esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis  
### Verification of the Numerical Simulation Results  
Reflecting the imaging log data for the 5182-5187 m section of Well GS10, the numerical model was designed to explore three situations: one without the influence of cracks and solution cavities, another considering only the presence of cracks, and the third one encompassing both cracks and solution cavities. In the latter two situations, the cracks and solution cavities were determined based on the imaging logging chart, as depicted in Figure 3. Two cracks with a dip angle of 19.81\({}^{\circ}\) were designed to cross the well in the model. The width of one crack was 10 mm, while the width of the other was 3 mm. Ten solution cavities were designed around the well, with diameters ranging from 3 mm to 20 mm and an average value of 10 mm. The third case was consistent with the actual situation on the site. Following the methodology mentioned in Section 3, the equivalent drilling fluid densities of collapse pressure (\(\rho_{\rm c}\)) were calculated for these varied conditions, as shown in Table 4. Notably, \(\rho_{\rm c}\) considering the impacts of both cracks and solution cavities increases by 6.42% compared to ignoring cracks and solution cavities. Furthermore, \(\rho_{\rm c}\) considering both cracks and solution cavities increases by 1.75% compared with the value for only considering cracks, and \(\rho_{\rm c}\) considering only cracks increases by 4.59% compared with the value for ignoring cracks and solution cavities. This indicates that cracks play a more significant role than solution cavities in affecting the wellbore stability.  
From a practical standpoint, as discussed in Section 3.1, the initial employment of an oil-based drilling fluid with a density of 1.14 g/cm\({}^{3}\), which overlooked the effects of cracks and solution cavities, resulted in significant wellbore collapse. Adjusting the drilling fluid density to 1.24 g/cm\({}^{3}\) subsequently alleviated this problem. This practical situation corresponds well with our simulation findings in Table 4, reinforcing the validity of our numerical approach. By accounting for both cracks and solution cavities, this method offers a more accurate and dependable means for assessing wellbore stability in the Dengying Formation carbonate reservoir in the Gaoshiti-Moxi area of the Sichuan Basin.  
Figure 10: Geomechanical parameters of the 5170–5190 m section of Well GS10 obtained from well logging interpretation.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 4 Results
Furthermore, there is a significant reduction in energy consumption as only 643 bytes needed to be transferred compared to the full firmware image size of 108 kBytes required for the update, a data reduction of 99.42%. Additionally, our method aligns with the objectives of [PERSON] [6],  
Figure 21: Time diagram of the firmware update process during runtime. where it aims to simplify the firmware update process without resorting to blockchain while maintaining update integrity and security. Finally, concerning the study by [PERSON] [23], our method eliminates the use of a bootloader, allowing firmware updates during runtime, further accelerating the process. Table 4 provides a comparative analysis between the different methods discussed in related works and the proposed method. Our method stands out for maintaining the device class type and substantially reducing downtime, primarily due to the Self-Programming-based update method during runtime, eliminating the need for rebooting after the update. Multicast distribution-based methods are advantageous for mass updates but have disadvantages such as high transmission time and the need for device reboot after updating. Packet transmission methods resulting from the difference between the original and updated firmware image (Delta Update) optimize transmission time, but reconstructing the firmware image from deltas results in high downtime for high-availability systems and requires more resources, especially memory and computational effort. Regarding energy efficiency, the proposed method stands out significantly. This efficiency gain is due to the fact that only the blocks of program code that need to be modified or added are transmitted. This metric aligns with Delta Update methods, which are noticeably more efficient than those requiring the entire firmware image to be updated. From the performance evaluation trials of the proposed system (Table 3), the update process achieved 100% efficiency. [PERSON] et al. [5] also reported 100% efficiency in network scenarios with more than 23 gateways. In terms of scalability, the proposed method does not have the capacity to handle the simultaneous update of thousands or millions of devices, in contrast to the analyzed methods based on multicast transmission topologies.
# Several Aspects of Application of Nanodiamonds as Reinforcements for Metal Matrix Composites
## 2 Materials and Methods  
Commercially available detonation nanodiamonds (produced by Electrokhimpribor, Russia), in which the diamond component was 95% \(\pm\) 2%, were used in this research. The average size of ND particles was 4 to 6 nm (Figure 1a-c). Part of the nanoparticles had defects in their crystal structure in the form of various twins (Figure 1b,c). Primary nanodiamond particles were consolidated into agglomerates (Figure 1d); for the present study, the agglomerates' sizes ranged from 20 microns to 200 microns; the average size of the agglomerates was 50 microns.  
