esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 2 The Development History of CT Technology  
### The Origin of X-ray CT  
Since [PERSON] first discovered X-rays, this radiation technology has been widely applied in numerous research fields. The principle of X-ray CT in detecting the structure of an object mainly utilizes the Compton scattering and photoelectric effect that occurs when the rays pass through the scanned object, during which the rays undergo energy attenuation [16]. When the ray beam passes through a substance with an attenuation coefficient greater than zero, its intensity attenuates exponentially, and the attenuation coefficient is determined by the variations in the material density and atomic composition of the scanned object [17]. Assuming that the incident beam is monochromatic and the irradiated object is homogeneous (i.e., it has a constant attenuation coefficient), then mathematically, it can be represented by the Beer-Lambert law of attenuation [18], which denotes the penetration intensity I of monochromatic X-rays as they pass through an object:  
\[I=I_{0}e^{-\int\mu(s)ds} \tag{1}\]  
Here, \(I_{0}\) represents the initial intensity of the incident beam, and \(\mu(s)\) is the local linear attenuation coefficient along path \(s\) of the X-ray through the object.  
This property was quickly applied to both medical and non-medical uses. In the field of geotechnical science, primarily over the past 30 years, numerous scholars have utilized X-ray imaging technology to extract and analyze the internal microstructures of a multitude of soil samples [19; 20; 21; 22; 23; 24; 25; 26].
## 1 Introduction
[26] provided a continuous SiC fibre-reinforced Ti-25-10 titanium alloy composite with tensile damage fracture characteristics, along with the damage mechanism at the strong and weak interfaces and the surface characteristics of the tensile fracture. A significant amount of research based on the analysis method of cross-scale mechanics has also been conducted [27; 28]. Most research in this field has focused on the failure mode of metal drive shafts and composite plates or shells. However, the damage process and the failure modes of composite shafts have not been studied sufficiently, especially in terms of the microscopic failure modes and failure processes, such as fibre fracture, matrix damage, and interface cracking. Therefore, it is imperative to conduct damage process analysis and failure mode research on SiC\({}_{\text{f}}\)/Ti composite shafts. Therefore, in this study, torsional strength experiments and failure mode analyses on SiC\({}_{\text{f}}\)/TC\({}_{4}\) composite shafts were carried out. The damage process trend of the shaft was monitored using acoustic emissions, and the onset of shaft fracture was examined by carrying out electron microscopy experiments. A macro-mechanics shaft model and a micro-mechanics RVE model were also established. Based on the two models, the damage processes of fibre fracture, matrix cracking, and interface debonding under torque loads were calculated by the finite-element method. The main goal was to calculate the progressive damage process under the same conditions as the experiment, verifying the effectiveness of the cross-scale models. This can provide design support for shaft applications in aero-engines.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 2 ECG Recordings  
All of the ECG data used in this study were obtained from the MIT-BIH arrhythmia database [21], which contains common and life-threatening arrhythmatic heartbeats. This database contains 48 recordings of two-channel ambulatory ECG recordings with a length of 30 min, a sampling frequency of 360 Hz, and 11-bit resolution over a 10 mV range. In most recordings, the upper lead is a modified limb lead II (MLII), and the lower lead is usually a modified lead V1 (occasionally V2 or V5, and in one instance V4). Each recording was independently annotated by two or more cardiologists. There are a total of 109,488 beats included in this study that were annotated as one of 15 possible heartbeat classes.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
## 3 Role of Blockchain in 6G Services
### Network Slicing  
Network slicing allows to create different virtual networks, where each network poses different network services from the physical networks infrastructure. This is made possible by integrating technologies, such as software-defined network (SDN) and network functions virtualization (NFV), that divide single network connections into multiple distinct virtual connections. It delivers virtual applications that are directly deployed on the slice for instant service delivery to the users. As an outcome, mobile applications that are running on slices have large-scale connections and low-latency services with a high-quality user experience. Network slices reduce the network complexity by virtualization and add automation to a complex network. However, it faces issues in dynamic slice creation, resource sharing, wireless resource virtualization, and data security. To mitigate the aforementioned issues, blockchain is integrated with network slicing to provide user trust, transparency, and privacy. Many researchers integrate blockchain as a service in network slicing. For example, [PERSON] et al. [39] focused on user authentication, load imbalance, and inappropriate network slicing issues in an edge-assisted 6G network. Initially, they designed a deep-learning-based adversarial network that predicts the efficient network slices and communication links based on the slice capacity, priority, and quality-of-service (QoS) demands for data transmission. Further, they applied DAG-based blockchain using the proof-of-space approach to enhance the scalability and overcome more resource consumption issues. Moreover, they introduced a soft actor-critic scheme for load balancing in the SDN controller. Finally, the paper concluded that the adopted scheme reduces handover latency, packet loss rate, and data transmission delay and enhances the system throughput and different attack detection rate.  
