esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods  
### Feature Selection  
Ensuring that the correlations between the features and the dependent variables are important in the prediction problem. Likewise, ensuring that the features are independent of one another is also important for improving the prediction accuracy. While modeling the forecasting method, both the features which exhibit strong, multiple collinearities and the features which have a low correlation with the dependent variable should be eliminated for enhancing the prediction accuracy. Thus, we choose the Pearson correlation analysis to test the correlation of all features and the dependent variable [33; 34]. The calculation of Pearson correlation analysis is as Equation (1).  
\[\rho_{\text{X,Y}}=\frac{\text{cov}(\text{X},\text{Y})}{\sigma_{\text{X}}\sigma _{\text{Y}}}=\frac{\text{E}[(\text{X}-\text{u}_{\text{X}})(\text{Y}-\text{u}_{ \text{X}})]}{\sigma_{\text{X}}\sigma_{\text{Y}}}, \tag{1}\]  
\(\text{cov}(\text{X},\text{Y})\) is the covariance between the features X and Y. \(\sigma_{\text{X}}\) and \(\sigma_{\text{Y}}\) indicate the standard deviations of the features X and Y. \(\rho_{\text{X,Y}}\) is the correlation value of the features X and Y. The value range of \(\rho_{\text{X,Y}}\) belongs to \((-1,1)\). If \(\rho_{\text{X,Y}}>0\), the two features are positively correlated: if \(\rho_{\text{X,Y}}<0\), the two  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field** & **Type** & **Sample** & **Comment** \\ \hline Driver ID & String & glox.jrrllBMvCh8n & \multirow{2}{*}{Anonymized} \\  & & xqkdtr2 dtopmlH & \\ Order ID & String & jkkt8 knoviIFuns9q & \multirow{2}{*}{Anonymized} \\  & & rrlvst@iqnpkwz & \\ Time Stamp & String & 1478223613 & \\  & & (4 November 2016 09:40:13) & \\ Longitude & String & 104.04392 & \multirow{2}{*}{GCJ-02 Coordinate System} \\ Latitude & String & 34.04392 & \multirow{2}{*}{GCJ-02 Coordinate System} \\ \hline \hline \end{tabular}
\end{table}
Table 2: An instance of online taxi-hailing GPS data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Indictors** & **Description** \\ \hline  & AQI & Air Quality Index \\  & CO & The concentration of CO (\(\text{ug}/\text{m}^{3}\)) \\  & NO\({}_{2}\) & The concentration of NO\({}_{2}\) (\(\text{ug}/\text{m}^{3}\)) \\ Air quality data & O\({}_{3}\) & The concentration of O\({}_{3}\) (\(\text{ug}/\text{m}^{3}\)) \\  & PM2.5 & The concentration of PM2.5 (\(\text{ug}/\text{m}^{3}\)) \\  & PM10 & The concentration of PM10 (\(\text{ug}/\text{m}^{3}\)) \\  & SO\({}_{2}\) & The concentration of SO\({}_{2}\) (\(\text{ug}/\text{m}^{3}\)) \\ \hline  & W & Weather. 1: Sunny 2: Cloudy 3: Raining 4: Haze \\  & WS & Wind speed (m/s) \\  & TEM & Temperature (\({}^{\circ}\)C) \\ Meteorological data & SSD & Sunshine duration (h) \\  & PRE & Precipitation (mm) \\  & TG & Sensible temperature (\({}^{\circ}\)C) \\  & VIS & Horizontal visibility (m) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environmental data structure description.  
features are negatively correlated. The larger absolute value of \(\rho_{\text{X,Y}}\) indicates a stronger correlation between the features X and Y.
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 3 Modeling of Vehicle Power System
### Battery Model  
The battery is an essential component of the vehicle power system. In this paper, a 12 V lead-acid battery is considered. The equivalent circuit model of the battery, as shown in Figure 6, is used widely in battery analysis [12]. In this circuit model, where \(V_{B}\) is the output voltage of the battery, \(V_{o}\) is the open circuit voltage (OCV) of the battery (i.e., electromotive force), \(R_{i}\) is the internal resistance of the battery, and \(I_{B}\) is the battery current. The relation among \(V_{o}\), \(R_{i}\), and \(I_{B}\) can be expressed as Equation (2).  
