esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Presentation of Components, Methodology of Study and System
### Structure of the Expert System  
Dedicated EmotivPRO software allowed one to archive a raw EEG signal with the Emotiv EPOC Flex Gel headset. The product allows one to export data to a selected file type, and then check its features and properties in any other software. Figure 3 shows the preview of the measured EEG signals in the EmotivPRO application.  
The designed system was composed of several components and its structure is presented in Figure 4. The signal collected from the BCI headset went to the computer connected via Bluetooth technology. The raw signal should be properly filtered (see Section 4), as its analysis is much more difficult.  
During the research, all analyses and classification methods were performed with the help of the Matlab programming and numerical computing platform. Thanks to this software platform, the authors had the opportunity to carry out appropriate modifications to the signals by filtering them. This enable an appropriate analysis and classification. All 32 electrodes available in the device Emotiv EPOC Flex Gel headset were used. It should be noted, however, that during the analysis of facial expressions for the purpose of generating movement commands, only 4 channels were used, which were: F8, Fp2, F7 and Fp1. Due to the fact that in order to create the control signal it was necessary to analyze the channels, the activity of which was the greatest when the muscles around the eyes moved. A completely different procedure took place in the case of the signal analysis in search of nervous tics. In this case, the authors decided to observe the potentials on all channels, due to the need for clarity and certainty of the appearance of the artifact.  
As can be seen in Figure 4, the raw signal was filtered twice (see Section 4). The first filtering of the signal was adapted to the needs of the classification of traffic commands. An approach was applied using the analysis of one-second samples of the signal coming from each of the 4 analyzed channels (F8, Fp2, F7 and Fp1). Then, waveforms showing the power spectrum estimation were obtained. The result was 4 waveforms of power as a function of frequency for each of the electrodes and for each one-second signal sample. Checking the signal at such times was not pointless. In this way, unambiguity could be obtained in the reading of the state at a given moment of the signal. The obtained spectra were analyzed by an expert system, in which the learned neural network evaluated them (Figure 4).  
As can be seen in Figure 4, the signal was refiltered to isolate the artifacts due to simulated neural tics. Then, the product of this operation went to the expert system, where it was analyzed by the second of the neural networks (Figure 4). This time, however, all 32 channels were taken into account.  
The next section effectively describes the procedure of preparing the EEG signal for filtering and thus the predas  
Figure 3: Signal analysis dashboard in EmotivPRO software.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 3 Transmission Scheme
### Proportional Fairness  
Proportional fairness (PF) is a scheduling scheme that finds a middle ground between two competing interests, namely, optimizing network throughput and maintaining a minimal service for all network nodes. To find the scheduled ISN \(l^{*}\) for a frame index \(t_{f}\), we define the average throughput of an ISN \(\tilde{T}_{l}(t_{f})\) over a window of time \(t_{w}=F\tau_{C}\), where \(F\) is the number of frames, as follows: [19]  
\[\tilde{T}_{l}(t_{f}+1)=\begin{cases}\big{(}1-\frac{1}{t_{w}}\big{)}\tilde{T}_{l }(t_{f})+\frac{1}{t_{w}}R_{l}(t_{f}),&l=l^{*}\\ \big{(}1-\frac{1}{t_{w}}\big{)}\tilde{T}_{l}(t_{f}),&l\
eq l^{*},l=1,\ldots,L \end{cases} \tag{24}\]  
Thus, the BS calculates a long-term average over a window of time \(t_{w}\). Scheduling occurs in every frame \(t_{f}\) as through the ISN with highest ratio of rate to average throughput, i.e.,  
\[l^{*}=\underset{l=1\ldots L}{\operatorname{argmax}}\ \ \frac{R_{l}(t_{f})}{\tilde{T}_{l}(t_{f})} \tag{25}\]
## 4 Application Studies
### Criticality Assessment
The comparison of four individual indexes to the OVERALL criticality is shown in Figure 8.  
