esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 1 Introduction
### State-of-the-Art  
Many approaches have shown how to manage omnidirectional receiving capability. Based on optical to electrical conversion, these approaches can be classified into three fundamental groups.  
The first group of receivers employs only one photodetector and some additional optical elements, for example, the fish eye optical system mainly used in robotics [5; 6]. A system using two mirrors,similar to telescope systems, serves as another example [7; 8]. An omnidirectional stereo imaging system uses a concave lens and a convex mirror [9]. Other assemblies use non-imaging collectors, such as compound parabolic concentrators (CPCs) in an array-like fashion [10; 11] or a fiber bundle that couples omnidirectional photons in its field of view (FOV) and delivers these photons to the detector [12; 13].  
The second group of receivers employs several photodetectors with a signal-combining technique. The first example of these is called selection combining (SC), which uses only one detector with the strongest instantaneous signal from \(s\)-detectors for signal detection [14]. The second technique is called maximum ratio combining (MRC), where all \(s\)-detectors are selected and coherently combined with the gain of each channel proportional to the root-mean-square (RMS) signal level and inversely proportional to the mean square noise level in that channel [15]. In other words, this technique follows the rule of optimal filtering. The third technique is known as switched combining. Detectors are switched once the signal collected by the active detector falls below a predetermined threshold. Because of this, switching does not occur as often compared to SC [16]. The last technique is equal gain combining. The signals from all \(s\)-detectors are summed coherently and with an equal gain [17].  
The third group consists of receivers with hemisphere-photomultiplier tubes. Typical examples were presented in [PERSON], [PERSON] et al. [2], [PERSON] et al. [18], [PERSON], [PERSON] et al. [19].  
Note, the overview given above is not comprehensive.
## 4 Results of Early Detection of Ethanol Gas Leaks
### Ethanol Gas Leak Detection During an Indoor Parcel Delivery
Figure 7 illustrates the raw classification results obtained when applying the pre-trained LDA model to classify all gas samples gathered by the eNose (shown in Figure 6) during the indoor delivery operation whose trajectory is described in Figure 5. The bold green line indicates the true or expected class of the gas samples through the experiment and the thin dark and red lines are the output of the early detection of an Ethanol gas leak. The bold green line has been generated manually by considering the possible extent of the Ethanol leak and when it should be detected. Figure 7 shows two interpretations of the LDA classification. Figure 7a displays the instantaneous real-time classification of each eNose data sample. As expected, during the transition intervals from Air to Ethanol and vice versa, the output of the LDA classifier oscillates back and forth between the two classes. As stated before, this effect is caused because the eNose data samples are transitioning between Air and Ethanol or vice versa. Alternatively, Figure 7b displays a windowed interpretation of the instantaneous real-time classification results of the eNose data samples, proposed to filter the LDA classifier results to avoid oscillating classifications. The windowed interpretation shows the most frequent results evaluated in the last N samples gathered from the eNose. The value of N for the eNose used in this work and the mobile robot application performed has been set to 19 samples using a holistic trial and error approach evaluated in different gas leak experiments. The drawback of this filter is that it adds a delay in the Ethanol gas leak detection of around 10 s although this is not a problem in this indoor delivery application as the mobile robot usually stays more than 60 s inside rooms and laboratories where a real gas leak could occur.
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 1 Introduction
The purpose of this study is to estimate ice production in the RISP and MSP by using a combination of Sentinel-1 SAR and AMSR2 data from 2017 to 2018. The Sentinel-1 SAR data are used to identify polynya area/extent and the AMSR2 data are used to derive thin ice thickness based on an algorithm presented in [19]. In the next section, the study area and datasets are described; Section 3 shows the method to identify a wind-driven polynya and the method to retrieve ice thickness; Section 4 presents the polynya areas, ice thicknesses, and ice productions of RSP; the results are discussed in Section 5; Section 6 gives a brief conclusion of this study.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 4 Design Parameters and Guidelines
### Unit Cell Size \(w\) and Central Element Width \(t\)
The first and maybe most important of the differences with the other equations is clearly visible in Figure 8d. The fitted plane decreases  
Figure 8: The effects of \(w\) [mm] and \(t\) [mm]. (**a**) Frequency shift around 800 MHz. (**b**) Frequency shift around 3.5 GHz. (**c**) Bandwidth shift around 800 MHz. (**d**) Bandwidth shift around 3.5 GHz. in bandwidth with higher \(t\) values. This is the opposite of the lower band response. This means that trying to achieve a wider bandwidth at the lower frequency yields a smaller bandwidth around the higher center frequency. It must be said that the precision of this equation is a lot lower than the precision of the other equations. The variability with this one is high. Using this equation for the bandwidth might result in a good approximation of the real-life response, but it is advisable to first look at the other equations before the fourth one. \[\mathit{BW}_{\mathit{high}}\left[\mathrm{GHz}\right]=-0.0708t\left[\mathrm{mm }\right]+0.0022w\left[\mathrm{mm}\right]+1.2205\qquad\quad p=61.558\ \% \tag{4}\]
## 6 Discussion  
In this section, we first analyze and compare the suppression attack with well-known jamming attack in terms of attack method, stealthiness, attack energy efficiency, and level of denial of service. Then, we further explore design issues and extensions of HED for future research.  
