esa-sceva/satcom-chunk-collection huggingface dataset:
# Product Improvement Using Knowledge Mining and Effect Analogy
## 3 Proposed Method
### Effect Scheme Acquisition  
#### 3.4.1 Analogy Object Selection  
One effect often corresponds to multiple actual engineering cases. The analogy for all cases in the knowledge base is inefficiency. In order to quickly and accurately find the most relevant analogy objects, cases need to be screened according to the problems of the target product. The similarity analysis between the engineering case and the target product is the premise of analogy design. According to the research in reference [89], the similarity between the two concepts can be expressed as the cosine value of the vector in n-dimensional Euclid space.  
Combined with the above mathematical principles, a case similarity calculation method based on the LT dimension is proposed. The specific application process of this method includes five steps. (1) The index number of the LT dimension in the problem root cause and engineering case is expressed in the form of a vector. The LT dimension of the problem root cause is extracted by NLPIR-Parser, and the LT dimension of the engineering case is recorded in the effect knowledge base. (2) The LT dimension feature set for the pairwise comparison is constructed to divide it into problem set P and case set C. Each element in the set is the modulus of the corresponding exponential vector, and the order is numbered according to the LT dimension. (3) Sets P and C are merged to obtain a new set U. Then, P and C intersect with U to obtain new sets P' and C', respectively, and the missing positions are filled with 0. (4) Equation (5) is used to calculate the similarity of cases, keeping two decimal places for the calculation results. Cases in the knowledge base are sorted by similarity. The cases with high similarity are more likely to produce effect schemes for the priority. If the number of analogy objects obtained by the first effect cannot meet the needs of the conceptual design, it can continue to screen in the engineering case base of the next effect according to the ranking results of effects.  
The parameters in Equation (5) are explained through examples of wind turbines and liquid flow meters. The former is the target product, and the problematic LT dimensions are [L\({}^{1}\)T\({}^{-1}\)] and [L\({}^{5}\)T\({}^{-5}\)]. The latter is an analogy object, and the LT dimensions are [L\({}^{2}\)T\({}^{-4}\)] and [L\({}^{5}\)T\({}^{-5}\)]. They are converted in the order of the vector form (1, \(-1\)), (5, \(-5\)), (2, \(-4\)), and (5, \(-5\)). Therefore, P = (\(\sqrt{2}\), \(\sqrt{50}\)) and C = (\(\sqrt{20}\), \(\sqrt{50}\)). The two newly processed sets are P' = (0, \(\sqrt{2}\), \(\sqrt{50}\)) and C' = (\(\sqrt{20}\), 0, \(\sqrt{50}\)). After substituting P' and C' into Equation (5), the similarity is calculated as 0.83.  
\[Sim(P^{\prime},C^{\prime})=\frac{p_{1}^{\prime}c_{1}^{\prime}+\cdots+p_{k}^{ \prime}c_{k}^{\prime}}{\sqrt{\left(p_{1}^{\prime}\right)^{2}+\cdots+\left(p_ {k}^{\prime}\right)^{2}}\times\sqrt{\left(c_{1}^{\prime}\right)^{2}+\cdots+ \left(c_{k}^{\prime}\right)^{2}}} \tag{5}\]
# Changing Dynamics of SARS-CoV-2: A Global Challenge
## 4 Variants of SARS-CoV-2
### Variants of Concern
#### 4.2.4 The Delta Variant (B.1.617.2)  
Yet another variant, Delta, emerged in India in December 2020 and was primarily accountable for India's deadly second COVID-19 wave, which spanned from March to July. On 7 May 2021, India reported the highest number of COVID-19 cases at 4,14,433 in a day, while the highest figure of 5015 deaths was reported on 23 May (Worldometer). The variant rapidly spread to almost 184 countries, including the UK, the USA, Israel, South Africa, Denmark, Germany, Spain and Italy (15 December, GISAID). The highly contagious Delta variant of SARS-CoV-2 has largely driven several waves of infection. Earlier this year, the Delta variant was responsible for 91% of new cases in the UK, displacing the Alpha strain, which previously dominated. The number of reported cases peaked on 17 July 2021, at 54,674 in the United Kingdom. Around the same time, the Delta variant was associated with 83% of all newly reported cases in the USA [59]. In Amazonas (Brazilian state) during September 2021, Delta surpassed the previously circulating variants of concern [57].  
