esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results
### Decoder Design Choices  
We change the channel combination of both the encoder features and the low-level features in the decoder structure. In order to evaluate the effectiveness of our proposed channel combination method, we compare the classification performances of four different channel combinations and the number of the network model parameters generated during their training. As shown in Table 3, the channel combination (64,16) has the highest OA and Kappa values and the best classification performance. This channel combination also requires the second lowest number of parameters, surpassed only by channel combination (32,8). Compared to the original structure, the number of parameters in combinations  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Conv 1\({}^{\star}\)1** & \begin{tabular}{c} **Conv 3\({}^{\star}\)3** \\ **Rate = 6** \\ \end{tabular} & \
\begin{tabular}{c} **Conv 3\({}^{\star}\)3** \\ **Rate = 12** \\ \end{tabular} &
\begin{tabular}{c} **Conv 3\({}^{\star}\)3** \\ **Rate = 18** \\ \end{tabular} & **Image Level** & **OA** & **Kappa** \\ \hline \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.55 & 95.18 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.68 & 95.44 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.61 & 95.34 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.58 & 95.26 \\ \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & 97.54 & 94.59 \\  & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.51 & 94.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the classification performance of the different ASPP branches.  
(32,8) and (64,16) are reduced by 25% and 22%, respectively. Channel combination (64,16) represents the optimal combination of strong classification performance and a reduced number of parameters in the network model.  
We also design different convolution structures for the decoder module, and report the findings in Table 4. The feature dimension of our convolution structure is 64; after concatenating the feature maps, we find that it is best to employ two 3*3 convolutions with 64 channels to refine the feature map.
###### Abstract  
Lower limb kinematics and kinetics during the landing phase of jumping might change because of localized muscle fatigue. This study aimed to investigate the acute influence of running-induced fatigue on the performance and lower limb kinematics and kinetics of a countermovement jump. A running-induced fatigue protocol was applied to fifteen male subjects. Participants were asked to perform three successful countermovement jumps before and after fatigue. Kinematic and kinetic data were collected to compare any fatigue influences. Wilcoxon signed-rank tests and paired-sample \(t\)-tests were used to analyze the data. Running-induced fatigue did not significantly change vertical jump height and peak vertical ground reaction forces (GRF) during the push-off and landing phases. Lower limb biomechanics significantly changed, especially kinematic parameters. During the push-off phase, fatigue resulted in an increased ankle peak inversion angle, knee minimal flexion angle, knee peak abduction angle, and hip peak flexion moment. In addition, the range of motion (ROM) of the ankle and knee joints in the frontal plane was also increased. Certain parameters decreased as a result of fatigue, such as the ankle peak internal rotation angle, hip peak abduction angle, the ROM of the ankle joint in the sagittal plane, and ROM of the hip joint in the frontal plane. During the landing phase, the peak inversion angle and peak external rotation angle of the ankle joint, peak abduction angle of the knee and hip joint, ROM of the ankle joint in the horizontal plane, ROM of the ankle and knee joint in the frontal plane were all increased as a result of fatigue. The knee peak flexion moment and hip peak extension moment, however, were decreased. Under fatigue conditions, lower limb kinetics and kinematics were changed during both the push-off and landing phases. More attention should be focused on the landing phase and the last period of the push-off phase due to potentially higher risks of injury. The findings of the current study may be beneficial to athletes and coaches in preventing jumping related injuries.  
running-induced fatigue; joint angle; joint moment; jump height; countermovement jump
## 2 Overview of Supplementary Damping Systems  
### Classification of Damping Devices  
Supplementary damping systems can be classified based on various criteria, including their energy dissipation mechanisms, activation methods, and functional characteristics [18]. This comprehensive classification (see Figure 2) provides a framework for understanding the diverse range of available damping technologies. The classification and characterization of supplementary damping systems form a complex framework that includes numerous criteria and operational principles. The classification system depicted in Figure 2 presents a systematic approach to analyzing these devices through three key perspectives: energy dissipation mechanisms, activation techniques, and functional features.
