esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 2 System Overview  
The schematic diagram of the hybrid mapping method with position and stiffness for manipulator teleoperation mainly consists of the master side and the slave side, which communicates with each other bidirectionally in real time, shown in Figure 1. The command data is transmitted by the Transmission Control Protocol (TCP) to guarantee the reliability of data transmission, and the feedback data is transmitted by the User Datagram Protocol (UDP) to obtain the low latency. With the vision and position/force feedback from the slave side, the operator utilizes the Haptic Workstation and sEMG electrodes at the master side to measure the position and stiffness at the human arm endpoint, and the hybrid mapping method with position and stiffness mapping is used to generate the position and stiffness commands of the slave robot, respectively.  
At the master side, the Haptic Workstation (CyberGlove Systems Inc., San Jose, CA, USA) consisting of dual CyberForce and dual CyberGrasp is a set of exoskeleton teleoperation device with force feedback [21]. The CyberForce is an arm exoskeleton, which tracks 6 DOFs (degrees of freedom) movement of the human wrist, and provides 3 DOFs force feedback along the coordinate axes directions.  
Figure 1: Schematic diagram of the hybrid mapping method for teleoperation system.  
The CyberGrasp includes a data glove with 22 sensors which measure the finger joint movement of human hand, and an exoskeleton device which provides force feedback for each fingertip. The TrignoTM Wireless system (Delsys Inc., Natick, MA, USA) [22] is used to achieve the muscle electrical signals of human arm in order to predict the arm stiffness.  
At the slave side, the HIT (Harbin Institute of Technology) humanoid robot consisting of an anthropomorphic upper body mounted on a wheeled mobile robot developed by HIT [23] is used as the slave robot. The robot is mainly composed of two 7 DOFs light-weight manipulators, two 15 DOFs five-fingered robot dexterous hands, 2 DOFs robot head with the stereo vision, 2 DOFs waist and 3 DOFs mobile vehicle. The manipulators have position and torque sensors in each joint, and can be operated in position control mode or Cartesian impedance control mode. The dexterous hands are the HIT-DLR (German Aerospace Center) II hand, which have five identical modular fingers and a palm. In this paper, we just focus on the position and stiffness teleoperation of the manipulator.  
The hybrid mapping method includes the motion mapping method for position teleoperation and stiffness mapping method for compliant teleoperation. The motion mapping method is utilized for noncontact tasks of manipulator, while the stiffness mapping method is used for contact tasks of manipulator. The two mapping methods are realized in the interaction software at the master side, and the operator can switch the methods manually through the software interface according to different situations.
## 3 Modelling of the Bus Transportation Network
### Bus Network Modelling
#### 3.5.2 Post-Treatment of GTFS Data  
The GTFS data are used to determine the list of all routes and trips. As GTFS data only consider trips to model buses' operations and do not use the concept of routes defined in Section 3.1, all trips must be analysed to identify their corresponding route. After that, the distance between consecutive stops of each route is determined using Open Street Map, which is a free editable geographic database of the world [53]. The elevation of each stop is retrieved from Open Elevation Software. GTFS data also allows analysing the time spentbetween two consecutive bus stops. This duration can vary during a day mainly due to the hourly/daily traffic conditions.  
Firstly, the distance between stops are determined. Based on the latitude and longitude information, a path between two bus stops is generated on Open Street Map using a \"bus\" transportation mode. To generate the path, Open Street Map creates a sequence of intermediate points on the road between the two stops with a higher density in curved portions (see Figure 4), and defines the total distance as the sum of distances between each intermediate point. This matches quite accurately the real path of the bus; however, it requires manual verification that the path proposed by Open Street Map fits the one on the bus network map.  
Secondly, determination of the altitude is performed. The bus consumption also depends on the slope of the road. Therefore, the Open Elevation software has been used to compute the altitude of all stops based of their latitude and longitude [54]. However, only the mean slope between consecutive stops is accessible in this way. The limit is the resolution of topographic map, around 200 m for open-source maps. This means that all points in a square of 200 m per side will have the same altitude, and that slope variations in this area might be hidden.
## 1 Introduction
### Background and Framework
#### 1.1.2 Disability Approach Models  
Theoretical models of disability are conceptual approaches that allow us to understand and examine the world of people with disabilities from different perspectives [9]. Over time, there has been an evolution in the definition of disability and the associated models of care. These models have adapted to reflect the advances in our understanding of disability and to more effectively address the needs and rights of people with disabilities.  
In the past, until the mid-20 th century, the prevailing theoretical model for addressing disability was the individualistic model. This approach viewed disability as a divine punishment, a personal tragedy, or an individual deficiency. Under this paradigm, people with disabilities were seen as objects of pity, incapable of fully participating in society. The focus was on \"fixing\" or \"normalizing\" the person with a disability, with little emphasis on the role of society in creating barriers and limitations.  
