esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 6 Conclusions  
Faults affect the transmission line and cause significant damage to equipment and power disruptions to the customers or end-users. These faults occur due to bad weather conditions or faulty equipment, and transient faults are the result of human interference. Hence, there is need to model a system that will classify, detect and isolate faults accurately within the shortest time of detection.  
This paper proposed the use of the CatBoost classifier as the preferred algorithm for fault classification because of its high accuracy and ease of training. This technique is achieved first by designing a 330 kV, 500 km transmission line using Matlab/Simulink to extract the fault current and voltage to identify the fault phase for each faulty voltage and current waveform. A 93,340 fault dataset was used to train the algorithm, and the result provided a better accuracy of 99.54%. The classifier algorithms are capable of training multi-dataset categorical data such as the SVM, ANN and XBoost classifiers.  
This paper addressed the classification of a multi-dataset of faulty voltage and current in transmission lines focusing on speed, accuracy and precision for fast detection and isolation of faults. The results also served as a guide on transmission line fault protection management systems and design. The CatBoost classifier was justified for the transmission line fault classification model after being compared to other methods in other literature. This paper can be improved by varying the fault resistance to different values from 0.01 to 50 \(\Omega\) and beyond. The model can also be optimised for real time data mining and automatic training for an effective fault protection mechanism.  
**Author Contributions:** supervision, S.H. and K.A.A.G.; writing--original draft preparation, V.N.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declares no conflict of interest.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 4 Experiments
### Comparison with State-of-the-Art Methods  
#### 4.3.1 Evaluations on the DVD Dataset  
To validate the effectiveness of our proposed method, we conducted comprehensive performance evaluations on the DVD dataset. As shown in Table 1, our approach outperforms state-of-the-art methods in both PSNR and SSIM metrics, demonstrating its superior performance in video deblurring tasks. To further illustrate the visual quality advantages of our method, we present comparative deblurring results from different approaches in Figure 4. Through magnified comparisons of specific regions, the differences in detail restoration between methods become more apparent. In the \"IMG_0030\" sequence, the intersection sign region suffered severe structural degradation due to motion blur, and existing methods struggled to accurately restore its complete contour. In contrast, our method successfully reconstructed clear outlines of the sign through a strategy that decouples structural and detailed information processing, validating the effectiveness of this design approach. The advantages of our method are even more pronounced in the \"IMG_0021\" sequence comparison. Not only did it accurately restore the overall shape of the wheel, but it also precisely reconstructed fine structures such as spokes, achieving results closest to the ground truth. These outcomes further confirm that our proposed method can maintain both structural integrity and detail authenticity when processing complex scenes, demonstrating excellent deblurring capabilities.  
Figure 4: Comparison of visual results on the DVD dataset. Zoom in for better visualization.
## 2 Related Research
### Greenhouses  
Automation and efficiency are crucial in greenhouse environment monitoring and control. In order to control and monitor the environmental factors, sensors and actuators are essential. Greenhouse crops can benefit a lot from the use of WSNs, because inside the greenhouse the crop conditions such as climate and soil do not depend on exterior factors and the implementations are thus easier than in outdoor applications.  
The first application of WSN in a greenhouse was reported in the year 2003. It was a monitoring and control system developed by means of Bluetooth [20]. Since then, several applications have been developed, most of them making use of IEEE 802.15.4/ZigBee. For example, [PERSON] and [PERSON] presented a proposal of a distributed greenhouse control and monitoring system using ZigBee [21]. [PERSON] _et al._ described the results of real deployment of a WSN IEEE 802.15.4 compliant system to monitor and control the environment in greenhouses where melons and cabbages were being grown [22]. [PERSON] _et al._ developed a WSN in a greenhouse, that integrates a variety of sensors which can measure substrate water, temperature, electrical conductivity, daily photosynthetic radiation and leaf wetness in real-time. Benefits came from an improved plant growth, more efficient water and fertilizer applications, together with a reduction in disease problems related to over-watering [23].  
[PERSON] _et al._ reported a WSN prototype with a two-part framework for greenhouses. In the first part, several sensor nodes were used to measure temperature, light and soil moisture. The other part consists of a GSM module and the management software based on a database running on the remote PC [24].  
[PERSON] _et al._ designed a monitoring system based on ZigBee, using a star network topology inside the greenhouse and a mesh topology for the connection between the greenhouses and the management system [25]. [PERSON] _et al._ reported a multi-functional remote sensing system that integrates RFID technology with spectral imaging and environmental sensing in a greenhouse. The multi-spectralimaging system was used for remote sensing of the canopy of cabbage seedlings. Greenhouse temperature, relative humidity, and lighting conditions were measured above the crop [26].
