esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Performance Verifications
#### 4.3.3 Different VM Sizes  
Considering that the packet processing consumes CPU resources, the next experiment tests the throughputs of VMs with different numbers of cores. Because the throughput of the VM with SR-IOV is too high while the throughput of the VM with OVS is too low, this experiment only presents the performance of applying OVS-DPDK, as shown in Figure 14. Results show that the throughput grows in all packet sizes when the VM size increases.  
The throughputs of a 2-core VM are 1.4 Gbps, 5.9 Gbps, and 9.93 Gbps when the packet sizes are 64, 512, and 1518, respectively. Regarding a 4-core VM, the throughputs are 1.42 Gbps, 6.7 Gbps, and 9.96 Gbps when the packet sizes are 64, 512, and 1518, respectively. The improvement ranges from 0.2% to 16% in this experiment. The reason is that if the VM only has two CPU cores, the preemption occurs frequently while the  
Figure 14: Throughputs of VMs with different cores in the PVP scenario.  
CPU core is processing. Therefore, the throughput is decreased if the CPU resources are limited. Increasing the number of CPU cores relieves the overhead of preemption so that the throughput is enhanced.
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
###### Abstract  
Energy is a fundamental tool for human development and this paper presents an approach that seeks to improve its use in Colombian off-grid communities. This approach is based on microgrid concepts where generation, storage, and consumption units interact with each other, and these interactions are presented through a linear programming model. In this approach, specific strategies are implemented according to the Colombian context, where some isolated communities already have diesel-based solutions for energy access, and the type of element that is studied, finding that the proposed optimization model is capable of adequately managing the loads of the microgrid,, thus improving the way in which the generated energy is stored and used through said horizon. Finally, different characteristics of the model are evaluated against multiple indicators and it is concluded that there may be much more specific strategies that improve its operation.  
microgrids management; renewable energy resources; isolated microgrids; optimization techniques +
Footnote †: journal: Applied  
acting as a single, controllable entity that is able to operate in either grid-connected or island mode [7]. A microgrid serves as a system that integrates the actions of all users connected to it and uses advanced information, control, and communication technologies to save energy, reduce costs, and increase reliability and transparency [8]. Microgrids are emerging as an alternative solution to the problem of accessibility to service isolated areas where the electric power system cannot reach, or even as a sustainable alternative that allows communities to autonomously manage part (or even all) of their energy requirements.  
This research studies the methods and tools used to make optimal use of renewable energy sources in microgrids within the context of isolated communities in Columbia in order to develop a new approach that could optimize the generation and use of electricity in off-grid areas where the current energy solution is the use of fossil sources such as diesel to supply energy demand.  
Consequently, this research presents a novel approach within a Colombian context for the use of renewable resources to produce energy and its storage capacity within a microgrid. This approach seeks to meet the energy demand from isolated communities at the lowest possible cost, implementing strategies that ensure good load distribution and the minimum amount of unattended demand.
## 4 Image Processing Methods of the Mode Shape
### Histogram Processing  
When plotting the vibration mode image of the rail, the displacement values in the \(x\), \(y\), and \(z\) directions are converted into the pixel values of the RGB image, respectively. The vibration distribution of the guided wave modes in the \(x\), \(y\), and \(z\) directions is analyzed with the histogram method. The histograms of three RGB colors are drawn, respectively. The abscissa represents the pixel value of the color from 0 to 255, and the ordinate represents the distribution of the pixel value. The more the distribution of the pixel values in the image is on the right side, the greater the vibration value of the mode in this direction.  
It can be seen from Figure 11 that when the pixel value is bigger than 120, the blue pixels in the \(z\) direction still has a 20-100 distribution in Figure 11d. Therefore, for mode 2, the histogram in the \(z\) direction of the blue pixels distributes more in the region of the high pixel value, so the main direction of vibration is the \(z\) direction.  
