esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background of Proposed Method  
### Emd  
The EMD was first proposed by [PERSON] et al. [16], in 1998, and uses a sifting process to extract intrinsic mode functions(IMFs) and a final residue, from a complicated multi-component signal \(x(t)\), i.e.,  
\[x(t)=\sum_{i=1}^{m}y_{i}(t)+r(t) \tag{1}\]  
where \(m\) is the total number of IMFs, \(y_{i}(t)\) (\(i=1\) to \(m\)) is the \(i\)th IMF, and \(r(t)\) is the final residue. Each IMF represents a different type of frequency in theory, and each IMF satisfies the following two features: (1) The maximum difference between the number of extreme points and the number of zero crossings is one; (2) At any data point, the average value of the local minimum envelope and the local maximum envelope of data are zero. The point is that the decomposition process of EMD is adaptive, which means that it does not need to select a basis function in the decomposition process, and is superior to wavelet transform.  
However, there are still some drawbacks to the results of EMD, i.e., the end-point effect, mode mixing, and real IMF determination [18; 19; 22]. Here, mode mixing is defined as any IMF consisting of oscillations of dramatically disparate scales, or similarly featured time scales that are distributed in different IMFs, resulting in the mixing and mutual influence of two adjacent IMFs, which is difficult to identify. Owing to the phenomenon of mode mixing, the decomposition of EMD loses its meaning, let alone the accurate identification of modal parameters, so mode mixing is a limitation of EMD. Therefore, it is necessary to improve the classical EMD to obtain a greater accuracy of modal parameter identification. To solve the limitations of mode mixing and real IMF determination in classical EMD, an IEMD is proposed in this paper.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 2 Related Works
A protocol called Clustered Routing for Selfish Sensors (CROSS) was presented in [19], where game theory was used to model the clustering for sensor networks. As CHs consume much more energy than normal nodes, a node selfishly abdicates from serving as the CH to other nodes for energy conservation. However, if each node refuses to be the CH, then all sensor nodes' payoffs are zero since their sensing data cannot be transmitted to BS effectively. To achieve a tradeoff between saving energy and providing the required services, each sensor node is considered as a player to campaign for the CH by joining in a clustering game. The mixed strategy is adopted in this game, and each player acquires an equilibrium probability to be CH after the game is finished. Each node decides whether to be a CH according to the corresponding equilibrium probability so that its payoff can keep the state of equilibrium. Though CROSS has shown a good paradigm to model the clustering in sensor networks by using game theory, the main problem is that it assumes all sensor nodes in the network can exchange information with each other which is not very realistic. To address this problem, a protocol called Localized Game theoretical Clustering Algorithm (LGCA) was presented in [20]. By playing a localized clustering game with the close neighbors, each sensor node can get an equilibrium probability to be the CH and selfishly decides whether to be the CH according to this probability. To avoid the case that more than one CH occur in a localized restricted area, the nodes successfully self-selected as CHs need to further run to become the final CHs. Then a MAC contention mechanism is adopted to insure that only one final CH is restricted in a specific zone. Compared with CROSS, LGCA is more feasible since it is completely distributed and easily extendible. As the payoffs for a sensor node when choosing different strategies are not specifically defined from the view of sensors' energy in CROSS and LGCA, a Hybrid, Game Theory-based and Distributed (HGTD) clustering protocol was proposed to address this issue in our previous work [30]. Each node plays the same localized clustering game as that in LGCA. Since the nodes with more neighbors and longer distances to BS have higher costs to serve as CHs, node degree and the distance to BS are considered simultaneously when defining nodes' payoffs. Moreover, an iterative algorithm is presented to restrict that only one CH can be selected in a localized restrict. Extensive simulation results validate that HGTD has further enhanced the network lifetime.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 4 3 GPP Release 17: Expansion of 5G Horizons
### Enhancements to Existing Features
These enhancements made IAB more resilient, efficient, and applicable across FR1 and FR2. Other enhancements to existing features in Release 17 included  
* **UE power savings**: In idle/inactive mode, new paging mechanisms reduced false alarms by subdividing UE groups and leveraging paging early indications. In connected mode, power-saving measures included reduced PDCCH monitoring with dynamic adaptations, search space set group switching, and PDCCH skipping;
* **Dynamic spectrum sharing (DSS)**: DSS offers an efficient migration path from LTE to NR by enabling both technologies to share the same carrier [61]. In Release 17, cross-carrier scheduling was enhanced for more flexible DSS;
* **Multi-subscriber identity module (SIM) support**: Release 17 introduced enhancements for multi-SIM devices to address paging collision and network switching issues [62];
* **Small data transmission**: Release 17 enabled UEs to transmit small data packets while remaining in the RRC_INACTIVE state, reducing the need for frequent transitions to RRC_CONNECTED.
# Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm
## Methodology
### Solution of Mathematical Optimization Model
#### 2.3.3 Sequential KSM  
In an SKSM, the infill criterion is the key factor affecting model performance. To balance the global search ability against the convergence speed of the optimization algorithm, this study combined the MP strategy [25] with the LCB strategy [26] to construct the SKSM by using sample points obtained from the domain of interest in each optimization iteration.  
**MP criterion.** The principle of the MP criterion is to find the minimum value of the objective function directly in a surrogate model and take the point that minimizes the value of the objective function as a new sample. Mathematically, the MP criterion can be expressed as follows:  
\[\text{min}\hat{g}(X) \tag{20}\]  
\[s.t.\{\begin{array}{l}\hat{h}_{w}(X)\leq 0\,w=1,2,\ldots N_{c}\\ X_{L}\leq X\leq X_{U}\end{array} \tag{21}\]  
where \(\hat{g}(X)\) and \(\hat{h}_{w}(X)\) are a surrogate model of the objective function and a constraint function, respectively. \(X_{L}\) and \(X_{U}\) are the lower and upper bounds of the design variable \(X\), respectively. \(N_{c}\) is the number of constrained functions.  
**LCB criterion.** To fully explore the optimal value of the objective function over the entire design space, the point that minimizes the LCB value can be taken as a new sample. The LCB function can be defined as follows:  
\[LCB(X)=\hat{g}(X)-As(X) \tag{22}\]  
where \(A\) is a constant and plays a major role in balancing global search and local search. According to the results of [PERSON] et al. [27], we set \(A\) to 2. \(\hat{g}(X)\) is the value predicted using the KSM, and \(s(X)\) is the standard deviation from the KSM.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 5 Experimental Results and Analysis
### Comparison of the Deblurring Performance
(3) Observed from Table 3 when restoring the eight degraded images with Gaussian blur kernel (G, 5, 5, 7) and Gaussian noise standard deviation of 1-5, the HTV-Lp model shows average PSNR values 0.531 dB higher than those of the ATV method, 0.391 dB higher than those of the ITV method, 0.300 dB higher than those of the ATpV method, 0.589 dB higher than those of the FAV4 Lp method, and 0.175 dB higher than those of the Hybrid TV method. Hence, we conclude that the high-order gradients sparsity of the image is helpful for image recovery and Lp-pseudonorm is better than L1-norm.
# Design of Time-Synchronized Switch Based on Zynq
## 1 Introduction
After extensive research, the following issues have been identified with WRS: (1) they use a discrete component architecture, specifically a combination of MCU and FPGA, which connects the two through an external EBI bus, making the communication process somewhat complex; (2) creating a Linux system on the MCU side is not sufficiently convenient; and (3) the expandability is limited, as the current hardware is inadequate to support the addition of new functionalities. To address these issues, a time synchronization switch with WR functionality has been designed based on the Zynq platform. This switch not only achieves independent control but also reduces the physical size of the design. The PS and PL communicate via an internal AXI bus, which can minimize external environmental interference, making time synchronization more precise and stable. Using the Xilinx built-in development tool simplifies the development process and improves development efficiency. The hardware-software co-design capabilities of Zynq allow developers to flexibly adjust task allocation and resource utilization based on application requirements [12]. The remainder of this paper is organized as follows. Section 2 introduces the principles of WR technology. Section 3 presents the hardware implementation plan for the time synchronization switch. Section 4 discusses the implementation of the time synchronization module. Section 5 validates the time synchronization functionality. Section 6 provides a conclusion.
