esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 3 Results and Discussion
### Failure Mode  
The process from the initiation and propagation of micro-cracks in rocks to the formation of macro-cracks is the essence of rock failure and plays an important role in strain energy consumption [18; 54]. In the biaxial compression tests, the propagation direction of macro-cracks in rocks with high dip fracture angles are generally perpendicular to the free surface, the main macro-crack surfaces are located at the \(\sigma_{1}\).\(\sigma_{2}\) plane, and the axial load can aggravate the propagation of macro cracks [24].  
The dip fracture angle and the mass and size of rock fragments after failure under different \(\sigma_{2}\) are statistically analyzed in this part. The fragments can be divided into 5 groups according to their size: \(>\)20 mm (big pieces), 12-20 mm (small pieces), 3-12 mm (coarse grain), 1-3 mm (fine grain), and \(<\)1 mm (powder). The fragments and the dip fracture angles of the three types of rocks after failure are shown in Figure 14. The weight contents of the fragments with different size ranges are shown in Figure 15.  
Figure 14: _Cont._  
Figure 13: Peak frequency distributions of (**a**) granite, (**b**) marble, and (**c**) sandstone.  
Figure 14: Fragments of (**a**) granite, (**b**) marble, and (**c**) sandstone after rock failure.  
Figure 15: Weight of fragments with different sizes: (**a**) granite, (**b**) marble, and (**c**) sandstone.  
Because of \(\sigma_{3}=0\), the propagation directions of the fracture planes of macro-cracks are generally perpendicular to the direction of \(\sigma_{3}\), as shown in Figure 14. The main micro-cracks of granite and marble obliquely intersect with the direction of \(\sigma_{1}\) at a small angle, and the angle tends to decrease with the increase of \(\sigma_{2}\), which indicates that the larger \(\sigma_{2}\) leads to a greater lateral constraint and the larger influence on the generation and propagation of vertical micro-cracks. The failures of granite and marble are more caused by tension cracks. The main micro-cracks of sandstone are in a shape of \"\(\Lambda\)\", and the dip failure angles are smaller than that of granite and marble, which indicates that the failure of sandstone is more caused by shear cracks. With the increase of \(\sigma_{2}\), the weight of big pieces increases at first and then decreases after the failure of granite, marble, and sandstone, indicating that the \(\sigma_{2}\) at a higher value can promote the generation and propagation of cracks in rocks. After the failure of marble specimens, the weight proportion of powder and small particles is obviously higher than that of granite and sandstone, indicating that the rock lithology has a great influence on the failure of rocks.
# Quantitative Scale Analysis of the Channel Bar in a Braided River and Its Internal Architecture
## 3 Results
### Quantitative Study on the Internal Architecture Elements of the Channel Bar
#### 3.3.1 Calculation of the Size of Single Accretion
The Occurrence and Density of Interlayer  
Through the comparison of dense well groups, it is concluded that when the interlayer is in the well pattern with a well spacing of less than 70 m, the interlayer usually has good comparability and continuity. Vertically, the upper and middle interlayers of the channel bar have better continuity, and the width is basically equivalent to the width of the channel bar. The continuity of the lower interlayer is poor, and the width is smaller than the width of the channel bar. According to the interlayers of 250 development wells in the Nanwu area, the density of a single well group is generally 2-3 per well. Vertically, the densest part of the interlayer is the middle of channel bar, which is as high as 80%, while the upper and lower parts account for 10%, respectively. The reason is the impact of river erosion, which makes the interlayers not easy to retain. On the basis of the architecture interface identification of the accretion and the calculation of its inclination angle, combined with the empirical formula and the interwell comparison method in the dense well area, the scale of the single accretion in the channel bar can be determined. According to the empirical formula for the size of the single accretion from Kelly [38], the width of the single accretion can be calculated through its thickness. \[w_{bi}=15.954l_{bi}^{1.3726}\ \ \ \ \ R^{2}=0.88 \tag{4}\]  
In the above formula, \(w_{bi}\) is the width of the sand body of the single accretion \(i\) (unit: m). \(t_{bi}\) is the length of the sand body of the single accretion \(i\) (unit: m). Concerning the lithological profile data of the core well in the PI3 sublayer of the Nanwu area, the thickness of the single accretion is 3-10 m. The width range of the single accretion can be calculated according to Formula 4, which is 100-400 m. On this basis, the length range of the single accretion can be determined; that is, in the water flow direction which may cause progradation, the length of the single accretion in the channel bar is 700-1500 m.
