esa-sceva/satcom-chunk-collection huggingface dataset:
# SimEx: A Tool for the Rapid Evaluation of the Effects of Explosions
## 3 Example of Application: Facade of a Building under Blast Loading
### Crater, Fragments, and Damage to People  
Figure 4 presents an estimation of the crater generated in the reference case on a sandstone soil, with an approximated radius of 1.6 m. For surface bursts, \(\text{HOB}=0\) m, as the one considered here, the equivalent charge radius is irrelevant, as it is only used to determine the dimensionless height of burst, which is identically zero in our example. The figure also shows that, for above-surface bursts, \(\text{HOB}>0\) m, the crater radius is significantly smaller for the same amount of explosive due to the air cushion that exists between the load and the ground, which reduces to a great extent the pressure that reaches the ground surface [6].  
Figure 5 shows the interface of the fragment assistant using the input data of the reference case. For the application of [PERSON]'s statistical theory for fragmentation of steel cylindrical shells [3, 26, 27, 28], the explosive charge is approximated to a cylinder of approximately 50 cm diameter surrounded by a steel fragmentation shell with a mass of the order of about 13% of the charge and a thickness of 2 mm.  
Figure 13: CW–S linear (**a**) and log-log (**b**) damage diagrams for reflected blast load on the facade of the first floor (Level 1): Case 1 (\(\bigcirc\)), Case 2 (\(\lozenge\)), Case 3 (\(\triangle\)).  
Finally, Figure 8 shows the calculating assistant for estimating damage to people in the reference case. As an illustrative example, the figure presents the results of lethality due to different types of injuries at a distance of 20 m from the origin of the explosion, assuming the worst-case scenario of an average person located close to the facade of the building being attacked. In the pressure-impulse graphs, representative distances are indicated using red dots plotted along the characteristic overpressure-impulse-distance curve [60]. As can be seen, at 20 m standoff distance, lethality due to lung damage or whole-body projection is negligible, but large primary fragments (e.g., CL 99%) may still produce secondary injuries with fatal results, as indicated by Figure 5.
## 2 Materials and Methods
### Data Preprocessing  
The preprocessing steps of the meteorological data were crucial to ensure the quality and reliability of the dataset used in this study. To address missing values, a full row imputation method was employed, where rows containing missing values were removed to maintain data consistency. This approach was deemed suitable given the high frequency of data collection, ensuring that sufficient data points remained for analysis.  
Outliers were detected and handled using a domain-specific range-based method. Given the high variability of climatic data, employing common statistical methods (e.g., interquartile range or z-scores) could risk removing valuable information that contributes to the model's learning process. Instead, our approach focused on eliminating data points outside plausible physical ranges, as determined by historical and domain knowledge. In our case, solar irradiance values below 0 W/m\({}^{2}\) or above 1000 W/m\({}^{2}\) were removed, as such extremes are not physically possible. Similarly, wind speeds below 0 m/s or above 30 m/s were excluded, as these values are inconsistent with historical observations in the region. Air temperature values below 0 \({}^{\circ}\)C or above 40 \({}^{\circ}\)C were also discarded, as such conditions have never been recorded in the study area.  
This range-based filtering minimized the influence of extreme and implausible values on model training while preserving the essential variability within the dataset.
## 3 Results  
### Study Selection  
The systematic search identified 228 studies, of which, 28 (12.28%) were duplicates and were removed. Of the remaining 200 studies, a further 9 (4.50%) were removed for being non-original or non-independent. A further 183 studies (91.50%) were removed as they did not meet the inclusion criteria. Of the remaining 8 studies selected for full text review, 5 (62.50%) were removed as they either did not meet the selection criteria or they failed the quality assessment. Figure 1 illustrates the process of identification, screening, eligibility and inclusion of papers in the review.  
Figure 1: Flow diagram illustrating the systematic review process, including identification, screening, eligibility and inclusion stages.
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 3 Method
### Problem Formulation  
We focus on undirected networks in this paper. In general, nodes in various networks do not relate to one other, hence not all nodes are shared by all networks. In this paper, we focus on aligning such partially aligned networks. In addition, a node in one network only has, at most, one corresponding node in another. For the NA task, we provide the following necessary definitions.  
