esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Modulating hemodynamic responses via NIRS biofeedback requires less training time than controlling electrical responses for an EEG-BCI, which can span 4 to 9 repetitions of a 40 min session [11]. These features can make NIRS-BCI more user-friendly and preferable for daily use in rehabilitation. To meet this demand, we developed a hand rehabilitation system comprising a robotic hand orthosis that is wirelessly triggered by a multichannel-NIRS-BCI. We evaluated system performance with seven neurologically healthy participants. The evaluation focused on how successfully the NIRS-BCI detected motor intention. The delay in hemodynamic response while training a classifier, the length of the feature window, and the method of feature vector composition determined the performance of each NIRS-BCI module. Feature vectors embraced preserving the NIRS channels, using concentrations of the two different types of hemoglobin types ([HbO] and [HbR]), and selecting NIRS channels. Accordingly, we tested and evaluated those conditions to attain the best performance of the NIRS-BCI by comparing them in terms of classification accuracy and detection latency. The evaluation was conducted in a real-time manner, in which moving feature windows were applied to the recorded data. A moving feature window was formed with every sampling timing. This is because our system should detect motor intention in a real-time manner during rehabilitation practice. The hand rehabilitation system presented here is a clinical testing-ready version that has been upgraded from our previously tested prototype, and some of the data has been presented in [13]. In the current study, we have improved upon the prototype system by evaluating the different window lengths and feature vector compositions. This has substantially improved classification performance in terms of accuracy and detection latency. Thus, we here demonstrate the implementation of our NIRS-BCI-triggered hand rehabilitation system that integrates a NIRS with our previously developed robotic hand orthosis [14]. We have evaluated how the following items have affected the performance (accuracy and detection latency) of the NIRS-BCI module: (1) different hemodynamic delays and lengths of the feature window, and (2) different feature vector compositions. The feature vector compositions that we tested were: (a) 'preserving channels' vs. 'averaging', (b) using [HbO] together with [HbR] vs. [HbO] or [HbR] separately, and (c) using all channels vs. criterion-selected channels. The purpose of this study was to describe the system implementation and to discuss the results of the evaluation as we develop our hand rehabilitation system.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Workflow
#### 4.2.2 Face Encryption  
After completing the registration process, users can protect facial information in shared images through encryption. For instance, as illustrated in Figure 4, when [PERSON] intends to share an image with a broad audience while ensuring the preservation of facial privacy, she uploads an image \(I\) containing facial regions via the client application to the server.  
Upon receiving the request, the server validates it and generates a unique identifier, \(a\), for image \(I\) using a UUID version 4 generator, denoted as \(\mathbf{UUID4}(16)\), where \(16\) specifies the byte length. Subsequently, the server retrieves [PERSON]'s identifier, \(ID_{\text{alice}}\), from storage and forwards the image \(I\) along with \(a\) and \(ID_{\text{alice}}\) to the CSP.  
The CSP processes the image \(I\) to identify facial regions using the function \(\mathbf{DETECT}(I)\), resulting in a set of facial regions, \(F=\left\{f_{1},f_{2},\ldots,f_{n}\right\}\). Each \(f_{i}\in F\) represents the bound  
Figure 4: Face encryption and decryption by owner.  
ing box coordinates of the \(i\)-th detected face, defined as \(f_{i}=(x_{i},y_{i},w_{i},h_{i})\), where \((x_{i},y_{i})\) denotes the top-left corner coordinates and \((w_{i},h_{i})\) specifies the width and height of the bounding box, respectively.  
Building on our prior work [17], this face detection process is designed to ensure that the byte size of each facial region matches the 16-byte block size of AES, thus eliminating the need for padding to ensure the compatibility of encrypted data with the original image format. This approach enables seamless reintegration of encrypted facial data into the original image, preserving the image's structural integrity.  
Subsequently, the CSP generates a symmetric key, \(K_{a}\), and an initialization vector, \(IV_{a}\), to encrypt the facial regions, \(F\), within the image, \(I\). Using these generated values (\(K_{a}\) and \(IV_{a}\)), the CSP encrypts the regions corresponding to \(F\), resulting in the encrypted image \(I^{enc}\). This encryption process follows the methodology outlined in our previous work [17], where facial regions are encrypted sequentially based on the order of \(f_{i}\) in \(F\). This sequential approach ensures that even overlapping facial regions can be fully reconstructed during decryption.  
