esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of Static Stiffness after Motion on a Three-Axis CNC Milling Table
## 3 Experimental Comparison of Static Stiffness Measurement Methods
### Control Measurements of Experiments 1 and 3 under Steady Load in the Opposite Direction  
In the opposite direction (force F\({}_{\text{a}}\)), the stiffness characteristics of other surfaces are shown. This is also confirmed by the experiments, because in this direction, the table showed higher stiffness at all positions. Figure 12 compares the static and the SSaS stiffness.  
As can be seen, here, too, the difference between the static and the SSaS stiffness is considerable. However, the case is a little more complicated. The stiffness of SSaS from the \(-\)X running direction is slightly greater than from the \(+\)X direction. Under opposite load application, this was reversed. What is more important to note, however, is that the sliding characteristic of stiffness showed considerable non-linearity when the load was applied in this direction. Therefore, describing stiffness with a single value is not quite right.  
For example, upon running to position 8, the SSaS stiffness from the \(-\)X direction is described by the value of 63 N/\(\upmu\)m as shown in Figure 13. The trend line's course is exponential. The resulting stiffness value was determined from the linear trend on the exponential curve by the least squares method. When looking at the stiffness characteristics, the stiffness is lower under application of a lesser load, and considerably greater under application of a greater load.  
Figure 12: Comparison of the degree of (a) static stiffness; (b) Experiment 3—Static Stiffness after Sliding (SSaS) in \(+\)X direction; (c) Experiment 3—SSaS stiffness in the X-direction (force F\({}_{\text{a}}\)).  
Figure 13: Static stiffness characteristic at position 8 when the latter is assumed in the \(-\)X direction (force F\({}_{\text{a}}\)).  
In this case, another phenomenon occurs. That is, if the motion axis in the correct direction is preloaded, the motion stiffness value approaches the static stiffness one. From the point of view of machine precision, this is an advantage.  
Application of load in two different directions shows essentially similar outcomes, but there are also significant differences. These differences arise from the fact that in the application of reverse load, the respective force impacts the opposite support surfaces of the kinematic members. These surfaces can be of different size, structure and surface quality, which is the reason why different results are generated.
# Quantifying Auditory Presence Using Electroencephalography
## 1 Introduction
### Summary  
This present study seeks to find the answers to the following questions. Besides subjective measurement by questionnaires, is it possible to objectively measure presence with EEG? If yes, which EEG index should we use specifically to reflect the level of presence, absolute band power or specific power ratio? Is there any correlation between these two measurement methods?  
To answer these questions, two experiments were conducted where eight loudspeakers were used to reproduce urban soundscapes to stimulate auditory presence. During the experiment, both subjective measurement (based on questionnaires) and objective measurement (based on EEG signal) of presence were carried on. Then, we analyzed the subjective presence levels and EEG indices via different statistical tests, and further evaluated the correlation between these two measurement methods.  
The rest of the paper is organized as follows: Section 2 explains the methodology that we used in the study. In Section 3, we present the results followed by a discussion in Section 4. Then, in Section 5, we draw the conclusion and give a few suggestions for future work.
