esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 2 Materials and Methods
### Statistical Analysis  
For Sentinel-1, the correlation analysis is used to select the optimal radar polarization index as the input parameter of the water cloud model. For Sentinel-2, the GRA and least squares method were used to establish a multiple linear regression model for vegetation water content inversion. 58 sample points in this study were selected. During construction of the winter wheat water content inversion model based on Sentinel-1 data, two-thirds of the measured data (38 sample points) was randomly selected to fit the water cloud model coefficients and one-third of the measured data (20 sample points) for model verification. For the model based on Sentinel-2 data, two-thirds of the measured data (38 sample points) was randomly selected to construct a multiple linear regression model for the wheat crop water content inversion and one-third of the measured data (20 sample points) for model verification. The evaluation indicators were Root Mean Squared Error (RMSE) and normalized Root Mean Square Error (nRMSE).  
\[\text{RMSE}=\sqrt{\frac{\sum_{\text{t}=1}^{\text{T}}\Big{(}\text{x}_{\text{t} }-\text{y}_{\text{t}}\Big{)}^{2}}{\text{T}}} \tag{12}\]  
\[\text{nRMSE}=\frac{\text{RMSE}}{\text{y}_{\text{max}}-\text{y}_{\text{min}}} \times 100\% \tag{13}\]  
where \(\text{y}_{\text{t}}\) is the measured crop water content, \(\text{x}_{\text{t}}\) is the estimated crop water content, \(\text{y}_{\text{max}}\) is the measured maximum crop water content, \(\text{y}_{\text{min}}\) is the measured minimum crop water content.
# An Investigation into the Influence of Sample Height on the Consolidation Behaviour of Dredged Silt
## 3 Test Results and Analysis
### Comparative Analysis of Consolidation Coefficient between Dredged Silt and Marine Silt
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Silt Sample**} & \multicolumn{2}{c}{**Composition Content of Soil Particles (\%)**} & \multicolumn{2}{c}{**Absolute Content of Clay Minerals (\%)**} \\ \cline{2-7}  & **Powder Particle (0.005–0.075 mm)** & **Clay Particle (0.002–0.005 mm)** & **Colloidal Particle (\textless{}0.002 mm)** & **Kaolinite (\textless{}0.002 mm)** & **Illite/Montmori-Lonite** & **Chlorite** \\ \hline Dredged silt in & & & & & & \\ Qianwan Bay, & 53.0 & 20.7 & 26.3 & 14.2 & 18.4 & 2.8 \\ Shenzhen & & & & & & \\ \hline Dredged silt in & & & & & & \\ Shenzhen airport & 51.8 & 21.5 & 26.7 & 13.8 & 17.9 & 3.0 \\ \hline Marine silt in the west & & & & & & \\ channel port of & 43.2 & 16.8 & 38.0 & 28.6 & 4.7 & 5.1 \\ Shenzhen Bay & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Material composition of three kinds of silt samples [16]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Silt Sample** & \(\mathbf{w\%}\) & \(\mathbf{\rho}\) (g/cm\({}^{3}\)) & Gs & \(\mathbf{\epsilon_{0}}\) & \(\mathbf{p}\) (kPa) & **Sample Size (cm)** \\ \hline Marine silt in the West Channel Port of Shenzhen Bay [44] & 99.2 & 1.420 & 2.67 & 2.703 & 25, 50, 100, 200, 400, 800 & 2 \\ \hline Dredged Salt in Shenzhen Airport [45] & 100.63 & 1.448 & 2.681 & 2.712 & 6, 12.5, 25, 50, 100, 200 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Physical properties and consolidation pressure of two kinds of silt samples. followed by illite and chlorite. The results also indicate that dredged silt predominantly comprises illite/montmorillonite and kaolinite. These distinctions yield marked differences in physical and mechanical properties. For example, dredged silt has a more substantial water content and void ratio than marine silt, making its consolidation coefficient less than that of marine silt. Table 6 offers a summary of the clay and colloid proportions from the Shenzhen Qianwan dredged silt, Shenzhen Airport dredged silt, and marine silt from Shenzhen Bay's western channel. The recorded values were 47.0%, 48.2%, and 54.8%, respectively. Whereas these figures closely align, it is evident that particle size composition profoundly affected the soil's drainage consolidation rate. In later testing stages under a high consolidation pressure, dredged silt's consolidation coefficient approached or even surpassed that of marine silt.
