esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 3 Experiment Results
### Accuracy Verification of the Static and Kinematic PPP  
#### 3.3.1 The Static PPP Tests  
An experiment is conducted to evaluate the accuracy of predicted clock correction in the static PPP application. First, a control group using the linear model is selected according to the preceding analysis. Second, the predicted clock correction for 1, 2, and 3 h from day 124 to day 139 in 2016 is calculated and tabulated at 30 s intervals based on IGS final products. Third, five random IGS global distribution-tracking stations (AMC2, CEDU, CHAN, NRC1 and YELL) are selected. Finally, the position and navigation data analysis (PANDA) software developed by Wuhan University is used to process the single-day solution of PPP. For the solution strategy, readers can refer to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Average (ns)**} & \multicolumn{3}{c}{**Max (ns)**} & \multicolumn{3}{c}{**Min (ns)**} \\ \cline{2-9}  & **1-h** & **2-h** & **3-h** & **1-h** & **2-h** & **3-h** & **1-h** & **2-h** & **3-h** \\ \hline Block IIF & 0.169 & 0.248 & 0.342 & 0.186 & 0.273 & 0.362 & 0.146 & 0.226 & 0.311 \\ Block IIR & 0.275 & 0.376 & 0.506 & 0.309 & 0.464 & 0.633 & 0.249 & 0.334 & 0.414 \\ Block IIR-M & 0.277 & 0.389 & 0.525 & 0.366 & 0.494 & 0.709 & 0.228 & 0.293 & 0.391 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The root mean square (RMS) of 1-h, 2-h, and 3-h prediction using the improved model and 3-h observation.  
Figure 7: Spectrum analysis for 2-h slipping prediction residuals of G25 after eliminating the clock reference.  
[PERSON] et al. [24]. The RMS of each station in North (N), East (E), and Up (U) directions is calculated from the station coordinates published on the IGS website.  
Figure 8 shows the static PPP precision of different clock prediction strategies. The old model is the result of the general linear model for 2-h prediction with 2-h observation. Compared with that based on the IGS final product, the accuracy of the static PPP solution based on forecast products is poorer. The accuracy of the horizontal direction is significantly asymmetric due to the impractical ambiguity resolution of the non-differential single station [9]. Compared with that of the old model, the accuracy of the improved model, the third set of histograms in Figure 8, increases by 47.4%, 47.9%, and 49.1% in N, E, and U directions, respectively. The positioning accuracy of the 2-h linear forecast is lower than that of the 3-h forecast when the improved method is used. The static PPP accuracy of the improved model is better than 0.1 m, which is in sub-decimeter.
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 5 Discussion
### Background Distractions  
One recurrent problem when users try to collaborate synchronously online is the inevitable background noises and visuals that interrupt their activities. The background noise can be subtle or too loud to ignore. In most cases, participants had to repeat their sentences due to their voice being muffled by the background noises:  
**G2F4:**: _I can draw using color, there is the box where the color located (There is some background noise behind the scene)_
**G2F5:**: _I use the box at the top, number 2 (Background noise--C draw orange rectangle, B draw blue rectangle) (G2F4 distracted by his little brother)_  
Sometimes, participants were distracted by something else while performing collaborative tasks on the virtual conference application. From the observation, one of the participants in Group 3 was interrupted while doing the collaborative activity. Distractions can also take the form of an unwelcome presence in the background:  
**G3F7:**: _Please wait for a moment. (Others were coloring the sea while [PERSON] is being interrupted by talking to someone behind the background)._
**G3F7:**: _Can you move over? (G3F7 was letting someone to move across behind the scene while the web camera was disable)_
**G4F10:**: _There might be distractions from brothers, or like maybe their parents always come into the room without knocking, without realizing they're in class, so that's why._  
Distractions can occur in the virtual world as well, such as when the default layout of the platform is perceived to be cluttered and interfering with the flow of the tasks.  
**G5F13:**: _Okay, I don't think that turning on the camera is, uh, it's not, it's not, it's unnecessary because I want the whiteboard to be full screen. I think having the, having the video on the side might clutter the experience._  
Another research study found that distraction can come from background sound, background video movement, or an unwanted presence [70]. Other factors that may cause distractions include a poor camera angle, bad lighting, walking while holding the camera, behavioral distraction (eating in front of the camera), and background noises [68]. There are also issues due to the home environment, such as seeing yourself on screen and other forms of interruption, which can be distracting.  