Let \(G^{s}\) and \(G^{t}\) denote the two networks to be aligned, respectively, where \(G^{s}=(V^{s},E^{s})\) and \(G^{t}=(V^{t},E^{t})\). \(V^{s}=\left\{v_{1}^{s},v_{2}^{s},\cdots,v_{n_{t}}^{s}\right\}\) is the node set of \(G^{s}\), where \(n_{s}=|V^{s}|\) represents the node number of \(V^{s}\). \(E^{s}=\left\{(v_{i}^{s},v_{j}^{s})\left|\,i,j\in\{1,2,\cdots,n_{s}\}\right.\right\}\) is the edge set among nodes in \(V^{s}\). \(V^{t}\), \(E^{t}\) and \(n_{t}\) have similar meanings.  
Anchor Nodes/Anchor Links. Anchor nodes refer to nodes that exist in multiple networks and represent the same real-world entities. The set of anchor nodes is denoted as \(V^{a}\), while anchor links refer to the corresponding relations between anchor nodes. Anchor links set is defined as \(E^{a}=\left\{(v_{i}^{s},v_{i}^{t})\left|\,v_{i}^{s},v_{i}^{t}\in V^{a}\right.\right\}\).  
Network Alignment. Given two partially aligned networks \(G^{s}\), \(G^{t}\), and a few labeled anchor links \(E^{a}\), the goal of network alignment is to discover all potential anchor links, that is, we aim to learn a predictive function: \(f:(G^{s},G^{t},E^{a})\to Y,Y\in\left\{0,1\right\}^{|V^{s}|\times|V^{t}|}\), \(Y_{ij}=1\) if \(v_{i}^{s}\) and \(v_{j}^{t}\) are predicted to be a pair of potential anchor nodes across networks \(G^{s}\) and \(G^{t}\), and 0 otherwise.
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Hydrodynamic Analysis
#### 3.3.1 Experimental A  
The distributions of surface residual currents in the surrounding before and after the change in the coastline in Area 1 are shown in Figure 8a,b. In comparison to currents before the coastline changes, one branch channel in that area decreased. Specifically, the narrow channel in the north of Meishan Island was closed off. Thus, the inflow and outflow of seawater could only occur through the Fodu channel below Meishan Island, which induced an increase in current velocity in the Fodu channel.  
Figure 7: Distribution of Eulerian residual current in the control experiment.  
The changes in flood and ebb surface current velocity before and after the coastline change in Area 1 are shown in Figure 8c,d. After the coastline changed, the main area of velocity change occurred below Meishan Island. Meanwhile, the changes in flood and ebb current velocity showed an alternating increasing and decreasing trend, with an increasing trend on the east side and a decreasing trend on the west side. In this study, Station A (29.75\({}^{\circ}\) N, 121.95\({}^{\circ}\) E) was selected to compare the changes in flood and ebb current velocity before and after the coastline change. Before the changes in the coastline, the maximum current velocities for flood tide and ebb tide at Station A were 0.77 m/s and 0.72 m/s, with the average velocities being 0.54 m/s and 0.48 m/s, respectively. After the changes in the coastline, the maximum current velocities were 1.01 m/s for flood tide and 0.92 m/s for ebb tide, with the average velocities being 0.67 m/s and 0.59 m/s, respectively. Compared to currents before the coastline change, the maximum flood and ebb current velocities at this station increased by 0.24 m/s and 0.20 m/s, and the average velocities increased by 24% and 23%, respectively.  
We used Section A (Figure 8c, 29.74\({}^{\circ}\) N, 121.89\({}^{\circ}\)-121.97\({}^{\circ}\) E) to show the vertical distribution of changes in velocity (Figure 9). On the western side of the section, velocity showed a decreasing trend, with the most significant changes occurring on the surface, reaching a maximum of 0.1 m/s. On the eastern side of the section, velocity exhibited an increasing trend. The area with the greatest variation was located at the surface around 121.94\({}^{\circ}\) E, reaching 0.12 m/s. In summary, the trends in changes in the surface and bottom were generally similar. The maximum changes in velocity occurred on the surface, gradually decreasing from the surface to the bottom.  
Figure 8: Current changes in Area 1. (**a**) Residual currents distribution before the change in coastline; (**b**) Residual currents distribution after the change in coastline; (**c**) Changes in the velocity of the peak flood tidal currents; (**d**) Changes in the velocity of the peak ebb tidal currents.
