esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Player Load  
This study aimed to define player load intensity zones for male U14 basketball players. As previously emphasized, conducting research in age-appropriate competitive contexts is essential for methodological rigor, athlete development, and injury prevention [14; 36; 55; 56]. Player load is a valuable metric for coaches, providing insight into the physical demands placed on athletes during training and competition [57].  
For the U14 category, five player load intensity zones were established: <1.07, 1.07-1.36, 1.37-1.63, 1.64-1.95, and >1.95 arbitrary units per minute (AU/min). The highest proportion of effort was observed in Zone 4 (1.64-1.95 AU/min), accounting for 30.80% of total load, followed by Zone 3 (1.37-1.63 AU/min) with 27.13%. Combined, these two zones represent 57.97% of the total player load. Notably, 19.00% of the total effort was performed in Zone 5 (>1.95 AU/min), indicating that U14 athletes are capable of sustaining high-intensity workloads.  
This finding contrasts with data from U18 and professional-level players (e.g., ACB/ EuroLeague), where a lower proportion of high-intensity player load is typically observed [11; 16; 55]. This discrepancy may reflect tactical differences in game models rather than physical limitations of senior players. For example, a study analyzing six official ACB League matches reported five player load zones derived from k-means clustering: 0.19-0.50, 0.51-1.01, 1.02-1.27, 1.28-1.56, and 1.57-1.93 AU/min [11]. These intervals have lower upper thresholds compared to those observed in U14 players. Furthermore, only 4.67% of ACB player load occurred in Zone 5 (1.57-1.93 AU/min), compared to 19.00% in the U14 group (>1.95 AU/min), highlighting a substantial difference in the distribution of physical demands.  
These findings highlight the physical capacity of U14 athletes to perform at intensities comparable to or exceeding those reported in older age groups [11]. The high representation of effort in Zones 4 and 5 underscores the importance of preparing youth players for the physical demands of competition through appropriately structured training programs that reflect these workload profiles. Coaches can better prepare youth athletes for competition while supporting long-term physical development and injury prevention.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 3 Range Extension of the Indoor Standard Device for Large-Scale Length Measurement
### Theoretical Extended Range  
We assume that the hollow pyramid plating is total reflection, without any light loss. The reflectors' reflectance ranged from 0.95 to 0.98; its transmitting power was between 0.8 mW to 1.0 mW; the detection capability of the detector varied from 150 \(\mu\)W to 200 \(\mu\)W.  
We assume the transmitting power is 1.4 mW, and the detection power is 150 \(\mu\)W, then the system's transmittance was expressed as T = 150 \(\mu\)W/1.0 mW = 0.15. Considering only the light loss due to corner reflectors, the number of times the light propagated within the prism was n = log(0.15)/log0.98 \(\approx\) 94. The multiplication factor of the light range extension followed an arithmetic progression: starting with 2, doubling to 6, tripling to 10, and so on. With 23 iterations, the range could be extended from 50 m to 1150 m. Therefore, using hollow corner reflectors, the measuring range can theoretically be extended by a factor of 23.
## 4 Experiment and Result Analysis
### Experiment Environment
In the LCF algorithm, each mobile agent starts its route from the sink node and searches for the next node with the shortest distance to its current location. Comparing the C/S model and the LCF algorithm [49], the C/S model is a more traditional network model, while the LCF model has better network performance and does not depend on the objective function and coding strategy.
## 2 System Model and Problem Formulation
### User Clustering Problem Formulation
In this work, we assume that subcarriers are pre-allocated to every user and they can share the subcarriers with other users once they form clusters. Unlike the problem formulated in (14), the number of clusters and the number of users in each cluster are unknown and not pre-determined. This relaxation can further enhance the utilization of channel diversity in improving throughput, but it makes the maximization an NP-hard complete problem. The throughput maximization is achieved by clustering users in their optimal group with optimal power allocations which results in the highest aggregate throughput of the system. In other words, user individual interest is not taken into account where some of the users may be deprived of their best individual throughput attainment for the sake of overall throughput improvement.
