esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 2 Materials and Methods
### Model Performance Metrics
#### 2.5.1 Silhouette Coefficient (SC)  
For a good cluster, the distance between samples of the same category is very small, and the distance between samples of different categories is very large. The silhouette coefficient (SC) can evaluate both characteristics at the same time. A higher silhouette coefficient score relates to a model with better clusters.  
The silhouette coefficient \(s\) for a single sample is given as:  
\[s=\frac{b-a}{max(a,b)} \tag{13}\]  
where \(a\) is the mean distance between a sample and all other points in the same class and \(b\) is the mean distance between a sample and all other points in the next nearest cluster.  
The silhouette coefficient for a set of samples is given as the mean of the silhouette coefficient for each sample [48].
## 6 Simulation and Results
### Average Energy Consumption  
Energy consumption is one of the main factors that matter when developing a new protocol or scheme for WSNs. To evaluate this factor, we have computed the average remaining energy of the whole network after each round. The results are shown in Figure 8.  
From Figure 8, we can see that our scheme performs better in terms of saving the energy of the network. RWFS can save as up to 85% more energy than the Cui et al. scheme. This is a direct impact of reducing the packet size. The larger the packet, the more energy is consumed by a node to send and receive packets.  
Figure 7: The network overhead analysis between RWFS (the proposed scheme) and the Cui et al. scheme. The two graphs show the total number of packets sent during 10,000 rounds.
## 7 Applications in SDN-based VANETs
### Post-Crash Warning  
This application highlight is utilized to forward a notice information to every one of the vehicles' drivers in foggy climate on the grounds that there might be an opportunity of a mishap because of substantial mist. This application will communicate something specific containing the most extreme speed limit and the path appropriate for driving the vehicle. This framework utilizes both the V2V and V2I correspondences.
###### Abstract  
This paper addresses the challenge of optimizing microgrid sizing to enhance reliability and efficiency in electrical energy supply. A comprehensive framework that integrates multiyear optimization with uncertainty considerations is presented to facilitate optimal microgrid design. The aim is to economically, safely, and reliably supply electrical energy to communities with limited or no access to the main power grid, primarily utilizing renewable sources such as solar and wind technologies. The proposed framework incorporates environmental stochasticity, electrical demand uncertainty, and various electrical generation technologies. Electric power generation models are developed, and a metaheuristic optimization method is employed to minimize total costs while improving power supply reliability. The practical utility of the developed computational tool is emphasized, highlighting its significance in decision-making for microgrid installations. Utilizing real-world data, the approach involves a two-stage process: the first stage focuses on installation decisions, and the second evaluates operational performance using an iterated local search (ILS) optimization algorithm. Additionally, dispatch strategies are implemented to optimize computational time and enable real-time network modeling. The proposed microgrid sizing approach is a valuable asset for optimizing decision-making processes, significantly contributing to extending electricity coverage in non-interconnected zones while minimizing costs and ensuring steadfast reliability.  
microgrid sizing; metaheuristic optimization; multiyear model; non-interconnected zones; stochasticity 1
## 2 Materials and Methods
### GA-BP Neural Network
#### 2.5.2 The Genetic Algorithm  
The GA algorithm is a global search computer algorithm derived mathematically based on inheritance laws in nature [50]. Its essence is selecting good individuals from the population; through crossover and mutation operations to obtain new individuals of good quality. The advantages of the genetic algorithm: (1) It can quickly search the whole solution in the solution space and has excellent global search capability. (2) It is suitable for distributed computing, and natural parallelism speeds up the convergence speed. (3) Simple, general, and wide range of applications. The GA algorithm is used to implement the optimization of BP neural network weights. The adaptive adjustment of the crossover and variance probability enables individuals to update the network weights continuously, thus improving the BP neural network's network convergence speed and algorithmic accuracy. For this reason, this paper uses the GA algorithm to optimize the BP neural network. The global search property of the GA algorithm is combined with the powerful nonlinear learning ability of the BP neural network to improve the training ability of the model. The BP neural network optimized by the GA algorithm is called the Genetic Algorithm Back Propagation neural network. The calculation process of the whole GA-BP neural network is shown in Figure 4.  
