esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology of the Study and Objective Functions Used  
We compare the PROS algorithm with the simplistic PRS approach and three other well-known algorithms, namely GA [1; 2; 3], PSO [4; 5], and DE [7; 8; 9; 10] in 12 mathematical test functions. All test functions are multi-dimensional and can be defined with any number of dimensions. We use a set of four different numbers of dimensions (\(D\)) in our study: (i) \(D=5\), (ii) \(D=10\), (iii) \(D=30\), (iv) \(D=50\). When \(D=5\) is used, the design vector of each problem has 5 elements, i.e., \(x=[x_{1},x_{2},x_{3},x_{4},x_{5}]\) and the optimization problem is relatively simple. This is not the case with higher values of \(D\) where the problem gets significantly harder. For example, in the case \(D=50\), each design vector has 50 elements to adjust (\(x_{1}\) to \(x_{50}\)). The five algorithms used are the following:  
1. Genetic algorithm (GA);
2. Particle swarm optimization (PSO);
3. Differential evolution (DE);
4. Pure random search (PRS);
5. Pure random orthogonal search (PROS). PRS and PROS use a single point for their search, while GA, PSO, and DE use a population of agents (or particles) at each generation (iteration). In order to compare the algorithms fairly we will use the maximum function evaluations as the convergence criterion for all cases, i.e., all algorithms will stop after a certain number of function evaluations is completed. GA and PSO are based on their MATLAB implementations and are executed using the MATLAB commands \(ga\) and \(particleswarm\), respectively, using their default parameters. DE, PRS, and PROS were programmed in MATLAB from scratch. For DE, a DE/rand/1/bin scheme is used with differential weight \(F=0.6\) and crossover probability \(CR=0.8\). Each optimization problem is run 10 times. The total number of optimization problems solved is 5 (methods) * 4 (different dimensions) * 12 (Problems) * 10 (Runs) = 2400. To maintain a consistency for all problems and all the different cases, in population-based algorithms (GA, PSO, DE), the population size is set to \(NP=10\cdot D\) and the maximum number of iterations (or generations) is set to \(MaxIter=20\cdot D-50\). Although some specific problems would actually require more iterations, we decided not to make exceptions to this rule, to keep things consistent and we kept these values that work well for most problems. Then the max. number of function evaluations can be calculated as \(MaxFE=NP\cdot MaxIter\). Table 2 shows the population size, max. number of generations/iterations and the max. number of function evaluations for each category of problems, based on the number of dimensions. For example, when we investigate the performance of the five algorithms in 30 dimensions (\(D=30\)), the population size used in the three population-based methods (GA, PSO and DE) is 300, while the maximum number of generations (or iterations) for
## 5 Proposed Work
### Task Scheduling
#### 5.2.2 Task Ranking  
The MHEFT algorithm is used to rank scheduled tasks generated using the DSC algorithm. The highest priority task among the scheduled tasks is selected for the upcoming processes, such as VM clustering and load balancing. The average execution and communication times for transferring data are calculated to rank tasks.  
MHEFT, which is a variant of the static list algorithm that prioritizes tasks with considerable influences on total workflow execution time, is the most efficient ranking algorithm. Procedures  
Figure 3: Task clustering.  
Figure 2: Task clustering and scheduling.  
that are static methodologies for mapping issues and that accept static condition for a given time are utilized. The upward rank of each task, which is defined as the critical path of the task and considers the maximum communication time and average implementation time from the start to the end of any task, is calculated to rank the tasks. If two tasks have the same priorities, then arrival time is checked, and first priority is given to the task that arrives first. The rank of task R(t\({}_{i}\)) is expressed as  
\[R(t_{i})=E(t_{i})+\underset{t_{j}=s(t_{i})}{max}(c_{i,j}+R\big{(}t_{j}\big{)}) \tag{2}\]  
where \(E(ti)\) is the average execution time of the task across all VMs, _S(ti)_ is the set of immediate successors of the task, \(C_{i,j}\) is the communication time that corresponds to the transfer of data _ij_ via edges \(i\) and \(j\), and _R(ti)_ is the computation of all of its children. Communication time \(C_{i,j}\) is computed using  
\[C_{i,j}=\text{y}+\frac{data_{i,j}}{b} \tag{3}\]  
where Y is the average latency, and b is the average bandwidth of communication links among VMs in the system. We can rank each scheduled task by computing Equations 2 and 3. The highest priority task is selected and transferred to the VM allocation process for further operations. Expression ranking and the selection of the highest priority task are completed via the MHEFT algorithm.
