esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Geant4--Simulation Results
### Identification of Light Ions at Backuard Direction for STF Characterization  
A string of two-stage telescopes centered at around \(90^{\circ}\) and covering a wide polar angle represents the element (iii). This detection element is mainly included for the characterization of STF events by measuring the angular and energy distributions of light fragments shown in Figure 10. The granularity of the array will provide the angular distribution, while the use of two-stage telescopes is motivated by the interest in both measuring the energies and identifying the \(Z\) of lighter fragments. Then, we have considered for the first stage a \(20\,\mathrm{\SIUnitSymbolMicro m}\) thick Si detector, which produces the (\(\Delta E\)) signal and only partially slows down the light ions, and for the second stage, a \(300\,\mathrm{\SIUnitSymbolMicro m}\) thick Si detector that is sufficiently thick for collecting the residual energies (\(E_{res}\)) of the ions expected. The dependence of the energy resolution of the Si detector on atomic mass (\(A\)) and energy (E) of heavy ions can be taken into account by considering the empirical formula proposed in [77]:  
\[\frac{\Delta E}{E}=5\times 10^{-3}\left(\frac{1+0.01A}{\sqrt{\frac{E}{A}}} \right).\]  
Accordingly, we considered an upper limit for our measurements of \(900\,\mathrm{keV}\), corresponding to ions with mass A = 50 at E = \(300\,\mathrm{MeV}\). The energy resolutions of detectors have been introduced in our simulations with the same approach described for the time and position resolutions of the TOF detector. The energy spectra at \(90^{\circ}\pm 15^{\circ}\) for ions with masses ranging from A = 48 to A = 176 are shown in Figure 14a,b for the thin (\(\Delta E\)) and thick (\(E_{res}\)) detectors, respectively. The two peaks in the \(E_{res}\) spectrum correspond to the light fragments of \({}^{48}\)Ca with energies larger than 100 MeV as those produced by DTF and STF decay. Indeed, only low \(Z\) ions can pass through the 20 \(\upmu\)m Si layer of \(\Delta E\) detector. The heavier fragments from SymBF and AsymBF reactions, due the larger energy loss, will be stopped and their kinetic energies will be collected in the \(\Delta E\) detector only.  
The energy peaks of the fragments from different processes are well separated in the (\(\Delta E\)) spectra, see Figure 14a. Although the use of a single thin detector would be sufficient to disentangle them, the use of a second detector stage is important for two reasons: (i) By measuring the total kinetic energies of all products, it is possible to achieve a comprehensive description of the reaction mechanism without making any further assumption on the reaction channel and kinematics; (ii) By using the \(\Delta E-E\) technique for the identification of the \(Z\) of the lightest fragments, it is possible to separate fragments having the same energy and mass but different \(Z\). For instance, in Figure 16, we present a \(\Delta E-E\) plot for several fragments of mass number A = 48 and energy ranging from 125 to 350 MeV.
# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 4 Discussion
NRPs, a vital class of antimicrobial agents, are synthesized by non-ribosomal peptide synthetase (NRPS) complexes independent of mRNA templates. The structural diversity and biological activity of NRPs contribute significantly to antimicrobial drug development [35]. However, the current system faces a notable limitation in directly identifyingand predicting NRPs due to the complexity of gene clusters involved in NRP synthesis, contrasting with a single open reading frame (ORF). This limitation suggests that, while deep learning provides a powerful tool for screening and identifying potential ribosomal peptides (RPs), further method development and algorithm optimization are required for the comprehensive exploration of antimicrobial peptide resources in the environment, especially NRPs. Therefore, in future research, exploring a broader range of antimicrobial peptides, including NRPs, will be a crucial direction for our work. At the same time, we must recognize that translating these assumed antimicrobial peptides from in vitro experiments to practical clinical applications still faces numerous challenges. Firstly, assessing the antimicrobial activity of these peptides relies on traditional microbiological techniques such as agar diffusion assays and minimum inhibitory concentration determinations. Furthermore, the transition from the laboratory to clinical application not only requires further stability and safety assessments but also includes pharmacokinetic studies and detailed clinical trials.
