esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Model  
Figure 1 shows the schematic diagram of the RIS-SHTLC system. In the downlink, the satellite transmits information to the terrestrial receiver with the help of the HAP and RIS. The satellite node is equipped with a laser. The HAP relay node with a multi-aperture  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Articles** & \begin{tabular}{c} **FSO Terrestrial** \\ **Communication** \\ \end{tabular} & \
\begin{tabular}{c} **Satellite–Laser** \\ **Communication** \\ \end{tabular} & **HAP** & **RIS** &
\begin{tabular}{c} **Multiple-Elements** \\ **RIS** \\ \end{tabular} & **RIS** \\ \hline
[10] & & & \(\surd\) & \(\surd\) & & & \\
[11] & & \(\surd\) & \(\surd\) & & & \\
[12] & & \(\surd\) & \(\surd\) & & & \\
[21] & \(\surd\) & & & \(\surd\) & & \(\surd\) \\
[23] & \(\surd\) & & & & \(\surd\) & & \\
[24] & \(\surd\) & & & & \(\surd\) & & \\
[25] & \(\surd\) & & & & \(\surd\) & & \\ This & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between this article and related articles.  
receiver adopts a decode-and-forward relay protocol (DF). The RIS module has N reflective elements, and the terrestrial receiver has a single aperture.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 1 Introduction
We develop the proposed model using MILP, and formulate optimization models for two cases: one in which the node selectively uses either MU-MIMO or OFDMA, and one where it can use both at the same time. We also provide a method of reducing the problem size. 3. We verify the feasibility of the proposed model through MATLAB numerical evaluations. By applying it to two different optimization problems, we show that the proposed model can be effectively used for diverse network scenarios. The optimization results provide insight into how the joint use of MU-MIMO and OFDMA affects the overall performance. The remainder of this paper is organized as follows. In Section 2, we summarize previous works related to this paper. Section 3 explains the network model, and the proposed optimization framework is described in Section 4. Section 5 presents the results of the MATLAB evaluations, and the paper is concluded in Section 6.
## 3 Integrated Problem Description and Effects Mathematical Models
### The Integrated Scheduling of Production and Maintenance Problem  
The PFSP is one of the most thoroughly studied scheduling problems in the operation research literature. Many production scheduling problems resemble a flowshop, and therefore, it has been extensively studied due to its application in industry [68].  
Formally, the PFSP can be described as follows. A set \(J=\{j,1\leq j\leq n\}\) of \(n\) independent jobs needs to be processed on a set \(M=\{M_{i},1\leq i\leq m\}\) of \(m\) independent machines in the same order on the \(m\) machines. We assume that all jobs are available at time zero and no preemption is allowed. In order to ensure the system's reliability and to prevent the occurrence of fatal breakdowns during the processing horizon, machines are monitored continuously by a PHM module. This module is able to predict, for each machine \(M_{i}\), the relative time before failure after processing a job \(j\), noted as \(RUL_{ij}\), and the resulting degradation value, \(\sigma_{ij}\), is then deduced. When the cumulative jobs degradation reaches a certain threshold, \(\Delta\), a maintenance operation should be planned.  
The resulting integrated sequence is denoted \(\Pi=\{\pi_{1},\pi_{2},\ldots,\pi_{m}\}\) where \(\pi_{i}\) represents the corresponding integrated sequence of \(n\) jobs and \(k_{i}\) (\(\geq 1\)) flexible maintenance on machine \(M_{i}\). Then \(\pi_{i}\) can be seen as a succession of \(k_{i}+1\) blocks of jobs (\(B_{il}=\{\text{subset of }J\}\)) separated by maintenance operations (\(M_{ic}\)):  
\(\pi_{i}\)= \(\{B_{i1},M_{i1},B_{i2},M_{i2},\ldots,B_{ik},M_{ik_{i}},B_{i(k+1)}\}\), where \(\cup_{l=1}^{k_{i}+1}B_{il}=J\).  
The general assumptions of the integrated problem are given as follows:  
* At time zero, all jobs are available and ready for processing and no preemption is allowed;
* Each machine can handle at most one action (production or maintenance) and each job can be processed by at most one machine;
* Each production job \(j\) (maintenance operation \(M_{ic}\)) requires a basic amount of processing time on each machine \(i\), represented by \(p_{ij}\) (\(pm_{ic}\));* We fix the degradation value associated to job \(j\) after being processed on machine \(i\), \(\sigma_{ij}=p_{ij}/RUL_{ij}\) where \(0<\sigma_{ij}<1\).
