esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement
\begin{table}
\begin{tabular}{l l} \hline
**Symbol** & **Definition** \\ \hline Sets & \\ \(G=(V,E)\) & HSR network, where \(V,E\) represent the station set and section set \\ \(L=\{l=\{l=(V_{I},S_{I},C_{l},D_{l},R_{l})\}\) & Train set, where \(V_{I},S_{I},C_{l},D_{l},R_{l}\) are the passing station sequence, stop symbol \\ sequence, capacity, departure time, and rank of train \(l\) \\ \(CS_{l}\) & Candidate stop set of train \(l\) \\ \(RS\) & O-D pair set \\ Parameters & \\ \(n^{0}_{rs}\) & Required direct service frequency between O and D \((r,s)\) \\ \(n^{0}_{v}\) & Required service frequency for station \(v\) \\ \(r(R_{l})\) & Highest stop ratio of train class \(R_{l}\) \\ \(\tau^{\text{stop}}_{vj}\) & Stop time at the \(j^{\text{th}}\) station along the route of train \(l\) \\ \(t^{\text{min}}_{l,j}\) & Running time of the \(j^{\text{th}}\) section of train \(l\) \\ \(\tau_{\text{ad}}\) & Acceleration/deceleration time \\ \(w_{i}\) & Weight of the \(i^{\text{th}}\) optimization objective \\ \(P\) & Population size \\ \(m\) & Elitist individual number \\ \(n\) & Tournament size \\ \(p_{c-\max},p_{c-\min}\) & Maximum and minimum crossover probabilities \\ \(p_{m-\max},p_{m-\min}\) & Maximum and minimum mutation probabilities \\ \(p_{skip}\) & Base skip probability \\ \(T_{0}\) & Initial temperature \\ \(\theta\) & Temperature drop ratio \\ \(g_{\max}\) & Maximum generation number \\ Variable & \\ \(x^{l}_{i}\) & The \(i^{\text{th}}\) binary 0–1 stop symbol of train \(l\): if train \(l\) stops at the \(i^{\text{th}}\) station, then \(x^{l}_{i}=1\); \\ \(\epsilon(l,v)\) & Otherwise, \(x^{l}_{i}=0\) \\ \(\epsilon(l,v)\) & Coefficient between train \(l\) and station \(v\): if \(v\) belongs to \(CS_{l}\), then the coefficient \\ \(\sigma^{l}_{v}\) & Coefficient between train \(l\) and station \(v\): if train \(l\) stops at station \(v\), then \(\sigma^{l}_{v}=1\), \\ \(\sigma^{l}_{v}\) & otherwise, \(\sigma^{l}_{v}=0\) \\ \(\phi^{l}_{rs}\) & Coefficient between the passing station order of train \(l\) and O-D pair \((r,s)\): if train \(l\) \\ \(d^{l}_{jr},a^{l}_{l}\) & The departure and arrival time of the \(j^{\text{th}}\) station along the route of train \(l\) \\ \(q_{rs},q^{0}_{rs}\) & Volume of total demand and detailed demand of O-D pair \((r,s)\) \\ \(l_{rs}\) & The shortest path length between the O-D pair \((r,s)\) \\ \(T(r,s)\) & Actual total travel time of passengers from station \(r\) to station \(s\) \\ \(T_{0}(r,s)\) & Total direct travel time of passengers from station \(r\) to station \(s\) \\ \(Z\) & Optimization objectives \\ \(Z_{i}\) & Multi-objective component \\ \(F\) & Fitness function \\ \(p_{c},p_{m}\) & Crossover and mutation probability \\ \(\overline{f}_{\max}\overline{f}_{\min}\) & Average fitness of the two chromosomes with the highest and lowest fitness \\ \(\overline{f}_{l}\) & Average fitness of selected chromosome pair \\ \(p^{l}_{skip}\) & Skip probability of train \(l\) \\ \(LF_{l}\) & Load factor of train \(l\) \\ \hline \end{tabular}
\end{table} 
Table 1: Input parameters and variables.
# Modeling and Simulation Tools for Fog Computing--A Comprehensive Survey from a Cost Perspective
## 3 Modeling and Simulation Challenges
### Fog Computing Infrastructure
#### 3.1.2 Computation  
Any device with computing capabilities could be used for compute. Generally speaking, any device in the fog layer which has computational power and any server on the cloud can act as a compute device. Based on them, fog computing systems provide new computational opportunities and more efficient services.  
