esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles and Concepts in Time Series Analysis
### What Is a Time Series?  
A time series can be represented as a sequence of scalar (or vector) values \(y_{1},y_{2},\ldots,y_{n}\) corresponding to contiguous, equally spaced points in time labeled \(t=1,2,\ldots,n\) (e.g., we could measure one \(y_{t}\) value each second, or each hour, or each week, or each month, etc., depending on the nature of the process, on the available technology to measure and store data, and on how we plan to use the collected data); this labeling convention does not depend on the frequency at which \(y_{t}\) values are sampled from a real-world process.  
Nowadays, time series have an impact in various fields. For example, they occur daily in economics, where currency quotes are recorded in time periods of minutes, hours, or days. Governments publish unemployment, inflation, or investment figures monthly. Educational institutions maintain annual records of school enrolment, dropout rates, or graduation rates [21]. Recently, international health organizations published the number of people who were infected by or deceased due to the COVID-19 pandemic daily. Several industries record the number of failures that occur per shift in production lines to determine the quality of their processes.  
Time series forecasting involves several problems that complicate the task of generating an accurate prediction; for example, missing values, noise, capture errors, etc. Therefore, the challenge is to isolate useful information from the time series, eliminating the aforementioned problems to achieve a forecast [22].
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
###### Abstract  
For the indoor navigation of service robots, human-robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human-robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments.  
Keywords:object semantic grid and topological map; indoor robot navigation; natural language interaction; auxiliary navigation point; segmented global path planning +
Footnote †: journal: Applied Sciences
## 2 Antenna Array Design
### Modeling of COTS GNSS Antenna
To understand the phase center variation, the plots for both the \(\varphi=0^{\circ}\) and \(\varphi=90^{\circ}\) elevation plane phase for LHCP and RHCP are presented in Figure 6. In the case of RHCP, in the plane \(\varphi=0^{\circ}\), the phase varies \(5.23^{\circ}\), and in the plane \(\varphi=90^{\circ}\) it varies \(0.68^{\circ}\), for \(\theta\) between \(-90^{\circ}\) and \(90^{\circ}\). These results show that the antenna has a small phase center variation (PCV). Figure 4: Axial ratio (solid line) at 1.57542 GHz in the elevation plane for \(\varphi=0^{\circ}\) (**A**) and \(\varphi=90^{\circ}\) (**B**). The blue dashed circles correspond to a 3 dB AR. This antenna showed good performance in most of its characterized parameters. The antenna can mitigate LHCP with low gains when compared to RHCP signals and even has low variation in the phase center, which is important for GNSS applications.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Case Design and Performance Metrics
#### 5.1.3 Performance Metrics  
In a cellular system, the CBP (\(P_{i}\)) in Cell\({}_{i}\) is an important performance metric for measuring the quality of service. The CBP is denoted as Formula (11). The number of call block (\(B_{i}\)) divided by the number of the Handover In (\(I_{i}\)) in Cell\({}_{i}\). The probability represents the fraction of the number of handovers that are blocked:  
\[P_{i}=\frac{B_{i}}{I_{i}}. \tag{11}\]
###### Abstract  
The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites.  
d Article
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 3 Simulation Results
### Performance Benefit of Prosumer Pairing  
The MPC algorithm can be operated in two ways, with or without sharing between the paired prosumers. To run the algorithm in unshared mode, the parameters \(P_{RC}^{max}\), \(P_{CR}^{max}\), \(P_{BCR}^{max}\), and \(P_{BRC}^{max}\) are all set to zero, which prevents the MPC algorithm from sharing energy between the prosumers. By running the algorithm for the full year, sharing and unsharing, comparisons can be made between the two scenarios to illustrate the benefits of sharing/pairing. For the paired mode of operation, Figure 5 shows the monthly total energies that flow between the paired prosumers. This figure illustrates some seasonal activity, in that more energy is transferred from the residential to the commercial prosumer in  
Figure 4: Two days of commercial demand and supply profiles used in simulation.  
