esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Subjective Perception of Support Force  
The subjective perceptions of the support force in the three motion phases were carried out with the help of a survey (see Figure 14). Sixty percent of the participants rated the support force during arm lifting as appropriate. Forty percent of them wished for stronger support force during lifting of the arm for condition S50 for both tasks. For one participant, the support force during the arm lifting process for condition S100 for task T2 was still too low. One participant was indecisive and was unable to give a ruling. Forty percent rated the support during arm lifting in task T1 condition S100 as too strong. The resistance during arm lowering was generally evaluated as low to appropriate by 60% of the participants. In both tasks, 40% of the participants rated the resistance during arm lowering in condition S100 as high or very high.  
Figure 14: Subjective perception of (**a**) the support force when lifting the arm, (**b**) support when screwing-in, and (**c**) effort when lowering the arm.  
Figure 13: Differences in the mean EMGs of the DAs and LDs under conditions S50 and S100 compared to S0 during the arm lowering phase of the motion in both tasks.  
The support force during the screw phase in task T2 was either too low, or way too low for 80% of the participants under both supported conditions (S50 and S100). In task T1, condition S50 was evaluated as too low or way too low by 80% of the participants. After increasing the support level to S100, 80% of the participants rated the support force as appropriate, but 20% still stated that it was too low.
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 3. Melt Pool Generation Mechanisms
### Thermal Behaviour
#### 3.1.3 Effects of the Travel Speed  
The temperature distributions measured at the centre of the melt pool at different travel speeds are reported in Figure 21[97]. It is possible to observe that increasing the travel speed results in a huge reduction in the temperature. Accordingly, by increasing the travel speed, a smaller width of the melt pool is expected.  
Figure 21: Effect of travel speed on temperature distribution along melt pool (reproduced with permission from [97]; Copyright © 2008 Elsevier).  
Figure 20: Effect of powder flow rate on temperature measurements using thermal monitoring (reproduced with permission from [97]; Copyright © 2008 Elsevier).  
[PERSON] [101] showed that for low values of laser power, the layer width decreases slightly by increasing the travel speed (Figure 22). This decrease has been confirmed by [PERSON], et al. [98], [PERSON], et al. [108], [PERSON] and [PERSON] [45], [PERSON], et al. [99], and [PERSON], et al. [100]. However, at a high level of laser power, the dimension of layer width is not significantly affected by the travel speed [101]. [PERSON], et al. [98] observed that after a critical value of travel speed, the layer width is unaffected by the variation of process parameters and is equal to the laser beam diameter. This behavior indicates that even in this case, it is not possible to consider the travel speed as an independent parameter, but it is advisable to indicate the laser power level to which it refers.  
[PERSON], et al. [98], [PERSON], et al. [108], and [PERSON] and [PERSON] [45] observed that the layer height is inversely proportional to the travel speed. [PERSON] [101] and [PERSON], et al. [99] showed that before a critical value, the layer height increases by increasing the travel speed. After this critical value, a decreasing trend is observed by increasing the travel speed. The critical value depends on the material, laser power, and z-increment of the deposition head.
## 2 Subject and Methods
### Cephalometric Rendering and Analysis  
Dolphin Imaging software was used to render a cephalometric image for each patient from the CBCT DICOM data. Each cephalometric image was generated from the algorithm proprietary to the imaging software and produced an orthogonal perspective with 0% magnification. The cephalometric image was digitally traced, and the following values were identified: sella-nasion-A point (SNA) angle, sella-nasion-B point (SNB) angle, A point-nasion-B point (ANB) angle, anterior facial height (AFH), posterior facial height (PFH), PFH to AFH ratio, mandibular plane angle (SN-MP), FMA (FH-MP), overjet (OJ), and overbite (OB). See Figure 1.
_applied sciences_  
_Article_  
**Contamination Levels and the Ecological and Human Health Risks of Potentially Toxic Elements (PTEs) in Soil of Baoshan Area, Southwest China**  
**[PERSON] 1,2,3,*(c)**, [PERSON] 1,2,3, [PERSON] 1,2,3 and [PERSON] 1  
1 Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences,  
Langfang 065000, China; [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (X.C.)  
