esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Simultaneously, based on the proposed lightweight Q-learning algorithm, the optimal number and position of reporting nodes for different paths in multi-service scenarios are dynamically solved, achieving efficient awareness timeliness and flexible dynamic selection of reporting nodes in LEO satellite networks. The main contributions of this paper are as follows:  
1. An APINT model is built for LEO satellite networks. Combining active and passive telemetry, the awareness path is shortened. Additionally, the optimal location for the reporting node is selected based on constraints related to the transmission time and status of SGLs, thereby reducing awareness time and enhancing awareness timeliness. 2. A dynamic reporting nodes selection method of network awareness based on active-passive integrated network telemetry (DRNSM-APINT) is proposed, introducing the concept of virtual boundaries to divide the awareness path into segments, and using the APINT model to find the optimal reporting node of each segment. By comparing the selection results of reporting nodes across various virtual boundary settings scenarios, the best reporting solution was chosen for the current awareness path, solving the problem of flexible selection of multiple reporting nodes. 3. A multi-reporting nodes selection algorithm based on lightweight Q-learning for various services is proposed. The dynamic pre-planning of virtual boundaries mitigates the computational complexity of Q-learning solutions. The proposed lightweight Q-learning algorithm dynamically determines the optimal number and location of reporting nodes for different service paths, achieving efficient timeliness of awareness and flexible selection of reporting nodes in dual time-varying scenarios of service and path. The remainder of this paper is organized as follows: Section 2 introduces the composition of LEO satellite network and presents the APINT model. Section 3 provides the details of optimized selection method for multiple reporting nodes. Section 4 introduces a lightweight Q-learning algorithm based on pre-planned virtual boundary. Section 5 validates the proposed method through simulation experiments. Section 6 provides a brief summary of the paper.
## 4 Hybrid H\({}_{\infty}\)/SMC Controller Design
To obtain the reaching condition of the SMC, a candidate quadratic Lyapunov function is suggested to be  
\[V_{L}(t)=\frac{1}{2}\;\mathbf{s}^{2}(t) \tag{44}\]  
To ensure the stability of the sliding manifold and the overall system rigorously, the time derivative of \(V_{L}(t)\) has to be negative definite for all \(\mathbf{s}\
eq 0\), so that \(V_{L}(t)\) keeps decreasing along any trajectory in the state space which, in turn, guarantees the stability of the sliding mode. It implies that  
\[\mathbf{s}(t)\dot{\mathbf{s}}(t)<0 \tag{45}\]  
The discontinuous term of the control law (\(\mathbf{u}_{\mathbf{d}}(t)\)), which ensures the systems converge to the desired equilibrium point by imposing the negative definiteness of \(\frac{dV_{L}(t)}{dt}\). in the presence of parametric uncertainty, disturbance, and noise, can be obtained by substituting Equations (19), (40) and (43) in Equation (45) and replacing the uncertainty matrix by its upper bound \(Q\):  
\[\mathbf{s}(t)[G\;Q\mathbf{x}(t)+GB\;\mathbf{u}_{\mathbf{d}}]<0 \tag{46}\]  
\[\mathbf{u}_{\mathbf{d}}(\mathbf{t})=-\Big{(}\;GB)^{-1}[(|G|\;Q|\mathbf{x}(t)| +\eta)sgn(\mathbf{s}(t))\Big{]}<0 \tag{47}\]  
where the sign function (\(sgn(0)\)) is used to bring the states back to the sliding manifold whenever they attempt to leave it due to uncertainty, and \(\eta\) (\(2\times 2\) diagonal matrix in this system) is a design parameter added to provide protection against disturbance and noise:  
\[\eta= \begin{bmatrix}\eta_{11}&0\\ 0&\eta_{22}\end{bmatrix}  \tag{48}\]  
Instead of using trial and error to find good values of the diagonal elements of \(\eta\), optimization is suggested, to tune them by searching for their optimal values that lead to both good robustness and performance of the system. Finally, PSO is applied to enhance the performance of the robust control system in terms of response errors and the overall energy of the needed torques. These characteristics are used to form the cost function  
\[J_{O}=\sum_{i=1}^{3}\int_{0}^{t_{f}}t\;|e_{i}(t)|dt+\sum_{i=0}^{2}\int_{0}^{t _{f}}t_{mi}^{2}(t)dt\;. \tag{49}\]  
where \(J_{O}\) represents the optimization cost function, \(e_{i}(t)\) represents the \(i^{th}\) link's angle response error, and \(t_{f}\) represents the final time used in simulations. The ITAE performance index of error is used to penalize the steady-state errors more than transient errors. Since the lower the control energy required, the more efficient the control system, then the ISCS performance index of the control signal is used.
