esa-sceva/satcom-chunk-collection huggingface dataset:
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology  
Spatial and temporal mismatches are the major challenges in scaling FAPAR data from the _in-situ_ measurements to a coarse resolution satellite product. The general scaling methodology in [30; 31] provides a conceptual framework to achieve the goal, via a combined use of prior knowledge and fine-resolution data. The validation framework by [PERSON] _et al._[25] holds a similar belief that the use of high resolution data could reduce the spatial scale discrepancy between _in-situ_ and coarse-resolution data. Borrowing the ideas from the two methods, this paper proposed a spatial and temporal scaling methodology for _in-situ_ FAPAR, as shown in Figure 2. The temporal and spatial mismatches were addressed in three steps as temporal normalization, temporal interpolation and spatial aggregation.  
After serious control over the spatial and temporal comparability between the pair of _in-situ_ and satellite data, the derived coarse-resolution FAPAR reference data were used to validate the MODIS FAPAR product.
## 3 Machine Learning (ML)
### Advanced ML Methods
#### 3.1.2 Auto Features Engineering Approach with Deep Neural Network (DNN)  
Deep learning, a subset of ML, has redefined the landscape of AI by enabling models to automatically learn intricate patterns and representations from raw data. Unlike traditional feature selection methods that aim to manually extract relevant input attributes, deep learning models are designed to autonomously discern significant features through successive layers of abstraction [48]. Although feature selection is bypassed in deep learning, preprocessing steps such as normalization often enhance the dataset's performance, resulting in improved DNN outcomes. While there is not a strict definition for the scale and size of a DNN, any ANN with more than one hidden layer holds potential for being considered a DNN. Technically, the number of DNN's layers should be large enough that allows efficient backpropagation across layers during training for learning deeper features of the dataset. Yet, as DNN size increases, the number of parameters and weights grows, demanding a larger training dataset [49]. Creating a DNN involves employing different types of layers in the ANN architecture. Common layer types include fully connected (dense), convolutional, dropout, pooling, recurrent, and normalization layers [48]. The selection of these layers' hinges on the model type (e.g., encoder-decoder), application (e.g., object detection, classification, and denoising), and dataset characteristics (e.g., videos, 2D images, 1D signals, and text). As such, numerous innovative architectural designs have been proposed to date that have revolutionized various domains, enabling more efficient and effective solutions (see Table 1). Figure 4: Illustration of the architectures of (**a**) a fully connected ANN, (**b**) U-Net, (**c**) GAN, and (**d**) Res-net. The U-Net architecture, recognized for its success in biomedical image segmentation tasks, presents a unique architecture that features a contracting and expansive path, culminating in a U-shaped network (Figure 4b) [79]. This design enables U-Net to effectively capture both local and global contexts, making it particularly adept at tasks involving pixel-wise classification. On another front, Generative Adversarial Networks (GANs; Figure 4c) introduce a pioneering concept of adversarial training, pitting a generator and discriminator against each other to create realistic content [48]. GANs have found applications in image reconstruction, denoising, and data augmentation, offering a powerful framework for creative synthesis. Meanwhile, Residual Networks (ResNet) address the challenge of vanishing gradients in DNN by introducing residual connections that facilitate training of substantially deeper architectures (Figure 4d) [80]. This architecture, featuring residual
