esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The existing shielding fiber manufacturing process involves the coating a polymer resin compound, blended with a shielding material, on a nonwoven fabric. This method is difficult to commercialize because of the lower uniformity of coating thickness and the reduced reproducibility of shielding performance [22,23]. In addition, because the proposed method involves combining fiber fabrics, such as nonwoven fabrics, on the shielding sheet, the sheet has a higher rigidity than flexibility, which is an inherent characteristic of fibers. Therefore, in this study, a flexible shielding fiber fabric was woven and its characteristics were evaluated. This study investigated the use of the melt spinning process during yarn processing, instead of the existing wet spinning process, with the aim of suggesting a method that would facilitate mass production and simplify the process technology. Barium sulfate and bismuth oxide were selected as shielding materials. Two fabrics were woven, each with a yarn containing one of the shielding materials, and the characteristics of the yarns and the shielding performance of the fabrics were compared. The study aimed to ascertain whether a shielding fabric, containing a radiation-absorbing material, can be woven using a process simpler than the previously proposed lyocell process [24]. This paper presents the fabrication method and characteristics of the shielding fiber that enabled user activity with convenience, and it is expected that the fiber has the potential to be adopted for shielding against low-dose radiation. Shielding fibers using radiation-impermeable substances, as proposed in this study, can be incorporated into clothing for shielding against both scattered and low-dose radiation generated in medical applications and into garments for shielding against natural radiation in the aerospace industry. In addition, they can be a useful component of laminated composite sheets, manufactured using shielding fibers.
## 4 Results of the Flow Field of the Model
### Flow Field of the Horseshoe Vortex  
When the upstream fluid arrives at the junction between the leading edge of the sail hull and the submarine body, the lateral vortex will be generated under the effect of adverse pressure gradient. The eddies propagate downstream under the impact of the incoming flow and are hindered by the bow of the sail hull. Then, the rotation of the eddies is deflected to generate the longitudinal vortices. When the vortices flow through the transition area, the velocity of the fluid increases and the longitudinal vortices will be stretched further. At last, the horseshoe vortex is formed around the sail hull. The horseshoe vortex promotes the flow separation between the sail hull and the submarine body, and the flow separation will enhance the strength of the horseshoe vortex further. Therefore,  
Figure 6: Contours of the flow field at plane A: (**a**) pressure contour; (**b**) velocity contour; and (**c**) kinetic turbulent energy.  
the horseshoe vortex has the properties of high strength and weak dissipation, and becomes one of the essential causes of the flow-induced noise.  
We have taken the \(Q\) criterion as the basis for the determination to show the three-dimensional structure of the horseshoe vortex, which is described as:  
\[Q=\frac{1}{2}\left(\|\Omega\|^{2}-\|S\|^{2}\right), \tag{23}\]  
where \(\Omega\) is the tensor of the eddy, and \(S\) is the tensor of the strain rate. Moreover, the region with \(Q>0\) in the flow field can be considered as the vortex core, which shows that the movement of the points is dominated by the rotation. The horseshoe vortex of the model at \(Q=500\) is shown in Figure 7a, of which the shape along the sail hull is like the alphabet \"U\". The horseshoe vortex is formed at the leading edge, developed in the downstream, of which the vortex surface remains steady, and dissipates slowly. Besides, the legs of the horseshoe vortex are stout. From the analysis, we may see that the adopted model in our study can capture the flow phenomenon of the horseshoe vortex. Therefore, the ignorance of some parts of the submarine body from the SUBOFF model is reasonable and feasible. However, the isolated sail hull cannot capture the horseshoe vortex, as shown in Figure 7b.