However, following the Second World War, advocacy movements for people with disabilities sparked a fundamental shift in perspective. These movements advocated for a social model of disability, which posits that disability is not simply an individual characteristic but is deeply rooted in the social environment and attitudes. According to this approach, disability is constructed and experienced based on the social context in which individuals live.  
In the social model of disability, it is emphasized that society is responsible for removing the barriers that limit the full participation of people with disabilities. These barriers can manifest in discrimination, lack of access to necessary services and supports, and negative attitudes and stereotypes perpetuating exclusion. This approach highlights the importance of ensuring equal opportunities, inclusion, and respect for the human rights of people with disabilities.
## 5 Experimentation and Analysis
### Analysis of Experimental Arithmetic  
#### 5.4.1 Comparison Results of the MODBO in Test Functions  
The CEC2017 test function set [47] is quite widely used, and it is challenging and academically recognized for algorithms. All the functions within the test set undergo rotations and shifts, intensifying the complexity of algorithmic optimization searches. The functions mainly contain 29 test functions: F1 and F3 are single-peak functions without local minima but only global minima, which can test the convergence ability of the algorithms; F4-F10 are multi-peak functions with local extrema, which are used to test the ability of the algorithms to jump out of the local optimum; F11-F20 are composite functions comprising three or more CEC2017 benchmark functions, where each subfunction is allocated a specific weight; F21-F30 are composite functions that incorporate a minimum of three hybrid functions or CEC 2017 benchmark functions after rotation/shift, with each subfunction having an additional bias value and a weight, and these combined functions further increase the optimization difficulty of the algorithm (where the F2 function of the original function set was officially removed due to F2 instability). F1-F7 are used in this study to show part of the test set to verify the feasibility of this improved algorithm.  
To verify the feasibility of the dung beetle algorithm for multi-strategy optimization, the algorithm is compared with the original dung beetle algorithm. The test set of CEC2017 is selected and the comparison results are shown below.  
As shown in Figure 3, the slope of the performance curve of the MODBO algorithm is larger, indicating that its convergence speed is faster, and compared with the DBO algorithm, the MODBO algorithm has better adaptability under the same number of iterations. After 500 iterations, the final adaptation value of the MODBO algorithm is lower than that of the DBO algorithm. Therefore, it can be concluded that based on the CEC2017 test function, after 500 iterations, the MODBO algorithm proposed in this study has a faster convergence speed, better stability, lower final adaptation value, and better performance compared with the DBO algorithm.  
Figure 3: _Cont._
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Distance Functions  
There is a very long list of possible distance functions among time series (see, e.g., ([PERSON], 2006), Table 3)). In this work, we focus on a prominent representative of shape-based distance functions for time series, the Dynamic Time Warping (DTW) distance. For completeness, we first introduce the simpler Euclidean distance for time and then present DTW as an elastic generalization of it.
## 1 Introduction
PVDF as a dielectric matrix possesses unique properties such as lightweight, flexibility, heat and chemical corrosion resistance, which are favorable for practical applications as absorptive material. The synergistic effect between Fe\({}_{3}\)O\({}_{4}\) nanoparticles and dielectric matrix is beneficial for enhancing the MAP of composites [13]. In this study, transmission line and waveguide method was used to measure the EM parameters (permittivity and permeability) of the C-Fe\({}_{3}\)O\({}_{4}\)/PVDF nanocomposites loaded in the rectangular waveguide at X-band (8.2-12.4 GHz) and Ku-band (12.4-18 GHz) frequency range. Furthermore, COMSOL Multiphysics based on finite element method (FEM) was used to simulate the MA properties of the synthesized C-Fe\({}_{3}\)O\({}_{4}\)/PVDF nanocomposites. The optimal loading of C-Fe\({}_{3}\)O\({}_{4}\) in the PVDF matrix for efficient EM wave absorption was investigated using the FEM formulation. The electric field (E-Field) variation within the different regions of the rectangular waveguide was analyzed. In addition, the relationship between the density of the EM energy and absorption coefficient was investigated. Conclusively, the MA properties of the sample containing 40 wt.% C-Fe\({}_{3}\)O\({}_{4}\) in PVDF nanocomposites displayed superior MAP. The good dissipation of EM wave by the sample can be associated to impedance matching, multiple polarization mechanism, and suitable EM attenuation ability.
## 2 Theoretical Framework
### Soil Treatment for Grounding Resistance Reduction  
Various methods and materials have been proposed for mitigating the problem of high soil resistivity and GR values [13]. Several studies, including [1; 4; 5; 12; 13; 14; 15; 16; 17; 18; 19; 20], have been conducted to provide LRMs with specific characteristics to further improve the performance of GSs, for example, by improving the ability to retain moisture in the soil near the ground rod, by decreasing the surrounding soil resistivity, and by creating a protective layer to prevent ground rod corrosion [15]. Therefore, an improved material suitable for GSs must be selected, depending on the soil type and local climate [12].  
