esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
**Background.** Arrival times at the scene and provision of initial emergency treatment have importance in pre-hospital care settings. Donning proper protective equipment by medical personnel, as was needed during the COVID-19 pandemic, prolongs the time between the arrival of medical personnel to a patient and provision of primary medical care. **Objective.** We examined the effect of a suggested personal protective equipment (PPE) wearing protocol (gown protocol) on shortening pre-hospital treatment times compared to the current overall protocol. **Method.** In this prospective simulation-based study, participants were instructed to inject a practice epinephrine syringe into a simulation mannequin after donning either a gown or a overall PPE kit in the shortest possible time. Participants performed the two protocols in a randomized order. Donning time, physiological measures, and participants' perceptions were measured after completion of each of the protocols. **Results.** Donning times and heart rates were significantly lower in the gown protocol compared to the standard overall protocol. In addition, participants reported that the gown protocol was more comfortable and allowed provision of better medical care. **Conclusions.** Advantages of using the new protocol included shortening the time until primary medical care can be provided, perceptions of greater comfort, less difficulty in administering medical care, and lower heart rate values.  
COVID-19; pre-hospital setting; personal protective equipment; PPE donning protocol; shortening time to treatment +
Footnote †: journal: Medical Care
First, we propose a neural network composed of a self-attention mechanism and a deep convolutional network which aims to automatically learn the correlation between traffic samples, capture the feature space's internal structure, and provide it downstream in the form of a sample embedding task. Secondly, we design a novel stacking integration method, which aims to detect and identify abnormal network traffic by integrating the sample embedding obtained above and the inspection results of the heterogeneous base learner. Finally, we design a new loss function, which fully and comprehensively considers the basic learner's influence on the model's overall performance by introducing the basic learner's loss value and the regular term composed of it and preventing the model from falling into an overfitting state. The main contributions of this paper are as follows:  
* We propose a neural network composed of a self-attention mechanism and a deep convolutional network, which learns from samples and converts them into sample embeddings. * We propose a stacking ensemble learning method composed of the autoencoder and base learner, using the autoencoder to remove irrelevant information in the samples and the stacking method to integrate the detection results of sample embedding and the base learner. * We design a novel loss function to observe the operation of the model through the introduced regularization term and base learner loss value. We use a network traffic dataset under an SDN architecture to evaluate the model's performance. The results show that the model has a better abnormal traffic detection effect than the comparison model. The structure of this paper is as follows: Section 2 briefly describes the research status of related work; Section 3 introduces the experimental environment, model framework, and specific design of TSMASAM; Section 4 details the experiments and performance evaluation of TSMASAM proposed in this paper. In Section 5, we conclude the paper.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 4 Experimental Results
### Robustness Test for Collusion Attack  
The experiments were divided into two test environments: (1) collusion attack test with a variety of compared methods; and (2) robustness test with collusion and noise addition attacks. To compare our method against the performance of a conventional scheme, we used a variety of robust watermarking methods as follows: [PERSON] et al. [11], and [PERSON] et al. [12]. Similar to our method, the method of [PERSON] et al. [11] is based on the mesh spectral decomposition. The method of [PERSON] et al. [12] embedded a watermark pattern by modifying the statistical features of the vertex norm. Anti-collusion  
Figure 4: Comparison between the original and the watermarked model: (**a**) original Bunny model; (**b**) watermarked model; (**c**) strongly watermarked model.  
Figure 5: Sample distortion maps of the watermarked meshes: (**a**) Bunny; (**b**) Cow; (**c**) Horse; (**d**) Rabbit; (**e**) Rameses; and (**f**) Venus. All watermarks were embedded with \(\alpha=0.05\) (MRMS = 0.02%).  
codes were adopted not only our method but also the compared methods [11; 12]. In the experiments, we generated 81 watermarked models by embedding a fingerprint code for each recipient of the models. Then collusion attacks were performed 100 times using a set of randomly selected watermarked models for each iteration. For the collusion attack for our tests, we averaged a set of selected models and obtained one attacked model. We also conducted experiments by varying the number of selected models \(k\) (\(1\leq k\leq 8\)). Robustness for collusion attack was evaluated by the number of times that tracing all attackers was successful.  
