esa-sceva/satcom-chunk-collection huggingface dataset:
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 6 MZAP Android Application--Adoption and the Effects of Digitalization
### Increase in Website Traffic  
The digitalization of match endgame reports has significantly improved their accessibility, resulting in an approximate 11% increase in traffic to the live score website and a corresponding rise in advertising revenue. Moreover, the searchable format of digitalized reports has proven beneficial to both sports statisticians and stakeholders in the betting industry.  
The increase in website traffic was calculated based on server logs of the Basketball League of Serbia from the first six months of the 2024/2025 season. The total number of unique visitors for each month was aggregated and compared to the corresponding period of the previous season, revealing an increase of 11.02% \(\approx\) 11%. Month-by-month comparisons revealed that the percentage increase in traffic, rounded up to 2 decimal places, varied between 8% and 12.69%. However, if the monthly increase was averaged, it amounts to 11.12%, which closely aligns with the previously stated figure of 11% (11.02%). Comparative Basketball League of Serbia's website traffic data are shown in Table 5.  
Given that an increase in traffic was observed consistently in each month when compared to the same month of the prior season, it is reasonable to project that this upward trend will continue for the remainder of the current season. Furthermore, it is anticipated that the traffic levels in the upcoming season will remain within a similar range as the current season or experience a slight decline.  
\begin{table}
\begin{tabular}{c c c} \hline
**Working Time Reduction** & **Number of Officials** & **Percentage** \\ \hline
40+\% & 107 & 39.48\% \\
40\% & 62 & 22.88\% \\
35+\% & 21 & 7.75\% \\
35\% & 27 & 9.96\% \\
30+\% & 13 & 4.8\% \\
30\% & 8 & 2.95\% \\
25+\% & 5 & 1.85\% \\
25\% & 10 & 3.69\% \\
20+\% & 7 & 2.58\% \\
20\% & 9 & 3.32\% \\
10\% & 2 & 0.74\% \\ \hline Total & 271 & 100\% \\ \hline \end{tabular}
\end{table}
Table 4: Decrease in working time survey results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Month** & **Season 2023/2024** & **Season 2024/2025** & **Increased Traffic** \\ \hline October & 37,142 & 41,854 & 12.69\% \\ November & 31,202 & 34,946 & 12\% \\ December & 32,202 & 36,388 & 13\% \\ January & 39,106 & 42,234 & 8\% \\ February & 36,645 & 40,310 & 10\% \\ Match & 37,254 & 41,352 & 11\% \\ \hline Total & 213,551 & 237,084 & \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline
**Traffic increase: 11.02\% \(\approx\) 11\%** & **Average: 11.12\%** \\ \hline \end{tabular}
\end{table}
Table 5: Comparative Basketball League of Serbia’s website traffic data.
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
###### Abstract  
Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation characteristics of the scrambling sequence symbols are deduced, the partial autocorrelation function is constructed, the superiority of the partial autocorrelation function is determined by derivation, and the two are combined as the input correlation characteristics. A shallow network combining a convolutional neural network and LSTM is constructed; finally, the linear block code scrambled dataset is input into the network model, and the training and recognition test of the network is completed. The simulation results show that, compared with the traditional algorithm based on a multi-fractal spectrum, the proposed method can identify a synchronous scrambler, and the recognition accuracy is higher under a high bit error rate. Moreover, the method is suitable for classification under noise. The proposed method lays a foundation for future improvements in scrambler parameter identification.  
