esa-sceva/satcom-chunk-collection huggingface dataset:
# Changes in Body Mass and Movement Strategy Maintain Jump Height Immediately after Soccer Match
###### Abstract  
A countermovement jump (CMJ) performed on a force plate is commonly applied in soccer to quantify acute neuromuscular fatigue (NMF), which may manifest immediately following soccer match play. Jump height (JH) is the main outcome variable reported for this purpose; however, it is sensitive to alterations in movement strategy, which may act to mask JH and, therefore, mask any presence of NMF. Acute reductions in body mass (BM) during match play could also lead to the maintenance of JH, but this is yet to be explored. This study sought to explore soccer-match-induced alterations to JH, movement strategy, and BM to inform future variable selection for the study of acute NMF. Fourteen male English National League soccer players performed three CMJs on a dual-force plate system immediately before and after a competitive soccer match. Differences in jump height were non-significant and trivial (\(p=0.924\), \(g=0.03\)) before and after soccer match play, but there was a large post-match decrease in BM (\(g=1.66\)). Furthermore, moderate decreases in jump momentum (\(g=0.56\)) and countermovement depth (\(g=0.72\)) were noted. As JH was determined by the take-off velocity, reduced BM could have augmented it (less mass to accelerate); however, reduced countermovement depth seemingly counteracted this (less distance to attain velocity). It may, therefore, be beneficial to report these variables when monitoring acute NMF via the CMJ.  
eurmuscular fatigue; countermovement jump; force plate; force platform; football Article
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 3 Visual Method for Measuring Crack Width in Concrete
### Measurement Process of Crack Parameters  
In this paper, using the above crack detection and binocular vision algorithm, combined with the experimental platform in Chapter 4, a crack parameter measurement method is proposed. Firstly, the concrete plane with cracks is captured by a binocular camera, and the left camera image is input into the improved DeeplabV3+ algorithm to segment the crack area, and the image coordinates of the crack boundary are input into the List. Then, the SIFT algorithm was used to match the feature points of the left and right images, and the coordinate transformation relationship between the left image pixels and the space plane was calculated by combining the calibrated internal and external parameters of the camera. Finally, each point in List was traversed according to Equation (13), and the spatial Euclidean distance was calculated to obtain the maximum width of the crack. The above process is shown in Figure 9.  
Figure 9: Flow chart of crack measurement.
## 4 Conclusions  
Antennas providing stable gain performance in all GNSS operational frequency bands are highly critical elements for practical space applications that require precise location determination. The capability to receive data from multiple GNSS providers with only a compact wideband antenna, compared to using multiple antennas for different GNSS bands, provides advantages such as elimination of the interaction problems between antennas, minimizing the volume occupied by the antennas on the satellite, and reducing the total platform weight, which are critical for space applications. In this study, a space vehicle compatible stable gain GNSS antenna operating in all GNSS frequency bands is presented. The proposed antenna provides a return loss better than 10 dB, an axial  
Figure 14: The measured radiation patterns for \(\phi=90^{\circ}\).  
Figure 13: The measured radiation patterns for \(\phi=0^{\circ}\).  
ratio lower than 3 dB, a stable RHCP gain higher than 7.2 \(\pm\) 0.3 dBic, and a low cross-polarization level over the entire frequency bands of GNSSs. The achieved overall volume reduction is 35%, and the weight reduction is 30%, compared to the second and third prototypes presented in [19; 20] and compared to the first prototype presented in [18], as 57% and 48%, respectively. The vibration resistance of the fabricated antenna is verified to qualification-level vibration testing, and the structural integrity and high functional performance of the antenna remain consistent. According to results, the fabricated and measured antenna is a very strong candidate for use in multi-band GNSS implementations for satellite applications, thanks to its simple design and satisfactory wideband performance. Moreover, the functional capability of the proposed new antenna structure and its structural resistance to environmental conditions of space applications have been operationally verified with a real spacecraft currently performing its mission.  
