esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Mapping of Forest AGB Using MCHM of UAV Stereo Imagery  
Considering the fact that the UAV stereo imagery should be available before the field work of tree measurements, this study proposed to determine positions of field sampling plots on the MCHM of UAV stereo imagery and ortho-rectified mosaic images, because they can provide information about the spatial distribution of forest canopy heights, forest densities, tree species and so on. For the coverage of as full a dynamic range of forest AGB as we can, four types of sampling plots were selected, including low and sparse forests (referred to as type A), low and dense forests (referred to as type B), two-layer forests with sparse higher trees (referred to as type C) and high and dense forests (referred to as type D). Six field sampling plots were selected for each type.  
The time-cost of field measurements for each plot is directly determined by the size of sampling plots. More time is needed for a larger sampling plot because more trees located within plots should be measured. What is the proper size of sampling plots of the field measurements for the mapping of forest AGB using UAV stereo imagery? In order to answer this question, considering the plot size of field measurement used in this study as in Section 2.2, three sizes of sampling plots were used in this study, i.e. concentric square plots with sizes of 15 m \(\times\) 15 m, 30 m \(\times\) 30 m and 45 m \(\times\) 45 m.  
The field sampling plots described in Section 2.2 were not located within the coverage of UAV stereo imagery, as show in Figure 1b. Therefore, the values of forest AGB of sampling plots had to be collected from the forest AGB map of lidar produced in the Section 2.3. For 15 m \(\times\) 15 m plots, the value of forest AGB can be directly collected from the forest AGB map of lidar. For 30 m \(\times\) 30 m (or 45 m \(\times\) 45 m) plots, the average of all four (or nine) pixels in forest AGB map of lidar covered by the sampling plot are used as a sampling of forest AGB in the model development. The prediction models were developed by taking the mean pixel values of MCHM of stereo imagery and the forest AGB of sampling plots as explanatory and response variables, respectively. The three MCHM maps with the spatial resolutions of 15 m \(\times\) 15 m, 30 m \(\times\) 30 m and 45 m \(\times\) 45 m were produced using the UAV MCHM with a resolution of 0.5 m in the same as the producing of lidar MCHM maps. The three forest AGB maps of UAV stereo imagery were further generated using the UAV MCHM maps and the corresponding prediction models. The three forest AGB maps of UAV stereo imagery were evaluated by taking that of lidar data as reference. The evaluation was carried out using the pixel by pixel comparison between the forest AGB maps of UAV and those of lidar.
## 5 Discussion
The proposed algorithm--employing the BB84 protocol in conjunction with a hybrid encryption scheme--presents an innovative solution for reliable data protection in LEO, thereby opening new opportunities for agricultural monitoring and other UAV applications. Nonetheless, achieving the full practical implementation of QKD will require overcoming several technical and engineering challenges related to the miniaturization of quantum devices the, optimization of data transmission protocols, and adaptation to diverse operational conditions. Continued research in this domain will be essential to developing robust and efficient quantum communication systems for UAVs, ensuring secure data transmission in agricultural monitoring and beyond.
# Effect of Harmonics on Ferroresonance in Low Voltage Power Factor Correction System--A Case Study
## 4 Experimental Results and Discussion  
The detailed measurement study was carried out under different operating conditions, and the experimental results for two operating points (C1--where the conditions for ferroresonance are not present and C2--where the conditions for ferroresonance are met) are presented in this paper. Voltage and current waveforms and electrical quantities on the low voltage and medium voltage sides of the transformer station are given for both operating points. The capacitor and reactor voltages and the stage current are also given. Based on the calculated reactor voltage, the reactor flux is calculated and evaluated. The subsequent data processing and further calculations are carried out in MATLAB.  
