esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Direct Shear Test
### Sample Preparation and Testing  
The dosage of polypropylene fiber used in the test was the percentage of fiber relative to the dry mass of iron tailings powder. According to the research results of fiber-reinforced sand, clay, expansive soil, and silt [10; 11; 12; 13; 14; 15; 16; 17], the polypropylene fiber dosages selected here were 0%, 0.25%, 0.5%, 0.75%, and 1%, respectively. In the sample preparation process, 17% water was first added to iron tailings powder and stirred for 3 min, and then let stand for 24 h. Then, the fibers were sprinkled into iron tailings powder and stirred for 3 min. The mixture is shown in Figure 3. The mixture was placed into a ring cutter of 61.8 mm in diameter and 20 mm in height, and a layer of Vaseline was applied inside the ring cutter before loading [31]. The sample was formed by compaction, and the upper and lower surfaces of the sample were flattened [32]. The formed sample is shown in Figure 4. The mold was released after 2 h, and the weight of each sample was weighed and controlled at 146 \(\pm\) 2 g. The sample was packed in cling film and placed in a natural environment for 24 h, and then subjected to a direct shear test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Particle Diameter (\(\upmu\)m)** & **\textless{}45** & **45–75** & **75–100** & **100–150** & **\textgreater{}150** \\ \hline Content (\%) & 69.57 & 8.76 & 5.18 & 6.98 & 9.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size analysis results of iron tailings powder.  
Figure 2: Polypropylene fiber.  
The full-automated quadruple direct shear apparatus produced by Nanjing TKA Technology Co., Ltd. was used in this test. It can perform direct shear tests under four normal stresses simultaneously, thereby improving the test efficiency. The normal stresses used in this test were 100, 200, 300, and 400 kPa, respectively, and the shear rate was 1 mm/min. Six shear forces (\(F\)) and the corresponding shear displacement (\(s\)) were recorded every second during the test, and the sample failure shear surface is shown in Figure 5.  
Figure 4: Direct shear test specimens of polypropylene-fiber-reinforced iron tailings powder with 1% fiber dosage.  
Figure 5: Shear surface of polypropylene-fiber-reinforced iron tailings powder with 1% fiber dosage.  
Figure 3: Mixture of polypropylene fiber and iron tailings powder.
## 3 A Network Intrusion Detection Method Incorporating a Bayesian Attack Graph and IL
### Path Prediction Experiments with Bayesian Attack Graphs
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature Field** & **Literature Number** & **Sketch** \\ \hline Introduction to Network Attack & [1; 2; 3] & Network intrusion security literature introduced in the background \\ Wireless sensor networks & [4; 5] & Modern English for Wireless Sensor Networks \\ DL & [6; 7] & Introduced as a Background \\ Network Intrusion Attack Graph Technology & [7; 8; 9; 10; 11; 20] & Application of DL in Network Technology \\ Other network security information technology & [12; 13; 14; 15; 16; 17] & Optimization results of attack graph technology by domestic and foreign scholars \\ Internet of Things and Computer Communication & [18; 19; 20; 21; 12; 13; 16; 17] & Domestic and foreign scholars’ detection methods \\ Internet of Things and Computer Communication & [18; 19; 20; 21] & Research on new application of network security \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of Cited References. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Host Name** & **Function Description** & **Host Number** & **Vulnerability Name** & **Vulnerability Number** \\ \hline Apache server & Provide Web Server services & H1 & CVE-2020-13942 & V1 \\  & Be responsible for email sending and & CVE-2020-15778 & V2 \\ Mail server & receiving management & & CVE-2018-19518 & V3 \\ DNS server & Domain name resolves to IP address & H3 & CVE-2020-1350 & V5 \\  & & & CVE-2019-0708 & V6 \\ PC & Personal office machine & H4 & CVE-2021-1675 & V7 \\ FTP server & Provide file storage and access services & H5 & CVE-2019-12815 & V8 \\ MySQL server & Provide database services & H6 & CVE-2016-6662 & V9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Host and Vulnerability Information of Network Framework. V1, V4, V5, V6, V8, and V9 were ultra-dangerous vulnerabilities, and the rest were high-order vulnerabilities. Based on the above network system environment and vulnerability information, this study used the Bayesian algorithm to construct the unweighted Bayesian attack graph, as shown in Figure 5.  
