esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Variable Universe Fuzzy Multi-Parameter Self-Tuning PID Control of Bridge Crane
## 3 Variable Domain Fuzzy Multi-Parameter Self-Tuning PID Controller
### Determining the Domain Scaling Factors  
In this article, the method of constructing domain scaling factors based on functions is utilized. The selected function must satisfy five conditions: coordination, monotonicity, regularity, duality, and avoidance of zero. Based on the above analysis, the function defined as satisfying these five conditions for domain scaling factors is the following.  
\[\begin{cases}\alpha(x)=1-\gamma exp\left(-kx^{2}\right)\\ \beta(y)=K\sum_{l=1}^{n}p_{l}\int_{0}^{t}e_{i}(\tau)d\tau+\beta(0)\end{cases} \tag{12}\]  
Figure 2: The control scheme for the bridge crane.  
In Equation (12) [30], \(x\) is the input variable; \(\gamma\) is the function coefficient, usually \(\gamma\in(0,1)\), as \(\gamma\) becomes larger, \(\alpha(x)\) becomes smaller, but the change in \(\alpha(x)\) is more dramatic, which can lead to the compression of the universe and the rapid response of the system; \(k\) is the exponent coefficient and \(k>0\), the larger \(k\), the larger \(\alpha(x)\). \(K\) is the coefficient of the integral term; \(n\) is the number of input variables, taken as 2 here; \(p_{i}\) is the \(i\)-th element in the constant vector; \(e_{i}\) is the \(i\)-th element in the input deviation vector; \(\beta(0)\) is the initial value of the output variable domain scaling factor.  
Drawing on the aforementioned principles and the results of multiple experiments, the following formulas have been derived to determine the input domain scaling factors for the positioning and anti-sway fuzzy controllers:  
\[\alpha(e)=1-0.65 exp\Big{(}-0.5e^{2}\Big{)} \tag{13}\]  
\[\alpha(ec)=1-0.6 exp\Big{(}-0.5 ec^{2}\Big{)} \tag{14}\]  
After analyzing the control effects of \(K_{P}\), \(K_{I}\) and \(K_{D}\) on the system, the domain scaling factors for the output variables have been determined. It has been found that the monotonicity of \(\mathrm{K_{P}}\) and \(\mathrm{K_{D}}\) is consistent with that of \(\left|e\right|\). Conversely, the monotonicity of \(\mathrm{K_{I}}\) is opposite to that of \(\left|e\right|\). After conducting multiple experiments, the following scaling factors have been selected:  
\[\beta_{1P}=0.4|e|,\beta_{1D}=6|e|,\beta_{1I}=1/(|e|+0.5) \tag{15}\]  
\[\beta_{2P}=15|e|,\beta_{2D}=0.5|e| \tag{16}\]  
Specifically, \(\beta_{1P}\), \(\beta_{1D}\) and \(\beta_{1I}\) denote the scaling factors for the output variable domain of the position PID controller, while \(\beta_{2P}\) and \(\beta_{2D}\) correspond to the scaling factors for the output variable domain of the anti-swing PD fuzzy controller.
# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 3 Definitions  
In this section, we introduce the basic terminology related to this paper.  
We consider a set of different social networks as \(G_{1},G_{2},\ldots,G_{s}\), each of which is defined as a Social Network Graph (SNG).  
