esa-sceva/satcom-chunk-collection huggingface dataset:
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
## 3 Experimental Analysis
### Flank Wear  
(1) Oxidation Wear  
During the intermittent machining of hardened steel, at a certain temperature, some elements in the tool material reacted with O in the air, which accelerated the tool wear. The wear morphology and element analysis results of the PCBN tool flank are shown in Figure 13. Element analysis results for unworn area A and worn area B are shown in Figure 13c,d. Compared with the elemental analysis at A, the content of oxygen at B was significantly increased. It could be seen from Figure 13b that a concave shape was present for the CBN grains, resulting from possible oxidation of boron nitride into B\({}_{2}\)O\({}_{3}\). Therefore, oxidation wear occurred during the intermittent machining of hardened steel.  
Figure 12: Chip free surface topography machined by the PCBN tool with different cutting speeds (Test 6): (**a**) v = 250 m/min, \(Pc\) = 69 \(\upmu\)m; (**b**) v = 314 m/min, \(Pc\) = 76 \(\upmu\)m; (**c**) v = 376 m/min, \(Pc\) = 85 \(\upmu\)m;  
(2) Diffusion Wear  
During the cutting process, under certain conditions, certain elements may spread from one region to another, thereby causing diffusion wear. Diffusion wear can cause the chemical composition of the tool, thus weakening the tool cutting performance. Figure 14 shows the change law of element contents from the unworn area (A) to the worn area (B).  
It could be seen from Figure 14b that the contents of iron and chromium were significantly increased, which indicated that iron and chromium diffused from the workpiece material to the cutting tool. Therefore, diffusion wear occurred during the intermittent machining of hardened steel.  
(3) Adhesive Wear  
In the high-speed milling of hardened steel, due to the high temperature and pressure between the chip and the flank face, chips were observed to adhere to the cutting edge easily, and chip and tool welding clearly occurred, as shown in Figure 15a. Chips on the cutting edge were introduced into the processing region and rubbed or collided with the workpiece surface before eventually falling off. When chips fell off, adhesive wear of the tool occurred. Elemental analysis was performed at point A, and the results are shown in Figure 15b. It was found that iron and chromium were detected on the top and bottom surfaces of the chips. Therefore, the wear mechanism of the PCBN tool included adhesive wear.  
In total, a combination of oxidation wear, adhesive wear, and diffusion wear was believed to control the flank wear of the PCBN cutting tool.  
Figure 14: Diffusion wear (Test 1 250 m/min): (**a**) the range of elemental analysis; (**b**) the change law of element contents.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 3 Numerical Simulations and Discussion
### Mission Applications
#### 3.3.2 Single SWA Transfer with \(\beta=0.19\)  
The presence of a SWA complicates both the transfer trajectory and the optimal control law. For example, assume \(\beta=0.19\), which is related to a medium performance E-sail. In the presence of a single SWA, numerical simulations give an optimal transfer with a minimum flight time \(\Delta t\simeq 7.68\,T_{0}\), a perihelion distance \(r_{p}\simeq 0.34\,r_{0}\), and an aphelion distance \(r_{a}\simeq 4.41\,r_{0}\); see also Figures 6, 7 and 9. The optimal COFT is shown in Figure 13, where the radial distance is in multiples of \(r_{0}\) and the red triangle indicates the perihelion point. Note how the Sun-perihelion distance is well below the radial distance of the starting point (the black circle in the figure), while the aphelion point reaches a high solar distance.  
Figure 12: Time variation of the sail pitch angle \(\alpha_{n}\) in a DT scenario with \(\beta=0.3\). Black circle \(\rightarrow\) start; red star \(\rightarrow\) aphelion point; black square \(\rightarrow\) arrival.  
Figure 13: Polar form of the optimal COFT with a single SWA when \(\beta=0.19\). Black circle \(\rightarrow\) starting point; red star \(\rightarrow\) aphelion point; red triangle \(\rightarrow\) perihelion point.  
The time variations of the state variables \(\{r,\,\theta,\,u,\,v\}\) are shown in Figure 14, where the presence of two red triangles indicates that the perihelion point is reached by the spacecraft at two separate instants of time, due to the inherent symmetry of the problem. A comparison between the curves in Figure 14 and those sketched in Figure 11 clearly shows that the general trends of the state variables in a DT and SWA case are profoundly different, even in the presence of a heliostationary condition at the aphelion point. Indeed, note how the two components of the spacecraft velocity are both zero at the midpoint of the COFT.  
