esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Hearing Aid Signal Processor  
With the increasing trend of deploying speech enhancement models on embedded platforms, various digital signal processor (DSP) chips [7; 8] have been widely adopted in real-time speech processing devices such as hearing aids. Medium-power chips, including Cadence HiFi 3z/4 [9; 10], XMOS-200 [11], and ARM Cortex-M55 [12], typically offer computational capabilities ranging from 100 to 500 MMACs, enabling support for moderately sized deep learning models. However, the relatively high power consumption of these chips limits device battery life, constraining prolonged wearability. To improve energy efficiency, a number of low-power chips have emerged in recent years, such as the TMS320C5505 DSP [13] and SmartHeaP [14], which operate at power levels below 10 mW while delivering computational power up to 100 MMACs. These chips are specifically designed for edge neural network inference and have demonstrated promising applications in tasks including voice wake-up detection and noise suppression for hearing aids.  
Table 1 shows the MMACs, power consumption, and estimated cost of the DSP chips.
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 3 Results
### Phenolic Content
#### 3.1.3 Total Ortho-Diphenolic Content  
Figure 4 shows that the highest concentration of TdOPC was found in macerated extracts, followed by alkaline- and acid-hydrolysed extracts. In fact, the highest concentration of TdOPC in macerated extracts was found in Leccino (126.519 \(\pm\) 8.80), whilst the lowest concentration was found in Coratina (16.50 \(\pm\) 2.73). This was followed by the hydrolysed extracts, in which the cultivar of 'Malti' had the highest concentration of TdOPC for both acid and alkaline hydrolysis (acid: 55.81 \(\pm\) 3.64 and alkaline: 65.31 \(\pm\) 4.30). The lowest concentration was found in Picholine for alkaline hydrolysis (12.80 \(\pm\) 1.45) and in Carolea for acid hydrolysis (27.53 \(\pm\) 2.64). All concentrations were expressed in mg PE/g.
## 3 Materials and Methods
### Model Training and Performance Evaluation
#### 3.4.3 XGBoost-Based Prediction of AI Literacy and SHAP Analysis  
The study employed an XGBoost regression model to predict the Self-Perceived Performance of AI Literacy among teachers. This model leverages the Gradient Boosting technique, which enhances predictions by iteratively minimizing errors from previous predictions. Here is a simplified explanation:  
The prediction update at iteration \(t\) is calculated by adding an improvement \(f_{t}(x_{i})\) to the previous prediction:  
\[\widehat{y^{(t)}}=\widehat{y^{(t-1)}}+f_{t}(x_{i})\]  
The final prediction \(\hat{y}_{i}\) after K iterations is the sum of all improvements:  
\[\hat{y}_{i}=\sum_{k=1}^{K}f_{k}(x_{i}),\ f_{k}\in\mathcal{F}\]  
The study further dissects the learning principles by examining the objective function, which includes both the prediction error and a regularization term to control model complexity:  
\[L(\theta)=\sum_{i=1}^{n}l(y_{i},\ \hat{y}_{i})+\sum_{k=1}^{K}\Omega(f_{k})\]  
where \(l(y_{i},\ \hat{y}_{i})\) represents the loss function. The optimization of the model involves approximating the loss function using its second-order Taylor expansion:  
\[L^{(t)}\approx\sum_{i=1}^{n}\left[g_{i}f_{t}(x_{i})+\frac{1}{2}h_{i}f^{2}_{t} (x_{i})\right]+\Omega(f_{k})\]  
To explain the predictions made by our model, we chose SHAP (Shapley Additive Explanations), a powerful tool particularly suited for tree-based models like XGBoost.  
SHAP values offer a detailed breakdown of feature contributions, enhancing interpretability. The formula to compute the Shapley value for a feature \(j\) is:  
\[\phi_{j}=\sum_{S\subseteq\{1,\ldots,n\}\smallsetminus\{j\}}\frac{|S|!(n-|S|-1)!}{n!} [v(S\cup\{j\})-v(S)]\]  
Although various explainability techniques, such as LIME and PDP, are available, SHAP analysis was adopted because it is particularly well-suited to tree-based ensemble models like XGBoost. SHAP can efficiently compute feature contributions by leveraging the model structure, enabling both global and local interpretation without approximation errors [64,74,75].  