Figure 6 shows the experimental results of collusion attack of the proposed method, [PERSON] et al. [11], and [PERSON] et al. [12]. In this figure, Cho-_mean_ and Cho-_var_ indicate the first and the second method of [PERSON] et al., respectively. The proposed method perfectly found attackers for every attack when the number of attackers was 1-7, and it also performed well when the number of attackers was '8'. Because the GD-BIBD codes in the test were generated with \(n=8\), which could protect the model against (\(n\leq 8\)) attackers, the results decrease drastically when \(n>8\). In contrast, Cho-_mean_ and [PERSON] et al. did not trace attackers for all attacks when the number of attackers was two or more. Cho-_var_ also did not work when the number of attackers was three or more. The results showed that proposed scheme was much more robust against collusion attack compared to the conventional schemes.
## 3 Results
### Primary Loop Optimization
#### 3.1.2 Pump Position  
Pump position in the reactor system plays a significant role as it dictates the flow characteristics in the reactor system. The steady-state velocity magnitude distribution is shown in Figure 4. The velocity contours are similar for both the pump positions, but the velocity magnitudes are quite different. The average velocity and maximum velocity (Table 7) of the salt is higher in the reactor system when the pump is placed after the HEX. Achieving higher velocities inside the reactor system for the same pump momentum source is ideal as it minimizes the risk of formation of recirculation zones leading to better thermal efficiency and performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Primary side pressure drop \(\left(\Delta P_{p}\right)\) & 1.24 \(\times\) 10\({}^{5}\) & Pa \\ Secondary side pressure drop \(\left(\Delta P_{c}\right)\) & 1.65 \(\times\) 10\({}^{6}\) & Pa \\ Overall heat transfer coefficient (U) & 2584.6 & W/m\({}^{2}\)K \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of PCHE design.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Primary inlet temperature \(\left(T_{in,p}\right)\) & 900 & K \\ Primary outlet temperature \(\left(T_{out,p}\right)\) & 835 & K \\ Secondary inlet temperature \(\left(T_{in,s}\right)\) & 800 & K \\ Diameter of micro-channel \(\left(d_{ch}\right)\) & 0.0021 & m \\ Length of micro-channel \(\left(L_{ch}\right)\) & 1.79 & m \\ Total reactor power & 2400 & MW \\ \hline \hline \end{tabular}
\end{table}
Table 5: Printed circuit heat exchanger design parameters.  
When the pump is placed after the heat exchanger, it copes with the pressure lost during the heat transfer to the secondary side, and if the pressure lost is not too high, then the pump can be placed before the heat exchanger too. The governing metric for the pump position will be flow distribution, average velocity, and maximum velocity of the fuel salt in the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pump Position** & **Average Velocity** & **Maximum Velocity** & **Units** \\ \hline Before HEX & \(4.80\times 10^{-1}\) & \(1.21\times 10^{0}\) & m/s \\ After HEX & \(6.21\times 10^{-1}\) & \(1.47\times 10^{0}\) & m/s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of pressure drop and heat transfer coefficient for different pump positions.  
Figure 4: Steady-state velocity magnitude distribution in the LFMSR primary loop with the pump position before the HEX (**a**) and after the HEX (**b**).
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Ablation Study
#### 4.5.1 Effect of SDAM  
The shallow detail aggregation module (SDAM) in ProSAF-Net is architecturally optimized to preserve high-frequency spatial features and refine edge localization accuracy. As shown in Table 4, when only SDAM is added, the mAP reaches 95.37% on the RSOD dataset and 94.39% on RSOD (+1.73% over baseline) and 94.39% on NWPU VHR-10 (+1.09% over baseline), empirically validating its efficacy in enhancing low-level feature discriminability. Furthermore, when SDAM is combined with DSFM, the mAP improves to 95.93% on RSOD and 95.30% on NWPU VHR-10, indicating that the hierarchical shallow-to-deep feature fusion mechanism implemented by SDAM and PSFM contributes to better detection performance.
# Camouflaged Object Detection That Does Not Require Additional Priors
## 3 Proposed Method
### Foreground Separation Module  
As mentioned earlier, AFFM extracts cues related to camouflaged objects from images to construct \(CC\). This information serves as a guide for model training and supplements the missing knowledge in each layer. In order to effectively utilize \(CC\), we designed the foreground separation module (FSM). The main goal of this module is to achieve a complete prediction of camouflaged objects by incorporating learning cues specific to camouflaged objects into the representation learning process and effectively separating the camouflaged object from the background in the image. More precisely, using \(CC\) to enhance the features of each layer can enhance the model's learning of the overall structure of the camouflaged object and effectively alleviate the problem of uneven pixel distribution. The overall structure of FSM is depicted in Figure 3.  
