esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Undirected Graphs  
The link puzzles concerned in this paper are played on undirected graphs. This paper uses ZDDs to represent subgraphs of graphs of puzzle instances. To avoid confusion among objects of those two kinds of graphs simultaneously in concern, we reserve words \"nodes\" and \"arcs\" for ZDDs, while we call the corresponding notions for graphs of puzzle instances \"vertices\" and \"edges\".  
An _undirected graph_ is a pair \(G=(V,E)\) where \(V\) is a set of _vertices_ and \(E\subseteq 2^{V}\) is a set of _edges_, which are sets of exactly two elements of \(V\). An edge \(e\in E\) is said to be _incident to \(v\in V\)_ if \(v\in e\). The  
Figure 3: The reduced ZDD representing \(Z(\mathbf{1})=\{\{\mathtt{a}\},\{\mathtt{b}\},\{\mathtt{c}\},\{\mathtt{a}, \mathtt{c}\},\{\mathtt{b},\mathtt{c}\}\}\). \(E=\{\mathtt{a},\mathtt{b},\mathtt{c}\}\) where \(\mathtt{a}<\mathtt{b}<\mathtt{c}\). Dotted lines represent \(0\)-arcs and solid lines represent \(1\)-arcs.  
_degree_ of a vertex \(v\) in \(G\) is defined by \(|\{\,e\in E\mid v\in e\,\}|\), that is, the number of edges incident to \(v\). For an edge set \(E^{\prime}\subseteq E\), we also define _the degree of \(v\) in \(E^{\prime}\)_ by \(|\{\,e\in E^{\prime}\mid v\in e\,\}|\). A vertex \(v\) is _isolated_ if its degree is \(0\).  
A nonempty edge set \(P=\{e_{1},\ldots,e_{|P|}\}\subseteq E\) is called a _simple path_ (or just a _path_) if there are pairwise distinct vertices \(v_{0},\ldots,v_{|P|}\in V\) such that \(e_{i}=\{v_{i-1},v_{i}\}\) for \(i=1,\ldots,|P|\). The path \(P\) is also called a \(v_{0}\)-\(v_{|P|}\)_path_.  
A nonempty edge set \(C=\{e_{1},\ldots,e_{|C|}\}\subseteq E\) is called a _(simple) cycle_ if \(|C|\geq 3\) and there are pairwise distinct vertices \(v_{1},\ldots,v_{|C|}\in V\) such that \(e_{1}=\{v_{1},v_{|C|}\}\) and \(e_{i}=\{v_{i-1},v_{i}\}\) for \(i=2,\ldots,|C|\).  
A possibly empty edge set \(P\subseteq E\) is called a _path matching_ if there are simple paths \(P_{1},\ldots,P_{k}\) with \(k\geq 0\) such that \(P=\bigcup_{1\leq i\leq k}P_{i}\) and \(\bigcup P_{i}\cap\bigcup P_{j}=\emptyset\) for distinct \(i,j\in\{1,\ldots,k\}\) (For a family \(P\) of sets over a universal set \(V\), we define \(\bigcup P=\bigcup_{e\in P}e=\{\,v\in V\mid v\in e\text{ for some }e\in P\,\}\).). We remark that for a path matching \(P\), a way of partitioning \(P\) into paths satisfying this definition is unique. We say that \(P\)_contains_ a \(u\)-\(v\) path if one of such paths is a \(u\)-\(v\) path.  
A family \(T\subseteq 2^{V}\) of sets over \(V\) is called a _pair matching_ if \(T\) consists of pairwise disjoint sets of size \(2\).  
**Lemma 1**.: _For a graph \(G=(V,E)\), an edge set \(P\subseteq E\) is not a path matching if and only if there is a vertex \(v\in\bigcup P\) of degree more than 2 in \(P\) or there is a subset \(C\subseteq P\) that is a simple cycle._
## 3 Communication Framework Overview for Sensor Data Loss Reduction
For this reason, the compression in the _SDSC_ mode is the most important task in our framework to reduce sensor data loss and the _SMEP_ method presented from Section 4 to Section 8 is one of such compression methods.