2 Key Laboratory of Geochemical Cycling of Carbon and Mercury in the Earth's Critical Zone,  
Chinese Academy of Geological Sciences, Langfang 065000, China  
3 Research Center of Geochemical Survey and Assessment on Land Quality, China Geological Survey,  
Langfang 065000, China  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract: The primary goals of this study were to reveal the environmental status of potentially toxic elements (PTEs) and their ecological risks, as well as their associated health risks in the Baoshan area, southwest China, which has been surveyed with the scale of 1:250,000 geochemical mapping. Based on a comparison of the PTE concentrations with the soil environmental quality of China and the enrichment factor (EF), geo-accumulation index (I\({}_{\text{geo}}\)), contamination factor (C\({}_{\text{d}}\)), and potential ecological risk indexes (E\({}_{\text{r}}^{i}\) and PERI), as well as the potential non-carcinogenic hazard indices (HI and CHI) and carcinogenic risks indices (TCR and CTCR), the following conclusions were drawn: The PTE concentrations in the surface soil samples that were collected from the investigated area (1.65% sites) exceeded the risk intervention values (RIV) for soil contamination of agricultural land of China. Cadmium (Cd) and mercury (Hg) posed higher ecological risks than other PTEs (arsenic (As), chromium (Cr), lead (Pb), copper (Cu), nickel (Ni), and zinc (Zn)), which was highlighted by their toxic response factor. Arsenic was the main PTE with a non-carcinogenic risk (19.57% sites for children and 0.25% sites for adults) and the only PTE that carries a carcinogenic risk (2.67% sites for Children and 0.76% sites for adults) to humans in the research area. Children are more vulnerable to health risks when compared to adults because of their behavioral and physiological traits. Geological genesis was responsible for the high concentrations, ecological risk, and health risk distribution patterns of the examined PTEs. Even though the present research highlights several important aspects related to PTE pollution in the research area, further investigations are needed, especially in mining areas.**  
**Keywords: soil PTEs; ecological risks; human health risks; the Baoshan area; southwest China**
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 3 Proposed Visibility Estimation Methodology
### Visibility Estimation Based on Image Analysis  
Image brightness and contrast contain the information about the fog concentration during image acquisition. We set a horizontal brightness histogram: the horizontal axis represents left-to-right information of the intermediate horizontal line selected in the image, and the vertical axis represents the pixel value of each point. For example, Figure 3 shows the horizontal brightness histograms of clear daytime, foggy daytime, clear evening, foggy evening, clear midnight, and foggy midnight. Figure 3a,b corresponds to a daytime image with good air quality and a foggy daytime image. The horizontal brightness histogram y-axis absolute value of the fog-free image is larger than the foggy image. The darkness of the foggy image is higher than that of the fog-free image. Since the darkness makes high-value pixels be fewer in the images, it can be seen from the histograms of the evening images given in Figure 3c,d that the y-axis absolute value of the foggy image with poor visibility is small, and the brightness of the bright portion of the foggy image is significantly reduced while the brightness of the dark part is significantly increased. The same pattern can be seen in the midnight images shown in Figure 3e,f. Therefore, image brightness histogram analysis can be used to estimate the visibility level of the given image.  
Develop the visibility indicators which are based on the image horizontal brightness histogram. Making use of the concept of image brightness, from the image get \(t_{l}\), the 20% darkest pixels, and \(t_{h}\), the 20% lightest pixels. In the heavy foggy days, \(t_{l}\) and \(t_{h}\) are around 0.5; in sunny and clear weather conditions, \(t_{l}=0\), \(t_{h}=1\). Additionally, using the concept of image contrast, in heavy foggy dense,\(t_{c}=0\); in clear weather dense, \(t_{c}=1\). However, these are ideal scenarios that do not often occur in the real world. For example, the values of \((t_{l},\ t_{h},\ t_{c})\) of Figure 3a-f are (0.311, 0.559, 0.248), (0.342, 0.516, 0.174), (0.251, 0.781, 0.467), (0.316, 0.476, 0.16), (0.182, 0.474, 0.292), and (0.208, 0.366, 0.158), respectively. \(t_{c}\) is significantly higher in the foggy images than that of the clear weather images, while \(t_{l}\), \(t_{h}\) mainly indicate the current ambient brightness, such as daytime, streetlight right or no streetlight night.  
