esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## 3 Simulation Environment
### OmNeT++  
Objective Modular Network NESTbed in C++ is a discrete event simulator. It is used for modeling wired and wireless communication networks as well as microprocessors, multiprocessors, and other distributed or parallel computing systems. It is based on the C++ language, and it consists of different basic modules that communicate with each other by passing messages among them. These basic modules are used to create larger modules [22].  
The Network Description (NED) language is used to simulate models in OMNeT++. The component modules described in this language can be assembled to create a compound (system) module. Models written in NED can be simulated by one of the network simulators, such as MiXiM [23], INET [24]/INETMANET [25], or Veins [26]. Veins is the open-source vehicular network simulator. The execution of a model by Veins is controlled by OMNeT++ while interacting simultaneously with a road traffic simulator (SUMO). Various submodules in Veins take care of setting up, running, and monitoring the simulation.  
When performing intravehicular communication (IVC) evaluations, both traffic and network simulators run in parallel since they are connected via a Transmission Control Protocol (TCP) socket. This communication network protocol is known as Traffic Control Interface (TraCI) [27].  
The vehicle movement in SUMO is directed by the movement of nodes in an OMNeT++ simulation environment. We have also used Plexe [28] for the movement of an automated vehicle in OMNeT++. Plexe is an extension of Veins in OMNeT++. It allows realistic and accurate simulation of the automated car. It structures real-time vehicle dynamics and several cruise control models, enabling the implementation of large-scale and mixed scenario control systems along with different networking protocols.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
###### Abstract  
The synchronous rectification (SR) strategy in CLLC resonant converters can effectively enhance operational efficiency, meeting the stringent requirements of application scenarios such as renewable energy systems, DC distribution systems, and electric vehicle-to-grid (V2G) technology. However, due to the complex mathematical model, achieving precise SR directly in CLLC resonant converters is challenging. To realize high-precision and low-complexity SR in CLLC resonant converters, this paper proposes a state-trajectory-based SR strategy. By leveraging geometric principles and tailored simplifications, the expression for SR timing is derived. The proposed SR strategy exhibits excellent versatility and is suitable for the entire frequency range of CLLC resonant converters. The feasibility and accuracy of the proposed SR strategy are validated through an 800 W CLLC resonant converter prototype, demonstrating a robust dynamic performance and achieving a rated efficiency of 97.38%.  
converter modeling: CLLC resonant converter; state trajectory; synchronous rectification (SR) +
Footnote †: journal: Applied sciences  
Article
## 3 Target Data Processing and Computing  
In this section, we first discuss the three data processing paradigms applied in most processing scenarios, such as IoT and big data processing. Then, we briefly describe computing in the edge/fog layer, which can bring benefits in terms of reducing the latency and bandwidth consumption when sending a large amount of data to the cloud.
## 3 Outage Rate Analysis  
At the receiver, the receiver vector \(\mathbf{r}(t)=[r_{1}(t),r_{2}(t)]\) is combined to benefit from micro- and macro-diversity. We consider two combininig methods: maximal ratio combining (MRC), which maximizes the received SNR, and selective combining (SC), which is easier to implement compared to (MRC). When MRC is adopted, the combined SNR is the sum of the two squared envelopes [2; 3; 4; 5] as  
\[\gamma_{mrc}=\gamma_{1}+\gamma_{2}. \tag{11}\]  
On the other hand, if selective combining is adopted, as noted in [2; 3; 4; 5], the corresponding combined SNR is given by  
\[\gamma_{sc}=\max\{\gamma_{1},\gamma_{2}\}. \tag{12}\]  
The outage rate for a given SNR threshold \(\gamma_{th}\) is expressed as  
\[P_{out}=\Pr[\gamma_{c}<\gamma_{th}], \tag{13}\]  
where \(\gamma_{c}\in\{\gamma_{mrc},\gamma_{sc}\}\) is the received SNR at the receiver. In the following sections, we derive the outage probabilities of non-cooperative and cooperative schemes.