Figure 11: Histogram of mode 2 image. (**a**) RGB image of mode 2; (**b**) histogram of mode 2 in the \(x\) direction; (**c**) histogram of mode 2 in the \(y\) direction; (**d**) histogram of mode 2 in the \(z\) direction.  
In terms of mode 3, as the pixel value increases, only Figure 12c has a distribution in the high-pixel region. That is to say, the histogram along the \(y\) direction of the green pixels distributes more in the region of the high pixel value, so the main direction of vibration is the \(y\) direction.  
Similarly, in terms of mode 7, only Figure 13d has a distribution in the high-pixel range of 145 to 240. This is, the histogram of the blue pixels distributes more in the region of the high pixel value, so the main direction of vibration is the \(z\) direction.  
Figure 12: Histogram of mode 3 image. (**a**) RGB image of mode 3; (**b**) histogram of mode 3 in the \(x\) direction; (**c**) histogram of mode 3 in the \(y\) direction; (**d**) histogram of mode 3 in the \(z\) direction.  
Figure 13: _Cont._  
In terms of mode 10, when the pixel value is bigger than 180, the green pixels in the \(y\) direction still has the distribution in Figure 14c. That is to say, the histogram of the green pixels distributes more in the region of the high pixel value, so the main direction of vibration is the \(y\) direction.  
Figure 14: Histogram of mode 10 image. (**a**) RGB image of mode 10; (**b**) histogram of mode 10 in the \(x\) direction; (**c**) histogram of mode 10 in the \(y\) direction; (**d**) histogram of mode 10 in the \(z\) direction.  
Figure 13: Histogram of mode 7 image. (**a**) RGB image of mode 7; (**b**) histogram of mode 7 in the \(x\) direction; (**c**) histogram of mode 7 in the \(y\) direction; (**d**) histogram of mode 7 in the \(z\) direction.
## 4 Results and Discussion
### RQ1: What NAS Approaches Are Applied to GANs in the Literature?
#### 4.1.3 Gradient-Based Approaches
In parallel, DA evaluates the outputs of multiple candidate operations to assign importance weights, replacing the conventional reliance on architectural parameters. This refined mechanism allows DAMGAN to efficiently determine the optimal candidate operations and construct a high-performing subnet with a remarkably low computational cost. Empirical results demonstrate that DAMGAN achieves competitive performance on benchmark datasets such as CIFAR-10--with while completing the search in only 0.09 GPU days. The method's scalability and transferability are further validated by its successful application to larger datasets like STL-10 and CelebA.
## 3 Results and Discussion
### Leaf N Predictive Models
The main shortcoming of the latter models is that they are site, data, and season specific and cannot be easily transferred. [PERSON] et al. (2018) demonstrated that machine learning techniques, such as random forest improved the estimation of leaf N by 49%, and are robust, if well parameterized. [PERSON] et al. (2018) and this study demonstrated that Sentinel-2 can be used to accurately estimate leaf N concentrations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & \(\mathbf{R^{2}}\) & **RMSE (\%N)** & **RRMSE (\%)** & \(\mathbf{p<0.05}\) \\ \hline
443 nm & 0.11 & 0.31 & 35.23 & No \\
