esa-sceva/satcom-chunk-collection huggingface dataset:
# Diffusion Model as a Base for Cold Item Recommendation
## 3 Cold Item Recommender with a Diffusion Model Base
### Cold Item Recommendation  
The cold item recommendation problem that we are trying to solve is defined as follows. Users in a user set \(U\) and items in an item set \(I\) make interactions such as clicks and ratings. Items in \(I\) are classified into one of the two mutually non-intersected subsets: warm item set \(I_{w}\) and cold item set \(I_{c}\). Regardless of their subset, each item \(i\) has its side information embedding \(c_{i}\). The interactions are represented as two rating matrices, \(R^{tr}\in\mathbb{R}^{|U|\times|I_{w}|}\) and \(R^{te}\in\mathbb{R}^{|U|\times|I_{c}|}\), whose elements indicate interactions between users and warm items and between users and cold items, respectively. Each user \(u\in U\) and warm item \(i\in I_{w}\) has its own collaborative embedding \(e_{u}\) and \(e_{i}\), which is calculated by a base CF method by using \(R^{tr}\) as the training data.  
Our goal is, for a given \(u\in U\) and cold item set \(I_{c}\), to calculate an ordered list of \(k\) cold items \(L_{u}^{k}\) that maximizes the prediction accuracy about \(u\)'s interaction in \(R^{te}\). To calculate \(L_{u}^{k}\), we can only use \(e_{u}\), and \(c_{i}\) of cold items as input. In the training, we exploit the information from \(e_{u}\) and \(e_{i}\), collaborative embeddings of users and warm items, \(R^{tr}\), and \(c_{i}\) of all items.
## 4 Analysis of the Simulation Signal
### Results of Simulation Signal Decomposition  
After determining the value of k using the FCI-VMD algorithm, the simulation signal is decomposed using VMD with a selected number of decomposition layers, namely, \(k=4\) (see Figure 2). The spectrogram reveals that IMF2, IMF3, and IMF4 correspond to the main frequencies \(\rho_{3}=110\) kHz, \(\rho_{2}=70\) kHz, and \(\rho_{1}=40\) kHz, respectively. In contrast, the IMF1 component exhibits a wider frequency band with high frequency and low energy, indicating it as the noise signal. To verify the effectiveness of the proposed optimization algorithm, we also decompose the signal with \(k=5\) using VMD, and the resulting components are shown in Figure 3. Notably, the main frequencies of the IMF2 and IMF3 components in Figure 3 are found to be relatively close, indicating the occurrence of mode mixing. Consequently, the optimal number of decomposition layers, k, for the signal is determined to be 4.  
To evaluate the advantages of the FCI-VMD method, a comparison is made with two alternative approaches: EMD and EVMD. The decomposition results obtained using EMD are depicted in Figure 4. As illustrated in the figure, IMF2 to IMF5 all exhibit varying degrees of mode mixing, making it challenging to distinguish between effective modes and noise modes. On the other hand, EVMD (empirical variational mode decomposition) is a variational mode decomposition method in which mode number \(k\) is empirically determined. Figure 5 showcases the decomposition results obtained using EVMD, clearly indicating excessive decomposition of the signal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\beta\) & \(A_{i}\) & \(P_{i}\) & \(t_{i}\)/ms & \(\rho_{m}\)/kHz \\ \hline
1 & 2 & \(6\times 10^{8}\) & 0.4 & 70 \\
2 & 2 & \(8\times 10^{8}\) & 0.6 & 40 \\
3 & 2 & \(7\times 10^{8}\) & 0.8 & 110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AE simulation parameters.  
Figure 1: Original simulation signal. SNR = 10 dB.  
Figure 3: _Cont._  
Figure 2: The modes obtained using FCI-VMD, with \(k=4\). (**a**) Distribution of different waveform components. (**b**) Spectrum of different waveform components.  
Figure 4: The modes obtained using EMD, with \(k=9\), IMF1\(\sim\)IMF5. **(a)** Distribution of different waveform components. **(b)** Spectrum of different waveform components.  
