esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods  
The AIS data structure is quite complex, consisting of 27 types of messages. Ship trajectories are recorded in messages of types 1, 2, 18, and 19, known as Position Report messages. Shore-based receiving stations capture these time-series data. Position Report messages contain information about the ship and its navigation, such as the seven-digit code issued by the International Maritime Organization (IMO) representing the ship's identifier and the 9-digit Maritime Mobile Service Identity (MMSI) issued by regional authorities. They also include the ship's position coordinates (longitude and latitude), speed over ground (SOG), course over ground (COG), heading (HDG), ship type code, and the time the base station received the message. In Taiwan, due to regulatory requirements, fishing vessels are required to install Vessel Monitoring Systems (VMSs), Digital Selective Calling (DSC) radios, and Emergency Position Indicating Radio Beacons (EPIRB). As a result, the data for fishing vessels often need to be completed or even contain null values. Furthermore, fishing vessels are typically distributed near shore and have disordered trajectories, so filtering out fishing vessels from the analysis is recommended. These Position Report messages represent the historical trajectories of ships at sea, providing valuable data for research and analysis. The data proportion for other vessels, such as passenger ferries, speedboats, and workboats, is less than 5%. Therefore, this study only uses the trajectories of cargo ships and tankers as research data. This section details the data used and the research area for this study. The study focuses on the busy waters west of Taichung Port (TC) in central Taiwan, with a spatial distribution ranging from 119.5 to 121 degrees east longitude and 23.5 to 25 degrees north latitude. This area connects the Taiwan Strait's north and south shipping lanes, linking Northeast Asia with Southeast Asia. The area has busy north-south shipping routes, as well as east-west routes to and from TC and Kinmen (KM) and the tracks along the coast of mainland China, as shown in Figure 1.  
Due to the vast amount of AIS ship trajectory data--take July 2020 as an example, when the total number of entries around Taiwan reached 320 million--employing fast and effective data cleaning methods is crucial for subsequent clustering analysis. Therefore, the experimental process in this work is divided into two stages: The first stage involves data cleaning, including trajectory segmentation, trajectory compression, and speed filtering. The second stage focuses on clustering analysis, involving the selection and normalization of three-dimensional features and an introduction to trajectory clustering algorithms. The overall research process framework is illustrated in Figure 2, and the details are explained in the following two paragraphs.
# Efficient Wheel-Rail Stick-Slip Numerical Modeling for Railway Traction Vehicles
## 3 Numerical Simulation Results
### Stick-Slip Simulation Using the Switch Model
Peak values exceed 1.5 times the mean value for the driving force, and this phenomenon is responsible for the axle damage. The value is not only remarkable in itself but also in correlation with the almost constant motor force. The culprit of this apparently incoherent dynamic is the friction force. The friction force plot is an illustration of the stick-slip mechanism. The discontinuous variation of friction at the transition from slip phase to stick is the cause of the huge variations in the drive force. The plot in Figure 7 exhibits such discontinuities at time moments around 0.025, 0.11, and 0.2 s. When the stick phase begins, friction is equal to the drive force and prevents the wheel from slipping on the rail as long as the adhesion threshold \(F_{sMax}\) is not surpassed. When the drive force is greater than the adhesion force, slip phase recommences (Equation (7)). Figure 7: System force variations during stick-slip oscillations; light blue for the driving force by \(F_{el}\), dark blue thick line for the friction force \(F_{p}\) and brown for the motor force \(F_{m}\). Corresponding to discontinuities of the friction force, gaps in acceleration value can be observed in its time history plot, shown in Figure 8. In the case of acceleration, at the slip to stick transition, the acceleration jumps to zero because friction equals the drive force in the stick phase (Equation (6)). The wheel begins to slip when the drive force becomes bigger than \(F_{sMax}\) and the absolute value of the acceleration increases again (Equation (7)).