The basic idea of suppression attack is that a malicious node multicasts a series of spoof data messages with continuous sequence number to increase the minimum sequence number stored in the Seed Set, which can prevent the legitimate nodes from accepting valid data messages and cause them to delete the cached data messages. For jamming attack, a malicious node needs to continually transmit a radio signal to block any legitimate access to the medium and interfere with reception. Compared to the jamming attack, the suppression attack is much stealthier. This is because the malicious node acts as a normal node but multicasts a limited number of data messages to legitimate nodes to increase the minimum sequence number stored in the Seed Set, which can cause the legitimate nodes to reject valid data messages by themselves. In addition, the suppression attack has lower energy consumption compared to that of jamming attack because the less number of attack packets (e.g., spoof data messages) are generated and multicasted by malicious node in the suppression attack. However, the jamming attack has to continually broadcast a radio signal, consuming more energy. The suppression attack makes all one-hop neighbor nodes reject valid data messages and delete the cached data messages. Thus, the valid data messages cannot be transmitted and shared further in the network. Eventually, the suppression attack can lead to an extremely severe denial of service in MPL-based LLNs.  
Figure 10: Changes of increment rate of minimum sequence number (\(R_{inc}\)) against simulation time and filter gain constant \(\alpha\).  
In the HED, each node individually monitors the malicious node to detect the potential multicasting misbehaviors. Similar passive monitoring-based approaches are also found in [41; 42]. Since the detection rate highly depends on how many spoof data messages are received from a single malicious node, it can be significantly reduced if multiple malicious nodes collude together. Inspired by the camouflage-based detection approach [15], in which each node pretends not to overhear on-going communication but monitors the forwarding behavior of its adjacent nodes to detect a deep lurking malicious node, we plan to extend the HED by deploying an active detection approach. The basic idea of active detection is that each legitimate node hides the information of MPL control messages, counts the number of forwarding misbehaviors, and rejects certain number of data messages from malicious node with a large number of forwarding misbehaviors.
# Rapid Soil Tests for Assessing Soil Health
###### Abstract  
Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl\({}_{2}\) extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R\({}^{2}\geq\) 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy.  
near infrared spectroscopy; 0.01 M Calcium chloride; Sustainable Development Goals; soil health indicators; translation functions; outcome-based policy Article
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 2 Materials and Methods
### Types of Interventions and Control
movements were initiated during the robotic training: (a) unilateral finger: one study, (b) unilateral hand and wrist: four studies, (c) unilateral shoulder and elbow: seven studies, (d) unilateral whole arm: two studies, and (e) bilateral wrists and hands: one study. The comparison control groups in the upper limb rehabilitation studies varied by two types: (a) nine studies provided robotic training with a sham tDCS condition; and (b) six studies administered only robotic training. All of the control groups experienced the same number of robotic training sessions as the combined-protocols treatment groups (Table S1). Post-stroke rehabilitation for the lower limbs consisted of robot-assisted gait training (four studies) and robotic gait orthosis on the impaired lower extremities (one study). Using anodal tDCS, four studies stimulated the leg area within the M1, located close to the midline, whereas one study used unilateral anodal tDCS on the ipsilesional hand area within the M1. These studies administered multiple sessions of the combined training protocols (e.g., 10-20 sessions). In addition, for lower limb rehabilitation, two studies with end-effector, two studies with exoskeleton, and one study with end-effector/exoskeleton robot device characteristics were included. Note that each of the five studies trained bilateral whole leg movements. For the controls, during lower limb rehabilitation periods, three studies provided robotic training with sham tDCS condition whereas two studies administered only robotic training. All studies used the same number of training sessions and robotic training protocols as the combined protocol treatment groups (Table S1). Table 3 shows the specific details of the tDCS intervention protocols.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 3 Method
### Training and Evaluation  
The model is trained using the cross-entropy loss:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log(p_{i,c}),\]  
where \(N\) is the batch size, \(C=4\) is the number of classes, \(y_{i,c}\) is the ground-truth label, and \(p_{i,c}\) is the predicted probability. For semi-supervised training, a weighted loss incorporates pseudo-labels, as detailed in Algorithm 1.  
