esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Redargeable Node Architecture  
In order to properly characterize the proposed WPT based approach, also the RN architecture has been defined. The related block diagram is described Figure 4. Each RN is equipped with a 2.45 GHz communication chain adopting an omnidirectional antenna and a 5.8 GHz harvester chain using a linear directional patch antenna designed according to a non canonical optimum impedance matching. The 5.8 GHz RF power, supplied by the ES during the WPT task, is collected by the RN and turned into DC power supply. The energy conversion within the harvesting chain is obtained by means of a solution proposed in [18].  
Figure 3: Energy Supplier node block diagram.  
Figure 2: 3D SBA composite radiation patterns of the Energy Supplier node at 2.45 GHz (**a**) and 5.2 GHz (**b**).  
The latter architecture, which is described in Figure 4, makes use of an 5.8 GHz linear optimum matched patch antenna connected to a voltage triplier and followed by a commercial DC-DC Boost BQ25570. The optimum matching impedance is obtained by means of a source-pull optimization carried out on Keysight Advance Design System (ADS) integrating the diode model and a custom BQ25570 behavioral model. The optimum impedance being the one maximizing the BQ25570 input current and, consequently, the power transfer performance.  
In the proposed solution, the antenna shows directly the optimum impedance to the voltage triplier minimizing the insertion loss and maximizing the DC-DC boost output current IDC. Taking into account a configuration as the one illustrated in [19], and considering for the present node the same output current parameter, a planar distribution of the current is made available to the node by the WPT block.
###### Abstract
Currently, more and more attention is paid towards the concept of hybrid communication solutions where envisioned use cases can leverage on a combination of multiple technologies, including long-range cellular communications, providing connectivity in a intelligent, reliable and optimal manner. In this paper, we propose the vehicular com  
Figure 1: Vehicle-to-Everything (V2X) technology classification. munication management framework (CAMINO), which empowers the use of hybrid V2X wireless technologies to support a multitude of CCAM services. CAMINO enables dynamic and flexible management of the different available V2X technologies and the services running on top of them. In addition, it allows integration with different vehicle/infrastructure sensors, actuators, Human-Machine Interfaces (HMIs) and external third-party service providers. CAMINO is an ITS device agnostic framework, meaning that it can be used on top of any network entity of the vehicular communication world such as vehicles, infrastructure, smartphones, Mobile Edge Computing (MEC) servers, service providers, etc. Furthermore, CAMINO allows monitoring and logging of valuable information like transmitted and received messages, Global Navigation Satellite System (GNSS) data, triggering of events, etc. This information can be used for real-time or offline analysis of various Key Performance Indicators (KPIs). The adoption of different technologies from different OEMs carries the risk of ending up with different groups of vehicles each using different communication technologies and as a result, only vehicles that belong to a certain group can communicate with each other. This could have a negative impact on the vision of the CCAM initiative for enabling enhanced safety and advanced services between connected and autonomous vehicles. OEMs could integrate multiple technologies in their vehicles, which requires the use of flexible hybrid communication solutions such as CAMINO. These solutions can provide the ability to use the same platform for supporting multiple communication technologies and using them in a dynamic way, acting as a low-complexity convergence layer for V2X interoperability and at the same time increasing reliability. The remainder of the article is organized as follows. Section 2 gives an overview of the current literature on V2X hybrid communication systems. In Section 3, we describe in detail the overall CAMINO architecture, the building blocks and how they interconnect with each other. Section 4 presents the different services that have been implemented within the CAMINO framework and how they can be used in a flexible manner on top of the different V2X technologies. Next, Section 5 describes how CAMINO can be integrated with different sensors, actuators and third-party service providers. In Section 6, we discuss the logging capabilities and the logging format of the framework for real-time or offline processing of KPIs. Section 7 shows how CAMINO can be used in practice for real-time CCAM experimentation. Finally, Section 8 concludes the article and discusses how CAMINO will be used in our future work.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 2 Materials and Methods
### Proposed Model
#### 2.2.2 Attention Mechanism  
The attention mechanism is an extensively employed technology in machine learning, which essentially involves the weighted summation of sequences. By adaptively assigning different weights to input variables, it distinguishes the importance of different variables on the output [35]. In this work, we utilize an attention mechanism to dynamically allocate varying weights to different input features, assigning higher weights to important features and lower weights to unimportant ones [36], highlighting the important parts of input features that affect wind power output. The input features are reconstructed into a new feature sequence based on the assigned weights. Its working principle is shown in Figure 2.  
