esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Traditional Recommendation Model
#### 3.2.1 Latent Factor Model  
The latent factor model is a collaborative filtering recommendation algorithm renowned for its high recommendation accuracy [21]. The fundamental concept behind the LFM model involves decomposing the user-item rating matrix into two low-dimensional matrices, which serve to capture the latent features of both users and items. By utilizing the product of these two low-dimensional matrices, the LFM model generates predictions for ratings.  
Consider a scenario where there are \(\mathbf{m}\) users, denoted as \(\mathbf{U}=\{\mathbf{u_{1}},\mathbf{u_{2}},\mathbf{u_{3}},\ldots\mathbf{u_{m}}\}\), and there are \(\mathbf{n}\) items, denoted as \(\mathbf{I}=\{\mathbf{i_{1}},\mathbf{i_{2}},\mathbf{i_{3}},\ldots\mathbf{i_{n}}\}\). Here, the rating matrix, denoted as \(\mathbf{R}\in\mathbf{R}^{m\times n}\), exhibits a high degree of sparsity with limited available data. The primary objective of the latent factor model is to predict ratings for users and items that have not yet interacted with each other based on the existing rating data.  
The LFM is comprised of \(\mathbf{K}\) latent factors, as illustrated in Figure 1. Consequently, for each user \(\mathbf{u}\), the vector \(\mathbf{p_{u}}=\{\mathbf{p_{u1}},\mathbf{p_{u2}},\mathbf{p_{u3}},\ldots,\mathbf{p_{uK}}\}\) represents their preference degree for each latent factor, while vector \(\mathbf{q_{i}}=\{\mathbf{q_{i1}},\mathbf{q_{i2}},\mathbf{q_{i3}},\ldots,\mathbf{q_{iK}}\}\) represents the preference degree of each item \(\mathbf{i}\) for the latent factors. Thus, the user's rating of the item can be predicted using the inner product of \(\mathbf{p_{u}}\) and \(\mathbf{q_{i}}\), as demonstrated in the following formula:  
\[\hat{\mathbf{r}}_{\mathbf{u}i}=\mathbf{p_{u}}\mathbf{q_{i}} \tag{5}\]  
where \(\hat{\mathbf{r}}_{\mathbf{u}i}\) denotes the predicted rating of user \(\mathbf{u}\) for item \(\mathbf{i}\). In the matrix form, the predictive score matrix \(\hat{\mathbf{R}}\) can be decomposed into the product of two low-rank matrices, as shown in the following formula:  
\[\hat{\mathbf{R}}=\mathbf{PQ} \tag{6}\]  
The solution method for LFM involves minimizing the mean square error. The calculation formula for the loss function is as follows:  
\[L=\sum_{\begin{subarray}{c}i\in I\\ \mathbf{u}\in\mathbf{U}\end{subarray}}(\mathbf{r_{ui}}-\hat{\mathbf{r}}_{\mathbf{u}i})^{2}+\lambda \Big{(}\|\mathbf{p_{u}}\|^{2}+\|\mathbf{q_{i}}\|^{2}\Big{)} \tag{7}\]  
where \(\lambda\big{(}\|\mathbf{p_{u}}\|^{2}+\|\mathbf{q_{i}}\|^{2}\big{)}\) represents a regularization term that is added to prevent overfitting in the model. \(\lambda\) is an overfitting penalty factor, which is a hyperparameter used to balance the data loss term and the regularization term.  
Figure 1: Rating prediction process based on latent factors. This figure mainly shows the rating prediction process of user \(\mathbf{u_{3}}\) for all items.