## 2 Defect Detection Technique Using Multi-Channel Eddy Current System
### 16 Ch Eddy Current System
#### 2.2.2 Frequency Setting  
In order to set the optimal frequency of the multi-channel eddy current system, the phase change was confirmed according to the frequency change, as shown in Table 1. When the amount of phase change is large, the resolution increases when converted to the depth of the defect, and the error of the defect decreases as the deviation value is minimized in the same defect. It was confirmed that the largest frequency change was measured at 300 kHz, and the deviation was minimized at 350 and 300 kHz. Therefore, 300 kHz was set as the optimal frequency for measuring defects.  
Figure 2: Multi-channel ECT sensor jig: (**a**) jig design, (**b**) jig maked.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Comparative Dataset Analysis  
The performance difference between iLIDS-VID and MARS highlights GAF-Net's strengths and limitations under varied conditions. iLIDS-VID's longer sequences allow GAF-Net to fully utilize its skeleton-based gait analysis. In contrast, MARS presents the following three key challenges: shorter sequences, a much larger number of tracklets (\(20,000\) vs. \(600\) in iLIDS-VID), and the presence of distractors and false detections.  
MARS's shorter sequences often hinder complete gait cycle extraction, while its larger dataset size increases scenario diversity and the likelihood of encountering distractors or false detections. These factors explain GAF-Net's smaller performance gains on MARS compared to iLIDS-VID while still demonstrating its adaptability to diverse conditions.
# Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM
## 3 Results and Discussion  
### Data Processing  
In this experiment, we selected 48,321 pieces of drilling data from eight wells in Xinjiang, China, as the model training data. We needed to process these data before applying them to the training model. Data processing mainly includes data preprocessing, cleaning, standardization, and other processes.  
The first step is preprocessing and cleaning the data. Data cleaning refers to using data analysis to change incorrect data into data that meets the requirements. The cleaning process of drilling data mainly includes detecting and deleting erroneous data and removing and completing missing data. We found that some of the data collected from the well had errors or missing data, so they were judged as invalid. After deleting these data, there were 41,242 pieces of data left, which were used to train the model.  
The second step is the standardization of data. The large data dimension gap between the parameters of data will cause hidden trouble for the subsequent machine learning modeling. So, we need to standardize the data to close the dimensional gap. The standardized formula is shown in Equation (1).  
\[y=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{1}\]  
where \(X_{\text{min}}\) is the minimum value in the dataset, \(X_{\text{max}}\) is the maximum value in the dataset, and \(y\) is the normalization result.  
The third and the last step is the segmentation of the data. In this experiment, we need to use the sliding window to train the timing model, so we need to divide the data to the same length. In this experiment, we took every 50 pieces of data as a new array, divided the data, and calibrated the corresponding ROP results.
## Materials and Methods
### Hyperspectral Images Acquisition and Calibration  
The samples were scanned by a Vis/NIR HSI system with a wavelength ranging from 380 nm to 1012 nm. The system was mainly composed of a spectrometer (G Series Image-\(\lambda\)-V10-IM, Spectral Imaging Ltd., Oulu, Finland), a CCD detector (Bobcat 2.0, Imperx Ltd, FL, USA), a variable-focal-length lens (Schneider, XENoPLAN, Bad Kreuznach, Germany), a translation stage (WN500 TA1000H, Beijing, China), a pair of 50 W Tungsten-Halogen lamps (Photoflex, Watsonville, CA, USA), and a computer to control the camera for acquiring images. Detailed parameters and its settings can be found in previous literature of [PERSON] et al. [21]. In this work, low signal-to-noise sub-images of the two ends were excluded and only the images in the wavelength ranging from 400 to 1000 nm (285 wavebands) were retained for the following data analysis. For a detailed explanation of the system correction and image acquisition, please refer to [PERSON] et al. [22].
## 3 Results and Discussions
### Permeability Measurements  
Through measurements, the inner diameter of the instrument was found to be D = 6.4 cm; the cross-sectional area of the quartz sand sample was found to be A = 0.0033 m\({}^{2}\), and the  
\begin{table}
\begin{tabular}{c c} \hline
**Grain Size (mm)** & **The Relaxation Time (s)** \\ \hline
0.1–0.2 & 0.410 \\
0.2–0.5 & 0.541 \\
0.5–1.0 & 0.857 \\
1.0–2.0 & 1.035 \\
2.0–3.0 & 1.273 \\
3.0–4.0 & 1.552 \\
4.0–6.0 & 1.705 \\
6.0–8.0 & 2.215 \\ \hline \end{tabular}
\end{table}
Table 1: Relaxation time of sand samples.  