# A High Signal-Noise Ratio UWB Radar for Buried Pipe Location Using Golay Complementary Sequences
## 3 Generation and Characteristics of the Probe Signal  
The Golay complementary codes are generated from a field-programmable gate array (FPGA, intel Cyclone V 5 CG) and the corresponding hardware structure diagram is shown in Figure 2. The source generator generates a pair of low rate Golay complementary codes: code A and code B. Their  
Figure 1: Experimental setup of the Golay complementary sequences radar system.  
code lengths were controlled by the variable controller. Through the multiplexer (MUX, Company, City, State Abbreviation if USA or Canada, Country), code A or code B was selected and then sent into serializer (Company, City, State Abbreviation if USA or Canada, Country), which was used to convert data into large bitwidth parallel data. The large bitwidth data was stored in the static random access memory (SRAM) using the SRAM controller (Company, City, State Abbreviation if USA or Canada, Country). When the transceiver receives a reset signal from the reset controller, it controls the SRAM controller and reads the data. After deserializing in the transceiver, the data is output through the low voltage differential signaling (LVDS) interface to obtain a high rate Golay complementary code. In the hardware implementation diagram, the reconfiguration controller was used to adjust the signal transmission rate. By using this FPGA board, Golay complementary codes with a maximum code length of \(2^{19}\) and tunable frequency between 615 MHz and 3.125 GHz can be achieved. In actual use, the signal frequency and code length can be flexibly selected according to the requirements of the detection environment. In our experiment, the code lengths of the Golay complementary codes are 1024 and the frequencies are 1 GHz.  
Figures 3 and 4 show the properties of Golay complementary codes. The time series of a pair of Golay complementary codes are shown in Figure 3a,b. The amplitude of code A and complementary code B are 0.1 V, and a symbol period of each code is 1 ns. Figure 3c,d show the power spectrums of codes A and B, respectively. The autocorrelation of each code is shown in Figure 4. The individual autocorrelation of A or B exhibits sidelobes. However, when the two autocorrelations were added together, the peak value was twice its single autocorrelation, whereas the sidelobes cancel exactly.  
Figure 3: _Cont._  
Figure 2: Hardware implementation diagram of the Golay complementary codes.
# Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations
## 2 Preliminaries and Problem Formulation  
### Notations and Graph Theory  
For a vector \(x\in\mathbb{R}^{n}\), \(x_{(i)}\) denotes the \(i\)th component of \(x\). For a matrix \(A\in\mathbb{R}^{n\times n}\), \(A^{T}\) and \(A^{-1}\) denote the transpose and the inverse of \(A\), respectively, and \(\lambda_{i}(A)\), \(i=1,2,...,n\), are the eigenvalues of \(A\) in ascending order, i.e., \(\lambda_{1}(A)\leq\lambda_{2}(A)\leq\cdots\leq\lambda_{n}(A)\). We say that \(A\in\mathbb{R}^{n\times n}\) is Hurwitz if every eigenvalue of \(A\) has strictly negative real part, and is neutrally stable if every eigenvalue of \(A\) has non-positive real part with those on the imaginary axis being simple. \(A\otimes B\) denotes the Kronecker product of \(A\) and \(B\). \(I_{N}\in\mathbb{R}^{N\times N}\) and \(\mathbf{1}_{N}\in\mathbb{R}^{N}\) denote the identity matrix and the column vector with all entries equal to 1. \(\text{blkdiag}(A_{i})_{i=1}^{N}\) represents a block-diagonal matrix with matrices \(A_{i}\), \(i=1,...,N\), on its diagonal.  