Figure 3: The modes obtained using VMD, with \(k=5\). (a) Distribution of different waveform components. **(b)** Spectrum of different waveform components.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 2 Materials and Methods
Using the DUD-E dataset, the performance of a scoring function in virtual screening could be expressed through a graphical representation of the true positive rate versus the false positive rate in terms of receiver operating characteristic (ROC) plots. In ROC plots, the true positive rate (TPR = TP/P) was plotted against the false positive rate (FPR = FP/N), where TP is the number of true positives, P is the total number of positives (actives), FP is the number of false positives, and N is the total number of negatives (decoys). A useful measure is the area under the curve (AUC). The higher the AUC value in a ROC curve, the better the discrimination between the true positive and the false positive poses. As previously mentioned, a successful scoring function for virtual screening should rank active compounds very early on a large score list, so metrics that emphasize early recognition of ligands are normally used. One of such measures is the enrichment factor at 1% (abbreviated EF1%). This value measures the number of active ligands recovered at 1% of the ligand/decoy database, over the number of active ligands that should be expected at the same fraction of the database with random selection. Other values such as the EF20% were also used sometimes. After an initial analysis of all the DUD-E targets, there was one (FGFR pdb:3C4F) that did not have the 1/50 proportion for active/decoys, so it was decided to exclude it from this test. For each target in the DUD-E dataset, an initial analysis of the PDB file associated was performed. The binding pocket was studied and evaluated. Similar PDB structures with co-crystallized ligands were also inspected. Re-docking of the ligands for which there was a ligand-target structure available was performed with AutoDock and with Vina. The docking protocol for both programs was adjusted so as to reproduce the known experimental binding poses for each target, a standard protocol, when validating a docking program/protocol for a specific target [24], as presented in Figure 2. Parameters adjusted in this process with Vina included in the box size and position, number of generated binding modes and exhaustiveness. In AutoDock, the parameters optimized also included the box size and position, number of grid points and spacing, number of genetic algorithm (GA) runs, population size, maximum number of energy evaluations, and maximum number of generations. After the first optimization stage performed for each target, the box dimensions and center coordinates used for both AutoDock and Vina were the same. The exhaustiveness value used for Vina was 8. As for AutoDock, the grid spacing was set to 0.375 A and the number of GA runs was set to 10. All this information is provided in Table S1 in the Supplementary Materials.
## 4 Results  
### Comparison of Mean Joint Angels with and Without AR System Application  
Table 2 presents the results comparing the mean joint angles with and without the application of the AR system. Our analysis revealed statistically significant increases in joint angles (\(p<0.05\)) when the AR system was applied during three specific movements: (1) standing arm abduction, (2) standing trunk lateral flexion, and (3) standing knee flexion to maximum height.
## 4 Experiments and Analysis
### Sensitivity Analysis  
The final experimental results demonstrate that the SSECRF algorithm efficiently integrates the respective advantages of the visible and thermal infrared hyperspectral imagery, and it shows a good performance in land-cover classification. However, two parameters \(\lambda\) and \(\theta\) are needed, as described in the SSECRF energy function \(E(\mathrm{x}|\mathrm{y})\), which have an effect on the final classification result. These two parameters mainly relate to the spatial interactions in the pairwise potential function. An additional 120 experiments were conducted to make a sensitivity analysis. Parameter \(\lambda\) ranged from 0.2 to 6.8 with a step length of 0.6, and parameter \(\theta\) varied from 1 to 5 with an interval of 1. Figure 8 shows the relationship between the OA and the parameter selection in the SSECRF algorithm.  
For Figure 8a, the classification map was obtained integrating the spatial-spectral feature set from the visible imagery and the emissivity from the thermal infrared hyperspectral imagery by the SSECRF algorithm. The OA from 60 experiments exhibits a tendency to first rise and then decrease slightly as parameter \(\lambda\) increases when keeping \(\theta\) unchanged. It can be concluded that parameter \(\lambda\) can make full use of the spatial-contextual information in the neighborhood, reducing the influence  
Figure 8: Sensitivity analysis of parameters \(\lambda\) and \(\theta\). (**a**) The spatial-spectral feature set from the visible imagery and the emissivity from the thermal infrared hyperspectral imagery input into the SSECRF algorithm. (**b**) The spatial-spectral feature set from the visible imagery and the raw radiance of the thermal infrared hyperspectral imagery input into the SSECRF algorithm.  