490 nm & 0.14 & 0.30 & 34.09 & Yes \\
560 nm & 0.01 & 0.32 & 36.36 & No \\
665 nm & 0.27 & 0.28 & 31.82 & Yes \\
705 nm & 0.15 & 0.30 & 34.09 & Yes \\
740 nm & 0.23 & 0.29 & 32.95 & Yes \\
783 nm & 0.30 & 0.27 & 30.68 & Yes \\
842 nm & 0.27 & 0.28 & 31.82 & Yes \\ REP & 0.70 & 0.18 & 20.45 & Yes \\ NDVI & 0.59 & 0.21 & 23.86 & Yes \\ NDVI1 & 0.60 & 0.21 & 23.86 & Yes \\ NDVI\_RE1 & 0.59 & 0.21 & 23.86 & Yes \\ NDVI\_RE2 & 0.51 & 0.23 & 26.14 & Yes \\ NDVI\_RE3 & 0.69 & 0.18 & 20.45 & Yes \\ NDVI\_RE4 & 0.73 & 0.17 & 19.32 & Yes \\ NDVI\_RE5 & 0.69 & 0.18 & 20.45 & Yes \\ NDVI\_RE6 & 0.22 & 0.29 & 32.95 & Yes \\ NDVI\_RE7 & 0.68 & 0.19 & 22.00 & Yes \\ SR & 0.70 & 0.18 & 20.45 & Yes \\ SR\_RE1 & 0.68 & 0.18 & 20.45 & Yes \\ SR\_RE2 & 0.56 & 0.22 & 25.00 & Yes \\ SR\_RE3 & 0.75 & 0.17 & 19.32 & Yes \\ SR\_RE4 & 0.74 & 0.17 & 19.32 & Yes \\ SR\_RE5 & 0.74 & 0.17 & 19.32 & Yes \\ SR\_RE6 & 0.22 & 0.29 & 32.95 & Yes \\ SR\_RE7 & 0.72 & 0.17 & 19.32 & Yes \\ CI\_red edge & 0.75 & 0.17 & 19.32 & Yes \\ CI\_green & 0.69 & 0.18 & 20.45 & Yes \\ MTCI & 0.75 & 0.17 & 19.32 & Yes \\ \hline \hline \end{tabular} REP = Red Edge Position; SR = Simple Ratio; NDVI = Normalized Difference Vegetation Index; RE = Red Edge; MTCI—MERIS Terrestrial Chlorophyll Index; CI = Chlorophyll index.  
\end{table} 
Table 3: Performance of bands and various vegetation indices on the estimation of leaf N using simulated Sentinel-2 spectral configuration from spectrometer data. Figure 4: Independent validation of the selected model (SR_RE3) for mapping leaf N concentrations. Figure 3: Comparison of the leaf N estimation performance using bands and various indices, including red edge based indices derived from Sentinel-2 data sets.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 4 Overview and Fundamental Results
### The \(k^{2}\)-Coloring Algorithm  
Now the \(k^{2}\)-coloring algorithm is introduced for the problem of hexagon tiling coloring. For a given distance \(d\in\Re^{+}\), the \(k^{2}\)-coloring algorithm requires \(k^{2}\) colors to paint the entire hexagon tiling \(h\) where \(k=\left\lceil\frac{2d}{\sqrt{3}}+1\right\rceil\) and guarantees that any two hexagons \(h_{1},h_{2}\in H\) with \(d(h_{1},h_{2})\leq d\) have different colors. Figure 3 indicates a coloring pattern of \(h\) generated by the \(k^{2}\)-coloring algorithm where \(k=4\) and \(d=\frac{3\sqrt{3}}{2}\). The \(k^{2}\)-coloring algorithm is employed by Smart Grid wireless device nodes in our proposed localized algorithm to locally identify the group they belong to.  
**Lemma 1**.: _Where a distance \(d\in\Re^{+}\), the \(k^{2}\)-coloring algorithm paints the hexagon tiling area \(H\), in a way that two different hexagons \(h_{1},h_{2}\in H\) have their own distinct colors if \(d(h_{1},h_{2})\leq d\)._  
Figure 3: The Coloring Pattern for \(k=4\).  
Figure 2: The Minimum Distance Between Two Nodes with Same Color.  
Proof.: The proof of this lemma can be shown as that in the color assignment, for two different hexagons \(h_{1}(i_{1},j_{1}),h_{2}(i_{2},j_{2})\in H\), painted by the coloring Algorithm 1, the distance \(d(h_{1},h_{2})\) will be larger than \(\frac{\sqrt{3}}{2}(k-1)\) (with \(k=\left\lceil\frac{2d}{\sqrt{3}}+1\right\rceil\)).  
