esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Protonic Conductivity
Furthermore, the increase of the polyelectrolyte content from 21 wt % to 50 wt % (P(_bis_MEP)/BC_1 and P(_bis_MEP)/BC_2, respectively) promoted an increase of the protonic conductivity from 9.12 \(\times\) 10\({}^{-2}\) mS cm\({}^{-1}\) to 23.6 \(\times\) 10\({}^{-2}\) mS cm\({}^{-1}\) at 94 \({}^{\circ}\)C and 60% RH. This is a solid evidence, apart from the low conductivity of BC (ca. 60 uS cm\({}^{-1}\) at 94 \({}^{\circ}\)C, 98% RH) [20], that the protonic conductivity of these nanocomposite membranes is related with the acidic groups of P(_bis_MEP) and the resulting water-uptake ability of the nanocomposites (Table 4). The protonic conductivities of the P(_bis_MEP)-based nanocomposite membranes are lower than those obtained for the cross-linked PMOEP/BC membranes prepared in our previous study (\(\sigma\) > 100 mS cm\({}^{-1}\) at 98% RH and 20-94 \({}^{\circ}\)C, membrane with 48 wt % polyelectrolyte, IEC = 3.38 mmol g\({}^{-1}\)) [21]. Since the IEC and the _WU_ values of both membranes are similar, the higher conductivity of the cross-linked version must result from significant differences in the structure and/or distribution of the polyelectrolyte and the hydrated domains within the structure forming the ion transport paths. These factors are, however, difficult to access in detail given the predominately disordered molecular structure of the materials. Although the values of protonic conductivity of the P(_bis_MEP)-based nanocomposite membranes are lower than those usually found for Nafion\({}^{\text{\textregistered}}\)[46; 47], the accomplished results may still be considered as promising in the present context. Figure 8: \(Cont.\)  
Figure 7: Nyquist plots of P(_bis_MEP)/BC_1 and P(_bis_MEP)/BC_2 collected at 80 \({}^{\circ}\)C and different RH (20 and 98%) (the values are normalized to the maximum in \(Z^{\prime}\)). Regarding the temperature dependence, the conductivity data can be fitted by the Arrhenius equation: \(\sigma=\sigma_{0}\) exp\([-E_{\text{a}}(RT)^{-1}]\), where \(\sigma_{0}\) is a pre-exponential term, \(E_{\text{a}}\) is the activation energy for proton transport, R is the gas constant and T is the absolute temperature. The estimated \(E_{\text{a}}\) values vary between 29-37 kJ mol\({}^{-1}\) for P(_bi_sMEP)/BC_1 and 21-51 kJ mol\({}^{-1}\) for P(_bi_sMEP)/BC_2, and this range of values is typically related with structural diffusion of protons as the leading transport mechanism. Data reported in literature for membranes composed of cross-linked PMOEP/BC [21] and poly(4-styrene sulfonic acid)/BC [20] display comparable \(E_{\text{a}}\) values. The activation energy is found to decrease with increasing humidity, particularly when reaching 98% RH. The behavior of P(_bi_sMEP)/BC_1 is similar to that typically found for Nafion(r) [47].
# Radiator Enablers for Wireless Communication Evolution
## 1 Introduction  
The wireless communication evolution exhibits a very high correlation with the respective progression of the employed radiating antennas. During their historical development, radiators have evolved from wires to printed patches and slots to MIMO arrays, reconfigurable intelligence surfaces and holographic metasurfaces, and even OAM radiators. The purpose of this review (which limits and entrenches its search space) is to focus on the innovations needed in order to place the next generation of wireless communication vision and concepts into technological reach. Stated differently, this review aims to identify challenges, trends, and directions and anticipate solutions that, from the authors' point of view, will enable and accelerate, in a catalytic way, the upcoming wireless communication generations.  