Figure 3: Two days of residential demand and supply profiles used in simulation.  
the summer compared to the winter months. We can also see that on average, the residential prosumer has more excess energy to transfer to the commercial prosumer. The upper plot in Figure 6 shows average daily behavior for the full year, illustrating RER energy surplus (production minus load) for both prosumers. These plots indicate when each prosumer has a surplus (more RER output than load) and when each has a deficit (less RER output than load). The solar activity is apparent in these plots, showing an average surplus energy in the afternoon. There are times during the day when there are significant differences between the surplus energy available in the two prosumers. During this time, it is expected that one prosumer will be likely to transfer energy to the other prosumer, confirmed by the positive energy transfers evident in the lower plot. Clearly, the commercial prosumer transfers more energy to the residential prosumer in the morning, and vice-versa in the afternoon. This indicates how the MPC algorithm exploits differences between the two paired prosumers. Figure 7 shows total purchases from external prosumers in the network for both paired prosumers, using both modes of operation (sharing versus unshared). Clearly, the amount of energy needed from outside sources drops when the two prosumers are paired.  
Figure 5: Monthly total energy transfers between the two prosumers.  
Figure 6: Average daily energy transfers between the two prosumers.  
Table 2 summarizes total results for paired versus unpaired MPC operation. If the two prosumers under consideration are unpaired, the combined percentage of the loads met with RER energy is 75%, which increases to 86% when the loads are paired. In other words, a greater percentage of the RER output remains within the two prosumer paired system. The total RER output that is transferred to outside prosumers in the network decreases for the paired case.
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 4 Experiment and Analysis
### Pest Image Preprocessing
#### 4.1.2 Image Denoising  
During digitization and transmission, images frequently encounter noise introduced by imaging equipment and external environmental factors. To ensure the model's accuracy and stability, noise reduction is a crucial step. This paper utilizes the Non-Local Means (NLM) filtering method for image denoising.  
The NLM filter emphasizes the self-similarity of images, leveraging redundant information to preserve detail during denoising. In agricultural pest monitoring applications, each image may contain one or several types of pests, with some images displaying multiple pests of the same type, exhibiting high self-similarity. Therefore, the use of NLM filtering significantly enhances image quality and analysis accuracy.  
Figure 6 shows the image post-NLM filtering with equalization. Following filtering, the pest features and contours become more precise and more distinct, while the overall image appears smoother.
#### 1.1.4 Solutions at a Micro Level  
Several scholars have tackled the problems at a micro level. They have proposed strategies and applications. Among the strategies and applications are design strategies and quantification of energy flexibility in buildings in Italy [23], an application of variable speed drive (VSD) in the energy saving of electric motors [24], a techno-economic assessment of thermal and combined photovoltaic/fuel cell/battery energy system in Malaysian hospitals [25], a multi-stage and multi-objective optimization for additional energy in stallations in developed hospital reference buildings as a new approach to cost-fitness assessment [26], an analysis of metal oxide nanofluids for flat-plate solar collectors [27], and energy saving and emission reduction for industrial motor rewinding and replacement [28]. Additionally, some scholars have offered novel alternatives such as zero net energy building improvement guidelines for office buildings [29], a method of linking residential electricity usage and tropical outdoor climate [30], and energy efficiency packages for tenant installations undergoing laboratory testing and verification of indoor energy efficiency and environmental quality [31].
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 3 W-\(\Delta\)DOR Hardware Implementation
### W-\(\Delta\)DOR DSP Core Architecture Description
#### 3.1.1 Rate Generator  
The different sampling rates and chip rates are derived from the main clock at frequency 8\(F\)1. The Rate Generator generates two enable signals: the first one is used by the PN generator to control the output chip rate, and the second one is used to tune the sampling rate of the output section of the RRC filter and the input section of the CIC filter.
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 1 Introduction
In contrast, visual assist was used to improve the operability of the remote-controlled robot. The force assist based on the time to collision (TTC) was applied to avoid collisions and achieve safe performance. The presence of a force assist is an important factor that influences the operability. The increased dependency on the force assist provides a more frequent force feedback to the control device compared to a system with decreased use of the force assist. To improve operability, a proposed technique that generates a lower dependence on the force assist was required. Furthermore, a visual assist, which shows the predicted trajectory of the mobile robot on the operator's monitor, was applied to improve the operability. The predicted trajectory was based on the TTC and mobile robot velocity. The proposed strategy can improve the operability while ensuring safety performance. The proposed method was evaluated by comparing the experimental results from start to finish, the number of back operations, and the number of collisions. The experimental results obtained using the proposed method show that the operability and safety performance are improved. In this study, an unconstructed environment such as a disaster and hazardous environment where there are many obstacles inside of buildings was considered for improving the operability and safety performance. The remainder of this paper is organized as follows. The remote-controlled system is described in Section 2. Section 3 proposes a remote-controlled method with force and visual assists for mobile robots. Section 4 presents the experimental results to confirm the validity of the proposed method. Finally, Section 5 concludes the study.
