esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 3 Results and Discussion
### Models and Data Processing
#### 3.3.2 A New Index on the Red Edge Region: Level Salinity Index LSI  
A new spectral index (combination of only few wavelengths) was considered for multispectral proposes; they could be cheaper and faster than hyperspectral vision systems. As previously explained, a slight displacement to the right of the red edge region was observed on the first derivative of the spectra of the S3 treatment, which could be related to the moderate increase in the salt concentration of the leaves. On the other hand, the PC1 loading showed the red edge region as the most sensitive area to changes in the spectra induced by salinity. Consequently, an index, Level Salinity Index (LSI), based on the most relevant wavelengths of PC1 loadings (Figure 5B) was proposed.  
This LSI index is an approximation to the second derivative at 710 nm (see explanation at Section 3.3.1) and it is a value related to the red edge displacements observed in the SG D2 SNV preprocessed spectra and also in the loadings of PC1. It is also very similar, but not identical, to the proposed by [28]:  
\[\text{LSI}=[(\text{R}_{675}+\text{R}_{745})/2]-\text{R}_{710} \tag{5}\]  
The LSI was applied to each pixel of the hyperspectral images of the calibration set leaves in order to generate corresponding virtual images and predict the salinity effects on the leaves (Figure 7).  
Figure 6: Virtual images of scores obtained by projection of the hyperspectral images, corrected with second derivative Savitzky–Golay algorithm and SNV normalization, on PC1. Leaves from calibration set. Pixels colored from blue to red are showing increasing levels of salinity affectation.  
Figure 7: _Cont._Clear differences between the LSI values from the pixels of the leaves corresponding to the control treatment (Ct) and the pixels of the leaves corresponding to the highest salinity treatment (S3) were observed (Figure 7). The increase in the LSI values noted in the images responds to a lateral displacement of the red edge to the right [28; 41], which concurs with the findings obtained from the first derivative spectra analysis.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 2 Methodology
### Derivation of Physical Grasp Pose  
The final step before carrying out the grasp action is to convert the image coordinates into the physical coordinates. Most of the algorithm logic branches give the position of two points for a two-fingered gripper to grasp. Let the coordinates of these two points be \((x_{1},y_{1})\) and \((x_{2},y_{2})\). The object position \((x_{obj},y_{obj})\) and orientation \(\theta_{obj}\) can be calculated from these two points for the robot to make the grasp attempt.  
\[(x_{obj},y_{obj})=(\frac{x_{1}+x_{2}}{2},\frac{y_{1}+y_{2}}{2}) \tag{12}\]  
\[\theta_{obj}=\arctan(\frac{x_{2}-x_{1}}{y_{2}-y_{1}}) \tag{13}\]  
Conventionally, images have their frame origin at the top left-hand corner with the \(x\)-axis pointing to the right and the \(y\)-axis downwards. However, as the camera is placed where the gripper is, its image frame origin is set at the center of the image with its \(x\)-axis to the right, and its \(y\)-axis upwards. As the images are of a fixed resolution of \(w\) (width) by \(h\) (height) pixels, the gripper's coordinates are at \((0.5w,0.5h)\) in the image frame. As such, further adjustments have to be made to account for this frame difference, as illustrated in Figure 14.  
The coordinates of the object \((x_{gripper},y_{gripper})\) in pixels can then be converted to the physical or real coordinates \((x_{real},y_{real})\) in cm as follows:  
\[(x_{gripper},y_{gripper})=(x_{obj}-0.5w,0.5h-y_{obj}) \tag{14}\]  
\[(x_{real},y_{real})=(x_{gripper}\times\frac{d}{f},y_{gripper}\times\frac{d}{f}) \tag{15}\]  
where \(d\) is the depth of the camera to the object in cm, and \(f\) is the focal length of the camera in pixels.  
**Remark:** The proposed algorithm in this work is developed for two-jaw parallel grippers. The main motivation for focusing on this type of gripper is its prevalence in practical applications compared to other types, such as multi-finger grippers, vacuum grippers, and magnetic grippers. While different gripper types offer their own benefits, two-jaw grippers are the most popular for robotic manipulations due to several key advantages: versatility (capable of handling a wide range of object shapes and sizes), precision (able to generate precise gripping force when handling delicate items), simplicity and reliability (in mechanical design, control, and maintenance), operation speed (especially compared to complex multi-finger grippers), and cost-effectiveness (simpler design, fewer components, and widespread use).  
