esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Data Features
### Parameter Selection of Adaptive Model Dataset  
An excessive number of features increases model complexity and may lead to overfitting, whereas too few features can result in underfitting. The primary goal of feature selection is to strike a balance by maintaining sufficient complexity for strong generalization performance while ensuring simplicity for efficient training, maintenance, and interpretation. When two or more features exhibit high correlation, multicollinearity arises, which can negatively impact model performance. Ideally, each feature in a machine learning model should be independent, minimizing or eliminating collinearity.  
To optimize the training data structure of the adaptive step length estimation model and improve the adaptability and estimation accuracy of motion features and step length,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Types** & **Acc** & **Gyro** & **Mag** & **Mean** \\ \hline chest & 0.2805 & 0.3105 & 0.2805 & 0.2905 \\ calf & 0.2505 & 0.2705 & 0.2805 & 0.2672 \\ foot & 0.2605 & 0.3005 & 0.2805 & 0.2805 \\ thigh & 0.2605 & 0.2605 & 0.2705 & 0.2638 \\ \hline \end{tabular}
\end{table}
Table 2: The feature discriminability across the four body parts.  
this study quantitatively analyzes and selects the most relevant feature parameters based on data characteristics in Figure 3. This approach strikes a balance between data discriminability and computational efficiency.  
To improve step length estimation, 12 key feature parameters with distinctive feature dissimilarity were selected from accelerometer, gyroscope, and magnetometer data from the device placed on the chest through data analysis. These features, chosen for their effectiveness in motion recognition and step length estimation, include six accelerometer-related parameters, three gyroscope features, and three magnetometer features. The selected features, which exhibit significant discriminability across 12 motion states, are as follows:  
* Accelerometer features: The peak values of the data along the Y-axis and Z-axis, respectively; the trough values of the data along the Y-axis and Z-axis, respectively; skewness; and frequency band energy.
* Gyroscope features: The trough values of the data along the Y-axis and Z-axis, respectively; and skewness.
* Magnetometer features: The peak values of the data along the Y-axis; the trough values of the data along the Y-axis; and skewness.  
Through this research, the optimal sensor placement and the most significant data types for input into the adaptive step length estimation model were identified. This approach enhances the model's ability to estimate step length across various motion states, significantly reduces fluctuations in step length estimation errors, and improves both accuracy and robustness. Consequently, this method provides high-quality data support for accurately and reliably tracking personnel trajectories in subsequent applications.
## 3 Results
### Measurements  
Using the setup described in Section 2.2, we realized a proof of principle test of WG loss in terms of transmitted power of some of the modeled structures on a large spectral range (from visible to MIR). In Figure 11, we report the average losses vs. the WG width of 6-mm-length SiN multilayer structures, for four different wavelengths. These values indicate that losses have a strong dependence on the wavelength of the propagating radiation and have a quasi-non-dependence on that range of WG widths, as expected. Moreover, from the measurements and from our FEM analysis, such WGs are demonstrated to work simultaneously in the NIR/MIR in single spatial mode.  
Figure 11: Coupling losses measured at 532 nm, 635 nm, 1550 nm, and 2640 nm for each WG width.  
Figure 10: Stress calculations in a SiN multilayer WG with a width of 10 μm: (**a**) component \(\sigma_{\rm xx}\) and (**b**) component \(\sigma_{\rm yy}\). Stress profile: (**c**) component \(\sigma_{\rm xx}\) along the y-coordinate in x = 0.
## 3 Results
### Dynamic Model Count Evaluation  
When using dynamic videos as input for COTTON-YOLO, the extracted cotton bill information is subsequently fed into ByteTrack. Each detected cotton bill is assigned a unique tracking number, and the corresponding cotton bill data is linked across different video results. The dynamic detection results are depicted in Figure 16. In the figure, red boxes signify the bouncing cotton bill IDs, while blue boxes denote missed detections.  