Then, for key storage, the symmetric key \(K_{a}\) is encrypted using [PERSON]'s public key \(pk_{\text{slice}}\), producing \(K_{a}^{enc}\). The CSP stores \(K_{a}^{enc}\) and \(IV_{a}\) securely for future decryption.  
Finally, the CSP returns the encrypted image \(I^{enc}\) and facial region metadata \(F\) to the server. The server associates these elements with identifier \(a\) to produce \(I_{a}^{enc}\) and \(F_{a}\), storing them in persistent storage for future retrieval. Subsequently, the server transmits \(I^{enc}\) to the client application for display to [PERSON], completing the encryption process.
## 3 Results
### Regression Tree Model
Time of year variables (Week in crop model and Day of Year (DOY) in grass model) were either the second or third most utilized spatial variables in both mapping models. Phenological variables (SOSN, SOST, AMP, MAXN, TMIN, and EOST) were much more important in the crop model (Table S4) than in the grass model and maybe implying that crop fluxes are more closely tied to phenology than grass or that crop phenological metrics are more accurately mapped from NDVI time series than grasslands. Precipitation was more influential in the grass model than in the crop model largely because precipitation was not used in the stratification of the crop model and it may be related to the higher probability of irrigation in croplands than in grasslands. AWC was the most important soil characteristic (better than SOC, BD, and CLAY) and was much more important in the grass model than in the crop model (32 versus 18 percent, respectively). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spatial Inputs** & **Attribute Conditions** & **Prediction** & **Mean Utilization** \\ \hline NDVI & 81\% & 83\% & 82\% \\ Week & 78\% & 0\% & 39\% \\ SOSN & 10\% & 65\% & 38\% \\ SOST & 2\% & 73\% & 38\% \\ AMP & 4\% & 70\% & 37\% \\ MAXN & 0\% & 69\% & 35\% \\ TMIN & 15\% & 51\% & 33\% \\ EOST & 1\% & 61\% & 31\% \\ DEM & 0\% & 62\% & 31\% \\ TMAX & 4\% & 52\% & 28\% \\ DUR & 0\% & 54\% & 27\% \\ TIN & 4\% & 47\% & 26\% \\ EOSN & 2\% & 48\% & 25\% \\ BD & 6\% & 40\% & 23\% \\ PCP & 0\% & 41\% & 21\% \\ MAXT & 0\% & 40\% & 20\% \\ SLP & 0\% & 38\% & 19\% \\ AWS & 0\% & 36\% & 18\% \\ C\_PPT & 0\% & 34\% & 17\% \\ SOC & 2\% & 24\% & 13\% \\ CLAY & 7\% & 17\% & 12\% \\ C\_TMAX & 12\% & 10\% & 11\% \\ Crop Type & 21\% & 0\% & 11\% \\ C\_TMIN & 0\% & 18\% & 9\% \\ MLRA & 8\% & 0\% & 4\% \\ C\_TMEAN & 0\% & 5\% & 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Utilization in percentage of the total weekly NEP fluxes for the spatial inputs used in mapping crop NEP.
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 2 Materials and Methods
### Study Area
In northern Italy, the Po River discharge was significantly reduced by the drought, which severely impacted the agricultural and energy sectors. The reduction in the water available for irrigation, the seawater intrusion, and the low flows affected food and energy production throughout the year [35; 41]. The mean flow during this period was about 30% lower than the second-lowest flow recorded in the past two centuries, observed in the summer of 2006 [38]. According to the 2023 ISPRA National Hydrological Balance Report, the Po River watershed experienced a 36% precipitation deficit, with 650.2 mm in 2022, compared with the long-term average annual precipitation of 1015.7 mm [38; 42]. Additionally, warmer temperatures induced a decline in snow precipitation with a consequent reduction in snowmelt contribution to the annual stream flow. The increase in temperature also resulted in enhanced evapotranspiration, which amplified the runoff deficit and shifted the timing of spring river flow. This led to an anticipated higher water demand, particularly for irrigation [9; 38]. These factors collectively reduced the average flow rate of many rivers, leading to the record-breaking drought event selected in this study for testing the effectiveness of the proposed methodology.
