esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of the Surface Roughness on the Shear Strength of Granular Materials in Ring Shear Tests
## 3 Results
### Effect of the Surface Roughness on the Shear Stress  
Figure 4 presents a comparison of the shear strength obtained from the smooth and rough surfaces with respect to the drainage condition drained or undrained) and shear velocity (0.1, 0.5, and 1 mm/s). The shear stress-time relationship was considered because the total displacements of the materials tested were not exactly the same for all test conditions, as they may have been different for a given shear velocity. As previously described, the tests were performed over a limited range of shear velocities because rapid shearing may have resulted in an unnecessary reduction in the shear strength of granular materials for higher velocities [32]. From the test results, the shear stress and shear deformation characteristics were very similar for both drainage conditions. The effect of surface roughness on shear stress is crucial when considering a relatively high shear velocity (i.e., \(>\) 0.1 mm/s). At 0.1 mm/s, the shear stress measured from the rough surfaces was slightly larger than from the smooth surfaces; however, at the end of the test, both values were similar at their closest point (20 kPa for the drained condition and 17 kPa for the undrained condition). At 0.5 mm/s, the difference in surface roughness was significant (Figure 4b,e). Compared to the results at 0.1 mm/s, the shear stress measured from the smooth surface only slightly increased (21-22 kPa), but the shear stress measured from the rough surface greatly increased (33-34 kPa). At the final stage of shearing (at 2200 s), the stress difference between smooth and rough surfaces was almost constant (12 kPa). For the shear velocity of 1 mm/s (Figure 4c,f), there was little difference in the stress-time relationships. The difference was small until the shearing time reached approximately 500 s; when shearing was continuously applied, dilatancy was prominent in the rough surfaces. In the drained condition, a larger difference in shear stress was observed when a larger shear velocity was applied, but the difference was small in the undrained condition. These results may be attributed to the presence of shear localization and water in the shear zone.  
There was no direct observation possible for the soil-structure interaction in ring shear tests using the developed apparatus. However, from the test results, it may be deduced that, in terms of the grain fragmentation, local rearrangement and sliding features were dominant for the shear velocity of 0.1 mm/s, while local abrasion and global fracture inside the grain were dominant for shear velocities of 0.5 and 1 mm/s. In brief, from the test results, the surface roughness was of significance, and this proves that the wall slip phenomenon was minimized. However, it is not easy to estimate the fragmentation effect based on shear velocity.  
Figure 4: Comparison between smooth and rough surfaces for different shear velocities of 0.1, 0.5, and 1 mm/s: (**a–c**) Drained and (**d–f**) undrained conditions. (**a,d**): 0.1 mm/s; (**b,e**): 0.5 mm/s; and (**c,f**): 1 mm/s.
## 3 Results
### NDVI Annual Variation on the Loess Plateau  
The mean NDVI value for 2011-2020 on the Loess Plateau was 0.58, slightly higher than 0.51 for 2001-2010, and the CV value for 2011-2020 was lower than that for 2001-2010, indicating a more uniform temporal distribution of NDVI for 2011-2020 (Table 2). The average annual increase rate of NDVI for 2011-2020 was 0.008, compared with 0.01 from 2001 to 2010, indicating a slight decrease in the increase rate.  
From 2001 to 2010, 81% of the areas on the Loess Plateau showed an increasing trend in NDVI, while only some areas in northeastern Inner Mongolia and a few areas in southwestern Gansu showed a slight decrease in NDVI. Among them, 18.3% of the areas showed a significant increase, mainly in Yan'an, Yulin, and Linfen, which also correspond to the areas with the most obvious vegetation restoration in the fall  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Period** & **2001–2010** & **2011–2020** \\ \hline  & Min & 0.007 & 0.010 \\  & Max & 0.940 & 0.946 \\ value & Mean & 0.507 & 0.584 \\  & STD & 0.203 & 0.198 \\  & CV & 0.062 & 0.052 \\ \hline  & Min & \(-\)0.090 & \(-\)0.097 \\  & Max & 0.090 & 0.099 \\ Growth rate & Mean & 0.007 & 0.006 \\  & STD & 0.009 & 0.008 \\  & CV & 0.066 & 0.058 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical table of the value and growth rate of NDVI in Loess Plateau.  