The video was recorded by a handheld camera moving at approximately 0.15 m/s, with a frame rate (FPS) of 30 f/s, which implies each frame appears for about 33.3 ms. The total processing time for COTTON-YOLO detection and ByteTrack for target matching and status updates amounts to 13.9 ms. Each cotton bill remains visible in the video for about 33.3 ms, exceeding the time required for target detection and tracking, thus meeting the real-time detection requirements of the input video. However, the blue boxes indicate a noticeable occurrence of missed detections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _RMSE_ & _MAE_ & _MAPE_ & \(\mathbf{R^{2}}\) \\ \hline YOLOv8 & 1.779 & 1.231 & 24.41\% & 0.543 \\ COTTON-YOLO & 1.008 & 0.587 & 14.01\% & 0.853 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect comparison between YOLOv8 and COTTON-YOLO.  
Figure 14: Image detection contrast effect: (a) Original image, (b)YOLOv8, (c) COTTON-YOLO.  
Figure 15: Cotton count detection comparison chart.  
Additionally, instances of cotton doll ID switching occur within the same image, often due to the target tracker mistakenly associating two or more different cotton bolls as the same one, causing the ID to jump from a higher to a lower number. Conversely, the similarity in appearance or movement of some cotton bolls leads to confusion and ID switches, resulting in an undercount. When targets re-enter the field of view or the tracker reinitializes after a failure, the same target might be counted multiple times. This situation typically occurs when a cotton doll briefly leaves the field of view or becomes obscured and then reappears, with the tracker unable to recognize it as the same target, thus reassigning a new ID. False cotton doll counts increase when the detector mistakenly identifies non-existent cotton bolls as actual targets, and complex backgrounds or changes in lighting may lead the detector to mistakenly identify background objects or shadows as cotton bolls, resulting in an overcount. In the final frame of the video, the highest detected cotton doll ID is 65, while manual counting averaged over three attempts yields a total of approximately 58 cotton bolls in the video, corresponding to a target detection tracking accuracy of about 87.93%. The maximum ID for cotton bolls tracked using YOLOv8 and ByteTrack is 34, with an accuracy of about 58.62%. Therefore, COTTON-YOLO combined with ByteTrack proves more effective in tracking and counting cotton bolls. The comparative results are depicted in Figure 17.
## 3 Principles and Modeling of an Adaptive Control Method for PMSMs Based on Super-Helical SMC
### Model-Referenced Adaptive Control System Based on Super-Helical Sliding Mode Control
\[\mathbf{V}(\mathbf{Z})=\mathbf{Z}^{\mathsf{T}}\mathbf{N}\mathbf{Z} \tag{32}\]  
The time derivative of V(Z) is given by the equation below:  
\[\dot{\mathbf{V}}(\mathbf{Z})=\dot{\mathbf{Z}}^{\mathsf{T}}\mathbf{N}\mathbf{Z}+\mathbf{Z}^{\mathsf{T}} \mathbf{N}\dot{\mathbf{Z}} \tag{33}\]  
Substituting Equation (31) into Equation (33) yields the following:  
\[ \begin{array}{l}\dot{\mathbf{V}}(\mathbf{Z})=\frac{1}{|x_{1}|^{2}}\mathbf{Z}^{\mathsf{T}} \mathbf{F}^{\mathsf{T}}\mathbf{N}\mathbf{Z}+\frac{1}{|x_{1}|^{2}}\mathbf{Z}^{\mathsf{T}}\mathbf{N} \mathbf{F}\mathbf{Z}\\ =\frac{1}{|x_{1}|^{2}}\mathbf{Z}^{\mathsf{T}}(\mathbf{F}^{\mathsf{T}}\mathbf{N}+\mathbf{N}\bm {F})\mathbf{Z}\\ =-\frac{1}{|x_{1}|^{2}}\mathbf{Z}^{\mathsf{T}}\mathbf{M}\mathbf{Z}\end{array}  \tag{34}\]  
In this context, \(\mathbf{M}=-(\mathbf{F}^{\mathsf{T}}\mathbf{N}+\mathbf{N}\mathbf{F})\). Substituting \(\mathbf{F}= \begin{bmatrix}-\frac{k_{1}}{2}&\frac{1}{2}\\ -k_{2}&0\end{bmatrix} \) and \(\mathbf{N}=\frac{1}{2} \begin{bmatrix}k_{1}^{2}+4k_{2}&-k_{1}\\ -k_{1}&2\end{bmatrix} \) into the expression for matrix M results in the following:  
\[\mathbf{M}=\frac{k_{1}}{2} \begin{bmatrix}k_{1}^{2}+2k_{2}&-k_{1}\\ -k_{1}&1\end{bmatrix}  \tag{35}\]  
