esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, three different bow-tie microstrip antennas that can operate in the 2-20 GHz range for ultra-wideband applications have been designed and experimentally implemented. Numerical analyses were made using the finite integration technique in the CST Microwave Studio simulation program. The designed antennas were selected from the non-slot state, and the radiating part was selected with two different slot sizes and produced from graphene filament. The substrate is produced from PLA material. It has been observed that all three designs perform quite well in ultra-wideband applications. The effect of design differences in antennas is reflected in antenna return losses. Especially for Antenna-1, the return loss seems to be quite good. In addition, copper is generally used for the radiating part in classical methods. This study has also shown that graphene performs better than copper because it is a good conductor, contrary to classical methods.  
Another critical point is that the production of 3D-printer technology is one of the new aspects of the proposed study, as it is cost-effective and easy to produce, unlike classical production methods. In addition, the voltages applied to graphene changes the chemical potential and cause changes in the permittivity of graphene. In this respect, it has been a critical study to provide the opportunity to create different frequency ranges. It is considered to use one or more of these produced antennas for biomedical UWB applications for future studies.  
This research received no external funding.  
Not applicable.  
We would like to thank Iskenderun Teknik University Engineering Faculty Electrical and Electronics Engineering Department for their support in the measurements.  
There is no conflict of interest.
## 5 Discussion  
In the future, as receiver computing power increases and orbit determination algorithms are further optimized, we believe that the real-time orbit determination accuracy of spaceborne receivers will be significantly enhanced. Moreover, the methods and strategies proposed in this paper are tailored to LEO satellites, which are more strongly influenced by the gravity field. As a result, the impact of different gravity field models on orbit determination accuracy is more evident. For high Earth orbit satellites and deep space exploration missions, perturbations from non-conservative forces such as solar radiation pressure (SRP) and antenna phase center variation (PCO) will increase, while the influence of gravity field perturbations will decrease. This shift will require further in-depth investigation in future studies. We look forward to applying these research findings to a broader range of aerospace fields to achieve more efficient and precise satellite orbit determination.  
Conceptualization, C.Z. and J.H.; methodology, [PERSON] and X.L. ([PERSON]). and M.W. and X.L. ([PERSON]); software, [PERSON] and X.L. ([PERSON]). and M.W. and X.L. ([PERSON]); validation, C.Z., J.H. and X.L. ([PERSON]). and [PERSON] and [PERSON]. ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]. ([PERSON]).; visualization, [PERSON] and [PERSON]. ([PERSON]). and [PERSON]; supervision, [PERSON] and [PERSON]. ([PERSON]). and [PERSON] and X.L. ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This study was funded by State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster (Grant No. MS01240105).  
Not applicable.  
The data used in this study comes from observation data obtained during the in-orbit operation of the Gaofen-7 satellite. According to national security regulations, the raw data are not publicly available. If necessary, please contact the corresponding author to submit a data access request.  
Authors [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company \"Aerospace Stellar Technology Co., Ltd., Beijing\". Author [PERSON] is employed by \"Tianjin University\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 1 Introduction  
Renewable energies have a noteworthy development worldwide. The International Renewable Energy Agency (IRENA) reported an increase of 8.8% in power generation with renewable energy in 2016 where hydraulic is the predominant one [1]. According to [2], in the last decade, the installed capacity of solar technology grew annually, with an average of 36.3%, in Mexico. This document foresees an annual average growth of about 21.1% in the photovoltaic solar generation capacity to be installed from 2017 to 2031 with an increase in the annual average rate of 29.3%. In Mexico, solar energy is an abundant resource, [3] estimates incident solar energy in the northeast of Mexico by an empirical model; while the global solar resources along the Gulf of Mexico were assessed by [4] confirming that this region offers a great solar potential. The incident solar radiation in the country is quantified in [5] with the mean irradiation estimated to be around 5.5 kWhm\({}^{-2}\) d\({}^{-1}\). Thus, these data encourage sustainable projects in the country in the medium-term. Therefore, well-trained and experienced personnel are needed, and the universities are the principal leaders in a position to prepare the students to enter the industry and train personnel, but the high costs of didactic equipment for teaching is unachievable for most public universities in emerging nations, such as in Mexico. Therefore, new training and education strategies are emerging, especially the use of virtual reality (VR) technology, whose use is growing dramatically.  
