esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### RNA Sequencing  
Garlic samples from different water stress treatments were exposed to Illumina-based RNA-Seq. The detailed sequencing results are displayed in Table 1. After filtering, 40,188,220, 41,264,246, 40,887,022, and 40,955,782 clean reads were obtained from the water deficit treatments, which were then assembled into 67,818, 65,943, 62,359, and 64,215 unigenes, respectively. BUSCO analysis revealed great completeness of the assembly (Figure S1). There were 32,091,884 (79.85%), 33,873,994 (82.09%), 33,316,246 (81.48%), and 32,943,202 (80.44%) total mapped reads in each sample, respectively. The GC content of the unigenes in each treatment ranged from 40.19% to 40.53%.  
A total of 96,712 unigenes were gained in the general library with a mean length of 803 bp and an N50 length of 1301 bp. The unigenes were annotated against NR, NT, SwissProt, KOG, KEGG, GO, and Pfam functional databases, and 28,014 (28.97%), 4493 (4.65%), 19,174 (19.83%), 31,955 (33.04%), 18,920 (19.56%), 11,801 (12.20%), and 27,195 (28.12%) unigenes obtained functional annotation, respectively. Approximately 31,404 predicted coding sequences (CDSs) were generated from Transdecoder software, and their length distribution is shown in Figure 1.  
As for species distribution, 36.9% of the 96,712 unigenes have high similarity to genes from _Asparagus officinalis_, followed by _Elaeis guinensis_ (7.6%), _Phoenix dactylifera_ (5.12%), and _Anna  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Raw Reads** & **Clean Reads** & **Q20 (\%)** & **Unigenes** &
\begin{tabular}{c} **GC Content** \\ **(\%)** \\ \end{tabular} \\ \hline D0 & 40,195,832 & 40,188,220 & 97.05 & 67,818 & 40.19 \\ D1 & 41,271,878 & 41,264,246 & 97.09 & 65,943 & 40.36 \\ D4 & 40,895,002 & 40,887,022 & 97.09 & 64,215 & 40.28 \\ D12 & 40,963,138 & 40,955,782 & 97.03 & 62,359 & 40.53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Illumina RNA-sequencing data.  
Figure 1: Length distribution of the unigene coding sequences generated from Transdecoder.
## 5 Performance Evaluation  
To evaluate the performance of BLJCAR, we implement the BLJCAR scheme embedded in coding-aware routing using NS-2, and compare it with the ONC scheme based on coding aware routing mechanism, which is represented in [6, 7, 8, 9, 10, 11, 12]. In the above two coding aware routing mechanisms, the routing metric design includes the coding opportunity related to local topology and the path length. This routing metric design is the common mechanism used in most present coding aware routing protocols.  
In our simulation, we consider the wireless topology in Figure 2. There are two flows in this topology. For flow 1, the source node is node 1 and the destination node is node 4. For flow 2, the source node is node 4 and the destination node is node 3. We use 802.11 at the MAC layer with the Two Ray Ground propagation model, and both data flows are generated by UDP traffic source with CBR model. The transmission range and the carrier sensing range are set to 250 meters and 550 meters, respectively. Nodes are not energy limited. We vary the offered load of both flows to test the performance of the two mechanisms in network congestion. For a more thorough understanding of the performance of BLJCAR, it is interesting to study the capability of BLJCAR to cope with the longer communication paths. To achieve this, we change the destination node of flow 1. In experiments 4, 5 and 6, the destination node of flow 1 is changed from node 4 to node 6. We assume the network is static so that the paths of both flows do not change during the simulation. Without loss of generality, flow 1 is considered as the specific real-time flow.
# Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
## 4 Conclusions  
In this study, an object detection model based on deep learning was trained to detect leaves with powdery mildew in wheat. Several YOLOv8 models were trained using a custom RGB image dataset. The YOLOv8m model was chosen for its superior performance on training and validation datasets. When applied to the test dataset, the YOLOv8m model achieved precision, recall, F1, mAP50, and mAP50-95 values of 0.796, 0.749, 0.770, 0.765, and 0.383, respectively. This model detects powdery mildew on wheat leaves with high accuracy. Its ability to identify diseased areas in images makes it suitable for integration into precision spraying equipment or mobile field research applications.  
