esa-sceva/satcom-chunk-collection huggingface dataset:
# The Electromagnetic Shielding of Optoelectronic Devices by Mesh Structures
## 4 Results and Discussion
At a frequency of 4 GHz, the attenuations are 38.9 dB and 34.2 dB, respectively, which are better than that for the filter based on a carbon nanotube layer with 10 nm thickness at the same frequency (30 dB). Numerical calculations of the shielding properties of structures is quite expensive in terms of machine time and resources. Moreover, a comparison with the numerical calculation of the transmission of a mesh structure on the substrate shows a mismatch in the results due to the presence of interference in the glass and absorption. However, this model can be used in a rough calculation of the shielding properties. The Kontorovich calculation of the shielding properties has a higher error compared with the one-mode calculation of the shielding properties; therefore, it is less preferable in calculating the shielding properties. However, one-mode calculation of the shielding properties can be used for a rough and quick calculation of the transmission in a frequency range close to the resonance peak. Therefore, the standard deviations from the numerical calculation of the transmission in the frequency range from 50 GHz to the resonance peak frequency (0.9 THz for sample 1 and 2.8 THz for sample 2) are 13.5% and 5.2% for samples 1 and 2, respectively. The resonance peak frequency can be determined using the one-mode calculation of the transmission: the mismatch with the numerical calculation at a frequency of 0.9 THz for sample 1 is 0.7%, and that at a frequency of 2.6 THz for sample 2 is 0.4%. The mismatch of the numerical calculation of transmission at higher frequencies can reach 40% when taking into account only the first Floquet mode. Despite the fact that the numerical calculation of transmission is optimal, for the estimation of the shielding frequency range, the best method is the one-mode model due to its easy and quick calculations.
# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 1 Introduction
### Short Literature Review
#### 1.1.1 Non-Optimal Algorithms  
Active sampling-based algorithms such as RRT expand randomly in the configuration space and rapidly construct tree-like structures over the entire search space; due to this nature, they can fetch a non-optimal random path in a short time compared to other algorithms [15; 20; 21; 22; 23; 24; 25]. Lately, many researchers have investigated a variety of sampling-based RRT path planning algorithms for industrial robot applications and their improved versions [26; 27; 28; 29; 30; 31] in terms of various parameters like computational time of an algorithm, path length, energy consumed by the path, length of the path, obstacle avoidance capacity, etc. RRT is one of the most influential algorithms in the sampling-based path planning algorithm family. It has an incremental search approach without any parameter tuning, and without reducing the resolution, it brings the randomly generated path without collision. This means that it constructs the search tree incrementally throughout the configuration space, without setting any resolution parameters. It randomly explores the search space and creates the number of nodes by checking for the obstacle in the configuration space between the nodes. It efficiently searches non-convex, high-dimensional spaces by randomly building a space-filling tree by creating the random nodes and making the edges between the nodes if no collision is detected between the two nodes.
# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
###### Abstract  
In order to promote the accurate recognition and application of visual navigation robots to the environment, this paper carried out research on the road pattern recognition of a vision-guided robot based on improved YOLOv8 on the basis of road pattern calibration and experimental sampling. First, an experimental system for road image shooting was built independently, and 21 different kinds of road pattern image data were obtained by sampling roads with different weather conditions, road materials, and degrees of damage. Second, the road pattern recognition model based on the classical neural network Resnet 18 was constructed for model training and testing, and the initial recognition of road pattern was realized. Third, the YOLOv8 target detection model was introduced to build the road pattern recognition model based on YOLOv8n, and the model was trained and tested, improving road pattern recognition accuracy and recognition response speed by 3.1% and 200%, respectively. Finally, to further improve the accuracy of road pattern recognition, improvement research was carried out on the YOLOv8n road pattern recognition model based on the C2f-ODConv module, the AWD adaptive weight downsampling module, the EMA attention mechanism, and the collaboration of the three modules. Three network architectures, classical CNN (Resnet 18), YOLOv8n, and improved YOLOv8n, were compared. The results show that four different optimization models can further improve the accuracy of road pattern recognition, among which the accuracy of the improved YOLO v8 road pattern recognition model based on multimodule cooperation is the highest, reaching more than 93%.  
