esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 4 Results and Discussion
### Rainfall Generation by WGEN  
Rainfall records from the Guanxi and Xinpu gauges were taken as inputs for the WGEN to produce 100 sets of rainfall forecasts over the subsequent 3 months of simulation according to the CWB's classification criteria of precipitation forecast probabilities: \"below normal\", \"normal\", or \"above normal\". A single precipitation forecast to assess groundwater level might produce an unreliable result and a huge discrepancy because of its predicting uncertainty. Thus, in order to minimize the error, the strategy involving 25 th to 75 th percentiles of simulated precipitation was used for the simulation of groundwater table. Tables 3 and 4 show the results of the seasonal rainfall forecast issued by the CWB in 2018. The monthly rainfall forecasts for the 100 sets of simulated data issued by WGEN were arranged in ascending order. Then, the 25 th and 75 th percentiles in ascending order were selected as future rainfall amounts and inputs applied to the hydrological models for simulating groundwater levels for the next season. As shown in Tables 3 and 4, the seasonal rainfall outlook announced by the CWB was mostly predicted as \"normal\" rainfall in 2018. To assess the performance of the seasonal rainfall forecasts, these two tables compare the 3 month rainfall predictions and the real rainfall recorded at the Guanxi and Xinpu rainfall gauges. For the Guanxi station, the 25 th and 75 th percentiles of simulated rainfall were 277.8 mm and 477.7 mm, respectively, from January to March, as the first seasonal rainfall outlook. However, the amount of real rainfall in the same period was 471.5 mm, which shows that the simulation corresponded with the actual situation. The same situation was found in the sixth and tenth seasonal rainfall outlooks from June to August and October to December  
Figure 6: (**a**) Validation results for groundwater level in 2017; (**b**) rainfall-induced flow hydrographs of surface water and groundwater from 2 June to 14 June in 2017, where the scale is 5x magnification in the \(z\)-direction. in 2018. On the other hand, the amount of seasonal rainfall in the second to fifth and ninth outlooks overestimated the real rainfall. In the seventh and eighth seasonal rainfall outlooks, the amount of real rainfall was underestimated. For the Xinpu station, the results indicate that the seasonal outlook in the first, sixth, seventh, and ninth outlooks successfully forecasted the real rainfall. The real rainfall in the second to fifth and tenth outlooks was overestimated. The eighth seasonal rainfall outlook underestimated the real rainfall.
# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 3 Experiments
### Results  
The results are labeled from a to d, where each label corresponds to a specific parameter:  
* a represents the distance (measured in meters);
* b represents the height (measured in meters);
* c corresponds to the azimuth (measured in degrees);
* d represents the side (also measured in degrees). To assess the model's performance, we varied several key parameters. These included the following:  
* Frequency sample rate--the rate at which the signal is sampled, which can impact the analysis's temporal and frequency resolution. * FFT size--the number of points used in the Fast Fourier Transform determines the resolution of the frequency domain representation. \begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Output** & **Filter** & **Stride** \\ \hline conv1 & 32 \(\times\) 256 \(\times\) 256 & 3 \(\times\) 3@32 & 1 \\ \hline conv2\_x & 64 \(\times\) 128 \(\times\) 128 & 3 \(\times\) 3@64 & 1, 2 \\ \hline conv3\_x & 128 \(\times\) 64 \(\times\) 64 & 3 \(\times\) 3@128 & 1, 2 \\ \hline conv4\_x & 192 \(\times\) 32 \(\times\) 32 &* Hop length--the number of samples between consecutive frames in time-frequency transformations, affecting both computational efficiency and the trade-off between time and frequency resolution. * Extra parameters--parameters typical to the feature, e.g., the number of MFC coefficients for MFCC or LFC coefficients for LFCC. The results for selected audio features are shown in Table 4.  