## 4 MPC Scheme Design
### Rollover Constraints  
A key challenge in preventing the rollover of the AGV is the prediction of rollover onset, especially in the presence of the road bank and the median slope. This work utilizes ZMP as a direct measure of a vehicle rollover threat index. The ZMP is defined as the point on the ground where the summation of tipping moments, due to gravity and inertia forces, equals zero, as illustrated in Figure 6.  
When the AGV is driving on a horizontal road, the reaction force \(F_{N}\) acting directly beneath the AGV's center of mass, this reaction point is the ZMP, as shown in Figure 6a. When driving on a road with a bank angle, as shown in Figure 6b, the reaction force \(F_{N}\) shifts in order to balance the vehicle's weight and satisfy the definition of ZMP. The lateral acceleration caused by turning will also shift the reaction force as shown in Figure 6c. Eventually, there exists an angle or lateral acceleration, as in Figure 6d, where the reaction force is no longer able to balance the vehicle's weight (or inertial dynamics) and the ZMP moves outside the support polygon, leading to the rollover of the AGV.  
Figure 5: Sideslip envelope constraint.  
Figure 6: Zero-moment-point (ZMP) illustration.  
Although the location of ZMP exists in three-dimensional space, only its coordinate along the vehicle's body-fixed \(y\)-axis, termed as \(y_{ZMP}\), is of interest for vehicle rollover prevention. The regularized \(y_{ZMP}\) with respect to half of the track width (\(T_{r}/2\)) is written as:  
\[g_{ZMP}=\frac{2}{T_{r}}\left(h_{sr}\phi+\frac{h_{sr}}{g}\left(\vartheta_{y}+ \omega_{z}v_{x}\right)-\frac{I_{x}}{mg}\omega_{x}\right) \tag{14}\]  
(14) can be rearranged as:  
\[\tilde{y}_{ZMP}=\textbf{N}_{1}\hat{\xi}^{(k)}+\textbf{N}_{2}\hat{\xi}^{(k)}+ \textbf{N}_{3}\textbf{u}_{2} \tag{15}\]  
with:  
\[\textbf{N}_{1}=\left[\begin{array}{cccc}2h_{sr}/\left(gT_{r}\right)&0&-2I_{ x}/\left(mgT_{r}\right)&0&0&0\end{array}\right],\textbf{N}_{2}=\left[ \begin{array}{cccc}0&2h_{sr}v_{x}/gT_{r}&0&2h_{sr}/T_{r}&0&0\end{array} \right],\]  
and \(\textbf{N}_{3}=\left[\begin{array}{cc}2h_{sr}/T_{r}&0\end{array}\right]\).  
Then, the constraint for rollover prevention is:  
\[-\tilde{y}_{ZMP,max}\leq\tilde{y}_{ZMP}\leq\tilde{y}_{ZMP,max} \tag{16}\]  
where \(\tilde{y}_{ZMP,max}\) is the threshold of ZMP.
###### Abstract  
Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.  
