esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To enhance the balance between lateral stability and energy efficiency, we propose an adaptive compound controller based on phase plane analysis for four-wheel independent drive electric vehicles (4 WID-EVs). The adaptive stability and energy-saving controller (SEC) is designed with a three-layer structure. The upper-layer controller employs model predictive control (MPC) to compute the external yaw moment based on the desired yaw rate and side slip angle derived from a reference model. The adaptive-layer controller utilizes a phase plane diagram to evaluate vehicle stability and reduces unnecessary external yaw moment consumption by accounting for the vehicle's steering state and battery's state-of-charge (SOC) level. The lower-layer controller implements an optimal torque distribution algorithm to minimize an objective function that considers tire workload, energy consumption, and smooth motor control. Numerical simulations are performed in MATLAB/Simulink using three distinct steering angles to evaluate the performance of the proposed control strategy. At each steering angle, the SEC's stability and energy efficiency are compared to those of the energy-saving controller (EC) and stability controller (SC) under varying battery charge levels. The results indicate that, at small steering angles, the vehicle operates in a highly stable state, enabling a reduction in the external yaw moment to achieve substantial energy savings. As the steering angle increases, the vehicle approaches a critical stability state, where the external yaw moment is applied to maintain lateral stability. Furthermore, as the SOC decreases, the SEC strategy will increasingly prioritize energy savings. Simulation results verify that the SEC strategy effectively balances lateral stability and energy savings while maintaining consistent performance across a range of operating conditions.  
e _Applied Sciences_  
2025  
28 February 2025  
10 March 2025  
**Keywords:** electric vehicle; phase plane analysis; vehicle stability; energy savings; model predictive control; torque distribution
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 3 Experimental Protocol  
The proposed system was tested in an experimental protocol in order to evaluate its performance. A total of seven volunteers PD subjects (aged over 70 years) took part in the experimental protocol of this study. All the PD subjects gave their informed consent before participating in the experimental protocol. In particular, the tests were carried out in both home and laboratory scenarios, and each subject was recruited for both trials. While the tasks of the laboratory scenario aimed to assess the developed algorithm performance in gait parameters detection, the data acquired over 12 h during tasks of the home scenario were used to test the proposed algorithm in a home environment. In each scenario, the tasks were video recorded through a GoPro to verify the correct functioning of the proposed algorithm. Subjects wore the camera attached to the lower abdomen through the velcro band used for the sensor and it was directed at the participant's feet. The tasks of the laboratory scenario were performed in the private clinic \"Villa dei Pini\" (Civitanova Marche, Italy) where, as shown in Figure 6, the subjects were invited to perform three different tasks as follows:  
* Timed Up and Go (TUG) task: subject started from sitting on a chair, then walked straight on for some meters, completed a turn of 180\({}^{\circ}\) around an obstacle, moved straight on for some meters again, and finally sat again on the chair. This activity was performed twice;
* Stair task: subject started from sitting on a chair, then walked straight on for some meters, ascended and descended the stairs four times, walked straight on for some meters again, and finally sat again on the chair;  
Figure 5: Graphical User Interface of the final report on the PD subject’s health state, where the extracted parameters (in the **bottom**) and the histograms of the patient status (on the **right**) are illustrated.  
* Free walking task: subject walked \"freely\" inside and outside the clinic for 15 min (so experiencing several straight walks, turns, stair ascending and descending activities, and pauses).  
In the task of the home scenario the subjects were asked to switch on the device and to wear it while performing their common ADLs during the day. At the same time, they annotated these activities in a diary which could also include additional information about problems with the device (improper device positioning or switching off due to the battery discharging) and the description of subject's health status during the day.
## 2 Data Description
### IRI F2-Layer Peak Modeled Characteristics  
IRI is an empirical climatological model of the ionosphere, which is considered the reference model by the ionospheric community [10; 41]. The last version of the model, i.e., IRI--2020, has been considered in this study to obtain information on the F2-layer peak characteristics _Nm_F2 and _hm_F2, which are the electron density and altitude of the F2-layer peak. IRI was run for the same time periods and locations covered by both CSES--01 and Swarm B datasets. _Nm_F2 values were modeled through the URSI coefficients [42]. _hm_F2 values are those given by the Shubin option [43]. Both are recommended options in IRI--2020. Information about the performance of IRI in describing the F2-layer peak characteristics for different conditions and on a global basis can be found in [44]. IRI--2020 Fortran code is available on the IRI website ([[http://irimodel.org/](http://irimodel.org/)]([http://irimodel.org/](http://irimodel.org/)), accessed on 7 April 2023).