## 2 Principles of Underwater Communication  
Due to the unique properties of water as a transmission medium, underwater communication has its own set of challenges and requirements, making it distinct from atmospheric communication. The main principles involved in underwater communication can be categorized as acoustic waves, radio frequencies, and optical waves.
# Marine Gelatine from Rest Raw Materials
## 3 Production Strategies for Gelatine
### Improving the Properties of Fish Gelatine
[76] examined the effects of natural phenolic cross-linkers (ferulic and caffe acid) on fish gelatines. In their study, caffe acid had notable effects in decreasing solubility, water vapour permeability, and oxygen permeability of fish gelatine films. Natural phenolic compounds may therefore be used as a natural ingredient for increasing safety of gelatine-based biodegradable packaging, by improving their barrier and physicochemical properties. Another natural material, chitosan nanoparticles (CSNPs), with excellent physicochemical properties, is known to be environmentally friendly, and bioactive, has been researched for improving properties of fish gelatine based films. [PERSON] et al. [77] have created novel bio-nanocomposite films by addition of CSNP particles (created by ionic gelation between chitosan and sodium tripolyphosphate) into fish gelatine film matrix. Newly created films had significantly increased tensile strength and elastic modulus, and decreased water vapour permeability compared to fish gelatine films. With the exception of its inferior physical properties when compared to mammalian counterparts, fish derived gelatine intended for food use often possesses undesirable sensory properties characterized by an unpleasant \"fishy\" flavour [78]. [PERSON], [PERSON], and [PERSON] [79] have investigated the effects of defatting and tannic acid incorporation during extraction on the properties and fishy odour of gelatine obtained from seagrass skin. They concluded that defatting by pre-treatment with citric acid and isopropanol and subsequent incorporation of tannic acid during the extraction prevented lipid oxidation and the subsequent development of volatile compounds and fishy odours in the resulting gelatine. The intensity of fishy odour may also increase if the storage of frozen raw materials is prolonged before processing, due to formation of volatile aldehydes and alcohols [78]. Therefore, delays in processing should be avoided in order to minimize formation of undesirable odour and further loss of technofunctioal properties of gelatine.
## 5 Food Safety and Quality Applications
The inspection system used a pair of high-power white LED line lights to illuminate chicken carcasses hung from shackles (Figure 11a). A sensing unit--which consisted of a C-mount lens, a transmission imaging spectrograph, and an EMCCD camera--was used to collect hyperspectral reflectance images line by line in 55 bands (389-753 nm) from the chicken carcasses that were moving at a speed of 140 birds per minute (bpm). Single-band 2-D spatial images (Figure 11b) were compiled from the acquired line-scan hyperspectral images for offline image and spectral analysis, such as region of interest (ROI) optimization and key band selection. Analysis of two-band ratios found that using the pairing of 580 and 620 nm (R580/R620) gave the largest difference between the averageROI reflectance spectra of the wholesome chickens and the systemically diseased chickens. The selected two wavelengths were then used by the same imaging system in multispectral imaging mode in a poultry plant. During continuous operation for over 100,000 chickens on a commercial 140-bpm kill line, the multispectral system achieved over 99% accuracy in identifying wholesome chickens and over 96% accuracy in identifying unwholesome chickens. The system can improve chicken product safety by preventing most unwholesome birds from entering the evisceration line and also by reducing the routine workload for food safety inspectors working in the chicken processing plants.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
###### Abstract  
This paper addresses the feasibility of implementing spatial diversity techniques to mitigate interference signals using low-cost GNSS receivers. Global Navigation Satellite Systems (GNSSs) remain at the core of navigation technologies and obtaining precise and robust positioning solutions in harsh scenarios becomes essential for the proper functioning of modern applications. Furthermore, this challenge is even more complex when mass-market receivers are addressed, since the previous requirements must be achieved while maintaining low-cost architectures. A promising solution is to use beamforming techniques, which exploit the spatial domain to achieve enhanced reliability and robustness. In this paper, the potential of beamforming in mass-market receivers is analyzed by implementing two interference mitigation techniques and using a five-channel low-cost software defined radio (SDR), KrakenSDR. The results show that the algorithms implemented are able to mitigate strong interference signals, allowing the GNSS receiver to compute an accurate positioning solution.  