Currently, bentonite is widely used as an LRM and recommended in IEEE standard 80-2013 [3]. Bentonite can absorb up to five times its weight in water. In addition, its dry volume can be increased by up to 13 times, and it can adhere to almost any surface [1; 5; 17; 18; 21].  
As reviewed by [1], bentonite perform well as an LRM in the presence of water, with a decrease in resistivity of up to 60%. Bentonite is a chemically inert material and, therefore, a noncorrosive agent for metal electrodes.  
In [15], the behaviour of bentonite was analysed on three different sites. The authors concluded that this LRM performed best in soils with a greater resistivity. In addition, they determined that the GR fluctuated over the year of testing on all rods due to seasonal variations.
## 2 Problem Formulation
### Environmental Settings
#### 2.2.3 Dynamic Environment with Current Fields  
During operations both aerial and underwater, the HAUV will always face current fields. When in the air, the air drag is normally much weaker than the vehicle's gravity, so we only consider the lift caused by currents. When there are currents, the lift force is as in the following Equation (2):  
\[Y_{air}=1/2*\rho_{air}*C_{air}*S_{air}*(v_{i}+v_{c})^{2} \tag{2}\]  
When the HAUV moves in a static environment, the lift force is as in Equation (3):  
\[Y_{air}=1/2*\rho_{air}*C_{air}*S_{air}*v_{i}^{2} \tag{3}\]  
where \(Y_{air/air}\) denotes the lift force when the HAUV is in the static environment or current field, and \(v_{i}\) defines the velocity of the HAUV in \(\Gamma_{i}\), which is expressed in m/s. \(\rho_{air}\) is the air density, affected by altitude. \(S_{air}\) is the reference area or the wing area of an aircraft measured in square meters, and \(C_{air}\) is the coefficient of lift, depending on the angle of attack and the type of airfoil. We roughly set \(C_{air}\) as 1 and \(\rho_{air}\) as 1.2 kg/m\({}^{3}\). When there are no current fields, \(v_{i}\) is the average velocity during the HAUV mission. When currents exists, \(v=v_{i}+v_{c}\), where \(v_{c}\) refers to the currents velocity. The size of our HAUV's wing area on each side is 750 mm \(*\) 300 mm; therefore, the \(S_{air}\) can be roughly set to 0.1-0.225 according to the direction of currents.  
Figure 5: Obstacle avoidance strategy: extend the outer circle of obstacles to \(CIR_{s}^{2}\) and find a free point set on the circle. If there is no solution found, \(CIR_{s}^{2}\) must be extended again.  
Likely, in an underwater environment, the power generated by the propeller needs to overcome the lift and frictional resistance. From our experimental data, for most scenarios, the lift is much more than resistance, so we only consider the lift and roughly regard the influence of current in as Equation (4):  
\[\begin{split} Y_{sea}=1/2*\rho_{sea}*C_{sea}*S_{sea}*(v_{i}+v_{c})^{ 2}\\ Y_{sea}=1/2*\rho_{sea}*C_{sea}*S_{sea}*v_{i}^{2}\end{split} \tag{4}\]  
where the \(\rho_{sea}\) is 1205 kg/m\({}^{3}\), \(S_{sea}\) is 0.33 m\({}^{2}\), and \(C_{sea}\) depends on the current fields and the HAUV's velocity, we calculate it bt ITTC recommend formula (5), where \(Re\) is the Reynolds number. In Sections 3.1 and 3.2 we will discuss the method to measure the influences of currents and the solution to avoid them.  
\[C_{sea}=\frac{0.075}{(lgRe-2)^{2}} \tag{5}\]
# Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food
## 3 Results and Discussions
### Fuzzy PID Controller Simulation Analysis  
The primary objective of this study is to implement a modified atmosphere preservation gas distribution system utilizing fuzzy PID control; thus, its actual performance is evaluated through simulation experiments. A simulation experimental platform is constructed in the Simulink function of the MATLAB software based on the transfer function of the gas distribution system, which allows for a comparison of the performance of the fuzzy PID controller with that of a traditional PID controller, as illustrated in Figure 16.  
To evaluate the adaptability of the fuzzy PID controller and the traditional PID controller to the gas distribution system under different parameter conditions, four comparison groups with step values of 30, 40, 50, and 60 were established. The results, as shown in Figure 17, reveal that the stabilization time of the fuzzy PID controller (represented by the red curve) is around 38 s, while that of the traditional PID controller (represented by the blue curve) is around 85 s. It can be observed that the fuzzy PID controller reaches a steady state more quickly and is able to adjust faster than the traditional PID controller.  
Figure 16: The simulation experimental platform.  
Figure 15: First-order pure lag system curve of the gas distribution system.  