## 4 Experiments and Analysis of Results
### Case Study 1: CWRU Dataset  
This case utilizes a bearing dataset published by Case Western Reserve University (CWRU), which is widely used to evaluate bearing fault diagnosis performance and is publicly accessible through the Bearing Data Center website. The data were collected using the equipment depicted in Figure 6, which includes motors, torque sensors/encoders, and control electronics. The dataset comprises vibration signals for four bearing states: ball fault (BF), inner ring fault (IRF), outer ring fault (ORF), and normal (N). Each condition includes various damage diameters (e.g., 0.007, 0.014, 0.021 inches), and the sampling frequencies available are 12 kHz and 48 kHz.  
In this paper, vibration signal data for four bearing states sampled at 12 kHz were selected. This selection resulted in a total of 10 bearing state categories. Each signal was divided into sample blocks of length 4096, and overlapping sampling (with an overlap size of 2048) was applied to mitigate boundary effects during preprocessing. A detailed description of the data is provided in Table 3.
## 5 Discussion
### Differences of Scan Matching at Sea and on Road  
The research on scan matching is mostly based on the land, such as wheeled robots, cars, and so on. Compared with the motion on land, the ASV sailing at sea is more violently affected by wind and waves, the motion is more complex, and it is more difficult to achieve accurate registration. In addition, the laser of the LiDAR hits the water surface, most of which is absorbed by water, and a small part of the mirror is reflected without an echo. Therefore, the laser point incident on the sea surface is almost not reflected back to the LiDAR. Compared with the LiDAR data on land, the point clouds scanned by the LiDARon the sea have no land information, and the number and characteristics of the point clouds are smaller, which further increases the difficulty of registration.
## 4 Discussion  
### Applications and Expected Effect  
An autonomous driving robot was employed in this study for underground mining using the developed road sign recognition algorithm; the robot not only drove in a straight tunnel, but also selected a path to drive at the intersection by recognizing the road signs without a global map. It was feasible to perform multipoint path planning to return to the tunnel entrance. In addition, if path-following technology can be used to drive and return to a desired point in an area that is difficult for humans to access and where the driving route changes frequently, such as in underground mines, the utilization of autonomous robots will be useful in fields such as safe exploration and tunnel surveying.  
Even if there is not enough learning image data for road signs due to the environmental characteristics of underground mines, if an image matching algorithm that applies only a single image as training data is used, road signs can be recognized efficiently. In addition, a stable recognition performance can be maintained if the geometric matching algorithm most suitable for an underground mining environment is used.
