esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Offline Changepoint Detection Ensembles
## 2 Materials and Methods
### Numerical Experiment
#### 2.2.2 Search Methods  
Opt  
The \"forward dynamic programming\" algorithm for optimal signal segmentation is initially presented in [33]. This algorithm provides finding the exact solution for the optimization problems stated earlier by calculating all possible partitions of the signal. Some of the observations about the algorithm and its extensions, as well as the computational complexity and other characteristics, are presented in [22].  
Win  
The window-based algorithm provides an approximate solution to the mentioned problems. It is based on calculating the dissimilarity of the two following each other window. The details are presented in [22].  
BinSeg  
The binary segmentation algorithm also provides an approximate solution to the problems. The signal is split into two parts for each step of this algorithm. For the details and additional information about the algorithm, an interested reader may refer to [34].  
Extension  
Our extension of the above-mentioned algorithms is as follows: in the part of cost function calculation, we simultaneously calculate costs for all partitions using each cost function separately, resulting in an \(N\times M\)-dimensional array of _scores_. Then all the _scores_ are scaled independently across the \(N\)th dimension, and, finally, they are aggregated into a single result of \(1\times M\) size. Finally, a search method is applied to the aggregated time series of _scores_ without any changes. Pseudocode of the proposed OptEnsemble, WinEnsemble, and BinSegEnsemble search methods is presented in the Appendix A.
# Finite Element Approaches to Model Electromechanical, Periodic Beams
## 3 Numerical Analysis
### Wave Propagation
The diagram below (Figure 10c,d) shows the changes in the excitation spectrum in the active periodic beam with the RLC circuits tuned to \(f_{R}=50\) kHz. As it can be noticed, the excitation wave (of the carrier frequency of \(f=50\) kHz) at that frequency was unable to propagate freely due to the dissipation of energy on the electrical resistance band gap that appears. Although the band gap was very narrow the amplitude of the wave decreased in a significant manner. On the other hand for this \(f_{R}=50\) kHz no significant changes were observed in the amplitude of the excitation signal of the carrier frequency of \(f=100\) kHz in comparison to the amplitude registered for the passive system. Finally, tuning of the RLC circuit to the frequency of \(f_{R}=100\) kHz (Figure 10e,f) did not cause any changes in the wave propagation of the excitation wave of the frequency of \(f=50\) kHz (also comparing to the passive structure). However, this value of the \(f_{R}=100\) kHz caused a significant widening of the band gap. The amplitude of the wave after passing through the structure with band gap decreased tenfold. It was caused by the synergy of the natural periodic structure band gap with energy dissipation caused by the active RLC circuit and its electrical resistance. Figure 8: Dispersion curves for the analysed beam, (**a**) RLC circuits off, (**b**) RLC circuits tuned to 100 kHz. Figure 9: Patterns of flexural wave propagation in an electromechanical periodic structure.
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 5 Simulation and Discussion
### Effect of the Wear Degree of Wheel Flats on Wheel-Rail Contact Geometry  
According to the wheel tread profile discretization method shown in Figure 9b, this section investigates the effects of different worn coefficients on the wheel-rail contact geometry. Taking \(d=0.6\) mm as an example, and without considering other geometric irregularities, the change in coordinates of the wheel-rail contact points in each iterative step during the forward rolling process of the complete wheel on the left side and the wheel containing a flat on the right side, when the wheel pair lateral displacement is 0, is shown in Figure 13.  
In Figure 13, the geometric model of the wheel flat is spatially symmetric, resulting in a symmetric process of contact point coordinate changes. The presence of a wheel flat on the right wheel affects the wheel-rail contact position of the complete wheel on the left side. As the iterative steps increase, the left wheel-rail contact point moves away from the flange and then gradually returns to the initial contact position, with continuous coordinate changes throughout the process. The right wheel-rail contact point also changes, shifting laterally towards the flange in step 5, after which the contact position remains almost constant until it returns to the initial position in step 56. Comparing the jump durations for different wear degrees, it is found that the maximum wear degree increased by 9.8% compared with the minimum wear degree.  