In the presented measurement study, the stage 3 of PFC system of transformer T1 was manually controlled (switched on and off) while the PFC controller automatically controlled the other PFC systems. The transformer current, transformer voltage, stage current and capacitor voltage were measured while the reactor voltage and flux were calculated. The currents of all significant loads connected to the low voltage side of the transformer station were also measured, but since they do not support ferroresonance, they are not presented in this paper.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### Segmentation of Demonstration  
Curvature serves as a quantitative measure of a trajectory's features and, inspired by [30], a curvature-based trajectory segmentation algorithm has been designed. The details are as follows:  
As illustrated in Figure 3, the blue dashed curve represents the curvatures of a trajectory. According to the changing trend of the curvatures, the trajectory can be divided into sub-regions represented as \(\mathrm{P_{i}}\). \(\mathrm{Ma_{i}}\) and \(\mathrm{Mi_{i}}\) denote the local maximum and local minimum curvatures in the ith region. \(\mathrm{Ma_{0}}\) and \(\mathrm{Mi_{3}}\) are the global maximum and global minimum curvature for the entire trajectory. The Euclidean distance (\(Eudis\)) between \(\mathrm{Ma_{i}}\) and \(\mathrm{Mi_{i}}\) in the ith region is defined as the curvature distance of the ith region \(\|\mathrm{P_{i}}\|\). To optimise the segmentation of the trajectory, a threshold \(\epsilon\) is defined. If \(\|\mathrm{P_{i}}\|<\epsilon\), the ith region and its \(\mathrm{Ma_{i}}\) and \(\mathrm{Mi_{i}}\) will be eliminated in the segmentation process. The rest of the local maximum and local minimum curvatures are selected as the segmentation points.  
Figure 2: The framework of the research methodology.
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 2 Related Work
### Deep Learning-Based Compression Methods for Natural and Remote Sensing Images
On the other hand, although the combined module of a CNN and a Transformer shows significant ability in feature extraction, in hyperspectral images, due to their large data volume and complex spectral information, the direct introduction of a high-complexity Transformer model may lead to the overall computational complexity being too high and the number of model parameters being too large, which in turn affects the efficiency of training and inference. In addition, traditional high-frequency and low-frequency feature division methods (e.g., DWT) and image post-processing techniques based on diffusion models, although effective in the compression of other types of images, are less applicable to hyperspectral images due to the specificity of their spectral information and spatial structure. Therefore,specific compression techniques for hyperspectral images need to be developed to better process their complex spectral and spatial characteristics.
# Design of a Broadband Transition from a Coaxial Cable to a Reduced-Height Rectangular Waveguide
## 3 Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Work (Year)**} & \multirow{2}{*}{\begin{tabular}{c} **Experiments** \\ **Included?** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(b/a\)** \\ **Range** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **\(\mathbf{Reflection}\)** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(\mathbf{Bandwidth}\)** \\ **(ratio/\%)** \\ \end{tabular} } & \multirow{2}{*}{**Complexity**} \\ \hline
[5] (2020) & Y & 0.143 & 5.0–7.5 & \(-21\) & \(1.50/40.0\) &
\begin{tabular}{c} Low (disk probe and \\ waveguide step) \\ \end{tabular} \\ \hline
[6] (2008) & Y & 0.193 & 6.1–10.6 & \(-30\) & \(1.74/53.9\) &
\begin{tabular}{c} Medium (additional step is \\ required) \\ \end{tabular} \\ \hline
[7] (2006) & N & 0.108 & 14.5–18.5 & \(-20\) & \(1.28/24.2\) &
\begin{tabular}{c} High (employs a conical \\ probe with a microstrip line) \\ \end{tabular} \\ \hline
[8] (1993) & Y & 0.120 & 1.2–2.3 & \(-20\) & \(1.92/62.9\) &
\begin{tabular}{c} Medium (dielectric-filled, \\ with airgap) \\ \end{tabular} \\ \hline
[9] (1996) & N & 0.102 & 0.70–2.50 & \(-10\) &
\begin{tabular}{c} High (employs a ridge \\ waveguide as input \\ interface) \\ \end{tabular} \\ \hline
[10] (2017) & Y & 0.0933 & 1.18–2.91 & \(-20\) & \(2.47/84.6\) &
\begin{tabular}{c} High (tapered double ridges \\ and tapered side walls) \\ \end{tabular} \\ \hline
[11] (2017) & Y & 0.500 & 1.47–2.56 & \(-15\) & \(1.74/54.1\) &