After model training, SHAP analysis was conducted to assess feature importance, utilizing Shapley Values to quantitatively evaluate each feature's contribution and enable interpretation that considers interactions between features.  
The study also utilized a SHAP Summary Plot to compare the relative impacts of major features across the dataset and created a Feature Importance Plot to analyze the contributions of features learned by the XGBoost model.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.3 Channel Estimation  
Estimation of future 6G radio communication channels is rather challenging, due to their growing complexity [16]. State-of-the-art unsupervised learning approaches (DL unsupervised model, CNN and RNN) have been used for channel detection in molecular communication [80; 81]. A DL-based detector called DetNet was proposed in [82] and is able to achieve similar accuracy as conventional algorithms with much lower computation time.  
The unsupervised DL-based detectors suggested in [81] can also outperform conventional detectors. Especially, the LSTM-based detector shows an outstanding performance for molecular communication use-cases, when dealing with inter-symbol interference [80].
###### Abstract  
Soil total arsenic (TAs) contamination caused by human activities--such as mining, smelting, and agriculture--is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470-900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on [PERSON]'s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (\(R^{2}\) = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (\(R^{2}\) = 0.78, RMSE = 0.42), random forest (RF) (\(R^{2}\) = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (\(R^{2}\) = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with [PERSON]'s rank correlation with machine learning models can be used to estimate soil TAs content.  
hyperspectral imaging; soil arsenic; extremely randomized trees regression +
Footnote †: journal: Applied Sciences
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 3 Data Augmentation and Pattern Recognition Method Based on DAE-GAN
### DAE-GAN Network and Its Training Process
\begin{array}{l}x_{f}=De(G(\boldsymbol{z}|\boldsymbol{c}))\\ \mathrm{dis}_{1}=E_{\boldsymbol{x}_{r}\sim P_{r}}[D(\boldsymbol{x}_{r}| \boldsymbol{c})]-E_{z\sim P_{z}}[D\!\left(x_{f}\middle|\boldsymbol{c}\right)]\\ \mathrm{dis}_{2}=E_{\boldsymbol{x}_{r}\sim P_{r}}[D(\boldsymbol{x}_{r}| \boldsymbol{c})]-E_{\boldsymbol{x}_{o}\sim P_{o}}[D(\boldsymbol{x}_{o}| \boldsymbol{c})]\\ \gamma_{1}=\sqrt{\frac{\{\mathrm{dis}_{1}\}^{2}}{\{\mathrm{dis}_{2}\}^{2}+ \beta_{1}}}\\ \gamma_{2}=\sqrt{\frac{\left[\sum\limits_{i=1}^{n}\left(\boldsymbol{x}_{ri}- \boldsymbol{x}_{ri}^{\boldsymbol{\wedge}}\right)^{T}\!\left(\boldsymbol{x}_{ri} -\boldsymbol{x}_{ri}^{\boldsymbol{\wedge}}\right)\right]^{2}}{E_{z\sim P_{z}} \!\left[D\!\left(x_{f}\middle|\boldsymbol{c}\right)\right]^{2}+\beta_{2}}}\\ L_{margin}=\max\left\{\sum\limits_{i=1}^{n}\left(\boldsymbol{x}_{ri}- \boldsymbol{x}_{ri}^{\boldsymbol{\wedge}}\right)^{T}\!\left(\boldsymbol{x}_{ri }-\boldsymbol{x}_{ri}^{\boldsymbol{\wedge}}\right)-\ ma,\quad 0\right\}\end{array} \right. \tag{5}\]  
In Equation (4), both \(L_{En}\) and \(L_{De}\) include \(\sum\limits_{i=1}^{n}\left(x_{ri}-\boldsymbol{x}_{ri}\right)^{T}\!\left(x_{ri} -\boldsymbol{x}_{ri}\right)\). This section is dedicated to mapping the decoder from low-dimensional latent vectors to their corresponding high-dimensional sample vectors. The primary objective of this process is to ensure that the generated samples closely resemble real ones by minimizing any discrepancies between them, thus enhancing the overall authenticity and accuracy of the generated data. The training process of the DAE-GAN network is as follows:  
Step 1: Begin by pre-training the DAE network with real samples, and establish the reconstruction error obtained at the conclusion of pre-training as the threshold for further processing. Step 2: Optimize the network modules based on Equation (4). Step 3: Repeat Step 2 until the GAN reaches Nash equilibrium, then stop the iterations.
