esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Property Evaluation
The C\({}_{2}\)AH\({}_{8}\) and C\({}_{2}\)AH\({}_{19}\) peaks that appear after the initial hydration disappear or weaken at 1400 \({}^{\circ}\)C. The multite phases, shown at 200 \({}^{\circ}\)C, transform to corundum and cordoerite phases when heated to 1400 \({}^{\circ}\)C. Then, the formation of corundum appears to affect the reduction in strength of the molded body [26]. However, at 1400 \({}^{\circ}\)C, the cordoerite phases are identified more prominently, thus confirming an increase in strength due to the sintering of multite up to 1400 \({}^{\circ}\)C [27]. Figure 6: XRD peaks after heat treatment at 200 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C of silica sand and CAC mixed molded body. Figure 8 depicts the XRD pattern of the A + C sample. Similar to the M + C sample, the C\({}_{2}\)AH\({}_{8}\) and C\({}_{2}\)AH\({}_{19}\) peaks that appear after the initial hydration disappear or weaken at 1400 \({}^{\circ}\)C. Owing to the compositional characteristics of the initial CAC, no evident crystal phase change is observed as a consequence of the increase in the existing alumina component. However, the CA, CA\({}_{2}\), and CA\({}_{6}\) components are indeed revived because CAC dehydrates at high temperatures. This seems to be a minor change that does not affect the strength increase caused by the sintering of alumina. In addition, the peaks of C\({}_{2}\)AH\({}_{8}\) and C\({}_{2}\)AH\({}_{19}\) that appear after the initial hydration disappear or weaken at 1400 \({}^{\circ}\)C, indicating sufficient dehydration due to high-temperature heat treatment [22; 36; 37; 38]. Figure 8: XRD peaks after heat treatment at 200 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C of alumina sand and CAC mixed molded body. Figure 7: XRD peaks after heat treatment at 200 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C of mullite sand and CAC mixed molded body. Every mixed powder sample for 3D printing was investigated by preparing a powder bed using a sessile drop tester (Figure 9a). Figure 9b shows the absorption area of the droplets formed at the powder interface in terms of the spread diameter. In addition, every absorbed region was separated from the powder bed after 4 h to measure the compressive strength. The average spread diameters of the S + C, M + C, and A + C samples are 7.4 mm (narrowest), 9.2 mm, and 8.3 mm, respectively. An inference from the spread diameters of the droplet is that the surface resolution of the molded body is adversely affected when the mold shows high spread diameters [17]. Therefore, the surface quality of the S + C sample is expected to be better. In Figure 9c, the strength values show the same trend as the strength results measured on the molded body, i.e., a lower strength despite the same 4 h curing time. It is only that the formation of forming strength after the hydration process by water drop is lower than the water reactivity by slurrying [5]. Figure 9: Powder bed sessile drop test: (**a**) Proceed with sessile drop test using sessile drop tester, (**b**) spread diameter as a result of sessile drop test, (**c**) compressive strength as a result of sessile drop test.
# Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
## Appendix A Support Vector Machine Algorithm  
As the process involves training procedures, the decoding processing on the receiver side of the encoder is based on support vector machine (SVM). SVM is a machine learning algorithm that employs the concept of the kernel function to map the data in a different dimensional space, such that the information is grouped according to similar attributes. The algorithm takes as input the raw data, which is classified depending on the kernel function. Then, the data are saved and compared with the original figure to identify similar patterns that are used for image identification. This process is repeated until the maximum number of iterations \(n\) is reached, as shown in Figure 10(a). As a result, a simplification of complex nonlinear decision boundaries is obtained to derive in a linear dimensional space [58]. Mathematically, the characterization is driven by the kernel that can take the form as presented in Table 11. For a better understanding, a flowchart of SVM is presented in Figure 10(b). The kernel used in the SVM algorithm for the proposed optical encoding model is the basis function (Gaussian).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of SVM** & **Kernel** & **Description** \\ \hline Base function (Gaussian) & \(K(x_{1},x_{2})=e^{-\frac{\left\|x_{1}-x_{2}\right\|^{2}}{2\sigma^{2}}}\) & Learning of one class, where \(\sigma\) represents the width of \\ \hline Linear & \(K(x_{1},x_{2})=x_{1}^{T}x_{2}\) & Learning of two classes \\ \hline Polynomial & \(K(x_{1},x_{2})=\left(x_{1}^{T}x_{2}+1\right)^{\rho}\) & \(\rho\) is the polynomial degree \\ \hline Sigmoid & \(K(x_{1},x_{2})=\) & The kernel is determined by \\ \(\tanh\bigl{(}\beta_{0}x_{1}^{T}x_{2}+\beta_{1}\bigr{)}\) & specific \(\beta_{0}\) and \(\beta_{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Brief description of the kernels that are used in the different types of SVM algorithms.