Previous research has suggested that using a larger face and reducing background visibility could help reduce distraction [70]. The larger face can help reduce background distraction by allowing users to focus more on its facial expression. The previous study designed its system to be as simple as possible to minimize distraction when performing video annotation [71]. That is, by reducing the number of features available on the screen, simplifying the icons, and reducing background visuals and noise in the video feed.
## 2 Materials and Methods
### Equalizations  
A total of six equalizations in the low-frequency range were used, whose frequency response curves are shown in Figure 1. These equalizations were performed through peak and dip parametric equalizer filters with a constant Q value of 1 at three different central frequencies: 40 Hz, 80 Hz, and 160 Hz. The gain of the filters was set as 6 dB, which was above the audible threshold according to our pilot experiment results.
## 3 Methodology  
### pHRI Assistive Strategies  
The main interaction controllers for pHRI are impedance and admittance control (see Section 3.2 for their mathematical definition). Controller stability issues are common with admittance control [21; 22]. When a human holds the tool at the end-effector, it results in a coupled system that can lose stability if the human operator stiffens his arm muscles, leading to robot vibrations. Hence for this task we chose impedance control. Investigating the stability issues of admittance control is a field in itself and there are several heuristic methods in the literature [20]. Impedance control was possible as the robot had a torque sensor in each joint, enabling torque control. Admittance control is preferred with robots that have only position control, by using an external FT sensor. As the task involved motion in the cartesian space hence we used the cartesian impedance controller that is explained later. We devised two assistive strategies:  
1. Force amplification strategy  
In this strategy we amplify the forces applied by the user on the knife's handle (see Section 4 for the experimental setup), detected by the FT sensor and input to our control scheme. The control diagram is shown in Figure 1. The appropriate parameter \(\eta\) has to be determined, as explained in Section 5.3.  
2. Intent prediction strategy  
In this strategy we predict the forces to be applied by the user using RNN-LSTM networks explained in Section 3.3. The control diagram is shown in Figure 2.  
Both Figures 1 and 2 are identical except for the module of their respective assistive strategies. In the force amplification strategy, the amplification module amplifies the human user's force input \(F_{h}\) at each time step through robot assistance \(\eta F_{h}\). In the intent prediction strategy, a trained RNN-LSTM network takes the human user's force input \(F_{h}\) at each time step to anticipate the user's input for the next time step \(F_{pred}\) and provides this force via robot assistance. In both cases the user can haptically sense this assistance being provided by the robot.
## 4 Algorithm Overview
### Lower-Level Decision
#### 4.2.2 Regular Vehicle Process Overview  
RVs receive input from EVs regarding EV decisions, traffic signals, and route priorities. The RV adjusts its route and speed based on priority routes for EVs and traffic signal conditions using this information. RV route selection is based on minimizing travel time while avoidingconflicts with priority EV routes. The congestion costs are also considered, and the speed is adjusted to maintain smoother traffic flows without affecting the priority EVs.  
_Objective for RVs_: Maximize the utility function using Equation (3).  
\[\mathrm{U_{RV}}=-\theta\times(\mathrm{Delay_{RV}})+\kappa\times(\mathrm{Rerouting \ Penalty}) \tag{3}\]  
where:  
* \(\theta\) represents the weight for minimizing the regular vehicle's delay.
* \(\kappa\) accounts for the inconvenience or penalty imposed by rerouting regular vehicles to prioritize emergency vehicles.