Different worn coefficients affect the start and stop moments of the wheel's lateral jump. During the contact point computation, the defect influences the contact point position when the defect depth on the tread exceeds the accuracy of the contact point search algorithm, which is a fixed value. In the same iterative step, the greater the maximum depth of the wheel flat as the worn coefficient increases, the fewer iterative steps are required to exceed the fixed value, leading to an earlier start moment of the lateral jump.
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
###### Abstract  
The fractional-order proportional-integral (FOPI) controller tuning rules based on the fractional calculus for the parallel cascade control systems are systematically proposed in this paper. The modified parallel cascade control structure (PCCS) with the Smith predictor is addressed for stable, unstable, and integrating process models with time delays. Normally, the PCCS consists of three controllers, including a stabilized controller, for a class of unstable and integrating models, a disturbance rejection controller in the secondary loop, and a primary servomechanism controller. Accordingly, the ideal controller is obtained by using the internal model control (IMC) approach for the inner loop. The proportional-derivative (PD) controller is suggested for the stabilized controller and is designed based on a stability criterion. Based on the fractional calculus, the analytical tuning rules of the FOPI controller for the outer loop can be established in the frequency domain. The simulation study is considered for three mentioned cases of process models and the results demonstrate the flexibility and effectiveness of the proposed method for the PCCS in comparison with the other methods. The robustness of the proposed method is also justified by perturbed process models with \(\pm 20\%\) of process parameters including gain, time constant, and delay time.  
fractional-order proportional-integral (FOPI) controller; parallel cascade control structure; internal model control; Smith predictor
## 6 Conclusions  
In this paper, we focused on the collaborative service mechanism between idle vehicles and an MEC server in an VEC network, and investigated the problem of jointly optimizing computation offloading and subcarrier allocation. Unlike existing works that primarily aim at latency minimization, we introduced AoI as the optimization objective to better characterize the timeliness of information. To address this optimization problem, a MHPPO method was proposed. Simulation results showed that the proposed MHPPO method outperforms other baseline methods in reducing the average AoI. The findings of this study may provide some reference value for improving the performance of VEC systems, particularly in applications where real-time data freshness is important, such as autonomous driving, traffic management, and emergency response systems. In the future, we will expand our research to address more complex scenarios, including bidirectional  
Figure 10: Rewards under different task arrival models.  
vehicle mobility and multi-RSU environments. Furthermore, we will investigate incentive mechanisms for service vehicles to promote active participation and optimize overall system performance.  
Conceptualization, X.Q. and K.X.; methodology, X.Q. and K.X.; software, X.Q.; validation, R.D.; formal analysis, X.Q., C.M. and K.X.; investigation, R.D. and Z.Z.; resources, Z.Z. and K.X.; data curation, X.Q. and C.M.; writing--original draft preparation, X.Q. and C.M.; writing--review and editing, C.M., K.X. and P.F.; visualization, X.Q.; supervision, K.X. and P.F.; project administration, Z.Z.; funding acquisition, K.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grants 2022 JBZY021, by the Changping Innovation Joint Fund of Beijing Natural Science Foundation (no. L234084) and also by the National Natural Science Foundation of China (no. 62071033).  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space
## 3 Calculation
### Autoencoder and Convex Hull-Based Clustering in Latent Layer  
The key feature of autoencoders is their ability to perform a nonlinear feature reduction. To use its power, we introduced a small modification to the model from the previous section. The autoencoder architecture for latent layer exploration was the same as previously, however, only the two neurons in the latent layer (with linear activation function) were used (Figure 14).  
The network was trained with the same data and methods described in Section 3.1. Within the latent space, clear clusters could be observed (Figure 15).  
The resulting confusion matrix is presented in Figure 17.  