Figure 7: Pressure distributions of (**a**) Network 1 and (**b**) Network 2. From Figure 8a,b, based on the \(SS\) and \(EVL\) indexes, it is observed that the pipes in Network 1 (red markers) are clearly divided into two groups: zero critical and highly critical pipes. Network 2 (blue markers) is not biased compared to Network 1, showing more variations in criticality. This different reaction is caused by the discrepancy in the network type. Network 1 is densely looped, still being able to supply water when the zero-criticality pipes are closed. In contrast, Network 2 is a hybrid-type, where pipe failure at the branch segment will disable access to water for downstream users. The most critical pipes might be identified using the \(SS\) and \(EVL\) indexes, but the rest of the pipes would not be ranked suitably since the indexes only react to a major failure. In Figure 8c, \(PD\) index values are evenly distributed for Network 1 (red markers), except several pipes with zero criticality. Meanwhile, in Network 2 (blue markers), many pipes show very low \(PD\) percentile rank values. For Network 2, it is observed that the pipes with low \(SS\) and \(EVL\) values also contain low \(PD\) values as well. These pipes can be simply regarded as less critical components using these three indexes. From Figure 8d, it is observed that \(WAD\) values are evenly distributed for the entire range for both networks. The results indicate that every single pipe in the network will surely have some effect on water age growth in the case of a failure. Figure 8: Comparison of the OVERALL criticality with **(a)** supply shortage (\(SS\)), **(b)** economic value loss (\(EVL\)), **(c)** pressure decline (\(PD\)), and **(d)** water age degradation (\(WAD\)) for Networks 1 and 2. As seen, all the indexes play an important role in determining the OVERALL criticality. By considering the \(SS\), \(EVL\), \(PD\), and \(WAD\) indexes simultaneously, the OVERALL index can achieve a balanced assessment of pipe criticality within a network.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 3 Yaw Position Anomaly Recognition Model
### Anomaly Detection Algorithm Process  
The specific process for identifying abnormal yaw position is as follows:  
(1) Data Acquisition: Retrieve the yaw position data from the SCADA system, along with the determined parameters through the SIG-Relief algorithm. These will serve as the input for the TSA-Informer model.  
Figure 2: The interval division of state parameter prediction residuals.  
(2) Predicting State Parameters: utilize the input parameters to predict the yaw position based on the TSA-Informer model.  
(3) Calculate \(e_{\text{AI}}\) and identify the abnormal timing of the yaw position.  
(4) Secondary feature processing for constructing true and specific abnormal features.  
(5) Based on the final model, train and obtain anomaly diagnosis results through the big data platform.  
(6) Verification and Analysis of Anomalous Results: if anomalies are confirmed, prompt on-site alerts should be issued, accompanied by corresponding solutions in a timely manner.
## 2 Techniques for Bessel and Perfect Vortex Beam Formation
### Methods of Forming Quasi-Bessel Beams
All the elements described above make it possible to form QBBs with the same intensity distribution in the cross-section, which coincides well with the Bessel function, and a phase function that corresponds to the exponent in Equation (1), but exists only on a certain limited length (after which they collapse):  
\[Z_{*}=D/2\tan(\theta_{k})\approx D/2\theta_{k}, \tag{11}\]  
where \(D\) is the diameter of the element. Hereinafter, we will assume that the angles \(\theta_{k}\) are small. In this case, beam propagation can be described in the approximation of the scalar theory of diffraction. The so-calculated intensity distribution over the beam cross-section  
Figure 1: Optical elements for formation of quasi-Bessel beams. (**a**) Spiral axicon forming a beam with a topological charge \(\ell=+1\). (**b–d**) Phase axicons forming beams with topological charges \(\ell=+3\): binary axicon (**b**), kinoform axicon (**c**), and holographic axicon (**d**). All phase axicons are 50 mm in diameter and designed for operation at wavelength \(\lambda=141\)\(\upmu\)m. (**e**) The calculated intensity distributions of quasi-Bessel beams with \(|\ell|=3\) formed by any of the phase axicons at a distance of 120 mm are identical. The size of frame is \(20\times 20\) mm. In (**b–d**), the phase grows from 0 (black) to \(2\pi\) (white). for the case of \(\ell=3\) is shown in Figure 1e. Hence, we can conclude that diffractive axicons can be used in any optical systems in which it is required to form Bessel beams whose cross-section practically does not differ from that of the ideal BBs. Later on, however, we will see that a more detailed analysis reveals important differences in the characteristics of these beams. Let us now discuss the technical side of the issue. In the visible range, phase transparencies can be generated, as discussed in the introduction, using commercially available spatial light modulators (SLMs) based on liquid crystals, whereas in the long-wave range, it is necessary to make optical elements from plastic or other materials transparent in this range. In the mid-infrared and near-terahertz ranges, materials absorb more strongly, and making helicoidal axicons with a continuous or step-wise profile requires the use of complicated technologies (multistep photolithography or laser ablation [25]). In addition, high-power free-electron lasers and wavelength-tunable sources of mid-infrared and terahertz radiation require elements with high radiation resistance and that are capable of operating with the radiation wavelength tuned within several tens of a percent. Hence, subject to the above requirements, binary axicons (Figure 1b) made of high-resistance silicon are the most suitable (at least in the first experiments). This work will mostly address these axicons. Bessel beams with an OAM (vortex beams) in the terahertz range were first obtained using such axicons [26].