This work was supported in part by the TUBITAK Space Technologies Research Institute (TUBITAK UZAY).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The author declares no conflicts of interest.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 3 Our Proposed AMC Method
### Hybrid Data Augmentation
#### 3.3.1 Rotation  
The rotation data augmentation method works directly on the original signal dataset and serves to expand the number of samples in the dataset to improve the generalization ability of the model. The principle of rotation data augmentation is shown in Figure 7. Specifically, for a given original signal sample \((I,Q)\), the augmented samples \((I^{\prime},Q^{\prime})\) can be obtained after the following rotational transformation:  
\[\begin{split}\left[\begin{array}{c}I^{\prime}\\ Q^{\prime}\end{array}\right]&=\left[\begin{array}{c}r\cos(\alpha+\beta)\\ r\sin(\alpha+\beta)\end{array}\right]\\ &=\left[\begin{array}{c}r\cos\alpha\cos\beta-r\sin\alpha\sin\beta\\ r\sin\alpha\cos\beta+r\cos\alpha\sin\beta\end{array}\right]\\ &=\left[\begin{array}{cc}\cos\alpha&-\sin\alpha\\ \sin\alpha&\cos\alpha\end{array}\right]\left[\begin{array}{c}I\\ Q\end{array}\right],\end{split} \tag{6}\]  
where \(\alpha\) refers to the angle of rotation, \(\beta\) and \(r\) refer to the inherent properties of the original signal sample. In this paper, the augmented signals are obtained by rotating the original signal by 0, 90, 180, and 270 degrees in the counterclockwise direction, so a set of original signal samples is augmented into four sets of signal samples.  
Figure 6: Hybrid data augmentation.  
Figure 7: Principle of rotation.
## 5 Materials and Methods
### Construction of AfsaKA Deletion and Complementation Strains  
The deletion mutant \(\Delta\)_AfsaKA_ was obtained by using homologous recombination method [42; 45]. Standard PCR and _A. flavus_ genomic DNA extraction were also carried out as formerly described [46]. Both 1307 bp upstream and 1096 bp downstream sequences of _AfsaKA_ gene were obtained from [[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)) and the primers used in this study were listed in Table 2. Overlap PCR was carried out to join them and form a knockout cassette as A-_pyrrG_-B. _A. flavus_ CA14 were used as a starting strain during the preparation of protoplast [47] and the knockout cassette transformation was conducted and successively verified as previously described [44; 47]. In order to obtain complementation strain \(\Delta\)_AfsaKA_::_AfsaKA_, we followed the method described by [PERSON] et al. [48] and transformants were verified by PCR and qRT-PCR. The primers used for qRT-PCR were listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Primers** & **Sequence (\(\mathbf{5^{\prime}}\)-\(\mathbf{3^{\prime}}\))** & **Application** \\ \hline P1 & TGTATAGGGACGCCTCTG & \\ P2 & CGACAGCTAGCTAGCTCCAAGAT & \\ P3 & CGCAGCAGCTGTCCTCTCACAT & \\ P4 & GAGGGAATTACTGTTTGAGT & _AfsaKA_ deletion \\ P7 & CATGTCGGAGAGTTTGTGTG & \\ P8 & GAGGGTTTTTCAGCGGT & \\ P9 & ATACGCGCCTCAACCCTAA & \\ P10 & ATCCGCCGAGAAGTC & \\ \hline _pyr_G-F & GCCTCAAAACATGCCTTTACC & _pyr_G amplification \\ _pyr_G-R & GCTGAGAGAGAGACACATG & _pyr_G amplification \\ \hline P801 & CAGCAGGTGTCCGGTGTGT & _AfsaKA_ mutant verification \\ P1080 & ATCGGAC
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 3 Methodology  
In this section, the methods for detecting collapsed buildings are described in detail. Firstly, building patches were extracted from pre- and post-event satellite images with manually prepared building footprints. Then, the building patches with \(96\times 96\) pixels are functioned as input data to be applied to explore the effect of dataset augmentation and the number of fine-tuning layers for the performance of applied pretrained VGGNet. Section 3.1 describes the basic concept of CNNs and the adopted VGGNet structure. The concept of fine-tuning method and the detailed fine-tuning process is demonstrated in Section 3.2. Six incremental fine-tuning methods were applied to study the effect  
Figure 1: Comparison of pre- and post-event remote sensing imagery for each damage level. (G1: negligible to slight damage; G3: substantial to heavy damage; G4: very heavy damage; G5: destruction.) [24].  
