esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Groundwater resource is crucial for the development of agriculture and urban communities in valley basins of arid and semiarid regions. This research investigated the groundwater chemistry of a typical urbanized valley basin on the Tibetan Plateau to understand the hydrochemical status, quality, and controlling mechanisms of groundwater in arid urbanized valley basins. The results show groundwater is predominantly fresh and slightly alkaline across the basin, with approximately 54.17% of HCO\({}_{3}\)-Ca type. About 12.5% and 33.33% of sampled groundwaters are with the hydrochemical facies of CI-Mg-Ca type and CI-Na type, respectively. Groundwater is found with the maximum TDS, NO\({}_{3}\)\({}^{-}\), NO\({}_{2}\)\({}^{-}\), and F\({}^{-}\) content of 3066 mg/L, 69.33 mg/L, 0.04 mg/L, and 3.12 mg/L, respectively. Groundwater quality is suitable for domestic usage at all sampling sites based on EWQI assessment but should avoid direct drinking at some sporadic sites in the urban area. The exceeding nitrogen and fluoride contaminants would pose potential health hazards to local residents, but high risks only existed for infants. Both minors and adults are at medium risk of these exceedingly toxic contaminants. Groundwater quality of predominant sites in the basin is suitable for long-term irrigation according to the single indicator of EC, SAR, %Na, RSC, KR, PI, and PS and integrated irrigation quality assessment of USSL, Wilcox, and Doneen diagram assessment. But sodium hazard, alkalinity hazard, and permeability problem should be a concern in the middle-lower stream areas. Groundwater chemistry in the basin is predominantly governed by water-rock interaction (silicate dissolution) across the basin in natural and sporadically by evaporation. Human activities have posed disturbances to groundwater chemistry and inputted nitrogen, fluoride, and salinity into groundwater. The elevated nitrogen contaminants in groundwater are from both agricultural activities and municipal sewage. While the elevated fluoride and salinity in groundwater are only associated with municipal sewage. It is imperative to address the potential anthropogenic contaminants to safeguard groundwater resources from the adverse external impacts of human settlements within these urbanized valley basins.  
hydrochemistry; groundwater resource; water quality assessment; agricultural pollution; urbanization +
Footnote †: journal: Applied Sciences  
1 Faculty of Engineering, China University of Geosciences, Wuhan 430074, China 12 Qinghai Provincial Land Remediation and Ecological Restoration Center, Xining 810008, China 12 Qinghai Provincial Land Remediation and Ecological Restoration Center, Xining 810008, China 2
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 4 Experimental Results  
### Datasets  
To conduct an experiment on a road vehicle tracking dataset, we used the KITTI (OD and MOT) [26] and VeRi-776 [27] datasets for tracking objects on a road. The KITTI OD dataset was used for pretraining the detector (YOLOv3), which comprises 7481 stereo images. The VeRi-776 dataset was configured for vehicle re-identification, and it comprises images of vehicles taken from 20 videos recorded in a city. Each vehicle image has an ID, model, license plate, color, etc., as labels, and images of each vehicle were taken from various positions, namely the front, rear, and side. The dataset was used to configure the pretrained model of the embedding network.  
KITTI Multi-Object Tracking Dataset  
The KITTI MOT dataset was used for fine-tuning and testing the proposed tracking model and other existing methods. However, since the dataset comprises video frame images, it should be created with a new structure suitable for ID information. As shown in Figure 10, object images corresponding to each ID were cropped and stored by using the ground truth information in the frames. Object images were extracted from multiple frames on the basis of their IDs and configured to be one class. To evaluate the performance quantitatively, we employed the recall, precision, Frag, MOTP, ID switch, and MOTA from [26,28].
# Effect of Heat-Treatment Remediation on the Mechanical Behavior of Oil-Contaminated Soil
## 2 Experimental Section  
### Specim Preparation  
Changes in the mechanical properties of petroleum-contaminated soils upon heat treatment were experimentally studied. To simulate actual conditions, the soil was obtained from an existing petroleum-contaminated site, a refinery plant in southern Taiwan, but the sampled soil was not polluted. To reduce soil heterogeneity, all soil specimens in this investigation were taken from the same location. The pollutants were commercially available gasoline and diesel. Particle size greatly influences the mechanical properties of soil. Therefore, a fixed particle size distribution was used to eliminate the influence of particle size distribution. The soil was sifted using an American Society for Testing and Materials (ASTM) standard sieve, and soil that passed through the No. 4 sieve (4.76 mm) was used. A single sieve analysis was conducted on all of the soil that passed through the No. 4 sieve (4.76 mm), yielding the particle size distribution curve in Figure 1.  