There is no doubt that the new generations of students have a great facility for the use of multimedia elements, which normally presents an attractive educational space, and motivates them to continue using them. These media allow the ability to analyze and produce visual messages, while being attractive and didactic for the students. The literature reports significant advances in this direction. Hence, VR has been explored in many topics including tourism [6, 7], medicine [8], improving creativity [9, 10], and in engineering [11, 12]. Thus, allowing the students and trainers to attract their attention, breaking the monotony of classical teaching, learning concepts or processes to solve problems, provide maintenance, and implementing practices in a safe environment, as if the students were immersed in the industry. Consequently, it would even be possible to face real technical dilemmas in their operation preparing them for future eventualities without spending a fortune on their purchase and maintenance, simply by maintaining them virtually. That is the intended purpose for the contribution of this paper: to be able to train engineering students in the installation of a photovoltaic farm, using the technological advances of VR that are available to us.
# Development of Multi-Item Air Quality Monitoring System Based on Real-Time Data
## 2 Materials and Methods  
### Process of Multi-Item Air Quality Monitoring System  
A multi-item air quality monitoring system component consists of a hardware part and a software part. The hardware part consists of a sensor unit and a communication control unit. The sensor unit consists of a total of 16 items: PM\({}_{10}\), PM\({}_{2.5}\), PM\({}_{4.0}\), PM\({}_{1.0}\), CO\({}_{2}\), CH\({}_{4}\), temperature, humidity, VOCs, O\({}_{2}\), H\({}_{2}\)S, NH\({}_{3}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), and CO. The total number of installed sensors are eleven. The communication unit consists of a Wi-Fi module and a Bluetooth module. The software part consists of a web program and a smartphone application. The measurement process is that the sensor unit of the hardware part measures 16 air pollution items, the measured data are transmitted to the cloud server through the communication unit, and the cloud service is transmitted to the smartphone application and the web program.  
Measured data can be saved in USB memory and a server at the same time during air quality measurement. In the smartphone application and web program, real-time measurement data can be checked daily and monthly, and an emergency alert can be transmitted to the user when the air pollution level is high. These monitoring and alerting systems have been studied by many researchers. [PERSON] et al. (2020) developed an open-source R package to meet data science needs of community air sensor networks [28]. The program allows users to access historical data, add spatial meta data, and create maps and plots for viewing community monitoring data [28]. [PERSON] et al. (2015) developed a smartphone software package for predicting air pollutant concentrations at mobile locations. They observed PM\({}_{10}\) and PM\({}_{2.5}\)[29]. [PERSON] et al. (2019) developed a smart air quality monitoring system that measures the PM\({}_{10}\), PM\({}_{2.5}\), PM\({}_{1.0}\), CO, CO\({}_{2}\), VOCs, temperature, and humidity. They connected the developed monitoring system to the smartphone application [30]. A schematic diagram of the developed multi-item air quality monitoring system is shown in Figure 1.
# A Review of Biochar Properties and Their Utilization in Crop Agriculture and Livestock Production
## 1 Introduction  
Biochar (BC) is obtained via the pyrolysis of organic material. In the process, biomass is treated by heat in very low oxygen (or other oxidizers) content, where complex chemical compounds are transformed into simpler ones, eventually yielding gaseous products such as water vapor, carbon dioxide (CO\({}_{2}\)), carbon monoxide (CO), hydrogen (H\({}_{2}\)), methane (CH\({}_{4}\)), ethane (C\({}_{2}\)H\({}_{6}\)), and also solid carbon residue often named as \"char\". \"Biochar\" indicates that the material has been obtained from biomass, such as hay, corn stover, bagasse, switchgrass, rice hulls, woodchips, animal manure, or even sewage sludge [1], with the primary purpose of utilization rather than as an energy source. Many of those materials are byproducts of agricultural production or municipal waste management, often treated or classified as a waste that needs to be disposed. Reusing this waste to produce BC that have outstanding properties and can be utilized in crop agriculture, animal farming, and environmental protection [2] is an excellent way of carbon and energy recovery.  