## 3 Finite-Time Disturbance Observer and Controller Design
### Finite-Time Disturbance Observer Design
\begin{split}\dot{\hat{x}}_{i}(t)&=-\lambda_{0}| \hat{x}_{i}(t)|^{2/3}\operatorname{sgn}(\hat{x}_{i}(t))-\varrho_{0}(\hat{x}_{ i}(t))+\hat{\omega}_{l1}(t)\\ \hat{\omega}_{l1}(t)&=-\lambda_{1}|(\hat{\omega}_{ l1}(t)-\hat{x}_{i}(t))|^{1/2}\operatorname{sgn}(\hat{\omega}_{l1}(t)-\hat{x}_{i}(t))- \varrho_{1}(\hat{\omega}_{l1}(t)-\hat{x}_{i}(t))+v_{i1}(t)\\ \hat{v}_{i1}(t)&=-\lambda_{2}\operatorname{sgn}(v_{ i1}(t)-\hat{\omega}_{l1}(t))-\varrho_{2}(v_{i1}(t)-\hat{\omega}_{l1}(t))-\frac{1}{L_{i}} \hat{\omega}_{l1}(t)\\ \hat{v}_{i}(t)&=-\lambda_{3}|\hat{v}(t)|^{1/2} \operatorname{sgn}(\hat{v}(t))-\varrho_{3}(\hat{v}(t))+\hat{\omega}_{l2}(t)\\ \dot{\hat{\omega}}_{l2}(t)&=-\lambda_{4}\operatorname{ sgn}(\alpha_{l2}(t)-\hat{v}(t))-\varrho_{4}(\alpha_{l2}(t)-\hat{v}(t))-\frac{1}{L_{i}} \hat{\omega}_{l2}(t)\end{split}  \tag{22}\]  
According to Lemma 1, by designing observer gains \(\lambda_{i}\), the observation error (22) is finite-time stable, which means that \(\hat{x}_{i}(t),\hat{\omega}_{l1}(t),\hat{v}_{i1}(t),\hat{v}_{i}(t)\), and \(\hat{\omega}_{l2}(t)\) converge to a bounded neighborhood of zero for \(t<T_{d}\)[30]. The proof of Theorem 1 is complete. Figure 3: FTDO-base sliding mode controller design block diagram.
## 3 Results and Discussion
### Phytochemical Attributes  
Phytochemicals, health-promoting compounds with potential applications in functional foods, are generally susceptible to degradation under industrial food processing conditions, affected by factors such as temperature and the presence of oxygen and light [65]. Thus, identifying the phytochemical composition is important to assess the potential applications of the spray-dried cashew particles.  
GA concentration was the most significant factor influencing TPC and ascorbic acid content (\(p<0.05\)) in spray-dried cashew particles (Table 2). Spray-dried particles prepared with 15% \(w/v\) GA presented a higher TPC (330.9-393.4 mg GAE 100 g\({}^{-1}\)) and ascorbic acid content (656.9-673.2 mg 100 g\({}^{-1}\)) than their counterparts with 25% \(w/v\) GA, which we hypothesize to be related to the diluting effect of the higher carrier addition ratio [38]. The TPC and ascorbic acid results reported here are higher than those previously reported for freeze-dried cashew particles (TPC: 65.32-67.64 mg GAE 100 g\({}^{-1}\); ascorbic acid: 72.83-96.46 mg 100 g\({}^{-1}\)) [48] and spray-dried cashew particles obtained from cashew pomace extract (TPC: 215.45 \(\pm\) 2.94 mg GAE 100 g\({}^{-1}\)) [11], highlighting the success of our spray-drying protocol in preserving valuable phytochemicals. A low carotenoid content was found in all spray-dried cashew particles (0.2-0.5 mg 100 g\({}^{-1}\), Table 2), similar to that previously reported for freeze-dried cashew particles (0.38-0.41 mg 100 g\({}^{-1}\)) [48].  
The inlet temperature affected the TPC of particles prepared with 15% \(w/v\) GA (groups C1 and C2, Table 2). For these groups, the inlet temperature increasing from 140 \({}^{\circ}\)C to 150 \({}^{\circ}\)C led to a significant (\(p<0.05\)) decrease in TPC from 393.4 \(\pm\) 23.8 mg GAE 100 g\({}^{-1}\) to 330.9 \(\pm\) 7.4 mg GAE 100 g\({}^{-1}\) for C1 and C2, respectively. This effect was not observed for groups C3 and C4 prepared with 25% \(w/v\) GA. Our hypothesis is that GA provided better protection to phenolics against heat degradation at the 25% \(w/v\) addition ratio when compared to 15% \(w/v\)[66; 67]. A similar effect was reported by [PERSON] et al. [24] for spray-dried cashew particles produced with an MD-starch 1:1 \(w/w\) blend that showed higher TPC retention (\(p<0.05\)) at a 15% \(w/v\) addition ratio (92.5 \(\pm\) 5.4%) than at a 10% \(w/v\) addition ratio (67.7 \(\pm\) 6.5%).  
The antioxidant activity of spray-dried cashew particles, assessed as DPPH radical scavenging activity, was in the range of 13.0-16.4 \(\upmu\)m TE g\({}^{-1}\) (Table 2), which is comparable to previously reported results for spray-dried elderberry particles (17-25 \(\upmu\)mol TE g\({}^{-1}\)) [17] and spray-dried coconut shell extracts (8-12 \(\upmu\)mol TE g\({}^{-1}\)) [68]. No clear influence of the tested parameters (GA concentration and inlet temperature) was found in the antioxidant activity results.