## 3 Experiments and Analysis
### Comparison of Dehazing Detection Algorithms  
#### 3.2.1 Comparison with Labeled Images  
The test dataset comprises clear original images and foggy images. The algorithms Cycle-GAN, AOD-Net, and the Dark Channel Prior method are compared with the previously mentioned dehazing algorithm. The source, total number, and partitioning method of the test image dataset are consistent with those employed in the passable area detection experiment. Cycle-GAN and AOD-Net are utilized as control groups after training to convergence.  
In the visualization Figure 11, the black transparent layer signifies impassable areas, while the red transparent layer represents passable areas. The closer the correspondence between the red regions and those in the original haze-free images, the stronger the alignment between the dehazing algorithm and the semantic segmentation algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mIoU (\%)** & **FPS** \\ \hline DeepLabv3+ & 92 & 35.3 \\ U-Net & 93.6 & 12.4 \\ PSP-Net & 87.84 & **52.6** \\ Proposed Algorithm & **94.54** & 39.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of network model performance.  
Figure 11: _Cont._  
The detection outcomes of the dehazing algorithm in this paper closely resemble those of the original image detection results, exhibiting optimal performance in areas with significant depth of field and along object edges. While minor instances of mis-segmentation occur in some images, the majority of pixels are precisely classified, leading to high accuracy.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 2 Background and Related Work
### Anomaly Detection  
In data mining and statistics literature, anomalies are also called abnormal data, outliers or deviant data. Anomalies can be caused by errors in the data, but also sometimes indicate the presence of new, previously unknown underlying processes. In fact, [PERSON] defines an outlier as an observation that is so different from other observations that there is good reason to speculate that it is produced by a different mechanism. As shown in Figure 1, the regions \(R_{1}\) and \(R_{2}\) that contain most of the observations are considered normal data instance regions, whereas the regions \(R_{3}\) and \(P_{1}\), which are far from most of the data points, contain only a few data points, and are considered exceptions. Often caused by system failures, illegal operations, external attacks, etc., this situation often conveys and reveals valuable information and exciting insights that exist in the data. Anomaly detection is an indispensable part of various modern discriminant models and decision-making systems. Traditional anomaly detection methods are mainly divided into four categories: based on the statistical distribution, based on distance, based on density, and based on clustering.
## 2 Research Area and Methodology
### ERT Configurations and Field Deployment  
The \"WGMD-9 High-Density Electrical Resistivity System\" used in this study is a novel detection system developed by the Chongqing Benteng Numerical Control Technology Institute in China. This system utilizes the WDA-1 Super Digital DC Resistivity Meter as the control unit, which integrates with the WDZJ-4 multi-channel electrode switcher, distributed high-density cables, and electrodes to achieve distributed two-dimensional high-density resistivity measurements. The spacing, \"a\" (unit: meters), between current injection electrodes (A/B) and potential measurement electrodes (M/N) serves as a core parameter, determining the detection resolution and minimum depth. The number of profile layers, \"n\", controls the vertical detection range through expanded electrode arrangements. During field measurements, electrodes are deployed at fixed intervals, \"a\", along survey points and were connected to a programmable multi-electrode switcher via  
Figure 2: Schematic diagram of high-density electrical resistivity tomography.  
multi-core cables. Measurement signals from the electrode switcher are transmitted to the engineering resistivity meter, with results sequentially stored in random-access memory. The data are then transferred to a computer for post-processing, where the geometric factor (\(K\)) is calculated based on the electrode geometry corresponding to the layer number, \"n\" (see Equation (2)), ultimately completing the data processing workflow.  
Based on the field conditions of the Baowu Carbon Industry site and the reconnaissance of the monitoring area, the high-density resistivity survey lines were systematically deployed. Monitoring wells MW4 and W5 are situated at the intersection of two main roads, leading to the arrangement of survey lines around the tar processing area, coal-refining area, and water management zone. A total of eleven survey lines were established: six in the tar processing area, including three transverse lines (each 60 m long with 2 m electrode spacing, labeled 1-1, 1-2, and 1-3), two densified lines (EL-1 and EL-2, measuring 60 m in length and with 2 m spacing), and one vertical line (20 m long with 1 m spacing, labeled a); three lines in the coal-refining area (each 60 m long with 2 m spacing, labeled 2-1, 2-2, and 3-1); and two lines in the water management zone (each 60 m long with 2 m spacing, labeled 2-3 and b).