Another evident difference between the two transfer concepts (DT and SWA) is in the time variations of the two control parameters \(\{\tau,\,\alpha_{n}\}\). For example, the SWA case with \(\beta=0.19\) has the optimal control law shown in Figure 15, where the two gray shaded areas indicate the presence of two coasting arcs (when the propulsion system of the electric sail is off), while in the rest of the transfer \(\tau=1\). Although the two coating arcs have a very small time length, they demonstrate that a condition with \(\tau=0\) can also be optimal from the viewpoint of the flight time minimization [62].  
Figure 14: Time variations of the spacecraft state variables in a SWA transfer with \(\beta=0.19\). Black circle \(\rightarrow\) starting point; red star \(\rightarrow\) aphelion point; red triangle \(\rightarrow\) perihelion point; black square \(\rightarrow\) arrival.
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 3 Modeling Scenarios and Data Processing
### Forecasted Gridded Weather Data
#### 3.2.3 Hurricane Hanna (2020)  
Hanna made landfall as a category 1 hurricane at Padre Island, Texas. The hurricane reached a peak intensity of 92 mph as it was located off the coast of South Texas at 1800 UTC on 25 July. Hanna weakened to a tropical storm by 0600 UTC on 26 July and dissipated at 1800 UTC on 26 July as it neared Monterrey, Mexico. The path Hurricane Hanna took can be seen in Figure 2.
## 3 WLTC-Based Analysis of Study Results
### Energy Performance
In this case, a higher bioethanol concentration in the fuel helps mitigate cooling losses by lowering the intake, compression, and combustion temperatures. Instantaneous air mass consumption (Figure 5b) correlates with throttle opening and engine speed (Figure 3a). The highest total air consumption during the WLTC test was observed with E0 fuel at 14.717 kg (Figure 5b). Air consumption with E10 fuel was 14.471 kg (approximately 1.67% lower), while E70 fuel resulted in 14.494 kg (approximately 1.52% lower). Theoretically, a stoichiometric mixture requires 14.70 kg of air per kilogram of E0 fuel, 14.10 kg of air per kilogram of E10 (4.1% less), and 10.65 kg of air per kilogram of E70 (27.6% less). The observed reduction in air intake mass was less than the theoretical amount per kilogram of fuel because the mass of E10 and E70 fuel consumed was \(\sim\)0.1% and \(\sim\)23.9% higher, respectively, due to their lower net calorific values than E0. Additionally, the \(\sim\)9% increase in \(BTE\_WITC\) resulting from a 70% bioethanol concentration in the fuel blend also contributed significantly to the observed differences; parallel trends have been noted in other research [38].
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 3 Results and Discussion
### Automatic Registration  
The automatic registration procedure described in Section 2.2 was applied to the six and eleven TLS point clouds acquired for each study case, after the application of the filtering procedure. That is, (i) one coarse registration based on the Harris 3D Noble detector and the PFH descriptor with which the point clouds are positioned close to their real position (Figure 10b) in the shared coordinate system; (ii) one fine registration procedure based on ICP for the positioning of the point clouds with millimetre precision  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mean laser range (m) & 8 \\ Security distance (m) & 0.8 \\ \% Scanning overlap & 60\% \\ Scanning resolution & 4 mm at 20 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scanning parameters for the acquisition with TLS.  
Figure 9: Optimal route followed by the unmanned ground vehicle (UGV) for data acquisition; (**a**) optimal route and scan points in the first study case; (**b**) resulting Faro point cloud of the first study case; (**c**) optimal route and scan points of the second study case; (**d**) resulting Faro point cloud of the second study case.  
(Figure 10c). The final 3D point clouds, after removing duplicated points, are formed by 44 million points and 73 million points for the first and second study cases, respectively.  
Regarding processing time, the total time for the automatic procedure required 23 and 48 min for the first and second study cases, respectively, in a PC Mountain Studio3D i5 Ivy with Intel Core i5-3570K processor and 32 Gb RAM with operating system Windows 10 64 bits, and no need for interaction by the user.
###### Abstract  
Integrated Sensing, Communication, and Computation (ISCC) has become a key technology driving the development of the Internet of Vehicles (IoV) by enabling real-time environmental sensing, low-latency communication, and collaborative computing. However, the increasing sensing data within the IoV leads to demands of fast data transmission in the context of limited communication resources. To address this issue, we propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism for ISCC to reduce the sensing tasks transmission delay. We formulate a joint task offloading and communication resource allocation optimization problem to minimize the total processing delay of all vehicular sensing tasks. To solve this mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes the original optimization problem into a task offloading subproblem and a resource allocation subproblem, which are solved iteratively. In the first stage, a Deep Reinforcement Learning algorithm is used to determine task offloading decisions based on the initial setting. In the second stage, a convex optimization algorithm is employed to allocate communication bandwidth according to the current task offloading decisions. We conduct simulation experiments by varying different crucial parameters, and the results demonstrate the superiority of our scheme over other benchmark schemes.  