fault diagnosis; vibration signal; rotating machinery; rotor +
Footnote †: journal: Applied Sciences
## Low-Cost Spin Pit Design
### Final Design
#### 4.2.1 Off-the Shelf Parts and Components  
In order to keep costs low, the number of custom parts was reduced to a minimum. This consideration is applied to the test rig design as well as to the test specimen. Table 3 shows the off-the-shelf components used in the final test rig (excl. test rotor, which is described in Section 4.3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Name or Brand** & **Price** & **Specifications** & **Ref. 1 ** \\ \hline Motor + variable frequency & Spindle: SDK80-48Z-15 & 900 \(\upxi\) & P = 1.5 kW & - \\ drive & VFD: FC300-2.2 TZ & 900 \(\upxi\) & n\({}_{\text{max}}\) = 48,000 rpm & - \\ \hline \multirow{2}{*}{Clamp set} & KTR 131 & 25 \(\upxi\) & Self-centering & [42] \\  & & & \(\upxi\) 0 \(\upxi\) mm & \\ \hline Shaft & 6 mm shaft, CF53, material code: 1.1213 & 5 \(\upxi\) & 300 mm \(\times\) 0 6 mm \(\times\) 6 h & - \\ \hline \multirow{2}{*}{Magnetic coupling} & MTD 0.3 _Mohac_ & 130 \(\upxi\) & n\({}_{\text{max}}\) = 26,000 rpm & [43] \\  & & M\({}_{\text{t}}\) = 1.12 Nm @ 3 mm gap & & \\ \hline Rolling bearing (2 required) & 71905 CDGA/PAA & 100 \(\upxi\) & n\({}_{\text{max}}\) = 44000 rpm & [44] \\ \hline Radiator & 240 mm computer water cooling radiator & 16 \(\upxi\) & 276 \(\times\) 120 \(\times\) 32 mm & - \\ \hline Adapter & 2\(\times\) external thread fitting adapter & 3 \(\upxi\) & G 1/4 Thread & - \\ \hline Coolant hose & 2M transparent soft PVC tube & 9 \(\upxi\) & 9.5 \(\times\) 12.7 mm tube & - \\ \hline Pump + expansion tank & Coolant water pump + expansion tank & 30 \(\upxi\) & Flow rate: 500 L/h & - \\ \hline Fan (2 required) & Standard 120 mm FC-Fan & 12 \(\upxi\) & 120 \(\times\) 120 \(\times\) 38 mm, 230 V AC & - \\ \hline \multicolumn{4}{c}{Total cost of off-the-shelf components} & 1345 \(\upxi\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Off-the-shelf components used in drive unit, mechanical connections, and cooling system.  
Figure 9: Low-cost spin pit and major components including periphery. Please note that the control desk (left) is moved away from the test rig when burst tests are conducted.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### Certainty Factor Method  
The certainty factor (CF) method is a probabilistic approach that is used to evaluate the contribution of different landslide conditioning factors to the landslide susceptibility. It is widely employed due to its ability to measure the degree of certainty regarding the occurrence of landslides based on statistical relationships between observed landslide events and contributing factors [35]. A notable example of the usage of CF in advance modeling approaches is the work by [PERSON] et al., who used CF for feature weighting in combination with a deep neural network (DNN) to evaluate landslide susceptibility [36]. The CF can be obtained using the following formula:  
\[f(x)=\begin{cases}\frac{\mathit{PPa}-\mathit{PPs}}{\mathit{PPa}(1-\mathit{PPs })},\textit{ if PPa}\geq\mathit{PPs}\\ \frac{\mathit{PPa}-\mathit{PPs}}{\mathit{PPs}(1-\mathit{PPs})},\textit{ if PPa}<\mathit{PPs}\end{cases}\]  
where PPa is the conditional probability of landslides in the \(a\)-th category, and \(\mathit{PPs}\) is the prior probability of the total number of landslides in the study area. The value of the CF is proportional to the probability of landslide occurrence, with values ranging between \(-1\) and \(1\). A positive CF value indicates a higher likelihood of landslides, while a negative value suggests a lower probability. If the CF value is close to \(0\), it implies a weak or no correlation, making the certainty of landslide occurrence statistically insignificant.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 6 Performance Analysis
### Energy Consumption  
In this section, we analyze the average energy consumption of one node to transmit data successfully in shared slots of the superframe. Taking \(T_{L}\) as the time required for transmitting a packet and \((T_{CCA})\) for each successful channel assessment, the average energy consumption for a node can be calculated as:  
\[\begin{split} Average\ Energy&=\alpha T_{CCA}P_{rx}+(1- \alpha)T_{S}P_{rx}\\ &\quad+(1-p_{b})T_{L}P_{tx}\end{split} \tag{23}\]  
where \(P_{rx}\), \(P_{tx}\) are the energy consumption to receive and transmit a packet, respectively. \(T_{S}\) is the time duration of transmitting the known signal.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement
### Cognitive Metrics
#### 3.2.1 Hop Count  
To reduce the latency for each transmission, each node should build a shortest path to the destination. In a tree-based routing, the number of hops to the sink node is a decisive metric to select a parent node; that is, each node can maintain a low latency by adopting the hop count to the sink node as a decision variable. In this work, we adopt the hop count (\(H\)) as a decision variable to form a hierarchical tree structure and reduce the end-to-end delay.
