esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation and Agreement Analysis  
Pearson's correlation and CCC values are reported in Table 3. The analysis showed strong to very strong relationships between IMU and MoCap for flexion and rotation (r = 0.703, \(p\) < 0.001 and r = 0.944, \(p\) < 0.001, respectively), correlations for extension (r = 0.564, \(p\) < 0.001), and lateral bending (r = 0.430, \(p\) = 0.003) were moderate. The CCC was highest for rotation (CCC = 0.927, 95% CI: 0.877-0.957), indicating strong agreement, while lower values were observed for lateral bending (CCC = 0.155, 95% CI: 0.046-0.260). Flexion and extension showed moderate agreement with CCC values of 0.262 and 0.375, respectively.  
Figure 5: _Cont._
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 2 The Campaign Sites and Instrumentations
### Instruments  
In this section, we describe the instruments and data employed in this work. Firstly, we present the ground-based system used, the mobile MSP-lidar II setup.  
In order to acquire information about the atmosphere of the Paraiba valley, such as composition, homogeneity and optical properties, we used the data basis from Sao Paulo Sanitation Technology Company--CETESB--and from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations--CALIPSO--satellite. We applied the Hybrid Single Particle Lagrangian Integrated Trajectory--HYSPLIT--model in order to study air mass transport. In this way, we also present, in the next subsections, the CETESB air-quality stations, the remote sensing system on board the satellite platform and a brief explanation of the HYSPLIT model.
# Versatile Video Coding-Post Processing Feature Fusion:
## 4 Experimental Evaluations and Discussion
### Rate Distortion Plot Analysis  
In video coding, a rate-distortion (RD) plot is a graphical representation that illustrates the tradeoff between the bitrate and the quality of a video compression algorithm. Here, the horizontal axis represents the bitrate, which is the amount of data required to represent the compressed video. The vertical axis represents the distortion introduced by the compression algorithm which is measured using PSNR metric. The RD plot is a useful tool for evaluating the performance of video compression algorithms, as it allows the comparison of the efficiency of different compression algorithms, the optimization of the compression settings to achieve a desired balance between bitrate and quality, and the analysis of the impact of different compression techniques on the video quality.  
The RD curves in Figures 9 and 10 compare the performance of RA and LD configurations, highlighting their tradeoffs between bitrate and PSNR. The proposed method consistently outperformed others in both RA and LD scenarios, demonstrating improved results across a range of quantization parameters (QPs) and variable resolution image sequences.  
Figure 8: Comparative visualization of (**b**) reconstructed frames from anchor VVC and (**c**) proposed methods for FourPeople sequence at QP 42 for LD configuration, alongside the (**a**) original uncompressed reference frame.  
Figure 7: Comparative visualization of (**b**) reconstructed frames from anchor VVC and (**c**) proposed methods for DaylightRoad2 sequence at QP 42 for RA configuration, alongside the (**a**) original uncompressed reference frame.
## 3 Testing Results
### Frequency Characteristics
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen No.** & \
\begin{tabular}{c} **Percentage of Low** \\ **Frequency Bands (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Percentage of High** \\ **Frequency Bands (\%)** \\ \end{tabular} \\ \hline DM1 & 17.43 & 77.03 \\ DM2 & 26.06 & 72.49 \\ DM4 & 42.91 & 53.41 \\ SM1 & 75.41 & 22.08 \\ SM2 & 68.25 & 25.24 \\ SM3 & 52.04 & 43.88 \\ SM4 & 86.29 & 12.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of high and low dominant frequency bands in dried and saturated rock specimens. Figure 7: The range and central frequency band of AE waveform in specimen: (**a**) Dried marble; (**b**) saturated marble; (**c**) explanation of different lines. The normalized applied stress is defined as the ratio of the current stress and the UCS. Figure 8 demonstrates the cumulative percentages of both low and high frequency AE versus increasing normalized applied stress in dried and saturated rocks, respectively. Both proportions of low and high frequency AE raise along with the increasing of normalized applied stress. The proportion of high frequency AE is generally higher than that of low frequency AE in dried rocks, whereas the magnitude of the relationship between the proportions of high and low frequency AE is the opposite. A significant growth in AE signals can be found in dried and saturated rocks before final failure. However, numerous AE signals occurred at the early loading stage in saturated rocks (Figure 8d,f). Figure 8: Characteristics of cumulative increasing curve of low frequency AE and high frequency AE, respectively, corresponding to the normalized applied stress. (**a**–**c**) DM1–DM4; (**d**–**g**) SM1–SM4; (**h**) explanations of different lines. Besides dominant frequency, the average frequency (AF) of the signals is another parameter to classify the failure mode of crack. This parameter can be calculated from the waveform's counts divided by its duration [21]. Figure 9 demonstrates the average frequency versus increasing normalized applied stress in dried and saturated rocks, respectively. In order to show the variation clearly, the curve is the moving average of the recent more than 50 hits. It can be observed that in the last stages prior to the sample failure for normalized applied stress higher than 80%, the range of variation of AF in dried rocks (generally from 20 to 140 kHz) is distinctly larger than the one in saturated rocks (generally from 40 to 260 kHz).