From Figure 5, nodes H6-V9 are the endpoints of the entire attack graph. The algorithm found all the attack paths from the attacker's initial node H0 to the endpoints of the attack graph H6-V9 and calculated the reachability probability of each path. The reachability probability of each node and each path in the graph are shown in Table 3.  
From Table 3, the reachability probability of nodes H1-V1, nodes H3-V5, and nodes H2-V4 were higher, so they were more likely to be compromised by attackers. In addition, Path-G had the highest path reachability probability, and the attacker was most likely to execute the attack according to Path-G, which contained the high-risk nodes H3-V5. Combining the node and path reachability probability, the node that the attacker was most likely to attack was H3-V5. Figure 4: Vulnerability CVSS Score and Availability Information. Figure 5: Attack Graph With Node Path Not Weighted.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 3 Results
### Comparison between Test Cases  
For comparison of the stitching algorithms between each test case, a standardization using the Z-score method (Equation (4)) was applied on mean repositioning error and stitching error estimator SEE for each test case.  
\[z=\frac{x-\mu}{\sigma} \tag{4}\]  
where \(z\) is the standardized value using Z-score method, \(x\) is the value to standardize, \(\mu\) is the mean of the population and \(\sigma\) is the standard deviation of the population. This method brings all standardized data to a mean \(\mu\) of 0 and to a standard deviation \(\sigma\) of 1. Figure 16 shows the standardized values of the mean repositioning error (Figure 16a) and stitching error estimator SEE (Figure 16b) for each test case.  
Figure 15: Bar charts of the quantitative criteria for test case D: run time (**a**), memory usage (**b**), mean repositioning error (**c**) and stitching error estimator (**d**). NA = Not Applicable.  
Successful stitching on each test case is then detectable with a low value of standardized mean repositioning error and low value of SEE. A better stitching quality is obtained with the novel 3D topography stitching algorithms (NR, EER and TSR), which highlights the relevance of multimap stitching based on reflectance maps, especially if the contrast of the reflectance map is optimized.
## 4 Results and Discussion
### Comparison of the Experiments and Simulations  
A comparison of the simulation and experimental results is shown in Table 4. For the simulation and experiments with the 100 mm polisher, the simulation results underestimated the experimental results of the R0 and R32 pads by 5.04% and 3.25%, respectively. Furthermore, the R32 pad was proved to own stronger slurry transportation ability with the evidence of eliminated bow wave. It can be concluded that the simulation results are consistent with the experiment and the error is acceptable. As for the 300 mm wafer polisher, the R32 pad had the advantage of an SST that was 13.57 s shorter than that of the R0 pad. The shorter SST can result in a higher updating speed of the slurry, which can be used to explain the increased MRR and NU.  
Figure 14: Bow wave images of the R0 pad and R32 pad along the polishing time.  
Figure 13: Schematic of the bow wave phenomenon.
## 4 Discussion
### Situation-Dependent Frequency Adjustments  
Interestingly, the frequency adjustments made by the participants did not show a strong correlation with the prevailing acoustic environment, which contrasts the results observed in controlled laboratory settings by [PERSON] [9]. In the aforementioned study, adjustments were closely tied to the simulated listening environment. The lack of a similar pattern in our study may have been due to the greater variability in real-life acousticenvironments, which are assigned to broad classification categories that might mask more specific contextual differences. In real-world settings, these environments likely vary significantly, potentially requiring more substantial deviations in settings than those observed under standardized lab conditions. Moreover, the participants in our study had the opportunity to experiment with a wider range of settings to approximate the best possible adjustments. This exploratory behavior contrasts that found by [PERSON] [9], where only one self-adjustment per acoustic condition was considered per participant, potentially limiting the ability to replicate those findings in a more dynamic, real-world context.
# Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments
## 2 Related Works  
### Human Fall Detection  
The problem of fall detection has been widely studied by the authors in [27, 31] using neural networks. Significant experiments have been carried out to study human falls in simulated laboratory conditions [32, 33, 34]. However, not all datasets are publicly available (for privacy reasons). The fall assessment methods presented in these studies have varied depending on the estimation-based data.  