## 8 Simulation Results  
This section analyzes the results of the three scenarios presented in Section 7, focusing on the system's ability to mitigate inter-area oscillations under varying levels of renewable penetration and its response to consecutive events. For all scenarios, two events are considered (see Figure 3):  
* A short-circuit at terminal 16 with a duration of 0.06 s.
* A short-circuit on line 03-04, followed by a switching event 0.25 s later.  
These events are simulated in a continuous-time domain, where the FFT function individually detects the oscillations in each of the four PV plants by analyzing the frequency at their connection terminals, triggering and deactivating the POD-P controller as required to damp inter-area oscillations effectively.  
The grid's electromechanical mode corresponds to an inter-area oscillation at 0.65 Hz, representing the system's natural oscillatory frequency. In the initial scenario, without PV generation, the electromechanical mode exhibits a damping ratio of 8.89%, which is above the stability threshold of 5% [51]. Variations in generation across scenarios significantly impact the stability of this mode, displacing it toward more unstable regions as renewable penetration increases and system inertia decreases. In all scenarios, both events generate inter-area oscillations, with Generator G01 oscillating against the rest of the system at 0.65 Hz.  
The simulations replicate real-world conditions with varying levels of renewable penetration and consecutive events, validating the POD-P controller's autonomous activation and its reliability in damping oscillations. The simulations are performed in the time domain (RMS). Additionally, eigenvalue analyses are conducted to determine the damping ratio of the electromechanical mode for each scenario. These analyses provide a complementary and quantitative measure of system stability, enhancing the robustness of this study's findings.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Methods  
NORCE's in-house python-based GDAR SAR processing system [38] was used in this study as it allows operational processing of big datasets. The process follows four steps: (1) geocoding and radiometric calibration of S1 data, (2) radiometric slope correction, (3) statistical analysis of long time series, and (4) mapping atmospheric exposure.  
Figure 3: 2019 tidal gauge observations from Hansjordnesbukta (Tromse) (red line) sampled every 10 min showing (**a**) the tidal range variation for the year 2019 and (**b**) tidal height observations during one S1 repeat cycle 1–12 August 2019. Tidal heights at Sentinel-1 acquisition times (see Table 1) are indicated as black dots. Tidal reference levels are indicated on the right. Observations in gray in (**b**) were filtered out because of the steep incidence angle (original tidal data ©Kartverket [12]).
## 3 Results and Discussion
### Antimicrobial Activity
sativa_, as well as on the target microorganisms. Literature studies have well documented that different organs of plants produce many different secondary metabolites with different biological functions [29; 47; 49]. Therefore, differences in the extract contents may determine the strong or mild antimicrobial and antifungal activity. This is supported by other studies showing that the choice of methanol as a solvent increases antifungal activity against _C. albicans_ and _C. parasilosis_, achieving MIC values of 31.2 ug/mL [47], in contrast to the aqueous and ethanol extracts used in our analyses. Furthermore, [PERSON] et al. (2016) demonstrated that a methanol extract derived from chestnut pollen grains exhibited mild antifungal activity against _C. albicans_ and _C. parasilosis_[50]. Another interesting thread suggesting that _C. sativa_ leaves exhibit antimicrobial activity against _Staphylococcus aureus_ is a study by [PERSON] et al. (2015). This research provided evidence that extracts from _C. sativa_ leaves can block virulence and pathogenesis in _Staphylococcus aureus_ by altering quorum sensing [38]. Quorum sensing is a critical mechanism by which the bacterium regulates its virulence and its ability to form biofilms [51]. The active substances in the extracts effectively prevented bacteria from communicating with each other and coordinating the reduction in virulence factors. Additionally, researchers found that the extracts of _C. sativa_ are not cytotoxic to human skin cells and do not inhibit the growth of normal skin microflora [38]. In conclusion, this study provides evidence that aqueous and alcoholic extracts of _C. sativa_ leaves can serve as a potential source of anti-staphylococcus agents to support alternative treatment options for infections caused by multidrug-resistant (MDR) bacteria.