## 3 Materials and Methods
### Dataset Collection and Preprocessing
#### 3.2.2 Constellation Phase Histogram Dataset  
The literature [16] indicates that distinct modulation formats result in the constellation diagram displaying varying shape characteristics. Therefore, the distribution characteristics of clustered points in constellation diagrams serve as features for modulation format recognition, a widely utilized approach documented in numerous studies [5; 16]. After receiving the I/Q signal, the constellation diagram is created by plotting the real \(I\) as the horizontal coordinate and the imaginary \(Q\) as the vertical coordinate of each sampling point. However, in this paper, we have introduced an offline processing module to reprocess the constellation diagram and extract its phase histogram as the dataset.  
In the offline processing module, we use the I/Q signal to obtain the phase histogram as follows. For each data point \((I_{i},Q_{i})\), the phase is calculated as  
\[\mathcal{O}=\arctan\left(\frac{Q_{i}}{I_{i}}\right) \tag{2}\]  
where \(I_{i}\) is the real part of the data point and \(Q_{i}\) is the imaginary part of the data point. Subsequently, the phase is divided into 1024 intervals, each with a width of \(\Delta\mathcal{O}=2\pi/1024\). Thus, the phase range of the \(k\)-th interval is  
\[[\mathcal{O}_{k},\mathcal{O}_{k}+\Delta\mathcal{O}] \tag{3}\]  
where \(k=0,1,2,\cdots,1023\). For each data point \((I_{i},Q_{i})\), if its phase \(\mathcal{O}_{i}\) falls within the k-th interval, the frequency \(f_{k}\) of the phase interval is incremented by 1. Thus, the frequency \(f_{k}\) of k within the interval is calculated as  
\[f_{k}=f_{k}+1 \tag{4}\]  
Eventually, by traversing all the data points and accumulating the frequencies, the frequency information within each interval can be obtained, thereby forming a phase histogram. Figure 3 depicts the constellation diagrams and the corresponding phase histograms for each modulation format.  
This paper investigates the phase histograms of three primary modulation formats (QPSK/8 PSK/16 QAM). These modulation formats span OSNR values ranging from 19 to 30 dB, with increments of 1 dB, and CD values ranging from \(-100\) to \(100\) ps/nm, with increments of 10 ps/nm. Each combination is replicated with 10 different random seed counts, resulting in a total dataset of 7560 samples (3 \(\times\) 12 \(\times\) 21 \(\times\) 10). Subsequently, 180 samples are designated as the test set, while the remaining data are randomly partitioned into training and validation sets in a 7:3 ratio.  
Figure 3: Constellation diagram **(top row)** and constellation phase histogram **(bottom row)** corresponding to **(a)** QPSK, **(b)** 8 PSK, and **(c)** 16 QAM.
