esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work and Methodology
### Categorization of Concentrated Activity Areas  
The categorization of hotspots or areas of heightened seismic activity using Getis-Ord \(Gi^{\star}\) and Kernel Density Estimation (K) involves a two-step process to identify and classify spatial patterns in a dataset. First, Getis-Ord \(Gi^{\star}\) is employed to detect statistically significant hotspots or coldspots (areas of elevated or diminished seismic activity in the Red Sea region) within the data. This statistic assesses the spatial clustering of high or low values and provides a measure of significance for each location. Positive \(Gi^{\star}\) values indicate hotspots of high concentrated seismic activity, while negative values indicate coldspots or dissipated seismic activity areas [45]. Second, Kernel Density Estimation (K) is utilized to create a smoothed density surface that highlights areas of high data concentration. This helps in visualizing the intensity and extent of the intensified seismicity identified by Getis-Ord \(Gi^{\star}\). Kernel Density Estimation provides a continuous representation of spatial patterns, allowing for a more nuanced understanding of the data distribution.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 4 Computational Experiments
### Dynamic RID Scheduling
#### 4.2.1 Dynamic RID Scheduling with Static Information  
In this dynamic RID model, static information is used to generate a complete set of distribution schedules covering the demand fulfilment across all the disaster regions. Here, the statistic information represents the situation where information updates are not considered while generating the complete distribution sequence over multiple periods to meet all the relief item demands across all the disaster regions. A limited number of heterogeneous fleet vehicles is considered. Distribution time is calculated as the summation of travel distance divided by vehicle speed, 30 min of service time for unloading items at a disaster region and an additional 30 min is considered as an offset time for a vehicle to be available for the next journey after completing a round trip. An hourly time slot is applied for dynamic scheduling; therefore, any vehicle's next availability time is considered by rounding up to the nearest next hour after adding travel time, offset time and rest period. Normalisation on time and cost is applied while implementing optimisation on the selection of the optimum sequence since the corresponding units of time and cost are different.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 5 Discussion  
The automatic HASTAC system is primarily built to track human heads and count the number of heads in depth imagery. We have tested our system on three benchmark datasets i.e., the PCDS dataset, the MICC dataset and the GOTPD dataset. The results acquired on the PCDS dataset and GOTPD dataset exhibit greater accuracy i.e., 98.40% and 99% respectively for head tracking and counting compared to the MICC dataset which produced an accuracy rate of 98%. The reason for getting lower head tracking and counting accuracy on the MICC dataset is the overlapping persons in the Queue condition when the persons are walking one after the other; our system causes some misdetection of heads which results in low tracking and counting accuracy. Secondly, the challenges we face during the processing of the MICC dataset is that it provides noisy depth images with greater illumination and complex backgrounds which hinders the detection of heads in some images. The heads in some of the images of this dataset are overlapped with the noisy background which results in mis-tracking and mis-counting of heads.
# Mode-Matching-Based Sound Field Recording and Synthesis with Circular Double-Layer Arrays
## 4 Computer Simulations
### Driving Signal Stability  
To evaluate the driving signal stability, Figure 10 plots maximum and minimum driving signal powers \(10\log_{10}\left\{\max(|D(r_{1,2},\phi_{l})|^{2})\right\}\) and \(10\log_{10}\left\{\min(|D(r_{1,2},\phi_{l})|^{2})\right\}\). The driving signals of the proposed method for all the layer intervals below the spatial Nyquist frequency are stable, similar to those of the LS method. These results suggest that the forbidden frequencies for \(J_{m}(kr_{1})\), \(J_{m}(kr_{2})\), \(J_{m}(kR_{1})\) and \(J_{m}(kR_{2})\) in Equations (16), (30) and (31) were effectively avoided by the proposed method in these array configurations.  
The driving signal dynamic ranges of the GSVD method for all layer intervals were larger than the others, although the driving signal stability was not evaluated in a previous work [38].
## 4 Discussion  
Our work shows that the schools' reopening had a clear impact on the overall contagion, since all the three analyzed regions had an increase in the number of cases two weeks after the schools' reopening. The time-lag detected for all the analyzed provinces is equal to 14 days, confirming our hypothesis that two incubation cycles are needed to perceive the impact of the contagion coming from schools. Moreover, the provinces that have had a large number of cases in the school environment are the ones that have subsequently had a higher total number of cases, and as expected, the contagion increased over time. The most significant example is Varese that with reference to the other provinces of the Lombardy Region is the one that had the highest incidence in schools' spreading over time throughout the entire provincial territory, thus leading to one of the most affected provinces in Italy during the second COVID-19 wave. Our study also suggests that population density is another driver of contagion by favoring the virus spread, while the mobility of population (that was already drastically reduced by the governmental restrictions with respect to the normal baseline) did not impact the COVID-19 spread.  