scrambled linear block codes; cross-correlation of symbols; partial autocorrelation function; convolutional long short-term memory neural networks +
Footnote †: journal: Applied  
1
Footnote 1: [[https://doi.org/10.3390/appl22111305](https://doi.org/10.3390/appl22111305)]([https://doi.org/10.3390/appl22111305](https://doi.org/10.3390/appl22111305))
## 3 Results
### Statistical Analysis
The occurrence of fractures in the ulna and pelvic bones is noteworthy, especially among females, with 15.19% (n = 156) for ulna fractures, which include fractures of the entire ulna, _processus coronoideus_, and _processus anconeus_, and 12.46% (n = 128) for pelvic bone fractures. These findings indicate that these regions are often affected by injuries. Tibia and femur fractures are equally notable, suggesting that these long bones are often impacted by trauma or external loads. Males have a greater incidence of tibia fractures (14.02%; n = 173) when compared to females (11.68%; n = 120). Similarly, males have a slightly higher frequency of femur fractures (9.24%; n = 114) than females (9.06%; n = 93). The lower frequency of fractures in bones such as the metacarpal and metatarsal bones and the even lower frequency of fractures in the jaw, hock, and radius indicates a reduced vulnerability to or incidence of external loads on these regions. The classification of fracture etiology, as shown in Figure 9, is significant in the context of identifying preventative interventions and comprehending the epidemiology of fractures in canines. Figure 8: Canine fractured bones according to gender. Figure 9: Distribution of canine fracture causes according to gender. The analysis of fracture causes in canine patients indicates a notable proportion of cases of unidentified origin, including 58.13% (n = 597) in females and 56.89% (n = 702) in males. This implies that, in many cases, the triggering incident resulting in the fracture is either not seen or not communicated during veterinarian consultation. Trauma is the second most frequent cause of fractures, accounting for 23.08% (n = 237) in females and 19.77% (n = 244) in males. This indicates the influence of several damage mechanisms that are not particular to trauma. Vehicle accidents have been identified as a particular cause, representing 15.00% (n = 154) of fracture cases in females and 18.56% (n = 229) in males. This finding underscores the significant risk associated with injuries resulting from traffic incidents. The least frequent documented cause of fractures is an assault by another dog, which accounts for 3.80% (n = 39) of cases in females and 4.78% (n = 59) in males. This suggests that aggressiveness or interactions with other dogs might increase the risk of fractures.
## 4 Three-Dimensional World Point Reconstruction and Structural Displacement Extraction
### Camera Calibration and Lens Distortion Correction  
Camera lenses, especially wide-angle lenses, introduce radial and tangential distortions in images. Radial distortion causes straight lines to appear as curved in the image,  
Figure 8: Framework adopted herein for UAV vision-based earthquake-induced structural displacement monitoring.  
because light rays passing through the lens refract differently based on their distance from the center of the lens. Tangential distortion occurs when the lens is not perfectly parallel to the image plane, causing the image to be slightly skewed. These distortions lead to inaccurate position and size of objects in the image. Lens distortion correction using accurate camera intrinsics is required to remove this lens distortion effect, making the image more representative of the real scene.  
Camera intrinsics can be obtained during the camera calibration process. In the proposed method, which is different from the checkerboard calibration procedure [42], camera intrinsics are estimated from a self-calibration process as part of the bundle adjustment in the structure-from-motion reconstruction using Agisoft Metashape 2.0.2 [43]. Self-calibration of a camera does not depend on any known reference objects. Instead, self-calibration relies solely on multiple images taken by the camera. Self-calibration uses feature points from the images captured from multiple viewpoints, combined with geometric constraints to estimate the camera intrinsics. For more details on this camera calibration procedure refer to [PERSON] [44] and [PERSON] et al. [45]. Given accurate camera intrinsics, lens distortions are corrected based on the procedures illustrated in [PERSON] [42].
# Safety Assessment of Microwave Breast Imaging: Heating Analysis on Digital Breast Phantoms
## 2 Materials and Methods
### Hardware Configuration  
MammoWave is one of the MI devices used in clinical studies [13]. Similar architectures can be found in the SAFE (MITOS medical technologies, Istanbul, Turkey) [22] and Wavelia (MVG industries, Villejust, France) [23] devices. All three operate with S-parameters: MammoWave uses two antennas, one for transmission and one for reception, while the  
Figure 1: Positioning of the horn antenna and an anatomical breast model. The orange dotted line highlights the region of interest (ROI).  