The battery SoC is an important parameter for the energy management system [13]. It can be calculated through Equation (3):  
\[V_{B}(t)=V_{o}(t)-R_{i}I_{B}(t), \tag{2}\]  
\[SoC\left(t\right)=SoC_{0}-\frac{\beta}{Ah_{nom}}\int_{t_{0}}^{t}I_{B}(t)dt, \tag{3}\]  
where \(SoC_{0}\) is the value of SoC at \(t_{0}\), \(Ah_{nom}\) is the nominal capacity of the battery, and \(\beta\) is the charge-discharge efficiency factor of the battery. When the battery is charging, \(\beta\leq 1\); when the battery is discharging, \(\beta\geq 1\). Through charging and discharging experiments, \(\beta\) can be obtained. Furthermore, the correlation curve of SoC-OCV, as established by experiments [14], is used to obtain \(SoC_{0}\) or the corresponding \(V_{o}\). Figure 7 shows the SoC-OCV curve at temperature 25 \({}^{\circ}\)C, \(-30\)\({}^{\circ}\)C, and 70 \({}^{\circ}\)C. Generally, the SoC-OCV curve at 25 \({}^{\circ}\)C is adopted in practical applications.
## 7 Conclusions  
In this paper, a trajectory optimization mechanism is proposed for UAV-assisted C-V2X communication that considers the Doppler effect caused by high-speed UAV and vehicle motion. Our findings demonstrate that jointly optimizing a vehicle's transmission time interval, UAV transmit power, and UAV trajectory reduces the weighted total energy consumption of the UAV. Based on past UAV trajectory, a fed-DDPG algorithm is proposed that allows the UAV to estimate a rewarding action by optimizing the trade-off between the maximum flying altitude, power consumption, and number of vehicles served. We formulate the above problem as a mixed-integer non-convex optimization problem and divide the problem into four optimization sub-problems. We utilize an actor-critic framework in fed-DDPG approach, where the actor-critic network comprises an LSTM to estimate and explore the optimal trajectory and altitude for the UAV. Depending on the vehicle's data distribution and traffic demand, the UAV trajectory is adapted based on the UAV energy consumption and buffer size. Compared to exhaustive search methods that have a high computational complexity, our algorithm reaches a globally optimal solution in polynomial time. Simulation results reveal that the OTFS-based fed-DDPG algorithm is superior to heuristic search algorithms in terms of throughput maximization and latency minimization.  
Conceptualization, A.G. and X.F.; methodology, A.G.; writing--original draft preparation, A.G.; writing--review and editing, X.F.; supervision, X.F.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This project is funded by Natural Sciences and Engineering Research Council (NSERC) of Canada, Project Number RGPIN-2024-04924.  
Not applicable.  
Data is contained within the article.  
The authors declare no conflicts of interest.
## 1 Introduction
### Contribution and Organization of Paper  
This paper presents a bi-level optimization model to determine the optimal operation strategy for both the DN and MG. The upper-level optimization determines the scheduling requirement injected from the DN to the MG by minimizing the operating costs of the DN. The lower-level optimization provides feedback information regarding the received scheduling requirement by maximizing the MG profit while considering the energy band operational scheme. Moreover, a modified version of the NSGA-II (MNSGA-II) is applied to solve the model, resulting in improvements in the profit of the MG while reducing the DN operational cost. Simulations are performed on an IEEE (Institute of Electrical and Electronics Engineers) test system and actual system to validate the model and highlight its advantages over other multi-objective approaches.  
The contributions of this study are as follows:  
* Strategic behavior is proposed for cooperation between each operator for both the DN and MG based on the tie-line control strategy using a power margin (energy band operation scheme) of the tie-line between the DN and MG, which aids in providing a reliable and economic reference for responsibility sharing between two operators.
* A bi-level optimization model with an operation scheme based on the energy band is presented for cooperation between the DN and MG, in which the upper-level sends/receives the scheduling requirement/feedback information to/from the lower-level to determine the amount of energy of the tie-line.
