esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Surface-Driven Mechanisms for Low-Temperature Sintering of Nanoscale Copper
## 3 Results and Discussion
### Sintering of Multiple Copper Nanoparticles
#### 3.1.2 The Structural Evolution During Sintering  
As mentioned above, the defects or pores are unavoidable when the temperature is relatively low. Understanding the evolution of the pore or defect is meaningful to predict the properties of sintered samples. We applied the surface extraction algorithm provided by OVITO software (Version 3.10.6) to extract the inner surfaces of nanoparticles and investigate the relationship between structural evolution during low-temperature sintering processes at different temperatures.  
Figure 4 illustrates the sizes of the pore regions and their corresponding internal surface areas in the simulated system during sintering. The black line graph depicts the decreasing volume of the pore sections in the sintered samples as the sintering temperature increases. The red line graph illustrates the reduction in the internal surface area of the pores in the sintered samples with rising sintering temperatures. The trends in surface area and pore volume changes are fundamentally similar, with both values approaching a relatively low level when the temperatures are larger than 600 K, which aligns closely with experimental observations. The same phenomenon can also be observed in the potential energy difference images before and after sintering presented earlier, as shown in Figure 3a.  
However, the trend of the internal surface area and pore volume does not completely align before 600 K; they only begin to converge closely after reaching 600 K. It is reasonable that the surface of a space is dependent on the topology of that space. Among these, a spherical space possesses the smallest surface area. In a microgravity environment, water droplets assume a spherical shape due to the influence of surface tension, which is consistent with the spherical surfaces observed in the molten nanoparticles after the temperature reaches 600 K. Essentially, this phenomenon aims to reduce surface area to  
Figure 4: The pore volume and internal surface area of sintered samples are obtained at different sintering temperatures. The illustration depicts the method of extracting the internal surface of the sample sintered at a temperature of 500 K.  
lower surface energy. When the temperature is lower than 600 K, the rapid dynamic state of the internal surface of the system has not been activated, and the atoms at the internal surface cannot easily adjust their positions; they primarily vibrate around their respective lattice equilibrium positions. Consequently, the morphology of the pore regions in the nanoparticles at this stage resembles polygons rather than spheres, as they struggle to minimize the internal surface area to a spherical shape. As outlined in the multi-stage low-temperature sintering mechanism presented in our previous work [29], the sintering process at this point relies heavily on the significant internal stresses generated by adsorption between particles, leading to twisting and sliding.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 6 Modeling Marine Transportation as Social Network  
Upon simulation, we aimed at extending the framework to support more advanced visualizations functionalities. Thus, we borrowed techniques from the area of Social Network Analysis (SNA). We model the ship traffic domain as an undirected network, whose nodes correspond to selected RoIs that need special attention (such as ports, regions that are beings crossed by major passaging vessels) and arcs represent the trajectories of vessels moving from one node to another. The network would be then analyzed by using conventional SNA analysis algorithms and visualized in constant time or in discrete timeslots, in order for domain and policy experts to observe dynamic changes over time. The idea is that transportation of ships may be considered as a form of social network where nodes represent points in the sea where significant traffic is being observed and arcs can be considered as preferences of ships over moving across specific points. In an attempt to model maritime flow, we should take the fact that a maritime network is an assemblage of individual vessel trajectories that are subjected to spatial constraints. Therefore, by analyzing such networks we could identify influential points that could potentially become areas of high risk in terms of accidents, touristic development, port authority strategic planning, etc.  
More specifically, historical AIS data were transformed into trajectories using the original geolocation information and applying a linear interpolation model. Upon trajectory construction, the network's nodes were divided into two categories, namely static (ports) and dynamic (areas where several trajectories of ships cross). The arcs corresponded to the passing of ships from one node to another, again based on their extracted trajectories (usually weighted by some criterion, such as average speed or average number of ships). The quality of this network was measured, utilizing metrics such as centrality, density, modularity, PageRank score, etc.  