To prove that matrices M and N are positive definite using [PERSON]'s criterion, it is necessary to verify that all principal minors of the matrices are positive. For a \(2\times 2\) matrix, [PERSON]'s criterion stipulates the following conditions: the first principal minor (i.e., the top-left element of the matrix) must be positive, and the determinant of the matrix must also be positive. For the matrix M, M\({}_{11}\) and det(M) are given by Equation (36). \[\left\{ \begin{array}{l}M_{11}=\frac{k_{1}}{2}\cdot\left(k_{1}^{2}+2k_{2} \right)\\ \det(M)=\frac{k_{1}}{2}\cdot\left[\left(k_{1}^{2}+2k_{2}\right)\cdot 1-k_{1}^{2} \right]=k_{1}k_{2}\end{array} \right. \tag{36}\]  
Since k\({}_{1}>0\) and k\({}_{2}>0\), it can be concluded that M is a positive definite matrix. For the matrix N, N\({}_{11}\) and det(N) are given by Equation (37). \[\left\{ \begin{array}{l}N_{11}=\frac{k_{1}^{2}}{2}+2k_{2}\\ \det(\mathbf{N})=\frac{1}{2}\cdot\left[\left(k_{1}^{2}+4k_{2}\right)\cdot 2-k_{1}^{ 2}\right]=\frac{k_{1}^{2}}{2}+4k_{2}\end{array} \right. \tag{37}\]  
From k\({}_{1}>0\) and k\({}_{2}>0\), it can be concluded that N is a positive definite matrix. Consequently, it follows that \(\dot{\mathbf{V}}(\mathbf{Z})<0\), indicating that \(\dot{\mathbf{V}}(\mathbf{Z})\) is a negative definite matrix. When the sliding mode gains k\({}_{1}\) and k\({}_{2}\) in the expression of the super-spiral sliding mode control (Equation (26)) are greater than zero, both matrices N and M are symmetric positive definite. Under these conditions, the matrix \(\dot{\mathbf{V}}(\mathbf{Z})\) is negative definite, which implies that the system is asymptotically stable at the equilibrium point (the origin).
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 6 Conclusions  
In this paper, an advanced path-planning method based on Dynamic Bridging RRT was successfully developed and demonstrated, dedicated to addressing the limitations of existing strategies in complex environments and real-time requirements. The research results indicate that, based on the dynamic sampling strategy of expanding speed, key achievements include faster search speeds in regions with dense obstacles or intricate bottleneck shapes. Meanwhile, in the concluding path-generation phase, a swift path-smoothing strategy was adopted, which further optimized the path, producing a more streamlined and high-quality route. This approach makes path planning for mobile robots in complex environments more efficient and is a useful complement to existing path-planning algorithms. However, due to time and resource constraints, we were not able to independently demonstrate the potential improvement effect that the path-smoothing component may have on the RRT-Connect algorithm. This is an important dimension of comparison that will be added in future work. Future research will include applying the smoothing strategy to RRT-Connect individually under the same conditions and performing a careful comparative performance analysis to fully assess the impact of the smoothing approach. Through such work, we hoped to provide deeper insights and further validate the stability and efficiency of the methods, providing a more complete performance evaluation framework.  
Conceptualization, S.Q. and R.T.; methodology, S.Q.; software, S.Q. and R.T.; validation, S.Q. and B.L.; formal analysis, S.Q.; investigation, S.Q., R.T. and C.T.; resources, S.Q., B.L., and X.H.; data curation, S.Q. and R.T.; writing\(-\)original draft preparation, S.Q.; writing\(-\)review and editing, S.Q., B.L. and C.T.; visualization, S.Q. and C.T.; supervision, B.L.; project administration, S.Q. and B.L.; funding acquisition, B.L.; All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (grant number 61973234 and 62203326), and in part by the Tianjin Natural Science Foundation (grant number 20 JCYBJC00180).  
Not applicable.  
The source code presented in this study is available on request from the corresponding author.  
The authors would like to thank Tiangong University for technical support and all members of our team for their contribution to the mobile robot experiments. The authors acknowledge the anonymous reviewers for their helpful comments on the manuscript.  
