esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The surface texture of asphalt pavement is of enormous importance to skid resistance. To investigate the degradation tendency of surface texture related to the skid resistance, four years of sensor measured texture depth (SMTD) panel data measured from 19 pavement structures including 4 types of surface asphalt layer are used to develop a surface texture prediction model. The panel data-based prediction model takes into account the dependence on time scale, diversified road sections, traffic factors, environmental factors, and the Bailey method-based aggregate gradation parameters. The regression and prediction capability of the surface texture model is evaluated from both short and long-term perspective. The results indicated that the random-effects model is the most suitable form to characterize the degradation of surface texture. The cumulative standard axle loads (CSAL), monthly average humidity (MAH), and the Bailey method-based aggregate gradation parameters (the coarse aggregate ratio (CA), the fine aggregate coarse ratio (FAC), and the fine aggregate fine ratio (FAF)) have significant influences on the sensor measured texture depth. The time scale of the input sensor measured texture depth (SMTD) data has influence on the accuracy of the surface texture prediction model; therefore, long-term data series can ensure the robustness of long-term prediction. The results of this study can benefit the material design, construction, maintenance of asphalt pavement, and its evaluation pavement for longer service life.  
Keywords: asphalt pavement; sensor measured texture depth; panel data analysis; RIOHTrack; prediction model +
Footnote †: journal: Applied Sciences
## 3 Field Experiments
### YA17 Pumping Test
wall gradually migrated downward because of the hindering effect of the wall. A 3D flow pattern was then formed. Figure 5: The drawdown at different depths of well YA17 in the three steps: (**a**) first dewatering; (**b**) second dewatering; (**c**) third dewatering and recovery stage. Figure 6 presents the drawdown distributions at various depths for wells YA17, YA19, and H2 during the first round of pumping. The drawdown in the pumping well was significantly larger than that in the observation well. The drawdown outside the pit was negligible, suggesting that the dewatering within the pit had minimal impact on the surrounding ground, likely due to the blocking effect of the diaphragm wall. The drawdown distribution in the observation well YA19 was also nonuniform, resulting from the pumping activity in YA17. The drawdown of YA19 mirrored that of YA17 near the top of the filter, following which the drawdowns in both wells increased with increasing burial depth. The maximum drawdown was located at a depth of 3 m below the bottom of the filter. However, the maximum drawdown of YA19 decreased by approximately 4.24 m. As such, the hydraulic gradient between YA17 and YA19 at a buried depth of 43 m was calculated as (26.55-4.24)/10.5 = 2, suggesting the presence of a cone of depression in the vicinity of the pumping well, accompanied by a hydraulic gradient of 2. Furthermore, the drawdown at the bottom of the diaphragm wall in pumping well YA17 was notably larger than that in well YA19. The drawdowns of wells YA17 and YA19 were 5 m and 1 m, respectively, resulting in a hydraulic gradient of 0.38 between YA17 and YA19 at the bottom of the diaphragm wall. Figure 7 shows the hydraulic gradient between the pumping well (i.e., YA17) and observation wells YA19 and H2 during the first round of pumping. The hydraulic gradient between the pumping well and observation wells, referring to the slope of the drawdown (change in water level per unit distance along the flow direction), showed similar patterns for YA17-YA19 and YA17-H2. Above the filter, the hydraulic gradient remained constant with increasing burial depth. At a buried depth of 45 m, the gradient peaked, sharply decreased with a further increase in buried depth, and finally stabilized below the bottom of the diaphragm wall. These findings suggest that a 3D flow was induced by the pit with an unclosed diaphragm wall and that such a wall was effective in blocking the dewatering effect outside the pit.
