esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The use of renewable energy such as wind power is one of the most affordable solutions to meet the basic demand for electricity because it is the cleanest and most efficient resource. In Algeria, the highland region has considerable wind potential. However, the electrical power system located is this region is generally not powerful enough to solve the problems of voltage instability during grid fault conditions. These problems can make the connection with the eventual installation of a wind farm very difficult and inefficient. Therefore, a wind farm project in this region may require dynamic compensation devices, such as a distributed-flexible AC transmission system (D-FACTS) to improve its fault ride through (FRT) capability. This paper investigates the implementation of shunt D-FACTS, under grid fault conditions, considering the grid requirements over FRT performance and the voltage stability issue for a wind farm connected to the distribution network in the Algerian highland region. Two types of D-FACTSs considered in this paper are the distribution static VAr compensator (D-SVC) and the distribution static synchronous compensator (D-STATCOM). Some simulation results show a comparative study between the D-SVC and D-STATCOM devices connected at the point of common coupling (PCC) to support a wind farm based on a doubly fed induction generator (DFIG) under grid fault conditions. Finally, an appropriate solution to this problem is presented by sizing and giving the suitable choice of D-FACTS, while offering a feasibility study of this wind farm project by economic analysis.  
wind farm; Fault Ride Through (FRT); Distributed-Flexible AC Transmission system (D-FACTS); Distribution Static VAr compensator(D-SVC); Distribution Static Synchronous Compensator (D-STATCOM)
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 3 Obtained Results
### High-Resolution Reconstruction of an Element of Interest for Virtual Tours Implementation  
Near the final chamber, an element of interest was noticed and used to focus on the experimentation of a high-resolution survey (Figure 9a). One of the most precious elements of the Gazzano cave is the presence of a big stalagmite that is located in the last part of the cave. It is close to the cave walls but its distance from the cave wall is sufficient to capture it in almost all directions as schematized in Figure 9b. According to the proposed approach, 92 photos of the stalagmite were taken, following a circular path around the stalagmite capturing it from different heights to frame it from different points of view. To scale the model, five distances were measured on the speleothem. Natural marks and recognizable features were selected without marking the object with the purpose of its complete preservation.  
The imagery was then processed in Agisoft Metashape as in the previous experiment. The distances were inputted in the processing procedures by collimating the relative features recognized on the photographs. The scaling procedure provided an overall centimeter-level accuracy (0.0135 m). The 3D point cloud was further processed to compute a 3D object (0.51 m high and with a maximum diameter of 0.7 m) with the survey picture mosaicked and draped on it (Figure 9c).
## 5 Experiments and Analysis
### Evaluation Measures  
Average displacement error (ADE) and final displacement error (FDE) are among the most commonly used performance metrics in the field of trajectory prediction, and in this paper we will primarily use these two metrics to evaluate model prediction accuracy. ADE measures the average difference between the predicted trajectory and the ground truth trajectory. ADE is calculated as follows:  
\[\text{ADE}=\frac{1}{m}\sum_{i=1}^{m}\sqrt{(X_{i}-x_{i})^{2}+(Y_{i}-y_{i})^{2}} \tag{12}\]  
FDE measures the Euclidean distance between the predicted position and the actual position at the last time step. FDE evaluates the difference between the predicted endpoint of the trajectory and the true endpoint. FDE is calculated as follows:  
\[\text{FDE}=\sqrt{\left(X_{f}-x_{f}\right)^{2}+\left(Y_{f}-y_{f}\right)^{2}} \tag{13}\]
## 5 Experiments
### Ablation Experiments and Complexity Assessment  
To verify the effectiveness of each module of the CDRNN proposed in this paper, ablation experiments were conducted, and the results are shown in Figure 4. The CDRNN was categorized into the model without using the CA mechanism and the model without using the CRJ. From the simulation results, it can be seen that the complete CDRNN model has the best recognition performance. Meanwhile, the model that does not use some of the modules has a poor recognition performance, which indicates that the model that adds the CA mechanism is more capable of focusing on the salient feature regions in the TFIs and weakening the aforementioned obfuscation cases.  