* Finally, the MNSGA-II is employed, which preserves the diversity of the non-dominated solution laterally, and yields a Pareto solution with high uniformity, owing to the trade-off between the operational cost of the DN and the profit of the MG resulting in improved performance, as well as faster convergence and divergence.  
The remainder of this paper is organized as follows: Section 2 addresses the methodology for the energy band. The bi-level optimization framework and multi-objective formulation is discussed in Section 3. Section 4 describes the proposed solution scheme and optimization process for cooperation based on the MNSGA-II. Section 5 presents a discussion of the obtained results. Finally, the paper is concluded in Section 6.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 1 Introduction
### Contributions and Outline of the Paper  
The primary objective of this work is to optimize the AOR approach by utilizing a novel PSO algorithm. This optimization aims to effectively balance the computational complexity and high detection accuracy in massive MIMO systems. Considering the challenges introduced by the increasing number of antennas, our approach seeks to address the need for an efficient signal detection method that can adapt to various channel conditions and system configurations. To achieve this goal, we propose two algorithms. The first algorithm is the modification of nature-inspired PSO, called novel PSO, which has improved inertial weights and acceleration coefficients. The second main algorithm is OAOR, which is based on the proposed novel PSO. The main contributions can be summarized as follows:  
* First, we introduce a PSO algorithm that is a modified variant called novel PSO, which incorporates adaptive acceleration coefficients to improve convergence in the context of MIMO detection. This enhancement contributes to both the efficiency and effectiveness of the optimization process. We individually evaluate the performance of this modified version of PSO for its effectiveness.
* Next, we propose the OAOR method utilizing the novel PSO that more efficiently handles the varying number of antennas. The scalability of the proposed novel algorithm can successfully address the growing demands of next-generation wireless communication systems. This method efficiently incorporates the adaptive acceleration coefficients and inertia weight, which further refines the optimization process and substantially improves convergence. Novel PSO-incorporated OAOR detectors significantly reduce the number of iterations to achieve the desired detection accuracy and consequently reduce the computational cost. In addition, the sensitivity of conventional AOR to relaxation and acceleration parameters is also more efficiently handled in novel PSO-based detection algorithms.
* A detailed evaluation of the OAOR method is conducted, focusing on both the computational complexity and error rate performance. The scalability of the OAOR approach is assessed across diverse antenna configurations using extensive simulations. Thisin-depth analysis highlights the method's robustness and flexibility, demonstrating its effectiveness in various scenarios. The numerical results reveal that the proposed algorithms outperform existing methods in terms of computational efficiency and symbol error rate (SER).  
The remainder of this paper has been arranged as follows: Section 2 provides a detailed system model for a massive MIMO system. Section 3 presents the proposed OAOR method, including its detailed derivation, convergence analysis, the modified novel PSO algorithm, and integration with OAOR. Section 4 describes the comprehensive analysis of computational complexity. The simulation results of detection performance in terms of SER are presented in Section 5. Section 6 provides the final observations and discusses future aspects.
## 4 Technical Review and Discussion
### Datasets and Open-Source Implementations  
Datasets play an essential role in evaluating watermarking methods for LDMs, providing a basis for assessing imperceptibility, robustness, and security under diverse conditions. The following datasets are commonly used in the evaluation of LDM-based watermarking:  
* **MS-COCO**[47]: The MS-COCO dataset, which contains over 328K images, is used extensively to evaluate the effectiveness of watermarking schemes. For LDM watermarking, a subset of 500 images is often used to assess both the imperceptibility and robustness of embedded watermarks.
* **StableDiffusionDB**[48]: This dataset includes images generated by Stable Diffusion based on prompts provided by users. It contains around 500 samples and is used for evaluating the quality of watermarked images under generative settings. The diverse nature of the images, influenced by user-generated prompts, allows for comprehensive robustness testing.
* **Flickr30k**[49]: This dataset is utilized for evaluating latent watermarking schemes. With captions provided for each image, it allows researchers to explore how well the watermark remains embedded in images generated under different textual conditions. Latent Watermark has demonstrated its effectiveness using this dataset, showing higher robustness compared to earlier methods.  
