esa-sceva/satcom-chunk-collection huggingface dataset:
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 2 Servomotors: Structure, Operating Method, Types, Main Characteristics
### Servomotor Characteristics  
The main technical characteristics of servomotors are as follows [18; 52; 53; 54; 55; 56; 57]:  
Figure 3: Using servomotors in various test benches: (**a**) as the digital twin of a wind generator; (**b**) in diagnosis of bearing faults.  
Figure 2: The servomotor used in a Hirata Cartesian robot.  
* Torque (shaft force).
* Operating voltage.
* Rotational speed.
* Maximum rotational angle.
* Dimensions and weight.
* The torque indicates the rate of acceleration of the output shaft and its ability to overcome the resistance to the rotation of the load. The ability to realize the full potential of the servomotor is directly proportional to the torque.
* The rotation speed of the servomotor indicates the time it takes for the output shaft to turn by 60\({}^{\circ}\). For example, a rotation speed of 0.07 s means that the servomotor shaft will turn by 60\({}^{\circ}\) in 0.07 s. The working voltage of the servomotor power supply affects both the rotation speed and the torque.
* The maximum rotation angle indicates the angle to which the output shaft of the servomotor can turn. In modern production, servomotors with continuous rotation are used, meaning that the maximum rotation angle is 360\({}^{\circ}\). However, in some mechanisms, motors with smaller rotation angles, such as 120\({}^{\circ}\), 180\({}^{\circ}\), 270\({}^{\circ}\), etc., are used.
* The dimensions of the servomotor affect the choice of the motor used to produce the mechanical structures in which they will be installed. This parameter is important for devices where speed, lightness, and compactness are crucial, such as drone models.
* Of the technical characteristics mentioned above, only three directly influence the control of servomotors: torque, rotation speed, and rotation angle. Depending on the selected control mode, the control parameter of the servomotor will differ.  
For example, the main technical characteristics of the servomotor mentioned above are presented in Table 1.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 4 Experiments
### Experiment Results  
#### 4.2.1 Comparison with Recent DETR  
Table 2 shows the results of comparing and analyzing the performance of various object detection models on the COCO 2017 validation dataset. As shown in the table, our proposed **SQ-DETR (Ours)** model, using the ResNet-50 backbone network and trained for 72 epochs, demonstrated outstanding performance comparable to other DETR-based models. SQ-DETR achieved an average precision (AP) of 52.6%, which is among the top-performing results compared to other models presented in the table. In particular, AP50, the average precision at an IoU threshold of 0.5, recorded a very high value of 71.3%. This indicates that SQ-DETR's ability to accurately predict object locations even under relatively loose IoU criteria is excellent. Even at AP75, which raises the IoU threshold more strictly, it maintained a respectable performance of 56.7%, showing that SQ-DETR exhibits stable performance even under difficult conditions that require very accurate object location prediction. Looking at the performance by object size, SQ-DETR recorded 34.6% in small object (S) AP, 57.1% in medium-sized object (M) AP, and 70.1% in large object (L) AP. In particular, the high AP value for large objects (70.1%) shows that SQ-DETR has the ability to detect large objects very effectively. Although the small object AP (34.6%) is relatively low, considering that small object detection is generally a difficult task, it can be interpreted that SQ-DETR has secured a certain level of detection ability for small objects as well. In Table 2, the number of parameters (Params) and FLOP information are not explicitly provided for SQ-DETR. However, since the core of the proposed SQ-DETR in the paper is to reduce the amount of computation by efficiently reducing the number of queries in the decoder stage, it is expected that efficiency will be improved compared to the existing DETR model in terms of the number of parameters and FLOPs. This will be covered in more detail in the subsequent experimental results analysis section. Overall, the performance analysis results in Table 2 show that SQ-DETR is a model with strengths in overall object detection performance, especially accuracy with high IoU criteria and large object detection ability. These performance results suggest that SQ-DETR can successfully maintain object detection performance while improving computational efficiency through the query selection method. To further verify the generalizability of SQ-DETR under different scenarios, we conducted additional experiments on the CrowdHuman dataset, which is known for its high-density pedestrian instances and frequent occlusions. This dataset poses a significantly more challenging setting than COCO and serves as a strong benchmark for evaluating detection robustness in crowded scenes.