Collaborative Sensing-Aware Task Offloading; integrated sensing, communication, and computation; resource allocation; Internet of Vehicles +
Footnote †: journal: Sensors  
00 Research Article  
1
Footnote 1: email: {[[https://cnetivecommons.org/licenses/by/4.0/](https://cnetivecommons.org/licenses/by/4.0/)]([https://cnetivecommons.org/licenses/by/4.0/](https://cnetivecommons.org/licenses/by/4.0/))}.
# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
###### Abstract  
The remarkable increase in published medical imaging datasets for chest X-rays has significantly improved the performance of deep learning techniques to classify lung diseases efficiently. However, large datasets require special arrangements to make them suitable, accessible, and practically usable in remote clinics and emergency rooms. Additionally, it increases the computational time and image-processing complexity. This study investigates the efficiency of converting the 2D chest X-ray into one-dimensional texture representation data using descriptive statistics and local binary patterns, enabling the use of feed-forward neural networks to efficiently classify lung diseases within a short time and with cost effectiveness. This method bridges diagnostic gaps in healthcare services and improves patient outcomes in remote hospitals and emergency rooms. It also could reinforce the crucial role of technology in advancing healthcare. Utilizing the Guangzhou and PA datasets, our one-dimensional texture representation achieved 99% accuracy with a training time of 10.85 s and 0.19 s for testing. In the PA dataset, it achieved 96% accuracy with a training time of 38.14 s and a testing time of 0.17 s, outperforming EfficientNet, EfficientNet-V2-Small, and MobileNet-V3-Small. Therefore, this study suggests that the dimensional texture representation is fast and effective for lung disease classification.  
deep learning; feed-forward network; image processing; feature extraction +
Footnote †: journal: Applied sciences
## 3 Results
### Skeletal Muscle Mass  
Significant changes were observed in the SMM (Tables 3 and 4, Figure 3). In SPA children, it increased by 0.43% from 38.85% to 39.28% (\(p\) = 0.147), and in EPA children, it increased by 2.51% from 40.27% to 42.78% (\(p\) = 0.001). In EPA girls, SMM increased from 38.54% to 40.12% (\(p\) = 0.001), and in SPA girls, it decreased from 38.66% to 38.10% (\(p\) = 0.024). SMM was significantly higher in EPA boys and increased by 3.44% from 42.01% to 45.45% (\(p\) = 0.000), and in SPA boys, this increased by 1.41% from 39.05% to 40.46% (\(p\) = 0.001). SMM increased by 12.50 kg in EPA boys and 6.95 kg in SPA boys (\(p\) = 0.007). The highest increase in SMM was recorded in EPA boys, and the highest decrease was in SPA girls. On average, boys had a higher SMM compared to girls (41.74% vs. 38.89%, respectively; \(p\) = 0.001).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SPA**} & \multicolumn{4}{c}{**EPA**} \\ \cline{2-13}
**Indicator** & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**Final**} & \multicolumn{4}{c}{**Initial**} & \multicolumn{2}{c}{**Final**} & \multirow{2}{*}{**Total \(p\)**} \\ \cline{2-13}
**Boys** & **Girls** & **Boys** & **Girls** & & & **Boys** & **Girls** & & **Boys** & **Girls** & \\ \hline BMI (kg/m\({}^{2}\)) & 26.18 & 22.34 & 27.94 & 25.16 & 0.120 & 23.12 & 22.57 & 25.12 & 24.40 & 0.002 & 0.002 \\ FM (kg) & 19.90 & 15.85 & 24.23 & 21.25 & 0.001 & 13.55 & 16.20 & 16.63 & 20.20 & 0.001 & 0.010 \\ FM (\%) & 30.61 & 31.95 & 32.25 & 33.05 & 0.010 & 27.22 & 32.23 & 25.13 & 31.20 & 0.000 & 0.031 \\ FFM (kg) & 40.36 & 31.22 & 52.59 & 40.36 & 0.041 & 35.47 & 31.73 & 49.33 & 41.85 & 0.050 & 0.001 \\ FFM (\%) & 67.17 & 68.13 & 66.45 & 66.58 & 0.001 & 71.95 & 65.31 & 72.90 & 67.03 & 0.002 & 0.000 \\ TBW (kg) & 29.78 & 23.16 & 38.50 & 29.61 & 0.032 & 25.81 & 24.15 & 35.00 & 30.00 & 0.000 & 0.002 \\ TBW (\%) & 50.10 & 49.17 & 49.11 & 48.41 & 0.001 & 52.31 & 50.62 & 53.27 & 51.06 & 0.000 & 0.002 \\ SMM (kg) & 23.13 & 18.23 & 29.95 & 22.89 & 0.041 & 20.65 & 18.73 & 33.60 & 24.23 & 0.001 & 0.000 \\ SMM (\%) & 38.75 & 38.52 & 39.95 & 37.95 & 0.050 & 41.10 & 38.37 & 42.86 & 39.25 & 0.000 & 0.001 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**EPA—**elevated PA group; SPA—**standard PA group; BMI—**body mass index (kg/m\({}^{2}\)); FM—**fat mass; FFM—**fat-free mass; TBW—**total body water; SMM—**skeletal muscle mass; \(p\)—ANOVA; \(p\) \(\leq\) 0.05.  
