esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Battery Mathematical Models
### KiBaM Model  
The KiBaM model is well known globally for its high accuracy and adaptability to various types of batteries. The operation of the battery is described as two tanks that are connected with a regulation valve. The first tank represents the charge that is available for use and the second one the charge that is chemically bound. The KiBaM model is described as:  
\[q_{1}\ =\ q_{1,0}e^{-kt}+\frac{(q_{0}kc-I_{ac})\big{(}1-e^{-kt}\big{)}}{k}- \frac{I_{ac}c\big{(}kt-1+e^{-kt}\big{)}}{k} \tag{1}\]  
\[q_{2}\ =\ q_{2,0}e^{-kt}+q_{0}(1-c)\big{(}1-e^{-kt}\big{)}-\frac{I_{ac}(1-c) \big{(}kt-1+e^{-kt}\big{)}}{k} \tag{2}\]  
where \(q_{1}\), \(q_{2}\) represent the available and chemically bound charge in Ah, \(q_{1,0}\) and \(q_{2,0}\) the initial values of \(q_{1}\), \(q_{2}\) in Ah, \(k\) and \(c\) are estimated based on manufacturer data, \(t\) symbolizes the time in h and \(I_{ac}\) the charging/discharging current in A.  
The maximum discharging current is calculated by (3) which is obtained by setting \(q_{1}=0\) in (1).  
\[I_{d,\max}\ =\ \frac{kq_{1,0}e^{-kt}+q_{0}kc\big{(}1-e^{-kt}\big{)}}{1-e^{-kt} +c(kt-1+e^{-kt})} \tag{3}\]  
Likewise, the maximum charging current is given by (4) which is obtained by setting \(q_{1}=cq_{\max}\) in (1).  
\[I_{c,\max}\ =\ \frac{-kcq_{\max}+kq_{1,0}e^{-kt}+q_{0}kc\big{(}1-e^{-kt}\big{)}}{1- e^{-kt}+c(kt-1+e^{-kt})} \tag{4}\]  
The parameter \(q_{\max}\) denotes the combined volume of the tanks and is given by (5).  
\[q_{\max}\ =\ \frac{q_{t}\big{[}\big{(}1-e^{-kt}\big{)}(1-c)+kct\big{]}}{kct} \tag{5}\]  
where \(q_{t}\) symbolizes the capacity that corresponds to current \(t\) at time \(t\).  
The battery's voltage is calculated as:  
\[V_{ac}\ =\ E_{ac}-I_{ac}R_{o} \tag{6}\]  
where \(E_{ac}\) represents the voltage in \(V\) and \(R_{o}\) the internal resistance in \(\Omega\).  
During discharge \(E_{ac}\) is defined as:  
\[E_{ac}\ =\ E_{\min}+(E_{od}-E_{\min})\frac{q_{1}}{q_{1\max}} \tag{7}\]  
where \(E_{\min}\) is the minimum permissible discharge voltage in \(V\) and \(E_{od}\) the maximum discharge voltage in \(V\). The \(q_{1\max}\) is defined as:  
\[q_{1\max}\ =\ cq_{\max} \tag{8}\]  
During charge \(E_{ac}\) is defined as:  
\[E_{ac}\ =\ E_{ac}+(E_{\max}-E_{ac})\frac{q_{1}}{q_{1\max}} \tag{9}\]where \(E_{\max}\) denotes the maximum permitted charge voltage in \(V\) and \(E_{\infty}\) the minimum charge voltage in \(V\).  
The state of charge (\(SOC\)) of the battery is defined as a linear combination of time and current:  
\[SOC_{(t+1)}\ =\ SOC_{(t)}(1-\sigma_{ac})+I_{ac}ef(\Delta t) \tag{10}\]  
where _ef_ represents the efficiency factor, \(I_{ac}\) the charge/discharge current in A and \(\sigma_{ac}\) the discharging rate of the accumulator. \(n_{ac}\) and \(\sigma_{ac}\) are estimated based on manufacturer data.