# Phytotoxic Substances Involved in Teak Allelopathy and Agroforestry
## 3 Allelopathic Property of Teak
### Extracts of Teak Leaves  
Aqueous extracts of fallen teak leaves inhibited the seedling growth and protein contents of _Vigna mungo_ ([PERSON] and _Vigna radiata_ ([PERSON] [42]. Methanol extracts of fallen leaves of teak reduced the germination of _Echinocchio colona_ L. and _Cyperus difformis_ L. [43]. Methanol extracts of fallen teak leaf powder were applied in a wheat field (10.8 g methanol extract residue/7.2 m\({}^{2}\)) and weed emergence in the field was monitored. The highest reduction (56%) in weed population was recorded 14 days after treatments. However, the treatment did not affect the growth of wheat [39].  
Aqueous extracts of fresh teak leaves also suppressed the germination and seeding growth of _Vigna mungo_ ([PERSON] [PERSON] [44] and _Plumbago zeylanica_ L. under laboratory conditions [45], and rice (_Oryza sativa_ L.), maize (_Zea mays_ L.), _Vigna radiate_ ([PERSON], _Vigna umbellate_ ([PERSON], and _Arachis hypogee_ L. under Petri dishes and pot culture conditions [46]. Aqueous extracts of fresh teak leaves recorded more than 30% germination inhibition of _Luffa cylindrica_ [PERSON], okura (_Abelmoschus esculentus_ ([PERSON]), and brown mustard (_Brassica juncea_ [PERSON]) at 10 days after sowing of these seeds [47]. In addition, aqueous extracts of teak leaf powder inhibited the seedling growth and the contents of photosynthetic pigments, such as chlorophyll and carotenoid in chilli (_Capsicum frutescent_ L.), _Vigna radiata_ ([PERSON] [48], _Pennisteum glaucum_ ([PERSON], and _Eleusine coracana_ [PERSON] [49]. Methanol extracts of fresh teak leaves also inhibited the growth of _Amarantthus spinosus_ L. [50].  
Aqueous extracts of teak roots suppressed the germination and seedling growth of _Hibiscus esculentus_ L. [51]. Inhibitory activity of the extracts of teak leaves, banks and seeds was compared against the growth of maize (_Zea mays_ L.), and leaf extracts showed the greatest inhibitory activity [52]. Those findings described in this section indicated that the extracts of the fallen and fresh teak leaves, roots and banks suppressed the germination and growth of many plant species, both weeds and crops. Those findings also suggested that the leaves, roots and banks may contain some phytotoxic substances, which are extractable with water and/or methanol. Allelopathic activity of the leachate, leaves, and extracts of teak and target plant species, are summarized in Table 1.
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 1 Introduction
Some researchers have studied the game process among airlines, ATC, or other participants in the process of resource allocation. The relevant results mainly focus on solving the problem of combining the CDM framework that the CTOP relies on and the game theory [17; 18; 19; 20]. Among them, the CDM model based on satisfaction game theory [21], the CDM model based on the deferred acceptance mechanism by using the bilateral market matching method of game theory [22], a Game Theory Model Applied to Resource Management of Large scale UAV Communication Networks [23], and the Bayesian game model for the game process of competing for advantageous resources of airlines around the three key issues in the CTOP implementation process [24] have been proposed. In summary, most of existing research tends to use multi-objective decision-making techniques to select the global allocation plan. The final alternative plans are numerous, and the uncertainty is larger, and the research on resolving the conflicts of interest among airlines during the implementation of the CTOP is still not fully developed, which is a loss of fairness to some extent. But the maturity of CTOP strategies is closely related to fairness, as a mature CTOP system should be capable of balancing fairness among different airlines, flights, and flight plans. It ensures the reasonable utilization of resources while maintaining efficient air traffic. Therefore, on the issue of balancing the global goals of CDM and the individual goals of airlines, game theory can be used to fully consider the impact of airline decisions and enhance airline participation. At the same time, calculating the preferred en-route resource allocation plans for each airline from the perspective of game theory can provide stronger quantitative indicators for ATC to choose the final plan, finally selecting an allocation plan that is acceptable to multiple parties. This paper introduces game theory into collaborative en-route network resource management. Combining with the CTOP framework and fully considering the role of airlines in the resource allocation process, it analyzes the roles played by ATC and airlines in the plan selection process and the game process and establishes the Stackelberg game model. Finally, the option range of selectable plans is narrowed down according to the obtained mixed strategy results, and then, an optimal allocation plan is obtained, which enhances the fairness of multi-party participation in the CTOP decision-making process and the allocation plans.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 2 e-Textile Sensor Design  
The soft force sensor we propose utilizes EeonTex NW170-SPLPA [25], a variable resistance stretchable sensor fabric created by Eeonyx. Comprising 72% nylon and 23% spandex fibers, this non-woven microfiber is coated with a conductive polymer that induces a piezoresistive effect. This property allows the fabric's electrical resistance to vary with deformation, enabling precise measurement of pressure, bending, stretching, or torsion. The fabric's flexibility and pliability, combined with its responsiveness to pressure, make it ideal for designing shape sensors. According to the manufacturer's specifications, the sensor is highly durable, with a tensile strength of over 450 N and a tear resistance of 12 N, capable of withstanding 1000 washing cycles. Given this data, the e-textile sensor is deemed suitable for use in soft robot curvature sensing applications.  