## 2 Research Methods
### Thermal Imaging Tests on Prototype Devices  
The operation of the cooling and freezing modules was tested in real weather conditions in the city of Lodz, Poland in July 2019, and the temperatures during this period are shown in Figure 3.  
The temperature distribution, heat transfer, and tightness in individual boxes of the innovative MainBox device were examined, depending on the type of stored products. These studies were carried out using thermal imaging techniques. For the analysis and monitoring of temperatures, a FLIR T1020 28\({}^{\circ}\) thermal imaging camera with a 1024 \(\times\) 768 pixel detector was used, characterized by the following parameters: sensitivity 20 mK (<0.02 \({}^{\circ}\)C), measurement accuracy \(\pm\)1 \({}^{\circ}\)C or \(\pm\)1%, measurement range 40 \({}^{\circ}\)C up to +2000 \({}^{\circ}\)C, and spatial resolution for 28\({}^{\circ}\) lens. The study used its own measurement methodology in the measuring range of the thermal imaging camera from \(-\)40 \({}^{\circ}\)C to +150 \({}^{\circ}\)C with the use of the HB-250 tape for identification and measurement of the emissivity coefficients [27].  
Figure 2: Arrangement of temperature sensors inside the device chambers, and dimensions in mm.
## 3 Results and Discussion
### Mutation Accumulation Analysis in Z-DNA-Intersected Enhancer Elements  
Given the significant enrichment of Z-DNA regions within enhancer elements, the potential functional significance of these intersections was explored by examining mutation accumulation patterns. For this purpose, we mapped mutations from 100 individuals of European ancestry (1000 Genomes Project) onto enhancer regions intersected by predicted  
Figure 2: Boxplots and scatter plots of—log\({}_{10}\) (\(p\)-values) from permutation tests showing the distribution of predicted Z-DNA overlaps across background regions, super-enhancers, enhancers within super-enhancers, and typical enhancers in six cancer cell lines.  
Z-DNA loci (SE-E, SE-S, TE) and compared their mutation distributions to those observed in background regions (see details in Section 3.4 of the Materials and Methods). To evaluate mutation densities, we calculated the number of mutations per individual in each region and normalized these values by region length. Due to the sparse nature of mutation data across all regions, a direct comparison proved challenging. To facilitate meaningful comparisons, we focused on regions with the highest mutation frequencies, specifically selecting the top quartile (top 25%) of regions from each category (SE-E, SE-S, TE, and BG).  
Comparative analysis of these mutation-enriched regions revealed clear differences (Figure 3). According to the Mann-Whitney test, all four distributions differed significantly from each other (pairwise \(p\)-values did not exceed \(10^{-73}\)). In terms of median mutation concentrations (mutations per unit region length), BG regions were the most variable, with a median of 0.108. TE and SE-E regions exhibited similar mutation frequencies with medians of 0.031 and 0.022, respectively, indicating comparable levels of evolutionary constraint. Visually, their distributions appear most similar among those examined. Notably, SE-S regions displayed the lowest mutation frequencies (median = 0.009), suggesting that they are the most conserved. Moreover, the mutation frequency distribution in SE-S exhibited a pronounced bimodal pattern, which may suggest the presence of both protein-coding segments, which are under stringent evolutionary conservation, and non-coding spacer sequences.
# Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition
## 3 Application of the MRSVD Method in the Incipient Fault Detection of the Rolling Bearings
### Determination of the Number of Decomposition Layers of MRSVD  
According to the analysis in Section 3.1, if the number of decomposition layers of the MRSVD is too small, the detailed signal obtained will contain more noise interference. If there are a large number of decomposition layers, the multiple decomposition layers will lead a larger calculation, which is inconsistent with the real-time requirement of the fault detection of a rolling bearing. However, if the number of decomposition layers is too large, the decomposed results will generate spurious signals. Therefore, the number of decomposition layers of the MRSVD in this study is adaptively obtained in the decomposition process when the kurtosis value of the detail signal reached a maximum. Therefore, if the current approximate signal continued to decompose, the kurtosis value of the next layer of detail signals would show a downward trend, so the number of decomposed layers was determined to be the current number of decomposed layers plus five.
## 4 Data Collection and Preparation
### Correlation between the Explanatory Variables  
Before proceeding with model estimation, it is important to check for correlation between the explanatory variables in the analysis dataset. The correlation was analyzed using the Pearson correlation coefficient matrix. Based on the correlation matrix, it was decided to exclude from further consideration the highway capacity (CAPLINK variable) as it was correlated with the number of lanes (LANES variable). The hourly volume (VOL variable) was also excluded from the models as it was correlated with the v/c ratio (v_ratio variable).
