esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Traditionally, fleet management in mining has relied on fixed routes and manual dispatching, which, while the cheapest to start, become costly over time due to their inefficiencies. In contrast, real-time fleet management systems offer unparalleled optimization but come with high costs and slow implementation, best suited for large mines with significant capital. A computer vision-based approach emerges as a compelling alternative--relatively inexpensive and faster to deploy, delivering actionable real-time insights for small-to-medium fleets. Compared to the CV approach as a baseline, real-time fleet management systems can range from 6 to 30 times more expensive per unit, reflecting their comprehensive hardware, software, and integration demands. For mining operations aiming to balance cost and efficiency without the complexity of enterprise solutions, CVprovides a practical, scalable starting point. For real-time fleet management by CV, it is critical to collect and use data from video recordings of operations. This study uses CCTV recordings for object detection to identify trucks and excavators, spatial-temporal analysis for equipment locations and interactions, and activity classification to classify activities like loading, hauling, or queuing. The YOLO model, a state-of-the-art object detection algorithm, is employed to analyze visual data from an operating open-pit mine.  
The steps of the method appear in Figure 1. The proposed system operates within a local CCTV network, ensuring continuous functionality without reliance on external internet connectivity. All video feeds from the CCTV cameras are transmitted to a Network Video Recorder (NVR) located in the system center. The NVR subsequently forwards the recorded footage to the central system computer, where the object detection algorithm processes the video data locally. The method focuses on setting the start times of excavators and trucks at dump places to help pick the best truck-to-excavator match-ups. This picking process is formulated as a two-goal math task: to cut down on the wait time of excavators and to lower the deviation from daily targets. To check the trade-offs between these goals, the epsilon limit method is used, making the Pareto line clear. This way shows the possible swaps and aids in making choices to keep work speed good while achieving work targets.
# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
## 1 Introduction
Among the sensors discussed earlier, the vibration sensor was chosen for the cutting force estimation algorithm because it does not require high sampling rates, effectively reflects tool changes through the vibration of the spindle near the tool, is free from structural limitations, and is both easy to install and cost-effective. Section 2 experimentally examines the relationship between cutting force and tool wear. Section 3 introduces the proposed method for cutting force estimation based on the LSTM time-series machine learning model. Finally, Section 4 evaluates the training process and performance of the cutting force estimation model, validating its estimation accuracy at other machining locations.
# Conceptual Design of an Extended Reality Exercise Game for the Elderly
### Aim and Research Question  
Based on the results of the previous studies, we find the gap between this paper and relevant previous work that motivates our aim and objectives. To discover the application and evaluation of VR exercise games for the elderly, this study proposes a conceptual design of an in-home VR exercise game for this user group, thereby aiming to improve their physical and mental health. As such, the primary research question is listed as follows:  
* RQ: How can one design an appropriate extended reality exercise game for the elderly?  
To address the research question, we present a novel conceptual design for a VR-based rowing exercise game for the elderly, _VRrowing_, and its system architecture, game structure, and user interface.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Schedulers  
MPTCP schedulers are mainly responsible for ensuring in-order delivery at the receiver. Among the available SFs, an MPTCP scheduler decides through which SF data should be sent so that no HoL blocking occurs at the receiver. We tested with five widely accepted MPTCP schedulers, as discussed followingly.
## 3 Results and Discussion
### Adsorption Kinetics  
In order to investigate the variation in the adsorption amount of the ionic liquid-modified magnetic polymer on FCF with time during the adsorption process, the kinetic behavior of the polymer on FCF was investigated, and the results are shown in Figure 9. It can be seen that the initial adsorption rate of the polymer to FCF is fast, the adsorption amount increases rapidly, and then the adsorption gradually slows down, and finally reaches an equilibrium state. This process is related to the gradual saturation of the adsorption sites of the polymer. In order to further illustrate the adsorption kinetics of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM on FCF, the pseudo-first-order kinetics and pseudo-second-order kinetic models were used to fit the adsorption kinetic data. The equations are as follows:  
\[\text{Pseudo-first order kinetic equation: Q}_{t}=\text{Q}_{e}(1-\exp^{-\text{k}_{1}t}) \tag{3}\]  
\[\text{Pseudo-second-order kinetic equation: Q}_{t}=(\text{k}_{2}\text{tQ}_{e} ^{2})/(1+\text{k}_{2}\text{tQ}_{e}) \tag{4}\]  
In the formulae, \(\text{Q}_{e}\) (mg/g) is the equilibrium adsorption capacity, \(\text{Q}_{t}\) is the adsorption capacity at time, and \(\text{k}_{1}\) and \(\text{k}_{2}\) are pseudo-first-order and pseudo-second-order rate constants, respectively.  