The simulated pollutants in the experiment were gasoline and diesel, and the pollution concentration was set to 0 (uncontaminated soil specimen), 5000, or 10,000 ppm, according to the literature [12; 13] and previous investigation data in the refinery plant. The simulated soil specimens in each experiment were mixed with gasoline or diesel to the experimental concentration. After stirring, they were placed in a high-temperature furnace for heat treatment.
## 2 Samples and Methods

Table 1: Sample location for the Permian Lucaogou Formation. Figure S3 (within the Supplementary Data). On this basis, the UCS, \(E_{\rm s}\), and \(\
u_{\rm s}\) can be calculated as follows [9]:  
\[UCS=P/A \tag{2}\]  
\[E_{\rm s}=\Delta\sigma/\Delta\epsilon \tag{3}\]  
\[v_{\rm s}=|\epsilon_{\rm r}/\epsilon_{\rm a}| \tag{4}\]  
where \(P\) is the load corresponding to the rock sample fracture, kN; \(A\) is the bottom area of the cylindrical rock sample, m\({}^{2}\). The calculation diagram of the \(E_{\rm s}\) and \(\
u_{\rm s}\) can be seen in Figure S4 (within the Supplementary Data). During the elastic stage of the stress-strain curve, \(\Delta\sigma\) is the axial stress, MPa; \(\Delta\epsilon\) is the axial strain, %; \(\epsilon_{\rm r}\) is the radial strain, %; and \(\epsilon_{\rm a}\) is the axial strain, %. In addition, the damage envelope can also be obtained through the relationship between the confining pressure and the peak strength. The slope \(k\) and intercept \(b\) were obtained using the linear regression method. Therefore, the \(\varphi\) and \(C\) can be calculated from the slope \(k\) and intercept \(b\) as follows [28]:  
\[\varphi=\arcsin\frac{k-1}{k+1} \tag{5}\]  
\[C=b\frac{1-\sin(\varphi)}{2\cos(\varphi)} \tag{6}\]  
For each sample, both \(DEN\), \(V_{\rm P}\) and \(UCS\), \(C\), \(\varphi\), \(E_{\rm s}\), and \(\
u_{\rm s}\) were obtained from laboratory tests. The relationships between physical parameters and rock mechanical properties should be discussed. The regression analyses (linear and non-linear) were applied to obtain the fit formula. Based on this, the empirical relationships for evaluating the rock mechanical properties of the shale oil reservoirs were established. In order to evaluate the prediction effect, statistical analysis was essential. The goodness of the fit for each empirical correlation was evaluated using the coefficient of determination (\(R^{2}\)). The \(R^{2}\) can be calculated as follows:  
\[R^{2}=1-\frac{\sum\limits_{n}^{i=1}{(y_{i}-\hat{y}_{i})}^{2}}{\sum\limits_{n }^{i=1}{(y_{i}-\overline{y})}^{2}} \tag{7}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) are the measured and estimated value of \(y\) for the \(i\)th data point, respectively, \(\overline{y}\) is the average value of the measured value, and \(n\) is the number of data points. Furthermore, the root mean square error (\(RMSE\)) and average absolute relative deviation (\(AARD\)) are also applied to estimate the goodness of the fit for each empirical correlation. The \(AARD\) and \(RMSE\) were calculated as follows:  
\[RMSE=\sqrt{\sum(y_{i}-\hat{y}_{i})^{2}/n} \tag{8}\]  
\[AARD=\frac{\sum|y_{i}-\hat{y}_{i}|/y_{i}}{n}\times 100 \tag{9}\]  
In addition, a number of empirical equations were introduced to evaluate the rock mechanical properties. Some empirical equations suggested in the literature between physical parameters and rock mechanical properties for different rock types are seen in Table 2. From Table 2, the empirical equations in the different literature for estimating the rock mechanical properties are different.