In [35], the authors used human motion segmentation with background subtraction. The main measure was a significant change in visual information between subsequent image frames. However, differences in the background and the presence of occlusion objects, different camera viewpoints, and variability of the person's appearance led to poor generalization of methods applied to the real world [27]. The authors of work [36] made a great contribution to the study of 3D information about a person falling from several cameras and sensors, and the analysis was carried out using depth maps. As noted by the authors in the work [37], systems with multiple cameras generated more accurate fall detection results. However, these methods have additional limitations, such as data synchronization and depth detection for 3D sensors, and it is not useful for cases in which cheapness, quick responses and simplified usage are required. Fall detection with a monocular camera has an advantage against other methods due to the absence of the above restrictions.  
One of the wider and open-source datasets that can be taken into account is [38], which concerns a human falling. Solving the detection task can be complex due to variable lighting and exposure conditions using such datasets. In most cases, these datasets are not fully able to provide fall detection from one camera because of the high occlusion coefficient when data from different cameras is analysed [39]. Comparable research to our approach was presented in [28], where the authors demonstrated fall recognition derived from synthetic data samples based on the alignment of MoCap poses and human models. They generated the values of the skeletal joints, the segmentation mask and the \"fall-no fall\" label in the created dataset. As a result, the authors [28] also introduced a deep learning framework for fall detection in complex non-obvious real-world conditions. However, the detection result does not provide the interaction masks with the environment during a fall.  
The presented approaches and datasets have two common disadvantages: (1) this is the intentional actions of persons in the process of reconstructing a fall (although the researchers note that they studied data on an unexpected and unintentional fall); (2) the presented variations of falls are often limited and difficult to use in the task of detecting the consequences of one. For example, it is difficult to evaluate what part of the body had been hit and the object of hit. In such tasks, human detections inevitably make mistakes in predictions and pixel segmentations [28].
## 4 Meteorological Analysis of the 28 December 2020 Sea Storm Event
### Anemometric Event Analysis at Sub-Regional Scale  
The diagrams in Figure 8 report wind data from CNR-ISMAR weather stations and show the average wind speed measured during the 10 min before the data sampling time (diagrams on the left) and the maximum gust speeds (diagrams on the right) over the same time interval.  
Figure 6: (a) Sea Level Pressure forecasting map valid for 28 December 2020 H18:00 UTC (BOLAM Model [45]); (b) map highlighting the high horizontal gradients over Campania region.  
The increase in wind intensity started just after 00:00 UTC and is well depicted by diagrams: at about 11:00 UTC, the wind speed reaches a relative maximum, then decreases up to about 13:00 UTC; later, the wind speed increases again, reaching its absolute maximum at about 19:00 UTC. The wind direction was between S and SW for the entire storm event. The classification of an anemometric event using the Beaufort scale [46] documents the relation between wind speed (and, therefore, its dynamic pressure) and the effects caused by the wind on the environment. The anemometric event that occurred on 28 December 2020 was characterized by gusts of maximum intensity (Force 11-violent Storm, Beaufort scale) at the location of Lago Patria, where wind speed maxima typical of a low-intensity hurricane were reached. On the other two locations, strong gale intensity was recorded.
## 3 Results and Discussion
### NP Yield and Related Energy Demand  
From the gravimetric measurements, it was determined that the mass of NPs actually obtained by aerosol-mediated synthesis is in the range 33-42% of the mass of aerosolised precursors. A possible explanation of such a phenomenon can be given by recalling that reaction (1), producing a solid phase that is very poorly soluble in water, is almost totally displaced towards the formation of the relevant products and does not influence the NP yield. On the other hand, the latter is strongly affected by fluid dynamic aspects related to duct wall wetting, leading to the formation of unwanted CaCO\({}_{3}\) microparticles remaining confined in the duct upstream of the Jet Venturi scrubber, actually reducing the NP yield. However, it should be pointed out that an NP yield not exceeding 42 percent was obtained in the total absence of capping agents, thus representing an infrequent finding in the literature concerning wet chemical methods. In addition, despite the need of improving an overall NP production yield in the perspective of a scale-up transfer, it is important to note that the solid phase abated by the jet Venturi scrubber and circulating in its carrier liquid is almost entirely made up of NPs, as shown by the results presented in Figures 6 and 7, proving a satisfactory selectivity of the collection apparatus.  