## 3 Results
### First Week of Observation  
At week one, a significant difference was observed in the inflammatory response scores between the groups (\(p\) = 0.00). The control group had more severe pulpal inflammatory reactions than the iloprost (\(p\) = 0.00), MTA (\(p\) = 0.04), and MTA-iloprost (\(p\) = 0.00) groups. Moderate to severe inflammation with areas filled with granulation tissue and degeneration was seen in the control group (Figure 1A,B). However, none of the specimens in the other groups showed a severe inflammatory response (Figure 2A-F). Furthermore, the odontoblastic layer was lost in the control group, but was mostly present in the other groups. A common finding that was observed in the iloprost- and MTA-iloprost-treated specimens was the presence of engorged and dilated blood vessels that were lined by endothelial cells (Figure 2B,F).  
Hard tissue formation was repeatedly found in the iloprost, MTA, and MTA-iloprost groups. A significant difference was detected among the groups for hard tissue formation (\(p\) = 0.00). The use of MTA and MTA-iloprost was associated with a higher rate of barrier formation than the control group (\(p\) = 0.00 and \(p\) = 0.00, respectively). For the iloprost group, the reparative dentin was mostly found on the lateral walls of the pulp chamber, rather than at the exposure site (Figure 2A,B).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Inflammatory Cell Response**} & \multicolumn{4}{c}{**Quality of Dentin**} \\
**Time** & **Group** & **No. of Specim** & & \multicolumn{4}{c}{**Score (\%)**} & \multicolumn{4}{c}{**Hard Tissue Formation Score (\%)**} \\ \cline{4-13}  & & & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **4** & **1** & **2** & **3** \\ \hline \multirow{4}{*}{Week 1} & Control & 9 & 0.0 & 0.0 & 33.3 & 66.7 & 0.0 & 0.0 & 55.6 & 44.4 & - & - & - \\  & llopost & 9 & 44.4 & 55.6 & 0.0 & 0.0 & 0.0 & 44.4 & 44.4 & 11.1 & - & - & - \\  & MTA & 7 & 28.6 & 28.6 & 42.9 & 0.0 & 0.0 & 85.7 & 14.3 & 0.0 & - & - & - \\  & MTA-ILO & 8 & 37.5 & 50.0 & 12.5 & 0.0 & 12.5 & 62.5 & 25.0 & 0.0 & - & - \\  & Control & 7 & 57.1 & 28.6 & 14.3 & 0.0 & 0.0 & 28.6 & 42.8 & 28.6 & 0.0 & 28.5 & 71.5 \\  & llopost & 10 & 50.0 & 40.0 & 10.0 & 0.0 & 50.0 & 30.0 & 20.0 & 0.0 & 10.0 & 70.0 & 20.0 \\  & MTA & 11 & 63.6 & 36.4 & 0.0 & 0.0 & 72.7 & 9.1 & 18.2 & 0.0 & 27.3 & 54.5 & 18.2 \\  & MTA-ILO & 9 & 77.8 & 22.2 & 0.0 & 0.0 & 77.8 & 0.0 & 22.2 & 0.0 & 55.6 & 22.2 & 22.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Histological section scoring of pulps capped with different test materials.  
Figure 1: Histologic features of maxillary first molars subjected to pulp exposure and restored with glass ionomer without the use of pulp capping agent at week one. Representative photomicrographs showing (**A**) diffuse inflammation and (**B**) degenerated pulp with granulation tissue filling the exposure area. H&E stain \(\times\)200 magnification. Arrows indicate the exposure area.
# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
###### Abstract  
Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable.  
5G NR; SLAM; sensor fusion; pose estimation; field robotics; radio mapping; iSAM; REM 2023
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 1 Introduction  
### Panama TR4  
_Fusarium oxysporum f._ sp. _cubense_ tropical race 4 (Foc TR4), is a soil-borne fungus that causes Panama TR4, a form of fusarium wilt that eventually kills infected banana plants [1; 2]. Since the 1980s, Foc TR4 has been regarded as the most important biosecurity threat to the global banana industry, and an unparalleled botanical epidemic [2], persisting indefinitely in the soil with no effective control method. There is currently no suitable replacement variety for Cavendish that can meet the needs of the market [1; 3; 4]. The disease can spread anthropogenically and naturally through the transportation of infected plant material, soil, and water [5].  