## 3 ISP-MCTS Algorithm Design
### ISP-MCTS Algorithm Optimization Process
#### 3.2.1 Selection  
The relationship between balanced depth exploration and breadth exploration is constantly explored in the existing state tree nodes, so that each exploration can reach a better solution as far as possible. The UCT (upper confidence bounds applied to trees) algorithm [33] is used to select the scheduling policy, and the improvements are as follows:  
\[Q^{\oplus}(s,a)\ =\ Q(s,a)+c\sqrt{2\ln N(s)/N(s,a)}\ +\ \sqrt{\big{(}\sum q(s,a)^{2}-N(s,a) \times Q(s,a)^{2}+D\big{)}/N(s,a)} \tag{9}\]  
\[\pi_{UCT}(s)\ =\ \underset{a}{\text{argmax}}Q^{\oplus}(s,a) \tag{10}\]  
The first two terms of the UCT algorithm in Equation (9) are reserved. N(s) represents the total times of that the state \(s\) is accessed, and N(\(s,a\)) represents the times during which the policy \(a\) is selected in the state \(s\). Q(\(s,a\)) represents the average score of the policy \(a\) that is executed multiple times in state \(s\). Considering a third item here, indicating the deviation obtained after the policy \(a\) is executed, where \(\sum\)q(\(s,a\))\({}^{2}\) represents the sum of the squared results which achieves at the state \(s\), constant D is used to ensure that policies with few choices are not underestimated [32]. The purpose of the deviation term without the particularity of the neighborhood is to fully consider the change in range of the score when the policy is selected, and select the policy by using Equation (10) in the end. As shown in Figure 1, taking node \(s_{1}\) as an example, when N(\(s_{1}\)) \(>\) P, every policy in the set (A\({}_{11}\), A\({}_{12}\),..., A\({}_{16}\)) is evaluated according to Equation (9), and the policy is selected on the basis of Equation (10).
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 4 Experiments and Analysis
### Simulation Results  
In order to confirm the high energy efficiency of the IS-MAC protocol, the IS-MAC and S-MAC protocols were simulated under the same network environment and the results were compared and analyzed from two aspects--energy consumption and throughput.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Initial energy & 1000 J \\ Receiving energy & 0.3682 W \\ Transmitting energy & 0.386 W \\ Idle energy & 0.7442 W \\ Sleep energy & \(5\times 10^{-5}\) W \\ Duty cycle & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters.  
Figure 4: **(a)** Energy consumption of S-MAC protocol and IS-MAC protocol under different duty cycles; **(b)** throughput of S-MAC protocol and IS-MAC protocol under different duty cycles.