Similar to the case of the autoencoder in Section 5, the proposed approach was very effective (100%) in failure detection in the case of true positive classification. Moreover, the number of normal state predictions in the case of true negative classification was increased to 98.9%. The method misclassified only 1.1% (false positive) of states where the gearbox was actually without damage. The above results showed a very high precision (0.98), F1-score (0.99), and recall (1.00).  
To show the nature of the latent data separation and compare the results with semi-supervised methods, we conducted supervised training of the support vector machine models. For this purpose, we reorganized the dataset and granted labels to normal and  
Figure 16: Results of the convex hull-based classification in latent space.  
Figure 15: Data in the latent space of the autoencoder.  
failure subsets. The labeled data were used with a 70-30% training-test split. Results for the linear and nonlinear support vector machine (SVM) models are given in Figure 18.  
In the case of the linear SVM model, the decision boundary takes the form of a straight line \(h_{2}=mh_{1}+n\) where m = 0.581 and n = \(-\)4.444. For nonlinear SVM, as a kernel, the polynomial of the fourth-order was used, resulting in a decision boundary that can be approximated by equation \(h_{2}=ah_{1}^{4}+bh_{1}^{3}+ch_{1}^{2}+dh_{1}+e\), where a = 0.0012, b = \(-\)0.0107, c = 0.0712, d = 0.2795, and e = \(-\)4.0032. The resulting confusion matrices are given in Figure 19.  
Figure 19: Confusion matrices for SVM classification: (**a**) linear SVM; (**b**) nonlinear SVM.  
Figure 17: Confusion matrix for convex hull classification of latent data.  
Figure 18: Decision boundaries and test data for: (**a**) linear SVM; (**b**) SVM with nonlinear kernel.  
The linear SVM model performed well, achieving high precision (1.00), recall (0.99) and F1-score (0.99), while SVM with the polynomial kernel separated the data perfectly (Figure 19b). The nonlinearity of latent data separation can be reflected by the R-squared measure between the linear and nonlinear decision boundary line. For the analyzed case, it was R2 = 0.98, so the data separation was highly linear, making it easily distinguishable.
## 4 Discussion
### Industry Transformation Framework and Future Development Trajectory  
Autonomous System Transition and Technical Evolution: The current manual control system provides a solid foundation for autonomous development using a dual-processor setup, consisting of a Raspberry Pi 3 B+ and an STM32F10 SC8T6. This setup allows for a clear distinction between high-level decision-making and low-level motor control, thereby facilitating the progressive integration of adaptive navigation algorithms tailored for dynamic aquaculture environments. Additionally, it incorporates visual positioning and SLAM technology to enhance spatial awareness, as well as AI-driven intelligent recognition using YOLO for autonomous anomaly detection. Industry Impact and Application Prospects: This integration model addresses critical industry challenges, including labor shortages, an aging workforce, and demands for precision management. The system transforms traditional aquaculture from location-dependent operations to technology-enabled remote management, which is particularly crucial for Taiwan's resource-limited coastal regions. Early adoption demonstrates the potential for global scalability while enabling operators to overcome location constraints and reduce the need for manual inspections.  
Advanced development directions encompass enhanced multi-platform collaboration featuring intelligent control processes informed by user behavior and environmental awareness. The system will incorporate Augmented Reality (AR) and Virtual Reality (VR) control interfaces that provide intuitive remote operation experiences, simulating physical presence on-site. Additionally, collaborative monitoring across aquaculture farms via cloud-based platforms will allow individual operators to manage multiple dispersed sites. Integration with Beyond 5G and NTN technologies will extend operational range to deep-sea environments while ensuring economic accessibility through a modular design that supports incremental capability expansion. This research establishes a new paradigm that bridges traditional aquaculture practices with advanced technology, offering a viable pathway for industry-wide digital transformation. By integrating progressive technologies, the system aims to achieve fully automated inspection tasks and wireless operational capabilities, advancing toward truly autonomous intelligent aquaculture management. This approach provides scalable, technology-driven solutions for precision monitoring across various operational scales.