The Delta variant has only one mutation, D614G, which is common with the Alpha, Beta and Gamma variants. The other 18 mutations are unique to the variant (Figure 6). The spike mutations include G142D, D614G, T19R, R158G, D950N and P681R (Figure 2e), and the specific RBD mutations are L452R and T478K (Figures 6 and 7). Both these RBD mutations help the virus escape from the neutralizing antibodies, hence making it fit and more infectious as shown in Table 1[37]. Additionally, it has deletions at positions E156 and F157. According to the Centers for Disease Control and Prevention (CDC), the Delta variant is two times more contagious than other variants. Two new sub-lineages of the Delta variant were reported, AY.1 and AY.2 (Delta Plus variant), which had an additional spike glycoprotein mutation, K417N, which exists in the Beta variant [58,60].
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 4 Experimental Verification
### Experimental Results
#### 4.3.5 Comparison with Other WPT Systems  
Compared with the two-coil PT-WPT system, the three-coil PT-WPT system significantly extends the distance between the special symmetry zones of the PT without adding complex controls or additional components. Table 5 contrasts the results in this paper with those in other published papers. The proposed system offers several advantages compared with other WPT systems: it achieves a consistent output independent of the coupling coefficient and load resistance.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference Output** & \multicolumn{3}{c}{**Total Transmission Distance**} \\
**Power** & \multicolumn{3}{c}{**(Coupling Coefficient of Adjacent Coils)**} \\ \hline \multirow{2}{*}{\(P_{ref}=48\) W} & \(\hat{d}=21\) cm & \(\hat{d}=25\) cm \\  & \((k=0.12)\) & \((k=0.08)\) \\ \hline \(R_{L}(\Omega)\) & \(P_{L-est}(\text{W})\) & \(\Delta P\) & \(P_{L-est}(\text{W})\) & \(\Delta P\) \\
10 & 46.17 & 3.819\% & 46.11 & 3.935\% \\
12 & 46.33 & 3.47\% & 46.34 & 3.453\% \\
14 & 46.45 & 3.23\% & 46.44 & 3.247\% \\
16 & 46.5 & 3.126\% & 46.4395 & 3.251\% \\ \hline \end{tabular}
\end{table}
Table 4: Error analysis of output power control.  
Figure 16: Dynamic waveform of the proposed system at startup. (**a**) Startup status waveform. (**b**) Changing \(R_{L}\) from 12 to 16 \(\Omega\).
## 4 Influence of the System Parameters
### MMED (or ERD)  
Figure 4 shows the ROC curves for MMED (or ERD), also considering the system parameters in Table 1, but only for \(n=50\). Most observations drawn from Figure 3 apply to Figure 4, but, as expected, MMED performs worse than GLRT, because the former has lower statistical power [6]. Furthermore, the differences between the results obtained with the _C_- and with the _R-model_ are less pronounced for MMED than for GLRT.  
From the results in Figures 3 and 4 one can notice the small variation in performance due to the variation in \(L\), the length of the impulse response of the MA filter adopted in the _R-model_. This is credited to the inherent ability of eigenvalue detection strategies in dealing with correlated samples, whose correlation information is somewhat transferred to the covariance matrix from where the eigenvalues are computed.
## 4 Establishment of Co-Simulation Platform
### Path Planning and Tracking Control Model Design  
We set the input and output parameters of the CarSim module. The input refers to the steering wheel angle, and the output represents the vehicle front axle midpoint coordinate. In the FCN module, the vehicle coordinate system in CarSim was converted to a global coordinate system. We recorded the real-time coordinates of the planned path and output the ideal coordinates in real time, which is different from the actual coordinates of the model car in CarSim. Finally, the steering wheel angle was controlled by the sliding mode algorithm, therefore the model vehicle travelled along the planned path. Figure 12 shows the parking path planning and tracking control model built in the CarSim and MATLAB/Simulink co-simulation platform.  
Figure 11: Model Vehicle Parameter Setting.  
Figure 12: Parking Path Planning and Tracking Control Model.  
As shown in Figure 12(a), the path tracking takes its effect when the speed is set as 1 m/s and the turning radius of the planned path is 37 m. Through the analysis of the data, it can be concluded that there is almost no deviation from the track of the planned path. It is proved that the model can track the parking path ideally at lower speed and a larger turning radius.  
