esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 4 Key Control Logic Design
### Analog-to-Digital Conversion Logic Design  
The analog-to-digital conversion acquisition process is shown in Figure 8. After the chip is initialized, the system enters a standby state, waiting for an external trigger signal to initiate the data acquisition process. Upon receiving the trigger command, the FPGA starts reading the preset channel addresses from the ROM. The system checks the validity of the channel addresses read from the ROM to ensure the correct target channel for data acquisition. Once the address is confirmed to be valid, the system prepares the signal channel for analog-to-digital conversion. Following the predetermined timing control, the analog-to-digital conversion begins converting the analog signal to a digital signal. The converted data are temporarily stored in the FIFO, and upon receiving a read signal, the system transfers the data from the FIFO to the flash storage module.  
Figure 8: Analog-to-digital conversion acquisition flowchart.  
Figure 7: Overall logic design flow.
# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 1 Introduction
The proposed model can adjust whether to focus more on global information or local information, and it is seen that the accuracy is higher than the existing models that use only global information. * In this study, the effect of dynamic routing on similarity evaluation was investigated. Since the similarity of the meaning of sentences is relatively free in the positioning of the corresponding phrases, it was found that dynamic routing hindered the correct evaluation. In addition, experiments were conducted on both English and Korean datasets to prove the language independency of the model proposed. This paper is composed as follows. Section 2 briefly describes the models of former studies and our proposed model in evaluating sentence similarity. Section 3 minutely describes the proposed model for global and local feature extraction. Section 4 describes the data used in this study, the hyperparameters used in the experiment, and the experimental results. Section 5 discusses the results. Finally, Section 6 discusses the summary of this study and proposes future studies.
## 3 Results and Discussion
### Radiation Characteristics  
Figure 12 shows the realized gain and radiation efficiency over the frequency of the proposed MIMO antenna. The MIMO antenna exhibits a peak gain of 7.5 dBi, along with a maximum radiation and total efficiency of 99% and 96%, respectively. The gain measurements of the antenna were obtained using a comparison method, where the gain of the antenna was compared to that of a reference antenna with a known gain. An Aaronia Hyperlog 60180 wideband log-periodic antenna (Aaronia, Strickscheid, Germany), which has a gain close to the designed antenna (approximately 5-6 dBi in the relevant frequency range) was selected as the reference antenna. During the measurements, the position of the Aaronia Powerlog 70180 wideband horn antenna, which has a gain of 10-13 dBi within the relevant frequency range, was kept fixed. Each of the MIMO antennas as well as the reference antenna were placed one by one along the broadside direction of the horn antenna, and the gain values of the proposed antenna were determined from the difference in S\({}_{21}\)(dB) readings on the VNA. The MIMO antenna's two-dimensional (2D) and three-dimensional (3D) radiation patterns at 2.5 GHz and 6.2 GHz are shown in Figure 13. It is noted that the radiation patterns show more directed features at 6.2 GHz compared to the omnidirectional characteristics at 2.5 GHz. This difference can be explained by the upper frequency band's greater gain over the lower frequency band. The simulation results are consistent with the measurements findings.  
Figure 11: S-parameters of the proposed MIMO antenna.  
Figure 12: Gain and efficiency variations in the MIMO antenna over the frequency.  
Figure 13: _Cont._
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 3 Three-Band Optimization Results
### Parameter Optimization Results
As shown in Figure 7, the sensitivity analysis indicates that the eight selected parameters have a certain influence on both the mass and the fundamental frequency. Figure 6: _Cont._  
For the parameters subjected to structural optimization, the Latin hypercube sampling method was adopted to generate the initial 86 sample data. Subsequently, these samples were optimized. The minimum first-order natural frequency of 200 Hz is set as the lower limit. Currently, a series of relatively mature and reliable solutions have been developed for multi-objective optimization problems, including algorithms such as the Multi-Island Genetic Algorithm, the Neighborhood Cultivation Multi-Objective Genetic Algorithm, and the Non-Dominated Sorting Genetic Algorithm (NSGA), among others [29; 30; 31]. In this study, the MOGA variant of the second-generation Non-Dominated Sorting Genetic Algorithm  
Figure 6: Geometric parameters: (**a**) geometric parameters of structure A\({}_{i}\) (**b**) geometric parameters of structure B; (**c**) geometric parameters of structure C\({}_{i}\) (**d**) geometric parameters of structure D; (**e**) geometric parameters of structure E.  