## 3 Proposed Methodology
### Learning and Locking Reference Systems with Machine Learning
#### 3.3.1. Dynamic Data Collection and Preprocessing  
Collecting sensor data is key to making on-site decisions and knowing what is happening. Therefore, the more sensors and devices that are enabled, the better the energy consumption patterns, operating modes, and parameters of the system will be defined.  
Typical sensors in building thermal systems are thermocouples, consumption meters, flow meters, and environmental sensors, which must be time synchronised. When necessary, interpolation is applied to estimate and impute missing data, and also data are normalised to ensure that all features have the same scale. It can also be interesting to create additional features to model components that influence the thermal dynamics, such as inertial data: these can be variables representing data from previous time steps,  
Figure 4. Summary of the proposed methodology.  
derivatives (e.g., the rate of change in temperature) or energy efficiency indicators (unit consumptions,\(k_{i}\)). Thus, it is necessary to process and analyse these data, filtering out noise and outliers, which will result in learning a more robust predictive model.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results
### Analysis of Implementation Results
#### 3.2.1 Impact of Sampling Method on Extraction Results
It can be seen from the red areas in the second and third columns in Figure 6 that method 6 has a higher recognition rate for buildings in more complex scenes such as shopping malls and factories. FRA-Net is added to introduce the idea of attention, and the details of building segmentation can be better processed through the further fusion of low-level and high-level features. Figure 6: Local prediction of 5–6 sampling methods and FRA-Net in the LandCover dataset, the meaning of colors is buildings (red), woodlands (green), water (grey), and backgrounds (black). (**a–e**) are the experimental results of 5–6 methods and FRA-Net on images from the LandCover dataset.
## 2 System Design
### The Custom-Made RWMS in Application  
In RWMS, the functions of the software and the hardware can be expanded to meet the user's requirements. In the plans to continuously monitor the groundwater levels in Quzhou, Hebei Province, China the monitoring frequency is combined with weather forecasting in the system design. The monitoring frequency of the system will increase automatically in case of heavy rainfall. Such work is undertaken by the data center and the data releasing module, as shown in the flow chart of the underground water monitoring (Figure 8).
# Reconstructing the 26 June 1917 Samoa Tsunami Disaster
## 3 Methods and Data
### Tsunami Modelling
#### 3.1.2 Tide Gauge and Runup Observations  
Available tide gauge records from Apia harbour as well as runup observations from different parts of Upolu and Savai'i were used to validate the inundation modelling and subsequent assessment of present-day exposure and impacts in the runup zone. Tide gauge readings for the 1917 event measured in Apia harbour were digitized using the analog maregraph provided in [23]. The maregram in Figure 2 shows the fluctuations of the tsunami waves within the harbour. The digitized maregram generally matches the predicted tide at Apia (predicted by analyzing tide constituent from recent tide record). However, the tide time reference given in [23] cannot be reconciled with an expected 30-40 min travel time for the tsunami to reach Apia. Therefore, either the tide time reference or earthquake time/location is inaccurate. Moving the tide time reference given in [23] to 20 min later improves the correlation between predicted and measured tide and resolves the arrival time inconsistency. Although there is no clear evidence that the tide time reference  
Figure 2: Tide gauge records for the 1917 tsunami in Apia harbour. (**a**) Original maregram taken from Angenheister (1920) [23]; (**b**) inverted and reprojected maregram for digitization; (**c**) digitized tide record (grey solid line) and predicted tide (black dashed line). The maregram shows the fluctuations of the sea in Apia harbour arriving only a few minutes after the earthquake. The earthquake occurred at 05:49 UTC (dashed red line), with the first noticeable sea level peak at 06:03 UTC. The maregram record prior to the earthquake better matched the predicted tide when shifting the record by 20 min (black solid line), which also produces more consistent arrival time for the tsunami.  
from [23] is incorrect, it appears to be the simplest explanation of the inconsistency, and it still provides the first indication of the likely tsunami arrival time in Apia.  
