esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Human Microbiome
### Microbiome Types
#### 2.1.3 Skin
Bacteria of the _Staphylococcus_ genus occupy an aerobic niche, and it is believed that they utilize the urea present in sweat as a source of nitrogen [56]. The areas that show the greatest diversity are dry regions such as the forearm, elbows, knees, buttocks, and various parts of the hand. In these areas, there is a predominance of large quantities of Proteobacteria and Bacteroidetes, but also some Firmicutes and Actinobacteria, in comparison to the humid and sebaceous zones. It is in these areas that some skin disorders such as psoriasis are observed, which result from a failure in immune tolerance to the microbiome [53, 57]. The most-predominant microbial species on the skin are Gram-positive bacteria, with _Staphylococcus epidermidis_ being particularly prominent, constituting almost 100% of the bacterial community on this surface. This bacterium is mostly harmless; however, when invading other regions, it can become harmful, namely in the case of catheters; when they are introduced into the body, _S. epidermidis_ can invade the host and cause nosocomial infections by forming a biofilm that protects it from immune responses and antibiotic action [48]. The use of antibiotics, cosmetics, and personal hygiene products, as well as lifestyle and dietary habits are factors that can also influence the skin microbiome. While most of these microorganisms are harmless or beneficial, some have recently been associated with skin conditions such as acne, psoriasis, and eczema [51]. Thus, the study of the variability of these microbial communities in different areas of the human body has helped to understand why eczema tends to manifest in humid areas, such as the folds of the arms and legs, and why psoriasis occurs in drier areas, such as the elbows and knees [53; 58]. Among epithelial surfaces, the skin is unique in its complex ecological interactions with the environment [59; 60]. Several external and host factors affect the microbial composition, namely temperature, humidity, and light exposure. Gender, genotype, immune status, and the use of cosmetics can also affect the microbiota. The microbial composition such as the population size and community structure is affected, as represented in Figure 1[48; 61]. The immune system of the host constantly adjusts itself, actively participating in the formation of a consistent and location-specific skin microbiome as individuals reach adulthood [62]. Moreover, the competition that occurs both within and among different microbial species plays a crucial role in the development and sustainability of a well-functioning microbiome [59]. Puberty has a profound effect on the composition of the skin microbiome. Sex hormones trigger the increased activity of the sebaceous gland and apocrine gland, resulting in increased skin lipids, which favor the colonization and growth of lipophilic bacteria, including _Cutibacterium acnes_[62].
## 4 Experimental Results
### Performance on Disturbance Defect Dataset
#### 4.5.2 In-Depth Analysis the Impact of Various Disturbance Factors
On the contrary, the pitted surface defect achieves the lowest recall among six kinds of defect. (94.12%). According to the examples in Figure 5, it can be observed that the motion blur will stretch the pitted surface defects into long strips which look similar to the inclusion defects. In Figure 9b,d, there are minority classes of pitted surface and scratches, which were misclassified as inclusion class. Hence, the motion blur disturbance factor will deteriorate the misclassification between the pitted surface, scratches, and the inclusion defects. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cr** & **In** & **Pa** & **Ps** & **Rs** & **Sc** & **Precision** \\ \hline
**Cr** & **90.44** & 0.76 & 0.00 & 2.96 & 2.64 & 0.24 & 93.20 \\
**In** & 0.40 & **79.60** & 0.00 & 5.92 & 13.64 & 7.12 & 74.62 \\
**Pa** & 0.04 & 0.00 & **99.20** & 0.48 & 0.00 & 0.00 & 99.48 \\
**Ps** & 2.44 & 10.20 & 0.56 & **88.00** & 0.84 & 1.00 & 85.40 \\
**Rs** & 5.96 & 5.64 & 0.00 & 1.60 & **82.48** & 1.88 & 84.54 \\
**Sc** & 0.72 & 3.80 & 0.24 & 1.04 & 0.40 & **89.76** & 93.54 \\
**Recall** & 90.44 & 79.60 & 99.20 & 88.00 & 82.48 & 89.76 & **88.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The confusion matrices when adopting the MRFFN+IA method on variance 0.3 task containing six types of defects (%), where Cr, In, Pa, PS, RS, and Sc denote the crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches defects respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cr** & **In** & **Pa** & **Ps** & **Rs** & **Sc** & **Precision** \\ \hline
**Cr** & **94.12** & 0.96 & 0.00 & 3.32 & 2.04 & 0.24 & 93.48 \\
**In** & 0.16 & **82.00** & 0.00 & 6.04 & 7.48 & 7.16 & 79.74 \\
**Pa** & 0.32 & 0.08 & **99.20** & 0.88 & 0.04 & 0.08 & 98.61 \\
**Ps** & 1.16 & 9.88 & 0.24 & **88.12** & 0.56 & 0.72 & 87.52 \\
**Rs** & 4.04 & 4.52 & 0.00 & 0.72 & **89.52** & 1.52 & 89.23 \\
**Sc** & 0.20 & 2.56 & 0.56 & 0.92 & 0.36 & **90.28** & 95.15 \\
**Recall** & 94.12 & 82.00 & 99.20 & 88.12 & 89.52 & 90.28 & **90.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The confusion matrices when adopting the MRFFN+IA method on density 0.3 task containing six types of defects (%), where Cr, In, Pa, PS, RS, and Sc denote the crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches defects respectively.