As shown in Figure 12(b), the path tracking takes its effect when the speed is set as 1 m/s and the turning radius of the planned path is 12 m. The plot shows that although the tracking trajectory slightly deviates from the planned path, it can still effectively track the planned parking path. The effectiveness of the model under a lower speed and a medium turning radius is demonstrated.  
As shown in Figure 12(c), the path tracking takes its effect when the speed is set as 1 m/s and the turning radius of the planned path is 6 m. It can be seen from the figure that the trajectory of the model vehicle clearly deviated from the planned parking path. It shows that the model fails to track the parking path under a tiny turning radius.  
In the process of parking, the steering wheel of the vehicle in the turning path is mostly close to the limit position, that is, driving at the minimum turning radius. Therefore, the control model has to be optimized to address the two main issues encountered in the model:  
1. The output coordinate parameters of CarSim are for the coordinate of vehicle front axle midpoint, so the midpoint of front axle of the vehicle is selected as the control target in the model to track the planned parking path. However, compared with the midpoint of front axle, the deviation of speed at the rear axle midpoint is smaller, which can better reflect the real trajectory of the vehicle. In that case, the selection of the midpoint of the front axle will produce certain error.
2. In the actual parking operation, the steering wheel angle, speed, deceleration and other factors affect each other. In the CarSim model, the speed is set as a constant value, and only the steering wheel angle is controlled in the path tracking process, which lacks authenticity and rationality.
## 3 Numerical Model
### Solids Stress Model  
#### 3.4.1 Kinetic Theory of Granular Flow  
In high-pressure dense-phase pneumatic conveying, it is necessary to consider the solids stress, including interparticle collisions and friction. The two-fluid model commonly employs the kinetic theory of granular flow (KTGF) to simulate particle collisions, and the basic parameter settings for KTGF are provided in Table 3. It is well-known that the classical kinetic theory of granular flow is based on the assumption that interparticle collisions occur randomly as instantaneous collisions, and it is therefore only suitable for dilute-phase flow. Therefore, this research introduces Savage radial distribution function \(g_{0,ss}\) to modify the classical KTGF and extend it to the field of dense-phase flow. [PERSON] et al. [26] have also confirmed the reliability of this approach.  
\[g_{0,ss}=\frac{1-7\alpha_{s}/16}{(1-\alpha_{s}/\alpha_{s,\max})^{2}} \tag{12}\]  
where \(\alpha_{s,\max}\) is the maximum solids volume fraction for the packing state, whose value is very close to the solids volume fraction of tap density. For pulverized coal, \(\alpha_{s,\max}\approx 0.63\).
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 4 Proposed Elliptic Curve Signcryption Based Security Protocol for Smart Card (ECSSP-SC)  
With the evolution of computing paradigms such as cloud computing, distributed computing, and edge computing, different services are provided by the enterprises simultaneously to the users. A secure and efficient mutual authentication scheme for multi-server environment is required to ensure the access of these services by the authorized users. In this segment of the paper, an Elliptic Curve Signcryption based Security Protocol for Smart Cards (ECSSP-SC) has been presented and explained. The main focus of the protocol presented here is to achieve mutual authentication in a multi-server setting.  
Although the ECSSP-SC presented here has been developed for multi-server environment, its mutual authentication phase can also be used for M2M and U2U communication systems, with a slight change in the registration phase. In this case, the machine or user should register only with the server and the presence of a Registration Center can be removed.  
The proposed protocol has been divided into a setup phase, registration phase, signcryption-based mutual authentication phase, and update phase for better understanding and explanation. The three entities participating in the proposed protocol are the user having a smart card, \(USR\), the server providing services, \(SVR\), and the registration center, \(RGC\). The symbols and denotations utilized in the presentation and description of the proposed protocol have been listed in Table 2. The operations described in the Section 3.1 have been used in the explanation of the proposed ECSSP-SC.
## 2 Materials and Methods
### Docking Studies  
Selection of the biological activity for docking study was done with the help of computer program PASS [20]. Software estimates predicted activity spectrum of a compound according to the structural formulas of synthesized fluorinated pyrazole aldehydes as probable activity (Pa) and probable inactivity (Pi). Activity with Pa \(>\) Pi is considered as possible for a particular compound. Phosphodiesterase inhibition has been selected for docking study since the PASS program calculated Pa \(>\) Pi of all compounds for that activity.  