Figure 3: Structure of GA-BP neural network.  
The content of the GA-BP neural network is divided into two parts: On the one hand, the GA algorithm is used to globally search for the optimal solution to find a set of optimal solutions. On the other hand, the optimal global solutions are used as the initial weights of the BP neural network. In this study, the initial population size of the GA algorithm is set to 50, the number of genetic generations to 100, the crossover probability to 0.3, and the variance probability to 0.09.
## 3 Problem Description  
Users apply to cloud service providers for virtual machine resources to deploy their own applications. To ensure good service performance, cloud service providers need to sign SLAs with users. The virtual machine placement problem is described as follows: cloud service providers design virtual machine scheduling strategies to meet the virtual machine resource requests of each user and at the same time reduce power consumption of the cloud data center, reduce resources waste, and reduce the SLA violations rate. The virtual machine placement problem can be formulated as a multi-dimensional bin packing problem, which is proven to be \(\mathcal{NP}\)-hard [19].  
Based on the above description, the following definitions are given:  
We are given a cloud data center sharing \(M\) physical machines, denoted as \(P=\{P_{1},P_{2},\ldots,P_{i},\ldots,P_{M}\}\). Each physical machine \(P_{i}\) has \(d\) available resources (such as CPU, memory, bandwidth, and storage). Each resource \(j\) has a capacity \(H_{ij}\), where \(j\leq d\). The users applied for a group of \(N\) virtual machines \(V=\{V_{1},V_{2},\ldots,V_{i},\ldots,V_{N}\}\). The required resource \(j\) of any virtual machine \(V_{i}\) is denoted as \(R_{i,j}\).  
**Definition 1**: _A cloud data center shared \(M\) physical machines, denoted as \(P=\{P_{1},P_{2},\ldots,P_{i},\ldots,P_{M}\}\). The number of types of available resources (such as CPU, memory, bandwidth, and storage) for a physical machine \(P_{i}\) is denoted as \(d\), and \(H_{i,j}\) is the capacity of the resource \(j\) on a physical machine \(P_{i}\), where \(j\leq d\)._  
**Definition 2**: _A user applies for a group \(V=\{V_{1},V_{2},\ldots,V_{i},\ldots,V_{N}\}\) of \(N\) virtual machines. The required resources of any \(V_{i}\) is denoted as \(R_{i,j}\), which represents the number of requirements for a resource \(j\) on a virtual machine \(V_{i}\), where \(j\leq d\). In this paper, we only consider CPU and memory as resources, and we do not consider disks. The reason is that cloud computing data centers generally use network-attached storage (NAS), and storage can be used as a separate module, so \(d=2\)._
## 2 Distribution Network Reliability Evaluation Method
### Fault Impact Analysis
Therefore, it is necessary to carry out a collaborative evaluation of the power supply reliability of the multi-voltage levels distribution network. Collaborative evaluation can fully consider the impact of each 10 kV low-voltage public distribution transformer on the distribution transformer area, and the calculated average reliability index can better reflect the actual system reliability. Additionally, with the gradual popularization of sensing terminals, the distribution network will have the ability to count the outage information of all power supply users in the region. Therefore, it is of great importance to consider the reliability evaluation of a multi-voltage levels distribution network with sensing and control terminals. At the same time, because the sense control-terminal also has the risk of failure, it is necessary to consider the impact of the sense-control terminal failure on the reliability of the distribution network so that the reliability evaluation result is closer to the actual operation.
## 3 Proposed Design Scheme  
### Hardware Design  
Figure 5 shows the complete hardware structure diagram proposed with reference to the standard documentation. The QSFP28 optical transceiver internal hardware of the proposed design scheme consists of PAM4 DSP IC for PAM4 and duplication, electro-absorption-modulated laser (EML)-transmitter optical sub-assemblies (TOSA) for transmission, and PIN-PD receiver optical sub-assemblies (ROSA) for reception. It also consists of a driver IC for driving one TOSA, a TEC controller for controlling optical transmitters wavelengths, and an MCU for overall operation of modules.  