## 4 Discussion: Crystal Structure Deformation as a Function of the Applied Pressure
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**P (GPa)** & **U-O3** & **U-O2** & **U-O2\({}^{\prime}\)** & **U-O1** & **U-C** & **C-O3** & **C-O2** & **+CO\({}^{\prime}\)** & **C-O1** & **C-C** & **R(IL)** \\ \hline
0.00 & 2.436 & 2.429 & 2.532 & 1.764 & 2.916 & 1.301 & 1.280 & 1.287 & 2.966 & 4.274 & 4.686 \\
0.48 & 2.433 & 2.428 & 2.534 & 1.764 & 2.915 & 1.301 & 1.279 & 1.286 & 2.961 & 4.277 & 4.683 \\
1.02 & 2.427 & 2.428 & 2.534 & 1.764 & 2.912 & 1.302 & 1.278 & 1.286 & 2.961 & 4.282 & 4.683 \\
1.54 & 2.423 & 2.427 & 2.532 & 1.764 & 2.909 & 1.302 & 1.278 & 1.286 & 2.960 & 4.285 & 4.682 \\
2.01 & 2.418 & 2.427 & 2.532 & 1.764 & 2.907 & 1.303 & 1.277 & 1.286 & 2.960 & 4.290 & 4.682 \\
2.51 & 2.414 & 2.427 & 2.532 & 1.765 & 2.904 & 1.303 & 1.276 & 1.285 & 2.960 & 4.295 & 4.682 \\
3.02 & 2.409 & 2.427 & 2.530 & 1.765 & 2.901 & 1.304 & 1.276 & 1.285 & 2.959 & 4.298 & 4.682 \\
4.00 & 2.400 & 2.428 & 2.529 & 1.765 & 2.895 & 1.305 & 1.274 & 1.284 & 2.959 & 4.305 & 4.682 \\
4.96 & 2.392 & 2.427 & 2.529 & 1.765 & 2.890 & 1.306 & 1.273 & 1.284 & 2.958 & 4.313 & 4.681 \\
6.00 & 2.384 & 2.427 & 2.527 & 1.766 & 2.884 & 1.307 & 1.272 & 1.284 & 2.958 & 4.319 & 4.682 \\
7.00 & 2.376 & 2.427 & 2.527 & 1.766 & 2.880 & 1.307 & 1.272 & 1.284 & 2.958 & 4.328 & 4.681 \\ \(\Delta\) & \(-0.060\) & \(-0.002\) & \(-0.005\) & \(0.002\) & \(-0.036\) & \(0.006\) & \(-0.008\) & \(-0.003\) & \(-0.008\) & \(0.054\) & \(-0.005\) \\ \hline \end{tabular}
\end{table} 
Table 4: Selected interatomic distances (in Å) in the rutherforine crystal structure at different applied pressures directed along \(U^{L}{}_{min}\). The last row in the table (\(\Delta\)) gives the variation of the bond distances at P = 7.0 GPa with respect to those at P = 0.0 GPa. Figure 3: View of the microscopic crystal structure of rutherforine showing the meaning of the U-O3, U-C, C-C, and interlayer R(IL) distances given in Table 4. Color code: U-Blue; C-Grey; O-red.
## 2 Theoretical Investigation of Monitoring Models
### Analysis of the Main Component Characteristics and System Performance of the Sensor Structure
#### 2.4.2 NdFeB Permanent Magnets  
The performance of permanent magnets is critical for sensor monitoring effectiveness, sensitivity, and stability as a magnetic field source, so a comprehensive consideration of factors such as performance and cost is necessary. In the 1980s, the neodymium-iron-boron (NdFeB) permanent magnet, as a third-generation rare-earth permanent magnet material, was developed, with a maximum magnetic energy product of 512 kJ/m\({}^{3}\). After nearly 40 years of application and development, NdFeB magnets continue to be used in numerous fields due to their outstanding performance; their key advantages are as follows: (1) Strong magnetism: As of now, NdFeB remains the most magnetic permanent magnet material, with high residual magnetization, magnetic energy product, and coercive force. (2) Stable magnetism: NdFeB permanent magnets exhibit good magnetic stability at room temperature, with minimal influence from external factors on their magnetization. As a result, they are extensively used in devices requiring long-term magnetic field stability, such as disk drives and MRI machines. (3) Low cost: China has rich reserves of rare-earth materials, and since NdFeB permanent magnets do not contain the valuable metals cobalt and nickel, their cost is significantly reduced, offering a high cost-performance ratio. Stress monitoring sensors are designed for long-term service, which requires high magnetic field stability. Given the strong magnetism and low cost of NdFeB permanent magnets, they were selected as the magnetic field source for the permanent magnet sensor. The key performance parameters of the NdFeB permanent magnets used here are listed in Table 1.