# On Applicability of Network Coding Technique for 6 LoWPAN-based Sensor Networks
## 3 Network Coding for 6 LoWPAN-Based Wireless Sensor Networks
### Network Coding for 6 LoWPAN (NC6 LoWPAN) WSNs
It accepts more errors, but it needs smaller memory size to perform network coding operations and increases sensors lifetime. Similarly to Algorithm 1, the time and space complexity of Algorithm 3 is also \(O(n)\) and it depends on the NC buffer length. ```
1:ReceivedPacket--packet received from MAC layer (encoded or native 6 LoWPAN packet), CurrentNodelD. 2:CurrentNativePacket prepared for sending to 6 LoWPAN layer. 3:Initialize:Decode,Matched = FALSE
4:Read DispatchCode from the dispatch header of the ReceivedPacket
5:ifDispatchCode = NLAPthen
6:for\(i\gets 1\) to 2 do
7:ifNID\({}_{i}\) = CurrentNodelDthen
8:Matched \(\leftarrow\) TRUE
9:endif
10:endfor
11:ifMatched = TRUEthen
12:for each BufferedNativePacket \(\in\) NCBufferdo
13:Decode \(\leftarrow\) Check decoding possibility (BufferalNativePacket, ReceivedPacket)
14:ifDecode = TRUEthen
15: BufferedNativePacket \(\leftarrow\) Extract BufferedNativePacket from NCBuffer
16:endif
17:endfor
18:ifDecode = TRUEthen
19:CurrentNativePacket \(\leftarrow\) Perform decoding (BufferedNativePacket, ReceivedPacket)
20:else
21: Discard ReceivedPacket
22:endif
23:endif
24:else
25:CurrentNativePacket \(\leftarrow\) ReceivedPacket
26:endif
```  
**Algorithm 3** Decoding Procedure at the NC Layer  
The algorithms presented in this section point that many mutually connected factors can influence the 6 LoWPAN-based WSN efficiency. For example, by increasing the NC buffer length, we can expect higher NC opportunity, lower value of network traffic, and decoding errors, but the sensors need more memory resources and higher computing capability. Additionally, through increasing the NC buffer length, more packet can be delayed. Thus, reasonable trade-off should be found between these factors. The goal of this paper is to confirm the applicability of the NC technique in a real 6 LoWPAN-based WSN, which is discussed in Section 4. Nevertheless, founding optimal values of the parameters used in the algorithms depending on the assumed requirements and constraints (applications, nodes, and network resources) requires further simulation investigations.
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 1 Introduction
These CADs are, obviously, configured as a decision support system (DSS) or as a second opinion for physicians. The development of CAD systems for the detection and diagnosis of skin lesions is a challenging task since low image contrast, artifacts (such as hairs and bubbles), blood vessels, and depigmented zones often feature in images to be processed. These details can affect the system's performance and reduce its accuracy. Thus, a preprocessing/segmentation phase is usually performed for image quality improvement to extract suitable features that efficiently characterize a lesion's nature. Various studies have shown the lack of an optimal set of features for melanoma detection, and, consequently, researchers have adopted features that they considered to be the most adequate. It is clear that for the optimization of CAD performance and CAD computational efficiency, the adoption of redundant and/or irrelevant features should be avoided. Although many studies are now available in the literature, skin lesion segmentation and classification can still be improved. In this paper, the authors propose a new pipeline strategy that adopts computer vision tools for accurate melanoma detection in dermoscopic images. The proposed CAD system uses a color-based k-means procedure for image segmentation and a support vector machine for the classification phase. The attribution of the detected skin lesion to a class of melanoma is driven by extracted features that are based on geometric properties and chromatic characteristics. The PH\({}^{2}\) dataset is adopted as a test-bench, and the reached performance is compared with some popular and recent approaches indicated in the literature [22]. A summary of the most recent classification methods which adopt the PH\({}^{2}\) dataset as a test-bench, is also included in Section 2. Section 3 of our paper deals with the techniques used for the method development. In Section 4, the implemented method is described, and in Section 5 the database adopted for the test of the proposed approach is presented. The achieved performance, discussions, and conclusions complete the paper.
