esa-sceva/satcom-chunk-collection huggingface dataset:
# Changing Dynamics of SARS-CoV-2: A Global Challenge
## 4 Variants of SARS-CoV-2
### Variants of Concern
#### 4.2.5 The Omicron Variant (B.1.1.529)  
The Omicron variant was reported in Botswana during early November 2021. The first case from South Africa was reported on 24 November 2021, and the WHO named this variant B.1.1.529 COVID-19 variant, followed by its declaration as VOC on 26 November 2021 [61]. Since then, cases due to B.1.1.529 have been reported in 89 countries, including the United Kingdom, Australia, Israel and the United States [62]. The appearance of this variant aggravated the situation concerning the COVID-19 pandemic, and has been persistent since 2019. Early sequence analysis suggests that this variant contains over 32 mutations in the spike glycoprotein, many of which are found in the RBD and NTD regions; therefore, some of these mutations are linked to the increased interaction of the SARS-CoV-2 spike glycoprotein with hACE-2 [63, 64] and neutralizing capability of SARS-CoV-2 antibodies and nanobodies [64, 65] (Table 1).  
Omicron has 3 common mutations with Alpha, Beta and Gamma, respectively, and 2 common mutations with Delta (Figure 6). Some of the prominent mutations found in the Omicron variant, N679K, P681H (next to the furin cleavage site), N501Y (inside the RBD) and D614G (spike glycoprotein), are among the polymorphisms that have been previously identified in other variants and shown to be more transmissible [66]. Most recently, genomic sequence analysis suggests that the 21K (Omicron BA.1) has sister clades, 21L (BA.2) and 21M (BA.3), thus indicating the presence of 3 sub-lineages. All of them comprise the Pango Lineage B.1.1.529. The spike glycoproteins of BA.2 and BA.1 (Omicron) share 21 amino acid mutations, but 21L carries 6 additional mutations and a deletion (CoVariants.org, accessed on 30 December 2021; GISAID).  
Moreover, the computational analysis suggests that Omicron and its sub-lineages were discovered to have less stable and closely packed RBD structures, which impeded antibody interactions more than wild-type and Delta (AY.1 and AY.2) variants, potentially leading to increased pathogenicity and immune escape [67]. Hence, it is likely that Omicron will spread quicker and elude antibodies more easily than prior variations, leading to a higher risk of re-infection [65, 68, 69]. Thus, multiple efforts are being made around the world to assess the comprehensive effect of the Omicron variant. Currently, the total number of sequenced Omicron strains are 5352, out of which the UK and South Africa have sequenced 2957 and 900 genomes, respectively, as of 15 December 2021 (Figure 5e) (GISAID).
## 3 Related Work and Discussions
### Privacy-Preserving User Identity  
Recently, the consent receipt specification [19] defines a record of authority when a user discloses his PII to an SP. This approach involves the user granting permissions for each SP explicitly. On the other hand, anonymity approaches [20; 21; 22; 23; 24], aggregated Zero Knowledge Proofs [25; 26; 27], and group signatures [28; 29] preserve complete anonymity for the user in a transaction with an SP. They require users to interact with an SP over the front-end. Therefore, they overload the IDM tasks to the user's decision in every transaction and do not support identity propagation over the unintended channel. Out of the methods mentioned, our approach involves the least user interaction. We require the user to select the intended purposes for his PII when he registers it to the system at the beginning. Afterwards, the authorisation is based on the defined purposes.  
In contrast to an anonymity approach, the confidentiality approaches in [45; 46; 47; 48; 49; 50; 51; 52] do not protect user anonymity but disclose PII to an entity if a disclosure policy is satisfied. [PERSON] et al. [46] encrypt user data with _Identity-based Encryption_[53]. If an SP wants to decrypt the data, it has to interact with a Trusted Third Party (TTP) to provide its policies and platform configurations. Our approach does not require SPs sending their configurations to the TTP. We borrow their idea to encrypt the data with the disclosure policies, but we use ABE as an encryption scheme with a more expressive policy.  