Figure 2: Distribution of collapsed and non-collapsed buildings in the study area. Red indicates collapsed buildings; green indicates non-collapsed buildings.  
of the number of fine-tuning layers for the performance of CNNs. Section 3.3 shows what dataset augmentation is and its characters. To assess the performance of applied methods, the evaluation metrics are described in Section 3.4.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 3 Visual Saliency-Based JND Model
### Spatial CSF Effect  
The human visual system is a multi-channel structure that breaks the input image into different sensory components. Each sensory channel has its own threshold (called the visual threshold). If the excitation value is lower than the visual threshold of the channel, the human eye can not feel the incentive. The visual threshold prevents the damage below the threshold from being perceived. On the other hand, the masking increases the visual threshold. The main features of the HVS show that HVS has a band-pass property. It is more sensitive to the noise injected in the DCT basis function along the horizontal and vertical directions than the diagonal direction in spatial frequency. The spatial CSF model describes the sensitivity of human vision for each DCT coefficient.  
The base threshold \(M_{base}\) is generated by spatial CSF based on a uniform background image and can be given by considering the oblique effect [27] as  
\[M_{base}(\omega_{i,j},\varphi_{i,j})=(M_{d}(\omega_{i,j})-M_{\flat}(\omega_{i, j}))\cdot\sin(\varphi_{i,j})^{2}+M_{\flat}(\omega_{i,j}) \tag{5}\]  
where \(M_{d}(\omega_{i,j})\) and \(M_{\flat}(\omega_{i,j})\) are found as  
\[\left\{\begin{array}{l}M_{d}(\omega_{i,j})=0.0293\cdot\omega_{i,j}^{2}+(-0. 1382)\cdot\omega_{i,j}+1.75\\ M_{\flat}(\omega_{i,j})=0.0238\cdot\omega_{i,j}^{2}+(-0.1771)\cdot\omega_{i,j }+1.75\end{array}\right. \tag{6}\]  
where the \(\omega_{i,j}\) is cycle per degree (cpd) in spatial frequency for the \((i,j)\)-th DCT coefficient and is given by  
\[\omega_{i,j}=\sqrt{i^{2}+j^{2}}/2N\theta \tag{7}\]  
\[\theta=\tan^{-1}[\frac{1}{2}\cdot R_{VH}\cdot H] \tag{8}\]  
where \(\theta\) indicates the horizontal/vertical length of a pixel in degrees of visual angle [28], \(R_{VH}\) is the ratio of the viewing distance to the screen height, \(H\) is the number of pixels in the screen height, and \(\varphi_{i,j}\) stands for the direction angle of the corresponding DCT component, which is expressed as  
\[\varphi_{i,j}=\sin^{-1}(2\cdot\omega_{i,0}\cdot\omega_{0,j}/\omega_{i,j}^{2}) \tag{9}\]  
Figure 1: The visual saliency-based JND model.
# Supervised Blockchain Anonymous Transaction Model Based on Certificateless Signcryption
## 5 Concrete Scheme
### Transaction Verification  
Considering the differences in transaction fee mechanisms across various blockchain platforms, this scheme temporarily excludes the execution costs associated with Ethereum and disregards processing fees. CLSC_SAT verifies the legality of transactions from two perspectives: First, it conducts a verification, ensuring that the sum of the transferred amount and the remaining account balance equals the original account balance. Second, it performs a legitimacy verification, confirming that the amount transferred is between 0 and the total account balance.  
For the verification of the transaction amount being \"sufficient\", the additive property of the Paillier algorithm is used. After the transaction, [PERSON] and [PERSON]'s account balances are updated to \(v^{\prime}_{A}\) and \(v^{\prime}_{B}\), respectively, by checking whether the following two equations are equal:  
\[Enc_{pk_{R}}(v_{A})\times Enc_{pk_{R}}(v_{0})=Enc_{pk_{R}}(v_{A}) \tag{10}\]  
\[Enc_{pk_{R}}(v_{B})\times Enc_{pk_{R}}(v_{0})=Enc_{pk_{R}}(v_{B}) \tag{11}\]  
If the equations are equal, then the transaction amount is \"sufficient\" for successful verification.  
The legality of the transaction is verified using the zero-knowledge range proof method based on FO commitments mentioned in reference [19], which will not be elaborated on here.
