esa-sceva/satcom-chunk-collection huggingface dataset:
# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 4 Experimental Results
### Design of Automotive Front Face Elements Using Semantic Segmentation and Image Restoration  
The MD-TransUNet image segmentation network trained in this paper is innovatively combined with the stable diffusion model. These masks are then in fed into the stable diffusion model, which leverages its powerful image generation capabilities to iteratively optimize and refine the image through a diffusion process, creating unique automotive front face designs. This process begins by merging the input mask with a noise image as the initial input. The model then corrects and improves the image through a reverse diffusion process at each layer, gradually generating high-quality images based on the mask features, ensuring that the design of the headlights and grilles meets expectations. The specific steps are illustrated in Figure 6, and the resulting automotive front face element designs are shown in Figure 7.  
This method also shows generalizability in practical applications. Different types of cars, such as sedans, SUVs, and high-performance sports cars, can have front face designs tailored to their unique styles and market demands by adjusting the parameters and input conditions of the stable diffusion model.  
For car manufacturers, this automated design generation can significantly shorten the design cycle, reduce costs, and quickly adapt to changing market demands. For designers, it allows them to focus on creativity and high-level design decisions while intelligent algorithms handle the tedious details. During the early design stages, the diffusion model can generate diverse designs, enabling extensive simulation and testing to select the optimal design solution.  
Figure 5: Ablation experiment result images. (**a**) Original Image; (**b**) Labeled Image; (**c**) TransUNet; (**d**) TransUNet + MSAG; (**e**) TransUNet + DCGCN; and (**f**) TransUNet + MSAG + DCGCN.  
Figure 6: Applying the trained segmentation model to automotive front face element design.  
Figure 7: Diversified designs of headlights and grilles on the automotive front face (**a**) Original Image; (**b**) Mask; (**c**) Front Face Diversified Design Schemes.
## 3 Results and Discussion
### Effect of Tea Extracts on Intestinal Absorption of \(\omega\)-3 FAs by Caco-2 Cells  
The intestinal transport of EPA and DHA from \(\omega\)-3 FAs, with and without various tea extracts, by Caco-2 cells was evaluated from the basolateral compartment after in vitro digestion and calculated as a percentage of the initial concentration, as shown in Figure 5. The values were 0.74 \(\pm\) 0.01%, 1.00 \(\pm\) 0.14%, 0.92 \(\pm\) 0.10%, 0.81 \(\pm\) 0.12%, 1.03 \(\pm\) 0.16%, and 0.98 \(\pm\) 0.01% for \(\omega\)-3 FAs treated alone and co-treated with GTE35, GTE65, NFGT100, NFGT30, and Adiphenon(tm), respectively. For EPA, the absorption from \(\omega\)-3 FAs significantly increased with various teas, except for NFGT100, compared to \(\omega\)-3 FAs alone (\(p\) < 0.05). The co-treatment with various teas resulted in significantly higher intestinal absorption compared to \(\omega\)-3 fatty acids treated alone (\(p\) < 0.05) in regards to DHA, which included values of 0.81 \(\pm\) 0.07% for \(\omega\)-3 FA, 1.30 \(\pm\) 0.03% for GTE35, 1.17 \(\pm\) 0.06% for GTE65, 1.18 \(\pm\) 0.15% for NFGT100, 1.20 \(\pm\) 0.19% for NFGT30, and 1.21 \(\pm\) 0.05% for Adiphenon(tm). The previous studies demonstrated that various polyphenols significantly enhanced the intestinal absorption of \(\omega\)-3 FAs [30; 31]. For instance, when DHA was co-treated with grape seed extract, its absorption in Caco-2 cells increased two-fold, whereas co-treatment with epigallocatechin gallate resulted in a 50% increase [31]. The current study, similar to these findings, showed that intestinal absorption of \(\omega\)-3 FAs was lower when administered alone compared to when co-treated with various tea extracts, suggesting that the antioxidant properties of the tea extracts could enhance the absorption of \(\omega\)-3 FAs.  
Figure 4: Changes in the cellular integrity measured using TEER values of \(\omega\)-3 FAs with and without various tea extracts during incubation in the Caco-2 monolayer. Transepithelial electrical resistance (TEER) values in the Caco-2 monolayers were estimated at various time points. The TEER values were calculated as the percentage of 0 min.  