# Detection of Smoking in Indoor Environment Using Machine Learning
## 1 Introduction
### Recent Studies on Cigarette Detection Sensing System  
Previously, various studies have been conducted to identify and detect pollutants in indoor smoking. First, by connecting the MQ-7 carbon monoxide gas sensor to the Raspberry Pi camera through Serial Peripheral Interface (SPI) communication, it is possible to monitor the smoking information in the indoor smoking area in real time by measuring the concentration of Carbon Monoxide (CO) [11]. Additionally, using the same Raspberry Pi camera, a smoke sensor and a gas sensor were connected through serial communication to determine whether smoking indoors in public places, while a separate study was conducted on a smoking detecting system in real time. This made it possible to search for images using 3D printing after detecting indoor smoking in public places [12]. In addition, a study introduces a new system for detecting human smoking behavior or small smoke using image techniques. Smoking or non-smoking were detected by using a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT 2.0). This proposed a method of integrating the information of the lighter and the Inertial Measurement Unit (IMU) on the wrist [13]. A similar study has developed a new system that uses images to detect human smoking or small smoke, in which motion detection and background removal functions are combined with motion area saving, skin-based image segmentation, and smoke-based image segmentation to provide potential. Selecting a smoke area made it possible to determine the occurrence of smoking [14]. Recently, smoking image detection model based on a convolutional Neural Network (NN) called Smoking Net, which automatically detects smoking behavior in video contents through images and images is suggested. This method has proven high accuracy and excellent performance for real-time monitoring as it can detect smoking images using only the smoking gesture information and cigarette image characteristics without detecting cigarette smoke [15]. Using the smoking gesture detection model is trained to detect smoking gestures as described above, it was confirmed that a gesture classification indicating whether or not a smoking gesture was indicated is received through sensor data [16].
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 5 Conclusions  
To ensure that the tumor tissue is destroyed and to minimize the damage to the surrounding healthy tissue, it is necessary to establish a more accurate model of lung tissue and obtain more accurate ablation parameters according to this model. In this paper, a biological heat transfer model based on porous media is established, and the microwave ablation of lung tissue containing tumor tissue is simulated by the finite element method. The effects of tumor diameter, tumor porosity, and microwave ablation power on the microwave ablation of porous lung tissue were studied in terms of tissue temperature distribution, ablation area, and ablation volume. The results showed that with the increase of tumor diameter, the tissue temperature and the longitudinal diameter of the ablation area decreased, but the ablation volume did not change significantly. The larger the tumor porosity, the lower the tissue temperature, and the smaller the ablation area and volume. Tissue temperature, ablation area, and ablation volume increased with the increase of ablation power. Therefore, increasing the power of ablation can ensure that the tumor is ablated to some extent. This paper provides the basic reference data for clinical treatment parameters of microwave ablation of lung tumors, which is beneficial to improve the treatment scheme. [PERSON] et al. [1] used DETECT-LC, an automatic segmentation method based on the threshold, to construct a three-dimensional tumor volume and reduce the burden of clinicians. In the future, we will also consider the three-dimensional reconstruction of tumors to obtain more generalized clinical treatment parameters.  
X.M., idea proposal, data analysis, simulation, writing-original manuscript preparation, review and editing; Q.N., supervision, conceptualization, investigation, project administration, funding acquisition, visualization, validation, writing--review and editing; N.W., conceptualization, review of the manuscript; S.B. conceptualization, review of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Fundation of China (31771021), Beijing Municipal Natural Science Fundation (3162006) and Jiangsu Science and Technology Department (Jiangsu Science and Technology Committee) (BE2017758).  
Not applicable.  
The authors declare no conflict of interest.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 4 Injection Experiment of 5G Signal Interference into an RA Receiver  
### Design of the Experiment  
In order to validate the theoretical analysis and simulation results, the experimental design utilizes the interference injection method to test the interference effect on a specific RA model. The experimental setup, including the RA, altitude simulator, interference source device, altitude data monitoring device, and IF signal monitoring device, is based on the RTCA DO-155, as shown in Figure 8[24]. The altitude simulator is used to receive the transmit signal from the RA and generate the echo signal from the RA at a set simulated altitude. The signal generator is used to generate a standard 5G NR TM1.1 waveform signal, which is injected into the signal loop of the RA through a coupler to apply interference to it. The altitude data monitoring device is responsible for monitoring and displaying the output data of the RA in real time. A spectrum analyzer is used to collect and analyze the spectrum of the RA IF output signal to observe the interference effect of the IF signal gain compression. The models of the experimental equipment and their functions are shown in Table 1.  
Figure 7: IF output with 5G signal injection at different center frequencies.
