esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 1 Introduction  
Even though the concept of virtual reality (VR) has been around since the 1960s [1], its usage in the field of robotics has only been widely adopted in robot-assisted surgery [2; 3; 4] and robot-assisted rehabilitation [5; 6; 7]. However, there is a great deal of synergy between VR and robotics [8]. Robots can be used in VR to help provide more immersive VR experiences by providing haptic feedback to users [9; 10] or by providing items for user interaction [11; 12]. On the flip side, VR is an enabling technology in robotics to provide immersive robot teleoperation [13], to aid in robot programming [14], to conduct human-robot interaction and collaboration studies [15; 16; 17], and even to train individuals on how to collaborate with robots [18].  
The lack of VR integration in the field of robotics has been largely caused by the absence of availability and affordability of commercial VR devices. In the past decade though, there has been considerable advancement to make VR devices with immersive visualization and 6 degree-of-freedom (DOF) tracking commercially available and relatively affordable. With these advancements, there has been an increase in utilizing VR in the field of robotics. One area that has seen particular growth is in utilizing VR devices for human-robot interaction and collaboration.  
VR provides an opportunity to create more natural and intuitive interfaces by immersing users in a 3D environment where they can view and interact with robots in 3D shared or remote environments. This can allow for better situational awareness and easier interaction. Robot interfaces can be of two types, either teleoperation, where an operator controls a robot's end-effector, or shared-control,where an operator provides high-level commands to the robot. Both interface types allow operators to control a robot from remote locations. This allows for robots to be used in dangerous, distant and daring jobs and removes risk from the operator but keeps their knowledge and expertise in the loop. By using VR these interfaces can be used in a wider application due to the immersive nature of VR.  
In this paper, we present a systematic review of the use of virtual reality in developing interfaces for interacting with robots. This paper has been motivated by the need to study this emerging field at the nexus of humans, VR, and robotics. The goal of this review is to consolidate the recent literature of VR interfaces for robots to identify the current state-of-the art and areas for future research.
# Multi-Objective Optimisation of Welding Parameters for AZ91D/AA6082 Rotary Friction Welded Joints
## 2 Materials and Methods  
### Materials  
The rotary friction welding was carried out on AZ91D magnesium alloy and 6082T6 aluminium alloy bars of 20 mm in diameter and 100 mm in length. Bars were obtained by the extrusion process. The chemical composition and mechanical properties of the base metals are shown in Tables 1 and 2.  
Before welding, the surfaces were prepared on the abrasive cut-off machine. After that, all contacting surfaces of samples were polished using grit silicon carbide papers to reduce the effect of oxide films and surface roughness. The specimens were cleaned in acetone to remove dirt and grease.
## 2 Materials and Methods
### Methods
#### 2.2.4 Py-GC/MS  
Py-GC/MS analysis was performed on an integrated system consisting of a Frontier EGA-PY3030D pyrolyzer and an Agilent 7890B/5977B gas chromatograph/mass spectrometer. The GC system was equipped with a capillary column HP-5 MS UI (30 m \(\times\) 0.25 mm \(\times\) 0.2 \(\upmu\)m) with a quadrupole mass analyzer. Online methylated Py-GC/MS analysis was operated as follows: less than 1 mg of sample together with 5 \(\upmu\)L of 10 wt.% methanolic solutions of tetramethyl ammonium hydroxide (TMAH, Aladdin Biochemical Technology Co., Ltd, Shanghai, China) as the methylation reagent were placed in the sample cup, which was placed on top of the pyrolyzer at near ambient temperature; when the furnace temperature and GC/MS system were ready, the sample cup was introduced into the furnace before starting the temperature program of the GC oven. The pyrolysis was performed at 550 \({}^{\circ}\)C for 0.2 min. The carrier gas of helium was adjusted at a flow rate of 1.0 mL/min and split of 5:1. The injector and GC/MS transfer line were controlled at 300 \({}^{\circ}\)C. Chromatographic conditions for the separation of pyrolysis products were controlled at an initial temperature of 50 \({}^{\circ}\)C, maintained isothermal for 2 min, increased by 4 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C, and maintained isothermal for 15.5 min. The MS detector was operated with an ionizing voltage of 70 eV; the ion source temperature was 230 \({}^{\circ}\)C and quadrupole temperature was 150 \({}^{\circ}\)C, with a mass range \(m/z\) of 29-550. The NIST library of mass spectra was used for identifying the compounds.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 8 Use Case Study: The DIAS Architecture  
