esa-sceva/satcom-chunk-collection huggingface dataset:
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 3 The Design of DAHC Protocol
### The Election of CHs  
The DAHC protocol uses a hybrid method to select CHs. In clustering protocols, the algorithm is cycled based on the round, and the operation of each round is the same. In our proposed protocol, the execution of the algorithm is also cycled by epoch. In the first round of an epoch, CHs are produced by the BS through centralized control. This is the same as the LEACH-C protocol, where each node sends its own energy information and geographic location to the BS, and the BS uses the simulated annealing algorithm to select the appropriate number and the optimal location of the nodes as the CHs. Only the first round of each epoch is generated by a centralized control method. After the selection, the BS will broadcast the CH set and the cluster structure, and the nodes will be assigned with roles according to the received information. After this step, the clustering process is completed.  
In the rest of the rounds of an epoch, aiming to save the communication cost between nodes and the BS, cluster heads are selected by a distributed method. As the number and location of CHs in the first round are optimal, the load of the whole network is more balanced. So, from the second round, the selection of the CHs is based on the cluster information of the previous round. After finishing the previous round of the cycle, the average residual energy of each cluster is calculated by the CH based on the residual energy of each member node attached to the data packet. In this round of election, the nodes whose remaining energy is not less than the average residual energy are considered as candidates. Then, the candidate who is closest to the previous CH is selected as the final CH. This method not only saves the communication cost between the nodes and the BS, but also ensures the reasonable structure of the cluster, and balances the energy load of the whole network. The process of clustering in a distributed method will be introduced in Section 3.4.  
Figure 1: The process of the DAHC protocol.
# The Performance of LiF:Mg-Ti for Proton Dosimetry within the Framework of the MoVe IT Project
## 1 Introduction
The goal of WP3 is the development of new devices for measurements of biological effects; that is, for RBE measurements and for radiobiological characterization of the proton beamline used for the exemplary selection of tissue types. A monoenergetic proton beam available in the experimental room at INFN-TIFPA (Trento Institute for Fundamental Physics and Applications) in the Proton Therapy Center was used to simultaneously irradiate the thermoluminescent dosimeters, which were provided and analyzed by the Naples Unit, while tumor cell lines were inserted in the bio-phantom designed by the BioTech (UniTN) partner. The accuracy and precision of the doses measured using TLD in the high-energy proton beams, in particular in the distal fall-off region of the Bragg peak, have been reported in several studies in the literature [23; 24; 25; 26; 27; 28; 29; 30]. The results indicate that for all commonly used materials, the sensitivity (TL response or absorbed dose) decreases with increasing LET; however, it has been demonstrated that the sensitivity of the high temperature peak for TLD-100 in the proton beam is stable, allowing the accurate determination of doses to within \(\pm 4.0\%\)[30]. This study reports the results of several exploratory tests of physical dosimetry along the proton beam profile to verify the performance of TLDs for radiobiological dosimetry. Depth-dose curves are reconstructed for several configurations and different planned dose values at the Bragg peak. The results of this investigation will help us to assess whether TLDs can be used as reliable in vivo dosimeters in proton beam therapy.
## 6 Strategy for Reliable Bridge Monitoring  
There are some simple practical ways to ensure the reliability and significance of monitoring results and minimize pitfalls, especially due to dynamic multipath.  
First, a monitoring survey should be planned only after the dynamic characteristics of a study structure are known or obtained as approximate values using rules of various regulations and Codes (see for example Section 2.1).  
Second, if possible, there should be made measurements at points with different dynamic behavior, especially points which are expected to have no deflections, for example at the edges of the deck, above pylons, and at points which have maximum deflections, usually at midspans. Alternatively, the same point(s) can be measured, both in intervals of loading and of no loading (mostly traffic), if this is possible. Deflection signal in points/intervals in which no deflections (are expected define the noise level of instruments/analysis (\"background noise\"), while the signal in points/intervals in which deflections are expected should exceed that noise level in order to document dynamic deflection. This requires of course that sensors have the necessary resolution to record deflections predicted for a specific structure, for example, based on Equations (1) and (2).  
Third, different sensors should be combined, for example GNSS and accelerometers, hopefully collocated with RTS (see Figure 2); or at least some control of GNSS data using RTS and other techniques permitting deflection measurements such as radar interferometers [22; 23] and optical techniques [2; 32; 33; 34; 35] during certain surveys is recommended. In our days, some of these studies can be based even on sensors embedded in smartphones; for example, smartphone accelerometers can easily permit to recognize a strong excitation with high deflections from a weak excitation with smaller deflections (e.g. [36]).  
