esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we proposed OFDM-based 1024-and 4096-QAM schemes that apply soft-decision Viterbi decoding for use in the 5G environment and beyond. Using link-level simulations, we presented the transmission performance of the proposed scheme under two multipath fading channel models, extended pedestrian A (EPA) and extended vehicular A (EVA), as functions of coding rate. Under EPA fading, the SNRs required to achieve a BER of \(1\times 10^{-2}\) were determined to be approximately 20 dB and 22 dB at a coding rate of 1/3 for 1024- and 4096-QAM, respectively, which are approximately 6 dB and 7 dB higher than what is required under the static condition. Similarly, under EVA fading, the SNRs required to achieve a BER of \(1\times 10^{-2}\) is approximately 22 dB and 24 dB at a coding rate of 1/3 for 1024- and 4096-QAM, respectively, which are approximately 8 dB and 9 dB higher than what is required under the static condition. We also found that, under EVA fading, 1024- and 4096-QAM can no longer achieve a BER of \(1\times 10^{-2}\) at coding rates greater than 2/3 and 1/2, respectively. We determined the relationship between phase error and the SNR penalty required to achieve a BER of \(1\times 10^{-2}\) for the proposed scheme as well as for the existing QAM and QPSK scheme. We found that 1024-QAM can no longer achieve a BER of \(1\times 10^{-2}\), at phase errors of 0.20 or above even if the SNR is increased. Similarly, 4096-QAM can no longer achieve a BER of \(1\times 10^{-2}\) at phase errors of 0.16 or above. To limit the SNR penalty to below 3 dB, the phase errors for 16-, 64-, 256-, 1024-, and 4096-QAM cannot be higher than 0.39, 0.31, 0.18, 0.12, and 0.11, respectively. In our future work, we will use system-level computer simulation to clarify the user throughput performance of mobile systems in which 1024- and 4096-QAM are incorporated into the modulation and coding sets (MCS).  
Conceptualization, H.O. and R.T.; Investigation, H.O. and R.T. and K.S.; Data curation, R.T. and K.S.; Writing--original draft preparation, H.O.; Writing--review and editing, H.O.; Visualization, R.T. and K.S.; Supervision, H.O.; Project administration, H.O.; Funding acquisition, H.O. This research was funded by JSPS KAKENHI Grant Number JP18K11277, Grant-in-Aid for Scientific Research (C). The authors would like to thank [PERSON] and [PERSON] who are graduates of Kogakuin University for their contributions to construct link-level simulations. The authors declare no conflict of interest.
# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 5 Discussion
### Future Directions  
In this exploratory study, we used only two background scenes and the image of a person as the target object, always with the same pose and viewed from the front in Experiments 1 and 2, and multiple poses (e.g., kneeling or a part of the body) in a naturalist environment in Experiment 3. Future studies should investigate whether YOLO can be applied to assess camouflage performance across a wider range of different backgrounds (such as arctic, and different weather conditions), target objects (different poses, camouflage patterns), viewpoints (from the front, observed from above), and combinations thereof.  
In this study, we applied YOLOv4-tiny to detect persons in the individual frames from a video sequence. It would be interesting to replicate the present study using a version of YOLO that incorporates motion detection. Motion is a special case in the visual search literature, as a minimal motion change is known to capture attention immediately [10; 78; 79; 80]. In the military context, it is also known that motion breaks camouflage [26], even if a person wears an optimal camouflage suit [29].  
Although we show that YOLOv4-tiny can assess the camouflage efficiency of static images, in the future, it would be an excellent idea to replicate the current study using different representative neural network models (such as Faster R-CNN, Mask R-CNN, etc.) to examine whether other models can do a better job in approaching human camouflage assessment (i.e., providing a better correlation between the model output and human performance). Additionally, a promising direction would be to train models directly on data from behavioral experiments like the present one, enabling them to more closely mimic human camouflage evaluation.
## 4 Research on Critical Technologies
### Lightweight and Miniaturization Technologies  
The weight of airborne laser communication terminals is generally in the tens of kilograms, with a power consumption of more than a hundred watts. Internationally, the weight of UAV laser communication loads has been reduced to less than 10 kg. Still, domestic airborne laser communication loads have not yet fully met the application requirements of various UAVs. Therefore, there is a pressing need to develop lightweight and miniaturized laser communication payload design technology. An integrated design approach can substantially increase the carrying space of UAVs, and due to a reduction in the load weight, it simultaneously improves the flexibility of UAV attitude adjustment and promotes the engineering application process of airborne laser communication payloads. Regarding miniaturization technology, the integrated design of optical communication terminals, such as the interstellar laser communication terminal developed by the Shanghai Institute of Optical Precision Machinery of the Chinese Academy of Sciences, can be considered. This integrated design without an independent beacon light not only maintains communication performance but also realizes the weight and volume reductions of the system. With a mass of 7.5 kg and a power consumption of 95 W, the terminal can recognize interstellar optical communication with a communication rate of 100 Mbit/s. The communication terminal has successfully established a stable double interstellar communication link through on-orbit self-test and optical axis calibration [28].  