Bananas are the world's most popular fruit and an important staple food [6], with a global industry worth $8 billion annually [7]. The potential impact of Panama TR4 is severe, because Cavendish accounts for approximately 47% of bananas produced globally, predominantly sourced from Asia, Latin America, and Africa [7].  
Foc TR4 was first identified in Sumatra, Indonesia, in 1992 [8], and to date, has spread across several continents [1; 9; 10] including Australia; the Northern Territory in 1997 and the state of Queensland in 2015 [4; 8]. The Queensland Government Department of Agriculture and Fisheries (DAF) initiated the Panama TR4 Program in response to the first detection in the Tully River catchment, within the Wet Tropics bioregion. The program successfully controlled and contained the impact of Panama TR4 within a section of the Tully River catchment, however, three additional plantations within this locationwere infested in 2017, 2018, and 2020 [9; 10]. At the time of the outbreak there was no accurate spatial dataset of all banana plantations [11]. The absence of this data jeopardized the banana industry and DAF's abilities to respond rapidly. Approximately 94% of the national banana supply is concentrated in the Wet Tropics, and is worth approximately AUD 480 million annually to the national economy [12]. Therefore, it is essential that the locations and extents of affected, and unaffected, banana plantations are monitored--particularly where vectors are likely to be transported, for example through erosion of contaminated soil, distribution and processing facilities, and machinery and equipment used across multiple plantations.
## 3 Evaluation of Service Life Using Different Design Approaches
### Service Life Variation with Random Variables (Probabilistic Methods)
#### 3.4.1 Service Life from the Different Methods Considering Temperature Effect
Over 0.2 of COV, the service life reduction is evaluated to be relatively large, which meaningfully suggests that the quality control of concrete mixing and construction is very important. After normalization of the design parameters, the changing service life ratio is shown in Figure 18 with increasing COV. Figure 17: Simulation results and service life with changing diffusion coefficient and COVs. Figure 18: Reduction ratio of service life with changing normalized design parameters and COV. LCC (life cycle cost) analysis for infrastructure has been studied widely. Before the construction stage, quantitative analysis for the priority of design parameters can be useful for cost-benefit analysis considering the exterior sulfate concentration.
## 3 Fabrication Procedure  
Figure 4 exhibits the fabrication process and structure of the HDSGRSO UMOSFET. The fabrication process of epitaxial layer is the only difference compared to traditional processes. First, a 3.2 \(\upmu\)m n-type epitaxial layer is formed on the N\({}^{+}\) substrate. Then, the middle and upper n-type epitaxial layer are formed on the first epitaxial in the same way. The doping concentration of the bottom and the upper epitaxial layers is the same, but the middle epitaxial layer has a higher doping concentration (a). Secondly, dry etching can be used to form the deep trench. An oxide layer is formed by thermally oxidized on the trench surface and then etched off to smooth the trench corner (b). Next, a 1.2 \(\upmu\)m oxide is deposited to form the field oxide (c). Polysilicon is then deposited in the trench to form the source electrode which is surrounded by the field oxide (d). Afterwards, a thick oxide is deposited and etched back to separate the gate and the source electrode (e). Following, the gate oxide is formed by thermally oxidizing for 25 min under 1050 degrees dry oxygen conditions, and the polysilicon is deposited again to obtain the gate electrode (f). Subsequently, the p-body and n\({}^{+}\) source region are formed by implanted ions, respectively (g). Finally, a thick oxide is deposited on the gate surface to separate the gate and source metallic electrodes, and then the fabrication of the device is finished (h).  
Figure 4: Fabrication procedure of the HDSGRSO UMOSFET.(**a**) Epitaxial layers are formed, (**b**) Trench is opened, (**c**) Thick oxide is deposited, (**d**) Polysilicon is deposited and etched back, (**e**) Thick oxide is deposited and etched back, (**f**) Gate oxide and gate poly are formed, (**g**) The p-body and n+ source region are formed, (**h**) Thick oxide and source metal are deposited.  
Figure 3: The three dimensions view of electric field distributions for the (**a**) SGRSO and (**b**) HDSGRSO UMOSFET with the same n-drift region doping concentration (\(N_{\text{D}}\) and \(N_{2}\)) \(5\times 10^{15}\)/cm\({}^{-3}\).
## 3 Material and Methods
### Application Case Study
#### 3.2.1 Data Retrieval  
In DMGs system, events from streaming services are daily imported and stored in a general format. Every event is a single streaming of a single song with annotated metadata (country, artist, etc.). These data are then regularly imported by the batch system. During the export, event data are converted into time-series data and grouped by song ID, resulting in a time series for each song with total number of streamings for each day.