Figure 14: The origin of an image (green) is at the top left-hand corner, whereas the origin of the gripper (blue) is at the center of the image, which is at coordinates \((0.5w,0.5h)\) in the image frame. For an object (red) in the image frame, adjustments are needed to convert it to the gripper frame.
# Neural Network-Assisted DPD of Wideband PA Nonlinearity for Sub-Nyquist Sampling Systems
## 3 Proposed NN-Assisted Dual-Stage DPD for Sub-Nyquist Sampling Systems
### Stage 2: An Attention-Based NN for the Virtual Training of DPD
#### 3.2.2 Attention Module  
The front part of the model comprises an attention mechanism featuring two layers. The first one is a nonlinear layer with the activation function tanh, where the size is equal to the inputs \(\tilde{\mathbf{u}}(n)\). Hence, the \(i\)-th output from the first layer of the attention module is represented as  
\[w_{i}(n)=tanh(\mathbf{a}_{i}^{T}\tilde{\mathbf{u}}(n)+\beta_{i}^{T}\tilde{ \mathbf{x}}(n)+b_{i}), \tag{18}\]  
Figure 3: The structure of the attention-based NN proposed for virtual training.  
where \(w_{i}(n)\) denotes the correlation between the \(i\)-th input and the pre-estimated DPD output, and \(\mathbf{u}(n)\) denotes the data input at the time instant \(n\). \(\left\{\mathbf{a}_{i},\mathbf{\beta}_{i},b_{i}\right\}\) are the parameters in the attention module. The second layer is a weight calculation layer, which is utilized to calculate the weight of each input neuron and reduce the importance of the redundant estimated signals. The output from the second layer of the attention module is denoted as  
\[\xi_{i}(n)=\frac{exp(w_{i}(n))}{\sum_{i=1}^{3Q+3}exp(w_{i}(n))},(i=1,2,\cdots,3 Q+3). \tag{19}\]  
Multiplying the calculated weights by the input \(U_{n}\) yields the weighted signal, as shown below:  
\[\chi_{i}(n)=\xi_{i}(n)\tilde{u}_{i}(n). \tag{20}\]
## 5 3 GPP Standardization for URLLC
### Possible 5G Integration Plan by 3 GPP
#### 5.4.1 Standalone (SA)  
In SA, the evolved LTE radio or the 5G NR cells and the core network are operated alone, so that the NR or evolved LTE radio cells are used for both user and control planes. The SA provides a simple solution for operators to provide services to both 4G and 5G customers using normal inter-generation handover as shown in Table 5. The three variations of SA, as defined by 3 GPP, are as follows:  
1. EPC and LTE Evolved Node B (eNB) access (i.e., based on current 4G LTE networks)
2. 5G core (5 GC) and NR 5G Node B (gNB) access.
3. 5 GC and LTE ng-eNB access
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 4 Discussion
### Meta-Regression--Dose-Response Relationship Between Sprint Speed and Muscle Activation  
A major contribution of this study was the meta-regression model, which confirmed a strong linear relationship between sprint speed and muscle activation (R\({}^{2}\) = 0.881, \(p\) < 0.001). The regression coefficient indicates a 6.32% increase in muscle activation per 1 m/s increase in sprint speed, quantifying the progressive neuromuscular demand at higher velocities. These findings build on previous reports but offer a more precise numerical estimate of activation changes per unit increase in speed. Given the absence of significant heteroscedasticity or residual bias, the model provides a statistically sound basis for predicting muscle activation at different sprint speeds [32]. This dose-response relationship has practical applications in sprint training periodization. Athletes can use progressive overload principles to gradually increase sprint velocity while ensuring sufficient neuromuscular adaptation [33; 34; 35; 43; 44].  