\end{table}
Table 6: The comparison of the body composition of children with excess FM (\(\geq\)85 percentile) between the initial and final measurements.
## 3 Results and Discussion  
### Experimental Setup and Datasets  
The experiment is carried out in Python3.7 with PyTorch framework on a PC equipped with an Intel i7-10700 2.90 GHz CPU, 16 GB RAM, and an NVIDIA GeForce GTX 1650 GPU. In this work, we employ the first four layers of the pre-trained ResNet50 as the backbone network to extract features. The target search region is cropped to \(255\times 255\). A binary mask output is obtained by thresholding the predicted segmentation at 0.15, the size of which is changed to \(15\times 15\) and then stored as a value in the memory network. The memory storage threshold \(\sigma\) is set to 0.8 by default.  
During training, the pre-training of the backbone network ResNet50 is performed on the dataset ImageNet [26] classification task. Using the stochastic gradient descent (SGD) method, a warmup period was first performed where the learning rate progressively elevates from \(10^{-3}\) to \(5\times 10^{-3}\) across the initial 5 epochs, followed by a logarithmic reduction to the learning rate of \(5\times 10^{-4}\) sustained over the subsequent 15 epochs. The datasets used for our algorithmic model to be trained are ImageNet [26] and COCO [27]. We then conducted experiments on several commonly used tracking datasets, including OTB-100 [28], TC128 [29], and UAV123 [30], all three of which are test sets. Moreover, we performed experiments on actual captured substation inspection videos to validate the effectiveness of our method. These videos are collected from real scenarios, which can provide a closer test environment. By conducting experiments on these inspection videos, we can better evaluate the applicability and robustness of the proposed methods in actual scenarios.  
Figure 3: The architecture of the method we propose. (**a**) The pixel-level equalized memory matching network (PEMMN), in which the query frame is matched to the dynamically stored reference frame using our matching method. (**b**) MQEM is the mask quality evaluation module to decide if the mask of the query frame can be included in the memory. (**c**) The process involves combining the enhanced feature \(X^{\prime}\) with the base segmentation tracking framework to generate the mask of the current frame and the tracking box of the target.
## 3 Results and Discussion
### Characterization of Volatile Compounds of FAA Tea in Four Harvest Times
#### 3.4.3 Key Flavor Characteristics of FAA Tea
These findings suggest that these compounds play pivotal roles in the antioxidant processes of FAA tea. Previous studies indicate that these compounds may synergistically enhance the antioxidant capacity through distinct mechanisms, thus protecting organisms from oxidative damage [50; 51; 52]. Specifically, honokiol has been confirmed to be a strong oxidant [53]. D-xylitol, L-glutamic acid, and costunolide might indirectly contribute to the antioxidant properties of FAA tea by participating in the regulation of the synthesis of antioxidant compounds in the body and stabilizing the microenvironment. Figure 9: Correlation analysis of antioxidant power, key flavor compounds, and differential non-volatile metabolites. The proportion of non-volatile compounds in FAA tea during different harvest periods could affect the perception of aroma. Non-volatile compounds often participate in metabolic pathways and metabolic reactions, resulting in the formation of volatile metabolites. After summarizing the influences of different harvest periods on the volatile and non-volatile metabolites of FAA tea, it was necessary to explore the correlation between non-volatile and volatile metabolites in FAA tea at different harvest periods. It can be seen in Figure 9 that there were different situations of strong positive correlations, strong negative correlations, and weak correlations between 30 key flavor compounds and 32 differential metabolites. Among them, the correlation between esters and ketones among flavor compounds and lipids and organic acids among metabolites was generally strong. This meant that the presence of certain compounds might enhance the flavor of FAA tea, while others might have an inhibitory effect on the flavor. Therefore, the difference in harvest periods affected the intricate biochemical reactions in FAA tea and played a crucial role in flavor formation. To our knowledge, there are currently no literature reports on the key flavor compounds and non-volatile metabolites in FAA tea at different harvest periods and their correlations. A better understanding of this topic would help enhance the key flavor components and produce the desired products by controlling the harvest period of FAA tea or regulating non-volatile metabolites.
# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 4 SMM: Contextually Salient Multi-Variate Motif (CS-motif) Discovery
### Salient Multi-Variate Sub-Sequences (SMSs)
#### 4.1.2 Identifying Contextually Salient Sub-Sequences  
Let \(\langle s_{t},s_{v},t_{i},v_{j}\rangle\) be a quadruple in the state-space visualized in Figure 6. As discussed above, here, \(s_{t}\) and \(s_{v}\) are the two parameters that control the amount of smoothing across time and variates, respectively, and \(t_{i}\) and \(v_{j}\) are the time instance and variate on which a multi-variate sub-sequence is centered. To identify whether this quadruple represents a contextually salient sub-sequence or not, we need to compare it to its 80 (\(=3^{4}-1\)) neighbors across time, variates and two scale spaces (with three neighbors along each of these four dimensions). In this paper, we note that we can use two alternative measures of comparison to identify contextually salient sub-sequences, described below.  
Smoothing-Based Contextual Saliency  
We define smoothing-based local saliency \(CS_{S}(s_{t},s_{v},t_{i},v_{j})\) as follows:  
\[=max\quad( \big{|}\mathcal{M}[s_{t}+1,s_{v}](t_{i},v_{j})-\mathcal{M}[s_{t},s_{v}](t_{i},v_{j})\big{|},\] \[\big{|}\mathcal{M}[s_{t},s_{v}+1](t_{i},v_{j})-\mathcal{M}[s_{t},s _{v}](t_{i},v_{j})\big{|},\] \[\big{|}\mathcal{M}[s_{t}+1,s_{v}+1](t_{i},v_{j})-\mathcal{M}[s_{t},s_{v}](t_{i},v_{j})\big{|}).\]  
Intuitively, \(CS_{S}\) measures the absolute difference between the smoothed entries at different scales around the quadruple \(\langle s_{t},s_{v},t_{i},v_{j}\rangle\). If \(CS_{S}(s_{t},s_{v},t_{i},v_{j})\) is larger than the \(CS_{S}\) values corresponding to some \(\Theta\%\) (for some user-provided \(\Theta\sim 100\)) of the neighbors of the quadruple \(\langle s_{t},s_{v},t_{i},v_{j}\rangle\) in the state space, then we can say that the multi-variate sub-sequence represented by this quadruple is (maximally) different from its neighbors.  
Note that the above definition of local saliency compares the actual values of the series smoothed at different degrees. Alternatively, we can define an entropy-based contextual saliency \(CS_{E}(s_{t},s_{v},t_{i},v_{j})\) by replacing \(\mathcal{M}\) with \(\mathcal{E}\), where \(\mathcal{E}[s_{t},s_{v}](t_{i},v_{j})\) is the Shannon entropy defined within the \((3\sigma_{t},3\sigma_{v})\) neighborhood of the smoothed (and suitably discretized) series \(\mathcal{M}[s_{t},s_{v}]\) around the time instance \(t_{i}\) and variate \(v_{j}\). Since each neighborhood of a smoothed series corresponds to a multi-variate subsequence, intuitively, \(CS_{E}\) measures how different the entropy of a given sub-sequence is relative to the version of the series further smoothed in time and/or in variates.
## 3 D2D-Based Resource Allocation Algorithms  
In this section, firstly, we briefly present the considered communication system model. Next, we introduce the algorithms that we consider in this work to compare their results against those of the proposed algorithm.  
The scenario considered in this paper has a single cell with \(M\) cellular users (CUEs), \(K\) D2D pairs, and \(BS\) base stations, as we can see in Figure 1. The index sets of CUEs is \(m\in\{1,2,...,M\}\), and the index sets of D2D pairs is \(k\in\{1,2,...,K\}\).  
Channel coefficients are calculated according to the distances between the cellular users, the base station, and the D2D pairs.  
Figure 1: System model.  
In this work, we consider three resource allocation algorithms. More specifically, in Section 3.1, we describe the greedy throughput maximization plus (GTM+) algorithm, in Section 3.2 the delay minimization conflict graph (DMCG) algorithm, and in Section 3.3 the power and delay optimization based uplink resource allocation (PDO-URA) algorithm.