esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Batch Adsorption Performance of EGS@APTES-GT
#### 3.2.2 Adsorbents Isotherms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Models and**} & \multicolumn{3}{c}{**Temperature (K)**} \\  & **Parameters** & **298** & **308** & **318** \\ \hline \multicolumn{5}{c}{As(V) adsorption} \\ \hline \multirow{5}{*}{Langmuir model} & q\({}_{\text{max}}\), (mg g\({}^{-1}\)) & 51.01 \(\pm\) 5.26 & 53.58 \(\pm\) 5.59 & 56.11 \(\pm\) 5.92 \\ \cline{2-5}  & K\({}_{\text{L}}\), (dm\({}^{3}\) mg\({}^{-1}\)) & 1.097 \(\pm\) 0.21 & 1.121 \(\pm\) 0.22 & 1.155 \(\pm\) 0.22 \\ \cline{2-5}  & R\({}_{\text{L}}\) & 0.159 \(\pm\) 0.01 & 0.156 \(\pm\) 0.02 & 0.152 \(\pm\) 0.02 \\ \cline{2-5}  & R\({}^{2}\) & 0.980 & 0.980 & 0.981 \\ \hline \multirow{5}{*}{Freundlich model} & K\({}_{\text{F}_{\text{F}}}\), (mg g\({}^{-1}\)) & 25.17 \(\pm\) 0.13 & 26.92 \(\pm\) 0.14 & 28.89 \(\pm\) 0.16 \\  & (dm\({}^{3}\) mg\({}^{-1}\))\({}^{1/\text{n}}\) & 25.17 \(\pm\) 0.13 & 26.92 \(\pm\) 0.14 & 28.89 \(\pm\) 0.16 \\ \cline{2-5}  & 1/n & 0.593 \(\pm\) 0.02 & 0.602 \(\pm\) 0.02 & 0.610 \(\pm\) 0.02 \\ \cline{2-5}  & R\({}^{2}\) & 0.999 & 0.999 & 0.999 \\ \hline \multirow{5}{*}{Temkin model} & A\({}_{\text{T}}\), (dm\({}^{3}\) g\({}^{-1}\)) & 23.01 \(\pm\) 6.90 & 24.13 \(\pm\) 7.30 & 25.47 \(\pm\) 7.76 \\ \cline{2-5}  & B, (mg g\({}^{-1}\)) & 7.855 \(\pm\) 1.17 & 8.116 \(\pm\) 1.23 & 8.377 \(\pm\) 1.29 \\ \cline{2-5}  & R\({}^{2}\) & 0.900 & 0.896 & 0.894 \\ \hline \multicolumn{5}{c}{Iprodione adsorption} \\ \hline \multirow{5}{*}{Langmuir model} & q\({}_{\text{max}}\), (mg g\({}^{-1}\)) & 94.28 \(\pm\) 7.04 & 103.7 \(\pm\) 8.39 & 114.0 \(\pm\) 9.70 \\ \cline{2-5}  & K\({}_{\text{L}}\), (dm\({}^{3}\) mg\({}^{-1}\)) & 2.087 \(\pm\) 0.43 & 2.012 \(\pm\) 0.43 & 1.963 \(\pm\) 0.43 \\ \cline{2-5}  & R\({}_{\text{L}}\) & 0.047 \(\pm\) 0.03 & 0.048 \(\pm\) 0.03 & 0.049 \(\pm\) 0.03 \\ \cline{2-5}  & R\({}^{2}\) & 0.957 & 0.955 & 0.956 \\ \hline \multirow{5}{*}{Freundlich model} & K\({}_{\text{F}_{\text{F}}}\), (mg g\({}^{-1}\)) & 54.19 \(\pm\) 1.26 & 59.23 \(\pm\) 1.15 & 65.09 \(\pm\) 1.09 \\ \cline{2-5}  & (dm\({}^{3}\) mg\({}^{-1}\))\({}^{1/\text{n}}\) & 54.19 \(\pm\) 1.26 & 59.23 \(\pm\) 1.15 & 65.09 \(\pm\) 1.09 \\ \cline{2-5}  & 1/n & 0.367 \(\pm\) 0.13 & 0.386 \(\pm\) 0.10 & 0.407 \(\pm\) 0.08 \\ \cline{2-5}  & R\({}^{2}\) & 0.988 & 0.992 & 0.994 \\ \hline \multirow{5}{*}{Temkin model} & A\({}_{\text{T}}\), (dm\({}^{3}\) g\({}^{-1}\)) & 40.75 \(\pm\) 10.6 & 42.98 \(\pm\) 13.2 & 43.44 \(\pm\) 14.1 \\ \cline{2-5}  & B, (mg g\({}^{-1}\)) & 16.04 \(\pm\) 1.43 & 17.13 \(\pm\) 1.85 & 18.56 \(\pm\) 2.20 \\ \cline{1-1} \cline{2-5}  & R\({}^{2}\) & 0.962 & 0.945 & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adsorption isotherm parameters (C\({}_{\text{H}[\text{As}(V)]}\) = 4.83 mg dm\({}^{-3}\), C\({}_{\text{II}[\text{prodione}]}\) = 9.81 mg dm\({}^{-3}\), pH 7, \(t\) = 120 min, \(m/V\) = 0.1–1.0 g L\({}^{-1}\)).