## 5 Experiments and Results
### Experimental Configuration  
The deep learning algorithms used in this experiment were executed on the same specifications. The specific configuration and experimental environment are detailed in Table 2. The Faster-RCNN model employed the VGG feature extraction network, while YOLOv5, YOLOv7, and YOLOv8 utilized YOLOv5s, YOLOv7-tiny, and YOLOv8s, respectively. The input image size of these models was unified to 640 px \(\times\) 640 px. The training iterations (Epoch) were set to 200, as depicted in Figure 9. The YOLO algorithm series models were trained with a batch size of eight, whereas the Faster-RCNN used a batch size of four. The experiment's hyperparameters were configured as follows: the initial learning rate was set to 0.01, the learning rate decay employed the Cosine Annealing algorithm, the optimizer used was SGD (Stochastic Gradient Descent), and the Momentum was set to 0.937.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Software** & **Configure** & **Matrix** & **Versions** \\ \hline Operating system & Windows10 & Python & 3.9 \\ CPU & Intel Core i5-9300H & PyTorch & 2.0 \\ GPU & NVIDIA GeoForce GTX 1660 Ti 6G & CUDA & 11.8 \\ \hline \end{tabular}
\end{table}
Table 2: Configuration of the experimental environment.  
Figure 8: Experimental road and scenario.
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 1 Introduction
Additionally, CubeSats' ability to image small areas at high frequency makes them especially useful for monitoring fires on islands and other small areas that may be difficult to monitor with other satellites. The growing interest in the potential of the PlanetScope constellation has sparked the interest of the scientific community. Thus, in [32], the authors examine the effectiveness of each of the eight bands available in PlanetScope images using a variety of feature selection methods, and then use these bands to map the extent of biomass burning and consumption for three forest fires. Their approach includes supervised classification methods, such as support vector machine and principal component analysis. In [27], a burned area mapping method to extract damaged areas using deep learning (DL)-based semantic segmentation, specifically, a U-Net that requires a unitemporal PlanetScope image, is presented. From another point of view, a framework for unattended wildfire damage assessment using VHR satellite imagery with PlanetScope data is proposed in [33]. The approach of these authors aims to take advantage of the high revisit frequency of this sensor. Alternatively, the authors in [34] use images from the PlanetScope sensor to generate high-resolution fire perimeters as a Sentinel-2 fire reference for the evaluation of burned-area products in Latin America and the Caribbean for the year 2019. Finally, a combination of DL Transformer methods and high-resolution PlanetScope images to map burned areas in the Brazilian Pantanal wetland is introduced in [35]. In this study, a proof of concept is presented for the detection of burned areas and the assessment of the severity of fires using different vegetation indices and the images of the SuperDove sensor of the PlanetScope constellation. To this aim, a methodology is developed that is then specifically applied to four fires in different areas of Greece and Spain. SuperDove's high-resolution imagery enables the detailed monitoring of wildfires, providing a unique opportunity to improve our understanding of these devastating events and develop more effective management strategies. Through this work, we seek to contribute to the existing literature on wildfire detection and severity assessment and demonstrate the usefulness of SuperDove imagery in this context. To the best of our knowledge, this contribution differs from previous studies by offering a robust approach to delineate burned areas and classify them according to severity level, especially for smaller fires, which is essential to understand the evolution of fire-climate relationships in the current world setting. It is also different from other methods that rely on fixed thresholds or supervised classifications, providing a flexible and potentially more accurate alternative.
## 4 Analysis and Interpretation of Results
### Correlation Links Between Individual Production Sources and Residual Load
#### 4.3.5 Czech Market Analysis  
In the case of the Czech Republic, correlation analyses between the main variables show several significant trends related to market dynamics and the integration of renewables. The correlation between wind and solar generation is consistently negative throughout the period under consideration, with an overall Pearson correlation coefficient of approximately \(-0.165\). This result suggests that higher wind generation generally corresponds to lower solar generation and vice versa. However, this effect is relatively modest, which is consistent with the seasonal and diurnal variation in the production profiles of these resources.  
The relationship between wind generation and residual load shows a slightly positive correlation (on average about +0.12), which is an interesting result since in most of the other countries analysed this relationship was negative. This phenomenon can be explained by the specificities of the Czech market, where wind power accounts for only a small share of total generation and often occurs during periods of higher overall demand, such as cold months.  