## 2 Materials and Methods
### Methodology
#### 2.4.2 The Analysis Method of Spatial Variation Characteristics of Cultivated Land  
Drawing on previous studies and other related technical regulations [63], we reclassed Hunan Province into four levels according to DEM \(<\) 100 m, 100\(\sim\)200 m, 200\(\sim\)800 m, and \(\geq\)800 m, with area ratios of 21.85%, 17.08%, 53.01%, and 9.06%, respectively. The slopes 0-2\({}^{\circ}\), 2-6\({}^{\circ}\), 6-15\({}^{\circ}\), 15-25\({}^{\circ}\), and \(\geq\) 25\({}^{\circ}\) were reclassed into five levels, and the area ratios were 5.16%, 20.54%, 34.58%, 23.22%, and 16.50%, respectively. When the daily average temperature rises steadily above 10 degrees Celsius, most crops can grow actively. An accumulated temperature (T) of \(>\)10 \({}^{\circ}\)C is the total of the daily average temperature during a period greater than or equal to 10 degrees Celsius. We reclassed Hunan Province into five levels according to T \(<\) 5500 \({}^{\circ}\)C, 5500-5600 \({}^{\circ}\)C, 5600-5700 \({}^{\circ}\)C, 5700-5800 \({}^{\circ}\)C, and \(\geq\)5800 \({}^{\circ}\)C, and the area ratios were 14.87%, 17.29%, 18.53%, 29.02%, and 20.29%, respectively. According to the annual average precipitations of 0-1500 mm, 1500-1570 mm, 1570-1640 mm, 1600-1810 mm, and \(\geq\)1810 mm, we reclassed Hunan Province into five levels, and the area ratios were 26.98%, 30.97%, 23.88%, 11.26%, and 6.92%, respectively. Then, we used the intensity of land-use change and the relative change index of land use to describe the characteristics of cultivated land change under different natural production conditions. _LUCI_ and _RLUCI_ were used to analyze the land-use change intensity variation in different regions.  
_LUCI_ is the index of the intensity of land-use change for land type \(j\) within spatial unit \(i\), e.g., in the zoning of agricultural characteristics factors. This index allows cross-sectional comparisons of the degree of variation in categories within different levels, and it can be calculated using Formula (2).  
\[LUCI=\frac{Area_{i,b}-Area_{i,a}}{TA_{j}}\times 100\% \tag{2}\]  
where \(TA_{j}\) is the total area of spatial unit \(j\), and \(Area_{i,b}\) and \(Area_{i,a}\) are the areas of land-use type \(i\) at the end of the study period (\(b\)) and the beginning of the period (\(a\)), respectively.  
_RLUCI_ is the relative change rate of land-use change, and it can better analyze the degree of local changes in land type \(i\) relative to the whole province and compare the differences in change intensity across regions. It can be calculated using Formula (3).  
\[RLUCI=\frac{\left|Area_{i,b}-Area_{i,a}\right|}{Area_{i,a}}/\frac{\left|TA_{i, b}-TA_{i,a}\right|}{TA_{i,a}} \tag{3}\]  
where \(Area_{i,b}\) and \(Area_{i,a}\) have the same meaning as previously mentioned, and \(TA_{i,b}\) and \(TA_{i,a}\) are the total area of land-use type \(i\) at the end of the study period (\(b\)) and the beginning of the period (\(a\)), respectively.
## 1 Introduction
The host image was decomposed by QDFT and quaternion QR, and a high-entropy block of the scalar part of the quaternion QR matrix was selected to embed the watermark. Over the last decade, various image-watermarking schemes based on tensor decomposition have been proposed [19; 20; 21]. Tensor decomposition can maintain the internal structure of the digital image and avoids the loss of important image information. [PERSON] et al. [19] pointed out a new blind watermarking scheme for color images based on the tensor domain. The scheme effectively considers the overall characteristics of color images, and propagates the watermark information to the three channels of the color image through tensor decomposition. [PERSON] et al. [20] used Tucker decomposition to decompose the luminance component, and then used adaptive dot matrix quantization index modulation to embed the watermark in the tensor domain. [PERSON] et al. [21] offered a watermarking scheme based on Tucker decomposition, and this method transformed the multi-spectral image and embedded the watermark into the element of the last frontal slice of the core tensor. From the above methods, some embed a watermark in single transform domain [6; 8; 15; 17; 19; 20]. Besides, in [2], the methods did not take efficient account of the correlation of frequency components. In [18], the scheme chose the high entropy block to embed a watermark, the block is unstable, which makes the watermark more vulnerable to attack. In a word, none of these methods takes full advantage of the three-dimensional \((3D)\) imaginary components of QDFT and the above methods suffer from watermark energy loss [6]. Based on [18; 19], the present paper proposes a hybrid transform color image watermarking scheme based on QDFT and tensor decomposition. The scheme considers the overall color image channels to improve attack resistance and decentralizes the distribution of the watermark further, and then enhances robustness. Furthermore, an appropriate strength is used to embed the watermark that satisfies the two conflicting factors, robustness and fidelity. The main contributions of the paper are as follows:* This paper blends QDFT with tensor decomposition (TD) and implements overall processing for a color image to embed a watermark. * The scheme proposed in this paper synchronously spreads the watermark to three RGB channels and enhances robustness performance. * This paper proves the correlation of three imaginary components of QDFT, using the QDFT's components to structure a tensor. The rest of this paper is arranged below. The relevant techniques are described in Section 2. The embedding and extraction processes of watermarking are provided in Section 3. The experimental part is provided in Section 4. Finally, the paper is summarized in Section 5.