The formula for allocating weights in the attention mechanism is as follows:  
\[e_{i}=u\ \text{tanh}(\omega_{1}x_{i}+b_{1}) \tag{1}\]  
\[a_{i}=\frac{\exp(e_{i})}{\sum_{i=1}^{j}\exp(e_{i})} \tag{2}\]  
\[Y=\sum_{i=1}^{i}a_{i}x_{i} \tag{3}\]  
Figure 1: Prediction flow of the proposed model.  
\(x_{i}\) represents \(i\)-th input feature data, \(e_{i}\) represents the attention probability distribution values corresponding to \(x_{i}\). \(u\) and \(\omega_{1}\) are weights, \(b_{1}\) represents bias. \(a_{i}\) is obtained by the exponential non-linear transformation of \(e_{i}\), aiming to make the attention probability distribution more flexible to adapt to different input data. \(a_{i}\) can be seen as the weight of each feature; the larger \(a_{i}\) is, the more contribution the input feature makes to the output. \(Y\) is the weighted new feature sequence.
# Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs
## 1 Introduction
### Multi-Attribute Decision Making (MADM)  
MADM junction selection routing (MADM)SR) is modeled as the MADM approach as it considers multiple attributes for junction selection. MADM has various approaches, but the \"Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)\" is applied in MADMJSR. TOPSIS is less complex than other techniques and has minimum network overhead. It estimates optimal solutions from multiple attributes. The property to separate the ideal solution from the concrete solution increases the accuracy of the scheme.  
The research paper is further divided into three sections. Section 2 discusses the system model. It also covers the proposed algorithms and the formulation of the solution in detail. In Section 3, the simulated environment and performance evaluation matrices are discussed, followed by a comparative analysis between the previously proposed scheme and our optimal junction selection protocol. In the end, research contributions are concluded in Section 4.
## 6 Discussions of Workshop Results
### CIREI Framework  
The development of the CIREI framework was shaped by a dynamic integration of systematic literature review and co-design workshops, each playing a distinct yet complementary role. The literature review established a theoretical foundation by identifying prevailing models, existing gaps, and critical factors within the field, which guided the initial direction and focus of our framework. Building on this foundation, the co-design workshops engaged key stakeholders, providing valuable practical insights and contextual feedback. This iterative interplay ensured that the resulting CIREI framework is both grounded in robust academic evidence and responsive to real-world needs, effectively bridging theory and practice to deliver a solution that is comprehensive, relevant, and user-informed.  
Figure 11: Summarised distribution of questionnaire responses for all participants (**left**), residents (**centre**), and experts (**right**) for each question.  
To organise the data and insights produced by the workshop, our framework was developed following a thematic analysis of the qualitative data. This framework features two main key components, or building blocks, as follows:  
1. \"Themes\", which are broad, non-situational and fewer in number (_Economic_, _Learning_, _Privacy_, _Trust_, _Usability_).
2. \"Concepts\", which are often based on recurring anecdotes or situations described by participants (_Aesthetic concerns_, _Agency_, _Current use of technology_, _Delegating tech actions_, _Individual limitations_, _Intergenerational interaction_, _Logistics of use_, _Low-tech use contexts_, _Online study/video tutorials_, _Personalisation_, _Rate of technological advancement_, _Social connectedness_, _Technology anxiety_).  
Some concepts are largely bound to a single theme, while others are cross-cutting or not clearly within the purview of a single identified theme.  