Figure 7: Contributions of parameters to various response functions. (NSGA-II) was applied to solve the optimization problem of the dimensional parameters. Using the MOGA algorithm in ANSYS 2024, the model reached convergence after 13 iterations, as shown in the iteration process in Figure 8. Table 2 presents the upper and lower limits of L1-L8 along with the optimized optimal dimensions. Compared to the optimization results, the thickness of the visible light supports, L1 and L2, was reduced to 1.6 mm and 4.1 mm, respectively, while the outer diameters of L4, L5, and L7 increased slightly. The thicknesses of the plate-type supports, L3, L6, and L8, were reduced to 3.1 mm, 3 mm, and 3.1 mm, respectively. Additionally, Table 3 lists the performance metrics before and after optimization. Although the first-order frequency decreased slightly, it still remained above 100 Hz. The structure achieved a total weight reduction of 40.3% based on topology optimization.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 4 Cloud Security Challenges and Proposed Solutions
### Technical Security Threat
#### 4.2.3 Data Loss  
The possibility of permanent or unintentional loss of data stored in the cloud is referred to as the data loss threat in cloud computing. While cloud services provide numerous data redundancy and backup systems, there are still circumstances that might cause data loss. The following are some of the most typical reasons for data loss in cloud computing [39, 43]:  
* Failures in hardware or infrastructure
* Accidental deletion or human error
* Malicious Activity  
**Mitigating Data Loss Risks:**  
Data backup and recovery: Implement frequent and automatic data backup processes to provide redundancy and multiple copies of vital data. Periodically test data restoration techniques to ensure backups are functional [44]:  
Redundant storage and replication: Use cloud services that provide data replication across several geographic locations or availability zones. This helps to defend against data loss caused by infrastructure failures or localized occurrences.  
Prepare a detailed disaster recovery strategy that specifies ways to recover data in the case of hardware failures, natural catastrophes, or other catastrophic occurrences. The rehabilitation strategy should be tested and validated on a regular basis.
# An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
###### Abstract  
Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate's matrices. Moreover, it deploys the Banachiewicz-Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.  
Keywords:beamforming; 5G; massive MIMO; mmWave communications; spectral efficiency +
Footnote †: journal:  
_Article_  
An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
# Thermal Control of CubeSat Electronics Using Thermoelectrics
## 2 Methodology
### Empirical Model of a Commercial Thermoelectric Module  
The analysis is based on the manufacturer's performance data for a commercial thermoelectric device (APHC-12706-S from European Thermodynamics Ltd., Kibworth, UK [12]) which is a 40 mm \(\times\) 40 mm \(\times\) 4 mm module. The module is of single stage geometry and is specifically designed for temperature cycling applications. It can provide a maximum temperature difference of 66 \({}^{\circ}\)C and is operational to a maximum hot-side temperature of 180 \({}^{\circ}\)C.  
In order to calculate the module's cold-side temperature as a function of the thermal resistance of the system, four empirical models were created. Module performance data was extracted from the data sheet [12]. The four models consist of linear fits for each of the supplied performance data sets for testing at 25 \({}^{\circ}\)C and 75 \({}^{\circ}\)C, and are shown in Figure 1. The fits calculate the total heat dissipated at the hot side of the module and the temperature difference (\(\Delta T\)) across the thermoelectric device, as a function of the heat pumped at the cold side, which was considered equivalent to the CPU power. The module data sheet provides constant-current performance data up to a maximum supplied voltage of 14.7 V. In this study we have imposed an upper bound on the on-board voltage of 12 V, based on what might be a realistic constraint on a CubeSat [13], meaning that module performance data for \(V\) > 12 V was ignored.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 5 Experimental Results
### Experiment Setting  
BOSSBase [32], Middlebury [33; 34], and Microsoft Research 3D Video [35] datasets with 5000 images with resolutions ranging from 512 \(\times\) 512 to 1800 \(\times\) 1500 were used for the experiment. A total of 3500 images were used for training, and the remaining 1500 images were used for performance testing. For network learning, 70,000 images were synthesized from the 3500 images with geometric and signal distortions. The geometric distortion used random parameters of 0 to 90-degree rotation, 0.7 to 1.5 times scaling, and 0 to 30% translation. The signal distortion also randomly applied a Gaussian noise of 0 to 200 variance and a JPEG compression factor of 30 to 100 to the geometrically distorted image.  
The proposed method was implemented with a tensorflow library [36] and Python. The network was trained using the adam optimizer [37], with a learning rate of \(10^{-3}\), epsilon of \(10^{-8}\), batch size of 32, and \(\lambda\) in (7) of 0.2. Until convergence occurred, the end-to-end network was trained and convergence typically occurred after 15 epochs. The training took about an hour per epoch using the Nvidia GTX 1080 single GPU.  