* The maximal authorized threshold degradation \(\Delta\) is fixed to 1 for all machines;
* At least one maintenance intervention is performed on each machine and no maintenance intervention is performed after the processing of the last job;
* After a maintenance intervention, the machine is recovered to an \"As good as new\" state.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 2 Preliminaries
### Formulations and Assumptions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Descriptions** \\ \hline \(VM(i)\) & The \(i_{th}\) VM (virtual machine), \(i\in[1,m]\) \\ \(PM(j)\) & The \(j_{th}\) PM (physical machine), \(j\in[1,n]\) \\ \(PM_{List}\) & list of all PMs \\ \(VM_{List}\) & list of all VMs on \(PM(j)\) \\ \(P_{j}^{*}\) & utilization ratio on \(PM(j)\) of parameter * \\ \(V_{i}^{*}\) & utilization ratio on \(VM(i)\) of parameter * \\ \(t_{s}\) & current lower threshold on parameter * \\ \(T_{*}\) & current upper threshold of parameter * \\ \(U_{pmj}^{*}\) & allocated resource * on \(PM(j)\) \\ \(C_{pmj}^{*}\) & total capacity of resource * on \(PM(j)\) \\ \(U_{p,v_{i}}^{*}\) & allocated resource * on \(VM(i)\) of \(PM(j)\) \\ \(R_{p,v_{i}}^{*}\) & request resource * on \(VM(i)\) of \(PM(j)\) \\ \(P_{j}\) & all resource utilization ratio on \(PM(j)\) \\ \(W_{VM}\) & migrate weights of all VMs on \(PM(j)\) \\ \(W_{PM}\) & allocate weights of all PMs \\ \(Source_{PM}\) & PMs need to execute migration \\ \(Select_{VM}\) & list of selected VMs to migrate out \\ \(Target_{PM}\) & list of target PMs to migrate in \\ \hline \hline \end{tabular}  
* can represent CPU(c), RAM(r) or Bandwidth(b).  
\end{table} 
Table 1: Notations and descriptions.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 3 Proposed System
### Modular Components for Road Sign Recognition
#### 3.2.2 Directional Information Extraction for Road Sign Objects  
Directional information is critical for understanding the spatial and contextual relationships among objects within a road sign image. As shown in Figure 4, directional information is indicated by colored circles, where each color consistently corresponds to a  
Figure 4: Examples of data acquired by the image recognition model. The red boxes indicate detected text or symbol objects within the road sign images. The different colored circles represent directional information, where each color corresponds consistently to a specific direction. Numbers represent road number objects identified by the model. The Korean text naturally appears because the images are obtained from actual road signs in the Republic of Korea (as described previously in Figure 1).  
specific direction, providing additional contextual meaning beyond basic text detection. The system utilizes DeepLabV3, a semantic segmentation model, to group objects based on their spatial alignment and orientation [6; 7]. This process involves segmenting road sign images into distinct regions, enabling the system to establish directional relationships between elements such as text and arrows.  
To further enhance segmentation performance, the system leverages a multi-scale approach to improve object boundary detection, inspired by techniques like FPNs (Feature Pyramid Networks), Panoptic FPNs, and PSP (Pyramid Scene Parsing) [14; 15; 16; 17]. These methods allow the system to better handle overlapping objects and maintain contextual coherence, especially in complex road sign layouts.  
By accurately associating each detected object with its corresponding directional context, this module forms the basis for downstream tasks such as contextual information integration. The directional grouping ensures that detected elements are interpreted correctly within the context of the road sign, enhancing the system's utility for navigation and traffic management [16].