Specifically, inspired by [45], we use channel attention (CA) for image features \(\{f_{i}\}_{i=1}^{3}\) to explore key channel features, and then obtain \(g_{coarse}\in\mathbb{R}^{\frac{H}{2^{i+1}}\times\frac{W}{2^{i+1}}\times 256}\) through a convolution operation. The \(g_{refine}\) is derived from \(g_{coarse}\) and \(g_{cc}\). \(g_{cc}\) is obtained by applying an up- or downsampling operation to \(CC\). Certainly, \(g_{refine}\) already encompasses fairly comprehensive information about the object. However, to attain precise separation of foreground and background features, we re-examine the channel features to filter out any camouflage features. Finally, the final output \(\{RF_{i}\}_{i=1}^{3}\) is obtained by using \(1\times 1\) convolution.  
The calculation process is depicted in Equation (3).  
\[\begin{split}& g_{coarse}=Conv_{3\times 3}(CA(f_{i})),i\in\{1,2,3\}\\ & g_{refine}=g_{coarse}\otimes Up_{2}/Dw_{2}(g_{cc})\\ & RF_{i}=Conv_{1\times 1}(CA(g_{refine})),i\in\{1,2,3\}\end{split} \tag{3}\]  
where \(f_{i}\) represents the image features output by the backbone. \(RF_{i}\) is the refined feature.
## 4 Enhancing the Dynamic Multi-Hop Routing (EDMR) Protocol
### The Routing Phase of the EDMR Protocol
#### 4.2.1 The Initial Routing Sub-Phase  
As previously mentioned, the different types of nodes (CHs, INs, and Ns) are ready to start their operations by the end of the table preparation sub-phase. The Ns begin sensing the environment, collecting data, and transmitting it to their CHs (intra-cluster). Each cluster is divided into two levels. Thus, the Ns of the first level transmit their data directly to the CHs, while the second-level Ns utilize their RTs to choose the first entry and route their data to the CH through it. The data gathered by the CHs must be sent to the BS (inter-cluster). Therefore, the CHs and INs located in the first level promptly send the collected data toward the BS, while the data from the CHs and INs in the second level are forwarded to other CHs and INs located at lower levels. The RTs of the CHs and INs are also used to select the first entry. Algorithm 2 summarizes the essential steps performed in this sub-phase. As a result, the data during the initial phase flow smoothly from all network nodes toward the BS, which in turn analyzes the collected data and determines the next structure of the network by selecting which clusters must remain active and which should deactivate their operations. This mechanism is employed in the following phase.
# Chlorobenzene Removal Using DBD Coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) Catalyst
## 3 Results and Discussion  
### Preparation of Catalysts  
According to the results of ICP-OES, the copper content of CuO/\(\gamma\)-Al2O3 catalyst was 4.5 wt%. Table 2 shows the structural properties of the CuO/\(\gamma\)-Al2O3 catalyst and \(\gamma\)-Al2O3 powder. Because the introduction of Cu will cover part of the pores, the specific surface area and the pore volume of \(\gamma\)-Al2O3 powder decrease from 168.6 m\({}^{2}\cdot\)g\({}^{-1}\) to 141.3 m\({}^{2}\cdot\)g\({}^{-1}\) and from 0.95 cm\({}^{3}\cdot\)g\({}^{-1}\) to 0.72 cm\({}^{3}\cdot\)g\({}^{-1}\), respectively.  
Figure 3 shows the XRD patterns of the CuO/\(\gamma\)-Al2O3 catalyst and the \(\gamma\)-Al2O3 powder. The formation of CuO crystals was distinguished according to the extra diffraction signals of the CuO/\(\gamma\)-Al2O3 catalyst at 20 = 35.6\({}^{\circ}\), 38.7\({}^{\circ}\), 48.7\({}^{\circ}\), 61.5\({}^{\circ}\), and 74.9\({}^{\circ}\) (JCPDS 48-1548) compared with the \(\gamma\)-Al2O3 powder.  