Figure 2: Validation diagrams of GPP and ET remote sensing products of PML data at 8 day scale (**a**,**c**) and monthly scale (**b**,**d**).  
This indicates that the initial revegetation of the Loess Plateau has achieved certain results. Compared with 2001-2010, the area of NDVI increase in 2011-2020 moved from southeast to northwest, and the area share increased from 81% to 83%, mainly in Hohhot, Inner Mongolia, and Pingliang, Gansu (Figure 3b). However, the vegetation cover in the Shanxi plateau, as well as the Guanzhong plain area, showed a decreasing trend from 2011 to 2020.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 4 Experimental Results  
### Experimental Environment  
The study simulated the network environment with the TC and used the Reno, BBR, BoostTCP, and standard TCP algorithm in the simulation tests. The performance indicators of throughput, fairness, and preemption were compared for scenarios with varying bandwidth, delay, and random packet loss rate. The experiment was conducted on five Inspur NF5280M5 servers equipped with two Intel Xeon-GoXD 6136 (3.0 GHz/12-core) processors and 64 GB memory. The simulation network structure is presented in Figure 4. In the presented structure, Server 1 acted as a sender, employing the Cubic and Reno congestion control algorithms; Server 2 acted as a sender, employing the BoostTCP algorithm; Server 3 acted as a sender, employing the BBR algorithm; Server 4 acted as a receiver; and, lastly, Server 5 acted as a simulated controller for the designed network environment. The two network ports on Server 5 were connected to the switch and Server 4, forming a network bridge. TC managed the delay and packet loss and simulated the environment of a wide-area network.  
Figure 4: The experimental network structure.  
Figure 3: Illustration of the BoostTCP position in the multi-layer network transmission architecture.  
The experimental topology was an end-to-end configuration with 1 Gbps network bandwidth. Multi data flows were sent from the sender to the receiver at the specified network delay and packet loss rate, with a default data packet size of 500 MB.  
Four well-known congestion control methods were used in the experiment: three lightweight learning-based methods (Reno, BBR, and BoostTCP) and the conventional Cubic algorithm as a contrast method. To simulate the characteristics of a long-distance, long-delay, and low-packet-loss-rate data transmission in a real aerospace business network, the network delay in the experiment was set to 5 ms, 10 ms, 20 ms, 30 ms, 40 ms, 50 ms, 80 ms, and 100 ms, and five different random packet loss rates were used: zero, 0.01%, 0.05%, 0.1%, and 0.5%.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### The Watermarking Performance Metrics
#### 2.3.1 Bit Error Rate (\(Ber\))  
This metric calculates invalid bits' percentage in comparison with the overall bits' number, which is sent through to the watermarking procedure. If the \(BER\) reaches 0, this implies that it indicates to the existence of a watermark. However, it can also indicate to the absence of a watermark if the \(BER\) does not reach 0 [53]. The \(BER\) is presented in Equation (4) as follows:  
\[BER=\frac{number\ of\ invalid\ bits}{total\ number\ of\ bits} \tag{4}\]
## 2 System Reliability Evaluation Method of a Single Failure Criterion  
In order to evaluate the system reliability, a model describing the relationship between the individual components and the overall system needs to be determined first, namely the failure criterion. For a bridge system, there are three commonly used failure criteria: failure of any one component, failure of any two adjacent components, and failure of any three adjacent components [20; 27; 28]. The bridge structure is composed of multiple components. Each component has its own failure mode, which is determined by the performance function. Because each component has the same material property and load source, the failure modes between the components have a nonlinear correlation. System reliability evaluation needs to consider the probability of the joint failure of multiple components. Therefore, it is necessary to construct a joint distribution function of multiple failure modes. However, it is very complicated to construct the joint distribution function. In order to consider the joint failure of multiple components, multivariate copulas were introduced to construct the joint distribution function of multiple failure modes and consider the nonlinear correlation between each failure mode. Then, the method of system reliability evaluation obtained by considering different failure criteria was proposed based on multivariate copulas.