# A Low Cost Vision-Based Road-Following System for Mobile Robots
## 3 Mobile Robot System
### System Architecture  
The system architecture consists of road detection system and road tracking system, see Figure 2. The overall system runs on the robot operating system (ROS) which provides a communication mechanism between different modules for the road-following system.  
\begin{table}
\begin{tabular}{c c} \hline \hline Sensor type & HIK Vision DS-2 ZCN2006 (C) \\ Resolution & 1280 \(\times\) 960 \\ Format & JPEG or BMP \\ Frame rates & 25 fps, 30 fps \\ Signal system & PAL/NTSC \\ Interface & 1 RJ45 10 M/100 M Ethernet interface \\ Voltage requirements & DC12 V \(\times\) 10\% \\ Power consumption & 2.5 W (static), 4.5 W (dynamic) \\ Gain & 0 to 16 DB \\ Shutter & 1/1 s to 1/30,000 s \\ SNR & 52 dB or better at minimum gain \\ Camera dimensions & 74.3 mm \(\times\) 81.1 mm \(\times\) 142 mm \\ Mass & 320 g \\ Operating temperature & \(-10\,^{\circ}\)C to 60 \({}^{\circ}\)C \\ Focal length & 4.7–94 mm \\ Max CCD format & 1/3\({}^{\prime\prime}\) \\ Aperture & F1.6–F3.5 \\ Maximum field of view & Horizontal: 58.9\({}^{\circ}\) \\ Video compression & H.265/H.264 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The camera and lenses specifications.  
Figure 1: Robot platform.  
Figure 2: Structure diagram of the vision-based road-following system.  
The road detection system is used to detect and track the border of unstructured roads, where the color-based road detector and texture line detector are implemented. Both methods work in parallel and are combined by a weighted average algorithm. The UKF is implemented to estimate and track the road borders. The final results of road detection system are the traversable road and the tracking waypoint on the road. The road tracking system is responsible for calculating the control commands and sending them to the robot base according to the tracking waypoint.
## 4 Materials and Methods
### RNA Extraction, Library Preparation and Transcriptome Sequencing  
Total RNA was isolated from the pulp of the two apple varieties (180 days after flowering) using the RNAprep pure Plant Kit (Tiangen, Beijing, China) according to the manufacturer's instructions. Quality and quantity of the total RNA were assessed using a Qubit 2.0 fluorometer RNA Assay Kit (Invitrogen Inc., Carlsbad, CA, USA) and Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). A total amount of 3 \(\mu\)g of the high-quality RNA per sample was used for the cDNA library preparation and 6 libraries (3 biological replicates) were constructed by NEBNext(r) UltraTM RNA Library Prep Kit for Illumina(NEB, Ipswich, MA, USA). Transcriptome sequencing was performed using Illumina Novaseq platform (Illumina, San Diego, CA, USA) to paired-end reads by Novogene (Beijing, China).  
Quantitative real-time reverse transcription PCR (qRT-PCR) was performed with an iQ5 Multicolor Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) using SYBR(r) Green Master Mix (TaKaRa, Kyoto, Japan). Each sample was assessed in three biological repeats and normalized using apple _MdActin_ and _MdE-1x_ genes as the internal control. The relative expression of the genes was analyzed by the \(2^{-\Delta\Delta{\rm CT}}\) method and the primers used for qRT-PCR were presented in Table S1.
# Multi-Identity Recognition of Darknet Vendors Based on Metric Learning
###### Abstract  
Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors' multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors' multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors' multiple identities.  
darknet; data mining; multi-identity recognition; metric learning; Siamese neural networks +
Footnote †: journal: Applied Sciences
# Analysis of Shield Tunnels Undercrossing an Existing Building and Tunnel Reinforcement Measures
## 8 Discussion
### Discussion on the Effect of the Excavation of Double Shield Tunnels on Soil, Segment and Building
When the grouting pressure increased to a certain value, the greater the grouting pressure, the greater the compression effect of the grouting body on the segment, resulting in an increase in the longitudinal and transverse stresses of the segment. From Section 5.3, the successive excavation of the double shield tunnels had an obvious nonlinear effect on the subsidence of the building and the lateral displacement of the pile along the excavation direction. Figure 37 shows the variation of maximum subsidence of the building and maximum lateral displacement of piles with grouting pressure. As shown in Figure 37, the subsidence of the building and the lateral displacement of piles decreased linearly with the increase of grouting pressure. According to the calculation, for every increase in grouting pressure of 100 kPa, the subsidence of the building decreased by approximately 2.138 mm, and the longitudinal displacement of the pile decreased by approximately 0.548 mm. This was because the increase in grouting pressure reduced soil subsidence, thereby increasing the support effect on the building, resulting in a decrease in building subsidence and pile lateral displacement.