## 3 Proposed Methodology
### Firmness of Spectrum  
When a signal is delivered, the fundamental frequency is \(\mathrm{f}_{0}=1/\mathrm{T}0\), where \(\mathrm{T}0\) is the signal's period. This translates to a frequency of \(1/\mathrm{N}\) for discrete-time transmissions. Since all other frequencies are multiples of this fundamental frequency, the resolution of the spectrum is determined by the number of samples N. At \(k=\) N/2, it is possible to determine the highest signal frequency that X[k] may represent. According to [PERSON]'s sampling theorem, the highest frequency of the original signal, which is assumed to be an audio stream sampled at 44.1 kHz, is 44.1/2, or 22.05 kHz. In the case of N = 1024 samples, the coefficient at \(k=\) N/2 corresponds to a frequency of 22.05 kHz, and the coefficient at \(k=\) 1 to a frequency of 22.05/512 43.1 Hz. The DFT coefficients would represent various frequencies (for instance, \(k=\) 1 corresponds to 22.05/500 = 44.1 Hz) if we sampled another area of an audio signal with just 1000 samples (at the same sample rate). According to the conclusion, the spectra can only be compared if the DFT spectra are computed using the same number of samples. The second numerical sample demonstrated another impact on the resolution. The DFT spectrum cannot fail to reflect the genuine contribution of the individual frequencies of an arbitrary signal x[n], even if the signal is periodic because it is sampled, i.e., it only has information about particular discrete frequencies.
## 2 Background Study
### Rain Attenuation Anomalies: Breakpoint  
At a breakpoint, the monotonous behavior of the time exceedance of rain changes abruptly in tropical regions. The breakpoint is more visible if the integration is considered over a smaller time duration; however, according to [29], a 1 min rainfall rate integration is the optimum time to clarify the rain structure to avoid noise. The main reason for modifying the model is considering the non-monotonous behavior of rain attenuation in tropical areas. It is found that there is a breakpoint of rainfall rate attenuation while moving from the temperate region to the tropical area, where the integration time is comparatively short. For such existing breakpoints, many of the temperate region models do not fit well in the tropical regions, and based on this, breakpoint idea-based models have been proposed in [30; 31].
## 3 Methodology
### Eca  
Effective Channel Attention (ECA) is an improved version of SENet, which removes the fully connected layer from the original SENet and replaces it with a \(1\times 1\) convolutional kernel for processing, making the model parameters smaller and lighter.  
As shown in Figure 3, obtain aggregated features of input features through Global Average Pooling (GAP), and ECA generates channel weights by performing fast 1D convolution with a size of \(k\). Among them, \(k\) is adaptively determined by mapping the channel dimension \(C\). The goal of the ECA module is to appropriately capture local interactions across channels, so it is necessary to determine the coverage range of interactions (i.e., the kernel size \(k\) of one-dimensional convolution). In different CNN architectures, the optimized interaction coverage can be manually adjusted for convolutional blocks with different channel numbers. However, manual tuning through cross validation will consume a significant amount of computing resources.  
Therefore, a possible mapping was introduced:  
\[C=\phi(k)=2^{(\gamma\times k-b)} \tag{1}\]  
In this paper, we set \(\gamma\) and \(b\) to 2 and 1 for all experiments.  
The kernel size \(k\) can be adaptively determined based on the channel dimension \(C\):  
\[k=\psi(C)=\left|\frac{\log_{2}(C)}{\gamma}+\frac{b}{\gamma}\right|_{old} \tag{2}\]
### Slope Stability Calculation Method  
The slope stability analysis method adopts the strength reduction method. In this method, the strength parameters \(c\) and \(\tan\varphi\) of the rock and soil mass are continuously reduced until the soil mass is destroyed, and the final reduced ratio \(F_{\text{s}}\) is the slope stability coefficient.  
\[\overline{c}=\frac{c}{F_{\text{s}}} \tag{1}\]  
\[\overline{\varphi}=\arctan\biggl{(}\frac{tan\varphi}{F_{\text{s}}}\biggr{)} \tag{2}\]  
In the formula, \(c,\overline{c}\) is the cohesion before and after the strength reduction, and \(\varphi,\overline{\varphi}\) is the internal friction angle before and after the strength reduction.  