## 3 System Model and Problem Statement
### Problem Statement  
Combining the fairness model and the energy consumption model, we define the problem under study as a multi-objective joint optimization problem and set a series of constraints as follows:  
\[\text{P1}:\quad max\Big{(}c_{D},\;\;f_{D},\;\;\;E_{solar}^{D},-E_{f}^{D}\Big{)} \tag{10}\]  
\[\text{s.t.}\;v_{f}^{v}\in[0,v_{vmax}] \tag{11}\]  
\[v_{f}^{h}\in[0,v_{hmax}] \tag{12}\]  
\[\theta_{f}^{h}\in[-\pi,\pi] \tag{13}\]  
\[z[t]\in\Big{[}h_{fmin},h_{fmax}\Big{]} \tag{14}\]  
\[\sqrt{\Delta x_{i,j}^{2}+\Delta y_{i,j}^{2}+\Delta z_{i,j}^{2}}>d_{min},\forall i,j\in\mathcal{N},i\
eq j \tag{15}\]  
\[\sqrt{\Delta x_{i,j}^{2}+\Delta y_{i,j}^{2}+\Delta z_{i,j}^{2}}<d_{comm}, \forall i\in\mathcal{N},\exists j\in\mathcal{N},i\
eq j \tag{16}\]  
Equations (11) and (12) represent the constraints on the horizontal flight speed \(v_{f}^{v}\) and vertical flight speed \(v_{f}^{h}\) of the UAV. Equation (13) represents the range of the UAV flight direction \(\theta_{f}^{t}\). Equation (14) limits the maximum and minimum values of the current flight altitude \(z[t]\) of the UAV. Equation (15) requires that the minimum distance between any two UAVs is greater than the maximum safety distance in order to prevent UAV collisions. Equation (16) requires that the distance between each UAV and at least one other UAV is within the communication range to ensure that each UAV in the UAV swarm can maintain communication; where \(\Delta x_{i,j}=\Big{|}x[t]_{i}-x[t]_{j}\Big{|}\), \(\Delta y_{i,j}=\Big{|}y[t]_{i}-y[t]_{j}\Big{|}\), \(\Delta z_{i,j}=\Big{|}z[t]_{i}-z[t]_{j}\Big{|}\).
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 5 Discussions
### Neural Networks in Other Space Missions  
The DEMETER satellite was equipped with an onboard neural network for efficient signal analysis beyond the waveform recording time [35]. This neural network-based system was utilized to detect various phenomena defined during the space mission based on the analysis of the received waveforms. Specific data processing is triggered to record only the relevant information. The neural network on the DEMETER satellite served the purpose of real-time detection and the characterization of whistling phenomena. Currently, there is no neural network for the real-time monitoring of the CSES-01. Therefore, our model improvement is optimized to address this limitation, resulting in significant improvements in both weight and detection speed. However, the neural network on the DEMETER satellite directly processes waveform signals, while the conversion of waveform signals into time-frequency maps requires more computational power and storage capacity, hindering real-time monitoring. The recognition of QP emission from waveform or audio data will also be considered in future missions.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
## 3 Results and Discussion
### Experiences  
The development team was significantly assisted by several farmers who implemented precision agriculture over the course of years, through their ideas and experience and by using and testing the module with authentic farming data from the 2022 cultivation year in Greece.  
Using real data combined with some notional options, for example, in the selection of the difficulty factors per field, to test the module under extreme data ranges, it was noticed that yield maps might be quite different from profitability maps (especially between fields) (Figure 6).  
Figure 8: The input data form for profitability assessment by ProFit (on farma).  
In a working example, 10% differences in rice yield between rice farms in the 2022 cultivation season may translate into 35% differences in profit and different spatial patterns within every field. These numerical differences can be explained by the fact that an amount of 6 t/ha yield in rice is considered a baseline in the cost/earnings balance of the annual rice cultivation budget in Greece.  