Commercially available metals for the matrix were aluminium and copper. Metal matrix composites were fabricated by mechanical alloying (ball milling) [27; 28; 29] in planetary mill Retch 400. The diameter of the balls was 12 mm. The \"weight of balls/weight of treated materials\" rate was 10. Mechanical alloying was executed in closed milling jars with a volume of 500 mL in argon atmosphere during 6-10 h (the stops for cooling were not calculated as milling time).  
For the annealing of nanodiamonds, the quantity of investigated material for each experiment was 25-30 g. Annealing was done using the crucible in vacuum furnaces at a residual pressure of 10\({}^{-5}\) millibar. Before evacuating, the furnace was filled with argon, i.e., the residual atmosphere was mainly argon. Then, the annealing temperature was raised at 20 degrees/min to 1200-1300 \({}^{\circ}\)C. The holding period of 1 h was followed by a cool-down to 800 \({}^{\circ}\)C at a 20 degrees/min rate, and the heating was then turned off. Samples were removed from the furnace after completely cooling.  
The materials were studied using the scanning electron microscope Helios Nanolab 600i, transmission electron microscopes JEOL JEM 2100 F/Cs, JEOL JEM 2100, FEI TITAN 80-300 with EELS (electron energy loss spectroscopy), X-ray diffractometer DRON-3, and the differential scanning calorimeter Netzsch DSC 404C.
## 4 Experiments and Results
### Setup for Collecting Our Custom-Made Driving Database  
Since our goal is to determine the driver's drowsiness using two drowsiness-related parameters, _i.e._, head nodding and eye-blink, we need a driving database that should be collected from a moving vehicle environment. However, it is not easy to collect such data simply because it could be very dangerous to manage sleepy drivers in the moving car. One possible solution is to let the subject drivers to simulate a drowsy driver in the vehicle according to the certain scenarios that may include a few drowsiness-related behaviors.  
Two scenarios are prepared: the normal driving and the drowsy driving. For the former, the subject is supposed to look at the side and rear mirrors sporadically with a normal attentive forward viewing and, yet, to drive with natural blinking. For the latter, the subject blinks often, like a drowsy driver.  
During the whole recording session, each subject wears a gyro sensor module mounted in a white box that is again attached to a black headband as shown to the left of Figure 8. The sensor is responsible to measure 3D head movement of the driver, and the collected data are sent on areal-time basis to the receiver, which connects to a notebook PC within the vehicle. This setup allows each subject to focus only on a given scenario, resulting in improved usability compared to the previous studies. We have collected two streams of video concurrently using two cameras: one for the color image and the other for black and white image. The former is used for the basis, whereas the latter works with an IR (infra-red) LED panel to dealing with the night driving. Each video was collected by synchronizing with the gyro-sensor. In building the DB, all videos are recorded with a 320 \(\times\) 240 image size, 200 frames, and two different lighting conditions. On the right of Figure 8, the whole setup is installed in front of the driver in the vehicle. Figure 9 depicts four images taken during the database-building process. Table 3 shows the head pose ranges for pitch, roll, and yaw, respectively. Notice that the yaw angle is  
\begin{table}
\begin{tabular}{c c c} \hline
**Face Model** & **MAE** & **RMSE** \\ \hline DB-ASM & 8.42288 & 11.08351 \\ AAM & 11.51482 & 13.04523 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of fitting performance between AAM (Active Appearance Model)and DB-ASM (Discriminative Bayesian-Active Shape Model).  
Figure 7: Robust fitting of DB-ASM against several occlusion cases.  
greater than other two head rotations because drivers mainly looking out the front window or at the two side mirrors while driving [20].  
Our computing setup for analysis consists of i7 CPU with 4 GB main memory, and the software package includes visual studio 2010, OpenCV, and OpenMP. The face tracker based on the present DB-ASM runs at 16-20 frames/second, in the real-time basis.