It is important to acknowledge a limitation of this meta-analysis regarding the assessment of fatigue. While fatigue-induced changes in muscle activation patterns and biomechanics were discussed, we did not include EMG-based fatigue estimation techniques such as spectral (e.g., median frequency shift) or time-domain (e.g., zero-crossing) analyses due to inconsistent reporting across studies. These methods are well-established in fatigue research and have shown strong validity for identifying neuromuscular fatigue in dynamic tasks [45; 46; 47]. Their absence in this synthesis may limit the depth of interpretation regarding underlying fatigue mechanisms. Future meta-analyses should consider incorporating these features to more comprehensively evaluate neuromuscular fatigue, particularly in differentiating central and peripheral contributions during high-intensity sprinting.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Responsive Web-Based Data Acquisition Platform
#### 2.8.2 Historical Data Section  
The platform also allows users to recover historical data as a report, which shows data tables of all nodes filtered by name, date, device, etc. This information can then be displayed and downloaded as a PDF, CSV, or Excel file for further processing and analysis.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 4 Results and Discussion
The consistent trends of these valleys across all classes suggest that the model is accurately detecting fault signatures, as valleys often correspond to significant changes or features in the data that are indicative of specific faults. The regularity and predictability of these valleys indicate the model's robustness and the high level of confidence in its classifications. The patterns are clear, providing strong evidence of the model's ability to detect faults accurately and consistently across different fault types, including insulation breakdown, loose connection faults, corrosion, and improper termination. The presence of these well-defined valleys in the graphical representation confirms the model's reliability in fault detection, contributing to the overall performance of the system. Figure 10: Lead connection issues in Power Transformer Detection Faults using Decision Tree with Fine Tree ML models. All classes, started between the magnitude of \(-40\) dB and \(-20\) dB. All classes formed a similar shape between the frequency band of 20 kHz and 60 kHz. From the frequency of 20 kHz, theC classes, split into groups of two, Class A and D showed positive gradients. Class B and C showed negative gradients, and they continued to drop gradually until reaching a frequency of 100 kHz.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Multiple Access Schemes  
Among the above positioning methods, different multiple access schemes have been deployed. In [60; 61; 62; 63], frequency division multiplexing (FDM) is used by different pairs of LEDs such that the various TDOAs can be calculated simultaneously. In the AOA-based systems [73; 90], FDM is also used for avoiding the inter-cell interference. Among RSS-based systems and their variants, the positioning schemes in [41; 51; 54; 82] use time division multiple access (TDMA), which would need very good time synchronization and a central unit to control all the transmitters. This often leads to a higher positioning accuracy of the system but at a high implementation cost. Note that a TDMA-based VLC system may often suffer from inter-pulse interference, which one can apply bit stuffing [92] to in order to migrate the interference between the light signals and to improve the illumination quality to allow a very short pulse width to be transmitted. To avoid stringent time synchronization, code division multiple access can be used to distinguish signals from different transmitters. In [93], each transmitter is assigned a unique optical orthogonal code (OOC). Signals from different transmitters can be separated at the receiver according to their correlation properties. In [94], a novel multiple access scheme for decentralized asynchronous VLC systems is proposed considering that the transmitters and receivers cannot be synchronized. Notice that there is often a trade-off between the number of available codes and their sequence length. To support a large number of transmitters, one can consider a spatial reuse method when there is a limited number of the codes for a large area with many transmitters. A fully distributed code division multiple access scheme is proposed in [95] that only requires chip-level synchronization. The simulation result shows that the average BER performance of the scheme is worse than TDMA but much better than FDMA. Even in the scenarios where time synchronization can be achieved, TDMA has been shown to be suboptimal [96]. Pilot sequence designs are proposed in [96] for simultaneously estimating the channel gains of multiple transmitters in a lighting system and satisfying the average and maximum transmit power constraint due to the illumination purpose. The paper illustrates how to design the optimal pilot sequences that minimize the noise variance experienced by a receiver, although the optimization problem is non-convex. For systems with ambient light and systems without ambient light, it also reveals the fundamental trade-off between noise variance, average transmit power, maximum transmit power, number of light sources, and pilot length. The system in [97] uses a dual-tone multi-frequency (DTMF) technique for multiplexing such that each LED will transmit using a unique ID of several digits that is rep
# Methacrylate Polymer Monoliths for Separation Applications
## 2 Methacrylate Polymer Monoliths
### Functionalization Routes and Functional Groups  
A large number of functional groups have been investigated and numerous chemical strategies have been developed over the years. The goal has always been to tailor the surface chemistries of polymer monoliths to make them suitable for the separation of a wide range of analytes, both polar and non-polar molecules, chemical and biological species, large and small molecules. Initially, polymer monoliths were not exhibiting acceptable separation efficiencies for smaller molecules, and instead performed better in the separation of larger biomolecules. In contrast, silica monoliths have traditionally shown better suitability for the separation of small molecules. Functional groups on methacrylate monoliths include amine groups [44], gold nanoparticles [25; 84], antibodies [61], thiol groups [85] and hydroxyl groups [43; 45].