The paper is structured as follows. In the Introduction, we set the stage by enlisting current and legacy evolution points, 6G use cases, and the key performance indicators and relative formulas that play pivotal roles in scheduling and resource allocation and thus in overall system performance evaluation. In Section 2, we focus on challenges, enabling technologies, and antenna developments. There, we organize our approach by addressing those issues for each of the key use cases independently. We follow this path since different use cases pose different challenges, which, in turn, call for diversified solutions. Section 3 is devoted to radiators' novel technology, design, and implementation issues. We study the increase in the degrees of freedom provided by gMIMO, RIS, holographic metasurfaces, and OAM. Then, we thoroughly examine the impact that those potent technologies have on mmWave, satellite, and THz wireless communications systems. Thepaper ends with the Section 4, where we reassess the presented information and identify a clear convergence path for the radiators driven by the need to enable further wireless communication evolution.
## 3 Methods  
### CTC-Transformer Based CSSR Baseline System  
We adopted a Transformer framework to build the CSSR system. However, the Transformer takes many more epochs for the monolingual ASR task to converge, let alone the CSSR task, which has many more model units. Inspired by [20], we added a CTC objective function to train the encoder of the Transformer. CTC helps the Transformer to converge with the forward-backward algorithm, enforcing a monotonic alignment between input features and output labels. The architecture of the CTC-Transformer baseline system is indicated in Figure 2.  
Specifically, let \(X\) be the input acoustic sequence, \(Y\) be the output label sequence comprising Mandarin modeling units or English modeling units, let \(L_{ctc}(Y|X)\) be the CTC objective loss [16] and \(L_{att}(Y|X)\) be the attention-based objective loss. The \(L_{att}(Y|X)\) loss is the cross entropy of predicted \(\hat{Y}\) and ground truth \(Y\). The combination of \(L_{ctc}(Y|X)\) and \(L_{att}(Y|X)\) is adopted for the ASR task:  
\[L_{ASR}(Y|X)=(1-\alpha)L_{att}(Y|X)+\alpha L_{ctc}(Y|X), \tag{7}\]  
where \(\alpha\) is a hyperparameter.  
We chose a Chinese character as the Mandarin acoustic modeling unit, as it is the most common choice for E2E Mandarin ASR and it has shown a state-of-the-art performance in Mandarin ASR [24; 25]. As for English, we chose the subword as the English unit. We adopted Byte Pair Encoding (BPE) [26] to generate subword units.
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 3 Proposed Approach Towards the Definition of a Space Sustainability Index
### Space Debris-Related Component  
\[e_{debris}=u_{1}(1-P_{coll})+u_{2}coll_{av}+u_{3}retr+u_{4}\hat{t_{do}} \tag{2}\]  
where  
* \(P_{coll}\) is the probability of one or more collisions between the satellites of a constellation and space debris.
* \(coll_{av}\) refers to the presence or absence of a collision avoidance system onboard the satellites. In case satellites are equipped with an advanced CA system, its value will be set \(coll_{av}=1\); if they are equipped with a simple CA system, \(coll_{av}=0.5\); when there is no CA system, \(coll_{av}=0\).
* \(retr\) is the retrieval possibility, i.e, the presence of grapple fixtures on each satellite to allow future active debris removal operations. \(retr=1\) if the necessary hardware is present on board and operational, and \(retr=0.5\) if it is present but not operational; \(retr=0\) if it is absent.
* \(\hat{t_{do}}\) is a number between 0 and 1 proportional to the time needed for a proactive controlled deorbit of a satellite.
* \(u_{i}\) are weighting factors, with unitary norm, for the previously introduced parameters.  
The mean number of collisions for all the objects having the same altitude and inclination in the selected constellation is given by [9]:  
\[N_{a}=F\cdot A_{c}\cdot T\cdot\alpha \tag{3}\]  
where \(\alpha\) is the number of satellites; the term \(F\) is the flux provided by MASTER8 [10], a tool provided by ESA for the characterization of the space debris environment and its future evolution. The flux is determined by the motion of the object through a stationary medium of uniform particle density at a constant speed, it is measured in m\({}^{2}\)/yrs and it is inversely proportional to the diameter of the debris. \(T\) is the considered time frame and it is set to 5 years.  