# RNA-Peptide Conjugation through an Efficient Covalent Bond Formation
## 2 Materials and Methods
### Construction of a Covalently Linked RNP  
The peptide subunit for the formation of a covalent linkage (Ac-TRQARRNRRRRWREQRGGSGGSGSG-HZ) was synthesized as follows. A HMBA-PEG resin was placed in a dry flask, and a sufficient amount of DMF was added to soak the resin; this mixture was allowed to swell for 30 min. \(N\)-\(\alpha\)-Fmoc-glycine (10 equivalent relative to resin loading) dissolved in DMF was mixed with a solution of DIC (5 equivalent relative to resin loading) in dry DMF on ice and then incubated for 20 min. The solution of an activated first amino acid was added to the resin prepared above. A DMF solution of DMAP (0.1 equivalent relative to resin loading) was added to the resin/amino acid mixture and incubated at room temperature for 1 h with occasional swirling. This procedure of coupling the first amino acid residue was repeated twice. The subsequent synthesis was performed on an automated peptide synthesizer (PSSM-8; Shimadzu, Kyoto, Japan) according to the Fmoc chemistry protocol using protected Fmoc-amino acids and HBTU. Acetylation of the \(N\)-terminal of peptide was performed by mixing with 1 M acetic acid anhydride and 1 M 1-methyl imidazole in DMF for 1 h at room temperature. The following procedures, cleavage of the protected peptide from the resin with hydrazine monohydrate, deprotection of the protected peptide, and purification of the peptide were performed as described previously [41]. The synthesized peptides were characterized by MALDI TOF mass spectrometry (AXIMA-LNR, Shimadzu), as follows: Acetylated Rev peptide hydrazide (Ac-Rev-(GGS)\({}_{3}\)G-HZ), m/z 3154.9 (calcd for [M+H]\({}^{+}\) 3155.5); Acetylated flexible peptide linker hydrazide (Ac-(GGS)\({}_{3}\)G-HZ), m/z [M+H]\({}^{+}\) 735.3, [M+Na]\({}^{+}\) 757.8 (calculated for [M+H]\({}^{+}\) 734.3).  
Crosslinking reaction of the RNA and the peptide subunits of RNP was carried out as described previously [41, 42] with a slight modification of the conditions. Freshly prepared 0.01 M sodium periodate (5 \(\upmu\)L; 50 nmol; 50 equiv) was added to 200 \(\upmu\)M RNA (5 \(\upmu\)L; 1 nmol) in 15 \(\upmu\)L of 0.03 M sodium acetate (pH 5.2), and the reaction mixture (25 \(\upmu\)L) was incubated for 1 h at 37 \({}^{\circ}\)C in the dark. After the reaction, 2.5 \(\upmu\)L of 10 M glycerol was added to the reaction mixture to reduce an excess amount of sodium periodate. The resulting oxidized RNA was purified by ethanol precipitation. A coupling reaction between the 3\({}^{\prime}\)-modified RNA (40 \(\upmu\)M) and Ac-Rev-(GGS)\({}_{3}\)G-HZ (88 \(\upmu\)M) was performed in 0.03 M sodium acetate (for pH 4 and 5) or 0.03 M sodium phosphate (for pH 6 and 7), containing 0.01 M NaCl (total 25 \(\upmu\)L) at 37 \({}^{\circ}\)C in the dark. The reaction mixture was extracted by phenol/chloroform and purified by ethanol precipitation to remove unreacted peptide, then dissolved in TE (25 \(\upmu\)L).
## 4 Performance Evaluation and Comparison
### Results and Comparison
#### 4.2.3 UNSW-NB15 Dataset  
Our LSTM-based IDS demonstrates exceptional performance on the UNSW-NB15 dataset, achieving an accuracy of 98.31%. The precision and recall for the \"BENIGN\" class are 97.87% and 98.74%, respectively, resulting in an F1-score of 98.30%. For the \"attack\" class, the precision remains high at 98.75%, while the recall reaches 97.89%, contributing to an F1-score of 98.32% as showing in Table 4, Confusion matrix for UNSW-NB15 dataset Figure 7, and ROC curve for UNSW-NB15 dataset Figure 8.  
Figure 6: ROC curve for NSL-KDD dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **Fl-Score** & **Support** \\ \hline BENIGN & 0.99 & 0.98 & 0.98 & 46,236 \\ \hline Attack & 0.98 & 0.99 & 0.98 & 46,236 \\ \hline Accuracy & & & 0.98 & 92,526 \\ \hline Macro avg & 0.98 & 0.98 & 0.98 & 92,526 \\ \hline Weighted avg & 0.98 & 0.98 & 0.98 & 92,526 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification report for UNSW-NB15 dataset.  
Figure 7: Confusion matrix for UNSW-NB15 dataset.
