esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 2 Materials and Methods
### Differential Algorithm  
The differential algorithm in DC systems is based on the vectorial sum of the input and output currents of a bus or a link. This calculation verifies that, under a fault condition, the circulating current entering and leaving the protection zone does not have the same value. In DC links, the differential current is calculated as in (7).  
\[\text{DiffCurrent}_{\text{Link}}=\text{I}_{\text{MP}}+\text{I}_{\text{NP}} \tag{7}\]  
where \(\text{I}_{\text{M}_{\text{P}}}\) is the measured current in the positive pole at one end of the link and \(\text{I}_{\text{NP}}\) is the current of the positive pole at the other end of the link.  
The same calculation is performed in the negative pole (\(\text{I}_{\text{M}_{\text{n}}}\) and \(\text{I}_{\text{N}_{\text{n}}}\)), but they are always treated as separated calculations. For the bus differential current calculation, the algebraic sum includes all the link currents connected to the bus. As in the previous calculation, a separate calculation is conducted for each pole.  
\[\text{DiffCurrent}_{\text{Busbar}}=\sum^{\text{n}}\text{M}=\text{I}_{\text{M}} \tag{8}\]  
where \(\text{I}_{\text{M}}\) is the link current and n is the number of links connected to the bus. The algorithm detects a fault when the measured current crosses a predefined threshold, as in [23].  
\[\text{Fault detection}=1\text{ if DiffCurrent}>\text{ DiffCurrent}_{\text{THR}} \tag{9}\]  
One of the main challenges of this algorithm is improving the fault detection speed of the differential algorithm. Due to the communication delay, it displays slower performance depending on the link length. In [8], the effect of the link distance on the detection time is analyzed with regard to the detection time. Apart from that, this method offers excellent selectivity and reliability.
# The Reverse Lunge: A Descriptive Electromyographic Study
## 3 Results
### Mean Peak Muscle Activity
#### 3.2.2 Mean Peak Muscle Activity Relative to Reverse Lunge Phase  
Visual analysis was performed of six random lunge trials to estimate where max descent occurred during the movement. This was conducted by comparing the location of the foot switch marks in the data in the six trials. From this analysis, max descent was estimated to occur at 50% of the reverse lunge movement; thus, the \"lowering phase\" represented the first 50% of the lunge, and the \"rising phase\" represented the last 50% of the lunge. Using this information, we were able to determine the phase of the reverse lunge where peak muscle activity occurred for each muscle. Figure 4 demonstrates a representative sample of raw RF EMG activity from a single participant's lead limb.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Stationary**} & \multicolumn{4}{c}{**Lead**} \\ \hline  & **Mean** & **SD** & **Phase** & **Mean** & **SD** & **Phase** \\ \hline Rectus femoris & 36.16 & 39.3 & Rising (73.68\%) & 105.67 & 32.77 & Rising (94.74\%) \\ Bicep femoris & 17.11 & 8.41 & Rising (76.32\%) & 36.61 & 15.78 & Lowering* (52.63\%) \\ Gluteus medius & 49.2 & 26.11 & Rising (100\%) & 34.29 & 38.6 & Lowering* (57.89\%) \\ Gluteus maximus & 36.61 & 22.96 & Rising (94.74\%) & 34.88 & 27.07 & Lowering (60.53\%) \\ \hline \end{tabular}  
Signifies that peak muscle activity occurred in the dominant phase for less than 60% of trials.  
\end{table}
Table 4: Peak muscle activity during the reverse lunge movement (% MVIC) and phase in which the peak occurred (% of participants).  
Figure 3: Stationary vs. lead limb peak muscle activation (% MVIC). Red bars indicate lead limb, blue bars indicate stationary limb.  
Figure 5 displays a representative sample of an RMS analysis, where 50 on the \(x\)-axis represents 50% of the reverse lunge (max descent). Qualitative analysis of all lunge trials determined the phase during which peak muscle activation occurred for each muscle and each limb. The majority of the participant trials displayed peak muscle activation in the lead limb RF during the rising phase (94.74%). However, the phase during which peak muscle activation occurred was far less consistent between participant trials for the remaining three muscles, occurring during the lowering phase for the lead GMed (57.89%), BF (52.63%), and GMax (60.53%), and during the rising phase in the remainder of the trials, indicating greater variability regarding which phase elicited peak activation. In the stationary limb, a majority of participant trials displayed peak muscle activation in the rising phase for all four muscles tested (RF [73.68%], BF [76.32%], GMed [100%], and GMax [94.74%]) (Table 4).
