esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### The Effect of Mooring Model  
The quasi-static mooring model based on the MSQS approach is an efficient and simple approach for integrating into FOWT simulation. However, the major disadvantage is that the computation of the MSQS approach ignores the effects of hydrodynamic forces and inertial forces on the mooring cables, which are highly correlated with the platform motion and are therefore important for estimating the mooring forces. The main feature of the dynamic mooring model based on the LM approach can be used to discretize the behaviors of mooring cables. For comparison, the platform hydrodynamic forces, including the 2 nd order wave exciting force and the quadratic viscous drag, would be coupled with both of the quasi-static and the dynamic mooring models.  
Figure 13a-f present the 6-DOF motion responses of the DeepCwind semi-submersible FOWT in the frequency domain by comparing LC4 with LC3. As can be seen, there are small discrepancies of 6-DOF motion responses between the quasi-static model and the dynamic mooring model. However, portions of fairlead tension responses in Figure 14a-c indicate that the MSQS approach predicts reduced tensions compared to the LM approach. [PERSON] et al. (2019) suggested that the platform motions were affected by the mooring dynamics only in harsh sea states but the mooring dynamics played a significant role in predicting the mooring loads in any sea condition. Our simulation results of the DeepCwind semi-submersible FOWT are evidently in good agreement with their findings.  
Figure 12: The effects of the QTFs on the motion responses in (**a**) the surge, (**b**) the sway, (**c**) the heave, (**d**) the roll, (**e**) the pitch, and (**f**) the yaw modes, respectively.  
Figure 13: Cont.  
Figure 13: The effects of the mooring models on the motion responses in (**a**) the surge, (**b**) the sway, (**c**) the heave, (**d**) the roll, (**e**) the pitch, and (**f**) the yaw modes, respectively.
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## Appendix A The Calculation of BER  
First of all, the SNR estimation is applied to each polarization by using the statistical moments method [38]. In order to calculate, the total SNR is supposed to be \(1/\sigma^{2}\) and the average symbol power of QPSK modulation is 2. Subsequently, we rescale the equalized signals differently according to the SNR of two polarizations. Particularly, the \(\triangle\)SNR varies due to the statistical PDL, and therefore the SNR estimation and rescaling should be updated periodically. The I/Q components are then deinterleaved, and the yielded I/Q parts suffer different noise levels. Subsequently, we apply the maximum likelihood detection (MLD) method to symbol decision:  
\[\begin{split}\mathbf{X}^{{}^{\prime}}&=\arg\min_{c _{k}}\{|\mathbf{X}^{{}^{\prime}}_{\theta}-\mathbf{D}_{k}|^{2}\},\mathbf{Y}^{{} ^{\prime}}=\arg\min_{c_{k}}\{|\mathbf{Y}^{{}^{\prime}}_{\theta}-\mathbf{D}_{ k}|^{2}\},\\ \mathbf{D}_{k}&=\Re(\mathbf{C}_{k}\cdot e^{i\theta} )\cdot\sqrt{\text{SNR}_{x}}+j\Im(\mathbf{C}_{k}\cdot e^{i\theta})\cdot\sqrt{ \text{SNR}_{y}},\end{split} \tag{1}\]  
where \(\mathbf{C}_{k}\) is the constellation alphabet, i.e., \([1+j1-j-1+j-1-1j]\) for QPSK modulation, and \(\mathbf{D}_{k}\) are the rotated and rescaled constellation points. The decisions are finally used for BER calculation. Then, the BER of optical pairwise-coded signals can be written as  
\[\text{BER}_{\text{paired}}=0.5(1+\text{erf}(\frac{-1}{\sqrt{2}\sigma}))+0.12 5(1+\text{erf}(\frac{-\sqrt{1+\eta}}{\sigma}))+0.125(1+\text{erf}(\frac{- \sqrt{1-\eta}}{\sigma})). \tag{2}\]  
Compared with the unpaired coding scheme, the average BER can be approximated to  
\[\text{BER}_{\text{unpaired}}=0.25(1-\text{erf}(\frac{\sqrt{1-\eta}}{2\sigma ^{2}}))+0.25(1-\text{erf}(\frac{\sqrt{1+\eta}}{2\sigma^{2}})). \tag{3}\]  
In Figure 11, we demonstrate the single-channel OSNR versus. \(\triangle\)SNR for the rotation angle \(45^{\circ}\), where interleaving and deinterleaving the I/Q components are performed at the transmitter and receiver, respectively. With the increased \(\triangle\)SNR between two polarizations, the PPC scheme increases the performance of the system in terms of BER compared with the unpaired signals. For \(\triangle\)SNR = 0, there is no penalty, and the performance is the same as that of unpaired signals. Whereas for \(\triangle\)SNR = 3/6/9 dB, we find the pairwise coded signals require the lower SNR to achieve BER to the same extent.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 7 Challenges and Prospects  
### Challenges  
From the advances in the tool wear monitoring method based on deep CNNs, many excellent methods emerged. However, many monitoring methods have limitations in practical applications, summarized as follows:  
(1) The existing monitoring model is established under fixed cutting conditions. Changes in the cutting process or parameters may cause the recognition model of the tool wear condition to fail. Thus, the model has a weak adaptability and generalization.  
