esa-sceva/satcom-chunk-collection huggingface dataset:
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 4 Experiments and Results  
This section presents a comprehensive evaluation of the proposed _XSQ-Learning_ algorithm, comparing its performance against classical _Q-Learning_ and state-of-the-art similarity-based methods: _QL-SmoothActions_, _QL-SmoothObservations_, and _QSLearning_. The experiments measure each algorithm's efficiency in terms of steps required to achieve the first stable maximum in cumulative reward. All algorithms were tested under identical conditions, including shared parameters (Table 2), implementation frameworks, and random seeds. The evaluation uses three progressively complex scenarios in a grid-world environment (Figure 1) to assess scalability and robustness.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 4 Simplification of Network Management
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**KPI** & **Part** & **Specialized KPI** & **Value** & **Description** \\ \hline \multirow{4}{*}{Speed} & DMS & Network & 4 min & Large, 50 tests \\ \cline{2-5}  & Test runs & Create node & P: 85, U: 15, C: 3 & milliseconds \\ \cline{2-5}  & \multicolumn{2}{c}{Validate node} & P: 85, U: 15, C: 3 & milliseconds \\ \hline \multirow{4}{*}{Scale} & Networks & Scale of networks & 2–2110 nodes & Small \(<\) 80, Large \(<\) 150 \\ \cline{2-5}  & \multirow{2}{*}{Networks} & \multirow{2}{*}{Scale of networks} & 207.300 nodes & Very large \(>\) 150 \\ \cline{3-5}  & & & 945.400 nodes & Aggregated extra large \\ \cline{2-5}  & \multirow{2}{*}{Test runs} & DMS & Very low & No requirements on scale \\ \cline{3-5}  & \multirow{2}{*}{DS, time and cost} & \multirow{2}{*}{Medium} & New APEX distribution \\ \hline \multirow{4}{*}{Complexity} & DMS & Lines of Code & 3019 & 2327 Bash, 375 Python \\ \cline{2-5}  & \multirow{2}{*}{Strategy} & \multirow{2}{*}{Lines of Code} & All: 3072 & JavaScript(JS): 916 \\  & & Compact JS: 517 & APEX CLI: 840, & APEX CLI: 840, \\ \cline{2-5}  & \multirow{2}{*}{Networks} & \multirow{2}{*}{Node Complexity} & Create: 5–28 & Min/max \\ \cline{3-5}  & & min/max (10–6180) & operations, ms \\ \cline{3-5}  & & & Validate: 3–48 & Min/max \\ \cline{3-5}  & & min/max (6–2037) & operations, ms \\ \cline{3-5}  & \multicolumn{2}{c}{Network Complexity} & 28 to 32468 & Tiny to very large \\ \hline \hline \end{tabular}
\end{table} 
Table 2: KPIs measured during the network management experiments. P = Raspberry Pi; U = large UNIX machine, C = Windows Cygwin machine.
# Development of Inclined Applicators for Flattened Beam Intraoperative Radiotherapy
###### Abstract  
A 3D-printed applicator for intraoperative radiation therapy (IORT) using the INTRABEAM system has been introduced, designed to protect normal tissue, expand indications, and assess its effectiveness in various intraoperative settings. The study involved designing and creating inclined applicators at 0\({}^{\circ}\) and 45\({}^{\circ}\) angles, which were capable of producing flattened and unflattened beams and shielding non-target areas from radiation. The applicator was evaluated based on dose uniformity, percentage depth dose, leakage, and dose rate. The findings showed that the applicator provided a uniform beam at various depths (1.23 to 1.30 for the flattened beam and 1.25 to 1.32 for the unflattened beam) and effectively shielded against leakage, with no leakage dose measured at 1 mm from the surface. Additionally, the dose rates after 3 min of irradiation for flattened and unflattened beams were 51.0 and 52.9 cGy for 0\({}^{\circ}\) and 48.5 and 54.8 cGy for 45\({}^{\circ}\) applicators, respectively. This suggests that the applicator can efficiently deliver IORT while minimizing exposure to normal tissues, particularly in treating chest and abdominal lesions near or on critical organs. The applicator has undergone dose calibration and has moved forward to clinical application.  