## 5 Results and Discussion
### PSO-SVR Models  
As stated above, the proposed PSO-SVR model was tried with the three kernel functions (i.e., RBF, P, and L) and their parameters, as shown in Table 2. For implementing the PSO-SVR models, the framework in Figure 6 was applied. The PSO algorithm and its parameters were needed to set up before searching the optimal hyperparameters of the SVR models. In which, the swarm size (s), maximum particle's velocity (\(V_{\text{max}}\)), the maximum number of iterations (i), individual and group cognitive parameters (\(\phi_{1}\), \(\phi_{2}\)), and inertia weight (\(w\)) were the parameters of PSO, which were used for the optimization process. According to previous works, s should be enough to ensure diversity [35; 99; 100]; thus, the swarm size of 100, 200, 300, 400, 500, respectively, was applied for the PSO procedure. The other parameters (i.e., Vmax, \(\phi_{1}\), \(\phi_{2}\), and \(w\)) was set to 2.0, 1.8, 1.8, and 0.9, respectively, according to recommendations of previous researchers [101; 102; 103; 104]. To check the termination criteria of the optimization process as well as ensure the optimal search process, \(i\) was set to 1000 iterations.  
Once the parameters of the PSO algorithm were established, the SVR models with various kernel functions were generated with initiation parameters. Then, the PSO procedure performed searching optimal values according to the hyper-parameters in Table 2. Finally, the optimal values of the hyper-parameters were determined with the lowest RMSE, and the PSO global search procedure was stopped after 1000 iterations. Table 3 shows the optimal values of the hyper-parameters with respectively kernel functions. In addition, Figures 5-7 show the performance of the optimization process based on kernel functions (i.e., PSO-SVR-L; PSO-SVR-P; PSO-SVR-RBF).
## 3 Joint Optimization of EE-SE  
### Quadratic Transform  
As the fractional expression in the problem brings difficulties in designing an algorithm, we can get the equivalently transformed expression of \(\mathrm{(P1)}\) by applying the quadratic transform in [30]. Therefore, we change the fractional problem into a new form without the fraction to simplify the optimization problem. The new problem is expressed as  
\[\mathrm{(P2)}\quad\max_{\mathbf{W},\mathbf{\Theta},y} \;2y\sqrt{a\sum_{k=1}^{K}\omega_{k}\log\left(1+\gamma_{k}\right)} \tag{7}\] \[\qquad-y^{2}\Big{(}\mu\sum_{k=1}^{K}\|\mathbf{w}_{k}\|^{2}+P_{ \mathrm{s,total}}\Big{)}\] \[\qquad+(1-a)\sum_{k=1}^{K}\omega_{k}\log\left(1+\gamma_{k}\right)\] s.t. \[\eqref{eq:P1},\eqref{eq:P2}\]  
where \(y\in\mathbb{R}\) is an auxiliary variable brought by the quadratic transform algorithm.  
Next, the square root of the SE part contained in the objective function in problem (P2) is replaced with variable \(t\). Furthermore, a new constraint on \(t\) needs to be added to ensure that the problem is reformulated equivalently.  
\[\mathrm{(P3)}\quad\max_{\mathbf{W},\mathbf{\Theta},y,t} \;2y\sqrt{a}\,t-y^{2}\Big{(}\mu\sum_{k=1}^{K}\|\mathbf{w}_{k}\|^{2}+P_{ \mathrm{s,total}}\Big{)} \tag{8}\] \[\qquad\qquad+(1-a)\sum_{k=1}^{K}\omega_{k}\log\left(1+\gamma_{k}\right)\] s.t. \[\eqref{eq:P3},\eqref{eq:P4}\] \[\qquad t^{2}\leq\;\sum_{k=1}^{K}\omega_{k}\log\left(1+\gamma_{k}\right)\]  
Therefore, optimizing the objective in problem (P1) over \(\mathbf{W}\) and \(\mathbf{\Theta}\) is equivalently turned to the process of finding the optimal \(\mathbf{W}\), \(\mathbf{\Theta}\), \(y\), and \(t\) for the problem (P3). As the problem (P3) makes it difficult to find the optimal values of the four variables simultaneously, the AO method is then adopted to conduct the optimization by organizing the original problem into several sub-problems. The iteration step is repeated until the convergence is reached. First and foremost, \(y\) and \(t\) are optimized, while the algorithm to optimize \(\mathbf{\Theta}\) and \(\mathbf{W}\) alternately will be introduced in Section 3.2. During the process of optimizing the objective over \(y\) and \(t\), we can have  
\[\alpha_{k}^{\text{opt}}=\frac{1}{e_{k}} \tag{14b}\]  
where \(k=1,2,\cdots,K\).