## 1 Introduction
The Ensemble Kalman filtering algorithm was applied to enhance the LAI estimation accuracy. Many other researchers have achieved notable results in this field. [PERSON] [25] used the China Land Soil Moisture Date Assimilation System (CLSMDAS) to perform an assimilation experiment on soil moisture based on the EnKF and Land Surface Process Model; their results reasonably reflected the spatial and temporal distribution of soil moisture. [PERSON] [26] used the EnKF algorithm coupled with the canopy radiative transfer model (ACRM) to accurately predict time series LAI data from a phenological model. [PERSON] [27] proposed an algorithm that integrated Direct Insertion (DI) and Deterministic Ensemble Kalman Filter (DEnKF) methods to assimilate snow depth with surface albedo; the solution was shown to improve the precision of snow depth simulations. Although the EnKF assimilation method has been widely used in the inversion of many surface parameters, it has not yet been applied to the estimation of land surface albedos. In this paper, we propose a time series high-resolution land surface albedo estimation algorithm based on the ensemble Kalman filter. The albedo dynamic model was established based on historical MODIS albedo products and Thematic Mapper (TM) observations, and albedo data was recursively updated via the EnKF method.
## 4 Fatigue Life Modelling and Prediction in Hydrogen Environment
\begin{tabular}{c c c c} \hline
**Categories** & **Author and Year** & **Title** & **Observation** \\ \hline [PERSON] et al. (2017) [69] & Fatigue crack growth modelling for pipeline carbon steels under gaseous hydrogen conditions & A corrosion–crack correlation model for modelling fatigue crack growth affected by hydrogen embrittlement in carbon steel of pipelines under gas–hydrogen conditions is proposed. \\  & & Crack growth modelling and life prediction of pipeline steels exposed to near-neutral pH environments: stage II crack growth and overall life prediction & A prediction model that considers load interactions and underload-type variable pressure fluctuations occurring during the operation of oil and gas pipelines is developed. \\  & & & \\ \multirow{4}{*}{Empirical models} & \multirow{4}{*}{[PERSON] et al. (2014) [21]} & \multirow{4}{*}{A review of the fatigue failure mechanism of metallic materials under a corroded environment} & In fatigue strength assessment, it employs methods such as probabilistic and statistically based assessments to provide reliable results. This paper presents a practical, parameter-poor numerical model of environmental hydrogen embrittlement. The approach uses a mechanism based on hydrogen-enhanced plasticity in the strain-at-tracture model to describe hydrogen embrittlement. \\  & & Low-cycle fatigue mechanical behaviour of 30 CrMo steel under hydrogen environment and numerical verification of chaboche model & A fatigue toughness model and a Chaboche kinematic hardening model were fitted based on low-cycle fatigue test data. It accurately describes the effect of hydrogen on the low-cycle fatigue mechanical behaviour of 30 CrMo steel. \\ \hline \multirow{4}{*}{Physical models} & \multirow{4}{*}{[PERSON] et al. (2016) [29]} & \multirow{4}{*}{Prediction of long-term fatigue life of CFRP oxygen storage vessel based on micromechanics of failure} & A comprehensive fracture mechanics life prediction method is proposed to mitigate subcritical crack growth in steels enhanced by hydrogen environment. \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \end{tabular} 
\begin{tabular}{c c}  & \multicolumn{1}{c}{Crack tip modelling of hydrogen environment embrittlement: application to fracture mechanics life prediction method is proposed to mitigate subcritical crack growth in steels enhanced by hydrogen environment.} \\  & & & \\ \cline{1-1}  & & & \\ \cl
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 4 Discussion  
This study integrates eye movement, EEG, and a subjective beauty questionnaire to construct and evaluate a multi-source affective computing model tailored for assessing rural visual landscapes. Model performance was optimized through feature selection, SMOTE, Bayesian optimization, and ensemble classification. Generalizability was evaluated via external validation, and the normality assumption underlying the subjective aesthetic questionnaire was empirically examined.