other two devices have multiple antennas and a switch to perform both functions. They operate within a similar frequency range. Differently from both MammoWave and SAFE, the Wavelia system has its antennas immersed in a coupling liquid. The MammoWave device works in the frequency range from 1 GHz to 9 GHz with an incident power of 1 mW and uses a moving Vivaldi antenna in reception to collect the signal scattered from the object under exam in various positions for every stationary configuration of the transmitting horn antenna. The five transmitting antennae assume five stationary positions uniformly distributed around the breast holder, while the receiving antenna moves in steps of 4.5\({}^{\circ}\) to measure the signal from each transmitting position [11]. In particular, experimentally, the device works by employing 10 transmitting positions, displaced in 5 sections, with a slight tilt from the central position of each section, in order to average the acquisitions two by two. Simulations were performed at the frequencies of 2 GHz, 4 GHz, 6 GHz, and 8 GHz to cover the wide range of frequencies used in the MI system. All the breast models were studied considering all five central positions of the horn antenna. These positions are presented in Figure 3 and are numbered from 1 to 5. The Vivaldi antenna was included in the simulated problem with model #1 at a later stage in order to determine its impact on the estimated temperature increases. Three angles between the transmitting and the receiving antennas were simulated. The letters A, B, and C denote the three positions of the receiving antenna, as shown in Figure 4.  
Figure 3: Studied positions of the transmitting horn antenna numbered from 1 to 5.  
Figure 2: Digital breast phantoms used in simulations. From left to right, a left healthy breast (**model #1**), a right breast with two benign tumors highlighted in red (**model #2**), a right breast with two malignant tumors highlighted in red (**model #3**), and two left healthy breasts (**models #4** and #5**).
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
## 6 Discussion
These answers are: 1. When explaining the radiation mechanism of a wire antenna in free space, it is expected to use current distribution along the antenna arm instead of the electromagnetic field distributions (modes). The present antenna is made of wires in free space. The wire radius is \(\rho=\lambda_{0}/200\), and the loops with a spacing of \(S<\lambda_{0}/30\) are located at \(h=\lambda_{0}/4\) above the ground plane. These values of (\(\rho\), \(S\), \(h\)) are not chosen for a conventional patch antenna design, where the radiation mechanism is often explained using the modes; 2. A reason for the decrease in the gain is that the CP wave frequency range for the Inner-PL extends towards the upper frequencies, as mentioned in Figure 8. As the frequency increases from \(f_{0}\), the loop height _electrically_ becomes more than a quarter of the wavelength, decreasing the gain since the physical height is a quarter of the wavelength at \(f_{0}\). Note that the antenna gain decreases as the antenna height above the ground plane increases from a quarter of the wavelength.
## 3 Minimum Nonlinearity PL Design Guidelines
### Extension of the IMPM to Account for Nonlinearities
As mentioned earlier, for the optimization stage the interest is on efficiently comparing several thousands of configurations; therefore, a valid indicator of the nonlinear performance of a given switch configuration is adopted instead of solving the nonlinear extended system. For this purpose, the power margin (\(\Delta P\)) is defined as the difference between the power delivered to the fed port and the power delivered to the switches, over the frequency range where the excited port is well matched:  
\[ \begin{split} f_{r}:|S_{ii}(f_{r})|<-10\text{dB}\\ \Delta P=\left\langle\frac{P_{in}(f_{r})}{P_{sw}(f_{r})}\right\rangle \end{split}  \tag{18}\]  
The definition and use of this metric is supported on the fact that for the \"ON\" and \"OFF\" switch states, up to moderate input powers, the power of the fundamental tone is much higher than that of the harmonics, as was confirmed by the X-parameters measurements performed. Therefore, the nonlinearities can be predicted by the switch power at the fundamental frequency. Likewise, as was evidenced for the antenna presented as case of study, although the maximum power through a switch is a significant driver of nonlinearities, the total power delivered to the switches set may be a more determinant factor.