## Abbreviations  
* AIoT Artificial Intelligence of Things IoT Internet of Things MQTT Message Queuing Telemetry Transport NTN Non-Terrestrial Network ROV Remotely Operated Underwater Vehicle RTSP Real-Time Streaming Protocol SIM Subscriber Identity Module 3 GPP 3 rd Generation Partnership Project FAO Food and Agriculture Organization Beyond 5G Post-5G/Next-generation 5G MCU Microcontroller Unit ESC Electronic Speed Controller AI Artificial Intelligence AR Augmented Reality VR Virtual Reality SLAM Simultaneous Localization and Mapping
# Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
## 6 Numerical Verification of Predicted MAV Behaviour
### \"Wing Rock\" Oscillation Simulation  
A typical phenomenon encountered when flying at large angles of attack is the so-called \"wing rock\". It is a self-excited phenomenon, which occurs at subcritical and supercritical angles of attack. Despite _\"wing-rock\"_ generally not being dangerous, it is advisable to examine it more thoroughly. Studying the dynamics of this motion was attempted in numerous research work (for example: [10, 55, 56, 57, 58, 59, 60]. All of the quoted work adopted quasi-static aerodynamic aircraft characteristics or presented different identification methods. However, due to the oscillatory changes in the angle of attack occurring at high, near-critical and supercritical angles of attack, the occurrence of phenomena associated with flow non-stationarity, including hysteresis, is obvious. _\"Wing rock\"_ oscillation simulations were conducted based on aerodynamic derivatives obtained during water tunnel testing and identified through a pulse function.  
Examples of simulation results are shown in Figure 9. The motion disturbance involved elevator displacement from \(8^{\circ}\) to \(7.8^{\circ}\). This caused MAV displacement into an area of unstable steady states (Hopf bifurcation). The outcomes were gradually increasing oscillations, turning into a limit cycle, with a period of about 0.2 s.  
Characteristic oscillations appeared as a result of the micro aerial vehicle moving into the area of unstable steady states. They primarily involve rocking of the MAV from wing to wing. That rocking has an amplitude of approximately \(39^{\circ}\) (for the banking angle), and \(6^{\circ}\) for the angle of attack and slip. The flight velocity is practically constant. Whereas the  
Figure 9: Simulation of _Wing-rock_ oscillations; waveforms of selected flight parameters.  
period of oscillations corresponding to MAV pitch angle change is equal to the period of phugoid motions. These oscillations are of limit cycle vibration character.
# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Proposed Network Architecture
By combining features from a small, focused region of the feature map, this layer refines and distills the information extracted by previous layers, resulting in a more robust and concentrated representation of the most critical features. In the final stage of the network, a convolutional layer with a \(1\times 1\) kernel size is employed, followed by a hyperbolic tangent (Tanh) activation function. The Tanh activation function is chosen for its ability to provide a more consistent gradient flow, which helps mitigate the vanishing gradient problem. This, in turn, enables faster and more stable convergence during the training process, ultimately leading to improved network performance. The proposed network adopts a residual learning approach, which involves adding a skip connection that directly links the input reconstructed frames to the final extracted residuals. This connection enables the gradients to flow unimpeded, facilitating the smooth transmission of information from earlier layers to later ones, and thereby promoting more effective learning and representation. The relationship between the input data, consisting of the reconstructed frame and QP map, and the output, which is the enhanced frame, is formally defined as Equation (4). \[\mathrm{Y}=\mathrm{Z}_{\Theta}(\hat{\mathrm{X}}(||)\mathrm{QP}_{\mathrm{map}} )\oplus\hat{\mathrm{X}} \tag{4}\]  
where \(\hat{\mathrm{X}}\) represents the reconstructed input frames, \(\mathrm{Y}\) denotes the enhanced output frame, \(\mathrm{Z}_{\Theta}\) symbolizes the operation performed by the CNN architecture, (\(||\)) means the concatenation operation, and \(\oplus\) symbolizes the element-wise addition.