Using the Java language and the freely available Gephi@library, visualization could take place, either in a static time period or in a sliding window of time.
###### Abstract  
A new configuration of a discharge chamber and power source for the treatment of up to 1 L of liquid is presented. A leakage transformer, energizing two metal electrodes positioned above the liquid, limits the discharge current inductively by utilizing the weak magnetic coupling between the primary and secondary coils. No additional means to avoid arcing (electric short-circuiting), e.g., dielectric barriers or resistors, are needed. By using this technique, exceeding the breakdown voltage leads to the formation of transient spark discharges, producing non-thermal plasma (NTP). These discharges effected significant changes in the properties of the treated liquids (distilled water, physiological saline solution, and tap water). Considerable concentrations of nitrite and nitrate were detected after the plasma treatment. Furthermore, all tested liquids gained strong antibacterial efficacy which was shown by inactivating suspended Escherichia coli and Staphylococcus aureus. Plasma-treated tap water had the strongest effect, which is shown for the first time. Additionally, the pH-value of tap water did not decrease during the plasma treatment, and its conductivity increased less than for the other tested liquids.  
Keywords:inductively-limited discharge; plasma-treated water; tap water; antimicrobial activity +
Footnote †: journal: Applied Sciences
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 2 Related Work
### Optical Flow for Visual Tasks  
Optical flow information is broadly applied during the course of conducting computer vision tasks. [PERSON] et al. [20] and [PERSON] et al. [21] applied optical flow networks for pose estimation. FlowTrack [22] trains an end-to-end optical flow network and utilities rich flow information in consecutive frames with the intention of boosting character representation and tracking performance. [PERSON] et al. [23] used an optical flow network for end-to-end training, which was able to forecast the movement trend of the target more precisely. [PERSON] et al. [24] fitted optical flow characteristics to a temporally noisy turbulent environment and constructed an online tracking algorithm.
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 2 Materials and Methods
### The 2D Device  
The 2D LC BS device is made up by cascading two orthogonal 1D liquid crystal cells. One is in charge of deviating the incident light beam in the azimuthal angle, while the other produces the deviation in the zenithal angle.  
Both cells were manufactured following the same steps, and both were based on the same working principle of the LC. Each grating is made of 72 parallel pixels, with a 15 \(\upmu\)m pitch. Interpixel size is kept smaller than 3 \(\upmu\)m, reducing any fringe effect. The resulting active area is \(1.1\times 1.1\) mm\({}^{2}\).  
A CAD application was used to develop and design the pixel layout, including the active area and the fan-out (Figure 6). In the final cascaded configuration, the pixels of the two cells are perpendicular to each other in the active area. The rubbing direction, however, is common to both of the cells (Figure 7).  
The incident light must be polarized in the same plane as the rubbing direction, determined in the manufacturing process.  
Figure 5: LC cell schematic, with their layer structure.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 6 Conclusions  
In this paper, for a network with a large number of satellites or aircrafts, we propose a BOCcos and 16 APSK integrated signal in the TDD mode. The signal can achieve TT&C and data transmission at the same frequency, which greatly improves spectrum utilization. The equipment can be easily integrated and miniaturized. In addition, this paper improves the TDD mode, which is commonly used in mobile communication systems to increase spectrum utilization, so that it can be applied to the TT&C of intersatellite links. The combination of the two can significantly reduce the costs of intersatellite links and inter-aircraft links. The acquisition and tracking process of BOCcos + 16 APSK is analyzed and simulated. Although the performance is reduced by approximately 2-3 dB, the acquisition and tracking process is fully applicable to high-CNR environments such as intersatellite links or inter aircraft links. In addition, to solve the problem in which the TDD mode is not received and received simultaneously, this paper uses an extrapolation method to cause the loop to keep tracking continuously without reacquisition, which reduces the constraint of the acquisition time on the TDD switching period. The ranging extrapolation error threshold is less than \(\pm 0.25T_{chip}\cdot c\), and the frequency extrapolation error threshold is less than \(\pm\frac{1}{4T_{chip}}\). Finally, the ranging accuracy of the system is less than 9 cm. In the future, we will improve the BOC signal acquisition method in this scenario. In this paper, BPSK-like is used to signal acquisition. Although the method is simple, the advantage of narrow correlation peak of BOC signal is abandoned.  