Promptly acting by closing the schools (as in the case of RC) was able to contain the COVID-19 spread (i.e., as listed in Table 2, RC was the region with the lowest Rt peak, and it was able to invert the trend of new daily cases before the other two regions RL and REm, as depicted in Figure 7).  
It is also interesting to observe, as reported in Table 3, that RL had the highest school contagion index and the highest overall contagion index, while REm had the lowest overall contagion index despite having the same school index of RC. This can be explained by the impact the retail and recreation mobility may have had for RC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **14 September 2020** & \multicolumn{4}{c}{**24 September 2020**} \\ \hline  & **Rt Peak Date** & **Rt Peak Value** & \multicolumn{2}{c}{**Rt Peak Date**} & **Rt Peak Value** \\ \hline Emilia & 10/10/2020 & 1.4 & Abruzzo & 10/10/2020 & 1.3 \\ \hline Lazio & 10/20/2020 & 1.4 & Basilicata & 10/20/2020 & 1.4 \\ \hline Liguria & 10/10/2020 & 1.4 & Calabria & 10/20/2020 & 1.4 \\ \hline Lombardia & 10/10/2020 & 1.5 & Campania & 10/20/2020 & 1.1 \\ \hline Marche & 10/20/2020 & 1.4 & Friuli * & 10/20/2020 & 1.3 \\ \hline Molise & 10/10/2020 & **1.8** & Puglia & 10/20/2020 & 1.2 \\ \hline Piemonte & 10/20/2020 & 1.5 & Sardegna * & 10/20/2020 & 1.2 \\ \hline Sicilia & 10/10/2020 & 1.3 & & & \\ \hline Toscana & 10/10/2020 & 1.4 & & & \\ \hline Trento & 10/20/2020 & 1.5 & & & \\ \hline Umbria & 10/10/2020 & 1.4 & & & \\ \hline Valle d’Aosta & 10/10/2020 & 1.6 & & & \\ \hline Veneto & 10/20/2020 & 1.4 & & & \\ \hline \multicolumn{4}{c}{**Avg.**} & **1.46** & **Avg.** & **1.27** \\ \hline \hline \end{tabular}  
* Friuli schools opening 16 September 2020. Sardegna schools opening 22 September 2020.  
\end{table}
Table 2: Rt peak values and dates associated to Italian Regions.
## 4 Discussion
### Discussion for Explaining \(T\)
In procedure 2, \(E_{sp_{1}}(A_{sp_{1}},\hat{A}_{sp_{1}})\) varies between 0.2390 and 0.4151 s, and \(\overline{E}^{2,1}=0.3188\) s is not distant from \(\overline{E}^{2,2}=mean\big{(}[E_{70}\ E_{80}]\big{)}=0.3839\) s, so the error of the directly modeled data \(E_{sp_{1}}(A_{sp_{1}},\hat{A}_{sp_{1}})\) is similar to the error of the not directly modeled data \(E_{sp_{1}}(A_{sp_{1}},\hat{A}_{sp_{1}})\). Figure 2 shows that, for the directly modeled data (\(\hat{A}_{sp_{1}}\)), the first three points are above the observed data (\(A_{sp_{1}}\)), point four is close (from above or below), points five to seven are below, and point eight is above. Comparing the indirectly modeled with the observed data, we notice that points 1, 2, and 3 of \(\hat{A}_{sp_{1}}\) are slightly above the corresponding points of \(A_{sp_{1}}\) (for \(sp_{1}\) cases \(\{70,80\}\)). For \(\hat{A}_{70}\), the estimated points 4 and 7 are visibly accurate, the estimated points 5 and 6 are below the observed value, and the estimated point 8 is above the observed value. For \(\hat{A}_{80}\), the estimated points 4 and 5 are visibly accurate, and the estimated points 6, 7, and 8 are below the observed value. With these results, we found procedures 1 and 3 to be suitable to model the data for modeling and to explain the data for validating. \begin{table}
\begin{tabular}{c c c c c} \hline
**Procedure \#** & \(E_{70}(A_{70}A_{70})\) & \(E_{80}(A_{80}A_{80})\) & \(\overset{\text{\hphantom{-}}}{E=mean}([E_{70}\ E_{80}])\) \\ \hline Procedure 1 & 0.1817 s & 0.2505 & 0.2162 \\ \hline Procedure 2 & 0.3582 & 0.4095 & 0.3839 \\ \hline Procedure 3 & 0.1892 & 0.2371 & 0.2132 \\ \hline \end{tabular}
\end{table} 