Our approach using 2D digital camera sensors aims to reach a wider audience, particularly in resource-limited settings where spectral imaging is financially unfeasible. By offering a simpler method, we strive to democratize technology for agricultural disease detection, allowing more users to benefit from advancements in the field. This inclusivity can lead to a more widespread monitoring and management of crop health, ultimately improving agricultural production outcomes. Our model addresses the immediate need for early and accurate disease detection in agriculture, essential for effective disease management and control. This research contributes to the development of future precision agriculture technologies, such as selective spraying systems. It also supports comprehensive disease monitoring in fields.  
However, the study has certain limitations. The model's performance can vary under different field conditions, such as varying light and shadow, which were not fully represented in the dataset. Additionally, although the dataset size was increased to 896 images, the model may be unable to capture the full spectrum of phenotypic expressions of powdery mildew. This limitation can potentially impact the model's generalizability to all wheat growth stages and environmental conditions.  
Conceptualization, E.O. and N.D.K.; methodology, E.O. and N.D.K.; software, E.O.; validation, E.O.; formal analysis, E.O. and N.D.K.; investigation, E.O. and N.D.K.; resources, E.O. and N.D.K.; data curation, E.O.; writing--original draft, E.O.; writing--review and editing, E.O. and N.D.K.; visualization, E.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.13137587](https://doi.org/10.5281/zenodo.13137587)]([https://doi.org/10.5281/zenodo.13137587](https://doi.org/10.5281/zenodo.13137587)).  
We are grateful to [PERSON] and [PERSON] for granting us permission to photograph their wheat trial fields in Tekirdag Namik Kemal University.  
The authors declare no conflicts of interest.
## 4 Simulation and Physical Experiments
### Feasibility Simulation of SAPSO  
The feasibility simulation experiment is designed as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Population & 80 & \(x_{B}\) & 0.98 \\ \(w_{max}\) & 0.42 & \(w_{min}\) & 0.1 \\ \(c_{1}\) & 1.8 & \(c_{2}\) & 2.2 \\ \(x_{min},x_{max}\) & \(\pm\)0.3 m or rad & \(v_{min},v_{max}\) & \(\pm\)0.03 m or rad \\ \hline \hline \end{tabular}
\end{table}
Table 2: SA–PSO parameters after optimization.  
Figure 4: The geometry and parameters of the SGP. (**a**) The vertical view of the platform. (**b**) The main view of the platform.  
Given the posture value \(q\) of the top platform. (_To be solved_)  
Calculate the \(I\) length vector of the six outriggers corresponding to \(q\) by IK function. (_Input_)  
Send \(I\) to the SA-PSO algorithm, find the optimal solution \(q_{SA-PSO}\) (_solving_).  
The SA-PSO algorithm parameters for FKP are set as Table 2. The target posture \(q\) is \(q1=0.1208\) m, \(q2=0.1009\) m, \(q3=0.0817\) m, \(q4=0.1824\) rad, \(q5=0.0634\) rad, \(q6=0.1504\) rad.  
While the corresponding \(I\) is calculated by \(IK(\ )\)  
11 = 0.3581 m, 12 = 0.3202 m, 13 = 0.3643 m, 14 = 0.3394 m, 15 = 0.2358 m, 16 = 0.4126 m. The value of six dimensions is obtained by the SA-PSO algorithm, and the convergence of each dimension is shown in Figure 5. When the iteration reaches 113 times, the target accuracy is almost achieved. Table 3 gives the final error of each dimension of the posture and the error of each dimension finally converges to the corresponding accuracy.  
The dimension error is a little larger than the Euclidean distance fitness accuracy \(\epsilon\), which is based on the structure of SGP. The following simulation is to analyze the relationship between fitness accuracy and iterations. Run the proposed SA-PSO algorithm 10,000 times, count average iterations and record the running time of each solving for different accuracy. It can be seen from Table 4, in order to achieve higher dimension accuracy, the fitness accuracy \(\epsilon\) will be higher, which means the iteration time will increase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accuracy \(\epsilon\)** & **Average Iterations \(K_{ev}\)** \\ \hline \(10^{-6}\) & 95.61 \\ \(10^{-7}\) & 107.10 \\ \(10^{-8}\) & 117.54 \\ \(10^{-9}\) & 127.16 \\ \(10^{-10}\) & 135.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average iterations and time for different accuracy.  
