{H}{2^{MSB-BH}}\right\rfloor \tag{4}\]  
where \(BH\) is the \"Bits to Hold\" value, \(H\) is the sample of the host signal in 16-bits format, \(\widetilde{H}\) corresponds to the modified \(H\) sample, \(MSB\) is the minimum quantity of bits to represent \(H\), and \(\left\lfloor\ \right\rfloor\) is the integer part operator.  
Reading the chain of binary values derived from the Binary Representation block. Selecting a quantity of bits equal to (\(MSB-BH\)).
6. Converting the previous binary value to decimal and adding the result to \(\widetilde{H}\). The result is called \(S\).
7. Repeating the previous steps until hiding all the bits of the binary chain in the host signal.  
In this way, the values of the stego signal are obtained when carrying out an addition operation between \(\widetilde{H}\) and the decimal value of the (\(MSB-BH\)) bits taken from the chain obtained in the \"Binary Representation\" (Step 6 of the operation).
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
## 8 Attack Behaviors Detection
### Edge-Level Attention  
A node \(v_{i}\) is typically associated with multiple hyperedges, which differ in their discriminative contributions to node behavior (e.g., cross-community hyperedges are more likely to contain attack features). Edge-level attention filters discriminative hyperedges by measuring their semantic relevance to nodes. For the hyperedge set \(\mathcal{H}_{i}=\{h_{j}\}_{j=1}^{M_{i}}\) associated with node \(v_{i}\), the attention coefficient \(\beta_{i,j}\) for hyperedge \(h_{j}\) is calculated as  
\[\beta_{i,j}=\frac{\exp\left(a_{e}^{\top}\cdot\mathrm{LeakyReLU}\left(W_{e} \left[f_{j}^{(l)}\,\|\,h_{i}^{(l-1)}\right]\right)\right)}{\sum_{h_{p}\in \mathcal{H}_{i}}\exp\left(a_{e}^{\top}\cdot\mathrm{LeakyReLU}\left(W_{e} \left[f_{p}^{(l)}\,\|\,h_{i}^{(l-1)}\right]\right)\right)}\]  
where \(f_{j}^{(l)}\in\mathbb{R}^{d^{\prime}}\) is the feature representation of hyperedge \(h_{j}\) at layer 1, \(h_{i}^{(l-1)}\) is the historical feature of node \(v_{i}\), \(\|\) denotes feature concatenation, \(W_{e}\in\mathbb{R}^{d^{\prime}\times(d^{\prime}+d)}\) is a cross-modal feature fusion matrix mapping hyperedge and node features to a joint semantic space, and \(a_{e}\in\mathbb{R}^{d^{\prime\prime}}\) is an edge-level attention parameter vector measuring the importance of hyperedges to node semantics. Node representations are updated by aggregating hyperedge features based on attention coefficients:  
\[h_{i}^{(l)}=\sigma\left(\sum_{h_{j}\in\mathcal{H}_{i}}\beta_{i,j}W_{e}\Big{[}f _{j}^{(l)}\,\|\,h_{i}^{(l-1)}\Big{]}\right)\]  
where \(\sigma\) is the Sigmoid activation function. This mechanism prioritizes feature propagation from cross-community hyperedges involving abnormal network connections like unknown remote IP communications or sensitive registry operations like permission modifications, suppressing interference from routine system behavior hyperedges and enhancing the semantic discrimination ability of node representations for attack patterns.
## 3 Results
### Amplification during Source-To-Detector Distance (SDD) Variation  
Increasing the SDD resulted in a signal fade with an exponential decay fit coefficient of 0.99781, when the detecting active area was 756 mm\({}^{2}\) (60 chips). However, as a consequence of increasing the active area by 756 mm\({}^{2}\) (60 chips), the exponential decay fit coefficient was reduced by 1.12%, thus a shift of the decay fit coefficient from 0. 99781 to 0.98668.  
With a detecting active area of 756 mm\({}^{2}\) (60 chips), the signal faded at a rate of 3.348 mV/cm, at the minimum SDD (60 cm). At the maximum SDD (90 cm), the signal fading rate reduced to 1.033 mV/cm. When the detecting active area was increased to 1512 mm\({}^{2}\) (120 chips), the signal faded at rates of 6.680 and 2.118 mV/cm at SDDs of 60 and 90 cm, respectively (Figure 8a). Therefore, at SDDs of 60 and 90 cm, 99.52%, and 105.03%, increases in the signal fading rate were respectively observed; this was due to an increase in the active detecting area by 756 mm\({}^{2}\) (60 chips).  
Figure 8: A graph of signal augmentation with (**a**) Number of LED chips/sensitive area and (**b**) Amplifier Board amplification; during source-to-detector distance variation (cm).  
Figure 7: A graph of signal augmentation with (**a**) Number of LED chips/sensitive area and (**b**) Amplifier Board amplification; with respect to absorbed dose variation (mGy).  
