esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Diffusion Coefficients from Desorption Kinetics
Therefore, the lower surface concentration at the beginning of the kinetics has no influence on the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Polymer** & **Temperature** & \multicolumn{8}{c}{**Diffusion Coefficient (cm\({}^{2}\)/s)**} \\ \hline  & **(\({}^{\circ}\)C)** & **Acetone** & **Ethyl** & **Toluene** & **Chlorobenzene** & **Phenyl** & **Penyl** & **Benzophenone** & **Steyne** \\ \hline GPPS & 85 & \(9.8\times 10^{-9}\) & \(1.1\times 10^{-9}\) & \(1.0\times 10^{-10}\) & \(2.0\times 10^{-10}\) & \(2.1\times 10^{-13}  
measured diffusion coefficients \(\mathrm{D_{P}}\), because the slopes after this initial phase were taken into account. The measured diffusion coefficients were determined in the range of 10\({}^{-8}\) cm\({}^{2}\)/s to 10\({}^{-16}\) cm\({}^{2}\)/s depending on the substance and the applied temperature. As expected, low molecular weight molecules show significantly higher diffusion coefficients \(\mathrm{D_{P}}\) than high molecular weight substances, especially at low temperatures. The glass transition temperature \(\mathrm{T_{g}}\) of polystyrene of around 100 \({}^{\circ}\)C is within the measuring interval for both GPPS and HIPS. In the case of GPPS sheet 1 spiked with \(n\)-alkanes, five kinetic points were determined below \(\mathrm{T_{g}}\) and three kinetic points were determined above \(\mathrm{T_{g}}\) (both include the kinetic point at \(\mathrm{T_{g}}\) of 100 \({}^{\circ}\)C). In case of sheet 1, it was possible to calculate the activation energies of diffusion below as well as above the glass transition temperature. Regarding GPPS sheet 2, only one kinetic point was determined above \(\mathrm{T_{g}}\). Therefore, activation energies of diffusion are not available above the glass transition temperature and the diffusion coefficients at 105 \({}^{\circ}\)C were applied to determine the activation energies of diffusion below the glass transition temperature. A similar situation was available for HIPS. Four (sheet 1) and five (sheet 2) kinetic points were available below, whereas two and three kinetic points, respectively, were available above the glass transition temperature. In the case of HIPS, the determination of the activation energies of diffusion was possible below and above the glass transition temperature. However, due to only a couple of kinetic points and the small temperature interval, the activation energies of diffusion above the glass transition temperature are less precise for HIPS compared to GPPS. Figure 1: Gas phase migration kinetics of **(a)**\(n\)-octane from general purpose polystyrene (GPPS) at temperatures between 80 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, **(b)** chlorobenzene from GPPS at temperatures between 85 \({}^{\circ}\)C and 105 \({}^{\circ}\)C, **(c)**\(n\)-dodecane from high impact polystyrene (HIPS) at temperatures between 80 \({}^{\circ}\)C and 100 \({}^{\circ}\)C and **(d)** toluene from HIPS at temperatures between 80 \({}^{\circ}\)C and 100 \({}^{\circ}\)C.
## 1 Introduction
Despite prior studies in the study area focusing primarily on land use/land cover changes across all classes [11; 16; 17; 62; 63], there is a noticeable gap in research specifically addressing the species-level identification of Acacia _decurrens_ plantations through the utilization of high-resolution satellite imagery and environmental variables combined with machine learning algorithms. Examining such a spatial pattern is crucial in any study aiming to ensure the provision of goods and services [61]. Therefore, the objectives of this research were (i) to model the spatial distribution of Acacia _decurrens_ plantation forests using high-resolution satellite imagery, (ii) to evaluate the performance of SDM algorithms for Acacia _decurrens_ distribution modeling, and (iii) to identify the relative importance of predictor variables for Acacia _decurrens_ distribution modeling. Modeling and understanding the spatial spread of such species are high priorities for resource managers to assess the environmental implications of the sustained use of plantation forests and to scale up for the other degraded areas of the country based on scientific findings and with great attention. This is because in Ethiopia, it is believed that small-scale tree plantations can contribute to addressing issues related to sustainable agricultural land use, mitigating the negative impacts of deforestation, and meeting the needs for the livelihood and energy of the growing population [64]. Having comprehensive information about its distribution enables effective control and management.