# Cartesian Aerial Manipulator with Compliant Arm
## 2 System Description
### Hardware Architecture  
The components and architecture of the Cartesian aerial manipulator are represented in Figure 6. The aerial platform consists of the S550 hexa-rotor integrating the NAVIO autopilot and a Raspberry Pi 3B+ board running the PX4 firmware [36] as well as the control program of the manipulator, developed in C/C++. This program is built around the Task Manager class [2], which implements the different functionalities used in the experiments: go to rest position, go to operation position, teleoperation, force control and monitoring, and Cartesian base calibration. The manipulator is interfaced through a USB-to-USART (Universal Synchronous-Asynchronous Receiver Transmitter) device connecting the Raspberry Pi board with the STM32F30S8 Nucleo microcontroller that takes care of the low level control of the XY-axes' micro motors and reads the measurements from the three sensors:  
* The two potentiometers used to estimate the displacement of the linear base.
* The magnetic encoder attached to the servo that measures the joint deflection.
* The linear potentiometer in the link used to estimate the pushing/pulling force.  
The microcontroller generates as output the PWM (Pulse Width Modulation) signals taken as input by the DRV 8833 dual H-bridge that controls the micro motors (Section 4.1), providing another PWM signal to control a gripper servo. The Herkulex servo of the compliant joint is controlled in position through a dedicated USART interface [7; 26]. The control references generated by the Task Manager and the feedback measurements collected by the microcontroller are sent at 100 Hz through the USART as data packets codified as C-type data structures.  
Figure 5: Magnetic gripper supported by the compliant linear joint (**left**). Compliant joint actuator (**right**).  
The Cartesian aerial manipulator is operated from the Ground Control Station (GCS) through a wireless link, typically a Wi-Fi network, that supports the SSH (Secure Shell) session used to launch and manage remotely the aerial manipulator control programs. The commands introduced by the operator to select the particular task to be executed or to tele-operate the manipulator using a joystick are sent as C-type data packets through UDP (User Datagram Protocol) sockets.
###### Abstract  
We present the results of silicon carbide (SiC) thin film synthesis on Si(111) substrates using chemical vapor deposition by decomposing CH\({}_{4}\) in H\({}_{2}\) at 1135 \({}^{\circ}\)C. The experiments were conducted in an Oxford Nanofab Plasmalab System 100 for carbon phase deposition times of 3, 5, 20, 60, and 90 min on Si(111) with or without native oxide, following established protocols. Our studies show that either predominantly crystalline SiC or a mixture of SiC SiC and Si-O/Si-O-C glass forms on Si substrates significantly doped with carbon and oxygen, depending on the presence or absence of native oxide. The thickness of the SiC film ranges from approximately 5-6 nm for films synthesized in 3 min to over 15 nm for those synthesized in 90 min, while the size of the crystal grains varies from a few to 110 nm depending on the synthesis duration. The findings suggest that the complex composition of the thin films and the region beneath them can more effectively compensate for the differences in lattice parameters and thermal expansion coefficients between the SiC film and the Si substrate; thus, this method is promising for depositing intermediate thin films of SiC on Si substrates.  
silicon carbide; chemical vapor deposition; X-ray diffraction; X-ray photoelectron spectroscopy; transmission electron microscopy Article
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 2 Problem Description and Related Work
### Multi-Objective VMP Optimization  
Multi-objective optimization (MOO) offers a framework for solving the VMPP by evaluating competing objectives simultaneously. Techniques such as genetic algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) have been applied to VMPP, each with strengths and limitations.  
GAs are commonly used for multi-objective VMP, evolving solution populations over generations [28; 29; 30; 31; 32]. However, GAs may suffer from slow convergence, especially in large solution spaces, and face challenges with maintaining population diversity, leading to premature convergence. PSO, inspired by social behaviors in nature, has been used to minimize energy consumption and optimize resource utilization [33; 34]. However, PSO can become trapped in local optima and is sensitive to parameter tuning. ACO, which simulates ant foraging behavior, has also been applied to VMPP, using pheromone trails to guide efficient placements [35; 36; 37]. However, ACO may struggle with balancing exploration and exploitation, leading to significant computational overhead in large environments. Hybrid approaches combining GAs with local search methods or PSO with heuristics have also been explored to overcome these challenges [38; 39; 30]. While these hybrid methods can improve solution quality, they may introduce additional complexity in implementation and tuning, and may struggle with real-time adaptation to dynamic environments.