road pattern; improved YOLOv8; recognition accuracy; road image +
Footnote †: journal: Applied Sciences
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 3 MILP Compact Formulations
### Four-Index MILP Formulation with Ordinality
Constraint (18) is equivalent with (7), observing that \(j\) is the first intervention of technician \(i\) if and only if there exist \(n\in\mathcal{N}_{i}\) such that \(z_{i,j,n,1}=1\), which is equivalent to \(\sum_{k=1}^{K_{i}}z_{i,j,j,k}=1\). Constraint (19) is equivalent with (8), observing that \(j\) is the last intervention of technician \(i\) if and only if there exist \(k\) such that \(z_{i,j,i,k}=1\), which is equivalent to \(\sum_{k=1}^{K_{i}}z_{i,j,k}=1\). As in the previous MILP formulation, time window constraints may induce impossible transitions. Relations (11) allows considering the same preprocessing rules than in the previous MILP formulation. A question is to analyze whether such formulation avoids sub-tours. Actually, feasible continuous sub-tours may occur, for example, \(z_{i,1,2,1}=z_{i,2,1,2}=z_{i,1,2,3}=z_{i,2,i,A}=0,5\). This configuration avoids paying a distance from 1 to 2, with a sub-tour of size 1. The following cuts can also be added to avoid such situations, similarly to previous subtour cuts \(u_{i,j,i^{\prime}}+u_{i,i^{\prime},j}\leqslant 1-u_{i,j,i}\). \[\min_{z,I}\sum_{i\in\mathcal{I}}\sum_{j\in\mathcal{J}_{i}}\sum_{n \in\mathcal{N}_{i}}\sum_{k=1}^{K_{i}}D_{j,n}z_{i,j,n,k}+ \sum_{i\in\mathcal{I}}\sum_{j\in\mathcal{J}_{i}}\sum_{n\in \mathcal{N}_{i}}D_{i,j}z_{i,j,n,1}+\sum_{j\in\mathcal{J}}P_{j}\left(1-\sum_{i,j,k}z_{i,j,n,k}\right) \tag{12}\]0  
Note that \(\sum_{k=1}^{K_{i}}\sum_{j^{\prime\prime}\in\mathcal{J}}z_{i,i^{\prime\prime}, i,k}\in\{0,1\}\) is the flow arriving at the arrival depot. It is zero if and only if technician \(i\) has an empty route; it is the equivalent of \(u_{i,j,i}\) in the first MILP formulation.
## 3 Sample Preparation Techniques and Development of Extraction Sorbents  
The selection of sample preparation technique is of utmost important to ensure that accurate and precise analysis of targeted analytes can be achieved. Different sample matrices require different sample preparation techniques. Highly complex matrices such as biofluids (i.e., blood, plasma, and urine) require a highly selective method to obtain high sensitivity and robustness for the analysis of small molecules due to the presence of endogenous (e.g., metabolites of the target analyte, proteins, or lipids) or exogenous (substances introduced during sample processing and analysis) compounds [24]. Fruits and vegetables contain carotenoids, carbohydrates, and fat, which also require selective extraction to eliminate the matrix interference. Therefore, consideration on the choice of sample preparation technique is vital. This paper will be focusing on the sample preparation techniques for food and water samples.  
