esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Models and Methods  
### Mathematical Model  
The purpose of this work is to study the acoustic field around an Ahmed body using available CFD tools. For this reason solving the differential equation is required. Motion of fluid is described by continuity and Navier-Stokes Equations (1)-(4). In order to reduce computational time a few assumptions are introduced to the present model. Fluid is viscous, incompressible and Newtonian, and gravitation is negligible and in this work is neglected. Use of the Reynolds Averaged Navier-Stokes (RANS) turbulent model in the first stage significantly improves model convergence. The results from \(k-\omega\) Shear Stress Transport (SST) are used as an initial condition for transient simulation. Continuity equation:  
\[\frac{\partial u_{x}}{\partial x}+\frac{\partial u_{y}}{\partial y}+\frac{ \partial u_{z}}{\partial z}=0 \tag{1}\]Navier-Stokes equation:  
\[\rho_{p}\left(\frac{\partial u_{x}}{\partial t}+u_{x}\frac{\partial u_{x}}{ \partial x}+u_{y}\frac{\partial u_{x}}{\partial y}+u_{z}\frac{\partial u_{x}}{ \partial z}\right) =-\frac{\partial p}{\partial x}+\mu_{p}\left(\frac{\partial^{2}u_ {x}}{\partial x^{2}}+\frac{\partial^{2}u_{x}}{\partial y^{2}}+\frac{\partial^{ 2}u_{x}}{\partial z^{2}}\right) \tag{2}\]  
\[\rho_{p}\left(\frac{\partial u_{y}}{\partial t}+u_{x}\frac{\partial u_{y}}{ \partial x}+u_{y}\frac{\partial u_{y}}{\partial y}+u_{z}\frac{\partial u_{y}}{ \partial z}\right) =-\frac{\partial p}{\partial y}+\mu_{p}\left(\frac{\partial^{2}u _{y}}{\partial x^{2}}+\frac{\partial^{2}u_{y}}{\partial y^{2}}+\frac{\partial^ {2}u_{y}}{\partial z^{2}}\right) \tag{3}\]  
\[\rho_{p}\left(\frac{\partial u_{z}}{\partial t}+u_{x}\frac{\partial u_{z}}{ \partial x}+u_{y}\frac{\partial u_{z}}{\partial y}+u_{z}\frac{\partial u_{z}} {\partial z}\right) =-\frac{\partial p}{\partial z}+\mu_{p}\left(\frac{\partial^{2} u_{z}}{\partial x^{2}}+\frac{\partial^{2}u_{z}}{\partial y^{2}}+\frac{\partial^{2}u_{z}}{ \partial z^{2}}\right) \tag{4}\]  
where:  
\(u_{x},u_{y},u_{z}\)--air velocity vector components  
\(p\)--pressure  
\(\rho_{p}\)--air density  
\(\mu_{p}\)--dynamic viscosity  
Received pressure and velocity distributions are used to calculate the acting drag force \(F_{d}\). To compare results with other studies, the non-dimensional drag coefficient \(C_{d}\) is calculated according to Equation (5). \[C_{d}=\frac{2F_{d}}{\rho_{p}Au^{2}} \tag{5}\]  
where:  
\(F_{d}\)--acting drag force  
\(A\)--frontal area of the vehicle  
\(u\)--relative air velocity. The initial calculations are conducted using the \(k-\omega\) SST turbulence model. Transport equations for this model are presented in (6) and (7).
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 4 Tracking Radar Design
### Radar Block Diagram and Operation
#### 4.3.2 Receiver  
The receiver subsystem amplifies, demodulates, filters, and digitally converts the \(Rx\)-antenna-received signals, preparing them for digital processing. The radar uses two receiving chains, one for the sum channel, or the main receiver (\(Rx\)), and one for the delta channel, or the tracking receiver (TRx). Each receiver comprises an analog front end and a digital back end. The tracking receiver (TRx) is used for autotracking and for processing the error signals produced by a TE21 tracking coupler installed in the \(Rx\) antenna feeder.  
The sum channel echo signals are received at the RHCP port of a 6 GHz OMT of the \(Rx\) Antenna and then are fed to the input of the main receiver. They are filtered, frequency translated and demodulated using the LO signal, and amplified to the value required by the optimal operation of an analog-to-digital converter (ADC). To mitigate the increase of phase noise and interferences due to \(Tx\)-\(Rx\) coupling through antenna spill-over, obtrusive objects, or transmission mismatch, the main receiver (\(Rx\)) employs digital self-interference mitigation phase noise cancellation techniques.  