The molecular docking of compounds (**3a-j**) was performed using iGEMDOCK (BioXGEM, Taiwan). Crystal coordinates of the catalytic domain of phosphodiesterase type 5 (PDE5) (PDB ID: 4 OEW) in the complex with monocyclic pyrimidinones (PDB ID: 5 IO) were downloaded from Protein Data Bank (PDB, [[https://www.rcsb.org/](https://www.rcsb.org/)]([https://www.rcsb.org/](https://www.rcsb.org/))). The PDE5 structure was prepared, including the removal of water molecules and optimized protein structure using BIOVIA Discovery Studio 4.5 (Dassault Systemes, San Diego, CA, USA). Avogadro 1.2.0 (University of Pittsburgh, Pittsburgh, PA, USA) was applied for optimizing the 3D structures of 28 molecules using the molecular mechanic's force field (MM+) [21]. In addition, the semiempirical PM3 method was used for geometry optimization of all structures [22].  
The protein binding site was outlined according to the bounded ligand (PDB ID: 5 IO) [23]. Genetic parameters were set (population size 200, generations 70, the number of a solution or poses: 2) after the preparation of the protein target and set of optimized structures of 10 fluorinated pyrazoles as ligands. Docking into the binding site and generation of protein-compound interaction profiles of electrostatic (_Elec_), hydrogen-bonding (_Hbond_), and van der Waals (_vdW_) interactions was performed for each compound in the library. Finally, by combining pharmacological interactions and energy-based scoring function, the compounds were ranked. Energy-based scoring function or total energy (\(E\)) is:  
\[E=vdW+Hbond+Elec. \tag{1}\]
## 3 Preliminaries
### Oversampling Techniques  
The problem of imbalanced datasets resides in the lack of minority class instances compared to those in the majority class. This problem results in wrong classification of the minority class instances to majority class instances having many false positives [59].  
Oversampling techniques can solve this problem by increasing the number of instances for the minority class. Figure 1 shows that the minority class (Class-2) can be enlarged by the oversampling technique to produce a class with a number of instances similar to the other class (Class-1). Oversampling can be done in many ways: randomly copying the minority class instances, synthetically creating new instances of the minority class based on the features of the instances, or creating new instances of the minority class from the instances that are harder to learn.
## 2 BDS/GNSS for Earth Observations
### GNSS Radio Occultation  
GNSS radio occultation refers to a remote sensing method to acquire atmospheric information. This process involves the use of a GNSS receiver installed on a Low Earth Orbit (LEO) satellite, which captures electromagnetic wave signals emitted by a high-orbit GNSS satellite, with the objective of probing the elements of the Earth's atmosphere. The LEO satellite orbits the Earth and consistently collects GNSS signals. As the GNSS signal travels toward the LEO satellite, it traverses the Earth's ionosphere and neutral atmosphere, resulting in a change in the signal's path due to refraction, which causes a phase delay.  
This technology does not rely on ground stations and can provide atmospheric profile data on a global scale with high levels of precision. GNSS radio occultation detection is not sensitive to weather conditions and can be observed under various climatic conditions. This technique is widely used in meteorology, climate research, and space weather monitoring. For instance, researchers can examine how solar activity impacts the Earth's electromagnetic environment by investigating variations in the ionosphere. Additionally, by utilizing refraction data from the neutral atmosphere, one can observe fluctuations in meteorological factors like temperature and humidity.  
GNSS radio occultation has been widely used in monitoring tropospheric and ionospheric changes. [PERSON] et al. [19] successfully developed a highly precise empirical three-dimensional Global Electron Density Model (GEDM) by utilizing the COSMIC GNSS radio occultation dataset, employing advanced techniques such as Fourier expansion and principal component analysis (PCA) for their analysis. Their work involved a rigorous evaluation of the GEDM model against the well-established IRI-2020 model. The results of the evaluation indicated that the GEDM model exhibited a significantly higher degree of consistency when compared to the IRI-2020 model. The statistical error associated with the GEDM model is markedly lower than that of the IRI-2020, particularly in mid-latitude regions where ionospheric conditions often vary. Furthermore, the GEDM model demonstrated an exceptional capability to accurately represent key ionospheric features, including the Equatorial Ionization Anomaly (EIA), the seasonal winter anomaly, and the annual anomaly. These findings underscore the GEDM model's potential as a superior tool for ionospheric research and applications. The role of GNSS radio occultation is significant in meteorological forecasting. [PERSON] and [PERSON] [20] examined how GNSS radio occultation observations influenced the prediction of the severe rainstorm that struck Henan, China, on 20 July 2021. Their findings suggest that the incorporation of GNSS radio occultation data can enhance the accuracy of precipitation distribution and amount forecasts, thereby improving the predictions for events such as this unprecedented rainfall event.