Compute devices provide services for storing, processing and analyzing data [28]. They could act as servers, coordinators or controllers depending on the given application and its requirements. They may also discover, monitor and manage other devices and their requests. In a real implementation compute devices need to be purchased, connected and configured, while in a simulation environment, they need to have specific features, such as management and mobility capabilities. It is desirable to deploy a fog system with various types of computing devices and predefined QoS levels, which enhances the performance metrics in a cost-effective manner. Here two different kinds of cost must be considered--the purchase cost and the operating cost. Notice that since compute can be placed on both fog and cloud nodes, the operating costs may also include latency, network congestion, and energy consumption, which are related to possible data transfers among compute nodes [29].
# Immobilized TiO\({}_{2}\)-Polyethylene Glycol: Effects of Aeration and pH of Methylene Blue Dye
## 3 Result and Discussion
### Effects of Aeration Rate  
Aeration flow rate affects the photoactivity efficiency of immobilized TiO\({}_{2}\)/PEG because it acts as an oxygen supply for the photocatalyst [31]. The effect of aeration flow rate was identified based on first-order rate constant (\(k\)) value against flow rate and is shown in Table 1, and decolorization (\(C/C_{o}\)) is shown in Figure 4, respectively. With PEG and DSAT in immobilized TiO\({}_{2}\)/PEG, the concentration of MB dropped from 12 to 0 ppm in one hour with the highest removal efficiency rate at 75 mL/min. As can be seen in Figure 4, the dye remaining was up to 0.29 in just 15 min as compared to 0.53, 0.36, and 0.62 for 100, 50, and 25 mL/min aeration, respectively. The value of photodegradation efficiency (\(k\)) at 0.0347, 0.058, 0.068, and 0.053 min\({}^{-1}\) for 25, 50, 75, and 100 mL/min flow rate accordingly indicates that the photocatalytic degradation of MB increased with increased aeration up to 75 mL/min; beyond that, however, the \(k\) value dropped by 0.22 to become 0.053 min\({}^{-1}\) at 100 mL/min. In other words, at 75 mL/min, optimum flow rate for photodegradation of MB reached as high as 0.068 min\({}^{-1}\) of removal efficiency. High aeration flow rate supply produced a high oxygen concentration and reacted with excited electrons from photon flux, producing O\({}_{2}\)\({}^{-}\) anions. The production of O\({}_{2}\)\({}^{-}\) caused the photodegradation rate of MB to increase as the aeration flow rate increased [32]. However, the excessive flow rate beyond 75 mL/min affected the penetration of light due to the production of a huge amount of bubbles during the photocatalysis irradiation.
## 3 Results
### Efficiency of DSSCs
This results in better light harvesting efficiency, leading to improved performance of DSSC devices based on ITO-nanorod deposited on a glass substrate. The result can be explained by the relatively low electrical resistance in Figure 15 of the ITO film, which significantly affects the measured current. When electrons are injected from the dye molecules into the TiO\({}_{2}\) nanoparticles with light energy, then the electrons are mobilized through the network of the TiO\({}_{2}\) and ITO-nanorod layers. Nonetheless, the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ITO Substrate** & \(\mathbf{J_{sc}}\) & \(\mathbf{V_{oc}}\) & **FF** & \(\eta\) \\  & [ **mA/cm\({}^{2}\)**] & [**mV**] & [**\%**] & [**\%**] \\ \hline ASD & 26.8 & 336 & 63 & 6 \\
20 sccm & 27.9 & 491 & 66 & 8.6 \\
40 sccm & 28.1 & 493 & 66 & 8.7 \\
60 sccm & 28.5 & 494 & 67 & 8.9 \\
80 sccm & 28.8 & 497 & 66 & 9.2 \\
100 sccm & 29.3 & 500 & 70 & 9.5 1  \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Photovoltaic parameters of DSSCs based on the ITO-nanorod films treated with oxygen plasma of varying gas contents at 20–100 sccm. Figure 18: Variability of data on graphs to indicate the error in the efficiency of DSSCs on ITO-nanorod treated with oxygen plasma of varying gas contents at 20, 40, 60, 80, and 100 sccm provided via bias-magnetron RF sputtering process. electrical entanglement of the TiO\({}_{2}\)/ITO-nanorod layers is not good enough to move across the interface during diffusion. In that case, the current density will decrease, resulting in a lower measured current density. Thus, solar cell efficiency can be improved by treating ITO with oxygen plasma. Since the ITO-nanorod was treated with oxygen plasma, the film structure changed, such that when the gas flow rate was increased, the size of the nanorods became smaller and separated because of the plasma treatment. Therefore, it affects the efficiency of converting solar energy into electrical energy for DSSC devices. In this, the obtained result can be explained that when the ITO film surface becomes rougher, sunlight (photon) will pass the device through the working electrode layer and react with the TiO\({}_{2}\)/dye layer, resulting in a large number of electrons that attach to the ITO film layer at the top layer. Thus, there is a high amount of electric current and Voc, resulting in electrons having much energy to transport to the counter electrode, which improved the ITO films' semiconductor properties as shown in Figure 19.