###### Abstract  
In this paper, a new multidimensional Takagi-Sugeno (T-S) identification technique is proposed for multivariable nonlinear systems. In this technique, multidimensional membership functions are designed using concepts from solid mechanics. The design of membership functions is carried out in multidimensional space, defining the principal axes from the eigenvectors of the inertia matrix, and it has the characteristic of dividing the space into regions with the same inertia. These regions are analyzed to define the center of gravity for each rule. Illustrative examples of multivariable nonlinear systems, such as a thermal mixing process and a binary distillation column, are selected to evaluate the effectiveness of the proposed method. The proposed method is compared with traditional T-S identification that uses one-dimensional membership functions and shows a reduction in the relative identification error and the algorithm execution time. Additionally, the proposed method prevents rules from being positioned outside the system's range, thereby avoiding the generation of unnecessary rules.  
Keywords:Takagi-Sugeno model; nonlinear systems; multidimensional membership functions; multivariable identification +
Footnote †: journal: Applied Sciences
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 3 Preliminaries
### Elliptic Curve Cryptosystem  
Elliptic curve cryptography (ECC) is a public key encryption algorithm based on the mathematics of elliptic curves. It uses points on an elliptic curve and their operations (such as addition and scalar multiplication) to construct encryption and decryption mechanisms. The general form of the elliptic curve equation is \(y^{2}=x^{3}+ax+b\), where \(a\) and \(b\) are constants. Specifically, let \(q>3\) be a prime number, and define an elliptic curve over the finite prime field \(Z_{q}=\{0,1,\ldots,q-1\}\) as the set of solutions \(E_{q}(a,b)\) to the congruence \(y^{2}\equiv x^{3}+ax+b(modq)\), where \(a,b\in Z_{q}\) are constants satisfying the non-singularity condition \(4a^{3}+27b^{2}\equiv 0\ (mod\ q)\).  
Compared to traditional RSA algorithms, ECC can achieve the same level of security with shorter key lengths, significantly improving computational efficiency and reducing storage requirements. This advantage makes ECC particularly suitable for resource-constrained environments.
# Voting-Based Document Image Skew Detection
## 4 Tests and Results  
Our test dataset contains over 300 images representing both documents scanned by hand and digital documents converted into images. By introducing a random skew angle on each, we were able to correctly determine the accuracy of the system, knowing a priori what it should return. In Table 1, we show a benchmark performed on our digital dataset with introduced skew. The test dataset contains a large diversity of document formats: from illustrated textbooks to scientific papers, poetry books, working sheets, art magazines, newspapers, CAD parts and more. It has been carefully selected such that there is diversity in structure, fonts, content (images/text) and abundance of orientation information. This dataset has been processed by applying random skew angles to each image and repeatedly fed to all three algorithms and the voting process while measuring accuracy and performance for each step. The results shown in Table 1 contain the following:  
* execution time on a 64-bit Windows 7 system, i7-8550U CPU, using CPU-only OpenCV 4.01 routines, Microsoft Visual Studio 2017, with full compiler optimization for speed;
* average error, computed as the average absolute value of the difference between the known introduced skew angle and the skew angle detected by our system, for success cases, i.e., if the confidence is greater than some threshold (empirically chosen 50%);
* average confidence, taking into account both success and failure cases;
* uptime: the ratio between the number of success cases and the number of total cases. For fine-tuning the exponent parameters of each algorithm's confidence, we used the brute force search method, that is: for every triplet of exponents \((\tau_{1},\ \tau_{2},\ \tau_{3})\ \in\ [1-\varphi,\ 1+\varphi]^{3}\), discrete interval, we compute the average error for the entire dataset for every voting process. We picked \(\varphi=0.8\) and the step between elements in the interval \(\varepsilon=0.025\). The brute-force search results in the \((\tau_{1},\ \tau_{2},\ \tau_{3})\) space are presented in Table 2. Anyway, the system is not extremely sensitive regarding \(\tau_{1},\ \tau_{2},\ \tau_{3}\) values, delivering robust results for most combinations as long as \(\tau_{1}<\tau_{2}<\tau_{3}\). The Section Supplementary Materials contains a reference to the source code and the benchmarking dataset for this project.