[PERSON] et al. in [40] integrated a multidomain network slice orchestration framework with a blockchain-based CoNet consensus algorithm for data consistency, scalability, and security. Further, they proposed a game-theory-based bilateral evaluation approach that provides system fairness with a QoS guarantee. The findings showed that the adopted scheme provides users' data security and privacy with high throughput and low latency. Then, [PERSON] et al. [41] formulated a blockchain-based optical network slicing scheme for IoT applications. They focused on the users' data security and trust issues while they utilized IoT services. The results showed that their presented scheme is able to complete slicing transactions with a high-quality dedicated communication network.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 2 Exploring the Options
### Option 2: User-Based Integrity with LEO-MEO Observables  
This option is based on monitoring the MEO satellites at user level using LEO-MEO L-Band observables. Each LEO-PNT satellite collects LEO-MEO observables and sends these observables or a suitable by-product to the user. Then, the user individually checks each MEO against a particular hypothesis, e.g., against a given threshold, and decides whether to use that MEO satellite for generating the navigation solution.  
The user monitors each MEO satellite by using the LEO satellites that are transmitting their products to the user as reference stations. This approach distributes the monitoring functionality between the LEO-PNT constellation and the user. The constellation performs MEO data collection and dissemination, while the user performs integrity processing.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods  
### Three-Dimensional Models  
The presented method was employed to watermark various 3D models in the glb file. The objects used in the research are presented in Table 1, where the abbreviation KB stands for kilobyte, where 1 KB equals 1024 bytes. Each model comprises vertices, and the underlying primitive topology is a triangle. A single vertex contains position information in the form of a three-dimensional vector. It may contain additional data useful in the rendering process, such as normal vectors, texture coordinates, and tangent vectors. Among the selected models are objects with different types of geometry, varying complexity, and different ways of creation, i.e., 3D scan, photogrammetry and computer-modelled objects. Some of the models are from the article [22] (gate.glb, statue.glb, pinecone.glb), which allows precise comparison of the proposed method with already existing solutions. The gate.glb model is the result of a 3D scan of a historic artifact [32]. The other models come from the 3D library available in Microsoft's Windows 10 operating system or are a combination of two models from the library.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Parameters** & **Model** & **Parameters** \\ \hline \multirow{5}{*}{} & Name: gate.glb & & Name: troll.glb \\  & Size: 25,030 KB & & Size: 25,438 KB \\  & Vertices: 362,771 & & Vertices: 48,624 \\  & Triangles: 499,976 & & Triangles: 81,831 \\  & Extras: texcoord & & Extras: texcoord, \\  & Source: scan 3D & & Source: computer \\ \hline \multirow{5}{*}{} & Name: statue.glb & & Name: duck.glb \\  & Size: 4393 KB & & Size: 23,955 KB \\  & Vertices: 98,453 & & Vertices: 12,258 \\ \cline{1-1}  & Triangles: 34,912 & & Triangles: 20,828 \\ \cline{1-1}  & Extras: normals & & Extras: texcoord, \\ \cline{1-1}  & Source: photogrammetry & & Source: computer \\ \cline{1-1}  & & & design \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 3D models used in the research.  