```
1:Labeled dataset \(D_{\text{labeled}}=\{(x_{i},y_{i})\}_{i=1}^{N_{l}}\), Unlabeled dataset \(D_{\text{unlabeled}}=\{x_{u_{j}}\}_{j=1}^{N_{u}}\), Test dataset \(D_{\text{test}}\), Epochs \(E\), Threshold \(\tau=0.9\)
2:Initialize Enhanced Swin Transformer parameters \(\theta\), optimizer, and scheduler
3:for epoch \(e=1,\dots,E\)do
4:if\(e<2\)then\(\triangleright\) Initial training on labeled data only
5: Train model on \(D_{\text{labeled}}\), update parameters \(\theta\)
6:else\(\triangleright\) Semi-supervised learning phase
7: Initialize PseudoLabels \(\leftarrow\)\(\emptyset\)
8: Initialize PseudoImages \(\leftarrow\)\(\emptyset\)
9: Generate pseudo-labels:
10:for all\(x_{u}\in D_{\text{unlabeled}}\)do
11: Predict class and confidence for \(x_{u}\)
12:if confidence \(>\tau\)then
13: Add \((x_{u},y_{u})\) to PseudoLabels
14:endif
15:endfor
16:Training: Train model on \(D_{\text{labeled}}\)
17:if PseudoLabels not empty then
18: Train model on pseudo-labeled data using weighted loss
19:endif
20:endif
21: Update learning rate scheduler
22:endfor
23:Testing: Compute predictions on \(D_{\text{test}}\) and evaluate metrics
24:return Classification metrics and trained model
```  
**Algorithm 1** Enhanced Swin Transformer with semi-supervised learning
## 2 Materials and Methods
### Configuration of Defect Models
#### 2.5.2 3-D Model  
Figure 4a shows the simulation setup for a 3-D plate of size 1200 \(\times\) 1200 \(\times\) 10 mm\({}^{3}\). The defect investigated is a complex Hann-shaped defect (60 mm diameter, 3 mm depth) combined with a flat-bottom hole (30 mm diameter, 5 mm depth), shown in Figure 4b. This defect shape was chosen because corrosion often results in smoothly varying thickness loss, while pitting corrosion produces defects similar to blind holes. Detecting and distinguishing both types of corrosion is crucial. The combination of these two types of weakening was selected to simulate a more realistic and complex corrosion profile, as real-world corrosion often involves irregular geometries. Our aim was to test the Mindlin model's capability to handle such complex defect configurations.  
The \(A_{0}\) mode was excited at Tx, and out-of-plane displacement components were measured along a circular array with a 700 mm diameter, as shown in Figure 4a. Some characteristic signals were recorded at the Tx and Rx points. The wave amplitudes were normalized by the maximum displacement from the intact plate measurements. Scattered wave fields were isolated by subtracting the intact plate waveforms from those measured in the defected plate, and the contour plots of plate normal displacements of the scattered signals were analyzed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Density (kg/m\({}^{3}\))** & **Young’s Modulus (GPa)** & **Poisson Ratio** \\ \hline
7850 & 210 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the steel.  
Figure 3: (**a**) Schematic of the simulation set-up of the plate for damage analysis; (**b**) Symmetric smooth defect; (**c**) One-sided smooth defect; (**d**) One-sided rectangular notch.
## 3 Results
### Effect of Technical Determinants on Performance  
Among the variables describing arm kinematics (Figure 4), reduced Elb\({}_{\text{Catch}}\) and Elb\({}_{\text{Finish}}\) (pointing to a straighter arm at the catch and reduced flexion at the finish) as well as decreased  
Figure 3: Average (bold line) \(\pm\) SD (shaded area) normalized muscle activations. Data were collected from all the twelve subjects and averaged after normalization.  