The adsorption kinetic simulation parameters are shown in Table 2. It can be seen from the data in the table that the adsorption behavior of the polymer to FCF is more in line with the pseudo-second-order kinetic equation (R\({}^{2}\) > 0.99). The results indicate that the FCF molecule from the solution to magnetic polymer surface is controlled by a chemisorption step. Consistent with the previous corollary, \"magnetic polymer adsorbs FCF mainly through electrostatic interaction between protic N on imidazole group and FCF\". The adsorption of magnetic polymer to FCF almost reached the maximum adsorption effect within 120 min. The equilibrium adsorption capacity fitted by the model is essentially consistent with the experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbate**} & \multicolumn{2}{c}{**Pseudo-First-Order Model**} & \multicolumn{2}{c}{**Pseudo-Second-Order Model**} \\ \cline{2-7}  & \(\mathbf{k_{1}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{R_{1}}^{2}\) & \(\mathbf{k_{2}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{R_{2}}^{2}\) \\ \hline FCF & 0.1966 & 190.8 & 0.988 & 0.00215 & 196.7 & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adsorption kinetics simulation parameters.  
Figure 9: The adsorption kinetics of FCF on \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM.
# Inner-Loop Controllers for Grid-Forming Converters
## 2 Materials and Methods
### Proposed Inner-Loop Voltage Controller with Current Limiter
#### 2.1.5 Virtual Impedance \(\times\) Virtual Admittance  
For comparison purposes, Figure 13 presents the virtual impedance and virtual admittance plotted at the same bode diagram. It can be observed that the magnitude and phase of the inverter with virtual impedance and admittance are similar below and around the grid nominal frequency, which in this case is 60 Hz.  
Alternatively, in the high-frequency range, the phase of the virtual impedance resulting from the Thevenin equivalent decreases at a higher rate compared to the Norton equivalent implementation. This is due to the fact that the Norton equivalent implementation of the inner-loop virtual impedance has a lower order. Consequently, fewer instabilities are expected in this frequency range when connecting multiple inverter-based DERs within the same network.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 3 Construction of EB-YOLOv8
### Embedding Multi-Scale Attention Mechanisms to Strengthen Feature Attention  
The small size of the target and the high similarity or complexity of the background can sometimes cause YOLO-series models to fail to focus on target details when detecting surface defects in steel, resulting in missed detections and false positives. The attention mechanism [24] is a data processing method that has made significant progress in solving the problem of detecting tiny features in small targets. They focus on important information and select key details from a set of information. These mechanisms can help models focus better on key features of the target. Additionally, incorporating appropriate attention mechanisms can reduce data fluctuations during model training and enhance model stability. However, EMA stands out among other attention mechanisms because it efficiently enhances features through group reconstruction, parallel multi-scale learning, and cross-spatial interaction. This makes EMA more suitable for steel surface defect detection tasks.  
Based on the above analysis, this paper introduces the efficient multi-scale attention (EMA) module, which is embedded in the C2f module (Figure 4). The EMA module is based on cross-spatial learning. The module outputs multi-scale feature maps that are grouped by channel dimension. C represents the number of channels, and H and W represent the height and width of the feature map, respectively. The parallel structure consists of two 1 \(\times\) 1 convolutions that extract micro-defect edges and a 3 \(\times\) 3 dilated convolution that captures the global context information of macro-defects. The 3 \(\times\) 3 branch uses a single 3 \(\times\) 3 convolution kernel to capture multi-scale feature representations and expand the feature space. The two 1 \(\times\) 1 branch paths implement channel encoding operations with a sigmoid function applied to the output of the 1 \(\times\) 1 convolution. The two-channel attention maps within each group are aggregated via simple multiplication, enabling cross-scale detection. Next, two-dimensional global average pooling (as in Equation (1)) encodes global spatial information. This guides the adaptive weight map, which is generated by spatial attention, to act as a high-pass filter. The filter adjusts features, enhances defect edges, and significantly improves detection accuracy in low-contrast images. The sigmoid function reduces the weights of low-gradient background regions, addressing defect detection tasks in complex backgrounds. Additionally, it smooths out high-frequency random noise in the time dimension by weighting the model's weights, thereby reducing noise in gradient descent and improving the model's stability.  
\[Z_{\text{C}}=\frac{1}{H\times W}\sum_{i}^{H}\sum_{i}^{W}x_{c}(i,j) \tag{1}\]  
Figure 4: Embedded EMA structure.