###### Abstract  
Given the high neuromechanical demands and frequent asymmetries in badminton, this study investigated the impact of a four-week asymmetry-targeted intervention on single-leg hop performance in elite junior badminton players and examined whether asymmetry-based indices could predict training responsiveness. Twenty-two national-level athletes (aged 15-18) were randomized into an experimental group (EG) undergoing neuromechanical training with EMG biofeedback or a control group (CG) following general byometric exercises. Key performance metrics--Jump Height, Reactive Strength Index (RSI), Peak Power, and Active Stiffness--were evaluated pre- and post-intervention. Two novel composite indices, Force Efficiency Ratio (FER) and Asymmetry Impact Index (AII), were computed to assess force production efficiency and asymmetry burden. The EG showed significant improvements in Jump Height (\(p\) = 0.030), RSI (\(p\) = 0.012), and Peak Power (\(p\) = 0.028), while the CG showed no significant changes. Contrary to initial hypotheses, traditional asymmetry metrics showed no significant correlations with performance variables (r < 0.1). Machine learning models (Random Forest) using FER and AII failed to classify responders reliably (AUC = 0.50). The results suggest that targeted interventions can improve lower-limb explosiveness in youth athletes; however, both traditional and composite asymmetry indices may not reliably predict training outcomes in small elite groups. The results highlight the need for multidimensional and individualized approaches in athlete diagnostics and training optimization, especially in asymmetry-prone sports like badminton.  
b adminton; asymmetry; impact index; biofeedback; neuromuscular asymmetry +
Footnote †: journal: Applied Sciences  
Article
## 3 Experimental Results and Analysis
### Evaluation Metrics
#### 3.2.3. The Number of Retained Non-Dominated Solutions  
This study utilizes non-dominated sorting [14] to assess the Pareto solution sets generated by different methods. The process is delineated in Figure 9. All Pareto solution sets from each technique are initially collected, and non-dominated solutions are preserved. Subsequently, the non-dominated solutions across all methods are amalgamated, uniquely identifying each method's solutions. Then, all solutions undergo non-dominated sorting. After sorting, each solution receives a rank, and those not at rank 1 are removed, leaving only dominant solutions. Finally, the quantity of retained non-dominated solutions per method is determined based on their assigned numbers.
# Characterizing the Tensile Behaviour of Woven and Composite Fabrics under UV Exposure
## 3 Experimental
### Equipment  
#### 3.3.1 Tensile Testing  
A constant rate of extension (CRE) tensile testing machine (Shimadzu AGX series) with a 600 kN load cell was adopted to study the tensile properties of engineered fabrics. The tensile test was conducted in accordance with ISO 13934-1 [25]. This test method determined the maximum force and its corresponding elongation by using the strip method. According to ISO 13934-1, the strip method required the full width of the test sample to be gripped within the jaws of the tensile testing machine. The test samples were 50 mm wide and 200 mm in gauge length, as specified in the test method. Test samples were mounted with a pretension of 0.5 N and subjected to an extension rate of 100 mm/min in the CRE tensile testing machine. A modification to the test method, ISO 13934-1, was necessary due to the constraints in the experimental study. Limited space available for outdoor UV weathering generally reduced the number of samples per test. At each level of exposure time, the number of samples was reduced from five, as required by ISO 13934-1, to three samples in both machine (MD) and cross-machine (CD) directions to check the consistency of the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & \multicolumn{2}{c}{**Woven**} & \multicolumn{2}{c}{**Composite**} \\
**MD** & & **CD** & **MD** & **CD** \\ \hline Tensile strength [kh/m] & 130 & 130 & 70 & 60 \\ Tensile strain [\%] & 10 & 10 & 10 & 10 \\ UV resistance 1 (\% retained) & 90 & & 90 \\ Thickness [mm] & 1.5 & & 6.3 \\ Mass per area [g/m\({}^{2}\)] & 535 & & 1200 \\ \hline \hline \end{tabular}  
* For a duration of 500 h.  
\end{table}
Table 1: Properties of woven and composite-engineered fabrics.  
Figure 2: Outdoor UV weathering test for woven (black) and composite (yellow) (Courtesy: Tencate Geosynthetics Asia).