Building on the results above, the Mel Spectrogram demonstrated the most favorable performance compared to the other feature extraction techniques. Notably, increasing the frequency sample rate from 16 kHz yielded a more significant error reduction compared to increasing the sample rate from 44.1 kHz to 96 kHz. However, it is also evident that raising the frequency sample rate to 96 kHz results in further improvements, particularly in the case of MFCC. This finding suggests that a higher sample rate can provide finer temporal details, contributing to more accurate estimations, especially in the context of certain features like MFCC. Given these observations, we decided to investigate further how other parameters, including the n_fft, hop length, and the number of Mel coefficients, influence the performance of the Mel Spectrogram. Since it has yielded the best results so far, we chose it as the focus for exploring the impact of these changes. To provide a more comprehensive analysis, we created a table with the results based on the following adjustments:  
* Frequency sample rate: 44.1 kHz and 96 kHz. * FFT size: 1024, 2048 and 4096. * Hop length: 50% and 75% overlap. * The number of Mel coefficients: 64, 128, and 256. By varying these parameters, we aimed to identify their individual and combined effects on the performance of the Mel Spectrogram and further fine-tune the model for improved accuracy in spatial parameter estimations. The results of these experiments are presented in Table 5 below.
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 6 Conclusions  
This study is based on six machine learning models (MLP, RF, AdaBoost, GBDT, XGBoost, LightGBM) and 14 impact factors (EVI, NDVI, LAI, LSWI, DEM, LULC, LST, FPAR, P, Tmax, Tmin, ET, PET, Time) to predict GPP in the Mongolian Plateau. Then, the prediction results are assessed in detail and comprehensively traded-off using five accuracy indicators (MAE, RMSE, RPD, R\({}^{2}\), and bias). Furthermore, the main impact factors and the contribution ratio of machine learning in GPP prediction are explored.  
The main conclusions of our research are as follows. The six machine learning models (MLP, RF, AdaBoost, GBDT, XGBoost, and LightGBM) achieved good results in GPP prediction, and the change trends of the predicted data and the reference data are in good agreement. Among them, LightGBM has the best overall performance, and is excellent in terms of absolute error, degree of deviation, model reliability, and degree of fitting with reference data. EVI, NDVI, P, LULC, Tmax, PET, and ET are the key inputs for GPP prediction and contribute the most to the prediction accuracy.  
This study is the first study on GPP prediction of the Mongolian plateau based on machine learning models. The technical route and conclusions can provide scientific references for research in this and related fields. More importantly, this study provides an alternative approach to GPP estimation and prediction for regions with few or no flux stations, namely based on remote sensing data. In future research, the limited measured data can be used to constrain and modify the remote sensing data to obtain a large number of training and reference data; on the other hand, trying different aggregation strategies, introduce emerging new algorithms and compare their results could help to provide more and better methods for the field of GPP prediction. This will help to further improve the accuracy and reliability of machine learning models in GPP predictions and ultimately service and production activities and scientific research.  
H.W.: Data Curation, Investigation, Validation, Visualization, Writing--Original Draft. W.S.: Data Curation, Formal Analysis, Investigation, Visualization, Writing--Original Draft. Y.H.: Conceptualization, Funding acquisition, Supervision, Writing--Review and Editing. W.C.: Supervision, Writing--Review and Editing. Y.Z.: Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Plan Program of China [2021 YFD1300501], the National Natural Science Foundation of China (41977421, 42130508), and the Key Project of Innovation LREIS (KPI011).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Numerical Modeling of Temperature Effect on Tensile Strength of Granitic Rock
## 2 Numerical Methods
### Solution of the Mechanical Problem Governing the Uniaxial Tension Test on Heated Rock  
The mechanical uniaxial tension test on heated numerical rock is carried out by explicit time marching simulation using the same scheme as in Figure 2 for solving the mechanical problem. Moreover, a criterion for introducing a new crack in an element with an unfavorably oriented thermally induced crack is needed here. The criterion in [26] is  
Figure 2: The flowchart of the global solution of the thermo-mechanical problem.  