## 2 Methods
### Methodology for Searching Representative Elements (MIRE)
#### 2.2.5 Step 5: Checking Material Flow Approximation Accuracy  
All representative products have been obtained and used to calculate an approximation of the total material flow. Since its accuracy is of interest, the equation below is used:  
\[\frac{\sum_{i=1}^{a}m_{R(i,a)}}{O}=\alpha \tag{8}\]  
\(m_{R(i,a)}\)--th element of the \(a\)th column in matrix \(\mathrm{M_{R}}\).  
The closest \(\alpha\) comes to value 1, the more accurate the material flow \(M_{\mathrm{R}}\) inventory is.  
\[If\;\alpha=1,\qquad then\;M_{R}=M \tag{9}\]  
Figure 4: Grouping within a family.  
\(M\)--actual material flow.  
The actual material flow or 100% accuracy of inventory of material flow \(M_{\text{R}}\) is obtained with the proviso that the number of families equals the number of all products in the selected time interval (\(K=n\)). In such a case, exactly one product is present within each family and this automatically means:  
\[x_{R,I}=\overline{x_{I}}\quad and\quad j=1 \tag{10}\]  
As there are no deviations in the values of the selected variable and FROM-TO matrices of representative products, the material flow is inventoried with 100% accuracy.  
The essence of this methodology to reduce the quantity of data to be processed in order to obtain a satisfactory material flow inventory. Therefore it is not reasonable, that \(K=n\).  
To obtain credible results consistency is needed:  
* In classifying the products into families based on a criterion of sequence of treatment processes;
* In grouping the products within families based on the criterion (selected variable); and
* In selecting a representative product based on \(x_{I}\).  
As described, MIRE helps us process large amounts of data and it is especially useful for cases where the elements are different and similar at the same time, depending on the criteria (size, weight, cost, etc.). By creating families and selecting their representative elements, the amount of data, that needs to be processed is significantly reduced. In this case, due to the variety of elements, the classic ABC analysis is not sufficient [17]. The result of the process is an approximation of the real material flow, and the deviations are checked by a feedback loop. All five MIRE steps are repeated as needed, until a satisfactory ratio between the accuracy of the approximation and the saved time is achieved.  
MIRE can also be used by companies that already had their material flow recorded, but need to quickly check how optimal their layout is. This is suitable after adding or updating the machinery or presenting a new product into the production line. In cases where the company never had their material flow recorded, MIRE can significantly shorten the time needed by reducing the quantity of data to be inventoried and analysed, because we do not need to consider all the material flow data, but only the elements, based on which we can satisfactorily describe the material flow. Needed input can be accumulated through positions of assembly drawings or any other source, that contains information about product parts, technological operations, etc.
## 3 Results and Discussion
### Test Results of Composite Activation Process  
By employing the compound activation method, the activity index was achieved at 86% within 28 days. Following the optimization of the activity enhancement scheme, this method was ultimately implemented in the Jiyin Water Conservancy Project, situated at an altitude of 2500 m in the Hotan region of Xinjang. A total of 12,000 cubic meters of concrete additions containing volcanic rock powder were utilized. Under extreme conditions, including temperature fluctuations, scouring, and cycles of freezing and thawing, the material exhibited excellent structural integrity along with favorable macro-mechanical properties and durability (Figures 27-29) [70]. These results confirm the technical feasibility of using volcanic rock powder as a concrete additive.  
Figure 28: Freeze–thaw test.  
Figure 27: Carbonization test.  
Figure 29: Resistance to sulphate attack test.