Figure 3: _Cont._  
Although the DCP algorithm and image brightness contrast analysis are effective methods to characterize the image visibility, in the high visibility environment, the gaps of image features are minimal. To optimize the results, the method of obtaining atmospheric transmissivity described in Section 3.1 is invoked to fuse the process as what is described in Section 3.3.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 3 VGRQ: The Proposed Routing Mechanism
### Assumptions  
* The sensor nodes follow a random distribution during deployment.
* Sensor nodes are static, homogeneous, and GPS-enabled.
* Every sensor node is allocated a distinct identifier (ID) to distinguish it from other nodes.
* The mobile sink follows a random mobility pattern to collect the data.
* There is no obstacle in the sensor field.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods  
### Rapid-DEM  
The developed framework combines multiple geospatial technologies to detect, prioritize, plan, fly, and fuse elevation data to update a DEM at a location with major topographic changes (Figure 1). Rapid-DEM works by (1) ingesting PlanetScope 3 m multispectral satellite data [9] from a user defined time period and area of interest into Google Earth Engine (GEE) [22] where post-classification thematic change detection is performed to identify land cover change. (2) Next, the classification data is exported to GRASS GIS [25] where a priority queue is generated. (3) High-priority locations are then surveyed with UAS and the data is processed in WebODM using structure from motion (SfM) to generate a DEM of the location. (4) The UAS DEM is fused with an existing lidar-based DEM and (5) finally, the fused DEM is optionally uploaded to an open data repository (e.g., OpenTopography) [26].  
Figure 1: Rapid-DEM is a five-phase process that detects, plans, flies, fuses, and updates urban topographic change. Phase (1) generates land cover maps used to identify land cover change. Phase (2) creates a priority queue that identifies the most important change. Phase (3) uses the data from the priority queue to fly high priority locations and generate updated elevation models. Phase (4) fuses UAS-based DEM with an existing DEM dataset and phase (5) shares the updated fused DEM with the broader community.
## 2 Methodology
### Experimental Procedure  
#### 2.2.1 Characterization Using Fourier Transform Infrared Spectroscopy (FTIR)  
Once the homogenous transparent liquid could be formed, FTIR equipment available in Centralised Analytical Lab (CAL), Universiti Teknologi Petronas (Seri Iskandar, Perak, Malaysia) was used to identify the functional group of the formulated DESs. The interaction between the Quaternary Ammonium Salt (QAS) and Hydrogen Bond Donor (HBD) when DES was formed could be analyzed based on the changes observed in the peaks. The intensity of the peaks changed when there was a change in chemical bonding. All analysis done in this part was based on the interpretation of infrared spectra by [PERSON] [41]. According to the literature, there are a few steps to analyze the infrared spectrum, as tabulated in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Name** & **Chemical Formula** & **Chemical Structure** \\ \hline Tetraethylammonium Acetate & (C\({}_{2}\)H\({}_{5}\))\({}_{4}\)N(OCOCH\({}_{3}\)) \(\cdot\) 4H\({}_{2}\  
The FTIR analysis was performed with Thermo Scientific(tm) Nicolet(tm) i55 FTIR Spectrometer that has Thermo Scientific iD7 ATR accessory with the diamond crystal. The Potassium Bromide (KBr) pellet method was implemented. Background scanning was performed to recognize the presence of air impurities, such as carbon dioxide peak for every sample. The spectrometer with a 2 cm\({}^{-1}\) resolution was used to generate spectra with patterns that provide structural insights in the wavenumber range of 4000-400 cm\({}^{-1}\) with 32 number of scans. The analysis of the DES samples available in liquid state was done at ambient temperature. Results of TEAAC:MEG, TEAAC:Glycerol, TEAB:MEG, and TEAB:Glycerol were compared to the FTIR of pure components TEAB, TEAAC, MEG, and Glycerol available on SpectraBase website provided by John Wiley and Sons, Inc. (Hoboken, NJ, USA).
