esa-sceva/satcom-chunk-collection huggingface dataset:
# Upmix B-Format Ambisonic Room Impulse Responses Using a Generative Model
## 5 Experiments Results
### Binaural Room Transfer Functions (BRTFs) Synthesis Error  
The binaural rendering technique used in this section is based on Ambisonics. This method involves convolving the spherical harmonic representation of the sound field with Head-Related Transfer Functions (HRTFs) by performing a dot product. The left and right binaural signals are then derived by summing the respective HRTF-convolved components. The synthesizing BRTF is expressed as follows,  
\[B(kr)=\sum_{n=0}^{N}\sum_{m=-n}^{n}b_{n}^{-1}(kr)\alpha_{nm}(kr)h_{nm}(kr), \tag{22}\]  
where \(\alpha_{nm}\) and \(h_{nm}\) represent the SH coefficients of the sound field and HRTFs, respectively. For further details on the binaural rendering algorithm, please refer to [35].  
To evaluate the improvement in rendering quality achieved by using the upmixed RIRs, we compare and analyze the synthesized BRTFs obtained through the Ambisonics-based binaural rendering method under three conditions:  
1. The fourth-order rendering using 32-channel signals generated by HiFi-GAN;
2. The fourth-order rendering using the estimated signals which are reconstructed to 32 channels by FOA;
3. The first-order rendering using FOA.  
The synthesized BRTFs are evaluated using the NMSE for the amplitude spectrum above the first-order aliasing frequency. As depicted in Figure 9, HiFi-GAN exhibits significant improvement in the synthesized BRTFs, with a maximum enhancement of approximately 3.2 dB. Furthermore, the result for Condition 2 in Figure 9 indicates that when employing fourth-order HRTFs for rendering, if the sound field only contains first-order information, the improvement in the rendered results, compared to Condition 3, is only marginal. However, when upmixed RIRs are utilized, the rendering quality experiences substantial enhancement. Therefore, upmixing FOA to a higher Ambisonic order proves highly effective for achieving significant improvements in binaural rendering quality. The HiFi-GAN algorithm used in this study as the upmixing method demonstrates effectiveness in enhancing the binaural rendering quality from FOA signals.  
Figure 8: The NMSE of reconstructed RIRs above the first-order aliasing frequency. (**a**) represents the results of the FOA reconstruction, and (**b**) represents the results of the HiFi-GAN output signals.
## 2 Materials and Methods
### Statistical Analysis  
The experimental data are presented as mean values, with the corresponding standard errors, to provide a comprehensive understanding of the variations in the results. To identify significant variations in the physicochemical composition and texture, the SPSS 27.0 software package (IBM Corp., Armonk, NY, USA) was utilized. The independent sample _t_-test and two-way ANOVA statistical methods were employed to determine whether there were statistically significant differences between the examined variables. A threshold of \(p<0.05\) was used to determine statistical significance, meaning that any differences below this value were considered noteworthy. Additionally, to assess the potential relationship between the metabolites and microorganisms present in the cheese samples, Pearson's rank correlation analysis was conducted, providing insights into the correlation between the microflora and their associated metabolic products.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Preprocessing
#### 3.1.1 Binarization and Thinning
An approach for binarization of non-uniformly illuminated document images to accurately recognize alphanumerical characters is presented in [53]. The proposed method combines local and global thresholding methods, i.e., Sauvola and Otsu methods to achieve robust binarization and improved performance compared to existing binarization methods. In the Sauvola binarization method, the local threshold is calculated using a Sauvola algorithm, which takes into account the local mean and standard deviation of the pixel intensities. In the Otsu binarization method, the global threshold is calculated using an Otsu algorithm, which maximizes the variance between the two classes of pixel intensities. Thinning, also known as skeletonization, reduces the thickness of the image by deleting the boundary pixels while preserving the shape and structure. The goal of thinning is to obtain the structure of the objects in the image. [PERSON] et al. [54] propose and compare different thinning algorithms for improving the performance of OCR for Arabic script. The results show that the Hybrid algorithm performed the best and improved the OCR accuracy by reducing the errors caused by variations in line thickness. The method can be effectively applied to different types of Arabic text, including handwritten and printed text. Results indicate that the Hybrid algorithm improved the OCR accuracy by reducing the errors caused by variations in line thickness. The results also show that the Hybrid algorithm can be effectively applied to different types of Arabic text, including handwritten and printed text.