\begin{tabular}{c} High (stable placement of a \\ printed circuit board) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance summary of the proposed transition and comparison to previous works. Figure 12: Higher-order rectangular waveguide modes in the proposed transition with \(b/a=0.375\). In Table 3, we note that the \(b/a\) ratio ranges from 0.0933 to 0.500 in previous works. For the \(b/a\) ratio less than 0.125, we did not go into the actual design optimization. Our intuition tells us that the proposed method will yield acceptable results with a reduced bandwidth and an increased reflection coefficient for the transition with \(b/a\) = 0.08 to 0.12. The bandwidth of the proposed CWT is significantly improved with a maximum bandwidth of 92.7% or a ratio bandwidth of 2.73 for \(b/a\) = 0.250. Using the method proposed in this communication, we have shown that a bandwidth of greater than 83% is possible for a \(b/a\) ratio ranging from 0.125 to 0.375. We believe that our work is significant since the proposed transition with \(b/a\) of 0.250 operates from 1.02 to 2.78 times the TE\({}_{10}\) mode cutoff frequency, while a standard commercial design operates from 1.1 to 1.9 times the TE\({}_{10}\) mode cutoff frequency.
## 1 Introduction
To realistically and quantitatively evaluate the reliability of redundant systems, in this paper, authors therefore propose to establish a time-dependent reliability model for redundant systems of a 5 MW offshore wind turbine based on component swapping. Reliability functions of load-sharing and standby redundant systems are derived. Effects of component swapping on the reliability of redundant systems are explored. Component reliability importance measure considering component swapping and imprecise failure rates is performed. The MRI and JRI of critical components considering component swapping are analyzed, and the lower and upper bounds of MRI and JRI of critical components are presented using imprecise theory. The rest of this paper is organized as follows: Section 2 introduces redundant systems and the fault tree of offshore wind turbines; Section 3 describes methods mentioned in this study including component swapping, reliability importance analysis, reliability functions of load-sharing redundancy and standby redundancy; Section 4 presents system reliability model and results analysis; Section 5 performs reliability importance analysis of redundant components of different types; Finally, some conclusions are summarized in Section 6.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 4 Attack Types
### Physical Attacks  
These types of attacks are not within the scope of a trust model, in the sense that they cannot be detected or mitigated by them. A brief examination of these attacks is offered here, as these attacks can be carried out easily on sensor or IoT networks and can provide a means of compromising nodes or taking them offline, which the trust model must then in turn be able to handle.  
**Device breaking.** A simple attack that should be considered when setting up sensor network environments, device breaking is as simple as smashing a physical device. This will, of course, knock the device off the network, so models should be prepared that are resilient in the case of one or more devices dropping off the network.  
**Device tampering.** Tampering can be used as a way of gaining easy access to a node through physically plugging a device (such as a laptop) into a node or modifying something on the hardware (swapping out memory cards, for example). There are many different ways of physically tampering with a device that will result in its compromise, giving attackers access to the network through the device or allowing them to insert worms or viruses into the network.  
**Jamming.** This type of attack makes use of intentional radio frequencies to harm wireless communication systems by keeping the communicating medium busy [48]. Thisphysical attack essentially blocks legitimate nodes from communicating by overpowering their signals. The attack can be executed in many ways, such as by simply emitting the frequencies used, by sending random packets or by more advanced methods that can evade detection for longer. The authors of [48] examine the various methods of jamming and their detection and countermeasures. Techniques such as link quality indicators (LQI) similar to those used in [25] could potentially be used to ensure that the significantly reduced communications quality caused by jamming will not greatly affect the trust values of the network.
# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 3 Materials and Methods  
### Data Collection  
In the experiments, we recruited four undergraduate students (one female, three male) for the experiment. The participants were 20 to 23 years old, weighed 45 kg to 75 kg, and were between 155 cm and 180 cm tall. Participants were able to walk normally and had no conditions that affected their gait.  