I ARTIC 2023 13, 9099. [[https://doi.org/10.3390/app1316/099](https://doi.org/10.3390/app1316/099)]([https://doi.org/10.3390/app1316/099](https://doi.org/10.3390/app1316/099))
# Chemical Analysis of Gunpowder and Gunshot Residues
## 2 Main Components of Gunpowder and Gunshot Residue
### Organic Compounds  
The organic compounds of GSR are present mainly in gunpowder [15; 31], acting as explosives, sensitiers, stabilisers, flash-inhibitors, moderants, coolants, anti-wear additives, and plasticisers [16; 28; 51; 52]. Organic compounds of gunshot residue (OGSR) originate from deflagration of these compounds. In specific cases, OGSR can also originate from the lubricants and other products used to clean firearms barrels [15; 31]. Currently, there are over 130 compounds associated with these residues detected and identified [15; 16; 51], including NC, NG, NQ, nitrobenzene (explosive), methyl centralite (MC--stabiliser and plasticiser), ethyl centralite (EC--stabiliser and plasticiser), akadrite I (AK I--stabiliser), akadrite II (AK II--stabiliser) 2,4-dinitrotoluene (2,4-DNT--flash suppressor), 2,6-dinitrotoluene (2,6-DNT--flash suppressor), dibutyl phthalate (DBP--plasticiser), diphenylamine (DPA--the primary stabiliser in single-base gunpowder) and its derivates, 2-nitrodiphenylamine (2-nDPA), 4-nitrophenylamine (4-nDPA), and N-nitrosodiphenylamine (N-n-DPA) [15; 16; 18; 20; 53; 54; 55; 56].  
The most common organic compounds in GSR are NG, EC, MC, and DPA and its derivates [54; 57], and consequently are usually selected as target analytes in many OGSR studies [57; 28; 31; 54; 58; 59; 60]. Interestingly, many of these additives do not go through the combustion process, which allows their detection on SG and GSR samples [16; 61]. Organic compounds can also originate from the primer, especially in newer ammunition models. Several patented primers use organic alternatives to lead styphnate [15; 46], such as nitropentene styptic acid, tetrazene, acetogen, polinitropolymerther, polinitrophenylether, hexogen, polyvinyl acetate, and red phosphorus stabilised with an acid scavenger and a polymer [15; 62].  
The analysis of these organic compounds is usually performed through infrared (IR) spectroscopy, Raman spectroscopy, and liquid chromatography (LC) or gas chromatography (GC), often coupled with mass spectrometry (MS) or tandem mass spectrometry (MS/MS) [18; 19; 25; 30], which are described in the following sections.
## 5 Connected Vehicles
### Benefits and Business Opportunities  
The IoV can be applied to various vehicle models and powertrain systems, but it has a more significant role in the field of EVs, mainly in the aspects of charging management and parking reservations, fleet management, predictive maintenance, driving behavior analysis, insurance, traffic control, and infotainment.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks  
This section presents a comprehensive overview of IM strategies in B5G wireless systems. It begins with a historical perspective on the evolution of IM techniques from earlier mobile generations, highlighting the transition from static and centralized methods used in 3G/4G to more adaptive, intelligent, and decentralized approaches in B5G. Following this background, the section provides a structured classification and detailed analysis of the key domains where interference significantly impacts system performance. These domains include D2D communication, HetNets, ICI, and AI-based frameworks. Each category is examined in terms of current research trends, mitigation strategies, and practical deployment challenges.
## 4 Parametric Analysis
### Distance from Tunnel Face  
Figure 10 shows the variations in the building response with different distances from the tunnel face. The remarkable increases in the building settlement with the advancing tunnel can be seen in Figure 10a, while the differential settlement of the building first increases and then decreases. The uniform building settlements are produced when the tunnel face is far away from the building (\(s_{1}=20.0\) m and \(s_{1}=-40.0\) m). The settlement difference between the two ends of the building reaches the maximum when the tunnel face is located at the middle of the building (\(s_{1}=-10.0\) m), coinciding with the results described in Section 3.2. As shown in Figure 10b, by decreasing the distance from the tunnel face from \(20.0\) to \(-5.0\) m, the rotation angles gradually increase along the direction of the tunnel. The trends are opposite as the distance from the tunnel face decreases from \(-15.0\) to \(-40.0\) m. In particular, the rotation angles are symmetric with respect to the building centerline where the maximum rotation angle appears, assuming that \(s_{1}=-10.0\) m.  