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results
### Research Hotspots in Axial Load Transfer Mechanism of FGRBSs
#### 3.2.1 Failure Modes of the FGRBSs  
[PERSON] [48] identified two fundamental failure modes for deformed reinforcing bars subject to axial loading. One is the radial splitting (dilation) of grout initiated by the wedge action of the surface ribs, while the other is the interfacial shearing of the bar surface against grout. [PERSON] [49] classified the failure mode of rock bolts into three main categories by conducting pull-out tests: failure along the B-G interface, failure along the R-G interface, and splitting failure of grout and surrounding rock. Many studies have confirmed that failure of FGRBSs occurs at the B-G interface [7; 39; 50; 51]. If the rock strength is relatively weak or if the borehole diameter is relatively small, the R-G interface failure is more likely to occur. Failures at the grout-rock interface and surrounded rock are also two other failure modes in rock bolting systems [7; 52; 53]. However, several researchers reported that the most common failure mode occurs at the B-G interface [43; 45; 54; 55].  
Figure 6: Co-occurrence mapping network of research hot spots tied to axial load transfer in FGRBSs.
## 4 Application to Rolling Bearings Fault Diagnosis
### Description of the Experimental Setup  
To validate our proposed multi-fault diagnosis method, the publicly available rolling bearing vibration data shared by the bearing data center of Case Western Reserve University (CWRU) [39] is researched in this paper. Since its availability, this data set has been intensively studied for bearing fault diagnostics [40,41]. The test stand of the rolling bearing experiment is illustrated in Figure 2, and the motor shaft (on the left side) support bearings were designed as the testing bearings, which are SKF deep groove ball bearings of 6205-2 RS JEM. During the experiments, single point faults ranging from 0.007 to 0.028 inches were introduced to the bearing elements (i.e., ball, and inner and outer raceway) using electro-discharge machining (EDM). Fainted bearings were then reinstalled into the test motor, and vibration data were recorded for motor loads of 0 to 3 horsepower (approximate motor speeds of 1797-1720 rpm).  
Specifically, the accelerator signals of the drive-end bearing were recorded with the sampling frequency 12 kHz and a duration of one second. In this scenario, there are a total of ten bearing fault classes. Besides the normal state, three defect severities are respectively simulated for the ball, inner raceway, as well as outer raceway. For each class, the original vibration signal is divided into a number of training and testing samples with a length of 2048 data points (about 0.171 s). Details of the analyzed multi-fault bearing data set are listed in Table 2.  
Figure 2: The rolling bearing experiment test stand of Case Western Reserve University (CWRU) bearing data center.
# Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN
## 2 Related Work
### Object Detection in Autonomous Driving Scenarios  
Object detection algorithms based on deep learning are mainly used in vehicle detection, pedestrian detection, and traffic sign detection in autonomous driving scenarios. [PERSON] et al. proposed a method that could effectively detect small vehicles in traffic scenes using multiscale feature fusion and focal loss [20]; [PERSON] et al. used group convolution to design a lightweight detection network for autonomous driving scenarios [21]; [PERSON] et al. proposed an efficient pedestrian detection algorithm based on SSD to solve the problem of missed detection in dense crowds [22]; [PERSON] et al. proposed the deconvolution of a region-based convolutional neural network to solve the problem of small traffic sign detection [23].  
Unlike previous work, this paper proposes an object detection algorithm based on Faster-RCNN in an autonomous driving scenario. The detected objects include vehicles, pedestrians, and common small objects such as traffic lights and stop signs. Through the improvement of each module of the original model, the previous shortcoming of poorly detecting small objects is solved, the detection performance of the model in automatic driving scenarios is improved, and the detection accuracy is improved while ensuring high speed.