The correlations between wind generation and electricity prices are mostly negative (\(-0.27\) on average), suggesting that increased wind generation tends to push wholesale prices down. This effect is strongest in 2022 (\(-0.44\)), which corresponds to a period of higher market volatility. In the case of solar generation and residual load, the correlation is consistently negative (around \(-0.13\)), while the relationship between solar generation and electricity prices is very weak and slightly positive on average (+0.03). This suggests that solar generation has a less pronounced price-setting effect than wind in the Czech Republic.  
The relationship between residual load and electricity price shows the highest stability and consistency, where the Pearson coefficient reaches approximately +0.55 in the long-term average. This strong positive relationship is in line with expectations, as residual demand reflects the need to cover consumption from conventional sources that have higher marginal costs, which directly affects pricing.  
Also notable are the year-to-year fluctuations in these correlations, with the negative impact of wind generation on price amplified in recent years (especially 2023 and 2024), while the correlation between residual load and prices weakens. This development may be an indicator of increasing market dynamics caused by higher RES variability, but also by increased reliance on cross-border flows.  
From the results of the research it can be concluded that the Czech electricity market has a structure where renewables, in particular wind and solar, play a secondary role in pricing for the time being. However, the evolution over time indicates a gradual increase in the influence of RES on the market dynamics, especially in the last years of the analysed period.
# Leveling Control of Hillside Tractor Body Based on Fuzzy Sliding Mode Variable Structure
## 5 Test Analysis
### Data Analysis of Auto-Leveling Tests
#### 5.3.2 Analysis of Horizontal Slope Field Spectrum Excitation Leveling Tests  
Hydraulic cylinder leveling displacement, velocity data, and real-time angle data of the vehicle body collected from a field spectrum excitation leveling test on a transverse slope (Figure 12) are compared with simulation data obtained under the same conditions, as shown in Figure 16. The test and simulation displacements exhibit the same trend, and have a higher leveling accuracy; however, some deviation is present within the range of  
Figure 15: Comparison of spectrum excitation test and simulation data on gravel pavement. (**a**) Front axle displacement speed. (**b**) Rear axle displacement speed. (**c**) Vehicle body real-time tilting.  
10-25 s due to the errors in the mathematical modeling of the fuzzy sliding mode variable structure control algorithm. During operation, the tractor body inclination angle is within the \(\pm 2^{\circ}\) safe range, then the chattering is not significant, the fuzzy sliding mode control algorithm has good leveling stability, and the tractor body leveling is safe and reliable.  
A comparison of the body leveling test and simulation results obtained under the two excitation conditions, indicates that the test displacements are essentially consistent with the simulation displacements and that the simulated and test vehicle body angle changes are essentially the same, which verifies the correctness, accuracy, and reliability of the auto-leveling control system and its fuzzy sliding mode variable structure control algorithm.
_applied sciences_  
_Article_  
**Improving Behavior Monitoring of Free-Moving Dairy Cows Using Noninvasive Wireless EEG Approach and Digital Signal Processing Techniques**  
**[PERSON] 1,*(c)**, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 1  
1 Basic Science Department, College of Zootechnics and Food Engineering, University of Sao Paulo, Sao Paulo 13635-900, Brazil; [EMAIL_ADDRESS] (A.I.C.A.); [EMAIL_ADDRESS] (E.J.X.C.)  