## 3 Numerical Example of Two-Dimensional Discrete Memristor Model
### Bifurcation with Coupling Strength  
The bifurcation diagram of a chaotic system is an important tool to analyze its characteristics. It can directly observe the period-doubling bifurcation and chaotic state of the system under different control parameters. The Lyapunov exponent (LE) is an important index to measure the separation rate of adjacent trajectories in phase space. In this paper, [PERSON]'s Jacobian algorithm is used to calculate LE. The LE can be calculated as follows  
\[LE=\lim_{n\rightarrow+\infty}\frac{1}{n}\sum_{i=0}^{n-1}\ln(\lambda_{i}), \tag{14}\]  
where \(i\) represents the number of iterations of the system, while \(\lambda_{i}\) is the eigenvalue of the Jacobian matrix of the system after \(i\) iterations. If a dynamic system has a positive Lyapunov exponent, the system is in chaos. The larger the value of LE is, the stronger the chaotic characteristics of the system will be. When the number of positive LE is greater than 1, the system is a hyperchaotic system, indicating that the system has stronger chaotic characteristics. Given control parameter \(\mu\) and initial conditions \((x_{0},q_{0})\), the bifurcation diagram and LE spectrum of the change of coupling coefficient \(k\) of the models are shown in Figure 3. As can be seen from Figure 3, under different coupling coefficients, the three models exhibit complex dynamic properties, such as periodic Windows, periodic or period-like states, chaos and hyperchaotic states. It is worth noting that 2D-DMLM and 2D-DMTM enter the chaos state from a period-doubling way, while 2D-DMSM enters chaos state from periodic state in period-like way. It can be seen from the LE spectrum that the system has a positive LE in most parameter intervals, and even two positive LEs in some intervals. Given the control parameter \(\mu\), initial conditions \((x_{0},q_{0})\), and coupling coefficient \(k\), the two Lyapunov exponents for the three models are given in Table 2. Note that the length of the sequence is set to \(10^{5}\). It can be seen that the three models all have two positive LEs, indicating that the three models are all hyperchaotic.  
Figure 3: The bifurcation and LEs of three models: **(a)** 2D-DMLM; **(b)** 2D-DMSM; **(c)** 2D-DMTM.
## 1 Introduction
For practical optical diagnostics, it is more feasible to obtain temporally resolved data, such as the spatial intensity distribution of transmitted photons or the angular distribution data as a function of time. These types of measurements have been proven to be practical in the existing literature [19]. In the previous Markov model, the scattering was set as the event which the algorithm would track. As a result, the scattering number (scattering order) of a photon will be preserved by the algorithm, but the total path length or the residual time cannot be kept in the current Markov model. In order to find temporal resolved angular distribution using the Markov chain model, the time or path length must be set as the event so that this information can be preserved as the photons are transmitted. In this work, the authors propose a so-called time-based Markov chain model, which aims to address the issues with the scattering-based model studied before. Because of the nature of the newly introduced time-based Markov chain model, the inversion or reconstruction can be done without using global optimization algorithms. Rather, using the backscattered angular distribution, the optical properties of the turbid slab can be inferred one-by-one using linear algebra operations, which benefits from the matrix form of the time-based Markov chain model. The rest of this work is organized as follows. Section 2 discusses the fundamentals and assumptions of the time-based model. Section 3 demonstrates the numerical verification and reconstruction results using the time-based model. Finally, Section 4 concludes this investigation.
## 3 Results
### Meta-Analyses
#### 3.6.2 Meta-Analysis on the Percentage of Residual Graft  
The graph obtained from the residual graft (%) meta-analysis is represented in Figure 10. The global meta-analysis mean residual graft was 15.2%, with a 95% confidence interval ranging from 7.2% to 23.2%.  
In this meta-analysis, [PERSON] et al. [47] and [PERSON] et al. [46] were the most discordant studies, positioned at opposite extremes. [PERSON] et al. [47] reported the lowest result, while [PERSON] et al. [46] reported the highest, with both findings falling outside the confidence interval (3.6% and 29.9%, respectively). Meanwhile, [PERSON] et al. [23] reported results below the meta-analytic mean, while [PERSON] et al. [48] reported results above it (12.2% and 17.1%, respectively). However, both were relatively close to the mean in this meta-analysis.  
The heterogeneity (I\({}^{2}\)) was 93.8%, indicating extremely high heterogeneity, demonstrating high discordance and variability between studies. Given the high heterogeneity, meta-regressions were performed to evaluate whether the covariate's mean age and sex ratio might have influenced the results.  
The first meta-regression examined the effect of mean age, shown in Figure 11. The second meta-regression investigated the effect of sex ratio, shown in Figure 12.  
Figure 8: Meta-regression of the effect of male/female sex ratio on the percentage of newly formed bone.  