## 2 Materials and Methods
### Teleoperation System Overview
#### 2.1.1 Robot and User Communication  
A Kinect V2 depth sensor (Microsoft Corporation, Albuquerque, NM, USA) publishes compressed RGB-D images from the perspective of the robot's head to produce a point cloud-based 3D visual feedback in the MR subspace. Compressed images enter the MR subspace through a custom image subscriber and depth image subscriber in Unity 3D. In Unity, RGB images and depth images are decompressed using OpenCV for C# (Microsoft Corporation, Albuquerque, NM, USA). A custom \"material\" shader in Unity combines these images into a 3D point cloud from the RGB image and the depth image, and the computation is done in parallel on the GPU.  
Onsite sensory information from the remote workspace is transferred back to the human operators. The operators act as part of the control loop to interact with the remote robotic arm and hand system via a computer-generated virtual environment and overlaid multisensory data. The 3D virtual scene is rendered according to the actual robot working environment and displayed to the user via the HMD. A force sensor attached to the robot finger is primarily dedicated to monitoring the grasping process. The user can directly observe the workpiece and end-effector status with the real-time overlaid sensory information in MR during operation. The depth sensor information is mapped and reconstructed in the point cloud form on the master side to be completely consistent with the environment of the slave side. The proposed teleoperation system allows information mapping between the user side and the robot side using an MR subspace [47].
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 2 Methods and Analysis
### Secondary Precise Tracking System  
The secondary platform is used to load the coronagraph payload and perform solar observation tasks. It can point and stabilize the optical axis on a moving balloon and track the sun by moving in azimuth and pitch. The index of the secondary platform is shown in Table 2.
## 3 Experiment
### Discussion  
In some lesions without tumors and after time delay, the signals of certain channels are highly coherent, or the signals of certain channels appear at non-overlapping times. The intensity of these signals remains high after pairwise multiplication and summation by the DMAS algorithm. Therefore, the image obtained using the DMAS algorithm has more clutter, and the coherent sum of these signals is high, which leads to high CF values. As a result, the images obtained using the CF-DAS and CF-DMAS algorithms also have some clutter. The MICF-DMAS algorithm retains the coherence of the DMAS algorithm and improves upon it using MICF weighting. In MICF, an ICF of lower weight is first  
Figure 13: Imaging results of the four algorithms for the heterogeneous two-tumor phantom (III) (Z = 45 mm).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MICF-DMAS** & **CF-DAS** & **DMAS** & **CF-DMAS** \\ \hline D1 (mm) & 8.1 & 9.7 & 10.1 & 9.6 \\ D2 (mm) & 9.2 & 12.1 & 12.6 & 11.6 \\ X1 (mm) & (22,\(-\)20) & (22,\(-\)20) & (21,\(-\)20) & (22,\(-\)21) \\ X2 (mm) & (\(-\)19,17) & (\(-\)19,18) & (\(-\)19,18) & (\(-\)19,17) \\ SCR (dB) & 10.26 & 4.03 & 4.21 & 5.91 \\ CR & 19.4 & 10.46 & 10.92 & 18.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation indicators of the four algorithms for the heterogeneous two-tumor phantom (III) imaging results.  
obtained by adding a time window to signals that do not overlap in time. The ICF values for multiple pairs of signals are then multiplied together to obtain the MICF. For each pair of signals with low coherence in the clutter focus, the MICF is multiplied by a factor of low value, which reduces the weight of the clutter focus. Compared to the original CF, the focus points of the improved MICF have lower weights in cases of clutter. As shown in Figure 12, high-intensity clutter is exhibited at (\(-23\) mm, \(-20\) mm) in the images acquired using the CF-DAS and CF-DMAS algorithms but not the MICF-DMAS algorithm. As a result, the MICF-DMAS algorithm yields images with less clutter and the highest SCR and CR. The MICF-DMAS algorithm does not change the focus of the signal, so the coordinates corresponding to the highest pixel value in the image are similar to those of the other three algorithms. The MICF is obtained by multiplying multiple ICF values and is more sensitive to signal coherence. Focused signals around the highest intensity point in the image are less coherent and have smaller MICF values. The focused intensity obtained after MICF weighting is small. The MICF-DMAS algorithm yields images with the smallest diameter of the high-intensity region around the point with the highest pixel value.