Figure 7: The performance of the proposed SSECRF method compared with the existing methods, by kappa coefficient.  
of the salt-and-pepper noise in the classification to improve the accuracy. However, when parameter \(\lambda\) is increased beyond a certain value, the effect of the spatial smoothing is too much, resulting in over-smoothing, so that the accuracy shows a downward trend. For parameter \(\theta\), which is a parameter in the spatial smoothing term, it has a similar function to parameter \(\lambda\). In addition, it has a fine-tuning effect on the classification accuracy when keeping parameter \(\lambda\) constant.  
For Figure 8b, the same experimental conditions were repeated with the same ranges of the two parameters \(\lambda\) and \(\theta\), but the emissivity feature was replaced by raw radiance data of the thermal infrared hyperspectral imagery when constructing the unary potential in SSECRF, and another 60 experiments were conducted. It is worth noting that the OA of the best classification result is no better than the best classification result obtained with the SSE feature set. Therefore, it can be proved that SSECRF is a relatively stable algorithm, and the classification performance changes regularly with the change of the parameters.
# Protein Assistants of Small Ribosomal Subunit Biogenesis in Bacteria
## 2 30S Ribosomal Subunit Biogenesis Factors
### YjeQ (RsgA)
The OB-fold domain of YjeQ stabilizes the A1492 position via hydrophobic and \(\pi\)-stacking interactions [30; 45; 46]. It should be noted that the 70S ribosome dissociates in the presence of YjeQ [37]. These data suggested that YjeQ sterically prevents premature 30S subunit from association with the 50S subunit and may act as a checkpoint protein that tests maturing 30S subunits for their ability to proofread. After correct 30S subunit maturation, the factor YjeQ dissociates, and mature 30S subunits become available for the translation. The YjeQ release from 30S subunit is accompanied by GTP hydrolysis [48]. Superimposition of YjeQ\(\bullet\)GMP-PNP (nonhydrolyzed GTP analog) and YjeQ\(\bullet\)GDP structures revealed the reorientation of both the N-terminal OB-fold and C-terminal zinc-finger domains [40; 45]. The trigger for GTP hydrolysis is conformational rearrangements of the helix 44 top that interacts with switches of the GTPase domain of YjeQ. A study on the compensation of slow growth of the \(\Delta yjeQ\) strain revealed suppressor mutations in the _rbfA_ gene [13]. It turned out that these mutations promoted spontaneous dissociation of RbfA from the 30S subunit, and only RbfA mutants, but not the wild-type RbfA, suppressed the 30S subunit maturation defects in the \(\Delta yjeQ\) cells. Prolonged association of RbfA with the 30S subunit aggravates defects in the _yjeQ_ null mutant. According to the structures of the 30S subunit\(\bullet\)RbfA complex [29; 30], it was suggested that proteins RbfA and YjeQ had overlapping sites. It was shown that the GTP-bound state of YjeQ enhanced the release of RbfA from the mature 30S subunit [13], and the C-terminal \(\alpha\)-helix of YjeQ was essential in this process [35]. However, IF3 may be the dominant factor that releases RbfA during stationary phase [32], when the GTPase activity of YjeQ is inhibited by the alarmore (p)ppGpp [49].
###### Abstract  
Given that aging deterioration significantly influences the structural behavior of reinforced concrete (RC) nuclear power plant (NPP) structures, it is crucial to incorporate changes in the material properties of NPPs for accurate prediction of seismic responses. In this study, machine learning (ML) models for predicting the seismic response of RC NPP structures were developed by considering aging deterioration. The OPR1000 was selected as a representative structure, and its finite element model was generated. A total of 500 artificial ground motions were created for time history analyses, and the analytical results were utilized to establish a database for training and testing ML models. Six ML algorithms, commonly employed in the structural engineering domain, were used to construct the seismic response prediction model. Thirteen intensity measures of artificial earthquakes and four material properties were employed as input parameters for the training database. The floor response spectrum of the example structure was chosen as the output for the database. Four evaluation metrics were implemented as quantitative measures to assess the prediction performance of the ML models. This study used multiple input variables to represent the characteristics of the seismic loads and changes in material properties, thereby increasing the minimum required database size for ML model development. This increase may extend the time and effort required to construct the database. Consequently, this study also explored the possibility of reducing the minimum required database size and the prediction performance through input dimension reduction of the ML model. Numerical results demonstrated that the developed ML model could effectively predict the seismic responses of RC NPP structures, taking into account aging deterioration.  