Table 27: Error between \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\) (procedures 1, 2, and 3). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Procedure \#** & \(E_{60}(C_{60}C_{60})\) & \(E_{65}(C_{65}C_{65})\) & \(E_{75}(C_{75}C_{75})\) & \(E_{85}(C_{85}C_{85})\) & \(E_{90}(C_{90}C_{90})\) & \(E\) \\ \hline Procedure 4 & 0.1165 s & 0.1117 & 0.0620 & 0.0616 & 0.1065 & 0.0917 \\ \hline Procedure 5 & 0.0753 & 0.0637 & 0.0362 & 0.0376 & 0.1004 & 0.0626 \\ \hline Procedure 6 & 0.0748 & 0.0956 & 0.0352 & 0.0281 & 0.0534 & 0.0574 \\ \hline \end{tabular}
\end{table} 
Table 28: Error between \(C_{sp_{1}}\) and \(\hat{C}_{sp_{1}}\) (procedures 4, 5, and 6). \begin{table}
\begin{tabular}{c c c c} \hline
**Procedure \#** & \(E_{70}(C_{70}C_{70})\) & \(E_{80}(C_{80}C_{80})\) & \(\overset{\text{\hphantom{-}}}{E=mean}([E_{70}\ E_{80}])\) \\ \hline Procedure 4 & 0.1569 s & 0.1070 & 0.1320 \\ \hline Procedure 5 & 0.1150 & 0.1223 & 0.1187 \\ \hline Procedure 6 & 0.1051 & 0.1285 & 0.1168 \\ \hline \end{tabular}
\end{table} 
Table 29: Error between \(C_{sp_{1}}\) and \(\hat{C}_{sp_{1}}\) (procedures 4, 5, and 6).
# Slope Failure of Noncohesive Media Modelled with the Combined Finite-Discrete Element Method
## 1 Introduction
Although there are some research endeavours conducted on rock or slope failure using the FDEM, seismic failure behaviour of slope media has not been prevalently included in the work aforementioned. Furthermore, quantitative comparison with existing literature is still high in demand. This paper focuses on the slope failure behaviour of noncohesive media considering both gravity and seismic excitations. In this study, nonparallel 2D FDEM program Y\({}^{\prime}\), which was implemented by [PERSON] [27], is used for the simulation. Computational work was performed on a Dell T7910 workstation with Intel Xeon E5-2630v4 2.2 GHz CPU, 32 GB memory and Microsoft Windows 10 operating system. Following the basics of the FDEM, two examples on noncohesive slope failure are given. One example is the collapse of a rectangular soil heap under gravity, and the other simulates a landslide disaster triggered by the 1999 Taiwan Chi-Chi earthquake. The noncohesive media is discretised into a number of discrete elements, and no further fracture of these discrete elements is considered. Results are compared and validated both qualitatively and quantitatively with data in existing literature. It is worth mentioning that the effect of water is not considered in this study, suggesting the slope media is well drained. This assumption is widely held and accepted in a good number of cases. In general, the combined FDEM is proven to be an applicable and reliable computational tool for modelling slope failure of noncohesive media.
## 4 Experiments and Performance Analysis
### Experimental Methodology  
Generally speaking, [PERSON]'s protocol is widely used for quality assessment of fused images [5; 25; 47], in which the real dataset is thought of as the reference image \(\mathbf{Z}\). According to the [PERSON] observed model, the observed HSI \(\mathbf{Y}_{h}\) and MSI \(\mathbf{Y}_{m}\), can be obtained by spatial and spectral degradation of high-resolution image \(\mathbf{Z}\), respectively. As shown in Figure 4, the fusion method is designed to simulate the degraded images \(\mathbf{Y}_{h}\) and \(\mathbf{Y}_{m}\) for the fused image \(\mathbf{\hat{Z}}\). This flowchart can solve two difficulties in image fusion, including the registration of observed images and the acquisition of the ground truth for performance evaluation.  