# Electromagnetic Particle Algorithm for Beam-Wave Interaction in Traveling Wave Tube of Symmetry
## 2 Coupling between Beam and Circuit  
It is assumed that the electron beam and the circuit will have a strong coupling effect. All beam charges in the traveling wave tube in Figure 1 generate charges on the equivalent circuit.  
\(I_{N-1/2}\) is the current between nodes \(N-1\) and \(N\); \(V_{N}\) is the voltage at node \(N\); \(Q_{\text{N}}\) is the charge on the capacitor at node \(N\); \(q_{N}\) is the line-deposited; \(q_{N}^{\prime}\) is the beam-deposited; and \(Q_{\text{N}}=q_{N}+q_{N}^{\prime}\).  
Figure 1: Transmission line model of traveling wave tube.  
Using Brillouin notation, the following spatial center equation transmitted along the route can be obtained:  
\[\begin{split} I_{N-1/2}-I_{N+1/2}=\frac{\partial q_{N}}{\partial t}, \hskip 14.226378 ptL_{0}(\frac{\partial l_{N-1/2}}{\partial t})=V_{N-1}-V_{N}\\ V_{N}=\frac{Q_{N}}{C_{0}},\hskip 56.905512 ptQ_{N}=q_{N}+q^{ \prime}_{N}=q_{N}+\rho_{N}d_{0}\end{split} \tag{1}\]  
However, in our proposed algorithm, the beam charge \(q^{\prime}_{N}\) is only a small fraction of the beam space \(G\) charge in the guide section and depends on the wavenumber of the space charge wave. The capacitor charge is shown in Equation (2).  
\[Q_{N}=q_{N}+\kappa(k)q^{\prime}_{N} \tag{2}\]  
Beam-circuit coupling coefficient \(\kappa(k)\leq 1\) reflects the nature of the space charge coupling constant of a finite-diameter traveling wave tube, where beam motion generates displacement currents, which in turn generate conduction currents in slow waveguides. The functional dependence of \(\kappa(k)\) on \(k\) is determined by the relative diameter of the electron beam and slow waveguide, as well as the transverse field distribution for the different modes.
# Infrared Fault Classification Based on the Siamese Network
## 4 Experiments  
This section validates the proposed method in this article on an infrared monitoring dataset (IMD) of solar panel farms. The dataset contains a total of 1394 infrared images of solar panels, divided into categories. The names and quantities of each category are shown in Figure 5. This dataset contains authentic infrared thermographic images of anomalies found in the solar system, including cell, cracking, diode-multi, hot-spot-multi, and offline-module anomalies, as shown in Figure 6. Each class consists of images corresponding to different anomalies.  
The experimental environment for this study is Anaconda 4.12.0 and PyTorch 1.10.2. The experimental platform is operating system Centos(r)7.9.2009, with a CPU of Intel(r) Xeon(r) CPU E5-2630 v4 (10 core, 2.4 GHz), and a GPU ofINVIDA(r) GeForce(r) RTX 2080 Ti. The input image size is 24 \(\times\) 40, with a single batch training quantity of 8, maximum iteration number of 75, and an initial learning rate of 0.01, using the Adam optimization algorithm.  
The training and testing procedures of this experiment are shown in Figure 7. The dataset is divided into the training set and the testing set in a ratio of 9:1. During the training phase, two randomly selected samples are used for building sample pairs. The labels for two samples of the same class are set to 1, and the labels for two samples of different classes are set to 0. After building sample pairs, the model is trained using the cross-entropy loss function and the Adam optimization algorithm. Training is finished after the best model weights are generated. In the testing phase, the failures form the diagnosis. To determine the failure cases, one example from the six types of training samples is randomly selected and bound with different class labels. Then, the two targetsamples are formed into sample pairs by dividing corresponding images from the selected examples. Adding the sample pairs to the trained model causes it to obtain corresponding similarity values. The label of the sample pair corresponding to the maximum similarity value is the class of the target sample.  