Keywords:array processing; spatial diversity; beamforming; mass-market; interference mitigation +
Footnote †: Academic Editor: [PERSON]
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 5 Conclusions  
The following conclusions can be drawn from the present study:  
1. Based on the rheological characteristics of Bingham fluid and the assumption of a spherical diffusion model of grouting, the flow equation of the Bingham fluid was obtained, and the formula for calculating the fracture diffusion radius of Bingham fluid was derived. The formula considers the influence of the main parameters, such as stone content, void ratio, water-cement ratio, and grouting pressure.
2. The slurry's diffusion mode in the soil-rock mixtures (stone content 30%) can be summarized as the main-fracture-sub-permeation mode. In the middle period before diffusion, the structure of the backfill of the soil-stone mixtures is loose before reinforcement, the initial kinetic energy of slurry diffusion is the largest, and the resistance to overcome is small. At this time, the midpoint of the slurry outlet of the cuff pipe is the midpoint, the main slurry veins and secondary slurry veins are formed rapidly along the soft surface, and fracture diffusion is dominant at this stage. At the later stage of diffusion, the void ratio in the reinforced area decreases, and the resistance of the slurry to continue expanding along the main slurry vein or secondary slurry vein channels increases so that the pores can only be filled along the vicinity of the main slurry vein or the boundary area, mainly by permeation diffusion. Finally, the slurry-rock cement skeleton is formed, and the skeleton is wrapped and packed with the soil in the grouting to strengthen the concretion.
3. The formula for fracture diffusion of a Bingham fluid is derived. The formula is brought into the test conditions under the corresponding working conditions for calculation. After comparing the calculated values with the test values for verification (the error of indoor model test results is less than 3%, and the error of field test results is less than 5%), it is confirmed that the formula applies to various working conditions of fracture grouting of the soil-rock mixtures and has a good prediction effect on the value of the fracture diffusion radius.  
[PERSON] designed the experiment. [PERSON] and [PERSON] carried out theoretical derivation and experimental analysis. All authors have read and agreed to the published version of the manuscript.  
This work is supported by The Natural Science Foundation of Chongqing (CSTB2022 NSCQ-MSX0887): Research on the construction technology of long and deep soil-rock mixed backfill tunnel composite shield.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding authors upon request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Molecular Docking
Epicatechin showed several hydrogen bonds with prominent amino acid residues of the PAS anionic subsite of AChE, namely Asp 74, Tyr 124 and Tyr 341, while van der Waals interaction with Trp 286. Hydrogen bond interactions of -OH groups of phenyl moiety with Asp 74 carboxylate group are established. In addition, a hydrogen bond between the Tyr 124-OH and the -OH group (in position 3) of chromene moiety, as well as between the Tyr 341 carbonyl group and the -OH group (in position 7) of chromene moiety is formed. Moreover, Tyr 341 makes pi-pi stacking with the phenyl ring of epicatechin. Interactions of epicatechin with Trp 86, Tyr 337 and Phe 338, all relevant amino acid residues of the anionic subsite (AS) are hydrophobic in nature. In addition, epicatechin showed hydrophobic interactions with some of the significant amino acid residues of the acyl binding pocket (ABP) of AChE, namely Phe 295 and Phe 297 (Figure 4). The lowest energy conformer of salicin exhibited hydrogen bond interactions with Tyr 124 and Tyr 341, van der Waals interactions with Tyr 72 and Asp 74, and pi-pi stacking interaction with Trp  
Figure 5: Active site of the AChE enzyme with salicin: (a) 3D view; (b) 2D view. Figure 6: Active site of the AChE enzyme with chlorogenic acid: (a) 3D view; (b) 2D view. 286, which are significant amino acid residues of the PAS. The interaction between salicin and Phe 338, important amino acid residue of AS, is hydrophobic. Interactions of salicin with amino acid residues of the ABP, Phe 295 and Phe 297, are hydrogen bonding and van der Waals, respectively (Figure 5). Chlorogenic acid was the only ligand, beside donepezil, that showed interactions with prominent residues of the catalytic site of AchE, Ser 203 and His 447, the former being hydrogen bonding and the latter pi-cation interaction. Chlorogenic acid also showed several van der Waals interactions with the important amino acid residues of AS of AChE, namely Trp 86, Tyr 337 and Phe 338, as well as of ABP, Phe 295 and Phe 297. Most of interactions between chlorogenic acid and significant amino acid residues of PAS peripheral anionic site, Tyr 72, Trp 286 and Tyr 341, are hydrophobic, while Tyr 124 forms a hydrogen bond. In addition, a hydrogen bond is formed between chlorogenic acid and the amino acid residue of the oxyanionic hole--Gly 121 (Figure 6). [PERSON] et al. (2016) [12] reported significant in vitro inhibitory activity of _S. alba_ bark extracts toward AChE and good correlations between anti-AChE IC\({}_{50}\) values and bioactive compounds--salicin, catechin, gallocatechin, procyanidins B1, B2, C1 and chlorogenic acid, confirming the role of these compounds in anticholinesterase activity, which is in accordance with our data.