(2) In recent years, there many recognition algorithms were applied to the field of tool wear monitoring, for example, fuzzy logic, the hidden Markov model, support vector machine and adaptive network-based fuzzy inference system. However, a single model algorithm is challenging for realizing the high-precision recognition of tool wear conditions [17].  
(3) The monitoring performance of the network model depends on the feature data collected by the sensors, and the accuracy of the feature data directly determines the prediction effect. At present, due to the influence of the processing environment, it is difficult for many sensors to collect high-precision feature data [28; 38].  
(4) Additionally, due to the investment cost and its increase with additional signal acquisition hardware and software equipment, the current situation of sensors used in the industry, for the most part, is not at a sufficient level. These factors lead to the lack of original information data, which limit the application of neural networks in this field [16].  
(5) The performance of multiple sensors may be better than a single sensor. However, using various sensors increases the complexity of noise and feature processing, resulting in reduced monitoring accuracy [101].
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 7 Results and Discussion  
### Cracking Behavior, Failure Mode, and Ultimate Load of the Tested Beams  
Figure 2 illustrates the cracking pattern of the six tested beams. All the beams failed in flexural mode when the steel reinforcement in the tension zone reached the yield point and concrete crushing occurred at the compression zone. For the control beams (ECCP and ECCS), the first crack developed when the tensile strength at the tension zone of the beam reached its maximum due to the applied load. After the formation of the first crack, vertical cracks on the tension side of the beam started to form. As the load increments increased, diagonal cracks in the shear span (the distance between the point load and support) started to develop towards the compression zone. The number of cracks and their widths increased further with higher loads. ECCP and ECCS exhibited 34 and 37 visible cracks, respectively. Table 4 shows the ultimate loads obtained for all beams. ECCP and ECCS had failure loads of 349.8 kN and 358.7 kN, respectively. The failure loads for both beams were very comparable, which could be attributed to the fact that SFs enhanced both the compressive and pullout strength of the ECCS, neutralizing the high dispersion effect of PVA fibers (a lower fiber density and consequently higher fiber count and dispersion) [9].  
Figure 2: Cracking pattern for all tested beams [9].  
Regarding beams repaired in the compression zone (ECCP-C and ECCS-C), cracks began developing in the LWSCC layer (tension zone) during the loading process. As the load increased, some cracks formed horizontally at the LWSCC-ECC interface, while others perforated the interface with very small widths (less than 0.1 mm). ECCP-C displayed 27 cracks, while ECCS-C displayed 30 cracks. The failure loads for ECCP-C and ECCS-C were 301.9 kN and 308.0 kN, respectively (Table 4). The failure load values were comparable to each other and lower than those of the control beams, likely due to the repair in the compression zone and the absence of fibers in the tension zone (LWSCC), which contributed to delaying crack formation [9].  
Crack development in beams repaired in the tension zone (ECCP-T and ECCS-T) was characterized by very fine vertical cracks in the ECC layer (tension zone). As the load increased, additional cracks developed at the ECC-LWSCC interface. These cracks initially propagated into the LWSCC layer and, with further loading, extended downward into the ECC layer with narrower widths due to the presence of fibers. ECCP-T and ECCS-T exhibited 30 and 37 visible cracks. These beams displayed failure loads of 313.7 kN and 330.4 kN, respectively, which were lower than those of the control beams. These results were attributed to the fact that the control beams were fully cast with ECC and lacked the LWSCC layer (42.5 MPa), compared to the high-strength layer of ECC (77.5 MPa to 82.5 MPa) [9].