## 4 Performance Evaluation
### Computation Overhead  
The cost of computation overhead takes into consideration the needed time to perform different cryptographic operations (hash function, encryption and decryption and ECC arithmetic operations). The estimation of the computational cost is based on the same hypothesis utilized in [17] as shown in Table 3.  
The obtained results in Table 4, show that the proposed scheme presents the lowest total computational cost (TCC) in Figure 11 including device computational cost (DCC), despite the fact that device computational cost is slightly higher comparatively to [18].  
\begin{table}
\begin{tabular}{c c} \hline
**Protocol** & **Communication Cost (bits)** \\ \hline Proposed protocol & 1840 \\ \hline
[8] & 1920 \\ \hline
[15] & 2560 \\ \hline
[16] & 2304 \\ \hline
[17] & 2528 \\ \hline
[18] & 2688 \\ \hline
[24] & 3152 \\ \hline \end{tabular}
\end{table}
Table 2: Communication cost.  
Figure 10: Communication overhead comparison.  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Function** & **Computational Time (s)** \\ \hline T (h) & Hash function time & 0.00032 s \\ T (sym) & Symmetric encryption/decryption time & 0.0171 s \\ T (eca) & ECC point addition time & 0.0044 s \\ T (ecm) & ECC point multiplication time & 0.0171 s \\ \hline \end{tabular}
\end{table}
Table 3: Approximate time taken by each cryptographic operation.
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
## 2 Background Theory
### UAV Dynamics and Control  
In this work, a multicopter is considered as the base of the aerial manipulator. We denote the fixed inertial reference frame by the subscript \(I\), and the body-fixed reference frame, whose origin is positioned on the base Center of Gravity (CoG), by the subscript \(B\). The vertical axis of the body frame, \(z_{B}\), is set to be parallel to the thrust force direction and is parallel to the inertial frame vertical axis, \(z_{I}\), when the UAV is in a hovering state. The inertial frame vertical axis, \(z_{I}\), is aligned with the gravity force vector and points in the opposite direction. These reference frames are illustrated in Figure 1.  
Multicopters are controlled acting on the thrust generated by each propeller; globally, the propellers generate a thrust force aligned to \(z_{B}\), and three torques around \(x_{B}\), \(y_{B}\), and \(z_{B}\). For the sake of simplicity, we consider a planar problem where the UAV is constrained to move in the \(x_{I}-z_{I}\) plane. The state of the UAV is described through the following generalized coordinates: \(x\) is the horizontal position of its CoG in the inertial frame, \(z\) is its vertical position, and \(\phi\) is the roll angle (about the \(y_{I}\) axis).  
The simplified dynamics of the UAV [3] are described by the following equations:  
\[\begin{split} m_{b}\dot{x}&=-U_{1}\sin(\phi)\\ m_{b}\dot{z}&=-m_{b}g+U_{1}\cos(\phi)\\ l_{b}\dot{\phi}&=U_{2}\end{split} \tag{1}\]  
Figure 1: Reference frames for the UAV. The inertial reference frame is denoted by the subscript \(I\), while the body-fixed reference frame is denoted by the subscript \(B\).  
where \(U_{1}\) and \(U_{2}\) are the thrust control force and the rolling control torque exerted by the propellers, respectively; \(g\) is the gravitational acceleration; \(m_{b}\) is the mass of the UAV; and \(I_{b}\) is the moment of inertia about the \(y_{B}\) axis.  