## 4 Result and Analysis  
Primary and secondary data were used in carrying out this report. A questionnaire survey was used to collect the primary data from the Xiaonan coal mine company. MATLAB software (64-bit (win64), R2019a) was used to train and test the network. Of the data sets, 329 were used to train and test the performance of the optimal model, with an optimal structure of 46 \(\times\) 14 \(\times\) 1. In order to show the prediction accuracy of the neural network models, a comparative analysis of the error between the predicted values of the traditional BP neural network, the GA-BP neural network, and the PSO-BP neural network is implemented in Figure 7. The comparison curves between the predicted and actual values of the coal mine safety risk for each model are shown in Figure 8.
# Electromechanical Behavior of Afyonkarahisar Clay Under Varying Stress and Moisture Conditions
## 3 Test Results and Discussions  
In this study, the clay soil under investigation was thoroughly analyzed in a laboratory setting with respect to its physical and mechanical properties. The soil was classified as a high plasticity clay (CH), and its fundamental engineering characteristics were determined through a series of tests conducted in accordance with relevant standards. The data obtained enabled a combined evaluation of both the mechanical behavior and electrical properties of the soil, thereby offering a novel approach for monitoring the electrical responses of soils under deformation.  
To determine the mechanical strength properties of the soil, unconfined compression tests were conducted. The unconfined compression test is a fundamental laboratory method widely used to determine the undrained shear strength of cohesive soils. In this test, a cylindrical soil specimen is subjected to axial loading without any lateral confinement. As the specimen deforms under the applied load, its peak compressive strength and deformation characteristics are recorded. The results provide essential data for assessing the engineering behavior of the soil, conducting bearing capacity analyses, and evaluating soil stability.  
Through this integrated approach, it becomes possible to monitor variations in the mechanical behavior of soils via electrical resistance data. This highlights the potential of electrical measurement techniques as an alternative monitoring method in geotechnical engineering for evaluating soil properties.  
The samples were subjected to uniaxial compression testing to investigate the relationship between water content and changes in electrical resistivity. The behavior of resistivity varied depending on the applied stress level, highlighting the complex interaction between mechanical and electrical properties of the clay. During the experiment, stress, strain, and electrical conductivity values were measured simultaneously to capture the full spectrum of variations occurring during compression.
## 2 Materials and Methods
### Theil-Sen Median Trend Analysis and Mann-Kendall Test  
The Theil-Sen Median was used to analyze the spatial dynamics trend of LCE from 2000 to 2019 in the GZA, while the significance of the change was determined by the Mann-Kendall test [33].  
\[S_{\beta}=Median\left(\frac{LCE_{j}-LCE_{i}}{j-i}\right),2000\leq i<j\leq 2019 \tag{3}\]  
where S indicates the trend of LCE; LCEj is the LCE value in year j, and LCEi is the LCE value in year i. When S > 0, LCE showed an increasing trend. Conversely, when S < 0, LCE indicated a decreasing trend.  
The Mann-Kendall test can be expressed as follows [34]:  
\[I=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}\big{(}LCE_{j}-LCE_{i}\big{)},2000 \leq i<j\leq 2019 \tag{4}\]  
\[\text{sgn}\big{(}LCE_{j}-LCE_{i}\big{)}=\left\{\begin{array}{l}1,LCE_{j}-LCE _{i}>0\\ 0,LCE_{j}-LCE_{i}=0\\ -1,LCE_{j}-LCE_{i}<0\end{array},2000\leq i<j\leq 2019\right. \tag{5}\]  
\[Z=\left\{\begin{array}{l}\frac{l-1}{\sqrt{Var(I)}},I>0\\ 0,I=0\\ \frac{l+1}{\sqrt{Var(I)}},I<0\end{array}\right. \tag{6}\]  
\[Var(I)=\frac{n(n-1)(2n+5)}{18} \tag{7}\]  
where I represents the test statistic; Z indicates the standardized test statistic at a certain significance level \(\alpha\), when \(\mid\)z \(\mid\) > z_(1 \(-\)\(\alpha\)/2) represents a significant change in the time series at the \(\alpha\) level. The variation trend of the LCE in this study can be divided into the 5 change types based on the 95% significance test (Table 3) [9].