## 6 Conclusions  
The article aimed to solve the hardware expenditure problem. Based on analyzing the pre-existing switching devices, a novelty electrode switching device HESD was developed for a distributed ERT instrument. It employs a new design style completely different from the conventional one. It uses a hierarchical structure in which the connections between electrodes and AMNB ports are not conducted directly, but instead it employs the four lines of L1-L4 as intermediates. The integrated HESD is composed of Series K, Series D, Series J and Series A switches, which belong to two layers: SS1 and SS2. In this article, the whole design is divided into three parts: SS1, SS2-1 and SS2-2.  
First is the SS1 structure or first layer design. Series K belongs to SS1. With 16 switches, they can connect AMNB to any combination of L1-L4. Other series all belong to SS2, which achieve the links between L1-L4 and four testing electrodes. The SS2 is divided into two stages.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ESD** & **4** & **8** & **16** & **32** & **48** & **64** & **128** \\ \hline traditional & 4 + e*4 & 20 & 36 & 68 & 132 & 196 & 260 & 516 \\ HESD-O & 16 + e*1 & 20 & 24 & 32 & 48 & 64 & 80 & 144 \\ HESD-A & 16 + e*3 & 28 & 40 & 64 & 112 & 160 & 148 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of different electrodes required in various ESD designs.  
Figure 9: Actual instrument: main control circuit (a); distributed circuit (b).  
Then there is the SS2-1 design. SS2-1 contains four switches of Series D for a four-electrode cell, which takes one electrode to one line. Applying SS1 and the first stage of SS2, HESD-O easily facilitated the electrode connection for the 1D profile test or odd layer's test. This could save up to three fourths the number of switches and related expenditure, including related hardware, power, and volume. It is especially beneficial in-situ tests and portable instruments.  
The third part is SS2-2. SS2-2 fixes the L1L3 problem, L2L4 problem, and one-line problem, and possesses a complex frame. Furthermore, applying the entire plan of HESD-A, all layers' 2D ERT tests can be settled, which also saves one fourth the number of switches. A saved hardware plan for the ESD design is provided in the article. The two designs of HESD, HESD-O and HESD-A, are suitable for different demands, respectively. Although they may bring new software designs, they significantly save on hardware expenditure and are more miniature, especially for multi-electrodes, e.g., several dozen or more single-channel ERT systems.
###### Abstract
When properly dosed, generally 3-8% by binder weight in the wet method and up to 10% by aggregate weight in the dry method, plastics can enhance the tensile strength, fracture resistance, and fatigue life of asphalt mixtures [8,22]. A high plastic content may increase binder viscosity to levels that hinder workability and compaction, or cause brittleness at low temperatures, hence elevating the danger of thermal cracking [11,21]. The compatibility of plastics with bitumen is a crucial issue, influenced by polymer polarity, molecular weight, crystallinity, and the presence of additives [10,16]. In addition to mechanical performance, environmental consequences have undergone more scrutiny. Research indicates that specific plastics can elevate emissions of volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) during the mixing process [20]. Furthermore, the prolonged emission of MPs from roadway surfaces due to traffic flow and environmental degradation has become a significant issue [14]. Leaching behaviour is another area of active research. Research indicates minimal leaching of heavy metals and nutrients from plastic-modified pavements, but higher concentrations of dissolved organic carbon (DOC) have been seen in some cases [23]. Standardized leaching tests, such as the US EPA Toxicity Characteristic Leaching Procedure (TCLP) [24] are con tributing to a better comprehension of potential groundwater impacts [25]. From a life cycle perspective, the incorporation of plastics into pavements presents distinct environmental advantages when properly optimized. Life cycle assessments (LCAs) consistently demonstrate decreases in greenhouse gas emissions, energy usage, and raw material consumption compared to traditional pavements [12; 13]. Nonetheless, these advantages may be offset if plastic feedstocks necessitate extensive transportation or energy-intensive preprocessing [26]. This review offers an updated perspective on the opportunities, hazards, and environmental consequences of plastic-modified pavements, acknowledging their potential to enhance sustainable material management and robust road infrastructure. Identifying this potential necessitates a comprehensive methodology that systematically assesses performance, environmental trade-offs, and long-term consequences. This study brings together recent research findings, highlights significant gaps, and explores demands for future research, technology advancement, and policy development. Figure 1 presents the conceptual framework outlining the key areas covered in this review, which serves as a guide to its structure and content flow.
## 3 Proposed Approach and Methodology
### Simulation and Validation  
The methodology is validated through simulations using the SUMO urban mobility simulator. The system's performance is assessed under various traffic conditions and intersection types, with metrics including vehicle delays, pedestrian waiting times, and safety incidents.  
Traffic demand data were synthesized based on expected vehicle movement patterns across different times of the day, simulating realistic urban scenarios. We incorporated historical traffic data, where available, to ensure the representation of typical flow distributions between origin and destination points.  
By integrating VLC, DRL, and connected vehicle technologies, this approach provides a scalable, adaptive, and efficient solution for modern urban traffic challenges.  