# Numerical Study on the Rising Motion of Bubbles near the Wall
## 2 Numerical Simulation Method  
### Governing Equation  
The problem of two-phase interface is usually calculated by capturing the phase interface and performing numerical simulation. The more commonly used methods are the Volume of Fluid (VOF) method [19; 20; 21] and the Level Set (LS) method [22; 23]. In this article, the VOF method is used to track the gas-liquid interface to study the motion of bubbles. The VOF method establishes the two-phase intersection interface based on the volume ratio function. The sum of the volume fraction of each phase is 1, so the maximum value is 1 [24]. Since the phases are not interspersed with each other, each additional phase only needs to increase the volume fraction function of the corresponding phase. In the VOF model, suppose there is a two-phase fluid, and mark the calculation domain as \(S\), then the domain where fluid \(A\) and fluid \(B\) are located can be represented by \(S^{A}\) and \(S^{B}\).  
For the continuity equation of incompressible fluid, the governing equation of bubble motion is:  
\[\
abla\cdot u=0 \tag{1}\] \[\frac{\partial\alpha_{A}}{\partial t}+u_{A}\
abla\alpha_{A}=\frac{S^{A }}{\rho_{A}}+\frac{1}{\rho_{A}}\sum_{B=1}^{n}=1(\dot{m}_{BA}-\dot{m}_{AB}) \tag{2}\]  
where \(\alpha_{A}\) is the volume fraction of the fluid A; \(u_{A}\) is the velocity vector of the fluid A, m\(\cdot\)s\({}^{-1}\); \(\rho\) is the density, kg\(\cdot\)m\({}^{-3}\); m\({}_{BA}\) is the mass transport from the B (A) phase to the A(B) phase, kg.  
Where \(\overline{V}(u,v)\) is the velocity field of the fluid, \(\Delta V_{ij}\) is the volume of a single grid, and \(Q_{ij}\) is defined on each grid \(I_{ij}\) as the integral of \(\alpha(\overline{x},t)\) on the grid:  
\[Q_{ij}=\frac{1}{\Delta V_{ij}}\int\limits_{I_{ij}}\alpha(\overline{x},t)dV \tag{3}\]  
Equation (3) is the VOF function, and it also satisfies:  
\[\frac{\partial Q}{\partial t}+u\frac{\partial Q}{\partial x}+v\frac{\partial Q }{\partial y}=0 \tag{4}\]  
Equation (4) is the VOF equation. From the above, it can be seen that the volume function of each phase is essentially the ratio of the phase volume to the grid volume in the grid.  
The momentum conservation equation considering surface tension is as follow:  
\[\rho\bigg{[}\frac{\partial u}{\partial t}+(u\cdot\
abla)u\bigg{]}=-\
abla p+ \
abla\cdot(2\mu D)+\rho g+\varphi \tag{5}\]  
where \(\varphi\) is surface tension, \(p\) is pressure, \(\mu\) is dynamic viscosity coefficient.
## 3 Proposed Optimal Orchestration Process
### Automation of Algorithm Selection
#### 3.5.3 Efficient Model-Hyperparameter Optimisation Using Ray Tune  
The choice of Ray Tune stems from its ability to efficiently manage hyperparameter searches through parallel execution and state-of-the-art optimisation techniques. This capability allows us to explore a wide range of hyperparameter configurations, ensuring models operate at peak performance in real-time environments. The adaptability and effectiveness of the resource allocation strategies further enhanced with the integration of Ray Tune into the eSS model pipeline.  
Together, the combination of LazyPredict, AutoRL, and Ray Tune not only streamlines the algorithm selection and tuning processes but also fortifies the framework's capability to dynamically allocate resources in response to varying network conditions. This automated approach facilitates an unassisted, adaptive end-to-end resource management system, addressing the pressing challenges of orchestrating 5G network slices effectively.  
Figure 8: AutoRL process flow.