## 5 Application of Marine Accident Occurrence Prediction Model and Results  
### Characteristics of Data U Set  
First, the characteristics of the Data U dataset were analyzed. In the original Data U set, the group in which related accidents occurred most had a travel distance of less than 50 km, accounting for 78.38%. Ships with a gross tonnage of 500 tons or more accounted for 43.24% of this group, indicating a relatively high presence of larger vessels. Moreover, approximately 67.57% of the vessels in this group had an annual sailing frequency of over 1600, indicating a correlation between higher sailing frequency and increased occurrence of MTSA-related accidents. The group where MTSA-related accidents occurred accounted for 37 cases (3.66%) within the original Data U set, which was relatively lower than the group where accidents did not occur. Table 7 illustrates the distribution of the independent variables after applying SMOTE. Using this SMOTE oversampling technique, the imbalance in the distribution of the original Data U set was effectively addressed. The newly  
Figure 9: Illustration of SMOTE process and distribution of the classes. generated dataset maintained the characteristics of the original dataset because there were no significant changes in the distribution.
## 3 THz Wave Modulation and Devices Based on ATR  
As mentioned before, amplitude and polarization modulation are quite important in THz spectroscopy and imaging. THz wave modulation through ATR geometry is proven feasible. Compared to the methods such as based on semiconductors, dielectric gratings, birefringent slides, metasurfaces, metamaterials, it is easy to fabricate and achieve high performance or multiple modulation [23,24]. In the following part, these emerging modulation methods and devices based on ATR are introduced in detail.
## 2 Materials and Methods
### Functional Region of Interest (Functional-ROI)  
Once DOT and fMRI data were preprocessed and normalized to standard MNI space, functional-ROI was calculated. This calculation is based on the assumption that brain regions involved in a particular function tend to exhibit similar temporal fluctuations in neural activity [15]. This implies that neighboring voxels within a brain region should exhibit similar patterns of activity over time. Therefore, it provides information about the coherence of neural activity within a specific brain region. The time series for each voxel within the DOT volume is obtained by extracting the hemodynamic (BOLD, HbO and HbR) signals, which reflects hemodynamic changes related to neural activity. Thus, for a voxel at time \(\mathrm{V_{i}}=1,...,n\), the rank R(t) is defined as:  
\[\mathrm{R(t)}\ =\ \frac{\sum_{i=\ 1}^{n}\left(\mathrm{V_{i}}\right)^{2}-n( \mathrm{V_{i}})^{2}}{\frac{1}{12}\mathrm{C}^{2}(n^{3}-n)} \tag{1}\]  
where C is the number of neighbors (7 voxels) of the voxel, and \(n_{i}\) is the mean across its neighbors at the ith time point. Then, Kendall's coefficient is used to measure the similarity of the time series among neighboring voxels within the ROI. The Kendall coefficient of concordance (KCC) is a statistical measure which provides a measure of how well the time-series data within a cluster of neighboring voxels are synchronized or correlated. In other words, KCC helps assess the homogeneity or regularity of neural activity within a particular region of the brain [16]. A KCC value of 0 indicates no concordance among the time series of neighboring voxels. This implies a lack of synchronization in neural activity in that region, suggesting that the activity patterns are dissimilar and irregular. Meanwhile, a KCC value of 1 indicates perfect concordance among the time series in the neighboring voxels. This signifies that the time-series data are highly synchronized, indicating a highly regular and coordinated pattern of neural activity in that brain region. Finally, the obtained KCC values are then transformed into Z-scores to allow for statistical comparisons based on one sample t-test (family-wise error rate; FWER threshold \(p<0.001\)). As a result, the statistical maps of the group analysis, which spatially represent the synchronization of HbO, HbR, and BOLD signals within the reconstructed DOT volume, serve as functional-ROIs during the general linear model (GLM) calculation.