## 3 Results and Discussions
### Fractographic Evaluation of the Aluminum/Composite Interface  
After obtaining the PEO process parameters and conducting the lap shear test, the fractured surfaces were visually characterized following the ASTM 5573-99 (2019) standard [40] to analyze the failure modes at the interface.  
Figure 5 shows the surfaces of the aluminum and thermoplastic composite samples, highlighting the joined regions. It is observed that there were mixed-mode fractures, with three different types. The yellow arrows indicate a small region with adhesive failure, as there was no polymer matrix in that region in the aluminum sample after the assembly rupture, representing 16.64%. The blue arrows indicate a polymer matrix's cohesive failure, where the matrix remained on the aluminum sample, representing 34.02%. Finally, the red arrows indicate a light fiber failure, with the reinforcement of the composite exposed both outside the laminate sample and on the aluminum sample, representing 49.34% of the total fracture [41]. It is also possible to observe the edge effect at the boundaries of the composite sample, indicating an excessive tightening in the assembly during the welding process, with the matrix reaching the glass transition temperature and flowing to the edges, a phenomenon previously observed in other studies [19; 24; 30; 39].  
Figure 4: Comparison of the contact angle value between untreated aluminum and PEO treatment.  
Figure 5: Fractography of the samples after the lap shear test. Adhesive failure (yellow arrows), cohesive failure (blue arrows), light fiber failure (red arrows), and edge effect (red circle).  
In Figure 6, the different stages of deformation in the samples during the lap shear test are shown, and on the right, a real image after fracture is observed. Noticeably, the aluminum sample exhibited considerable deformation after the test, which is attributed to the material condition. In this study, AA2024-O aluminum was used, where the \"O\" designation signifies annealed material, characterized by high ductility and significant plastic deformation (purple arrow in Figure 6) [42; 43; 44].  
Although the PEI/glass fiber sample did not show deformation like the aluminum alloy, it exhibited an edge effect (green arrow), which is attributed to excessive tightening and the thermoplastic matrix softening phase, leading to flow towards the edges of the sample, as shown in Figure 6 and observed in other studies [19; 24; 30]. Studying this effect is crucial because it can result in delamination in the composite material, impacting the integrity of structures and/or substructures [45; 46; 47].  
Figure 6: Fractures on the interface surface. Region where the deformation was verified (red circle), plastic deformation in AA2024-O (purple arrow), edge effect in PEI/glass fiber (green arrow), and material anchor points (yellow arrows).
# Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies
## 3 Materials and Methods
### Creating a Survey  
The table used in the questionnaire is taken from the main European PQ standard EN 50160:2010 (Table 5). It is worth mentioning that other voltage sag tables, such as those listed in IEEE Std 1564-2014, could be used as well.  
The following categories of equipment were included in the survey by default: MV three-phase electric motors, LV three-phase electric motors, LV single-phase electric motors, variable-frequency drives, heating elements, controllers, lighting, and servers. Other categories could be added by the companies. They had to mark all voltage sags and supply interruptions that are dangerous to each category separately. In each table cell, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Voltage, p.u.**} & \multicolumn{5}{c}{**Duration, s**} \\  & **0.01 \(\leq\) t \(\leq\) 0.2** & **0.2 \(<\) t \(\leq\) 0.5** & **0.5 \(<\) t \(\leq\) 1** & **1 \(<\) t \(\leq\) 5** & **5 \(<\) t \(\leq\) 60** \\ \hline \multicolumn{5}{c}{**Voltage swells**} \\ \hline
**U \(\geq\) 1.2** & Cell S1 & Cell S2 & Cell S3 \\
**1.2 \(>\) U \(>\) 1.1** & Cell T1 & Cell T2 & Cell T3 \\ \hline \multicolumn{5}{c}{**Voltage sags and supply interruptions**} \\ \hline
0.9 \(>\) U \(\geq\) 0.8 & Cell A1 & Cell A2 & Cell A3 & Cell A4 & Cell A5 \\
0.8 \(>\) U \(\geq\) 0.7 & Cell B1 & Cell B2 & Cell B3 & Cell B4 & Cell B5 \\
0.7 \(>\) U \(\geq\) 0.4 & Cell C1 & Cell C2 & Cell C3 & Cell C4 & Cell C5 \\
0.4 \(>\) U \(\geq\) 0.05 & Cell D1 & Cell D2 & Cell D3 & Cell D4 & Cell D5 \\ U \(<\) 0.05 & Cell X1 & Cell X2 & Cell X3 & Cell X4 & Cell X5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification of short-duration RMS voltage variation according to EN 50160:2010.  