## 4 Conclusions  
Light use efficiency (LUE) is an important factor in designing the light environment in plant factories with electrical lights (PFELs). To analyze LUE, 3D-scanned plant models (3D-SPMs) for lettuce and ray-tracing simulation were used. The leaf areas of 3D-SPMs were slightly larger compared with the measured data, but considering the curled and convex structure of leaves, those of 3D-SPMs are thought to be more adjacent to actual ones. Additionally, in a growth chamber with an LED source, the simulation results were well matched with the measured light intensities and photosynthesis. When planting density decreased, the light interception of the central plant was increased due to a decrease in the mutual shading effect, but that of border plants was decreased by receding from the center of the light source. Different lighting distances, light arrangements, and floor reflectances also affected light interception, and the results indicated that cases with lower deviations in intercepted light showed larger light interception and LUE. The optimal photosynthetic photon flux density (PPFD) level for achieving maximal LUE was able to be obtained, which can support the control of light sources. Our study suggests a basic insight into designing the light environment in PFELs for maximization of LUE.  
**Author Contributions:** J.K. and J.E.S. conceived the research; J.K. performed the experiments; J.K. and W.H.K. analyzed the results; J.K. and J.E.S. prepared the manuscript; and J.E.S and W.H.K. revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry, and Fisheries (IPET) through Agriculture, Food, and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader, funded by Ministry of Agriculture, Food, and Rural Affairs (MAFRA) (717001-7).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Architecture and Test Environment
### Collection and Description of Data  
Before deciding to build a custom detection model, we tested several existing models based on other datasets, such as COCO. However, these models did not meet the desired level of accuracy due to the constraint of having only one camera covering all the lanes of the intersection. The camera captures large vehicles that are close but the problem arises with vehicles in other lanes that are further away and appear smaller in size. To address this issue, we decided to build a dataset that met our needs by using the same camera deployed in the intersection. This approach ensured that the detection and tracking were optimized for the specific camera and consequently improved the accuracy of the counting, which is the primary purpose of our application.  
To build our dataset, we used an embedded system with a Linux system based on the Allwinner A133 motherboard. We programmed the board to capture video footage throughout the day from the IP camera installed at the intersection. The videos were stored on a memory card and sent to the server via FTP with a Python program that removed the successfully transmitted videos.  
We carefully filtered the collected video sequences to obtain images that contained diversified sets representing different classes (cars, trucks, buses, and motorcycles) at different times of the day and in varying weather conditions, ensuring that all possible variations of the real world were included.  
The dataset contains 13,000 images, which were manually annotated using the labeling tool with the text extension of YOLO.  
Figure 3 shows the system architecture used for the dataset collection.  
Figure 2: The proposed architecture for traffic flow detection. Vehicle detecting, tracking, and counting are directly performed in the Nvidia Jetson Nano.
## 2 Line-Scan Hyperspectral Measurement Techniques
### Spectroscopy Techniques
#### 2.1.1 Reflectance and Transmittance Spectroscopy  
Light interaction with biological materials, such as food and agricultural products, is a complicated phenomenon. When a biological sample is exposed to an illumination source (e.g., halogen light), a small amount of the incident light is reflected at the outer surface, causing specular reflectance. The remaining incident energy goes into the sample, and the photons undergo multiple scattering actions before they are absorbed or reflected back to reemerge from the sample surface, generating diffuse reflectance. Hence the reflectance measurement generally involves both specular and diffuse reflectance signals. When the sample is small and the illumination light is strong enough, the incident light can also result in diffuse transmittance through the sample. Reflectance and transmittance signals are normally acquired as a function of wavelength and presented in the form of spectra. Reflectance and transmittance spectroscopy techniques can work in a broad wavelength range from ultraviolet to infrared. The spectral constitution of the illumination light is not altered during light-sample interactions. Sample information can be obtained based on the intensity changes due to light reflection, absorption, and scattering at different wavelengths. Reflectance spectroscopy is usually used for sample surface inspection, while transmittance spectroscopy is useful for evaluating internal attributes of the sample.
## 3 Data Routing Approaches in WSNs
### Delay-Aware Approaches  
As illustrated before, according to the vital role of energy in sensor networks, any data routing approach in WSNs should be aware about the energy levels of the nodes beside its routing goal. Therefore, most of the proposed delay-aware data routing approaches considered energy in their mechanism, too.  