## 3 Results
### AI Development Description
#### 3.4.1 Fault Detection Network
* Networks with 1 to 10 layers were evaluated, and generally, better results were obtained with an intermediate number of layers. There was no conclusive evidence that using the minimum or maximum number of layers improved the network's performance. * For the different sample sizes, there are one or several combinations of the number of layers and neurons that minimize the training error.
## 4 Results and Discussion
### Results of Camera's Orientation, Accuracy, and Precision around the X, Y, and Z Directions
This means the precision error for the sensor-based approach is less, as the participants could repeat the orientation of the camera regarding (yaw) with less error when using the sensor-based approach. The other interesting results presented in Table 2 could be the range values (range = max - min) of the changes in the camera's position and orientation when the sample pictures were captured. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Image** & **Rotation** & **Pitch (Ratio), Degree** & **Roll Degree** & **Yaw or Head (Ratio), Degree** \\ \hline Reference image & & (0.97), 7 & 0 & (0.96), 10 \\  & Avg. & (0.98), 5 & 1 & (0.93), 17 \\  & Min. & (1), 0 & 0 & (1), 0 \\ Group 1 & Max. & (0.85), 38 & 6.5 [90 *] & (0.69), 80 \\ (without sensor) & Precision Error & (0.03), 7 & 1.5 & (0.08), 20 \\  & Accuracy Error & (0.01), 2 & 1 & (0.03), 7 \\  & Range & 38 & 6.5 [90 *] & 80 \\  & Avg. & (0.99), 2 & 1 & (0.94), 15 \\  & Min. & (1), 0 & 0 & (0.99), 2 \\ Group 2 & Max. & (0.96), 10 & 4.5 & (0.85), 38 \\ (with sensor) & Precision Error & (0.01), 2 & 1.2 & (0.03), 7 \\  & (SD) & (0.02), 5 & 1 & (0.02), 5 \\  & Accuracy Error & (0.02), 5 & 1 & (0.02), 5 \\  & Range & 10 & 4.5 & 36 \\ \hline \hline \end{tabular}  
* Two of the pictures captured by participants had a 90 degrees rotation around the Y-axis of the tablet. Although these two exceptional pictures were discarded because of the high statistical skews that could impose on the affect calculations, this can occur again in real situations if the crews are not warned in advance.  
\end{table} 
Table 2: Comparison between accuracy and precision of sensor-based and non-sensor-based approaches regarding the camera’s orientation factors (i.e., pitch, roll, and yaw/head). The maximum range in orientation occurred around Y (\(90-0=90\) degrees) and Z (\(80-1=79\) degrees) when the non-sensor-based approach was used. When the sensor-based approach was used, the maximum range in orientation occurred around Z (\(38-2=36\) degrees). As was previously indicated, the SIFT algorithms cannot correctly identify the distinct points if the orientation is more than (30 degrees). Therefore, based on the results, the sensor-based approach can prevent this issue during the image capturing phase. Logically, the analysis of the captured images during the image matching phase by image matching algorithms is errorless.
## 4 Case Study
### Performance of Proposed Model
#### 4.3.4 Dab Diameter  
The total error for the dab diameter is 4.72% (accuracy 95.28%), with the error for each type detailed in Table 4. Figure 11 illustrates the ground truth and predicted values of dab diameter for each type, offering a clear comparison of the model's performance. Each rectangle represents the bounding box of the dabs, providing a visual comparison of the model's performance.  
Figure 10: Scatter plot for dab location.
## 2 Materials and Methods  
### Characteristics of the Database  
Parameters related to rock bursts in the underground openings of coal mines or preventing their occurrence were selected. The database contained a total of 627 samples, of which 53 were cases of rock bursts (labelled as 1), and in the remaining cases (labelled as 0), despite the tremors, the excavation was not damaged or destroyed. All dynamic phenomena that led to any damage to the opening were classified as rock bursts. Thus, samples labelled as 1 include all cases of damage or destruction of underground infrastructure on a local scale or with a range of several dozen to several hundred meters (e.g., floor uplift, damages of support (steel arches, mesh, props), and coal ejection into the opening). In this way, the definition of a rock burst was extended, which, according to Polish mining regulations, is considered only to be a case of complete loss of functionality by an opening.  