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 6 Outcomes of QKD Mono-Authentication
Figure 6: The plot illustrates the signature and verification times for two post-quantum algorithms, computed for the mono-authentication scheme based on the QKD-SARG04 protocol. The impact of the proposed mono-authentication paradigm on QKD security encompasses its repercussions and influences on the security framework of QKD systems. Unlike conventional methods, mono-authentication streamlines the authentication process, consolidating it into a single step at the conclusion of communication. This consolidation offers potential benefits by simplifying authentication and minimizing vulnerabilities that adversaries could exploit. Evaluating the impact involves examining how the approach affects the overall security posture of QKD systems, considering its strengths, weaknesses, and implications for securing quantum communication. While our study predominantly focuses on numerically analyzing post-quantum algorithms within mono-authentication scenarios in QKD protocols, the broader implications of our approach on QKD security warrant thorough exploration. Mono-authentication, as advocated in our study, represents a significant shift in the authentication process within QKD systems, optimizing efficiency by condensing authentication into a singular, conclusive step. This streamlined process not only enhances protocol efficiency but also introduces potential security benefits by reducing opportunities for adversary intervention during authentication. Furthermore, our findings highlight the superior performance of CRYSTALS-DILITHIUM and its efficiency in communication steps, bolstering the security and resilience of QKD implementations. Additionally, our study underscores the importance of considering real-world factors, such as noise in QKD scenarios, which may influence the effectiveness of authentication methods. Moving forward, exploring the broader implications of our approach on QKD security entails assessing its resilience against quantum attacks, scalability in large-scale QKD deployments, and compatibility with emerging quantum technologies. Through addressing these aspects, our aim is to not only advance QKD security but also contribute to the broader field of quantum cryptography, facilitating secure communication in the quantum computing era.
## 6 The Framework of the Charge Controller System  
Overall, after the long review illustrated in this paper, the proposed framework contains an EMS stored on the cloud computing service. This system serves three different goals. The first is to monitor and combine different energy sources in order to obtain the best optimized system. The second goal is to control the switches in the energy hub, and the third goal is to manage the charging and discharging process. The system will yield many benefits:  
* Reduce the carbon footprint by including RESs such as solar plants (photovoltaic), WTs, and other RESs;
* Enhance the demand power by monitoring and controlling the power balance at the same time;
* Introduce an intelligent system and cloud computing to the power management field, and make the system manageable.  
It is difficult to carry out an actual optimization charge controller on an intelligent power system via cloud computing, as it is based on numerous nonlinear parameters and contains many genuine bonds and limitations. Furthermore, because many actual characteristics are stochastic, handling a power system as a plant (dynamic systems) is problematic. Therefore, there are two suggestions: the first is to plan the optimization algorithm for the charge controller based on the real parameters; the next is to implement this proposed algorithm as a practical system that offers optimal interventional treatment solutions for all protection requirements. Therefore, this study focuses on presenting a final chart of the model that will consider three aspects: power demand management, RE, and cloud computing, which will be the main contribution of the future study conducted in Figure 9.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Category** & & \multicolumn{1}{c}{**Challenges**} \\ \hline \multirow{4}{*}{**Smart grid**} & \(\bullet\) & Heterogeneous \\  & \(\bullet\) & Energy storage systems are insufficient. \\  & \(\bullet\) & Not combining energy storage with the cloud and sharing it \\  & \(\bullet\) & Big data management and analytics \\ \hline \multirow{4}{*}{**Cloud computing**} & \(\bullet\) & Data-sharing issues \\  & \(\bullet\) & Lack of integration of multiple mobile agents with the cloud \\  & \(\bullet\) & Dependability not sufficient \\  & \(\bullet\) & Insufficient platform Implementation for offering long-term evolution \\  & \(\bullet\) & Unsynchronized function \\  & \(\bullet\) & Risk of DDoS \\  & \(\bullet\) & Any error leads the system to fail \\  & \(\bullet\) & Insufficient methods data analytic and storage methods \\ \hline \multirow{4}{*}{**Big data**} & \(\bullet\) & Memory storage limitations \\  & \(\bullet\) & Stability \\  & \(\bullet\) & Management \\  & \(\bullet\) & Insufficient methods for handling massive amounts of data \\  & \(\bullet\) & Information and data management challenges \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main features of big data in smart grids.
