esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Detection Model for Apparent Defects in Metro Tunnel Liniings
#### 3.1.1 Basic Framework for Modeling the Defects Detection Model
As shown in Figure 3, the YOLOv8 model builds upon its predecessors by incorporating significant enhancements to its architecture, which is composed of three primary components: the feature extraction backbone, the feature enhancement module (neck), and the detection head. Upon receiving an input image, the YOLOv8 model processes it through the feature extraction backbone. Here, the cross-stage partial bottleneck (CSPBottleneck) with a dual conversion (C2f) module is employed to extract essential features such as edges and textures. This stage efficiently captures both local and global features while optimizing gradient flow for effective training. Next, the extracted features are passed to the neck, where cross-stage feature transmission occurs. This step integrates features from different layers, allowing the model to handle objects of various scales by combining detailed high-resolution features with deeper, more abstract representations. Finally, the refined features are directed to the detection head, where the decoupled-head module separates the tasks of classification and localization. This separation enhances the precision of both tasks by reducing interference, enabling the model to output accurate bounding boxes and class labels for objects within the image. As an advanced single-stage object detection model, YOLOv8 demonstrates superior performance in terms of detection accuracy, speed, and model parameters. Considering the inherent challenges presented by the metro tunnel environment, such as restricted spatial constraints, limited data transmission bandwidth, and the complex visual conditions marked by low illumination and intricate background structures, YOLOv8's compact architecture proves particularly advantageous. Its design facilitates precise and efficient detection while ensuring seamless deployment on lightweight, portable edge devices, which is essential in the spatially confined and operationally demanding context of metro tunnels. Therefore, YOLOv8 was selected as the basic framework in this study, and a detection model tailored to apparent-defect detection scenarios in metro tunnel linings was designed based on this framework.
## 9 A Complete Pattern: WS-Trust
### Solution
#### Dynamics  
We describe the dynamic aspects of the WS-Trust using sequence diagrams for the use cases \"_create security token_\" and \"_access a resource using a token_\".  
_Create a security token (Figure 6):_  
Summary: STS creates a security token using the claims provided by the requester.  
Actors: A Requester  
Precondition: The STS has the required policy to verify the requester claims and the requester provides parameters in form of _claims_ and _RequestType_ signed by a _signature_.  
Description:  
* The requester requests a security token by sending the required _claims_ and _RequestType_ signed by a _Signature_ to the STS. The signature verifies that the request is legitimate.
* The STS contacts the Trust Engine to check the requester's claims.  
Figure 5: Class Diagram for the WS-Trust Pattern.  
* The Trust Engine contacts the web service's policy to verify the claims including attributes and security token issuers of the requester.
* Once approved, the STS creates a security token containing the requested claims.
* The STS sends back its _SecurityTokenResponse_ with a security token issued for the requester.  
Postcondition: The requester has a security token that can be used to access resources in a trusted unit.  
_Access a resource using a token (Figure 7):_  
Summary: A STS allows the use of resources by establishing trust by verifying _proofOfClaims_ sent by the requester.  
Actors: A Requester  
Precondition: The Trust Engine has the required policy to verify the requester' security token.  
Description:  
* The requester asks for a service access by providing the required security token.
* The receiver sends the security token to the STS for verification.
* The STS use its Trust engine to verify the security token claims.
* Once approved, the STS notifies the receiver that the security token is valid and verified.
* The receiver gives the requester a token that implies the right to use the service.  
Postcondition: The requester has a security token that can be used to access services in a Receiver web service.  
Figure 6: Sequence Diagram creating a security token.
## 4 Experimental Studies  
A semi-physical simulation experiment was designed to verify the effectiveness of the MPC controller. The controllable pitch propeller was implemented in the form of a virtual prototype, and the controller used the embedded pitch control board designed in the current work. Both achieved bi-directional data communications through TCP/IP.
## 2 Results and Discussion
### Volatile Composition of Investigated Fruits and Vegetables
#### 2.1.4 Garlic  
Like onions, garlic is rich in organosulfur compounds that are responsible for the characteristic odor and taste of this vegetable, as well as bioactive properties [47, 48]. Of the 21 compounds identified in the garlic samples, 99.7% of the volatile profile is constituted by organosulfur compounds, with the remaining 0.3% belonging to the esters, ethers, aldehydes and benzene derivatives.  