## 2 Materials and Methods
### Gene Expression Analysis in hGFs  
A QuantStudio(r) 3 PCR system (Applied Biosystems Real-time PCR systems, Thermo Fisher Scientific) was utilized to analyze the expression of IL-6 and IL-8, which are inflammatory markers induced by IL-1\(\beta\) stimulation. The RNeasy Mini Kit (Qiagen, Venlo, The Netherlands) was employed to extract total RNA, which was then converted to cDNA using the PrimeScript RT Reagent Kit (TAKARA Bio, Shiga, Japan). The TaqMan(r) Gene Expression Assay (Thermo Fisher Scientific) was used to quantify the expression of IL-6, IL-8, and Glyceraldehyde-3-phosphate dehydrogenase (GAPDH). For miRNA analysis, total RNA was isolated using the miRNeasy Mini Kit (Qiagen). Subsequently, cDNA was generated using TaqMan microRNA assays and a microRNA Reverse Transcription Kit (Thermo Fisher Scientific). Specific reverse transcription primers were employed for hsa-miR-146a-5p and U6.
## 2 Related Works
### Sensors Used for Hand Gesture Recognition Interface  
Typically, there are two main types of sensors used for hand gesture recognition: gloves-based sensors and vision-based sensors. The gloves-based techniques require users to wear a hand glove fitted with sensors for recognizing the hand posture. By contrast, the vision-based approaches require only a camera, consequently achieving natural communication in human-computer interaction without the need for any extra devices.  
Many previous studies on hand gesture recognition have been conducted using gloves fitted with sensors, such as the works of [13; 14; 15; 16; 17; 18; 19]. However, despite some remarkable successes, this method remains challenging owing to its complexity as well as variable glove sizes between users. Consequently, many recent efforts have been focused on vision-based systems. These methods require no gloves or hardware except for a sensor, so these systems are substantially cheaper and can be easily accepted by any field.  
Vision-based sensors can be categorized into two types, namely the traditional camera (RGB)-based systems and depth sensor (RGB-D)-based systems, respectively. Using traditional cameras, the model-based approaches [7; 20; 21; 22; 23; 24; 25; 26; 27; 28] generate hand model hypotheses and evaluate them with the available visual observations. These approaches exhibited clear detection difficulty when the light levels were changed or a complex background was used, and also required a fixed distance from the camera to the users. In order to overcome these limitations, some studies have used depth cameras, for example, PrimeSense, AsusensXtion Pro, and Microsoft's Kinect [29]. These cameras have advanced significantly over the past few years, and their performances have increased, while their prices have lowered. Compared with traditional RGB cameras, depth cameras offer many advantages: 30 frames per second with depth resolution, working in low light levels, and tracking at a longer distance. Some systems use depth images from Kinect and achieve high speeds, while avoiding the disadvantages of traditional RGB cameras by tracking depth maps from frame to frame [30; 31; 32]. These methods use a complex mesh model and achieve real-time performance. However, such systems only work for hand tracking, not hand gesture recognition such as swiping left, right, up, down, or zoom in and out, and so on.
## 4 Method for UAV Electromagnetic Interference Suppression
### Anti-Electromagnetic Interference Method of UAV Data Link  
The UAV data link needs to face the electromagnetic interference caused by the converter station during operation, as well as the multipath fading, power attenuation and Doppler shift caused by high-speed movement due to the surrounding complex environment. Research on anti-jamming technologies applied to UAV data links are mainly divided into three categories: related technologies to improve system reliability and effectiveness, cooperative communication technologies and anti-jamming technologies based on cognitive radio.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 5 Conclusions  
The automatic derivation of dense GCP information from aerial orthoimages facilitated a detailed error pattern analysis of Kompsat-3A data. During bias analysis, most Kompsat-3A data exhibited geometric skewness, necessitating the implementation of an affine model for accurate bias compensation. The dense GCPs were categorized based on their respective zones within the Kompsat-3A image space. Subsequent error analysis was conducted for various experimental cases, ranging from GCPs occupying only one zone to those covering all nine zones. As the number of zones increased, the errors in bias modeling decreased, reaching a stabilization point observed in the cases involving three zones. These empirical findings lead to the conclusion that for reliable RPC bias compensation, especially when aiming for high accuracy with an RMSE of one pixel, GCPs covering at least four or five zones should be utilized. Notably, it was observed that GCPs covering three zones yielded satisfactory results as a minimum requirement. However, their effectiveness was contingent upon their distribution, avoiding a straightforward zoning pattern.  