# Transformative Technology for FLASH Radiation Therapy
## 2 Beam Conditions for the FLASH Effect
### FLASH with Proton and Ion Beams
Appear at average dose rates of >30 Gy/s, with the apparent optimal at 100 Gy/s; b. FLASH effect is likely highly dose-, tissue-, and end-point-dependent. * Dependence of beam delivery on the beam structure and uniformity in dose deposition a. Typical dose delivery time for a consistent (electron) FLASH effect is ~100 ms (best <250 ms); b. Most positive FLASH studies used a modified pulsed clinical electron linac with a beam pulse length of ~microseconds and a repetition rate of 100-400 Hz; c. Instantaneous (within the pulse) minimum FLASH dose rate is 106 Gy/s (again, a characteristic of clinical electron linacs). * Dosimetry and treatment-planning questions a. Observed volumetric dose-deposition dependence. b. Low-dose-rate areas not tolerated during FLASH--toxicity reappears? c. Bragg peak and pencil-beam scanning questions: do distal edge and penumbra effects and associated lower-dose rate beam \"halos\" create a problem? \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dose Delivery Mode** & **Protons** & **Helium** & **Carbon** \\ \hline Conventional: 2.6 Gy/fraction & \(2\times 10^{9}\) p/s & \(5\times 10^{8}\) He/s & \(1.7\times 10^{8}\) C/s \\ Delivery time: 100 s & 0.4 nA & 0.2 nA & 0.2 nA \\ FLASH: \(\geq\)10 Gy/fraction & \(1\times 10^{13}\) p/s & \(2.5\times 10^{12}\) He/s & \(0.8\times 10^{12}\) C/s \\ Delivery Time: 100 ms & 1.6 nA & 0.8 nA & 0.8 pA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dose delivery requirements for FLASH for protons and ions extrapolated from electron FLASH studies. Figure 2: Schematic view of pulsed beam delivery inducing the FLASH effect. (Reproduced from [PERSON] et al. [12] under the terms of the Creative Commons Attribution License (CC BY), [[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)]([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))) (accessed on 9 September 2022). * Can a relatively large target volume be uniformly irradiated by fractionated FLASH-compatible deliveries over a longer time frame? * Instantaneous FLASH dose rate and delivery time for 10 Gy--is it consistent for all radiation types?
## 3 Results and Discussion
### Droplet Impact on Horizontally Moving Micro-Pillar Surfaces
#### 3.2.2 Velocity Restitution Coefficient and Energy Dissipation
Since the change in gravitational potential energy in this study is two orders of magnitude smaller than that in kinetic energy and surface energy, the gravitational potential energy \(E_{g}\) is neglected. The dissipated energy \(E_{\mu}\) is obtained with the total energy in the initial state minus the sum of kinetic energy and surface energy. By normalizing these energy terms with respect to the total energy in the initial state, dimensionless quantities are obtained. As shown in Figure 12, along with \(We_{n}\) increasing, the proportion of energy dissipation also gradually increases. This is because the increases in droplet penetration depth and spreading diameter lead to greater viscous dissipation during the spreading and retraction processes. Meanwhile, the dimensionless kinetic energy decreases rapidly with increasing \(We_{n}\), which explains the variation in the velocity restitution coefficient. The greater the initial kinetic energy of the droplet, the deeper the penetration depth, resulting in increased dissipated energy and ultimately causing the dimensionless kinetic energy of the droplet to remain almost unchanged upon leaving the surface. Figure 11: Velocity restitution coefficients for different cases. The pink region indicates a rapid decrease in \(\varepsilon_{n}\) and the light blue region indicates fluctuating \(\varepsilon_{n}\).