The maximum bending moments first increase and then decrease with the decrease in the distance from the tunnel face (Figure 10c). Both the maximum positive and negative bending moments appear at \(y=10.0\) m, corresponding to \(s_{1}=-20.0\) m and \(s_{1}=0\), respectively. For \(s_{1}=0\) and \(s_{1}=-20.0\) m, the maximum negative shear forces appear at \(y=4.0\) and \(y=16.0\) m, respectively. For \(s_{1}=-5.0\) m and \(s_{1}=-15.0\) m, the maximum positive shear forces appear at \(y=17.0\) m and \(y=3.0\) m, respectively (Figure 10d). In the case of \(s_{1}=-10.0\) m, the bending moments and the shear forces are antisymmetric and symmetric with respect to the building centerline, respectively. Moreover, when the tunnel face is located outside the two ends of the building (\(s_{1}=20.0\), \(0\), \(-20.0\), \(-40.0\) m), the shear forces at the middle of the building are about zero.  
Figure 9: Building response with different alignment angles: (**a**) settlement; (**b**) rotation angle; (**c**) bending moment; (**d**) shear force.
# Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
## 5 Simulation Results
### Performance Evaluation
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Error Measure** & **LASSO** & **SVR** & **RF** & **LSTM** \\ \hline MAE & 9.6 & 11.7 & 5.1 & 4.9 \\ \hline RMSE & 13.3 & 33.7 & 7.9 & 7.1 \\ \hline nRMSE [\%] & 7.4 & 39.8 & 4.4 & 4.2 \\ \hline Max Error & 95.2 & 122.7 & 62 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction errors for the DAM. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Error Measure** & **LASSO** & **SVR** & **RF** & **LSTM** \\ \hline MAE & 18.9 & 21.4 & 26.4 & 21.2 \\ \hline RMSE & 59 & 54 & 71.9 & 48 \\ \hline nRMSE [\%] & 5.1 & 5.0 & 6.2 & 4.4 \\ \hline Max Error & 1064 & 1060 & 1058 & 1040 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction errors for the RTM. renders the lowest MAE, nRMSE, and Max Error values among all the learning methods used to predict the gap prices. In addition, the LSTM network also outperformed all the methods in predicting the DAM and RTM electricity prices individually.
## 2 Effective Diffusivity
### Effective Diffusivity in a Continuous Domain  
Normal diffusion describes diffusion via random walks of a particle in a continuous domain. The mean-squared displacement \(\langle r^{2}\rangle\), relative to a fixed starting location at \(t=0\), is calculated by averaging displacement trajectories over a large number of realizations. In prior studies of 2D single particle random walks, it was shown that \(\langle r^{2}\rangle\) grows linearly with time (\(t\)) after a sufficiently large number of time steps have passed [10; 11; 12]. The general formula for normal diffusion is written in terms of an effective diffusivity (\(\kappa\)) as [11; 12; 13]  
\[\langle r^{2}\rangle=(2d)\kappa t, \tag{1}\]  
where \(d\) is the dimension of the medium. In our study, all simulation results are sampled in a stationary time regime where this observed relationship is linear; the effective diffusivity is estimated by rearranging (1) to obtain  
\[\kappa=\frac{\langle r^{2}\rangle}{(2d)N\Delta t}, \tag{2}\]  
where \(N\) is the number of time steps (of size \(\Delta t\)) in the simulation.
## 6 Experiments and Discussion
### Field Experiments  
A tightly-coupled integration system platform was designed and implemented within a vehicle to test the proposed architecture. The platform was mainly comprised of a Crossbow IMU-440 MEMS sensor (Milpitas, CA, USA), a differential GNSS receiver and a single chip MS5803 low-cost barometer, which is shown in Figure 10a. The performance indexes of the IMU-40 are listed in Table 6. Moreover, a NovAtel IMU-ISA-100C device (Calgary, AB, Canada) is utilized to provide high accuracy reference navigation solutions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gyroscope Performance** & **Accelerometer Performance** \\ \hline Bias stability: 10 \({}^{\circ}\)/h & Bias stability: 1 mg \\ ARW: 4.5 \({}^{\circ}\)/\(\sqrt{\hbar}\) & VRW: 0.5 m/s/\(\sqrt{\hbar}\) \\ Input range: \(\pm\) 200\({}^{\circ}\)/s & Input range: \(\pm\)10 g \\ Scale factor non-linearity: \(\leq\)100 ppm & Scale factor non-linearity: \(\leq\)100 ppm \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance parameters of the IMU sensor.  