###### Abstract  
Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler algorithm, Chirp Scaling algorithm, and Omega-k algorithm, with these algorithms being the most used and traditional in SAR raw signal processing. The most prominent algorithm that operates in the frequency domain for focusing SAR raw data obtained by a synthetic aperture radar with large synthetic apertures is the Omega-k algorithm, which operates in the two-dimensional frequency domain; therefore, in this paper, we used the Omega-k algorithm to produce SAR images and modify the Omega-k algorithm by adding the Doppler factor to improve the accuracy of SAR raw data processing obtained by the continuous wave and pulsed frequency modulated linear frequency modulated radar system from the surfaces of interest. On the other hand, for the case of unmanned aerial vehicle-borne linear frequency modulated continuous wave (LFM-CW) SAR systems, we added motion compensation to the modified Omega-k algorithm. Finally, the testing and validation of the developed Omega-k algorithm used simulated and real SAR raw data for both pulsed synthetic aperture and continuous wave radars. The real SAR raw data used for the validation of the modified Omega-k algorithm were the raw data obtained by the micro advanced synthetic aperture radar (MicroASAR) system, which is an LFM-CW synthetic aperture radar installed on board an unmanned aerial system and the raw data obtained by European remote sensing (ERS-2) satellite with a synthetic aperture radar installed.  
omega-k algorithm; doppler factor; SAR raw data; motion compensation Article  
Doppler Factor in the Omega-k Algorithm for Pulsed and Continuous Wave Synthetic Aperture Radar Raw Data Processing
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Fanet  
A FANET consists of a group of homogeneous or heterogeneous flying agents that are capable of communicating with each other as a team, interacting with their neighboring to acquire valuable information [35]. The flight speed can be in the range from 0 to more than 100 m/s with two-dimensional (2D) or three-dimensional (3D) movements, which are commonly controlled according to the mission. The topology changes can be stationary, slow or very fast, with disorderly or synchronized movements depending on the objective [13].  
In a FANET, the network architecture design takes into account a routing protocol with the capacity to reorganize itself in the case where one or more UAVs fail. Protocols with proactive or reactive routing algorithms may not work as a consequence of low processing capacity and low memory of the nodes. When such factors exist, the FANET must be capable of forwarding packets between the source and destination of the nodes, thus optimizing the chosen metric and incorporating mechanisms to save energy and maximize the network lifespan.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Runtime Test Nr.** & **SARGAS** & **YOLOv5** & **Faster-RCNN** & **cutLER** \\ \hline
1. & 66 m 7 s & 7 m 40 s & 79 m 40 s & 47 m 46 s \\
2. & 64 m 36 s & 7 m 38 s & 74 m 94 s & 46 m 55 s \\
3. & 61 m 5 s & 7 m 20 s & 75 m 83 s & 48 m 57 s \\
4. & 64 m 31 s & 7 m 47 s & 78 m 83 s & 49 m 6 s \\
5. & 65 m 1 s & 7 m 35 s & 79 m 21 s & 49 m 1 s \\
6. & 64 m 36 s & 7 m 33 s & 78 m 21 s & 48 m 30 s \\
7. & 65 m 22 s & 7 m 36 s & 79 m 18 s & 46 m 59 s \\
8. & 64 m 55 s & 7 m 46 s & 78 m 00 s & 47 m 50 s \\
9. & 65 m 16 s & 7 m 34 s & 77 m 55 s & 47 m 48 s \\
10. & 65 m 3 s & 7 m 44 s & 78 m 34 s & 48 m 41 s \\ \hline Average time (s) & 64 m 39 s & 7 m 37 s & 78 m 14 s & 48 m 9 s \\ \hline FPS & 0.371 & 3.145 & 0.306 & 0.498 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Algorithm runtime comparison on a 1438 frame video footage analysis. Figure 11: RGB format (**left**) and grayscale with Canny edge detection applied (**right**). only a modest decline of approximately 5% in recall--underscoring its robustness to distributional shifts. In summary, while Faster R-CNN achieves the highest detection accuracy, the SARGAS framework offers a favorable trade-off between generalization, detection stability, and computational cost. These characteristics make it a strong candidate for deployment in dynamic, real-world environments such as aerial search and rescue, where labeled training data is scarce and adaptability is crucial.
## 5 Computational Implementation
### Comparison with the GAMS Optimization Package  
To corroborate the effectiveness and robustness of the proposed hybrid optimization algorithm so as to locate and size D-STATCOMs in radial distribution grids, we implemented the exact MINLP model defined from Equations (1) to (9) in the GAMS optimization package by using the BONMIN and the COUENNE solvers. Table 6 presents the numerical results obtained after implementing MINLP solvers in GAMS.  