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 5 Transmission Latency Analysis
### Cloud-Based FL  
Hierarchical cloud-based FL [10] architectures incorporate both edge servers and a cloud server, operating two distinct aggregation processes: edge aggregation and global aggregation.  
* Edge aggregation: Exactly as in MS-FL and single-server FL, clients exchange updates with their local server. The per-round \"edge\" delay is therefore the same \(\tau_{\text{edge}}(t)\) defined in Section 5.1.
* Global aggregation: After edge aggregation, the cloud server aggregates the models from the various edge servers. The transmission latency for global aggregation is expressed as follows: \[\tau_{\text{cloud}}(t)=\max\limits_{s}\Bigl{(}\tau_{s}^{U}(t)\Bigr{)}+\max \limits_{s}(\tau_{s}^{D}(t)),\] (25) Here, \(\tau_{s}^{U}(t)\) and \(\tau_{s}^{D}(t)\) denote the uplink and downlink latencies from edge server s to the cloud server in round t, respectively.
* Overall Transmission Latency: The end-to-end transmission time to achieve the targeted accuracy is the sum of the latencies over all edge and global aggregation rounds. Assuming the two layer federated learning process requires \(\text{T}_{\text{cloud,based}}\) global aggregation rounds to reach the specified performance level, the total transmission latency is given by the following: \[\tau_{\text{cloud,based}}^{\text{Total}}(t)=\sum\limits_{t=1}^{\text{T}_{ \text{cloud,based}}}\left(\tau_{\text{edge}}(t)+\tau_{\text{cloud}}(t)\right)\] (26) It is important to note that the RSU-to-cloud (global) transmissions typically incur higher latencies due to the longer distances between regional servers and the cloud compared to those between clients and regional servers. Consequently, \(\tau_{\text{cloud}}(t)>\tau_{\text{edge}}(t)\). This increased latency contributes to a longer overall convergence time for cloud-based FL compared to single-server FL or MS-FL architectures.  
In summary, while cloud-based FL enhances scalability and allows for more efficient local aggregation, its overall communication delay is higher due to the additional global aggregation phase and the larger transmission distances involved. This trade-off must be considered when designing FL systems in large-scale networks.
# Therapeutic Potential of Chrysin in Improving Bone Health
###### Abstract  
Vitamin D deficiency is endemic worldwide. Although several strategies have been established to enhance vitamin D\({}_{3}\) levels, studies specifically focusing on the inhibition of vitamin D metabolism, which may prolong the availability of active vitamin D in pathological conditions, have been less explored. Studies also suggest that higher doses of vitamin D\({}_{3}\) fail to achieve optimum vitamin D levels. In this context, we focused on the enzyme CYP3A4, which promotes the inactivation of active vitamin D. The current study aimed to decipher the impact of chrysin, a proven CYP3A4 inhibitor, as an intervention and its effects in combination with low-dose vitamin D\({}_{3}\) (40 IU) and bone health in vitamin D deficiency conditions. The in vivo activity of chrysin was evaluated in female Wistar albino rats fed a vitamin-D-deficient diet to attain vitamin D deficiency for 28 days. Chrysin was given alone and in combination with calcium carbonate (CaCO\({}_{3}\)) and/or vitamin D\({}_{3}\). All therapeutic interventions were assessed for serum 25-hydroxyvitamin D3(25-OH-D\({}_{3}\)) by LC-MS and biochemical, urinary, and bone parameters. Animals treated with chrysin alone and in combination with low-dose vitamin D\({}_{3}\) and/or CaCO\({}_{3}\) showed an eminent rise in serum 25-OH-D\({}_{3}\) levels along with increased serum biochemical parameters. In contrast, a significant decrease in the urinary parameters followed by beneficial effects on bone parameters was noticed in contrast with the vitamin-D-deficient diet group. Our findings revealed that although chrysin alone showed a notable effect on 25-OH-D\({}_{3}\) and osseous tissue, comparatively, it showed an intensified therapeutic effect in combination with vitamin D\({}_{3}\) and CaCO\({}_{3}\), which can be employed as a cost-effective option to improve bone health.  