Figure 8: 3D positioning errors of the selected schemes during [2900 s, 2960 s].  
Figure 10: The test vehicle platform and equipment. (**a**) Designed hardware platform; (**b**) GNSS antennas and the NovAtel device.  
Figure 9: Comparison of the residual sequence and SOMD sequence of satellite #24 when the number of visible satellites changes from 1 to 8 at the 2960-th second.  
To make effective comparisons, GDOP based 4-satellite and 5-satellite selection standard tightly-coupled positioning schemes, denoted as STC4 and STC5 are carried out; and CNE-TC is also implemented to show the contributions of RMNCE-TC over the SNR/CNR and elevation based methods. More detail settings about the compared schemes are listed in Table 7. Different schemes during the test. The NovAtel reference trajectory is shown in Figure 11.
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 2 Materials and Methods
### Model Training and Evaluation  
Several machine learning algorithms (i.e., Lasso, Ridge, SVR, GPR, DT, and RF) are used for model training. Lasso and Ridge regressions are types of linear regression, while SVR and GPR are capable of solving both linear and non-linear problems. DT is a non-parametric supervised learning method that is easy to interpret and understand, and requires less effort for data preprocessing. On the other hand, RF is an ensemble learning method that creates a number of decision trees at training. Compared to DT, RF has a more complex structure, but it prevents the overfitting issue and has a higher prediction accuracy. The selection of these different machine learning algorithms aims to explore and validate their effectiveness for the creep rupture life prediction of modified 9 Cr-1 Mo steels. More description about the selected machine learning models and hyperparameters optimization can be found in Appendix A.  
The optimization of the hyperparameters of each regression model is conducted to acquire the parameters that exhibit the highest \(R^{2}\) and lowest \(RMSE\). After the hyperparameters are optimized, the \(k\)-fold cross-validation (\(k\) = 5 in this study) is performed on the training dataset. That is to say, the training dataset is split into k equal folds. The (\(k\)-1) folds are chosen as the training set to train the model, and the remaining fold is used for validation. This process is repeated \(k\) times and the error is calculated. In this work, two error indicators such as \(R^{2}\) and \(RMSE\) are calculated to evaluate the trained model. These error terms can be obtained by the following equations:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}}}{\sum_{i=1}^{n}{(y_{i}- \overline{y})^{2}}} \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}}}{n}} \tag{3}\]  
where \(n\) is the total number of data samples, \(\hat{y_{i}}\) represents the predicted value, \(y_{i}\) represents the actual value, and \(\overline{y}\) is the mean of the observed data \(y\). The higher \(R^{2}\) represents a better model fitting, while the lower \(RMSE\) indicates a higher model accuracy. By comparing the error indicators among the built models, the most appropriate machine learning algorithm that shows the lowest error is determined for the final prediction of creep rupture life. In addition, the accuracy of life prediction results is also assessed using the two-factor bands.
## 8 Discussion and Conclusions  
### Discussion  
This study investigated the geological and environmental conditions of the Dashiling Gully debris flow through field geological surveys. The debris flow type was identified as a rainfall-induced gully debris flow, and the formation conditions were analyzed. Numerical simulations were conducted using FLO-2D software to assess debris flow movement characteristics under three different rainfall frequencies (1 in 30 years, 1 in 50 years, and 1 in 100 years). Hazard zones were delineated based on debris flow velocity and deposition depth, with a focus on the 100-year return period event. The necessary parameters for debris flow mitigation were calculated, and a safe and feasible mitigation plan, consisting primarily of check dam construction, was proposed and evaluated. This study employed numerical simulation techniques to investigate the movement velocity and deposition depth of mudslides in Dashiling Gully. However, due to inherent limitations of the FLO-2D software, the model did not account for channel erosion during debris flow initiation and internal water body oscillations within the flow. Future research efforts will aim to incorporate these processes into mudslide modeling. The sudden and unpredictable nature of mudslide events presents challenges for obtaining real-time field data. While numerical  
Figure 10: Mudslide hazard zone map following the implementation of check dams.  
simulations offer a valuable tool for hazard assessment, they currently lack comprehensive field data for validation and calibration. Future research will focus on collecting extensive field data to improve the accuracy and reliability of mudslide models. It is important to note that this research has limitations, as it does not explicitly depict the erosive effects of the debris flow on the channel bed and banks, nor does it simulate phenomena such as liquid-phase oscillations and jumping.  