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 4 Experiments
### Implementation Details  
#### 4.2.1 SegDepth Module and Heatmap Module  
Given an RGB image I (\(R^{256\times 256\times 3}\)) as input, the architecture uses two modified ResNet-50 models [39] to encode the image into two embedding features. The skip-connected low-level features and the embedding features are input to the segmentation depth decoder and the heatmap decoder, respectively, which infer depth map (\(R^{256\times 256\times 1}\)), segmentation (\(R^{256\times 256\times 3}\)), and heatmap (\(R^{64\times 64\times 15}\)), respectively. The heatmap decoder trains the network using the following mean square error (MSE) function:  
\[L_{H}=MSE(HM,HM),\]  
\begin{table}
\begin{tabular}{l c c c} \hline
**Action** & **Frames** & **Train Size** & **Test Size** \\ \hline Gaming & 14,171 & 10,186 & 1,594 \\ \hline Gesticulating & 18,634 & 13,429 & 2,082 \\ \hline Greeting & 6,653 & 4,798 & 742 \\ \hline Lower stretching & 52,056 & 33,482 & 15,235 \\ \hline Patting & 10,054 & 7,052 & 1,592 \\ \hline Reacting & 24,282 & 17,492 & 2,716 \\ \hline Talking & 11,072 & 7,987 & 1,234 \\ \hline Upper stretching & 170,723 & 109,560 & 50,564 \\ \hline Walking & 33,430 & 21,468 & 9,862 \\ \hline \end{tabular}
\end{table}
Table 1: Total number of frames according to the action types and the distribution between training and test sets.  
\(HM\) is the ground truth (GT) and \(H\)\(M\) is what the decoder predicted. Since the depth estimation and segmentation are performed simultaneously in a single process, we used a unified loss function for depth and segmentation, as follows:  
\[L_{D}=L_{segmentation}+L_{depth}, \tag{2}\]  
The depth loss function uses the mean square error with GT, and the segmentation loss function uses cross-entropy, as follows:  
\[L_{depth}=\lambda_{d}MSE(D,\hat{D}), \tag{3}\]  
\[L_{segmentation}=\lambda_{s}CE(S,\hat{S}), \tag{4}\]  
\(D\) and \(S\) are the GT of the depth map and the segmentation, and \(\hat{D}\) and \(\hat{S}\) are the predictions of the decoder.
## 3 Chaos-Based Parameter Estimation and Stability Analysis
### Dynamical Analysis for Parameter Tuning via Bifurcation and Chaotic Behavior
\begin{array}{c}-1+3x^{12}+14x^{11}+20x^{10}-6x^{ 9}-47x^{8}-44x^{7}\\ +(-8\alpha+8)x^{6}+44x^{5}+29x^{4}-2x^{3}-12x^{2}-6x)\\ \end{array} \right)},\] (38) \[O^{[*]}(x) = \frac{x^{4}\Bigg{(} \begin{array}{c}x^{12}+6\alpha^{11}+12x^{10 }+2x^{9}-25x^{8}-28x^{7}+\\ 1+(-8\alpha+8)x^{6}+28x^{5}+7x^{4}-10x^{3}-4x^{2}+2x\\ \end{array} \Bigg{)}}{\left( \begin{array}{c}-1+3x^{12}+14x^{11}+20x^{10}-6x^{ 9}-47x^{8}-44x^{7}\\ +(-8\alpha+8)x^{6}+44x^{5}+29x^{4}-2x^{3}-12x^{2}-6x\\ \end{array} \right)},\] (39) \[O^{[*]}(x) = -\frac{x^{3}\Bigg{(}\begin{array}{c}x^{8}-1+(6\beta-2)x^{7}+(16 \,\beta^{2}-8\,\beta-2)x^{6}+\\ \phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-} \phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-} \phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-}\phantom{-* **Smooth, Connected Basins:** Selecting values of \(\alpha\) and \(\sigma\) from the stable region (Table 1, Column 4; Figure 2a-g), where no bifurcations are present, leads to stable dynamical behavior in the iterative schemes (Figure 3a-e). * **Robust Convergence near Bifurcations:** When \(\alpha\) is chosen near the first or second bifurcation thresholds (Table 1, Columns 5 and 6), the dynamical plane (Figure 3f) still exhibits stability. In these cases, large basins indicate robustness, and convergence can be achieved even from coarse initial guesses, albeit at a slower rate. * **Higher-Period Bifurcation Regions:** For parameter values corresponding to higher-order bifurcations (e.g., four- and eight-period cycles; see Table 1, Column 7), the schemes continue to display stable behavior, as shown in Figure 3g. However, convergence is typically slower and involves a higher computational cost. * **Disconnected or Scattered Basins:** In regions associated with chaotic or unstable behavior (Table 1, Column 8), the iteration may converge to unintended solutions or fail to converge altogether. These behaviors are illustrated in Figure 4a-e, where the parameter \(\alpha\) lies within the chaotic regime. Figure 3: (**a–g**) Dynamical planes illustrating stable convergence behavior of the fractional-order scheme.