Figure 5: Convergence of Six–DOF and fitness by SA–PSO method. (**a**) Posture convergence progress with iteration. (**b**) Fitness convergence with iteration.
## 2 Methods  
We developed a comprehensive Exploratory Data Analysis (EDA) to reveal hidden information contained in the satellite-derived measurements and to refine the analysis to discriminate slicks by category, as proposed in our earlier studies [19; 20; 21; 22]. The design of our EDA focuses on a data-driven scheme to investigate possible ways to improve the seepage-spill discrimination with the simplest possible analysis and the lowest satellite-imaging cost. The research strategy employed herein is a development of our previous investigations [19; 20; 21; 22], and consists of four distinct Data Processing Segments (i.e., A, B, C, and D in Figure 2)--devised in eight individual Phases--separately described in detail and introduced in a complete manner easily enabling replicability of our data mining exercise. A summary of our EDA design is depicted in Figure 2. While in-house Python codes are used to run the oil slick RADARSAT-2 related analyses (i.e., Phases 1-4), PAST (PAleontological STatistics: version 3.20, Oslo, Norway [26]) is used in the implementation of Phases 5-8.  
Figure 1: Campeche Bay located off the Mexican coast on the southernmost bight of the Gulf of Mexico. The highlighted region shows the location of the analyzed oil slicks. Courtesy of [PERSON] (LabSAR/UFRI).  
Figure 2: Research strategy developed to refine the ability to discriminate between two petrogenic oil-slick categories (i.e., seeps versus spills), as proposed in our previous studies [19; 20; 21; 22]. The proposed Exploratory Data Analysis (EDA) has four distinct Data Processing Segments defined as: (**A**) Input Data Repository (Phases 1–3); (**B**) Data Treatment Practice (Phases 4–5); (**C**) Multivariate Data Analysis Techniques (Phases 6–7); and (**D**) Oil-Slick Discrimination Algorithm (Phase 8).  
A multi-year dataset of RADARSAT-2 scenes imaged between 2008 and 2012 gave rise to the oil slick data archive analyzed in our earlier investigations [19; 20; 21; 22]. This data archive consists of polygons representative of oil slicks that had been identified and field validated as seeps and spills by domain experts. For more information about this dataset, see [19; 20; 21; 22]. The workable dataset explored herein is defined after fine-tuning this data archive along the 1 st Data Processing Segment (Figure 2A: Input Data Repository--Phases 1-3).
# Internal Interface Diversification as a Security Measure in Sensor Networks +
## 2 Interface Diversification
### Attack Scenarios Mitigated by Diversification
#### 2.2.1 Infecting the Node  
Internal interface diversification protects against scenarios where the adversary has succeeded in slipping a harmful executable into the system and attempts to run it. The aim of the attacker is to get full control over the IoT device, which can then be used according to his or her objectives. However, if the malicious executable fails to successfully invoke the diversified system calls (or run commandson a diversified command shell), the malicious executable cannot operate in the system. Regardless of whether the attacker has gained physical access to the IoT device or is operating remotely from another machine in the network, interface diversification makes running malicious executables in the system considerably more difficult. Therefore, although internal interface diversification does nothing to stop malicious programs from being planted in the system, diversification prevents them from running. This also applies to many previously unknown pieces of malware that would go unnoticed by anti-virus software. In this respect, internal interface diversification is a proactive measure.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 6 Experimental Simulation Results
### Evaluation of Simulation Results  
The above results approach the PLC simulations issue in the BPL area for low-voltage networks. The purpose of this paper is to demonstrate the potential of the NS-3 framework in cooperation with the PLC module and the G.hn extension. Presented results serve towards the development of complex PLC simulators with advanced possibilities of PLC network models including PLC devices, parameterized power lines and overall topology.  