Dially disulfide (175) is the major compound found in the investigated garlic samples, contributing to 70.4% of the total volatile composition. This is one of the most important organosulfur compounds present in garlic, being formed by the decomposition of allicin, an unstable and highly reactive molecule. It is the most common bioactive compound present in garlic and accounts for about 70% of the sulfur compounds family [49]. Allicin is produced enzymatically by the interaction of the non-protein amino acid allin with the enzyme allinase, common in all Allium species [50]. During the enzymatic reaction, ammonia and pyruvate are also formed. Due to its instability, alicin degrades easily over time, forming a collection of allyl sulfates and second generation polysulfides (commonly called garlic organosulfides) [51, 52]. The chemical structures of the main second generation organosulfides are shown in Figure 3.  
Figure 2: Distribution of chemicals identified in red and yellow onions.  
Diallyl trisulfide (260) was also identified in a considerable percentage (12.4%), being the second most dominant volatile in garlic. Both diallyl disulfide and diallyl trisulfide, which are associated with anticancer properties [53; 54; 55], are partly responsible for the bioactive effects of garlic.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 2 LDPC Codes Constructed from BIBDs and Their Tanner Graphs  
### LDPC Codes and Tanner Graphs  
An LDPC code is defined by the null space of a sparse matrix (\(\mathbf{H}=[h_{ij}]\)), known as a parity-check matrix. If every row of \(\mathbf{H}\) contains \(\rho\) nonzero elements and every column of \(\mathbf{H}\) contains \(\gamma\) nonzero elements, the resulting LDPC code is referred to as \((\gamma,\rho)\)-regular. The Tanner graph of an LDPC code is a bipartite graph, where the two types of vertices correspond to the row indices and column indices of \(\mathbf{H}\), respectively. An edge connects the row index (\(i\)) and the column index (\(j\)) if and only if element \(h_{ij}\) of \(\mathbf{H}\) is nonzero. A cycle in the Tanner graph is a closed path of interconnected edges that starts and ends at the same edge, traversing no other edge more than once. The length of the shortest cycles in the Tanner graph of an LDPC code is known as its girth.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## Materials and Methods
### Multi-GNSS Positioning Models
#### 2.1.1 Single-Frequency SPP  
It is possible to find a SPP solution based on at least 4 pseudo-range observations and by utilizing dual- or multi-frequency IF combinations, which eliminate the ionospheric delay. For a single-frequency SPP, ionospheric corrections are usually computed by an external model. In general, with \(m\) observed satellites and considering the satellite orbits and clocks, satellite timing group delay and atmosphere delays, the single-frequency SPP model can be given for a single epoch as [15]:  
\[\left[\begin{array}{c}p_{r,1}^{s,1}-\overline{\rho}_{r}^{s}\\ \vdots\\ p_{r,1}^{s,m}-\overline{\rho}_{r}^{s}\end{array}\right]=\left[\begin{array}{ ccc}-\mu_{r}^{s}&\mathbf{1}\end{array}\right]\left[\begin{array}{c} \boldsymbol{x}\\ d\overline{t}_{r}\end{array}\right]+\left[\varepsilon_{p}\right],\boldsymbol{Q _{L}} \tag{4}\]  
\[d\overline{t}_{r}=dt_{r}+d_{r,1} \tag{5}\]  
where \(\overline{\rho}_{r}^{s}\) denotes the geometrical range including the measurement errors; \(\mu_{r}^{s}\) is the unit vector of the component from the receiver to the satellite; \(\mathbf{1}\) is a vector with corresponding rows and one column; \(\boldsymbol{x}\) is the vector of the receiver position increments relative to the a priori position; \(\boldsymbol{Q_{L}}\) denotes the stochastic model of observed minus computed values of observations; The estimable receiver clock \(d\overline{t}_{r}\) is a combination of true clock offset plus the receiver hardware delay.  
The redundancy of the SPP model is the number of observables minus the number of estimable parameters:\(m_{s}-(3+1)=m_{s}-4\) (satellites), \(m_{s}\) denotes the number of observables. Hence, the SPP model by least square (LS) is solvable for \(m_{s}\geq 4\).
