esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents an electromechanical coupling model integrating an equivalent magnetic network (EMN) model of a dual three-phase permanent magnet synchronous motor (DTP-PMSM) with the dynamic model of a helical planetary gear transmission system. Using this model, this study analyzes the dynamic characteristics of an electric drive system, specifically motor phase current, electromagnetic torque, and gear meshing force, under self-fault-tolerant control strategies. Simulation and experimental results demonstrate that the self-fault-tolerant control strategy enables rapid fault tolerance during open-phase faults, significantly reducing system fault recovery time. Meanwhile, compared to the open-phase faults conditions, the self-fault-tolerant control effectively suppresses most harmonic components within the system; only the second harmonic amplitude of the electromagnetic torque exhibited an increase. This harmonic disturbance propagates to the gear system through electromechanical coupling, synchronously amplifying the second harmonic amplitude in the gear system's vibration response. This study demonstrates that self-fault-tolerant control strategies significantly enhance the dynamic response performance of the electric drive system under open-phase faults conditions. Furthermore, this study also investigates the electromechanical coupling mechanism through which harmonics generated by this strategy affect the gear system's dynamic response, providing theoretical support for co-optimization electromechanical coupling design and fault-tolerant control in high-reliability electric drive transmission systems.  
electromechanical coupled dynamic model; self-fault-tolerant control; dynamic response; vibration characteristics
## 3 Secondary Optical Design
### TIR Lens Design
#### 3.1.2 Design of Side Total Reflective Surfaces  
When the light source divergence angle (\(\theta_{1}\leq\theta\leq 90^{\circ}\)), the rotating surface \(AB\) does not play an optical role, and the light rays become parallel rays by refracting through the rotating surfaces \(AC\) and \(BE\) and then reflecting through the rotating surfaces \(CD\) and \(EF\). Therefore, the curvature of the outer surface needs to be calculated. The design of the lateral total reflective surface is shown in Figure 7, and a point \(P\) is taken on the curve \(EF\), according to the law of refraction:  
\[n_{0}\sin\ (\ 90-\theta)=n_{1}\sin\ (\ \beta^{{}^{\prime}}) \tag{7}\]  
According to the sum of the interior angles, the triangle is \(180^{\circ}\), the angle of reflection: \(\beta_{2}=\frac{\beta^{\prime}+90^{\circ}}{2}\)  
Suppose the horizontal coordinate of point \(P\) is \(x_{i}\), then the vertical coordinate \(y_{i}=x_{i}\tan(90^{\circ}-\theta)\), from the definition of slope,  
\[\frac{y_{i+1}-x_{0}\tan(90-\theta)}{x_{i+1}-x_{i}}=\tan\beta^{\prime} \tag{8}\]  
According to the iterative calculation,  
\[x_{i+1}=\frac{x_{0}\tan\beta^{{}^{\prime}}_{i+1}-k_{i}x_{i}-x_{0}\tan(90- \theta_{i+1})}{\tan\beta^{{}^{\prime}}_{i+1}-k_{i}} \tag{9}\]  
\[y_{i+1}=\frac{K_{i}x_{0}\tan(90-\theta_{i+1})-yi\tan\beta^{{}^{\prime}}_{i+1}+ k_{i}x_{i}\tan\beta^{{}^{\prime}}_{i+1}-k_{i}x_{0}\tan\beta^{{}^{\prime}}_{i+1}}{k_{i}- \tan\beta^{{}^{\prime}}_{i+1}}\]  
Figure 7: Side reflective surface optical design principle.  
After the above calculation, the discrete points of the intermediate aspheric curve and the side total reflection curve are obtained.
## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Multi-Sensor Information Fusion Technology  
[PERSON] et al. [15] comprehensively adopted monocular cameras, binocular stereo cameras, and LiDAR as environmental sensing devices to develop an autonomous intelligent excavator based on a comprehensive sensing system. Through multi-sensor information fusion, they realized the detection of obstacles and mining targets in the mining operation scene and the real-time three-dimensional modeling of mining results. [PERSON] et al. [17], addressing the challenge of complex and dynamic environments that are difficult to perceive during earthmoving operations of intelligent excavators, employed both cameras and LiDAR to sense the surroundings. They used the iterative closest point (ICP) algorithm to fuse image and point cloud data, effectively enhancing the environmental perception capabilities of the excavator during operation. [PERSON] et al. [60] developed a local terrain reconstruction system for intelligent excavators by comprehensively using LiDAR and binocular cameras, which effectively improved the environmental perception ability of intelligent excavators. At present, the research on multi-sensor information fusion technology for intelligent excavators is still relatively lacking, but the relevant research in other fields also has certain reference significance for the research and development of intelligent excavators. [PERSON] et al. [61; 62] comprehensively adopted LiDAR and a camera for data acquisition, and projected point cloud data onto images for data fusion, which added depth information to image pixels and effectively improved the ability of environmental perception. However, the resolution of the camera was higher than that of the radar, resulting in the loss of depth information of some image pixels. To solve the problem of resolution matching between LiDAR and the camera, [PERSON] et al. [63] calculated the geometric transformation relationship between different sensors and adopted the Gaussian interpolation algorithm to compensate for the image pixels lacking depth information, effectively
## 2 Materials and Methods
### Data Analysis  
All of the data from the study were analyzed using IBM SPSS Statistics 25. Quantitative variables were tested for normal distribution using the Shapiro-Wilk test and were written as averages with standard deviations or medians with interquartile ranges. Quantitative independent variables were tested using Kruskal-Wallis H tests/Welch ANOVA tests, according to their distributions and their inequality of variances. Post-hoc analyses were made using Dunn-Bonferroni tests or Games-Howell tests. Statistically significant (SS) differences were established at \(p\) values less than 0.05 (\(p\leq 0.05\)).