If the colors from the \(k^{2}\)-coloring algorithm painted in both \(h_{1}(i_{1},j_{1})\) and \(h_{2}(i_{2},j_{2})\) are the same, then \(j_{1}\pmod{k}\times k+i_{1}\pmod{k}+1=j_{2}\pmod{k}\times k+i_{2}\pmod{k}+1\). This is if and only iff  
\[i_{1} \equiv i_{2}\pmod{k}\] \[j_{1} \equiv j_{2}\pmod{k}\]  
Let \(x=i_{1}-i_{2}\) and \(y=j_{1}-j_{2}\), and it follows that \(x\) and \(y\) will be multiple of \(k\). The distance between the centers of \(h_{1}(i_{1},j_{1})\) and \(h_{2}(i_{2},j_{2})\) is given by  
\[d_{c}(h_{1},h_{2})=\frac{\sqrt{3}}{2}\sqrt{x^{2}+xy+y^{2}}\]  
Consider the following cases as:  
* \(|x|\geq 2k\): \(d_{c}(h_{1},h_{2})=\sqrt{\frac{3}{4}(\frac{x}{2}+y)^{2}+\frac{9}{16}x^{2}}\geq \sqrt{\frac{9}{16}(2k)^{2}}>\frac{\sqrt{3}}{2}(k-1)+1\) where \(d_{c}(h_{1},h_{2})\) represents the Euclidean distance between the centers of two hexagons \(h_{1}\) and \(h_{2}\). Since the distance from the center of a hexagon to a certain point inside cannot exceed \(\frac{1}{2}\), the distance between them \(d(h_{1},h_{2})\) will be at least \(d_{c}(h_{1},h_{2})-2(\frac{1}{2})>\frac{\sqrt{3}}{2}(k-1)\).
* \(|y|\geq 2k\): We also obtain the same result as in the case \(|x|\geq 2k\).
* \(|x|\) and \(|y|<2k\): If \(x=y=\pm k\), then the distance \(d(h_{1},h_{2})\) will be larger or equal to \(d_{c}(h_{1},h_{2})-2(\frac{1}{2})>\sqrt{\frac{9}{4}k^{2}}-1>\frac{\sqrt{3}}{2 }(k-1)\). Otherwise, only six cases of \(x,y\) are left as depicted in Figure 2, and \(\frac{\sqrt{3}}{2}(k-1)\) is the only distance between two hexagons with all the cases.  
Consequently, the lemma is proved completely.
## 1 Introduction
In [22], a multi-modal contrast learning strategy, DHT-CL (neighborhood attention-driven multi-modal contrast learning strategy), is proposed, using multiple sets of LiDAR sensors mounted on trackside signal poles. Ref. [23] presents a system for obstacle detection in railway level crossing, with the main focus being the detection of smaller objects, such as rocks, using 3D point clouds acquired with tilting 2D laser scanners. However, such stationary approaches only monitor a reduced section of the train track, like, for example, a level crossing where the flow of vehicles and people across the tracks is expected to be high, but disregard the rest of the train's path, where obstacles like landslides can also appear. On the other hand, most automotive applications follow a machine-learning-based approach [24; 25], where the availability of large amounts of good data is paramount. In road-based transportation, it is significantly easier to acquire such data, including situations with obstacles, than in railway-based ones. Therefore, this paper follows our previous work [26] and describes a data-fusion framework that combines on-vehicle data from LiDAR and GNSS (Global Navigation Satellite System) with pre-existing path knowledge to detect obstacles deterministically. The proposed data fusion approach combines two commonly used information sources, LiDAR and GNSS, with an uncommon one, path maps. This is only feasible in a railway environment, as, unlike cars, trains will usually follow a predetermined path with little deviation, especially in single- or double-track railways. This enables the use of simplified methods that require only moderate amounts of data for development, thus circumventing the large data requirements of a machine learning algorithm, which would not be feasible to obtain in the project's context.