## 4 Experiments
This is because with a smaller step size in the array motion, the beam sampling of the optical system becomes more densely  
Figure 11: The reconstructed wavefront distributions of optical system (**a**) using interferometer data, (**b**) using iterative method with step size of 20 mm, (**c**) using iterative method with step size of 40 mm, (**d**) using averaging method with step size of 20 mm, (**e**) using averaging method with step size of 40 mm, (**f**) without correcting for multi-beam pointing error E and with step size of 20 mm, and (**g**) without correcting for multi-beam error E and with step size of 40 mm. Figure 10: Experimental setup of (**a**) multi-beam array and (**b**) MASTS. distributed. As a result, the reconstructed wavefront has a higher resolution. A higher wavefront resolution implies more accurate calculations of wavefront aberrations, leading to more accurate PV and RMS values. Certainly, choosing an even smaller motion step size can provide a higher resolution wavefront and consequently more accurate data. However, the data shown in Figure 11b are already comparable to interferometer data. Opting for a smaller motion step size would extend the testing time to over 3 h. Comparing the results of the iterative method with the averaging method, it shows that under the 20 mm step size condition, the RMS values from the iterative method are closer to the interferometer data. Under the 40 mm step size condition, the averaging method yields an RMS value slightly closer to the interferometer data than the iterative method. This discrepancy may be attributed to insufficient wavefront resolution, causing minor errors in the RMS value calculations. In terms of RMS value calculations, the iterative reconstruction results are superior to the averaging reconstruction results. The wavefront distributions reconstructed under different motion step sizes exhibit high repeatability for the array. Figure 11f,g demonstrate that if multi-beam pointing errors are not compensated for, directly plugging raw test data into Equation (5) will result in incorrect reconstruction. This highlights the necessity of using our method to handle MASTS test data. The differences in the wavefront distribution between MASTS test results and interferometric test results may be attributed to the fact that the interferometer test results likely incorporate the wavefront distribution of the reference flat mirror. Moreover, variations in the positions of the CCD during MASTS and the interferometer during interferometric testing can also contribute to variations in the wavefront distribution of the test results.
## 4 Experiment and Result Analysis
### Ablation Experiment  
This section analyzes the impact of each component on recognition accuracy in the KITTI 3D object validation dataset. The replication results of PointPillars on the validation dataset are taken as the baseline. As shown in Table 5, the average recognition accuracy under three levels of difficulty for cars, pedestrians, and cyclists improved by 1.14%, 0.96%, and 1.2%, respectively, after implementing the ASP module. This suggests that multi-scale pillar sampling is effective for recognizing small and distant objects. After implementing the CPA module, the average recognition accuracy for the three categories increased by 3.03%, 2.09%, and 2.74%, respectively. This indicates that the CPA module can effectively strengthen the feature correlation of point clouds in each pillar, enabling the network to learn richer contextual features, and thereby improving recognition accuracy. When combining these two modules, ASCA-PointPillars achieved an average improvement of 4.23%, 3.1%, and 3.58% in the three categories, respectively, achieving the highest average recognition accuracy for the car category. This demonstrates the effectiveness of the ASP and CPA modules. Using the RS-Aug algorithm alone increased the average recognition accuracy of the three categories by 2.59%, 2.49%, and 2.96%. When ASCA-PointPillars was combined with RS-Aug, it achieved the highest average recognition accuracy for pedestrians and cyclists, with improvements of 4.09%, 5.08%, and 5.07% in the three categories, respectively. This shows that RS-Aug can effectively improve the recognition performance of categories that have fewer instances in a dataset.
# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 2 Method
### Edge Detection Branch  
RPN is used to generate target candidate regions. It first maps back to the original image from the feature map using rectangular boxes with artificially defined scales and aspect ratios to obtain a feature map including the initial candidate boxes. Then a 256-dimensional feature vector is generated on this feature map using a sliding window of size 3 \(\times\) 3. Finally, two branches are combined on this feature vector to obtain more accurate object candidate boxes. The first branch is to determine whether the candidate box belongs to the object or the background by the Softmax function. The second is to calculate the border offset of the candidate frame by the Smooth\({}_{L1}\) loss function (see Equation (13)) to realize the border regression. However, the RPN cannot generate accurate candidate frames due to the dense stacking of parcels and the phenomenon of mutual occlusion. To address this problem, the proposed method adds an edge detection branch to the RPN and combines the edge features of the parcels to generate more accurate candidate boxes.  