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 4 Results and Discussion
### Rotational Speeds  
When the chamfer of the end covers is C0.5 and the interference amount is 60 \(\upmu\)m, the effect of rotor speeds on the stress of the interference fit structure is analyzed. The simulation data results are shown in Table 4. It is known that the relationship between the centripetal force and rotational speed is quadratic. In addition, the stress generated by the sheath and the magnet is the compression force among the layers, and the sum value of the compression force of each layer is equal to the centripetal force and the direction is opposite. So, the maximum principal stress is also a quadratic correlation with speeds, as shown in Figure 13. Therefore, the speeds are also one of the factors for the comprehensive consideration of the rotor interference fit strength.  
\begin{table}
\begin{tabular}{c c} \hline
**The Rotor Speed (rpm)** & **The Maximum Principle Stress (MPa)** \\ \hline
60,000 & 286.6 \\
70,000 & 293.44 \\
80,000 & 301.34 \\
90,000 & 310.29 \\
100,000 & 320.3 \\
110,000 & 331.36 \\ \hline \end{tabular}
\end{table}
Table 4: The simulation results of stress under different rotor speeds.  
Figure 12: The relationship between the interference and stress at different rotational speeds. (a) 2D plane curve; (b) 3D Surface Plot.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Amount of** & \multicolumn{4}{c}{**The Maximum Principle Stress (MPa)**} \\ \cline{2-5}
**Interference (\(\upmu\)m)** & **60,000 rpm** & **88,000 rpm** & **96,000 rpm** & **112,000 rpm** \\ \hline
10 & 85.20 & 189.29 & 226.26 & 309.83 \\
20 & 121.17 & 186.24 & 223.33 & 306.95 \\
30 & 167.15 & 190.12 & 221.16 & 30
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 3 Evaluation Framework
### Use Cases  
We define three use cases for the analysis of network performance:  
Use Case 1 (UC1) is focused on supporting last-mile connectivity in rural scenarios of already connected areas. For Belgium, the number of rural houses per km\({}^{2}\) is 150. It is assumed that the operator has a penetration of 50% of the homes, so the total density of served houses is 75 per km\({}^{2}\). The rural town of Least in Flanders is selected with an area of 1.33 km\({}^{2}\) to account for the 100 CPE served.  
Use Case 2 (UC2) is focused on connecting new urban cities with a home density of 1100 houses per km\({}^{2}\). The city centre of Liege is selected, with an area of approximately 0.45 km\({}^{2}\) and operator penetration set to 20%. Similar to UC1, UC2 also serves 100 CPEs.  
Use Case 3 (UC3) studies the impact on the beam elevation service for taller buildings and larger aggregated traffic. Specifically, in each tall building, the CPE provides internet service for eight apartments with an internal cable connection between them and the CPE. This implies that the CPE must serve an aggregated traffic of 250 Mbps for the internet plus 48 Mbps for television and headroom of 1 Gbps. A tall building is defined as an apartment construction with an altitude larger than 50 m. An urban residential area in Ghent is selected, where up to 12 residential towers, equivalent to 96 apartments, are found. Table 2 summarises the use cases, and Figure 4 depicts the areas.  
A final evaluation of the performance of the losses due to reflection is performance. Note that up to now UC1 to UC3 only considers direct link on the 60 GHz band. To evaluate if improvement on the performance of the calculation, a multi-ray bouncing analysis is performed to UC1 and UC2 where up to two reflected rays are accounted for the total path losses as described in Equation (3).  
Figure 4: Use cases simulated areas of interest. (**a**) Least—Rural. (**b**) Liege—Urban. (**c**) Ghent—Tall Buildings.
# Revisiting the Detection of Lateral Movement through Sysmon
## 5 Lateral Movement Categories on Windows Environments
### Exploitation of Remote Services  
Cybercriminals may exploit MS Windows systems' remote services to gain unauthorized access to the various nodes of a network. This kind of exploitation is successful when the adversary takes advantage of a revealed vulnerability of the targeted system, known or currently unknown. To determine whether the system is in a vulnerable state, the assailant makes use of numerous information-gathering techniques to reveal unpatched OS components or even the lack of updated IDS and antivirus software. Among the most common, port scanning and vulnerability assessment tools are included, which are loaded and installed remotely on the system under attack. Server nodes are considered the most high-value targets during the execution of LM. Mostly, they comprise the final destination of an attacker after gaining initial access to a network node and hopping from one system to another.  