The coarse and fine two-stage ATP trapping system is bulky, and its optical-mechanical structure is very complex, which makes it difficult to use for maintenance and rapid deployment. Therefore, the two stages can be simplified into a first-stage capture mechanism using a flow-checking galvanometer and a manually assisted capture method to accomplish the ATP task.
# Dynamic Community Detection Based on Evolutionary DeepWalk
## 3 The DeDW Algorithm
### Problem Description and Symbol Definition  
**Definition 1**.: _Dynamic network. In a dynamic network \(G=(V,E)\), \(V=\{v_{1},\,\cdots,\,v_{n}\}\) represents the set of nodes, \(E=\{e_{ij}\mid\ \forall i,j\in\{1,\,\cdots,\,n\}\), \(i\
eq j\}\) represents the collection of edges. Dynamic networks can be divided from time step 1 to the time step T of a fixed-length temporal snapshot network \(G=\{g_{1},\,\ldots,\,g_{t},\,\ldots,\,g_{T}\}\), \(1\leq t\leq T\). In the temporal snapshot network \(g_{t}=(V_{t},\,E_{t})\), \(v_{i}^{t}\in V_{t}\) represents node \(i\) in \(g_{t}\), \(e_{ij}^{t}\in E_{t}\) represents the connection edge between node \(i\) and node \(j\) in \(g_{t}\)._  
Figure 1: The research framework of this article.  
In a dynamic network, the attribute of each node is represented as a feature vector. In this paper, we suppose the attribute of each node at different time step is stable and invariable. The original community structure is represented as adjacent matrix, due to the nodes and their relationship are sparse, graph embedding is used to map high dimensional sparseness adjacent matrix to low dimensional dense vector space.  
**Definition 2**.: _Dynamic communities. the temporal snapshot network \(g_{t}=(V_{t},E_{t})\) can be divided into \(k\) communities, the community \(C_{t}\) can be expressed as \(C_{t}=C_{t}=\{c_{t}\},c_{t}^{2},\ldots,c_{t}^{k}\},I\leq t\leq T\), and \(c_{t}^{i}\cap c_{t}^{j}=\varnothing\), \(\forall i,j\in\{1,\cdots,n\}\), \(i\
eq j\). The dynamic communities divided by the dynamic network \(G=(V,E)\) can be represents by \(C=\{C_{1},C_{2},\cdots,C_{T}\}\)._  
In order to capture the change in dynamic networks, the dynamic graph \(G\) is partitioned into different time steps. In each time step, there are nodes added to or removed from \(G\), meanwhile edges associated with nodes are added or removed. For simplification, we generated embedding matrix of \(G\) at each time step. At each time step \(t\), the comprehensive features of nodes are represented as their current state (namely current features) and previous state (namely historical features). Table 1 shows the symbols commonly used in this article and their corresponding descriptions.  
In this paper, graph embedding is used to map adjacent matrix to feature vector. Graph embedding as one of the technologies of graph feature learning, attracts great attention. Given graph \(G\), the aim of graph embedding is to learn a map function \(f\), \(f:G\rightarrow\mathbb{R}^{d}\), that can map high dimensional sparseness adjacent matrix into low dimensional dense vector space. In the map function, \(d\) is embedding dimension and \(d<<|V|\). In this way, the dense and low dimension matrix is used for graph analysis instead of a sparse and high dimensional adjacent matrix. Moreover, the graph model can represent the relation of nodes with those with no direct edges, which can remit the sparse node interaction problem.