**Author Contributions:** Conceptualization, X.L. and W.W.; methodology, Y.Y.; software, L.Y.; validation, L.X. and X.L.; formal analysis, L.X.; investigation, Y.Y.; resources, W.W.; data curation, L.X.; writing--original draft preparation, L.X.; writing--review and editing, L.X. and X.L. visualization, Y.Y.; supervision, Y.Y.; project administration, W.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Technology and Industry for National Defense, grant number No. 060301 and No.B0110.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Related Work
### Work on the Algorithmic Layer for UAV Payload Functions  
Ref. [9] proposes a UAV-based target detection technique for enhancing the efficiency of rescue missions. The YOLO-v5 algorithm in the field of computer vision is introduced to improve the accuracy of search and rescue. However, the work does not consider the cost overhead, and the stability of the YOLO-v5 algorithm under different lighting conditions deserves further discussion. Ref. [10] proposes a technique for road extraction and road condition detection based on videos captured by UAV multispectral cameras, or pre-downloaded satellite multispectral images to provide optimal route planning to humans. The accuracy of route planning in wilderness scenarios is improved. However, processing a large amount of multispectral data requires high computational resources, and the system latency is difficult to be effectively guaranteed. Ref. [19] describes the organizational use of UAVs as base station nodes to expand network coverage, allowing UAVs to carry the appropriate payloads to enhance 5G networks as lift-off base stations, UAV relay stations, and UAV aggregators, respectively, and points out the deployment methods, citing application scenarios such as civil communications, the Internet of Things (IoT), and sensor networks.
# AG1478 Elicits a Novel Anti-Influenza Function via an EGFR-Independent, GBF1-Dependent Pathway
## 1 Introduction
Besides their passive role as a physical barrier, epithelial cells actively produce mucins, surfactants, interferons, and proinflammatory cytokines to direct innate and adaptive immune responses to viral infection [11]. Human epidermal growth factor receptor (EGFR)-mediated cellular signaling is critical to maintain epithelial homeostasis. However, its function in influenza infection remains unclear. In one study, EGFR was found to promote IAV internalization by host cells [12]. In the second study, however, EGFR was demonstrated not to affect viral internalization, but instead to facilitate IAV infection by repressing IRF1-dependent interferon (IFN) production [13]. Thus, EGFR was implied as an IAV promoting protein in both studies but via different mechanisms. Our initial objective is to sort out these discrepancies by investigating the mechanistic link between influenza infection and the EGFR signaling pathway. Interestingly, and also surprisingly, EGFR _per se_ did not have any effect on influenza infection in our study; rather, the off-target effect of an EGFR inhibitor, AG1478, led to an exciting discovery of a novel anti-influenza pathway.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 5 Experiment and Analysis
### The Performance Comparison of WFO, FQ, and FCFS
#### 5.3.1 WFO vs. FQ and FCFS: Impact of Data Volume on Delay  
We compared the changes in the average delay and data quantity of all the nodes after receiving data under the WFO, WFO(HPC), FQ, and FCFS strategies. Figure 7 shows the experimental results.  
The experiment was comprised of four scenarios with different bandwidths and nodes. The path and forward delays are consistent with the experiment described in Section 5.2. The delay in this experiment refers to the time difference between the node receiving data at the beginning and the reception of all data. The delay in Section 5.2 refers to the delay of the data from the CC to a node. The bandwidth of the CC was set to 200 MB/s and 150 MB/s. The number of nodes was set to 100 and 150, respectively. The amount of data from the CC varied from 100 MB to 700 MB.  