The delta channel error signals are received at the RHCP port of a TE21 tracking coupler installed in the \(Rx\) antenna feeder. They are processed in the same way as the echo signals.  
The main receiver (\(Rx\)) digital baseband output signal is fed to the main signal processor (SP), while the tracking receiver (TRx) digital baseband output signal is fed to the tracking signal processor (TSP) for subsequent processing.
# Evaluation of LoRa Network Performance for Water Quality Monitoring Systems
#### 3.2.3 Path Loss  
Identifying path loss for wireless network applications in urban environments with possible power attenuation is important for defining the budget link. The path losses in this study were analyzed using the propagation of free space (FSDP), referring to (10), and the Okumura-Hata (Oku-Hata) model, referring to (12). The path losses are presented in Table 9.  
Figure 15: Integration K-means trend on water quality data and packet loss at Water Station 3.  
Because the FSDP method includes antenna gain and feeder losses, the path loss with an antenna gain of 3 dBi showed a slight decline from each end node compared to the implementation of an antenna gain of 2.1 dBi. However, there were no differences in path loss when the antenna gain changed with the Oku-Hata method because its significant elements were the height of the transmitter and receiver, frequency, and distances. However, the Oku-Hata method results in a higher path loss than the FSDP method because it accounts for the effects of diffraction, penetration, and reflection of multiple obstacles in the exterior realm. The obtained FSDP for distance with an estimation of 1 km in this study was significant, as calculated by [PERSON] et al. for a similar omnidirectional antenna used for both the receiver and transmitter [49].  
Nevertheless, the use of the FSDP model can provide information for modifications to reduce the average free-space path loss.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 3 Numerical Example
### Modal Identification  
Data measured by sensors were needed for modal identification but were generated analytically here. An initial velocity of \(-0.1\) m/s was given vertically to nodes 2 and 3 in the model shown in Figure 3 to obtain the time histories of the displacement and acceleration at the sensor locations. These generated values were then adopted as measured data to perform modal identification, which provided the mode shape \(\phi_{ei,maa}\) for the strain, the mode shape \(\phi_{ai,maa}\) for the acceleration and the eigenvalue \(\lambda_{i,maa}\). For simplification, only the first mode was considered. For the first mode, \(\lambda_{maa}=1738.8\ \sec^{-2}\) and the mode shapes for the strain and the acceleration are as follows.  
\[\phi_{e,maa}=\left[\begin{array}{ccc}0.082&0.993&0.082\end{array}\right]^{T} \tag{42}\]  
\[\phi_{a,maa}=\left[\begin{array}{ccc}0.408&0.816&0.408\end{array}\right]^{T} \tag{43}\]
# Modeling of Fatigue-Strength Development
## 2 Materials and Methods
### Prediction of Fatigue-Strength Development in Emulsion Asphalt  
The combined effects of cure temperature and cure duration on fatigue-strength development in the cold-emulsion reclaimed asphalt pavement mixture was studied using the maturity method. Fatigue-strength evolution with time in the cold-emulsion asphalt mix was tracked using Equation (14). This study evaluated both linear hyperbolic fatigue-maturity function, Equation (8), and the parabolic hyperbolic fatigue-maturity function, Equation (9). Model coefficients, \(\text{k}\) and \(\text{F}_{\text{u}}\), for Equations (8) and (9), were obtained from fatigue strengths of cold-emulsion asphalt specimens cured at 5 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, and validated with fatigue-strength results of specimens cured at 50 \({}^{\circ}\)C. Keeping in mind that asphalt fatigue test results are sensitive to test strain levels, the models were replicated at each of the six strain levels adopted in the four-point bending (4 PB), test to establish the range of their validity.
## 2 Related Works  
### Deep Learning for Robot Grasping  
In recent years, deep learning has gained prominence in the field of robot grasping because of its superior feature extraction and generalization capabilities. Deep learning methods for robot grasping can be broadly categorized into regression-based, classification-based, and detection-based approaches.  
Regression-based approaches resemble object detection bounding box regression and have demonstrated effectiveness, but they often involve a two-stage process, leading to slower detection speeds. For instance, ROI-GD [8] utilizes features within the Region of Interest (ROI) for grasp detection, initially extracting ROIs from a scene and subsequently employing a grasp detector based on the extracted ROI features. Inspired by Fast-RCNN, [PERSON] et al. [16] employed a grasp proposal network to identify grasping regions and then used a filter to determine the optimal grasping pose. Similarly adopting a two-stage approach, SISG-Net [17] incorporates a semantic segmentation capability, albeit at the expense of increased computational costs. [PERSON] et al. [18] integrated YOLO with grasp detection, but this method is limited to grasping single objects and cannot handle grasping in cluttered environments. Additionally, [PERSON] et al. [19] applied Mask-RCNN for grasp feature extraction and introduced Y-Net for angle estimation and Q-Net for quality evaluation.  