For comparison, four state-of-the-art methods were taken as the baseline, including the original CNMF [25], BSR [16], HySure [15] and tensor-based CSTF [20]. To drop the effect of randomness, the performances in the experiments were the means of multiple measurements. All the methods were tested on a laptop equipped with Intel CPU Core-3210 with 2.5 GHz speed and 16 GB RAM, using Mathworks Matlab R2015a.
## 4 Design and Measurement
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Operating Frequency (GHz)**} & \multicolumn{2}{c}{**Return Loss**} & \multicolumn{1}{c}{**Isolation**} & \multicolumn{1}{c}{**Coupling**} & \multicolumn{1}{c}{**Coupling**} \\  & \(S_{11}\) & \(S_{32}\) & \(S_{21}\) & \(S_{31}\) \\  & **(dB)** & **(dB)** & **dB** & **dB** & **dB** \\ \hline \multirow{3}{*}{3} & Simulated & \(-28.18\) & \(-29.36\) & \(-3.12\) & \(-3.17\) \\  & Measured & \(-24.01\) & \(-27.65\) & \(-3.23\) & \(-3.24\) \\  & Error (\(\pm\)dB) & \(-4.17\) & \(-1.71\) & \(-0.11\) & \(-0.07\) \\ \hline \multirow{3}{*}{2} & Simulated & \(-20\) & \(-35.42\) & \(-3.13\) & \(-3.13\) \\  & Measured & \(-30.95\) & \(-29.53\) & \(-3.24\) & \(-3.30\) \\ \cline{1-1}  & Error (\(\pm\)dB) & \(10.95\) & \(-5.89\) & \(-0.11\) & \(-0.17\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of S-parameters at the resonating frequencies of 2 and 3 GHz of the simulated and fabricated circuits. Figure 13: Photographs of the fabricated WPD circuit designed at 2 GHz. Figure 14: Simulated and measured S–parameters results of the proposed WPDs. Figure 15: MeasuredS–parameters results of the fabricated conventional WPDs and the proposed WPDs with DGS.
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 2 The Structure of the Subsea Connector  
The metal lenticular sealing gasket is usually used as the main seal for the subsea collect connector and clamp-type connector, which are commonly used in deep sea environments. This article takes the subsea collect connector as an analysis case, as shown in Figure 1. The core sealing structure of the subsea collect connector is composed of a lenticular sealing gasket, top hub, and bottom hub. The external installation tool drives the press ring to move downward to make the fingers clap the top and bottom hubs and exert pre-tightening force on the sealing structure to complete the connection and sealing. In the whole structure, the parts directly exposed to the oil and gas medium include the top hub, bottom hub, and lenticular sealing gasket. Therefore, these three parts are greatly affected by the change in the temperature load. The applicable range of underwater temperature of the whole underwater connector system is 3-150 \({}^{\circ}\)C, which is also the temperature load change range of the subsea connector thermal structure coupling analysis in this paper. When the sealing structure of the subsea connector is subjected to external load and temperature load, the structural stress and thermal stress will overlap. If the coupling stress is too large, it will have an irreversible impact on the sealing structure, especially on the sealing contact surface. Therefore, the mechanical and sealing contact characteristics of the sealing structure of the subsea connector under the thermal-structural coupling effect should be analyzed in detail.