The authors declare no conflicts of interest.
# Towards Cognitive Recommender Systems
## 3 A General Framework for Cognitive Recommender Systems
### Knowledge-Driven: Intelligent Knowledge Lakes
#### 3.2.1 Domain Knowledge  
As discussed in Section 3.1, Knowledge Lakes are used to facilitate the content and context analytics for the RSs, as they contain virtually inexhaustible amounts of both data and contextualized data that is readily made available anytime to anyone authorized to perform analytical activities. Intelligent Knowledge Lakes ([PERSON], 2018) to facilitate linking Artificial Intelligence (AI) and Data Analytics, to enable learning from domain expert's experience, and use this knowledge to annotate the contextualized information stored in a knowledge lake. The goal is to enable RSs to learn from contextualized data and use them to develop cognitive assistance for RSs' users for: facilitating the recommendation processes and learning from the users' cognition over time (by generating new rules for future predictions). Accordingly, Intelligent Knowledge Lakes can help RSs to understand the big data, use it in a smart way, and feed it into its (Intelligence) engine. One of the main challenges to build an Intelligent Knowledge Lake is to mimic the knowledge of domain experts using techniques such as feedback, surveys, and interviews; to build a domain-specific Knowledge Base. A Knowledge Base (KB) typically consists of a set of concepts organized into a taxonomy, instances for each concept, and relationships among the concepts.  
As an example, a domain knowledge can be a crucial source of data when optimizing a Recommender System when personalizing digital government services, that is, to decide what a user sees. It should be highlighted that, this would different from customization, where the user decides what they want to see. In building a domain knowledge, we need to capture all important entities, relationships, and events that are happening in real time in the specialized discipline or field, such as health, education, retail and financial domain. For example, to construct a domain-specific Knowledge Base in banking, the Concepts may include products (e.g., accounts, cards, checks, loans, etc.), customers (e.g., Personal, Business, Government, etc.), processes (e.g., sales, customer service, risk management, debt management, product management, campaign management), analytics (Customer Analytics, Credit Card Analytics, Loan Analytics, Economy Analytics, etc.), and more. The instances of such concepts would be, for example, the list of customers (instances of the concept Customer) or different Fraud detection operations (instances of the concept Process, e.g., operations such as data analysis algorithms to detect patterns and anomalies). To identify the instances, it is also important to consider the entities related to those concepts and instances. For example, fraud detection can be applied to several entities such as credit cards or insurance. In this context, connected entities will form a (Knowledge) graph including financial transactions, location, devices used, initiated sessions and authentication systems.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 2 Related Works
Initially, the researchers looked at single-cell achievable data rates, deter mining a trade-off between information rates, latency, and supported devices. Aiming to improve the system sum rate, they proposed NB-IoT-based QoS-aware resource allocation in a multi-cell situation. They created an optimization framework as well as a non-optimal solution for a large number of users in this regard. Finally, the authors developed a cooperative game strategy to achieve a certain system performance and evaluate their concept with evidence-based findings. Their findings revealed that providing higher data rates with NB-IoT comes at a cost, including fewer supported devices and increased delays. This paper focused on jointly optimizing the data rate, delays, device density, and resource allocation to achieve a better solution using NB-IoT. Moreover, in the case of healthcare applications, data sizes are small, requiring less power and long-distance communication, both of which are well supported by NB-IoT. In this paper, we emphasize on resource allocation methods for smart healthcare system using NB-IoT addressing parameters that were not reflected in previous works. This paper discovers the idea of event categorization to identify an appropriate user for data transmission in a certain time slot for the allocation of radio resources. We explore the factors that affect event-influenced access priority in the NB-IoT healthcare environment and formulate an optimization framework to maximize user utility in networks along with some constraints. In this proposed model, prioritized users (uplink cases) communicate with the base station with interference-aware radio resource allocation while considering the appropriate users, data rate, time slot, and QoS constraints. Such a model is expected to offer higher performance for healthcare applications.