Let \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\) be an undirected graph, which represents the communication between agents, with a set of nodes (or agents) \(\mathcal{V}:=\{1,2,...,N\}\), a set of undirected edges \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\), and an weighted adjacency matrix \(\mathcal{A}=[\alpha_{ij}]\in\mathbb{R}^{N\times N}\). For the undirected graph, if \((i,j)\in\mathcal{E}\), then \((j,i)\in\mathcal{E}\), which means the agents \(i\) and \(j\) can communicate with each other. The weights \(\alpha_{ij}=\alpha_{ji}>0\) if and only if \((i,j)\in\mathcal{E}\) and \(\alpha_{ij}=\alpha_{ji}=0\) otherwise.  
The Laplacian matrix of the graph \(\mathcal{G}\) is denoted by \(L=[l_{ij}]\in\mathbb{R}^{N\times N}\), where \(l_{ii}=\sum_{j=1,j\
eq i}^{N}\alpha_{ij}\), \(l_{ij}=-\alpha_{ij}\), \(i\
eq j\), and, thus, \(L\) is a positive semi-definite matrix with \(0=\lambda_{1}(L)\leq\lambda_{2}(L)\leq\cdots\leq\lambda_{N}(L)\). The undirected graph is connected if there exists a path between any two distinct nodes. For the connected graph, \(L\) has a simple zero eigenvalue that is \(0=\lambda_{1}(L)<\lambda_{2}(L)\).
# TDM-PON PAM Downstream Transmission for 25 Gbit/s and Beyond
###### Abstract  
The optical access network is currently driving studies on transmissions beyond 10 Gbit/s. This paper reports an analysis of Pulse Amplitude Modulation (PAM), seen as a promising candidate for future Passive Optical Networks (PON). Previous 25 Gbit/s real-time PAM4 results are extrapolated here with simulations to higher bit rates and a higher number of PAM levels. Our main goal is to evaluate the compliancy of PAM with the existing standards and legacy networks as far as fiber length, optical budget class, and wavelength plan are concerned. The simulations enlighten us as to the challenges of multilevel modulation formats, such as noise and jitter, compared to the currently adopted Non-Return-to-Zero (NRZ).  
access networks; Intensity Modulation Direct Detection (IMDD); optical fiber communication; Pulse Amplitude Modulation (PAM); Passive Optical Networks (PON) +
Footnote †: journal: _photonics_
## 4 Cascaded Weighted Sum and Its Field of Application
### Comparison of Pareto Optimization and CWS in Different Application Scenarios
#### 4.3.3 Repeated Optimization, also as Part of an Automated Process  
Another domain of CWS-based optimization or planning is project type III, where the same task is executed repeatedly with minor modifications and, thus, known areas of interest. If a major change occurs, the area of interest can be adapted by using Pareto optimization. A typical example is the scheduling of jobs to the heterogeneous resources of a computational grid, as was introduced before and described in detail in [6]. It is a permanent replanning process, because events demanding a change of the schedule may occur long before a plan is completed. Examples are the introduction of new jobs or new resources, unexpected deactivations of resources, changes of the cost profiles of  
Figure 10: Both diagrams show a sample population of an advanced search more or less shortly before convergence. The CWS concentrates the best individuals (**black dots**) more or less on the region of interest, as shown in the left diagram. In contrast to that, Pareto-based optimization procedures attempt to distribute their solutions along the Pareto front as best as they can, see the right diagram. Thus, fewer solutions will be found in the area of interest.  
resources, early completion of jobs, or the like. As described in [6], five objectives are optimized and four penalty functions are used to handle the restrictions. Because planning time is limited and thousands of jobs and hundreds of resources must be handled, the planning must be stopped (long) before the used Evolutionary Algorithm converges. Thus, it is important to explore the region of interest as well as possible, see Figure 10. Additionally, there is no human expert to check the results several times per hour. For this automated scheduling process, the determination of the Pareto front makes no sense and the CWS is a meaningful alternative. These considerations also apply to many other scheduling tasks like the one described in [15,19].  