## 4 Application Trends
### Analysis and Prediction of Frequency-Usage Behavior Based on Spectrum Knowledge Graph
Frequency-usage behavior prediction can help overcome the sensing capacity limitations and provide active, predictive, and enhanced information support for the LEO constellation resource scheduling, improving frequency efficiency. Figure 8: Prediction of frequency-usage behavior based on spectrum knowledge graph.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 3 Methodology and Experimental Procedure
### Methodology  
In a positioning system, its accuracy is studied analysing the distance error, \(\epsilon_{p}\), made in the estimation of the position. In this paper, this error is defined as:  
\[\epsilon_{p}=\sqrt{(x-\hat{x})^{2}+(y-\hat{y})^{2}}, \tag{1}\]  
where \((x,\,y)\) are the real Cartesian coordinates of the tag position and \((\hat{x},\,\hat{y})\) represent the estimated coordinates of the tag position. The figures of merit selected to evaluate the performance of the proposal are the mean distance error, the standard deviation of the distance error (and the variance) and the RMSE explained in detail in [17].
## 2 Overview of Multiobjective Path Problems and Algorithms
### Path Problems Dealing with Uncertainty--An Outline  
The classical single objective or multiobjective path problems assume that the parameters associated with the arcs are deterministic. However, setting the parameter values is often very difficult, since these values may be subject to several sources of uncertainty and/or imprecision.  
Having in mind that path problems dealing with uncertainty/ imprecision are not in the core subject of this work, we just briefly refer to shortest path problems involving probabilistic network parameters and to cases where the imprecision of parameters is modelled using fuzzy sets or rough sets.  
1. Stochastic Path Problems  
In the work in [135], the authors develop a method for solving a stochastic multiobjective shortest path problem and present an application to a hazardous material transportation case. In [136], a biobjective path problem on a directed multigraph with cycles is studied, where each arc is associated with a survival probability of moving along the arc parameter and the length of the arc. The authors intend to maximize the path survival probability, and to minimize the path length. As it involves the resolution of a NP-complete problem, even in the acyclic case, approximation algorithms are developed. The papers [137; 138] deal with an uncertain MOSPP in a 'weighted connected direct graph' where two uncertain parameters, cost and time, represented by uncertain variables are associated with the arcs. The authors proposal consists of solving the deterministic transformations of two models. Namely, an expected value model and a chance constrained model of the multiobjective uncertain shortest path problem. Furthermore, the deterministic models resolution uses two multiobjective genetic algorithms, namely nondominated sort ing genetic algorithm II (NSGA-II) and multiobjective cross-generational elitist selection, heterogeneous recombination and cataclysmic mutation (MOCHC). (b) Imprecision in Shortest Path Problems A shortest path problem on a network in which fuzzy numbers are assigned to the arc lengths is addressed in [139]. An order relation between fuzzy numbers is introduced, allowing us to build an auxiliary problem which enables a resolution for the fuzzy shortest path problem to be derived. The auxiliary problem consists of the calculation of the efficient paths set of a MOSPP, based on the order relation between fuzzy numbers referred to above. Other variants of this problem have been approached in [140; 141]. Paper [137] deals with a multiobjective path problem in a directed network, and the associated arc weights are represented as rough variables in order to model the imprecision. The proposed problem resolution involves a modified rough [PERSON]'s labeling algorithm; or, alternatively, a method which considers a rough chance constrained programming technique solved by a Goal Attainment Method and the NSGA-II.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 3 Methodology
### Graph-Based Convolutional Operations:  
Presently, two fundamental approaches are being explored to generalize convolutional neural networks (CNNs) to accommodate structured data formats since utilizing a standard convolution technique, regular general graphs. One strategy involves broadening the spatial definition of convolution, which requires rearranging the vertices into specific grid-like configurations suitable for conventional convolutional operations. Another crucial avenue of exploration lies in graph-based convolutional operations, which leverage the inherent structure of graphs to perform convolutions directly on irregular data representations, such as road networks or social networks. This innovative approach allows for the incorporation of spatial dependencies and local connectivity patterns inherent in graph data, enabling more effective analysis and prediction tasks. The alternative approach operates within the spectral domain, leveraging graph Fourier transforms, often termed spectral graph convolution. This method applies convolutions in spectral domains, offering a promising avenue for graph convolution, particularly as subsequent studies have managed to reduce computational complexity from quadratic to linear. Based on spectral graph convolu  
Figure 1: Graph-structured traffic data, denoted as \(\mathrm{v_{t}}\), represents the current traffic status at time step \(t\) in matrix format.  