The term \(A_{c}\) is the cross-sectional area and can be calculated using the radii of one satellite of the constellation (the target), \(r_{tar}\), and the one of space debris (the impactor), \(r_{imp}\); \(A_{c}=\pi(r_{imp}+r_{tar})^{2}\).  
The probability of collision can be calculated using Poisson statistics:  
\[P_{i=n}=\frac{N_{a}^{n}}{n!}\cdot e^{N_{a}} \tag{4}\]  
where \(n\) is the number of impacts. Therefore, the probability of one or more impacts will be:  
\[P_{i\geq 1}=1-e^{-N\alpha} \tag{5}\]  
Finally, the probability of one or more collisions in a constellations with \(\alpha\) satellites is then given by:  
\[P_{coll}=P_{i>\alpha}=1-e^{-N_{a}} \tag{6}\]The term related to the deorbiting time is defined as:  
\[\hat{t_{do}}=\begin{cases}0,&\text{if }t_{do}\geq 60,\\ 0.2,&\text{if }36<t_{do}<60,\\ 0.5,&\text{if }12<t_{do}\leq 36,\\ 0.8,&\text{if }6<t_{do}\leq 12,\\ 1,&\text{if }t_{do}\leq 6.\end{cases} \tag{7}\]  
where \(t_{do}\) is the deorbiting time in months; its maximum value is set to 60, because this is the average estimated time for an LEO satellite to passively deorbit due to atmospheric drag.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 4 Data Processing
### Preprocessing
#### 4.1.2 Instance Selection  
According to [99], the selection of instances is another preprocessing step that usually differs from the traditional offline setting. Selecting appropriate data points is vital for the training phase of an algorithm. Especially after the appearance of a drift in the data stream, it is necessary to select new instances for training and discard data points that belong to outdated concepts in the stream. While drift detection methods are discussed in detail in Section 6, here, the focus is only on the training instance selection methods. The goal is to evaluate which data points should be used for the training, or in other words, which data records represent the current status of the data stream. One way to identify a change in the data stream is to analyze data points regarding their distance to other stream data records. Nearest neighbor methods, for example, bring data points into spatial dependencies. In addition, the temporal component can be considered by sliding a fixed-size window over a data stream or by weighing instances according to their age (see Section 4.2).  
An influential work in this context is the IBL-DS (Instance-based learning on data streams) algorithm by [101]. The algorithm follows instance-based learning, which means that predictions are made ad hoc based on current data--called the case base--rather than using a previously created model. Thus, the temporal component and possible changes in the data stream are not disregarded, which is beneficial, especially in scenarios with concept drift. The case base is the pivotal point in this approach. Since not all data points can be stored, the selection must be made carefully. The instances should reflect the characteristics of the current data stream, and the feature space should be represented as balanced as possible. Thus, neither regions are over- or under-represented. Furthermore, only instances that belong to the current concept in the data stream should be included. The approach is implemented via the WEKA interface. It has been evaluated on many synthetic datasets such as STAGGER, Gauss, Sine2, or Hyperplane, as well as on real-world stream datasets (the stream datasets were artificially generated based on static datasets of the UCI package. IBL-DS is evaluated on the car, nursery, and balance dataset). A drawback of nearest neighbor approaches is the computational complexity [99].  
Ref. [64] proposed an instance selection method with two levels, called Competence-Based Editing (CBE). The first stage is noise removal and the second cope with redundancy reduction. The authors present the algorithm in the context of spam detection. Within their benchmarking study, ref. [99] points out that CBE is evaluated as the best option, for instance, in model accuracy and reduction.