Fourth, GNSS sensors should be mounted on stiff bases (in the simplest of the cases, poles stiffened laterally with tensioned wires) and at elevations ensuring no secondary reflections from passing vehicles to the antennas (see Figure 5).  
Fifth, after a conventional analysis of satellite signals leading to dubious peaks, sky-plots (projections of tracked satellites on a sky map) just before, during and just after dubious peaks must be examined in order to investigate whether one or more satellites were shadowed by vehicles (see [28], Figure 5). These satellites should be removed from the analysis because transient changes in the geometry of observations (satellites) may modify solutions and give the impression of displacements.  
These simple, practical rules, listed not in the order of their significance, are expected to permit safe GNSS bridge monitoring.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on Hyperspectral Images  
Multispectral remote sensing images have been used in road extraction because of their high spatial resolution and multiple spectral features. The commonly used data sources are the satellite multispectral images including QuickBird, IKONOS [180; 181], Worldview 2 satellite [182], Landsat satellites [183] and Gaofen 1 and Gaofen 2 satellites. In addition, because of the large number of bands (generally more than 100 bands) and continuous spectrum bands, hyperspectral images are used for conventional road extraction as well as show great potential for road condition detection, road material identification, road pothole detection and crack detection.
# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 5 Results and Discussion
A clear advantage that can be gained from the radio mapping process is the employment of a loop closure mechanism in order to provide scene recognition capability within the radio environment. We believe that this is an important next step within the current methodology that can provide significant information gains for operational environments and correct the poses of the graph in places that have been revisited based on RSSI values. Summing up, NR5G-SAM is a method of localization and mapping that uses radio signals instead of visual or laser sensors. It involves measuring the signal strength and time of arrival of 5G NR radio waves from a known location to a robot's receiver. By comparing the characteristics of these waves to an estimated map of signal strengths, the robot can estimate its own pose relative to the map. This technique has the advantage of being able to work in environments where visual and laser sensors may be impeded. We foresee its potential in areas with limited GNSS coverage as it provides absolute measurements that are geographically referenced to nearby gNodeB stations in urban or rural scenarios. Figure 12: Individual REMs for FR2 setup along with the 2D global 5G REM.
# Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos
## 2 Preliminaries
### Basic Definitions
#### 2.1.2 Grunwald-Leitnikov (G-L) Definition  
The G-L integral is given by  
\[I_{GL}^{\lambda}g(t)=D^{-\lambda}g(t)=\lim_{h\to 0}h^{\lambda}\sum_{j=0}^{k}(-1)^{j} \left(\begin{matrix}-\lambda\\ j\end{matrix}\right)g(kh-jh) \tag{4}\]  
Here, the coefficients \(\omega_{j}^{(-\lambda)}\) verify \(\omega_{0}^{(-\lambda)}=\left(\begin{matrix}-\lambda\\ 0\end{matrix}\right)=1\) and belong to the following polynomial,  
\[(1-z)^{-\lambda}=\sum_{j=0}^{\infty}(-1)^{j}\left(\begin{matrix}-\lambda\\ j\end{matrix}\right)z^{j}=\sum_{j=0}^{\infty}\omega_{j}^{(-\lambda)}z^{j} \tag{5}\]  
The G-L derivative is:  
\[D_{GL}^{\mu}g(t)=\frac{d^{\mu}}{dt^{\mu}}g(t)=\lim_{h\to 0}h^{-\mu}\sum_{j=0}^{k}(-1)^{j} \left(\begin{matrix}\mu\\ j\end{matrix}\right)g(kh-jh) \tag{6}\]  
where  
\[\omega_{j}^{(\mu)}=\left(\begin{matrix}\mu\\ j\end{matrix}\right)=\frac{\Gamma(\mu+1)}{\Gamma(j+1)\Gamma(\mu-j+1)}\]  
and \(\omega_{0}^{(\mu)}=\left(\begin{matrix}\mu\\ 0\end{matrix}\right)=1\). Two general properties of the fractional-order derivative are used:  
**Property 1**.: _The additive index law_  
\[D^{\alpha}\left(D^{\beta}f(t)\right)=D^{\alpha}D^{\beta}f(t)=D^{\alpha+\beta} f(t) \tag{7}\]  
**Property 2**.: _Caputo fractional derivative operator is a linear operator_  
\[D^{\alpha}\left(af(t)+bh(t)\right)=aD^{\alpha}f(t)+bD^{\alpha}h(t) \tag{8}\]  
_(\(a\), \(b\) are real constants)._
# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 4 Case Study and Cross-Domain Examples
### Cross-Domain Example: Customer Churn Prediction  
To illustrate WISFC's versatility beyond healthcare, we present a hypothetical churn-prediction scenario, no real data were used. Imagine a customer dataset with features such as tenure, number of products, account balance, credit score, age, and online service usage labeled for churn. A data scientist might train three models: logistic regression, decision tree, and XGBoost. Each of those produces its own feature-importance ranking (e.g., standardized coefficients, Gini importances, SHAP values). While all three may concur that tenure belongs in the top ranks, they could disagree on other predictors such as credit score, number of products, or age. Applying WISFC follows the same workflow:  
1. **Collect importance outputs** from each model.