## 3 Design of Trajectory Tracking Control System
### Design of MPC Controller
The following improvements will be made to the inertia factor: In the early stage of optimization, choosing a more significant factor can enhance the global search ability of the particle and then find the optimal point position faster. In the later stage of the algorithm, a smaller factor is more conducive to improving the convergence speed and local search ability. Therefore, the inertia factor is set as follows in this paper:  
\[\omega_{\rm n}(t)=0.9-0.5(t-MaxIter) \tag{19}\]  
The PSO algorithm is used to optimize the MPC controller. The primary weight coefficients are in the MPC control process. The PSO algorithm is used to optimize and modify the weighting coefficient, and the iterative optimization is used to reduce the ITAE index. Until the iteration number is satisfied, the optimization is stopped in order to realize the purpose of adaptive optimization of the MPC control parameters. Figure 4 shows the process diagram of the PSO algorithm used to implement the optimal design of the MPC parameters. In the particle swarm optimization (PSO) algorithm, the timeliness and the quasi-certainty of the optimization results are ensured. The initial population number N is set as 30. The maximum iteration number MaxIter is 100. The position limit is 0.5-2. The speed limit is \(-\)0.2-0.2. The acceleration constants c1 and c2 are 0.5 and 0.4, respectively. The inertia factor is given in Equation (19).
## 3 Discussion
In R-HJH, the transcription of PSY, which catalyzes the production of the first carotenoid molecule phytoene, was higher than that in wild-type HJH. Several studies reported that the expression level of PSY was related to carotenoid accumulation in citrus [23; 24]. Moreover, the expression of CHYB and CCD7, which play an important role in downstream lycopene biosynthesis, was upregulated in R-HJH compared to wild-type HJH. Previous studies demonstrated that the CHYB gene affected the color of citrus fruit by converting orange \(\beta\)-carotene into yellow \(\beta\)-cryptoxanthin and zeaxanthin through a hydroxylation reaction. It was reported that the CCD gene family was related to the cleavage of carotenoids into apocarotonoids at different double-bond positions [25; 26]. [PERSON] et al. found that four carotenoid biosynthetic pathway genes, 1-deoxy-D-xylulose-5-phosphate synthase (DXS1), deoxyxylulose 5-phosphate reductoisomerase (DXR), geranylgeranyl diphosphate synthase (GGPPS2), and phytoene synthase (PSY1), were highly and positively related to lycopene levels in Cara Cara orange by a combination of metabolomic and transcriptomic analyses [27].Two upstream genes (PSY and \(\zeta\)-carotene desaturase, ZDS) were upregulated and two downstream genes (LCYB and capsanthin-capsorubin synthase, CCS) were downregulated. These genes might be involved in the massive accumulation of lycopene in Hong Anliu sweet orange, consistent with the mechanism regulating lycopene accumulation in Cara Cara orange [28; 29]. Similar results were reported in 'Siam Red Ruby', a new lycopene-accumulating pumpmelo variety in Thailand. Gene expression results showed that increases in the expression of upstream genes (CitPSY, CitPDS, CitZDS, CitZISO, and CitCRTSIO) and decreases in the expression of downstream genes (CitLCYB1, CitLCYb2, CitLCYe, CitHYb and CitHYe) were the main mechanisms controlling lycopene accumulation in the pulp of 'Siam Red Ruby' [30].
## Design and Implementation of a Hybrid Ontological-Relational Data Repository for SIEM Systems  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Laboratory of Computer Security Problems, St.Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 th Liniya, Saint-Petersburg, Russia;  
E-Mails: [EMAIL_ADDRESS] (O.P.); [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS] (I.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +7-812-328-2642; Fax: +7-812-328-4450.  