The findings of this study suggest that tea extracts could enhance the bioavailability of \(\omega\)-3 FAs through mechanisms such as oxidation prevention, micelle stabilization, and intestinal efflux minimization. However, further studies, such as in vivo models, are required to confirm the observed effects and explore their broader implications for human health under physiological conditions.
## 1 Introduction
We evaluate the key techniques discussed in the literature to see how effective each of them is for edge computing and which would be most helpful for future research. We examine and evaluate the corpus of contemporary research on workload distribution on edge intelligence devices. Our work provides a thorough assessment of the main methods and algorithms used in edge computing for efficiently executing difficult workloads [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 52; 59; 53; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79]. Figure 1: Edge, fog, and cloud computing architecture. The main contribution of this paper is that it provides a review of the state-of-the-art task allocation methods. More precisely, this paper only used research published in the last three years and it highlights the most frequently used task allocation methods and optimization algorithms in combination with the network types which are used in edge computing systems according to the application and the task allocation algorithm used. The purpose of this work was to show the task allocation and all the optimization algorithms and methods that have been, very recently, the most frequently used in order to advance the current understanding of choice-making when it comes to adopting the optimal task allocation method, algorithm, and networks according to the use case. All the task allocations methods reviewed in this paper tried to give a solution to the edge servers or the devices located in the edge computing layer by distributing their workloads to the end devices, fog layer devices, and cloud computing layer devices in order to provide optimizations according to the use cases of the applications. The document is divided into six sections. The systematic literature review (SLR) approach employed in this article is described in Section 2, including the definition of research questions (RQs). Next, in Section 3, we provide the list of findings in the literature. In Section 4, we provide a summary of the collected papers. In Section 5, we provide a critical discussion of our findings and identify future work challenges. We finally conclude this review in Section 6.
_Appl. Sci._**2014**, \(4\), 42-54; doi:10.3390/app4010042  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Underwater Superoleophobicity Induced by the Thickness of the Thermally Grown Porous Oxide Layer on C84400 Copper Alloy  
**[PERSON] * and [PERSON]**  
Department of Materials Engineering, University of Wisconsin-Milwaukee, Milwaukee 53211, WI, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +1-414-514-0491. Fax: 414-229-6958  
_Received: 30 September 2013; in revised form: 16 January 2014 / Accepted: 22 January 2014 / Published: 20 February 2014_  
**Abstract:** The underwater contact angle behavior on oxide layers of varying thicknesses was studied. These oxide layers were grown by thermally oxidizing C84400 copper alloys in N\({}_{2}\)-0.75 wt.% O\({}_{2}\) and N\({}_{2}\)-5 wt.% O\({}_{2}\) gas mixtures at 650 \({}^{\circ}\)C. Characterization of the oxidized specimens was effected using X-ray diffraction, scanning electron microscope (SEM) and contact angle goniometer. The results from the X-ray diffraction analyses confirmed the formation of CuO, ZnO and PbO. The average sizes of the oxide granules were in the range of 70 nm to 750 nm, with the average thickness of the oxide layer increasing with the increase in the weight percent of oxygen in the N\({}_{2}\)-O\({}_{2}\) gas mixtures. The results showed that the oxide layer growth followed the parabolic law. The underwater oil contact angles increased, due to the change in the surface morphology and porosity of the oxide layer. The small sizes and irregular packing of the oxide granules cause hierarchical rough surface layers with pores. The estimated pore sizes, in the range of \(88\pm 40\) to \(280\pm 76\), were predominant on the oxide layers of the samples processed in the N\({}_{2}\)-5 wt.% O\({}_{2}\) gas mixture. The presence of these pores caused an increase in the porosities as the thickness of the oxide layers increased. At oxide layer thickness above 25 microns, the measured contact angle exceeded 150\({}^{\circ}\) as underwater superoleophobicity was recorded.  
**Keywords:** thermal oxidation; superoleophobicity; oxide layer; porosity
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results
### Performance of the Proposed Defense  
To ascertain the efficacy of the proposed methodology, this study chose two filter-based defense strategies (DIP [39] and LPIF [40]) for a comparative analysis. To guarantee the thoroughness and dependability of the experimental outcomes, six prevalent adversarial attack techniques were selected: FGSM, PGD, BIM, C&W attack, AutoAttack, and VNIFGSM. These attack methods are capable of providing a comprehensive assessment of the performance of various defense strategies across diverse attack scenarios.  