Classification-based approaches, pioneered by [PERSON] et al. [9], treat grasping as a classification problem. It is noteworthy that SAE [9] is the first approach that employed deep learning methods to address robot grasping problems, avoiding the laborious process of manual feature engineering. [PERSON] et al. [5] developed a model known as the Grasp Quality Convolutional Neural Network (GQ-CNN) for the classification of stable grasps in depth images. Recently, [PERSON] et al. [20] developed an angle-matching strategy based on the oriented anchor box mechanism.  
Detection-based approaches treat grasping as a pixel-level grasp configuration detection problem, as illustrated by [PERSON] et al. [4]. [PERSON] et al. [4] pioneered an approach that involves regression to determine grasp configurations by predicting heatmaps for grasp confidence, angles, and widths. Other studies, such as [PERSON] et al. [6], introduced a residual convolutional neural network for the task of antipodal robot grasping, achieving high accuracies of 97.7% on the Cornell dataset and 94.6% on the Jacquard dataset. [PERSON] et al. [21] introduced a Gaussian-based grasp representation method based on a lightweight generative structure network. This method achieved accuracies of 97.8% on the Cornell dataset and 95.6% on the Jacquard dataset. [PERSON] et al. [7] utilized depth-wise separable convolutions to make a grasping network more lightweight. [PERSON] et al. [22] also utilized depth-wise separable convolution to create a lightweight network. [PERSON] et al. [23] employed an RGBD dense fusion approach, but the grasping performance was mediocre.
## 3 Results
### FEM Deformation Analysis  
The structural FEM analyses were carried out in ANSYS software. The calculations were performed for both orientations of the magnets: vertical and horizontal magnetization. As it was revealed in the previous subsection, the direction of the magnetization affects the direction and magnitude of the magnetic force. The values of these forces and their directions for each case are given in Table 2. Both above-mentioned cases were calculated for two exemplary adjustment forces in adjusting screws--\(F_{r}\) = 0.5 kN and \(F_{r}\) = 1.5 kN. The main aim of this study was to compare the influence of the magnetic force on the deformation of the whole magnet holder structure and changes in the position of the magnet.  
The deformations of the holder for the above-described cases are presented in Figures 13 and 14 for pressed and pulled magnets, respectively. Deformations of the outer frame in all cases are very small. The inner frame is subjected to bending due to the presence of the pretension force in adjustment screws. The gap between the inner and the outer frame allows for the deformation of the inner U-shaped frame and through it the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Orientation of Magnet** & \begin{tabular}{c} **Magnetic Force (kN)** \\ **Gap 100 mm** \\ \end{tabular} & \
\begin{tabular}{c} **Magnetic Force (kN)** \\ **Gap 200 mm** \\ \end{tabular} &
\begin{tabular}{c} **Magnetic Force (kN)** \\ **Gap 600 mm** \\ \end{tabular} \\ \hline Vertical magnetization (down) & 3.82 & 3.71 & 3.79 \\ Horizontal magnetization (right) & \(-\)3.64 & \(-\)3.70 & \(-\)3.78 \\ Vertical magnetization (up) & 3.81 & 3.71 & 3.76 \\ Horizontal magnetization (left) & \(-\)3.61 & \(-\)3.70 & \(-\)3.72 \\ \hline \end{tabular}
\end{table}
Table 2: Magnitudes of magnetic forces acting on particular magnets of the Halbach array.  
Figure 11: Field distribution in Halbach array calculated in FEMM for THZ undulator for a gap of 100 mm.  
Figure 12: Magnetic forces for individual magnets pairs, measured for different undulator’s gaps; magnets with remanence 1.28T.  
movement of the magnet. As observed, the movement of the magnet was constant in the whole volume.  
In the first case of the pressed magnet, the extreme displacements of the magnet under magnetic load \(F=-3.64\) kN were as follows: \(-0.306\) mm for the adjustment force \(F_{r}=0.5\) kN and \(-0.494\) mm \(F_{r}=1.5\) kN. In the second case of the pulled magnet, the extreme displacements of the magnet under magnetic load \(F=3.82\) kN were as follows: \(0.202\) mm for adjustment force \(F_{r}=0.5\) kN and \(-0.006\) mm \(F_{r}=1.5\) kN. The direction of the magnetic force does not have any significant influence on the mechanical response of the holder. Only a slight initial displacement of the magnet was observed. It was proven that in both cases, calibration of the magnet position is possible.