## 2 Materials and Methods
### Standard Solution and QC Samples  
The calibration standard solutions at levels of 0.1, 0.2, 0.5, 1, 2, 5, 10, 20 and 50 ng mL\({}^{-1}\) for each analyte were made by serial dilutions of the mixed standard solution. Each calibration standard solution contained 25 \(\upmu\)g mL\({}^{-1}\) of \({}^{13}\)C\({}_{18}\)-ZEN as internal standard. QC samples were prepared at low, medium and high concentrations (0.5, 1 and 5 ng mL\({}^{-1}\)) by spiking blank serum matrix with standard mixture.
OST patients, especially those in BP-treatment for a period of time greater than 3 years or shorter than 3 years and simultaneously affected by systemic or local risk factors, bear a not well defined risk of developing BRONJ, but surely lower than that in ONC patients [2,9]. BPs main function is strong inhibition of the osteocl activity with the suppression of bone turnover; moreover, BPs also possess anti-angiogenic properties. The multiple and different pharmacological actions of BPs may delay or inhibit the wound healing of tooth extraction sockets, and they may be involved in BRONJ development [2,10,11,12,13]. Among the local risk factors, dental extraction has been described as the main trigger event in the majority cases of reported BRONJ [2,7,12,14,15,16]. However, to date, the exact prevalence of BRONJ after tooth extraction is still not well known, and several surgical protocols for dental extraction have been proposed, with different rates of ONJ onset [2,7,9,16,17]. Recently, many authors have investigated the application of autologous platelet concentrates (APC) in post-extraction sockets in patients at risk of BRONJ [18]. The application of APC was first proposed by [PERSON] et al. in 1998; from then APC have been applied in various fields of regenerative medicine, including oral surgery [19,20]. Platelet-rich plasma (PRP), belonging to APC class, is an autologous product that consists of a high concentration of platelets and a native concentration of fibrinogen [21,22]. Given the high concentration of platelets, the PRP is rich in platelets granules, which contain many substances fundamental to the promotion of the healing process (e.g., adhesive proteins, pro-coagulant factors, cytokines and chemokines), and a great number of growth factors, such as platelet-derived growth factor (PDGF), transforming growth factor-beta (TGF-b), epidermal growth factors (EGF) and vascular endothelial growth factors (VEGF) [19,21,22]. Thanks to all these components, PRP promotes angiogenesis and mucosal healing, making it an attractive product in surgical procedures on patients at risk of BRONJ [19,21,22]. The aim of the present study was to evaluate the outcome at 2 years of a standardized medical-surgical protocol for dental extraction, including PRP application in post-extraction sockets, compared with conventional protocol not combined with PRP or any other APC, both in ONC and OST patients at risk of BRONJ.
# Multi-Phase Stator Current Tracking with Gradual Penalization of Commutations
## 3 Proposal  
The optimization phase of FCSMPC is accomplished by computing the value of the CF for each candidate control move. This entails a high computational load when the number of possible control moves is high (e.g., in multi-phase and/or multi-level systems). In [28], a method is provided for avoiding the exhaustive minimization of the CF. The method relies on a vector quantity referred to as \"gap\". Knowing the gap vector \(g\), it is possible to determine the control move that reduces the predicted error the most. However, the method considers just one plane, either \(\alpha-\beta\) or \(x-y\), at a time. In order to provide current tracking in both planes, the method must shift objectives in different sampling periods. This is achieved by computing which plane (\(\alpha-\beta\) or \(x-y\)) will develop a larger weighted error in \(k+2\). Also, the commutation frequency of the VSI is not explicitly considered. The proposal, which is put forward in the following, avoids such change in objectives and is able to consider penalization on commutation frequency.