tion principles, denoted as \"G', it involves multiplying a signal \(x\) in \(R^{n}\) with a kernel \(\theta\), expressed as:  
\[\theta*Gx=\theta(L)x=\theta\Big{(}U\wedge U^{T}\Big{)}x=\bigcup\theta(\Lambda)U^ {T}x \tag{2}\]  
where \(U\) is the graph Fourier basis matrix, \(L\) is the normalized graph Laplacian matrix, \(\wedge\) represents the diagonal matrix of the eigenvalue of \(L\), and \(\theta(\Lambda)\) is diagonal matrix. This operation effectively filters graph signal \(x\) by convoluting it with the kernel \(\theta\) using the graph Fourier transform \(U^{T}x\).
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 4 Results and Discussion
### Latency Overhead Analysis
#### 4.2.1 Computational Latency  
The proposed method aims for seamless integration with the transformer architecture, leveraging its capability for parallel computation. The latency related to computation is primarily affected by the complexity of operations that must be performed in an encrypted state. Mathematically, the computational latency \(L_{c}\) can be expressed as:  
\[L_{c}=O(n\cdot d^{2}+m\cdot d) \tag{26}\]  
where \(n\) represents the sequence length, \(d\) the dimensionality of the model, and \(m\) the number of operations per layer. The method optimizes this aspect through effective algorithmic modifications that are compatible with the FHE scheme while maintaining the core functionality of the transformer model.
## 2 Design of Robotic Laser Welding Process for LNG Cargo
### How to Secure the Precision of Mobile Robots  
Precise positional movement of mobile robots in relation to the welding target is currently a challenging issue, particularly in expansive areas where minor errors in robot posture can lead to significant inaccuracies. To address this, it is crucial to accurately determine the initial position of the mobile robot. Many previous studies have explored mobile robot navigation, such as [7; 8]. In this study, an omni-directional wheel was used to achieve flexible motion in a narrow space. Although omni-directional wheels have been extensively studied as described in [9], their mechanical limitations make it difficult to achieve precise movement. To correct the position error of the mobile robot, a one-degree-of-freedom slide was added to the end welding head. This approach is different from conventional stationary laser-welding robots, where high positional accuracy is achieved through pre-workspace calibration. Instead, the aim of this study is to overcome position errors arising from movement and seam tracking errors through end-effector correction actions.  
To address the issue of determining the position and orientation of a mobile robot in complex indoor environments, QR codes are utilized. The robot analyzes the distortion of the QR codes within its field of view and adjusts its orientation accordingly to estimate and correct its position and orientation errors. Conventional image recognition methods may not be sufficient in such environments, and installing additional devices for high  
Figure 3: Mobile robot for laser welding consists of manipulator with laser welding assembly, robot controller, laser source, etc.  
cost indoor positioning is not practical in vast open spaces such as LNG cargo bays. In challenging conditions where installation of network devices and additional equipment is difficult, QR code-based location recognition provides a cost-effective and efficient solution for explicit robot position identification, including position correction. This method is particularly useful in situations with complex environments, such as those encountered in LNG cargo bays.  
The movement of the robot is also a challenging aspect, particularly in the case of LNG cargo tanks where the ground environment is comparable to outdoor conditions, making it difficult for the robot to move in a straight line at a consistent speed, which is essential not only for precise positioning, but also for achieving high-quality welding, as planned. There are no large robots available for laser welding within vast LNG cargo holds that are capable of movement between different levels. Although studies such as [10; 11; 12] have been conducted on robots with elevator-like movements for logistics and transportation purposes, laser welding robots face the challenge of accommodating very thick cables due to issues such as wiring, cooling, and power requirements. Technical and procedural solutions are required to address these issues.