Definition 1 (Social Network Graph): Let \(G=(V,E)\) be a social network, where \(V\) represents the set of nodes, each node \(v_{i}\in V\) represents a user, \(E\subseteq V\times V\) is the set of edges representing the set of social relationships connecting users, and \(e_{i,j}\in E\) indicates the social relationship between nodes \(v_{i}\) and \(v_{j}\). We represent each SNG with a unique latent user space by network embedding. This method learns the probabilistic distributions of nodes and uses low-dimensional vectors to represent them in a latent space._  
**Definition 2** (Network Embedding Mode [35]).: _Given network \(G=(V,E)\), network embedding aims to encode each node \(v_{i}\in V\) into a low-dimensional latent space with a mapping function \(f:v_{i}\to R^{d}\), where \(d\) is the dimension of the latent space. It is synonymous with node embedding or node representation._  
**Definition 3** (User Identity Linkage [34]).: _Given two social networks \(G^{s}\) and \(G^{t}\), the task of UIL is to find every pair of users \(v^{s}_{i}\in G^{s}\) and \(v^{t}_{j}\in G^{s}\) that belong to the same person. We utilize an alignment matrix:_  
\[P_{N\times M}=\{p_{ij}\} \tag{1}\]  
_where \(N\) and \(M\) denote the number of nodes \(G^{s}\) and \(G^{t}\), respectively, and the matrix can be represented as follows:_  
\[p_{i,j}=\begin{cases}1,&\text{if $v^{s}_{i}$ and $v^{t}_{j}$ belong to the same person}\\ 0,&\text{otherwise}\end{cases} \tag{2}\]
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 2 State of the Art
### Cloud Computing and IoT  
The cloud computing paradigm is one of the most positive aspects of ICT development for the IoT insofar as it allows independent and virtualized execution environments to host multiple applications in an isolated fashion on the same hardware platform, typically in large data centres. Additionally, cloud computing has the advantage of providing interconnection to different businesses if they are running on the same platform.  
Cloud computing is also a key enabler when moving from a product-centred to a services-centred approach due to its elasticity that allows companies a \"pay-as-you-grow\" offer.  
Closely related to the issue of data centres, intelligence software and data processing will play an increasingly important role in IoT solutions. A popular concept today is _Big Data_ which refers to the growing number and size of data sets available to businesses and individuals for compilation and analysis. New paradigms have emerged in the field of Big Data. _MapReduce_[43] is one that has generated high expectations in scenarios where latency is not a critical requirement. Naturally, these technologies are therefore key IoT enablers allowing the aggregation and collection of massive sets of data that devices and sensors are able to produce. The IoT is unique compared to other Big Data applications such as social network analysis because, as described above, communication between machines is different from communication between humans.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Data Preparation and Generation Module  
The data preparation and generation module is the foundation of the SSB-PPRL method, a privacy-preserving record linkage approach based on secret sharing and blockchain. It transforms raw records into a distributed computing format and generates masks via smart contracts for matching and auditing. The module includes data encoding, Boolean secret sharing generation, mask generation, computing node selection, and data distribution. Data is split into distributed shares and managed by blockchain and IPFS, as shown in Figure 2.
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 1 Introduction
FF-MR takes the session as a representation of encrypted DNS covert channel traffic, fuses statistical features with byte sequence features extracted by Residual Neural Networks, and focuses on important features through a Multi-Head Attention mechanism to detect and identify three encrypted DNS covert channels. * We conduct comprehensive experiments to evaluate the performance of FF-MR by comparing it with other encrypted DNS covert channel detection methods. We establish four baselines to measure the improvements achieved by the detection model in FF-MR and verify the validity of the model. Finally, recommended values for the hyperparameters are identified using a parameter sensitivity experiment. The rest of the paper is organized as follows: Section 2 introduces related work on DoH-encrypted DNS covert channel detection. Section 3 summarizes and analyzes the command control process of DoH-encrypted DNS covert channel and introduces the research and application of Multi-Head Attention. In Section 4, we present the design details of FF-MR. In Section 5, we experimentally evaluate the performances of FF-MR on the publicly available dataset CIRA-CIC-DoHBrw-2020. Section 6 concludes this paper.
## 3 System Design and Implementation
### Client Server Model
#### 3.4.1 Python--A High-Level Programming Paradigm  
The entire application is built on Python, which is an elegant open-source, cross-platform, high-level, dynamic interpreted language [28]. Being a multi-platform-supported language, it runs equally well on Windows and Linux. Debian, the Raspberry Pi operating system, contains a Python application out-of-the-box. Python is a very powerful, yet flexible and widely-used procedural language that serves various purposes, such as web and desktop application development, system administration tasks, scientific research, data analysis and management, and event-driven applications.  