Table 4 depicts the accuracy performance of image classifiers utilizing diverse defense strategies within six unique attack scenarios. Our research findings suggest that our method surpasses the other two defense approaches, exhibiting enhanced accuracy and robustness. Notably, DIP experiences a considerable drop in performance when handling color images, such as those in the CIFAR-10 and CIFAR-100 datasets, due to the reliance on fixed threshold techniques that frequently lead to the loss of critical details in color images. Furthermore, we subjected our defense technology to rigorous testing across multiple  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**First Layer**} & \multicolumn{6}{c}{**Second Layer**} \\ \cline{2-7}  & **Null** & **Max** & **Min** & **Midpoint** & **Average** & **Gaussian** \\ \hline Max & 0.86349 & 0.80559 & 0.79002 & 0.23039 & 0.81714 & 0.85185 \\ Min & 0.24844 & 0.23592 & 0.03955 & 0.01234 & 0.23602 & 0.23961 \\ Midpoint & 0.02344 & 0.01972 & 0.01234 & 0.01327 & 0.01921 & 0.01810 \\ Average & 0.85433 & 0.88133 & 0.81258 & 0.07882 & 0.84395 & 0.86244 \\ Gaussian & 0.89178 & 0.86613 & 0.63420 & 0.01234 & 0.85877 & 0.91329 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average accuracy of defense models with different image filter combinations on the MNIST dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**First Layer**} & \multicolumn{6}{c}{**Second Layer**} \\ \cline{2-7}  & **Null** & **Max** & **Min** & **Midpoint** & **Average** & **Gaussian** \\ \hline Max & 0.56602 & 0.47965 & 0.58282 & 0.06860 & 0.55302 & 0.54232 \\ Min & 0.59879 & 0.60677 & 0.50553 & 0.08838 & 0.56375 & 0.57001 \\ Midpoint & 0.07436 & 0.20019 & 0.08563 & 0.05761 & 0.05438 & 0.14775 \\ Average & 0.62835 & 0.58955 & 0.59644 & 0.08838 & 0.61992 & 0.62959 \\ Gaussian & 0.63335 & 0.59730 & 0.62383 & 0.06546 & 0.62801 & 0.66676 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average accuracy of defense models with different image filter combinations on the CIFAR10 dataset.  
datasets and various deep learning architectures, and the results confirmed its robustness in practical deployment.
## 4 Optimization Model  
In this section, the lifetime optimization models are proposed. As [PERSON] and [PERSON] stated in [2], the lifetime of the WSN is largely determined by energy depletion. Therefore, the energy consumption  
Figure 2: Reduced CSMA/CA protocol without RTS/CTS.  
at each sensor location in a data gathering cycle is applied to calculate the lifetime of the network. Our goal is to cover the entire target area with full connectivity, and the WSN life ends when it cannot provide such coverage or connection function. By combining Equations (1) and (2), to ensure the coverage of a WSN, at least one sensor should be working in each hexagonal grid. To prolong the lifetime, as mentioned in Section 3.2, \(n_{i,j}\) sensors are deployed in grid \((i,j)\), among which only one is active and the rest are in sleeping mode due to the sleep/wake-up protocol. The lifetime of the WSN can be calculated by  
\[\tau=\min_{i=1}^{L}\left[\min_{j=1}^{6i}\left(\frac{n_{i,j}E_{0}}{E_{i,j}} \right)\right]t, \tag{6}\]  
where \(E_{0}\) is the initial energy of a sensor, \(E_{i,j}\) is the energy consumption in a data gathering cycle at location \((i,j)\), \(t\) is the data gathering cycle and \(L\) is the number of layers in the target area. If the distance between sensors, \(d\), is too small, the overlap of coverage will be very large, which results in huge amounts of redundant sensing information and energy waste. On the other hand, an extremely large value of \(d\) will dramatically increase the transmission energy of a single hop (since the transmission distance is increased, see Equation (2)). From all of the aforementioned perspectives, our objective is to achieve the optimal \(d\) and \(n_{i,j}\) as well as maximize the WSN lifetime (which ends when the coverage, the connectivity or the success transmission rate falls below the designated threshold).  