## 2 Cybersecurity in the Maritime Sector
### Basic Concepts Related to Cyber Risk  
The notion of risk may differ among various industries but typically involves evaluating the probability and consequences of potential incidents that could impact assets [19]. In the context of Industry 4.0, risk managers have placed significant emphasis on \"cyber risk\", which pertains to potential damage arising from vulnerabilities in technological assets. Cyber-attacks are intentional actions carried out by malicious individuals aiming to disrupt the confidentiality, availability, or integrity of information systems [16]. These attacks may target maritime operations such as unauthorized access or deliberate sabotage, resulting in navigational errors or service disruptions [19]. Additionally, managers face cyber-physical risks, which involve the control of physical processes through digital components [17]. These risks arise when malicious actors manipulate computer systems that control physical infrastructure, such as pipelines or valves, potentially causing harm to both property and human lives [20].  
Assessing cyber risks entails comprehending the likelihood of incidents and their outcomes, including collisions, operational disruptions, and economic losses [21]. To conduct an effective risk analysis, it is necessary to evaluate both the likelihood and the potential impact of these events. The maritime industry faces unique challenges, in which cyber risks may have significant consequences. Failure can lead to higher expenses, delivery delays, and potential harm to both people and the environment, underscoring the importance of implementing a comprehensive cyber risk management strategy. For example, the 2017 NotPetya malware attack resulted in an estimated USD 300 million of  
Figure 2: SLR Methodology.  
direct economic damage and USD 8.4 billion in losses for stakeholders, demonstrating the serious operational and financial effects of cyber-attacks [22; 23].
## 2 Mechanism Description  
A structure diagram of a space camera with a two-axis pointing platform is given in Figure 1. The platform consists of two axes, namely the elevation axis and azimuth axis. The two axes are orthogonal and rotate independently, allowing for the simultaneous adjustment of imaging directions. The base is installed on the spacecraft. The azimuth mechanism is assembled on the elevation mechanism, and the camera is assembled on the azimuth mechanism. When the elevation axis rotates, the azimuth axis will rotate accordingly. The rotation range of the elevation axis is \(-10^{\circ}\) to \(35^{\circ}\), and the rotation range of the azimuth axis is \(-40^{\circ}\) to \(40^{\circ}\). A large rotation range ensures that the camera can capture targets that require a large angle of rotation, thus increasing the detection range. Meanwhile, the camera achieves IMC through the rotation of the two-axis pointing platform. The setting of the rotation range can include the entire stroke needing rotation by the IMC.  
Permanent magnet torque motors were used to drive the shaft of each axis directly, which eliminate transmission clearance and guarantee rotation accuracy. High-precision encoders were installed on the shaft for the real-time acquisition of the mechanism rotation angle. The direct drive mechanism improves structural stiffness and accordingly increases the closed-loop bandwidth of the servo system.  
During the in-orbit imaging period, the IMC calculation module calculates the pointing platform guidance position in real time, guiding the platform to rotate (this will be discussed in the next section).
## 4 Branch-and-Price-and-Cut Algorithm
### Label Setting Algorithm
#### 4.4.1 Label Definition  
A feasible partial route from the depot node \(N_{0}\) to any node \(i\in\{P\cup D\}\) is denoted by the label \(L_{i}=\{i,Y_{i},AC_{i},RC_{i},V_{i}\}\). Here, \(i\) is the last visited node; \(Y_{i}\) stands for the service beginning time of node \(i\); \(AC_{i}\) keeps track of the total cost of the partial route; \(RC_{i}\) records the accumulative reduced cost of the partial route; \(V_{i}\) is a binary vector, where each entry reflects the visit state of a specific node. Within \(V_{i}\), a value of 1 indicates that the node has been visited, while a value of 0 means it has not been visited.
## 3 Dissimilarity Metrics Based on Neighboring Information
### Definition of Classical Dissimilarity Metrics  
Depending on the value of \(\lambda\) in (6) several classical similarity/dissimilarity metrics can be obtained. In addition, it is essential to notice that the relation between a similarity metric \(S\) and its equivalent dissimilarity metric DM is DM = \(1-S\) if \(S\in[0,1]\).  