Another example already mentioned is the planning of collision-free movement paths for an industrial robot [14,15,24,25]. Depending on the task at hand, we have four or five objectives and at the minimum one penalty function to handle collisions. As robot movements can be simulated and visualized, the results are checked by a human expert mostly on the level of robot movements rather than of objective figures. As areas of interest usually are also known in advance and new solutions should be generated fast, the CWS is suited here as well and for the same reasons as with the task before.
## 3 Measurement Method Design  
### Measurement Method Summary  
The main method of the pore water pressure measurement is to directly embed the sensor in the pavement structure. There are two main methods of embedding sensor. Method 1: Post-construction embedding method. Holes are drilled on the already-passed road surface, and the sensors are embedded in holes. This method has good flexibility and can be freely chosen to measure the position. At the same time, it is the most convenient and the easiest to conduct the measurement, but it has the greatest disturbance to the road surface. Method 2: Pre-construction embedding method. In the process of road construction, the sensor is embedded in a predetermined structural layer. The measurement results of this method are closest to the actual situation, and the method has the least disturbance to the pavement structure, but it is the most difficult to achieve. During the roller compaction process, it is difficult to ensure that the sensor is not damaged, but in fact the sensor can be damaged in the process. In addition, during the roller compaction process, the position of the sensor can be changed due to non-uniform force. As a result, the measurement result has a large error.  
Therefore, the measurement methods currently used have great problems. Optimizing the measurement method, minimizing the disturbance to the road surface, and preventing the sensor from being damaged are the key problem to be considered in the field measurement of pore water pressure.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 1 Introduction
### The History and Evolution of GPUs  
Originally conceived for real-time graphics rendering, GPUs have since outgrown their narrow origins to become indispensable engines for a wide spectrum of compute-intensive tasks. Their story begins in 1968 with the founding of Evans & Sutherland Computer Corporation to build custom graphics hardware [1]. A defining moment arrived in 1999 when NVIDIA introduced the GeForce 256--often credited as the first \"modern\" GPU, which established the benchmark for future GPU designs and capabilities [2].  
Over the following decades, GPU architectures have seen dramatic enhancements, most notably in programmability, allowing developers to exploit massive parallelism for non-graphical workloads [3]. Today, GPUs drive applications across gaming, professional visualization, high-performance scientific computing, machine learning, and even cryptocurrency mining [4; 5; 6]. The market has diversified into several segments--consumer and gaming GPUs, professional and workstation cards, data-center artificial intelligence (AI) accelerators, and mobile/embedded solutions--each optimized for distinct performance, power, and feature requirements [7; 8; 9].  
A pivotal milestone arrived in 2006 with NVIDIA's unveiling of the Tesla architecture, which for the first time incorporated unified shader functionality [3]. Shaders--small programs executed on the GPU to manipulate graphics data--had until then been processed through distinct vertex and pixel pipelines [10]. By unifying these pipelines, every core gained the ability to execute any shader type, vastly increasing both flexibility and programmability. This architectural evolution laid the groundwork for general-purpose GPU computing frameworks such as CUDA (compute unified device architecture) [3; 11].  
In the years since, GPUs have become omnipresent, powering devices from high-end desktop workstations to smartphones [2; 12]. High-performance systems typically employ dedicated GPUs, while integrated GPUs (iGPUs), embedded within the CPU and sharing system memory, are widespread in entry- to mid-level consumer hardware [2; 13]. Although iGPUs offer advantages in cost and energy efficiency, they lag significantly in raw compute power: for example, a consumer-grade dedicated GPU of a given generation can deliver between four and twenty-three times the single-precision floating-point throughput of its integrated counterpart [13; 14].