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 4 Performance Evaluation
### Considered Scenario
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** \\ \hline \multicolumn{2}{c}{Cell configuration} \\ \hline PRB bandwidth (\(B_{n}\)) & 360 kHz \\ \hline Number of available PRBs in a cell (\(W_{n}\)) & 65 PRBs \\ \hline Average spectral efficiency (\(S_{n}\)) & 5 b/s/Hz \\ \hline Total cell capacity (\(c_{n}\)) & 117 Mb/s \\ \hline \multicolumn{2}{c}{SLA configuration} \\ \hline \multirow{2}{*}{\(SAGBR_{k}\)} & Tenant \(k=1\) & 60\% of system capacity \(C\) \\ \cline{2-3}  & Tenant \(k=2\) & 40\% of system capacity \(C\) \\ \hline \multirow{2}{*}{\(MCBR_{k,n}\)} & Tenant \(k=1\) & 80\% of cell capacity \(c_{n}\) \\ \cline{2-3}  & Tenant \(k=2\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cells and SLA configuration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** \\ \hline Number of cells & \(N=4\) & \(N^{\prime}=5\) \\ \hline Initial training steps & 1000 & 3000 \\ \hline \multirow{3}{*}{ANN config.} & Input layer (nodes) & 22 & 27 \\ \cline{2-3}  & Fully connected layer & 1 Layer (100 nodes) \\ \cline{2-3}  & Output layer(nodes) & 81 & 243 \\ \hline Experience replay buffer maximum length (\(l\)) & \multicolumn{2}{c}{\(10^{7}\)} \\ \hline Mini-batch size (\(J\)) & \multicolumn{2}{c}{256} \\ \hline Learning rate (\(\tau\)) & \multicolumn{2}{c}{\(10^{-4}\)} \\ \hline Discount factor(\(\gamma\)) & \multicolumn{2}{c}{0.9} \\ \hline \(\epsilon\) value (\(\epsilon\)-Greedy) & \multicolumn{2}{c}{0.1} \\ \hline Reward weights & \(\varphi_{1}=0.5\), \(\varphi_{2}=0.4\) \\ \hline Time step duration (\(\Delta t\)) & \multicolumn{2}{c}{3 min} \\ \hline Action step (\(\Delta\)) & \multicolumn{2}{c}{0.03} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DQN configuration parameters.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 6 3 GPP Release 19: 5G-Advanced Growth
### Enhancements to Existing Features
Other enhancements to existing features in Release 19 include  
* **Sidelink**: This work focuses on multi-hop UE-to-network sidelink relay for mission-critical communications, especially in public safety and out-of-coverage scenarios;
* **Network energy savings**: Key enhancements include on-demand SSB in SCell for connected-mode UEs configured with CA, on-demand system information block type 1 (SIB1) for UEs in idle and inactive modes, and the adaptation of the transmission of common signals and channels;
* **Multi-carrier enhancements**: One enhancement is to use a single DCI to schedule multiple cells with different subcarrier spacing values or carrier types. Another enhancement is to use a single DCI to schedule multiple cells with multiple PDSCH/PUSCH transmissions on each cell.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 4 Analysis of Constellation Collision Maneuver
### Phase 3: Collision Avoidance Maneuver in the Stage of Derailment Disposal  
As can be seen from Figure 4, the fading speed accelerates when the satellite orbit altitude is lower than that of 350 km, and the debris is highly affected by the atmosphere and the space object flux is low, and the collision between the two is relatively small. The orbit of the satellite in the out-of-orbit phase is approximate to the elliptical orbit of 550 km \(\times\) 350 km, the semi-major axis is 6828 km, the eccentricity is 0.01465, the inclination of the orbit is 53\({}^{\circ}\), and the calculation time is 293 days. the annual collision probabilities and fluxes of detectable objects and all objects are obtained as shown in Table 8.  
Figure 8: The relationship between the mean number of avoidance manoeuvres and ACPL during Phase 2.  
Figure 9: The relationship between risk and the mean number of avoidance manoeuvres during Phase 2.  
Figure 10 shows the relationship between the overall avoidance times and the ACPL value of the satellite in the out-of-orbit disposal phase. When the ACPL value is \(10^{-4}\), all satellites need to perform 15 avoidance maneuvers at this stage. As shown in Figure 11, the risk after the maneuver is reduced by 1%, and the collision remaining risk caused by all space objects is 5.7%. By increasing the number of avoidances, the remaining risk can continue to be reduced.
## 4 Discussion
### Remote Sensing for Biodiversity Conservation  
#### 4.3.1 Biomimetic Loggers Development to be Even More Similar to Remote Sensing Tools  
In the broadest sense, remote sensing tools allow to collect data about an object without contact with it [24; 25]. Various methods can be used, such as reception of electromagnetic radiation emitted or reflected by the object [24] or use of a biomimetic model that mimics the physical properties of the object you wish to study coupled with a data-logger [11]. This second method permits the acquisition of animal-specific data which is not possible with classical methods. However, it requires at least the biomimetic logger deployment in the habitat of the studied species. The use of more expensive cloud data-loggers that send data to a server eliminates the need for data collection in the field (as done in our study) [125]. In this sense, coupling a biomimetic model with a cloud data-logger allows data to be directly obtained on a server as with conventional remote sensing tools, further facilitating multi-site and multi-year studies.