# Metallic Nanoparticles Obtained via \"Green\" Synthesis as a Platform for Biosensor Construction
## Materials and Methods  
### Reagents  
Chloroauric acid (HAuCl\({}_{4}\)), silver nitrate (AgNO\({}_{3}\)), cobalt (II) sulfate (CoSO\({}_{4}\)), manganese(II) chloride (MnCl\({}_{2}\)), potassium chromate (K\({}_{2}\)CrO\({}_{4}\)), palladium (II) chloride (PdCl\({}_{2}\)), chloroplatinic acid (H\({}_{2}\)PtCl\({}_{6}\)), neodymium(III) sulfate (Nd\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)), sodium ethylenediaminetetraacetate (EDTA), 2,2\({}^{\prime}\)-azinobis (3-ethylbenzothazoline-6-sulfonate) diammonium salt (ABTS), methanol, and all other reagents and solvents used in this work were purchased from Sigma-Aldrich (Steinheim, Germany). All reagents were analytical grade and were used without further purification. Purified medium-size fragments of chitosan from crab shell were obtained as described earlier [57]. All solutions were prepared with ultra-pure water prepared with Milli-Q (Millipore, Molsheim, Germany).
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 1 Introduction
With a depth of nearly 900 m and a continuous observation time of more than six months, the data of seafloor chemical substances such as chlorophyll were obtained successfully [18]. In 2013, the Sanya submarine observation network system was put into use, building a solid foundation for the larger seabed observation network construction of China. However, the above-mentioned submarine observation networks are built on a wired network such as optical fiber, which can meet the long-term real-time observation requirements, but has limitations such as deployment difficulty, high deployment cost, and low degree of freedom in the sea area. For the problems existing in the above-mentioned seabed observation system, it is necessary to develop a seabed long-term in-situ real-time observation system with easy deployment, low deployment cost, and high freedom degree difficulty. The rapid development of satellite communication and underwater acoustic communication technology provides the possibility for building a seabed real-time observation system based on wireless communication. In this paper, aiming at long-term in-situ real-time observation of seabed engineering geology, a seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO) is proposed and developed. The proposed SRSS/ILMO can also realize the observation of different fields by carrying different sensors such as those based on marine chemistry, biology, and environment.
## 4 Results and Discussion
Figure 9 showcases a comparative \(accu_{y}\) and \(F_{score}\) examination of the ODAE-WPDC methodology with recent techniques. The figure exposes that the ODAE-WPDC system has resulted in enhanced performance with regard to \(accu_{y}\) and \(F_{score}\). Interms of \(accu_{y}\), the ODAE-WPDC system has obtained enhanced \(accu_{y}\) of 99.28%, whereas the DL-SGD, DL-RMSProp, DL-Adam, SI-BBA, PDGAN, and NIOSELM algorithms have reached \(accu_{y}\) of 94.64%, 92.84%, 94.69%, 94.93%, 94.12%, and 93.40%, correspondingly. With regard to \(F_{score}\), the ODAE-WPDC system has obtained higher \(F_{score}\) of 99.27%, whereas the DL-SGD, DL-RMSProp, DL-Adam, SI-BBA, PDGAN, and NIOSELM systems have reached \(F_{score}\) of 94.50%, 95.52%, 95.27%, 94.78%, 92.21%, and 90.86%, correspondingly. From the detailed results and discussion, it is clear that the ODAE-WPDC model has shown effectual phishing WS detection and classification performance.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.1 Denoising Diffusion Process  
At the core of DDPM is the denoising diffusion process, a probabilistic sequence of transformations that gradually introduces controlled noise to an image [47]. The process aims to model the generative path from a clean image to a noisy version. Formally, the process is defined as:  
\[X_{t}=f_{\theta_{t}}(X_{t-1})+\sqrt{2\eta_{t}}\varepsilon \tag{1}\]  
where \(X_{t}\) is the image at time \(t\), \(f_{\theta_{t}}\) is the denoising function parameterized by \(\theta_{t}\), \(\eta_{t}\) is a schedule of diffusion noise, and \(\varepsilon\) is a sample from a unit Gaussian distribution.
## 4 Subsidence Results and Comparison
LT-1's higher resolution, enhanced coherence, and reduced unwrapping errors \begin{table}
\begin{tabular}{c c c} \hline \hline
**Compared Indicators** & **LT-1** & **Sentinel-1** \\ \hline RMSE (m) & 7.7 & 19.4 \\ SD (rad) & 0.2 & 1.6 \\ SNR & 18.6 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Table of phase-unwrapping results. Figure 6: Subsidence results along the A-A\({}^{\prime}\) cross-section for LT-1 and Sentinel-1. result in more accurate and reliable subsidence detection, even in challenging environments with dense vegetation. The significant differences in RMSE, SD, and SNR further emphasize LT-1's advantages, making it a more precise and effective tool for subsidence monitoring compared to Sentinel-1. These findings highlight the potential of LT-1 for providing detailed and accurate ground deformation insights, essential for effective risk management in mining regions.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 2 Generalized Hilbert Transform (GHT)
### Analysis of GHT  
According to Equations (1)-(4), the GHT result \(g(t)\) depends on the time-frequency spectra \(S(t,f)\) and the order parameter \(p\). For a chosen order parameter, the output resolution of GHT is unique, which limits its application. As \(S(t,f)\) is related to the time-frequency analysis methods [3; 4; 5; 6; 7; 8; 9; 10], here we only analyze the impact of the chosen order parameter \(p\) on the GHT result \(g(t)\).  