Conceptualization, J.O. and C.L.; methodology, J.O. and C.L.; software, H.J., J.O. and C.L.; validation, H.J., J.O. and C.L.; formal analysis, J.O. and C.L.; investigation, H.J., J.O. and C.L.; resources, J.O. and C.L.; data curation, H.J., J.O. and C.L.; writing--original draft preparation, H.J.; writing--review and editing, J.O. and C.L.; visualization, H.J., J.O. and C.L.; supervision, C.L.; project administration, J.O.; funding acquisition, J.O. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Research Foundation of Korea, grant number 2019R11A3A01062109, and the Korea Aerospace Research Institute.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from [Korea Aerospace and Research Institute] and are available with the permission of [Korea Aerospace and Research Institute].  
The authors declare no conflict of interest.
## 1 Introduction
Consequently, under realistic conditions, the performance of the proposed algorithms is verified using test data prepared with a software-hardware-emulated electrical grid testbed. This testbed uses various sample rate meters, including Schweitzer Engineering Laboratories' SEL735, SEL421, SEL734, SEL700 GT, and SEL351S, positioned near and far from an IB-PV array farm.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
###### Abstract  
The challenge in reusing high-impact recorders lies in developing an efficient and accurate failure prediction model under small-sample conditions. To address this issue, this study proposes an IPSO-SVM model. First, the particle swarms in the IPSO algorithm were grouped based on their exploration and exploitation functions, and dynamic inertia weight mechanisms were designed accordingly. The grouping ratio was dynamically adjusted during iterations to enhance optimization performance. Tests using benchmark functions verified that this approach improves convergence accuracy and stability compared to conventional PSO algorithms. Subsequently, the 5-fold cross-validation accuracy of the SVM model was used as the fitness value, and the IPSO algorithm was employed to optimize the penalty and kernel parameters of the SVM model. Trained on high-impact experimental data, the IPSO-SVM model achieved a prediction accuracy of 90.5%, outperforming the PSO-SVM model's 85%. These results demonstrate the potential of the IPSO-SVM model in addressing failure prediction challenges under small-sample conditions.  
fault prediction; high-impact recorder; particle swarm optimization algorithm (PSO); support vector machine (SVM) 2020 IEEEexample:BSTcontrol
## 3 Results and Discussion
### Machine Learning Classification
#### 3.2.3 Feature Extraction Using Local Binary Pattern  
In contrast to the baseline model, a more advanced approach involves extracting features from the brain heatmaps using the Local Binary Pattern (LBP) technique, as depicted in Figure 7. LBP is efficient for summarizing texture information in images, including EEG heatmaps and has been used in EEG classification in different applications [36, 37]. The method involves dividing each heatmap into regions and computing the LBP for each pixel, creating a binary code that captures the relative intensities in a local area. These binary patterns are then aggregated into histograms, each serving as a distinct signature of the activity in a specific brain region. The LBP histograms, with their reduced dimensionality yet rich information content, serve as input fora SVM model designed to classify different states of brain activity.
## 4 Numerical Simulations
### Depth Control Simulations  
Similar to the previous simulation, the AUH was interfered with by a 10 N co-directional force, with a holding time of 4 s and a cycle of 20 s. In practice, the depth control of the AUH is mostly a step response and therefore, the long-span-step and multi-step simulations are performed in this section (Figure 9).  
Figure 8: Simulations of heading control with PID, LADRC, and LADRC-TD; (**a**) heading tracking with the sinusoidal signal; (**b**) long-span-step response; (**c**) multi-step response.  
Similar to the heading control simulation, PID and LADRC demonstrated an overshoot in the multi-step responses. Moreover, LADRC and LADRC-TD exhibit small fluctuations, while PID fluctuates wildly under disturbance. Although the response speed of LADRC-TD is slightly lower, it shows no overshoot.  