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 4 Numerical Results and Discussion
### LOO Error  
As a global indicator for the method accuracy, the LOO error of the first group of variables is shown in Figure 4.  
As seen in Figure 4, the LOO error decreases effectively as more samples are included into the database. When 200 sample point are taken into account, the LOO error is \(1.2\times 10^{-7}\). This validates the reliability of the established PCE model and ensure the correctness of the following results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Bone Radius \(B\)** & **Reflector to Skin Distance \(R\)** & **Fat Thickness \(F\)** & **Muscle Thickness \(M\)** & **Implantation Depth \(I\)** \\ \hline Range (mm) & 20–40 & 0–20 & 1–20 & 25–40 & 10–20 \\ \hline \end{tabular}
\end{table}
Table 3: The ranges for the 5 analyzed variables.
## 2 Formulations
### Band Structures  
In this model, \(E_{z}\) and \(H_{z}\) both are periodic with period \(\Lambda\). According to the Bloch theorem, the electric and magnetic fields in a periodic layered medium are \(E_{z}(z)=E_{z}(z+\Lambda)\) and \(H_{z}(z)=H_{z}(z+\Lambda)\), respectively. The column vector of the Bloch wave satisfies the following eigenvalue equation for consistency  
\[\left(\begin{array}{c}a_{3}\\ b_{3}\end{array}\right)=\frac{1}{4}\left(\begin{array}{cc}A_{TE/TM}&B_{TE/TM} \\ C_{TE/TM}&D_{TE/TM}\end{array}\right)\cdot\left(\begin{array}{c}a_{1}\\ b_{1}\end{array}\right)=e^{ik_{B}\Lambda}\left(\begin{array}{c}a_{1}\\ b_{1}\end{array}\right). \tag{26}\]  
For the TE mode:  
\[A_{TE}=e^{j(k_{1}t_{1}-k_{2}t_{2})}\left(\frac{\mu_{1}}{\mu_{2}} -\frac{k_{1}}{k_{2}}\right)\left(\frac{\mu_{2}}{\mu_{1}}-\frac{k_{2}}{k_{1}} \right)+\] \[e^{-j(k_{1}t_{1}+k_{2}t_{2})}\left(\frac{\mu_{1}}{\mu_{2}}+\frac {k_{1}}{k_{2}}\right)\left(\frac{\mu_{2}}{\mu_{1}}+\frac{k_{2}}{k_{1}}\right), \tag{27}\]  
\[B_{TE}=e^{-j[k_{1}t_{1}-k_{2}(2t_{1}+t_{2})]}\left(\frac{\mu_{1}}{\mu_{2}}+ \frac{k_{1}}{k_{2}}\right)\left(\frac{\mu_{2}}{\mu_{1}}-\frac{k_{2}}{k_{1}} \right)+\] \[e^{j[k_{1}t_{1}+k_{2}(2t_{1}+t_{2})]}\left(\frac{\mu_{1}}{\mu_{2}} -\frac{k_{1}}{k_{2}}\right)\left(\frac{\mu_{2}}{\mu_{1}}+\frac{k_{2}}{k_{1}} \right), \tag{28}\]  
\[C_{TE}=B_{TE}^{*}, \tag{29}\]  
and  
\[D_{TE}=A_{TE}^{*}. \tag{30}\]  
For the TM mode:  
\[A_{TM}=e^{j(k_{1}t_{1}-k_{2}t_{2})}\left(\frac{\varepsilon_{1}}{ \varepsilon_{2}}-\frac{k_{1}}{k_{2}}\right)\left(\frac{\varepsilon_{2}}{ \varepsilon_{1}}-\frac{k_{2}}{k_{1}}\right)+\] \[e^{-j(k_{1}t_{1}+k_{2}t_{2})}\left(\frac{\varepsilon_{1}}{ \varepsilon_{2}}+\frac{k_{1}}{k_{2}}\right)\left(\frac{\varepsilon_{2}}{ \varepsilon_{1}}+\frac{k_{2}}{k_{1}}\right), \tag{31}\]  