2. **Normalize** and (optionally) **weight** them by validation performance (e.g., slightly favoring XGBoost if it achieved the highest AUC).
3. **Compute WIS** (average normalized rank) and **FC** (fraction of models ranking each feature in their top-k).  
A plausible WISFC outcome might rank tenure highest (\(WIS\approx 1.3\), \(FC=3/3\)), followed by the number of products (\(WIS\approx 2.1\), \(FC=2/3\)) and credit score (\(WIS\approx 2.4\), \(FC=2/3\)). Age may appear lower (\(WIS\approx 3.0\), \(FC=1/3\)), reflecting its sole prominence in XGBoost. Compared with naive top-k overlaps or single-model explanations, WISFC would deliver a richer, more stable set of churn predictors to guide business analysts toward the most reliable factors for customer retention strategies. This demonstration confirms that WISFC applies to any structured-data domain where multiple models and interpretability are required.  
WISFC yields a broader, more reliable feature set than rigid overlap rules while guarding against model-specific anomalies. Uncorroborated features (such as a lone genetic marker in the medical example or age in the churn scenario) remain visible but receive low FC scores as a cautionary signal. Conversely, predictors that never top any single model yet rank consistently high across several (e.g., BMI or credit score) are promoted by WISFC as robust candidates. This dual emphasis on consensus and magnitude provides the balanced strategy required for dependable knowledge discovery from small or noisy datasets.
###### Abstract  
Eccentric muscle actions are integral to human movement, rehabilitation, and performance training due to their characteristic high force output (overload) and low energy cost and perceived exertion. Despite the growing use of eccentric devices, a gap in the research exists exploring multi-muscle activation profiles during multi-joint eccentric-only, isokinetic exercise. This study aimed to quantify and compare surface electromyographic (EMG) activity of four leg muscles--vastus lateralis (VL), tibialis anterior (TA), biceps femoris (BF), and medial gastrocnemius (GM)--during a standardized (isokinetic) submaximal eccentric multi-joint exercise using the Eccentron dynamometer. Eighteen healthy adults performed eccentric exercise at 40% of their maximal eccentric strength. Surface EMG data were analyzed using root mean square (RMS) and integrated EMG (iEMG) variables. Repeated-measures ANOVAs and effect sizes (ES) were used to evaluate within-subject differences across muscles. Results showed significantly greater activation in the VL compared to all other muscles (\(p<0.05\); and ES of 1.28-3.17 versus all other muscles), with the TA also demonstrating higher activation than the BF (\(p<0.05\)). The BF exhibited the lowest activation, suggesting limited hamstring engagement. These findings highlight the effectiveness of the multi-joint isokinetic eccentric leg press movement (via an Eccentron machine) in targeting the quadriceps and dorsiflexors, while indicating the possible need for supplementary hamstring and plantar flexor exercises when aiming for a comprehensive lower body training routine. This study provides important insights for optimizing eccentric training protocols and rehabilitation strategies.  