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{
\begin{tabular}{c} **Conc.** \\ **(\#g/mL)** \\ \end{tabular} } & \multicolumn{2}{c}{**Recovery**} & \multicolumn{2}{c}{**Intra-Day**} & \multicolumn{2}{c}{**Inter-Day**} \\ \cline{3-8}  & & **Mean (\%)** & **RSD (\%)** & **Accuracy (\%)** & **Precision** (RSD\%)** & **Accuracy (\%)** & **Precision** (RSD\%)** \\ \hline Basic Blue 26 & 0.30 & 100.90 & 1.26 & 82.85 & 4.07 & 82.93 & 2.79 \\  & 3.00 & 101.05 & 0.67 & 103.90 & 1.25 & 103.74 & 0.26 \\  & 15.00 & 103.03 & 0.96 & 100.17 & 0.48 & 99.72 & 0.75 \\ \hline Basic Red 2 & 3.60 & 99.48 & 2.30 & 80.23 & 4.25 & 80.70 & 0.58 \\  & 36.00 & 100.01 & 0.75 & 103.84 & 1.23 & 103.55 & 0.25 \\  & 180.00 & 101.61 & 0.82 & 100.24 & 0.49 & 99.91 & 0.59 \\ \hline Basic Yellow 28 & 1.50 & 100.39 & 1.44 & 80.38 & 4.71 & 80.85 & 0.68 \\  & 15.00 & 98.09 & 0.94 & 103.60 & 0.98 & 103.35 & 0.25 \\  & 75.00 & 98.98 & 0.94 & 99.65 & 0.21 & 99.48 & 0.60 \\ \hline Disperse Brown 1 & 0.90 & 100.37 & 1.51 & 86.19 & 9.34 & 87.98 & 2.89 \\  & 9.00 & 100.71 & 1.02 & 100.31 & 1.22 & 99.92 & 0.35 \\  & 45.00 & 101.47 & 0.33 & 99.02 & 0.39 & 99.97 & 0.94 \\ \hline Disperse Orange 3 & 0.53 & 97.96 & 0.88 & 86.31 & 9.68 & 88.85 & 2.93 \\  & 5.30 & 98.44 & 0.93 & 100.33 & 1.15 & 100.41 & 0.12 \\  & 26.50 & 99.19 & 0.46 & 99.07 & 0.38 & 100.09 & 0.95 \\ \hline Disperse Yellow 3 & 1.20 & 100.73 & 1.86 & 87.81 & 8.20 & 88.74 & 1.60 \\  & 12.00 & 99.36 & 1.12 & 101.06 & 0.99 & 100.25 & 0.70 \\  & 60.00 & 99.52 & 0.64 & 99.35 & 0.45 & 100.05 & 0.73 \\ \hline HC Blue No. 2 & 0.72 & 104.71 & 2.69 & 95.79 & 8.32 & 97.94 & 3.54 \\  & 7.20 & 105.33 & 1.74 & 98.47 & 1.27 & 98.46 & 0.02 \\  & 36.00 & 101.80 & 0.31 & 99.30 & 0.18 & 99.85 & 0.53 \\ \hline HC Yellow No. 5 & 1.20 & 83.32 & 3.25 & 97.20 & 7.71 & 100.39 & 5.33 \\  & 12.00 & 86.57 & 1.34 & 91.85 & 3.44 & 96.06 & 7.64 \\  & 60.00 & 90.39 & 1.34 & 96.58 & 4.39 & 100.29 & 6.40 \\ \hline Solvent Orange 4 & 0.90 & 97.50 & 3.58 & 100.17 & 6.39 & 102.94 & 4.58 \\  & 9.00 & 96.41 & 1.18 & 97.34 & 1.23 & 101.07 & 3.22 \\  & 45.00 & 93.42 & 1.32 & 95.92 & 1.49 & 100.21 & 3.83 \\ \hline Solvent Orange 7 & 2.01 & 98.77 & 1.88 & 93.00 & 5.09 & 90.86 & 2.07 \\  & 20.10 & 97.85 & 0.54 & 100.86 & 0.88 & 100.28 & 0.54 \\  & 100.50 & 98.33 & 0.38 & 99.54 & 0.33 & 100.12 & 0.55 \\ \hline \end{tabular}
\end{table} 
Table 3: Recovery, accuracy, and precision of colorants in tatto eyebrow sample with different concentrations using HPLC-DAD.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 6 Error Analysis
### Correct and Error Distribution  
To investigate the two models further, we manually inspected the first 50 sentences from each model's output. Following standard classification taxonomy, we identified four types of classification outcomes in the propaganda detection task:  
* Type-1: TP--A news text containing propaganda is correctly classified as propaganda. * Type-2: TN--A news text not containing propaganda is correctly classified as nonpropaganda. * Type-3: FP--A news text not containing propaganda is incorrectly classified as propaganda. * Type-4: FN--A news text containing propaganda is incorrectly classified as nonpropaganda. Table 13 shows the distribution of the correct and erroneous results in all the models tested. Additionally, Figure 6 shows a visualization of the statistics. By calculating the distribution of types, we observed that for both assessed models, the vast majority of