# Target Image Mask Correction Based on Skeleton Divergence
## 3 Results and Discussion
### Single Target Mask Correction Effect  
As shown in Figure 3, Comparing a and b, the contour of the human body of b is clearer, because the shape and structure of the binary mask based on the edge similarity of the skeleton region are well corrected. Comparing c and b, the plaid pattern on the man's shirt and the outline of the human body are clearer in c, and many edge pixel masks in b are corrected, which fully reflects TOPSIS and points. The cloud regularization constraint plays a positive role in the elimination of error accumulation in b. Comparing c and d, it can be seen that the outline, clothing, and facial details of the man in d are more complete, and the hole filling technique improves the accuracy of the binary mask correction.
## 1 Introduction
Based on these techniques, a practical UWOC system with high reliability and low power consumption is realized in this paper and comprehensively evaluated in the laboratory environment. The experimental results show that the system reliability is better than several recent implementations based on FPGA platforms, and the results are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Light** & **Rate** & **Distance** & **BER** \\ \hline
2022 [35] & LED & 5 Mbps & 20 m & \(\sim\)\(10^{-6}\) \\
2023 [44] & LD & 25 Mbps & 10 m & \(1.0063\times 10^{-2}\) \\
2023 [45] & LED & 4 Mbps & 12 m & \(\sim\)\(10^{-3}\) \\
2024 [36] & LED & 22 Mbps & 12 m & \(2.467\times 10^{-4}\) \\ This work & LD & 10 Mbps & 10 m & \(2\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of system performance. The rest of the paper is organized in the following manner: Section 2 provides a comprehensive description of the system architecture and optimized design methodologies. Section 3 details the experimental setup, presents the results, and analyzes the system performance. Section 4 concludes the paper by summarizing the findings, discussing potential application domains, highlighting system limitations, and outlining future research directions.
## 4 HMBAC Design and Implementation
### Janus Implementation
#### 4.3.1 Frontend Application  
This is a client-side Electron application, running locally by each user. The _User Interface_ (UI) implements the presentation layer through a web-based interface developed in React.js. enables users to log into the system using their appropriate credentials and, upon successful validation, to submit their requests.  
In addition, the Electron app implements various support frontend services, developed in Node.js and running in the background at the client side. The _Blockchain-Related Mechanisms_, encompass all the functionality required for a user to communicate with the middleware. These mechanisms enable a user to create and sign a query and commit it to generate a new transaction.  
The _Authentication Mechanisms_ supports user authentication to the system. To authenticate the credentials (attribute certificates) of a user, the Certificate Authority (CA) of the relevant stakeholder must be involved. The frontend authentication mechanisms pass the credentials along with a signed challenge to the Frontend API, which communicates with the relevant CA in order to issue a token for the user and establish communication. The  
Figure 5: Janus implementation design based on the HMBAC architecture.  
authentication mechanisms also support communication with the user's Vault instance, securely storing the user-side attribute keys.  
Finally, _Endpoints_ represent the communication points between the UI and the Frontend API. Any traffic towards the API will be handled by Kubernetes and the Ingress web server.