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 4 Numerical Results and Discussion
### Energy Consumption  
In this section, a comparison in terms of the transmission energy consumption will be discussed between the EFPR and DSR routing protocols. Tables 4 and 8 show the number of sensor nodes participating in the path between the source node and destination, and also the residual energy in these sensor nodes before sending the desired packet in both approaches (EFPR and DSR protocol), where Tables 5 and 9 show the total of residual energy in each sensor node that participated in the path after sending a packet from the source (S) to the BS using both techniques, respectively. Based on the readings in Table 9, it is observed that the total energy consumption using the DSR protocol is high, and the reason for this is, of course, the technique that the DSR protocol adopts to select the next hop.  
Figure 13 shows the two different paths to deliver the packet from the source to the destination using the EFRP (red path), and the DSR approach (black path). Figure 14 shows the total residual energy in each sensor node that participated in the transmission process before and after (using the DSR approach). Despite the low residual energy in sensor node 18, the DSR protocol has selected it in the route towards the BS. The reason for this is that the DSR algorithm did not take into consideration the residual energy of sensor nodes before involving them in the transmission process.  
Figure 15 illustrates a comparison between the EFRP and DSR routing protocol in terms of the total energy consumption. It is obvious that the energy consumption rate of the sensor nodes used in the transmission process using DSR approach is higher than the total energy consumption that resulted from the use of EFRP. The reason behind this is that the packet header size in DSR increases with route length due to source routing. It is easy to see that the source sensor node has spent a considerable amount of energy in order to deliver the packet to the next sensor node (18) in the study area. Furthermore, we see that each sensor node needs to send the packet a very long distance in order to deliver it to the next hop.
## 2 Materials and Method
### Interface Shear Test  
For each relative density, the internal friction angle (\(\phi\)) of the sand and the interface friction angle (\(\delta\)) between the sand and the pile materials were measured using the shear box test (6 cm \(\times\) 6 cm \(\times\) 2 cm). To determine the angle of internal friction (\(\phi\)) of sand at different relative densities, a shear box test was performed in accordance with ASTM D-3080 [63]. Interface friction tests were performed according to ASTM D5321/D5321M-17 [64] to find the interface friction angle (\(\delta\)) in-between sand and different pile materials at four different relative densities. The internal friction angle of sand and the interface friction angle between sand and model pile materials, which correspond to different relative densities, are summarized in Table 5. As can be observed, the angle of internal friction increases as relative density does as well. The interface friction angle also increases with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relative Density (Dr), (\%)** & \
\begin{tabular}{c} **Average Dry Unit** \\ Weight (\(\gamma_{\text{d}}\)), (\(\delta\)N/m\({}^{3}\))** \\ \end{tabular} & **Void Ratio (e)** &
\begin{tabular}{c} **Compaction Process** \\ \end{tabular} \\ \hline
10 & 14.6 & 0.86 & From a height of 10 to 15 cm, test sand was poured until the model tank was filled to the necessary depth. \\ \hline
40 & 15.5 & 0.75 & The test sand was poured from a height of 5–10 cm and the tank was filled with layers every 10 cm, each point (15 cm \(\times\) 15 cm) was compacted for less than a second (for a moment). \\ \hline
65 & 16.3 & 0.66 & The test sand was poured from a height of 5–10 cm and the tank was filled with layers every 10 cm, each point (15 cm \(\times\) 15 cm) was compacted for two seconds. \\ \hline
90 & 17.3 & 0.57 & The test sand was poured from a height of 5–10 cm and the tank was filled with layers every 5 cm, each point (15 cm \(\times\) 15 cm) was compacted for eight seconds. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Void ratio, compaction method, and dry unit weights of test sand corresponding to different relative densities.  
