esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To address the multi-mode operating characteristics of trains, such as acceleration, deceleration, and constant speed, [PERSON] et al. successfully improved train operational efficiency and safety through fuzzy control [15]. To overcome challenges related to high-precision speed tracking under uncertainties in train parameters and external resistance, [PERSON] et al. proposed a robust adaptive control algorithm [16]. Additionally, [PERSON] et al. developed a data-driven thrust allocation method that simultaneously ensures the tracking safety of multiple trains and the operational efficiency of individual trains [17]. However, most existing thrust allocation methods for land trains do not consider thrust constraints, making them unsuitable for direct application to the steady control of MATC systems. Furthermore, current models of tandem underwater vehicles are relatively simple, with fewer platform thrust units, and existing research has primarily focused on attitude control rather than thrust allocation for steady sailing. AUV units in MATC systems face significant challenges due to thrust input saturation and uncertain ocean current disturbances [18], which complicate effective thrust allocation. The key innovation of this study lies in its hybrid approach to thrust allocation in MATC systems. For smaller systems with thrust constraints, an improved Genetic Algorithm (GA) is introduced, specifically designed to account for constraint conditions, ensuring precise and feasible solutions [19,20]. For larger systems, the Harris Hawks Optimization (HHO)+BP algorithm is employed, enabling efficient offline thrust allocation and significantly reducing computational overhead for real-time implementation. The advancements of this study lie in the development of the hybrid approach that effectively addresses both discrete and continuous optimization problems in thrust allocation for MATC systems. This method demonstrates remarkable scalability, seamlessly adapting to systems with varying numbers of units, from small configurations to large-scale setups. In certain practical application scenarios, such as fixed-altitude seabed exploration, the motion of the MATC system is primarily confined to the horizontal plane, where the effects of vertical motion are minimal. In addition, by simplifying the model to concentrate on horizontal dynamics, we can more effectively analyze how thrust allocation influences system speed and inter-unit distance. Therefore, this study focuses on controlling straight-line motion within a two-dimensional horizontal plane. This study not only provides a novel solution to the steady straight-line sailing challenge in MATC systems but also establishes a theoretical foundation for multi-body coordination control in complex underwater environments. The successful application of this method paves the way for the broader deployment of multi-body vehicles in future deep-sea exploration, environmental monitoring, and other underwater tasks, offering substantial academic and practical significance.
## Literature Review
### Major Concerns in Current Scheduling Research
But they did not consider the hot-charging situation between continuous casting and hot rolling, only scheduling the inventory of cold slabs, and this did not match well with the hot-rolling scheduling, which is not suitable for the actual production of the steel plant. [PERSON] et al. (2018) assigned a slab sequence for each furnace and determined the feed-in and residence times for each slab, ultimately achieving the reduction of unnecessary energy consumption in the reheating furnace. They solved the scheduling problem in the reheating furnace well by specifying a scheduling scheme with the objective of minimizing the total penalties, but their research did not integrate well with the scheduling of the hot-rolling mill. [PERSON] and [PERSON] (2018) established an integrated scheduling model for reheating furnaces and hot-rolling mills and adopted a multi-objective differential evolution algorithm for different optimization objectives of reheating furnaces and hot-rolling mills. They considered reducing unnecessary heating time in the reheating furnace and minimizing total changeovers in the hot-rolling mill to achieve optimal scheduling. Due to the lack of consideration for the conversion of different slab types and the impact of hot charging in the reheating furnace, it also consumes a large amount of energy. The above-mentioned planning and scheduling models for reheating furnaces generally aim to increase the hot-charging ratio or minimize the time of the slab in the furnace in order to optimize the overall model of the reheating furnace and hot-rolling mill. Most of the heat absorption of the slab and gas consumption in the heating furnace are judged based solely on the unit consumption of the slab and gas, without considering the excessive energy consumption between the conversion of cold- and hot-charging slabs in the same furnace. These schedules do not really match the actual situation of the hot-rolling process. Therefore, it is of great significance to study the cold-hot slab conversion mode and form a functional relationship around the different gas consumption during slab type conversion. This study can reasonably schedule the production of the continuous casting and hot rolling process while meeting process constraints and realize collaborative optimization of multiple processes, including continuous casting, the reheating furnace, and hot rolling. Moreover, it can also reduce production costs and achieve overall energy conservation and consumption reduction.