Henceforth, both client-side and server-side applications are briefly explained, along with their key concepts for successful completion of the task.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm
### Minimum-Energy-Based Imaging Algorithm
#### 3.2.1 Range Compression  
The range compression filter \(\mathrm{H}_{\mathrm{com}}(f_{\tau})\), as shown in Figure 6, can be constructed by  
\[\mathrm{H}_{\mathrm{com}}(f_{\tau})=\exp\left(-j\tau\frac{f_{\tau}^{2}}{k_{\tau }}\right) \tag{9}\]  
where \(f_{\tau}\) is the range frequency. After range compression, \(S_{1}(\eta,\tau)\) is obtained.  
Figure 6: Procedure of the proposed algorithm, where \(\mathrm{H}_{\mathrm{com}}(f_{\tau})\) is the matched filtering function, \(\hat{\sigma}_{\mathrm{sum}}\) is the reconstructed scene, \(\hat{\sigma}_{\mathrm{sum}}(\cdot\,\ i)=\hat{\sigma}_{i}\) indicates that \(\hat{\sigma}_{i}\) is assigned to the \(i\)th column of \(\hat{\sigma}_{\mathrm{sum}}\), and \(N_{g}\) is the number of range gates, the numbers 1–5 mark the main steps of the algorithm.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 2 Problem Statement
### Impacts of Subset Size on Accuracy and Integrity Performances  
Although it is commonly believed that more satellites in view are considered and the more accurate and reliable solution will be achieved under fault-free conditions, the performance improvement caused by the satellite increase might be not always worthy of the increased channel and computation capability demand caused by that. Thus, it is necessary to investigate the relationship between subset size growth and its performance improvement and load increase effect.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Experiments  
This section explains the three types of experiments that were performed to analyze the performance of proposed LEACH-MEEC against three algorithms in the literature, namely, LEACH-MAE, Mobile-LEACH and LEACHDistance-M.  
1. Time: The impact of different time duration ranging from 0 to 1000 s over the performance of LEACH-MEEC was studied.
2. Number of Nodes: The impact of different numbers of nodes from 0 to 100 measuring the significance of packet delivery ratio over LEACH-MEEC was studied.
3. Sensitivity Analysis: Different statistical estimation techniques were applied on results to measure the significance of connectivity feature over the performance of our algorithm to strengthen our claim.  
Figure 3: Flow diagram of LEACH-MEEC clustering.
## A Secure Routing Protocol for Wireless Sensor Networks  
Considering Secure Data Aggregation  
**[PERSON], [PERSON] * and [PERSON]**  
Department of Ubiquitous IT, Dongseo University, Busan 617-716, South Korea;  
E-Mails: [EMAIL_ADDRESS] (T.M.R.); [EMAIL_ADDRESS] (H.-J.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-51-320-1760; Fax: +82-51-327-8955.  
Academic Editor: [PERSON]  
_Received: 9 April 2015 / Accepted: 19 June 2015 / Published: 26 June 2015_  
**Abstract:** The commonly unattended and hostile deployments of WSNs and their resource-constrained sensor devices have led to an increasing demand for secure energy-efficient protocols. Routing and data aggregation receive the most attention since they are among the daily network routines. With the awareness of such demand, we found that so far there has been no work that lays out a secure routing protocol as the foundation for a secure data aggregation protocol. We argue that the secure routing role would be rendered useless if the data aggregation scheme built on it is not secure. Conversely, the secure data aggregation protocol needs a secure underlying routing protocol as its foundation in order to be effectively optimal. As an attempt for the solution, we devise an energy-aware protocol based on LEACH and ESPDA that combines secure routing protocol and secure data aggregation protocol. We then evaluate its security effectiveness and its energy-efficiency aspects, knowing that there are always trade-off between both.  
**Keywords:** wireless sensor network; security; energy-efficient; secure routing protocol; secure data aggregation; LEACH; ESPDA
## 3 5G Technology Enablers for V2X Use Cases
### The Evolution of V2X Communications
#### 3.1.3 3 GPP Release 15  
Release 15 (released in 2019) is basically an enhanced version of Release 14; the communication did not change in principle [30; 74]. The vehicular communication is now referred to as eV2X. New requirements were specified in [5; 28], such as vehicle platooning, advanced driving, extended sensors, and remote driving, in order to enhance support for V2X scenarios. While keeping the full backward compatibility with Release 14, Release 15 introduced the following advancements [123]:  
* Carrier aggregation support for Mode 4 communication (Mode 3 CA was already supported in Release 14).