If \(c\) denotes the collection of positive numbers, then it can be expressed as  
\[c=\Big{\{}\ \ \ \ (2k_{1}-1)\cdot(2k_{2})^{-1},\ \ 2k_{1}\cdot(2k_{2}-1)^{-1}\ \ \,\ \ (2k_{1}-1)\cdot(2k_{2}-1)^{-1}\ \ \ \Big{\}} \tag{6}\]  
where \(\{\ \}\) takes the number collection; \(k_{1}\) and \(k_{2}\) are positive integer numbers; if \(A\) and \(B\) both are real numbers, and  
\[B=A^{c}=\left\{\begin{array}{c}\
icefrac{{(2k_{1}-1)}}{{A^{(2k_{1}-1)}}}\\ \
icefrac{{(2k_{2}-1)}}{{A^{(2k_{1})}}}\\ \
icefrac{{(2k_{2}-1)}}{{A^{(2k_{1}-1)}}}\end{array}\right. \tag{7}\]  
then, the following four conclusions are drawn:  
(i) For \(A<0\), then \(B=\
icefrac{{(2k_{1}-1)}}{{A^{(2k_{1}-1)}}}\) does not exist. Therefore, when \(S_{n}(t,f)<0\) or \(g_{n}(t)<0\) (\(n=\{1,2\}\)), \(p\) cannot be chosen as \((2k_{1}-1)\cdot(2k_{2})^{-1}\).  
(ii) For \(A<0\), then \(B=\
icefrac{{(2k_{2}-1)}}{{A^{(2k_{1})}}}>0\). Therefore, when \(S_{n}(t,f)<0\) or \(g_{n}(t)<0\) and \(p\) is chosen as \(2k_{1}\cdot(2k_{2}-1)^{-1}\), the signs of \(S_{n}(t,f)\) and \(g_{n}(t)\) are changed and GHT obtains instability result (as shown in Figure 1d).  
(iii) For \(A<0\), then \(B=\
icefrac{{(2k_{2}-1)}}{{A^{(2k_{1}-1)}}}<0\). Therefore, when \(S_{n}(t,f)<0\) and \(g_{n}(t)<0\), \(p\) can be chosen as \((2k_{1}-1)\cdot(2k_{2}-1)^{-1}\); and correspondingly, GHT preserves the signs of \(S_{n}(t,f)\) and \(g_{n}(t)\).  
(iv) For \(A\geq 0\), then \(B=A^{c}\geq 0\). Therefore, when \(S_{n}(t,f)\geq 0\) and \(g_{n}(t)\geq 0\), \(p\) can be chosen as any number; and correspondingly, GHT preserves the signs of \(S_{n}(t,f)\) and \(g_{n}(t)\).  
Above all, the order parameter \(p\) can be only chosen as \((2k_{1}-1)\cdot(2k_{2}-1)^{-1}\) and \(2k_{1}\cdot(2k_{2}-1)^{-1}\); the GHT result with \(p=2k_{1}\cdot(2k_{2}-1)^{-1}\) usually has a serious instability problem, which limits its further application.  
Figure 1: (**a**) Synthetic 1D channel data. Instantaneous amplitudes obtained by: (**b**) HT; (**c**) GHT with \(p=1.0\); (**d**) GHT with \(p=0.8\) (ratio of an even number 4 and an odd number 5); (**e**) GHT with \(p=0.6\) (ratio of two odd numbers 3 and 5).
# Solving Optimal Electric Vehicle Charger Deployment Problem
## 2 Related Work
### Problem Formulation Approaches  
#### 2.1.1 Facility Location Problem (FLP)  
The latest research introduced a body of prior work that formulates the optimal deployment of EV chargers as a multi-objective optimization problem [8], considering various factors such as battery types [9] and distances to nearby energy sources [10].  