# Robotic System for Post Office Package Handling
## 3 Architecture
### Hardware Setup
#### 3.1.4 Processing Units  
As depicted in Figure 4, the proposed system is composed of two processing units, one for robot control and the other for computer vision, both of which were configured as a multi-ROS-PC setup. This was carried out to make each of the units independent from one another in terms of computing, so no unexpected interruptions would occur. To ensure that the robot control is not affected by system latencies, the robot control PC was set up with real-time capabilities by using Ubuntu 18.04 with an enabled real-time kernel. The real-time kernel has compatibility issues with NVIDIA GPU drivers, which are needed for computer vision tasks, which is why a two-unit setup was used. The robot control PC runs ROS Melodic. It has two Ethernet cards to ensure a direct connection with the robot's controller and ROS multi-master ecosystem. Apart from the two Ethernet cards, it can be an ordinary desktop computer, as the main task is to generate feasible trajectories of an industrial robot, and there are no other processes that require high-performance capabilities. In this case, it had the following specifications: Intel Core i7-8400K CPU@ 3.70 GHz and 16 GB RAM.  
The processing unit for computer-vision-related computations, on the other hand, requires a graphics processing unit, both for running ZIVID and for grasp point detection. It runs on regular Ubuntu 18.04, also with ROS Melodic. In this case, the computing capabilities can have a big influence on the overall cycle times of the pick and place process. The computer vision PC has the following specifications: AMD Ryzen 5 3600@ 3.60 GHz, 32 GB RAM, and NVIDIA GeForce RTX3060 Ti.
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 8 Optical Application with Tunablility
### Fluid-Filled Elastomeric Lens  
An electrically tunable optical lens using DE actuation inspired by the human eye was suggested [10]. In this work, a transparent fluid is filled between two membranes (VHB 4905, bi-axially pre-stretched 400%), forming a symmetrical biconvex lens. In the remaining part of the membranes, a compliant electrode (carbon grease) is coated on both sides. When an electrical field is applied to compliant electrodes, the membrane contracts in thickness and expands in the planar area, as shown in Figure 12(a). Thereby, the radius of the curvature of the lens changes, which causes a variation in focal length from 22.73 mm to 16.72 mm, i.e., -26.4% under the applied voltage of 3.5 kV. Like the human crystalline lens, the diameter at the rest of the tunable lens is 7.6 mm. This tunable lens shows advantages in low power consumption, fast response, no overheating, and shock tolerance. However, since the electrode part encircles the lens part, the usable optical area is reduced. By using transparent compliant electrodes for the DEA lens, this issue can be solved [133]. The transparent DEA lens consists of a transparent liquid, one transparent passive membrane, and one transparent electroactive membrane (VHB 4910, bi-axially pre-stretched 300%), which is coated with transparent compliant electrodes (singled walled carbon nanotube).  
In a similar way, the focal length of the lens varies under the voltage applied across the electroactive membrane, as shown in Figure 12(b). The assembled lens has an optical transmittance of 88% at 550 nm, while the focal length variation is observed as 103% under the operating condition of 4.9 kV, and the response time is measured to be less than one second. Although the active-lens shows a high focus tuning range, it requires a high voltage of over 4 kV. To decrease its driving voltage, a new type of fluid-filled elastomeric lens was introduced, which is driven by a concentric annular DEA [134]. This lens module has a circular reservoir and a concentric annular reservoir, which are connected by fluidic channels. The circular liquid-filled lens, with a diameter of 5 mm, shows a large focal length tunability of 300% within 1.0 kV. Similarly, to reduce the operating voltage of the fluid-filled elastomeric lens, various approaches were suggested, such as separating the DEA diaphragm and the active-lens part [135] or using multi-stack DEAs [136]. However, a fluid-filled elastomeric lens needs  
Figure 13: An operation principle of two fluid-filled elastomeric lenses using DEAs: (**a**) bioinspired lens; (**b**) transparent DEA lens.  
to be improved to overcome gravitational stability for a large-aperture lens, as well as liquid leakage or diffusion problems across the membrane [137; 138].