The first use case of this simulator is oriented to making predictions based on statistical data, which will be used to measure real topologies, to some extent without the need of a real measurement. The main area of application is in smart-grid networks and smart-mtering. The second use case is the design and testing of sub-sections of smart-cities, smart-home or industry 4.0. The third use case is for developing adaptive methods of PLC communication generally, intended for broadband power line technologies (ITU-T G.hn, IEEE P1901, HomePlug AV2) and narrowband power line technologies (PRIME, G3-PLC, ITU-T G.hnem).  
The achieved results demonstrate their usability with measurements under real conditions. This is ensured by verifying the theoretical assumptions of basic simulations mentioned in this paper and focusing on the influence of several basic parameters on the application rate in UDP/IP networks. Future work will be focused on improving the simulators for advanced topologies in accordance with PLC under real conditions, especially in smart-grid areas for power distribution and management. Different calculation and definition methods were created for the G.hn PLC module in NS-3. The next steps in this work should improve the overall behavior of the simulated PLC network. This creates a possibility of implementing predictive algorithms, for a specifically defined environment. The main goal of the results was achieving the correct trend of simulations, corresponding to theoretical assumptions which allows the simulation of real smart grid systems under real conditions in comparison with measurement.  
Figure 9: **(a)** Comparison of throughput with cable type sweep; **(b)** Comparison of throughput with bandwidth sweep.
## 6 Conclusions  
In this paper, performance of vehicle roll behavior under various road excitation and steering wheel inputs was studied using 6-DOF half-car and 9-DOF full-car models. With the proposed method, influence of half-car and full-car models on vehicle roll behavior and coupling relationships between vehicle vertical and lateral response was comprehensively studied. The main conclusions are as follows:  
1. Based on the generating theories, a 3-D road profile model was first developed with road level ISO-\(A/C\) as the half-car and full-car dynamics model input. State response of vehicle roll behavior relationships between road excitation and steering wheel input were calculated with half-car and full-car models. From the results, it was concluded that road excitation and steering wheel input have a significant influence on vehicle roll behaviors, i.e., 6-DOF model was suitable for small steering wheel input without road excitation. Average accuracy of this model was determined to be a maximum of 65%. A 9-DOF model was more accurate for large steering input with road excitation with an average accuracy higher than 85%.
2. Calculated vehicle response of vehicle roll behavior was validated using CARSIM(r). Compared to calculation results, CARSIM(r) simulation results showed that proposed half-car model can provide a satisfying result without road excitation. However, full-car model can provide better description of system dynamics under various road excitation and large steering wheel input conditions to further validate the conclusions.  
In the future, the proposed model will be applied to an actual road profile and a practical full-car will be realized using the proposed 9 DOF full-car model. In addition, our research will extend to state estimation and control of a full-car nonlinear suspension system and focus on application of these results for improving vehicle performance, especially on the aspects of ride comfort and road handling.  
This work was supported by the National Natural Science Foundation of China (Grant No. U1564210), Innovative Talent Support Program for Post-Doctorate of China (Grant No. BX201600017) and China Postdoctoral Science Foundation (Grant No. 2016M600934).  
[PERSON] and [PERSON] conceived and designed the simulations; [PERSON] wrote the paper and analyzed the data; and [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] provided some suggestions and revised the paper.  
The authors declare no conflict of interest.
# Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems
## 3 Classical Controller Methods for Coupled Tank System  
In this section, the liquid level system has been subjected to a classical PI controller design with a feedforward application in order to provide real-time position control. In order to improve the accuracy of controlling the liquid level system, a controller design called cascade nonlinear proportional integral retarded (CNPIR) has been developed. This design takes into consideration the dynamic models of the system.