Three-dimensional models boast a broad spectrum of applications. Consequently, the information contained within the vertices can vary depending on the intended use. For objects created by 3D scanning, such as for cultural heritage preservation [33], it is sufficient for vertices to store only information about their position and texture coordinates. Texture coordinates are a pair of real numbers \((u,v)\), where \(u,v\in[0,1]\). They are used to determine the colour of a given vertex based on the applied texture. On the other hand, the vertices of models used in rendering, in addition to position and texture coordinates, may contain normal vectors, which are necessary in calculating illumination. Among other things, there are also tangent vectors, which are used to simulate the refraction of light. It is imperative to note that both normal vectors and tangent vectors are elements of \(\mathbb{R}^{3}\).
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 3 Framework Architectures
### Security Integration via RBAC-IoT  
Inspired by RBAC-IoT microgrid security [1], LEAD-RP implements lightweight role-based access control through three mechanisms:  
1. Node Role Assignment: During cluster formation, nodes are assigned the following roles: \[\text{Role}(i)=\begin{cases}\text{CH}&\text{if }T(s_{i})>\tau\\ \text{CM}&\text{otherwise}\end{cases}\] with role-specific communication privileges encoded in join-request messages.  
Dynamic Privilege Update: Using energy factor \(E_{f}\) as trust metric,  
\[\text{Privilege Level}=\lfloor 3\times E_{f}\rfloor\quad(0=\text{revoked},1=\text{ data},2=\text{control})\]
3. Cross-layer Validation: Each data packet includes \(\text{Hash}(\text{data}||E_{f}||\text{role})\), which was verified at CH using local energy maps.  
This approach incurs <5% energy overhead versus cryptographic methods [9] while preventing 92% of spoofing attacks in simulations.
## 3 Results and Discussion
### HS-SPME-GC-MS Volatile Fingerprinting
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**PG-O Soup**} & \multicolumn{3}{c}{**INS Soup**} \\ \hline
**VID** & **Compound** & **RI** & **VID** & **Compound** & **RI** \\ \hline \(-0.853\) & 4,7-dimethyl-undecane1  & 1091 & \(-0.746\) & 2-nonanol & 1524 \\ \(-0.892\) & 3,5-dimethyl-octane 1  & 1017 & & _Aldehydes_ & \\ \multicolumn{2}{c}{_Terpenoids_} & & 0.742 & hexanal & 1089 \\ \(-0.711\) & 3-carene 1  & 1140 & & _Hydrocarbons_ & \\ \(-0.853\) & \(\alpha\)-phellandrene & 1158 & 0.726 & Tridecane & 1301 \\ \multicolumn{2}{c}{_Unidentified_} & & \(-0.732\) & 3,3-dimethyl-octane 1  & 998 \\ \(0.940\) & unidentified & - & \(-0.732\) & 4-methyl-decane & 1002 \\ \(-0.711\) & unidentified & - & \(-0.758\) & 3-methyl-undecane & 1170 \\ \(-0.760\) & unidentified & - & & _Terpenoids_ & \\ \multicolumn{2}{c}{} & & 0.708 & \(\beta\)-pinene & 1096 \\ \multicolumn{2}{c}{_Sulphur compounds_} & & \multicolumn{1}{c}{} & _Sulphur compounds_ & \\ \multicolumn{2}{c}{} & & \(-0.778\) & Allyl methyl disulphide & 1281 \\ \multicolumn{2}{c}{_Unidentified_} & & \multicolumn{1}{c}{} & _Unidentified_ & \\ \multicolumn{2}{c}{} & & \(-0.711\) & unidentified & \\ \multicolumn{2}{c}{} & & \(-0.723\) & unidentified & \\ \multicolumn{2}{c}{} & & \(-0.726\) & unidentified & \\ \multicolumn{2}{c}{} & & \(-0.842\) & unidentified & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._These alcohols and aldehydes probably resulted from the oxidation of lipids during the production process [57]. The soup containing INS held higher concentration of several ketones, two aldehydes, a lactone, a terpenoid, an alcohol and a hydrocarbon. All ketones, aldehydes and the alcohol were also present in higher concentration in the INS flour compared to the other chickpea flours and PS. As the supplier informed us that the INS flour production included a soaking step of the ground chickpea, these compounds could have resulted from enzymatic lipid breakdown during the flour production [58]. The \(\gamma\)-nonalactone and \(\beta\)-pinene were derived from the other soup ingredients but were adsorbed to a larger extent by other starch sources (PS, NG-O, and PG-O) compared to INS. (\(E\),\(E\))-3,5-octaiden-2-one, (\(E\),\(Z\))-3,5-octaiden-2-one, 3-octen-2-one, benzaldehyde, and \(\beta\)-pinene are reported as having earthy, woody, mushroom, resin, and turpentine-like aromas and therefore the addition of INS flour to instant soups might induce off-flavours [23; 59; 60; 61]. However, as noted before, odour threshold, concentration, and interactions between volatiles should be considered [22; 23; 24].
# Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator
###### Abstract  
To address the problems of mismatch, poor flexibility and low accuracy of ordinary manipulators in the complex special deflagration work process, this paper proposes a new five-degree-of-freedom (5-DOF) folding deflagration manipulator. Firstly, the overall structure of the explosion-expulsion manipulator is introduced. The redundant degrees of freedom are formed by the parallel joint axes of the shoulder joint, elbow joint and wrist pitching joint, which increase the flexibility of the mechanism. Aiming at a complex system with multiple degrees of freedom and strong coupling of the manipulator, the virtual joint is introduced, the corresponding forward kinematics model is established by D-H method, and the inverse kinematics solution of the manipulator is derived by analytical method. In the MATLAB platform, the workspace of the manipulator is analyzed by Monte Carlo pseudo-random number method. The quintic polynomial interpolation method is used to simulate the deflagration task in joint space. Finally, the actual prototype experiment is carried out using the data obtained by simulation. The trajectory planning using the quintic polynomial interpolation method can ensure the smooth movement of the manipulator and high accuracy of operation. Furthermore, the trajectory is basically consistent with the simulation trajectory, which can realize the work requirements of putting the object into the explosion-proof tank. The new 5-DOF folding deflagration manipulator designed in this paper has stable motion and strong robustness, which can be used for deflagration during the COVID-19 epidemic.  
Keywords:COVID-19; 5-DOF manipulator; forward and inverse kinematics analysis; trajectory planning; MATLAB simulation +
Footnote †: journal: Applied Sciences
## 2 Inter-DCI Link Modeling and OP Estimation
### PAM4 Signal Characterization  
In this subsection, the characterization of the PAM4 signal generated at the output of each one of the optical transmitters is provided.  
In the simulator, the PAM4 symbols sequence is generated using four level deBruijn sequences of maximum length \(4^{N_{reg}}\), obtained from Galois arithmetic, where \(N_{reg}\) represents the length of the offset register used to generate the sequence. The PAM4 symbols \(a_{k}\) (with \(k\) = 0, 1, 2, and 3), with corresponding power levels \(P_{k}\), are equally likely to occur [38]. The representation of the power levels of a PAM4 signal for a non-zero extinction ratio is presented in [38]. The PAM4 signal extinction ratio is defined as \(r=P_{0}/P_{3}\)[38]. The power of the intermediate levels of the PAM4 signal is controlled by the constants \(A\) and \(C\), with \(P_{1}=CP_{3}\) and \(P_{2}=AP_{3}\)[38]. To minimize the BER in optically amplified PAM4 links with dominant signal-ASE beat noise, the constants \(A\) and \(C\) can be optimized, being the optimal \(A\) and \(C\) given by \(C=(1+4\sqrt{r}+4r)/9\) and \(A=(4+4r+r)/9\)[38]. After signal generation, to model the amplitude and phase distortion induced by the filtering and parasitics at the electrical part of the transmitter, the ideal PAM4 signal is filtered by a 3 rd order Bessel filter. The \(-3\) dB bandwidth of the Bessel filter is set the same as the symbol rate, \(R_{s}\).
## 4 Methodology
### Fuzzy Rank Ordering Criteria Weighing Method
In the Fuzzy Rank Ordering Criteria Weighing Method, the weights calculated for each criterion are represented as triangular fuzzy numbers (TFNs), as in Figure 3.  