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 2 Materials and Methods
### Measurement Equipment and Methods
#### 2.2.2 Measurement of Physicochemical Index Parameters  
In accordance with the provisions of the current national standard for passion fruit quality grading (GB/T 40748-2021) regarding the physicochemical requirements for passion fruit, this study determined the physicochemical characteristics of the samples, including \(PP\), \(SSC\) and \(Acidity\). A total of 192 fruit samples were analyzed, with the following ranges observed: \(PP\) ranged from 44.1% to 61.8%, \(SSC\) ranged from 9.9% to 23.8% and \(Acidity\) ranged from 1.19% to 6.46%.  
The following describes the \(PP\) measurement process: Using an electronic balance, weigh the whole passion fruit. Then, cut the passion fruit in half along its longitudinal axis. Gently scoop out the pulp, thoroughly clean the outer shell of the fruit and then weigh the passion fruit peel. Record measurements in grams (g).  
\[PP=\frac{W-W_{p}}{W} \tag{3}\]  
where \(W\) is the total fruit weight and \(W_{p}\) is the peel weight from each fruit.  
The following describes the \(SSC\) measurement process: A digital refractometer/acid meter (Model PAL-BX/ACID-F5, ATAGO, Tokyo, Japan) is used. To obtain the juice for SSC measurement, the passion fruit is manually pressed and filtered through two layers of cotton-linen cloth. At least 3 mL of this extracted passion fruit juice is placed in the measurement area of the instrument, and then the \(SSC\) value of the juice is measured. This process is repeated three times for each sample and then the average value is taken.  
The following describes the \(Acidity\) measurement process: Using a digital refractometer/acid meter (Model PAL-BX/ACID-F5, ATAGO, Japan), to perform the acid measurement, after diluting 1 g of juice 50 times, at least 3 mL of the diluted solution is placed in the measurement area of the instrument to measure the acid value of the juice. This process is repeated three times for each sample and then the average value is taken. The effectiveness of the digital acid measurement has been confirmed in the research conducted by [PERSON] et al. [25], as referenced in their study.
## 3 Results and Discussion
### Antimicrobial Activity  
The diameter of the inhibition zones of the pectin film containing different concentrations of TC against _E. coli_ and _L. innocua_ is shown in Figure 4. The pure pectin film was considered as a control to investigate the potential antimicrobial activity of pure pectin. No inhibition zone appeared for pectin film without TC indicating no antimicrobial activity of the control. The diameters of the inhibition zones increased significantly by increasing the concentration of TC in the films for both bacteria tested. This is because more TC is available for release. Larger inhibition zone diameters were observed against _L. innocua_, a Gram-positive bacterium, compared to _E. coli_, a Gram-negative bacterium. The diameters of the inhibition zones varied from 37.41 to 30.46 mm for P/NE:1 to P/NE:4 for _L. innocua_. The same trend was obtained from 33.68 to 27.53 mm for P/NE:1 to P/NE:4 tested against _E. coli_. The presence of an extra hydrophilic membrane in Gram-negative bacteria hinders hydrophobic compound penetration like TC, leading to reduced inhibition zone diameters [71]. This result is consistent with the findings reported by [29], who incorporated a nanoemulsion of cinnamaldehyde into a pectin film. The larger diameters of the inhibition zones against _L.innocua_ compared to _E.coli_ were reported. It is reportedthat enhancing the concentration of clove bud essential oil in pectin film led to greater antimicrobial activity [57]. Another study also reported the antimicrobial activity of pectin based films containing _Cinnamomum verum_ against _Staphylococcus aureus_ (_S. aureus_), _Listeria monocytogenes_ (_L. monocytogenes_), _E. coli_, and _Salmonella typhimurium_ (_S. typhimurium_) [71].