###### Abstract  
This article concerns research on the use of two types of coatings (parylene C and TiO\({}_{2}\)-ZrO\({}_{2}\)-Al) in multilayer composites with potential use in metallurgical protective gloves to improve their insulation against contact heat and radiation heat. To evaluate the thermal safety of the glove user, the composites were examined under the conditions of exposure to contact heat (using a heating cylinder, according to EN ISO 12127-1) and radiant heat (using a copper plate calorimeter, according to EN ISO 6942). Moreover, heat transfer through composites exposed to the heat of a hot plate was examined using thermography. The experimental studies were supported by heat transfer simulations through 3D models of composites. The contact heat method showed that composites achieved insulation against contact heat for both contact temperatures \(T_{\rm c}\), but composites with parylene C have a longer \(t_{\rm t}\) of 9 s (for \(T_{\rm c}\) = 100 \({}^{\circ}\)C) and 7 s (250 \({}^{\circ}\)C) compared to composites with TiO\({}_{2}\)-ZrO\({}_{2}\)-Al. The radiant heat method showed that composites achieved the fourth (highest) level of \(RHTl_{24}\) under exposure to a radiant heat flux of 20 kW m\({}^{-2}\). The modeling results showed that the parylene C coating increases the thermal barrier of the composite by approximately 10%, while the TiO\({}_{2}\)-ZrO\({}_{2}\)-Al coating increases it by 2%. The applied research techniques demonstrated the usefulness of using both types of coatings in the design of metallurgical protective gloves based on multilayer composites.  
thermal insulation; contact heat; radiant heat; protective gloves; basalt fibers; parylene C; TiO\({}_{2}\)-ZrO\({}_{2}\)-Al coating; simulation; thermography; micro-CT Article
## 3 Results and Discussion
### Thermal Analysis of CSR
Thermal Efficiency of CSR  
The experiment conducted on one of the sunny days (24 March 2018) with average DNI recoded as 800 Wm\({}^{-1}\) and the total energy available from sun for a concentrator surface area of 10 m\({}^{2}\) was 8000 W. Unlike the other solar collectors, the optical losses of [PERSON] concentrator are a little bit complicated and were calculated by adding the actual available aperture of the concentrator (7 m\({}^{2}\)), reflectivity of the reflecting surface (glass mirrors > 93%), precision and geometry of the reflector (9%), and the absorbance of the casted aluminium surface of the heat receiver (0.90). Under the available configurations of CSR, these losses were calculated to be 2370 W, 394 W, 524 W, and 471 W respectively, using Equations (2-5). It was determined that out of total DNI energy, the total optical and thermal losses were found to be 3759 W and 2120 W respectively. Out of total available energy from DNI (8000 W), about 2127 W power was utilized for groundnut roasting (Equation (5)) in CSR contributing 26.6% overall thermal efficacy of the system (Equation (6)). The complete energy distribution has been shown in Figure 9.  
Figure 10 shows the solar thermal energy utilization in kWh during the roasting of groundnuts in kg of light, medium and dark quality attributes. From these curves, one can assess the different weight of the groundnuts of desired qualities at different amounts of available energy and this research can be modeled to design various sizes of solar roasting machines on the basis of thermal energy available for processing/ roasting. It has been observed that \(30\pm 1\), \(21\pm 1\) and \(15\pm 1\) kg of groundnuts can be roasted in CSR by utilizing 1 kWh of solar thermal power subjected to light, medium and dark roasting quality respectively. Also considering the results under Section 3.4, CSR has the capacity to produce about 10 kW/day useful thermal energy for the roasting of groundnuts on a sunny day. Figure 10: Solar thermal power utilization for roasting of groundnuts. Figure 9: Energy distribution and losses for groundnut roasting (dark condition) in CSR with average incoming DNI of 800 Wm\({}^{-2}\). Figure 10 shows the solar thermal energy utilization in kWh during the roasting of groundnuts in kg of light, medium and dark quality attributes. From these curves, one can assess the different weight of the groundnuts of desired qualities at different amounts of available energy and this research can be modeled to design various sizes of solar roasting machines on the basis of thermal energy available for processing/ roasting. It has been observed that \(30\pm 1\), \(21\pm 1\), and \(15\pm 1\) kg of groundnuts can be roasted in CSR by utilizing 1 kWh of solar thermal power subjected to light, medium and dark roasting quality respectively. Also considering the results under Section 3.4, CSR has the capacity to produce about 10 kW/day useful thermal energy for the roasting of groundnuts on a sunny day.