* Support for 64 QAM.
* A reduction in the maximum time between packet arrival at Layer 1 and the resource being selected for transmission.
* Radio resource pool sharing between Mode 3 and Mode 4 UE.
* Transmission diversity.
* Other RF and RRM requirements.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 3 Barrier Function-Based Multivariable Super-Twisting Guidance Law Design  
In this section, a novel multivariable adaptive super-twisting controller based on barrier function is proposed. Then, following a nonsingular sliding mode surface, a novel guidance law based on the proposed controller with terminal angle constraint is formulated. Finally, the stabilization of the guidance law is proved based on Lyapunov theory.
## 2 High-Level Framework Description
### Embedder  
The Embedder block takes as input the alphabet as returned by the Granulator block and runs an embedding function in order to cast each graph (belonging to an input graph set, e.g., \(\mathcal{S}_{\text{tr}}\)) towards the Euclidean space.  
The mapping function yields the so-called symbolic histogram [33] by transforming each input graph \(\mathcal{G}\) into an \(n\)-length feature vector of the form:  
\[\mathbf{h}(\mathcal{A},\mathcal{G})=[\text{occ}(s_{1},\mathcal{G}),\ldots, \text{occ}(s_{n},\mathcal{G})] \tag{1}\]  
where \(\mathcal{A}=\{s_{1},\ldots,s_{n}\}\) and \(\text{occ}:\mathcal{A}\times\mathcal{G}\rightarrow\mathbb{N}_{0}^{+}\) is the enumeration function that counts the number of times each symbol \(s\in\mathcal{A}\) appears in \(\mathcal{G}\).  
The counting procedure operates as follows:  
1. The input graph \(\mathcal{G}\) is decomposed into its constituent parts, yielding a decomposition \(\mathcal{G}^{\prime}=\{g_{1},\ldots,g_{k}\}\). For the sake of consistency with the Extractor and Granulator modules, in case of path-based subgraphs, the decomposition follows a Breadth First Search strategy, whereas in case of cliques-based granulation, the decomposition follows the maximal clique decomposition.
2. For the \(i^{\text{th}}\) symbol in \(\mathcal{A}\): 1. The pairwise dissimilarities between \(s_{i}\) and all subgraphs in \(\mathcal{G}^{\prime}\) are evaluated; 2. All dissimilarities below a threshold \(\zeta_{i}\) are retained and considered as a 'hit'; 3. The \(i^{\text{th}}\) entry in \(\mathbf{h}\) is filled with the number of occurrences (i.e., the number of 'hits'); 3. Repeat step 2 for \(i=1,\ldots,n\).  
There are two important aspects in this procedure that need to be addressed. First, the threshold \(\zeta_{i}\) for scoring a 'hit' is defined as:  
\[\zeta_{i}=1.1\cdot\Phi(\mathcal{K}_{i}) \tag{2}\]  
where \(\mathcal{K}_{i}\) is the cluster whose representative element is \(s_{i}\) and \(\Phi(\cdot)\) is a function that evaluates the compactness of the cluster (further details in Section 3). In this manner, the threshold scales in a symbol-aware fashion by accounting the size of its cluster.  
Second, the pairwise dissimilarities between symbols and subgraphs is evaluated by means of the very same nBMF dissimilarity measure already used by the Granulator block.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 3 RFF Identification Scheme
### Index-Based DCTF Identification Scheme  
An index-based DCTF identification scheme is proposed for Scenario 1 using a multi-channel CNN. The multi-channel CNN accentuated the device disparities by emphasizing the transient parts, which led to performance enhancement compared with the single-channel CNN. The experimental results are presented in Section 4. In the index-based DCTF identification scheme, because the complete knowledge of all the preamble sequences was available at the gNB, the CNN model was trained separately for each preamble sequence. Once the testing signal was received, the index of the preamble sequence could be estimated by computing the cross-correlation with the standard preamble sequences. Then, the extracted DCTFs would be entered into the corresponding CNN model for device classification.