As shown in Table 2, we measured the average time to process a single TFI sample in the first epoch to evaluate the required training time. The processing times for CDRNN, AlexNet, ResNet50, MobileNet, TFA-SCNN, STFT-CDSC and VViT models were, respectively, 2.12 ms, 13.7 ms, 5.01 ms, 1.71 ms, 2.03 ms, 2.75 ms and 3.61 ms. The results of the simulation in Table 2 show that in terms of the number of parameters, the CDRNN was significantly lower than ResNet50, AlexNet, VViT and STFT-CDSC. It was only slightly lower than MobileNet and TFA-SCNN, demonstrating its lightweight advantage. The optimal design of the CDRNN model not only reduces the computational burden of the model but also improves its applicability in resource-constrained environments. Compared with image recognition models such as AlexNet, ResNet50, MobileNet, TFA-SCNN, STFT-CDSC and VViT, our proposed CDRNN has a lower overall complexity, is more lightweight, and has a smaller resource overhead.
## 2 Background
### Work Design in HRCS
#### 2.1.5. Emerging Applications  
Aside from assembly applications, collaborative systems are being tested for a wide variety of tasks that do not yet have a distinguishable work design strategy. However, common practices have been identified in the case of welding [32], polishing, and drilling [69], in which robots are used as intelligent fixtures for ergonomic reasons. Similar methods of searching to reduce physical fatigue are seen in engine cover lifting [70] and metal surface finishing [71] tasks.  
Promising efforts have been made in quality inspection applications. Although their presence in HRCS is still moderate, considerable potential is seen within sequential, simultaneous, and supportive interactions alike. In terms of sizes and weights, visual screening tasks from wheel bearings [72] to end-off-line automobiles [73] are feasible. Moreover, peer work roles are well exercised, for example, in the quality evaluation of metallic surfaces after polishing treatments [74]. Safety assurance, in this case, is deeply coupled with an intuitive communication interface based on human body gestures, as a conventional industrial robot is directly involved in the quality inspection process.  
A successful application outside the automotive industry lies in the manufacturing of medical equipment. In [75], after pre-filled syringes are inserted in autoinjector devices, a thorough quality inspection of their injection parameters has to be assessed. Similar to [48], a decision authority scheme is available for human operators with the intention of fostering a more managerial relationship with robot actions. In general, anthropocentric work design strategies for quality inspection tasks a subject to further study in the literature of HRCS.
## 2 Results and Discussion
### LiARG Inhibition In Vitro  
The inhibitory activity of _C. longa_ extracts and **1** against purified recombinant _Li_ARG was assessed across a range of concentrations (Supplementary Material, Figure S2). Among the samples tested, DCCURC showed the lowest IC\({}_{50}\) value (10.04 \(\pm\) 1.5 \(\upmu\)g/mL), followed by HEXCURC (14.4 \(\pm\) 2.4 \(\upmu\)g/mL), **1** (17.55 \(\pm\) 0.4 \(\upmu\)g/mL) and ETOHCURC (52.64 \(\pm\) 0.3 \(\upmu\)g/mL) (Table 1). Statistically, the activity of DCCURC, HEXCURC, and **1** was similar, while ETOHCURC was less active. The similar inhibitory activity of HEXCURC and DCCURC to that of **1** suggests that sesquiterpenes turmerones, abundant in HEXCURC, and 2, present in DCCURC, may also contribute to LiARG inhibition as part of their mechanism of action.  
There are few reports regarding curcuminoids' effect on arginase. The treatment with **1** was able to reduce mouse arginase activity by 73% in a colorectal cancer model [28]. There is also a lack of **1** inhibitory activity on _Leishman_ arginase. Its antileishmanial effects have previously been associated with the induction of apoptosis [29] and the reduction of plasma membrane fluidity [30].  