Figure 4: QSFP28 internal register architecture and page accessing [17].  
The PAM4 DSP IC functions to modulate two 25 Gbps NRZ signals with 50 Gbps PAM4 signals and, vice versa, that is, to convert PAM4 signals to NRZ signals. The EML-TOSA is a 25/28 G single channel TOSA and applies an electro-absorption modulator and distributed feedback laser diode (DFB-LD), monitor photodiode, embedded optical isolator, and TEC to a gold box-type package. The TEC controller is used to control the TEC inside the TOSA. Because the TOSA laser diode has the characteristic that the wavelength varies according to temperature, the internal TEC controls the laser diode to maintain a constant temperature without being affected by external temperature. The linear PIN-PD ROSA for receiving PAM4 optical signals consists of a photodiode of 25 GHz bandwidth and a trans-impedance amplifier (TIA) capable of receiving up to 2.5 mA inputs and is adjustable from 31 to 37 GHz. The MCU controls laser diodes, linear drivers, and linear TIA using a built-in analog-to-digital converter (ADC) and digital-to-analog converter (DAC). The MCU is also responsible for operation of the PAM4 DSP IC and operation of the QSFP28 optical transceiver.  
For optical transceivers manufactured with the QSFP28 form-factors, a number of ICs must be integrated in a limited printed circuit board (PCB) area. Therefore, we optimized the PCB design by selecting each IC for the QSFP28 design in consideration of its small size and high-efficiency characteristics. In particular, DC-DC converters for power supply are more than 90% efficient and the inductor is applied with switching converters integrated inside the IC to minimize the area. This can reduce the heat in the power supply and lower the power consumption.
## 1 Introduction
Nonetheless, the application of deep learning models to achieve convergence across all layers of the network is computationally expensive. Computational costs can, however, be reduced by using pre-trained models that draw on the rich hierarchical features of a large dataset, such as ImageNet, Common Objects in Context (COCO) or Microsoft COCO. Pre-training serves as a good starting point for other tasks and improves a model's ability to capture meaningful patterns and features in the specific segmentation task, even when the available labeled data for that task are limited [27]. Thus far, the combined use of U-Net and pre-trained ResNet models integrated with UAV remote sensing datasets in the forestry sector has been limited with respect to the segmentation of individual tree species in uneven-aged mixed forests. Thus, the present study explored the applicability of high-resolution UAV imagery and the ResU-Net model (U-Net model with ResNet101, pre-trained on large ImageNet datasets, as backbone) to predict the distribution of a high-value deciduous broadleaf tree crowns in an uneven-aged mixed forest located at the University of Tokyo Hokkaido Forest (UTHF). Specifically, because Japanese oak is not only a high-value timber species of northern Japanese mixed forests but also a dominant tree species in the UTHF [1], it was selected for our individual tree segmentation task. In addition, we used UAV images taken before and after the color change of Japanese oak leaves to determine the optimal timing of UAV image collection for integration with the ResU-Net model. We also investigated the influence of data augmentation techniques on the accuracy of the ResU-Net model with a limited training dataset. The rest of this paper is organized as follows: Section 2 describes the detailed information regarding the study area, data collection (including field data, UAV image acquisition and processing, and data augmentation), the applied deep learning algorithm (ResU-Net), the identification of Japanese oak using the ResU-Net model, and the evaluation metrics for the trained ResU-Net model. Section 3 presents the results of the identification of Japanese oak using UAV datasets without and with data augmentation, as well as the performance of the ResU-Net model with each of the UAV datasets. The discussion and conclusions, based on the key findings, are presented in Sections 4 and 5, respectively.