## 3 Results and Discussion
### Prediction of Antibiotics Removal Efficiency in Absence of Br\({}^{-}\)In Synthetic Water
#### 3.3.1 Formation of Bromine in Seawater Ozonation  
Figure 4 shows the bromine concentration after injecting ozone into synthetic sea water containing bromide ions at pH 8. Reacting ozone with bromide ions in synthetic seawater, 3.1 mg/L of bromine was generated. A previous study [21] predicted ozone decomposition and bromine formation in synthetic seawater. The predicted ozone formation indicated that ozone decomposed rapidly within 20 s, while bromine was generated and became stable after 40 s. The predicted bromine formation of 3.1 mg/L is the same as the experimental value for a dosage of 1 mg/L ozone into water containing 60 mg/L bromide ions at pH 8.  
To assess the effect of antibiotics in water on bromine formation, the bromine formation was evaluated in water containing 1 uM of FF or OA. Table 3 shows the comparison of apparent rate constants at given condition. In the water containing 1 uM FF, 3 mg/L of bromine was generated with 1 mg/L ozone. However, in the water containing 1 uM OA, a lower bromine concentration (2.6 mg/L) was generated compared to that in the pH 8 buffer containing only bromide (3.1 mg/L). This is because the rate constant for the reaction of ozone with OA (2400 M\({}^{-1}\)s\({}^{-1}\)) is 15 times that with bromide ion (160 M\({}^{-1}\)s\({}^{-1}\)). Because ozone reacted competitively with bromide ion and OA, less bromine was formed in the water containing OA. In the case of FF, the reaction rate between ozone and FF was extremely low at 3.2 M\({}^{-1}\)s\({}^{-1}\). Therefore, the effect of FF is negligible in bromine formation.
## 7 Concluding Remarks and Future Work  
In this paper, we propose the MPMF routing policy which can perform efficiently and cost-effectively for message forwarding in OppNets. The MPMF policy is based on control-based replication that distributes only a limited number of copies of a message in the network. The MPMF policy selects the profitable relay node according to the nodes' moving direction and two-hop neighborhood information. Relays moving closer to a destination node and having higher weight values are selected to carry messages in the network. When relays in the vicinity of a message-carrying node are not qualified due to sparse node density and nodal motion status, the moving direction of the destination node, the location information of a one-hop node, and a centrality value of a node are jointly utilized to improve the message delivery service. The use of joint utilization design can avoid messages being kept longer in a buffer in the absence of better candidate nodes. In comparison with Epidemic, TCCB, GSaR, TC, and PRoPHETv2, the MPMF policy is feasible for distributing long-TTL messages in OppNets, resulting in a low messaging overhead ratio and high message delivery rate. Our future study will focus on developing enhanced methods for relay node selection and will adopt additional application scenarios such as unmanned aerial vehicles into OppNets.
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 2 Background
### Multi-Agent Framework
#### 2.1.2 Markov Game  
When more than one agent is involved, an MDP is no longer suitable for describing the environment, given that actions from other agents are strongly tied to the state dynamics. A generalization of MDP is given by Markov games (MGs), also called stochastic games. A Markov game is defined by the tuple \((N,S,\{A^{i}\}_{i\in N},P,\{R^{i}\}_{i\in N},\gamma)\), where  
* \(N=1,2,3,\ldots,N\) is the set of \(N>1\) agents;
* \(S\) is the space observed by all agents;
* \(A^{i}\) is the action space of the i-th agent and \(A:=A^{1}\times A^{2}\times\cdots\times A^{N}\) is called the joint action space;
* \(P:S\times A\rightarrow\delta(S)\) is the transition probability to each state \(s^{\prime}\in S\) given a starting state \(s\in S\) and a joint action \(\mathbf{a}\in A\);
* \(R^{i}:S\times A\times S\rightarrow\mathbb{R}\) is the reward function of the i-th agent representing the instantaneous reward received, transitioning from \((s,a)\) to \(s^{\prime}\);
* \(\gamma\in[0,1]\) is the discount factor.