## 5 Proposed Solutions and Policy Recommendations
### Demystifying Controversy between Principles of Equitable Access and Non-Appropriation  
#### 5.3.1 Ensuring Equitable Access through Coordinated Efforts  
In order to ensure equitable access for all states, the ITU RR 2016, after the WRC 2015, provided a measure stating that all filings for orbital slots must be accompanied by coordinated efforts considering the potential stakeholders' activities actively or passively impacted by the proposed satellite [28]. These coordinated efforts ensure that the satellite operator takes measures to refrain from causing radio interference to other satellites in orbit [109] for a period of up to seven years [28]. These measures, although pledged to cater to the radio astronomy needs of astronomers, have proven insufficient due to the prevalent congestion in both the LEO and MEO, causing major interference and thus inhibiting equitable access [110]. Therefore, in addition to the filling and coordinated efforts, it is recommended that the ITU reduce the number of satellite constellations and satellites in LEO and MEO equitably.
## 3 Methodology  
We conducted two parameter studies on AI-based activity recognition and DES to validate the proposed DTC (Figure 1). The input data are from a completed construction project on special foundation engineering in Rosenheim, Germany. This project involved the construction of a bypass road including two bridges near the German-Austrian border. The project consists of 32 bridge piers, each including 5 to 17 large diameter bored piles of the same type, and ranging from 26 m to 50 m in length. Due to challenging soil conditions [38], the project owner mandated comprehensive process documentation from the pile producer, BAUER Group. This documentation provided the data for our research. We used three data sources:  
1. **Activity data:** While producing the pile, workers manually recorded activities on site with a tool provided by fielddata.io (a German start-up, now acquired by the BAUER Group). They had a choice of 27 predefined activities. The tool was connected via the equipment's Wi-Fi to have the same time stamps as the sensor data.
2. **Equipment sensor data:** We used data from sensors already installed on the equipment and sent via telematics to the proprietary platform every 1 Hz. Measurements included pump pressure, rotary torque, winch forces, and mast inclination (Table 1 and Figure 3 (black circles)).  
3. **Production log:** Every pile was documented in a handwritten report. This report gave insight into the bored pile sequence and start and end times. Thus, the duration of the following seven subprocesses is derived: (1) drill, (2) idle between drill and reinforce, (3) reinforce, (4) idle between reinforce and install contractor pipe to fill in concrete, (5) install contractor pipe, (6) idle between install contractor pipe and concrete, and (7) concrete. Data from 232 bored piles were analyzed. We used the activity and equipment sensor data to test and train the DL models for automatic activity recognition. We used the production log input for the DES model.
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
## 4 Surface Tracing Strategy for Long Scan Length
### Profile Restoration  
Using Equations (2) and (6), linear misalignment can be well compensated. However, misalignment could be eliminated only when the linear guide had perfect straightness and repeatability. In practical tests, further compensation on the residual mismatch was still needed. Figure 7 shows the concept of compensating the residual mismatch, demonstrated by simulation data. The residual mismatches were compensated by adjusting every alternate data section. Assuming \(k\) is an odd number, the mismatches between the \(k\)th, \((k-1)\)th and \((k\pm 1)\)th sections are  
\[\left\{\begin{array}{l}\Delta d_{k}=\frac{1}{N}\sum\limits_{i=0}^{N}{z^{ \prime}_{i+(n-N),k-1}-z^{\prime}_{i,k}};\\ \Delta d_{k+1}=\frac{1}{N}\sum\limits_{i=0}^{N}{z^{\prime}_{i,k+1}-z^{\prime }_{i+(n-N),k}}.\end{array}\right. \tag{9}\]  
Then, the further corrected height values are  
\[z^{\prime\prime}_{k}=z^{\prime}_{k}+\Delta d_{k}+\frac{i}{N}(\Delta d_{k+1}- \Delta d_{k}). \tag{10}\]  
Considering the redundant data in the overlapping area, in each adjusted section \(\{z^{\prime\prime}_{k}\}\), the first \(N\) data points and the last \(N\) data points were removed to connect with the neighboring sections.