Samples in the database represent points in underground mining excavations for which geological, geomechanical, mining and technical conditions were determined and correlated with the occurrence of tremor with known energy and the corresponding value of peak particle velocity. These data come from one of the hard coal mines in Poland, in the USCB. The risk of rock bursts in the selected mine remains high due to, for example, complex geological structure of the deposit (thick layers of sandstone, faults, folds, washouts etc.), mining remnants (edges of coal seams, pillars), and the proneness of coals to accumulate strain energy and burst. In the selected mine, coal seams are extracted using a longwall system with caving. Mining is carried out at large depths, exceeding even 1 km. Such large depths determine a high level of primary stress, which also adversely affects the occurrence of rock bursts. The previously mentioned thick sandstone layers are also very unfavorable in the selected mine. The fracturing of these sandstones is a source of strong tremors threatening underground openings. The data collected in the database embraced a period of the last 25 years of mining in the selected mine. Therefore, the geological, mining and technological conditions, rock burst prevention methods, and seismological observations can be considered comparable. For each of the 627 samples in the database, 15 parameters (features) related to the occurrence of rock bursts or their prevention were included.
## 1 Introduction
Therein, the filters are expected to work as P sinks, where the P load would gradually decrease from the inlet to the outlet of the system through retention mechanisms deemed low cost. However, edge-of-field technologies present large variability in P retention as discussed in the next sections, which is commonly the case for systems receiving event-driven drainage discharge. In addition, their retention performance for P may be favored or limited depending on the catchment characteristics, e.g., P load and dominant P form, and design. Thus, it is important that the operating capacity and lifetime of these systems are fully understood before implementation, so the problem with P loss can be properly targeted in different agricultural catchments. This approach would consequently improve the cost-efficiency for P retention in critical source areas and ultimately support compliance with the P retention goals at the catchment-scale and sustainable agriculture. Therefore, this review aims to (i) provide an overview of currently used edge-of-field technologies for P retention from agricultural drainage discharge, (ii) describe their applicability and limitations, (iii) discuss the P retention performance and causes of variability, and (iv) suggest targeting approaches for P retention under specific catchment conditions. The review indirectly supports the use of site-specific regulations for P use at the farm-scale based on the catchment characteristics and efficiency of edge-of-field technologies, as opposed to general regulations. This would enhance the retention of P in critical source areas with minimal impact on the agricultural practices. This review avoided citing studies dealing with other water runoff pathways other than agricultural drainage discharge as the effluent treated, and has no focus on P management or processes at the farm-scale. However, studies dealing with agricultural surface runoff were sometimes cited to provide complementary information or to cover the lack of studies with agricultural drainage discharge.
## Materials and Methods
### Reflectance-Based Datasets  
We acquired optical photos from the PhenoCam data repository ([[http://phenocam.sr.unh.edu](http://phenocam.sr.unh.edu)]([http://phenocam.sr.unh.edu](http://phenocam.sr.unh.edu))) at Harvard Forest [(13)]. Digital cameras at the sites provide continuously visible photos in red, green and blue channels (hereafter, RGB values) on the top of canopy. We used photos captured during daylight hours (07:00 a.m.-5:00 p.m. local time) and excluded photos which were taken under poor weather conditions or snow.  
We used observations from a NDVI sensor installed on the top platform of the tower [(18)]. This sensor (Tetracam Agriculture Digital Camera, Tetracam, Inc., Chatsworth, CA, USA) took a shot at midday every day and provided images in the NIR, red and green bands. The sensor was mounted about 6 m above the canopy and has a viewing zenith angle of 30 degrees, which makes its footprint cover a rectangle of 150 m \(\times\) 300 m at the ground surface. Radiances in the red (550-700 nm) and NIR (720-1000 nm) channels are used to calculate NDVI in the region of interest (ROI) which covered the canopy.  