## 5 Conclusions and Future Work  
This paper systematically investigates the introduced LORSAL algorithm for the EEG-based emotion class. Additionally, the critical frequency bands, Delta, Theta, Alpha, Beta, Gamma, and the effectiveness of different features, PSD, DE, DASM, RASM, and DCAU, on emotion recognition are also analyzed. The LORSAL classifier performs better than the compared shallow methods and has the superiority of time efficiency compared to the recent DL approaches.  
The performance and application of LORSAL-based emotion recognition should be further researched in future work. More informative and representative features can be used in LORSAL. As shown in Table 5, in the research by [PERSON] et al. [33], SVM achieved higher values of AUC (Area Under ROC Curve), 0.9234 and 0.9426, for classifying LV/HIV and LA/HA emotions by concatenating PSD and raw pre-processed EEG signals than with other features. We will try to integrate different features to train the LORSAL classifier. The future attempts include the application of LORSAL for 4-class emotion classification in VA space, as the studies in [48]. A further comparison of LORSAL and DL methods, and the combination of their advantages in feature extraction and avoiding overfitting will be investigated. Future work could also include applying LORSAL on multimodal information, e.g., fNIRS, and other physiological signals in brain activity analysis [79; 80].  
Conceptualization, C.P. and C.S.; methodology, C.P. and C.S.; software, C.P. and C.S; validation, C.P.; formal analysis, C.P. and C.S; investigation, C.P.; writing--original draft preparation, C.P.; writing--review and editing, C.S.; supervision, H.M., J.L. and X.G.; funding acquisition, C.P., C.S., and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant 61902313, the Fundamental Research Funds for the Central Universities, Xidian University, No. RW190110, and the Construction Project Achievement of College Counselor Studio of Shaanxi Province: Reach Perfection with Morality Studio.  
The authors would like to thank [PERSON] for providing the source codes of the LORSAL algorithm on the websites ([[http://www.lx.it.pt/~jun/](http://www.lx.it.pt/~jun/)]([http://www.lx.it.pt/~jun/](http://www.lx.it.pt/~jun/))).  
The authors declare no conflict of interest.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Driving Behavior Characteristics Analysis Results
#### 3.1.3 Front Time Feature  
The amount of time given as a headway is an essential parameter for the analysis of the safety of the following behavior of drivers. The researcher analyzed 14,760 simulated headway time measurements, and video analysis techniques were applied to 2340 field observation measurements. The driving simulation data were analyzed, and the statistical results of the headway time characteristics of drivers in the sharp-curve sections are shown in Table 5. The results indicate that the average headway time in curved sections is significantly smaller than that in straight sections, and more than 25% of drivers have a dangerous headway time of less than 1 s, reflecting a higher risk of rear-end collisions. A safety headway time model based on vehicle speed and reaction time was constructed:  
\[THW_{s}=\frac{V_{f}}{3.6}T_{r}+\frac{V_{f}^{2}-V_{l}^{2}}{25.92(a_{f}-a_{g})} \tag{6}\]  
where THW\({}_{s}\) is the safe headway (s); V\({}_{f}\) and V\({}_{l}\) are the speeds of the front and rear vehicles, respectively (km/h); and T\({}_{r}\) is the driver's reaction time (1.5 s), which represent the accelerations of the front and rear vehicles respectively (m/s\({}^{2}\)). The model comprehensively considers the influence of speed difference and reaction time on the following safety.  
\[THR=\frac{THW_{s}-THW_{a}}{THW_{s}} \tag{7}\]  
where \(THW_{a}\) is the actual time headway (s). The larger the \(THR\), the more the actual time headway deviates from the safety threshold and the higher the risk of a rear-end collision.