## 3 Results
### LSS Adoption in the Manufacturing of Chemicals  
As seen, the area delimited by LSSM covers similar sectors and follows a publication trend comparable to that of LSS. In this section, a special focus on the application of LSS in the chemical manufacturing industry is given. Chemical manufacturing holds great potential for the adoption of these methodologies. The widespread use of chemical products across multiple fields represents a clear example of customer service, making this sector a promising candidate for the implementation of LSSM. Nevertheless, the search carried out on this topic shows a dramatic decrease in the number of research papers compared to other manufacturing sectors (Figure 6). This suggests that the application of the LSSM techniques in this sector has not yet been widely developed. Figure 5: Network visualization of keywords cited in LSSM more than 30 times. Groups of repeated keywords are shown in the same color (clusters). Figure 6: Number of published articles between 2002 and 2022 on the application of LSS in manufacturing and non-manufacturing environments (**left**), and on the top ten subject areas of LSSM adoption (**right**). By searching LSSMch in the Scopus database, the results hardly offered 22 scientific papers published between 2002 and 2022. This clearly means that the research and development of LSS methodology applied to the chemical manufacturing environment is in a very early stage. Table 10 shows the year of publication, citations, and the most important contributions of all works published on this topic. In short, 18 articles deal with sustainable development, 9 with innovation and infrastructure and 9 are related to nuclear facilities; it should also be noted that the most productive country is the USA (12 articles), followed by Malaysia (2 articles). The publication trend in the field of LSS applied to the manufacturing of chemicals does not show a positive trajectory. The first study on this topic was published in 2006, and there have been fluctuations in the number of studies over the years. The years 2011 and 2013 recorded the highest number of publications, with five and four studies, respectively. However, in the last six years, only four works have been published, three of which were published in 2020 and one in the last year. This indicates low adoption and limited development of LSS methodologies in the chemical manufacturing sector. Furthermore, it is notable that only half of the published works received more than 10 citations, suggesting a relatively low impact of these studies within the research community. Despite the promising results reported by other authors in different sectors and the clear advantages that this sector
###### Abstract  
Additive Manufacturing (AM) methods are generally used to produce an early sample or near net-shape elements based on three-dimensional geometrical modules. To date, publications on AM of metal implants have mainly focused on knee and hip replacements or bone scaffolds for tissue engineering. The direct fabrication of metallic implants can be achieved by methods, such as Selective Laser Melting (SLM) or Electron Beam Melting (EBM). This work compares the SLM and EBM methods used in the fabrication of titanium bone implants by analyzing the microstructure, mechanical properties and cytotoxicity. The SLM process was conducted in an environmental chamber using 0.4-0.6 vol % of oxygen to enhance the mechanical properties of a Ti-6 Al-4V alloy. SLM processed material had high anisotropy of mechanical properties and superior UTS (1246-1421 MPa) when compared to the EBM (972-976 MPa) and the wrought material (933-942 MPa). The microstructure and phase composition depended on the used fabrication method. The AM methods caused the formation of long epitaxial grains of the prior \(\beta\) phase. The equilibrium phases (\(\alpha+\beta\)) and non-equilibrium \(\alpha^{\prime}\) martensite was obtained after EBM and SLM, respectively. Although it was found that the heat transfer that occurs during the layer by layer generation of the component caused aluminum content deviations, neither methods generated any cytotoxic effects. Furthermore, in contrast to SLM, the EBM fabricated material met the ASTMF136 standard for surgical implant applications.  
SLM; EBM; Ti-6 Al-4V; electron beam melting; selective laser melting; microstructure +
Footnote †: journal: Applied Physics Letters  
_Article_
## 3 Conventional Geolocation Techniques
### Hybrid Technique of Geolocation Measurements
#### 3.7.2 TDOA/FDOA  
This technique is a combination of FDOA and TDOA measurements. Employing this measurement can increase the accuracy of geolocation and reduce the number of sensors needed. The Newton-Raphson method is the most common method used as the geolocation engine to solve this over-constrained problem [70,71].  
[PERSON] et al. demonstrated that the hybrid TDOA/FDOA measurement is more precise and better-performing compared to TDOA and FDOA measurements individually, which utilize three sensors. Figure (a)a shows the geometry of the geolocation technique based on the TDOA and FDOA measurements. Figure (b)b shows the CRLB comparison between TDOA, FDOA, and the hybrid (TDOA/FDOA) measurements [21].  