The _Learning and Usability_ themes are most closely connected to our research questions, but the presence of other themes in the data justifies their inclusion in the framework. Social factors were not prominent or distinct enough to form a separate _Social_ theme. However, many items coded under _Agency_ reflected participants' agency regarding social pressure, such as their assertion that they would not be influenced by subjective norms.  
Most connections between themes and concepts are one-to-one, as illustrated in Figure 12. However, there is a notable three-way intersection _between Intergenerational Interaction_, _Agency_, and _Usability_. Participants reported that usability issues led them to rely on younger relatives for using technology, hindering their proficiency and confidence. This intersection was significant in the data, with multiple data points across groups and activities, warranting its inclusion. The other three-way intersections were not prominent enough to be shown here.
## 1 Introduction
The PFG on the system at different positions may also change by some extents when the power flow references are being regulated by different the ranges, scales, steps and directions. Accordingly, the resulting influences on different positions of the system by the PFG are different and uncertain. It is requisite to outline the PFG at several representative and critical positions of the power system, to evaluate their severities of influences on the power system. Furthermore, when the power flows in the system are being regulated by the magnitude and phase angle of OSIV individually or comprehensively, there may be multiple regulation modes by the two variables of OSIV, and the regulation effectiveness or efficiencies are also different by different regulation modes. When the phase difference of the system varies, the regulation efficiencies may also change by some extent even the regulation modes remain the same. Therefore, it is also necessary to explore and compare the different PFG dependent on different CIVs including the magnitude of OSIV, the phase angle of OSIV, and the phase differences of the system in different operation conditions. For the purpose of achieving the above objectives, this paper is intended to research and compare the regulation principles and efficiencies of the power flow gradients to multiple characteristic independent variables by multiple regulation modes at several critical points of the UPFC embedded power system. The mathematical model of the PFG to each CIV at each SCP are constructed based on a specific real circuit of UPFC embedded system, and the tests and analysis are conducted in different typical operation conditions, so as to outline the PFG principles more practically and comprehensively. The organization of this paper is as below. In Section 2, the mathematical model and theoretical analysis for the PFG to CIVs are achieved. In Section 3, case studies for the PFG to each CIV at different SCP are carried out and analyzed in two-dimensional planes and three-dimensional spaces. The main conclusions are summarized in the last section.
# Practical Adaptive Sliding-Mode Control Approach for Precise Tracking of Robot Manipulators
## 2 Proposed SS-ASMC Approach
### The Dynamics of Robot Manipulator
The proposed SS-ASMC approach can be depicted with a block diagram as shown in Figure 1, which provides that the sliding variable \(\mathbf{s}_{t}\) enters the vicinity of the sliding manifold within a finite time \(t_{\tilde{z}}>0\), as shown in Theorem 1. Then, the switching gains \(\hat{\mathbf{K}}_{i,t}^{s}\) can be upper-bounded by the unknown positive values as in Lemma 1. **Theorem 1**. : _For a robot manipulator controlled by the proposed SS-ASMC approach (Equation (12)), the sliding variable \(\mathbf{s}_{t}\) enters the vicinity of the sliding manifold within a finite time \(t_{\tilde{z}}>0\), which is guaranteed to be uniformly ultimately bounded for \(t>t_{\tilde{z}}\) as_  
\[\|\mathbf{s}_{t}\|_{2}\leq\sqrt{\sum_{i=1}^{n}\tilde{\epsilon}^{2}+\sum_{i=1}^ {n}\frac{1}{\tilde{\delta}_{i}}\big{(}\hat{K}_{i,M}^{*}\big{)}^{2}+\sum_{i=1}^ {n}\frac{1}{\tilde{\gamma}_{i}}\big{(}\tilde{\eta}_{i}^{*}\big{)}^{2}}\]  
_where \(\hat{K}_{i,M}^{*}\) is the maximum value of \(\hat{K}_{i,t}=\bar{E}_{i}^{*}-\hat{K}_{i,t}^{s}\). The proof is given in Appendix B._  