Similarly, to the best of our knowledge, there are no reports on the inhibitory activity of sesquiterpene compounds, such as turmerone, on _Leishman_ arginase. A previous report demonstrated that the sesquiterpene alismoxide, isolated from _Curcuma comosa_, showed inhibitory activity against human arginase I [31]. The mechanisms of action attributed to turnerone against _Leishmania_ include morphological and ultrastructural alterations [20] and an increase in metacaspase expression, which acts in cell death pathways [32]. In macrophages, turmerones have antioxidant and immunomodulatory activities, such as NO inhibition [33], and a significant increase in gene expression for IL-12 p40, 1L-1B, and iNOS, indicating a TH1 response, important for leishmaniasis control [32].
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 3 Literature Review
[38] used machine learning algorithms with radio spectrum monitoring to classify and mitigate interference in aviation channels. They applied CNNs and RNNs to distinguish legitimate signals from interference, reducing false positives. A multi-tier framework enhanced spectral efficiency and clarity by adjusting detection thresholds based on conditions. However, its high computational cost limits its suitability for low-latency, resource-constrained aviation systems. [PERSON] et al. [39] introduced a GNSS-jamming protection method that refines interference mask models to improve the detection of intentional jamming. By using probabilistic modeling and adaptive filtering, the method creates accurate interference masks, enhancing jamming protection around critical navigation zones. Simulated tests showed a major decrease in false jamming alerts while maintaining high sensitivity to real threats. However, optimization of the model's computational complexity is needed for real-time use. [PERSON] et al. [40] studied UAV interference in urban settings using deep learning detection methods, noting the vulnerability of command links to electromagnetic environments. They relied on predefined models, lacking adaptive real-world testing. [PERSON] et al. [41] investigated cognitive radio for mitigating aviation communication interference dynamically with a real-time adaptive prototype. The system is effective but has a high demand for computational resources, limiting its onboard application. [PERSON] [42] introduced AIMDS for FAA flight inspection, integrating real-time spectral monitoring with machine learning to enhance RFI tracking and impact assessment on communication and navigation systems. AIMDS used onboard sensors and ground stations for accurate interference localization and classification. However, it did not fully address the challenge of detecting low-power interference below conventional equipment noise floors, which can go unnoticed until avionics performance degrades. Table 2 presents a comprehensive analysis of the research papers discussed above, addressing various aspects of RFI in aviation. It outlines the technologies utilized, research focus areas, identified gaps, and the equipment or simulations used in each study. The entries cover a wide range of RFI sources, including 5G's impacts on radar altimeters, GNSS system vulnerabilities, and interference from co-located transmitters and receivers. Key themes include the importance of developing effective mitigation techniques, implementing real-time monitoring systems, and improving EMC standards. Identified gaps, such as a lack of field validation, limited automation for anomaly detection, and inconsistencies in regulatory standards, are emphasized. The testing methods highlighted in the table include spectrum analysis, EMI/EMC simulations, UAV-based monitoring, and advanced filtering techniques.
## 2 Materials and Methods
### Estimation of the Interferometric Phase of the Source of Radiation
To simplify the calculation, one can repeat this process along the azimuth direction at a suitable PRT interval and use interpolation to achieve IPSR of each PRT. To verify this method, a preliminary simulation based on real data is conducted. The data were obtained using an L-band airborne SAR, whose main parameters are listed in Table 1. The original data were achieved without jamming, and the spectrum is shown in the upper side of Figure 6. In the simulation, a jamming signal with 30 MHz bandwidth is added to the original data, in which the JSR is 0 dB. The shape of the spectrum is then distorted, as shown on the lower side of Figure 1.0 After the jamming operation, we traverse the IPSR from 0 to 2\(\pi\), perform DCC, and examine the shape of the spectrum until a close shape is matched. In this simulation, the ideal IPSR is set to 90\({}^{\circ}\), and the IPSR interval is set to 1\({}^{\circ}\). Under this situation, the cosine similarity of different IPSR values could be obtained, as clearly shown in Figure 1.1 As we can see from the Figure 7, the cosine similarity will reach the peak value of 0.8414 when the IPSR is 90\({}^{\circ}\), which is consistent with the simulation condition. To verify the validity of the cosine similarity method in different situations, we perform IPSR estimation simulations under different JSR, as shown in Figure 1.2 In Figure 8, it is demonstrated that the IPSR estimation results are quite accurate under different IPSR angles. Moreover, we can find that the cosine similarity method can also achieve good performance, even at \(-10\) dB and \(-20\) dB for different JSRs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation** & **Input Data Size** & **Output Data Size** & **Time Cost (\(\upmu\)s)** \\ \hline Azimuth FFT & \(N\times K\) & \(N_{2}\times K\) & \(K\times N_{2}/128\times 4\) \\ Range \(K\) points dot product & \(N_{2}\times K\) & \(N_{2}\times K\) & \(N_{2}\times K/128\times 2\) \\ Range FFT & \(N_{2}\times K\) & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}/128\times 4\) \\ Range \(K_{2}\) points dot product & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}/128\times 2\) \\ Range IFFT & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}/128\times 4\) \\ Azimuth \(N_{2}\) points dot product & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}/128\times 2\) \\ Azimuth IFFT & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}\) & \(N_{2}\times K_{2}/128\times 4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The major time cost of the CS algorithm. Figure 6: Spectrum without jamming and with 0 dB jamming.