For fair comparison, all experiments were performed on a gray channel. The bit capacity for [PERSON]'s method and the proposed watermarking method were set to 256 bits. For the proposed watermarking method, the image was divided by 8 \(\times\) 8 to create 64 blocks. Among these, 32 blocks were used to insert bit information and the remaining blocks were used for the template. The proposed watermark was inserted on scale 3 in the curvelet domain divided by scale 5 and direction 8. [PERSON]'s method inserts the watermark on scales 3 and 4 in the curvelet domain divided by scale 5 and direction 128. The quantization step \(q\) for the proposed method and [PERSON]'s method was set to 3.  
In the experiment with the DIBR watermarking method [31], all other parameter values were set to default, and only the block size was adjusted. In the DIBR watermarking method with the template, the image was divided by 8 \(\times\) 8 to create 64 blocks. Among these, 32 blocks were used to insert bit information and the rest were used for the template. In the method without a template, the image was divided by 5 \(\times\) 6 to create a total of 30 blocks, and all 30 blocks were inserted with bit information. With this block size adjustment, the bit capacity of the DT-CWT method with a template and the DT-CWT method without a template were set to a similar level.
## 4 Materials and Methods
### Proposed Architecture
#### 4.2.2 AI Component  
Statistical and AI techniques are increasingly used with numerical models to pro-duce more accurate forecasts. Linear regression (LR) forecasting is one of the AI models used more frequently in electricity consumption and generation forecasting due to the good results obtained.  
LR is a data analysis technique that predicts the value of unknown data by using another related and known data value. It mathematically models the unknown or dependent  
Figure 3: Initial programming of the energy meter.  
Figure 2: Proposed Architecture.  
variable and the known or independent variable as a linear equation. The regression model consists of an approach to model the relationship between a dependent scalar variable \"Y\" and one or more explanatory variables named with \"X\" and then plots a line that will indicate the trend of a set of continuous data, whose formula is:  
\[Y=mX+b\]  
where \(Y\) is the result, \(X\) is the variable, m is the slope (or coefficient) of the line, and b is the constant or also known as the \"point of intersection with the \(Y\)-axis\" on the graph (when \(X=0\)).  
The predictive models are obtained using simple linear regression using the demand data of each distribution company EDECHI, EDMET, and ENSA provided by Empresa de Transmision Electric S.A. (ETESA) [69]. Table 1 shows the data used to train the model.  
The demand of the model variables y, x, and z are obtained by applying simple linear regression on the data set that constitutes the annual electricity demand of each of the distributors.  
The models for each distributor are as follows:  
\[\mathrm{x}=\mathrm{i}_{1}+\mathrm{m}_{1}\mathrm{D}_{\mathrm{EDECHI}}\]  
\[\mathrm{y}=\mathrm{i}_{2}+\mathrm{m}_{2}\mathrm{D}_{\mathrm{EDEDET}}\]  
\[\mathrm{z}=\mathrm{i}_{3}+\mathrm{m}_{3}\mathrm{D}_{\mathrm{ENSA}}\]  
where \(\mathrm{i}_{1}\), \(\mathrm{m}_{1}\), \(\mathrm{i}_{2}\), \(\mathrm{m}_{2}\), \(\mathrm{i}_{3}\), and \(\mathrm{m}_{3}\) are the coefficients of the linear regressions of the distributors.  
Using simple linear regression and the demand values of each distributor, the predictive models are obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Maximum Demand (MW)**} \\ \hline
**Year** & **EDECHI** & **EDMET** & **ENSA** \\ \hline
1998 & 50.88 & 354.14 & 274.59 \\
1999 & 55.97 & 369.67 & 295.49 \\
2000 & 58.07 & 394.35 & 314.83 \\
2001 & 60.10 & 412.81 & 298.63 \\
2002 & 62.39  
The coefficient of determination (r\({}^{2}\)) is used to evaluate how well the data of each model fit. In this, a value of 1 is equivalent to an optimal fit. The coefficients of determination (r\({}^{2}\)) for each distributor are shown in Table 5.  
The data set extracted from the national ETESA source and the sensor test data set is verified by the model used to generate the recommendation data displayed in the mobile application.
# Throughput Capacity of Selfish Wireless _Ad Hoc_ Networks with General Node Density
## 3 Achievable Rate  
In order to derive the per-node rate of the networks, we leverage the routing strategy illustrated in [5] and show that there also exists a routing scheme in SWNETs.  