## 4 RF Concepts and Principles
### Radio Frequency Energy Harvesting in the Near Field  
Electromagnetic induction and magnetic resonance are two techniques that are commonly used in near-field applications to produce electric power and the ability to power devices that are within a wavelength of each other [56]. Ref. [57] explains an example of near-field RF-EH in which unintentional RF energy from 10 kHz to 1 GHz (wideband) was scavenged and converted to usable energy in a railway environment. A metamaterial with a wideband frequency of 350 MHz and a load of 8.5 Kohms was used to deliver up to 84% of the energy.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.1 Image Denoising  
DDPM excels in the task of image denoising by utilizing its denoising diffusion process. The model is trained to effectively reverse the diffusion process, removing noise from observed images. By learning the intricate probability distribution of clean data and accounting for the noise introduced during the diffusion steps, DDPM can produce denoised images with enhanced clarity [13; 43].
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Sensing Applications Based on Radiation Pressure
#### 4.2.5 Optomechanical Detection on Angular Momentum of Light  
Photons carry linear momentum and spin angular momentum when they are circularly or elliptically polarized. In the light-matter interaction, the transfer of linear momentum creates optical forces, whereas transfer of angular momentum induces optical torque. A wealth of applications of photon angular momentum have been widely proposed. Methods for measuring photon angular momentum have been rapidly developed but rarely explored in integrated photonics. Recently, the photon angular momentum measurements were set out by using the on-chip optomechanical systems. For PhC optomechanical cavities, the first integrated optomechanical resonator to detect the spin angular momentum and optical torque [146] was proposed using a silicon nano-optomechanical device as shown in Figure 26. There was a suspended nanobeam in which two PCN nanocavities were embedded on each side and attached to a birefringent waveguide. The core element of the device was a birefringent suspended structure due to the angular momentum exchange between the light and the waveguide. As a result, the photons applied an optical torque on the birefringent waveguide to twist or float. The resonance modes of the nanocavities were particularly sensitive to their distance from the substrate, so they could provide very sensitive detection to the rotation of the waveguide actuated by the optical torque.  
In addition, light with a helical wavefront also possess orbital angular momentum (OAM). As presented in Figure 27, an optomechanical PhC cavity to detect the optical torque allowed measurement of the OAM of light [147], as well as non-absorbing optical field detection via light's OAM degree of freedom. One of the nanobeams was attached to the central pad, while the other was fixed to the surrounding chip. Optical actuation of the central pad induced by OAM could shift the nanobeam's center of mass position through twisting of the central pad, and through excitation of the mechanical bouncing mode of nanobeam. The device had a torque sensitivity of \(\tau_{min}=3.22\times 10^{-21}\) N\(\cdot\)m/Hz\({}^{1/2}\), allowing OAM detection of optical fields with a sensitivity of \(P_{min}=8.7\) uW/Hz\({}^{1/2}\). Research on optical torque and optomechanical interplay with photon angular momentum can contribute to torsion-based cavity optomechanics and photon spin-orbit optomechanical coupling, as well as sensing applications such as gyroscope and torsional magnetometry.  
Figure 25: (**a**) Schematic of a chip-scale 2D PhC slot cavity for acceleration sensing; (**b**) close-up view of optomechanical cavity with colored arrows denoting three different photonic crystal lattice perturbations resulting in optomechanical stiffening [144; 145].