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 6 Conclusions  
This work contributes a practical deep learning framework that advances the state of the art in remote sensing-based forest carbon estimation, particularly under conditions of ecological complexity and data limitations. This study proposed an enhanced ResNet-based deep learning framework for estimating forest aboveground carbon stock using Sentinel-2 multispectral imagery in the mountainous region of the Liuchong River Basin, Guizhou Province. By incorporating multiscale residual blocks and channel attention modules, the model effectively captured spatial heterogeneity and spectral relevance across forest types. Experimental results demonstrated superior accuracy and spatial consistency compared to conventional machine learning methods and standard ResNet baselines. The findings highlight the potential of integrating satellite data and deep learning for scalable, high-resolution carbon estimation, offering practical implications for ecological monitoring and climate policy support in complex forested landscapes.  
Conceptualization, methodology, validation, formal analysis, visualization, writing--original draft preparation, J.R.; writing--review and editing, L.L., Y.W., L.O. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by High-level innovative talents in Guizhou Province (BKRCH [2024] No.7), The Science and Technology Project of Bijie city of open competition mechanism to select the best candidates (Grant No: BKHZDZX[2023]), Dongfeng Lake and Liuchong River Basin of Observation and Research Station of Guizhou Province (Grant No: QKHPT \(Y\)WZ[2025]002), Bijie Scientist Workstation Project (BKHPT[2025]NO.2), Karst Plateau Resources and EnvironmentRemote Sensing Talent Team (BWRLT [2023] No.14), Intelligent Geospatial Information Application Engineering Center (BKLH [2023] No. 8), Intelligent Geospatial Information Application Engineering Center (BKLH [2023] No. 8), and the Fundamental ResearchFunds for the Liaoning Universities (LJ202410166034).  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 5 Integration with IoT, Alerts, and Energy Consumption
### Feedback and Alerts  
Sitting posture recognition systems often incorporate feedback mechanisms to help users maintain or correct their posture. The type of feedback provided can vary depending on the system's design, intended application, and user preferences. Visual feedback involves providing information through visual cues that the user can see. This can be performed via screens, indicators, or graphical interfaces. Auditory feedback involves using sound cues to notify the user of their posture status. This can include tones, spoken messages, or alarms. Haptic feedback uses tactile sensations to alert the user, typically through vibrations or gentle pressure applied to the body. Delayed feedback provides an overview or summary of the user's posture over a specific period, rather than immediate alerts. This can be used for longer-term posture training or assessment. The choice of feedback type in a sitting posture recognition system depends on the specific use case, user preferences, and the desired outcomes. Combining multiple types of feedback can be particularly effective, providing a comprehensive approach to posture correction and long-term habit formation. The examples of feedback used in existing literature are given in Table 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Algorithm** & **Data Size** & **Accuracy** \\ \hline
[95] & Random forest, decision tree, KNN & 7 videos & 95\% \\
[116] & VGG13, Resnet, Vit, & 1440 frames & 98.6\% \\
[119] & RNN, & 120 frames & 88\% \\
[121] & Uniform local binary pattern (uLBP) & 1499 frames & 98.4\% \\  & histograms for pattern extraction & 1200 frames & 99.82\% \\
[127] & Convolutional Stems & 1200 frames & 99.82\% \\
[142] & Neural networks, decision tree, & 10,800 frames & 93.72\% \\
[143] & Support Vector Machine & 120 videos & 98.20\% \\
[132] & Support Vector Machine (SVM), & 12 videos & 97.10\% \\
[126] & Convolutional neural network & 408 frames & 82.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Algorithms in Kinect camera-based technologies.
## 4 Analysis Example and Methods
### Results from the BHP Bidirectional FSI Simulation  
#### 5.2.1 Simulation Results for the BHP Vibration with Different Elastic Layer Thicknesses  
The curve for the radial acceleration at the middle node (point P) in the BHP with different thicknesses of the elastic layer is shown in Figure 14.  
As can be seen in Figure 14 that the amplitude of the radial acceleration in the BHP decreases with the increase in the thickness of the elastic layer.  
Figure 15 shows the comparison curves for the radial acceleration at point P in the BHPs with RTV or SWCNTs added when the thickness of the elastic layer is the same.  