To simulate the behavior of the UAV, these control actions are determined through a PID controller, which aims to minimize the UAV vertical displacement and roll angle, i.e., correct any deviations from the hovering state. The PID controller equations are as follows:  
\[U_{1} =-k_{P_{2}}z-k_{D_{2}}\dot{z}-k_{I_{z}}\int_{0}^{t}z\,dt,\] \[U_{2} =-k_{P_{\phi}}\phi-k_{D_{\phi}}\phi-k_{I_{\phi}}\int_{0}^{t}\phi\,dt. \tag{2}\]  
It has to be noticed that, since multicopters are underactuated systems, the horizontal position of the UAV cannot be controlled independently but only by applying a combination of of thrust force and roll torque. The coefficients of the PID controller used in the simulations are summarized in Table 1.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Spatial Features
#### 3.1.3 Spatial Feature Enhancement Based on Lightweight Networks
Efficient-GhostNet extracts spatial features from image sequences, reducing the model's parameters and computational load without the need for dimensionality reduction, by employing a local cross-channel interaction strategy. (4) ShuffleNet  
Unlike the above Lightweight networks, ShuffleNet primarily consists of pointwise group convolution and channel shuffle. It transforms pointwise convolution, typically used for dimensionality reduction, into pointwise group convolution. Additionally, before the 3 \(\times\) 3 depthwise convolution, the input feature map is shuffled along the channel dimension to prevent the solidifying of channel information caused by two Group convolutions. Based on ShuffleNet, [PERSON] et al. [80] designed a lightweight Chinese lip-reading model by replacing the residual network. To help the network focus on the most informative features, a Convolutional Block Attention Module (CBAM) was incorporated into ShuffleNet. Invalidation experiments, the method outperformed the baseline models of MobileNet v2 and ShuffleNet v2. By combining ShuffleNet with DCTCN, CFS-DCTCN, a lip-reading model for virtual scenes, was designed to model both visual and temporal features while integrating a context understanding mechanism [81]. Experimental findings demonstrate that CFS-DCTCN extracts visual features that are strongly aligned with the training data, achieving a lip-reading accuracy of 98%. Several works [83; 84] have demonstrated that lightweight networks, such as ShuffleNet, significantly reduce the parameters and computational load of lip-reading models while maintaining high recognition accuracy. These advancements position lightweight networks as a promising direction for research and application in lip-reading technology.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions
### Quality of the Marked Image  
We decrypted the stego image directly and then obtained marked image \(\mathbf{M}\) (see examples in Figure 8). The different PSNR values of marked images are shown in Figure 9a for the six test images under different embedding capacity. Besides, the structural similarity index (SSIM) was also utilized to evaluate the quality in Figure 9b  
\[SSIM(\mathbf{M},\mathbf{O})=\frac{(2\mu_{M}\mu_{O}+C_{1})(2\sigma_{MO}+C_{2})}{(\mu_{M} ^{2}+\mu_{O}^{2}+C_{1})(\sigma_{M}^{2}+\sigma_{O}^{2}+C2)} \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block Size** & **Block Amount** & **Size of \(\mathbf{m}\)** & \(\mathbf{E_{c}}\) & \(\mathbf{E_{r}}\) \\ \hline \(1\times 8\) & 98,304 & 4 & 1,179,648 & 1.5 \\ \(1\times 8\) & 98,304 & 2 & 589,824 & 0.75 \\ \(4\times 4\) & 49,152 & 3 & 442,368 & 0.5625 \\ \(8\times 8\) & 12,288 & 20 & 737,280 & 0.9375 \\ \(16\times 16\) & 3072 & 32 & 294,912 & 0.375 \\ \(16\times 16\) & 3072 & 16 & 147,456 & 0.1875 \\ \(16\times 16\) & 3072 & 8 & 73,728 & 0.0938 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Embedding capacity of different sizes of blocks.  
Figure 7: Cover image \(\mathbf{I}\) of: (**a**) Baboon; (**b**) Beeflower; (**c**) Goldgate; (**d**) Lena; (**e**) London; and (**f**) Peppers.  
where \(\mu_{M}\), \(\mu_{O}\), \(\sigma_{M}\), \(\sigma_{O}\), and \(\sigma_{MO}\) are the local means, standard deviations, and cross-covariance for images \(\mathbf{M}\) and \(\mathbf{O}\), respectively.  
It can be found in the comparison results that, when we adopt a lower capacity such as 73728 bits, the quality of the marked image \(\mathbf{M}\) will be better.  
Figure 8: Examples of marked images \(M\) of: (**a**) Baboon; (**b**) Beeflower; (**c**) Goldgate; (**d**) Lena; (**e**) London; and (**f**) Peppers.