nuclear power plant; aging deterioration; machine learning; seismic response prediction; feature selection; feature extraction Article
## 3 Learning Model
### Model to Estimate the Beam Quality by Deep Learning  
The proposed system not only measures the received up-pass frequency signal of each broad beam, but it also builds a beam-domain image without high resolution. The inputs to the machine-learning-based model are images that present the acquisition power and the wide beam. The learning framework that is envisaged in this paper outputs input data with high resolution onto the beamfield image that represents the acquired power, as well as the narrow beam.  
A model is used to estimate the power of the received narrow beam whenever an upstream frequency signal is received. The construction of the model that is referred to is shown in Figure 4. As is shown in Figure 4, a 2D convolutional layer and a convolutional layer with subpixel upsampling are used, before the input to a normal convolutional layer.  
Figure 3: The beam-selection scheme in this paper.  
A powerful super-resolution technique is used in these two layers, which can learn to improve the fractional variability in the picture. Subpixel convolution can reduce the computational complexity and can upgrade maps by using complex filters that are trained with special inputs. This information can be used to increase the resolution of the image through the upsampling layers. As described above, two 2D convolutional layers make up the subnetworks below. Spatial correlations in images can be used by these layers. Thanks to the specific input type that was designed, the acquired power of the wide beam depends on the beam direction. The fully connected (FC) layer is the last layer of the neural network. As you can see, Table 2 shows the number of neurons in the fully connected layer, each layer of the neural network, and the size of the filter. Our proposed scheme uses a trained model to estimate high-resolution beamfield images, and it chooses the narrow beam that obtains the highest energy.
## 3 Results  
### Water Vapor Transmission Rate (WVTR)  
In an ideal wound dressing, the WVTR should be between 2000 and 2600 g/m\({}^{2}\)/day in order to avoid dryness and moisture loss, which could result in bacterial infections [32]. However, depending on the type of skin wound, the WVTR may need to be higher; for illustration, in order to prevent granulating wounds, first-degree burns require WVTR of 3000 g/m\({}^{2}\)/day, whereas severe burns require 5000 g/m\({}^{2}\)/day [32; 33]. After combining the nZnO particles with the PVA/starch blend (SPGC), the new solutions' (S1-S4) WVTRs were recorded from 2691 g/m\({}^{2}\)/day to 2728 g/m\({}^{2}\)/day (Figure 3). When compared to the SPGC blend, there was a reduction in WVTR from 4146 to an average of 2704 g/m\({}^{2}\)/day. Therefore, the measured WVTR demonstrates that the prepared material could be used as an ideal wound dressing.  
Figure 3: Biodegradable starch/PVA wound dressings’ WVTR before (SPGC) and after adding nano-ZnO particles (S1–S4) to the starch-based/polyvinyl alcohol composite.
## 5 Discussion  
### Generalization Ability  
#### 5.1.1 Native Models  
The SQLCoder model excels in handling direct queries, specific attribute queries, and complex spatial-related query tasks, achieving nearly perfect exact and Execution Accuracy. These results are attributed to its design, specifically tailored for natural-language-to-SQL conversion, and the incorporation of a grouped query attention mechanism and intermediate padding techniques, enhancing the model's ability to manage complex SQL queries.  
Although ERNIE's precision in certain tasks, such as querying specific shapes of components or identifying wall openings, falls short compared to the other two models, it exhibits strong generalization capabilities when dealing with ambiguous and colloquial natural language queries. This is due to ERNIE's superior ability to comprehend and restructure natural language inputs.  