## 2 Rectifier Analysis
### Output Load  
The rectifier is an alternating current (AC)/DC converter circuit. The output voltage will drive the load for specified applications, such as charging batteries or connecting it directly to electronic devices. Consequently, the output load or load resistance plays a critical role in the overall performance. As seen in (2), the load resistance, \(R_{L}\), highly affects the overall PCE and output voltage. Therefore, an optimal value of \(R_{L}\) should be determined to assure maximum power conversion efficiency.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 4 Experimental Results  
We confirmed the effectiveness of the proposed method in terms of hiding capacity, marked-image quality, and lossless compression performance using JPEG-LS [37] and JPEG 2000 [38]. In the experiments, we used three databases: Kodak [44], SIPI [45], and IHC [46]. The Kodak database consists of 24 color images with \(512\times 768\) or \(768\times 512\) pixels, while the SIPI database consists of 6 color images with \(512\times 512\) pixels. The IHC database contains 6 color images with \(4680\times 3456\) pixels. To explore the influence of image size, we used two different sizes for the IHC test images: he original size and \(1/16\) of the original. Figure 8 shows example images from each database.  
We horizontally concatenated three color components in the order of R, G, and B, as shown in Figures 9a, 10a, 11a and 12a. The block size for encryption was \(8\times 8\) pixels. Since the RDH-MSB method [29] was designed for grayscale images, the algorithm of [29] was independently applied to each color component. For data hiding, threshold \(L\), which controls the hiding capacity, was defined as 1, 3, and 10. Figures 9-12 exhibit marked encrypted images derived with the proposed method.  
Figure 8: Examples of test images. (**a**) Kodim4 from Kodak [44]; (**b**) peppar from SIPI [45]; (**c**) hhc5 with \(1/16\) of original size from IHC [46]; (**d**) hhc5 with original size from IHC.  
Figure 11: Marked encrypted images (ihc5 (1/16)). (**a**) Grayscale-based image; (**b**) \(L=1\), (**c**) \(L=3\); (**d**) \(L=10\).  
Figure 12: Marked encrypted images (ihc5 (original)). (**a**) Grayscale-based image; (**b**) \(L=1\); (**c**) \(L=3\); (**d**) \(L=10\).  
Figure 10: Marked encrypted images (peppar). (**a**) Grayscale-based image; (**b**) \(L=1\), (**c**) \(L=3\); (**d**) \(L=10\).
# Defense and Security Mechanisms in the Internet of Things: A Review
## 6 Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{227.6 pt}} \hline
**Solution** & **Type/Application/Advantages/Limitations** \\ \hline PUF-Based Authentication & • Authentication Protocol \\ • IoT in Healthcare & • Strong anonymity, resistance to physical attacks \\ • High computational requirements for resource-constrained IoT devices \\ \hline Blockchain-Based Authentication & • Authentication Protocol \\ • Data immutability, transparency, user identity protection \\ • High computational and energy costs, especially in low-power IoT environments \\ \hline Schnorr Signature Protocol & • Key Agreement Protocol \\ • IoT in Smart Meters & • User privacy, untraceability, low computational complexity \\ • Limited scalability in large IoT networks \\ \hline Lightweight IDS with Machine Learning & • Intrusion Detection System \\ • Cryptote IoT Networks & • High accuracy in detecting threats, flexibility for new attack types \\ • Requires large datasets for training, potential for false positives \\ \hline TIML & • Malware Detection System \\ • Dynamic IoT Environments & • Adaptability to changing data, high classification accuracy \\ • Challenging implementation in resource-constrained environments, high resource demands for training \\ \hline ECC-Based Key Agreement Protocol & • Key Agreement Protocol \\ Protocol & • IoT in Decentralized Edge Computing \\ • Efficient session key generation, low power consumption \\ • Vulnerable to post-quantum attacks without additional safeguards \\ \hline \end{tabular}
\end{table} 
Table 10: Comparison of proposed IoT security solutions.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 7 Results and Discussion
### Image Recognition
#### 7.3.3 Identifying Benthic Species  
This test aims to identify stationary benthic species represented by various shapes, such as lines, polygons, and circles. Python uses OpenCV to construct a computer vision system to quickly identify, count, and categorize multiple species. The algorithm seeks each shape's contours (i.e., lines representing boundaries) and highlights them. Subsequently, the shape is identified by counting the vertices of each contour. For example, a shape is recognized as a triangle if it has three vertices. Figure 18 demonstrates a sample of the benthic species identification through image processing. Here, the image processing first identified the different shapes of benthic species (Figure 18a), then counted the number of species according to the defined shapes (Figure 18b).  
Figure 17: Crack length detection test. The blue line represents the crack.