As depicted in Figure 7, the average delay after offloading data for the three strategies exhibited a linear increase with an increase in data volume. Notably, the WFO strategies (optimal and heuristic) demonstrated lower delays compared to FQ and FCFS strategies. The delay in implementing WFO strategies increased at a slower rate compared to FQ and FCFS strategies which means that as the volume of data increases, the benefits of using WFO become more evident. A lower delay indicates that data can be efficiently distributed across the cloud-edge collaboration network architecture using the WFO strategy. The comparison of FCFS and FQ performances shows sensitivity to the number of nodes while having little impact on bandwidth and data volume. The influence of the number of nodes on WFO performance was relatively small under the same conditions. However, the optimized approach may occasionally result in worse outcomes than the heuristic-based approach. Because both the optimal approach and the heuristic are used to select the  
Figure 6: Number of users that the WFO strategy served in different tolerance times. When the tolerate delay is less than 3 ms, the number of nodes means offloading the data directly from the CC.  
shortest data path in a given queue, in the scenario of this paper, there are multiple queues. The optimal approach must be better than the heuristic for one queue, not necessarily for the whole. Therefore, the optimized approach occasionally reaches worse results than the one using heuristics.
## 2 Preliminaries
### BPL Decoding Algorithm for Polar Codes  
#### 2.1.1 BP Decoding Algorithm for Polar Codes  
In the BP decoding algorithm, information is propagated through computational units as shown in Figure 1. \(L_{i,j}\) and \(R_{i,j}\), respectively, represent the left-going and right-going soft information for node \((i,j)\) on the factor graph, often expressed in terms of log-likelihood ratios. Therefore, the information update process for a computational unit can be represented as follows:  
\[\left\{\begin{aligned} L_{i,j}&=f(L_{i+1,2j-1},L_{i+1,2 j}+R_{i,j+N/2})\\ L_{i,j+N/2}&=f(R_{i,j},L_{i+1,2j-1})+L_{i+1,2j}\\ R_{i+1,2j-1}&=f(R_{i,j},L_{i+1,2j}+R_{i,j+N/2})\\ R_{i+1,2j}&=f(R_{i,j},L_{i+1,2j-1})+R_{i,j+N/2} \end{aligned}\right. \tag{1}\]  
where  
\[f(\alpha,\beta)=\ln(\frac{e^{\alpha+\beta}+1}{e^{\alpha}+e^{\beta}})\approx sign (\alpha,\beta)\min(|\alpha|,|\beta|) \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Symbol** & **Meaning** \\ \hline \(L\) & Number of BP Decoders \\ \(y\) & Signal Received from the Channel \\ \(N\) & Code Length \\ \(K\) & Length of Information Bits \\ \(x\) & Codeword \\ \(\hat{x}\) & Estimated Codeword \\ \(u\) & Information Sequence \\ \(\hat{u}\) & Estimated Information Sequence \\ \(G\) & Generator Matrix \\ \(L,R\) & Soft Information for Leftward and Rightward Propagation \\ \(l_{i}\) & Factor Graph at the i-th Layer \\ \(\mathbb{R}^{n}\) & n-Dimensional Euclidean Space \\ \(\lambda\) & Parameter of the PDF 1 for Bernoulli Distribution \\ \(\lambda\) & Parameter of the Joint PDF for Bernoulli Distribution \\ \(I(\lambda)\) & Fisher Information Matrix \\ \(G(\lambda)\) & Metric Matrix \\ \(w\) & Weight \\ \(\Phi\) & Cumulative Distribution Function of the Standard \\  & Normal Distribution \\ \hline \end{tabular} \({}^{1}\) PDF: Probability Density Function.  
\end{table}
Table 1: Symbol comparison table.