There are many well-known vulnerabilities concerning the various MS Windows OS versions. Server Message Block (SMB), Authenticated Remote Code Execution (RCE) and Remote Desktop Protocol (RDP) are two of the most popular misconfigurations. Within the context of this paper, exploitation of remote services was performed against the testbed described in Section 4. Precisely, the EternalBlue vulnerability was used in three different ways to exploit the SMB of an unpatched version of Windows Server 2019, which was left vulnerable on purpose, while the RDP exploit was tested with BlueKeep (CVE-2019-0708) RCE. Overall, four different instances of the Exploitation of Remote Services attack were executed. First, SMB was exploited via the Metasploit's ms17-010 module. Next, the SMB EternalBlue vulnerability was used towards the final execution of the arbitrary remote commands on the targeted system via Metasploit's _smb_login_ auxiliary service. To expand the analysis of the remote exploitation on the targeted server, the testbed was infected with the worm-like _WannaCry_ malware. To allow the propagation of the malware, User Account Control (UAC) of the server was disabled remotely with the use of the _smbexec 2.0_ tool. Finally, Metasploit's CVE-2019-0708 dedicated module was utilized to leverage the targeted Server's RDP misconfiguration.
## 4 Experimental Results and Discussion
### Training Set  
The training set used in this study included various weather conditions (daytime, nighttime, rainy day, foggy day). Daytime was defined as sunny weather with clear visibility, nighttime as good weather but dim lighting, rainy day as rainy conditions, regardless of bright or dull weather, foggy day as poor visibility in the absence of rain. A total of approximately 3300 images were collected, around 2700 of which were Class A (around 300 each in Class A 30, Class A_40, Class A_50, Class A_60, Class A_70, Class A_80, Class A_90, Class A_100, and Class A_110) and around 600 were Class B. Table 17 shows the number of Class A and Class B images used in all four weather conditions. Furthermore, we randomly selected 10% of the images in the training set to serve as the validation set.  
The images in the training set were mostly screen captures of dashcam footage. The training set was also enriched using images obtained from the TT100K dataset, Google Street View, and Google Images searches. The images captured from dashcam footage accounted for 70% of the training set, and the images from the TT100K dataset, Google Street View, and Google Images searches each accounted for 10%.  
\begin{table}
\begin{tabular}{c c} \hline \hline Processor & Intel(R)Core(TM)i5-8500 CPU @ 3.00 GHZ * 6 \\ \hline Display card & NVIDIA GeForce RTX 3060 12 GB \\ \hline Operating system & Windows 10 \\ \hline Memory & 32 GB (RAM) \\ \hline \hline \end{tabular}
\end{table}
Table 16: Computer hardware configurations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **M1** & **M2** & **M3** & **M4** \\ \hline \multirow{6}{*}{Components} & \multirow{6}{*}{YOLOv7} & \multicolumn{3}{c}{Rotation angle and Gaussian noise} \\  & & \multicolumn{1}{c}{YOLOv7} & + & Canny edge detection \\  & & \multicolumn{1}{c}{} & YOLOv7 & + \\  & & Mask R-CNN & + & YOLOv7 \\  & & \multicolumn{1}{c}{} & Mask R-CNN & + \\  & & \multicolumn{1}{c}{} & Mask R-CNN & Mask R-CNN \\ \hline \hline \end{tabular}
\end{table}
Table 15: Models used in this study.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 6 Experiment  
### Experiment Setting  
#### 6.1.1 Track Parameter Setting  
The intelligent satellite used in the simulation experiment is set as a sun-synchronous orbit satellite, and its orbital parameters are selected from the STK satellite database (see Table 1 for the specific settings). The planning period is set to 1 June 2019 00:00:00 and 1 June 2019 00:00:00, with a total of 24 h. The observation time window of the satellite to the ground target is calculated by STK. Due to the orbital height of the satellite and the angle of view of the observed load, the ground point target usually has only 1 or 2 observation time windows within 24 h. In this chapter, we assume that each observation target has only one valid observation time window, that is, each observation target corresponds to an observation task.