**Lemma 1**. : _The switching gains \(\hat{\mathbf{K}}_{i,t}^{s}\) are upper-bounded by the unknown positive values \(\bar{K}_{i}^{s,*}\) as follows,_  
\[\hat{K}_{i,t}^{s}\leq\bar{K}_{i}^{s,*}\]  
_for all \(i=1,2,\cdots,n\) and \(t>0\). The proof is given in Appendix C._
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 6 Performance Evaluation  
### Platforms  
The experimental platform is shown in Figure 7. We use the Universal Software Radio Peripheral (USRP) N210 software-defined radios and WISP as backscatter nodes for our instantiation of our scheme. The USRP N210 is equipped with an SBX40 daughter board that can be used as a detector and reader. We use the open source code written by [PERSON] on GitHub to use USRP as a reader. Based on the experimental conditions and design, the SBX-40 daughter board provides the multiple-input multiple-output (MIMO) function and provides 40-MHz bandwidth. The working frequency is 400 MHz to 4400 MHz. The platform used in the experiment is 64-bit Ubuntu 14.04, GNU Radio 3.7.4. The selected CRFID tag is WISP4.1 with dipole antenna, and the microcontroller is MSP430F2132. The commercial reader model used is the Impinj Speedway R420, which is connected to the Laird circularly polarized directional antenna S9028 PCL. Up to 4 antennas can be connected at the same time, and the antenna gain is 9 dBi. Our goal in the evaluation is to demonstrate that our proposed scheme can significantly improve system goodput.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Physical Implementation Model
#### 3.2.3 Emulation Node  
All emulation nodes run real network protocol stacks and applications to complete network traffic exchange. Due to the advantages of flexible configuration, easy extension  
Figure 2: Physical implementation model of the proposed platform.  
and convenient management, virtual nodes are generally used to simulate the nodes in the target network. For some special needs or cases where emulation can not be supported by pure virtual nodes, physical nodes are used, including embedded devices, satellite links, and etc.
## 3 Results
### General Feature Comparison  
The packages vary greatly regarding most aspects of the comparison, such as theoretical background, programming requirements, data handling, and output (Figure 4). A detailed table with the description of each package is available in Appendix C.  
In total, nine packages explicitly refer to DQ, but only three were developed with reference to a published DQ framework (dataquieR [6], DQAstats [17], and MOQA [7]). Merely three packages offer a GUI, yet about half (n = 15) provide an option to use a single function to generate comprehensive output. For the most part, programming knowledge is necessary to perform a wide range of DQ checks. A report outside an R console, for example as PDF or HTML files, is provided by 13 packages.  
While the majority of packages (n = 16) allows at least for some metadata to be provided through functions, only few are able to cover more than four domains with at least some indicators (illustrated in Section 3.3). Only four packages handle compiled metadata in a separate file. Six packages provide a grading of DQ issues for at least selected indicators.  
Most packages (n = 19) provide summaries of a given dataset; for example, the number of variables and data records, data types, and the percentage of missing values. Many (n = 18) also compute summary statistics and (n = 17) univariate descriptive statistics graphs (e.g., mean, median, mode, standard deviation, quartiles, and distributional characteristics) that may serve as descriptors, but only a third (n = 10) provides multivariate visualizations. All packages handle numerical values, but only a minority (n = 13) offers functionalities to  
Figure 3: Number of data quality dimensions and domains for which inferences can be made based on the output of each of the 27 packages included in this review. For each package, the numbers correspond to the number of covered domains within each dimension. If a domain is covered by indicators and descriptors, the descriptors are not mentioned separately, yet Tables A1–A7 provide the detailed information. 1 identical with dataMaid, 2 first published as mosaicQA, 3 includes integration with errorlocate.  
describe and assess strings, such as consistent upper and lower case usage or data fields with blanks.