The input is produced as a list of prioritized '\(n\)' fuzzy-ranked criteria, with weights and ranks inversely correlated. The criteria are assigned fuzzy ranks in ascending sequence, with the most essential criterion given rank 1 and the least essential criterion given rank \(n\). The formula, fuzzy rank sum (FRS), deals with fuzzy weighting in the context of FMCDM problems. The weights obtained for each criterion are represented by triangular fuzzy numbers (\(l\), \(m\), \(u\)). \[w_{k}(RFS)=(l,\;m,\;u)\;w_{k}(RFS)=\left(\frac{n-k+0.5}{n-k+0.5+\sum_{j=1,j\
eq k }(n-j+1.5)},\frac{n-k+1}{\sum_{j=1}^{n}(n-j+1)},\;\frac{n-k+1.5}{n-k+1.5+\sum_{j =1,j\
eq k}^{n}(n-j+0.5)}\right)\;k=1,2,\ldots,n \tag{2}\]  
This formula results in weights that are not crisp values but fuzzy numbers. The three-dimensional nature of the weights comes from the fact that each weight in the fuzzy rank sum method is represented as a triangular fuzzy number, which includes three values:  
The lower bound (\(l\)) (left end of the triangular fuzzy number):  
\[l=\frac{n-k+0.5}{n-k+0.5+\sum_{j=1,j\
eq k}^{n}(n-j+1.5)}\;\;\;\;\;\;k=1,2, \ldots,n\]  
The middle value (\(m\)) (the peak of the triangle):  
\[m=\frac{n-k+1}{\sum_{j=1}^{n}(n-j+1)}\;\;\;\;k=1,2,\ldots,n\]  
The upper bound (\(u\)) (right end of the triangular fuzzy number):  
\[u=\frac{n-k+1.5}{n-k+1.5+\sum_{j=1,j\
eq k}^{n}(n-j+0.5)}\;\;\;\;\;\;k=1,2, \ldots,n\]  
The FRS method provides triangle fuzzy numbers for weights, which is why the result is three-dimensional. This approach allows decision-makers to reflect the inherent vagueness and uncertainty in their judgments, which is crucial when precise information is unavailable or difficult to obtain.
## 2 Materials and Methods
### Proposed Methods
#### 2.2.1 Reconstruct the Channel Attention Module  
The channel attention mechanism is to focus on the relationship between the channels and to use their dependence relationship to strengthen the ability of the feature screening and the extraction of the network. By grasping the global information, the network can screen out useful information to strengthen the extraction of this part of the feature and suppress useless features. The channel attention mechanism consists of squeeze and excitation. First, the global space characteristics of each channel are compressed. The information is compressed into a descriptor to represent the information for each channel. Then, the dependence of the channels is learned, the weights of the different channels are obtained, and the feature graphs are adjusted according to the weight information.  
In this paper, based on the channel attention mechanism, the SEB attention convolution block is constructed to fuse global information and capture rich context associations to solve the problem of image classification. This module is added to deepen the connection between the channels on the basis of the original channel attention mechanism; so, a \(1\times 1\) convolution is first used for information fusion for each channel when obtaining the global information. The operational differences between the original channel attention module and the channel attention module in this paper are shown in Figure 3. Figure 3a represents the operation of the SE block. Firstly, the global spatial information is compressed into a channel by generating statistics through global average pooling. Then, two fully connected (FC) layers are added before and after the non-linear to control the change of dimension; that is, the first fully connected layer is a dimension reduction layer, and then, Relu is  
Figure 2: Residual convolutional neural network architecture based on channel attention.  
added. Then, another dimension is connected to add a layer back to the channel dimension of the transform output. Finally, the sigmoid activation function is used to learn the non-linear interactions between the channels, ensuring that multiple channels are allowed to be emphasized. The final output is to rescale the feature by multiplying the output activated by the previous layer. Before global pooling, a \(1\times 1\) convolution operation is added to the SEB convolution block in Figure 3b for channel fusion, and then, the number of channels is halved to reduce the computation of the model.