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 2 Functions of Six Different Types of Bacterial TA Systems
### Types III-VI  
Type III TA system is composed of the protein toxin and the RNA antitoxin with multiple tandem repeats, which can form the non-toxic protein-RNA complex in a reversible manner [75; 126]. The short palindromic repeat between toxin and antitoxin genes can form a transcriptional terminator, which controls the relative level of toxin mRNA and antitoxin RNA [69]. The toxin can function as an endoribonuclease that processes the antitoxin precursors into small RNAs and cleaves host RNAs. In addition, this system plays an important role in abortive phage infection (abi) by degrading phage RNAs, which promotes altruistic suicide of phage-infected cells. The _toxIN_ pair located on a plasmid from _Pectobacterium atrospticum_ was first identified as type III TA system [69]. The antitoxin ToxI folds into an RNA pseudoknot, and its full-length sequence (200 nt) is composed of 5.5 sequence repeats of 36 nt, but even a single repeat unit is able to annul ToxN toxicity. The crystal structure of the ToxIN complex showed a triangular heterohexamer which consists of three ToxN toxins and three ToxI RNA pseudoknots [127]. Another known type III TA system is the _antiQ-abiQ_ pair on a plasmid of _Lactococcus lactis_, which is structurally and functionally similar to the _toxIN_ pair although their gene sequences show only 31% similarity [128]. In type IV TA system, the protein antitoxin does not bind directly to the toxin but instead competes for the same cellular target, which indirectly antagonizes toxin function [70]. For example, the _E. coli_ TA modules _yeeUV_ and _cptBA_ encode the toxins YeeV and CptA, respectively, which block the polymerization of bacterial cytoskeletal proteins MreB and FtsZ. The corresponding antitoxins YeeU and CptB counteract the toxicity by stabilizing the filamentous polymers [70]. Interestingly, YeeU has a similar fold to those of the YoeB and RelE toxins [125], suggesting evolutionary exchange between toxin and antitoxin families. Type V TA system has the protein antitoxin that degrades mRNAs of the corresponding toxin, suppressing the cellular expression of the toxin. This system was exemplified by the GhoST module encoding the small peptide toxin GhoT that damages the cell membrane and the antitoxin GhoS that cleaves the toxin mRNA [72]. Interestingly, under unfavorable conditions, the type II toxin MqsR cleaves the GhoS mRNA, thus promoting the translation of GhoT [129]. The _socAB_ operon was most recently identified as a type VI TA system [73]. The protein toxin SocB binds tightly to the \(\beta\) sliding clamp DnaN and thus represses replication elongation, and the antitoxin SocA behaves as a proteolytic adaptor protein to bind the toxin, which expedites protease-mediated degradation of the toxin.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 5 Results  
Using the equations presented previously and applying the chosen parameters, several simulations were made, obtaining the results presented in this section. First, we present the beamforming performance under sensor position errors, followed by the CRB results, based on the system specifications shown in Table 1. To analyse the impact of these errors, three distinct deployments, each with significantly different sensor distributions, were considered. By introducing errors, we expect the resulting BER graphs to reveal variations in system performance. This can be confirmed by analyzing the results presented in Figure 6.  
Figure 6 presents BER performance assuming a random Gaussian error in each sensor's position, with the variance expressed as a function of the carrier wavelength. The case where the variance is zero represents the ideal scenario with no error in sensor position estimation. The performance gap between the ideally scenario (where sensor positions are accurately known) and the non-ideal cases widens as the SNR increases. This indicates that, in high-SNR conditions, performance can be severely degraded due to destructive beamforming caused by position errors. These differences highlight the importance of analyzing the sensor distribution before deploying a non-uniformly spaced WSN, as our objective is to minimize the transmitted power while maintaining a minimum SNR that satisfies the BER threshold through beamforming, ensuring reliable data reception.  
Figure 5: Beam pattern for linear array with: (a) 4 elements; (b) 8 elements.  
As previously discussed, network topology is crucial in system performance, particularly concerning received signal power. To illustrate this, in Figure 7, we present the BER performance for three different deployments. We assume that all sensors transmit at  
Figure 6: BER performance of a 40–node WSN under different position error variances, applied to the sensor positions across three distinct deployments.  
maximum power and that the drone is positioned at the center of the field. The results are plotted as a function of the noise power upon the receiver drone.  
As anticipated, the received signal power decreased significantly with variations in network topology for a fixed number of sensors. This is a result of the increased distance between transmitters and the receiver, which leads to higher path loss throughout the system. Therefore, receiver position and position estimation accuracy are critical considerations for successfully implementing such systems, reinforcing the need to simulate the system before applying it in a real-world scenario.  
Finally, for the CRB, we calculate the FIM and the CRB as the inverse of the FIM [20]. The results show the normalised trace values of the CRB in the \(x\),\(y\), and \(z\) directions.  