The DIAS research project stands for Drone Innovation in saffron Agriculture Surveillance. It is co-funded by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation. University of Western Macedonia (UOWM) in cooperation with Kozani Saffron Producers Cooperative (KSPC) aims at developing an integrated automated surveillance system for saffron cultivation. This 24 h real-time saffron cultivation surveillance system is relied on signal and image collecting and processing, which is derived from advanced surveillance, risk identification and early warning systems, based on integrated sensor networks, and aerial unmanned vehicles.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 3 Directed Graphs and Distributed Low-Density Parity-Check Codes for Data Collection
### Directed Graphs for Multi-Source Sparse Cooperation
#### 3.1.2 Two-Round Cooperation Graph  
In this model, the moving sink node traverses twice along the distribution region of the active source nodes. In this way, the source nodes cooperate during the two rounds and the cooperation relation can be also denoted using the directed graphs. Taking the two-round cooperation model in Figure 3 as an example, the directed graph and the cooperation matrix are illustrated in Figure 5. It is noted that there are two self-loops eliminated at each source node for conciseness.  
For the directed graph in Figure 5a, define the matrix \(G_{2}^{1}=[g_{j,i}^{2,1}]_{N\times N}\) and the matrix \(G_{2}^{2}=[g_{j,i}^{2,2}]_{N\times N}\) to denote the cooperation relation in the first round and the second round, respectively, as illustrated in Figure 5b. Here, the element \(g_{j,i}^{2,1}=1\) in the matrix \(G_{2}^{1}\) and \(g_{j,i}^{2,2}=1\) in \(G_{2}^{2}\) denote the directed edges from the node \(s_{i}\) to the node \(s_{j}\) for the directed graph in Figure 5a. The element \(g_{i,i}^{2,1}=1\) and \(g_{i,i}^{2,2}=1\) mean that \(s_{i}\) participates its own data transmission in the both rounds, though the self-loops are omitted in Figure 5a for conciseness. In the following, let the overall cooperation matrix \(G_{2}=\left[G_{2}^{1},\ G_{2}^{2}\right]\) as the basic matrix and efficient distributed LDPC codes can be designed in the following.  
Figure 4: Directed graph and cooperation matrix for the one-round cooperation model (**a**) directed graph; (**b**) cooperation matrix.  
Figure 5: _Cont._
# Usage of V2X Applications in Road Tunnels
## 2 Background  
### Positioning Approaches  
Over the years, several approaches have been developed and applied with the main goal of ensuring additional data for the estimation of an accurate position based on video processing [20], sensor data acquisition [21], 3D accelerometers [22], gyroscope and odometry [23; 24], beaconing [25], and other communication methods, such as wi-fi signal strength and fingerprinting [26], etc.  
Moreover, various methods, such as Kalman filtering [27], Dead Reckoning [28], Map matching [29], and others, have been devised. These methods enable computation and filtering of input data and provide position refinement. Each of them has a specific value and benefit for certain purposes and usages, and each method calculates the position with an error.  
The results of the experimental measurements are mentioned in [30], where the evaluation of GPS, Kalman filtering, and Dead Reckoning is performed and compared to the ground truth reference.  
Each of the methods, including GNSS, respectively GPS, show an error. This implies that their combination could be a way to obtain a better accuracy estimation, especially when using Map matching, which is based on the map layout and reference positions in the map. These methods are relevant and are used in mobility solutions and applications, where the position of the vehicle could be calculated and pinned on the map [27; 28; 29; 30]. This can also work in the tunnel, where the path is clearly defined.  