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 3 Multiple Target Tracking  
A block diagram of multiple target-tracking using the new track association scheme is shown in Figure 3. Each step on the block diagram is described in the following subsections.  
Figure 3: Block diagram of multiple target tracking.
# Foremost Walks and Paths in Interval Temporal Graphs +
## 4 Min-Hop Foremost Paths
### Algorithm to Find mhf Paths in Interval-Temporal Graphs
#### 4.1.1 Data Structures Used by \(mhf\) Algorithm 1  
1. We use the same data structure to represent the interval temporal graph as we used in our earlier paper [13]. The data structure comprises a (say) C++ vector with one slot for each vertex in the graph. The slot for any vertex \(u\) itself contains a vector of vertices adjacent from \(u\). Associated with each adjacent vertex \(v\) from \(u\), there is a vector of time-ordered tuples for the edge \((u,v)\).
2. \(incSt\) is a structure that keeps track of vertices discovered in every hop. The fields in this structure are as follows: 1. \(curVtx1d\) is the current vertex. 2. \(arrTm\) is the time of arrival at the current vertex. 3. \(refProIncSt\) is reference to previous \(incSt\) that stores similar information about previous vertex on this path.
3. \(allHopPath\)s--array of lists that stores a list of vertices discovered at every hop. This array has, at most, \(H\) lists, where \(H\) is the maximum number of hops in min-hop paths from source vertex, \(s\), to any of the vertices \(v\in V\). Every element of the list is an instance of the structure \(incSt\).
4. \(t_{EKA}\)--array that stores for each vertex \(v\) tuples of: 1. earliest known arrival time \(t_{f}\). 2. number of hops in which earliest time found \(h_{f}\). 3. index, \(indx\), into allHopPaths to the structure \(incSt\) in the list at \(h\).  
Figure 7: Example \(mhf\) paths from vertex \(a\).
## 4 Results
### Building Net Saving Analysis  
Figure 10 analyzes the economic feasibility of the scenarios. The analysis considered a discount rate range from 1% to 19% to observe the minimum rate at which it is not considered profitable to make the investment. For the case where modifications are made exclusively to the envelope of the medical center (Figure 10a), it appears that the use of reflective paint is the most attractive option. The best results are obtained with this material used on the roof (Case A-I), facade (Case A-V), and combined on the roof with thermal insulation (Case A-III), in that order. For its part, the use of the green roof proposal did not show the feasibility of implementation. The above can be explained by the reflective coating being the cheapest material of the three technologies evaluated. In addition, the Campeche area has high solar radiation, so reflecting about 90% of direct radiation contributes significantly to energy savings. It is important also to analyze the discount rate results. Given that discount values below inflation (6%) do not represent a real benefit from the investment approach, the moderate viability region was proposed, enclosed in the yellow rectangle, which is between 6 and 7%. According to the image, the use of polyurethane insulation and its simile combined with the reflective coating are not only below Cases A-II and A-V, but it is also seen that they are susceptible to falling within the zone of infeasibility upon reaching the region of moderate viability. Therefore, it may not represent an attractive investment option.  
Figure 10: _Cont._  
For its part, integrating technological substitution with inverter equipment and passive strategies (Figure 10b) increases the range of total net savings and the profitable alternatives for energy savings. Five of the six cases that make up Lot B may be in the moderate viability region. Of these, it is interesting that Case B-III, corresponding to the reflective coating on the roof linked to the inverter system, provides better economic performance than the simple integration of the new HVAC system alone (Case B-I). Despite case B-III, the above is a more significant initial investment by the medical center's administration. This demonstrates the economic benefits of using the correct passive system for the envelope. Finally, for Lot C, although there are certain benefits from the energy and environmental perspective, its integration entails a higher cost that is not solved in the long term, as seen in Figure 10c.
## 3 Proposed Solution Method
### Master Stage: GNDO
#### 3.2.3 Implementation of the GNDO Approach  
As explained by Equations (9)-(22), to illustrate the application of the GNDO approach to the problem regarding the optimal placement and sizing of D-STATCOMs in electrical distribution grids, Algorithm 1 is used.  