When calculating the slope deformation of the model, the Mohr-Coulomb criterion needs to be followed. When the shear stress exceeds the shear stress of the rock and soil mass determined by internal friction and cohesion, the rock and soil mass will produce shear failure. The formula for calculating shear stress \(f_{\text{s}}\) is as follows:  
\[f_{\text{s}}=\sigma_{1}-\sigma_{3}\frac{1+sin\varphi}{1-sin\varphi}-2c\sqrt{ \frac{1+sin\varphi}{1-sin\varphi}} \tag{3}\]  
In the above formula, \(f_{\text{s}}\) is the shear stress, \(\sigma_{1}\) is the maximum principal stress, and \(\sigma_{3}\) is the minimum principal stress.
# Prediction of Residual Stress of Carburized Steel Based on Machine Learning
###### Abstract
[23] found that as the carburizing temperature and carbon potential increase, the amount of carbon in the carburized layer increases, which in turn leads to an increase in residual stress and inhibits the initiation of cracks. [PERSON] et al. [24] studied the calculation model for retained austenite, martensite, and residual stress by using the finite element method under different carburizing processes. The results show that the established model is effective at calculating the residual stress field of the martensitic transformation during the quenching process of the steel specimen. However, the above description stems from only a simple study of the relationship between residual stress and microstructure or residual stress and carbon content; there has been no systematic study of residual stress and microstructure or carbon content. Because residual stress depends primarily on the cooling process, the volume expansion of the martensite transformation caused by the different surface carbon contents expands by different degrees. In addition, after quenching, the surface of the martensite structure is inconsistent with that of the transition layer and the core. Using the C content and the tissue gradient to predict residual stress should improve the accuracy. In the work detailed in this paper, the carbon content of the surface-to-subsurface areas of the carburized and quenched surfaces of 23 CrNi3 Mo steel was gradually reduced. The volume expansion caused by martensitic transformation was gradually reduced from the surface to the core, resulting in greater residual compressive stress at the surface than in the core. The material organization structure segmentation model (SegModel-MOS) model used for semantic segmentation of optical micrographs was based on AlexNet and trained with 1200 homemade optical microstructure images. The experimental results demonstrate that SegModel-MOS effectively identifies needle-shaped martensite, residual austenite, and lath martensite from microstructure images. The main contributions of this article are as follows:1. A high-precision semantic segmentation model for optical micrographs, named SegModel-MOS, is established. This model combines migration learning and a residual network to achieve accurate image segmentation after training with small numbers of data samples. 2. In this paper, the SVM algorithm is first used to establish a mapping relationship with the residual stress based on the percentage and carbon content of acicular martensite, retained austenite, and lath martensite steel microstructures; then, it predicts the residual stress.