Figure 9: Leave-one-out meta-analysis for newly formed bone (%). The dashed red line represents the overall meta-analytic mean [23; 46; 47; 48].  
In this meta-regression, with the increase in mean age, there is a decrease in the quantity of residual graft (%). However, this relationship did not show significant results (Wald test, \(p\) = 0.569).  
This meta-regression shows that with the increase in males in the study, there is an increase in the percentage of residual graft at the follow-up. This relationship is significant (Wald test, \(p\) < 0.001).  
A leave-one-out meta-analysis was also performed, as shown in Figure 13.  
Once again, it is clear from Figure 12 that, even when one study is removed, the 95% confidence interval still includes the overall result, suggesting that no single study drastically changes the overall effect estimate.  
Figure 11: Meta-regression of the effect of mean age on the percentage of residual graft.  
Figure 12: Meta-regression of the effect of the sex ratio on the percentage of residual graft.  
Figure 10: Forest plot graph of residual graft (%). I\({}^{2}\) represents the heterogeneity index. The dashed red line represents the meta-analytic mean [23; 46; 47; 48].
## 3 Results
### Vehicle Panel Measurements  
The previously described processing (convolution, selection of the linear IRs and FFT) was applied on data measured on the existing vehicle panel, thus obtaining the spectrum of sound pressure measured by the B&K microphone positioned on-axis at a distance of 1 m and the spectrum of acceleration in each of the \(N=368\) points measured with the four monoaxial accelerometers. Then, both the experimental measurement (Figure 6a) and the numerical solution of the model (Figure 8) were processed to reconstruct the sound pressure at the microphone position by applying the [PERSON]'s integral, with the formulation for the acceleration data:  
\[p(r_{2},f)=\frac{\rho_{0}}{2\pi}\sum_{n=1}^{N}a(r_{n},k)\ \frac{e^{-i\cdot k \cdot d}}{d}\Delta S_{n} \tag{4}\]  
where \(d\) is the distance between observing point (\(r_{2}\)) and vibrating point (\(r_{n}\)), \(\rho=1.2\) kg/m\({}^{3}\) is the air density, \(a\) is the acceleration, \(n\) is the node index, \(\Delta S_{n}\) is the area of each element and \(k\) is the frequency index. In the case of a numerical solution, one can note that \(a\) is directly obtained in the frequency domain, therefore making the FFT unnecessary. SPL curves are then obtained with Equation (3). In Figure 11, the microphone recording at a distance of 1 m on-axis is superimposed onto the calculated SPL for the measurement case in Figure 6a. It is visible how, despite some small frequency shifts, the curves' trends are in good accordance in the whole frequency range of interest. No effect of the IR cut in the time domain can be seen in this case, since the acoustic measurements were performed in the free field, thus allowing to keep the IR longer.  
In Figure 12, eventually, the SPL curve reconstructed from the numerical simulation of the vehicle panel (Figure 8) is compared with the recorded SPL at a distance of 1 m (Figure 6b). In this case too, a very good matching between the output of [PERSON]'s integral and the acoustic recording was obtained in the whole frequency range, thus validating the developed approach.  
Figure 11: SPL recorded by a microphone (solid line) superimposed with SPL calculated by [PERSON]’s integral (dash line) from real panel measurements at McLaren Automotive.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 3 Research Method
### Location Preference Evaluation  
As mentioned in Section 3.3.2, opportunity items are those that are re-organized, while attraction items and trivial items are kept in their original locations. To maximize cross-selling, an opportunity item should be moved to a cell close to that of its related attraction items. For example, bottle openers should be placed near bottled beers instead of canned beers. Therefore, the location preference for opportunity items will be evaluated according to purchase association and physical proximity.  
Figure 3 illustrates a typical layout consisting of cabinets and aisles in a retail store. A cabinet consists of several cells where a cell is a space used to display a product item. Let \((x_{m},y_{m})\) and \((x_{n},y_{n})\) respectively be the center coordinates of cell \(m\) and cell \(n\). The physical distance between the two cells is defined as:  
\[g_{m,n}=\left\{\begin{array}{ll}|x_{m}-x_{n}|+\left|y_{m}-y_{n}\right|&\text{ if }\quad A_{m}=A_{n}\\ |x_{m}-x_{n}|+\min\left((2Y-y_{m}-y_{n}),(y_{m}+y_{n})\right)&\text{if }\quad A_{m}\
eq A_{n}\end{array}\right. \tag{4}\]  
where \(A_{m}\) is the aisle number of cell \(m\), \(A_{n}\) is the aisle number of cell \(n\), and \(Y\) is the length of a cabinet. Based on Equation (4), we can derive a physical distance matrix \(G=\left[g_{m,n}\right]\) that shows the physical relationship among all cells.  