_Active bundles_[48] are mobile agents that use disclosure policies to authorise requests from the host and disclose the data to the host. However, the execution of a mobile agent's plaintext code in an untrusted host is not secure [54]. Recent work also took advantage of _Blockchain_ technology to store data (e.g., an authentication claim [23], or an access control list [24]) in a Blockchain network. However, PII is stored in an external database. While Blockchain brings an auditable history to the stored data, it cannot protect PII from an untrusted host of the external database and the authorisation server. Our solution avoids the above issues of active bundles and Blockchain because our authorisation does not rely on the execution of plaintext code. Instead, the cryptographic computing itself performs the authorisation. If the host changes the disclosure policies (e.g., the access control list) or the cryptographic computing, the decryption simply fails.  
Finally, research in the past has _investigated Purpose-aware Access Control_ to prevent one organisation from misusing or accidentally transferring user data to another one [49; 50; 51; 52]. A subject may have access to an object if the given _access purpose_ corresponds to the _intended purpose_ for which data were collected. However, these solutions have difficulties in determining the access purpose of a request. In our research, we add a _purpose authorisation request_ that determines an access purpose based on an authentication statement and the requesting service.
# DNA Barcoding as a Plant Identification Method
## 8 Mini Barcoding  
DNA mini barcoding has developed over the previous 10 years to either overcome the limitations of DNA barcoding or as an extension of it. DNA mini barcoding uses a DNA sequence shorter than conventional barcodes, usually equal to or less than 200 bp, which can easily and rapidly be amplified. Instead of achieving universality for most species, the major goal of creating a mini barcode is to identify specific target species of herbal plants to prevent adulteration of natural herbal goods. Nonetheless, in the case of complex herbal mixtures made, for instance, from 10 different species, mini barcoding is challenging. Considering the position and the length of the specific mini barcode sequence to distinguish between various species is very important, since certain DNA sequences may contain unstable mutation sites [67; 68; 69].
## 2 Formulation of the Problem and Proposed Solution
### Formulation of the Problem
,\] (2b) \[\underline{E}_{i,j}^{(\mathrm{S})}(\underline{t})=\iint\limits_{-\infty}^{+ \infty}\underline{\widetilde{G}}_{i,j}^{(\mathrm{S})}(u,v)\widetilde{ \underline{\int}}_{j}(u,v)e^{-\mathrm{i}(ux+vy+R_{i}(u,v)|z|+R_{j}(u,v)|z|)} dudv,\] (2c) where the following definition of a double Fourier transform has been used: \[\widetilde{f}(u,v)=\frac{1}{4\pi^{2}}\iint\limits_{-\infty}^{+\infty}f(x,y)e^{ \mathrm{i}(ux+vy)}dxdy\Leftrightarrow f(x,y)=\iint\limits_{-\infty}^{+\infty} \widetilde{f}(u,v)e^{-\mathrm{i}(ux+vy)}dudv,\] (3) and \[\underline{\widetilde{G}}_{i,j}^{(\mathrm{T})}(u,v)= \begin{pmatrix}v^{2}A_{i, j}^{(\mathrm{T})}(u,v)+u^{2}B_{i,j}^{(\mathrm{T})}(u,v)&uv\Big{(}B_{i,j}^{(\mathrm{T})}(u,v)-A_{ i,j}^{(\mathrm{T})}(u,v)\Big{)}&uC_{i,j}^{(\mathrm{T})}(u,v)\\ uv\Big{(}B_{i,j}^{(\mathrm{T})}(u,v)-A_{i,j}^{(\mathrm{T})}(u,v)\Big{)}&u^{2}A_ {i,j}^{(\mathrm{T})}(u,v)+v^{2}B_{i,j}^{(\mathrm{T})}(u,v)&vC_{i,j}^{(\mathrm{ T})}(u,v)\\ uC_{i,j}^{(\mathrm{T})}(u,v)&v\overline{C}_{i,j}^{(\mathrm{T})}(u,v)&D_{i,j}^{( \mathrm{T})}(u,v)\end{pmatrix} ,\] (4a) \[A_{ij}^{(\mathrm{T})}(u,v)=-\frac{\omega\mu_{j}}{2R_{j}(u,v)(u^{2}+v^{2})} \begin{cases}1&T=\mathrm{P}\\ T_{ij}^{(\mu)}(u,v)&T=\mathrm{S}^{\prime}\end{cases} \] (4b) \[B_{ij}^{(\mathrm{T})}(u,v)=-\frac{R_{i}(u,v)}{2\omega\epsilon_{i}(u^{2}+v^{2}) } \begin{cases}1&T=\mathrm{P}\\ T_{ij}^{(\epsilon)}(u,v)&T=\mathrm{S}^{\prime}\end{cases} \] (4c) \[C_{i,j}^{(\mathrm{T})}(u,v)=\frac{R_{i}(u,v)}{2\omega\epsilon_{i}R_{j}(u,v)} \begin{cases}\begin{array}{c}\mathrm{sgn}\big{(}z-z_{j}\big{)}&T=\mathrm{P} \\ (-1)^{j}T_{ij}^{(\epsilon)}(u,v)&T=\mathrm{S}^{\prime}\end{array}\end{cases} \] (4d) \[\overline{C}_{i,j}^{(\mathrm{T})}(u,v)=\frac{1}{2\omega\epsilon_{i}} \begin{cases} \mathrm{sgn}\big{(}z-z_{j}\big{)}&T=\mathrm{P}\\ (-1)^{j}T_{i,j}^{(\epsilon)}(u,v)&T=\mathrm{S}^{\prime}\end{cases} \] (4e) \[D_{i,j}^{(\mathrm{T})}(u,v)=\frac{u^{2}+v^{2}}{2\omega\epsilon_{i}R_{j}(u,v)} \begin{cases}-1+\mathrm{i}\frac{2\mathcal{R}_{i}(u,v)}{u^{2}+v^{2}}\delta \big{(}z-z_{j}\big{)}&T=\mathrm{P}\\ (-1)^{j}T_{ij}^{(\epsilon)}(u,v)&T=\mathrm{S}^{\prime}\end{cases}  \tag{4f}\]  
\[ \begin{split}\sum\limits_{n=-\infty}^{+\infty}\delta^{\text{j}\text{i} \
eq\phi}\int\limits_{0}^{+\infty}\underline{\mathbf{H}}^{(n)}&(w \rho)\widetilde{\underline{\mathbf{G}}}(w)\widetilde{\underline{\mathbf{J}}}^{( n)}(w)wdw\\ &=-\frac{1}{4\pi^{2}}\begin{pmatrix}c_{\phi}&s_{\phi}\\ \mathrm{j}s_{\phi}&-\mathrm{j}c_{\phi}\end{pmatrix}\int\limits_{0}^{+\infty} \int\limits_{0}^{2\pi}\widetilde{\underline{\mathbf{G}}}_{2,1}^{(\mathrm{S})}( w,\psi)e^{-\mathrm{j}(\mathrm{i}\pi c_{\phi-\phi}+\pi c_{\phi}c_{\phi-\phi}+R_{2}(w)d+R_{1}(w)z_{ 0})}wdwd\psi\underline{\underline{\mathbf{J}}}_{0^{\prime}}\\ &\text{where }x_{0}=\rho_{0}c_{\phi_{0}},y_{0}=\rho_{0}s_{\phi_{0}}, \text{and}\\ &\widetilde{\underline{\mathbf{G}}}(w)=w^{2}\begin{pmatrix}B_{2,2}^{(\mathrm{ P})}(w)+B_{2,2}^{(\mathrm{S})}(w)e^{-\mathrm{j}2R_{2}(w)d}&0\\ 0&A_{2,2}^{(\mathrm{P})}(w)+A_{2,2}^{(\mathrm{S})}(w)e^{-\mathrm{j}2R_{2}(w)d} \end{pmatrix}.\end{split}  \tag{10}\]
## 3 State of Technology
#### Identified Existing Challenges  
Based on the experimental work presented and discussed in Table 1, the following challenges for the realization of ACHP systems can be identified:  
* **Compressor discharge temperature:** Occurring discharge temperatures constrain the achievement of higher sink temperatures, as they can cause problems, such as the decomposition of used lubricating oils and material problems in the compressor.