## 3 Incremental Pyraformer-DCCA
### Pyraformer
#### 3.1.2 Coarse-Scale Construction Module  
The Coarse-Scale Construction Module (CSCM) plays a crucial role in building the hierarchical structure of the PAM by efficiently aggregating information from lower-resolution nodes. The goal of the CSCM is to abstract features from finer scales and propagate them to coarser levels, thereby constructing a pyramidal representation of the input sequence. This approach effectively captures temporal dependencies at multiple resolutions, reducing computational complexity and ensuring a broader view of the temporal structure.  
As shown in Figure 6, the CSCM utilizes a convolution operation to progressively abstract features from lower-resolution nodes, which form the foundation for nodes at coarser resolutions. This hierarchical construction allows the model to understand temporal dependencies across different time scales. Specifically, the CSCM takes, as input, the lower-scale nodes and processes them using a convolutional operation to generate nodes at a coarser level.  
Considering an input sequence \(X\in\mathbb{R}^{L\times D}\), the CSCM aims to create a coarser representation of the input sequence by reducing the sequence length while maintaining crucial temporal information.  
It applies a convolutional layer with stride \(C\), which effectively reduces the sequence length by a factor of \(C\). Let the convolutional filter be represented by \(W_{C}\in\mathbb{R}^{k\times D}\), where k is the kernel size of the convolution. The convolution operation is represented as follows:  
\[X^{(s+1)}=f\Big{(}W_{C}*X^{(s)}\Big{)} \tag{15}\]  
where \(X^{(s)}\in\mathbb{R}^{L_{s}\times D}\) is the input sequence at scale \(s\), \(X^{(s+1)}\in\mathbb{R}^{L_{s+1}\times D}\) is the output at the next coarser scale, and f represents a nonlinear activation function, such as ReLU. The stride of the convolution C determines the length of the output sequence at scale \(s+1\), which can be expressed as:  
\[L_{s+1}=\left[\frac{L_{s}}{C}\right] \tag{16}\]  
By applying the convolution operation with a stride greater than 1, the CSCM could effectively reduce the temporal resolution, creating a hierarchical representation of the  
Figure 6: Coarse-Scale Construction Module.  
sequence. The resulting nodes at the coarser scale contain aggregated information from the finer-scale nodes, enabling the model to represent broader temporal patterns.
## 3 Attitude Controller Design
### Experiment Results and Discussion
#### 4.2.1 I&I Control Experiment  
The parameters of the I&I controller are selected as follows: \(K_{1}=diag\{0.02,0.1,0.03\}\), \(K_{2}=diag\{5,5,5\}\), \(\gamma=[0.4,0.4,0.4]^{\top}\).  
To validate the anti-disturbance performance of I&I controller, disturbances are applied to each axis. Each axis is disturbed by a collision. The I&I controller regulates the state to \(\eta_{2}=[0,0,0]^{\top}\). The roll, pitch, and yaw control results of I&I control are shown in Figure 4.  
The experiment results show that: (1) the attitude error generated by the collision is near \(10^{\circ}\); (2) the attitude converges to \(\eta_{2d}\) in less than \(0.8\) s; (3) the roll and pitch control have shorter adjust time than the yaw control, which is because the yaw axis has a higher moment of inertia and a lower control moment, resulting from the mechanical design of the rotor arrangement; and (4) when no disturbance is applied, the control accuracy of roll and pitch is better than yaw; this is mainly for the unmodeled dynamics of the tail servo.  
The control torque is shown in Figure 5. The roll and pitch control toques are generated by the change in the speed of the rotors, which is quick, whereas the yaw control torque is generated by the servo sway, which is slow and has a slight chattering.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit (SI)** \\ \hline \(l_{1}\) & 0.13 & m \\ \(l_{2}\) & 0.075 & m \\ \(l_{3}\) & 0.15 & m \\ \(m\) & 1 & kg \\ \(B\) & 3 & N \\ \(I_{xx}\) & 0.0061 & kg/m\({}^{2}\) \\ \(I_{yy}\) & 0.006 & kg/m\({}^{2}\) \\ \(l_{zz}\) & 0.0118 & kg/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of NTAUV.  
Figure 3: Testbed.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### HMAC Calculation and Steganographic Key  
The HMAC code is generated from the entire glb file, which was created after zeroing out the bits in the model intended to hide the watermark. To obtain the HMAC, the OpenSSL library was used, and the SHA-512 hash function was applied to compute the HMAC. A secret key, which is part of the steganographic key, is also required. Its use ensures that third parties will not be able to adulterate the HMAC without access to the correct key.  