Open-source implementations are vital for promoting transparency and reproducibility in the development of watermarking techniques for LDMs. Table 2 shows key open-source projects related to LDM watermarking. These datasets and implementations provide a foundation for evaluating the practicality and robustness of different watermarking methods in latent diffusion models, enabling broader community involvement and further advancements in watermarking technology.
## 4 Discussion
### Discussion of the Lake Markakol MP Analyses
According to oral communication from the heads of community administrations of the surrounding communities, the number of tourists visiting Lake Markakol has increased substantially over the last three decades. Despite the remoteness, inaccessibility, low population and harsh natural and climatic conditions, according to reference data of the \"Markakol State Nature Reserve\" RSE, the growth of tourists to Lake Markakol over the last 5 years increased from 661 to 892 people per season. Another reason for the large number of mesoplastic particles and MP traces found in our investigations might be the close proximity of the settlement to the lake. Inefficient household waste management, insufficient infrastructure for waste collection and recycling and the widespread use of plastic products in everyday life are obvious reasons for the plastic and MP distribution in the study area. The low specific gravity of plastic particles can cause their widespread distribution in aquatic systems, since they can be transported to various zones of lakes, reservoirs or rivers and deposited in bottom sediments and soils [104; 105; 106] under the influence of water flow and air streams. The used methods were suitable for the first inventory of MP occurrence in the Lake Markakol study area, including the tributaries and the outflow. But using color alone to identify MPs may not be sufficient, especially in cases where the plastic is obscured by a large amount of biological residue. In such situations, additional analyses, such as by microscopy or chemical methods, are required to accurately identify plastic and minimize errors in the MP analysis process. In some cases, it is suggested to disregard transparent and white and black color particles because they interfere with biological material and other substances which can be misidentified as plastic [76; 99]. For reliable and rapid identification of large MP particles, it is advisable to use techniques such as pyrolysis-gas chromatography with mass spectrometry (Pyr-GC/MS), Fourier transform infrared spectroscopy (FTIR, u-FTIR) or Raman spectroscopy (Raman, u-Raman). This may pose a problem with water pollution over large areas. In addition, long-range atmospheric transport of small plastic particles is possible [107; 108]. This problem also requires further analyses.
## 5 Dynamic Equation for the Anti-Swaying System in the Frequency Domain  
Based on Equation (10), the structure parameter \(k\) significantly influences the swaying-reduction effect of the hydraulic anti-swaying system. Then, the \(k\) value must be determined in such a way that the swaying-reduction effect of the hydraulic anti-swaying system can reach an ideal state.  
In Equation (9), let \(Mk=\varepsilon\). \(\varepsilon\) is called the structure parameter load ratio and has units of Hz. By performing a Laplace transform on Equation (9), a complex domain function for the load swaying angle can be obtained:\[\Phi(s)=\frac{(s+2\xi\omega_{n})\varphi_{0}}{s^{2}+2\xi\omega_{n}s+\omega_{n}^{2}}. \tag{11}\]  
In Equation (11), \(s\) is a complex variable and \(s=\sigma+j\tau\). \(\sigma\) and \(\tau\) are real variables, and \(j\) is an imaginary unit. \(\omega_{n}=\sqrt{\frac{2 Mm_{2}s}{l}}\) is the system frequency in units of Hz, \(m_{1}\), \(m_{2}\), and \(l\) together affect the system frequency. \(\xi=\frac{\varepsilon}{2\omega_{n}}\) is called the dimensionless damping coefficient, or the damping coefficient for conciseness, and \(\xi>0\). Equation (12) is an obvious result of these definitions:  
\[\xi=\frac{k}{2}\sqrt{\frac{Ml}{2m_{2}g}} \tag{12}\]  
The denominator in Equation (11) can be set equal to zero, as in Equation (13):  
\[s^{2}+2\xi\omega_{n}s+\omega_{n}^{2}=0 \tag{13}\]  
Equation (13) is called the characteristic equation of the hydraulic anti-swaying system, and the corresponding root of Equation (13) is called the characteristic root of the hydraulic anti-swaying system.  