The amplifier board output signal had a signal fading rate of 9.399 mV/cm at 60 cm and 2.945 mV/cm at 90 cm of SDD. This implied a 40.70% and 39.05% increase in the signal fading at SDDs of 60 cm and 90 cm, respectively.
## 2 Characteristics of the Gas Bearing  
The research object was an aerodynamic radial bearing lubricated with vapor from a low-boiling medium. The bearing was designed to support the shaft of a high-speed ORC (organic Rankine cycle) microturbine, on which the turbine rotor disc and a 10 kW electric generator rotor are mounted. The shaft of this machine is supported by the same two radial bearings and two thrust bearings, but only the radial bearings are the subject of the research discussed in this article. The microturbine is part of a cogeneration system that enables electricity generation from waste heat produced by industrial processes. The working medium of the ORC system is a low-boiling fluid with the trade name HFE-7100. The microturbine was designed as an oil-free machine [5; 12]. This means that inside the hermetic body there is a low-boiling medium which, in addition to driving the turbine, also lubricates the gas bearings. There is, therefore, no need to supply the bearings with any typical lubricant.  
During the design of the bearing system, the following operating conditions were assumed to meet the requirements of the microturbine:  
* Nominal rotational speed--40,000 rpm;
* Static load on the radial bearing--approximately 18 N;
* Ambient pressure of the bearing--1 bar;
* Lubricant--vapor of the low-boiling medium called HFE-7100;
* Lubricant temperature--170 \({}^{\circ}\)C;
* Dynamic viscosity of the lubricant (at 170 \({}^{\circ}\)C)--1.39792 \(\times\) 10\({}^{-5}\) N\(\cdot\)s/m.  
During the bearing design process, the following dimensions were specified: nominal journal diameter--49 mm, bearing length--44.5 mm, nominal radial clearance--35 \(\upmu\)m. Figure 1 shows the geometry and basic dimensions of the bearing and explains the designations used in the subsequent sections of the article. Figure 1b shows the coordinate system that served to describe the data and present the calculation results. The point indicating the center of the journal is marked as C, and \(\omega\) indicates the direction of rotation of the shaft.  
In performing the calculations, discussed later in this article, the position of the journal in the bearing's lubrication gap where the bearing achieved the required load capacity of 18 N was sought. The calculations for the gas-dynamic bearing were independently performed using two methods, and the results obtained were compared with each other. The first method employs the in-house developed program GAZBEAR [29; 30], which solves the Reynolds equations using the finite difference method with the alternating direction implicit (ADI) algorithm [20; 29]. In the second method, the solution was obtained using algorithms implemented in the commercial program ANSYS CFX [31], which operates based on the finite volume method. The details concerning each of these methods and the models used are discussed in the following sections.
# Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
## 2 Materials and Methods
### Accuracy Evaluation Method  
In this study, the coefficient of determination (\(R^{2}\)) and root mean square error prediction (RMSEP) were used to comprehensively evaluate the accuracy of wheat take-all calibration model [30; 31]. The coefficient of determination (\(R^{2}\)) is used to characterize the degree of fitting between the regression line and the observed value, meaning the accuracy of fitting degree between spectral index and DI value of each sample. The value of the coefficient of determination is between 0 and 1. The higher the \(R^{2}\) is, the better the fitting effect of the model is. That is, the better the spectral index model interprets the disease index, the greater is the reference value. The root mean square error prediction (RMSEP) reflects the degree of sample dispersion and can be used to observe the deviation between the true value and the prediction value of the disease index. For the disease index, RMSEP is very sensitive to large errors. When the RMSEP is smaller, the estimating accuracy of disease index is higher. When \(R^{2}\) is larger, RMSEP is smaller, which is the optimal model.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y}_{i})^{2}} \tag{4}\]  
Figure 2: ASD FieldSpec HandHeld spectrometer (**left**) field data measured scene (**right**).  
where \(y_{i}\) is the value to be fitted, its mean value is \(\overline{y}_{i}\) and the fitted value is \(\hat{y}_{i}\).  
\[\text{RMSEP}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}X_{i}-\hat{X}_{i}\bigr{)}^{ 2}} \tag{5}\]  
where \(X_{i}\) and \(\hat{X}_{i}\) are the values obtained in practice and by theoretical calculation, respectively and \(n\) is the number of observations of practice testing or theory calculation.
## 4 Performance Analysis of QoS Metrics
### Evaluation of PDR for the Transmission Power Level (\(P_{\text{Trans}}\)) and Energy Consumption  
Multiple tests are performed with PDR and energy consumption at different \(\text{P}_{\text{Trans}}\), which allow drawing a relationship between PDR and energy consumption of a node. Thus, this analysis would also facilitate finding the optimized network lifetime and the optimized \(\text{P}_{\text{Trans}}\) where the communication is also reliable with decent PDR. To eliminate any sort of bottleneck from the hardware perspective of the modules the baud rate and bit rate are set to the maximum value supported by the modules, which are 115,200 and 250,000 b/s, respectively.