## 3 Smart and Secure HIE Systems: SSHES
### Blockchain and Integration into HIE Systems  
Blockchain is typically a series of blocks, with each block containing a collection of transactions. Such transactions are assembled and appended to the blockchain in a sequential manner. Additionally, each block possesses a distinctive identifier known as a hash, which is formulated based on the block data. This hash for each block serves to connect it to the preceding block, thus forming a chain-like structure. Figure 4 shows the blockchain structure and components after being integrated into HIE systems. Moreover, there exist various categories of blockchain that can be employed in HIE, encompassing public, private, consortium, and hybrid blockchains [24; 25; 26; 27]. Each variant carries its own merits and considerations concerning factors such as data privacy, transparency, and governance. The selection of blockchain type hinges on the specific prerequisites and goals of the healthcare entity or consortium implementing the system.  
Figure 4: General structure of blockchain with integration into HIE systems.
## 2 Principle of Detection
### Detection Method
Under the Kolmogorov turbulent power spectrum, for the propagation process from \(z=0\) to \(z=L\), the logarithmic amplitude fluctuation variance in the spherical wave is calculated by  
\[\sigma_{\chi}^{2}(L)=0.5631k^{7/6}\int_{0}^{L}C_{n}^{2}(z)[\frac{z(L-z)}{L}]^{ 5/6}dz \tag{4}\]  
where \(k\) is the wave number and \(k=2\pi/\lambda\), \(C_{n}^{2}\) is the refraction index structure constant of the atmosphere. On the vertical detection path, the scintillation index of the spherical wave is written as  
\[\beta_{I}^{2}(L)=4\sigma_{\chi}^{2}=2.25k^{7/6}\int_{0}^{L}C_{n}^{2}(z)[\frac {z(L-z)}{L}]^{5/6}dz \tag{5}\]  
In summary, the atmospheric turbulence detection method adopted is as follows: based on RTS theory, first, the scintillation index \(\beta_{I}^{2}\) at each distance is obtained according to Equation (2); then, the variation trend of \(\beta_{I}^{2}\) and \(C_{n}^{2}\) along the propagation path is obtained according to Equation (5), and finally, the atmospheric turbulence profile can be obtained. Before the experiment, we carried out some systematic simulation studies to ensure the feasibility of the experimental scheme and the selection of system parameters [30; 31]. Yinchuan is located in northwest China in the central and northern parts of the Ningxia Plain, surrounded by the Yellow River in the east and the Helan Mountains in the west. It is also surrounded by the Badain Jaran, Ulanbuh, Tengger, and Maowusu deserts and has a typical temperate continental semi-arid climate, as shown in Figure 2. Its geographical coordinates are \(35^{\circ}14^{\prime}\)-\(39^{\circ}23^{\prime}\) north latitude and \(104^{\circ}17^{\prime}\)-\(107^{\circ}39^{\prime}\) east longitude. According to the long-term observation of aerosol particle size distribution, the average aerosol particle size is usually \(\overline{u}=\)1.0 \(\upmu\)m [32]; however, in spring, the aerosol particle size is slightly larger than average, but from an order of magnitude perspective, the conditions of \(L\gg\overline{u}^{2}/\lambda\) are clearly met. Figure 2: Geographical location map of Yinchuan.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 4 System Architecture
### Advanced Software Architecture and Optimization
#### 4.2.1 Dual-Thread Processing Framework  
The ESP32 firmware leverages FreeRTOS for true multitasking with mathematically optimized resource allocation (Figure 4):  
* Operates at 100 Hz with guaranteed worst-case latency of 15 ms.
* Implements novel command buffering algorithm ensuring command ordering.
* Processes single-character commands: 'F', 'B', 'L', 'R', and 'S'.
* Uses priority inheritance mechanism preventing priority inversion.  
Medium-Priority Audio Processing Thread:  
* Manages 16 kHz I2S sampling with 99.2% buffer efficiency.
* Implements adaptive dynamic range compression continuously monitoring signal amplitude.  
Figure 4: Dual-thread architecture and data flow.  