The cross ambiguity function (CAF) is used to determine the time of the emitted signal of TDOA and FDOA using at least two sensors. The CAF measurement is applied, and the surface is formed based upon functional offsets. From the CAF function, the dominant peak value formed on the surface determines the real computed values of TDOA/FDOA integrated into signals (\(signal_{1}\), \(signal_{2}\)) and maps these values with the CAF magnitude by means of sensor and emitter geometries [72].  
Figure 9: The geometry and Performance verification of hybrid TDOA/FDOA measurements compared to TDOA and FDOA individually [21].  
Figure 10a shows the geometry of the TDOA/FDOA geolocation measurement in terms of CAF. Mathematically, CAF can be formulated as  
\[\text{CAF}(\tau,f)=\sum_{N=1}^{n-1}sig_{1}[n]sig_{2}^{*}[n+\tau]e^{\frac{-2\pi nf }{\tau_{\text{s}}}} \tag{10}\]  
where \(\tau\) and \(f\) are time and frequency of arrival, \(sig_{1}[n]\) and \(sig_{2}[n]\) are analytic signals received at sensor #1 and sensor #2, the (\(*\)) denotes a complex conjugate, \(F_{\text{s}}\) is the sampling frequency of the collected signals [72,73].
## 3 The Gradient-Based Iterative Algorithm
\right]\] (24) \[Y(N) = [y(N),y(N-1),\cdots,y(1)]^{\mathrm{ T}}\] (25) \[\hat{\vartheta}_{k} = [\hat{\vartheta}_{s,k},\hat{\vartheta}_{\gamma,k},\hat{\vartheta }_{n,k}]^{\mathrm{ T}}\] (26) \[\hat{\phi}_{k}(t) = [\hat{\phi}_{s,k}(t),\hat{\phi}_{\gamma,k}(t),\hat{\phi}_{n,k}( t)]^{\mathrm{ T}}\] (27) \[\hat{\phi}_{s,k}(t) = [-\hat{m}_{k-1}(t-1),-\hat{m}_{k-1}(t-2),\cdots,-\hat{m}_{k-1}(t- n_{a}),\] (28) \[u(t-1),u(t-2),\cdots,u(t-n_{b})]^{\mathrm{ T}}\] \[\hat{\phi}_{\gamma,k}(t) = [\hat{m}_{k-1}^{2}(t),\hat{m}_{k-1}^{\mathrm{ T}}(t),\cdots,\hat{m}_{k-1}^{\mathrm{ \eta}_{\gamma}}(t)]^{\mathrm{ T}}\] (29) \[\hat{\phi}_{n,k}(t) = [\hat{v}_{k-1}(t-1),\hat{v}_{k-1}(t-2),\cdots,\hat{v}_{k-1}(t-n_{ d})]^{\mathrm{ T}}\] (30) \[\hat{m}_{k}(t) = \hat{\phi}_{s,k}^{\mathrm{ T}}(t)\hat{\vartheta}_{s,k},\ t=1,2,\cdots,N\] (31) \[\hat{v}_{k}(t) = y(t)-\hat{\phi}_{k}^{\mathrm{ T}}(t)\hat{\vartheta}_{k} \tag{32}\]  
The steps involved in computing the parameter estimate \(\hat{\vartheta}_{k}\) in the GI algorithm are listed as follows:  
1. Collect the input-output data \(\{u(t),y(t):t=1,2,\cdots,N\}\) and form \(Y(N)\) by Equation (25). 2. To initialize, let \(k=1\), \(r_{0}=1\), \(\hat{\vartheta}_{0}=10^{-6}1_{n_{0}}\), \(\hat{m}_{0}(t)=10^{-6}\), form \(\hat{\phi}_{1}(t)\) by Equations (27) to (30) and \(\hat{\Psi}_{1}(t)\) by Equation (24). 3. Form \(\hat{\phi}_{k}(t)\) by Equations (27) to (30) and \(\hat{\Psi}_{k}(t)\) by Equation (24). 4. Compute \(r_{k}\) by Equation (23) and update the estimate \(\hat{\vartheta}_{k}\) by Equation (22). 5. Compute \(\hat{m}_{k}(t)\) and \(\hat{v}_{k}(t)\) by Equations (31) and (32), respectively. 6. Compare \(\hat{\vartheta}_{k}\) with \(\hat{\vartheta}_{k-1}\): if \(\|\hat{\vartheta}_{k}-\hat{\vartheta}_{k-1}\|^{2}\leqslant\varepsilon\), then terminate the procedure and obtain the iterative times \(k\) and estimate \(\vartheta_{k}\); otherwise, increment \(k\) by 1 and go to step 3.