boone; chrysin; CYP3A4; vitamin D deficiency; osteoporosis; 25-OH-D\({}_{3}\)
## 8 Conclusions  
This paper presents a comparative study of different machine learning models for detecting GPS spoofing attacks, aiming to identify the most suitable model for use in UAVs. An Optimized LSTM model was proposed in this paper. The proposed model was compared to a standard LSTM model where the obtained results validated the initial proposition that the integration of a GA with the LSTM model would significantly improve the performance metrics, including accuracy, detection speed, and memory utilization. Specifically, the LSTM model optimized by a GA showed a high improvement in detection accuracy, which increased from 88% with the regular LSTM model to 92% and finally to 93% after the GA's search space was expanded. This finding highlights a direct correlation between the breadth of the search space used for hyperparameter optimization and the effectiveness of the model. However, it is important to recognize that while the GA enhanced model precision, it also increased the computational complexity and the memory requirements. Due to its inherent limitations in handling multiple model configurations simultaneously, a GA can become computationally and memory-intensive. This trade-off is particularly significant in the context of UAV applications, where size, weight, and power constraints are critical.  
Conceptualization, A.A.-S. and M.E.; methodology, A.A.-S. and M.E.; software, A.A.-S., S.E.-K. and A.J.; validation, A.A.-S., S.E.-K., A.J. and M.E.; formal analysis, A.A.-S. and A.E.-B.; investigation, A.A.-S., A.E.-B., S.E.-K. and A.J.; resources, A.A.-S. and M.E.; data curation, A.A.-S., A.E.-B., S.E.-K. and A.J.; writing--original draft preparation, A.A.-S., A.E.-B., S.E.-K. and A.J.; writing--review and editing, A.A.-S. and M.E.; visualization, A.A.-S., A.E.-B., S.E.-K. and A.J.; supervision, A.A.-S. and M.E.; project administration, M.E. and A.A.-S.; funding acquisition, M.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. The dataset used for web phishing detection is available for download from [[https://data.mendeley.com/datasets/z7](https://data.mendeley.com/datasets/z7) dj3 yyz18/3]([https://data.mendeley.com/datasets/z7](https://data.mendeley.com/datasets/z7) dj3 yyz18/3) (accessed on 25 June 2024).  
We acknowledge the responsible use of the AI language model ChatGPT for assistance with proofreading, editing, and language refinement in this manuscript. This tool was used responsibly to enhance the clarity and readability of our work, while all original ideas, analyses, and conclusions remain the sole product of the authors. We affirm that the use of this AI tool does not detract from the originality or integrity of our research.  
The authors declare no conflicts of interest.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 5 Conclusion and Shortcomings  
### Conclusions  
This paper proposes IFNPMH to address the limitations of existing fusion methods (FHM and FHP) for fusing multispectral images (MSI), panchromatic images (PAN), and hyperspectral images (HSI). Firstly, employing dual channels to extract spatial information characteristics from MSI and PAN, as well as spectral information features from HSI; Furthermore, the CBAM attention mechanism module utilizes channel and spatial attention units to improve the capability of representing features. Ultimately, the reconstruction module is employed to acquire high-resolution hyperspectral remote sensing images with enhanced spatial details. Jumping connections have been incorporated into certain sections of the network, enabling the transmission of more detailed information to subsequent feature layers. This allows for the optimal utilization of effective information, resulting in a more efficient training process for the network. Furthermore, this research introduces a novel spectral spatial combination loss function to enhance the network's ability to generate fused images that possess precise spectrum information and distinct spatial details. Evaluation on simulation datasets demonstrates IFNPMH's advantages over other advanced methods.  
Due to the physical limitations of satellites, high spatial resolution and high spectral resolution remote sensing image data are extremely scarce, making it impossible to conduct some long-term research. The fusion method proposed in this article can utilize a small amount of existing high spatial resolution and high spectral resolution remote sensing image data, as well as PAN, MSI, and HIS images from different satellites, to generate the missing high spatial resolution and high spectral resolution remote sensing images in research.