```
Data: Chosse the AC network under study
1; Find the per-unit network equivalent;
2 Select the values of \(N_{i}\) and \(t_{\text{max}}\);
3 Select the \(\mu\) parameter as \(\frac{1}{2}\), and \(t=0\);
4 Create the initial population with the structure defined in (13);
5 Apply the slave stage (power flow) to know the fitness function value for each individual solution \(x_{i}^{t}\), that is, \(A_{f}\big{(}x_{i}^{t}\big{)}\);
6 Select \(x_{\text{best}}^{t}\) as the individual with the minimum value of the fitness function;
7 for\(t\leq t_{\text{max}}\)do
8 for\(i=1:N_{i}\)do
9 Obtain a random value \(\gamma\) between 0 and 1;
10 if\(\gamma>\frac{1}{2}\)then
11 /*Local exploration search*/;
12 Select the best current solution \(x_{\text{best}}^{t}\);
13 Compute the value of the vector \(M\) using Equation (17);
14 Calculate the generalized mean value \(\mu_{i}\) using Equation (14);
15 Obtain the generalized standard variance \(\delta_{i}\) through Equation (15);
16 Compute the penalty factor \(\eta\) with Equation (16);
17 Make the local exploration using Equation (12);
18
19 else
20 /*Global exploration search*/;
21 Obtain three random integers \(j\), \(k\), and \(m\) that are different from each other and from \(i\);
22 Compute the value of the vector \(v_{1}\) with formula (19);
23 Compute the value of the vector \(v_{2}\) with formula (20);
24 Evaluate the global exploration rule (18) to obtain \(v_{i}^{t}\);
25 Revise the upper and lower bounds of \(v_{i}^{t}\) using rule (21);
26 Evaluate the slave stage to obtain the fitness function value \(A_{f}\big{(}v_{i}^{t}\big{)}\);
27 Select the next individual \(x_{i}^{t+1}\) through Equation (22);
28
29 Result: Report the final solution value \(x_{\text{best}}^{t+1}\).
```  
**Algorithm 1**General application of the GNDO approach to select nodes and define sizes for D-STATCOMs in distribution networks.  
Note that Algorithm 1 uses all of the formulas involved in the GNDO approach (9)-(22) in order to explore and exploit the solution space by dividing the search into local and global explorations.
## 4 Experimental Results and Analysis
### Evaluation Measure  
We use the uniformity measure as the objective evaluation index in the experiments [42]. The uniformity measure is used to quantitatively evaluate the degree of similarity in different classes divided by the segmentation algorithm. It is defined as follows:  
\[U=1-2*n*\frac{\sum\limits_{j=1}^{K}\sum\limits_{i\in R_{j}}\left(I_{i}-\mu_{R_{j }}\right)^{2}}{Num*\left(I_{\text{max}}-I_{\text{min}}\right)} \tag{10}\]  
where \(n\) represents the number of thresholds; \(Num\) represents the number of all pixels in the original image \(I\); \(I_{\text{max}}\) and \(I_{\text{min}}\), separately, represent the maximum gray value and minimum gray value of the pixels in the image \(I\); \(R_{j}\) denotes the \(j_{\text{th}}\) segmented area; \(I_{i}\) represents the gray value of pixel \(i\); and \(\mu_{R_{j}}\) represents the grayscale mean of the pixels in the segmented region \(R_{j}\). The value range of \(U\) is \([0,1]\). The larger the \(U\) value, the better the regional uniformity in the segmentation results, and the better the segmentation effect and vice versa.