quadriceps; hamstrings; plantar flexors; dorsiflexors Article
# Effects of the Damping Parameters on the Opening and Closing Characteristics of Vent Valves
## 3 Results
### Different Diameters of Damping Hole B  
Damping hole B is located in front of the steady flow plate. Figure 12 shows the opening and closing characteristic curves of the valve under different damping hole diameters for damping hole B. According to Figure 12a, as the diameter of damping hole B changed, the middle pressure of the support changed with the same rule over time. It can be seen from Figure 12b that with the change in diameter of damping hole B, the change law of the piston chamber pressure did not change in the closing process of the valve. In the opening process, the lag phenomenon occurred when the piston chamber reached the control pressure with the decrease in diameter of the damping hole, which was more obvious when Z\({}_{2}\) was equal to 0.2 mm. It can be seen from Figure 12c,d that as the diameter of damping hole B decreased, the opening time of the valve would increase a little, and the control pressure in the piston chamber decreased when it was fully opened because the pressure in the control chamber rose slowly with time. To ensure that the control gas  
Figure 11: Opening and closing characteristics of valve with different damping hole diameters: (**a**) The variation law of middle bearing pressure with time; (**b**) Control the change of cavity pressure with time; (**c**) The variation law of piston displacement with time; (**d**) The variation of piston displacement with pressure in control chamber.  
pressure could be smoothly transferred to the piston chamber, the diameter of damping hole B should be greater than 0.4 mm.
## 6 Scanning the Coverage Area with Directional BS Antennas (NLOS Channels)
Similar with the LOS case, Figure 13 shows the misdetection probability versus the distance of a UE from the BS for three scenarios: (i) omnidirectional BS, (ii) random scanning with an \(8\times 8\) BS antenna array, and (iii) scanning with the proposed pattern of an adaptive array as described in Table 4.  
Figure 12: Deterministic optimal scanning pattern for BS using adaptive directional antennas under NLOS condition; (**a**) slot 1–2 with \(8\times 8\) antenna array; (**b**) slots 3–5 with \(8\times 8\) array; (**c**) slots 6–15 with \(8\times 8\) array (**d**) slots 16–25 with \(12\times 12\) array; (**e**) Coverage for 25 slots over the entire cell segment. As observed, under NLOS assumption, the proposed deterministic scanning scheme using an adaptive array achieves low misdetection rates (red dashed line) even for UEs located at \(\sim\)40 m away from the BS. With an omnidirectional antenna at BS (blue line), the detection of PSS signals does not perform well since the misdetection probability at all UEs is 1 meaning that they are incapable of detecting the signals. This shows the drawback of omnidirectional antenna under NLOS channels. For the random scanning case using an 8 \(\times\) 8 array antenna at BS, the misdetection rate (green-dashed-x line) fluctuates over the region. Although the performance of the random scanning concept is better than using omnidirectional antenna, the performance is still much less efficient than the use of the deterministic scanning scheme.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 2 Preliminaries
### Re-Encryption Scheme for QKD Network  
In re-encryption scheme, a secret message is encrypted by sender's local random key \(s\). The delivery of the random key \(s\) can be executed by the following protocol. After the protocol, two endpoints can share the key \(s\). The following is the main steps of the re-encryption protocol with two endpoints.  
Protocol 1: re-encryption scheme for the relay QKD network:  
1. Each node executes QKD protocol with its neighbor nodes, which generates n pairs of QKD keys \(Qk_{i}\) as shown in Figure 1.
2. [PERSON] encrypts the random key \(s\) with her QKD key \(Qk_{1}\), which is shared with the first relay node. Calculate the first ciphertext \(c_{1}=s\oplus Qk_{1}\), where \(\oplus\) denotes bitwise module-2 addition.
3. The relay nodes do the XOR operation with the QKD keys, which are shared with the upstream relay nodes and the downstream relay node, to obtain the XOR keys \(a_{i}\), i.e., \(a_{i}=Qk_{i}\oplus Qk_{i+1}\), then immediately dismiss the QKD keys, \(Qk_{i}\), \(Qk_{i}+1\). When relay node \(i\) receives the incoming ciphertext, it calculates \(c_{i+1}=c_{i}\oplus a_{i}\). It is easy to see that \(c_{i}=s\oplus Qk_{i}\).
4. When [PERSON] receives the incoming ciphertext, \(c_{n}=s\oplus Qk_{n}\), he decrypts the ciphertext with his QKD key \(Qk_{n}\) to obtain \(s\). Then [PERSON] and [PERSON] share the common key \(s\). [PERSON] can transmit the message secretly to [PERSON] with the key latter.