## 3 Results
### Precise Point Positioning  
To estimate the GPS positioning accuracy, we calculated the PPP error (5) for the worldwide network. The dynamics for the PPP error spatial distribution can be found in the Supplementary Materials (Video S1). Figure 5 shows the average PPP error vs. latitude. The latitude bin size is 2.5\({}^{\circ}\). PPP errors are averaged over \(-0^{\circ}\)... 180\({}^{\circ}\) (European-Asian sector, upper panels a-b) and \(-180^{\circ}\)... 0\({}^{\circ}\) (American-Atlantic sector, bottom panels c-d). The dashed line on panels b and d marks 18:30 UT. Panels a and c show the data for the reference day (21 June 2015, 172 nd Day of the Year, DOY), while panels b and d show the data for the 22 June 2015 storm (173 DOY).  
The results for the reference day show that the PPP error increases due to satellite geometry or data processing peculiarities. Such effects have similar patterns for both the storm and the reference days. For example, there is a PPP error increase at \(\sim\)10 UT in the Northern Hemisphere. Note that, over 0-1(2) UT, the quality of the PPP solution obtained by the GAMP software is significantly worse than that at other times. So, we do not consider this time. In Figure 5, the navy blue regions (zero values)  
Figure 5: Average precise point positioning (PPP) error (obtained by GAMP software) vs. latitude. The latitude bin size is 2.5\({}^{\circ}\). The longitude region was 0\({}^{\circ}\)... 180\({}^{\circ}\) (European-Asian sector, upper panels _a-b_), \(-180^{\circ}\)... 0\({}^{\circ}\) (American-Atlantic sector, bottom panels _c-d_). The dashed line on panels b and d marks 18:30 UT. Panels a and c show the data for the reference day (172 DOY), while panels b and d show the data for the 22 June 2015 storm (173 DOY).  
mark lack of the data. Data gaps can be seen on both the reference and the storm days. One can see such patterns at the North Pole region, as well as at \(\sim\)60\({}^{\circ}\)S, where there are no stations for calculations.  
The typical average errors are less than 0.5 m (and even less than 0.1-0.3 m). From Figure 5, in the Northern Hemisphere after 18:30 UT (dashed line), one can see a sharp decrease in the PPP quality at 65-75\({}^{\circ}\)N. The error exceeded 0.5 m. So, we recorded a precision deterioration, which results in fivefold worse positioning accuracy. There is an equatorward \"propagation\" of the error with time. In the American-Atlantic sector (Figure 5d), we can observe such a \"propagation\" as far as \(\sim\)50\({}^{\circ}\)N. The PPP error maximum appears at \(\sim\)20 UT at this latitude. In the European-Asian sector, we observed a similar pattern with an increased error at \(\sim\)20 UT, but not in such details due to fewer stations. There is a signature that propagated to the equatorial region in the Northern Hemisphere, but the effect is not so pronounced, and it is observed during the reference day.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 2 Data and Methodology
### Methodology
#### 2.2.3 Training Object Detection Model  
Object detection is defined as the process of accurately identifying and localizing objects within an image [49; 50]. It primarily concerns itself with two fundamental questions: what the target object is and where it is located. Research in detection and classification is predominantly centered around two critical issues: accuracy and processing speed. Achieving an appropriate balance between these two elements is vital in object detection. There are many machine learning models used for object detection [51; 52; 53; 54]. YOLO mod  
Figure 2: The system architecture.  
els demonstrate superior computational time and efficiency compared to state-of-the-art models such as Faster R-CNN, EfficientDet, and SSD [54; 55]. The first YOLO model, introduced by [PERSON] in 2016, was designed to address the limitations of two-stage detectors, specifically, their inadequate convergence speed [51; 56]. Over the years, subsequent versions of YOLO have sought to mitigate the shortcomings of earlier models by modifying the architecture to develop an optimal network that effectively balances accuracy and speed [57]. In the study, the YOLOv10 model was preferred. YOLOv10 employs innovative strategies to enhance both computational efficiency and performance. Previous YOLO architectures demonstrated inevitable redundancies in their designs. In the YOLOv10 model, improvements have been made in the downsampling layers to maximize information retention during this process. As a result, the model delivers a competitive performance while reducing latency. Additionally, it incorporates stages with fundamental building blocks that achieve a higher efficiency without compromising performance. The head components are designed for efficiency, ensuring the model minimizes the overhead of the classification head without significantly impacting performance. This comprehensive approach results in a more streamlined and efficient architecture [58].