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 3 Materials and Methods
### Preprocessing  
We applied preprocessing steps that are widely used in Arabic sentiment classification as follows:  
* Normalization: to unify different forms of some letters, we replaced \(\tilde{\text{(i)}}\), \(\tilde{\text{(>)}}\) and \(\tilde{\text{(<)}}\) with \(\text{(a)}\) and replaced \(\tilde{\text{(p)}}\) with \(\text{(h)}\), where the symbols correspond to Buckwalter Arabic transliterations;* Elongation removal: we removed repeated letters (used in social media to show strong feelings) and kept a maximum of two letters;
* Stop-words removal: we removed stop-words listed in both NLTK and Arabic stop-words libraries (we tested both options of removing or keeping stop-words and chose the best option);
* Cleaning: we removed numbers, URLs, punctuation, diacritics and non-Arabic letters. These steps were needed for TF/IDF text representation only. For AraBERT-based experiments, we kept the original texts and used the preprocessing method of AraBERT.
# Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
## 3 Experiments
### Experiment on Manga109
#### 3.1.3 Logical Reasoning Mechanism  
In this dataset, the logical inference machine identifies label noise data samples based on three rules as follows:  
* Rule one: \"One author only works for one publisher\".
* Rule two: \"One author only draws manga towards one target reader\".
* Rule three: \"One author only draws manga towards one genre\".  
In this dataset, several authors do not satisfy these rules, and the logical inference machine not take into account them. The logical inference machine identifies label noise data samples by checking rules in first-order logic (FOL) format. To realize this, first of all, three databases: \"author-publisher database\", \"author-target database\", and \"author-genre database\" consisting of many predicates are built using SWI-Prolog [31]. The \"author-publisher database\" lists the publishers that each author works for, using the predicates company \"company (authorA, companyB)\". The \"author-target database\" lists the target readers of each author (young man, girl,...), using the predicates target \"target (authorA, targetB)\". The \"author-genre database\" lists the genre of each author (battle, sports, lov-romance, etc.), using the predicates target \"genre(authorA, genreB)\". Furthermore, each image in the non-expert training set also belongs to one publisher, target reader, and genre. Therefore, three other databases: \"image-publisher database\", \"image-target database\", and \"image-genre database\" are also built the same way as for authors.  
During the training phase, the classification result of the temporary model, \(Model_{t}\), is converted into predicates, as shown in Figure 6. The first argument, receiving values  
Figure 4: The distribution of number of samples each class in Manga109 dataset.  
Figure 5: Architecture of classification model  
\"img0, img1, img2\", corresponds the images included in the mini-batch data \(X_{t}\), and the second argument, receiving values \"kazuna-kei, deguchi-ryusei, shinzawa-motoei\", are the predicted names \(Y_{t}\) of the authors that drew the corresponding images.  
The Logic Inference Machine checks each image of mini-batch based on its predicted author to determine if that result violates any rules. Figure 7 shows an example for the rule one. First, the Logic Inference Machine finds the company, \(Company\), that owns the image \(IMG\) in the image-publisher database. It then checks if the predicted author, \(Author\), works for different publishers by the predicate \"workFor(Author, Company)\". This predicate returns \(True\) if \(Author\) works for another company, and the \(IMG\) is considered violating the rule one. Other rules are similarly implemented. Each image is checked with each rule, and is added to \(D_{t}\) if it violates one of the three rules.
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
## 6 Conclusions  
In order to demonstrate the significance of spinning magnetic bearings for achieving stable pointing of loads, this work first offered a novel seven-degree-of-freedom Lorentz force stabilized platform. Next, it simulated the dynamics of rotating magnetic bearings. Analyzing the structural characteristics and functionality of the spinning magnetic bearing allowed for the creation of an adaptive controller based on the RBF network. The experiment's results showed how effectively the adaptive controller tracks angular velocity signals, suppresses disturbance signals, and tracks angular displacement signals. This study is crucial for improving the pointing stability and precision of the satellite/payload since it offers solutions for related problems in the realms of laser communication and high-resolution reconnaissance.  