Since the object edge is generally located at pixels with a large first-order differential, we employ the Sobel operator to calculate the first-order differential of the image [30]. Edge detection is achieved by calculating the weighted gray value of the upper, lower, left, and right four neighborhoods of each pixel, with the calculation process being as follows. First, two 3 \(\times\) 3 matrices \(S_{x}\) and \(S_{y}\) weight the input image to obtain the image gradients \(G_{x}\) and \(G_{y}\) in the horizontal and vertical directions, respectively, and then the gradient value \(G\) and  
Figure 5: Overall framework of the proposed method.  
gradient direction \(\theta\) of each pixel are calculated according to \(G_{x}\) and \(G_{y}\). When a point's gradient value exceeds a certain threshold, the point is at the object's edge.  
\[S_{x}=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix},S_{y}=\begin{bmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{bmatrix} \tag{1}\]  
\[G=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{2}\]  
\[\theta=\tan^{-1}\biggl{(}\frac{G_{y}}{G_{x}}\biggr{)} \tag{3}\]  
where \(S_{x}\) is a horizontal filter to obtain the horizontal edge information of a pixel point in the image \(G_{x}\) and \(S_{y}\) is a vertical filter to obtain the vertical edge information \(G_{y}\).  
Equation (2) involves square and square root calculations that increase the computational overhead. Therefore, we optimize the calculation process of the gradient value \(G\):  
\[G=\left|G_{x}\right|+\left|G_{y}\right| \tag{4}\]  
The edge detection branch is shown in Figure 6. The input to this branch is the parcel candidate box result and the manually annotated Ground Truth, which is then convolved with a set of edge detection filters to determine the edge difference between the parcel candidate box and the Ground Truth.
## 3 Results  
Table 2 shows the numerical values of the computed parameters. The local SAR is shown as 2D maps for each plane (axial, coronal, and sagittal plane) using a color scale (Figure 3). Values are in dB relative to the maximum value estimated in the model. The color range is between 0 dB (red) and \(-\)350 dB (dark purple).  
To better visualize the SAR distribution, in the representation of the phone call scenario, the smartphone is not included.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Phone Call**} & \multicolumn{2}{c}{**Message Writing**} & \multicolumn{2}{c}{**Browsing**} \\ \hline  & **Max Local** & **Sab** & **Max Local** & **Sab** & **Max Local** & **Sab** \\  & **SAR (W/kg)** & **(W/m\({}^{2}\))** & **SAR (W/kg)** & **(W/m\({}^{2}\))** & **SAR (W/kg)** & **(W/m\({}^{2}\))** \\ \hline Duke & 2.82 & 0.13 & 0.17 & \(8.30\times 10^{-3}\) & 0.02 & \(2\times 10^{-3}\) \\ \hline Ella & 2.60 & 0.05 & 0.37 & \(32.10\times 10^{-3}\) & 0.06 & \(6.10\times 10^{-3}\) \\ \hline Billie & 12.75 & 0.43 & 0.12 & \(11\times 10^{-3}\) & 0.02 & \(2.20\times 10^{-3}\) \\ \hline Thelonious & 8.44 & 0.21 & 0.10 & \(14.60\times 10^{-3}\) & \(7.15\times 10^{-3}\) & \(0.5\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical results of finite-difference time-domain (FDTD) simulations.  
Figure 3: Specific Absorption Rate (SAR) distribution in a phone call scenario in three different planes: axial, coronal, and sagittal.