Figure 5: The names and quantities of each category in the IMD dataset.  
Figure 6: Typical examples from the IMD dataset.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Data
#### 2.2.2 In Situ Data  
To validate GRACE-derived GWS estimates, in situ groundwater level measurements were obtained from the Peruvian National Water Authority (ANA, Autoridad Nacional del Agua), available at: [[https://www.gob.pe/ana](https://www.gob.pe/ana)]([https://www.gob.pe/ana](https://www.gob.pe/ana)) (accessed on 19 March 2025), corresponding to the periods from 2003 to 2023. The validation process utilized data from over 1700 wells across six major aquifers (Caplina, Tumbes, Chillon-Rimac, Lurin, Chilca, Locumba), reporting annual decline rates (m/year) and extraction volumes (hm\({}^{3}\)/year).  
These measurements, reported as annual changes in water table depth (m/year), were converted into equivalent volumetric changes (mm/year) using aquifer area data. This conversion allowed for a direct comparison with satellite-derived GWS estimates.
## 5 Experiment
### Experimental Results and Analysis
Figure 8 shows the execution time of the intelligent algorithm for the MAFS traffic scheduling scheme and other comparative traffic scheduling methods during the network model training process. Specifically, it starts from the agent obtaining network state information and continues until a new routing strategy is calculated through an intelligent network model and converted into flow rules for issuance. The experimental results show that the CTN-MADDPG traffic scheduling algorithm of the MAFS method has a shorter execution time, which means that it can complete the model learning and routing decision faster and can respond more quickly to the changes in the network environment to reduce the routing decision delay. Therefore, the MAFS traffic scheduling method has higher computational efficiency. Figure 6: Packet loss rate. Figure 7: Flow completion time. Figure 9 records the changes in the throughput of MAFS, DDPG, and MADDPG with the training episodes. In the early stages of system operation, the throughput changes in DDPG, MADDPG, and MAFS are not very different due to the limited number of algorithm training steps. With the increase in the training period, the network throughput of MAFS is significantly better than the other two traffic scheduling methods, and the throughput continues to improve and tends to stabilize. Figure 10 records how the delay varies for MAFS, DDPG, and MADDPG with the training episodes. Compared with the DDPG and MADDPG algorithms, the delay of MAFS is kept at a small value, which steadily decreases and stabilizes over time. Figure 11 records the changes in the reward values of MAFS, DDPG, and MADDPG with the training episodes. Although the reward values of the MAFS method in the training process have a certain jitter, they are relatively stable after 425 episodes and reach a convergence state. Various agents cooperate with each other to complete the task of maximizing the network utility. The experimental results demonstrate that the MAFS traffic scheduling method can effectively reduce network delay and improve network throughput with good convergence and stability. Based on the above results analysis, the MAFS method can formulate optimal traffic scheduling strategies based on real-time state information. Compared with the ECMP, DDPG, and MADDPG methods, it can effectively improve network performance and avoid congestion in the network to ensure effective data transmission and achieve load balancing. Figure 8: The execution time varies with the episodes. Figure 9: Throughput varies with the episodes.
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 2 Materials and Methods
### Algorithm
#### 2.1.1 Input Parameters  
The basic input parameters shown in Table 1 are necessary to perform the optimization. These are categorized into track parameters and train parameters.  
The parameters were chosen to balance between the minimum amount necessary to perform the optimization and the desire to be flexible in respect to the scenarios that can be covered by the algorithm presented. Therefore, not all requirements from [42] are explicitly modelled, but can be respected by means of the available input parameters. These parameters impact the exact optimal positions of the infill balise groups as they each influence the possible trajectories of the train, be it through changes in the braking curves, the deceleration, the acceleration, or the exact position where data are transmitted to the train. The applicable National Values (NV) for a particular question need to be considered in preprocessing, as they can have significant impact on the shape of the braking curves and therefore can shift the optimal positions of infill balise groups.