## 6 Experimental Evaluation
On the other hand, the test set consists of utterances contaminated according to the eight noisy environments defined in Table 1. This set is intended to evaluate the different algorithms proposed in this work. Noises were added to the reverberated speech at six different SNRs from \(-\)5 dB to 20 dB, so a total of 7200 test utterances was obtained. In order to simulate reverberation, ten different \begin{table}
\begin{tabular}{c c} \hline
**Reverberation** & **Noise (Test Only)** \\ \hline (A) No reverb. & Car, Street, Pedestrian street \\ (B) Low & Bus, Cafe \\ (C) Medium & Babble, Bus station \\ (D) High & Mall \\ \hline \end{tabular}
\end{table} 
Table 1: Predefined acoustic environments: each environment combines a reverberation environment with a given noise. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **N\({}^{o}\) Utterances** & **N\({}^{o}\) Speakers** \\ \hline
**Training set** & 700 & 440 \\
**Test set** & 150 & 93 \\ \hline \end{tabular}
\end{table} 
Table 2: Distribution of clean speech utterances and speakers across training and test sets. acoustic impulse responses, in turn also different from those of the training set, were considered for each acoustic environment and, again, randomly applied. For STFT computation, a 512-point DFT was applied using a 32 ms square-root Hann window with 50% overlap. This results in a total of 257 frequency bins for each time frame. The noisy speech and noise SCMs were estimated using an updating constant \(\tilde{\alpha}=0.9\). The parameters for CDR-based _a priori_ SAP calculation were set as [5]: \(d_{mic}=13\) cm, \(q_{min}=0.1\), \(q_{max}=0.998\), \(c=3\) and \(\rho=2.5\). The SPP threshold for RTF updating was set as \(p_{\text{thr}}=0.9\). Finally, for the SPP-driven trade-off parameter of the parametric Wiener filter, the following parameter values were used [5]: \(\mu_{min}=1\), \(\mu_{max}=4\), \(c=-3\) and \(\rho=4\). The algorithm was implemented in Python and it is available at [42]. The computational burden of the implementation was evaluated on a PC with an Intel Core i7-4790 CPU. The algorithm works on a frame-by-frame basis, so that the algorithmic delay is in this case the duration of a frame, that is, 32 ms. The average performance of the whole system (i.e., including SPP and RTF estimation, MVDR beamforming and postfiltering) on this machine achieved approximately 8x faster than real-time. In the following, the performance of the different techniques is tested. In order to simplify the reading of the results tables, an acronym is provided (in parentheses) for each considered technique after its description.
## 5 Results and Discussion
### Ecotoxicological Characterization (EC) of RA, FA, A1, A2 and A3 Eluate Samples
Based on the data presented in this work and on other studies [19; 26], it can also be suggested that the alkaline pH of eluates from FA (pH 11.8) and concrete (pH 12.5 and 12.4 for A1 and A2, respectively) may be relevant in this respect and may contribute to possible environmental risks. Such risks can be especially important if leachates or eluates from concrete or FA are produced in buildings in service (e.g., due to rain events) and/or in landfills and can reach freshwater ecosystems leading to water alkalinisation. Therefore, results obtained in this study highlight the importance of integrating data from CC and EC of materials' eluate samples aiming at assessing the possible environmental risk of the construction materials, as suggested by other authors [26; 50]. The incorporation of raw materials that have a low ecotoxicity level (e.g., FA, RA) in construction materials can however generate construction materials leading to eluates with a significantly higher toxicity level. For example, A1, coming from a concrete with 100% NA and 100% cement, has a toxicity level lower than that of A2 and A3 where natural aggregates and Portland cement were partially replaced with non-conventional RA and FA. In fact, it seems relevant that although FA has a low ecotoxicological potential (Table 6), when this raw material was incorporated in concrete mixes in a given quantity (60% in the present study) it did not contribute to diminish the potential ecotoxicity of the respective eluates. The literature states that the cement-based construction materials that raise the most concerns in terms of ecotoxicological risk are those that incorporate RA. However, the incorporation of by-products (FA) in such materials may also contribute to some non-negligible environmental risk. These raw materials have a variable chemical composition that is often unknown.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 2 Distributed GEP Algorithm and Its Extensional Applications
### Problem Formulation  
We consider a kind of distributed generalized eigenvalue problem, where the data of the same \(d\) observations are provided from \(N\) local servers to their shared believable central server. That is, the centered data are established as \(X=[X_{1},X_{2},\ldots,X_{N}]\in\mathbb{R}^{d\times num_{1}}\) and \(Y=[Y_{1},Y_{2},\ldots,Y_{N}]\in\mathbb{R}^{d\times num_{2}}\), where \(d\) is the feature dimension and \(num_{1}\) and \(num_{2}\) are the number of data \(X\) and \(Y\). \(X_{i}\) and \(Y_{i}\) are i.i.d, respectively, for \(i\in\{1,2,\ldots,N\}\). Each local server prepares data matrices \(A_{i}\in\mathbb{R}^{d\times d}\) and \(B_{i}\in\mathbb{R}^{d\times d}\) according to the different tasks of GEP from \(X\) and \(Y\). The centered GEP is formulated as a maximum optimization as below:  
\[\max_{w\in\mathbb{R}^{d}}\leavevmode\
obreak\ w^{*}\Big{(}\sum_{i=1}^{N}A_{i} \Big{)}w\leavevmode\
obreak\ \leavevmode\
obreak\ \text{s.t.}\leavevmode\
obreak\ w^{*}\Big{(}\sum_{i=1}^{N}B_{i}\Big{)}w=1. \tag{3}\]  
It is equivalent to:  
\[\max_{w\in\mathbb{R}^{d}}\leavevmode\
obreak\ w^{*}M_{0}w\leavevmode\
obreak \ \leavevmode\
obreak\ \leavevmode\
obreak\ \text{s.t.}\leavevmode\
obreak\ w^{*}w=1, \tag{4}\]  
where the covariance matrix \(M_{0}=\Big{(}\sum_{i=1}^{N}B_{i}\Big{)}^{-\frac{1}{2}}\sum_{i=1}^{N}A_{i} \Big{(}\sum_{i=1}^{N}B_{i}\Big{)}^{-\frac{1}{2}}\) and \(A_{i},B_{i},M_{0}\in\mathbb{R}^{d\times d}\) are all symmetric. The solution to (4) is the ground truth of the centered GEP.  