For the simulation environment, a three-connected, four-arm intersection scenario with two lanes in both directions is considered (Figure 4). Each one of these intersections is controlled by an agent that maps the environment around it in cells, acquiring different information about the vehicles and pedestrians travelling through the intersection (Figure 8). Each of the three intersections is divided into 3 layers made up of 164 cells. The first layer is made up of 80 cells, with 10 for each lane routing vehicles to the junction, indicating their presence. If a vehicle is inside of the cell, it is filled with '1'; otherwise, it is filled with '0'. The second layer, made up of the same number of cells, indicates the normalized speed of the cars in each cell, if any are present. The third layer, made up of just 4 cells, represents the waiting zones, indicating the number of pedestrians standing still waiting for their phase to become active. This state representation helps the agent map the environment around the intersection and ends up being very similar to the states observed via VLC, illustrated in Figure 9. In this case, the vehicles are identified over time by the lane they are in and by the traffic light they are communicating with. Pedestrians, on the other hand, are identified over time by the waiting zone they are in, as well as the traffic light they are communicating with.
## 2 Materials and Methods
### Pressure Swing Adsorption Steps  
The PSA process has many steps, of which the pressure changing inside the adsorption bed, the step time and the valve acting sequence for each step are different. Normally, four operation steps are performed in one cycle of the PSA process. In this study, the pressure equalization steps (including depressurization equalization step (DPE) and pressure equalization step (PPE)) were added to a basic four-step process proposed by [PERSON] to improve process efficiency, as shown in Figure 1.  
First, biogas flows into the adsorption bed (Bed1) and increases the pressure to the desired pressure (PR). The process enters the adsorption step (AD) where the second bed contains more CO\({}_{2}\) and the outlet stream of Bed1 is enriched in less CO\({}_{2}\) components (high CH\({}_{4}\)). Then, a portion of CH\({}_{4}\) product is moved to Bed2 to assist in the regeneration of the adsorbent. After that, Bed1 is loaded and Bed2 is regenerated. The depressurizing equalization step (DPE) is then taken place. This step is important to reduce energy consumption due to the quantity of the gas to be compressed. After the equalization step, the process repeats with the reversion of the two bed roles. The basic process includes the following six steps [13]:  
Figure 1: The improved Skarstrom cycle in the pressure swing adsorption (PSA) process—symbols are defined in the text.  
1. Pressurization step, PR;
2. Adsorption step, AD;
3. Depressurization equalization step, DPE;
4. Blowdown step, BL;
5. Purging step, PU;
6. Pressure equalization step, PPE.  
According to the real process, twelve adsorption columns are used, in which six coupled adsorption and desorption pairs work in parallel. Therefore, the inlet biogas flowrate is equal to 1/6 of each adsorption column.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 4 Evaluation
### Ablation Study
Designing separate sub-models for each variable ensures that the temporal dependency of each feature is extracted and no information is lost, which plays a key role in recognition, while the multi-head self-attention mechanism can effectively fuse the features to generate more recognizable fingerprints, which is highly effective in recognition. The Inception structure is used to extract the information of different time steps, which has the same function as the TCN model to some extent, so less improvement in the recognition effect was observed. Figure 7: Plot of changes with the number of iterations during the training of each indicator. (**a**) Change in loss; (**b**) change in accuracy; (**c**) change in balanced accuracy; and (**d**) change in \(F1\). We compared our results with past research using traditional machine learning algorithms, and the results are shown in Table 5. IoT-Sentinel was the earliest method that used fingerprints, IoT-Sense further improved the fingerprint extraction, IoTDevID adopted more comprehensive features and performed a strict dimensionality reduction, SysID [19] adopted a two-phase classification method, and DOCAT [20] joined the protocol information. All of the above methods were manual feature extraction methods, meaning they suffered from information leakage. INTCN_AT automatically extracts the timing features of packets and extracts more abstract features as device fingerprints, achieving a better device identification effect.