The wearable sensors were triaxle acceleration sensors. Due to the improvement of the sensor manufacturing process and optimized detection algorithms, more data can be obtained today using fewer sensors [33], which is one of the important reasons why inertial sensors are increasingly used in gait events in recent years.  
The acceleration sensors were placed on the foot instep [34], calf [19], and thigh [18]. The direction of the acceleration sensor on the instep, calf, and thigh is shown in Figure 1a. The acceleration resolution of the three-axis accelerometer was \(6.1\times 10^{-5}\)g, the attitude measuring stability was \(0.01^{\circ}\), and the baud rate range was 2400-921,600 bps. The baud rate used in the experiment was 115,200 bps and the sampling frequency was 50 Hz.  
The toggle switch of the triaxial acceleration sensor was used to collect the acceleration data. Participants were then asked to walk for at least 120 s on the already configured treadmill. In addition, the treadmill speeds were set at 0.78 m/s, 1.0 m/s, and 1.25 m/s, respectively. Participants walked normally three times on a treadmill at each speed, with all settings being the same in each state. In order to prevent the participants from affecting the gait due to fatigue, the experiment requires the participants to rest for 2 min for each walking test. In addition, we only start saving data when the treadmill's running speed reaches the set speed. Similarly, when the experiment was stopped and the treadmill began to slow down, we stopped collecting data. Moreover, each participant was asked to perform the same experiment under the same conditions to ensure the reliability and validity of the collected data.
## 6 Application to Experimental Data
### Simulated Training Data  
The simulated data for training the logistic regression model are generated by using the approach presented above. The four path models [2; 23] are made according to the size of aluminum cylinder used in the experiment. Unfortunately, the accurate information on the type and property of sediment in the experiment are not provided, and thus the reflection coefficients of the multi-paths are not known. For this reason, when generating simulated signals, 31 possible combinations of the coefficients are considered, which are listed in Table 3. The rotation angle varies from 0 degrees to 90 degrees with 0.1 degree increment. Thus, the total number of the simulated data is 29,700. Since the simulated data are made without the exact information on the experiment environment, there may be simulated signals that are not correlated to the experimental data. In order to remove such data, the logistic regression model is trained with the experimental data consisting of the aluminum cylinder and Rock 1 measurements, in which the principal component analysis (PCA) [33] is applied to reduce 451 components to 52 components with explained variance of 0.99. This trained model is applied to the simulated data to select feature vectors, which are applied to train the model for the experimental data. In this selection procedure, the average number of the simulated data selected for training data as the cylinder is 21,098 for 1000 trials. The remaining simulated data are used as clutter signals since these simulated data are classified as clutter, which implies that these are more similar to the rock measurement. Note that the measurements of Rock 2 are also used for the clutter signals when testing the model.
## 2 Materials and Methods  
### Participants  
The pilot study was conducted from 2017 to 2019. Children from 6 elementary schools in Siedlce--a medium-sized city in Poland--took part. There were 148 overweight and obese children in two groups. The children taking part in the study came from similar backgrounds and were similar in terms of socio-economic status.  
In the first group, children took part in PE lessons for the standard amount resulting from the school curriculum, which was 4 h a week (SPA--standard PA group; n = 88, girls 50, boys 38). In the second group, children took part in a significantly elevated amount of 10 h of PE per week (EPA--elevated PA group; n = 60, girls 40, boys 20). When the study began, the children started the 4 th grade of elementary school (age 10), and the study ended when the children started the 6 th grade (age 12). The sampling strategy was convenience sampling; therefore, the selected groups were not equal. The sample size was justified by power analysis in the G*power software (version 3.1.9.7; Universitat Kiel, Kiel, Germany) [23].  
Before starting the study, the children were informed about the confidentiality of the results and the purpose of the study. The project received a positive opinion from each school's management, teachers, and parents/guardians. All parents or guardians gave written consent for their children to participate in the study. The parents declared that they would prepare the child for measurements in accordance with specific guidelines. A positive effect of parents' involvement in preparing children for measurements has been previously demonstrated [24]. The study was approved by the Research Ethics Committee at the University of Siedlce (No. 2/2016).  
