esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Comparative analysis is conducted based on key performance metrics, including sensitivity and communication distance, to identify the optimal design for wireless passive temperature sensors in demanding environments. The remainder of this work is organized as follows. Section 2 outlines the sensor antenna design process, detailing the theoretical and the optimization of sensor dimensions. Section 3 explains the antenna backscattering principle and identifies the parameters influencing sensor sensitivity. Section 4 describes the experimental setup used for communication distance and sensitivity measurements. Section 5 presents the results and discussion, including an analysis of the S\({}_{11}\) magnitude under various scenarios and a comparative analysis of the findings. Finally, Section 6 concludes the study by summarizing key findings and suggesting potential future improvements.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 3 Risk Analysis
### Quantitative Risk Assessment (QRA)  
QRA is to be applied whenever the analyzed risks need further detailed analysis. QRA assesses risks to identify and prioritize technology needs and evaluate regulatory alternatives [60]. Quantitative methods used in the literature can be analytical (such as the probability of failure POF, second-order reliability method SORM), probabilistic (Monte-Carlo simulation MCS, stochastic response surface methods SRSM), or sophisticated (fuzzy set theory FST, multi-criteria decision analysis MCDA) [61]. Those reliability-based methods are then categorized into FM analyses (FMEA), tree and diagrammatic analyses [62; 63; 64; 65] (FTA, DFT, ETA, and BT), and hazard analyses (HAZOP). Ref. [66] contains further details of risk assessment methods. Complex mathematical and statistical problems can be easily represented and solved using Monte Carlo simulation. It was applied in many fields such as Energy, finance, project management [67], engineering [68], insurance, transportation [69], human health risk assessment [70], and manufacturing. [PERSON] et al. [71] declare that MCs come with high computational effort, which is considered the main disadvantage.
## 1 Introduction
In order to further improve the clustering performance of the algorithm, some studies [34; 35] have utilized genetic algorithms to optimize the initial clustering center of FCM. Unfortunately, combination algorithms' complicated structure slows down the algorithm's execution. Hierarchical agglomerative clustering (HAC) has found various applications in data science, particularly in exploratory data analysis, machine learning, and pattern recognition [36; 37; 38], including the estimation of the optimal number of clusters in categorical data clustering using a silhouette coefficient [39]. However, the simple upward agglomerative HAC algorithm has a high time complexity and space complexity when dealing with large-scale datasets. To address this, the combination of HAC and fuzzy c-means (FCM) algorithms can make comprehensive use of their advantages and compensate for their shortcomings. Specifically, preliminary clustering of data using the HAC algorithm can improve the stability of clustering by removing noise data, shorten the calculation time by reducing the computational complexity of the FCM algorithm, and improve the accuracy of clustering by dividing the data into relatively small cluster sets. This algorithm is particularly useful for handling spatial debris characteristic data, and can significantly improve the stability, computational efficiency, and accuracy of clustering. This paper focuses on the spectral polarization characteristics of space debris and utilizes collected characterization data for feature extraction and cluster analysis. A fuzzy C-means (FCM) algorithm model based on hierarchical agglomerative clustering (HAC) is proposed, and the accuracy of the algorithm structure is validated using the Kosko subset measure formula. The experimental results, including data and images from both inner and outer fields, demonstrate the accuracy and superiority of the proposed algorithm. By extracting characteristic parameter combinations that effectively describe spectral polarization and determining the corresponding clustering characteristic matrix, an important contribution is made to the practical application of space debris classification and identification.
