esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Performance Self-Evaluation  
This performance evaluation compares graph processing execution times to determine the optimal partition size for the proposed scheme. The evaluation employed the Single Source Shortest Path (SSSP) algorithm with the sk-2005, twitter7, and soc-LiveJournal1 datasets. Figure 6 presents the execution times for each dataset. The x-axis represents the number of partitions, ranging from 2, 4, 8, 16, to 32. For the sk-2005 dataset, the fastest execution time was achieved with 32 partitions, while the slowest occurred with only 1 partition. For the twitter7 dataset, the fastest execution time was recorded with two partitions, and the slowest with eight partitions. Similarly, for the soc-LiveJournal1 dataset, the fastest execution time was also observed with two partitions, while the slowest occurred with eight partitions. The optimal number of partitions for the proposed scheme depends on both the dataset size and the capacity of the GPU's global memory.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data** & \(|V|\) & \(|E|\) & **Description** \\ \hline soc-LiveJournal1 & 4,847,571 & 68,993,773 & LiveJournal online \\  & & & social network \\ twitter7 & 41,652,230 & 1,468,365,182 & SNAP network: Twitter \\  & & & follower network \\ sk-2005 & 50,636,154 & 1,949,412,601 & 2005 web crawl of \\  & & &.sk domain \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset.  
Figure 6: Graph processing execution time according to the number of partitions.  
This performance evaluation also examines graph processing execution times for each algorithm when applying the operation reduction method. The GPU processing times are compared with and without operation reduction to assess its impact. Figure 7 presents the processing times, including the preprocessing time required to apply the operation reduction method and the resulting reduction in execution times. The evaluation was conducted using the sk-2005 and twitter7 datasets, with the graph modification rate set to 1%. Both preprocessing time and operation reduction time were measured. The x-axis of Figure 7 represents the algorithms being compared, listed in the following order: Single Source Shortest Path (SSSP), Single Source Widest Path (SSWP), Breadth-First Search (BFS), and CC. The y-axis of Figure 8 represents the net benefit time, calculated as the reduced execution time achieved through operation reduction minus the preprocessing time. The results confirm that the time savings from operation reduction significantly outweighed the preprocessing time. This performance evaluation demonstrates the superior efficiency of the proposed operation reduction method.  
Figure 7: Time savings through operation reduction methods.
## 2 Related Work
ACOBF [21] combines ACO with bacterial foraging to enhance [21] energy efficiency and resource utilization; however, it focuses on task-level scheduling without addressing VM placement dynamics and scalability considerations across multi-datacenter environments. AP-ACO [22] enhances ACO convergence and search capabilities via adaptive parameter settings [22] but focuses on traffic-aware placement without renewable factors. Hybrid ACO+Sine Cosine [23] improves exploration and exploitation for VM placement, targeting power consumption and resource wastage; yet, it lacks renewable energy and SLA constraints. Inverted ACO (IACO) [24] improves cloud workflow scheduling, [24] reducing the total execution time and operational cost, but it does not incorporate renewable energy sources or SLA constraints. ETA-ACO [25] optimized both the power consumption of PMs and networking components concurrently using a weighted sum, outperforming several heuristics and metaheuristics. ACOGA [26] combines ACO and GA principles in a communication-aware framework to optimize the total traffic flow (TTF) and reduce active servers. Improved ACO for virtual resource scheduling [27] addresses node distribution challenges but lacks broader environmental and cost objectives, limiting its applicability to green cloud computing. In this paper, we propose a novel scheme that incorporates renewable energy sources (solar) into VM placement through an ACO-based algorithm using solar irradiance data. Our energy-aware algorithm prioritizes minimizing energy consumption, supplemented by bandwidth optimization. We evaluate metrics such as the carbon footprint, energy consumption, and total cost to ensure both environmental and economic benefits. In addition to this, we implement VM migration for server consolidation to improve resource utilization, contributing to an effective and more sustainable cloud computing operation.