## 4 Experimental Results and Discussion
For voiced /u/ vowels in men, the utilization of the CNN classifiers and LPCC parameters obtained the highest accuracy, 82.69%, compared to the other vowels, classifiers, and parameters. For women, the accuracy of each classifier is higher in vowel /i/ than in other vowels. Another finding is that the fusion of the HOS parameters in a particular vowel has a positive effect on the overall accuracy of the classification. The best results for mixed samples were achieved by the CNN classifier, although most of the results were very similar to those of the FNN classifier. To further analyze the behavior of the model, the author investigated the confusion matrix and relationship between the loss and learning rates. Figure 5 shows the relationship between the loss and learning rates when the highest performance was 82.69%. In this experiment, the epoch was 100 and the learning rate was 0.001. As the epoch runs from 1 to 100, the learning rate tends to decrease from 0.001 to 0.0081, and the loss value tends to decrease from 0.66 to 0.58. The classification results represented by the confusion matrix are described in Figure 6. The confusion matrix of the testing set shows that an
## 1 Introduction  
Various global automobile companies are actively conducting research on development of an autonomous vehicle. The autonomous vehicle, known as the self-driving vehicle, is a vehicle which can perceive an environment and navigate without human intervention [1]. According to the Society of Automotive Engineers (SAE), the technology of the autonomous vehicle is categorized into six levels, and most global automobile companies have a plan to release a fully autonomous vehicle by 2020 [2]. Those six levels are summarized in Table 1[3].  
One of the well-known electric automobile companies in the USA has the most advanced autonomous vehicle technology in the current market. Vehicles in this company have numerous driving assistant features such as autopilot, summon, etc. [4]. Moreover, the autonomous vehicle division of the most famous IT company located in Mountain View, USA, has conducted testing of the fully self-driving vehicle in Arizona, USA since October 2017 [2].  
However, the most important technical aspect to achieve fully autonomous driving is safety. According to the accident report published in May 2016, a driver who operated an electric vehicle was killed due to a collision with a semi-trailer on a highway in Florida, USA [3]. The main reason for this crash was that the vehicle was driven by the autopilot feature consisting of three systems: auto-steer, Auto Lane Change System (ALCS), and Traffic-Aware Cruise Control (TACC), but the driver heavily depended upon vehicle automation technologies which belong to level 2 in Table 1. One interesting point from the accident report is that the level 2 technology does not show high system reliability. This limitation of the vehicle automation technologies injects motivation into this research. Especially, this research focused on the safety feature related to blind spot detection.  
Blind spot detection is a core technology in terms of the ALCS, as mentioned above. In addition, 4-10% of all traffic crashes refer to side-swipe accidents caused by blind spot detection [5]. For this reason, giving an audio-visual warning to drivers is necessary when misdetection of the blind spot occurs. Therefore, this study proposed development of the camera-based blind spot detection system using a deep learning methodology.  
This paper is organized as follows: Section 2 describes the technological aspects of the blind spot detection system and provides a brief literature review. Section 3 presents methodologies and a research framework for system development. Experimental results with pre-recorded video images and live video images on a real road are discussed in Sections 4 and 5, respectively. Section 6 describes conclusions, research limitations, and future work.
## 3 Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSECRF)
### Methodology of SSECRF  
In this paper, in order to make full use of the spatial-spectral-emissivity information for precise land-cover classification, the SSECRF algorithm is proposed to integrate the emissivity information retrieved from the thermal infrared hyperspectral imagery and the spatial and spectral information from the visible data. The emissivity and the spatial-spectral feature set are fused in the SSECRF algorithm using different potentials. The main procedure of the proposed method is illustrated in Figure 3. Firstly, the emissivity information is retrieved from the thermal infrared hyperspectral imagery data, and is upsampled to the same spatial resolution as the visible data. The spatial-spectral feature set is then constructed by extracting the different features from the visible data. Furthermore, the unary potential is modeled by integrating the emissivity and the spatial-spectral feature set, and the pairwise potential is modeled to consider the spatial interactions of the visible data in the neighborhood pixels. The final classification map is obtained when the SSE energy function is minimized.  
Figure 2: Schematic diagram of conditional random fields (CRF) in image classification.