## 4 Exploring the TES Performances Improvement by a Numerical Parametric Study
### Model Definition  
The TES system is described by a C++ code, where the first law of thermodynamics is applied to the storage as an open system. Several hypotheses are considered.  
First, for the HTF and the storage tank:  
* the HTF is incompressible, and its flow is axial;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Flow Rate Influence**} & \multicolumn{2}{c}{**Inlet Temperature Influence**} \\ \hline \(T_{in,i}\) (\({}^{\circ}\)C) & & 43.8 & & 44.7 \({}^{\circ}\)C & \\ \(T_{in,f}\) (\({}^{\circ}\)C) & & 65.8 & & 72.7 & 67.9 \\ \(\hat{m}_{\textit{HTF}}\) (L/h) & 626 & 353 & & 288 & \\ \(E_{\textit{stored}}\) (kWh) & 4.2 & 4.1 & 5.0 & & 4.4 \\ \(E_{\textit{losses}}\) (kWh) & 0.8 & 0.9 & 0.6 & 0.8 \\ \(\tau_{95\%}\) (h) & 3.0 & 3.5 & 1.9 & 2.5 \\ \(\eta_{\textit{stored}}\) (\%) & 84 & 82 & 89 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Flow rate and inlet temperature influence on the TES storage time and efficiency.  
the kinetic and potential energy variation are negligible;
* the thermo-physical properties are independent of the temperature;
* the tank is divided in \(N\) meshes of same size;
* the meshing is small enough to consider the temperature as uniform with one consistent mesh volume. Second, for the PCM and the nodules:
* the PCM volume expansion during the phase change is neglected, thus the solid and liquid densities are identical, and there is no supercooling;
* the thermo-physical properties of each phase are independent of the temperature;
* the conductive heat transfer between nodules is neglected, and inside a nodule the heat transfer is only conductive;
* each tank mesh includes \(N_{\text{od}}\) nodules and each nodule includes \(M\) concentric meshes.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 4 Simulation Results and Performance Analysis
### Tracking Performance  
The tracking performance for the BOC signal is mainly determined by two factors: the ranging error, which is reflected in the ranging error of the code tracking loop, and the velocity measurement error, which is reflected in the Doppler measurement error.  
The ranging error of the code tracking loop is shown in Equation (23):  
\[\varepsilon_{DLL}=\left[\frac{d\cdot B_{\mathit{dll}}}{2\cdot C/N_{0}}\left(1+ \frac{1}{T_{\mathit{coh}}\cdot C/N_{0}}\right)\right]^{\frac{1}{2}}\cdot \lambda_{\mathit{boc}} \tag{23}\]  
where \(d\) is the chip interval of the code tracking loop, \(B_{\mathit{dll}}\) is the DLL bandwidth, \(C/N_{0}\) is the CNR, \(T_{\mathit{coh}}\) is the integration time of the loop, and \(\lambda_{\mathit{boc}}\) is the wavelength of the BOC signal.  