The classical similarity and/or dissimilarity metrics can be formulated as follows:  
Jaccard metric: This similarity metric \(Jac\) and distance \(Jacd\) between two vehicles \(i\) and \(k\) can be formulated as:  
\[Jac=\frac{a_{ik}}{a_{ik}+a_{i}+a_{k}} \tag{7}\]  
Figure 1: An illustration of a shared node between two vehicles. The vehicle in yellow is shared by both \(i\) and \(k\) vehicles.  
\[\mathbf{Jack}=\mathbf{1}-\mathbf{J}=\frac{\mathbf{a}_{i}+\mathbf{a}_{k}}{\mathbf{a}_{ik}+\mathbf{a}_{i}+\mathbf{a}_ {k}} \tag{8}\]  
Dice metric: This similarity metric \(\mathbf{Dic}\) and distance \(\mathbf{Dicd}\) between two vehicles \(i\) and \(\mathbf{k}\) can be expressed as:  
\[\mathbf{Dic}=\frac{\mathbf{2}\mathbf{a}_{ik}}{\mathbf{2}\mathbf{a}_{ik}+\mathbf{a}_{i}+\mathbf{a}_{k}} \tag{9}\]  
\[\mathbf{Dicd}=\mathbf{1}-\mathbf{Dic}=\mathbf{1}-\frac{\mathbf{2}\mathbf{a}_{ik}}{\mathbf{2}\mathbf{a}_{ik}+\bm {a}_{i}+\mathbf{a}_{k}}=\frac{\mathbf{a}_{i}+\mathbf{a}_{k}}{\mathbf{2}\mathbf{a}_{ik}+\mathbf{a}_{i}+ \mathbf{a}_{k}} \tag{10}\]  
Kulczynski metric: This similarity metric \(\mathbf{Kul}\) and distance \(\mathbf{Kul}\) between two vehicles \(i\) and \(k\) can be formulated as:  
\[\mathbf{Kul}=\mathbf{0.5}\bigg{(}\frac{\mathbf{a}_{ik}}{\mathbf{a}_{ik}+\mathbf{a}_{i}}+\frac\mathbf{{a}_{ik}}{\mathbf{a}_{ik}+\mathbf{a}_{k}}\bigg{)} \tag{11}\]  
\[\mathbf{Kul}=\mathbf{1}-\mathbf{Kul}=\mathbf{1}-\mathbf{0.5}\bigg{(}\frac{\mathbf{a}_{ik}}{\mathbf{a}_{ik }+\mathbf{a}_{i}}+\frac{\mathbf{a}_{ik}}{\mathbf{a}_{ik}+\mathbf{a}_{k}}\bigg{)} \tag{12}\]  
Fowlkes-Mallows metric: This similarity metric \(\mathbf{Fow}\) and distance \(\mathbf{Fowd}\) between two vehicles \(i\) and \(\mathbf{k}\) can be expressed as:  
\[\mathbf{Fow}=\frac{\mathbf{a}_{ik}}{\sqrt{(\mathbf{a}_{ik}+\mathbf{a}_{i})(\mathbf{a}_{ik}+\mathbf{a} _{j})}} \tag{13}\]  
\[\mathbf{Fowd}=\mathbf{1}-\mathbf{Fow}=\mathbf{1}-\frac{\mathbf{a}_{ik}}{\sqrt{(\mathbf{a}_{ik}+\mathbf{a} _{i})(\mathbf{a}_{ik}+\mathbf{a}_{k})}} \tag{14}\]  
Sokal-Sneath metric: This similarity \(\mathbf{Sok}\) and dissimilarity \(\mathbf{Sokd}\) can be determined as:  
\[\mathbf{Sok}=\frac{\mathbf{a}_{ik}}{\mathbf{a}_{ik}+\mathbf{2}(\mathbf{a}_{i}+\mathbf{a}_{k})} \tag{15}\]  
\[\mathbf{Sokd}=\mathbf{1}-\mathbf{Sok}=\frac{\mathbf{2}(\mathbf{a}_{i}+\mathbf{a}_{k})}{\mathbf{a}_{ik}+\bm {2}(\mathbf{a}_{i}+\mathbf{a}_{k})} \tag{16}\]
## 4 Numerical Application
### Evaluation Metrics
#### 4.1.2 Structural Similarity Index Measure (SSIM)  
The SSIM was used to evaluate the structural similarity between the inversion results and the true model, which is defined as follows:  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{18}\]  
where x and y represent the velocity values of the inversion results and true model within a local window, respectively, \(\mu_{x}\) and \(\mu_{y}\) represent the mean values of \(x\) and \(y\), respectively, \(\sigma_{\text{x}}\) and \(\sigma_{y}\) represent the standard deviations of \(x\) and \(y\), respectively, \(\sigma_{\text{xy}}\) represents the covariance between \(x\) and \(y\), and c\({}_{1}\) and c\({}_{2}\) are constants introduced to avoid zero denominators. The range of SSIM is [0, 1], where values closer to 1 indicate a higher structural similarity between the inversion results and the true model, and vice versa.