Figure 4 shows the XPS patterns of the Cu 2p in the CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalysts before and after the DBD discharge. The spin-orbit split on the 2p spectrum of Cu was observed. The 2p orbit of Cu before discharge was shown in figure (a). The binding energy of Cu (932.4 eV for Cu2O and 933.9 eV for CuO) and the existence of the satellite peak suggest that the main chemical state of surface Cu is Cu\({}^{+}\)(Cu\({}_{2}\)O) and Cu\({}^{2+}\)(CuO). The mass ratios of Cu\({}^{+}\)(Cu\({}_{2}\)O) and Cu\({}^{2+}\)(CuO) are 23.1% and 76.9%, respectively. The results of XPS analysis indicate that the mixture of Cu\({}^{+}\) and Cu\({}^{2+}\) forms the upper layer of the CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst, and Cu\({}^{2+}\) is the main state of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\). The 2p orbit of Cu after DBD discharge was shown in Figure 4b. The binding energy of Cu (932.4 eV for Cu\({}_{2}\)O and 934.6 eV for Cu(OH)\({}_{2}\)) and the existence of the satellite peak suggest that the main chemical state of surface Cu is Cu\({}^{+}\)(Cu\({}_{2}\)O) and Cu\({}^{2+}\)(Cu(OH)\({}_{2}\)). The mass ratios of Cu\({}^{+}\)(Cu\({}_{2}\)O) and Cu\({}^{2+}\)(CuO) are 81.6% and 18.4%, respectively. The increase in the Cu\({}^{+}\) ratio is mainly due to the reduction in Cu\({}^{2+}\) as an oxidant during the discharge process [44; 53]. The production of Cu\({}^{2+}\) (Cu (OH)\({}_{2}\)) is mainly due to the adsorption of water from the degradation of chlorobenzene on the catalyst surface.
## 2 Shaking Table Test Design
### Test Model Construction  
According to the in situ geological survey data, artificially prepared geotechnical materials were used for the model test. The gravel soil was simulated by the mixture of wetted gravel and pea gravel. The physical and mechanical parameters of the soil are shown in Table 6, and the gradation curve is shown in Figure 4. A mixture of bentonite and fine sand with a mass ratio of 1:2 was used to simulate the potential sliding surface [20; 21]. The bedrock was a stirred mixture of different materials, including red clay, fine sand, cement, and water with a mass ratio controlled as 1:0.55:1:0.25. It was constructed using the layer-filling method and compacted in layers. The elastic modulus of the hardened rock mass reached 16.4 GPa, which provided the necessary strength and stiffness.  
On the basis of the condition of the prototype worksite, the test model was appropriately simplified in the design of the front and back rows of anti-slide piles to reinforce the bridge foundation on the gravel soil slope. According to the reinforcement ratio and the stirrup ratio of the prototype structure, the reinforcement cages of the pier pile foundation and anti-slide piles were tied and formed, and micro-concrete was poured to construct a reduced scale model, as shown in Figure 5a,b. The geometry and concrete grades of the front-row and back-row anti-slide piles as well as the pier pile foundation are shown in Table 7. Findings from the existing research have shown that the well-graded micro-concrete has similar stress-induced deformation characteristics as ordinary concrete [22]. A steel box with a sliding track was welded on the top of the pier, and two counterweights were hung on the track to simulate the dynamic response of the adjacent box girders during vibration [23], as shown in Figure 5c. The weights and the spacing of the counterweights were determined on the basis of the similarity relationships. The photograph of the full test model is given in Figure 5d.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structure** & **Length** & **Sectional Dimension** & **Pile Spacing** & **Concrete Grade** \\  & **(cm)** & **(cm)** & **(cm)** & **Concrete Grade** \\ \hline Front-row stabilizing pile & 38.6 & \(2.9\times 4.3\) & 8.6 & C35 \\ Back-row stabilizing pile & 65.7 & \(3.6\times 5.0\) & 8.6 & C35 \\ Bridge pile foundation & 57.1 & 46.4 & 39.3 & Diameter 2.9 & 6.6 or 9.3 & C30 \\ Cushion cap & 5.7 & \(23.1\times 30.9\) & — & C30 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Structural dimensions of bridge foundation and anti-slide piles.  
Figure 4: Grading curve of gravel soil.