Here, we analyze two types of sensor deployments, uniform and non-uniform, which are usually discussed and compared in the deployment-based lifetime optimization. In a uniformly deployed WSN, the sensor density will not be changed by its distance to the base station. Such a deployment is simple, but it may cause energy hole problem. Thus, non-uniform deployment scheme was proposed (see (1)), in which more sensors are deployed near the base station, while less ones are located at the edge.
## 3 The CsrA-Containing Ribonucleoprotein Complexes  
### Structural Information  
The CsrA protein is unrelated to typical regulators in terms of amino acid sequence but contains a KH motif (single-strand RNA binding domain) [76]. NMR-based structure shows that CsrA is composed of 5 \(\beta\)-strands and a short \(\alpha\)-helix followed by an unstructured carboxy-terminal region (Figure 2) [36]. The functional CsrA dimer is formed by interdigitation of two CsrA monomers, resulting in a hydrophobic core composed of 10 \(\beta\)-strands and two wing-like \(\alpha\)-helices, forming a barrel-like structure [77]. Using comprehensive alanine-scanning mutagenesis, two critical regions for regulation and RNA binding were identified [78]. These two RNA-binding surfaces are located within the first and last \(\beta\)-strands, which lie parallel to each other on opposite sides of the dimer and form two positively charged regions [78].  
Figure 2: Structure of the RsmE-RNA complex of _P. fluorescens_. Solution structure of the 2:2 complex of RsmE with 20-nucleotide _hcnA_ sequence. Protein ribbons belonging to each monomer are shown in green and grey. Heavy atoms of the two RNAs are shown in yellow (carbon and nitrogen) and red (oxygen and phosphorus). The linking phosphates are shown in orange. From [42].  
Several amino acids residues are involved in RNA-binding, with R44 being the most important, and less importantly Arg7 and Ile47 [78]. CsrA preferentially binds GGA motifs (see below) located in single-strand regions of short hairpin structures [42; 78; 79]. Sequence-specific recognition of this motif is mostly mediated by carbonyl oxygen and amide groups of the main chain, indicating that CsrA-fold itself is responsible for RNA binding specificity [42].
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
###### Abstract  
This paper presents an innovative approach towards space-ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user's and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods.  
space-ground integrated system; resources allocation; network slicing; GNSS +
Footnote †: journal:  
0000-0001-588-227-1000][PERSON]; [PERSON] Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System. Sensors.
# Ce- and B-Doped Silica Fibers for Monitoring Low-Energy Proton Beams on a Medical Cyclotron
## 2 Materials and Methods  
### Fiber Optic Setup  
The doped silica fibers provided by D-Pace were fabricated by the University of Milano-Bicocca's Department of Material Science (UMB-DMS) using the sol-gel process [6; 14]. The fibers were SiO\({}_{2}\) doped with Ce\({}^{3+}\) ions and were drawn to a diameter of 200 \(\upmu\)m or 600 \(\upmu\)m. The fiber produces radio-luminescence (RL) emitted by the Ce\({}^{3+}\) dopant ions due to excitation by ionizing radiation, with an emission band centered at 450 nm. The SiO\({}_{2}\) fiber also produces luminescence due to Cherenkov radiation, which is characterized by a broad spectral emission in the blue-UV wavelength region [7]. The boron-doped fibers are manufactured from commercial grade borosilicate glass (model 0500, Hilgenberg, Germany). None of these fibers had a cladding or protective layer. Each fiber was terminated with an SMA 905 connector. The fibers used are summarized in Table 1.  
As the fibers are delicate, the unit was then slid into a custom plastic housing, as shown in Figure 1b. To reduce the noise due to background light, the detector assembly was subsequently  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fiber** & **Dopant** & **Length** & **Diameter** \\ \hline Ce-600 & Ce\({}^{3+}\) & 8.0 cm & 600 \(\upmu\)m \\ Ce-200 & Ce\({}^{3+}\) & 3.0 cm & 200 \(\upmu\)m \\ B-200 & B\({}^{3+}\) & 7.5 cm & 200 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fiber samples.  