## 1 Introduction
On the other hand, in [25], a system using vibration, current, AE, and shock pulses is developed to diagnose different levels of Silica and Ferric Oxide contamination using various contaminant particle sizes to determine the best magnitude for the detection, concluding on the effectiveness of the peak amplitude on AE signals to differentiate between the conditions. Similarly, the authors of [26] studied the effect of contamination with steel and corundum particles on the wear and fatigue life of bearings, reporting the use of time and frequency indicators on vibration signals to identify the damage of the particles, concluding on the effect of the hardness of the particles over their size related to the damage produced inside the bearings. As shown in the literature, bearing failure is a critical phenomenon as it presents in different ways, with the defects frequently being related to lubricant contamination. Most of the methods presented focus on one type of fault at the time, mainly defects on the outer and inner race of the bearings, while few works study the presence of multiple simultaneous defects; in addition, most of the proposals use vibration signals as they are captured directly from the motor, while other sources provide information that could relate to different conditions and require more advanced processing or the fusion of data from various physical magnitudes to complement the information from the measured sources. Considering these facts, this research presents a bearing fault diagnosis method based on the fusion of time domain statistical and fractal features from raw vibration and current signals, capable of detecting the presence of black iron oxide in the lubrication of bearings with OR damage. The features fused using Linear Discriminant Analysis (LDA) allow an improvement in classification with an SVM model, as the fusion creates clusters that maximize the separation between conditions while compacting them, in comparison to the analysis of the individual signals, showing the method's capability to differentiate between the conditions.
Another study revealed the capacity of genus _Virgibacillus_ members to synthesize hydrolytic enzymes applicable in biotechnological processes, particularly in the food industry [16]. However, there are no data in the literature concerning the production of pectinase enzymes by _Virgibacillus salarius_ strains for use in the food industry, but many studies have reported that _Virgibacillus salarius_ strains expressed biotechnological potential in a variety of metabolic products with important applications in biotechnology. The halophile _Virgibacillus salarius_ (strain BM02) was reported to utilize substrates including cellulose, corn stalks, sugarcane bagasse, and wheat straw as a sole carbon source without a requirement for pretreatment [17]. _Virgibacillus salarius_ BM02 was also identified as a potent producer of exopolysaccharides [18]. _Virgibacillus salarius_ KSA-T is capable of reducing surface tension under a wide range of pH values, salinities, and temperatures, thus giving this bacterial isolate an advantage in many applications in the pharmaceutical, cosmetic, and food industries, as well as for use in the bioremediation of marine environments [19]. In our study, we investigated, in detail, _Virgibacillus salarius_ strain 434 as a potent pectin-nase producer. The cultivation conditions for a maximum pectinase yield were optimized, and then the extracellular haloalkaliphilic pectinase was purified and its properties were further characterized.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
###### Abstract  
In video human action-recognition tasks, motion tempo describes the dynamic patterns and temporal scales of human motion. Different categories of actions are typically composed of sub-actions with varying motion tempos. Effectively capturing sub-actions with different motion tempos and distinguishing category-specific sub-actions are crucial for improving action-recognition performance. Convolutional Neural Network (CNN)-based methods attempted to address this challenge, by embedding feedforward attention modules to enhance the action's dynamic representation learning. However, feedforward attention modules rely only on local information from low-level features, lacking contextual information to generate attention weights. Therefore, we propose a Sub-action Motion information Enhancement Network (SMEN) based on motion-tempo learning and feedback attention, which consists of the Multi-Granularity Adaptive Fusion Module (MgAFM) and Feedback Attention-Guided Module (FAGM). MgAFM enhances the model's ability to capture crucial sub-action intrinsic information by extracting and adaptively fusing motion dynamic features at different granularities. FAGM leverages high-level features that contain contextual information in a feedback manner to guide low-level features in generating attention weights, enhancing the model's ability to extract more discriminative spatio-temporal and channel-wise features. Experiments are conducted on three datasets, and the proposed SMEN achieves top-1 accuracies of 52.4%, 63.3% on the Something-Something V1 and V2 datasets, and 76.9% on the Kinetics-400 dataset. Ablation studies, evaluations, and visualizations demonstrate that the proposed SMEN is effective for sub-action motion tempo and representation learning, and outperforms compared methods for video action recognition.  
action recognition; convolutional neural network; feedback attention; motion tempo; sub-action +
Footnote †: journal: Applied sciences
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Rates of Change  
Rates of change (\(\delta Q\)) is introduced as a new model development method to replace the conventional method of utilizing flow or water level as the prediction model output. The current research on streamflow forecasting concentrates mainly on the prediction of the flow or water level as the output variables of the forecasted value (\(Q_{f}\)). The mathematical expression of a forecast flowrate is as follows:  
\[Q_{f}=Q_{i}+\delta Q \tag{8}\]  
where:  
\(Q_{f}\) is the forecast flowrate;  
\(Q_{i}\) is the initial flowrate at the time, \(t\);  
\(\delta Q\) is the rate of change.  