###### Abstract  
Efficiently reporting soil-specific information is of key importance for plant growth but can be quite demanding as well. Indeed, it may require expensive digitizers, subscriptions to services for communication links between each sensor and the cloud, and the incorporation of power-hungry elements. Added to this, soil sensors may vary drastically, e.g., in terms of power characteristics, response times, or interfacing options. The need for improved energy autonomy increases reporting complexity, as it presupposes that the participating components will enter a low-power (sleep) state when not in action. Furthermore, the IoT nodes hosting the sensing instruments should be able to work unattended for long periods under varying environmental conditions. In response to the aforementioned physical and technical challenges, this work highlights the details behind the cooperation of a cost-effective microprocessor equipped with a radio transceiver and some simple and widely available electronic components to form nodes that can host a diverse set of soil sensors and deliver reliable data in satisfactory ranges. The sensitivity and power efficiency of the LoRa protocol make it ideal for rural agri-field use; in the meantime, optimized action/sleep management, along with tiny solar panels, guarantee sustainable operation. The proposed system was tested utilizing various typical soil instruments, and its range coverage, consumption, and measurement quality were thoroughly evaluated under different installation settings, thus providing guidance for similar implementations and indicating its suitability for a wide set of monitoring applications.  
wireless sensor networks; energy consumption; energy harvesting; TEROS 10; TEROS 12; 5 TE; 10 HS; ThetaProbe ML2; environmental monitoring; smart agriculture
# The Applications of Plasma Techniques
### Tar Removal by Nanosecond-Pulsed Dielectric Barrier Discharge  
[PERSON] and [PERSON] in [9] presented results about plasma-catalytic reforming of simulated biomass tar composed of naphthalene, toluene, and benzene in a coaxial plasma reactor supplied with nanosecond high-voltage pulses. The study evaluated the effect of Rh-LaCoO\({}_{3}\)/Al\({}_{2}\)O\({}_{3}\) and Ni/Al\({}_{2}\)O\({}_{3}\) catalysts covering high-voltage electrodes on tar conversion efficiency. Compared to the plasma reaction without a catalyst, the combination of plasma with the catalyst significantly enhanced the conversion of all three tar components, achieving complete conversion when a Rh-based catalyst was used. Apart from gaseous and liquid samples, char samples collected from five locations inside the reactor were also analyzed for their chemical composition. The authors stated that char was not formed when the Rh-based catalyst was used. Different by-products were detectedfor the plasma reactor without a catalyst or with the Ni- and Rh-based catalysts. A possible reaction pathway in the plasma-catalytic process for naphthalene, as the most complex compound, was proposed through the combined analysis of liquid and solid products.  
Plasma Electrolysis Spraying \(Al_{2}O_{3}\) Coating onto Quartz Fiber Fabric for Enhanced Thermal Conductivity and Stability  
[PERSON] et al. in [10] reported the synthesis of Al\({}_{2}\)O\({}_{3}\) coating onto quartz fiber fabric by plasma electrolysis spraying for enhanced thermal conductivity and stability. The nano- and micro-sized clusters were partially observed on the coating, while most of the coating was relatively smooth. It was suggested that the formation of a ceramic coating was followed as the nucleation-growth raw; that is, the formation of the coating clusters was partially dependent on the fast growth, implying an inhomogeneous energy distribution in the electrolysis plasma. The deposition of the Al\({}_{2}\)O\({}_{3}\) coating increased the tensile strength from 19.2 to 58.1 MPa. The thermal conductivity of the coated quartz fiber was measured to be 1.17 W m\({}^{-1}\) K\({}^{-1}\), increased by \(\sim\)45% compared to the bare fiber. The formation mechanism of the Al\({}_{2}\)O\({}_{3}\) coating was preliminarily discussed. The authors state that the thermally conductive quartz fiber with high thermal stability by plasma electrolysis spray will be widely used in flexible thermal shielding and insulation materials.