## 2 Methods
### Motion Control Strategy
#### 2.3.1 Trajectory Design Based on Gait Parameters.  
In physical therapy, the gait pattern is defined by the medical personnel and should be adapted to each patient's needs. Therefore, an adaptable trajectory generator is necessary to satisfy the flexibility required in an assistant exoskeleton. In this sense, a target trajectory for each joint is defined through the selection of the coefficients of smooth polynomials, which satisfy a set of kinematic constraints defined by the physician. To guarantee a smooth motion, the target trajectories are determined based on the mathematical structure of Bezier polynomials, which have bounded derivatives and allow us to define the trajectory as a function of the step time, \(T\)[43]. Then, the reference trajectories are given by the following:  
\[q_{j}^{*}(s)=\sum_{i=0}^  
where using (2), the position reference vector is defined as \(\mathbf{q}^{*}(s):=[q_{1}^{*}(s)\ q_{2}^{*}(s)\ q_{3}^{*}(s)]^{T}\) and its first and second-time derivatives are defined as  
\[\mathbf{\dot{q}}^{*}(s)=\frac{\partial\mathbf{q}^{*}(s)}{\partial s}\frac{ds}{ dt}, \tag{5}\]  
and  
\[\mathbf{\ddot{q}}^{*}(s)=\frac{\partial\mathbf{\dot{q}}^{*}(s)}{\partial s} \frac{ds}{dt}, \tag{6}\]  
respectively. Thus, the coefficients of the Bezier polynomials are selected by solving the optimization problem:  
\[\begin{array}{llll}\text{find}\ \beta_{i,j},&\forall\ i,j,&\text{such that:}\\ \text{min}&J(\beta)\\ \text{subject to:}&\text{dynamic constraints,}\\ &\text{kinematic constraints,}\\ &\text{safety constraints,}\end{array} \tag{7}\]  
where the dynamic constraints include the exoskeleton and child's dynamics computed in (1), the kinematic constraints are selected according to the therapy conditions, and the safety constraints are set to prevent injuries due to over-elongation of the muscles or the transition through non-anatomical positions.  
In order to evaluate the exoskeleton's motion control strategy, the kinematic constraints are defined to set a gait pattern, which uses as input parameters the step length, step angle, cadence, speed, and time of each step. Table 3 shows the parameters of a healthy person's gait, which are used to define a nominal gait pattern. Based on the defined gait parameters, a trajectory is planned to perform a forwarding motion, as shown in Figure 7. This trajectory considers a sagittal walking that ensures smooth motion through the imposition of initial and final velocities and accelerations equal to zero.
## 5 Conclusions  
In this paper, we propose the Estimated Signal Padding algorithm, which enhances range resolution by extending the signal length in the time domain, thereby achieving a broader bandwidth. This algorithm has been validated through MATLAB simulations and actual FWCW radar tests. Performance results based on the padding length and inter-target distances indicate that the highest accuracy is achieved with four times padding, given the same distance error tolerance. The proposed algorithm improves range resolution by at least 2.25 times compared to existing algorithms. Target detection was performed using OS-CFAR, and the proposed algorithm is capable of measuring up to approximately four targets. When two targets of equal size are present, the detection rate of the proposed algorithm is improved by 29.5% compared to Zero Padding and by 7.5% compared to Neural Network Padding. If the target sizes are different, the detection rate improves by 27.3% compared to Zero Padding and by 29.3% compared to Neural Network Padding. The proposed algorithm is currently limited to measuring the distance between the radar and the target without determining the exact direction of the target. Further research is required to accurately determine not only the distance to the radar but also the precise direction of the target [34; 35]. In addition, compared to the indoor environments, outdoor environments are more strongly multipath signal and interference and require compensation for moving objects [36; 37]. Further research is required on algorithms that eliminate multipath signals and are related to measurements of moving objects to improve range resolution in both indoor and outdoor environments.  
Conceptualization, S.L. ([PERSON]) and S.L. ([PERSON]); formal analysis, S.L. ([PERSON]); investigation, S.L. ([PERSON]), M.K. and S.L. ([PERSON]); methodology, S.L. ([PERSON]) and Y.J.; project administration, S.L. ([PERSON]); software, S.L. ([PERSON]) and M.K.; supervision, S.L. ([PERSON]); writing--original draft, S.L. ([PERSON]); writing--review and editing, Y.J. and S.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Institute of Information & communications Technology Planning & Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2024-RS-2023-00254529) grant funded by the Korea government (MSIT), in part by the NRF grant funded by the Korea government (MSIT) (No. 2023R1A2C1006340), and in part by the Basic Science Research Program by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540). The EDA tools are supported by the IC Design Education Center (IDEC), Korea.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.