Existing mudslide mitigation strategies often fail to achieve a balance between safety and cost-effectiveness, and they may not fully consider the dynamic characteristics of mudslides. Frequently, designs rely on past mudslide events and mitigation parameters, which can lead to either over-engineering or inadequate safety factors. By analyzing the dynamic behavior of mudslides, this study provides a theoretical basis for the design of future mitigation projects in Dashing Gully, addressing these shortcomings and promoting more effective and efficient mitigation strategies.
## 2 Materials and Methods
### Methods
#### 2.3.2 [PERSON]'s Slope Estimator and Mann-Kendall Trend Test Methods  
[PERSON]'s slope estimator and the Mann-Kendall trend test were used to analyze the seasonal spatiotemporal variation in the WUE within Anhui Province. [PERSON]'s slope estimator is robust for estimating trends in time series data, is independent of data distribution assumptions and insensitive to outliers, thus providing reasonable estimates even for non-normally distributed data. The Mann-Kendall trend test method is robust and non-parametric and can produce reliable results in the presence of outliers or data discontinuities [43; 44].  
The standardized statistic \(\mid\!Z\!\mid\) obtained from Sen+MK at significance levels of 0.05 and 0.01 (Table 1) indicates that a \(\mid\!Z\!\mid\) value exceeding 1.65, 1.96, and 2.58 suggests that a trend is significant at the 90, 95, and 99% confidence level, respectively. Trends were thus categorized into nine classes: extremely significant decrease, significant decrease, moderately significant decrease, no significant decrease, no change, no significant increase, moderately significant increase, significant increase, and extremely significant increase [45; 46].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Z Value** & **WUE Trend** \\ \hline \(2.58\leq\mid\!Z\!\mid\) & Extremely significant trend \\ \(1.96<\mid\!Z\!\mid<\!2.58\) & Significant trend \\ \(1.65<\mid\!Z\!\mid<\!1.96\) & Moderately significant trend \\ \(\mid\!Z\!\mid\leq\!1.65\) & No significant trend \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sen+MK trend test change division criteria.  
Figure 2: Technical flowchart of the study (Among them, RN is net radiation, LAI is leaf area index, LST is land surface temperature, GPP is total primary productivity, and ET is evapotranspiration).
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
###### Abstract  
As telecommunication systems evolve towards new-generation architectures, likewise, new protocols are created in order to improve efficiency. One of these protocols is Transmission Control Protocol (TCP), which controls the transmission bit rate in function of network congestion. Nevertheless, in wireless communications, there appear problems such as noise and interference, for which TCP was not designed. Based on these problems, there exist some methods trying to mitigate congestion, such as explicit loss notifications and the use of end-to-end codification. The aim of this work was to propose a wireless TCP protocol improvement, considering a negative acknowledgment (NACK), which allows to differentiate between losses due to congestion and losses due to wireless channel issues. NACK employs a small protocol packet and produces improvement in the quality of service metrics. The experiments were carried out in in-door and out-door environments, over an online video game scenario, and over a long-distance wireless link between two islands. The average results show a 25-percent delay improvement and a 5-percent jitter improvement when compared to the original TCP Reno protocol, while for throughput a 90-percent improvement was achieved for distances between 100 and 414 m.  
C 2020 12 101 10.3390/fi12060101 9 June 2020
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 2 Data and Methods  
In this paper, CALIOP vertical feature mask (VFM) products were used to validate MODIS cloud products during the period from 1 January 2013 to 31 December 2016 over the high aerosol concentration region of Eastern China (110 to 126\({}^{\circ}\) E, 25 to 45\({}^{\circ}\) N, Figure 1).