After fine-tuning in a Chinese context, the Llama model excels in specific tasks, such as identifying individual components within a building and querying wall openings and theirconnections to other components. However, its accuracy declined when handling queries involving multiple attributes or specific conditions. This indicates that Llama adapts well to the Chinese language environment after appropriate fine-tuning, but its ability to handle complex attribute queries is somewhat limited.
## 5 Results and Discussion
### Potential Applications of the Sealing Technique  
As the global demand for energy continues to grow, the depth of resource acquisition is also increasing, especially in the extraction of non-renewable resources such as oil, natural gas, and oil shale. These resources are often located deep underground, where the HTTP environment poses great difficulties for mining. Hydraulic fracturing technology, as a method for increasing reservoir permeability and enhancing the capacity of production, plays an important role in the extraction of deep resources. Additionally, climate change is a global issue and clean energy must be developed to replace traditional fossil energy to reduce CO2 emissions. HDR geothermal energy is an emerging geothermal energy source with huge energy and is also a substitute for fossil energy used for heating and generating electricity. In HDR reservoirs, the temperature of the rock is generally between 150 \({}^{\circ}\)C and 650 \({}^{\circ}\)C, and this kind of rock is buried 3-10 km underground. HDRs possess limited fractures or pore spaces and hence have no or little water, or no unified rock porousness. To harvest this geothermal heat, dry rocks are fractured by circulating cold liquid water and hydraulic pressure down one well to harvest the heat from these fractured dry rocks, and heated water is extracted from an additional geothermal well in a locked system. Therefore, whether it is the efficient extraction of deep fossil fuels or the development of renewable new energy from deep HDRs, hydraulic fracturing technology is required. However, facing HTHP environments deep underground, conventional hydraulic fracturing techniques  
Figure 11: Hydraulic fractured granite rocks under high-temperature and high-pressure conditions. (**a**) Hydraulic fractured rocks at 100 \({}^{\circ}\)C; (**b**) hydraulic fractured rocks at 200 \({}^{\circ}\)C; (**c**) hydraulic fractured rocks at 300 \({}^{\circ}\)C; (**d**) Hydraulic fractured rocks at 400 \({}^{\circ}\)C.  
often fail to work, necessitating the development of new sealing equipment to adapt to such conditions.  
Currently, hydraulic fracturing under HTTP conditions cannot be conducted smoothly on site. Many studies are focused on developing HTTP hydraulic fracturing isolation devices in the laboratory to achieve field-scale promotion and application. In laboratory hydraulic fracturing experiments, commonly used materials such as cement or rubber easily fail at high temperatures. Therefore, the development of a sealing technology that can remain stable in HTTP environments is particularly important. This technology can effectively solve the failure problems in high-temperature environments and is significant for both laboratory research and actual mining.
## 2 Materials and Methods  
Gene Preparation, Cloning of Nucleocapsid (N) in pEAQ-HT, and Agroinfiltration of Nicotiana benthamiana  
The nucleotide sequences of the whole Nucleocapsid (N) of the SARS-CoV-2 strain Wuhan-Hu-1 (GenBank accession number: MN908947.3) with a six-histidine tag on the C-terminus were plant codon optimized and synthesized by GenScript Biotech (Piscataway, NJ, USA). The N-C dish tag gene was cloned into pEAQ-\(HT\)[36] using the restriction sites _Agel_/_Xhol_. The construct pEff-HEV/RBD containing the RBD (from V319 to V524) of the SARS-CoV-2 strain Wuhan-Hu-1 (GenBank QJE37812.1) and HEV capsid protein 110-610 (GenBank accession number DQ079627.1) was produced as previously described by our groups [30; 31]. Briefly, the recombinant plasmids pEAQ-\(HT\)-N and pEff-HEV/RBD were transformed into competent _Escherichia coli_ XL1 Blue; putative clones harboring the expression vectors (pEAQ-\(HT\)-N and pEff-HEV/RBD) were verified by sequencing (Eurofins, Hamburg, Germany). Recombinant vectors were transformed into the electrocompetent _Agrobacterium tumefaciens_ strain LBA4404.