The HMAC is calculated from the complete glb file due to the presence of numerous potential attack vectors. For instance, the JSON section contains a description of the complete binary buffer as well as other data used in the rendering process (rendering mode, use of vertex indices, etc.). Other sensitive metadata includes the glb file header, which stores information such as the overall size of the file. This comprehensive approach guarantees that all aspects of the model are protected, thereby increasing the method's robustness. However, this universality also means that even minor changes to metadata--such as those introduced during file saving--will be flagged as modifications.  
The steganographic key (stego key) is a secret element that should only be in the possession of authorised persons. It contains information about the number of bits for HMAC embedding, the seed of the pseudorandom number generator, and the name of the vertex attribute used to attach the watermark data. The key structure is shown in Figure 4.  
The proposed steganographic key begins with two characters that specify the number of bits used to embed the HMAC code. This is followed by a numeric string of arbitrary length ending with an asterisk (*), which serves as the seed for the pseudorandom number generator. Next is the name of the vertex attribute--such as POSITION, NORMAL, or TEXCOORD_0--conforming to glTF 2.0 standards. The final part is another arbitrary-length string that serves as the HMAC key.
## 5 Discussion  
### Benefits of Gradient-Guided Resblock  
In the proposed network, the super-resolution subnet and the gradient modeling subnet are trained jointly. In Figure 5, we present some feature maps of the last convolutional layer in the gradient modeling subnet. The feature maps contain sufficient diversity for representing the high-frequency details in T2w MRI, which supports the validity of the gradient-guided strategy. Facilitated by these feature maps, the edge and texture areas can be reconstructed explicitly. In DGGRN, the SR subnet consists of eight gradient-guided Resblocks. The ResNet and the proposed network share the same initial parameters and hyperparameters, such as the learning rate and the number of epochs. Table 1 summarizes experimental results on BrainWeb and NAMIC of ResNet(the third column to last) and DGGRN (the last column). Compared with ResNet, in terms of PSNR, our network with gradient-guided realizes average improvements of 1.0 dB on BrainWeb, 0.4 dB on NAMIC and 0.3 dB on IXI.  
In Figure 4, three examples of real images with an upscaling factor of 4 are presented. Facilitated by the gradient modeling subnet, the proposed method outperforms ResNet in recovering sharp edges and tiny textures.
## 2 Overview
### Artificial Neural Networks
#### 2.2.1 Tree Parity Machine  
A specific type of ANN, Tree Parity Machine (TPM), was first introduced in [32]. TPM specifically has only one hidden layer and one output, usually called _\(\tau\) (tau)_. The total number of input neurons is equal to \(\texttt{N}\times\texttt{K}\), where N is the number of neurons connected to each hidden layer neuron, and K is the number of neurons in the hidden layer. An example TPM with 3 hidden neurons and 3 \(\times\) 4 input neurons is presented in Figure 1. Each connection between the input and hidden layers has a parameter called _weight_. The weight values are discrete and are chosen from the interval set by the L value: \([-\texttt{L},\texttt{L}]\). The input values (denoted as \(x\)) are either \(-1\) or \(1\)[33].  
Figure 1: Tree parity machine with 3 hidden neurons and 3 \(\times\) 4 input neurons.  
The learning process of the TPM begins with computing the output values of the hidden layer neurons. The value for the \(k\)-th neuron is calculated using the formula presented in Equation (1) [23]:  
\[\sigma_{k}=\text{sgn}\left(\sum_{n=1}^{N}x_{kn}*w_{kn}\right) \tag{1}\]  
where \(n\in\{1...N\}\), \(k\in\{1...K\}\), \(x\) is the value of the n-th input neuron of the \(k\)-th hidden neuron, \(w\) is the weight value on the connection between these neurons, and the signum function is calculated as shown in Equation (2):  
\[\text{sgn}(z)=\left\{\begin{array}{ll}-1&\quad z\leq 0\\ 1&\quad z>0\end{array}\right.. \tag{2}\]  
The final output is then calculated based on the previously computed hidden layer outputs, as shown in Equation (3):  
\[\tau=\prod_{k=1}^{K}\sigma_{k} \tag{3}\]  
where \(k\in\{1...K\}\). Based on Equations (1) and (3), it can be concluded that the final output value of the TPM is either \(-1\) or \(1\).