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 4 Sample Preparation Strategies for MALDI-Based Bacterial Identification
### On-Plate Method  
Formic acid is added before applying a drop of the MALDI matrix in the cell smear method [50; 51]. The colonies grown on the medium are recovered with a toothpick and applied to the MALDI sample plate in a thin layer. A drop of formic acid is applied and allowed to dry. The cell smear method identifies gram-negative rods, such as _Enterobacteriaceae_, with a high probability, but is less accurate for the identification of gram-positive bacteria, such as _Staphylococcus_ and _Enterococcus_, and of yeasts. This distinction may reflect insufficient extraction of proteins from the bacterial body due to differences in the cell wall structure, resulting in a lack of spectral information for identification. The formic acid extraction (on-plate) method, in which the colony is applied to a MALDI sample plate and then formic acid is applied to the colony to promote cell wall destruction, increases the identification probability.
## 2 Background
### GM-PHD Algorithm  
The PHD filter is a recursion algorithm that propagates the intensity function \(D_{k}(x)\) associated with the multi-targets posterior probability. To implement the PHD filter, under a linear target system, the Gaussian mixture (GM) method has been introduced in the PHD filter. Additionally, it forms the GM-PHD filter algorithm. The relationship between the PHD and GM-PHD is shown as in Figure 2.  
In order to implement the GM-PHD filter algorithm, some additional assumptions of Gaussianmulti-targets model are required besides the PHD filter, which is described as below.
###### Abstract
To enhance both the model performance for detecting the damage location and the damage severity, in this paper, we propose a two- stage structural damage detection method in which the selection of a classifier for each stage is very important. We choose 1D-CNN to detect the damage location because 1D-CNN has been widely adopted in the field of SDD with good results. It was also discovered in [17] that 1D-CNN is more accurate for identifying the damage location than the damage severity. This is because the variety of structural vibration responses on different damage locations is much larger than that of the damage severity. In addition, there is a very small difference between the vibration response corresponding to different damage severity at the same location. In this case, a CNN-based model may need more complex structures and more training data. Therefore, after detecting the damage location, we can choose another method that may produce better results with fewer samples and classes to detect the damage severity instead of detecting the damage location and severity at the same time. SVM is a strong classification machine for small-scale sample learning problems. It is a sparse kernel decision machine that avoids computing posterior probabilities when building its learning model. SVM has been extensively used for classification, regression, novelty detection tasks, and feature reduction [18]. Compared with 1D-CNN, SVM is more suitable for damage severity identification, and the computational cost and required training samples of SVM are less than those of 1D-CNN. In summary, the novelty and the main contributions of this paper are as follows:* We propose a new two-stage structural damage detection method which follows the strategy of \"divide-and-conquer\" to solve the problem of insufficient training data and enhance the model performance for multi-level structural damage detection. * Our method fully combines the advantages of 1D-CNN and SVM, reducing computational costs and eliminating the need to rely on expertise to design complex feature extraction methods. * We verify the proposed model on an eight-level steel frame structure. The experimental results show that the proposed method outperforms the state-of-the-art methods in terms of both damage location detection and damage severity detection.
## 4 Detailed Proposed Technology and Test Results
### Acquisition of High-Quality Deep Learning and Inspection Data
The inspection image of the part was extracted from the registered image by using the ROI coordinates of the part set in the initially acquired image of SPOT, and high-quality learning data necessary for AI algorithm development could be obtained, as shown in Figure 5.
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 2 The Properties of the LHP Materials
### Optical and Electrical Properties of LHP
For instance, the diffusion length for MAPbI3-xCClx is more than 1 \(\mu\)m [40], while in CH(NH2)2 PbI3 (FAPbI3) films the effective diffusion length for electrons and holes are 177 \(\pm\) 20 nm and 813 \(\pm\) 72 nm, respectively [42]. When the film processing condition and procedures are optimized, the diffusion length could be further elongated [43; 44]. The dependence mentioned above could be finally attributed to intrinsic factors such as Frohlich interactions and extrinsic factors such as charge-carrier scattering by grain boundaries and dopants [45].