# Shadow Fleets: A Growing Challenge in Global
## 4 Approaches to Detecting Dark and Gray Fleet Characteristics
### Flag Choice  
The use of flags of convenience has long been a contentious issue in maritime trade, but it has taken on new significance in the context of sanctions evasion. Dark and gray fleets often register their vessels under flags of convenience, such as Panama, Liberia, and the Marshall Islands, among others. This choice is strategically made to benefit from the legal advantages and lax regulatory practices associated with these flags, facilitating the evasion of sanctions and reduced oversight of their operations [51]. Also, countries with less stringent regulatory oversight, such as Cameroon and Gabon, have been implicated in registering vessels linked to sanctioned oil trades, complicating enforcement efforts [52].  
Flags of convenience offer a regulatory environment that allows vessel operators to circumvent stricter labor, safety, and environmental standards that might be enforced under more stringent registries. This practice not only undermines international efforts to promote maritime safety and environmental protection but also complicates the enforcement of sanctions and other regulatory measures.
# Long-Term EEG Component Analysis Method Based on Lasso Regression
## 2 Methods
### EEG Component Decomposition and Feature Extraction
#### 2.2.2 PSD Feature Extraction  
As discussed in Section 1.1, [PERSON]'s method is commonly used in power spectrum estimation. It allows for the partial overlap of data segments and non-rectangular window functions, which could improve spectral distortion [29]. If the original length of \(N\) data is  
Figure 4: The process of EEG component decomposition and feature extraction.  
divided into \(K\) segments, the length of each segment is \(L=N/K\). The spectrum estimation of each segment is the total average, which is given by:  
\[PSD(\omega)\ =\ \frac{1}{K}\sum_{i\ =\ 1}^{K}\hat{P}_{i}(\omega) \tag{5}\]  
where \(\hat{P}_{i}(\omega)\), \(i=1,2,3,\ldots,K\) is the power spectrum of the \(i\)-th segment, which is defined as follows:  
\[\hat{P}_{i}(\omega)\ =\ \frac{1}{LV}\left|\sum_{n\ =\ 1}^{L}w(n)x_{i}^{c}(n)e^{-jwn }\right|^{2} \tag{6}\]  
where \(w(n)\) is the window function, in which the Hamming window is used, and \(V\) is the normalized definition of \(w(n)\).  
Finally, the power spectral density curve is obtained. The band power of the EEG rhythm is calculated by the average of the PSD value in the corresponding frequency bands as: \(\delta\) (1-3 Hz), \(\theta\) (4-7 Hz), \(\alpha\) (8-13 Hz), \(\beta\) (14-30 Hz), and \(\gamma\) (31-50 Hz). The frequency bands of the EEG signals are related to the different functional states of the brain: (1) the delta band (0.5-4 Hz) usually occurs during deep sleep; (2) the theta band (4-8 Hz) often occurs when the human brain is idle or meditating; (3) the alpha band (8-13 Hz) can be detected in a relaxed state; (4) the beta band (13-30 Hz) is linked to logical thinking, emotional fluctuation, vigilance, or anxiety; and (5) the low gamma band (30-50 Hz) will appear when people are usually very excited, excited, or strongly stimulated. Frequency bands represent different cognitive processes, which are used as indicators or features and are mentioned in a variety of literature [30; 31; 32].