A rate of change is proposed in this study based on the mathematical relationship as follows:  
\[\delta Q=\frac{Q_{t}-Q_{i}}{t-t_{i}} \tag{9}\]  
where:  
\(\delta Q\) is the rate of change;  
\(Q_{t}\) is the flowrate at current time, \(t\);  
\(Q_{i}\) is the initial flowrate at a previous time interval;  
\(t\) is the current time;  
\(t_{i}\) is the last time interval.  
By applying the rates of change (\(\delta Q\)), the fluctuation can be controlled to improve the model's accuracy. For this research, the \(\delta Q\) will be based on 30 min.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Random Topologies' Generation  
The random topology generation process plays an important role in testing and validating the ML and DL models. Creating diverse network scenarios establishes a robust foundation for the precise detection and prediction of faults.
## 5 Analysis of Computational Complexity
### Reasons for DAWPC-MAC's Superiority  
* **Deep Q-Learning (DQN)**: Enables adaptive scheduling and power control, reducing computational overhead.
* **Collision Avoidance**: Minimizes retransmissions and interference, lowering complexity.
* **Energy Efficiency**: Reduces computational burden on nodes, enhancing scalability.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACF & Autocorrelation Function \\ CCF & Cross-Correlation Function \\ DFT & Discrete Fourier Transform \\ FHSS & Frequency Hopping Spread Spectrum \\ OFDM & Orthogonal Frequency-Division Multiplexing \\ PRBS & Pseudo-Random Binary Sequence \\ PSD & Power Spectral Density \\ TVIR & Time-Varying Impulse Response \\ UAC & Underwater Acoustic Communication \\ WSS & Wide-Sense Stationary \\ WSSUS & Wide-Sense Stationary Uncorrelated Scattering \\ \end{tabular}
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 3 Results and Discussion
### Assessment of Rainwater Resources of the Qaidam Basin  
#### 3.3.1 The Average Amount of Precipitation  
The spatial distribution of the mean annual precipitation from 2001 to 2016 before and after the correction of 3B42 RT and 3B43 in the Qaidam Basin is shown in Figure 7. It can be seen that the precipitation on the eastern, southern and southeastern edges of the Qaidam Basin is high, while in the center and northwest it is small. The original distribution of precipitation in the east and west of the Kunlun Mountains is not even, but the trend of decreasing from east to west is enhanced significantly after the correction. The main reason for this is the increase of precipitation in the eastern Kunlun Mountains after the TRMM products corrected. The precipitation in the Qilian Mountains and the southeastern edges of the Qaidam Basin was also relatively high and increased after correction. At the same time, there was a higher consistency between precipitation and elevation in those regions, that is, precipitation increased with elevation.  
For the 12 subregions of the basin, including MA, LE, DCD, DLH, WL, DL, GEM, QML, MD, ZD, XJR and GSR as shown in Figure 1, the areal precipitation in each region is shown in Table 3.  
We can see from Table 3 that the average annual precipitation of the Qaidam Basin based on the original 3B42 RT and 3B43 are 148.45 mm and 146.26 mm, respectively, while the new assessment value is 186.01 mm and 174.76 mm based on corrected TRMM 3B42 and 3B43. It is obvious that the annual precipitation (104.34 mm) based on the interpolated precipitation by gauges is significantly less than that of original and corrected TRMM products.  
Figure 7: Comparison of spatial distribution of TRMM precipitation before and after correction. (**a**) Original 3B42 RT precipitation; (**b**) Corrected 3B42 RT precipitation; (**c**) Original 3B43 precipitation; (**d**) Corrected 3B43 precipitation.  