* Processes 64-byte chunks (8 ms audio) optimizing CPU utilization and Bluetooth efficiency.
* Achieves 40% computational load reduction compared to standard approaches.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### Activate Confirmation  
In the original protocol specification, no provision was made for a server to request confirmation that a device had successfully received and acted upon the SRUP_ACTIVATE message; rather, it was assumed that the server would be able to identify that the activation had occurred via other (non-protocol) means. For completeness of the protocol, this capability has now been added.  
Whilst this could be done by adding an additional message-type (for example a type such as SRUP_MESSAGE_TYPE_ACTIVATE_RESPONSE), such a message-type would however have a structure identical to the existing SRUP_MESSAGE_TYPE_RESPONSE; so a more elegant solution would be to use the existing message-type, but to add two new responses to the previously defined statuses. This approach has been adopted in the revised version of the protocol.  
In keeping with other enhancements to the protocol, which seek to minimize the number of different message types required, this approach helps to keep the protocol as simple as possible to implement, whilst providing all of the functionality it requires.
## 2 Materials and Methods
### Database Search Protocol  
The search strategy for the systematic review and meta-analysis on the associated effects of static magnetic fields on orthodontic tooth movement was conducted across six electronic databases, including PubMed, MEDLINE, Scopus, Google Scholar, Web of Sciences, and Embase. The search was performed using Boolean operators (AND, OR, NOT) and MeSH keywords. All types of studies, except for case reports, literature reviews, editorials, seminar articles, and studies with sample sizes of fewer than 10 were included in the search. The search terms used for PubMed and MEDLINE included (((\"Orthodontic Tooth Movement\"[Mesh] OR \"Tooth Movement, Orthodontic\"[Mesh]) AND (\"Magnetic Fields\"[Mesh] OR \"Static Electricity\"[Mesh] OR \"Magnets\"[Mesh]) NOT (Review[ptyp] OR Case Reports[ptyp] OR Editorial[ptyp] OR Seminar[ptyp] OR Letter[ptyp] OR News[ptyp] OR Historical Article[ptyp]) AND ((Humans[Mesh]) AND English[lang]). The search terms used for Scopus included (TITLE-ABS-KEY((\"Orthodontic tooth movement\" OR \"tooth movement, orthodontic\") AND (\"magnetic fields\" OR \"static electricity\" OR magnets)) AND NOT (LIMIT-TO (DOCTYPE, \"re\") OR LIMIT-TO (DOCTYPE, \"ar\") OR LIMIT-TO (DOCTYPE, \"ed\") OR LIMIT-TO (DOCTYPE, \"le\") OR LIMIT-TO (DOCTYPE, \"no\")) AND (LIMIT-TO (LANGUAGE, \"English\") AND LIMIT-TO (SUBJAREA, \"DENT\"))). The search terms used for Google Scholar included (((\"Orthodontic Tooth Movement\" OR \"Tooth Movement, Orthodontic\") AND (\"Magnetic Fields\" OR \"Static Electricity\" OR \"Magnets\")) NOT (Case Reports OR Reviews OR Editorials OR Seminars OR Letters OR News OR Historical Articles)) AND (Humans AND English). The search terms used for Web of Science included ((\"Orthodontic Tooth Movement\" OR \"Tooth Movement, Orthodontic\") AND (\"Magnetic Fields\" OR \"Static Electricity\" OR \"Magnets\")) NOT (Case Reports OR Reviews OR Editorials OR Seminars OR Letters OR News OR Historical Articles)) AND (Languages: (English) AND Document Types: (Article OR Proceedings Paper OR Review)) AND (Database: (WOS Core Collection)). The search terms used for Embase included ((\"orthodontic tooth movement\" OR \"tooth movement, orthodontic\") AND (\"magnetic fields\" OR \"static electricity\" OR magnets) AND [humans]/lim AND [embase]/lim AND [English]/lim AND NOT ([case report]/lim OR [review]/lim OR [editorial]/lim OR [letter]/lim OR [news]/lim)). The search was conducted without any time restrictions, as the literature on the topic is limited.