Figure 12: Angular velocity tracking performance. (**a**) Angular velocity tracking chart. (**b**) Error curve.  
**Author Contributions:** F.C.: writing--review and editing, software, data curation; W.W.: writing--original draft preparation, funding acquisition, formal analysis, project administration; S.W.: methodology, validation, resources. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the National Natural Science Foundation of China (no. 52075545).  
**Institutional Review Board Statement:** This study did not require ethical approval.  
**Informed Consent Statement:** This study did not involve humans.  
**Data Availability Statement:** In this study, no new data were created.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Comparison of Ice Velocity Products  
#### 3.3.1 Individual Measurements  
The large number of acquisitions allows the formation of dense time series to observe temporal variations in ice velocity over shorter periods. We compare results from offset maps (without mosaicking) in a time series of Zachariae Isstrom, in Northeast Greenland. Figure 4 shows the evolution of ice speed of Zachariae near its 2015 grounding line using historical data [34], Landsat-8 (blue dots) and Sentinel-1 (orange triangles). The two datasets, Sentinel-1 and Landsat-8, are consistent with each other and with measurements from preceding years based on TerraSAR-X and RADARSAT-2. The number of measurement points increased significantly with Landsat-8 and Sentinel-1. Landsat-8 is only acquired in summer and is complemented by Sentinel-1a year round. In the case of Zachariae Isstrom, we observe that the glacier did not exhibit seasonal velocity variations until about year 2005 when we detect significant seasonal variations, up to 20%, which increase till present. We attribute this behavior to the progressive disappearance of its floating ice shelf and to the increase in calving events and subsurface melting by the ocean of its ice front [34]. A similar behavior was observed on Jakobshavn Isbra after its ice shelf broke off in year 2002 whereas no seasonal variation was detected on this glacier in the past [35; 36].  
Figure 4: _Cont._
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### Architecture
This allows for detailed and precise information to be provided to the competent authorities for a quick response and efficient management of the emergency. As we can see in Figure 2, heterogeneous devices communicate and collaborate with each other to accomplish specific tasks, supported by a publish-subscribe mechanism to exchange information through topics. The next section will describe all the frameworks used for the development of our architecture. We will provide a thorough explanation of each node, what data it processes, the important code snippet and algorithm, and how it interacts with other nodes and have a complete overview of the architecture.
## 5 Experiments
### Experimental Results
#### 5.4.4 Effect of Initial Translation Distances on Registration Results  
In this experiment, the initial rotation angles were fixed at \([-90^{\circ},90^{\circ}]\), and the initial translation distances were systematically varied from \([-0,0]\) to \([-20,20]\) to explore the relationship between registration results and initial translation distances. The experimental results are shown in Figure 6.  
The analysis reveals that all algorithms demonstrate a lack of sensitivity to variations in initial translation distances. For rotation errors, the results of SGC, NIGCN, GDC, and our method are basically unaffected by changes in initial translation distance, maintaining a consistent level of rotation error. This observation suggests that these algorithms exhibit commendable stability across different initial translation distances. In contrast, our method showcases enhanced robustness and more consistent outcomes when addressing varying initial rotation angles and translation distances. Furthermore, our approach reaches a stable state without the need for iterative processes, thereby exhibiting greater efficiency. Specifically, our method achieves a standard deviation of translation vector error of \(3.56\times 10^{-4}\), which is nearly an order of magnitude lower than the standard deviation of \(2.35\times 10^{-3}\) observed in the suboptimal method, indicating a significantly higher level of stability. Figure 7 shows the registration results of two images with an initial translation error of \(10\) m. The color value of the vertex represents the normalized value of the distance error. The redder the color, the larger the relative error, and the lighter the color, the smaller the error.  
Figure 5: Effects of initial rotation angle on rotation error, translation error, and running time.