The previous studies on precipitation in the Qaidam Basin were mostly based on the gauged precipitation data to get the areal precipitation of the Qaidam Basin according to the traditional interpolation method. The pity is that the gauge distribution is too sparse and uneven, causing an unreliable interpolation result. It is obvious that the precipitation in the area was underestimated in the past. The fusion remote sensing precipitation with local ground information in high mountainous areas is helpful.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 4 Results and Discussions
### Theoretical \(z_{ROC}\) Curves  
The theoretical values of the slope, \(C\), and intercept, \(M\), of the new \(z_{ROC}\) plots for each device (Section 3.4.2) can now be estimated using the \(u\) and \(SL\) values for each device  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Device Type**} & \multirow{2}{*}{\(\theta_{TPR}\)} & \multicolumn{3}{c}{**TPR**} & \multicolumn{3}{c}{**FPR**} & \multirow{2}{*}{**m**} & \multirow{2}{*}{**c**} & \multirow{2}{*}{**c**} & \multirow{2}{*}{**SL**} & \\  & & & & & & & & & & & \\ \hline Device 1 & \(-0.85\) & \multirow{2}{*}{0.16} & \(-5.80\) & \multirow{2}{*}{35.45} & \multirow{2}{*}{6.11} & \(-3.73\) & \multirow{2}{*}{\(-0.29\)} & \multirow{2}{*}{5.30} & \multirow{2}{*}{18.43} & \(-3.47^{3}\) \\  & (14) & & & & & & & & & \\ Device 2 & \(-0.87\) & \multirow{2}{*}{0.16} & \(-5.92\) & \multirow{2}{*}{36.18} & \multirow{2}{*}{6.11} & \(-4.48\) & \multirow{2}{*}{\(-0.29\)} & \multirow{2}{*}{4.53} & \multirow{2}{*}{15.75} & \(-3.47\) \\  & (12) & & & & & & & & & \\ Device 3 & \(-0.84\) & \multirow{2}{*}{0.16} & \(-5.89\) & \multirow{2}{*}{36.00} & \multirow{2}{*}{6.11} & \(-3.67\) & \multirow{2}{*}{\(-0.29\)} & \multirow{2}{*}{5.49} & \multirow{2}{*}{19.07} & \(-3.47\) \\  & (14) & & & & & & & & & \\ Device 4 & \(-1.08\) & \multirow{2}{*}{0.16} & \(-6.13\) & \multirow{2}{*}{37.46} & \multirow{2}{*}{6.11} & \(-1.62\) & \multirow{2}{*}{\(-0.30\)} & \multirow{2}{*}{6.59} & \multirow{2}{*}{22.21} & \(-3.47\) \\  & (12) & & & & & & & & & \\ Device 5 & \(-1.17\) & \multirow{2}{*}{0.16} & \(-6.22\) & \multirow{2}{*}{38.01} & \multirow{2}{*}{6.11} & \(-1.54\) & \multirow{2}{*}{\(-0.29\)} & \multirow{2}{*}{7.49} & \multirow{2}{*}{26.03} & \(-3.47\) \\  & (12) & & & & & & & & & \\ \hline \hline \end{tabular}  
*obtained by linear regression of each coordinate \(z_{TPR}\) and \(z_{FPR}\), respectively, versus the target value, \(x\) (Figure 5b-d). For example, for device 1:  
\[M_{1}=\frac{u_{fp,1}}{u_{tp,1}}=\frac{-3.47}{6.11}=-0.57\;logits/\left(\frac{IU}{L }\right)\]  
and  
\[C_{1}=-\frac{SL_{tp,1}-SL_{fp,1}}{u_{tp,1}}=-\frac{35.45-18.43}{6.11}=-2.78\;logits\]  
Table 4 shows a satisfactory agreement between C and \(M\) for the theoretical and experimental curves (Pearson correlation coefficients 0.91) for all device types, respectively, as also shown in corresponding plots (Figure 6a,b).
## 3 Results
### Analysis of the Water Absorption of the Fibres  
As illustrated in Figure 10, the samples exhibited a rapid absorption phase during the initial 20-30 min of soaking. This behaviour is comparable and consistent with other research on natural fibres [53]. The saturation point was generally reached between 40 and 50 min of soaking, except for cucumber fibres, which demonstrated an ongoing increase in absorption. The average water absorption values after 50 min of soaking demonstrated that the fibres belonging to the Cucurbitaceae plant family exhibited the highest absorption capacity, with zucchini and cucumber reaching mean values of 268.57 \(\pm\) 2.03% and 266.31 \(\pm\) 1.88%, respectively (Figure 10 and Table 4). Conversely, the fibres with the lowest mean absorption values were tomato and pepper. The percentage of water absorbed is similar to that of other natural fibres, such as banana, sisal, bagasse and jute fibres [35; 54; 55].  