used here with a modified tensile strength for the new crack. Accordingly, a new crack is introduced in an element already having a crack when the following criterion is fulfilled:  
\[\text{If}\,\sigma_{1}>\sigma_{\text{t}}^{*}\ \&\ |\mathbf{n}_{\text{1}}\cdot \mathbf{n}^{*}|<C_{\alpha}\ \text{with} \tag{13}\]  
\[\sigma_{\text{t}}^{*}=(1-|\mathbf{n}_{\text{1}}\cdot\mathbf{n}^{*}|)\sigma_{ \text{t}}^{ic}+|\mathbf{n}_{\text{1}}\cdot\mathbf{n}^{*}|\sigma_{\text{t0}} \tag{14}\]  
where \(\mathbf{n}_{\text{1}}\) is the normal of the initial crack, \(\mathbf{n}^{*}\) is the principal direction corresponding to the present major principal stress, \(\sigma_{\text{1}}\). The modified tensile strength, \(\sigma_{\text{t}}^{ic}\), is a convex combination of the strength of the initial crack, \(\sigma_{\text{t}}^{ic}\), and the intact tensile strength, \(\sigma_{\text{t0}}\). The meaning of the second inequality in (13) is that the new crack is introduced (only once) when the angle between the old crack normal and the new principal direction is greater than \(\alpha=\text{acos}(C_{\alpha})\), with \(C_{\alpha}\) being an adjustable parameter. A value of \(1/\sqrt{2}\) corresponding to \(\alpha=45^{\circ}\) is used in this study.
# The Effects of Polyphenol-Rich Black Elderly on Oxidative Stress and Hepatic Cholesterol Metabolism
## 3 Results
### Effects of BEE on the Genes Involved in Cholesterol Biosynthesis and Absorption  
The mRNA abundance of genes involved in de novo synthesis and flux of cholesterol was measured to investigate the effects of BEE on hepatic cholesterol metabolism. SREBPP2, the critical transcription factor for cholesterol metabolism, modulates the expression of HMGR and LDLR. HMGR is the rate-limiting enzyme controlling cholesterol biosynthesis, and LDLR is the receptor responsible for LDL-derived cholesterol uptake. BEE significantly (\(p<0.05\)) and dose-dependently decreased the expression of SREBPP2, with a concomitant decrease in the expression of HMGR and LDLR. The expression of LDLR-related protein 1 (LRP1), a member of the LDLR family, was not altered by BEE treatment. Hepatic SR-B1, the receptor for HDL uptake, plays a crucial role in mediating reverse cholesterol transport (RCT). ABCA1, the transport for cholesterol efflux, is involved in the biogenesis of HDL and promotes RCT. In BEE-treated HepG2 cells, there was a significant (\(p<0.05\)) induction of the expression of SR-B1 and ABCA1 compared with control (Figure 1). Furthermore, the protein levels of cholesterol metabolism altered by BE exerted consistent results with mRNA results. The protein abundance of SREBPP2, HMGR, and LDLR was noticeably  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Total** & **Total** & **Total** & **Total** & **Total Antioxidant Capacity** \\  & **Phenolics** & **Flavonoid** & **Anthocyanin** & **DPPH** & **ABTS** & **FRAP** \\  & **(mg GAE/g)** & **(mg QE/g)** & **(mg CGE/g)** & **(mM VCE/g)** & **(mM VCE/g)** & **(mM FeSO4/g)** \\ \hline BEE & 222.8 \(\pm\) 15.1 & 522.9 \(\pm\) 43.9 & 242.9 \(\pm\) 7.7 & 199.2 \(\pm\) 21.2 & 369.1 \(\pm\) 30.6 & 3.0 \(\pm\) 0.3 \\ \hline \hline \end{tabular} Data represent mean \(\pm\) SEM. DPPH radical scavenging activity, ABTS radical scavenging activity, and FRAP were measured for the total antioxidant capacity analysis. GAE, gallic acid equivalents; QE, quercetin equivalents; CGE, cyanidin-3-glucose equivalents; VCE, vitamin C equivalents.  
\end{table}
Table 1: Composition and total antioxidant capacity of BEE.  
decreased, whereas the SR-B1 and ABCA1 proteins were markedly increased in the BEE-treated HepG2 cells (Figure 2).
# An Investigation into the Influence of Sample Height on the Consolidation Behaviour of Dredged Silt
## 3 Test Results and Analysis
### Compression Strain  
Figure 4 displays the cumulative stable strain of samples of various heights under different consolidation pressures. From this figure, it is evident that, under a constant consolidation pressure, the relationship between the sample height and the stable strain of the soil sample was roughly linear. Specifically, as the sample size (or height) increased, the cumulative stable strain of the soil sample diminished. This linear reduction was subtle under lower consolidation pressures but became more pronounced as the pressure escalated.  