## 2 Sensor Network Design and Upgrade Problem  
Let us assume the operation of a process under steady state conditions can be represented by the following set of \(m\) non-linear algebraic equations **R**  
\[\textbf{R(z)}=\textbf{R(x, u)}=\textbf{0}, \tag{1}\]  
where **z** stands for the n-dimensional vector of process variables, and **x** and **u** represent the vector of measured and unmeasured variables respectively. The problem of optimal selection of instruments during plant design or upgrade consists of determining the optimal partition of vector **z** in vectors **x** and **u**, and it is formulated as follows  
\[\begin{array}{ll}Min&f(\textbf{q})\\ s1.&\\ &g_{{}_{j}}(\textbf{q})\leq g_{{}_{j}}^{{}^{*}}(\textbf{q})&\forall\ j\in S\\ &q_{{}_{i}}=1&\forall\ i\in I_{{}_{0}}\\ &\textbf{q}\in\big{\{}0,1\big{\}}^{\ast-|I_{{}_{0}}|}&\end{array} \tag{2}\]  
where **q** is a \(n\)-dimensional vector of binary variables such that: \(q_{i}=1\) if variable \(i\) is measured, and \(q_{i}\) = 0 otherwise. For the sake of simplicity it is assumed that there is only one potential measuring device for each variable; if it is not the case the number of binary variables increases significantly. Furthermore \(f(\textbf{q})\) represents a one-dimensional objective function, \(g_{{}_{j}}(\textbf{q})\) indicates the constraint imposed on the quality of the \(j\)-th key variable estimate, considering there exists only one constraint for each key variable, \(S\) is the set of key process variables, and \(I_{{}_{0}}\) stands for the initial set of instruments that is empty at the network design stage. No instruments' localization restrictions are imposed in this formulation but they may arise in practice.  
Different performance criteria of the sensor structure, \(f(\textbf{q})\), are used depending on the specific application. Frequently the life cycle instrumentation cost for the design or upgrade project leads the selection; nevertheless reliability measures are sometimes preferred for safety reasons. A wide variety of objective functions have been used: instrumentation cost, global error of variable estimates, system reliability, variable reliability and availability, the economic value of instrumentation projects, etc, [4], [9], [26].  
Regarding the set of constraints, \(\textbf{g(q)}\), engineers not only require to know the value of key variables for economic, safety or environmental reasons (estimability constraints), but impose conditions about the precision, reliability or availability of variable estimates.  
In general nonlinear discrete optimization problems arise. For large scale processes the dimension of the search-space for optimization models represented by equation (2) increases significantly. In consequence the design turns out to be a huge combinatorial optimization problem that may have many local optima. In these cases, it is really valuable that the solution procedure provides at least a good solution, if not the global optima, and that also can be run in parallel computers to reduce execution times.
## 3 Results
### Model and Validation  
#### 3.3.1 LAI Inversion Model Based on NDVI and Its Derivative Parameters  
The NDVI and its derivative parameters were inversed with the leaf area index in the growing cycle of rapeseed based on the 150 samples. Also, optimal regressive equations are all exponential functions (shown in Figure 2). Through data analysis, it can be concluded that: (1) the coefficient of determination R\({}^{2}\) of each regression equation is greater than 0.83. This shows that the NDVI series of the vegetation index has a good inversion effect on LAI. The inversion effect of A\({}_{\rm CRNDVI}\) and A\({}_{\rm GNDVI}\) is the best, with values of 0.8932 and 0.8906, respectively. Compared with the derivative parameters of the same type, it is easy to find that the coefficients of determination R\({}^{2}\) satisfy the following relationships:  
Figure 1: Trends between A\({}_{\rm VNDVI}\) and reflectivity of the near-infrared, green, blue, and red bands.  
Figure 2: _Cont._  
Figure 2: Inversion models between NDVI, its derivative parameters, and rapeseed LAI. Note: significant level is 1% (n = 150).  
these three parameters gradually becomes close to NDVI. Moreover, the relative sensitivity of all the accumulative derivative parameters of \(\mathrm{A_{VNDVI}}\) decreases gradually, indicating that the sensitivity and anti-interference of \(\mathrm{A_{VNDVI}}\) gradually tend to be stable with the increase of the accumulative effect. In general, the sensitivity of cumulated parameters has the same law as NDVI.