Because of the limitation of the simulations, the accuracy advantage of the LADRC-TD algorithm in the heading control and depth control cannot be reflected when compared with other algorithms. However, the simulations demonstrate that the anti-interference performance of LADRC-TD and LADRC is superior to PID in heading control and depth control. Moreover, LADRC-TD performs better than LADRC under the step response, as LADRC-TD shows no overshoot phenomenon. Therefore, the LADRC-TD algorithm is employed by the AUH for depth control and heading control in practical applications. In the next section, the corresponding steady-state accuracy of each algorithm can be obtained by virtue of the pool experiments.
## 3 Results and Discussion
### Ambient Concentrations  
The seasonal PM\({}_{2.5}\) was collected to determine the contribution of causes to resulting PM\({}_{2.5}\) in the subject area of the present study. A total of 160 chemical components, comprising PM\({}_{2.5}\), OC, EC, 6 ionic components, 23 PAHs, 16 kinds of hopanes and steranes, 33 kinds of alkanes, 6 kinds of cyclo-alkanes, 33 kinds of alkanoic acids, 8 kinds of benzene carboxylic acids, 8 kinds of alkanoic diacids, levoglucosan, cholesterol and 20 kinds of the other chemical components, were analyzed (Table 4). Annual average concentration of PM\({}_{2.5}\) in the subject area of the present study was 25.44 \(\mu\)g/m\({}^{3}\), whereas those in residential area and on roads were 19.07 \(\mu\)g/m\({}^{3}\) and 31.81 \(\mu\)g/m\({}^{3}\), respectively. Seasonal PM\({}_{2.5}\) in the subject area appeared as in the following order of summer (22.71 \(\mu\)g/m\({}^{3}\)) \(>\) autumn (18.59 \(\mu\)g/m\({}^{3}\)) \(\geq\) winter (18.50 \(\mu\)g/m\({}^{3}\)) \(>\) spring (16.47 \(\mu\)g/m\({}^{3}\)), whereas it appeared on roads as in the following order of winter (62.79 \(\mu\)g/m\({}^{3}\)) \(>\) spring (29.27 \(\mu\)g/m\({}^{3}\)) \(>\) summer (23.17 \(\mu\)g/m\({}^{3}\)) \(>\) autumn (12.02 \(\mu\)g/m\({}^{3}\)). Annual average concentration of OC and EC in the subject area of the present study appeared as 6.37 \(\mu\)g/m\({}^{3}\) and 1.50 \(\mu\)g/m\({}^{3}\), respectively. The annual average concentration of OC and EC in the residential area were 4.99 \(\mu\)g/m\({}^{3}\) and 1.10 \(\mu\)g/m\({}^{3}\), respectively, whereas those on roads appeared as 7.75 \(\mu\)g/m\({}^{3}\) and 1.91 \(\mu\)g/m\({}^{3}\). In general, EC refers to the primary particles emitted from the biomass burning, coal and diesel oil, etc. [7; 9]. On the contrary, OC is classified into the primary organic carbon (POC) and secondary organic carbon (SOC) according to respective processes of creation [8]. Thereby, the ratio of OC to EC as well as EC tracer method can be employed for the prediction of the ratio of SOC in OC [50]. According to the EC tracer method, the ratio of OC/EC over 2.5 is generally known that it contributes largely to the creation of the secondary OC. The ratio of annual average OC/EC appeared in residential area as 5.35, while it appeared in roads as 4.24; this suggests comparatively higher content of SOC therein.
## 2 Materials and Methods
### On-Chain and Off-Chain Data Management
For example, in the case of the proposal for traceability systems discussed in this paper, smart contracts specify which data should be stored on-chain, such as batch hashes and identifiers, while allowing larger payloads or user data to be stored off-chain [46]. This selective approach helps maintain the integrity and security of essential data while alleviating the burden on the blockchain itself, while facilitating seamless interactions with off-chain storage solutions, such as IPFS or traditional databases [47]. When data are stored off-chain, the blockchain can maintain a cryptographic hash of that data on-chain for the detection of any changes to the off-chain data by comparing hashes, thereby ensuring that the data remains trustworthy and tamper-proof [48]. However, one significant concern of off-chain data storage is the potential for trust and security issues. When data are stored off-chain, users must rely on the security and integrity of the off-chain solution, which may not offer the same level of decentralisation and immutability as the blockchain itself, and regulatory and compliance challenges could also be faced. This reliance can create vulnerabilities that need to be carefully managed. Moreover, implementing this model can introduce complexity into the system architecture because developers must ensure that off-chain data are properly bound to the on-chain elements, which can complicate the overall design. There is also the risk of data loss in cases when the off-chain storage solution fails or is compromised, undermining the reliability of the blockchain.