Lastly, in the following figure (Figure 1), a schematic layout of the Dead Reckoning method and its functioning is presented. This functional schema was selected because it directly shows the use case for positioning in the tunnel and also because the Dead Reckoning approach was used within the experimental analysis presented in the article.
# A New Approach for Environmental Risk Assessments of Living Modified Organisms in South Korea
## 3 Results and Discussion
### Vip3 Aa Protein Expression and Stability  
In transient protein expression using agroinfiltration, the introduced gene directly expresses the desired protein without fusion into the plant genome. This strategy provides the advantage of a significantly improved protein accumulation level and faster protein production timeline [34]. Thus, the production level of the LMOs protein can be easily amplified by increasing the number of host plants. In order to evaluate the risk of Vip3 Aa protein, the Vip3 Aa-agroinfiltration transient plants were extensively engineered to achieve high protein accumulation levels in tobacco leaves. To take advantage of the risk assessment tool based on agroinfiltration, it is essential to confirm the concentration of Vip3 Aa protein expressed in real LMOs and infiltrated plants as LM mimic organisms.  
Figure 1: The safety-management framework of the Ministry of Environment and the National Institute of Ecology for LMO risk review and assessment.  
The expression level of Vip3 Aa protein in infiltrated leaves was investigated using ELISA assay and compared with those of LM crops such as Bt corn (MIR162) and cotton (COT102) (Table S1, Figure 3) [35; 36]. The expression level of Vip3 Aa in the agroinfiltrrated plants (170-220 ng/mg, fresh weight) was similar to that of LM corn MIR162 (87.5-339 ng/mg, dry weight). Moreover, Vip3 Aa in the agroinfiltrrated plants was stably expressed until 5 d (Figure 3). The standard deviation of the Vip3 Aa protein expression in the agroinfiltrrated plant was smaller than that of the two LM plants.  
Figure 3: The expression level of Vip3 Aa protein in the agroinfiltrrated tobacco leaves. The red arrow indicates the starting point of Vip3 Aa expression.  
Figure 2: Scheme of the new risk assessment strategy. (**a**) Traditional risk assessment methods include laboratory and field trials. (**b**) A new risk assessment approach based on transient expression models of plants using an agroinfiltrration method.  
To investigate the stability of the Vip3 Aa protein, we analyzed its expression levels in the recombinant Vip3 Aa-treated artificial diet (100 ng/mg) once and in the infiltrated plant leaves (the 3 rd-day sample after infiltration) using western blot analysis for 60 h (Figure 4). While the Vip3 Aa protein in the agroinfiltrated plant leaves slightly decreased until 60 h, the recombinant Vip3 Aa in the artificial diet significantly reduced after 15 h and was barely detectable after 21 h.
## 5 Testing, Results, and Analysis
### Analysis and Results
In addition to the graphs, qualitative feedback was collected from the five experts regarding the simulation, the accessibility of the housing unit, and the use of the virtual reality (VR) tool. Together, their insights help identify both the strengths and the areas for improvement of the developed virtual environment. In terms of simulation, the experts agreed that visual aspects such as flat colors, uniform lighting, and low-quality textures negatively affected the sense of realism. Although some interactions were described as immersive, they were also noted to lack technical refinement and physical consistency. For example, the automated repositioning of the wheelchair after collisions was perceived as unnatural, and the turning speed and movement dynamics were suggested to be improved. The addition of contextual sounds--especially during collisions or when activating switches--was recommended to enhance immersion. Regarding accessibility, there was consensus that certain areas, such as the kitchen, bathroom, and small bedroom, were difficult to navigate using a wheelchair. While the design was acknowledged to follow regulatory guidelines, several experts pointed out that furniture layout and narrow space widths often hinder effective maneuverability. This highlights a gap between regulatory compliance and real functional use. As for the VR tool itself, experts recognized its potential as a resource for evaluating accessibility and supporting inclusive housing design. However, they also identified technical limitations that should be addressed: challenges in spatial perception, physical discomfort (e.g., fatigue or dizziness) during use, and a learning curve that may affect the user experience--especially for individuals unfamiliar with this type of technology. Clear instructions before beginning the experience and more flexible control schemes were also recommended. Figure 16: Chart showing responses based on the questionnaire used to evaluate symptoms caused using virtual reality. Figure 14: Chart showing responses based on the questionnaire used to evaluate the realism of the experience. Figure 15: Chart showing responses based on the questionnaire used to evaluate the user experience of the simulation. In summary, the qualitative evaluation suggests that the tool holds strong potential to support accessible design, but it requires technical improvements and enhanced usability to reach its full effectiveness. The expert feedback provides valuable direction for a more user-centered development process, grounded in real-life accessibility conditions within the domestic environment.