Figure 3: A typical store layout showing the cell relationship.  
It is clear that an opportunity item \(i_{i}\) should be placed in a cell close to the set of attraction items, \(AI\). In addition, if the purchase association between the attraction item \(i_{j}\) and the opportunity item \(i_{i}\) is stronger, items \(i_{j}\) and \(i_{i}\) should be placed closer together. Thus, location preference in which opportunity item \(i_{i}\) is placed to cell \(m\) is defined as:  
\[l_{i,m}=\sum_{j\in AI}p_{i,j}\times g_{m,Cell(j)}\qquad\text{for all }m\in Cell(k) \text{ where }k\in OI \tag{5}\]  
where \(p_{i,j}\) is the support count for itemset \(\{i_{i},i_{j}\}\) defined in Equation (1), \(g_{m,Cell(j)}\) is the physical distance between cells \(m\) and \(Cell(j)\), and \(Cell(j)\) is the function returning the cell to which item \(i_{j}\) belongs.  
Product items belong to specific product categories such as beverages, snacks, or fresh food. For management purposes and shopping convenience, a store layout is divided into many zones, each of which is devoted to a single product category. Therefore, the cells to which opportunity items can be moved should be limited by management constraints. Let \(a_{i,m}\) be the likelihood availability that opportunity item \(i_{i}\) is placed in cell \(m\):  
\[a_{i,m}=\left\{\begin{array}{ll}1&\text{if item }i_{i}\text{ is allowed to be placed on cell }m\\ 0&\text{if item }i_{i}\text{ cannot be placed on cell }m\end{array}\right. \tag{6}\]  
Finally, the final location preference \(lp_{i,m}\) can be derived as:  
\[lp_{i,m}=l_{i,m}\times a_{i,m} \tag{7}\]
## 4 IoT-Enabled Traffic-Aware Telemedicine Architecture (ITTA)
### IoT-Based Telemedicine Services Framework (ITSF)  
The architecture ITTA provides multi-tier IoT services for reliable mobile telemedicine networks in the form of edge, fog, and cloud services. For such service provisioning, we have proposed a multilayer IoT-based Telemedicine Services Framework (ITSF), as shown in Figure 2. Each of these layers is envisioned as a service layer to provide multiple services, which can be assumed to be the sub-components of the respective layers. The real-time health monitoring service enables the continuous monitoring of the vital biosignals of the patients. First-level health risk assessment service takes the data provided by the real-time health monitoring service to yield a better understanding of the patient's current condition. At the edge layer, the information dissemination service coordinates with the other services to ensure that relevant alerts reach the concerned doctor and the paramedics in real time. The fog layer provides the dissemination of patient-level health risk service, which enables the reception of the first-level health assessment from IM\({}^{2}\)E and reliably transmits the same to the cloud. The cloud layer provides the services of data storage and data processing with analysis, which aids in the diagnosis of the disease/issue with the patient. Since it is important to deliver emergency patients' vital biosignals within the QoS requirements of the medical data, all the layers provide QoS-aware communication service.
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 5 Conclusions  
The main purpose of this paper is to apply adaptive equalizers to SerDes receivers. On the 112 Gb/s high-loss channel (greater than 30 dB), the signal transmission method adopted in this paper can effectively reduce the signal bandwidth and reduce the equalization strength required by the receiving end. The two-step equalization method effectively improves the convergence accuracy of the algorithm, and in the pre-equalization stage, accurate reception of the signal at the receiver is not required, and the algorithm has strong robustness. Compared with high-end processes, the equalizer can achieve comparable performance.  
Z.W.: Conceptualization, Methodology, circuit design and simulation. M.L.: Methodology, Software. F.L.: Software, Conceptualization. X.H.: Software, circuit design and simulation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China under Grant 2018 YFB2202302.  
The authors declare no conflict of interest.