\[B_{TM}=e^{-j[k_{1}t_{1}-k_{2}(2t_{1}+t_{2})]}\left(\frac{\varepsilon_{1}}{ \varepsilon_{2}}+\frac{k_{1}}{k_{2}}\right)\left(\frac{\varepsilon_{2}}{ \varepsilon_{1}}-\frac{k_{2}}{k_{1}}\right)+\] \[e^{j[k_{1}t_{1}+k_{2}(2t_{1}+t_{2})]}\left(\frac{\varepsilon_{1}} {\varepsilon_{2}}-\frac{k_{1}}{k_{2}}\right)\left(\frac{\varepsilon_{2}}{ \varepsilon_{1}}+\frac{k_{2}}{k_{1}}\right), \tag{32}\]  
\[C_{TM}=B_{TM}^{*}, \tag{33}\]  
and  
\[D_{TM}=A_{TM}^{*}. \tag{34}\]  
The phase factor \(e^{ik_{B}\Lambda}\) is thus the eigenvalue and satisfies the secular equation  
\[\left|\begin{array}{cc}A_{TE/TM}-e^{ik_{B}\Lambda}&B_{TE/TM}\\ C_{TE/TM}&D_{TE/TM}-e^{ik_{B}\Lambda}\end{array}\right|=0. \tag{35}\]  
Finally, the dispersion relation for the Bloch wave function is  
\[k_{B}(k_{z},\omega)=\frac{1}{t_{1}+t_{2}}\cos^{-1}[\frac{1}{2}(A_{TE/TM}+D_{TE/ TM})]. \tag{36}\]
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
###### Abstract  
AMIs (Advanced Metering Infrastructures) present an important role in Smart City environments, especially from the point of view of distribution and customers, offering control and monitoring capabilities. The use of PLC (Power Line Communication) technology offers a wide range of advantages in AMI, including not needing to deploy an additional communication infrastructure. However, the electrical network was not initially designed for communications, as these networks pose problems depending on the connected loads, such as network impedance variation, frequency selectivity or noise. For this reason, the use of simulators is proposed to facilitate the deployments based on PLC networks, and analysis and diagnosis tools for the identification of problems in operating networks are also required. This paper presents a toolkit for evaluating and analyzing the performance of PLC networks. This toolkit is composed of SimPRIME, a simulator for the evaluation of NB-PLC PRIME (PowerR line Intelligent Metering Evolution) networks' performance; SimBPL, a simulator for the evaluation of MV-BPL (Broadband Power Line over Medium Voltage) cells' performance; and PRIME Analytics, a forensics tool that allows diagnosis of communication problems in PRIME operational networks based on traffic traces. The toolkit has been developed throughout several research projects in close collaboration with DSOs (Distribution System Operators) and equipment manufacturers, so they provide solutions to actual problems of these industry key players and have been adapted to facilitate their use. As a result, the tools are accessible through web applications to increase their usability, portability, and scalability. These applications represent the first steps in offering PLC simulation and analysis as a service that could benefit the research community, academia, and industry.  