## 6 GAN Architecture and Perceptual Guidance  
The generative adversarial network (GAN) framework adopted in this study leverages a dual-stream encoder-decoder structure with modality-specific branches. Each branch is based on a modified ResNet-18 backbone pre-trained on ImageNet and adapted to accept single-channel inputs. These branches independently extract hierarchical features from MR and PET modalities, capturing distinct anatomical and functional cues. Feature maps are fused via a transformer-inspired multi-head attention module designed to enhance inter-modality contextual relevance. The decoder reconstructs super-resolved outputs by integrating fused representations with skip connections to retain fine-grained spatial detail.  
A multi-scale PatchGAN discriminator operating at scales 64, 128, and 256 ensures high-frequency texture realism. The training was guided by a composite objective function:  
* **Adversarial loss (LSGAN)** : \(\mathcal{L}_{adv}=\mathbb{E}[(D(G(x))-1)^{2}]+\mathbb{E}[D(y)^{2}]\) ensures output indistinguishability from real samples.
* **Perceptual loss**: computed over VGG19 activations (layers relu1_2, relu2_2, relu3_4) to preserve semantic and texture fidelity.
* **Reconstruction loss**: L1 pixel-wise loss \(\|G(x)-y\|_{1}\) minimizes low-level dissimilarity.
* **Joint Hankel rank loss**: \(\|H_{x}(MR)\|_{*}+\|H_{x}(PET)\|_{*}+\lambda\|H_{x}(MR)-H_{x}(PET)\|_{1}\) reinforces inter-modality structure sparsity.  
Hyperparameter settings were as follows: initial learning rate = \(2\times 10^{-4}\) with cosine annealing decay, batch size = 8, patch size = 64\(\times\)64, optimizer = Adam (\(\beta_{1}=0.5\), \(\beta_{2}=0.999\)). Early stopping was applied after 15 epochs of no SSIM improvement on a held-out validation set. Total training was conducted over 150 epochs using an NVIDIA A100 GPU.
# Language Model Using Neural Turing Machine Based on Localized Content-Based Addressing
## 5 Experiments and Discussion
### Experimental Results in enviks LM Task
An important finding was observed regarding the performance of the LCA-NTM-based LM. The BPC of the proposed architecture was lower than that of the MDM-NTM-based LM, except when the selected memory address was 97. The performance improvement was not insignificant compared to that observed through the experimental results on the PTB LM task. We analyzed the errors with cosine similarities and discovered that more negative values existed than those in the experimental results on the PTB LM task. Therefore, the proposed NTM architecture applied vanilla content-based addressing, not LCA. These drawbacks led to the performance degradation of the proposed LCA-NTM architecture. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**nWP**} & \multirow{2}{*}{**nRV**} & \multirow{2}{*}{**nVEM**} & \multirow{2}{*}{**WD**} & \multirow{2}{*}{**IT**} & \multicolumn{3}{c}{**BPC**} \\ \cline{5-8}  & & & & & & & **Validation** & & **Test** \\ \cline{5-8}  & & & & & & & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline LSTM & 18.1 M & - & - & - & - & - & - & 1.4610 & - \\ Transformer & 44.1 M & - & - & - & - & - & - & 1.1120 & - \\ MDM-NTM & 30.2 M & 1 & 128 & \(1\times 10^{-7}\) & 2940 & 1.4502 & 0.0010 & 1.4475 & 0.0007 \\  & 34.9 M & 2 & 128 & \(1\times 10^{-7}\) & 3118 & 1.4473 & 0.0009 & 1.4419 & 0.0012 \\  & 37.8 M & 3 & 128 & \(1\times 10^{-7}\) & 3397 & 1.4185 & 0.0011 & 1.4117 & 0.0010 \\  & 42.5 M & 4 & 128 & \(1\times 10^{-7}\) & 3644 & 1.3970 & 0.0008 & 1.3922 & 0.0009 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of MDM-NTM-based LMs on the enwik8 LM task (nWP, number of weight parameters; nRV, number of read vectors; nVEM, number of vectors in the external memory; WD, weight decay; IT, inference time (ms/batch); \(\mu\), mean of BPC results; \(\sigma\), standard deviation of BPC results).