Delay-aware approaches can be classified into two types: hard delay-aware and soft delay-aware. In hard delay-aware routing approaches, deterministic end-to-end delay bound should be supported and the main goal is ensuring the on-time delivery of real-time data, whereas in soft delay-aware routing, a probabilistic guarantee is required and the goal is just selecting the shortest available path.
# Ergonomic Panel Angles for Kitchen Appliances with Cooker Knobs
## 1 Introduction
Therefore, usability evaluation research focusing on panel angle can provide valuable data for designing the control panels of kitchen appliances. In response to the identified gap, [PERSON] and [PERSON] (2022) conducted an experimental investigation to determine the appropriate and optimal panel angles for touchscreen panels [34]. The study focused on offering ergonomic recommendations for the panel angles of touchscreen kitchen appliances by examining how different angles impacted subjective evaluations of visibility, physical comfort, and preference. The findings revealed that: (1) the panel angle significantly influenced all three subjective measures; and (2) panel angles between 15 to 42\({}^{\circ}\) were recommended as the appropriate range, with 15 to 19\({}^{\circ}\) as the optimal range. However, this research still leaves a knowledge gap, as it only focused on touchscreen panels, one of the abovementioned two primary control types commonly used in kitchen appliances. The distinctive mechanical characteristics between knob panels and touchscreen panels can lead to differences in the grip, posture, viewing angle, and actions users adopt during panel operation, suggesting that the appropriate and optimal panel angles for knob panels could differ from those for touchscreen panels. Therefore, this study experimentally identified the ergonomically appropriate and optimal panel angle ranges for knob panels. We aimed to address an unexplored research question: 'How does the usability of knob panels vary depending on the panel angle?' Accordingly, a research hypothesis was established that the panel angle would affect three types of usability. For usability evaluation, subjective ratings of visibility, physical comfort, and preference were collected, similar to previous studies on control panel usability [1; 2; 3; 4; 5; 6; 7; 8; 9]. The experimental setup and procedures, including participants, study design, experimental variables, and data analyses, were conducted in the same manner as in the [PERSON] and [PERSON] (2022) study [34]. Six panel angles--0, 15, 30, 45, 60, and 90\({}^{\circ}\)--were selected to cover the range of design parameters commonly used in existing slide-in/freestanding ranges.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 4 NSGA-II for the Multi-Objective Path Planning Problem
### Invalid Solution Operator  
The invalid solution operator is used to convert the path (\(p=[S,p_{1},p_{2},...,p_{i},p_{i+1},...,p_{n},T]\)) that collides with the obstacles (\(O=O_{1},O_{2},...,O_{m}\)) into a feasible path. The design process of this operator is expressed as follows:  
Figure 2: The crossover operator is shown. In the left sub-figure, the yellow circles are the two rotation points randomly selected on the path. Similarly, the black circles are the two rotation points on the other path. The line segments between the yellow circles are exchanged with the line segments between the black circles. Finally, two new paths are generated and displayed in the right sub-figure.  
Along the path, it is determined in turn whether the line segment (\(\overline{p_{i}p_{i+1}}\)) of the path collides with the obstacles. If so, the intersection points and the obstacles (\(O(i)\)) that collide with the line segment are found.  
Then, the vertices of the obstacles (\(O(i)\)), the two rotation points (\(p_{i}\), \(p_{i+1}\)) and the intersection points form the vertices of the graph. Any two vertices within the set of vertices are linked to form the set of edges. Meanwhile, the edges that collide with all obstacles (\(O\)) are deleted. Then, based on the vertices and edges of the graph, the Dijkstra algorithm can be applied to attain a feasible shortest path (\(q=[p_{i},q_{1},q_{2},...,q_{k},p_{i+1}]\)) from \(p_{i}\) to \(p_{i+1}\). Next, the line segment (\(\overline{p_{i}p_{i+1}}\)) of path (\(p\)) can be replaced with the path (\(q\)), and the path (\(p^{\prime}=[S,p_{1},p_{2},...,p_{i-1},q,p_{i+1},...,p_{n},T]\)) is generated. Finally, the viable path (\(p^{\prime}\)) is made by repeating the above operation from \(i=0\) to \(n\). The invalid solution operator is presented in Figure 3.