* **Compressor lubrication:** When oil is used to lubricate the compressor, additional components are needed for separation and cooling, raising the complexity and costs. In addition, the oil tends to penetrate the whole circuit, which requires a recirculation system and can have a negative impact on the heat pump performance.
* **Oil-free operation of the system:** Compressors that can be operated oil-free or for which lubrication is done by the solution are often associated with higher equipment costs due to necessary modifications or are unavailable for commercial use.
* **Absorber and desorber design:** An efficient design of the absorber and desorber is an important factor in improving the performance of the system. Therefore, an advanced understanding of the occurring heat transfer phenomena is essential, especially for the absorber at high temperature and high-pressure operation.
* **Liquid-vapor mixing and distribution process:** When using PHE, an appropriate selection of the operation mode together with effective liquid-vapor mixing and distribution are important to achieve high overall heat transfer coefficients and system performances.
* **Solution pump:** Cavitation caused by changing low pressure conditions related to rapid changes in compressor operation and operating conditions is a major challenge for the solution pump in ACHP cycles due to the saturated liquid leaving the liquid-vapor separator.
###### Abstract  
Estimating the exact residual lifetime of wire rope involves the security of industry manufacturing, mining, tourism, and so on. In this paper, a novel testing technology was developed based on unsaturated magnetic excitation, and a fabricating prototype overcame the shortcomings of traditional detection equipment in terms of volume, sensibility, reliability, and weight. Massive artificial discontinuities were applied to examine the effectiveness of this new technology with a giant magneto resistance(GMR) sensor array, which included types of small gaps, curling wires, wide fractures, and abrasion. A resolution enhancement method, which was adopted for multiframe images, was proposed for promoting magnetic flux leakage images of a few sensors. Characteristic vectors of statistics and geometry were extracted, then we applied a radial basis function neural network to achieve a quantitative recognition rate of 91.43% with one wire-limiting error. Experimental results showed that the new device can detect defects in various types of wire rope and prolong the service life with high lift-off distance and high reliability, and the system could provide useful options to evaluate the lifetime of wire rope.  
wire rope; nondestructive; magnetic flux leakage; super-resolution; quantitative recognition Article
# Super-Resolution Remote Imaging Using Time Encoded Remote Apertures
## 3 Reconstruction Algorithm  
In this section, we design a trilateration based reconstruction algorithm for our imaging system. There exist algorithms (TRIBOND [20] and LIGA [21]) that reconstruct a point-cloud given list of unassigned edge measurements. TRIBOND is a deterministic algorithm that addresses the unassigned distance geometry problem (uDGP). It has been successfully applied to reconstruct the structure of molecules and nanoparticles while using edge distance lists extracted from X-ray or neutron diffraction data. However, we can not apply the TRIBOND algorithm directly to our data, because it contains not only the first bounce, but also the second bounce. Moreover, these bounces are unlabeled. It requires modification. [PERSON] et al. [19] showed that it is possible to reconstruct point-cloud using unlabeled edge measurements. Here, we modify the TRIBOND algorithm [20] with method described in [19] to process data that were acquired by our imaging system. The modified TRIBOND algorithm (see Algorithm 1) consists of two parts: core finding and adding a vertex.