Figure 6: 3D Schematic diagram of the soil tank.  
increasing relative density, but it begins to decrease under very dense conditions, such as at 90% relative density. This behavior has also been observed in previous research [22; 32; 65]. It can be seen that the internal friction angle of sand for different relative densities is quite high due to the shape of the sand particles. The shape of the sand particles has a considerable effect on the angle of internal friction. The sand with high internal friction angle was also observed by [PERSON] et al. [66].
## 2 Theories
### Three-Dimensional Reconstruction  
In this section, for the goal of 3D reconstruction, two maps generated separately by two SLAM methods are briefly introduced.  
1. RGB-D Point Cloud  
An RGB-D point cloud is a data representation that combines color information from a traditional RGB (Red, Green, and Blue) image with depth information to create a three-dimensional reconstruction of a scene or object. Figure 1 shows a typical RGB-D point cloud reconstruction process.  
Initially, the preprocessed input data are aligned and used for local reconstruction. Next, we can determine the position and orientation of the cameras by establishing associations between visual features. This involves the process of matching observed features or landmarks in the environment with their corresponding features in a known or reference map. And loop closure detects and corrects errors in the estimated trajectory. Finally, based on the estimated camera pose, the input data are integrated into the 3D reconstruction mapping.  
2. LiDAR Point Cloud  
LiDAR Point cloud maps, generated by LiDAR SLAM, provide information regarding the positions of objects. This is achieved through the emission of laser beams by the LiDAR sensor, which is then reflected back from the objects to calculate their respective positions. Due to the properties of 3D LiDAR beams being emitted in a specific angular range and covering multiple directions at the same time, the point cloud maps possess the advantage of fast and wide-ranging simultaneous generation. As a result, it is well-suited for applications involving large-scale environments. To alleviate computational load and down-sampling, some mapping algorithms will preserve features like points, lines, and surfaces as data points.
# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 2 Literature Review
### Prediction of Bike Sharing Demand  
Based on their spatial granularity, the existing methods and approaches of predicting bike sharing demand can be classified into three groups: system-level prediction, cluster-level prediction, and station-level prediction [19].  
For system-level prediction, [PERSON] et al. [20] analyzed a public bike sharing scheme in Lyon, France, using a combination of statistical signal processing tools to derive temporal usage patterns for a typical week. While including internal (subscribers, number of available bikes) and external (temperature and precipitation) factors, those tools were also used to estimate the number of rentals within the next hour.  
Additional research on predicting bike sharing demand at the system level was proposed by [PERSON] and [PERSON] [21]. They applied different regression models to predict the hourly bike sharing usage up to 24 h ahead. The used regressors (weather, previous bike usage, and holiday) were obtained from a public dataset containing two years of information on the Washington DC bike sharing system. Within their study, Ridge Regression and AdaBoost Regression were shown to have the best performance.  
An approach for system-wide usage prediction over a longer time period was proposed by [PERSON] et al. [22]. Within their study, they introduced a clustering technique for synthesizing mobility data in order to obtain recursive mobility patterns. By validating their approach with existing data from New York City, [PERSON] et al. could show that combining those patterns with weather data enables the accurate prediction of daily bike sharing demand [22].  
However, concerning the objective of our study, the presented methods cannot be applied to OSABS demand generation, since the usage can be predicted either for only a few hours ahead [20; 21] or with insufficient temporal granularity [22]. An additional impediment to transferability emerges from the constraint that employing data derived from already implemented conventional bike sharing systems only allows for the prediction of demand within these particular systems. This also applies to the multitude of cluster-based (e.g., Refs. [9; 11; 23; 24]) or station-based (e.g., Refs. [8; 19; 25; 26; 27; 28; 29; 30; 31; 32; 33]) approaches, as they rely on already existing stations (or subsets of stations).