2 Escuela de Ingenieria de Industrias Allimentaria, Universidad Nacional de Jaen, Carmetera Jaen-San Ignacio Km 24-Secure Yannyacu, D6801, Peru; [EMAIL_ADDRESS]  
3 Bioprocess and Biotechnology Department, School of Agriculture, Sao Paulo State University, [PERSON], 1780, Jardim Paraiso, Botucat 18610-307, Brazil; [EMAIL_ADDRESS]  
4 Federal Institute of Sao Paulo, Piracicaba 13400-000, Brazil; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +55-1935654255  
**Featured Application: A method that allows brain signals to be monitored in freely moving bovines is described. The method uses noninvasive electrodes to minimize stress during EEG monitoring and allows bovines to behave normally during the process. The method can be applied to investigate changes in brain electrical activity under normal handling conditions.**  
**Abstract:** Electroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and extract brain signal characteristics in farm animals are not as developed as those for humans and laboratory animals. The objective of this study was to develop a noninvasive method for monitoring brain signals in cattle, allowing the animals to move freely, and to characterize these signals. Brain signals from six Holstein heifers that could move freely in a paddock compartment were acquired. The control group consisted of the same number of bovines, contained in a climatic chamber (restrained group). In the second step, the signals were characterized by Power Spectral Density, Short-Time Fourier Transform, and Lempel-Ziv complexity. The preliminary results revealed an optimal electrode position, referred to as POS2, which is located at the center of the frontal region of the animal's head. This positioning allowed for attaching the electrodes to the front of the bovine's head, resulting in the acquisition of longer artifact-free signal sections. The signals showed typical EEG frequency bands, like the bands found in humans. The Lempel-Ziv complexity values indicated that the bovine brain signals contained random and chaotic components. As expected, the signals acquired from the retained bovine group displayed sections with a larger number of artifacts due to the hot 32 degree C temperature in the climatic chamber. We present a method that helps to monitor and extract brain signal features in unrestrained bovines. The method could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain pathologies, and to other situations related to animal behavior.  
**Keywords:** cattle; complexity; EEG; Lempel-Ziv; surface electrodes; wireless
# Development of a Thermal Analysis Tool for Linear Machines
## 3 Description of the Thermal Networks
### Thermal Network of the End Windings  
Similar to the thermal network of the primary core, the thermal conductivity of the end windings has been taken as infinite. Thus, the temperature of the copper of the end windings is the same as the average temperature of the copper in the slot. The only thermal resistances in the thermal network of the end windings (Figure 5) are those corresponding to the radiation and the convection. This is a simplification that has been made to reduce the complexity of the model. Obviously, there is an error in the calculation of the temperature of the copper in the end windings. But this can be assumed to be generally small. However, when the heat flow through the primary core is very high, the end windings can contain the actual hot-spot of the copper. Due to the simplification, this kind of hot-spot issue cannot be detected with this model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Configuration** & **Characteristic Length** & \(Ra\) **Interval** & \(Nu\) \\ \hline Vertical Plate & Height of the plate & Whole interval \(Nu=\left\{0.825+\frac{0.387\cdot Ra_{L}^{1/6}}{\left[1+(0.492/Pr)^{9/16} \right]^{8/27}}\right\}^{2}\) \\ \hline Hot Horizontal Plate, Top & \(A/p\) & \(10^{4}\)–\(10^{7}\) & \(Nu=0.54\cdot Ra_{L}^{1/4}\) \\  & \(p\): perimeter of the plate & \(10^{7}\)–\(10^{11}\) & \(Nu=0.15\cdot Ra_{L}^{1/3}\) \\ \hline Hot Horizontal Plate, Bottom & \(A/p\) & \(10^{5}\)–\(10^{11}\) & \(Nu=0.27\cdot Ra_{L}^{1/4}\) \\ \hline Cold Horizontal Plate, Top & & Same as bottom of hot plate \\ \hline Cold Horizontal Plate, Bottom & & Same as top of hot plate \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlations for horizontal and vertical plates.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Material** & **Emissivity** \\ \hline Copper & 0.1 \\ Aluminium & 0.09 \\ Steel & 0.2 \\ Surrounding surfaces (whitish) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Emissivity of materials in the model.  
As it can be seen in Figure 1a, the end windings have been modeled as rectangular prisms. The dimensions of the prism depend on the type of winding that is being modeled. Figure 6 shows the length of the prism (see \(l_{ew}\) in Figure 1a) for a double-layer concentrated winding and a single layer distributed winding. The height of the prism is the height of the slot minus the thickness of the wedge, and the length is obtained by subtracting the width of a primary tooth to the active length of the machine. The convective heat transfer coefficients for the faces of the prisms are calculated with the process from Section 3.2.