# Isogeometric Schwarz Preconditioners with Generalized B-Splines for the Biharmonic Problem +
## 2 GB-Splines: Definition and Basic Properties  
### Generalized Polynomial Spaces  
In this section, we outline the definition of GB-splines and their fundamental properties, following [38] (Section 4). Throughout the paper, GB-splines are basis functions in IGA discretizations (see [7] for a detailed description).  
**Definition 1**.: _Let \(2\leq p\in\mathbb{N}\) and \(\Delta\) be a partition over the interval \([0,1]\),_  
\[\Delta:=\{0=\eta_{0}<\eta_{1}<\cdots<\eta_{\ell+1}=1\},\quad\ell\in\mathbb{N}. \tag{2}\]  
_A generalized polynomial space of degree \(p\) is defined as a space of the form_  
\[\mathbb{G}\mathbb{P}_{p}^{U,V}(\Delta):=\langle 1,\zeta,\cdots,\zeta^{p-2},U( \zeta),V(\zeta)\rangle,\quad\zeta\in[\eta_{0},\eta_{\ell+1}] \tag{3}\]_where \(U,V\in C^{p}[\eta_{i},\eta_{i+1}]\) and \(\langle D^{p-1}U,D^{p-1}V\rangle\) is an extended Tchebycheff space on \([\eta_{i},\eta_{i+1}]\) for all \(i=0,\ldots,\ell\)._  
In various kinds of spaces \(\mathbb{G}\mathbb{P}_{p}^{U,V}(\Delta)\) (see [38] for more details and also [39; 40; 41] for a complete description of Tchebycheff space), the popular choices for the functions \(U,V\) on \([\eta_{i},\eta_{i+1}]\) are  
\[\mathbb{P}_{p}([\eta_{i},\eta_{i+1}]):=\langle 1,\zeta,\ldots,\zeta^{p-2}, \zeta^{p-1},\zeta^{p}\rangle, \tag{4}\]  
\[\mathbb{E}_{p,\alpha_{i}}([\eta_{i},\eta_{i+1}]):=\langle 1,\zeta,\ldots, \zeta^{p-2},\exp(\alpha_{i}\zeta),\exp(-\alpha_{i}\zeta)\rangle,\quad\alpha_{ i}\in\mathbb{R}_{>0}, \tag{5}\]  
and  
\[\mathbb{T}_{p,\alpha_{i}}([\eta_{i},\eta_{i+1}]):=\langle 1,\zeta,\ldots, \zeta^{p-2},\cos(\alpha_{i}\zeta),\sin(\alpha_{i}\zeta)\rangle,\quad 0< \alpha_{i}(\eta_{i+1}-\eta_{i})<\pi. \tag{6}\]
## 3 Materials and Method
### Dataset Annotation and Preprocessing
#### 3.2.1 Dataset Annotation  
Before the start of the annotation work, all team members underwent a training series which included the use of annotation tools, understanding annotation rules, and practice with actual annotation operations. This training process ensured that each annotator could accurately understand the requirements of the annotation task and how to apply the annotation rules. To ensure the accuracy and consistency of data annotation, a detailed set of annotation rules was established. These rules were based on the best practices and latest research findings in the field of cybersecurity, covering scenarios from simple network access behaviors to complex financial fraud and malicious software attacks. For example, any abnormal access pattern in server log data, such as a large number of requests in a short time or abnormal request paths, was marked as an \"attack\". For financial behavior data, transactions were judged as \"credit card fraud\" based on transaction frequency, amount, and location. In the actual annotation process, each piece of data was independently reviewed and annotated by at least two annotators in order to reduce subjective bias and improve annotation accuracy. The annotation results were determined by majority vote, that is, when the majority of annotators' results were consistent, that result was determined as the final annotation. To ensure consistency and accuracy of the annotation standards, the following measures were taken:  
1. Regular Review: Annotation teams held regular review meetings to discuss and resolve issues and disagreements encountered during the annotation process. This helped to continuously refine the annotation rules, ensuring a unified understanding of the annotation standards among all personnel.