# Determining the Quality of a Dataset in Clustering Terms
## 2 Materials and Methods
### Methodology
#### 2.2.5 Graph Model  
A graph is a mathematical structure \(G=(V,E)\) consisting of a nonempty set \(V\) of objects called vertices and a family \(E\) of two-element sets of vertices called edges. These sets represent the presence of some relations between two vertices. For this purpose, we can assume that clients are vertices and two clients are joined by an edge if their features or shopping habits are similar. The graph of clients can be constructed in different ways depending on the structure of a data set. If only the information on products that were bought by specific clients is available, then we construct firstly an auxiliary graph of clients and products, where clients are joined with the product they bought. On this basis, we construct the main graph where two clients are joined with an edge if they bought the same product. When looking for clusters, we are looking for similar clients. Firstly, we must define what it means for two clients to be similar. Similarity \(sim(x,y)\) is a function on the set of clients fulfilling the following conditions: \(sim(x,y)=1\) (or maximum similarity) if only \(x=y\) and \(sim(x,y)=sim(y,x)\) for all pairs \((x,y)\). There is no one universal similarity function (see e.g., [23]). For the purpose of this research, the cosine similarity was applied [24].  
Let \(x=(x_{1},x_{2},\ldots,x_{n})\) and \(y=(y_{1},y_{2},\ldots,y_{n})\) be the vectors one wishes to compare. The similarity function expressed as a cosine measure is of the form:  
\[sim(x,y)=\frac{x\times y}{\sqrt{\sum_{i=1}^{n}x_{k}^{2}\sqrt{\sum_{i=1}^{n}y_{k }^{2}}}},\]  
where \(x\times y\) is the scalar product of the vectors \(x\) and \(y\).  
The measure calculates the cosine of the angle between \(x\) and \(y\). A cosine value of 0 means that the two vectors are at an angle of \(90^{\circ}\) to each other (orthogonal) and are not similar. The closer the cosine value is to 1, the smaller the angle and the greater the similarity of the vectors.  
Using the similarity function, a so-called similarity matrix can be constructed, where each entry is a value of the similarity function for an appropriate pair of customers. Based on that matrix, a customer graph can be constructed, in which an edge is placed between customers if their similarity function is sufficiently large. This cut-off threshold is a meta-parameter of the construction. It may vary and depends on the data set. It is convenient that the output graph is connected, which means that there is a connection (not necessarily direct) between each pair of vertices.
# Highly Sensitive Liquid M-Z Waveguide Sensor Based on Polymer Suspended Slot Waveguide Structure
## 5 Data Analysis and Comparison of MZI
As shown in Figure 9, when the reflective index varies from 1.33 to 1.34, the corresponding \(S_{d}\) for wavelengths of 1500, 1550, and 1600 nm are 1049.4, 972.1, and 935.9 dB/RIU, respectively. It can be concluded that the shorter wavelength will lead to better mode confinement, resulting in a higher normalized output energy. Therefore, the sensing accuracy can be enhanced by reducing the wavelength. In addition, the rapid response of polymer slots to changes in wavelength enables the polymer slot to be applied in the field of spectral detection. Figure 8: The simulated optical field of the suspended MZI with a slot. Figure 9: The relationship between the normalized output energy with the refractive index for the structure at wavelengths of 1500, 1550, and 1600 nm.
## 2 Method
### Optimization Algorithm
#### 2.4.2 Hybrid Optimization Scheme  
A hybrid optimizer is used in this study, as shown in Figure 5. In this figure, the hybrid optimization implies that the PSO-based optimization process is applied firstly to its variables to obtain their optimized values. Since the first optimization process may not be accurate as it would result in low variations of values to be optimized, a second optimization process would then be applied to the results of the first optimization process. In this case, PSO, a non-elitist algorithm, is then followed by Pattern Search, using hybrid optimization. The PSO and Pattern Search can then be used to find a local optimum in a limited search area; the use of both PSO and PS algorithms through hybrid optimization ensures better results than a single optimization process. The independent variables given as inputs to the optimization process are also limited due to their upper and lower bounds. These bounds ensure that the search space for both the PSO and Pattern Search algorithms remains within them. During the PSO algorithm execution, if a solution is found outside the bounds of the search space, any such solution outside these bounds will have its value replaced by that of its given upper and/or lower bounds, without any influence on other solutions. The hybrid optimizer's procedure is shown in Figure 5.