# Large Language Models as Evaluators in Education: Verification of Feedback Consistency and Accuracy
## 5 Experimental Results and Analysis
### Comparison Between Human and LLM Evaluations  
LLMs showed consistent results for certain criteria such as Correct and Revealing; however, from a comprehensive perspective, there are still issues with consistency and reliability. In this section, we conducted an analysis comparing human evaluation results with those from LLMs. We verified the reliability of the ensemble LLM evaluation by assuming the majority vote results from human annotators as the true label. We then compared this true label with the accuracy of the predicted label, which was determined by the majority vote of LLM evaluators.  
We proposed two majority voting methods:  
* Internal (Macro) majority voting: This method involves performing majority voting within each LLM and then combining the majority results from multiple LLMs to make a final decision.
* Global (Micro) majority voting: This method gathers \(M\) evaluators and \(N\) responses, applying a single majority vote across all at once to make a final decision.  
Figure 12 presents the accuracy comparison between human evaluations and LLM evaluations on the MC160 dataset with the generation prompt strategy set to w/ criteria. Overall, regardless of the majority voting method or the type of feedback generator model used, high accuracy was observed for the Correct, Revealing, and Guidance criteria. This indicates alignment between the majority vote results of human annotators and those of LLM evaluators. In contrast, lower accuracy was noted for the Diagnostic and Encouragement criteria, suggesting that both humans and LLMs struggle to provide consistent responses for these specific feedback criteria, highlighting the need for further research to address these challenges.  
Of particular note is the discrepancy between the high agreement among human annotators for the Guidance criterion, as shown in Figure 7(a), and the very low inter-agreement observed in Figure 10(a). Despite this contradictory result, the accuracy for Guidance in Figure 12 remains high across models, indicating the need for additional research.  
Table 4 presents the results of majority voting based on LLM-generated feedback evaluations on the MC160 dataset. Among the five feedback evaluators, Correct and Revealing scored in the high 4-point range on average, while Guidance and Encouragement  
Figure 12: Accuracy between human and LLM evaluations for the generation prompt strategy w/ criteria in the MC160. The legend represents the feedback generation models. MV stands for majority voting.  
averaged in the high 3-point range, and Diagnostic scored in the low 3-point range. When considering the average voting counts, agreement among LLM evaluators appeared low. However, after performing majority voting, the results were similar to the majority vote of human annotators, leading to a high overall accuracy.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
## 2 Permanent-Magnet Halbach Array Design
### Halbach Array Configurations  
The configuration of the magnets in the array plays a critical role in determining the field distribution and strength. This array configuration has the unique property that produces higher flux density on the stronger side and nearly becomes zero on the weaker side. This asymmetry is achieved by carefully orienting the magnets so that their magnetic fields combine constructively on the desired side. In a Halbach array, permanent magnets are arranged in a linear or cylindrical configuration to produce a stronger flux density and a uniform magnetic field.