power line communications; power line intelligent metering evolution; simulations; traffic analysis; web applications +
Footnote †: journal: Computer Science  
e1 FSI de Telecomunicacion, Universidad Politecnica de Madrid (UPM), Avda. Complutense 30, 28040 Madrid, Spain; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] (J.B.)1
# Image Human Thorax Using Ultrasound Traveltime Tomography with Supervised Descent Method
## 1 Introduction
Thus, it is possible to measure the amount of gas and fluid in the thorax by reconstructing the acoustic velocity distribution, which is crucial in the diagnosis of many diseases [42; 43]. Machine learning techniques develop quickly in recent years and it has been widely used in inverse problems. One of the reasons for its popularity is that, through the training process, lots of work can be completed offline, which makes the online predicting process simple and efficient. Supervised descent method is one of these techniques, which is firstly used in face alignment [44]. The average descent direction of the cost function is learned from the training process and used to update the model in the online predicting process. The time-consuming matrix inversion in traditional gradient-based method is no longer needed. Previous studies show that SDM performs well in imaging, such as 2D magnetotelluric imaging [45], microwave thorax imaging [46], EIT thorax imaging [47], and corrosion imaging [48]. In this paper, we study the feasibility of imaging human thorax using ultrasound traveltime tomography with SDM. Numerical experiments based on normal thorax model and synthetic patient thorax model are conducted. The remaining of the paper is organized as follows. In Section 2, the overview of the forward modeling is described. In Section 3, details of traveltime tomography with SDM are discussed. In Section 4, our method is compared with the traditional gradient-based inversion method and its ability to detect the change of acoustic velocity distribution is tested.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 3 Resource Allocation  
### User Grouping  
In NOMA, multiple users use the same time-frequency resource to transmit signals and different users on the same subcarrier will inevitably lead to a great difference in system performance. Although the exhaustive search method can optimize this, the algorithm complexity is too high to be used in practical scenarios. To ensure the accuracy of the SIC at the receiver and reduce the signal separation delay, we decided to superpose only the signals of two users on each subcarrier, \(M_{n}=2\). The greater the channel gain difference between the multiplexed users, the greater the throughput improvement of the NOMA systems compared with the OMA systems [23; 24]. On the contrary, when the channel gain difference is small, the throughput cannot be significantly improved. In [9], users with medium channel quality may obtain a relatively small channel gain difference between the multiplexed users when they are grouped, which will increase the interference between users. We changed the grouping method to increase the channel gain difference when remaining users with medium channel quality are grouped.  
Based on user instantaneous CSI obtained by the BS, the channel gains of all users are sorted in ascending order. The number of users \(M\) is even. User grouping consists of two steps, as shown in Figure 2. First, the two users with the largest difference in channel gain are divided into a group; that is, the user with the largest channel gain is paired with the user with the smallest channel gain, and the user with the second largest channel gain is grouped with the user with the second smallest channel gain, and so on. In the second step, another matching method is used when the middle six users are grouped. The first user is paired with the fourth user. The second user is paired with the fifth user. The third user and the sixth user are paired.  
Figure 2: Proposed user grouping method.
## 3 Results and Discussion
### Luminescence Properties  
Photoluminescence spectra of the PS glass system obtained at \(\lambda\) excitations = 300 nm are illustrated in Figure 10. The emission spectra revealed several sharp and intense emission bands characteristic of different Pb\({}^{2+}\) centers, including isolated Pb\({}^{2+}\), the Pb\({}^{2+}\) dimer, or more complex Pb\({}^{2+}\)-based centers [37; 43; 44].  
The emission band located at 367 nm is ascribed to the electronic transitions of isolated Pb\({}^{2+}\) ions. It is enhanced progressively with increasing SiO\({}_{2}\) content up to the PS6 sample and slightly decreases in the PS7 sample due to concentration quenching phenomena. The emission bands in the region of 397-450 nm are attributed to the Pb\({}^{2+}\) dimer center, and the emission peak centered at 468 nm is associated with ligand-to-metal charge transfer  
Figure 9: Variation in (**a**) \(\rm E_{g}\) and (**b**) \(\rm E_{U}\) with the chemical composition.  
(LMCT) [37]. The emission bands in region 480-750, with a dominant band centered at 507 nm, are characteristic of defects in the network and oxygen vacancies [43]. The incorporation of and increase in the SiO\({}_{2}\) content in the host matrix shift these emission bands towards a longer wavelength and induce emission quenching. The enhancement of the emission is related to the isolated Pb\({}^{2+}\) center; as the SiO\({}_{2}\) concentration increases, it is accompanied by quenching of the emission of more complex Pb\({}^{2+}\)-based centers (clusters), indicating the existence of energy transfer [44]. The variations in PL bands' intensity on the glass matrix are due to structural modifications and depend on the role of the Pb\({}^{2+}\) ions.  