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
###### Abstract  
This work presents a novel self-organizing control method for mega constellations to meet the continuous Earth observation requirements. In order to decrease the TT&C pressure caused by numerous satellites, constellation satellites are not controlled according to the designed configurations but are controlled with respect to intersatellite constraints. By analyzing the street-of-coverage (SOC) of coplanar constellation satellites, the continuous coverage constraint of the mega constellation is transformed into constraints of the right ascension of ascending node (RAAN) and relative motion bound between every two adjacent coplanar satellites. The proposed continuous coverage constraint can be satisfied by most ongoing or planned mega constellations. Artificial potential functions (APFs) are used to realize self-organizing control. The scale-independent relative orbital elements (SIROEs) are innovatively presented as the self-organizing control variables. Using the Gaussian equations and [PERSON]'s theory, the stability of the APF control in quadratic form is proven, from which it can be concluded that the APF control variables of the controlled satellite should have the same time derivative as the target satellite states under two-body Keplerian motion condition, and SIROEs are ideal choices. The proposed controllers and self-organizing rules are verified in the sub-constellation of the GW-2 mega constellation by simulation. The results demonstrate the goodness in control effect and ground coverage performance.  
mega constellation; continuous Earth observation; self-organizing control; artificial potential function +
Footnote †: journal: Remote Sens.  
2022148996103399/rs14225896
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 3 DIR-Net Method  
The proposed DIR-Net consists of two subnetworks, a denoising subnetwork, and a decoding subnetwork. The cores of both networks are the attention modules. These two parts utilize a specially designed attention structure to refine the input information gradually and complete the process of decoding in the end. We first introduce the overview processing of our polar encode/decode framework. Then, the denoising subnetwork and decoding subnetwork are introduced. In the end, we introduce the training details.
## 2 Materials and Methods
### Modeling of Decimal Reduction Times  
The first-order kinetics model was used to calculate the D-value for the reduction of _S. aureus_. The D-value represents the FE-DBD treatment time required to reduce 1 log\({}_{10}\) CFU of microorganisms. The closer the R\({}^{2}\) value is to 1, the better the data point fits the regression line.  
\[log\frac{N_{0}}{N}=\frac{k}{2.303}\times t\]  
* The initial microbial population (CFU/g) is \(N_{0}\).
* The microbial population multiplied by FE-DBD plasma treatment time (CFU/g) is \(N\).
* The FE-DBD plasma treatment time (min) is \(t\).
* The reduction rate constant is \(k\).  
For the determination of the D-value of \(S\). Typhimurium, the Weibull model, a two-parameter nonlinear model, was used. The calculation formula is expressed as follows:  
\[log\left(\frac{N_{t}}{N_{0}}\right)=-b\mathbf{t}^{n}\]  
* \(N_{t}\) is the number of microorganisms (log CFU/g) after the FE-DBD plasma treatment time, \(t\).
* \(N_{0}\) is the initial microbial population (log CFU/g).
* \(t\) is the FE-DBD plasma treatment time (min).
* \(b\) indicates the time needed to reduce the population for one log unit.
* \(n\) (parameter) indicates the shape of the survival curve.  
An n value of 1 corresponds to a linear survival curve, and n values correspond to downward and upward concavity.  
To obtain the D-value from the Weibull parameters, we used the equation of Buzrul and [PERSON] [20]:  
\[D=\left(\frac{1}{b}\right)^{1/n}\]  
\(D\) represents the time required to decrease the microorganism by 90%. The Weibull model was analyzed using the GraphPad Prism software for Windows, version 5.0 (GraphPad Software, San Diego, CA, USA).