Instead of using the official MODIS NDVI product, we calculated the NDVI from the 8-day MODIS nadir BRDF-adjusted surface reflectance (MCD43A4, 500 m, collection 5) [(38; 39)] to minimize potential seasonal effects of the illumination geometry. The reflectance data were downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center ([[http://daac.ornl.gov/](http://daac.ornl.gov/)]([http://daac.ornl.gov/](http://daac.ornl.gov/))) for 5 \(\times\) 5 pixel (\(\sim\)2.3 m \(\times\) 2.3 km) regions centered over the location of the tower. After removing pixels contaminated by cloud and snow by using quality control flags associated with each pixel, the remaining pixels were used to calculate the NDVI:  
\[\mathrm{NDVI}\ =\ \frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\mathrm{Red}} \tag{1}\]  
where NIR and Red represent band 2 (near infrared: 780-900 nm) and band 1 (red: 630-690 nm) reflectance, respectively.
## 4 The Forward Kinematics Analysis  
The six rotating joints, which are shown in Figure 3, can be found in the structural diagram of UR10 which is shown in Figure 4, where the pose of the tail end of each joint is represented by one of the six numbers 1 to 6, and 0 represents the position of the origin of the standard 3-dimensional Cartesian coordinate system on the base of the robot.  
Figure 4: The pose diagram of the UR10 robot.  
Figure 3: The six joint axes of the UR10 robot.  
For the pose change from the coordinates of the UR robot's base to the end effector, the pose change matrix \(i_{i}^{-1}T\) between adjacent joints can be obtained through the DH model, as is shown in Equation (4).  
\[i_{i}^{-1}T=Rot(z,\theta_{i})Trans(0,0,d_{i})Trans(a_{i},0,0)Rot(x,\alpha_{i}) \tag{4}\]  
The expression \(Rot(z,\theta)\) is shown in Equation (5).  
\[Rot(z,\theta)=\begin{bmatrix}\cos\theta&\sin\theta&0&0\\ -\sin\theta&\cos\theta&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{5}\]  
In Equation (4), \(\theta_{i}\) is the angle for which \(x_{i-1}\) spins around \(z_{i}\) to become \(x_{i}\); \(d_{i}\) is the distance between \(x_{i-1}\) and \(x_{i}\) along \(z_{i}\); \(a_{i}\) is the distance between \(z_{i}\) and \(z_{i+1}\) along \(x_{i}\), and \(\alpha_{i}\) is the angle for which \(z_{i}\) spins around \(x_{i}\) to become \(z_{i+1}\). \(Rot(z,\theta_{i})\) represents the rotation matrix of the \(z\)-axis; \(\theta_{i}\), \(d_{i}\), \(\alpha_{i}\), and \(a_{i}\) are shown in Figure 5.  
Equations (2), (3), and (5) are introduced into Equation (4) to obtain Equation (6), where \(c\) represents the cosine function and \(s\) represents the sine function.  
\[i_{i}^{-1}T=\begin{bmatrix}c\theta_{i}&-s\theta_{i}c\alpha_{i}&s\theta_{i}s \alpha_{i}&a_{i}c\theta_{i}\\ s\theta_{i}&c\theta_{i}c\alpha_{i}&-c\theta_{i}s\alpha_{i}&a_{i}s\theta_{i}\\ 0&s\alpha_{i}&c\alpha_{i}&d_{i}\\ 0&0&0&1\end{bmatrix} \tag{6}\]  
Successive multiplication is conducted on \(i_{i}^{-1}T\). Then, the end effector's transformation matrix, which is shown in Equation (7), relative to the base of the UR10 robot can be obtained.  
\[T_{robot}=\prod_{i=1}^{6}i_{i}^{-1}T \tag{7}\]  
Equations (6) and (7) are utilized to establish the transformation matrix of the UR10 robot's forward kinematics model.