Biochar properties are dependent on the type of feedstock material used to obtain the BC and the conditions under which the pyrolysis is conducted, especially its temperature and duration of the process. Different types of biomass yield BC of different characterization, but for most of the feedstock materials, a general rule applies--with an increase of pyrolysis temperature, the obtained BC is characterized by higher C content, energy content, porosity and surface area with lower O and H content, and lower BC bulk density [3]. An increase of pyrolysis time increases C content and surface area of BC [4].  
When it comes to the use of BC in agriculture, a subject that was thoroughly investigated by research during the past decade is the use of BC as a soil amendment. Parameters that are dependent on each other, such as the improvement of soil quality and microbial activity, the increase in crop yields and the reduction of greenhouse gases emissions associated with the extensive use of fertilizers, have been intensively investigated. In this paper, the last decade of research on BC in agriculture is summarized and also new possible ways of utilizing BC, that are recently emerging as research subjects, especially in the field of livestock production, are reviewed.  
A summary of the scope and content of this review paper is presented in Figure 1. Information is summarized in tables at the end of each section.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Reverse Process of Compression and Encryption  
The complete reverse process of compression and encryption is illustrated in Figure 6. This is the opposite process of the compression and encryption algorithm.
## 3 Novel Taxonomy for SSNs
### High Level Virtualization
#### 3.2.3 An Insight on the Application of HLV
This system supports up to 16 groups that coexist in SSN, but the sensors only execute one application task at a time. * Clustered-based solutions:  
This solution is preferred when the deployed SSNs targets are dynamic events, like mobile WSNs and vehicular ad-hoc networks. * Cluster Tree Based Self Organization: This approach presents a dynamic formation VSNs creation that is based on event is detects. Thus, it provides a sequential execution of the desired applications. But it is not clear how it is supported by sensors. * Closed-Loop System for Subsurface Contaminant Plume Monitoring: This approach is an extension to the previous one that improves the event tracking based on the event location, where the VSNs creation mechanism will select the nodes closest to it to save the energy. For the discussed System Level, the following is concluded:  
In the recent future, this level of virtualization will be applied at the SSNs providers. There are real SSNs examples that apply this level of virtualization. However, all of them are still for research purposes, as GENI networks, FIT IoT-LAB; each of them provide a huge SSN equipment in multiple sites for researches all over the world to implement and test different issues that are related to the field of WSNs. Researchers can create an account on the FIT IoT-LAB easier than GENI. Additionally, he can use a readymade firmware or build his own. While the WISEBED project has simple SSN equipment that is not published for researches. Regarding SSN participation, the listed approaches are educational test-beds for research purposes. There is no external access for CPN, which is built for studying the behavior of communication between the VSN, while the WSNTS are built for studying the behavior of energy consumption and security issues in VSNs. Additionally, it provides a web portal API for researchers to give them external access to their experiments.
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Ultra Dense Networks
#### 2.1.3 Mobility Support  
Due to increase in heterogeneity and densification in network, mobility support has become a big challenge. As the are many data served to users, mobility support and always connectivity have become features of critical importance for cellular networks. An ad-hoc solution referred as the concept of specific virtual cell is adapted in LTE--_Release 11_[23]. With the support of virtual cells, the user can move or communicate with any BS without the need of hand-off. However, in 5G, where there will be mmWave spectrum, the hand-off is more challenging and difficult. In mmWave, there will be narrow frequencies beams which must be aligned to communicate, so hand-off more likely will not exist in 5G. For communication with multiple BS, a scheme for beam alignment has been proposed in [24].