Sensors_**2015**, _15_, 1964-1997; doi:10.3390/s150101964  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 School of Electronics and Information Engineering, Harbin Institute of Technology,  
Harbin 150001, China; E-Mails: [EMAIL_ADDRESS] (L.J.); [EMAIL_ADDRESS] (G.R.); [EMAIL_ADDRESS] (Y.Z.)  
2 School of Information and Communication Engineering, Dalian University of Technology,  
Dalian 116024, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-451-8640-3135.  
Academic Editor: [PERSON]  
_Received: 29 September 2014 / Accepted: 12 January 2015 / Published: 16 January 2015_  
**Abstract:** Interference alignment (IA) has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs). However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC)-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN) code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER) expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS) algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.  
keywords: wireless sensor networks; interference alignment; clustering; pseudo noise (PN) code; spatial-code domain; bit error rate (BER); spread spectrum +
Footnote †: journal: Journal of Signal Processing
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 1 Introduction
However, they must also be small enough not to deteriorate living tissues physically upon entering the body, and they must be large enough to handle both endogenous and exogenous signals from multiple sensing systems [19]. From the design aspect, the nanoscale structures required for different applications have not been developed due to the lack of effective processes in the field of nanotechnology. The manufacturing technique of the bottom-up approach--the idea of the atom-by-atom building of devices--is accepted by physicists, but due to the high reactivity of most atomic species, has not been preferred by chemists [2; 20]. Other challenges faced by researchers include designing and fabricating nanorobots at dimensions below the nanometer range, and programming and coordinating a large number of nanorobots (known as swarms) [16]. Moreover, other specific design issues like sensing, navigation, power communication, locomotion, and the manipulation of components are severe challenges in the field of nanorobotics [4]. Another issue in terms of the nanorobotic structure is controlling matter at the molecular scale to affect the nanorobots' behaviour (dynamics and properties). Overall, the automation, power, and manufacturing of nanorobots is a challenging and very novel area [13]. The main focus of the paper is on challenges in the design and development of nanorobots in terms of sensors, actuators, powering, and data transmission. To the best of authors' knowledge, these challenges have not been classified and reviewed as they are in the current paper. The aim of this review paper is to provide some useful and classified information about the available options explored by other researchers for new designers. The information provided in this paper will hopefully help others to expedite their research and develop new concepts and avenues. Challenges related to design and development, including sensors, actuators, communication channels for data transfer, power and navigation are explored in detail in Section 2, followed by an overall review of these challenges concerning various applications in Section 3. The risks and hazards associated with the application of nanorobots are briefly discussed in Section 4. Section 5 summarizes the conclusions made in this review study, followed by proposing future work.
## 3 Proposed Framework
### Gateway  
The WSN control unit is a multi-protocol WSN coordinator equipped with a WiMAX gateway device. The gateway is composed of the WSN control unit and the WiMAX gateway. Several manufacturers have tried to create wireless sensor network devices which combine WSN sink coordinator and other wireless communications technologies. Such devices include the Centre Suisse d'Electronique et de Microtechnique (CSEM) WiseGSM (Global System for Mobile Communications) [33] and BeanAir BeanGateway [34] devices which combine WSN with General Packet Radio Service (GPRS). Also studies such as [35; 36; 37] utilize GSM/GPRS modem with embedded Transmission Control Protocol and the Internet Protocol (TCP/IP) stack to provide wireless TCP-IP connectivity to WSN for remote monitoring. This study proposes to use Waspmote gateway [38] by libelium which has a dedicated socket for solar panel input, hardware interfaces for a 3G plus GPS module, and a maximum communication range of 7 to 12 km. The Waspmote will integrate all the information received from the sensor nodes and calculate their locations and send the text data to the database using the WiMAX gateway. Multiple gateways should be deployed in the neighboring villages using a square grid in which the number of gateways will be determined a priori when planning the WSN and depending on the village size. The initial deployment should maintain network coverage and connectivity using a minimum number of gateways. The static gateway maintains a connection to the server using WiMAX connectivity. The gateways should be fitted with a renewable energy source such as solar power for longer term autonomous operation. The gateways should have a priori information about their coordinates. A schematic diagram of the gateway is shown in Figure 2.
## 3 Analysis of the Main Geomorphological Features of the Pit Rim Structure
### Extraction and Analysis of Valley Lines and Ridge Lines
#### 3.4.1 Extraction and Analysis of Valley Lines  
The valley lines inside the pit rim structure were primarily distributed on the mountains in the northwest and southwest (Figure 10a). A significant water flow path was formed due to the low topography in the central area. The extracted 23 main valley lines (G1-G23) were evenly distributed outside (Figure 11a), and most of the valley lines were consistent with the extracted ridge boundaries. The valley lines formed between the ridges on the left were shorter, and these lines were largely distributed in the upper part of the ridges. The valley lines on the right were more densely distributed and then extended to the bottom (Figure 11b), and the catchment area on the right was more significant. In general, the valley lines formed a significant vertical drop in a relatively short horizontal distance (Figure 11c).  