## 3 Results
### Acoustic Field and Energy Dissipation
Therefore, we adjusted the transducer position, which was originally designed to be perpendicular to the xy-plane at z = 0. The adjusted position moved the transducer \(-\)3 mm along the x-axis, setting the new focal point at (\(-\)3, 0, 42). Based on this modified setup, the acoustic waves at a frequency of 400 kHz were analyzed. As shown in Figure 5a, a maximum acoustic intensity at the SCC was observed near the ampulla (a thicker area containing the copula). The maximum intensity at the focal point was calculated to be 1.03 W/cm\({}^{2}\). The maximum intensities for 600 kHz and 800 kHz were 3.14 W/cm\({}^{2}\) (Figure 5b) and 4.21 W/cm\({}^{2}\) (Figure 5c), respectively. As the frequency increased, the intensity also increased, and the region of concentrated intensity became smaller with increasing frequency. Figure 4: **(a)** Axial direction results showing the focal distance and normalized intensity for transducers at 400 kHz, 600 kHz, and 800 kHz. Normalization was applied using the maximum intensity of 38.21 W/cm\({}^{2}\) at 800 kHz. **(b)** Lateral direction results calculated along the line defined by y = 0, corresponding to the z-axis where the intensity was maximum. Figure 5: Acoustic intensity profile showing the maximum intensity near the ampulla in the SCC at (**a**) 400 kHz, **(b)** 600 kHz, and **(c)** 800 kHz. This trend was similarly observed in the acoustic energy dissipation density. The maximum dissipation density was calculated to be 0.43 W/cm\({}^{3}\) at 400 kHz (Figure 6a), 1.22 W/cm\({}^{3}\) at 600 kHz (Figure 6b), and 1.71 W/cm\({}^{3}\) at 800 kHz (Figure 6c). Similarly to the intensity, the area of concentrated dissipation density decreased as the frequency increased. This reduction in the dissipation region with higher frequencies indicates that the acoustic energy is more focused at higher frequencies, which can lead to a more localized heating and a higher streaming velocity gradient. Thus, the precise control of frequency is critical for optimizing the distribution of dissipation and its corresponding effects on both acoustic streaming and temperature rise. As described in Equation (8), this calculated dissipation will be used in the following sections to further analyze its effects on both acoustic streaming and temperature rises.
## 2 Background
### Deployment in Wireless Sensor Network
#### 2.5.3 Dynamic Deployment  
Dynamic deployment is used with mobile WSNs, when the objective is to monitor an event or increase the network's coverage, connectivity, and lifetime issues. In dynamic deployment or self-deployment, the nodes are randomly deployed first; then, the nodes change their locations to increase the coverage, which means each point in the area of interest should be covered. This type of deployment can be either centralized or distributed. In centralized deployment, the sink node should be a powerful node because it is responsible for finding the new location of each sensor node through deployment algorithms and sending the required location to each sensor. This type of deployment saves energy but may fall to single-point failure problems and cannot be implemented in disasters or battlefields. While the distributed deployment lets each node determines its new position in the monitoring area, this may consume more energy [27]. A summary of deployment methods is shown in Figure 7.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 2 Materials and Methods
### sRNA Sequencing  
Total RNAs from five plants were pooled. The pooled samples were quantified by spectrophotometry (Nanodrop, Thermo Scientific), and the quality was verified by agarose gel electrophoresis. After purification and verification, the samples were sent to the Genomic Services Laboratory (Labsergen, Irapuato, Guanajuato, Mexico) for library construction (TruSeq Small RNA) and sequencing (Illumina NextSeq-500 platform; SE, 1 \(\times\) 36, 10 Mb). The read quality control was performed in the Galaxy platform (usegalaxy.org) with the \"FastQC\" program, and sequencing adapters (small RNA) were removed with \"Trim Ga-lore\". The resulting sequences were filtered by \"Manipulate Fastq\" based on an 18-24 nt size using regular expressions. The resulting files were used for the abundance estimation by single ends in \"Salmon\" with the coding sequences of the EuMV-YP viral components (Component A, No. DQ318937; component B, No. DQ318938) in sense and antisense.  
The siRNAs were mapped by HISAT2 on the Galaxy platform using components A and B of EuMV-YP as the reference genome. The alignment was carried out with the default parameters of the platform, and the resulting file was converted to SAM format for further processing and visualization. The number of reads of each sequence was arranged in a Microsoft Excel(r) sheet to generate the graphs and the heatmap.  
Clusters containing more than 700 vsiRNAs were deemed vsiRNA hotspots. The guanine and cytosine contents of each sequence were calculated with ApE software. A two-tailed \(t\)-test was performed to compare the percentage of guanine and cytosine between the vsiRNAs.
## 1 Introduction
Results obtained from experimentation using the proposed framework demonstrate that the quantity and quality expansion of images and labels using GANs improve accuracy and convergence compared to existing semantic segmentation models using source images and labels. This paper is structured as follows. Section 2 briefly reviews state-of-the-art data augmentation methods based on image style transfer and GANs. Subsequently, Sections 3 and 4 present our framework for semantic segmentation combined with data augmentation using two GAN models with different mechanisms and their setup properties, including benchmark datasets of three types and evaluation metrics. The preliminary and evaluation experiment results are presented in Sections 5 and 6. Finally, Section 7 presents the conclusions and explains some intentions for future work.