The inclusion criteria for the research were the following: (1) overweight (BMI \(\geq\) 85 \(<\) 95 percentile) or obesity (BMI \(\geq\) 95 percentile), determined based on BMI percentile charts [17; 25; 26]; (2) good health (no newly diagnosed chronic diseases, injuries, or wounds and good well-being); (3) age of 10 years at the beginning of the study; (4) consent of the management, teachers, and parents to take part in the study. Failure to meet any of the criteria in any measurement session prompted exclusion from the research. Exclusion criteria were the following: (1) age other than 10 years at baseline; (2) lack of consent from the child or parent to take part in the study or one of the measurement sessions; (3) absence from one of the measurement sessions; (4) no promotion to the next grade of school; (5) chronic disease; (6) having an implanted pacemaker, which could have influenced the BIA measurement result; (7) BMI other than overweight or obesity; (8) change in BMI during the study period from excessive to normal body weight; (9) hospitalization or newly diagnosed disease; (10) lower extremity injury precluding PA or disability; (11) resignation from participation in the research at any time; (12) injuries, wounds, and feeling unwell during the measurement session.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 6 Open Issues in AMR Models and Future Work  
Although there are many studies on modulation recognition, several open issues remain that require solutions in future studies. With the recent development of DL-based algorithms, efforts are being made to obtain an effective, reliable, and robust classifier. The remainder of this section discusses the open issues and future work.  
Radio signals vary widely based on the modulation scheme, noise levels, and present transmission conditions. Classification is difficult in advanced communications systems that use OFDM- or M-ordered QAM schemes for modulation. Real-world signals are often corrupted by noise and interference from other transmitters, resulting in difficulties in signal classification. Datasets that include all types of modulation signals with appropriate labels are required. Recent models, particularly DL-based models, are computationally complex and require high processing powers and large amounts of memory; therefore, they often cannot be used in real-time processing. It is also observed that models have improved performance for known data but often show poor performance with unknown data. It is sometimes difficult to identify the relevant features that contribute to an accurate classification. Another issue is the fixed length of the input signal; however, real-time signals have varying frame lengths, which may affect the model's performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SNR**} & \multirow{2}{*}{**Purely Convolutional Model**} & \multicolumn{2}{c}{**Pure Transformer**} \\  & & **Encoder Model** & **Proposed HCTC** \\ \hline \(-20\) & 9.21 & 8.97 & 10.18 \\ \(-18\) & 8.97 & 9.33 & 9.82 \\ \(-16\) & 9.82 & 9.58 & 10.42 \\  
Future work should concentrate on the combination of feature extraction methods, such as statistical, time, and frequency domain features, or the fusion of these feature extractions for AMR. Furthermore, the image-based representation of input signals, such as eye diagrams, constellation images, AF images, and their combination, should be studied. Image-based representation models provide innovative ways to visualize modulation schemes by transforming time-domain or frequency-domain data to visual formats. These formats are essential for understanding and extracting features from signals prior to applying deep-learning techniques. By employing image representation models, we can enhance our understanding of the underlying patterns within the data, facilitating more intuitive and effective classification processes. There is also a scope for working with the hybrid DL method, in which multiple methods are combined for modulation classification tasks.
## 4 Case Study
### Comparison  
To verify the performance of the proposed strategy, three different cases were examined:  
* Case 1: inverters with volt-var curve controlled by the proposed method
* Case 2: inverters with volt-var curve with a fixed value
* Case 3: inverters with no reactive power compensation  
In case 1, \(V_{1},~{}V_{2},~{}V_{3},~{}V_{4}\) at each inverter volt-var curve were tuned by the proposed method. In case 2, the \(V_{1},~{}V_{2},~{}V_{3},~{}V_{4}\) at each inverter volt-var curve were 0.92, 0.98, 1.02, and 1.08, respectively. In case 3, inverters with no reactive power compensation were used as the base case for comparison with cases 1 and 2.