## 3 Problem Description and Modeling
### Problem Description
\begin{cases}1&s\geq s_{\text{rated}}\\ \alpha\cdot s+\beta\cdot e^{-\lambda(s_{\text{rated}}-s)}&s_{\text{critical}}<s <s_{\text{rated}}\\ v_{min}\cdot tanh\left(\frac{s-s_{\text{dead}}}{s_{\text{critical}}-s_{\text{ dead}}}\cdot\pi\right)&s_{\text{dead}}\leq s\leq s_{\text{critical}}\\ 0&s<s_{\text{dead}}\end{cases}  \tag{1}\]  
The FJSPLSP-LTCC problem studied in this paper involves several interdependent decision subproblems, including: (1) selecting a processing machine for each operation; (2) determining the processing sequence of operations on each machine; (3) assigning AGV resources to each operation; and (4) scheduling charging actions for each AGV. To facilitate the modeling of the FJSPLSP-LTCC, the following assumptions are made:  
1. All jobs, machines, AGVs, and chargers are initially available;
2. The sequence of operations within each job is predetermined and must be followed;
3. Each operation can only be processed on one machine at a time;
4. Each machine and each AGV can process/transport only one job at a time. 5. Once an operation (processing, transportation, or charging) starts, it cannot be interrupted until completion;
6. AGVs can only travel along predefined paths;
7. AGV speed is updated prior to each movement based on the current battery level and remains constant during the trip;
8. Jobs do not need to be returned to the L/U station after the final operation;
9. AGVs can only be charged prior to transporting jobs; after each transport task, a forced battery swap is evaluated;
10. Each charging station can serve only one AGV at a time;
11. Machine failures, job insertions, and AGV path conflicts are not considered.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 3 RFID Link Budget Calculation  
The operation of the proposed system involves passive RFID tag readings at a maximum distance and at high speed of the vehicle inspection. To theoretically validate this idea, in this section we present a reliable radio wave propagation model that can be used to estimate the maximum range at which the tags can be detected and determine the speed limit for the inspection vehicle. The fundamentals of the RFID link budget calculation can be found in [38] for static applications. In addition, simple models have been used in the related papers [12; 13] to calculate the RFID coverage range in roads. Specifically, [13] proposes an unobstructed free-space model for estimating the signal strength received from the RFID tags. A slightly more sophisticated approach is used in [12], which takes into consideration the possible interference caused by the reflection on the ground of the radiowaves. This approach serves to conclude that both the reader and the RFID tags should be as high above the ground as possible. Although not explicitly stated, a basic assumption in the propagation models in [12; 13] is again that both the reader and the RFID tags are static, without movement. The contribution of this section is to generalize previous approaches in [12; 13; 38] by taking into account vehicle's location and speed in the equations governing the link budget. In addition, the effect of multipath interferences in complex scenarios, such as urban environments, is modeled statistically for a more accurate description of reality. Therefore, the exposition is organized as follows: a basic background on the RFID link budget calculations (for the case of the downlink communication) is given in Section 3.1.1. The extension to in-motion scenarios under real conditions is described in Section 3.1.2.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 3 Data and Methodology
### Methodology
#### 3.2.1 Network Structure  
YOLOX-Nano is architecturally segmented into three primary components: the backbone, dedicated to feature extraction; the Neck, responsible for augmenting the feature extraction process; and the Prediction, serving as the detection head. The model employs \(CSPDarkNet53\)[20] as its core feature extraction network, which produces feature layers of three distinct scales. These layers undergo further processing in the Neck's advanced feature extraction layer (\(FPN\)) [21], facilitating multi-scale feature fusion and in-depth feature extraction. Subsequently, the extracted feature maps, comprising three layers, are fed into the Prediction segment to perform regression prediction, as illustrated in Figure 3.
## 5 Engineering Validation
### Data Acquisition from Sensing Subsystem  
Structural health monitoring systems were installed on the bridge for about ten years. From the sensing subsystems, the dynamic responses of cables and the main structure were measured by sensors. The type of accelerometer is the same as that in the workshop test. An accelerometer was installed near the 1/3 location of the cable. The sampling frequency of the accelerometer is 20 Hz. The measured acceleration response of the cable is shown in Figure 14.  
Figure 12: Basic information on the experimental bridge and sensors.  
Figure 13: Loading trucks in the field experiment.  
Limited to the in-service conditions, it is difficult to install a load cell or other sensors on the cable to acquire the cable tension directly. In this situation, a GPS sensor and a strain gauge were installed on the top of the South Tower and the girder near the bottom of the experimental cable as a reference, respectively. Stress was calculated from the strain data based on the calibration in the construction stage. The sampling frequencies of the GPS sensor and strain gauge are 1 Hz and 20 Hz, respectively. The GPS displacement of the tower and the stress of the girder are shown in Figure 15a,b, respectively.