## 4 Complexity and Performance
### Experimental Setup  
In this simulations, the standard deviation and write-voltage of the erased state are set to 0.35 and 1.4, respectively, while the standard deviation of the programmed state is 0.05 and the write-voltages are 2.6, 3.2, and 3.93, respectively. For data retention noise, the constant parameters \(\alpha_{i}\), \(\alpha_{o}\), \(A_{t}\), and \(B_{t}\) are set to 0.62, 0.3, \(3.5\times 10^{-5}\), and \(2.35\times 10^{-4}\), respectively, where the relationship between \(\sigma\) and \(\mu\) is set to 0.3 [30]. A 2-level soft quantization voltage is used for adjacent states. The LDPC code used is a regular code (4000, 3600) generated by the Progressive Edge Growth algorithm [31; 32], with row weight \(d_{c}=30\) and column weight \(d_{v}=3\). The maximum number of decoding iterations is set to 15, and the number of error frames collected at each noise point is 30.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 3 Main Error Sources and Suppression Methods of Dynamic Measurement
### Error from Accelerometer  
Whether it is the compensation of vibration noise or the rough estimate of gravity value, the accelerometer is required to have high precision, so the error of the accelerometer itself cannot be ignored. However, due to the influence of environmental factors such as temperature or the structure of the device itself, the accelerometer drifts. For example, the commonly used Nanometrics Titan accelerometer has a temperature drift coefficient of 320 mGal/\({}^{\circ}\)C [56], which affects the measurement accuracy of the final gravity value. The following methods can be used to inhibit accelerometer drift: The first is to add a temperature control system; for example, the atom gravimeter laboratory transformed from a container in ZJUT installs an air conditioning temperature control device to maintain the ambient temperature and humidity and make the instrument work normally [38]. The second is to model and compensate the temperature drift of the accelerometer, collect the drift data at different temperatures, establish the drift model, and compensate through the algorithm, which can effectively suppress the temperature drift and obtain high-precision acceleration signals. Figure 7 shows the scene of our accelerometer temperature experiment. The third is to compare the exact gravity value obtained by the atom gravimeter with the one obtained by the accelerometer to get the drift value and deduct it. The three methods involve the whole process of measurement, do not conflict with each other, and can be used all or only a few of them.  
Figure 6: Raman reflecting mirror generates vibration noise with ground vibration.  
In addition, due to the different installation positions of the accelerometer and the Raman reflecting mirror, there is a mechanical transfer function. If the output of the accelerometer is directly used for vibration compensation, it cannot reflect the real vibration noise. This is an important factor affecting the vibration compensation, so the transfer function needs to be calibrated before measurement. Many scholars have carried out relevant studies on the transfer function. For example, based on the simplified transfer function model including delay and gain coefficients, [PERSON] et al. analyzed the interference fringes of the atom gravimeter and the output signal of the accelerometer and searched for the optimal values of the two coefficients in the current environment, which can more accurately speculate the real vibration of the mirror in the current environment [57]. [PERSON] et al. used the improved chaotic sparrow search algorithm to optimize the above two coefficients, which can achieve a good compensation effect in a short period of time. After compensation, the uncertainty of fringe fitting was attenuated by 70.21%, and the search time was shortened nearly six times, effectively improving the accuracy and stability of the atomic gravimeter [58].
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 5 Discussion  
In this study, we proposed a novel method for enhancing the resolution and quality of medical images using a convolutional self-attention block (CSBlock). The experimental results demonstrated that the proposed method significantly improves PSNR and SSIM metrics compared to conventional approaches, such as VDSR and EDSR. This indicates that our model effectively preserves fine details, such as texture and contours, which arecritical for medical image analysis. The structural innovations introduced in the proposed method play a key role in achieving these improvements:  
* Dynamic Attention Mechanisms: The CSBlock effectively combines channel and spatial attention to prioritize diagnostically relevant features. This allows the model to emphasize high-frequency details, such as lesion boundaries, while suppressing irrelevant noise.
* Subpixel Convolution for Upsampling: Unlike deconvolution-based methods, the use of subpixel convolution reduces artifacts and improves the clarity of restored images, particularly in high-frequency regions.
* Stable Training with L1 Loss: The use of L1 loss mitigates over-smoothing effects, resulting in sharper and more realistic outputs compared to VDSR and EDSR.  
From a clinical perspective, enhanced image resolution can significantly improve the detection of subtle lesions and structural abnormalities. For example, sharper lesion boundaries and improved texture clarity assist dermatologists in distinguishing malignant melanomas from benign lesions. Preliminary qualitative feedback from medical experts has highlighted the diagnostic value of these improvements, though further blinded evaluations are required to validate these findings quantitatively.  