The graph shows that if we have a high SNR, the accuracy would almost remain constant even if more sensors were added. Thus, we can reduce the number of sensors if we correctly choose the sensors associated with the drone, which all contribute constructively.
## 3 Method
### Encoder-Decoder Network Architecture (Supervised Learning)  
We suggest a new deep convolutional neural network (DCNN) encoder-decoder architecture to segment the RV and FAZ area. The encoder is responsible for extracting a collection of features from the input image to produce encoded data, which are then used by the decoder to provide a segmented result. The encoder portion creates a low-resolution feature map of the input image using a generic convolutional neural network (CNN) architecture without completely connected layers. On the other hand, in the decoder part, a parallel architecture is used to replicate a high-resolution feature map [35]. Each encoder consists of various convolutional layers responsible for producing feature maps [36]. Then we used a batch normalization layer to enhance model generalization [37]. Then to intro  
Figure 2: Flow chart of the proposed K-means implementation.  
duce nonlinearity, a rectified linear unit (ReLU) max(0,x) is applied to an efficient CNN activation function.  
After that, a low-resolution feature map is obtained using several convolutional layers. Next we use a flattening transformation [38] to reduce the dimension of the feature vector obtained in the last layer and input it to the decoder part, which is then up-sampled using transposed convolution in the decoder part. The last layer in the encoder part is the flattening layer which reshapes the feature vector into a 1-dimensional vector to avoid loss of information [39].  
The same structure is used in the decoder part, starting from an unflattened layer. The next layer is to perform transposed convolution [40] to up-sample the feature map, followed by ReLU activation function and batch normalization to obtain a high-dimensional feature representation. The final decoder output is fed to a sigmoid activation function which is the last layer to obtain the final segmented output. The model summary is shown in Tables 1 and 2.
# Buckling Analysis of CNTRC Curved Sandwich Nanobeams in Thermal Environment
## 5 Results and Discussion
### Parametric Study
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{_L/h_} & \multicolumn{2}{c}{\(V_{cnt}^{*}\)=12} & \multicolumn{2}{c}{\(V_{cnt}^{*}\)=17} & \multicolumn{2}{c}{\(V_{cnt}^{*}\)=28} \\ \cline{3-10}  & & \(\varepsilon_{zz}\)=0 & \(\varepsilon_{zz}\)\(\
eq\)0 & \(\varepsilon_{zz}\)\(=\)0 & \(\varepsilon_{zz}\)\(\
eq\)0 \\ \hline \multirow{10}{*}{UD} & 5 & 73.7930 & 73.4424 & 120.6917 & 120.1610 & 146.3642 & 145.5590 \\  & 10 & 49.0266 & 49.0250 & 77.6401 & 77.6399 & 101.4712 & 101.4484 \\  & 20 & 21.9451 & 21.9242 & 33.2103 & 33.1708 & 48.7346 & 48.7136 \\  & 30 & 11.4565 & 11.4369 & 17.0329 & 16.9992 & 26.2191 & 26.1931 \\ \hline \multirow{10}{*}{FG-X} & 5 & 79.5433 & 79.1094 & 128.5687 & 127.9463 & 149.0114 & 148.3479 \\  & 10 & 57.1285 & 57.1134 & 90.5184 & 90.5055 & 111.0156 & 110.9778 \\  & 20 & 28.9721 & 28.9611 & 44.1395 & 44.1160 & 61.6479 & 61.6372 \\  & 30 & 15.9804 & 15.9653 & 23.8972 & 23.8700 & 35.6810 & 35.6608 \\ \hline \multirow{10}{*}{FG-O} & 5 & 58.0980 & 57.9593 & 96.1446 & 95.9410 & 128.1600 & 127.5412 \\  & 10 & 33.6793 & 33.6650 & 52.7577 & 52.7221 & 75.9965 & 75.9952 \\ \cline{1-1}  & 20 & 12.7261 & 12.6870 & 18.9830 & 18.9144 & 29.3401 & 29.2905 \\ \cline{1-1}  & 30 & 6.2518 & 6.2261 & 9.1882 & 9.1452 & 14.5124 & 14.4755 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of thickness ratio on the buckling load of a single layer CNTRC curved beam \(\alpha=\frac{\pi}{3}\), \(T=300\) K.  