## 2 Passive Wake-Up Radio
### Passive Wake-Up Radio Hardware Architecture
#### 2.2.2 Matching Network  
For maximum power delivery to the rectifier stage and reduced transmission loss from the antenna, it is essential that the antenna impedance is matched with the impedance at the input of the rectifier diode. This process is called impedance matching and it is achieved by using the impedance matching network [62; 63]. The matching network consists of inductors and capacitors connected in series or parallel. These passive components are used because they do not add noise to the circuit and do not waste power as opposed to the active components.  
The matching network is connected between the antenna and the rectifier block of the energy harvesting circuit. The energy harvesting circuit shows nonlinearity due to the presence of nonlinear components such as the diode. This results in the rectifier circuit impedance to differ from the antenna load impedance and the input RF signal frequency [64], and hence reduces the circuit efficiency. This setback can be addressed by the matching network using components such as inductors, resistors, capacitors, or microstrip lines in '\(L\)', '\(\pi\)', or '\(T\)' networks according to the shape in which they are arranged. For example, the values of the components in an L-matched network with a shunt capacitor and series inductor can be obtained using the following equations.  
\[B_{C}=\pm\frac{\sqrt{(Z_{0}-R_{L})/R_{L}}}{Z_{0}} \tag{1}\]  
\[B_{L}=\pm\sqrt{R_{L}(Z_{0}-R_{L})}-X_{L} \tag{2}\]  
where \(Z_{L}\) is the load impedance. \(Z_{0}\) is the antenna impedance (typically 50 \(\Omega\)). \(R_{L}\) is the real part of the load impedance. \(X_{L}\) is the imaginary part of the load impedance.  
To match the impedance of the antenna with the diode, the values for the shunt capacitor (BC) and the series inductor (BL) are determined from Equations (1) and (2), respectively.
## 2 Materials and Methods
### Geoaccumulation Index (Igoo)  
This index integrates the effects of anthropogenic factors and soil matrices on the distribution of heavy metal(loid) contents. Igoo is widely used to study soil heavy metal(loid) pollution caused by anthropogenic activities [26]. The formula is as follows.  
\[\mathrm{I_{geo}}=\mathrm{log_{2}[C_{n}/1.5\times B_{n}]} \tag{4}\]  
where Igoo is the geoaccumulation index, \(\mathrm{C_{n}}\) is the measured value of elements in soil (mg/kg), \(\mathrm{B_{n}}\) is the background value of heavy metal(loid)s in the soil (mg/kg), and 1.5 reflects the fluctuation of the background value in different areas due to differences in soil matrices. The evaluation levels are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Geological** & **Degree of** & **Nemero Combined** & **Degree of** & **Pollution Load** & **Degree of** \\
**Cumulative Index** & **Contamination** & **Pollution Index** & **Contamination** & **Index** & **Contamination** \\ \hline Igoe \(\leq 0\) & Uncontaminated & \(\mathrm{P_{N}\leq 0.7}\) & Uncontaminated & PLI \textless{}1 & Uncontaminated \\ \hline \(0<\mathrm{Igeo}\leq 1\) & Low pollution & \(0.7<\mathrm{P_{N}\leq 1}\) & Warning Level of & \(1\leq\mathrm{PLI}<2\) & Low pollution \\ \hline \(1<\mathrm{Igeo}\leq 2\) & Medium pollution & \(1<\mathrm{P_{N}\leq 2}\) & Low pollution & \(2\leq\mathrm{PLI}<3\) & Moderate pollution \\ \hline \(2<\mathrm{Igeo}\leq 3\) & Medium-strong pollution & \(2<\mathrm{P_{N}\leq 3}\) & Moderate pollution & PLI \textgreater{}3 & High pollution \\ \hline \(3<\mathrm{Igeo}\leq 4\) & Strong pollution & \(\mathrm{P_{N}>3}\) & High pollution & \\ \hline \(4<\mathrm{Igeo}\leq 5\) & Strong-extreme pollution & & & \\ \hline Igeo \textgreater{} 5 & extreme pollution & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation and classification of soil heavy metal(loid) pollution.