# An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks
## 4 Numerical Tests
### MNIST with CNN  
MNIST dataset are popular for machine learning and consist of a 10-class gray-scale image with a size of 28 \(\times\) 28. Figure 5 shows part of the MNIST data.  
Figure 4: Visualization of the changing parameters by each method.  
Figure 3: Stylbinski–Tang function.  
For a performance comparison of each method, a simple structured CNN consisting of two convolution layers and one hidden layer was used. For the convolution, a \(5\times 5\times 32\) sized filter and \(5\times 5\times 32\times 64\) sized filter were used, the batch size was set to 64, and a dropout of 50% was performed. Learning was conducted 8800 times, and the learning rate was 0.001. Figure 6 shows the results of the experiment.  
Figure 5(a) shows the change in cost according to the learning using each method, and shows that all of the methods minimized the cost. Figure 5(b) shows the accuracy using the learning data as the learning progresses, and Figure 5(c) shows the validation accuracy using the test data as the learning progresses to check if it is over-fitting. For a more detailed comparison, Figure 7 shows the accuracy after a certain iteration.  
Figure 6: Results of MNIST with a convolutional neural network (CNN).  
Figure 6(a) shows the training accuracy after conducting 1000 iterations, while Figure 6(b) shows the validation accuracy after conducting 2000 iterations. The training accuracy was high for all methods; however, Adam and our proposed method were particularly high. The order of validation accuracy from high to low is: our proposed method \(>\) Adam \(>\) Adagrad \(>\) GD.
## 3 Results
### Interaction of Berberine Hydrochloride and the Recombinant FtsZ Protein  
The effects of berberine hydrochloride on FtsZ polymerization were analyzed by SDS-PAGE. As shown in Figure 4A, the amount of FtsZ pelleted increased slightly with GTP concentrations ranging from 0.25 to 1 mM, but decreased with increasing GTP concentrations (from 2 to 8 mM). The amount of FtsZ pelleted was the highest in the presence of 1 mM GTP. As shown in Figure 4B, compared to the control, FtsZ polymerization was inhibited by berberine hydrochloride in a concentration-dependent manner. Meanwhile, one procedure in the reaction was superior to two (Figure 4B).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sanguinar**} & \multicolumn{2}{c}{**Tetrandrine**} & \multicolumn{2}{c}{**Berberine Hydrochloride**} & \multicolumn{2}{c}{**Theophylline Sophoridine**} & \multicolumn{2}{c}{**Matrine**} & \multicolumn{2}{c}{**Sinomenine**} & \multicolumn{2}{c}{**Betanicine**} & \multicolumn{2}{c}{**Nonivamide**} \\  & **Chloride** & & & & & & & & & & & & & & & & & & & & & & \\ \hline
**(mg/mL)** & **MIC** & **MBC** & **MIC** & **MBC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MBC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** & **MIC** \\ \hline _E. coli_ & & & & & & & & & & & & & & & & & & & & & & & & \\ CVCC1515 & 0.04 & 0.16 & 0.16 & 0.16 & 0.64 & 0.64 & 2.56 & \(>\)5.12 & 5.12 & 10.24 & 5.12 & \(>\)5.12 & 8.88 & 5.12 & \(>\)10.24 & - & \(>\)10.24 & - & \(>\)5.12 & - \\ _E. coli_ & & & & & & & & & & & & & & & & & & & & & & \\ CVCC195 & 0.16 & 0.32 & 0.16 & 0.16 & 0.64 & \(>\)0.64 & 2.56 & \(>\)5.12 & 5.12 & 10.24 & 5.12 & \(>\)5.12 & \(>\)5.12 & - & - & - & - & - & - & - \\ _S. enteritidis_ & & & & & & & & & & & & & & & & & & & & & & \\ ATCC14028 & & & & & & & & & & & & & & & & & & & & & & & \\ _S. aureus_ & & & & & & & & & & & & & & & & & & & & & & & & \\ ATCC43300 & & & & & & & & & & & & & & & & & & & & & & & & \\ \hline \end{tabular} Note: \(\gamma\), no detection.  