To construct the e-textile force sensor, the fabric was cut into a 2 cm by 2 cm square. This sensor layer was then placed between two vertically aligned copper electrodes, acting as a piezoresistive pressure sensor, as depicted in Figure 1. The electrodes were connected to a breadboard and a resistance measuring circuit, incorporating a Wheatstone bridge for signal conditioning and an Arduino UNO with a 10-bit Analog-to-Digital Converter (ADC). In data acquisition systems, ADCs play a crucial role in capturing and digitizing analog signals from sensors and transducers. Since the e-textile sensor readings in terms of resistance change is an analog signal that is continuous in time and amplitude, the ADC converts the sensor readings into a digital signal, which is discrete in both time and amplitude to be processed by the digital controller. Upon applying pressure, the material's resistance changes accordingly.  
To increase the ability of our soft sensor to measure a wider range of forces, we use a multi-layered design. Each layer is a 2 cm by 2 cm square of conductive textile material. The first and last layers are connected to the copper electrodes, while the intermediate layers are placed in between, as depicted in Figure 2. Adding more layers extends the sensor's dynamic range [26; 27], although it may affect the response time for detecting applied forces. Achieving the right balance of layers based on response time and dynamic range needs is crucial for optimal performance in specific applications. This balance is a common challenge in designing soft force sensors [28]. To enable more sensing points on the e-textile shape sensor, a 4 \(\times\) 4 grid of conductors is arranged into a matrix above and below the soft sensing material, creating 16 sensing points, as shown in Figure 3.  
Figure 1: The sensor design utilizes piezoresistive textile material and copper electrodes to detect changes in pressure and strain.  
Figure 2: Diagram depicting the design of our soft force sensor with stacked layers [27].
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments
### Performance Analysis  
We generate instances with scales of 40, 60, 80, and 100 to test the efficiency of the proposed method. Instances with different scales are solved by the GDNN trained on the corresponding scales. For comparison, we divide the competitors into construction heuristics and high-quality competitors.
## 2 Basic Structure and Key Technologies
### The Metrological System and Its Layout  
The metrological system consists of three laser heterodyne interferometers, developed by [PERSON] _et al._[PERSON] _et al._ (2012); [PERSON] _et al._ (2013) to measure the movements of the 3D stage in \(X\), \(Y\) and \(Z\) directions, respectively. The reflectors of the interferometers are plane mirrors. The location of reflection mirrors are shown in Figures 1 and 2. The laser frequency of the three interferometers is stabilized at the level of about \(10^{-7}\). The resolution of the interferometer is 1 nm.  
The layout of the metrological system is well arranged so that all interferometers are fixed on the granite base and independent from the 3D stage, as shown in Figure 1. The three measuring lines, represented by the three laser beams of the three corresponding interferometers, are parallel to the movement directions of the 3D stage and orthogonal to each other. They intersect at the measuring point, which is corresponding to the center of the probe's tip-ball, as shown by the dotted lines in Figure 2. The measuring lines of \(X\)- and \(Y\)-interferometers, \(X\)-guideway and \(Y\)-guideway are in the same plane. Therefore, such a metrological system obeys the Abbe principle and the crosstalk is minimized.
## 4 Prediction Model for Roadway Surrounding Rock Stability
### Data Discretization  
In Table 3, we define the domain \(U=\{1,2,\ldots,20\}\), rock quality designation (RQD), uniaxial compressive strength (Rc), integrity coefficient of rock mass, groundwater seepage, and joint condition as belonging to condition attribute C. Meanwhile, decision attribute \(D\) represents the stability grade of the roadway surrounding rock. According to the classification standard of evaluation indexes for roadway surrounding rock stability in Table 1, we discretize the evaluation indexes and construct a knowledge expression system, as the statistics shown in Table 3, where the evaluation indexes are categorized into stability grades from 1-5. Different index categories lead to different corresponding stability grades; thus, this enables knowledge discovery of the relationship between indexes and decisions.
# Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples
## 1 Introduction
Inspired by the idea of generative adversarial networks (GANs) [9], we believe that classes that have similarities to the target class can be regarded as fake samples generated by the generator, and we only need to implement the function of the discriminator to enable the model to distinguish between real/fake samples. In this paper, we propose an adversarial prototype generator, which refers to the support images as positive examples, and introduces different categories with similarities as negative examples. The module obtains the respective prototype features from the feature maps of positive and negative examples, and conducts a confrontation between positive and negative prototypes to make the final generated adversarial prototype focus on the positive features but not the negative examples, and obtains the feature differences between the positive and negative examples from the perspective of the prototype features. Inspired by SAM [10], we design a corresponding decoder, use cross-attention between feature prototypes and query image embedding, guide the segmentation of the query image through the interaction between them, and obtain more accurate segmentation results under the guidance of the adversarial prototype. In addition, we note that, in most of the methods, the encoders used are complex and pretrained, and we assume that the encoder can capture features of any class. But, in fact, in the supervised learning settings, the pretrained encoder will focus more on the training class, will be less effective at extracting features from unknown classes, and may lead to overfitting. One solution to this problem is to make the model not memorize the semantic information learned during training, which is difficult to achieve with pretrained encoders. In our work, we reduce the emphasis on the feature extraction capabilities of the encoder, we make simple modifications to the encoder of Vision Transformer (ViT) [11], we remove some learnable parameters to reduce the encoder's attention to the image content, and we make the model have better generalization performance for the new classes that are not trained. Our main contributions are as follows:  
1. We propose a novel few-shot semantic segmentation model. Specifically, we design a prototype extractor to obtain class-related prototype features, introduce positive and negative examples, design an adversarial prototype generator to learn the feature differences between positive and negative examples, and design a corresponding decoder to segment the query image under the guidance of prototype features. 2. We reduce the focus on feature extraction effects and make simple modifications to the encoder so that the model has better generalization performance for new classes. 3. A large number of experiments on the FSS-1000 dataset show that the proposed model has strong generalization ability for new classes, and the segmentation effect is not inferior to the FSS methods with the encoder.
## 5 Performance Evaluation
### Discussion  
Given the source to sink node distance and the hardware parameter values in Table 1, we can determine the transmission manner, the optimal multi-hop number as well as the corresponding individual distances. The key difference between DEAR and other energy efficient routing algorithms is that we try to let each node consume the energy at similar rate rather than to minimize the total energy consumption during each routing process.  
The selection criterion of the next hop node is the key problem during routing process. In this paper, we treat the distance metric as the primary parameter and we also consider node residual energy as the secondary parameter. Thus, the energy consumption can be balanced well based on the distance and residual energy distribution. From this paper, we can see that DEAR algorithm is a distributed, localized, energy efficient and balancing algorithm which can be easily used in real applications.  
The shortcoming of the DEAR algorithm is the requirement of knowing the source to sink node distance, which can be obtained through GPS devices, certain localization or positioning techniques with additional computing and communication overhead. Also, the DEAR algorithm is not usable in sparse network environments or when there are obstacles between the neighboring nodes. In both cases, the next hop node based on our DEAR algorithm might not be found.
# 3-D Integral Formulation for Thin Electromagnetic Shells Coupled with an External Circuit
## 5 Numerical Examples  
In this section, we consider two numerical examples. To valid our formulation, three numerical methods are compared. The first one is a 3D shell element FEM coupled with the circuit equations [5]. In this modelling, a special care is proposed for the mesh around the conductor to ensure accurate results. The second one is a 2D FEM formulation. This approach has already shown its good precision with a few numbers of elements and is considered as the reference. The last one is our coupling integral formulation.
# An IoT System for Remote Monitoring of Patients at Home
## 5 Protocol Conversion in the IoT System
### Protocol Conversion Cases
For an ISO/IEEE 11073 Present (Store Sensing Data) Request message received, the gateway or the IoT server stores the measured biomedical information contained in (\"Observed Value\", \"Absolute Time Stamp\") fields into the appropriate places in its resource tree designated by \"URL&Value\". Figure 12 shows an example of the process described in Figure 11. In the upper image of Figure 12, \"APDU Choice Type\" on the first line and \"Event Type\" on the ninth line indicate that this message is an ISO/IEEE 11073 Present Request message and a Store Sensing Data message, respectively. Two measured (observed) values are delivered in the dotted box of the upper figure: 90 is measured at time 6 December 2016, T12:10:0000 and 72 at time 6 December 2016, T12:40:0000. These values and measured times are transmitted in two oneM2M Update primitive Request messages to update the resource tree. Only one oneM2M Update primitive Request message is shown in the dotted box (No. 1) of the lower figure. Figure 11: Conversion of an ISO/IEEE 11073 Present (Store Sensing Data) Request message to oneM2M Update primitive Request messages. Figure 5:0 An example of the process described in Figure 8 (oneM2M Create primitive Request message).