## 4 Results and Discussion
### PCM Sample Test  
In this test, 250 mL of PCM sample was taken in two different containers, which were made of stainless steel and copper material. The test was carried out to identify the material suitable to hold the PCM to enable better charging and discharging efficiency and, thus, could be recommended as PCM heat exchanger material to have longer thermal backup properties. Various temperature sensors were placed to monitor the room temperature, the surface temperatures of copper and stainless steel, along with the temperature of PCM in both these containers. Figure 13 displays the observation that shows that the temperature difference between the metal and the PCM it contains is only 3 \({}^{\circ}\)C in the case of copper container in comparison to stainless steel, which is 7 \({}^{\circ}\)C. This complements the results obtained by numerical simulation that gives a clear indication that since copper has higher thermal conductivity (398 W/mK) than stainless steel (15 W/mK), it aids heat transfer across it when used as PCM heat exchanger material for improved charging and discharging effectiveness. Another significant outcome of the PCM sample test is that it helps understand that a temperature difference exists between the phase change temperature of the PCM and the effective cold room temperature, which is in the range of 5-7 \({}^{\circ}\)C. This acts as an important parameter in selecting the appropriate PCM for a  
Figure 12: Water sample test: (**a**) Position (**A**)—in front of evaporator; (**b**) Position (**B**)—corner of cold room below evaporator.  
given storage application, especially in the food industry, where maintaining a particular recommended temperature is essential to increase the shelf life of the product and maintain its nutritional value.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 3 The Software-Defined Media Ontology
### Description of Class Definitions
#### 3.3.1 **Context**
MPEG-7 is a standard that has extensive definitions of semantic information of media data. For example, by describing semantic information using an ontology such as the MOWL [37], which describes the semantic information defined in MPEG-7, and linking it from a **Context** class object, the extensive semantic information description of MPEG-7 can be incorporated into the SDM Ontology.
## 1 Introduction  
Copper is a biologically-active element, whose compounds have a strong influence on the vital activities of organisms. Copper plays an essential role in biological processes including metabolism, nerve function, hemoglobin synthesis, and bone development [1; 2; 3]. However, an increase in the amount of copper in the human body may lead to serious problems such as heart disease, anemia, and liver damage [4].Therefore, the copper concentration in environmental samples--especially water--should be monitored continuously, preferably by simple and effective analytical methods. Many analytical techniques including microwave plasma--AES [5], AAS [6], potentiometry [7; 8], voltammetry [9], ICP-OES [10], and ICP-MS [11] have been developed and used to monitor copper concentration levels in a variety of samples. Actually, most AAS and ICP-OES methods recently developed use preconcentration methodology to improve sensitivity and selectivity. However, such methodology is highly costly, and time-consuming. Spectrophotometric techniques generally are preferred for metal ion determinations in aqueous media, because of their simplicity, low cost, and availability [12]. Among the many spectrophotometric reagents used to determine copper are 1,4-dioxane [13], 4-(2\({}^{\prime}\)-benzothiazolylazo) salicylic acid (BTAS) [14], 2,4-dinitro APTPT [15], diethyldithiocarbamate (DDTC) [16; 17], 2-amino-4-(_m_-tolylazo)pyridine-3-ol (ATAP) [18], 2-(5-Bromo-2-Oxoindolin-3-ylidene) hydrazine carbothioamide (HBITSC) [19], 4-(4\({}^{\prime}\)-nitrobenzylideneimino)-3-methyl-5-mercaptoethanol-1,2,4-triazoleole (NBIMMT) [20], 2-acetylpyridine thiosemicarbazone (2-APT) [21], N,Nbissalcylidene-2,3-diaminopyridine(H2 IF) [22], and rhodamin derivative with 5-(_a_-methyl-3 hydroxybenzylidene) (5M, 3H-BR) [23]. However, most of these reagents suffer from shortcomings such as a narrow pH range, interference from common ions, the need to heat the reaction mixture, and lengthy color development [24]. Thus, there is an urgent need to prepare selective and sensitive chromogenic reagents that can operate over a wide range of concentrations and pH values.  
Although reagent 4-amino-3-mercapto-6-[2-(2-thienyl)vinyl]-1,2,4-triazin-5(4H)-one (AMT) has been previously reported [25; 26; 27; 28], as far as we know, there is no analytical use of this reagent yet. Therefore, the present work will discuss the preparation and characterization of a copper(II) complex with AMT and the use of this ligand as a spectrophotometric reagent for developing an analytical method for determining copper in certified reference materials and water samples.