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 5 Discussion
### Improvement for IMERG  
The evaluation of this study indicates that there is still much room for improvement in the performance of IMERG for the southeast coast of China. The most obvious shortcoming is the overestimation of weak precipitation and the underestimation of heavy precipitation, especially at sub-daily scales. Although this deficiency has been noted in previous studies [39; 92; 93], it is particularly evident in this coastal urban region. This issue may be addressed by segmental correction, as proposed by [PERSON] et al. [46], by building separate correction models to adjust precipitation estimates for different intensities. Since the errors of IMERG for different precipitation intensities vary in different regions, this study suggests that it is necessary to design a spatial distribution map of the calibration model parameters to localize the correction algorithm, which can improve the IMERG's ability to detect regional precipitation (especially high-intensity precipitation) and to optimize its application in operations such as rainstorm warning, flood prevention and urban management in coastal areas.  
Furthermore, contrary to expectations, the performance of the GPCC-calibrated IMERG FR has not significantly improved in the coastal region compared to the near-real-time products, IMERG ER and IMERG LR. This may stem from the limited number of GPCC stations (19 stations in the region), which do not represent well, the spatial variability of precipitation across the region [94]. Moreover, the GPCC data used for product adjustments are monthly records, which may not help much in improving performance at daily and sub-daily scales. Therefore, this study also suggests using GPCC daily observations, or even integrating locally available sub-daily measurements for calibration, to further enhance the product's ability to observe precipitation over short periods in some key areas.
## 3 Method
### Network Architecture  
In this section our proposed approach is provided and described in Figure 2. As the figure shows, the architecture consists of four main processes. Starting with the 20 CT scan cases, we portioned the data to be 70% for the training dataset and 30% for the validation dataset. First, we started with the lung segmentation phase. We used the Contrast Limited Adaptive Histogram Equalization preprocessing method to remove noise and intensity inhomogeneity. Then, we removed all the black slices to only crop the region of interest containing the lungs.  
A Unet architecture, based on CNN encoder and CNN decoder approaches, is then introduced for fast and precise image segmentation to obtain the lung segmentation model. We then proceeded the exact same way to obtain the infection segmentation model in the second phase, and to further improve the model and to better estimate its skill on unseen data, we used fourfold cross-validation as a resampling procedure for the evaluation.  
In the third phase, we augmented the data which was then fed to our proposed three layer CNN architecture, with additional fully connected layers, followed by a Softmax layer for the COVID-19 classification. The last phase is the volume reconstruction of the twenty cases. We reconstructed the lungs' volumes first, then the infection volumes for each  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original CT Scan** & **Lung Mask** & **Infection Mask** & **Lung and Infection Masks** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three sample images from the used dataset. First column: original CT scans. Second column: lung masks. Third column: infection masks. Fourth column: superposition of the lung and infection masks.  
case, in order to calculate the volume ratio to obtain, at last, the corresponding infection rates. A detailed description of the four processes' components will be described in the later sections.
## 3 Methods  
### Problem Formulation  
In order to optimize the joint antenna selection and user allocation, the maximization problem is formulated, which aims to select \(N_{RF}\) antennas from \(N_{t}\) transmitting antennas and select \(K\) users from all \(U\) users to obtain a maximum sum rate.  
The joint antenna selection and user allocation problem is formulated as  
\[\begin{array}{ll}\max\limits_{\mathcal{A},\mathcal{T}}R_{sum}(\mathcal{A},& \mathcal{S})=\max\limits_{\mathcal{A}\mathcal{S}}\sum\limits_{i=1}^{K}\,\log _{2}(1+\gamma_{i}(\mathcal{A},\mathcal{S}))\\ \text{subject to}&|\mathcal{A}|=N_{RF},\\ &|\mathcal{S}|=K,\\ &\sum\limits_{i=1}^{K}\,p_{i}\leqslant P_{total},\end{array} \tag{6}\]  
where \(P_{total}\) denotes the overall transmission power. \(p_{i}\) is determined by water filling method.  
The optimization objective of the water-filling method is to determine the power allocated to each scheduled user in order to achieve a considerable system sum rate [26].