## 3 Results
### Results of Test Time Augmentation  
Table 11 shows the precision, recall, and mAP@50 of all the classes, as calculated using the mean ensemble of the base models. The mAP@50 for 'all class' of the TTA is 0.847, which outperforms the mAP@50 of any of the base models, as shown in Table 1. Precision also witnessed an increment from 0.808 to 0. 867, and recall increased from 0.755 to 0.72.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP@50** & **FPS** \\ \hline Base model & 0.837 & 0.772 & 0.84 & 31.95 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of ‘all class’ for mean ensemble model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Metrics** & **Yolov5n** & **Yolov5s** & **Yolov5m** \\ \hline \multirow{4}{*}{Occluded} & Precision & 0.923 & 0.832 & 0.869 \\  & Recall & 0.567 & 0.667 & 0.736 \\  & mAP@50 & 0.773 & 0.8 & 0.808 \\  & P & 0.896 & 0.804 & 0.845 \\ Displaced & R & 0.615 & 0.7 & 0.703 \\  & Map@50 & 0.786 & 0.971 & 0.80 \\  & P & 0.833 & 0.896 & 0.959 \\ Perforated & R & 0.82 & 0.82 & 0.768 \\  & mAP@50 & 0.876 & 0.903 & 0.919 \\  & P & 0.76 & 0.938 & 0.959 \\ Faded & R & 0.652 & 0.696 & 0.667 \\  & mAP@50 & 0.719 & 0.795 & 0.832 \\  & P & 0.843 & 0.801 & 0.886 \\ Rusted & R & 0.883 & 0.917 & 0.917 \\  & mAP@50 & 0.927 & 0.892 & 0.919 \\  & P & 0.856 & 0.857 & 0.838 \\ Defaced & R & 0.722 & 0.727 & 0.704 \\  & mAP@50 & 0.785 & 0.827 & 0.798 \\  & P & 0.644 & 0.724 & 0.684 \\ Good & R & 0.896 & 0.881 & 0.866 \\  & mAP@50 & 0.861 & 0.864 & 0.841 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of models on each class after improvement.  
Figure 8 shows the F1 scores curve of TTA, which peaks at 0.80 at a confidence threshold of 0.736. The result implies that the proposed model minimizes both false positives and false negatives across all classes. The model is effective in detecting good traffic signs with a peak F1 score of 0.85.
## 2 Results
### Identification of Allium Species Non-Volatile Metabolites via UPLC/PDA/qTOF-MS  
Phytoconstituents of _A. cepa_ red cv. and _A. sativum_ were analysed via reversed-phase UPLC/PDA/ESI-qTOF-MS, using a gradient mobile phase consisting of acetonitrile and formic acid. Complete elution of metabolites was achieved within a short time (ca. 20 min). UPLC-qTOF-MS using electrospray ionization (UPLC-ESI-MS) is regarded as a particularly well accepted platform for untargeted plant metabolite profiling [10]. The technology has been recently applied to assess the effect of fermentation on organosulphur compounds in garlic [11]. In the current study, a total of 51 metabolites were detected as listed in Table 2. Metabolites assignment was made by comparing retention times, UV-vis spectra, MS data (accurate mass, isotopic distribution and fragmentation pattern) with the reported literature of _Allium_ and by searching the phytochemical dictionary of natural products database. Metabolites belonged to various classes including sulphur and non-sulphur containing peptides, flavonoids, phenolic acids, and fatty acids (Figure 3). Metabolites were eluted in a decreasing order of polarity, whereby dipeptides and phenolic acids appeared first in the chromatogram followed by flavonoid di- and monoglucosides, aglycones, and finally fatty acids (Figure S3).  
Figure 2: GC/MS based PCA score plot derived from modelling drying effect on _A. sativum_ (**A**); _A. cepa_ red cv. (**C**) one at a time separately to assess the effect of drying on metabolite composition (\(n=3\)). The loading plot from _A. sativum_ (**B**) and _A. cepa_ red cv; (**D**) shows the most variant masses detected using GC/MS and contributing to the samples segregation. Volatiles are denoted with m/z/retention time (sec) pair and identifications are discussed in text. M6—diallyl-disulphide, M21—3-Vinyl-l,2-dithiacyclohex-5-ene, M28—diallyl trisulphide, and M40—diethyl phthalate. Peak numbering follows those listed in (Table 1) for volatiles identification using SPME-GC/MS.