# Test Center Location Problem: A Bi-Objective Model and Algorithms
## 5 Simulation Results
### Comparison Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline _(n,m,k)_ & **\#T** & **\#G** & _scm (T,G)_ & _scm (G,T)_ & **\(\alpha\beta\) (T,G)** & **\(\alpha\beta\) (G,T)** & **t (T)** & **t (G)** \\ \hline (40,20,5) & 4 & 4 & 0 & 0 & (0,0) & (0,0) & 3.81 & 14.48 \\ (40,20,8) & 8 & 7 & 0 & 0 & (0,0) & (0,0) & 13.53 & 15.7 \\ (40,20,10) & 10 & 8 & 0.25 & 0 & (0,1.31) & (0,0) & 23.47 & 22.68 \\ (40,20,12) & 7 & 6 & 0 & 0 & (0,0) & (0,0) & 12.47 & 11.38 \\ (100,40,10) & 10 & 8 & 0 & 0 & (0,0) & (0,0) & 135 & 151 \\ (100,40,15) & 8 & 9 & 0 & 0.11 & (0,0) & (2.23,0.21) & 207 & 105 \\ (100,40,20) & 5 & 5 & 0.2 & 0.2 & (3.67,0.85) & (0,0.35) & 237 & 114 \\ (100,40,25) & 8 & 9 & 0.22 & 0.125 & (0,0.12) & (0,0.12) & 216 & 88.9 \\ (200,20,5) & 5 & 4 & 0 & 0 & (0,0) & (0,0) & 17.98 & 89.06 \\ (200,20,8) & 10 & 6 & 0 & 0 & (0,0) & (0,0) & 64.68 & 101.9 \\ (200,20,10) & 9 & 7 & 0 & 0 & (0,0) & (0,0) & 96.29 & 109.5 \\ (200,20,12) & 9 & 7 & 0.43 & 0.11 & (0,0.77) & (0,0.24) & 95.76 & 123.5 \\ (500,40,10) & 7 & 6 & 0 & 0 & (0,0) & (0,0) & 277.4 & 1494 \\ (500,40,15) & 6 & 5 & 0.2 & 0.17 & (0,0.35) & (0,0.27) & 576 & 1086 \\ (500,40,20) & 6 & 5 & 0.2 & 0.17 & (0,71,0.29) & (1.39,0.03) & 727 & 1884 \\ (500,40,25) & 7 & 6 & 0.17 & 0.26 & (0,0.14) & (0.78,0.32) & 637 & 1452 \\ \hline (500,100,50) & 8 & 8 & 0 & 0.25 & (0,0) & (1.50,0.14) & 1061 & 12,841 \\ (1000,100,50) & 9 & – & – & – & – & – & 1737 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results for TCLA (denoted by \(T\)) and Gurobi (denoted by \(G\)). Columns #T and #G show the number of obtained solutions by each method. the \(scm\) and \(\alpha\beta\) metrics are shown in the middle columns, and finally, the running time of the algorithms is reported in the last columns. For simplicity, \(\alpha\beta(,. )\) values are represented as percentage (\(\times\)100). TCLP within a reasonable time frame, whereas TCLA demonstrates competent performance and successfully identifies acceptable non-dominated solutions. One way to tackle this issue is by improving the formulation in Equation (5). For example, [PERSON] [13] added some valid inequalities that help efficient branching and pruning in the branch-and-bound algorithms. Unfortunately, such improved formulation works only for unweighted demand points and it is not straightforward to extend this approach to the TCLP presented in this paper. Figure 6: Obtained set by TCLA (blue-color diagram) and Gurobi (black-color diagram) in objective space for small instances. There is no outcome for Gurobi for the last instance.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 3 The Proposed Channel Synthesizer
### Inputs  
In general, we assume that the position of both the UAV and the corresponding ground terminal may change in time and we design the synthesizer so that these positions are required as an input. The conversion from position to time is then straightforward by accounting for the particular velocities of the UAV or the terminal.  
Another input is formed by the fixed position of tree canopies where their center, geometrical shape and dimensions (e.g., radius and height for a cylindrical shape) need to be provided. After that, their surface is sampled for the purposes of PO with samples of dimension below half the wavelength. Further, the volume of the canopies needs to be spatially sampled (voxelized) to enable MST incoherent field calculations. The voxels may be, for example, cubes with sides exceeding half the wavelength as the incoherent field calculations are not very sensitive to this parameter [6]. Regarding the MST calculations, the branches and leaves parameters such as length, radius, complex permittivity, number density, and spatial orientation probability need to be provided as well [4; 5; 6].  
The synthesizer is proposed for an incident plane wave, so the corresponding frequency, amplitude and polarization must be given as an input, as well.