Using the emission spectra data, the color coordinates (x,y) of the PS glass system were calculated to reflect the luminescent colors. The obtained values are represented in CIE 1931 (Commission Internationale de l' Eclairage) color diagrams, illustrated in Figure 11.  
As may be
## 5 Proposed MOEA/D-AMSF Algorithm  
In this section, we propose an MOEA/D with adaptation and multi-strategy fusion (MOEA/D-AMSF) for solving the optimization model effectively, in which a joint routing and core coding scheme with core switching is designed for the first time. The proposed scheme uses the variable core index along a path and encodes the routing and core index into a chromosome to co-evolve, resulting in a higher possibility of obtaining the globally optimized solution and increasing the algorithm convergence when compared with the bi-population coding scheme. As we stated in Sections 1 and 2, research on a multi-objective optimization model and an algorithm with core switching has not been considered in existing works.  
The key elements and advantages of the proposed MOEA/D-AMSF are as follows:  
1. The proposed algorithm adopts multiple-strategy fusion and adaptive mechanisms to accelerate the algorithm convergence and enhance solution diversity. It has the following characteristics: 1. A hybrid algorithm combining a heuristic algorithm and uniform design method is used to generate the initial population, making the generated initial solution set distributed more uniformly; 2. For the proposed coding scheme, the adaptive crossover and bi-layer mutation with repair operators are designed to solve each subproblem, and the crossover and mutation probabilities are adaptively adjusted according to the iteration status in different evolution stages; 3. Simple single-point crossover and single-point mutation strategies are used in the early search stage of the algorithm, while multi-point mutation is employed after it evolves to a certain extent, which increases the probability that the search jumps out of the local optimum, finding more global solutions; 2. The Tchebycheff (TCH) decomposition method is used to decompose the proposed bi-objective optimization problem into multiple single-objective optimization subproblems, which makes it easy to apply the single-objective optimization method to each subproblem and to solve all the subproblems in parallel, accelerating the algorithm convergence.  
The chromosome coding and decoding strategy, initial population generation method, adaptive evolution operators, and the proposed algorithm flow are described in detail below.
# Interconnected Government Services: An Approach toward Smart Government
## 2 Related Work
### Privacy Issues  
Implementing technical solutions that simultaneously solve the lack of interoperability within government institutions can create other non-technological challenges, such as data privacy. These solutions generate an increasing number of real-time data. Making these data open to create more transparency can be a positive goal. However, it can also endanger personal data privacy and lead to illegal data use [28].  
Digital technologies and services based on them give governments additional power, which can sometimes affect the degree of democracy in a country. That is why, in [29], the authors point to the potential privacy issue when the government implements the unethical usage of personal data by digital tools. Such privacy concerns are not new in the literature as several cases of personal data misuse exist. For instance, the Swedish government has faced the challenge of leaking personal data related to vehicles. This leak forced the Swedish government to restrict outsourcing private and sensitive data to third parties [30].  
Achieving high degrees of privacy and transparency is a big challenge [29]. Many authors think implementing services based on advanced technologies can overcome technical challenges. There are also legal, ethical, and political issues related to the government and the citizens' relationship [31]. For these reasons, it is necessary to define a suitable approach that will find the right balance between the need to control government work (which refers to the governance method and division of responsibilities) and secure the individual's privacy. The key idea is to create an access policy to prevent any entity from accessing all of the data. Thus, the risk is not the data itself but how is the data are used, which includes the data-sharing method and the underlying infrastructure [32].  
In [33], the author proposes developing a legal framework to deal with various data sources and government services. In Europe, personal data usage is defined according to the general data protection regulation (GDPR). This regulation has motivated many governments to consider introducing a consistent framework for exchanging, sharing, and purchasing data to ensure the ethical use of new technologies [34].
## 4 Experiments
### Results on COVID-SemiSeg Dataset
#### 4.2.2 Quantitative Results on COVID-SemiSeg  
Table 1 illustrates quantitative results. As observed intuitively, the proposed trans-sYLmtMT significantly outperforms U-Net, Attention-U-Net, Inf-Net and Trans-Inf-Net across key metrics including _Dice_, \(S_{\alpha}\), \(E_{q}{}^{mean}\), and _MAE_.  