cases were correctly classified, with Type-1 and Type-2 together representing over 75% of the outputs. In contrast, error types with Type-3 and Type-4 together accounted for less than a quarter of the total. This highlighted the models' strong ability to achieve correct classification, while errors remained relatively infrequent and consistently distributed across both models. Table 14 presents an example of the five errors produced by MTL models, including both false positives and false negatives, and the justifications for these classification errors. It is clear from the table that the FPs arose from neutral or factual statements containing terms associated with propaganda, whereas the FNs involved subtle propaganda lacking explicit cues. These findings highlight the inherent challenges in accurately distinguishing propaganda within Arabic text and emphasize the need for improved contextual and linguistic modeling in future work. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Type-1** & **Type-2** & **Type-3** & **Type-4** \\ \hline MTL-Sent-UW & 52\% & 25\% & 10\% & 13\% \\ \hline MTL-Emotion-UW & 53\% & 27\% & 9\% & 11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Percentage of correct and error types produced by each model.
## 6 Implementation
### Performance Evaluation  
The prototype implementation was tested to measure the performances of the SE operations in the proposed architecture. A specific NodeJS [52] client was created to interact with the SE. This client generates the unsigned transaction, hashes it, and sends it to the smart card to be signed. Then, a blockchain verifies the transactions, confirming physical ownership of the part. Due to the internal functionality of Hyperledger Fabric, when sending a transaction, a second signature is needed. The execution of both actions has been tested and measured from a performance point of view on 20 iterations. Table 3 presents the measurements showing the time required for each operation and the overall transaction submission time. Notice that the worst case scenario overhead introduced by adding the two crypto-operations performed by the SE into the Hyperledger client transaction is less than 0.6 seconds (accounting of 17.1% of the total procedure time).  
The security/performance trade-offs introduced by this architecture are considered acceptable for this use case, where security is a big concern and there is no real-time requirement, considering the use case where humans are interacting and managing a physical part. Additionally, this process removes the necessity of the handwritten signatures in the supply chain, which saves time and enhance consistency. Finally, it is important to notice that not only do the two parties agree to the part transference, but all the authorized partners are remotely verifying the process and the part's genuineness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & **Avg. Time** & **Min. Time** & **Max. Time** \\ \hline HSM Sign 1 & 267.5 ms & 264 ms & 281 ms \\ \hline HSM Sign 2 & 267.7 ms & 263 ms & 281 ms \\ \hline Sum of Sign Actions & 535.2 ms & 527 ms & 562 ms \\ \hline Overall Transaction Time & 3290.1 ms & 3262 ms & 3307 ms \\ \hline HSM Overhead & 535.2 ms & 527 ms & 562 ms \\ \hline HSM Overhead (\%) & 16.3\% & 16.0\% & 17.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: HSM performances while interrogated through NodeJS API.  
Figure 5: Prototype setup for laboratory evaluation.