wrapped in black electrical tape. The fibers were attached to the outside of the \({}^{18}\)O(p,n)\({}^{18}\)F gas target in position \(D\) with an inner length of 12 cm, routinely in use at our facility, see Figures 1a and 2. To transport the light output to the photodetector, 13.5 m long plastic fibers of 1 mm in diameter were then attached to the SMA connector, and on the other end to a Multi-Pixel Photon Counter (MPPC, Hamamatsu model C11208-01(X); 100 ms gate time and 0.5 photon equivalent threshold selected for all experiments). To further reduce the noise from ambient light, the SMA connectors were also covered in black electrical tape as shown in Figure 1a. The MPPC was connected to a PC via a USB cable and the data from the MPPC was displayed and recorded via the provided software. The entire configuration is summarized in Figure 1c.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 2 Literature Review and Related Work
### Existing Studies on the Use of mmWave Automotive Radars for UASs  
Academicians and researchers have investigated the potential use of mmWave automotive radars in enhancing the flight performance of small-sized UASs. These studies leverage the SWaP aspects combined with deep-learning (DL) elements for autonomous flying, obstacle avoidance, and indoor navigation [33; 34; 35]. Specifically, on the subject of altitude estimation, experiments were conducted in [36]. The use of the constant false alarm rate (CFAR) algorithm for the detection of the ground surface was presented. However, no mathematical basis nor discussion on the performance metrics was furnished. The effectiveness of radar sensors in AGL altitude estimation was explored in another relevant study [37]. However, the text offered limited depth, with a greater emphasis on potential applications. The research most closely aligned with this study pertaining to the intended application was carried out in [38]. A 77 GHz mmWave FMCW radar chipset was employed to estimate the altitude of a small-sized UAS. A modification of the CFAR algorithm with a window size spanning the entire range profile with an empirically chosen threshold was presented. Moreover, a range compensation method was offered to improve the consistency of measurements for low altitudes. The cited article serves as a valuable source to inspire a comprehensive experimental work. Though the work provides a proof of concept regarding the applicability of mmWave automotive radars as a tool for altitude estimation, there is substantial room for more directed discussions, including but not limited to ground clutter estimation and performance metrics.
## 3 Fault Diagnosis Transfer Model
### Fault Recognition Module  
The output of the feature extractor is \(X_{D,fea}^{i}=X_{y,out}^{i}(y=5)\), which contains feature information learned from both domains. \(X_{s,fea}^{i}\) represents the features learned from source domains that contain useful knowledge. The fault identification module consists of two fully connected layers, a \(Dropout\) layer and the output layer. The \(z\)th fully connected layer processes \(X^{i}_{D,feat}\), as shown in Equation (14).  
\[X^{i}_{FC,\beta,z}=h\Big{[}X^{i}_{s,feat};\theta^{FC}\Big{]}=\rho\Big{[}\omega^{ FC}*X^{i}_{s,feat}+b^{FC}\Big{]} \tag{14}\]  
where \(\theta^{FC}=\big{\{}\omega^{FC},b^{FC}\big{\}}\) represents the parameters of the fully connected layer. The dropout layer's process for the second fully connected layer \(X^{i}_{FC,z}(z=2)\) can be expressed as Equation (15).  
\[X^{i}_{s,Dropout}=Dropout\Big{(}X^{i}_{FC,s,z},v\Big{)} \tag{15}\]  
where \(v\) is the deactivation rate, which is taken as 0.5 in this paper. The softmax function is used as the output layer to identify the class of faults. The softmax function can be expressed as Equation (16).  
\[Softmax\Big{(}C^{i}_{s}=j\Big{|}X^{i}_{s,Dropout}\Big{)}=\frac{e^{X^{i}_{s,Dropout}}}{\sum_{j=1}^{k}e^{X^{i}_{s,Dropout}}} \tag{16}\] \[(j=0,1\cdots 9)\]  
where \(c^{i}_{s}\) denotes the fault label recognized by the output layer. The loss function of the fault recognition module is expressed as Equation (17).  
\[L_{C}=-\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\sum_{j=1}^{k}I[C^{i}=k]log\frac{e^{X^ {i}_{s,Dropout}}}{\sum_{j=1}^{k}e^{X^{i}_{s,Dropout}}} \tag{17}\]  
where \(k\) is the quantity of fault categories in the data sample, and \(I(\cdot)\) represents the indicator function.