Without loss of generality, the strategy of obtaining per-node achievable rate operates as follows: Firstly, using percolation theory, we construct a backbone network which is composed of many horizontal and vertical highways. Then, according to the density of AN, we partition the network area dynamically to ensure each square contains an AN _w.h.p._, such that the transmission would not be terminated by the selfish nodes. Since we begin with percolation theory in the SWNETs model, we will take a brief look at it here.  
Percolation theory [23; 24] is a field of mathematics and statistical physics that provides models of phase transition phenomena. For example, assume that water is poured on top of a porous stone--will the water be able to make its way from hole to hole and reach the bottom? By modeling the stone as a square grid, each edge can be open and traversed by water with probability \(p\), or closed with probability \(1-p\), and they are assumed to be independent. For a given \(p\), what is the probability that an open path exists from the top to the bottom? That is, is there a path of connected points of infinite length through the network? In fact, there exists a critical \(p_{c}\) below which the probability is always 0 and above which the probability is always 1. In some cases \(p_{c}\) may be calculated explicitly; for example, in a two-dimensional square lattice \(\mathbf{Z}^{2}\), when \(p>1/2\), water percolates through the stone with a probability of one. One can then ask at what rate the water percolates and how it depends on \(p\). In other words, how rich in disjoint paths are the connected component of open edges? To maximize the information flow, we want to operate the network at \(p>1/2\), above the percolation threshold, so that we can guarantee the existence of many disjoint paths that traverse the network.  
In this paper, we merely consider the case of \(\frac{1}{n}\leq p(n)\leq 1\). When \(p(n)<\frac{1}{n}\), following the Chernoff Bound, we know that there are few ANs in the network, and each node transmits the packets directly to the destination or via constant hops by increasing the power. In this case, the asymptotic throughput is similar to the broadcast capacity [25], which is \(\Theta\left(\frac{1}{n}\right)\).
# Graph Neural Network for Senior High Student's Grade Prediction
## 4 Discussion  
### The Application of Deep Learning Method in High School Education  
Dealing with the complexity, nonlinearity, and unclear mechanism of the student performance prediction problem, in this study, a kind of deep learning method, i.e., graph neural network is applied to the senior high student's personalized grade prediction and performs well.  
However, a satisfying prediction performance is achieved with the student's personal examination data extended by the linear interpolation method. Whereas, the prediction performance degenerates rapidly with the data of only nine examinations. Admittedly, the deep learning method is very good at learning (or fitting) the complex function (mechanism) hidden in the data. However, this ability is based on big data and increasing computation force.  
Therefore, the main limitation of deep learning methods applied in education currently is the amount of data. In order to meet the requirement of the amount of data, the linear interpolation method is employed to extend the data, in this study. The reason for employing the linear interpolation method is that we assume students' grade performance would not change rapidly during a short time. The prediction error reflected by the curve of the loss function in Figure 10 converges around 300 iterations and finally reaches 1.2070. That means with the linear interpolation data between the second semester mid-term exam in the 2016 school year (date 20 April 2017), and the second semester mid-term exam in the 2017 school year (date 23 April 2018), which is about 300 data points, the graph neural network based personalized grade prediction model has been trained well. The trained model with about 200 data points should be able to perform well, considering the curve of the loss function shown in Figure 10.  
Despite the data being extended by the linear interpolation method, the graph neural network based prediction model eventually performs well in this study. It is still recommended to collect more real data, which could be more diverse and informative, to support the application of the deep learning method in the education field.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Proposed Method** & \multicolumn{2}{c}{**LR**} & \multicolumn{2}{c}{**SVR**} & \multicolumn{2}{c}{**MLP**} \\ \hline grade & rank & grade & rank & grade & rank & grade & rank \\
0.22\% & 1.67\% & 9.31\% & 44.4\% & 7.18\% & 36.1\% & 4.25\% & 23.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction Performances (MAPE) of Different Methods for Student 14295.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Proposed Method** & \multicolumn{2}{c}{**LR**} & \multicolumn{2}{c}{**SVR**} & \multicolumn{2}{c}{**MLP**} \\ \hline  & grade & rank & grade & rank & grade & rank & grade & rank \\ Mean & 0.29\% & 10.87\% & 2.32\% & 43.48\% & 4.44\% & 60.59\% & 2.43\% & 43.99\% \\ SD & 0.0051 & 0.1164 & 0.0104 & 0.3271 & 0.0147 & 0.4634 & 0.0119 & 0.3763 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prediction Performances (MAPE) of Different Methods for 100 Students.