## 3 Materials and Methods
### Tests and Measurements Carried Out  
In order to verify the adopted assumptions, the following tests and measurements of the power supply system with an induction generator and a converter system were carried out:  
* on-grid operation: tests in the on-grid mode were aimed at checking the possibility of power control at the grid connection points. The generator control system stabilized the active power value at the grid connection by controlling the torque. The tests were carried out: (1) in a steady state for the maximum active power generated to the grid, without additional load and stabilization of the power value at the connection point (contactor K1 was on and K2 off); and (2) in a transient state during the change in resistive load (contactors K1 and K2 were on, during the measurement the value of the load power was changed by adding more resistors). In on-grid mode, the battery is not used. Power control was carried out at a constant rotational speed and variable torque;
* automatic switching between modes: the tests were aimed at measuring the value of the time of switching between the on-grid and off-grid modes during a power failure in the network (turning off the K1 contactor) and the appearance of voltage in the network (turning on the K1 contactor);
* off-grid operation: (1) measurements of the values of the generated phase voltages and active power at symmetrical and asymmetric resistive loads and non-linear loads; (2) measurements in transient states with changes in the load power values-during the tests, the load power values were changed step by step and the generator power regulation time and battery current were measured. The battery voltage and current limits were also tested.  
Dynamic parameters of voltages and currents waveforms were measured using a Tektronix oscilloscope (e.g., switching and setpoint settling times). Electricity quality parameters (voltage, current and power values) were measured with a Fluke power quality analyzer. For the measurements carried out, the influence of the accuracy of the measuring instruments on the values of the generated voltage, current, and power was checked. Detailed information about the measurement procedures and results are provided in Section 4.
## 2 Flywheel Theory and Structure
### Components of FESS
#### 2.2.1 Flywheel Rotor  
The rotor of the flywheel is a rotating mass \(m\) (kg) with inertia \(J\) (kg\(\cdot\)m\({}^{2}\)) that spins at an angular velocity of \(\omega\) (rad/s) and stores kinetic energy \(E\) (joules) as given by Equation (1).  
\[E=\frac{1}{2}J\omega^{2} \tag{1}\]  
The useable stored kinetic energy of a flywheel spinning between a minimum and maximum velocity of \(\omega\) is given by:  
\[E=\frac{1}{2}J(\omega_{max}^{2}-\omega_{min}^{2}) \tag{2}\]  
where the moment of inertia \(J\) is a function of flywheel geometry and mass. In case of a disc type flywheel with mass \(m\) (kg), density \(\rho\) (kg/m\({}^{3}\)) and length \(l\) (m), the angular velocity can be expressed by Equation (3):  
\[\omega=\frac{2}{r}\sqrt{\frac{E}{\rho\pi l}} \tag{3}\]  
Therefore, the stored energy is expressed by Equation (4) [51].  
\[E=\frac{1}{4}mr^{2}(\omega_{max}^{2}-\omega_{min}^{2}) \tag{4}\]  
In the case of a cylindrical type flywheel with an inner radius \(r_{1}\) and outer radius \(r_{2}\), the stored kinetic energy is given by Equation (5).  
\[E=\frac{1}{4}m(\omega_{max}^{2}-\omega_{min}^{2})\Big{(}r_{2}^{2}-r_{1}^{2} \Big{)} \tag{5}\]
## 3 Results and Discussion
### Compression Tests  
Table 2 shows the average values, depending on the type of reinforcement, for the results obtained in compression testing up to breaking point for all of the pieces tested along with their density (\(\rho\)). The table sets out the breaking strain (\(\sigma_{\text{max}}\)), the average strain for this strength value (\(\epsilon_{\text{max}}\)), the longitudinal modulus of elasticity obtained in the range  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\sigma_{\text{max}}\)* & **E (N/mm2)** & \(\epsilon_{\text{max}}\)* & \(\epsilon_{\text{u}}\)* \\  & **(N/mm2)** & (N/mm2) & **(\%)** & **(\%)** \\ \hline WR & 23.70/1.52 & 5532 & 5.84/0.34 & 67.25/2.78 \\ FC300 & 46.14/3.89 & 7934 & 11.11/0.72 & 19.74/3.95 \\ FB280 & 54.64/4.00 & 6912 & 8.88/0.78 & 25.30/4.95 \\ FB600 & 55.16/4.49 & 7362 & 8.61/0.15 & 21.62/3.37 \\ \hline  & \(\text{A}  
of between 0.15 times and 0.35 times the maximum test load (E), and the ultimate strain obtained in the test (\(\epsilon_{\text{u}}\)). The ductility of the pieces was calculated, taking into consideration the strain and the strain energy density reached in the tests. The ratio between the ultimate strain and the strain under maximum load is a measurement of ductility, which appears as D\({}_{\epsilon}\) in the table. The ratio between the total area enclosed by the strength-strain graph (A\({}_{\text{u}}\)) and the area enclosed by the graph up until the value of the maximum strength (A\({}_{\text{e}}\)) is an indicator of the ductility in terms of strain energy density (D\({}_{\text{A}}\)).  