## 2 Materials and Methods
### PV Output Interval Prediction
#### 2.2.3 QRLSTM PV Interval Prediction Model  
The QRLSTM model can be obtained by combining the quantile regression method with the long short-term memory neural network. The expression of the loss function of the QRLSTM model is shown in Equation (5).  
\[\begin{array}{ll}f_{\text{cost}}&=\sum\limits_{i=1}^{N}\rho_{\tau}[Y_{i}-f(X _{i},W,b)]\\ &=\sum\limits_{i|Y_{i}\geq f(X_{i},W,b)}\tau|Y_{i}-f(X_{i},W,b)|+\sum\limits_ {i|Y_{i}<f(X_{i},W,b)}(1-\tau)|Y_{i}-f(X_{i},W,b)|\end{array} \tag{5}\]  
where \(N\) represents the number of samples \(Y_{i}\), and \(Y_{i}\geq f(X_{i},W,V)\) represents that the estimated value of the \(i\)-th regression model is less than or equal to the actual value of the explained variable. The objective function of the QRLSTM model is given by Equation (6).  
\[\min\limits_{W,b}f_{\text{cost}}+\frac{\lambda}{2}\Big{|}\Big{(}\hat{W}(\tau ),\hat{b}(\tau)\Big{)}\Big{|}^{2} \tag{6}\]  
where \(\hat{W}(\tau)\) is the weight under the quantile condition and \(\hat{b}(\tau)\) is the bias item weight under the quantile condition. Using the Adam stochastic gradient descent algorithm, the parameters \(\hat{W}(\tau)\) and \(\hat{b}(\tau)\) can be solved, and then the results are put into Equation (7), where the condition quantile estimate of the explained variable can be obtained.  
\[\hat{Q}_{Y}(\tau|X)=f\Big{(}X,\hat{W}(\tau),\hat{b}(\tau)\Big{)} \tag{7}\]  
The quantile \(\tau\) is continuously valued in the interval (0, 1), and the model learns the nonlinear relationship under different quantile conditions so as to obtain the corresponding optimal estimated value under different quantile conditions. The conditional quantile curve is called the conditional distribution function, and the conditional density P can be derived from the distribution function, as shown in Equation (8).  
\[P\big{(}\hat{Q}_{Y}(\tau|X)\big{)}=\frac{d\tau}{d\hat{\mathcal{Q}}_{Y}(\tau|X)} \tag{8}\]  
where \(\hat{\mathcal{Q}}_{Y}(\tau|X)\) is the conditional quantile curve. Then, after conditioning \(X\) and discretizing \(\tau\) in Equation (8), the probability density function of the explained variable can be obtained through Gaussian kernel density estimation. The specific flow chart is shown in Figure 2.
## 3 Theoretical Foundation  
The microseismic signals were received by the microseismic monitoring system, and the rich information, such as the temporal and spatial intensity, contained in each event were obtained after manual processing. By analyzing the changes in the number, density, and concentration of the microfracture, the macroscopic development trend of rocks could be inferred, and the analysis process was based on statistics to convert the rich information of the microseismic events. In this study, we mainly used the cumulative apparent volume and energy index analysis, which were based on the statistical analysis of the microrupture magnitude, microrupture energy, apparent stress, apparent volume, and energy index.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Coordinates**} & **Actual** & \multicolumn{2}{c}{**Microseismic**} & \multicolumn{2}{c}{**Error/m**} \\  & & **Blasting** & **System Positioning** & **Coordinates/m** & **Coordinates** & **Straight Line** \\ \hline \multirow{3}{*}{18 March 2017 19:01:04} & E & 17,597,327.3 & 17,597,330.8 & 3.5 & \multirow{3}{*}{5.9} \\  & N & 3,103,174.1 & 3,103,172.3 & 1.8 & 5.9 \\ \cline{1-1}  & U & 3731.0 & 3735.5 & 4.5 & \\ \cline{1-1}  & E & 17,597,296.4 & 17,597,294.9 & 1.5 & \\ \cline{1-1}  & N & 3,103,174.1 & 3,103,174.0 & 0.1 & 6.1 \\ \cline{1-1}  & U & 3731.0 & 3725.1 & 5.9 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of blast event positioning accuracy.  
Figure 5: Positioning error analysis and sensitivity analysis of the 3720 m bottom structure microseismic monitoring system. (**a**) Error analysis. (**b**) Sensitivity analysis.  