###### Abstract
Broader studies focusing on the processes of sorption and release of CBM from lignite deposits would play an important role in evaluating the potential LCBM (lignite cooled methane) production from fossil coal-natural gas systems, relying heavily on laboratory core analysis (using crushed coal samples) and reservoir characterization, in view of a future methane exploitation. However, there are very few methane sorption/desorption studies on coal samples derived from lignite reservoirs so far [14; 20; 21; 22; 23; 24]. Lignite is one of Greece's most valuable mineral resources used for electric power production. Greek lignite reserves are in terrestrial or xylitic deposits and occur mainly in the western part of the country (Region of Western Macedonia, Greece). According to the Public Power Corporation (PPC), the original resources have been estimated at 5 billion tons. The Region of Western Macedonia and, in particular, the areas of Ptolemaida, Amynteo, and Florina have the largest lignite capacity in Greece, with total deposits of 1.9 billion tons [25]. In the Ptolemaida basin, a total of eight lignite deposits have been recorded, with three lignite mines (south field mine, Ypsilanti or southwest mine, and Mavropigi mine; Figure 1) near the Ptolemaida power station (Figure 2), which is a 1210-megawatt coal-fired power station with five units [26]. Considering that the Greek plan to stop burning lignite to produce energy according to the \"Just Transition Development Plan 2021-27\" of Western Macedonia, this has not yet been seen [27; 28]; the recovery of an exploitable energy source from its lignite deposits will accelerate the forthcoming exit from the era of coal use. Thus, the beneficial exploitation of lignite deposits could reduce the climate change impact through the mitigation of GHG sources [29]. However, the unconventional hydrocarbon potential resources, i.e., LCBM, of the country are not well known, since detailed investigations are lacking. Based on the above, the aim of the present study was to evaluate the presence of methane in the lignite deposits of the Ptolemaida basin. For this purpose, three lignite subsoil samples were collected from different sites of the southern field mine, reflecting the average petrographic composition of the lignite coal. Desorption experiments were conducted using the gas channeling process at four different temperatures, in order to evaluate the LCBM that could be extracted from the deposits of the area. Taking into account the results from the desorption process, a SWOT analysis was performed focusing on the strengths, weaknesses, opportunities, and threats of potential LCBM exploitation in Greece as a supplement to natural gas for enhancing energy security. Figure 1: Map of Ptolemaida’s active mines and lignite deposits. Figure 2: Ptolemaida’s power station from PPC archives.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems  
The proposed algorithm for fault location is capable to estimate the fault current with the DER's available information. The generalized algorithm for estimating the fault distance is divided in 5 steps, as illustrated in Figure 3. The algorithm is explained in detail in the following.  
Figure 3: Flow chart of the adaptive fault location algorithm for active distribution networks.
# Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
## 1 Introduction
Unlike traditional approaches, YOLO introduces a real-time detection system that divides the input image into a grid and predicts bounding boxes based on the grid cell content. Real-time performance is achieved by sacrificing fine-grained localization accuracy, but remains competitive in detecting objects, making it a popular choice for various applications [31]. Over the past few years, researchers have released multiple YOLO versions, each exceeding its previous version. YOLOv2 implemented batch normalization and anchor boxes, but YOLOv3 integrated a more intricate backbone network (Darknet-53) and multi-scale predictions. YOLOv4 improved performance by implementing novel data augmentation techniques and architectural modifications. YOLOv5 has demonstrated significant improvements in both speed and accuracy, as well as being more user-friendly for developers. The most recent versions, including YOLOv7 and YOLOv8, have significantly improved limits, providing cutting-edge performance in terms of speed and accuracy [35; 36; 37]. YOLO and other deep learning-based object recognition techniques have significantly influenced agricultural applications. They have helped implement more effective and precise identification of plant diseases, pests, and crop health problems. Consequently, they have helped advance intelligent farming systems and precision-based agriculture methods. As these algorithms progress, they have the potential to improve crop management and optimize productivity in the agricultural industry [31; 38]. In this study, an object detection model that identified wheat powdery mildew disease in digital images of wheat leaves was developed. For this purpose, YOLOv8 models of different sizes were trained accurately to detect and locate regions affected by powdery mildew. To train, test, and validate the model, wheat leaf images were collected from the trial area of the Faculty of Agriculture at Tekirdag Namik Kemal University. Data augmentation methods were used to enrich the collected data and make the model more robust.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction
### Considered Research Gap  
In the field of industrial robot gear condition monitoring no combined AD and TD model has been presented up to now to the best of our knowledge. Therefore, the research objective of this publication is to present such a system. For the detailed design of this system, a suitable AD and TD model must be chosen. As no comparison of AD and TD models for univariate time series of HIs derived from acceleration sensors has been  
Figure 1: Example for the trend detection method using moving averages.  