## 3 Virtual Admittance Feedforward Compensation and Phase Correction to Reduce Current Distortion
### Reference Current Phase Correction  
With the analysis in Section 2.2, the input filter capacitor generates capacitive current, which degrades the power factor. To eliminate the influence of the input capacitor, the reference current phase should be lagged by \(\varphi\), i.e.,  
\[i_{ref}=\frac{v_{in,m}}{R_{in}}\sin(\theta-\varphi), \tag{12}\]  
where \(\theta\) is the phase of the input voltage. Therefore, the total input current is calculated using  
\[\begin{split} i_{in}&=i_{cin}+i_{ref}=\frac{v_{in,m }}{R_{in}}\sin(\theta)\cos(\varphi)-\frac{v_{in,m}}{R_{in}}\sin(\varphi)\cos( \theta)+2\pi f_{line}C_{in}v_{in,m}\cos(\theta)\\ &=\frac{v_{in,m}}{R_{in}}\sin(\theta)\cos(\varphi)+[\tan(\varphi )-\sin(\varphi)]\frac{v_{in,m}}{R_{in}}\cos(\theta)\end{split}. \tag{13}\]  
Since \(\varphi\) is usually small, the total input current \(i_{in,total}\) is simplified as (14), where the input current phase error caused by the input capacitor is eliminated.  
\[i_{in,total}=\frac{v_{in,m}}{R_{in}}\sin(\theta-\varphi). \tag{14}\]
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 1 Introduction
Owing to the advantages of volume sampling, remote sensors are becoming more widely recognized, and the availability of improved resolution and Doppler capabilities in these remote sensors have greatly increased their utility, thus making remote sensors the observational platform of choice in many situations. [3; 18; 19; 20; 21; 22; 23; 24; 25]. Among the remote sensors, lidar is uniquely capable of collecting an all-weather, high-accuracy, large detection range high, spatio-temporal resolution and other excellent characteristics, and is a more powerful instrument for measuring wind parameters than others. [PERSON] [26] used the Doppler wind lidar (DWL)to research a wildland fire plume in complex terrain east of San Jose, CA, USA. Vertical wind and turbulence profiles within 200 m were measured to study the kinematic structure and spatial and temporal evolution of the fire plume and found that the velocity accelerates at the plume boundary. [PERSON] et al. [24] used the data measured by a WindCube lidar installed in mountain valley  
Figure 1: Difference in wind speed between open and mountainous valley terrain. and turbulence characteristics of the wind were analyzed. [PERSON] [27] et al. studied the wind characteristics in a mountainous valley by 2D anemometers and 3D Doppler radar. At present, the measured research on wind parameters of mountainous valleys at bridge site areas mostly adopts anemometers or anemometer mast. The spatial range of the measured data is limited, and it is difficult to arrange multiple wind towers in the spanning direction of the bridge due to the constraints of terrain and equipment. There are limited reports about the wind field measurement study of U-shaped valleys through lidar. The aim of this paper is to obtain the wind characteristics at a bridge site in a U-shaped valley and provide a reference for the wind-resistant design of related bridges in similar valleys. Therefore, in this paper, a high performance Wind3D 6000 lidar was used to study the wind field characteristics in a U-shaped valley at bridge site. Five virtual wind towers were set up at the key position of the main girder for continuous observation for about six months. Then, through screening the original data, the measured data in the range of 0-810 m were analyzed, and wind speed and direction were analyzed from the perspectives of time, space, and statistical distribution. The research method and results can provide reference for the study of mountain canyon wind fields, and it provides a wind parameter basis for bridge design.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 3 Grounding Network Topology Detection Method  
The current injection method and magnetic field excitation method are the main detection methods of grounding grid topology structure.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 1: What Is the Current State of the Usage Scenarios for Blockchain in Education?
#### 4.1.2 Record of Learning Trajectory  
Twelve articles (42%) model blockchain-based learning platforms to trustfully register student's achievements by academic organizations. Note that the information managed in this case extends the transcripts and certificates discussed in the in the previous category.  
The idea behind these articles is that learners would store their learning evidence from any source (formal, non-formal or informal) on a blockchain-based system to keep the information trustful and tamper-proof, and verifiable by third parties.  
Authors consider and propose different approaches about how these learning records can be used, managed and protected. For example, thanks to the platform modeled by [PERSON] et al. [31], each student can have their learning records stored and transferred from one institution to another in a safe form. [PERSON] et al. [16] model a system where learners obtain badges according to their achievements.