## 2 Results and Discussion
### Insect Attraction to VOCsB  
In _C. lunulata_ (Figure 2a), 90% of males and 73.6% of females selected the VOCsB emitted by _M. morganii_ rather than those from the control; both results were statistically significant: \(\chi^{2}=6.40\), \(p=0.01\), degrees of freedom (df) = 9, n = 10, and \(\chi^{2}=4.36\), \(p=0.03\), df = 18, n = 19, respectively. This demonstrates that the VOCsB from _M. morganii_ attract both sexes. The results regarding attraction to the VOCsB obtained from _Klebsiella_ sp. were inconclusive, and therefore, studies are still in progress. In _C. barrerai_ (Figure 2b), 77.7% of males significantly preferred the compounds emitted by _Ci. freundii_ rather than those of the control (\(\chi^{2}=5.56\), \(p=0.01\), df = 17, n = 18). In regard to the VOCsB emitted by _K. oxytoca_, 61% of male individuals preferred these over the control; however, this result was not statistically significant (\(\chi^{2}=0.89\), \(p=0.34\), df = 17, n = 18). There were no attraction tests for females of this species due to the low number of female insects captured.  
In tests on the attraction of _C. lunulata_ to bacteria from _C. barrerai_ (Figure 3), 77.7% of males selected the VOCsB emitted by _Ci. freundii_ rather than those from the control (\(\chi^{2}=5.56\), \(p=0.01\), df = 17, n = 18), 61% of males selected the VOCsB emitted by _K. oxytoca_ rather than those from the control (\(\chi^{2}=0.89\), \(p=0.34\), df = 17, n = 18). For the females of _C. lunulata_, 77.7% selected the VOCsB emitted by _Ci. freundii_ rather than those from the control (\(\chi^{2}=5.56\), \(p=0.01\), df = 17, n = 18), and 73% of  
Figure 2: Attraction of _Cyclocephala lunulata_ and _Cyclocephala barrerai_ towards VOCsB from the bacteria resident of the genital chamber of females. Asterisks indicate significant differences in \(\chi^{2}\) test and \(p>0.05\). (**a**) Males (n = 10) and females (n = 19) of _C. lunulata_ attracted to VOCsB from _Morganella morganii_; (**b**) Males of _C. barrerai_ (n = 18) attracted to VOCsB from _Klebsiella_ _oxytoca_ and _Citrobacter freundii_.  
females selected the VOCsB emitted by _K. oxytoca_; however, this result was not statistically significant (\(\chi^{2}=3.56\), \(p=0.59\), df = 17, n = 18).  
Although is not possible the compounds are part of the pheromones, our results confirm that VOCsB act as attractants [7], because adults of _C. lunulata_ responded to VOCsB obtained from the bacteria isolated from _C. barrerai_; in particular, to those that had 2-phenyl ethanol, phenol, and sulfur derivatives. In this work, it was observed that _C. lunulata_ and _C. barrerai_ display attraction behaviors to VOCsB in the bioassays, walking against the wind towards the source of emission [60,61].
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 4 Simulation Results  
In this section, various simulation results are presented to validate the effectiveness of the proposed multi-UAV wildlife monitoring scheme. Two major factors considered as simulation parameters are the number of agents and exploration time (caused by energy limit). To compare the performance of the OT-based multi-UAV monitoring scheme with time-varying spatial distribution OT (TV-Gauss), two other exploration strategies are employed: Lawn Mower method with time-invariant uniform exploration, LM (TI-Uni), and OT-based multi-UAV monitoring strategy with time-invariant uniform distribution, OT (TI-Uni). For all simulation scenarios, the unicycle robot dynamics is considered for the UAV dynamics. A brief description of the unicycle robot dynamics is provided below.