Moreover, we performed a comparative analysis of PSNR and SSIM values across multiple subsets of the ISIC 2020 dataset to assess the statistical significance of the observed improvements. Repeated measurements revealed consistent gains (within a confidence interval of 95%), suggesting that the proposed approach reliably outperforms baseline methods. However, further statistical tests on larger and more diverse datasets are needed to confirm the robustness of our model. Future work could incorporate advanced statistical methods (e.g., ANOVA or mixed-effects modeling) to better account for inter-sample variability and further validate the clinical relevance of our findings.
## 3 Risk Assessment of Technical Constraints Violation
The uncertainty set size of the technical constraints depends on the network configuration considered. For instance, in ordinary conditions, \(|J_{n}|=9\) for feeder \(F1\) and \(|J_{n}|=15\) for feeder \(F2\). All the main data used for the simulations are summarized in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Descriptions** & **Symbols and Values** & **Notes** \\ \hline \hline \multicolumn{4}{c}{Technical Constraints} \\ \hline range of nodal voltage & \(V^{min}\); \(V^{max}\) & \(\begin{bmatrix}0.96;1.04\\ 0.90;1.10\end{bmatrix}\) & ordinary conditions \\ \hline maximum branch current & \(I^{max}\) & \(\begin{bmatrix}1.00\\ 1.10\end{bmatrix}\) & \(\begin{bmatrix}0.00\\ 1.10\end{bmatrix}\) & \(\begin{bmatrix}0.00\\ 1.10\end{bmatrix}\) \\ \hline \multicolumn{4}{c}{DER available} \\ \hline n. of generators & \(N_{RES}\) & 3 & all generators provide flexibility services \\ n. of DR resources & \(N_{DR}\) & 21 & all loads are involved in DR programme \\ max generation curtailment & \(Fmax^{RES}\) & 100\% & related to the nominal rate of the generator \\ max demand curtailment & \(Fmax^{DR}\) & 40\% & related to the nominal rate of the load \\ uncertain behavior & \(\epsilon_{g,h}\), \(\epsilon_{d,h}\) & 20\% & for any resource in any hour \\ \hline \multicolumn{4}{c}{Flexibility services remuneration} \\ \hline capacity price [\(\xi\)/kW] & \(p^{RES}\) & 25 & \(\hat{p}^{RES}\) & \(\pm 20\%\) & for any generator \\ \(p^{DR}\) & 40 & \(\hat{p}^{DR}\) & \(\pm 10\%\) & for any load \\ energy price [\(\xi\)/kWh] & \(c^{RES}\) & 0.06 & \(\hat{p}^{RES}\) & \(\pm 20\%\) & for any generator \\ \(c^{DR}\) & 0.10 & \(\hat{e}^{DR}\) & \(\pm 10\%\) & for any load \\ \hline \multicolumn{4}{c}{BESS} \\ \hline maximum Power rate [MW] & \(P^{B}_{max}\) & 3000 & \\ duration range [h] & \(d_{min}d_{max}\) & [1; 8] & nominal BESS capacity \(E_{n}{}^{B}=P^{B}\times d\) \\ admissible range of operation & \(\sigma_{m}\); \(\sigma_{M}\) & [10\%; 90\%] & related to \(E_{n}{}^{B}\) \\ roundtrip efficiency & \(\eta_{trig}\) & 97\% & \\ initial state of charge & \(SoC_{0}\) & 50\% & of \(E_{n}{}^{B}\) (equal for every BESS installed) \\ power unitary cost of BESS [\(\xi\)/kWh] & \(c_{p}\) & 160 & \\ energy unitary cost of BESS [\(\xi\)/kWh] & \(c_{\varepsilon}\) & 240 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main data adopted for the simulations. Figure 8: (**a**) Network profile of the minimum nodal voltage at the 19 th hour during the emergency configuration with line 1–2 out of service and emergency tie 4–15 closed; (**b**) Daily profile of the minimum voltage of node 22 under the same network configuration.