Figure 6: Relaxation time of sand samples.  
pressure measurement interval was found to be L = 10 cm. The outdoor temperature was continuously measured for three days, and the average temperature during this period was 20 \({}^{\circ}\)C; thus, the hydrodynamic viscosity coefficient was 0.001 Pa-s. The constant head method was used to perform this experiment. After the water flow remained stable, the experimental data were recorded when the amount of water flowing out of the drain reached 1000 mL. The permeability coefficient of the sample can be calculated through the measurement results of the Darcy seepage experiment, and the permeability of the quartz sand samples of this grain size can be obtained by conversion via Equation (3). The experimental data are shown in Table 2.  
It can be seen that, with the increase in the grain size of the quartz sand samples, there was a significant difference in the time required to achieve the same seepage flow, and the difference between the minimum and maximum time was close to 10 times more. The reason for this phenomenon is that the pore throat diameter and the permeability have a positive relationship, indicating that the permeability is smaller in the small grain size, resulting in a slower flow rate. Through data such as flow, time, pressure head, etc., it is possible to calculate parameters such as seepage flow, seepage velocity, water level difference and hydraulic gradient, and it is also possible to obtain the permeability coefficient and permeability from [PERSON]'s law. The experimental results show in Figure 7 that the evolution of the permeability is closely correlated with the grain sizes of the sand samples.
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 2 Methods
### Establishment of Vehicle Distance Measurement Model
#### 2.2.4 Improved Geometric Ranging Method
It can be seen from the above analysis that the position of the feature point of the vehicle on the camera's imaging plane will change when \(\theta\) exists, resulting in the deviation of the ranging results using the traditional geometric ranging method. The degree of deviation is related to the position of the feature point of the vehicle in the image plane. It changes with the position of the feature point of the vehicle. In addition, the larger the roll angle, the greater the deviation. Therefore, the roll angle of the camera needs to be taken into account when using the geometric ranging method based on a monocular vision to measure the longitudinal distance between vehicles in the multi-lane and multi-vehicle scenes with high accuracy. By Combining Equations (7), (13), and (14) when the camera produces a left deflection angle, or combining Equations (7), (16), and (17) when the camera produces a right deflection angle, the position of the feature point of the vehicle that changes due to the roll angle of the camera can be corrected. Moreover, then combined with the traditional geometric ranging method, the influence of the roll angle of the camera on distance measurement can be eliminated, and the distance between vehicles can be calculated more accurately in theory. This is the improved geometric ranging method proposed in this paper. The improved geometric ranging method has lower requirements for camera installation and considers the influence of the attitude angle of the camera on ranging more comprehensively.
## 1 Introduction  
_Clustridioides difficile_ is an anaerobic, spore-forming, toxin-producing, Gram-positive bacillus bacterium. Infection with _C. difficile_ can result in gastrointestinal symptoms, including diarrhea, pseudomembranous colitis, and toxic megacolon [1]. These symptoms are largely due to the presence of _C. difficile_ virulence factors such as toxin A (TcdA), toxin B (TcdB), and/or the binary toxin (CDT) [1, 2]. The virulence genes, _tcdA_ and _tcdB_ are encoded by the pathogenicity locus, while the _cdt_ genes are encoded by the binary toxin locus. Strains that lack the toxin genes are considered non-pathogenic [1].  
Historically, cell culture cytotoxicity neutralization assays have been used for the detection of _C. difficile_. However, this method is labour-intensive, has a long turnaround time, and is not cost-efficient. Currently, the majority of diagnostic laboratories have switched to enzyme immunoassay (EIA) for the detection of _C. difficile_ toxins [3], or nucleic acid amplification tests (NAAT) with DNA extracted directly from stool samples which offer greater detection sensitivity [3, 4, 5, 6, 7]. Automated multiplex syndromic panels utilizing polymerase chain reaction (PCR) to detect multiple pathogens simultaneously within a few hours [6; 7; 8] have been implemented in the majority of high-volume frontline diagnostic laboratories. Multiplex panels have been evaluated for their detection of _C. difficile_ toxin gene targets [8; 9; 10; 11; 12; 13; 14; 15], but primarily in adult cohorts [8; 12; 13; 16].  