The OPPs' determination in food samples is challenging due to the presence of matrix interference in complex matrices and low analytes' concentration. Therefore, development  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pesticides** & **K\({}_{\text{OW}}\)** & **M.W** & **Action** & **Toxicity Class WHO** \\ \hline Cadusafos & 3.90 & 270.40 & Nematicide & 1b \\ Diazinon & 3.30 & 304.30 & Insecticide & 2 \\ Chlorpyrifos-methyl & 4.24 & 322.50 & Insecticide & \(\underline{\text{U}}\) \\ Malathion & 2.75 & 330.40 & Insecticide & 3 \\ Chlorpyrifos & 4.70 & 350.60 & Insecticide & 2 \\ Phenthoate & 3.69 & 320.40 & Insecticide & 2 \\ Profenofos & 4.44 & 373.6 & Insecticide & 2 \\ Ethion & 4.28 & 384.5 & Acaricide & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties and toxicity class of organophosphorus pesticides [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**OPPs**} & \multicolumn{2}{c}{**MRL (mg/kg)**} \\ \cline{2-3}  & **Apple** & **Grape** \\ \hline Chlorpyrifos & 0.01 & 0.01 \\ Profenofos & 0.01 & 0.01 \\ Malathion & 0.02 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum residue level (MRL) of OPPs in fruits (European Union (EU) Regulations (EC) No. 2020/1085, No. 2017/978, and No. 2015/399).  
of a facile and sensitive detection method is crucial for determination of OPPs [25]. Sample preparation must also be fast, easy, simple, low-cost, and amenable to a broad range of analytical instruments.  
The accuracy and precision of an analytical method is highly dependent on selection of sample preparation techniques [26]. Over the past few years, numerous extraction techniques for determination of OPPs in food and environmental samples have been reported. Trends in sample preparation for pesticides' residues have been focusing toward miniaturization of analytical methods and development of selective sorbents. Extraction techniques can be categorized into two categories, which are liquid-based extraction and solid-based extraction.
## 5 Experiment Results
### The Performance of the Onboard Computer  
Most drone surveillance applications use a Raspberry Pi as the onboard computer because it is cheaper than the Nvidia Jetson NANO. In this study, we applied the Nvidia Jetson NANO as the onboard computer equipped with the drone. It requires more hardware and power than the Raspberry Pi. We compared the processing delay between Nvidia NANO and Raspberry Pi 3B+, as shown in Figure 7. The process executed on the NANO was recorded in real time with no processing delay during the aerial SWIR image-capturing task. However, executing the same process on a Raspberry Pi 3B+ caused a processing delay. Note that at 3000 frames, the processing delay was 60 s, making the Raspberry Pi 3B+ unsuitable for handling the SWIR image process in real-time applications. Although the Raspberry Pi has the advantage of a small size, light weight, and low power consumption, it had lower graphic computing performance than the Nvidia Jetson NANO. Thus, the Nvidia Jetson NANO was more suitable to mount on an UAV to perform the SWIR image collection task.
## 1 Introduction
Solar-induced fluorescence (SIF) signals from satellite remote sensing platforms have been reported to be a novel methodology for monitoring the variations of GPP [32, 33, 34, 35, 36, 37]. SIF is a remitted electromagnetic radiation in the wavelength range of 650-800 nm during the photosynthetic processes [38, 39]. Due to its direct linkage to vegetation photochemistry, SIF provides an early and more physiologically-based method to access the functional status and changes of vegetation [39, 40]. It has been reported in several studies that SIF and GPP have strong empirical linear relationships [32, 33, 40, 41, 42, 43, 44]. Environmental conditions had a greater impact on SIF than GPP [45], but the correlation in the responses of SIF and GPP to environmental variables has rarely been studied quantitatively with continuous and long-termobservations [46]. Thus, a comparison of PRI-based LUE model and SIF-based linear model could be helpful to better estimate GPP under varying environmental conditions. In this study, we assessed the performance of a PRI-based LUE model in estimating GPP under various environmental conditions in a maize field, and compare it with SIF-based linear model. We integrated a set of field observation data, including multi-angle spectral observations, eddy flux measurements, environmental variables, and canopy structural parameters. The objectives of our study are: (1) to explore the angular variations of observed PRI and evaluate the performance of single-angle and multi-angle spectral observations of PRI in estimating LUE for a maize canopy, respectively; (2) to evaluate and compare the performance of PRI-based LUE model and SIF-based linear model for the estimation of GPP in the maize field; and (3) to explore the model preferences under different environmental conditions.