The apparent volume refers to the source volume, which is the volume of the rock in the inelastic deformation zone of the source, and it can be estimated by Equation (1), where \(\Delta\varepsilon\) is the incremental body strain in the inelastic zone of the source.  
\[V_{\rm A}=\frac{P}{\varepsilon_{\rm A}}=\frac{\mu P^{2}}{E} \tag{1}\]  
where \(\mu\) is the stiffness of the rock (shear modulus). The apparent volume is a scalar quantity, which is closely related to the microseismic body potential and microseismic energy and can be expressed by methods such as the cumulative volume or contour plots.  
The energy index \(EI\) of a microseismic event is the ratio of the measured radiative microseismic energy \(E\) generated by the event to the average microseismic energy \(\overline{E}(P)\) of all events in the region, which corresponds to the driving stress of the source at the time of the microseismic event, as shown in Equation (2).  
\[EI=\frac{E}{\overline{E}(P)}=\frac{E}{10^{4\log_{p}+c}}=10^{-c}\frac{E}{Pd} \tag{2}\]  
In Equation (2), \(C\) is the correction factor, and the average energy \(\overline{E}(P)\) can be obtained from the actual measured average energy in the region and the microseismic body variation potential \(P\) in relation to \(10^{d\log_{p}+c}\). When \(d=1\), the average energy is proportional to the apparent stress.
## 1 Introduction  
Trichloroethylene (TCE) has been widely used primarily as degreasing solvent by industry, since the 1900s [1]. TCE was considered as one of the most frequently detected organic contaminants in groundwater [2,3]. Various methods have been developed for the determination of TCE including gas chromatography (GC) coupled with either electron capture detectors (ECD) [4; 5; 6], mass spectrometers (MS) [7; 8; 9; 10], or flame ionization detectors (FID) [11]. Traditional sample preparation methods for TCE such as liquid-liquid extraction and solid phase extraction are labor-intensive and time-consuming [5]. Solid-phase microextraction (SPME) is suitable for the extraction of volatile organic compounds (VOCs) and has been introduced for the analysis of TCE in recent years [6; 7; 9; 10; 11]. Solid phase microextraction was devised by [PERSON] and co-workers in 1989 [12] and has been widely used for food, environmental, and bioanalytical applications [13]. As a volatile compound, TCE is generally extracted from the sample headspace with faster extraction times and improved selectivity [14]. The condensed phase, the headspace gas phase, and the SPME polymer film are involved in a regular headspace SPME process and the diffusion of analytes happens across two interfaces, the condensed/gas interface and the gas/polymer interface [15]. The extraction efficiency is limited by mass transfer between the two interfaces, especially between the condensed/gas interface [15]. Once the extraction conditions; such as extraction temperature, extraction time, sample agitation, pH, ionic strength, volume, _etc._; for an analyte are optimized, it is difficult to improve the extraction efficiency further. A widely accepted standard by researchers for SPME method development is that the amount of organic solvents in the sample matrix should be kept to a minimum [16]. However, it is not always true. In our study, we found by introducing a microliter quantity of organic solvent that the extraction efficiency of SPME could be significantly improved. TCE in water was extracted by the liquid-liquid microextraction assisted headspace SPME (LLME-SPME) and was determined with a portable GC/MS instrument. A similar success for the improvement on the extraction efficiency was also found for the extraction of toluene and 1,2,4-trichlorobenzene (TCB) from water by LLME-SPME.
## 2 Methodology
### Study Area  
The Region of Interest (ROI) selected for the study is an irrigated crop area located in Buenos Aires Province, in the southeast of Argentina (see Figure 1). The extension of the cultivated area of the Bonaerense Valley of Colorado River (BVCR) is approximately 91,163 ha, comprising winter crops such as wheat, barley, forage, and cereals and horticulture [52]. Since 2016, a team of experts from Argentina's National Institute of Agricultural Technology (INTA) Hilario's Ascasubi Experimental Station (HAEE) has been gathering in situ data to create a land cover map each year [25]. The appointed study area mainly corresponds to a dryland agri-environment, and gravity irrigation has enabled most of the agricultural practices in the region [53]. This study focused on two winter wheat fields (_triticum aestivum_) belonging to the BVCR's 2020 and 2021 crop campaigns [54].