# Design and Shear Analysis of an Angled Morphing Wing Skin Module
## 2 Materials
### Fabrication  
Silicon rubber (MVQ-4012), KFRP fibers (1414), and CFRP rods (T300) were adopted to fabricate the skin through a four-step procedure illustrated in Figure 2. First, the CFRP rods were passed through the pins on the frame. It should be noted that two manganese steel rods (diameter = 1.5 mm, modulus = 265 GPa) were installed on the two sides instead of CFRP rods to strengthen the boundaries where overstretching of the rubber matrix is likely to happen. The CFRP skeleton is shown in Figure 2a. Second, a mold with pillars on two sides was fabricated. The KFRP fibers were wound around the pillars and laid evenly on the top and bottom sides of the CFRP rods. The initial angle was determined by the shaping plate, as shown in Figure 2b. Third, silicon rubber was stirred fully and injected into the mold, as shown in Figure 2c. Finally, the silicon rubber was cured at room temperature for 12 h, and then the specimen shown in Figure 2d was obtained by removing the mold and pruning. Five specimens with different initial angles \(\theta_{0}\) = 50\({}^{\circ}\), 60\({}^{\circ}\), 70\({}^{\circ}\), 80\({}^{\circ}\), 90\({}^{\circ}\) were manufactured and named M50, M60, M70, M80, M90, respectively. The other geometric parameters are listed in Table 1. Note that the distance \(l\) was chosen as 4.5 mm, indicating that the CFRP rods were closer to the bottom surface of the rubber matrix. This was because an offset of the CFRP rods led to a smoother top surface (detailed experimental results in Appendix A).  
Figure 1: Design of the morphing wing skin module.  
Figure 2: Fabrication procedure of the morphing wing skin specimen: (**a**) skeleton made of CFRP rods, manganese steel rods, and the frame; (**b**) mounting KFRP and shaping the initial angle of the specimen; (**c**) injecting rubber and curing; (**d**) removing the mold, pruning and obtaining the specimen.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 2 Materials and Methods
### Modelling Methodology
#### 2.2.3 Model Evaluation and Hyperparameter Tuning  
All models were evaluated in terms of their generalisation performance using a class-stratified randomly selected test set, which accounted for 30% of the overall data. Traditional ML models were optimised by tuning their relevant hyperparameters through 10-fold cross-validation on the remaining data. For the DL models, hyperparameter tuning was performed using a randomly selected validation subset that constituted 20% of the remaining data. Table 1 displays the considered hyperparameter values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Hyperparameter** & **Options** \\ \hline \multirow{4}{*}{SVM} & Kernel & Radial basis, polynomial and sigmoid \\  & Technique & Grid search cross-validation \\  & Gamma & 1, 10, 0.1 and auto \\  & Cost & 1, 0.1 and 0.01 \\ \hline \multirow{4}{*}{RF} & Bootstrap & True, False \\  & Technique & Randomised search cross-validation \\  & Maximum Depth & 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None \\  & Max features & Auto, sqrt \\  & Minimum leaf samples & 1, 2, 4 \\  & Minimum sample split & 2, 5, 10 \\  & Number of trees & 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000 \\ \hline \multirow{4}{*}{LSTM} & LSTM units & 240, 64, 120 \\  & Dropout Layers & 0.5, none \\ \cline{1-1}  & Dense Layers & 180, 100, 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter values for the different methods used.  
Model performance was measured using balanced accuracy, positive and negative predicted values (PPV and NPV), the area under the precision-recall curve (AUC-PR), the area under the receiver operating characteristic curve (AUC-ROC), and the Matthews correlation coefficient (MCC). The Youden's J statistic [15] was used to select the optimal ROC's cut-off point.
## 3 Results
### Analysis of the flgM Gene of _A. aneurinibacillus Strain NH_  
The _flgM_ gene of _A. aneurinibacillus_ strain NH was identified by conducting a Smith-Waterman local alignment of the NH whole-genome sequence with the _flgM_ gene coding sequence of _A. aneurinibacillus_ strain XH2 (GenBank accession no. CP014140). The resulting alignment (Figure S2) had no gaps, a reasonably high alignment score (193), and 74.2% nucleotide identity. Translation of the NH _flgM_ gene was performed to obtain the derived amino acid sequence. This sequence was then compared to that of flgM proteins of several species from the _Aneurinibacillus_ genus (Figure S3). Although the NH strain's FlgM protein is highly related to that of species _Aneurinibacillus_. aneurinibticus_ (98% amino acid identity), these two species exhibit several non-conservative substitutions from the consensus sequence compiled by the alignment algorithm (Figure S3). These substitutions are H8Q, T39N, E51T, Q64K, and I76D.