The findings of this study are consistent with those reported in Section 3.1, as the fibres with the highest water content demonstrated the greatest absorption capacity after undergoing the drying process. This behaviour is a critical factor in determining the most suitable fibre type, given the moisture conditions to which the building materials intended  
Figure 10: Water absorption (%) of fibres by immersion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Residue Type** & \
\begin{tabular}{c} **Percentage of Moisture in** \\ **Stems and Leaves (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Drying Time of** \\ **Leaves/Stems (Days)** \\ \end{tabular} \\ \hline Tomato & 83.68 \(\pm\) 2.43 & 3.5/4.5 \\ Pepper & 84.44 \(\pm\) 2.00 & 3.5/3.5 \\ Zucchini & 90.43 \(\pm\) 3.05 & 3.5/4.5 \\ Cucumber & 89.02 \(\pm\) 2.00 & 2.0/3.5 \\ Aubergine & 85.57 \(\pm\) 1.62 & 2.0/3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average moisture content of fresh leaves/stems from horticultural residue and drying time to constant weight.  
for development are exposed. The absorption capacity of each type of fibre defined its expansion and contraction behaviour in the presence of moisture.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Epipolar Geometry-Based Extrinsic Parameters Determination  
Once the intrinsic parameters of each camera in a binocular stereo camera system are calibrated, the determination of the extrinsic parameters of the binocular stereo camera system becomes a matter of positional estimation, i.e., estimating the rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{t}\) between two cameras from a set of image point correspondences between two cameras.
## 2 Problem Statement: Nonlinear Submerged Arches Design
### Funicular Equilibrium of Submerged Arches  
The funicular equilibrium of a submerged arch is governed by the following set of differential equations in Cartesian coordinates \((x,y)\)[1]:  
\[\begin{split}&\frac{dN}{dx}=\frac{N\gamma_{s}}{\sigma_{adm}} \frac{dy}{dx},\\ &\frac{d^{2}y}{dx^{2}}=-\left(\frac{\gamma_{h}(D_{p}-y)}{N}+\frac{ \gamma_{s}}{\sigma_{adm}}\left(1+\left(\frac{dy}{dx}\right)^{2}\right)^{-1/2} \right)\left(1+\left(\frac{dy}{dx}\right)^{2}\right)^{3/2},\\ &\frac{dy}{dx}|x=0;\ y(0)=H;\ N(0)=\sigma_{adm}h,\end{split} \tag{1}\]  
where \(D_{p}\) is the depth (i.e., the distance from the water surface to the supporting line, see Figure 1); \(N\) is the axial internal compression force; \(\sigma_{adm}\) is the compression stress in the arch cross section under a fully stressed condition; \(h\) is the arch thickness at the apex; and, finally, \(\gamma_{h}\) and \(\gamma_{s}\) are the unit weights of the water and the arch's material, respectively. Solutions to Equation (1) may be obtained by numerical integration until \(x=B\) (see Figure 1), at which \(y(x)=0\).  
Figure 1: Geometry of a submerged arch.  
The funicular arch shape can be represented in Cartesian coordinates, simplifying the traditional geometric expressions based on the arch length and the angle of the tangent [30]. From these analytical expressions, the maximum span and height of the funicular submerged arch, as well as the minimum compression force (and therefore, the minimum amount of airspace), can be determined. The hydrostatic pressure and self-weight load were considered in [31] to show the influence of the self-weight in the funicular shape, resulting in a sufficiently accurate solution for practical applications.
## 4 Experimental Results
### Network Parameters  
As shown above, our proposed model consists of D-units (four for rough transmission map and two for refined transmission map). The choice of number of D-units was investigated through an experimental comparison in terms of number of D-units and quality loss results, as summarized in Table 2.  
According to Table 2 and Figure 11, we deduce that four D-units are enough to achieve maximum performance to produce \(t_{r}\) (minimum MSE) and two D-units to refine it (maximum SSIM), considering the computational costs.