# Study of the Impact of PV-Thermal and Nanofluids on the Desalination Process by Flashing
## 6 Model Validation  
In order to validate the numerical model presented herein in Equations (1)-(16), work research published in the literature on the subject in question was examined, and it was found that very limited experimental data were published on the subject of desalination by the flashing technique. Only water as the base heat transfer fluid was reported. However, to the best of our knowledge, none was reported in the literature on the desalination by flashing technique using nanofluids. Therefore, it was decided to use the data published in reference [PERSON] et al. [7] for validation purposes. Figure 25 has been constructed to show a comparison between results predicted by this model for the flashed flow and the data reported in reference [7]. As can be observed from this figure, the amount of the produced distillate by flashing decreased along the different chambers/stages progressively and consequently, the flash temperature and pressure profiles decreased. This is due to the increase of the heat of vaporization at lower saturation temperatures and consequently, less amount of vapor was extracted from brine.  
Figure 25 depicts the flashed flow reduction ratio across the different flashing chambers from the first to the last chamber. The data reported by reference [7] on the reduction ratio of the flashed flow were compared to the predicted results by this model chamber by chamber. The comparison shown in this figure clearly shows that the model compared well with the data of reference [7]; however, the figure shows that data were underpredicted by our model. This can be attributed to the energy lost to the surroundings in the form of irreversible energy during the flashing process taking place in each chamber and this was not fully taken into account by the mass and energy balances built in the present model, as can be seen in Equation (16).  
Figure 24: Brine flow out (kg/s) at 1200 w/m\({}^{2}\) at 20% concentration and different nanofluids.
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
## 2 Filtering Antenna Design and Analysis  
The proposed arrow-shaped flexible filtenna is designed and simulated by using the EM software tool CST Studio Suite(r) version 2023, and its final layout is depicted in Figure 1. It can be seen in the figure that proposed filtenna consist of a coplanar waveguide (CPW) feed line and an arrow-shaped patch. Two slots are etched from the tail of the arrow-shaped patch, and a rectangular stub is loaded to further optimize the performance of filtenna. The overall size of the proposed filtenna is L\({}_{\rm X}\)\(\times\) L\({}_{\rm Y}\)\(\times\) h = 23 mm \(\times\) 30 mm \(\times\) 0.254 mm. The structure of the antenna is designed over flexible substrate material Rogers RT/duroid 5880 with 0.254 mm thickness, a relative permittivity of 2.2, and a loss tangent of 0.0009 [45]. The optimized parameters and final layout of the proposed design are provided in Figure 1.
# Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion
## 4 Experimental Setup  
### Feature Extraction  
The audio file is resampled to 16 kHz. We employed the Mel-Frequency Cepstral Coefficients (MFCC) as the acoustic features in the AAI experiments. We used a Hamming window with 25 ms frame length and 10 ms frameshift in the feature extraction stage, and extracted 13 cepstral coefficients per frame, where delta and 2-delta are computed. We also conducted the Cepstral Mean and Variance Normalization (CMVN) and Vocal Tract Length Normalization (VTLN) to normalize the feature and to reduce the differences among speakers [29].  
For the articulatory trajectories, we did a smoothing process with the low-pass filter mentioned above and resampled to 100 Hz sequences, assuring the synchronization of the acoustic feature and articulatory trajectories. The additional 4 dimension articulatory trajectories are computed after the pre-processing stage.