Figure 14: Radial acceleration at point P with different thicknesses of the elastic layer. (**a**) BHP with RTV as the elastic layer; (**b**) BHP with RTV-SWCNTs composite materials as the elastic layer.  
Figure 15: Radial acceleration at point P with different materials. The thickness of the elastic layer is (**a**) 5 mm; (**b**) 10 mm; (**c**) 20 mm; (**d**) 25 mm.  
Figure 15 shows that when the thickness of the elastic layer is the same, the vibration acceleration in the three-layer BHP with SWCNTs added is lower than that of the double-layer BHP and the vibration absorption effect is better. Adding the SWCNTs to the elastic layer increases its tensile strength and the smooth inner wall of the pipeline can not only reduce the fluid resistance but also stabilize the flow.  
The vibration reduction is 'the standard deviation of the radial acceleration of the BHP with RTV as the elastic layer minus the standard deviation of the radial acceleration of the BHP with RTV-SWCNTs composite material as the elastic layer'. The vibration reduction reflects the degree of vibration suppression in the BHP with the same elastic layer thickness and different materials, as shown in Table 6.  
It can be seen from Table 6 that the standard deviation for the radial acceleration in the three-layer BHP with SWCNTs added is significantly lower than that of the double-layer BHP with RTV as the elastic layer. And with the increase in the pipeline thickness, the suppressive effect of the vibration increases.
# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 4 Crowdsourcing Recommendation System
### Feature Representation and Classification  
In this section, we focus on details of the data modeling for both users and images, with the goal of representing each as a collection of features that characterize them, and with the final goal of transforming text data into numerical features that can be processed with machine learning (ML) techniques as classifiers. In this feature extraction process, uncorrelated or superfluous features will be removed to simplify the job and avoid noise. The BoW and TF-IDF methods were tested to represent the data, and the BoW technique was sufficient in our work as the TF-IDF did not produce better results. This way, the BoW technique was used to pass from text descriptions to numeric vectors for both users and images, denoting with 1's and 0's the presence or absence of the extracted features in the frequency vector. Previous to any ML process, including classification, some preprocessing to remove meaningless symbols and some normalization of data to set a common scale for input values are usually necessary, so similar ranges are used for similar features to avoid making some features dominate over the others and to resemble standard normally distributed data in some way to allow the classification model to converge faster.  
In order to compare different techniques to build the recommendation system, three different well-known classification methods were used: random forests (RFs), support vector machines (SVMs), and neural networks (NNs). Some implementation and testing details for each one are included in Section 5, where the input data for all of them are the numerical vectors describing users, images, and the set of historically completed tasks, annotated, or rejected.  
According to our previous definitions about accepted or rejected tasks, the _Status_ value for each user across all images is used as a classification label, dividing the input records randomly into training and testing sets as usual to feed the classification process. For the training phase, historic records of accepted/rejected tasks according to the user and image features are fed to the classifier. In the testing phase, the trained model is used to predict some randomly selected images across the user's features to see if the prediction is correct or not, or, in other words, whether the user has taken the same decision as the classifier prediction, being this a binary value for acceptance (classifier output is '1') or rejection (classifier output is '0').
## 3 Test Results and Discussion
### Analysis of Cooperative Failure Characteristics in Jointed Rock Mass during the Instability Process
#### 3.2.2 Analysis of Cooperative Failure Characteristics in Jointed Red Sandstone  
The stress-time trends during the instability stage and the corresponding sample failure modes are illustrated in Figure 9, with crucial points labeled using letters in the stress-time curves. Samples No. 1-1 to 1-4 feature jointed regions of varying sizes, each displaying distinctive failure modes during the loading process.  
Figure 8: Stress–time curve and instability stage division.  
In the case of 1/D = 0.7 (No. 1-1, Figure 9b), initial cracks emerge at the tips of the lower jointed section of the sample and steadily propagate along the axial stress direction. During the O-A stage, the stress curve exhibits a linear decline, and axial stress experiences a sudden drop. Nevertheless, no failure occurs in this stage, as no fresh cracks develop on the sample surface, and existing cracks do not propagate further. Beyond point A in the stress curve, axial stress remains stable and fluctuates until point B, when tensile cracks connect. It is clear that after point B, material failure escalates, and tensile failure becomes the primary mode of specimen failure.  