sandwich beams. Note that the critical buckling load increases significantly for a decreased opening angle and increased CNT volume fraction. As summarized in Table 7, the dimensionless critical buckling load of curved sandwich (\(0^{\circ}/90^{\circ}/0^{\circ}/c/0^{\circ}/90^{\circ}/0^{\circ}\)) nanobeams could be affected by nonlocal and length scale parameters as well as by the core-to-face sheet thickness ratio, \(h_{c}/h_{f}\), and thermal condition. A meaningful reduction of the critical buckling load is observed for higher temperatures for a fixed geometry and nonlocal parameters \(\mu\), \(\lambda\). An increased value of \(\mu\) and a reduced value of \(\lambda\) reduce the critical buckling load of the nanostructure under the same thermal and geometric assumptions. Moreover, Table 8 summarizes the sensitivity of the buckling response of CNTRC sandwich (\(0^{\circ}/c/0^{\circ}\)) beams to different elastic foundation parameters and boundary conditions, with an increased stability of the structure for more rigid boundary conditions and foundation.
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 1 Introduction
However, we propose a novel approach: using RSRP and SINR for throughput and jitter estimation in UL and DL, respectively. The main aim of this paper is an analysis of relationships between radio reference signal parameters (such as RSRP, SINR, or Reference Signal Received Quality (RSRQ)) and QoS metrics (i.e., throughput and jitter) in the 5G network. We analyzed the scenario of throughput assessment by using the iPerf application [26; 31; 32] and a measurement testbed based on R&S equipment. Measurements in Warsaw for four MNOs were performed by the Systemics-PAB company as part of the drive test campaign. Systemics-PAB provides QoS/QoE assessment services in mobile networks. In the analyzed scenario, throughput and lost datagrams rates are measured for Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) in both uplink (UL) and DL. Additionally, jitter is measured for UDP. In the drive tests, the measurement of signal metrics, i.e., RSRP, SINR, and RSRQ, was performed. The measurements were carried out at the 2.1 and 2.6 GHz bands. In the mobile network, the coverage and capacity issues are connected with the QoS and radio signal parameters. However, based on the collected measurement data, we cannot unambiguously draw conclusions about coverage and capacity. From a propagation viewpoint, the range of base stations for the analyzed bands are similar. Therefore, we may expect the areal aspect of the coverage to be similar for these bands and all MNOs. We calculate correlation coefficients for various pairs of measured parameters, similarly to [13]. The obtained results are the basis for selecting the best signal parameters for estimating throughput or other signal metrics. This approach can also be used in modeling some parameters based on others. Finally, the obtained results allow for the assessment of the MNOs regarding 5G network availability and provided QoS. Our key contributions are as follows:  
* Selection of measurement data and correlation analysis of QoS metrics for iPerf scenario, four MNOs, TCP and UDP, and UL based on drive-test measurement campaign in Warsaw;
* Analysis of linear regression between QoS parameters, which can be used in their modeling or estimation;* Linking the obtained results with radio resources used by individual MNOs and their radio access network (RAN);
* Discussion of the results and MNO comparison based on the assessment of the correlation level of QoS parameters;
* Original approach to the classification/identification of MNOs in Poland based on the RSRQ-5G measurement. The remainder of the paper is organized as follows. In Section 2, we describe the measurement scenario, testbed, iPerf application, and the QoS and reference signal metrics measured in the drive test campaign. The correlation analysis and relationships between QoS parameters are presented in Sections 3 and 4, respectively. A discussion of the results, including the comparison of MNOs, is contained in Section 5. Section 6 provides a summary.
## 4 Discussion
### Effect of Increasing LIPUS Intensity and Exposure Time  
The effect of LIPUS has been addressed in different cell types with varying effects [1,6,8]. In this paper, MIO-M1 response to LIPUS appeared not to be detrimental for cell viability in the range of 0.2-1.2 W/cm\({}^{2}\) acoustic intensities at 10%-20% DF when a water immersion setup was used (Figure 1b). Nevertheless, we were unable to detect an increased cell proliferation effect at these experimental conditions, as reported in HepG2 and 3T3 cells. While [PERSON] et al. [10] registered a statistically significant proliferation increase in cell cultures treated for 1 min with 0.2 and 0.43 W/cm\({}^{2}\) at 50% DF and 100 Hz PRF, we did not detect cell proliferation at almost the same ultrasound conditions in MIO-M1 cells with 10 Hz PRF.  