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 2 GNSS-R Data and Reflection Events  
### LEO Data  
The LEO data used in this study consist of a total of 1188 reflection events on 1 March 2021, sourced from Spire Global Inc. Currently, the Spire Lemur-2 constellation comprises more than 80 GNSS radio occultation CubeSats, out of which about 30 have been adapted to acquire GNSS reflectometry measurements at grazing angles [19]. The Lemur-2 satellites follow a Sun-synchronous orbit, with altitudes ranging from 400 to 600 km and varying orbit inclinations. This orbital configuration enables them to conduct GNSS-R measurements, encompassing all latitudes of the Earth.  
The Spire grazing angle GNSS-R products are collected with a focus on specific regions, including the polar areas, the Gulf of Mexico, and southeast Asia. These regions are selected due to their favorable characteristics, such as sea ice surfaces and calm ocean surfaces, which enable the best performance of coherent reflectometry measurements [10]. Figure 2 displays the track positions of the specular points distributed across both polar regions, as well as in the mid-latitude and tropical regions at different local times. Given the geographical distribution of the events, the dataset has been categorized into three distinct regions: north, covering latitudes between 40\({}^{\circ}\)N and 90\({}^{\circ}\)N; tropics, spanning latitudes between 40\({}^{\circ}\)N and 40\({}^{\circ}\)S; and south, covering latitudes between 40\({}^{\circ}\)S and 90\({}^{\circ}\)S.  
Each Lemur-2 satellite event lasts an average of 4 min, resulting in a total of about 80 h of recorded data. The recording durations vary, with a minimum of 1 min and a maximum of 6 min. Table 1 shows the number of events per region (north, tropics, and south) along with their corresponding durations in minutes.  
A total of 21 CubeSats from the Spire constellation are evaluated. The metadata include the space vehicle number (SVN) of the Lemur-2 satellite, as well as information about the GNSS satellite and constellation from which the CubeSat receives the reflected signals. For the simulation in this study, the GNSS constellation employed is GPS (global positioning system). Upon analyzing the Spire data, it is found that each Lemur-2 satellite receives the reflected signal from 4 to 19 GPS satellites during different time windows, depending on the positions of the transmitters and the receiver. The Spire SVN and GPS pseudo-random noise code (PRN) are presented in Table 2.
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 4 Experiments
### Results  
#### 4.3.1 Evaluation Metrics  
A well-known metric of MOT is the multiple object tracking accuracy (MOTA) [55], which shows its expressiveness as it combines three sources of errors (false negatives, false positives, and identity switches):  
\[\text{MOTA}=1-\frac{\sum_{t}(c_{1}\cdot fn_{t}+c_{2}\cdot fp_{t}+c_{3}\cdot idsw _{t})}{\sum_{t}g_{t}} \tag{13}\]  
where \(fn_{t},fp_{t},idsw_{t}\), and \(g_{t}\) represent the numbers of false negatives, false positives, instantaneous identity switches, and ground truth, respectively. In order to highlight the influence of identity switches which is the primary problem this paper aims to solve, the weighting factors are specially set as \(c_{1}=c_{2}=1\), and \(c_{3}=10\) in this paper, while many other related works set \(c_{3}=log_{10}\)[56; 11; 57]. Anyway, experiments indicate that our setting can better discriminate the MOTA score under different methods.  