## 3 Tolerancing Informatics
### Tolerancing Informatics Workflows
#### 3.4.1 Automatic Transfer of Specification-Related Tolerancing Information  
It is widely acknowledged, that, due to the strong repercussions of tolerancing decisions on manufacturing, inspection, and assembly cost as well as on the product function, tolerance specification (deriving adequate tolerancing schemes) and tolerance allocation (allocating adequate values to these tolerances) are highly responsible design tasks. Though these tasks are typically performed in late design stages based on expert knowledge, current research efforts focus on the early and automatic generation of functional specifications from design knowledge [18] and on the knowledge-based tolerance allocation [19].  
However, nowadays in many companies, the information transfer regarding these tolerance specifications from design to manufacturing and inspection is highly dependent on error-prone manual interventions. This is because these tolerance specifications from design have to be manually entered in software tools for the computer-aided process planning or computer-aided inspection. In contrast to this manual process, tolerancing informatics workflows allow the automatic transfer of specification-related tolerancing information to all downstream activities. In order to achieve this, several research works have been proposed, which focus on the interoperability between product definition and measurement planning [20] as well as on selecting and developing adequate exchange formats between computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-aided inspection (CAI) tools [21; 22]. Moreover, approaches for the mapping of these tolerance specifications from higher-level CAD exchange formats, such as STEP (Standard for the Exchange of Product model data), to lower-level geometry exchange formats, such as STL (Stereolithography), have been proposed [23] as well as methods to include such STL files in modern tolerancing informatics workflows [24].  
The automatically transferred specification-related tolerancing information can be modelled using different representation schemes [11] and used for the automatic quality assurance of parts [25]. Moreover, recent advances regarding the management of obtained measurement data [26] help to further process the obtained verification-related tolerancing information to assembly and testing, where this information can be used for applying assembly adjustments and advanced matching strategies.
## 4 Conclusions  
In this paper, a new technique to perform pattern shaping by evaluating the vicinity of the EBG phase region is proposed. The whole structure consists of a top antenna and a reflector surface. The reflective surface was designed to resonate at a close but lower frequency than the radio altimeter frequency. The top antenna's operating frequency was selected to be lower than the target radio altimeter frequency and around the highest end of the resonance band of the reflector surface. Then, the top antenna was put over the surface and brought closer to increase the frequency of the whole structure. This approximation process continued until the target radio altimeter frequency was found. During the design, it was aimed that, at the new height of the antenna, the final operation frequency remains outside the resonance region, but is not far away from the reflector's low-surface-impedance region to have impedance low enough for imitating the PEC reflector characteristics, ensuring that the antenna's diffuse pattern is gathered up.  
With the proposed technique, a space-saving MIMO antenna is designed by gathering the pattern of a diffused pattern antenna. The lowest gain of the resultant structure, whose top antenna-reflector distance is 6 mm, is 10.1497 dBi and the directivity is 11.5165 dBi. The half-power beamwidth angle is around 60\({}^{\circ}\). When the distance is precisely adjusted between 6 mm and 7 mm, the exact 4.3 GHz value is reached. It should be noted that the distance between the top antenna and the reflector, which is 6 mm, is 8.6% of 69.72 mm, which is the wavelength of a 4.3 GHz target frequency, ensuring the compactness. It can be concluded that a MIMO radio altimeter antenna meeting the ITU criteria has been designed following the proposed technique, successfully.  
Conceptualization, S.K. and M.K.; Methodology, S.K.; Software, S.K.; Validation, S.K. and F.O.A.; Formal analysis, S.K. and M.K.; Investigation, S.K.; Resources, S.K. and M.K.; Data curation, S.K. and F.O.A.; Writing--original draft preparation, S.K.; Writing--review and editing, S.K. and M.K.; Visualization, S.K.; Supervision, M.K.; Project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Miscellaneous Solutions
#### 4.5.2 Downward Path Construction Mechanism  
Author [68] has described issues with downward route cancellation in which nodes send No-path DAO messages, which means that DAO message with lifetime zero to parents to cancel the path to a specific node. The packet from the no-path message is delivered to all parents informing them about the cancellation of the downward route to the original node. Issues such as the old downward path still exist until the expiry of the route lifetime due to no-path DAO message is not reached because of packet loss or when the mobile node is not reachable to the old parent, causing inconsistent routing states. Another problem is node deleting the old parent but it is out of reach of the new parent selected from the neighbor list resulting in loss of connectivity.  