The processing energy demand for this aerosol-mediated synthesis, taking into account the yield range given above, is estimated to be in the range of 1830-2330 MJ/kg of CaCO\({}_{3}\) NPs produced. The energy demand for the production of both reagents (precursors) does not exceed approximately 30 MJ/kg [52], as the energy consumption for Na\({}_{2}\)CO\({}_{3}\) production, according to the Solvay process, can be estimated to be lower than 15 MJ/kg. Namely, the energy consumption for reagents manufacturing in the present study represents only a negligible fraction of the processing energy demand. Intriguingly, approximately 80% of the total impinged energy is dissipated in the Venturi jet scrubber. [PERSON] et al. [53] discussed the cumulative energy demand related to the synthesis of TiO\({}_{2}\) NPs according to different production techniques, including reagents and processing energy burden. Although CaCO\({}_{3}\) and TiO\({}_{2}\) are different chemical species, a few observations can be made. It can be observed that the processing energy demand of the present technique is in line with the corresponding value typical of a biological method and is considerably lower than that required by flame pyrolysis and thermal plasma for titania NP production.  
Figure 8: Plot of EDS analysis of elements detected by FESEM analysis of nanosized phase corresponding to AER-1 sample.
## 3 Results
### Hydrogel Swelling Behaviour  
Studies on the swelling behaviour of the obtained hydrogels were carried out under various conditions: in deionised water, and in aqueous solutions at pH = 3.0 (phthalate buffer) and pH = 7.4 (phosphate-buffered saline). Samples of the swollen hydrogels, as well as the ones dried in a desicator, were transparent. For each tested hydrogel, the SR was the highest after soaking in deionised water (see Figure 3). The swelling ratio for each hydrogel soaked in phthalate buffer and in phosphate-buffered saline was significantly lower than the SR achieved in deionised water.  
As seen in Figure 3, the highest swelling ratio among all tested hydrogels and conditions, reaching almost 350 g/g (345.7 \(\pm\) 5.4 g/g), was observed for hydrogels with the PEG1000 cross-linker after soaking in deionised water. The SR of the hydrogel with PEG1000 after incubation in deionised water was more than twice as high as the SR of the hydrogel with PEG400, while using 3-arm PEG1000 cross-linker resulted in hydrogel with the lowest SR under those conditions. For the tested hydrogels, swelling ratio increased with the increase of the molecular weight of the linear cross-linkers. However, for hydrogels containing cross-linkers with the same molecular weight, linear PEG1000 and branched PEG1000, the cross-linker structure seemed to be the determining factor on the swelling behaviour of the hydrogels. A similar influence of cross-linker chain length and structure on hydrogel swelling behaviour was observed under each tested condition. Those results could be linked with the porous structure of the hydrogels observed in SEM images. A hydrogel-containing cross-linker with the longest chains, PEG1000, had the highest SR among the tested hydrogels. As can be seen on SEM images, the \(\gamma\)-PGA-PEG1000 hydrogel had a meshlike structure with the biggest pores. Such a structure provided more space for water than a cross-linker with shorter chains does.  
To establish changes in swelling behaviour over soaking time, the SR values of the \(\gamma\)-PGA-based hydrogels soaked in deionised water were measured at different time intervals. The experiment was carried out for 240 min. As seen in Figure 4, in the first part of the experiment, the swelling ratio increased with increasing length of soaking time. Then, between 120 and 150 min, the SR achieved almost constant values, which were similar with the SR values after soaking for 24 h (see Figure 3).  
Figure 3: Studies on swelling behaviour of \(\gamma\)-PGA-based hydrogels carried in phthalate buffer at pH = 3.0, deionised water, and phosphate-buffered saline at pH = 7.4 (\(n\) = 3; error bars, standard deviation; ns = nonsignificant with \(p>0.05\)).
# Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network
## 4 Experiments and Results
### Time and Memory Usage  
Finally, we compared the time complexity of the proposed methods and other style transfer algorithms, as shown in Figure 11a. Although the computation time between [PERSON] et al.'s [PERSON] et al. (2018) and our method is similar (the comparative results are shown in Figure 12), our result can keep the structure of the input image. Table 5 compares the runtime of our method and other methods for several image sizes. Compared to [PERSON] et al. [PERSON] et al. (2018), our method is three orders of magnitude faster. Our method processes \(512\times 512\) images at about 30 FPS, making it feasible to run in real time or on video.  
Figure 10: Transforming an aerial photograph into the Chinese painting style video. For the whole video, please see the additional files.  
Figure 9: Transforming a real image into a Chinese painting. Our network can handle any size of images. (**a**) Input image; (**b**) Chinese painting produced by our network. The input image has an enormous size of (2300, 768); because of the graphics memory constraints, we divided this huge images into three parts to process.  
Regarding memory, our generative model requires 2 MB to save its parameters. In the process of generating Chinese paintings, the required memory is linearly related to the size of input sketch, as shown in Figure 11b: for \(256\times 256\) pictures, it takes about 600 Mb; for \(512\times 512\) pictures, it consumes 2.5 Gb memory.  
Figure 11: (a) Speed comparison (in log space) between our method and [PERSON] et al. [2], [PERSON] et al. [38] and [PERSON] et al. [39]. The feed-forward methods (ours and [PERSON] et al. [39]) are significantly faster than [PERSON] et al. [2] (500-times speed up) and [PERSON] et al. [38] (5000-times speed up). (b) Memory usage vs. image size, the required memory is linearly related to the size of input image.  
Figure 12: Example results of style transfer using our image transformation networks. (left) Input image; (middle) results of [PERSON] et al. [39]; (right) ours.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Size** & **Methods** & **[PERSON] et al. [2]** & **[PERSON] et al. [39]** & **Ours** \\ \hline \(128\times 128\) & \(1.542\) s & \(0.004\) s & \(0.002\) s \\ \(256\times 256\) & \(6.483\) s & \(0.015\) s & \(0.008\) s \\ \(512\times 512\) & \(25.23\) s & \(0.051\) s & \(0.032\) s \\ \(1024\times 1024\) & \(106.2\) s & \(0.212\) s & \(0.122\) s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speed (in seconds) for our style transfer network vs. the optimization-based baseline for varying numbers of iterations and image resolutions. Both methods are benchmarked on a GTX 1080 GPU.
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 2 Model
### Model Analysis  
Before calibrating the parameters, a sensitivity analysis was conducted for the link performance function to examine the effects of the parameter settings. In this instance, the Sobol sensitivity analysis method was applied [41; 42]. Table 2 illustrates the results, where \(S_{i}\) stands for the first-order sensitivity index, and \(ST_{i}\) stands for the total sensitivity index. The portion of the output variance that is only attributable to changes in the input factor \(X_{i}\) is described by \(S_{i}\). The total of \(S_{i}\) plus all \(X_{i}\)-related interaction effects is \(ST_{i}\).  
\(\alpha_{1}\) has the biggest impact on the link performance, as seen in Table 2. \(\alpha_{1}\) is equivalent to a scaling factor for regulating the cost when expressed as a link performance function. In fact, \(\alpha_{2}\) and \(\alpha_{3}\) are utilized to regulate the curvature or growth trend of the link performance function, since they have relatively little impact on the cost and are both formally in the exponential section. We depict the link performance function for various parameter values,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(S_{i}\) & \(ST_{i}\) \\ \hline \(\alpha_{1}\) & 1.697 & 1.008 \\ \(\alpha_{2}\) & \(5.5\times 10^{-5}\) & \(2.406\times 10^{-4}\) \\ \(\alpha_{3}\) & \(2.00\times 10^{-6}\) & \(4.335\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the sensitivity analysis.  
as shown in Figure 1, where the free-flow time and \(\alpha_{1}\) are kept at 1, to see how different parameter sets affect the model.  
The function index term grows as \(\alpha_{2}\) and \(\alpha_{3}\) increase, since \(v/c\) is less than or equal to 1. In this instance, the cost approaches the free-flow time when the saturation is low and develops more quickly when the saturation is close to 1, making the entire function's curve steeper where \(v/c\) tends to 1.