The cell viability dependence on PRF has been addressed by [20]. They observed that a high percentage of viable cells is achieved in treatments with 5-100 Hz PRF and further decreases at lower PRF, probably due to ultrasonic streaming. Despite they did not show significant viability differences between 5 and 100 Hz PRFs, we suggest that streaming may impact the ultrasound mediated proliferative effects. Nevertheless, further studies are suggested to determine PRF influence in LIPUS cell proliferation capacity.  
Moreover, differences in LIPUS biological effects between cell populations cannot be discarded. Other authors did not observe changes in the cell proliferation rate in different cell lines [5] or primary cell cultures [11] exposed to a repetitive daily-based LIPUS regime. On the other hand, additional extracellular components could contribute indirectly to the LIPUS proliferative stimulation by releasing paracrine signaling factors [11].
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 4 Conclusions  
In the paper, the 8 \(\times\) 8 MIMO antenna system with high isolation and small size (0.0595 \(\times\) 0.106A\({}^{2}\)) is presented for 5G metal frame smartphone applications. The designed MIMO antenna system has an ideal antenna efficiency of more than 55% in the N79 Band frequency band. The isolation below \(-\)22 dB and typical ECC (<0.049) can be achieved by two methods of the spatial diversity and the combined slot. Furthermore, the influence of the user's hand on the antenna and the radiation effect on the user's hand is further considered. Plus, the low peak SAR is obtained, which meets the US standard of 1.6 W/kg, and the Chinese and European standards of 2.0 W/kg. Compared to other reported MIMO antennas, the presented antenna has smaller size and leaves more design space for other antennas and components. To validate the simulation results, the prototype was made and measured. What is more, it is found that the simulated results for various key performance parameters are in very good agreement with the measured results. Based on the performance and the measured results, we believe this structure still holds great promise in the next-generation metal frame mobile terminals. Meanwhile, the proposed MIMO antenna has good scalability, and it can be designed in combination with other methods, such as introducing the higher-order modes by adding shorting pins to generate resonances.  
Writing--original draft preparation, Y.G.; writing--review and editing, Y.G., J.W. and X.W.; visualization, Y.G. and R.S.; investigation, Y.G., J.W., X.W. and R.S.; for other cases, all authors have participated. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We choose to exclude this statement.  
Authors wish to express their thanks to the technical support provided by [PERSON], RF engineer.  
The authors declare no conflict of interest.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Data Preprocessing  
In order to remove the effects of water vapor and clouds and to convert the digital counts to surface reflectance, the ASTER and OLI bands were preprocessed using Environment for Visualising Images (ENVI). All the ASTER bands were radiometrically calibrated using the closest neighbor resampling approach and were resampled to 15 m resolution. The final nine-band ASTER picture was created by combining the three VNIR bands and six SWIR bands from the ASTER data, and a separate layer stack of TIR bands was also created. The first 7 OLI bands were also radiometrically calibrated using the closest neighbor resampling approach and were resampled to 30 m resolution. The Fast Line of Sight Atmospheric Analysis Spectral Hypercubes (FLAASH) atmospheric correction model incorporates the Moderate Resolution Transmittance (MODTRAN) radiation transfer code to remove the atmospheric attenuations to produce reflectance imagery, which is then used to calibrate the cross-talk-corrected nine bands of the ASTER image and seven bands of the OLI image to surface reflectance.  
To obtain spatially and radiometrically corrected images for spectral data analysis and comparison, preprocessing processes were necessary. The ML task consisted of preprocessing, training, and testing the model. To transform the given data into a format that only provides relevant details about the problem, data preparation was necessary.
(1) First, a noise covariance matrix is used to rescale and decorrelate noise in the data, a process known as noise whitening. Consequently, the noise has a unit variance, and no band-to-band correlations are present. (2) The noise-whitened data are often modified using the PCA approach.