# Elastoplastic Analysis of Plates with Radial Point Interpolation Meshless Methods
## 2 Meshless Methods
### Discrete System of Equations
Substituting the stress and the strain in Equation (23) allows us to obtain  
\[\int_{\Omega}\delta(\mathbf{B}(\mathbf{x}_{l})\cdot\mathbf{u})^{T}\cdot( \mathbf{c}(\mathbf{x}_{l})\cdot\mathbf{B}(\mathbf{x}_{l})\cdot\mathbf{u}) \cdot d\Omega-\int_{\Omega}\delta(\mathbf{H}(\mathbf{x}_{l})\cdot\mathbf{u}) ^{T}\cdot\mathbf{b}\cdot d\Omega-\int_{\Gamma_{t}}\delta(\mathbf{H}(\mathbf{x }_{l})\cdot\mathbf{u})^{T}\cdot\mathbf{\bar{t}}\cdot d\Gamma=0 \tag{28}\]  
because only the small strain will be considered, \(\delta\mathbf{B}(\mathbf{x}_{l})=0\) and \(\delta\mathbf{H}(\mathbf{x}_{l})=0\), the expression can be presented as  
\[ \begin{split}&\int_{\Omega}\delta\mathbf{u}^{T}\cdot\mathbf{B}( \mathbf{x}_{l})^{T}\cdot\mathbf{c}(\mathbf{x}_{l})\cdot\mathbf{B}(\mathbf{x}_ {l})\cdot\mathbf{u}\cdot d\Omega-\int_{\Omega}\delta\mathbf{u}^{T}\cdot \mathbf{H}(\mathbf{x}_{l})^{T}\cdot\mathbf{b}\cdot d\Omega-\int_{\Gamma_{t}} \delta\mathbf{u}^{T}\cdot\mathbf{H}(\mathbf{x}_{l})^{T}\cdot\mathbf{\bar{t}} \cdot d\Gamma=\\ &=\delta\mathbf{u}^{T}\int_{\Omega}\mathbf{B}(\mathbf{x}_{l})^{T} \cdot\mathbf{c}(\mathbf{x}_{l})\cdot\mathbf{B}(\mathbf{x}_{l})\cdot d\Omega \cdot\mathbf{u}-\delta\mathbf{u}^{T}\int_{\Omega}\mathbf{H}(\mathbf{x}_{l})^ {T}\cdot\mathbf{b}\cdot d\Omega-\delta\mathbf{u}^{T}\int_{\Gamma_{t}}\mathbf{ H}(\mathbf{x}_{l})^{T}\cdot\mathbf{\bar{t}}\cdot d\Gamma=0\end{split}  \tag{29}\]  
leading to its final form, representing the system of equations  
\[\mathbf{K}\cdot\mathbf{u}-\mathbf{f}_{b}-\mathbf{f}_{t}=0\quad\Rightarrow\quad \mathbf{u}=\mathbf{K}^{-1}\cdot(\mathbf{f}_{b}+\mathbf{f}_{t}), \tag{30}\]  
in which \(\mathbf{K}\), \(\mathbf{u}\), \(\mathbf{f}_{b}\) and \(\mathbf{f}_{t}\) are the global stiffness matrix, the global displacement field, and the global body and external force vectors, respectively. The discretization procedure allows us to obtain these entities by using a numerical integration:  
\[\mathbf{K}=\int_{\Omega}\mathbf{B}(\mathbf{x}_{l})^{T}\cdot\mathbf{c}(\mathbf{ x}_{l})\cdot\mathbf{B}(\mathbf{x}_{l})\cdot d\Omega=\sum_{I=1}^{N_{Q}}\mathbf{B}( \mathbf{x}_{l})^{T}\cdot\mathbf{c}(\mathbf{x}_{l})\cdot\mathbf{B}(\mathbf{x}_ {l})\cdot\hat{\omega}_{I} \tag{31}\]  
\[\mathbf{f}_{b}=\int_{\Omega}\mathbf{H}(\mathbf{x}_{l})^{T}\cdot\mathbf{b}\cdot d\Omega =\sum_{I=1}^{N_{Q}}\mathbf{H}(\mathbf{x}_{l})^{T}\cdot\mathbf{b}\cdot\hat{ \omega}_{I} \tag{32}\]  
\[\mathbf{f}_{t}=\int_{\Gamma_{t}}\mathbf{H}(\mathbf{x}_{l})^{T}\cdot\mathbf{\bar{t} }\cdot d\Gamma=0=\sum_{J=1}^{n_{Q}}\mathbf{H}(\mathbf{x}_{J})^{T}\cdot\mathbf{ \bar{t}}\cdot\hat{\omega}_{J}. \tag{33}\]  
Because the RPI function possesses the Kronecker delta property, the essential and natural boundary conditions can be directly imposed by using the direct imposition method or penalty methods, such as in other discrete numerical methods using interpolation functions.