The following list discusses the system operation for different value ranges of the damping coefficient, \(\xi\).
## 1 Introduction
For a better analysis in different data scenarios, we used five datasets in the experiment: Bentham [35]; Department of Computer Science and Applied Mathematics (Institut fur Informatik und Angewandte Mathematik, IAM) [36]; Recognition and Indexing of Handwritten Documents and Faxes (Reconnaissance et Indexation de donnees Manuscrites et de fac similES, RIMES) [37]; Saint Gall [38]; and Washington [39]. In addition, we also use three optical models as HTR system: Bluche [40]; Puigcerver [15]; and the proposed model. In this way, we created a wide variety of combinations between datasets and applied techniques, creating various analysis workflows. An open source ([[https://github.com/arthurflor23/handwritten-text-recognition](https://github.com/arthurflor23/handwritten-text-recognition)]([https://github.com/arthurflor23/handwritten-text-recognition](https://github.com/arthurflor23/handwritten-text-recognition)), [[https://github.com/arthurflor23/](https://github.com/arthurflor23/)]([https://github.com/arthurflor23/](https://github.com/arthurflor23/)) spelling-correction) implementation for the reproducibility of results is also provided upon request. The remaining of this article is organized as follows: in Section 2, the process of the offline Handwritten Text Recognition system is detailed. Then, in the Section 3, the spelling correction process is presented through statistical and neural networks approaches. In Section 4, the overall best HTR system produced in our analysis is presented, detailing the optical model and the proposed encoder-decoder model for spelling correction. In Section 5, the methodology and experimental setup are explained. In Section 6, the results obtained from the experiment in each dataset and technique are presented. In Section 7, the results are interpreted and discussed. Finally, Section 8 presents the conclusions that summarize the article.
# Intraoperative Use of Mixed Reality Technology in Median Neck and Branchial Cyst Excision
## 2 Materials and Methods  
Here we present 3 clinical cases: 2 median neck cysts in female patients and 1 branchial cyst in a male patient.  
All approaches listed below are based on a marker system that uses special AR markers [18] as reference of a 3D model to a certain point in space. These markers have a specific texture that the software can recognize by means of computer vision algorithms; this allows the software to identify its location and orientation relative to the observer. The overall system flowchart is illustrated in Figure 1.  
* The main aspects of application for this approach: The markers themselves are made of PLA plastic, while the image-texture coating is applied by means of UV print, which allows for the sterilization of the markers before their application in the operating room (Patent [19]);
* We transformed the CT into a 3D model using a segmentation approach with respect to the set density threshold or the area of interest in the 3D Slicer. This allowed us to isolate all key anatomical structures of the neck as a series of separate three-dimensional models. After that, resulting 3D models were optimized and cleaned up using the procedural toolkit in Houdini (up to 75-95% polygon reduction); we then transformed the MSCT into a 3D model using a segmentation approach with respect to the set density threshold or the area of interest. This allowed us to isolate all key anatomical structures of the neck separately as a series of three-dimensional models;
* We visualized 3D models using mixed reality smartglasses and the developed software, which has preset parameters for referencing the markers to the three-dimensional model. It has an integrated basic interface [20], which allows the user to display the required anatomical elements as well as customize the parameters of the marker tracking system;
* The 3D models were transferred to the HoloLens glasses using TCP via the Wi-Fi network, which alleviated time-consuming app compilations for each procedure.  
The approach to marker tracking is based on a combination of the method of analyzing the camera image on the glasses and the spatial mapping system [21], which supports the further positioning of the 3D model using the environment mapping data in case the marker leaves the field of view.  
Subjects gave their written informed consent, and the study protocol was not in conflict with the institute's committee of ethics. The developed approaches of the current study did not impose any additional risks to patients or surgeons because they were not subject to any specific procedure, nor were they required to follow any new rules of behavior. Diagnostics and surgery were performed without any deviation from the standard procedure. All  
Figure 1: Three main stages of the developed workflow.  
decisions of the surgeons were based on the patient's medical conditions as well as on the analysis of the obtained CT and MRI data.