## 5 Mathematical Models
### Models at the Macro-Scale
#### 5.1.1 The Navier-Stokes Equations for Viscoelastic Fluids
The ratio of \(\mu_{s}\) to \(\mu_{p}\) is defined as viscosity ratio of viscoelastic fluid also known as the retardation ratio and denoted as \(\beta=\frac{\mu_{s}}{\mu_{p}}\leq 1\). Different boundary conditions can occur on the boundary \(\partial\Omega\) of \(\Omega\) depending on the different physical conditions. However, it is important to consider that while the Neumann condition specifies the gradient of the velocity perpendicular to the boundary, the Dirichlet boundary condition expresses the value of the velocity on the boundary. The decomposition of the velocity \(\mathbf{v}\) in its normal component \(v_{N}:=\mathbf{v}\cdot\mathbf{n}\) and its tangential part \(v_{T}:=\mathbf{v}-v_{N}\mathbf{n}\) (where \(\mathbf{n}\) is the vector normal to boundary \(\partial\Omega\)) allows the description of five different types of boundary conditions [98] as reported in the Table 5. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & Oldroyd B & FENE-P & FENE-CR & Linear PTT \\ \hline \(f_{R}(\mathbf{C})\) & \(C-I\) & \(\frac{C}{1-\text{tr}(\mathbf{C})/L^{2}}-I\) & \(\frac{C-I}{1-\text{tr}(\mathbf{C})/L^{2}}\) & \([1+\text{tr}(\mathbf{C}-I)](C-I)\) \\ \(f_{S}(\mathbf{C})\) & \(C-I\) & \(\frac{C}{1-\text{tr}(\mathbf{C})/L^{2}}-I\) & \(\frac{C-I}{1-\text{tr}(\mathbf{C})/L^{2}}\) & \(\mathbf{C-I}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The relaxation and strain functions for some constitutive models are denoted by \(f_{S}(\mathbf{C})\) and \(f_{R}(\mathbf{C})\). \(L\) is the ratio of the length of a fully expanded polymer dumbbell to its equilibrium length, and \(\text{tr}(\mathbf{C})\) denotes the trace of the tensor \(\mathbf{C}\). \begin{table}
\begin{tabular}{l l} \hline \hline \multirow{2}{*}{_No-slip_} & The boundary \(\partial\Omega\) is considered as a rigid wall where no fluid comes out of the wall. On the boundary, the following equation is valid [98]: \\  & & \(\mathbf{v}=0\) \\ \hline \multirow{4}{*}{_Free-slip_} & No fluid comes out of the wall, but in contrast to the no-slip condition, no friction acts along the wall. Therefore, on the boundary, the following equation is valid [98] \\  & \(v_{N}=0\) and \(\mathbf{\tau}\cdot\mathbf{T}\cdot\mathbf{n}=0\) for each \(\mathbf{\tau}\perp\mathbf{n}\) \\ \cline{1-1}  & With the symbol \(\cdot\), we intend to represent the matrix-vector operation and the vector dot product operation depending on the type of the operands. \\ \hline \hline \multirow{2}{*}{_Inflow_} & In this case, all velocity components are stated [98], i.e., \\  & \(\mathbf{v}=\mathbf{v}_{in}\) where \(\mathbf{v}_{in}\) is given \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Boundary conditions that are possible on the boundary \(\partial\Omega\) of \(\Omega\) depending on the different physical conditions. If the velocity is stated on the whole boundary of the domain (i.e., when pure Dirichlet boundary conditions are considered), the additional condition  
\[\int_{\partial\Omega}\vec{v}\cdot\vec{n}\ dA=0\]  
must be satisfied because the velocity field is divergence-free [98].
# A Study on the Impact of DPF Failure on Diesel Vehicles Emissions of Particulate Matter
## 2 Experimental Apparatus and Procedure
### Experimental Apparatus
#### 2.1.2 Vehicle Emission Test  
In this study, an HDC-1000 (DASAN RND, Inc., Gyeonggi, Korea) was used as the chassis dynamometer. A chassis dynamometer is used for the \"exhaustive gas inspection of running vehicles\", as stipulated in May 2002 in the \"Air Quality Conservation Act\" under the jurisdiction of the Ministry of Environment, Republic of Korea [12]. After the image acquisition of the damaged DPF, exhaust gas analysis was conducted by installing the damaged DPF on the experimental vehicle. A smoke concentration meter (OPA-102, DANSAN RND, Inc.) and a nanoparticle emission tester (3795-HC, NPET TSI, Inc., MN, USA) were used to measure the exhaust gases. The OPA-102 is a device that measures the concentration of smoke generated by diesel vehicles using the light transmission method. This method not only eliminates the measurement error associated with reflective meters but also measures the exhaust generated during operation in real time. The NPET-3795-HC is a vehicle-mountable Portable Emissions Measurement System (PEMS) device that measures the number of particles in the exhaust gas emitted by a diesel vehicle while driving on a real road. This device can sample exhaust gases at temperatures of up to 300 \({}^{\circ}\)C and remove volatile particles using a catalyst-based remover to measure only solid particles. The measurable particle diameter range was 23-1000 nm, and the number concentration range was 10\({}^{3}\)-10\({}^{6}\) particles/cm\({}^{3}\). As the experimental vehicle, a 2005 Santa Fe-SM 2 WD model equipped with a 2013 compound regenerative exhaust gas reduction device (DOC + DPF) (Econics, Inc.) was prepared, and its exhaust gas characteristics resulting from DPF damage were analyzed [13]. The vehicle had traveled 134,000 km at the time of testing, and it was equipped with a 2.0 L VGT D4 EA engine. The specifications of the experimental vehicle are listed in Table 1.