## 3 Methodology
### Feature Extraction
#### 3.3.1 Hand-Crafted Features
First, speeded-up robust features (SURF) [41] are used as feature descriptors to detect interesting key points. Then, \(k\)-means clustering [42] is used to generate a visual vocabulary by reducing the dimensions of the features. The center of each cluster refers to a feature or visual word. We extracted 500 BoVW features, using 500 clusters. A summary of the feature descriptors, along with the number of extracted features, is in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Feature Descriptors** & **Features** & **Number of Features** \\ \hline \multirow{4}{*}{**Dataset**} & \multirow{2}{*}{**Dataset**} & Statistical Textures & First order statistics, GLCM, GLRLM & 140 \\ \cline{3-4}  & & LBP & Texture histogram & 256 \\ \cline{2-4}  & & HOG & Occurrences of oriented gradients & 10,800 \\ \cline{2-4}  & & \multirow{2}{*}{PHOG} & Occurences of oriented gradients at & \multirow{2}{*}{168} \\  & & each pyramid resolution level & & \\ \cline{3-4}  & & GIST &
\begin{tabular}{c} Information of the gradients, \\ orientations, and scales of the image \\ \end{tabular} & 512 \\ \cline{2-4}  & & BoVW & Image features as ‘words’ & 500 \\ \hline \multirow{4}{*}{**Dataset**} & \multirow{2}{*}{**Dataset**} & Alex & Deep-activated features & 1000 \\ \cline{3-4}  & & GoogLeNet & Deep-activated features & 1000 \\ \cline{3-4}  & & InceptionV3 & Deep-activated features & 1000 \\ \cline{2-4}  & & XceptionNet & Deep-activated features & 1000 \\ \cline{2-4}  & & ResNet-50 & Deep-activated features & 1000 \\ \cline{2-4}  & & SqueezeNet & Deep-activated features & 1000 \\ \cline{2-4}  & & ShuffleNet & Deep-activated features & 1000 \\ \cline{2-4}  & & MobileNet & Deep-activated features & 1000 \\ \cline{2-4}  & & DenseNet & Deep-activated features & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of feature descriptors used in our study.
## 2 Materials and Methods
### GC-EAD Detection  
GC-EAD (gas chromatograph Clarus 500, PerkinElmer, Waltham, MA, USA; EAD, Ockenfels Syntech GmbH, Buchenbach, Germany) was used to identify EAD-active compound(s) in the headspace of _R. batava_ flies and to test EAD activity of synthetic compounds.  
The GC effluent was divided using a splitter into two equal parts, allowing simultaneous flame ionization and EAD detection of the separated volatile compounds. One part of the column effluent was directed to the flame ionization detector (FID). A nitrogen make-up gas at a flow rate of 5 mL/min was used to enhance FID performance. Another part of the effluent was introduced into a purified and humidified air stream flowing at 0.5 m/s through a glass tube over antenna preparation. The flies used in GC-EAD analyses were not chilled or anesthetized prior to use. Glass capillary electrodes were filled with 0.9% NaCl saline (Illsanta, Vilnius, Lithuania) and grounded via a silver wire. An indifferent electrode was inserted into the severed head of the fly. The recording electrode, connected to a high-impedance DC amplifier (IDAC-4, Ockenfels Syntech GmbH, Buchenbach, Germany) with automatic baseline drift compensation was brought into contact with the distal end of the fly antenna. The antennal and the FID signals were recorded simultaneously, stored and analyzed using GeEad V. 4.4 software (Synthech, Ockenfels Syntech GmbH, Buchenbach, Germany). The male and female antennae responses to male odors and to synthetic (+)- and (-)-\(\delta\)-heptalactones, as well as male antenna responses to female odors were tested. Flies from 4 to 7 days old were used in the tests. Each EAD test was replicated three times and each antenna used was from a different fly.  
For headspace analysis, the GC was equipped with a DB-Wax capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA). The injector and the detector temperatures were set to 240 \({}^{\circ}\)C. The oven temperature was maintained isothermally at 40 \({}^{\circ}\)C for 1 min; afterward, it was raised to 240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, then maintained isothermally for 13 min. Hydrogen, at a flow rate 1.5 mL/min, was used as a carrier gas.  
After the identification of EAD active compounds and revealing the potential presence of enantiomers, a Rf(r)-bDEXsm column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Restek Corporation, Bellefonte, PA, USA) was used instead of the previous column. The stationary chiral phase of the column was composed of 2,3-di-O-methyl-6-O-tert-butyl dimethylsilyl \(\beta\) cyclodextrin, added into 14% cyanopropylphenyl/86% dimethyl polysiloxane. Cyclodextrin-based GC stationary phases provide excellent separation for a wide range of chiral compounds and are the most widely used [17]. In this case, the injector and the detector temperatures were set to 230 \({}^{\circ}\)C. The oven initial temperature was 90 \({}^{\circ}\)C, then programmed to 200 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments  
This section aims to verify the performance of our proposed MASA algorithm. Firstly, we compare the performance of the proposed algorithm with that of current resource-scheduling algorithms. Next, ablation experiments are conducted to verify the effectiveness of the proposed training optimization method. To demonstrate the scalability of the proposed algorithm for complex mission environments, the experiments simulate the task-scheduling problem in streaming mode using six real-world RF applications in the field of wireless communication and radar processing.