respondent could select only one option out of three: (1) dangerous, (2) partially dangerous, or (3) non-dangerous. Option \"unknown\" was not allowed to be selected in order to prevent speculation and cases of abuse. It is noteworthy that a sag and a swell of the same percentage will have different effects on grid equipment, and therefore must be treated differently.  
More than 60 industrial companies, which are clients of the Lithuanian DSO (\"Energijos skirstymo operatorius\", AB), were surveyed, including the largest such as the nitrogen fertilizer company in Jonava (\"Achema\", AB), the phosphate fertilizer company in Kedainiai (\"Lifosa\", AB), the national state-owned railway company Lithuanian Railways, and companies of the Port of Klaipeda. It should be noted that the Mazeikiai refinery (\"Orlen Lietuva\", AB), the largest Lithuanian industrial company, was not surveyed because it is fed directly from the TSO grid. In the case of this study, similarly to [8], in order to properly represent the population, the importance is not on the quantity of respondents, but on their quality, i.e., priority must be given to the most important and most competent companies.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 5 Conclusions  
Selecting an appropriate CV method for classification tasks significantly depends on the size and availability of the labeled image dataset. If there is an abundance of labeled images, it is recommended to use approximately 2-3 thousand or more per weed category, utilizing CNN-based neural network models. However, if the available dataset contains fewer than a hundred images per category, it is pragmatic to initially employ traditional feature-based computer vision methods.  
When constructing the dataset, particularly if images are collected via automated methods such as robots or drones, one can amass thousands of images. These images, however, require extensive labeling and segmentation from the background, demanding significant plant expert hours. In such scenarios, we propose the use of traditional computer vision techniques to initially extract individual plants and weeds. This process should be followed by involving a plant expert to select approximately 40 representative samples for each class.  
At this juncture, the methodologies described in this study can be applied to classify an additional 100 images per weed category. The experts only need to verify and correct the misclassified images, typically around 15% of the dataset. Following this correction process, the classification scripts can be re-run, this time utilizing several thousands of images, achieving classification accuracies of approximately 95-96%. Consequently, each class directory will predominantly contain high-quality images, with about 4-5% that are inaccurately classified. These erroneous images can be swiftly identified and corrected by a plant expert, thereby ensuring a high-quality and sizable dataset suitable for training both SVM- and CNN-based classification networks.  
LM.: writing--original draft preparation; P.A.M.: writing--review and editing; G.T.: writing--review and editing; A.N.: writing--supervision. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Szechenyi Istvan University.  
Not applicable.  
Data Availability StatementData is contained within the article.  
This research was carried out by Precision Bioengineering Research Group, supported by Szechenyi Istvan University Foundation.  
Author [PERSON] was employed by the company Leader of precision agriculture team, Axial Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Building ZTD Height Change Grid Model Based on ERA5
### Selection of Fitting Model for ZTD Variation with Height  
Since the ZTD in the region above 10 km from the ground is very small, the ZTD below 10 km is selected for discussion and analysis of the ZTD variation with height. The relationship between ZTD and height calculated by ERA5 below 10 km in 2018-2020 is shown in Figure 6.  
As can be seen from Figure 6, ZTD below 10 km clearly shows an exponentially decreasing form with height change, which can be fitted using an exponential model, and  
Figure 5: Elevation difference.  
an exponential model of ZTD with height change for some regions in China (70\({}^{\circ}\)-140\({}^{\circ}\) W, 15\({}^{\circ}\)-55\({}^{\circ}\) N) is constructed using the inverse ZTD from the 2018-2020 ERA5 reanalysis data.
## 3 Analysis of Semiconductor Power Loss  
The power loss of a TSBB converter includes the losses of the switch, diode, inductor, and capacitor when various parasitic components in the circuit are ignored. The power loss of the two converters is determined by the power loss of the semiconductors assuming that the losses of the inductor and capacitor are the same between the conventional and proposed TSBB converters. Therefore, the increase or decrease in power efficiency is determined by the operation of the switch and diode.