_Received: 26 April 2013; in revised form: 10 June 2013 / Accepted: 17 June 2013 / Published: 9 July 2013_  
**Abstract:** The technology of Security Information and Event Management (SIEM) becomes one of the most important research applications in the area of computer network security. The overall functionality of SIEM systems depends largely on the quality of solutions implemented at the data storage level, which is purposed for the representation of heterogeneous security events, their storage in the data repository, and the extraction of relevant data for analytical modules of SIEM systems. The paper discusses the key issues of design and implementation of a hybrid SIEM data repository, which combines relational and ontological data representations. Based on the analysis of existing SIEM systems and standards, the ontological approach is chosen as a core component of the repository, and an example of the ontological data model for vulnerabilities representation is outlined. The hybrid architecture of the repository is proposed for implementation in SIEM systems. Since the most of works on the repositories of SIEM systems is based on the relational data model, the paper focuses mainly on the ontological part of the hybrid approach. To test the repository we used the data model intended for attack modeling and security evaluation, which includes both ontological and relational dimensions.  
**Keywords:** ontology; security information and event management; data model; data representation; logical inference; repository
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## Materials and Methods  
### Experimental Setup  
Figure 1 shows the layout of our laser speckle imaging. The details of the experimental setup are described in our previous publication [35]. The light source was a 640 nm single-frequency laser with 25 mW. The laser intensity illuminated on the sample can be controlled by the half-wave plate (HWP) and polarizing beam-splitter (PBS). A cylindrical lens was used to focus the light in 1-D and generate an illumination line. A 1-D galvo mirror was used to scan the illumination line across the sample. The backscattered photons from the sample were de-scanned by the same galvo mirror and captured by the camera (Andor, Zyla 5.5, scientific CMOS). The camera supports both global shutter and rolling shutter. It has two operation modes. One is the traditional 2D camera mode for surface illumination laser speckle contrast imaging (SI-LSCI) without the cylindrical lens in the light path, and the galvo mirror is set at a stationary position without scanning. The other is operated as the line camera for the line scan laser speckle contrast imaging. Additionally, based on the current setup, we are also able to obtain the morphological images. A white light source (Leica CLS 150 XE) is used to form a uniform illuminated area to obtain the conventional morphological microscope images.  
Particularly, different from previous acquisition modes, the system has been upgraded to three imaging modes. One is surface illumination laser speckle contrast imaging (SI-LSCI). The other two are line scan temporal speckle contrast imaging (LS-TSCI) and line scan spatial speckle contrast imaging (LS-SSCI). As illustrated in the black dashed box in Figure 1, in LS-TSCI mode, the galvo mirror is driven by a periodic stair-step waveform; in LS-SSCI mode, the galvo mirror is driven by a periodic sawtooth wave signal, which varies linearly with time in each frame period. Both in LS-TSCI and LS-SSCI mode, the sensing element of the camera is conjugated to the illumination line. In LS-TSCI mode, assuming that the number of steps per frame is 100 and the width of each step is 15 with the exposure time 1 ms, 15 consecutive raw speckle intensity data can be used to calculate one temporal speckle contrast. Thus, it will take 1500 ms to acquire one speckle image. In LS-SSCI mode, assuming that the number of lines per frame is 100 and the integral time of line camera is 1 ms, it will take 100 ms to obtain one speckle image. In the experiments, a data acquisition board (NI, PCI-6115) was used to produce both the trigger signals and the driving waveform of the galvo mirror to synchronize the galvo mirror to the camera. We designed a homemade LabVIEW program for hardware control and image acquisition.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 4 Establishment of LTE-R Time Synchronization SPN and MC Models
### SPN Model of LTE-R Time Synchronization Process
If they are illegal, this indicates that the attacker launched a delay attack in the synchronization process, causing the synchronization process to fail, and the token reaches P\({}_{15}\) and enters the next cycle.
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 1 Introduction
* We modify the FUSegNet model [4], originally developed for wound segmentation, for teeth instance segmentation by introducing grid-based attention gates in skip connections. * We extend our approach to find horizontal bounding boxes (HBB), oriented bounding boxes (OBB), and missing teeth detection. * We generate the OBB coordinates for each of the teeth in the DNS dataset [5], and these coordinates are available in our GitHub repository, as referenced in the _Data Availability Statement_. * We provide comprehensive experimental findings, including a comparison with the state of the art, and an in-depth examination of our technique with a comprehensive ablation study to show the efficacy of our approach. To highlight the motivation, approach, and impact of this study, we present a structured summary in Figure 1.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction
### Related Works
#### 1.2.3 Correlation Processes  
The survey by [PERSON] et al. [3] is one of the most referenced works on alert correlation techniques. They reviewed several works, and they summarized the alert correlation processes to propose a process model that comprised (1) alert processing, (2) alert reduction, (3) alert correlation, and (4) alert prioritization. Each of these process blocks expanded to other subprocesses. For example, preprocessing constituted normalization and feature construction, while alert reduction was made up of filtering and reduction. The issue with correlation processes is that until now, there is no standard approach, as researchers tend to follow a process that is convenient and proper for the problem they are trying to solve, and usually, the approach is targeted at a specific part of the entire process. The process model by [5] is supposed to address the end-to-end process; hence, it is more elaborate with up to 10 steps. However, most, if not all, of the steps in that process are covered by the consolidated model by [PERSON] et al. Most recently, [PERSON]. [20] proposed a process model that was specific for the identification of multistep attacks. This process model had three main steps (1) alert clustering (2) context supplementation, and (3) attack interconnection. This process model assumed all other necessary and preceding steps such as feature selection, normalization, and fusion had already occurred.