Figure 3 shows all of the strength-strain graphs obtained in the tests.  
The results obtained in the compression tests (Table 2 and Figure 3) show that all of the reinforced pieces share very similar behavior. Furthermore, there are highly significant differences between the reinforced and unreinforced pieces, both in terms of the shape of the graphs and the values attained.
## 6 Methods
### Vessel Enhancement Filters
#### 6.1.1 Hessian Matrix Computation  
The Hessian matrix serves as a fundamental computational method used in all Hessian-based filters. It calculates the local gradation change by performing the second-order partial derivatives of the input image voxel \(X=(x,y,z)\) in nine directions. It is defined as  
\[H(X)=\begin{bmatrix}f_{xx}&f_{xy}&f_{xz}\\ f_{yx}&f_{yy}&f_{yz}\\ f_{zx}&f_{zy}&f_{zz}\end{bmatrix}=\begin{bmatrix}\frac{\partial^{2}f}{\partial x ^{2}}&\frac{\partial^{2}f}{\partial x\partial y}&\frac{\partial^{2}f}{ \partial x\partial z}\\ \frac{\partial^{2}f}{\partial y\partial x}&\frac{\partial^{2}f}{\partial y^{2} }&\frac{\partial^{2}f}{\partial y\partial z}\\ \frac{\partial^{2}f}{\partial z\partial x}&\frac{\partial^{2}f}{\partial z \partial y}&\frac{\partial^{2}f}{\partial z^{2}}\end{bmatrix} \tag{1}\]  
Eigenanalysis is used to avoid computing derivatives in many directions, and to extract only the principal directions. Let \(e_{1}\) be the eigenvector representing the axial direction, let \(e_{2}\), \(e_{3}\) be the cross-sectional direction of \(H(X)\), and the associated \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) are the eigenvalues. The eigenvalues should meet the following condition: \(|\lambda_{1}|\leq|\lambda_{2}|\leq|\lambda_{3}|\). The vessel centerline voxel should satisfy the following: \(\lambda_{2}\approx\lambda_{3}\ll 0\), \(\lambda_{1}\approx 0\).
## 5 Experiments and Results
### Experimental Settings  
In order to verify the efficiency of the method proposed in this paper, we implemented a data controller based on a Xilinx Virtex-7 FPGA chip (Xilinx, San Jose, CA, USA). The hardware platform is a processing board equipped with a Xilinx XC7 VX690T FPGA (Xilinx, San Jose, CA, USA) and two clusters of Micron MT41K512M16 HA-125 DDR3 SDRAM (Micron Technology, Boise, ID, USA) particles. Each cluster of DDR3 SDRAM has a capacity of 8 GB and a bitwidth of 64 bit. A SAR data matrix with a granularity of 16,384 \(\times\) 16,384 is used, and the data is a complex number of single-precision floating-point numbers (the bit width is 64 bit). Two independent data controllers are implemented in FPGA, and two clusters of DDR3 are connected.  
The experiment was completed in the Vivado 2018.3 software with the Very-High-Speed Integrated Circuit Hardware Description Language (VHDL), and the Xilinx MIG v4.2 IP core (Xilinx, San Jose, CA, USA) was used. The parameters used in the experiment are as follows: the working clock of DDR3 is 800 MHz, the working clock of the data controller is 200 MHz, the working clock of the processing engine is 100 MHz, and the DDR3 burst transmission length is eight.  
The experimental procedure is designed according to the standard CS algorithm. In the first step, the original data are stored in DDR3A by FPGA according to the range direction. The second step is to read data from DDR3A in the azimuth direction, and store them in DDR3B in the azimuth direction after caching. The third step is to read data from DDR3B in the range direction and, after caching, write these data to DDR3A in the range direction. The fourth step is to read data from DDR3A in the azimuth direction. After completing all the above steps, we should compare whether the final read data is the same as the original data, measure the data bandwidth of each step and record it.