## 4 Discussion
The kinematics determines the angle and position of the joints, while the kinetics predicts the muscle forces required to maintain or perform the necessary motion. Simultaneously, EMG sensors need to be attached to the muscles of interest along with the motion capture system to measure their activation level during the CBA and HBB tests. Finally, we can compare the muscle forces/activation obtained from musculoskeletal modeling software and EMG output to identify the primary and accessory muscles involved during the CBA and HBB tests. This would not only validate the obtained results but also provide a more comprehensive understanding of shoulder biomechanics and muscle dynamics during the CBA and HBB tests. Musculoskeletal modeling also offers significant potential for enhancing both clinical and non-clinical aspects of orthopedic practice. Clinically, these models can improve diagnostic accuracy by quantifying muscle forces, activation patterns, and joint mechanics, enabling data-driven decisions, particularly in cases where subjective pain responses or imaging inconsistencies cause uncertainty. They also aid preoperative planning by simulating surgical outcomes, optimizing rehabilitation strategies, and tailoring interventions to individual patients. Non-clinically, musculoskeletal models serve as educational tools, helping clinicians and trainees visualize joint interactions and biomechanical principles while providing researchers with a platform to test hypotheses and refine diagnostic methods. Incorporating subject-specific data into these models would further improve their accuracy in clinical settings, where patient-specific anatomical variations are critical for effective diagnosis and treatment planning. Additionally, a pilot study to gather preliminary data on muscle forces using experimental methodswould validate these predictions and serve as a foundation for integrating musculoskeletal modeling with finite element analysis (FEA). Using muscle force predictions as input for FEA can provide insights into joint mechanics, especially about the contact area and stress levels at the AC joint during the CBA and HBB tests, enhancing their clinical utility. Future research should then assess the reliability of the HBB test as a standalone diagnostic tool for AC joint OA. One approach could involve performing CBA and HBB tests on patients with AC joint OA and comparing the outcomes with radiographic studies to evaluate test sensitivity. Another idea could be comparing the stress level at the AC joint during CBA and HBB tests using the FEA mode. The test that results in a greater reduction in AC joint separation distance would have increased contact area at the joint and experience higher stress levels, indicating that it may be more effective for diagnosing AC joint OA. This will eventually contribute to advancing both clinical practice and orthopedic biomechanics of the AC joint.
## 2 Specified Dynamic Model and Preliminaries  
Based on the control objective of this work, the dynamics for an \(n\)-degree-of-freedom, revolute-joints, serial robot arm in the presence of external vibration and uncertain payload can be expressed in joint-space coordinates as follows:  
\[M(q)\ddot{q}+V_{m}(q,\dot{q})\dot{q}+G(q)=\tau-\tau_{0}-\tau_{l} \tag{1}\]  
where \(q\), \(\dot{q}\), and \(\ddot{q}\in\Re^{n}\) denote the angular displacement, angular velocity, and angular acceleration vectors, respectively; \(M(q)\in\Re^{n\times n}\) represents the inertia matrix; \(V_{m}(q,\dot{q})\in\Re^{n\times n}\) represents the Coriolis/centripetal matrix; \(G(q)\in\Re^{n}\) represents the gravity vector; \(\tau\in\Re^{n}\) represents the torque input of the joints; \(\tau_{0}\in\Re^{n}\) represents the disturbance due to external vibration; and \(\tau_{l}\in\Re^{n}\) represents the load torque.  
For the purpose of controller design and analysis, we state some properties and assumptions that are used in this work. The properties are applicable for the dynamics of a standard fixed-base n-link, revolute, direct-drive robot manipulator in Equation (1) [27; 28]:  
**Property 1**.: _The inertia matrix \(M(q)\) is a positive-definite and symmetric matrix that satisfies_  
\[\lambda_{m}\left\|\chi\right\|^{2}\leq\chi^{T}M(q)\chi\leq\lambda_{M}\left\| \chi\right\|^{2},\ \forall\ \chi\in\Re^{n} \tag{2}\]  
_where \(\chi\) is a vector; \(\lambda_{m}\) and \(\lambda_{M}\in\Re\) are known, positive, real, and bounding constants; and \(\left\|\cdot\right\|\) is the Euclidean norm._  
**Property 2**.: _The inertia and Coriolis/centripetal matrices satisfy the following skew-symmetric relationship:_  
\[\chi^{T}(\dot{M}(q)-2V_{m}(q,\dot{q}))\chi=0,\ \forall\ \chi\in\Re^{n} \tag{3}\]  
_where \(\dot{M}(q)\) is the derivative of the inertia matrix._  
**Assumption 1**.: _The friction effects are beyond the scope of this study; hence these effects are neglected in (1)._
# A Novel Deep Learning Approach for Deepfake Image Detection
## 5 Applied Transfer Learning Neural Network Techniques
### Mobile Net Technique  
The Mobile Net [23] is a transfer learning model we have built for deepfake detection in our research study. Google open-sourced the Mobile Net model. The Mobile Net is highly used for vision-related applications with fast processing. The model architecture reduces computation costs and has less complexity. The architecture is built using depthwise separable convolutions [24]. The two operations, depthwise and pointwise, are performed in depthwise separable convolutions. This reduces the number of parameters compared to regular convolutions. Our research study replaced the top layers of Mobile Net with MLP block.  