In this study, we evaluated the diagnostic characteristics of an in-house direct-from-stool real-time PCR (DFS RT-PCR) assay for _C. difficile_\(tcdB\) detection and compared performance between the DFS RT-PCR and the Luminescence xTAG(r) Gastrointestinal Pathogen Panel (GPP) (Luminex Corporation, Toronto, ON, Canada), a multiplex gastrointestinal syndromic panel, for the detection of \(tcdB\) using stool samples previously enrolled in the Alberta Provincial Pediatric EnTeric Infection Team (APPETITE) study in Alberta, Canada [17]. Additionally, we employed an enrichment culture approach to further characterize the differences observed between \(tcdB\) detection by DFS RT-PCR and GPP.
## 1 Introduction  
Wheat is among the most important cereal crops, and its worldwide production makes it the second world's most important grain after corn [1]. Currently, wheat is the most widely grown crop in the world and its world trade is greater than for all other crops combined. This is likely due to wheat's agronomic adaptability to different latitudes and altitudes, growth temperatures, humidity conditions, and types of soils [2].  
Wheat is also the most important food grain source, and thereby also occupies a central place in human nutrition. More than the 90% of the wheat produced in the world is common wheat (_Tritcium aestivum_ L.), which can be classified as \"hard\" or \"soft\", depending on grain hardness, and is mostly used for breadmaking [3] whilst the rest of the production consists of durum wheat (_Tritcium turgidum_ subsp. _durum_ (Desf.) Husn.).  
Nowadays, there is an increasing interest in wheat volatiles variation due to species [4; 5] and variety [6; 7; 8]. Since wheat is cultivated over a wide range of altitudes (from 0 to 4570 m a.s.l) and in different geographical locations characterized by different climates and soils [2], some authors also studied the effect of cultivation area and altitude on VOCs profile [6; 9; 10; 11].  
Since selected compounds characterizing the VOCs profile of wheat were found also in wheat flour and bread, as well as in semolina, pasta, and wheat beer [12; 13; 14; 15; 16], a deeper knowledge of VOCs profile evolution along the wheat foods chain is crucial both for the improvement of the quality of wheat products and their quality control since they contain several flavor-active volatiles.  
Most of the studies focused on VOCs of wheat or wheat flours, and their derivatives were aimed to track selected compounds along the food processing chain [12; 13; 16].  
The aim of this work was to study the changes of the volatilome from wheat to wheat beer by a unifying approach that considers the overall profile of the volatiles.  
To this purpose, common wheat varieties grown in different geographical locations sited between 70 and 1200 m a.s.l. within the Abruzzo region (Italy) and wheat beers obtained by them were analyzed by solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS), and the obtained datasets were processed with multivariate statistics.
## 2 Materials  
### Study Area and Data  
The CZT metropolitan area is located in Hunan Province, China, and consists of three cities, i.e., Changsha, Zhuzhou, and Xiangtan, which are less than 40 km away from each other. Changsha is the capital city with more than 10 million people, whereas Zhuzhou and Xiangtan are smaller with 3.8 million and 2.7 million people, respectively. As of the end of May 2024, Changsha has opened 7 metro lines with a total mileage of 226.6 km, namely Line 1, Line 2, Line 3, Line 4, Line 5, Line 6, and the Xihuan Line (Figure 1). The Xihuan Line was the only intercity metro line and opened on June 28, 2023. It connects Changsha and Xiangtan with 8 stations from south to north, i.e., Xiangtan North Railway Station, Chuamxingshan Station, Huanggiiawan Station, Shuanghu Station, Pingtang Station, Hongqiao Station, Tongxi Station, Dawangshan Station, and Shantang Station. The first 3 stations are located in Xiangtan, and the other 5 are located in Changsha. The Xihuan Line connects with Line 3 at Shantang Station, and thus, passengers from Xiangtan can access the whole metro network of Changsha.  
To analyze the travel patterns of intercity metro ridership, this study collected the smart card data of the Changsha metro on Sept 6 th (Wednesday), 7 th (Thursday), 9 th (Saturday), and 10 th (Sunday), 2023, from the metro company. As shown in Table 2, all data contain the unique passenger ID, boarding time, boarding line, boarding station, alighting time, alighting line, alighting line, alighting line, alighting station, etc. Here, trips entering one city and leaving from another city are taken as intercity travels. Finally, 36,670 intercity trips were obtained.