Runup observations derived from historical records of eyewitness accounts documented in [4; 30; 31] provided benchmarks to infer the extent of wave runup onto land. These observations were digitized to help validate the tsunami runup modelling.
# An Aeromagnetic Compensation Algorithm Based on a Residual Neural Network
## 2 Compensation Model and Method
### Res-Bp
The Res-Bp structure is as shown in Figure 2. Suppose there are two sets of data: a training set (\(X_{train}\), \(y_{train}\)) and a test set (\(X_{test}\), \(y_{test}\)). After the training set is trained by the network model (\(C_{train}\)), the predicted interference field of the test set is:  
\[\hat{y}_{test}\ =\ X_{test}C_{train}. \tag{21}\]  
The compensated aeromagnetic data are obtained by subtracting the predicted value (\(\hat{y}_{test}\)) from the real value (\(y_{test}\)). The improvement ratio (IR) is a measurement scale proposed by the Canadian Aviation Research Center (NAE). At present, this measurement scale is adopted internationally and can reflect the performance of the aeromagnetic measurement system [20; 21]. Therefore, the standard deviation (STD) and IR of the aeromagnetic data before and after compensation are used to evaluate the magnetic interference compensation algorithm:  
\[\text{STD}= \sqrt{\frac{1}{n}\ \sum_{i=1}^{n}\left(x_{i}-\mu\right)^{2}} \tag{22}\] \[\text{IR}= \frac{\text{STD}_{u}}{\text{STD}_{c}}, \tag{23}\]  
where \(\mu\) is the arithmetic mean of the variable, and \(\text{STD}_{u}\) and \(\text{STD}_{c}\) are the standard deviations of the uncompensated and compensated data, respectively.
## 4 Survey Results and Analysis
### Preliminary Data Analysis
#### 4.1.3 Checking Normality of Data Distribution  
In order to check the normality of the data distributions, the Shapiro-Wilk test was applied for all the research variables.  
For this test, the null hypothesis of normality data distribution (H0)\({}_{\text{N}}\) specifies that the distribution of the data belongs to a normally distributed normal distribution.  
If the significance threshold value '\(p\leq 0.05\)', the null hypothesis (H0)\({}_{\text{N}}\) is rejected, and in this case the alternative hypothesis (H1)\({}_{\text{N}}\) is accepted, because statistical significance was obtained.  
When the significance threshold value '\(p>0.05\)', then the null hypothesis (H0)\({}_{\text{N}}\) is accepted since statistical significance was not obtained.  
The results of the Shapiro-Wilk test (a test considered as more powerful in determining normality by statisticians [53; 54; 55]) indicate that none of the variables is normally distributed, all variables having \(p\)-value < 0.001.
###### Abstract  
A single frequency of 500 Hz is used as the equivalent frequency for traffic noise to calculate the approximate diffraction in current road barrier designs. However, the noise frequency changes according to the different types of vehicles moving at various speeds. The primary objective of this study is the development of a method of calculating the insertion loss based on frequencies. First, the noise emissions of a large number of vehicles classified by speed and type were measured to obtain data the noise spectrum. The corresponding relation between vehicle type, speed, and noise frequency was obtained. Next, the impact of different frequencies on the insertion loss was analyzed and was verified to be reasonable in experiments with different propagation distances compared to the analysis of a pure 500 Hz sound. In addition, calculations were applied in a case with different traffic flows, and the effect of a road noise barrier with different types of constituents and flow speeds were analyzed. The results show that sound pressure levels behind a barrier of a heavy vehicle flow or with a high speed are notably elevated.  
Keywords:noise barrier; insertion loss; vehicle frequencies; diffraction; flow speed +
Footnote †: journal: Applied Sciences
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 4 Analysis, Comparisons, and Future Plans  
### Simulation Environment  
In order to validate the proposed 5 GsatEC framework, a test environment was built on the ground.  
The full architecture, including hardware, software and services, will be verified in a prototype system in the future. The test platform will include eight nodes (Table 1). All these nodes will be developed in the embedded platform.  
The key of the hardware platform in the 5 GsatEC framework is the combination of heterogeneous embedded resources. Through the above demonstrated system, software based on microservices can shield the underlying hardware information, which can promote deployment of the framework in the 5G network.  