Figure 3: \(e\)-\(\lg p\) curves of soil samples with different heights.  
Figure 2: \(e\)-\(p\) curves of soil samples with different heights.  
With the increase of sample height, the change of cumulative strain is shown in Table 3. It can be seen from Table 3 that as the sample height expanded from 2 cm to 3 cm, the smallest strain difference was 0.318% at a consolidation pressure of 5 kPa. Conversely, at a consolidation pressure of 120 kPa, the strain difference peaked at 2.022%. When the sample height transitioned from 3 cm to 4 cm, the minimal strain difference stood at 0.242% under a 5 kPa consolidation pressure. Yet, at a pressure of 25 kPa, this difference reached its zenith at 1.482%. An increase in sample height from 2 cm to 4 cm led to the least strain difference at 0.56% under 5 kPa of consolidation pressure. When subjected to 120 kPa, the most pronounced strain difference was measured at 2.997%.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Available Bandwidth  
The primary objective of many tasks is to identify viable routes from source to destination, regardless of current network traffic or application demands. Consequently, networks are susceptible to overloading due to excessive traffic, thereby resulting in applications being unable to improve their performance. To ensure access to network bandwidth, it is imperative to ascertain the available end-to-end bandwidth along the route from source to destination. End-to-end throughput is a parameter with a concave profile [45], which is determined by the bottleneck bandwidth of the intermediate hosts in the routing path. Thus, to guarantee that the data transmission has ample usable bandwidth, it is essential to consistently opt for higher bandwidth environments along the end-to-end route. However, determining the remaining bandwidth using the IEEE 802.11 MAC protocol remains a challenging issue. Here, this study adopts the method proposed by [46] for estimating the medium radio occupancy factor in the network, thereby defining the medium's occupancy factor as the percentage of channel utilization by mobile nodes within the same interference region. By continuously monitoring the radio activity on the channel through ongoing listening, the local assessment of medium radio occupancy allows for the evaluation of available bandwidth. To obtain the medium's occupancy rate, each mobile node maintains its occupied period within an equal time interval (\(T_{BW}\) seconds), which is referred to as the observation period. Here, \(T_{BW}\) is chosen to be 1 s.  
Regarding the idle and busy states of wireless communication channels, IEEE 802.11 utilizes both physical-carrier sensing and virtual-carrier sensing for determination [46]. Physical-carrier sensing involves energy detection and carrier sensing, while the 802.11 standard employs the network allocation vector (NAV) to implement virtual-carrier sensing. In this study, the wireless channel is considered idle when both the receiving and transmitting states of a node are idle, and the elapsed time exceeds the value set by the NAV. Conversely, the wireless channel is considered busy when the node is in a receiving or transmitting state, or the elapsed time is less than the value set by the NAV.  
The counting of the occupation factor \(COF\) of the channel is accomplished according to Equation (10):  
\[COF(v)=\frac{channel\_busy(T_{BW})}{T_{BW}} \tag{10}\]According to Figure 7, \(channel\_busy\) is determined by Equation (11):  
\[channel\_busy(T_{BW})=\sum_{i}t_{BC_{i}} \tag{11}\]  
To ensure an accurate assessment of the \(COF\), a windowed mean method is employed for calculation. Hosts estimate their available bandwidth for new data transmissions as the product of channel bandwidth and the ratio of idle time to total time. Therefore, the available bandwidth factor can be conveniently defined as Equation (12):  
\[ABWF(v)=\frac{available\_bandwidth}{channel\_bandwidth}=1-COF(v). \tag{12}\]
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 4 Results and Discussion
### Double Channel Spectral Efficiency
However, it is possible that for higher \(f_{Dm}\)s, more frequent shadowing adds a level of order to the noisy signal, making the NN2 better at predicting what future SNR values will be. The \"shadowed\" parts of the signal provide very low SNR values, and if the NN2 can predict these values to be quite low, then the margin that is introduced might not need to be as high, and the overall performance could be better. The shadowed intervals have accurately low predicted SNRs, but regular parts of the signal could also have adequate predictions that will not be hindered by an extremely high margin. When observing the error transmission ranges and the obtained results, it is important to note that the next order of magnitude, i.e., having a transmission error rate of 0.0001 or lower, was only unobtainable since the current test set has 25,000 samples, and such an error rate would imply no more than two samples could be allowed to be incorrectly transmitted. Furthermore, even with a larger test set, two channels and the considered shadowing conditions would probably not allow for such a low error to be theoretically obtainable anywhere where there is average or heavy shadowing. This could direct future work towards analyzing three or more channels or simply analyzing the performance obtainable when two channels of different shadowing levels are combined. These scenarios are understandably of interest but would simply be out of scope for this paper as the goal was to perform a sort of grid analysis in terms of ML algorithm performance for various channels and to evaluate whether a neural network that purposefully underestimates values could be of interest considering fixed transmission error rates. Figure 12: The achieved spectral efficiencies on the test set for various scenarios under average shadowing conditions using two channels, comparing OI and NN2. The color of each square represents a relative improvement of the achieved spectral efficiency calculated as \((M_{i}^{NN2}-M_{i}^{OI})/M_{i}^{OI}\).