# Application of Bonded-Block Models to Rock Failure Analysis
## 2 Discrete Element Modeling of Rock Fracture
### Bonded-Particle Models  
In the early [PERSON] paper [1], both polygonal and circular particles were already proposed. Rock mechanics codes typically resorted to polygonal or polyhedral blocks to simulate jointed rock, while soil mechanics made more use of circular particles. Circular-particle models have a high computational advantage since the procedures for contact detection and the calculation of contact forces are much faster than in the case of more complex shapes. Therefore, much larger systems can be addressed. By applying breakable bonds to the circular particles, [PERSON] and [PERSON] [3] were able to study the fracture of intact rock. These models were able to reproduce the main features observed in laboratory tests. However, it was soon found that more complex particle shapes were necessary to fully account for the frictional effects, particularly for higher confinement stresses. The use of macro-particles or particle clumps allowed a more realistic representation of the various traits of the physical evidence [11; 12; 13] by using a higher bond density to improve the model performance. Various alternative formulations have been proposed for circular-particle models intended to more closely simulate the response of polygonal-block systems, namely, \"grain-based models\" [14], multiple-contact models [15], or the \"flat-jointed material\" concept [16]. The use of more elaborate contact relations was also proposed, namely, by introducing a tensile softening model to analyze the fracture initiation and propagation in tensile and compression lab tests [15; 17]. Inserting the rock mass compartmentation and planar features, such as joints and faults, into the particle assemblies led to the \"Synthetic rock mass\" concept, accounting for the nonlinear behavior both at the block interfaces and in their interior [4].
# Multi-Angle Fast Neural Tangent Kernel Classifier
## 1 Introduction
### Our Contributions  
* The traditional SVM is a single-kernel learning model, the SVM model with multi-kernel learning is introduced, using NTK, which is equivalent to an infinitely wide neural network, as the base kernel function for multi-kernel learning, and multi-kernel-NTK, RBF-Boolean, and POLY-Boolean models are selected for the classification task, where RBF- Boolean utilizes the multiscale multi-kernel idea, multi-kernel-NTK, POLY-Boolean utilizes the linear synthetic kernel idea.
* The HMPSO algorithm uses EDA to estimate and preserve the information of the historical optimal distribution of particles, which can avoid premature convergence of the model by competing among the information and maintaining fast convergence.  
Its performance is better than most of the PSO improvement algorithms. We use the HMPSO algorithm to select various parameters in multi-kernel-NTK and other multi-kernel models.
* The single kernel SVM model has a limited search space, and the diagnostic results of the model are more sensitive and less robust to the kernel parameters and penalty parameters. The sensitivity of multi-kernel-NTK and NTK-SVM models to the penalty parameter C and network depth L is studied visually and comparatively.
* The NTK kernel has a multi-layer structure, which consumes a lot of time in its computation. Because of its special function construction (four-dimensional GRAM matrix), matrix decomposition and chunking acceleration techniques are difficult to be applied to the NTK kernel function. We extract the features belonging to the Boolean type from the original dataset and use the Boolean kernel (Monotone Disjunctive Kernel) to encode them into a binary square array for training, which improves the model accuracy and compresses the model training time at the same time.
* The Multi-NTK model is accelerated regarding the number of features and the multi-kernel model. We also used the data-cutting technique to construct the Block-NTK model to accelerate the model regarding the number of samples.
## 3 Methodology
### GitHub Modeling Using DTMC
#### 3.2.1 State Configuration  
In this study, the states of DTMC used are defined by a 3-tuple (participant, location, action), structured as follows:  
\[State=participant\times location\times action\]  
* \(participant=\{p1,p2,p3,\dots,pn\}\)
* \(location=\{master,branch\}\)
* \(action=\{create,commit,pullRequest,accepted,merge,rejected\}\)  
In addition, the state _month_ has been added. It is used to distinguish intervals of one month. Participant represents individuals who have participated in activities within a GitHub repository. For anonymity, each participant is assigned a sequential identifier from p1 to pn based on their order of participation. The initial participant, p1, is typically the individual who originally created the repository for the project. Location denotes the branch where the activity took place. The default branch used for major project activities is commonly named main or master; however, in this paper, it is uniformly referred to as \"master\". Any other branches are defined as \"branch\". Actionsignifies the essential activities undertaken to contribute to the GitHub repository. These include the following:  
* _create_: Action to initiate a new branch.
* _commit_: Action to upload changes to the repository, reflecting modifications made to the code.
* _pullRequest_: A request to incorporate code changes into the master branch or another branch.
* _accepted_: Feedback indicating that a pull request has been approved.
* _merge_: Action to approve and integrate proposed changes, thereby applying new features or bug fixes.
* _rejected_: Feedback indicating that a pull request has been denied.