The advantages of the 5G satellite network over the ground 5G satellite network and existing satellite network are illustrated by two specific examples. The experimental simulation scenario is shown in Figure 4. Users in remote areas are not covered by the ground 5G communication network. Space networks are divided into the 5 GsatEC network and satellite network, based on edge computing capabilities. In the simulation experiment, the satellite's characterization parameters include the number of satellites, satellite height, processing power (calculation and storage), and number of supported users. The characterization parameters of the 5G communication network include coverage, number of nodes available, and number of supported users. The meaning of each time cost is explained in Table 2.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 4 Results and Discussion
### Influence of Kv/Kh on the Production Curves
Unfortunately, logging data are absent for this well to compare the petrophysical properties with those from other wells (Figure 10b). Figure 9: Comparison of observed and simulated oil production rate (OPR) and water (WPR) productions designated as historical and simulated values using a Kv/Kh of 1 and 0.1: (a) Well G, (b) Well B. The Kv/Kh = 1 scenario better matched the WPR than the 0.1 scenario, indicating vertical water inflow. Thus, in Wells G, C and B, Kv/Kh = 1/1 provides a better match to the observed WPR because of the higher WPR than predicted using 1/10, while using 1/1 predicted a better match in Well G, because of lower WPR. Conversely, Kv/Kh = 1/1 indicates water inflow in both the lateral and the vertical directions. However, the case using 1/10, which indicates lateral direction, was a better predictor for Wells E and A because the WPR predicted by 1/1 was too low in Well E and too high in Well A. In conclusion, a comparison of observed WPR with the simulated WPR using two Kv/Kh ratios of 1/10 and 1/1 can help in evaluating whether the water inflow is vertical or lateral. For all the Kv/Kh scenarios, a higher WPR was predicted at the beginning and a lower WPR was predicted at the end of the assumed water breakthrough periods. However, WPR increased while OPR decreased, often linearly, e.g., in Well B, during the first 4-5 years. Such a consistent decline in OPR and increase in WPR can be attributed to the depletion of the reservoir rather than water breakthroughs due to the borehole's poor integrity. Moreover, the repairs of the borehole cementing carried out later during the many years of production often did not lead to a reduction in WPR. It can be assumed that water intervened in the porous intervals of LHS due to the good hydrodynamic conductivity of the reservoir in all directions, leading to earlier waterflooding than expected. The vertical water inflow may show that the mostly wet underlying Al Khlata Formation or the overlying Upper Haushi reservoirs are hydrodynamically connected with the LHS reservoir. A bottom and edge water Carter-Tracy aquifer model for the Lower Haushi was implemented in the model previously and showed that influx can be attributed to the aquifers from the north and west [16]. However, the volumes of water produced from the Lower Haushi wells are much higher and could not be justified from a material balance standpoint using any of the attempted models. Although a full match of WPR was not obtained by changing Kv/Kh values only, a substantially better match for WPR was obtained using Kv/Kh = 1. For comparison, primarily to facilitate the pressure match in the simulation, the previous investigators reduced Kv globally in all of the reservoirs by a factor of 0.01, which led to a significant WPR mismatch [16].
## 3 Suspension Design and Modelling
### Modelling of the Hydroneumatic Suspension
#### 3.2.1 Mathematical Model of the Actuation Cylinder  
Assumptions are Made as Follows in the Proposed Model:  
1. All parts of the cylinder have been effectively sealed. No gas dissolved into the oil;
2. The cylinder components are rigid since the working pressure only varies within a range of 40 bar in the pneumatic chamber;
3. The oil is incompressible since the oil is just the damping medium in this system;
4. The mass and the inertia of the oil is negligible.  
Figure 4: Hydroneumatic suspension system for the half-vehicle.  
The exerted force of suspension in this test rig is related to gas pressure \(P_{1}\) in the pneumatic chamber, oil pressure \(P_{2}\) in the hydraulic chamber, and seal element friction force \(F_{f}\).  
\[F=P_{1}A_{1}-P_{2}A_{2}-F_{f} \tag{1}\]  
where \(A_{1}\) and \(A_{2}\) are the sectional areas of the pneumatic and hydraulic chambers, respectively.