# Magnetoelectric Nanoparticles for Wireless Peripheral Nerve Stimulation: A Computational Study
###### Abstract  
This study aims to precisely characterize the use of magnetoelectric nanoparticles (MENPs) for stimulating peripheral nerves. Numerical methods were employed to quantify the interaction between MENPs and nervous tissue. The influence of MENPs' orientation, concentration and distance was assessed in terms of the external potential distribution exerted by the MENPs, the amplification of the exerted MENPs' stimulation required to excite the neural fibers and the current injected into the intracellular space. The results highlight the significance of MENPs' concentration for stimulation accuracy and efficiency, the impact of MENPs' orientation on the electric potential distribution sensed by the nervous tissue, as well as the importance of the MENPs' distance over the fibers' recruitment. Given the considerable variability in the interaction between MENPs and nerves, our research provides a crucial step towards understanding this interaction, offering quantitative support for the application of MENPs in nervous tissue stimulation.  
magnetoelectric nanoparticles; peripheral nerve stimulation; numerical methods Article
## 1 Introduction
According to previous studies, this technique can discriminate geological site structures [22], hydrogeology [23], contaminated materials, and biogeochemical processes [24] based on altered electrical properties. This research emphasizes the importance of integrated use of geological, hydrogeological, chemical, and geophysical data to achieve high-resolution characterization of underground geological heterogeneity, aquifers, and contaminated areas [25]. The proper collection, storage, representation, and integration of geothematic data from different sources are key aspects in defining reliable remediation strategies for contaminated sites [14]. The creation of a \"4D\" geographical database (which also considers the time factor) enables the integrated management, representation, and analysis of different data (geological, hydrogeological, geophysical, and chemical) [26]. The multitemporal and multidisciplinary geodatabase assumes the role of an effective \"near real time\" decision support system (DSS) [26; 27; 28] able to manage and release data from characterization to implementation of the technique. The 3D georeferenced model extracts useful information for the decision-making process quickly and in a versatile way. The hydrogeological uniqueness and chemical peculiarities orient the selection of a remediation technology and identification of the application points [14]. The selected remediation technology was verified through a pilot test, which was monitored through ERT in near real time, with water and soil sampling, to obtain technical and operational information for optimization of the intervention procedures on a larger scale. Based on the satisfactory results of laboratory investigations (microbiological and microcosm tests) [29; 30; 31], a field test was designed and implemented as the final step of the evaluation process, which led to the selection of an appropriate remediation technology according to the hydrogeochemical peculiarities of the site [15]. The possibility of scaling up the process was evaluated using the pilot test. Full-scale results provide information on the long-term trend of the groundwater treatment onsite. The work illustrates the remediation measures adopted and the results of post-treatment monitoring for a period of two years. The results that led to project closure derive from the integration of multidisciplinary data, using a multiscale approach. This research represents the first completed example in European territory of remediation of an aquifer contaminated with chlorinated solvents by a combination of adsorption and biodegradation.
## 2 Materials and Methods
### DBN-Based Path-Tracking Deviation Pattern Identification Model and Dataset Preparation
#### 2.3.4 Dataset Segmentation  
To ensure the reasonableness of the dataset, firstly, the dataset of each model is randomly divided into 10 parts, of which 9 are the training set and 1 is the testing set; then, the training set is further divided into 10 parts, of which a certain 1 part is rotated as the validation set, and the remaining 9 parts are used as the training set. Each model has three types of data set types, and each type has 1000 data samples, which totals 9000 data samples for the three models. The data set division for each model is shown in Table 7.
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 5 Analysis of the Experimental Results  
### The Influence of Data Set  
In this paper, all the experiments are carried out on a computer with Core I7700K CPU and GTX 1080 Ti GPU. First, the influence of data set size on the model is considered, as shown in Table 1. From this, we can see that when only 60 kilometer post images obtained from the Internet and the literature were used as a data set, we randomly selected 80% of them to train the model, and the remaining 20% were used for validation. The accuracy of the obtained model was only 75.65%. However, when the kilometer post data generated by the DCGAN algorithm were mixed with the original 60 images, and 500 images were obtained as the data set to train and verify the model, the accuracy of the model was significantly improved, reaching 98.92%. Therefore, it can be seen that the size of the data set has a direct impact on the accuracy of the model and that the method of generating kilometer post data using the DCGAN algorithm proposed in this paper is effective.