2. Double-Blind Annotation: A double-blind mechanism was implemented during the annotation process, meaning that each annotator could not see the results of others, reducing mutual influence and ensuring the objectivity and independence of the annotation.
3. Quality Control: A quality control step was introduced in which annotation results were regularly evaluated through random checks. Problems identified during checks were corrected and feedback was provided promptly, ensuring annotation quality.
## 2 Data and Methods
### Data
#### 2.1.1 Description of the NIER Operational Forecast Model  
The NIER operates an air quality forecast system using the CMAQ model [21; 22] along with meteorological inputs provided by the Weather Research Forecasting (WRF) model ([23]). An operational forecast system has been introduced [24; 25] and the model configurations are summarized in Table 1. CMAQ v4.7 was used to estimate the concentrations of atmospheric chemical species. The chemical mechanism in the CMAQ was the Statewide Air Pollution Research Center, Version 99 (SAPRC99; [26]), and the 5 th generation CMAQ aerosol module (AERO5) was used for aerosol simulation. Air pollutant emissions include both anthropogenic and natural emissions. MIX 2010 [27] and the Clean Air Policy Support System (CAPSS) was used to measure anthropogenic emissions. Anthropogenic emissions were allocated spatiotemporally using Sparse Matrix Operator Kernel Emissions (SMOKE; [28]) and then utilized in the air quality model. Furthermore, biogenic emissions were utilized based on the Model of Emission of Gases and Aerosols from Nature (MEGAN) version 2.04. [29; 30].  
The spatial resolutions of the WRF-CMAQ model operated by NIER are 27 km (D01), 9 km (D02), and 3 km (D03), as shown in Figure 1a. The models forecast 76 h from 03 UTC every day. In this study, we analyzed AOD concentrations from the 27 and 9 km grid domains for East Asia and the Korean Peninsula, respectively.
## 6 Simulation Results and Discussions  
Wind regimes applied to each of the two WFs in the simulations are depicted in Figures 7 and 8 [31]. Different WTGs in a WF experience different wind regimes, depending on their location. Therefore, in the simulation procedure, wind regimes in Figures 7 and 8 have been adjusted for each of the WTGs in the WF applying Equation (29).  
In order to get a close estimation of the real power system on one hand, and applying extreme conditions to ensure good performance of the proposed control system on the other hand, it has been assumed that two sudden changes occur in load demand of two simulated zones in the proposed power system. Zone one experiences a sudden change of 0.01 P.U at 50 th second and zone two experiences a sudden change of \(-0.01\) P.U at 100 th second in the simulations [32]. The line power transfer between the two zones, considering different control strategies, is demonstrated in Figure 9. As time passes and wind velocity varies, the transferred power between the two zones varies. It is obvious that the highest range of transferred power oscillations between the zones is in the case of applying lookup table control strategy to allocate set points to individual WTGs. By applying just FLC in allocating WTGs' set points, the domain of oscillations is decreased. Finally, applying dual control strategies, FLC and EL, decreased the oscillations to a maximum of 0.004 P.U.  
Figure 8: Wind regime applied to zone two.  
Figure 7: Wind regime applied to zone one.  
Figure 10 demonstrates the output power of conventional thermal plant in zone one. It can be noted that the power oscillation in the output power of this plant is the least in the case of applying dual control FLC and EL. Without the FLC and EL, wide range of the oscillations my result in frequency stability problems.  
The output power of the thermal plant in zone two is depicted in Figure 11. In this case, similar to the previous case, power oscillation is the least in the case of applying dual control FLC and EL.  
Frequency deviations from the base value of 1 P.U in two zones are depicted in Figures 12 and 13. It is noted that in the case of applying a lookup table to allocate best set points to individual WTGs, great deviations in frequency occurs and this may result in frequency instability. Applying FLC decreases these oscillations, but the best result among these three solutions is achieved in the case of applying FLC and EL controller.  
Figure 10: The output power of thermal unit in zone one.  
Figure 9: Transferred power between the two zones.