## 2 System Modeling and Problem Placement
### System Modeling  
Applying the second [PERSON]'s law, the motion dynamics of the inertial load can be derived by  
\[J\ddot{\theta}=D_{m}P_{L}-B\dot{\theta}-A_{f}S_{f}(\dot{\theta})-\tau_{d} \tag{1}\]  
where \(J\) and \(\theta\) denote the inertial moment and angular position of the load, respectively; \(D_{m}\) and \(P_{L}=P_{1}-P_{2}\) correspond to the radial displacement and pressure difference, i.e., load pressure, between two chambers with \(P_{1}\) and \(P_{2}\) signify the pressures in the forward and reverse chambers of the HRA, respectively; \(B\) reflexes the total viscous friction coefficient; \(A_{f}\) represents the magnitude of Coulomb friction with known shape defined by the \(S_{f}\) function [52]; and \(\tau_{d}\) is the lumped uncertainty due to unknown external load, model uncertainties, and parametric deviations.  
The load pressure dynamics are given by [53]  
\[\frac{V_{t}}{4\beta_{e}}\dot{P}_{L}=-D_{m}\dot{\theta}+Q_{L}-C_{t}P_{L}+q(t) \tag{2}\]  
where \(V_{t}\) is the total control volume of the HRA, \(\beta_{e}\) represents the effective buck modulus of the hydraulic fluid, \(C_{t}\) denotes the internal leakage coefficient, \(q(t)\) reflexes the grouped uncertain term caused by modeling errors and parameter deviations, and \(Q_{L}\) is the load flow rate which is defined by [53]  
\[Q_{L}=k_{q}x_{v}\sqrt{(P_{S}-\text{sign}(x_{v})P_{L})} \tag{3}\]  
where \(k_{q}=C_{d}w\sqrt{1/\rho}\) is the flow gain, \(C_{d}\) represents the discharge coefficient, \(w\) is the spool valve area gradient, and \(\rho\) is the density of the hydraulic fluid. \(x_{v}\) denotes the valve spool displacement which is proportional to the control signal \(u\) applied to the servo valve [54; 52; 44], i.e., \(x_{v}=k_{u}u\), \(k_{u}>0\) is the servo valve coefficient; \(k_{t}=k_{q}k_{u}\) signifies the total flow gain with respect to \(u\); \(P_{S}\) is the supply pressure; and \(\text{sign}(\bullet)\) is the standard signum function.  
Figure 1: The studied electro-hydraulic system configuration. (**a**) The architecture of the EHS control system; (**b**) The hydraulic circuit of the considered EHS.  
Set \(\mathbf{x}=[x_{1},x_{2},x_{3}]^{T}\stackrel{{\Delta}}{{=}}[\theta, \dot{\theta},D_{m}P_{L}/J]^{T}\) as the system state vector. According to (1)-(3), the whole system dynamics can be obtained in the state-state representation as  
\[\dot{x}_{1} =x_{2}\] \[\dot{x}_{2} =x_{3}+f_{2}(\mathbf{x})+d_{1}(t) \tag{4}\] \[\dot{x}_{3} =f_{3}(\mathbf{x})+g_{3}(\mathbf{x},u)u+d_{2}(t)\]  
where \(f_{2}(\mathbf{x})\), \(f_{3}(\mathbf{x})\), \(g_{3}(\mathbf{x},u)\), \(d_{1}(t)\), and \(d_{2}(t)\) are given by  
\[f_{2}(\mathbf{x}) =-\frac{B}{J}x_{2}-\frac{A_{f}}{J}S_{f}(x_{2})\] \[f_{3}(\mathbf{x}) =-\frac{4D_{m}^{2}\beta_{e}}{JV_{t}}x_{2}-\frac{-4\beta_{e}C_{t} }{V_{t}}x_{3}\] \[g_{3}(\mathbf{x},u) =\frac{4D_{m}\beta_{e}k_{t}}{JV_{t}}\sqrt{\frac{1}{\rho}(P_{S}- \text{sign}(u)\frac{J}{D_{m}}x_{3})}\] \[d_{1}(t) =\tau_{d}/J;d_{2}(t)=\frac{4D_{m}\beta_{e}}{JV_{t}}q(t)\]
# Identification of Complicated Lithology with Machine Learning
## 3 Data Selection and Practical Application
### Model Construction  
This study selects three machine learning algorithms--random forest, support vector regression, and XGBoost--to predict felsic content in the study area. For the training dataset, two different ratios for the predictor variables and measured felsic content data were selected: 20% and 80%. After training the models, the remaining data was used as the validation set. The predictor variables in the validation set were then input into the models, and the felsic content was predicted using the three aforementioned machine learning methods.  