However, in order to better evaluate the influence of identity switches, [PERSON] [11] introduced a new term _gidsw_ instead of _idsw_ for measuring the proportion of identity switches in a global way. In fact, the _idsw_ only counts the number of instantaneous identity switches, but does not account for the proportion of a trajectory that is correctly labeled over a whole sequence. In contrast, the _gidsw_ would count all the mismatches when the identity is changed. Thus, a trajectory with an identity switch in the middle will be counted wrong for half of its length, instead of just once for the _idsw_. Also, to balance the influence of each count, the weighting coefficients can be adjusted accordingly, as can the global multiple object tracking accuracy (GMOTA), by replacing the multiple object tracking accuracy (MOTA) formula:  
\[\text{GMOTA}=1-\frac{\sum_{t}(c_{1}\cdot fn_{t}+c_{2}\cdot fp_{t}+c_{3}\cdot gidsw _{t})}{\sum_{t}g_{t}} \tag{14}\]  
where the weighting factors are set to \(c_{1}=c_{2}=c_{3}=1\) since the _gidsw_ is much larger compared with _idsw_; hence, no need for extra penalty.
## 3 Knowledge-Guided Parallel Hybrid Local Search Algorithm  
We propose a knowledge-guided parallel hybrid local search (KG-PHLS) algorithm for solving the AEOS scheduling problem based on the established agile satellite scheduling model. The framework of the proposed algorithm is illustrated in Figure 2. The algorithm is built upon the framework of a hybrid local search algorithm and integrates four strategies to enhance algorithm efficiency:  
1. A knowledge-guided initial solution generation method;
2. A knowledge-guided new solution construction method;
3. Parallel search strategy;
4. Data mining techniques.  
Figure 2: The flowchart of the KG-PHLS algorithm.
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 3 Materials and Methods
### Image Data Preprocessing  
The original dataset for fine dandruff provided by AI-Hub consisted of a total of 17,434 images, as shown in Table 3. Due to limitations in the given development environment, the quantity of data used in this study was reduced. The number of training images for the fine dandruff diagnosis model was reduced to 5034, and the number of validation images was reduced to 1259. The test dataset was composed of 1007 images, extracted at a 5:1 ratio from the training data. The preprocessing results are shown in Table 4.  
The original dataset for perifollicular erythema provided by AI-Hub consisted of a total of 67,414 images, as shown in Table 3. Due to limitations in the given development environment, the number of training images for the perifollicular erythema diagnosis model was maintained at 4692 and the number of validation images was reduced to 1174, extracted at an 8:2 ratio from the training data. The test dataset comprised 652 images, extracted at a 9:1 ratio from the training data. The preprocessing results are shown in Table 5.  
The dataset exhibited a significant imbalance among the different class types, which can degrade the performance of machine learning classification models [32; 33]. To address the class imbalance, two measures were implemented. First, data augmentation techniques were used to increase data diversity by transforming the data. Initially, the normalization of the image data was performed by converting pixel values from 0 to 255 to a range of 0 to 1. Subsequently, to augment the data, the images were rotated by 20 degrees, shifted horizontally and vertically by 10%, sheared by 20%, and horizontally flipped, with the fill mode set to \"nearest\" to increase data diversity, as shown in Figure 4. Second, weights were assigned to address the data imbalance. These calculated class weights served as a means to assign appropriate importance to each class during the model training, mitigating the effects of the unbalanced class distribution. In the case of fine dandruff, the assigned weights for each class were 2.36 for good conditions and 0.84 for mild, moderate, and severe conditions. For perifollicular erythema, the assigned weights for each class were 2.75 for good conditions, 0.59 for mild, 0.59 for moderate, and 4.43 for severe conditions.
## 3 Results and Discussion  
### Experimental Platform Construction  
In order to verify the feasibility and practicability of the air compressor pipeline leak identification system, an experimental system is built in the air compressor room of Sanmenxia Longwangzhuang Coal Industry Co., Ltd. The tests are conducted on five working conditions, including normal pipeline, leaking pipeline, worn pipeline, normal valve and leaking valve. The layout of the test bench is shown in Figure 6 and the captured infrared images are shown in the Figure 7.  
Figure 5: The recognition process of the proposed method.  
Figure 6: Pipeline leak experiment platform for mine air compressor.