## 4 Impossibility Results
### Synchronization and State Observation Problems
#### 4.1.6 Causal Ordering of Messages (CO)  
The CO problem is specified as follows [19; 20; 21; 22].  
* **Safety specification of CO** states that if the send event of message \(m\) causally happens before send event of message \(m^{\prime}\), then at each common destination of \(m\) and \(m^{\prime}\), \(m^{\prime}\) cannot be delivered before \(m\).
* **Liveness specification of CO** states that a message sent by a correct process to another correct process should be eventually delivered.  
In a system with even one Byzantine process, the distributed CO problem is subject to the same limitations (exposure to false positives and false negatives) as the CD problem, resulting in safety and liveness violations.  
Solving CO requires satisfying  
\[\Phi_{CO}(e_{i}^{\psi})\stackrel{{ def}}{{=}}\bigwedge_{e_{h}^{x}}(e_{h}^{x} \to e_{j}^{z}|_{E}=e_{h}^{x}\to e_{j}^{z}|_{F})\wedge\phi(e_{i}^{\psi}),\]  
where \(e_{h}^{x}\) is a send event of a message to \(p_{i}\), \(e_{j}^{z}\) is an event where \(p_{j}\) sends a message \(m^{*}\) to \(p_{i}\), \(\phi\) is a predicate on when/whether \(p_{i}\) can safely deliver \(m^{*}\) sent at \(e_{j}^{z}\) to itself (i.e., has received and determines it is safe to give \(m^{*}\) with respect to all other messages sent to itself in the execution to the application), \(e_{i}^{\psi}\) is an event where \(p_{i}\) delivers the message \(m^{*}\) from \(p_{j}\), and \(\rightarrow\) is defined on application messages.  
As \(e_{h}^{x}\) is a send event, from [4] for the CD problem, detecting \(e_{h}^{x}\to e_{j}^{z}\) is susceptible to false positives and/or false negatives. Thus, it cannot be guaranteed that the predicate \(e_{h}^{x}\to e_{j}^{z}|_{E}=e_{h}^{x}\to e_{j}^{z}|_{F}\) in the formula \(\Phi_{CO}\) can be satisfied. Hence, CO cannot be solved.  
A false positive of the CD problem can result in a liveness violation--waiting indefinitely at \(p_{i}\) for the delivery of \(m^{*}\) until the prior delivery of \(m^{\prime}\) that was never sent by \(p_{h}\)--in the CO problem. A false negative of the CD problem is a safety violation--not waiting for the delivery of \(m^{\prime}\) that was sent by \(p_{h}\) at \(e_{h}^{x}\) to \(p_{i}\)--in the CO problem.
###### Abstract
Another data fusion approach is high-level (decision-level) data fusion, and it handles each data source separately and fuses the results of corresponding models to provide final decisions to improve the results [28]. Although data fusion has been widely used in remote sensing for quite some time [26], the application of data fusion to research for food safety and quality assessment is relatively recent. Data fusion in the food analysis applications has been applied to measure and evaluate many different quality and safety parameters, including the freshness of pork meat [29; 30], the water-holding capacity of chicken breast fillets [31], hazelnut adulteration [32], green tea quality [33], bruising of blueberries [34], and the freshness of tilapia fillets [35]. However, research that uses data fusion of two different hyperspectral imaging systems measuring in the VNIR range of 400-1000 nm and the SWIR range of 1000-2500 nm for the detection of foreign materials (FMs) has not yet been conducted. Therefore, the purpose of this paper is to demonstrate the potential of using two different HSI systems (VNIR and SWIR) using data fusion for detecting the various types of small sized FMs on the surface of chicken meat and to visualize the detection of FM. The specific objectives of this study were, first, to build and study a spectral library of chicken meat (muscle and fat) and FMs; second, to develop an FM-blob detection method using hyperspectral images; third, to spatially register the VNIR and SWIR images from fillet segmentation masks for blob matching; finally, to apply data fusion for decision making about the presence of FM.
## 4 Discussion
### Model Performance
In an STN prediction study in Fuyang, Zhejiang Province, China, [PERSON] et al. [62] found that the RF and BRT models explained approximately 50% of the STN variability. In contrast, [PERSON] et al. [63] used the RF model to predict STN content in northeast China, obtaining R\({}^{2}\) values that were higher than this study that explained 69% of the STN variation. These different predictive performances may be due to differences in the type and quality of the ancillary data, the study area, and the number of field observations.