In our distributed setting of GEP, the difficulty lies in estimating the centered covariance \(M_{0}\) with local data. Hence, based on the trace maximization method, we formulate the central optimization problem in the central server as follows:  
\[\max_{w\in\mathbb{R}^{d}}\leavevmode\
obreak\ \leavevmode\
obreak\ w^{*}\Big{(}\sum_{i=1}^{N}M_{i}\Big{)}w\leavevmode \
obreak\ \leavevmode\
obreak\ \text{s.t.}\leavevmode\
obreak\ w^{*}w=1, \tag{5}\]  
where \(M_{i}=(B_{i})^{-\frac{1}{2}}A_{i}(B_{i})^{-\frac{1}{2}}\) is the local covariance, calculated and stored locally.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 2 Research and Applications of RO in NWP
### Impact of the Amount of RO Data on NWP and Future Trends
Commercial RO data from Spire and other companies are not included. As shown in Figure 9, the possible amount of RO observations may reach 14,700 soundings per day in 2025. Moreover, it may reach a maximum of approximately 18,400 soundings per day in 2027. According to [46], if the assimilated number reaches 16,000 soundings per day, the error in the NWP can be reduced by 25% compared with the error in 2013. This can probably be achieved by 2027.
# Experimental Study of LoRa Transmission over Seawater
## 5 Conclusions  
The paper presented results acquired in the systematic measurement campaign which investigated LoRa transmission properties over seawater. A variety of hardware was deployed to operate in two different ISM bands, while different LoRa parameters were probed. The values reported by transceivers' RSSIs were recalculated to correspond to the absolute values of the signal strength at the transceivers' inputs, while the saturation threshold for SNR measurement chain was determined for each device. We were able to investigate short and long-term fluctuations of RSS, SNR, and PRR thanks to high sending rate and a huge number of packet sent in each of the experiments. Our main conclusions are: (1) RSSI of each transceiver in the test has a wide dynamic range, up to \(-50\,\mathrm{dBm}\), and exhibits linear behavior; (2) SNR measurement chain of each transceiver reaches saturation for RSS that are higher than \(-100\,\mathrm{dBm}\); (3) LoRa links over \(22\,\mathrm{km}\) long LOS propagation path over seawater are fully feasible for every combination of LoRa parameters, and in both ISM bands (\(868\,\mathrm{MHz}\) and \(434\,\mathrm{MHz}\)); and (4) communication over \(28\,\mathrm{km}\) long obstructed LOS path is possible in \(434\,\mathrm{MHz}\) band, but using costly and bulky antennas. The significance of the results for LOS propagation path is particularly relevant due to the use of low-cost off-the-shelf rubber duck antennas.  
Conceptualization, N.J., E.P. and V.D.; Methodology, N.J., E.P. and V.D.; Software, N.J.; Validation, V.D. and M.Z.; Formal Analysis, N.J.; Investigation, N.J., E.P., M.Z. and I.D.; Writing--Original Draft Preparation, N.J. and E.P.; Writing--Review & Editing, V.D., I.D., and M.Z.  
The authors are grateful to Trival Antene, d.o.o. for the loan of three element collinear dipoles and carbon antenna masts used in the experiments.  
The authors declare no conflict of interest.