The authors proposed a modification in the cancellation mechanism for downward routes in which a common ancestor of old and new routes send a no-path DAO message instead of a mobile node upon receiving an updated DAO message. This no-path message is forwarded to the old parent downward. Therefore, packets stored between the mobile node and common ancestor can be used by the mobile node as the old downward path still exists until the installation of the new downward route. In addition, lost no-path DAO packets will not result in the state of inconsistency as the route is removed in the sub-DODAG of the old parent path to the mobile node in the proposed mechanism.  
Table 5 illustrates the working principles, issues addressed, strengths, and weaknesses of the solutions under the category 'Miscellaneous Solutions'.
## 3 Proposed Framework  
Figure 2 shows the framework that we aimed to build to predict the propagation scale of situational information. The framework consists of the following tasks:  
1. Collecting the COVID-19-related datasets consisting of tweets.
2. Applying pre-processing steps to remove noisy data.
3. Manually annotating the random 3000 tweets by different annotators according to different situational information categories.
4. Feature extraction through TF-IDF, as machine learning classifiers need data in the form of feature vectors.
5. Applying supervised machine learning classifiers and obtaining accuracy scores to check the classification performance of different classifiers.
6. Choosing the classifiers with the highest classification accuracy score to label the remaining data.
7. Extracting content, user-related linguistic and cognitive features to predict the propagation scale.
8. Applying Machine learning regression algorithms to predict the retweeted amount of every situational information separately.
9. Presenting the results to analyze the propagation scale through ML regression algorithms evaluation parameters such as co-efficient values of all features.
## 2 Methodology
### Spatial Division and Link Section Extraction Method  
A dynamic segmentation approach was applied in this study using the standard deviation range set in the previous section to determine the hierarchical segmentation of vehicle speed data within each space. Specifically, a division function based on the time mean speed (TMS) and space mean speed (SMS) was used to evaluate the stability of each grid in terms of stable flow (SF). Grids identified as SF were excluded from further segmentation, whereas those not identified as SF underwent additional segmentation to capture unstable flow dynamics.  
Figure 4 illustrates the dynamic segmentation process. If a grid, as shown in Figure 4a, includes areas without roads, these areas are excluded initially to ensure the segmentation focuses only on road networks. Next, each grid is evaluated for SF status; if the grid is identified as SF, no further segmentation is applied. However, for unstable flow (UF) grids, additional divisions are carried out, as shown in Figure 4b, until all sections are classified as SF or the preset maximum number of divisions is reached. Figure 4c illustrates the segmentation progression, with a minimum of 5 divisions and a maximum of 12. These limits were set to prevent overly large grids, which result from fewer than five divisions, and to avoid segments shorter than an average of 100 m, deemed unsuitable for microsegment speed data on expressways.  
Figure 4: Process of stable flow (SF), unstable flow (UF), and low-speed (SF-L) segmentation based on data distribution.  
Figure 3: Boundary between SMS, TMS, and unstable flow.  
Low-speed sections were then defined as areas with speeds below 50 km/h, reflecting the minimum speed allowed on expressways. Excluding UF spaces, SF-identified spaces were further assessed for low-speed zones, labeled as SF-L. The final segmented spaces were analyzed to assess overlap with road links, segmented further by spatial criteria, as shown in Figure 4d. In Figure 4, as well as in all subsequent figures, the colors follow a spectral scheme, where red denotes low speeds, blue indicates high speeds, and yellow represents medium speeds, highlighting sections of unstable traffic flow.