## 4 Discussion and Limitations  
Previous studied on grouting were based on the simulated single plate device. The experimental device used in this paper realized the visualization of the grouting process, and allowed the value of grout diffusion to be observed. The rock fracture in Bardon's standard roughness coefficient curves were used as a simulation model, as rough fractures are more similar to real fractures. The sealing effect of grouting in orthogonal experiments was acquired by monitoring the changes in the initial water flow rate, and the sequence of the influential factors in the sealing effect was obtained to provide the basis for choosing factors during actual grouting engineering. However, the scale model of the experiment was simplified and idealized and only four factors that affect the sealing effect were considered. In further studies, experiments on grouting in rough fracture networks with flowing water should be carried out, and 3D printing technology could be used to simulate rock masses. Moreover, an experimental scheme should be combined with an actual engineering process.
## 3 Materials and Methods
### Defense Integration
#### 3.4.1 Traditional Defense Mechanisms  
We implemented sensor-specific defense mechanisms in the sensor callback functions to provide real-time protection:  
* **RGB Camera Defenses**: We implemented a decision-based adaptive median filter that dynamically adjusted its kernel size (\(3\times 3\) to \(7\times 7\)) based on the detected noise level. The filter specifically targets salt-and-pepper noise by
* Identifying potential noise pixels (values of 0 or 255),
* Growing the filter window until valid median values are found, and
* Preserving edge details by only replacing confirmed noise pixels.
* **DVS Defense**: We introduced a system to track event counts, setting a threshold of 5000 events per frame to identify flooding attacks, along with a dual-phase defense strategy composed of
* Spatial clustering analysis using KD-tree data structures to identify dense noise patterns and
* Morphological operations (dilation) for standard noise smoothing when clustering is not detected.
* **Depth Camera Defense**: A multi-stage validation approach including the following:
* Range clipping to valid depth values (0-100 m),
* Gradient-based tampering detection using Sobel operators, and
* Adaptive Gaussian smoothing with intensity based on detected gradient magnitude.
* **LiDAR Defense**: A point cloud filtering pipeline with
* Distance-based filtering (maximum range 50 m),
* Density-based clustering using KD-tree analysis to detect phantom objects, and
* Removal of points in clusters exceeding density thresholds.
* **GPS Spoofing Defense**: Route consistency validation and velocity checks:
* Position validation against planned route (deviation threshold: 5 m),
* Velocity consistency checks (maximum realistic speed: 20 m/s), and
* Automatic reversion to true GPS coordinates when anomalies are detected.
* **Steering Bias Defense**: Statistical anomaly detection for control commands:
* Maintains 50-tick steering command history,
* Z-score analysis for outlier detection (threshold: \(|\,Z\,|>3\)), and
* Automatic correction to historical mean when a bias is detected.
* **DoS Defense**: Sensor update monitoring and rate limiting:
* Timestamp tracking for all sensor updates,
* Rate limiting enforcement (maximum 20 Hz), and
* Buffering mechanisms for delayed sensor readings.