Specifically, we achieve improvements of 1.9%, 3.1%, and 1% for the Dice score, Specificity, and \(E_{\phi}{}^{mean}\) compared to prior state-of-the-art Trans-Inf-Net. Meanwhile, we reduce the mean absolute error by 4.4% over the baselines.  
These quantitative gains highlight the benefits of our multi-task semi-supervised approach. By incorporating edge information and unlabeled data in a teacher-student framework, transYLmtMT produces more accurate and consistent segmentations. The fusion of complementary regional, boundary, labeled and unlabeled cues enables robust generalization even to subtle infection characteristics.  
Together, the numerical metrics validate transYLmtMT's ability to push the state of the art forward for this critical pandemic visualization task. The system delivers reliable, precise, and interpretable CT scan infection maps to aid radiologists.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Method** & **Dice** & **Sen.** & **Spec.** & \(S_{\alpha}\) & \(E_{\phi}^{mean}\) & **MAE** \\ \hline \multirow{3}{*}{COVID-SemiSeg Dataset} & U-Net [25] & 0.574 & 0.561 & 0.949 & 0.706 & 0.744 & 0.105 \\ \cline{2-8}  & AttU-Net [26] & 0.582 & 0.508 & 0.961 & 0.684 & 0.762 & 0.096 \\ \cline{2-8}  & Inf-Net [50] & 0.647 & 0.709 & 0.910 & 0.737 & 0.827 & 0.103 \\ \cline{2-8}  & Trans-Inf-Net [19] & 0.695 & **0.721** & 0.939 & **0.778** & 0.854 & 0.083 \\ \cline{2-8}  & transYLmtMT & **0.714** & 0.651 & **0.970** & 0.741 & **0.863** & **0.061** \\ \hline \hline \end{tabular}  
* Red represents the best, blue represents the second best.  
\end{table}
Table 1: Results on COVID-SemiSeg dataset.  
Figure 3: Segmentation results for visual comparison. [PERSON]. [25], [PERSON]. [26], [PERSON]. [50], [PERSON]. [19].
# Broadband Holography via Structured Black Silicon Nano-Antennas
## Material and Methods
### Fabrication of Binary Computer Generated Hologram  
To clean the Silicon wafer, it was first submerged in Acetone, and then in Isopropyl Alcohol (IPA), and subsequently rinsed with deionised water and dried with a \(N_{2}\) gun-blower. Then, a 10 \(\upmu\)m thick layer of AZ9260 positive photo-resist was spin-coated onto the sample. The sample was then baked for 180 s at a temperature of 110 \({}^{\circ}\)C. The lithography process starts fabricating a chromium photomask by means of direct laser writing with a resolution of 1 \(\upmu\)m. The Si wafer is then exposed through the mask by Ultraviolet (UV) light with a dose value of 1800 mJ/cm\({}^{2}\), in order to transfer the calculated interference pattern on the Si wafer. The sample was then submerged into a developing bath using a AZ726 developer for 6 minutes. The developer removes the exposed area, leaving the sample with the desired pattern. Reactive Ion Etching was applied in order to transform the photoresist free area into black silicon. The gasses used to perform the etching procedure were \(O_{2}\) and \(SF_{6}\) ions, with a dose of 20 sccm each. The chamber pressure was set to 30 mtorr, with a RF power of 50 W and Inductively Coupled Plasma (ICP) power of 50 W. The recipe used was discovered through an optimisation procedure against the the following parameter, \(O_{2}\) from 10 to 20 sccm keeping the \(SF_{6}\) at 20 sccm, while RF power was swiped from 50 W to 100 W, and the same for the Inductively Coupled Plasma (ICP). After the etching process was concluded, the sample was exposed to UV light for 120 s. Finally, the sample was submerged again into the AZ 726 developer for 6 minutes in order to remove the rest of the photo-resist material, leaving reflecting and absorbing surfaces on the sample.