performed up to date, a method to select suitable AD and TD models for the application of industrial robot gear condition monitoring is formulated. Afterwards, it is applied to choose models for the presented combined system. In the context of the framework presented in [36], we address the question of algorithm selection for the inference task. By doing so, we support the transfer of state of the art AI models into practice and reduce the effort of model selection for practitioners. The identification of suitable data acquisition systems or the selection of features is not considered in this publication. This is e.g., considered in [3]. Therefore, the presented work builds up on assumptions derived from this publication. These assumptions are summarized in Section 2.1.1. Furthermore, we limit our research frame to the field of six-axis articulated robots as we can not provide comprehensive experiments for other asset classes and hence validate our approach for such assets.
# A Spatial 4-DOF Laser Collimation Measurement System
## 4 Experiment and Results
### Stability Test  
The stability of the system was tested under laboratory conditions. The ambient temperature range at the time of measurement was 20 \(\pm\) 0.5 \({}^{\circ}\)C. The experiment lasted for 30 min, and the data were collected every 10 s. The experimental results are shown in Figure 17. The standard deviations of horizontal and vertical straightness were 1.11 \(\upmu\)m and 0.94 \(\upmu\)m, respectively. The standard deviations of yaw angle and pitch angle were 0.91 arcsec and 0.90 arcsec, respectively. From the above data, it can be seen that the proposed system has good stability.  
Figure 16: Calibration experiments: (a) X straightness calibration curve; (b) Y straightness calibration curve; (c) calibration curve of pitch in the X direction; (d) calibration curve of yaw in the Y direction.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 6 Future Work and Challenges
### Challenges  
Amongst the challenges inherent in integrating a microstrip patch antenna and radar chip on the same printed circuit board PCB at 77 GHz, radome design and fabrication is crucial for deployment on UAS. The purpose of a radome is to protect the antenna and electronics from environmental effects. The design and fabrication of a radome is a potential cost overhead. Moreover, the inclusion of MIMO in the next phase of the work implies that an array of multiple Tx and Rx antennae shall be deployed. While horn antennae may be considered, their installation and calibration on UAS airframes entail substantial cost, effort, and volume implications. The requirement of precise antenna separation deems conformal microstrip patch antennae the optimal choice. Nevertheless, their manufacturing and characterization is a daunting task. There is a need to explore cheap substrates with a small Young's modulus [71] for bending and rolling to fulfill the said task. Traditional testing methods with a vector network analyzer (VNA) and anechoic chamber are expensive. A cheaper alternative could be to employ the radar unit itself as a test bench by incorporating targets with a known RCS at a calibrated distance to gauge the practical gain against simulated results since the rich software control offered by Texas Instruments allows for real-time SNR evaluation [72]. Nevertheless, the characterization of the radiation pattern for HPBW evaluation is a research venture of its own.  
Another challenge is that there is no evidence of the long-term performance and reliability of mmWave automotive radars for airborne applications. Although automotive-grade electronics have a wide operating temperature range and experience significant vibration through the course of the life cycle, the vibration spectrum of a broad scale of UAS platforms may pose additional stress. Moreover, if the scope is extended to commercial aviation, long-term exposure to very low temperatures at high altitudes may warrant accelerated life testing. These are unexplored areas and a thorough investigation is necessary for the mmWave band to become a viable avenue given the 5G interference and inherent benefits associated with this migration.