# Study on Mechanical Properties of Fractured Sandy Mudstone Based on Triaxial Compression Experiment
## 3 Analysis of Triaxial Test Results
### Effect of Confining Pressure on Crack Propagation Characteristics of Specimens  
The process of ongoing crack growth and buildup that leads to macroscopic fracture is fundamental to rock failure. Based on the mechanical reasons of fractures, cracked and undamaged sandy mudstones are classified into three types to explain their failure patterns under varying confining pressures: Three types of cracks: shear, tensile, and secondary. The tensile crack typically runs parallel to the direction of the maximum principal stress, the shear crack typically runs at a specific angle to it, and the secondary crack, which is related to the degree of stress concentration at the crack tip, typically widens after the main crack forms. Figure 10 displays the fracture development features of several rock specimens.  
The failure mechanisms of entire specimens under various confining pressures are W100-1-5. Figure 10a illustrates that the majority of the specimen's fractures are found on the cylinder's top and bottom surfaces. On the side of the cylinder of specimen W100-1, there were two conjugate shear fractures and two secondary cracks after the specimen's failure under uniaxial compression. The shear fracture and one secondary crack were oriented at a 23\({}^{\circ}\) angle with respect to the specimen's axis. A single shear crack dominates the fracture pattern as confining pressure increases, and the angle between the shear crack and the axial stress is 45 degrees, suggesting a deflection of the maximum shear stress in one direction. Simultaneously, the surrounding secondary cracks almost vanish, suggesting that the presence of confining pressure raises the rock's particle density and the shear fracture tip's initiation stress.  
The failure forms of specimens with fractures under various confining pressures are WL100-1-5. Figure 10b illustrates that the primary failure mode of specimens with cracks is tensile fractures near prefabricated cracks. At the prefabricated fracture tip of the  
Figure 9: M value of rock specimen under different confining pressures.  
WL100-1 specimen, four secondary tensile cracks developed, and the crack propagation route progressively advanced towards the cylinder's top and lower boundary surfaces. In comparison to the intact rock specimens, the WL100-2-5 specimens produced more shear cracks with increasing confining pressure, extending from the specimen boundary to the precast crack and connecting with the tensile crack surrounding the precast crack. This suggests that the rock fracture occurs in a tension-shear mixing mode under varying confining pressures.
## 3 Materials and Methods
### Data Preprocessing and Analysis
#### 3.3.1 Extracting Text and Image Data from Memes  
The first step in data preprocessing involved extracting text and images from memes. To do so, a tool called Optical Character Recognition (OCR), integrated into Python [33] using the _Pytesseract_ package, was used to extract text from memes. Before extracting the text, the quality of the images in memes was enhanced using the _OpenCV_ library. To enhance the quality of memes for text extraction, three key techniques were used. The first one was _bilateral filtering_, inspired by [34], to minimize noise while preserving edges. The second technique was _grayscale conversion_, used to convert image format into grayscale, and the third technique was _thresholding_, used to make the text more distinct from the background. Once the text was extracted, it was saved in a structured format in a folder named \"text_data\", separate from the original memes in both the public and private datasets.  
For extracting images from memes, we resized and standardized the images using techniques from the OpenCV library, including cv2.imread(), cv2.resize(), cv2.cvtColor(), and cv2.bilateralFilter(). After the images were processed, they were saved in a folder called \"image_data\", separate from the original memes in both the public and private datasets. Finally, both the public and private dataset folders each contained two sub-folders: \"text_data\" and \"image_data\". These sub-folders were used for further preprocessing.  
Figure 2: The data extraction and preprocessing pipeline.
# Thermophilic Methane Production from Hydrothermally Pretreated Norway Spruce (_Picea abies_)
## 4 Discussion
### Effect of Sugar Degradation Products  
Higher pretreatment severity leads to higher sugar dissolution which increases the concentration of sugar degradation products such as furfural and HMF which inhibit the AD process [2]. Furfural is formed in the hydrolysate as degradation product from pentose sugars (xylose and arabinose) while HMF is formed from hexose sugars (mannose, glucose, and galactose) [43]. Increase in severity leads to low arabinose concentration in hydrolysate pretreated at 170 \({}^{\circ}\)C due to its assumed conversion to furfural. Since only arabinose and xylose can be converted to furfural as they are pentose sugars, and arabinose has the lowest activation energy in the degradation reaction of arabinose to furfural when compared to that of xylose to furfural a conversion of arabinose is assumed [19].  