## 3 Results
### Questionnaire
Figure 9: Monitoring and supporting the family. Continuing to explore the home activities, we also tried to assess, through a Likert scale, the importance of having the possibility of verifying if the activities had been done and what difficulties the child might have encountered. No negative answers were given, with the majority of the SLTs answering that it is \"very important\" (16 answers) or \"Important (6 answers). Throughout this subsection it was clear that being able to verify the execution of home activities was important, and also the importance and impact of involving the family. Following these very positive answers, the next question asked what kind of difficulties the SLTs faced regarding the continuation of the intervention session at-home. Most of the answers mentioned the lack of time from parents, difficulties in coordinating routines, placing the focus on the SLTs or an excessive amount of school work. Some of  
Figure 11: How do SLTs verify if the activities sent home were done? Figure 10: Most common type of activities or exercises sent home. the answers also mentioned the lack of understanding from the parents of what is needed from them, parents reprehending children when they \"underperform\" in therapy, and the lack of adequate and timely clinical feedback regarding activity completion acting as a demotivation for some families. The last part of the questionnaire focused on new approaches to intervention and continuation of the intervention session. All 33 participants were asked if they knew or used any tool similar to what usually supports telepractice, a term used here as placeholder of sorts, to try and determine how \"at ease\" were SLTs with technologies that allow at-a-distance intervention and to set the stage to discuss a new tool that would behave in a similar fashion. There was an overwhelming number of negative answers, with 27 SLTs saying they never used telepractice. The reasons alleged ranged from considering that face-to-face contact is preferable, or that the families do not have the technical skills or the resources to support a telepractice. A final multiple-choice question, with a set of characteristics that they could pick and choose in order to define the ideal tool to support the continuation of the intervention at-home, was presented to the SLTs. As with previous data, a simple coding was used in order to reduce the answers to one-word descriptors, as shown in Figure 12. Difficulty refers to the tool permitting varying or adjustable difficulty levels; adjustable if the tool could adapt to the users' capabilities, e.g., fine motor skills; wholesome if it behaved as a family-friendly activity, that all can play and enjoy; reset referred to its ability to be re-set at-a-distance; the tool should create and send logs to the SLT without user interaction; and be simple as in simple to use--enabling low cognitive effort.
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 3 Results and Discussion
### Results for Maximum Allowable Users  
The average Q factor was considered to determine the allowable number of users under a certain configuration. The Q factor is measured with respect to the change in the bandwidth for several targeted users.  
Table 1 shows the Q factor versus the number of users at different bandwidths, from 100 GHz to 300 GHz. It seems that 196,608 users always fulfil the minimum allowable Q factor (\(\geq\)6). The average Q factor is 6.185766 (no. of users = 196,608), as compared to 5.892776 (no. of users = 393,216). Figure 10 depicts the average Q factors at different numbers of users. We can conclude the maximum number of users for this configuration (6 \(\times\) 32 \(\times\) 32 \(\times\) 32) is 196,608. The Q factor for the user number (equal to 393,216) is less than 6; therefore, it cannot be considered for effective communication.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Users**} & \multicolumn{4}{c}{**6144 Users**} & \multicolumn{4}{c}{**49,152 Users**} & \multicolumn{4}{c}{**98,304 Users**} & \multicolumn{4}{c}{**196,608 Users**} & \multicolumn{4}{c}{**393,216 Users**} \\ \cline{2-19}  & **a** & **b** & **c** & **d** & **a** & **b** & **c** & **d** & **a** & **b** & **c** & **d** & **a** & **b** & **c** & **d** & **a** & **b** & **c** & **d** \\ \hline
**Bandwidth (GHz)** & 6 & **x** & **32** & **32** & **6** & **8** & **32** & **32** & **6** & **16** & **32** & **32** & **6** & **32** & **32** & **32** & **6** & **64** & **32** & **32** \\ \hline
100 & & 5.84612 & & & 5.37531 & & & 6.41122 & & & 5.65825 & & & 5.17017 & & & & & & & & & & & & & & & \\ \hline
150 & & 6.67228 & & & 5.97101 & & & 5.83785 & & & 6.49439 & & & & & & & & & & & & & & & \\ \hline
200 & & 5.59458 & & & 6.42985 & & & 6.51374 & & & 5.49055 & & & & & & & & & & & & & & & & \\ \hline
250 & & 6.8909 & & & 6.3323 & & & 6.58316 & & & 6.84947 & & & & & & & & & & & & & & & & \\ \hline
300 & & 6.72808 & & & 5.75616 & & & 6.56407 & & & 6.43617 & & & & & & & & & & & & & & & & \\ \hline Q factor (Average) & & 6.346392 & & & 5.972926 & & & 6.382008 & & & 6.185766 & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of Q factor in accordance with the number of users and bandwidth (P\({}_{\rm{in}}\) = 0 dBm).  
Figure 9: No. of users versus Q factor (bandwidth = 300 GHz).