As illustrated by each graph, the traditional PID controller (represented by the blue curve) initially exceeds the setpoint and gradually returns to it, while the peak of the fuzzy PID controller (represented by the red curve) aligns with the setpoint. This demonstrates that the fuzzy PID controller is capable of achieving minimal overshoot during gas distribution. In general, the fuzzy PID controller demonstrates superiority over the traditional PID controller in terms of increasing the speed of gas distribution, enhancing the accuracy of gas distribution, reducing overshoot during gas distribution, and adapting to the different parameters of the gas distribution system.
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 2 Dataset
### Industrial Process
#### 2.1.2 Coke  
The burning of coke supplies the heat required to decompose calcium carbonate. The size of the coke is kept about half of the size of limestone used, i.e., 35-55 mm. The coke containing high ash percentages is troublesome as ash is composed of heavy metal oxides and silicates. Therefore, the size, quality, moisture content, and calorific value of the coke are essential factors. Coke which is unsorted and has both undersized and oversized particles, is known as Unsorted Coke.  
Unsorted coke is processed by crushing and sieving at Coke Sieving Plant. It is fed to Hopper Conveyor, which leads to two vibrating screens. The upper one is 44 mm in size, whereas the lower one is 16 mm in size. Coke which passes through both screens, is called \"Undersize\" and is collected at the end of the Undersize Conveyor. \"Size\" coke is the one that passes through the upper screen and is retained on the lower screen. In case any \"Oversize\" coke does not pass through any of the screens, Coke Crusher and Oversize Conveyor arrangements are also present. This Sorted Coke is transported from the yard to the Coke Bins with the help of dumpers. Dumpers are weighed at the Weigh Bridge to calculate their Gross Weight. The moisture content of the coke is deducted from this weight. Upon exit, the dumper is weighed again, and the net weight of the coke unloaded is then calculated.  
A fixed ratio of coke and limestone is maintained using a stone plate feeder stroke and totalizer. Coke and limestone mixture is then sent to the top of kilns via belt conveyor, from where all six top kiln feed bunkers are filled using a tripping trolley. Bunkers are to be filled manually by the belt operator. It is made sure that the Bunker's level is not less than half.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 3 Methodology
### Attitude Control

Table 2: The effect of UAS altitude on landmark matching efficiency by comparing the number of matches (SuperGlue). \begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{**Altitude (m)**} & \multicolumn{4}{c}{**Not Adjusted 1**} & \multicolumn{4}{c}{**Adjusted**} \\ \cline{2-7}  & **Feat. 2** & **Matches 3** & **Pct. (\%) 4** & **Feat.** & **Matches** & **Pct.** \\ \hline
50 & 4000 & 838 & 20.95 & 2872 & 909 & **31.65** \\
60 & 4000 & 968 & 24.20 & 3398 & 993 & **29.22** \\
70 & 4000 & 953 & 23.83 & 3753 & 955 & **25.45** \\
80 & 4000 & 996 & 24.90 & 3893 & 1008 & **25.89** \\
90 & 4000 & 994 & **24.85** & 3965 & 931 & 23.48 \\
100 & 4000 & 1000 & **25.00** & 4000 & 925 & 23.13 \\ \hline \end{tabular} \({}^{\dagger}\) Disabling altitude control applied using Equation (3). 2 Extracted landmarks from UAS imagery. 3 Matched landmarks to the reference dataset. 4 Valid landmarks pct., matches/feat.  
\end{table} 
Table 3: The effect of UAS altitude on landmark matching efficiency by comparing the number of matches (ORB). While we conjecture the following should not differ for various platforms, in Algorithm 1, \(T\), the threshold for matching landmarks, is empirically set to 50 for the UAS we use in our experiments. To ensure the attitude control module works on other UAS platforms, the scale-altitude linear relationship in Equation (2) must be estimated due to camera focal length changes.
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 4 Open Problems and Ongoing Trends
### Map Representation Challenges
#### 4.2.2 Context-Aware Map Representation  
Some of the existing semantic-mapping systems build metric semantic maps, such as semantic occupancy grid maps, which allow the robot to interpret the positions of represented elements using their associated labels. Other works build hierarchical semantic maps, which give a context-aware representation, where the detected features are associated using their labels to a common-sense database giving a more detailed description of the context, especially about the concepts involved in the environment and the relationships between them. For single-robot semantic-mapping systems, there are many works that focus on building a context-aware map representation, but for collaborative systems, most of the existing works focus on fusing local geometric-semantic maps and do not focus on building a shared contextual database of the environment. There are only a few works that have built and maintained a common-sense database on the server that is shared by all robots. This database is populated using data collected by multiple robots. Therefore, this can cause some problems due to multi-robot data dissimilarities, but it permits to build a much detailed and extensive description of the environment to expand the knowledge database of individual robots.