The Mobile Net model configuration parameters and layer architectural analysis are analyzed in Table 4. The analysis describes the first layer as the input layer with an output shape of (100, 256, 256, 3). Then, the Mobile Net layers are involved with 1024 units  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr No.** & **Layers** & **Unit** & **Activation Function** & **Output Shape** & **Parameters** \\ \hline
1 & The Sequential Input Layers. & None & None & (100, 256, 256, 3) & 0 \\ \hline
2 & The Xception Layers. & 2048 & None & (None, 8, 2048) & 20,861,480 \\ \hline
3 & The Dropout Layers. & 0.2 & None & (100, 8, 8, 2048) & 0 \\ \hline
4 & The Flatten Layers. & None & None & (100, 131,072) & 0 \\ \hline
5 & The Dense Layers. & 64 & RELU & (100, 64) & 8,388,672 \\ \hline
6 & The Output Layers. & 1 & SIGMOID & (100, 1) & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Xception model configuration parameters and layer architectural analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sr No.** & **Layers** & **Unit** & **Activation Function** & **Output Shape** & **Parameters** \\ \hline
1 & The Sequential Input layers. & None & None & (100, 331, 331, 3) & 0 \\ \hline
2 & The NAS-Net layers. & 4032 & None & (None, 11, 11, 4032) & 84,916,818 \\ \hline
3 & The Dropout layers. & 0.2 & None & (100, 11, 11, 4032) & 0 \\ \hline
4 & The Flatten layers. & None & None & (100, 487,872) & 0 \\ \hline
5 & The Dense layers. & 64 & RELU & (100, 64) & 31,223,872 \\ \hline
6 & The Output layers. & 1 & SIGMOID & (100, 1) & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The NAS-Net model configuration parameters and layer architectural analysis.  
and 3,228,864 total parameters. The parameters of Mobile Net are less as compared to other techniques. To prevent the overfitting of the model, the dropout layer is added. The deepfake prediction is performed by a family of dense and flattened layers in the architecture. The dense layer has 64 units with relu activation. The architecture's output layer with sigmoid activation is used for deepfake classification.
## 3 Method: Automated Target Detection
### Lake Shapes for Different Water Occurrences  
The target detection is performed based on a fixed water occurrence threshold of 50% (see Section 3.1). In order to conduct overflight statistics (Section 3.4) and to study the impact of different thresholds (Section 5.2), isolated water occurrence masks are required. Performing the morphological operations repeatedly for different thresholds (i.e., repeating the full image processing procedure) incurs great time and computational costs. Instead, the binary land-water mask (output of Section 3.1)  
Figure 2: Visualization of the morphological operations used in the automated target detection: (**a**) initial land–water mask, (**b**) after erosion, (**c**) after dilation, (**d**) final land–water mask.  
covering the entire MRB is vectorized to reduce the computational time necessary to process the raster data. The result is one polygon feature per isolated water body. In order to extract the isolated water occurrence, the water occurrence mask is clipped to the bounding box of each feature with a buffer of \(0.15^{\circ}\), which is required because narrow ends of dendritic reservoirs may have been lost due to the morphological operations. The clipped water occurrence mask is again transformed to a binary land-water mask, but this time with an occurrence threshold of 5%. Afterwards, each binary mask is labeled (i.e., connected pixels are marked with a consistent integer), and the label of the corresponding water body is identified using the pole of inaccessibility (POI) of the respective polygon; i.e., the spot furthest from the lakeshoe [29]. The POI is very likely to be located above a pixel with a high occurrence. We isolate the water occurrence of each target using the respectively labeled pixels as a mask.  
Due to the morphological operations, contiguous water bodies may be separated, especially at the shore of dendritic reservoirs. This results in duplicate isolated water occurrence masks. In order to detect these duplicates, intersections between the shorelines of all isolated masks are searched, and the smaller intersecting masks are removed.  
Based on the isolated water occurrence masks, the water extent and surface area of each target can be extracted for different occurrence thresholds. In this study, we used thresholds of 5%, 25%, 50%, 75% and 95% in order to calculate the satellite altimetry overflight statistics (Section 3.4).