Although the concentration of furfural and HMF is within the threshold value (2 g/L for furfural and 0.8 g/L for HMF) for both the samples [44], inhibition could be expected from the synergistic effect of both inhibitors [45]. Although inhibitors are also considered to be more pronounced at thermophilic condition than mesophilic condition [46; 47], this may not be a problem in continuous flow industrial processes since the microorganisms can get acclimatized to toxic compounds over time. The inhibitors are themselves degraded in an adapted culture, but if furfural is present in the HMF, the conversion rates of both decrease significantly and HMF degradation started only with the complete degradation of furfural in batch experiments [48]. Continuous flow experiments are needed to better understand the effects of hydrolysate inhibitors on AD and culture adaptations to such.
## 4 Experiment and Results
### Experiment Design  
#### 4.2.1 Experiment Environment and Strategy  
The experiment was carried out on a PC with a processor of Intel Core (TM) i7-6700, RAM of 32 GB and GPU of NVIDIA GeForce GTX 1080 Ti. In addition, Python 2.7.15 was utilized as programming language on the operating system of Ubuntu 16.04, and structural component detection was performed using the deep-learning framework of Tensorflow-gpu 1.1.0. Moreover, CUDA 8.0 was applied to make GPU acceleration, and OpenCV [54] was used for image pre-processing and image visualization during the process of training and testing.  
When training YOLO in this article, a weight decay of 0.0005 was applied, and 0.9 for the momentum. Moreover, epoch and batch size were manually set to 160 and 32, respectively. The learning rate was initially set to 0.01 since a high initial learning rate made the neural network of YOLO converge faster. This learning rate maintained for 60 epochs and was adjusted to 0.001, and then 0.0001 after 90 epochs. The reason to decrease the learning rate with the process of training is that it is beneficial for fine-tuning of parameters in YOLO.
## 4 Discussion
### Limitations  
The current study has some limitations. First and foremost, the rigid wall assumption constrained the capability of predicting accurate WSS distribution. Several studies have reported that the neglecting of IAs wall elasticity would lead to an overestimation of WSS [63; 64; 65; 66]. However, the quantitative impacts of wall elasticity on WSS distribution is unclear due to the difficulties in obtaining the patient-specific aneurysm wall properties from in vivo measurements. In addition, more samples should be included in future studies to systemically evaluate the impacts of space characteristics of ACoAAs on flow characteristics. Furthermore, the blood was simplified as a Newtonian fluid in the present study.  
The effect of rheological models on flow characteristics in IAs has been discussed in several studies [65]. Even though their impacts remain inconsistent [67; 68; 69], some recent studies showed that the influence of using the Newtonian model on hemodynamics characteristics is small [70; 71].
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 3 Materials and Methods
### Problem Statement
In this approach, the indirect utility function is formulated in its simplest form as a meta-criterion function, derived by summing the individual objectives with assigned weights:  
\[\mathbf{C}(\mathcal{S},\mathbf{p})=C(\mathcal{S},\mathbf{p})=\sum_{n=1}^{N_{c} }\omega_{n}c_{n}. \tag{5}\]  
Here, \(\omega_{n}\) represents the weight assigned to the \(n\)-th criterion \(c_{n}\), and \(N_{c}\) denotes the total number of criteria. The optimal solution is achieved when the criterion function \(C\) reaches a relative minimum at \(\mathbf{p}^{*}\), expressed as  
\[\mathbf{p}^{*}=\underbrace{\text{argmin}}_{\mathbf{p}\in\Omega}\big{(} \mathbf{C}(\mathcal{S},\mathbf{p})\big{)}. \tag{6}\]  
Various optimization algorithms can be employed to address the problem formulated, (1) or (5). Since the objectives are stochastic and the decision variable vector \(\mathbf{p}\) includes both continuous and discrete elements, the optimization process presents additional challenges. The environment model utilized in this research relies on numerical data and does not have an analytical representation. As a result, classical hard computing optimization techniques, such as those requiring analytical computation of gradients (e.g., Jacobian or Hessian matrices), cannot be applied. Furthermore, purely stochastic methods, such as Monte Carlo techniques, are insufficient for obtaining accurate solutions with guaranteed polynomial-time convergence. Instead, soft computing methods offer a viable alternative for identifying the global minimum of the cost function. In this study, several heuristic algorithms are utilized, including Improved Grey Wolf Optimizer (I-GWO) [32; 33], particle swarm optimization (PSO) [34], genetic algorithm (GA) [35], and Ant Colony Optimization for continuous domains (ACO\({}_{\mathbb{R}}\)) [36].