By exploring the profitability maps, the farmers can allocate zones that are more profitable than others--and not only more productive; parameters affecting profitability include specific fertilizer types, fertilization rates, soil types (e.g., heavy or light), cultivars, etc.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 2 Materials and Methods
### Dynamic Models of 2-DOF Vehicle  
The 2-DOF vehicle model can reflect the lateral characteristics of the vehicle accurately with a small calculated load and is often utilized to analyze the relationship between the yaw rate and the sideslip angle when the vehicle is moving on a plane. The 2-DOF model describing vehicle motion is based on the following idealized assumptions:  
1. Vehicle driving on a flat road, no vertical road uneven input;
2. Ignore the steering transmission system and apply the input directly to the wheel;
3. Longitudinal velocity as a constant, and ignore the effect of aerodynamics;
4. The lateral acceleration is limited to less than 0.4 g, and the tire cornering characteristics are in a linear range.  
Therefore, the car is simplified into a two-wheeled bicycle model, as shown in Figure 3, and its dynamic model is represented as:  
\[\left\{\begin{array}{c}ma_{y}=F_{yf}+F_{yr}\\ I_{z}\dot{\gamma}=F_{yf}l_{f}-F_{yr}l_{r}\end{array}\right. \tag{16}\]  
where \(m\) is the mass of the vehicle; \(a_{y}\) is the lateral acceleration; \(F_{yf}\) and \(F_{yr}\) are the lateral tire forces; \(I_{z}\) is yaw moment of inertia; \(\dot{\gamma}\) the yaw acceleration the yaw acceleration; \(l_{f}\) and \(l_{r}\) are the distance from the vehicle's center of mass to the front and rear axes. By using the kinematic formula, we can rewrite \(a_{y},F_{yf},F_{yr}\) in terms of \(u,v,\ \beta,\gamma\).  
\[a_{y}=v+u\gamma,\ \beta=\frac{u}{v},F_{yf}=k_{1}\alpha_{f},F_{yr}=k_{2}\alpha_{ r},\ \alpha_{f}=\beta+\frac{l_{f}\gamma}{u}-\delta,\alpha_{r}=\beta-\frac{l_{r} \gamma}{u} \tag{17}\]  
The linear 2-DOF vehicle model can be obtained by arranging Equations (16) and (17). This lays the foundation for analyzing the impact of the steering wheel angle and torque on the vehicle's sideslip angle.  
\[\left\{\begin{array}{c}mu\Big{(}\dot{\beta}+\gamma\Big{)}=(k_{1}+k_{2})\beta +\frac{l_{f}k_{1}-l_{r}k_{1}}{u}\gamma-k_{1}\delta\\ I_{z}\dot{\gamma}=(k_{1}+k_{2})\beta+\frac{l_{f}^{2}k_{1}+l_{r}^{2}k_{1}}{u} \gamma-l_{f}k_{1}\delta\end{array}\right. \tag{18}\]
# Coupled Model of Heat and Power Flow in Unventilated PV/PCM Wall-Validation in a Component Scale
## 2 Materials and Methods  
In order to verify the technical concept and validate the numerical model of the described above En-ActivETICS, pilot tests were conducted. For this purpose, a small-scale wall component was made using the oriented strand board (OSB) supporting construction. The test panel consisted of thermal insulation covered with the 3 cm layer of PCM mortar and the flexible PV panel (Figure 1).  
For the mock-up development, the thin-film CIGS (copper indium gallium selenide) flexible panel with 30 W peak was selected.  
Based on preliminary analyses conducted previously, three types of paraffins were selected to effectively absorb excess heat from the PV panel. The selection was made in accordance with the weather data for the location of the experiment. It was concluded that due to high temperature fluctuations on the backside of the PV panel, three materials characterized by overlapping phase transition range should be applied: RT25 HC, RT28 HC and RT35 HC [33]. The thermal conductivity of all three PCMs is at the level of 0.2 [W/mK]. The partial enthalpy at specific temperatures for all three materials are presented in Figure 2. Selected materials are pure organic paraffins that require further treatment to be applied in building construction. Thus, paraffin was initially encapsulated using high-conductivity material as a core-shell and then hand-mixed with the pre-prepared mortar. The paraffin mass content in the prepared granules was approximately 40%, and these were also added to mortar in equal mass proportions (1:1). This means that the weight of the pure paraffin in the PCM-mortar was approximately at the level of 20%.  
Figure 2: Partial enthalpy of three selected paraffins.