# Electroless Copper Patterning on TiO\({}_{2}\)-Functionalized Mica for Flexible Electronics
## 2 Materials and Methods
### Characterization Methods  
Surface morphology was observed using a TESCAN Lyra I XMU scanning electron microscope (SEM) (Brno, Czechia) operating at 20 kV. Atomic force microscopy (AFM) was performed using a Nanosurf FlexAFM (Liestal, Switzerland). Energy-dispersive X-ray (EDX) analysis was conducted using a Quantax 200 EDX detector (Bruker Nano GmbH, Berlin, Germany) coupled to the SEM. Raman spectroscopy was performed using a TO-ERS-532 spectrometer (Thunder Optics, Montpellier, France) with a 532 nm laser source and a \(\times\)20 objective. UV/Vis transmittance spectra were measured using a SP-V1100 spectrometer (DLAB Scientific Co., Ltd., Beijing, China). X-ray diffraction (XRD) patterns were collected using a D8 Advance diffractometer (Bruker AXS GmbH, Karlsruhe, Germany) with a CuK\(\alpha\) source and LynxEye PSD detector. The sheet resistance of the samples was measured using a Veeco FPP-5000 four-point probe (Veeco Instruments Inc., Plainview, NY, USA).
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 3 Results and Discussion  
### Distribution of Lateral Earth Pressure in T Mode  
As shown in Figure 7, the coordinate of schematic diagrams of the trap-door test was determined and is discussed in the following. The center of the movable plates for each trap-door test was selected as the zero point of the abscissa. The earth pressure at the height of 30 cm was selected for analysis while the data at other heights were similar. The maximum distance of the trap-door was marked as \(S_{\text{max}}\), the height of the trap-door was noted as \(H\), and the width of trap-door was \(w\), as shown in Figure 3. The coordinate diagrams in other displacement modes were the same. Because the load cells were only arranged on the semi-length wall, the earth pressure data on the other half length could be recorded symmetrically.  
The distribution of the lateral earth pressure along the X axis under the condition of different widths of T mode is shown in Figure 8. Once moveable plates of any width produced an outward translation movement, earth pressure behind them decreased rapidly. With the development of movement, the minimal pressure behind moveable plates approached 0. However, the earth pressure behind the fixed wall increased sharply and surpassed the static earth pressure. It can be seen that the distribution of the lateral soil pressure along the X axis was approximately an \"inverted bell\" curve. The gap of peak pressure behind the fixed wall in different trap-door widths was less than 11%.  
Figure 6: Experimental process.  
Figure 7: Schematic diagrams of coordinate axes. (**a**) The width of trap-door was 20 cm and 40 cm. (**b**) The width of trap-door was 10 cm and 30 cm.  
The primary reason accounting to this phenomenon is uneven deformation of the soil, which meant that the deformation of soil behind the movable plates was larger than that behind the fixed wall. The horizontal soil arching effect was thus generated in the soil body [6], as shown in Figure 9a. The soil arching effect can redistribute the soil stress and can fully mobilize the shear strength. The shearing resistance caused by uneven displacement reduced the earth pressure on the yielding soil and increased the pressure on the adjoining stationary part. The redistribution of earth pressure behind the movable plates and fixed wall, as shown in Figure 9b, revealed that the earth pressure behind moveable plates would be lower than the static earth pressure, \(p_{0}=k_{0}\gamma h\). However, the earth pressure behind the fixed wall would be higher than the static earth pressure, \(p_{0}\).
## 2 Materials and Methods
### Materials Characterization  
Scanning electron microscopy (SEM) was utilized for the purpose of sample analysis. It provides information about the surface topography and composition of the sample. The experimental procedure was performed using NovaNanoSEM 450 microscope (FEI Europe B.V., Eindhoven, The Netherland). Microscopic images were taken using the following detectors: TLD (Through the Lens Detector, topographic contrast) and CBS (Circular Backscatter Detector, material contrast) at accelerating voltages of 5 kV and 15 kV, respectively. Distance to sample (Working Distance, WD) was 4 mm for TLD detector and 5 mm for CBS.  
To determine the specific surface area of the initial powders, the BET (Brunauer-Emmett-Teller) method was used, with nitrogen (N2) being the widely accepted standard adsorptive [36]. For that purpose, the device BELSORP-mini II produced by BEL Japan, Inc. (Toyonaka, Japan) was employed. All samples were measured by degassing at 100 \({}^{\circ}\)C for 3 h.  