\end{table}
Table 1: MIC and MBC values of alkaloids against different bacterial pathogens.  
Molecular docking showed that one hydrogen bond was formed between berberine hydrochloride and FtsZ protein and the binding energy was \(-26\) kJ/mol; berberine hydrochloride was predicted to bind to the Asp253 residue of FtsZ (Figure 4C,D).
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 4 Approaches for Preventing Sticking of MXenes
### Integration with Polymer Matrices or Carbon Nanomaterials
#### 4.2.2 Carbon Nanomaterials  
Integration of MXenes with carbon nanomaterials, such as carbon nanotubes or graphene, can also prevent sticking in MXene electrodes. Carbon nanomaterials possess excellent electrical conductivity, high aspect ratios, and large surface areas, making them ideal candidates for dispersing MXene flakes. Carbon nanomaterials effectively act as scaffolds, providing a three-dimensional network structure that facilitates the dispersion and stabilization of MXenes. The high surface area and strong interfacial interactions between MXenes and carbon nanomaterials hinder the agglomeration of MXene flakes, mitigating sticking.  
MXene foams with varying pore structures have been synthesized in [77] using polystyrene (PS) spheres with different diameters of 80, 310, and 570 nm (Figure 18a,d,g). These foams exhibit uniform and interconnected pores that offer ample active sites and a strong electrical connection for electron transfer. Of particular interest is the finding that when the sizes of the MXene flakes and templates (310 nm) match, the resulting foam demonstrates the highest gravimetric capacitance of 474 \(\pm\) 12 F g\({}^{-1}\) at 2 mV s\({}^{-1}\), outperforming the others. Furthermore, the ratio of MXene to PS mass influences the packing density of the foams and the sticking trend of the nanosheets (Figure 18b,c,e,f,h,i), which in turn affects the inner resistance of the foam electrodes. Introducing carbon nanotubes further enhances the electrical conductivity of the foams, resulting in a capacitance of 462 \(\pm\) 8 F g\({}^{-1}\) at 2 mV s\({}^{-1}\) and retaining 205 \(\pm\) 10 F g\({}^{-1}\) at 1000 mV s\({}^{-1}\). This research holds promise for energy storage applications and provides valuable guidance for the design of porous electrodes based on 2D nanomaterials for supercapacitors without sticking.
###### Abstract  
In this work, the seismic response of a multi-story, multi-bay special truss moment frame (STMF) with Ni-Ti shape memory alloys (SMAs) incorporated in the form of X-diagonal braces in the special segment is investigated. The diameter of the SMAs per diagonal in each floor was initially determined, considering the expected ultimate strength of the special segment, developed when the frame reaches its target drift and the desirable collapse mechanism, i.e., the formation of plastic hinges, according to the performance-based plastic design procedure. To further investigate the response of the structure with the SMAs incorporated, half the calculated SMA diameters were introduced. Continuing, three more cases were investigated: the mean value of the SMA diameter was introduced at each floor (case DC1), half the SMA diameter of case DC1 (case DC2), and twice the SMA diameter of case DC1 (case CD3). Dynamic time history analyses under seven benchmark earthquakes were conducted using commercial nonlinear Finite Element software (SeismoStruct 2024). Results were presented in the form of top-displacement time histories, the SMAs force-displacement curves, and maximum inter-story drifts, calculating also maximum SMA displacements. The analysis outcomes highlight the potential of the SMAs to be considered as a novel material in the seismic retrofit of steel structures. Both design approaches presented exhibit a certain amount of effectiveness, depending on the distribution, with the placement of the SMA bars and the seismic excitation considered. Further research is suggested to fully understand the capabilities of the use of SMAs as dissipation devices in steel structures.  
Keywords:special truss moment frames (STMFs); shape memory alloys (SMAs); superelasticity; dynamic time history analysis; energy dissipation +
Footnote †: journal: Applied sciences