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 4 Design
### Markov Decision Process
#### 4.2.4 Reward  
In this task, we want agents to cover as much of the map as possible. Therefore, we can use the coverage area of the agents as part of the reward. Specifically, we can evaluate the performance of the agents by calculating the number of grid cells covered by the agents on the map. Then, we can divide this number by the total number of grid cells to obtain a coverage rate. This coverage rate is the value of the reward and can be represented as:  
\[r_{rew}=\frac{1}{N}\sum_{i=1}^{N}\left[\rho_{i}==1\right] \tag{32}\]  
where \(N\) is the total number of grid cells on the map, \(\gamma_{i}==1\) is the \(i\)-th grid cell covered by the agents, and \(\left[\rho_{i}==1\right]\) is an indicator function, taking the value of \(1\) when \(\rho_{i}==1\) and \(0\) otherwise. If an agent collides with another agent or an obstacle or flies into a no-fly zone, it is penalized. Thus, we can add the penalty value for such situations to the reward value to obtain the final reward value. The penalty value can be represented as:  
\[r_{pun}=\left\{\begin{array}{ll}-1,&\text{collide or in no flyzone}\\ 0,&\text{otherwise}\end{array}\right. \tag{33}\]  
To ensure that UAVs can work continuously during the mission, the UAV's energy consumption needs to be considered. We can use the energy consumption of the UAV as part of the reward and encourage agents to plan their paths efficiently by limiting the UAV's energy consumption.  
\[r_{pun}=\left\{\begin{array}{ll}-k_{energy}\cdot E_{t},E_{t}>E_{c}\\ c_{energy},E_{t}\leq E_{c}\end{array}\right. \tag{34}\]  
where \(E_{t}\) represents the current energy consumption value of the UAV, and \(E_{c}\) represents the upper limit of the UAV's energy consumption. When the current energy consumption value (\(E_{t}\)) exceeds the energy consumption limit (\(E_{c}\)), the reward value corresponding to the reward function is \(-k_{energy}\cdot E_{t}\), where \(k_{energy}\) is a positive number representing the penalty coefficient for energy consumption. When the energy consumption of the UAV is less than or equal to the energy consumption limit (\(E_{c}\)), the reward value corresponding to the reward function is \(c_{energy}\), where \(c_{energy}\) is a positive number representing the additional reward value after the energy consumption reaches the limit, aiming to encourage agents to better control the energy consumption of the UAV.  
The final reward value is the sum of the reward part and the penalty part:  
\[reward_{n}=r_{rew}+r_{pun} \tag{35}\]
## 3 Analysis of Original Research Articles
### Machine Learning Techniques
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Refs.** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline
[18] & 2011 & Eggs, Meat, Milk, Oilseeds, Rice, Sugar, Wheat & Global macroeconomic and food price data & PCA & Oil prices indirectly affect food prices via the food production index (corr.: 0.87 with GDP, 0.36 with CPI). & Historical macroeconomic relationships may not account for future structural changes \\ \hline
[19] & 2022 & Rice & Monthly average prices from 1995 to 2014, Commission for Food Security (Dakar retail level) & & Local prices are affected by world prices, more so by increases than by declines; 11.80\% of positive deviations and 39.50\% of negative deviations are eliminated after \\ \hline
[20] & 2024 & & Futures and cash daily price data for corn and soybean (1991–2006) & Semiparametric Regression & Confidence bands for yield and price forecasts & The accuracy of the forecasted cash price depends largely on the current futures price \\ \hline
[21] & 2016 & Corn, Soybean & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[22] & 2024 & & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[22] & 2016 & & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[22] & 2024 & Rice & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[23] & 2024 & Wheat & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[23] & 2024 & Wheat & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[24] & 2021 & Wheat & \begin{tabular}{} \end{tabular} & BPPNN & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of machine learning techniques in wheat, rice, and corn price prediction. forecast cash prices. [PERSON] et al. [21] developed a Backpropagation Neural Network (BPNN) model using Big Data to forecast corn prices in South Africa. Their approach achieved high accuracy for 1-month predictions both for in-sample and out-sample predictions. Additionally, their model outperformed eight volunteer expert commodities traders in real-time prediction.