Wri\({}_{\text{Catch}}\) were indicative of higher power output and speed. Higher values of \(\text{Sho}_{\text{Catch}}\) and \(\text{Sho}_{\text{Finish}}\), as well as \(\text{Elb}_{\text{ROM}}\) led to higher speed but not to increased power. The greatest effect was observed for \(\text{Elb}_{\text{Catch}}\) with a 1 SD increase in flexion, resulting in a power output change of \(-2.6\%\) and in change in speed of \(-5.9\%\). Regarding the movements of the pelvis and spine segments, \(\text{Pelv}_{\text{ROM}}\) was the most important parameter for power production, with increased RoM associated with more power produced (5.9% for 1 SD change), and \(\text{L2}_{\text{Catch}}\) was the most important for speed with a 1 SD change resulting in a 1.7% change. \(\text{T6}_{\text{ROM}}\) and \(\text{L2}_{\text{ROM}}\) also had positive effects on power and speed. A more forward-tilted pelvis at the catch (lower \(\text{Pelv}_{\text{Catch}}\)) was favorable to power but had no impact on speed. A more backward-leaning pelvis at the finish (higher \(\text{Pelv}_{\text{Finish}}\)) was beneficial to speed but not to power. Regarding the parameters describing the oar angles, asymmetries had no significant influence (\(\text{Length}_{\text{As}}\), \(\text{EFA}_{\text{As}}\), \(\text{FSA}_{\text{As}}\) and \(\text{CSA}_{\text{As}}\)) on performance. EFA was the most important parameter to power output and speed (an angle increase of 1 SD was associated with 7.2% and 1.9% changes, respectively). Increased CSA and FSA negatively impacted power (\(-2.6\%\) and \(-4.0\%\)) and speed (\(-0.4\%\) and \(-1\%\)). A less negative peak of boat acceleration mAcc was associated with decreased performance in power and speed (\(-1.7\%\) and \(-1.4\%\)). A higher \(\text{Force}_{\text{M2P}}\) had a positive effect on both power and speed (2.7% and 0.7%). Also, an earlier \(\text{Force}_{\text{T2P}}\) had a positive effect on both power and speed (0.4% and 0.7%). Finally, there was no significant effect on performance for \(\text{Force}_{\text{AP}}\) (Figure 4).  
Figure 4: Effect of one standard deviation (1 SD) change in technical determinants on two measures of performance: average power and average speed over the rowing cycle. Only the effects for the coefficients with \(p\)-value \(<0.05\) are reported.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 3 Comparison of the Solutions
### Criteria for Comparing Solutions
#### 3.1.4 The Cost of the Telecommunication System
Once deployed, its reliability is high and human intervention is very limited unless a repeater breaks, for example. Breakings occur very rarely because the optical fiber is underground. Furthermore, telecommunication towers do not require significant interventions except possibly to repoint the antennas. In this specific case, human intervention on large towers is delicate. Therefore, the intervention cost can be high if it necessitates the use of a helicopter or the transportation of the intervention team. The balloons seem to be a good alternative even if these solutions are emerging and it is too early to assess their reliability. However, if the strong winds do not alter the balloon's structure, the only periodic intervention is to recover the balloon, re-inflate it, and send it back. As for the drone, it requires an important intervention cost, especially as a permanent solution. The flight time is on average 3 h; this requires untimely recovery of the drone, recharging the batteries, and probably repairing the drone's light elements if the weather conditions are harsh.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 5 Simulation and Analysis
### Simulations over Different Trajectories  
Furthermore, we add more simulations over different node trajectories, and the corresponding results are presented in Figure 12. As for the first simulation case, the azimuth angle increases along with the simulation time and a bending curve can be observed as shown in Figure 12a. The relevant stability CDF results based on estimated and measured positions and speeds are plotted in Figure 12c. Figure 12b shows a case of time-varying motion directions and the corresponding stability CDF results are presented in Figure 12d. Compared with the measured data results, we can learn that our proposal shows a good approximation and it is still validated at different node trajectories.  
Figure 11: Comparisons with different algorithms, (**a**) CDF of average stability, (**b**) ratio CDF of changed edges.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Computational Complexity** & **Time Consumption** \\ \hline Our proposal & \(\mathcal{O}(akdN^{2}+(1-\alpha)kdN)\) & 0.93 s \\ Method 1 & \(\mathcal{O}(kdN)\) & 1.04 s \\ Method 2 & \(\mathcal{O}(kdN)\) & 0.51 s \\ Method 3 & \(\mathcal{O}(kdN^{2})\) & 2.62 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The computational complexity comparisons of different algorithms.  
Figure 12: The comparison results over different node trajectories, (**a**) curves of Trajectory 1, (**b**) curves of Trajectory 2, (**c**) stability CDF of Trajectory 1 based on our proposal, (**d**) stability CDF of Trajectory 2 based on our proposal, (**e**) stability CDF comparisons of four algorithms for Trajectory 1, (**f**) stability CDF comparisons of four algorithms for Trajectory 2, (**g**) ratio of changed edges CDF for Trajectory 1 of 4 algorithms, (**h**) ratio of changed edges CDF for Trajectory 2 of 4 algorithms.  
We further investigate the networking performance over different node trajectories from aspects of network stability and the ratio of changed edges to validate our proposal. Figure 12e,g represent the CDF results over Trajectory 1 of these two metrics, respectively, and Figure 12f,h denote that of Trajectory 2. Indeed, our proposal shows marginal improvements or even approximate performance compared to the existing methods based on the metric of average stability. Nonetheless, it presents almost the lowest result regarding the ratio of changed edges and low computational complexity as shown in Table 3. Considering the metrics of network stability, the ratio of changed edge number, and the computational complexity comprehensively, it is reasonable to state that our proposal outperforms the existing methods. Consequently, our proposal can still provide some insights into further FANET networking research.