For 1/D = 0.5 (No. 1-2, Figure 9c), following the attainment of peak strength, initial cracks manifest first at the tips of both the upper and lower jointed sections of the sample. These cracks extend along the axial stress direction after point A. In the A-B stage, the sample undergoes crack propagation and increased damage. Beyond point B, the sample  
Figure 9: The change of stress at the key points with time and the corresponding failure mode. (**a**) Complete red sandstone; (**b**) No. 1-1 (1/D = 0.7); (**c**) No. 1-2 (1/D = 0.5); (**d**) No. 1-3 (1/D = 0.3); (**e**) No. 1-4 (1/D = 0).  
rapidly dissipates a substantial amount of energy and experiences abrupt failure, leading to a sharp reduction in load-bearing capacity.  
In the case of 1/D = 0.3 (No. 1-3, Figure 9d), during the OB stage, the stress trends and failure modes of sample No. 1-3 resemble those of No. 1-2. The primary failure mode for samples No. 1-2 and No. 1-3 is tensile failure, with rock damage initially occurring at the ends of the jointed regions.  
For 1/D = 0.3 (No. 1-4, Figure 9e), where the joint completely traverses the red sandstone, its failure mode differs from the other samples. Shear failure predominates in this case, with the sample undergoing sliding shear failure along the separation planes of the jointed and intact sections. Localized shear failure gradually progresses to shear failure across the entire cross-section. After loading the sample to point O, numerous cracks develop and extend within the jointed portion until point B, at which the entire sample experiences sliding.
## 3 Results and Discussion
If one intends to compensate for secondary radiation accumulation, one must add a buildup factor. The accumulation factor is thus a multiplicative factor that incorporates the contribution of the scattered photons into the reaction to the uncollided photons. Thus, the accumulation factor may be calculated as the ratio of the entire dosage to the uncollided dose-response [27]. Meanwhile, the term of buildup factor can be classified as to exposure buildup factor (EBF) and energy absorption buildup factor (EABF) [28]. In parallel to the given description, the lower buildup factor values can be considered a superiority pattern against gamma-rays since the uncollided photon amount in the successful shields would be low. Figures 9 and 10 depicts the variations of EBF and EABF values as a function of energy at various mean free paths (i.e., from 0.5 to 40). The highest values were seen in both EBF and EABF in the mid-energy range, where Compton scattering is the main interaction between the gamma-ray and the material (see Tables S1-S5). In other words, this area has a high amount of uncollided photons. As a result, relatively higher values for the buildup factor are required to correct the transmission calculations. Our results indicate that SM1.5 has the lowest accumulation factors in both EBF and EABF. Thus, incoming gamma-rays may interact with the SM1.5 sample more often than with other samples. The effective removal cross-section, R (cm\({}^{2}\)/g), refers to the possibility that a fast or fission energy neutron would undergo the first collision, thus excluding it from the group of penetrating, uncollided neutrons. For neutron energies between 2 and 12 MeV, it is believed to be roughly constant. Moreover, the materials with the highest \(\sum\)R values can be considered as superior materaials against hazardous neutron particles. In this study, \(\sum\)R values of SM glasses were determined, and the results have been demonstrated in Figure 2:1 Following the gamma-ray attenuation characteristics, a distinguished synergistic impact of increasing Sm\({}_{2}\)O\({}_{3}\) was eventually found. As shown in Figure 11, the SM1.5 sample clearly differs from the SM0.2 sample. Therefore, one can say that Sm\({}_{2}\)O\({}_{3}\) can be considered a useful tool to improve the gamma-ray and fast neutron shielding properties of HMO glasses. Figure 2:2 _Cont._  
Figure 2:3 _Cont._  
Figure 2:4 (**a–e**). Variation of exposure buildup factor (EBF) against Photon energy for all glasses. Figure 2:5 _Cont._  
Figure 2:6 (**a–e**). Variation of energy absorption buildup factor (EABF) against Photon energy for all glasses.
# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 3 Results
### Relationships Between SIF and Ecosystem Fluxes
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **PFTs** & & **CRO** & **DBF** & **EBF** & **ENF** & **GRA** & **MF** & **SH** & **SAV** & **WET** & **WSA** \\  & & & **N = 17** & **N = 23** & **N = 13** & **N = 49** & **N = 37** & **N = 9** & **N = 16** & **N = 9** & **N = 18** & **N = 6** \\ \hline \multirow{4}{*}{CSIF} & \multirow{4}{*}{GPP} & r & 0.62 & 0.84 & 0.59 & 0.81 & 0.81 & 0.91 & 0.81 & 0.68 & 0.75 & 0.82 \\  & & Slope1 & 19.12 & 24.94 & 15.30 & 23.74 & 29.21 & 21.58 & 20.25 & 16.56 & 17.95 & 26.84 \\  & & Slope2 & 18.65 & 24.22 & 22.99 & 27.11 & 22.84 & 21.55 & 20.90 & 20.30 & 18.74 & 25.43 \\  & & r & 0.58 & 0.75 & 0.71 & 0.69 & 0.71 & 0.75 & 0.70 & 0.59 & 0.51 & 0.69 \\  & & Slope1 & 0.21 & 0.26 & 0.34 & 0.25 & 0.27 & 0.24 & 0.34 & 0.29 & 0.25 & 0.51 \\  & & Slope2 & 0.29 & 0.29 & 0.38 & 0.35 & 0.34 & 0.27 & 0.46 & 0.44 & 0.36 & 0.58 \\ \hline \multirow{4}{*}{GOSIF} & \multirow{4}{*}{GPP} & r & 0.61 & 0.77 & 0.21 & 0.69 & 0.78 & 0.90 & 0.61 & 0.69 & 0.71 & 0.77 \\  & & Slope1 & 20.78 & 24.49 & 5.38 & 22.19 & 26.65 & 23.21 & 18.38 & 20.59 & 18.34 & 26.96 \\  & & Slope2 & 22.51 & 27.56 & 25.75 & 30.48 & 28.15 & 25.93 & 23.45 & 24.39 & 21.00 & 27.19 \\  & & r & 0.59 & 0.74 & 0.41 & 0.61 & 0.71 & 0.77 & 0.52 & 0.65 & 0.50 & 0.67 \\  & & Slope1 & 0.24 & 0.28 & 0.19 & 0.25 & 0.33 & 0.27 & 0.30 & 0.38 & 0.27 & 0.52 \\  & & Slope2 & 0.35 & 0.34 & 0.44 & 0.39 & 0.43 & 0.32 & 0.51 & 0.54 & 0.40 & 0.63 \\ \hline \multirow{4}{*}{RTSIF} & \multirow{4}{*}{GPP} & r & 0.61 & 0.77 & 0.19 & 0.68 & 0.77 & 0.90 & 0.61 & 0.71 & 0.70 & 0.60 \\  & & Slope1 & 10.95 & 13.79 & 3.25 & 13.00 & 14.93 & 13.97 & 11.34 & 13.15 & 10.57 & 13.68 \\ \cline{1-1}  & & Slope2 & 11.53 & 15.03 & 16.93 & 17.67 & 15.09 & 15.08 & 13.28 & 14.90 & 11.57 & 15.89 \\ \cline{1-1}  & & r & 0.56 & 0.73 & 0.40 & 0.60 & 0.67 & 0.75 & 0.51 & 0.64 & 0.49 & 0.48 \\ \cline{1-1}  & & Slope1 & 0.12 & 0.15 & 0.12 & 0.14 & 0.18 & 0.16 & 0.18 & 0.24 & 0.15 & 0.25 \\ \cline{1-1}  & & Slope2 & 0.18 & 0.18 & 0.29 & 0.23 & 0.23 & 0.19 & 0.30 & 0.33 & 0.22 & 0.37 \\ \hline \hline \end{tabular} Notes: N represents the number of sites; the sloped (slope2) indicates the linear regressions coefficient of GPP and SIF or ET and SIF with (without) intercepts, respectively.  
\end{table} 
Table 1: The relationships of GPP, ET, and SIF across PFTs at FLUXNET sites. across various PFTs, with the highest slopes observed in woody savanna (WSA, Table 1). Forest ecosystems showed higher SIF-ET correlations (0.63-0.76) and slopes (0.21-0.23) compared to non-forest ecosystems (r: 0.5-0.7; slope: 0.19-0.43), though these differences were less pronounced than those observed for the SIF-GPP relationships.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible Array Antennas for a Sub-6 GHz 5G Band  
An array antenna is the combination of multiple antenna elements arranged in a particular orientation where the radiation characteristics of the antenna is determined by the orientation and the separation of the antenna elements. In array antennas, all of the antenna elements can be fed to single port. This section gives a review of various flexible array antennas for a sub-6 GHz band.