# Micro-Fabrication of Components for a High-Density Sub-Retinal Visual Prosthesis
## 4 Discussion  
Microfabrication technology for retinal prostheses, while not as mature as processes for integrated circuit fabrication, has nevertheless progressed in important ways to fill the design input requirements for visual prosthetics. In particular, our team has evolved segmented, serpentine high-density multi-electrode arrays with >256 electrodes for sub-retinal stimulation, having excellent SIROF charge capacity and demonstrated biostability. These SiC-encapsulated electrodes have leveraged virtually all of the technologies that are incorporated in current clean rooms for semiconductor fabrication, including photolithography, physical vapor deposition, and chemical and reactive ion etching, to name a few. While silicon wafers are used as host substrates, the microfabricated components are removed from their host surfaces upon completion; precision lithography, which is necessary to create high-density arrays, would not have been possible without the Si starting material.  
Visual prostheses will likely remain technology drivers for implantable medical devices for the foreseeable future. The high channel counts required to create complex patterns of phosphenes that correspond to images that are captured in the user's environment will likely drive the fabrication of even greater density visual prosthesis components in the future--if the packaging of the implantable electronics can keep pace. This is especially true as visual prostheses increasingly focus on treatment not only of severely blind patients, but also of individuals with age-related macular degeneration who retain some useful peripheral vision but require restoration of higher-acuity central visual function. Future work, in fact, will likely lead to the manufacture of integrated medical devices for vision restoration that incorporate not just micro-fabricated electrodes, but also stimulation and recording electronics and fully integrated leads and chip scale packaging; such implantable prostheses are indeed already under development.
## 2 Materials and Methods
### Methods
As segmentation tasks grow in complexity and dataset sizes expand, SegFormer's scalable and adaptable approach positions it as a viable solution. The proposed approach is also compared to the original Mask2 Former architecture [32]. This model retains the original MaskFormer [32] architecture in general, but uses Masked Attention in the decoder block instead of the traditional Cross Attention mechanism. This feature allows it to overcome pixelwise errors, such as boundary artifacts or fragmented estimates, which are common in segmentation tasks, particularly in object interiors and along their edges. The backbones used in the models are given in Figure 3:2  
The two primary metrics commonly employed to assess semantic segmentation models are accuracy and mean Intersection over Union (mIoU). These metrics offer valuable insights into the model's performance by indicating its overall accuracy and its ability to precisely determine object boundaries. Accuracy is a straightforward metric that quantifies the ratio of correctly predicted pixels to the total number of pixels in the dataset. The accuracy metric is calculated as follows:  
Figure 3:3  
In the equation, _TP_ and _TN_ represent the true positive and true negative rates, respectively. _FP_ and _FN_ represent false positive and false negative rates, respectively. While accuracy is straightforward and simple to calculate, it might not offer a complete perspective on the model's performance, particularly in cases of dataset imbalance, where certain classes are significantly more prevalent than others. Mean Intersection over Union (_mIoU_) offers a more comprehensive evaluation across all classes by normalizing the overlap area between the predicted segmentation and the actual segmentation by their union. It is computed by averaging the _IoU_ for all classes in the dataset. For a single class, IoU is defined as follows:  
Figure 3:4  
In the equation, the overlap area is the area of pixels correctly identified to a given class, and the union area is the combined area of the actual and predicted segments for that class. The _mIoU_ excluding overlap is given by  
Figure 3:5  
In the equation, \(N\) denotes the number of classes. _mIoU_ is particularly useful in assessing the ability of the model in terms of how it handles the distinction between different classes and how it deals with the complexity of object boundaries. It is less sensitive to class imbalance than accuracy, which is why it is commonly used to evaluate semantic segmentation models. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Backbone** \\ \hline DeepLabV3+ & ResNet-101 \\ \hline Mask2 Former & Swin-S Transformer \\ \hline ConvNext & UperNet \\ \hline SegFormer & Mix Transformer Encoder \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The backbones used in this study.