From Table 6, it can be concluded that (i) GAMS BONMIN and COUENNE solvers get stuck in local optimal solutions compared to the solution informed by the proposed hybrid optimization approach; (ii) the total operating cost reductions achieved by the BONMIN and COUENNE solvers are 16.12% and 15.94%, respectively, while the proposed approach reaches a value of 16.20%; and (iii) the GAMS solvers and the proposed genetic-convex approach identify the commercial area in Figure 3 as the most important zone to inject reactive power, since the largest sizes of D-STATCOMs are concentrated in nodes 30 and 32.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solver** & **Location and Size Node (MVAr)** & \(\mathbf{A_{\text{cost}}}\) **(US$/year)** \\ \hline Benchmark case & — & 130,613.90 \\ BONMIN & \{8(0.2980), 25(0.0920), 30(0.5127)\} & 109,560.85 \\ COUENNE & \{13(0.1850), 16(0.0825), 32(0.4478)\} & 109,791.14 \\ Genetic-Convex & \{14(0.2896), 30(0.5593), 32(0.1177)\} & 109,455.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical results obtained after implementing mixed-integer nonlinear programming (MINLP) model in GAMS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sol. No.** & **Location (Node)** & \(\mathbf{A_{\text{cost}}}\) **(US$/year)** & **Sol. No.** & **Location (Node)** & \(\mathbf{A_{\text{cost}}}\) **(US$/year)** \\ \hline
1 & \{14, 30, 32\} & 109,455.96 & 11 & \{13, 16, 30\} & 109,530.77 \\
2 & \{14, 30, 31\} & 109,472.54 & 12 & \{8, 14, 30\} & 109,713.95 \\
3 & \{11, 14, 30\} & 109,496.84 & 13 & \{13, 29, 30\} & 109,720.81 \\
4 & \{12, 14, 30\} & 109,498.91 & 14 & \{12, 30, 31\} & 109,770.17 \\
5 & \{12, 16, 30\} & 109,501.60 & 15 & \{13, 28, 30\} & 109,905.94 \\
6 & \{10, 14, 30\} & 109,504.58 & 16 & \{10, 12, 30\} & 109,908.50 \\
7 & \{13, 30, 31\} & 109,511.63 & 17 & \{10, 30, 31\} & 109,955.44 \\
8 & \{13, 30, 33\} & 109,513.62 & 18 & \{9, 12, 30\} & 109,960.30 \\
9 & \{13, 17, 30\} & 109,515.72 & 19 & \{13, 29, 31\} & 109,961.47 \\
10 & \{10, 16, 30\} & 109,525.51 & 20 & \{8, 12, 30\} & 110,069.41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best solutions found by the proposed CBGA-SOCP approach with a population size of 20.
## 2 Materials and Methods
### Data Acquisition
#### 2.3.4 Sentinel-2 2A  
Sentinel 2 2A imagery was exported using Google Earth Engine (GEE) [44] API. A geometry polygon was first constructed as an area of interest around Wheat Maid tailing repository encompassing the central lower lagoon and used a date filter between 1 July 2018 and 1 August 2018. For JavaScript code, see 'Data Availability Statement'.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 4 Experiments
### Experimental Setup  
In the experiments, three metrics, OA, AA, and Kappa, are used as the evaluation criteria, and two typical traditional classifiers, KNN and SVM, are also used. The number of selected bands is systematically varied from 5 to 50, with increments of 5, to observe the impact of band selection on classification performance. The parameter \(k\) of KNN is set to 5 in our experiments. As for the SVM classifier, the cross-validation process determines that the Radial Basis Function (RBF) with the kernel parameter \(\gamma\) = 0.5 performs best for our classification tasks. In order to reduce the sampling random bias, each classification algorithm is run 10 times, and the average classification accuracy is reported.  
There are two critical parameters, \(\lambda_{1}\) and \(\lambda_{2}\), in our model. We meticulously tune these parameters within a wide range of values, specifically from \(10^{-4}\) to \(10^{4}\). The optimal settings of these parameters that yield the best classification results are determined, that is, \(\lambda_{1}\) = 0.5 and \(\lambda_{2}\) = 0.1.