For each machine learning algorithm adopted in this study, the grid search method was used to determine the hyperparameters of the model. For the XGBoost model, grid search explored the learning rate [0.01, 0.1, 0.2], the maximum tree depth [3; 5; 7], the number of estimators [50; 100, 200], the column sampling rate [0.3, 0.5, 0.8], and the regularized alpha value [1; 5; 10]. For the random forest and support vector regression models, the hyperparameter optimization of the models is carried out in the same way. Finally, the support vector regression model adopted the radial basis function (RBF) as the kernel function and set the regularization parameter, C, to 12.53 and the epsilon value to 0.31. The XGBoost model sets the maximum depth of the tree to 3 to prevent overfitting. The learning rate was selected as 0.01, and 200 decision trees were used for integration simultaneously. In total, 50% of the features of each tree were randomly sampled, and an l1 regularization coefficient of 5 was applied. The random forest regression model consists of 179 decision trees. The maximum depth limit of each tree is five layers, and the minimum number of samples required for internal node splitting is four. The model sets the random seed to 42.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 4 Deep-Learning-Based Cooperative Spectrum Sensing  
All the previously introduced approaches are designed for the study and application of spectrum sensing in the context of non-cooperative spectrum sensing, whereas in real-world application scenarios, there are, in general, multiple PUs or multiple SUs communicating with each other, which can have a lot of complexity and conflict possibilities.  
Nowadays, there are several modes and applications of cooperative spectrum sensing, which have been introduced earlier, in Section 2.3. The findings indicate that the hidden node problem has emerged as a prominent problem in cooperative spectrum sensing. This is also true in real-world communication environments, where shadowing and multipath effects may affect the accuracy of sensing results. How to efficiently and rationally allocate resources to each sensing node is also an urgent issue to be addressed.  
Recently, scholars have started to introduce deep learning and deep reinforcement learning into the field of cooperative spectrum sensing to alleviate or solve the aforementioned problems. For example, deep learning can optimize data fusion by automatically extracting features from the data of multiple sensing nodes, enabling the system to make more accurate judgments. Another example is that, with proper training, deep learning models can better handle and predict uncertainty in dynamic environments. Deep reinforcement learning, on the other hand, can recognize potential security threats or learn to make effective and robust decisions in CSS security scenarios through continual learning and adaptation. Thus, the security issues in cooperative spectrum sensing can be mitigated to some extent, and the current state and progress of research in recent years will be reviewed below.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 2 Theoretical Background  
### Coupled Vibration of Fluid and Pipe  
In the vibration of the water supply pipe, waves transmitted in the longitudinal direction of the pipe and transverse waves transmitted in the out-of-plane direction are generated [13]. To measure the leak signal using an accelerometer sensor, out-of-plane vibration must be measured because it is related to the coupled vibration of the pipe [13].  
The coupled vibration of the fluid and the pipe can appear in several modes, as shown in Figure 1. The basic forms of coupled vibration are the n = 0 'breathing mode' and n = 1 'bending mode' of the pipe. If the pipe is a single beam, it corresponds to the bending vibration of the single beam. In general, because water supply pipes are buried and constrained, it is difficult to detect a large-amplitude bending vibration. The n = 2 mode is the 'ovalling mode', which is mainly seen at high frequencies.  
Of these, the breathing mode (n = 0) is related to the transmission of fluid sound waves in the longitudinal direction. The fluid sound transmitted by a leak is a longitudinal wave whereas what is to be measured is the transverse vibration of the pipe [14; 15].  
When a strong leakage occurs at a buried water supply pipe, a leak signal with a wide frequency range is generated from the inside of the high-pressure pipe to the outside of the low-pressure pipe. Unlike temporary noise, the leak signal can be clearly distinguished because it continuously generates a signal when a leak occurs [16; 17].  
Figure 1: Coupled vibration modes of fluid and pipe.