# 3D-Printed Lightweight Foamed Concrete with Dispersed Reinforcement
## 2 Materials and Methods
### Methods
#### 2.3.5 Water Absorption Water Absorption Measurement  
To evaluate the capillary water absorption of foamed concrete samples, specimens were placed in water-filled containers supported by plastic grids to ensure they remained elevated above the container's base. This setup restricted the water-absorbing surface to the base of the sample, with an area of 40 mm\({}^{2}\). The samples, fabricated using both 3D printing and conventional casting methods, were standardized to the dimensions of \(40\times 40\times 160\) mm. During the initial rapid absorption phase, measurements were recorded at regular intervals over a 7-day period. The absorption process was monitored by periodically weighing the specimens using a high-precision scale with a sensitivity of 0.01 g, enabling accurate tracking of mass variations. The water depth was consistently maintained at 3 mm throughout the experiment to ensure uniform testing conditions. The experimental procedure was conducted in both vertical and horizontal orientations. In the vertical orientation, standard protocols were followed to measure capillary water absorption along the sample's height. Conversely, the horizontal orientation was employed to investigate potential variations in water absorption across the layers of the 3D-printed samples. This approach is particularly relevant as the layer-by-layer deposition process inherent to 3D printing can introduce structural inhomogeneities that may influence the material's water absorption behavior. Following data collection, the water absorption coefficient (A) for each sample was determined based on the linear relationship between the increase in mass and the square root of time. The water absorption coefficient was calculated using the following Formula (2):  
\[A=\frac{\Delta m_{l}}{F\times\Delta\sqrt{t}} \tag{2}\]  
where  
A--water sorption coefficient, (kg/(m\({}^{2}\)h\({}^{1/2}\)));  
\(\Delta\)m\({}_{\rm t}\)--sample mass increase, (kg);  
F--suction surface, (m\({}^{2}\));  
\(\Delta\)\(\sqrt{t}\)--time square root increase, (h\({}^{1/2}\)).  
The observed linear relationship between the increase in mass and the square root of time enabled the calculation of the absorption coefficient for each sample, providing a precise quantification of its capillary water absorption properties.
## Data and Methods
### SMAP Global FT Datasets  
The SMAP global FT data records are archived and distributed by the National Snow and Ice Data Center (NSIDC). The SMAP global FT datasets are available in both 9 km (L3_FT_P_E) and 36 km (L3_FT_P) grids, and global and polar EASE-Grid 2.0 projection formats [16; 17; 45]. The SMAP L3_FT_P[E] (R16010) products in the global EASE-Grid 2.0 format were used for this study, spanning a 2-year daily record (2016-2017). The SMAP FT products distinguish 4 levels of FT conditions determined from the descending (6 AM) and ascending (6 PM) orbit retrievals, including frozen (both AM and PM overpass times), non-frozen (AM and PM), transitional (AM frozen; PM non-frozen) and inverse-transitional (AM non-frozen; PM frozen) states.  
Grid cells dominated by permanent snow/ice cover were masked from the SMAP FT domain, which excludes most of Greenland and Antarctica. Grid cells dominated by urban development and excessive open water (i.e., fw \(>\) 50%) were also excluded from the domain to reduce the influence of radiofrequency interference (RFI) and water contamination of the Tb and FT retrievals. Land cover conditions and large water bodies are identified in the L3_FT products using static ancillary 250 m MODIS IGBP landcover maps [46; 47]. The FT accuracy assessment was carried out by selecting WMO stations representing surrounding SMAP EASE-Grid cells without considering additional screening or weighting of the FT accuracy using the assigned quality flags. A set of retrieval quality flags are assigned in the product to indicate the relative conditions and quality of the FT results, and to identify other factors influencing classification accuracy. The quality flags include: (1) masked areas for excessive (\(>\)50%) urban and fw cover, (2) cautionary high-water cover conditions (20% \(<\) fw \(\leq\) 50%), (3) permanent snow/ice cover, (4) and areas with low FT-SCV and GMAO temperature correlation (\(|\)R\(|\) \(<\) 0.5). Other product quality flags identify the presence of seasonal snow cover, false FT mitigation areas (Section 2.2.3), active precipitation, mountainous terrain (\(>\) 250 m elevation gradient within a cell), and SMAP identified RFI [48].