Figure 11: (**a**) The valley lines combined with point-cloud display, (**b**) the valley lines XY projection, and (**c**) the valley lines XZ projection.  
According to statistics (Table 4), the mean length of the morphological parameters of the valley lines was determined as 202.53 m, the mean maximum elevation difference was 93.31 m, and the mean dip angle reached 29.37\({}^{\circ}\). In general, the longer valley lines were distributed between the ridges at the outer two ends, and there were shorter valley lines in the central area. To be specific, G23 achieved the longest length of 581.88 m, a vertical drop of 219.17 m, as well as a relatively gentle dip angle. The valley lines (G20 and G11) close to the end of the ridge line achieved the smallest dip angle.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Semantic Segmentation Module
#### 3.1.4 Attention Mechanism ECA
The adaptive channel weighting of feature maps enhances correlation between different channels, allowing for a better exploration of details and texture information and thus improving the recognition of object boundaries and shapes.
# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 4 Results and Discussion
### Reliability Assessment of Expert Decision for \(L_{s}\)  
Figure 5 shows the results of normalizing the remote sensing results for \(L_{S}\). No \(L_{S}\)1 operators were observed in terms of time and quality, somewhat different from the expert judgment. In addition, comparing the expert decisions with the test results for \(L_{S}\)1-\(L_{S}\)5 indicates that the operator \(L_{S}\) was only correctly identified by the experts 26.7% of the time for any work time or quality. This confirms that expert judgments are subjective and may not accurately reflect the actual skill level of an operator.  
Figure 4: \(L_{S}\) and \(L_{D}\) weight factors according to experts.  
The experimental results show that an operator's \(L_{S}\) can be accurately measured by considering the working time (as measured by the PMS) and quality of the workpiece. In addition, the results of the experiments show how the working time and quality of each operator compared to the general levels, helping to identify the strengths of individual operators. Operators OP3, OP7, OP9, OP10, and OP12, located below and to the left of the diagonal midline in Figure 5, can be seen to have relatively fast work times compared to quality at each skill level, whereas the remainder of the operators can be seen to provide higher relative quality than faster work time. These operator characteristics can be an important consideration when designing an optimal production line.
## 3 Materials and Methods
### Comparison with Existing Policy Analysis Frameworks  
To highlight the advantages of our proposed framework, we compare it with existing policy analysis methodologies, particularly decision support systems (DSSs), evidence-based policy (EBP), and institutional modeling frameworks. Table 1 presents a structured comparison of these approaches.  
Our framework extends traditional DSS models by incorporating LLM-powered analysis [31] to generate structured recommendations, ensuring contextual adaptation. Additionally, while EBP frameworks primarily rely on retrospective data, our approach integrates predictive analytics, enabling policy-makers to simulate interventions before implementation. This combination allows for an advanced decision-making paradigm that balances institutional stability with adaptive policy innovation.  
The integration of AI, visualization techniques, and statistical modeling follows a structured analytical pipeline. Visualization enables pattern discovery and human interpretability, statistics provides rigorous empirical validation, and AI enhances predictive modeling and scenario-based decision-making. This multi-layered approach ensures that decision-makers receive actionable insights while maintaining institutional transparency and robustness. For example, in the hospitality use case, interactive visualizations identify pricing patterns, while graph neural networks model complex booking dynamics, and large language models synthesize policy recommendations grounded in institutional memory.  
The framework operates on structured time intervals based on the policy domain. For example, in the hospitality sector use case, booking data includes arrival dates (check-in), departure dates (check-out), and cancellation dates to capture customer behavior. Similarly, in the food security and education sectors, data are analyzed annually, focusing on trends across pre-crisis, crisis, and post-crisis periods (e.g., 2019-2022 for food security and 2000-2020 for education). These structured temporal dimensions allow for robust historical analysis, predictive modeling, and intervention design, ensuring that insights remain policy-relevant.  
While this paper incorporates existing analytical techniques such as statistical modeling, visualization methods, and AI-based decision support, it proposes a novel framework that integrates institutional theory with data-driven approaches. Unlike prior studies that focus on either policy modeling or AI-driven decision support in isolation, our framework introduces the concept of institutional intelligence--a synergistic combination of human expertise, AI-assisted modeling, and structured policy analysis. This framework is not merely descriptive; it is prescriptive, providing policy-makers with a structured methodology for adapting AI-powered decision tools to institutional constraints, hierarchical decision-making, and policy design. To further emphasize this contribution, we introduce a step-by-step implementation guide outlining how each component of the framework can be practically deployed across various policy domains.