## 2 Problem Formulation and Preliminaries
### Problem Formulation  
#### 2.2.1 Control Objective  
Consider the heterogeneous linear SISO (Single Input and Single Output) discrete-time multi-agent system of \(n\) agents with topology graph \(G\). The dynamic of the \(i\)th agent is modeled by the following dynamic system.  
\[\left\{\begin{array}{c}x_{i,k}(t+1)=A_{i}x_{i,k}(t)+B_{i}u_{i,k}(t)\\ y_{i,k}(t)=C_{i}x_{i,k}(t)\end{array}\right. \tag{1}\]  
where \(i=1,\ 2,\ \cdots,\ n\) is the index of the \(i\)th agent, \(t=0,\ 1,\ 2,\ \cdots,\ T\) is the discrete-time index, and \(k=1,\ 2,\cdots\) is the index of iteration. \(x_{i,k}(t)\in\mathbb{R}\) is the state of the \(i\)th agent in time \(t\) and \(k\)th iteration. Similarly, \(u_{i,k}(t)\in\mathbb{R}\) is the control input signal and \(y_{i,k}(t)\in\mathbb{R}\) is the output signal. Multiple agents have different dynamics which are denoted as \(A_{i}\), \(B_{i}\), and \(C_{i}\). Taking the Z-transformation of Equation (1), we get  
\[Y_{i,k}(z)=\frac{C_{i}[B_{i}U_{i,k}(z)+x_{i,k}(0)]}{z-A_{i}} \tag{2}\]  
where \(\mathbf{z}=e^{i\omega},\ \omega\in[-\pi,\pi)\) with \(j=\sqrt{-1}\) and \(\omega\) is frequency, and \(x_{i,k}(0)\) is the initial state of \(i\)th agent in the \(k\)th iteration; let \(G_{i}(z)=\frac{C_{i}B_{i}}{z-A_{i}}\), \(\gamma_{i,k}(z)=\frac{C_{i}}{z-A_{i}}x_{i,k}(0)\), then (2) can be rewritten as  
\[Y_{i,k}(z)=G_{i}(z)U_{i,k}(z)+\gamma_{i,k}(z) \tag{3}\]  
The desired formation is defined as the relative state values between agents. The relative state values could be position, velocity, and so on. The control objective for the multi-agent systems in this paper is to find an appropriate control input sequence \(u_{i,k}(t),\ i=1,\ 2,\ \cdots,\ n,\ t=0,\ 1,\ 2,\ \cdots,\ T\) such that every agent can move in the desired formation tracking trajectory accurately in the sense of expectation as the iterative number \(k\) tends to infinity. That is, \(\lim\limits_{k\rightarrow\infty}y_{i,k}(t)=y_{i}^{d}(t)\). \(y_{i}^{d}(t)\) is the desired output of the \(i\)th agent and denoted as  
\[y_{i}^{d}(t)=r(t)+d_{i}(t) \tag{4}\]  
where \(r(t)\If the following condition (7) is met  
\[\lim_{k\rightarrow\infty}E_{i,k}(z)=0 \tag{7}\]  
that is, \(\lim_{k\rightarrow\infty}Y_{i,k}(z)=Y_{i}^{d}(z)\), the multi-agent systems realize formation tracking control.