## 2 Constructing the Evaluation Index System
### Selection of Evaluation Factors  
Selecting appropriate evaluation factors is a critical step in landslide susceptibility assessment, which requires a detailed evaluation of the factors related to landslides in the study area through field surveys and remote sensing observations [3]. According to [PERSON] et al. [15], there are 596 factors influencing landslide occurrence. However, considering all of them in practical work projects would be time-consuming and labor-intensive. In landslide susceptibility assessment, selecting more evaluation factors does not necessarily lead to higher accuracy [26]. The selection of evaluation factors needs to consider factors such as dependency, measurability, non-redundancy, and relevance to geological features. Currently, there is no unified standard for selecting evaluation factors. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Year** & **Author** & **Method** \\ \hline
2022 & [PERSON] [19] & raster units \\
2003 & [PERSON] et al. [21] & raster units \\
2023 & [PERSON] et al. [22] & slope units + MSS \\
2017 & [PERSON] [23] & slope units + r.slogeunits \\
2021 & [PERSON] et al. [24] & slope units + MSS \\
2015 & [PERSON] et al. [25] & slope units + r.slogeunits \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature on the selection and acquisition of mapping units. It is mainly performed by analyzing the relevant factors that trigger landslides, calculating the correlation coefficients between factors, and then removing highly correlated factors to reduce the influence of data redundancy. Some scholars [15; 27; 28; 29; 30] have indicated that terrain, meteorological and hydrological factors, geology, human activities, and vegetation cover have significant impacts on landslide occurrence (Table 2). Table 2 lists the most commonly applied evaluation factors that have significant impacts on landslides. Here, further explanations are provided for several evaluation factors. Slope angle: Landslides are more likely to occur when the slope angle ranges from 10\({}^{\circ}\) to 45\({}^{\circ}\)[33]. [PERSON] et al. [34] found that the geological units of different lithologies exhibit varying sensitivities to landslide susceptibility. Landslides often occur in loose geological formations, especially in relatively fragmented and hard ancient strata. The study found that in formations with alternating soft and hard layers, such as sandstone, mudstone, shale, and coal-bearing strata, slope deformation and failure are more intense, often leading to large or medium-sized landslides. In contrast, in formations composed of carbonate rocks, slope deformation is weaker, and landslides are less likely to develop. Regarding ground curvature, previous studies have shown that when the curvature is greater than 0, indicating a convex slope, landslide occurrences are more common. This is because convex slopes experience distortion and deformation, leading to stress concentrations within the slope body, resulting in slope instability phenomena [31]. Research has indicated that shallow movements are less likely to occur on slopes with dense vegetation and deep root
## 4 Results and Discussion  
### Effect of Hydrostatic Pressure  
#### 4.1.1 Experimental Results  
It can be seen from Figure 3 that when the hydrostatic pressure was 0.5 MPa, the depth of chloride ion diffusion in the concrete changed with different w/c ratios. Specifically, when the w/c ratio was 0.35, the chloride ion diffusion depth was 11.3 mm; when the w/c ratio was 0.45, the chloride ion diffusion depth was 16.7 mm; and when the w/c ratio was 0.55, the chloride ion diffusion depth was 19.4 mm. In addition, the chloride ion diffusion depths of the concrete with different w/c ratios under different hydrostatic pressures are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **w/c Ratio** & **Porosity** & **Permeability Coefficient** & **Diffusion Coefficient** & **Volume Fraction of** \\  & **(\%)** & **(\(\times 10^{-13}\) m/s)** & **(\(\times 10^{-12}\) m\({}^{2}\)/s)** & **Fine Aggregate (\%)** \\ \hline S-0.3 & 0.3 & 19.67 & 2.47 & 0.98 & 28.2 \\ S-0.4 & 0.4 & 22.98 & 5.90 & 1.71 & 27.9 \\ S-0.5 & 0.5 & 30.67 & 14.7 & 2.53 & 27.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model parameters of the cement mortar block.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **w/c Ratio** & **Hydrostatic Pressure (MPa)** & **Duration (d)** & **Chloride Concentration (\%)** \\ \hline S-0.35/S-0.45/S-0.55 & 0.35/0.45/0.55 & 0.5 & 30 & 5\% \\  & & 1.0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: External environment parameters of the simulation.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Method
#### 3.2.2 Ship Plume Image  
Utilizing the knowledge of a ship's position summarized in its _ship track_ and _wind-shifted ship track_, we are able to focus our attention on the area that lies within immediate proximity to the analyzed ship. For this, the concept of a _ship plume image_ (see Figure 5a) is introduced. The area of the _ship plume image_ is determined based on the _wind-shifted ship track_ as follows: the average coordinate of the studied _wind-shifted ship track_ defines the center \((longitude_{centr},latitude_{centr})\) of the _ship plume image_; the borders of the image are defined as \(longitude_{centr},latitude_{centr}\pm 0.4^{\circ}\) (For the area in the Mediterranean Sea, in horizontal direction \(0.4^{\circ}\approx 37.4\) km, in vertical direction \(0.4^{\circ}\approx 44.2\) km). This particular size of a _ship plume image_ was determined in order to allow for optimal plume coverage for the most typical range of ship speeds (14 kt-20 kt) (kt--knot, a unit of speed equal to a nautical mile per hour; 14 kt \(\approx 26\) km/h; 20 kt \(\approx 37\) km/h). Given the size of the pixel grid, such an offset results in an image of a maximal dimension of \(18\times 18\) pixels.