The elemental microanalysis was performed using Octane energy dispersive X-ray detector (AMETEK, Inc., Berwyn, PA, USA). The applied Octane Elect Plus silicon drift detector (SDD) was equipped with a chip of 30 mm\({}^{2}\) area. The X-ray diffraction analysis (XRD) was performed using Philips X'Pert Pro Materials Powder Diffractometer (Philips Analytical, now PANalytical, Almelo, The Netherlands) in order to evaluate the phase composition of the zirconia powder. The CuK\(\alpha\) radiation source was used in XRD, with an anode voltage of 45 kV and a current of 40 mA.  
The strength analysis was based on the results of three-point bending strength tests and Vickers hardness measurements. For that purpose, Bending Testing Machine DELTA 5-200 was used (Form+Test Seidner&Co. GmbH, Riedlingen, Germany). For the hardness, the tester type FALCON 400G2 produced by Innovatest (Maastricht, The Netherland) was applied.  
Figure 2: Electroconsolidation apparatus: (**a**) Overall view; (**b**) Disassembled graphite mold. 1—Pressing unit, 2—Built-in mold, 3—Vacuum chamber, 4—Electrical current filter, 5—Temperature control unit, 6—Current transformer.
## 2 Literature Review
### Related Work
In [37], the author proposed a machine learning approach to predict the optimal storage format for the SpMV kernel on a GPU (COO, CSR, ELL, and HYB). Support vector regression (SVR) and multilayer perceptron neural network (MLP) were used, and the performance results of the experiment on two separate GPUs (Fermi GTX 512 and Maxwell GTX 980 Ti) show that the average prediction error ranges between 7% and 14% [38]. In [39], a machine learning-based tool was proposed to automatically predict the best process mapping strategy and data distribution based on the structure of the matrix. [PERSON] et al. [37] proposed high level abstraction of the structure of sparse matrix based on fitting blocks of non-zero elements to convolutional neural network (CNN). The results demonstrate the robustness of CNN model and predicted the performance of CNN on intel HASWELL core and ARM A57 core. [PERSON] et al. [38] proposed CNN based model to estimate the execution time of SpMV and energy consumption of DRAM at variable processor frequencies with over all relative error ranges between 0.5% to 14%. [PERSON] et al. [40] proposed Block CSR based optimization of SpMV for sparse matrix with dense block substructures. They proposed algorithms for both CPU intel many integrated core and GPU architectures and demonstrated that the performance of SpMV up to 4X faster than NVIDIA and cuSPARSE, 3x faster than MKL, and 147X times faster than intel math kernel library. The machine learning based optimizations for SpMV computation mostly focused on selection of optimal storage format, execution time or performance prediction and some efforts dedicated to energy consumption. Achieving higher performance usually requires carefully choosing the sparse matrix storage format and fully utilizing the underlying system architecture [41; 42; 43]. Over the years, SpMV's optimization has revolved around ideas of improving memory bandwidth, matrix representation, instruction throughput, matrix reordering, cache, and register blocking, etc. The structure of the matrix is an unknown entity before runtime which motivates the idea of using machine learning based approach to predict the optimal block size before actual execution of SpMV computation using BCSR storage format. Block sizes are selected randomly and manual selection is time consuming and error prone. Even after trying different combinations of block sizes (random and manual selection in practice), we may settle with the block size which is not optimal. To the best of our knowledge, our work is the first attempt at using machine learning techniques to optimize the performance of the SpMV kernel using the BCSR storage format and predict the near-optimal block size based on the structure of the matrix.
## 2 Related Work
### Mobile Sink  
Another problem in WSNs is the hot-spot issue, in other words, sensor nodes near the sink node tend to drain their battery more quickly than other nodes. A mobile sink is an effective solution to this problem. In mobile sink methods [3], a sink node can move in the network. Then, routes between sensor nodes and the sink node change accordingly, which distributes the load of packet forwarding among sensor nodes. Nevertheless, a mobile sink introduces two new problems. One is routing overhead. Here, each sensor node maintains a route to the sink node and this route needs to be updated when the sink node changes its position. The other is the increase of a node's active time in monitoring aperiodic queries from the sink node. With a fixed sink and periodic data collection, each sensor node can perform sleep/wake-up scheduling to save power. However, with a mobile sink, each sensor node does not know when a query from the sink node will come and needs to be awake for a longer time.