The Doppler measurement error is determined by the carrier's third-order PLL and includes thermal noise vibration error and dynamic stress error, as shown in Equation (24):  
\[\varepsilon_{PLL}=\varepsilon_{tPLL}+\frac{1}{3}\delta_{e}=\left[\frac{B_{ \mathit{pll}}}{C/N_{0}}\left(1+\frac{1}{2\cdot T_{\mathit{coh}}\cdot C/N_{0}} \right)\right]^{\frac{1}{2}}+\frac{\tilde{\kappa}\cdot 2\pi}{3\left(1.2B_{ \mathit{pll}}\right)^{3}} \tag{24}\]  
where \(\varepsilon_{tPLL}\) is the thermal noise vibration error, \(\delta_{e}\) is the dynamic stress error, \(B_{\mathit{pll}}\) is the third-order PLL bandwidth, and \(\tilde{\kappa}\) is the acceleration of the system.  
The Doppler frequency is calculated as shown in Equation (25):  
\[f_{\mathit{doppler}}=\frac{\sqrt{2}\cdot\varepsilon_{PLL}}{2\pi\cdot\Delta t} \tag{25}\]  
where \(\Delta t\) is the update time of the Doppler measurement.  
Figure 13 illustrates the results for the ranging error of the code tracking loop under different power ratios and different CNRs. Figure 14 exhibits the results for the Doppler frequency measurement error under different power ratios and different CNRs. We can draw the following conclusion: when \(CNR<63\,\mathrm{dB}\)-Hz, the ranging error of the code tracking loop and the Doppler frequency measurement error increase with the decreasing power ratio. Otherwise, the ranging error and Doppler frequency measurement error are almost unaffected by the power ratio.  
Figure 13: Ranging error under different conditions.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 3 Results of Predicting the Number of Heatwave-Related Patients
### Feature Importance  
Figure 5 shows the estimated variable importance rankings corresponding to the model. The weekly mean temperature variable, which had a value of 0.440, contributed the most in this model, followed by the vulnerable occupational groups (0.129), weekly median temperature (0.102), floating population (0.098), and weekly max temperature (0.085) variables. These five variables can be considered the main variables for prediction, whereas the rest are less important. Interestingly, the variable importance rankings proved that the floating population variable, which changes with time, had a greater effect on prediction than the population of registered residents. However, regional economic indicators had less impact on diseases related to heatwaves, as observed from the low values for income (0.020) and insurance (0.013).
# New Methods of Series Expansions between Three Anomalies
## 1 Introduction
proposed an explicit integral solution, utilizing methods recently applied to the 'geometric goat problem' and to the dynamics of spherical collapse [19]. [PERSON] and [PERSON] proposed two programs to solve [PERSON]'s equation with high computational speed and optimal accuracy [20]. Furthermore, this also involves the conversion between three types of anomalies. The conversion expressions among the three types of anomalies are generally complex and extremely inconvenient in numerical calculations. We can express it in the form of series within an appropriate range of accuracy to improve its computational efficiency and accuracy [21]. With the application and development of space technology and computer technology in satellite ephemeris calculations, studying the relationship among the three types of anomalies of satellite motion (true anomaly, elliptic anomaly, and mean anomaly), as well as the extreme value problem of their differences, has a more important practical value. Many scholars at home and abroad have conducted in-depth research on the relationship among the three types of anomalies in satellite motion, but there are few studies in the literature that deeply analyze the extreme value problem of the differences among these three types of anomalies. In this paper, we mainly use the Lagrange conjugate series to derive the series expansion formula between the true anomaly and the elliptic anomaly. Then, we derived the series expansion between the elliptic anomaly and the mean anomaly using the symbolic iteration method. We further use the series expansion between the true anomaly and the elliptic anomaly and the series expansion between the elliptic anomaly and the mean anomaly to derive the relationship between the true anomaly and the mean anomaly. And we analyze the accuracy and characteristics of the obtained expansion formulas. The expression derived in this paper is easy to compare and analyze. And the calculation results of this paper have, to some extent, expanded the scope of satellite motion analysis theory.