## 3 Communication Performance
### Results
To illustrate the improvements using different receiver elements, we present scatterplots of equalized QPSK signals of the first packet using the IPNLMS channel estimation algorithm in Figure 11, which includes the following: (a) MRC-FDE using the 1 st receiver element; (b) MRC-FDE using receiver elements 1 and 2; (c) MRC-FDE using receiver elements 1 to 3; (d) MRC-HTFDE using the 1 st receiver element; (e) MRC-HTFDE using receiver elements 1 and 2; and (f) MRC-HTFDE using receiver elements 1 to 3. The top panel displays the results of using MCR-FDE; the bottom panel displays the results of using MRC-HTFDE. The output SNRs for MRC-FDE were 7.0 dB, 8.1 dB, and 8.6 dB, whereas the output SNRs for MRC-HTFDE were 8.5 dB, 11 dB, and 12.3 dB, respectively. The output SNR improvements were 1.5 dB, 3.1 dB, and 3.7 dB. Figure 10: Output SNR as a function of the number of transmissions and the number of elements used. (a) FDE results and (b) HTFDE results  
Figure 9: BER performances as a function of the number of transmissions and the number of elements used: (a) the performance with MRC-FDE and convolution code and (b) the performance with MRC-HTFDE and convolution code.
## 2 Materials and Methods
### Satellite Data
To match the Terra/MODIS data at a 500 m resolution with the Spot/Vegetation data and MOD13A3, the Terra/MODIS NBR2.1, NBR1.6, and GPP data were unified to 1 km spatial resolution, and a geometric projection was used. The July and August data were averaged for each year because that is the most active vegetation growth period in South Korea (Table 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Sensor** & **Band/Product** & **Spatial Resolution** & **Period** \\ \hline \multirow{4}{*}{Terra} & \multicolumn{3}{c}{NIR (Band 2)} & \multirow{4}{*}{\(1\) km} & \multirow{4}{*}{May 2000} \\  & & & SWIR\({}_{1.64\,\mathrm{\SIUnitSymbolMicro m}}\) (Band 6) & & May 2000 \\  & & SWIR\({}_{2.13\,\mathrm{\SIUnitSymbolMicro m}}\) (Band 7) & \(1\) km & May 2012 \\  & & NDVI (MOD13A3) & & July–August, 2000–2017 \\  & & GPP (MOD17A2H) & & \\ \hline SPOT & Vegetation & NDVI & \(1\) km & July–August, 1999 \\ \hline \multirow{4}{*}{Landsat} & \multirow{4}{*}{TM} & Blue (Band 1 or 2) & & August 1999 \\  & & Blue (Band 1 or 2) & & August 2000 \\ \cline{1-1}  & & Green (Band 2 or 3) & 30 m & August 2004 \\ \cline{1-1}  & & Red (Band 3 or 4) & & August 2007 \\ \cline{1-1}  & & & & August 2010 \\ \cline{1-1}  & & & & August 2016 \\ \hline Terra \& Aqua & MODIS & Land Cover & \(1\) km & 2013 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite data. Figure 1: (**a**) Study area; (**b**) Three-band composite image of Landsat-5 consisting of Band 7 (short-wave infrared (SWIR)), Band 4 (near infrared (NIR)), and Band 3 (Red) in 13 August 2000; (**c**) Land type according to the International Geosphere-Biosphere Program (IGBP) land cover classification scheme of MODIS Land Cover.