## 4 IDAIC Framework
### IDAIC Phases  
#### 4.1.1 Domain Understanding  
The first phase in the IDAIC framework aims at collecting the necessary information to obtain a comprehensive understanding of the asset or system under study. In accordance with the Industrial AI paradigm [27], we categorize the required inputs as people, systems and things. The key people, or the roles that must be fulfilled, are those of a domain expert and a data expert. The understanding of the domain and the understanding of data analytics are two key skills needed for successful analysis of industrial data. These people must then integrate with the systems (such as data from heterogeneous sources), and things (such as documentation and equipment specifications). This phase is similar to Business Understanding in CRISP-DM; however, given the engineering context, Domain Understanding is considered to be more aptly named.
## 2 Materials and Methods
### The Simulated Exposure Scenario  
As for the V2X scenario, we considered the case of a road user (e.g., a pedestrian) standing in the vicinity of a car equipped with two 5G-V2X antennas. We implemented the computational simulations using the finite-difference time-domain (FDTD) solver of the simulation platform SIM4 Life (ZMT Zurich Med Tech AG, Zurich, Switzerland, www.zurichmedtech.com, accessed on 18 July 2022) [48].  
To assess the impact of the position of the phantom on the dose of RF field absorbed, five different configurations of the human model with respect to the car body were considered, as can be seen Figure 2. The chosen configurations are listed in the following:  
1. CONF 1: the Ella model is placed next to the frontal car hood, at its midline;
2. CONF 2: the Ella model is placed laterally to the car body, in front of the antenna array mounted on the windscreen;
3. CONF 3: the Ella model is placed laterally to the car body, at an equal distance from the two antennas;
4. CONF 4: the Ella model is placed laterally to the car body, in front of the array antenna mounted on the roof top;
5. CONF 5: the Ella model is placed next to the car trunk, at its midline.  
Figure 2: The exposure scenario with the car model, the phantom Ella at the five analyzed configurations and the two antenna arrays (the small dark rectangles at the front and rear of the car). The Ella model is placed in the ‘FRONT’ (**top** panel) and ‘ORTG’ orientation (**bottom** panel) with respect to the car body.  
Moreover, for each of the five configurations above, two different orientations--frontal and orthogonal--of the human phantom with respect to the car body were assessed. In the first orientation (i.e., \"FRONT\" orientation, illustrated in the upper part of Figure 2), the eyes of the model are directed towards the car body, whereas in the second orientation (i.e., \"ORTG\" orientation, illustrated in the lower part of Figure 2), the [PERSON] model is placed orthogonal to the car body. In each configuration and orientation, the Ella model was placed at the minimum distance from the car model, that is, the outermost point of the human phantom (namely, the toes in the FRONT orientation and the right/left shoulder in the ORTG orientation) was placed 1 mm away from the same line as the outermost point of the car model (namely, the frontal and rear bumpers in CONF1 and CONF5 and the lateral side of the car body in CONF2-4).  
For each configuration, the computational domain included the whole human body, the whole car model and the two array antennas. The computational domain was discretized with a nonuniform grid with a maximum step of 1.408 mm for all body tissues, apart from the eye tissues (i.e., cornea, lens, sclera and humor vitreous), which were discretized with a finer grid of 1.043 mm. The total number of discretization cells varied from \(857\times 10^{6}\) to \(1.102\times 10^{9}\) across configurations. Furthermore, the computational domain was truncated by assuming a perfectly matched layer (PML) absorbing condition at the domain boundaries.
## 4 Experimental Results and Discussion
### Training and Implementation  
The experiments are implemented using the Pytorch framework. All of the models were optimized via the Adam optimizer [35] with a learning rate of 0.0001, batch size of 8. The datasets are splited into two set: training set and validation set with the ratio of 8:2. The Dice overlap coefficient was adopted as a regional loss function [12]. The Dice score is not only a measure of how many positives are found, but it also penalizes for the false positives, similar to precision. Thus, it is more similar to precision than accuracy. The only difference is the denominator, where the true positives are replaced by the total number of positives. The Dice score is also penalizing for the false positives. We did not incorporate any transfer learning or use any data augmentation in order present a fair evaluation. Furthermore, the average results from 5-fold cross-validation were used as the overall performance of our proposed method. The models are trained until they cannot achieve further improvement. In this research, we adopt the Dice similarity coefficient, Precision, and Recall as evaluation metrics to validate the effectiveness of the proposed method. Three metrics described in the formulas:  
\[Dice\,score=\frac{2\times|X\cap Y|}{|X|+|Y|} \tag{8}\]  
where \(\cap\) denotes the intersection operator, \(X\) and \(Y\) are the predicted segmented and ground-truth, respectively.  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
where TP is score of identifying ground truth pixels exactly, FP is score of failing in identifying ground truth pixels, and FN is score of failing in identifying background pixels into ground truth pixels.