## 6 Key Challenges and Future Directions
### Role of WebAssembly in Advancing IoT
This predictability allows developers to create applications with consistent performance regardless of the platform on which they are running. * **Promoting Interoperability*
* Wasm promotes interoperability among IoT devices. It can serve as a universal runtime for executing IoT applications, allowing devices from different manufacturers and platforms to run the same application code and communicate more seamlessly with each other. * **Resource Management in IoT** Wasm's efficient memory and resource utilization could play a critical role in managing the constrained resources of IoT devices, thereby enhancing their performance and capabilities. In IoT systems, resource management is a critical consideration. IoT devices, ranging from small sensors to more complex machines, usually operate under tight resource constraints, including limited processing power, memory, storage, and energy. Wasm, with its compact, low-level format and efficient execution model, offers several advantages for resource management in IoT. Efficient Use of Processing Power  
Wasm's compact binary format and efficient execution model ensure that it uses processing power optimally. Unlike interpreted languages, which require parsing and interpreting at runtime, Wasm's bytecode is pre-compiled and can be executed quickly by the Wasm virtual machine. This reduces the processing overhead, making it ideal for resource-constrained IoT devices. * **Compact Memory Footprint** Wasm has a compact memory footprint. It uses a linear memory model, which provides a single, contiguous array of bytes representing the application's memory. In combination with Wasm's efficient use of memory, this model reduces the amount of memory needed to run applications, an important advantage for memory-limited IoT devices. * **Energy Efficiency** Efficient use of processing power and memory translates to energy efficiency, a critical consideration for IoT devices, many of which are battery-powered. By minimizing the computational resources needed to run applications, Wasm helps to reduce the energy consumption of IoT devices, extending their battery life and reducing the overall energy footprint of the IoT system. * **Granular Control over Resources** Wasm's low-level nature provides developers with granular control over system resources. For example, developers can control memory allocation and deallocation explicitly in Wasm, which is often not possible in high-level languages. This control allows for more efficient use of resources and can lead to better performance and reduced resource consumption. * **Future Developments** Proposed enhancements to Wasm, such as the introduction of garbage collection and improved resource management primitives, are expected to further enhance Wasm's resource management capabilities for IoT. These developments can help to address the current limitations and make Wasm an even more attractive option for resource-constrained IoT devices.
# Voltage Optimization in PV-Rich Distribution Networks--A Review
## 2 PV Prosumers in Distribution Network
### PV System Capabilities for Voltage Optimization  
This paper deals with PV prosumers with reactive power capability, i.e., other prosumer types are not considered. DSOs have the main responsibility for voltage optimization in DNs. Traditionally, the available variables are limited to the capacitor bank placement, tap changing transformer, network reconfiguration, cross-section enhancement, etc. However, some researchers [30; 31] suggest that the aforementioned strategies may not be effective for prosumer-based DNs due to their slow response. The application of PV inverters represents a promising solution and in combination with already present control mechanisms can give results, so several studies have proposed their use for voltage optimization [32; 33; 34; 35; 36]. Different modes of operation are possible for PV inverters and the authors of [30] distinguish the following:  
* fixed power factor mode;
* volt-VAR control;
* volt-watt control;
* mode for power rate limit;
* voltage balance mode.  
In the fixed power factor mode, the power factor is maintained at a constant value and thus voltages are directly affected. Voltage control in the volt-watt control mode is achieved by active power from PVs. In the power rate limit mode, the rate of active power  
Figure 1: Prosumer configuration and interconnection with DNs.  
output from the PV inverter is limited. In volt-VAR control, reactive power from the PV inverter is used for voltage optimization. The general operating principle of volt-VAR control is described using a volt-VAR curve shown in Figure 2[37]. The volt-VAR curve represents a relation between a voltage value at the point of common coupling (PCC) and reactive power from the PV inverter. If the voltage value on PCC is lower than the specific threshold, the PV inverter injects reactive power. On the other hand, in the case of the higher voltage value on PCC, reactive power is absorbed.  
Reactive power capability determines the amount of reactive power available from the inverter [38]. Figure 3 represents reactive power capability determined with vectors of apparent power \(S\) and active power \(P\)[8,38,39]. The reactive power of the PV inverter depends on the active power and can be determined as:  
\[Q_{1}^{2}\leq\sqrt{S_{1}^{2}-P_{1}^{2}} \tag{1}\]