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 4 Experimental Result
### Baseline  
In this study, the performances of the models for bug report priority prediction were compared with the following baseline models:  
Naive Bayes (NB) [15]: NB is a conditional probability-based classification method that calculates the probability that a feature of the data belongs to each class (label). This simplifies the probability calculations based on the assumption that the data features are mutually independent. The model determines the probability of belonging to a feature class relative to a class-wide probability distribution.  
Decision Tree [16]: A Decision Tree model classifies and predicts datasets using rules in the data. The model determines the target through questions in a series of filtering processes.  
Random Forest [16]: This creates a Decision Tree and randomly selects and repeats only certain features that form multiple Decision Trees. Subsequently, the most frequently generated value is selected as the final predicted value based on the predicted values from several Decision Trees.  
Support Vector Machine (SVM) [15]: This model converts the data into higher dimensions using nonlinear mapping. This method determines a linear separation that optimally separates the hyperplanes in a new dimension and determines the optimal decision boundary.  
CNN [4]: A CNN extracts data features through a convolutional layer. This creates a feature map with the extracted features. Max pooling is performed by inputting this into the pooling layer. Through this process, the largest value can be extracted from the feature map. Finally, data classification is performed based on the characteristics of the networks connected through a fully connected (FC) layer.  
LSTM [7]: The LSTM comprises a structure in which the internal layers exchange information. It is also a Long-Term/Short-Term Memory model that predicts results by considering past data.  
CNN-LSTM [17]: CNN-LSTM uses the results of the CNN as the LSTM layer input. The LSTM layer performs repetitive operations on the input data. This model learns the relationship between time points in the data after receiving feedback from the previous data.
# Shear Strength Improvement of Clay Soil Stabilized by Coffee Husk Ash
## 2 Materials and Methods
### Coffee Husk Ash  
Indonesia holds an important place as the fourth biggest coffee exporter in the world, behind Brazil, Colombia, and Vietnam, supplying 11% (600,000 tons) of the world's coffee each year [43]. The primary production areas are Sumatra, Java, and Sulawesi islands, which differ in terms of processing methods and variety of production [44]. Aceh Province, located on Sumatra Island, is one of the coffee-growing provinces in Indonesia, which accounts for 28.23% of national coffee production [43]. Additionally, around 80% of all coffee planted in Aceh, Indonesia, is in Takengon, Central Aceh [44]. Takengon is well-known for the Gayo Mountain Coffee, which is grown on the hills at an altitude range of 1100-1300 m above sea level [44].  
The coffee husk is considered an agricultural waste product of coffee production [45; 46; 47; 48]. The coffee husk employed in this study was obtained from farms and factories around the Takengon area and then burned for 3-4 h to obtain the ash. Subsequently, the dried CHA was ground powder and sieved using a 2 mm sieve to remove suspended solids. Potassium oxide, silica, iron oxide, and phosphor pentoxide are major components in the mineral compositions of CHA (Table 3). Prior studies have documented a similar range of percentages for CHA's chemical composition [22; 37; 49]. Typical CHA micromorphology is shown in Figure 3 by using SEM and TEM images. The texture of CHA is corrugated, and the grains are jagged, crushed, modular, and rough, which is suitable as a stabilizing agent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Symbol** & **Value** \\ \hline Liquid Limit (\%) & LL & 70.90 \\ Plastic Limit (\%) & PL & 27.77 \\ Plasticity Index (\%) & PI & 43.13 \\ Soil Classification: & & \\ USCS & & CH \\ AASHTO & & A-7-6 \\ Maximum Dry Density (kg/m\({}^{3}\)) & MDD & 1220 \\ Optimum Moisture Content (\%) & OMC & 36.3 \\ Specific Gravity & Gs & 2.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical characteristics of untreated clay.  
Figure 2: Grain size distribution of the untreated clay.