## 3 Materials and Methods
### Overview of the Proposed Approach
#### 3.1.1 Network Architecture  
The proposed approach is based on the U-Net architecture given by [PERSON] et al. [11], which is drawn schematically as a \"U\"-shape network. This shape results from intermediate connections between the encoder and decoder, which help preserve the spatial information and fine details between the encoder and decoder. This feature makes the U-Net capable of performing precise pixel segmentation. Moreover, these connections allow versatility in integrating attention mechanisms, which focus on specific regions of the image, adding contextual information to the model.  
The proposed network architecture uses the intermediate connections to feed an attention mechanism proposed by [PERSON] et al. [15], as illustrated in Figure 1.  
The proposed architecture comprises an encoder formed by five convolutional layers, which transform the input into 32 channels and end with 512 channels. Then, the output of each encoder layer is given to the attention module. The decoder layers are fed with the output of the attention module and an upsampling of the previous decoder layer. Finally, the segmentation map is delivered with two channels: foreground and background probabilities.
## 3 Results and Discussions
### UAE Effect on Almond Oil  
In order to understand ultrasound effect on almond oil, the remaining almonds from UA, BA, and NA were recovered and used up with their fat using automatic Soxhlet extraction with n-hexane. Yields and fatty acid profile were then compared.  
According to Table 3, heat treatments do not alter almond oils. Visibly, the yield of extracted oils between conventional and ultrasonic system comparing to natural almonds remained stable with an average of 53% of total almond oil extracted. Fatty acid (FA) profile was constant as well; oleic acid (C18:1 n9) was the most abundant FA in the samples studied with 71%, followed by linoleic acid (C18:2 n6) with ~20%, palmitic acid (C16) with ~6%, stearic acid (C18) with ~1.1%, and palmitoleic acid (C16:1) with ~0.5%. Across all previous research articles that probed the fatty acid profile of almonds, oleic acid was found to be the major FA with 57.54% to 73.94% [30; 31]. Considering these results, UAE presents the advantage of being a non-destructive extraction technique since it does not damage essential fatty acids which could be used for its numerous health benefits [32].
## 5 In-Line Application of LIBS in Industrial Production
### Minerals  
For the exploration and efficient excavation of mineral quarries the composition of rock has to be measured during the drilling process. Analysis of minerals inside the drill hole is not feasible; however, the drill dust can be extracted with a dust hose and analyzed. Figure 10a shows a LIBS analyzer for continuous in-line analysis of the drill dust [102]. The LIBS system is mounted on a mobile drill rig.  
The results from a LIBS measurement campaign in a quarry are shown in Figure 10b. Dust samples were collected at different horizontal positions of the drill rig and at various drill depths. The Aluminum concentration measured as a function of drill depth and horizontal position varied from 0 to 1.5 m\(\%\). From such elemental maps a spatial model of the mineral deposit can be determined, and the excavation process can be optimized.  
In another study, the ability of LIBS to provide in-line analyses of phosphate ores under industrial conditions was demonstrated [233]. Impurities in the phosphate rock significantly affect the ability to efficiently recover phosphate from the rock in the production plant and produce on-grade products. The most significant variables are CaO, MgO, Fe\({}_{2}\)O\({}_{3}\), and Al\({}_{2}\)O\({}_{3}\). A rugged LIBS sensor was developed and installed above a conveyer belt in an open phosphate mine (Four Corners Mine, FL, USA). A photograph of the installed LIBS system is seen in Figure 11a. The system enabled automated measurements of several elements (Mg, Fe, Al, Si, Ca) and the on-belt evaluation of phosphate ores. The variation of LIBS-measured signals for MgO (square symbols), Fe\({}_{2}\)O\({}_{3}\) (up triangle), bone phosphate lime (BPL, down triangle), and metal impurity ratio (MER, circle) over 27 h of a test run are shown in Figure 11b. During the test run two different rock portions were detected as evidenced by varying signals of MgO, Fe\({}_{2}\)O\({}_{3}\), BPL, and MER. The real-time detection of unwanted material by LIBS enables removing this material from the conveyer before further processing.  
Figure 10: LIBS in-line measurement of drill dust in a quarry. (**a**) LIBS system mounted on a drill rig. (**b**) Measured spatial variation of Aluminum concentration as a function of drill depth and horizontal position of the mobile drill rig. Adapted from [102].