## 2 System Model and Proposed Scheme
### Proposal: Blind Adaptive Array with Subcarrier Transmission Power Assignment  
Conventional BAA algorithms such as CMA and PI cannot suppress the interference enough when \(\text{SIR}_{\text{total}}\) is equal to 0 dB [18]. Focusing on multicarrier systems, the secondary transmitter assigns different power densities to each subcarrier, that is, higher and lower level. At the secondary receiver, CMA and PI are selectively applied according to assigned power densities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DSA** & **Null-Steering** & **Proposed** \\ \hline Spectral efficiency & \(\leq\)100\% & \(\geq\)100\% & \(\geq\)100\% \\ Sharing domain & Time/Frequency & Space & Space \\ Requirement & Spectrum usage of primary system & Inter-system CSI & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons in spectrum sharing/superposing approaches. DSA: dynamic spectrum access, CSI: channel state information.  
The concept of the proposed scheme is shown in Figure 2. \(G\) is defined as the power density difference between high and low-level subcarriers. \(N_{\text{High}}\) is also defined as the number of high-level subcarriers. The pattern of one high-level subcarrier and multiple low-level subcarriers is assumed to be repeatedly arranged and total transmission power is controlled so as to equal that of the conventional one. When the interference from the primary system, transmitted at equal power spectral density, reaches the receiver of the secondary system, we can obtain the situation that \(\text{SIR}_{n}\) of high-level subcarriers exceeds 0 dB while that of low-level subcarriers drops below 0 dB. The secondary system applies CMA to high-level subcarriers and PI to low-level subcarriers. This approach enables the interference suppression when \(\text{SIR}_{\text{total}}\) is almost equal to 0 dB. Detailed algorithms of CMA and PI follow.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 2 System Architectures
### Coded Aperture Digital Holography
#### 2.1.1 Coded Aperture Correlation Holography Architectures
As a result, only part of the incident light is modulated by the SLM while the remaining part is not. On the sensor plane, two patterns are detected, namely, a uniform signal from the plane wave and a pseudorandom complex function \(G(u,v)\) generated by the CPM modulation. Figure 3: Optical configuration of COACH. CPM—Coded phase mask; \(L_{1}\), \(L_{2}\)—Refractive lenses; \(P_{1}\), \(P_{2}\)—Polarizers; SLM—Spatial light modulator; Blue arrows indicate polarization orientations. A second polarizer P\({}_{2}\) oriented 45\({}^{\rm o}\) along the active axis of the SLM creates interference between the two beams on the sensor plane to yield the following intensity,  
\[l_{k}(u,v)=\left|A+G(u,v)\exp(i\theta_{k})\right|^{2},\qquad k=1,2,3, \tag{1}\]  
where \(\theta_{k}\) is the \(k\)th phase value of the three-phase shifts. A complex point spread hologram \(H_{PSH}\) = \(A^{*}G(u,v)\) is produced by the phase-shifting procedure. An object is placed at the axial location of the point object and the complex object hologram is synthesized from three intensity recordings similar to the process of Equation (1). Any cross-section intensity of the object can be represented as a collection of uncorrelated points. For simplicity, let us assume that the object plane \(o(x,y)\) is located at the front focal plane of the lens \(L_{2}\), a distance \(f_{o}\) from \(L_{2}\). Each \(j\)-\(th\) object point, at (\(x_{j}\),\(y_{j}\)), on the object plane, generates two mutually coherent beams on the sensor plane. One is a tilted plane wave \(A_{j}\)exp\([i2\pi(ux_{j}+vy_{j})/\lambda f_{o}]\) and the other is a shifted version of \(G(u,v)\) multiplied by the same plane wave as the following: \(B_{j}\)exp\([i2\pi(ux_{j}+vy_{j})/\lambda f_{o}]\)(\(\text{$u$-$\!$}u_{j}\),\(v\)-\(v_{j}\)), where (\(u_{j}\),\(v_{j}\)) = (\(x_{j}\),\(y_{j}\))\(x_{h}\)/\(f_{o}\). The intensity pattern on the sensor plane, resulting from the object in the input, is given by  
\[ \begin{split} I_{k}(u,v)&=\sum_{j}\left|A_{j}\exp \left[\frac{i2\pi\left(x_{j}u+y_{j}v\right)}{\lambda f_{o}}\right]\right.\\ &\quad+\exp(i\theta_{k})B_{j}\exp\left[\frac{i2\pi\left(x_{j}u+y _{j}v\right)}{\lambda f_{o}}\right]G\left(u-u_{j},v-v_{j}\right)\right|^{2} \end{split}