We extended our investigation to the literature on facility location problem (FLP), which is analogous to our problem in the sense of having to determine optimal deployment plans including locations and expansion patterns [11]. It took our attention that the FLP is formulated as the mixed integer programming [12], wherein the constraints are reduced to continuous-variable linear equations. The literature goes on to generalize the formulation to the mixed integer linear programming [13] and the mixed-integer nonlinear programming (MINLP) [14].
## 3 Results
### Performance Comparison of Optimization Algorithms after Integrating Security Layers  
In this paper, the SC-MADDPG algorithm proposed (i.e., the MADDPG algorithm that integrates a safety layer based on the incorporation of communication mechanisms)  
Figure 8: Average latency under different numbers of UAVs.  
Figure 9: Average latency of different algorithms.  
is compared with the following two joint optimization algorithms for task offloading and UAV trajectory:  
1. MADDPG algorithm (with UAV communication mechanism): execute joint UAV actions directly obtained from the Actor network without considering safety constraints.
2. Hard-MADDPG algorithm (with UAV communication mechanism): consider safety constraints and require the output safe joint action to satisfy all constraints.  
The cumulative number of collisions in the test phase after convergence of the three algorithms is shown in Figure 10. From the experimental results, it can be seen that the cumulative number of collisions between UAVs increases gradually with the increase of training rounds. The MADDPG algorithm without safety constraints has the highest number of UAV collisions, followed by the Hard-MADDPG algorithm, and the SC-MADDPG algorithm has the least. Therefore, the SC-MADDPG algorithm with a safety layer has a better effect in avoiding UAV collisions.  
Figure 11 shows the average energy consumption of the SC-MADDPG, Hard-MADDPG, and MADDPG algorithms. The experiment iterated 1800 rounds. Experimental results show that the energy consumed by the SC-MADDPG algorithm is less than the energy consumed by the Hard-MADDPG and MADDPG algorithms, as after a UAV collision, the task on the UAV needs to be re-uploaded from the mobile user device to another UAV for task offloading, which increases the transmission energy consumption. The SC-MADDPG algorithm reduces the probability of UAV collisions, resulting in lower energy consumption.  
In this section, the average delay of the SC-MADDPG, hard-MADDPG, and MADDPG algorithms are compared in different task data sizes. The experimental results show that  
Figure 11: Average energy consumption of different algorithms.  
Figure 10: Number of UAV collisions under different algorithms.  
the MADDPG algorithm has the worst performance and the highest average delay. The average task processing delay of the SC-MADDPG algorithm is less than the other two algorithms. The SC-MADDPG algorithm provides safe joint actions, prevents UAVs from colliding, and avoids the delay of task retransmission, resulting in the lowest delay and optimal performance. The specific effect is shown in Figure 12.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Integration between Sensor and Textile
#### 3.6.3 Integration at the Yarn/Fabric Level  
A fabric embedded with a pressure sensor was developed in [140] by selectively placing polyurethane-coated Ni-Ti alloy yarns during the weaving process of a fabric made of predominantly woolen yarns, as presented in Figure 17. The capacitance of the dielectric material sandwiched between two electrodes varied with the thickness of the material. The fabric enables developing a real-time sitting posture correction system. Conducting thread such as copper (Cu) or silver (Ag) fibres were utilized for the electronic textile [6, 141].  
Electrical engineers from ETH Zurich have developed intelligent textiles with sensors and conductive filaments woven into textiles. This technology is developed by attaching thin-film electronics to the textile. Microchips and other microelectronic elements were integrated directly into the architecture of the material. The microchips were placed on plastic strips and encapsulated [142].  
Coating fibres, yarns and fabrics with metals, galvanic substances or metallic salts can create conductive textiles. Electroless plating, evaporative deposition, sputtering and the chemical coating of textiles with a conductive polymer are popular coating processes [5]. To produce the desired geometry of the wearable sensor, pattern transferring is used as  
Figure 17: Textile pressure sensor based on Ni-Ti alloy fibre coated with polyurethane rubber (adapted from [140]).  
the fabrication method. The available techniques of pattern transferring are microscale modelling, lithography, screen printing, inkjet printing, 3D printing and handwriting. Researchers at the University of Southampton developed textile-based screen-printed network for electrodes and associated conductive tracks used in healthcare applications. A polyurethane paste was screen printed onto a woven textile to create a smooth and conductive track. A wearable device was developed using silver pastes printed onto nonwoven textiles for health monitoring [6]. The laser scribed (LS) technique is useful for manufacturing sensors, for example, predesigned graphics are set to move the laser beam accordingly and exposed materials are sensitized for sensing different stimuli [5].