## 2 Interference Signal Models
### Specific Steps of the Proposed Algorithm  
#### 3.2.1 Steps for Calculating the Relatively Flat Noise Data Segment \(R\)  
1. Divide the FFT-transformed data of length \(N\) into \(p\) segments, where each segment has a length of \(l\). Calculate the mean amplitude of each segment as follows: \[A^{\prime}(k)=\tfrac{1}{l}\sum\limits_{j=1}^{l}A(l\times(k-1)+j)\quad k=1,2, \ldots,p\] (1)
2. Compute the differential value between the mean amplitude of two adjacent segments: \[D(l)=A^{\prime}(k+1)-A^{\prime}(k)\quad l=1,2,\ldots,p-1\] (2)
3. Take the absolute value of the calculated differential value and identify the segment \(L\) with the largest absolute differential value. If the differential value is positive, the corresponding segment is considered the relatively flat noise data segment \(R\). If the differential value is negative, the subsequent segment is considered the relatively flat noise data segment \(R\).  
The process for calculating the relatively flat noise data segment \(R\) is illustrated in Figure 2.
## 6 Conclusions  
In this study, by considering the effect of \(OCR\) on the parameter \(\Lambda\) and the nonlinear relationship between \(OCR\) and the thermal pore water pressure, a calculation method of thermal pore water pressure considering overconsolidation effect for saturated clay is proposed. Compared with previous calculation methods, the proposed calculation method has higher accuracy and the required parameters are easy to obtain. Moreover, through applying the calculation method and comparing the predicted results of the calculation method with the experimental data, the following conclusions can be drawn:  
* Based on the proposed calculation method, the critical threshold of \(OCR\) (4.3) for determining whether the value of \(\Lambda\) will vary with \(OCR\) is obtained. When the clay is in slightly overconsolidated state, i.e., \(1<OCR\leq 4.3\), the value of \(\Lambda\) is the same as that of normally consolidated clay. However, when the clay is in a highly overconsolidated state, i.e., \(OCR>4.3\), the value of \(\Lambda\) of overconsolidated clay is smaller than that of normally consolidated clay, and gradually decreases with the increase of \(OCR\).
* The proposed calculation method can accurately predict the evolution of thermal pore water pressure under undrained conditions, and is supported by some undrained heating test results of overconsolidated saturated clay, which can provide a theoretical basis for the design of thermo-active structures.
* The effect of \(OCR\) on thermal pore water pressure is related to the heating rate. However, the calculation method proposed in this study does not consider the influence of heating rate. The follow-up work can explore the comprehensive effects of \(OCR\) and heating rate on the thermal pore water pressure under undrained conditions theoretically and experimentally.  
Conceptualization, Z.Z.; methodology, Z.Z.; validation, Z.Z. and G.T.; formal analysis, Z.Z. and G.T.; investigation, Z.Z. and G.T.; writing--original draft preparation, Z.Z. and G.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (No. 2019 QZKK0905).  
Not applicable.  
Data are contained within the article.  
[PERSON] is supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (No. 2019 QZKK0905).  
The authors declare no conflict of interest.
## 2 Methods and Materials
The centroid of previously identified important binding residues (ResIDs from PDB 1 BHF: Arg134, Lys135, Arg154, Glu157, Ser158, Thr159, Ser162, Lys179, His180, Tyr181, Lys182, Ile193, Ser194, Tyr209, Asp214, Gly215, and Leu220) was used as the docking box center [7]. The dimensions of the docking grid size used were 30 \(\times\) 30 \(\times\) 30 A\({}^{3}\) (inner box) and 35 \(\times\) 35 \(\times\) 35 A\({}^{3}\) (outer box). First, the ligands shortlisted after screening through the generated e-pharmacophore model were docked using the Glide high-throughput virtual screening (HTVS) protocol. The OPLS_2005 force field was used for the HTVS docking. The HTVS screened ligands were then subjected to Glide standard precision (SP) docking, followed by Glide extra precision (XP) docking. In the virtual screening protocol, it is standard practice to redock the top 10% of the HTVS scored structures with Glide SP, and then redock the top 10% of SP scored structures with Glide XP [31; 32]. Therefore, in our work, 10% of the best docked ligands were retained at each step. The OPLS_2005 force field was used for the docking runs [33]. During each docking step, the ligands were considered flexible and post-docking minimization was performed. **Structure-based drug design:** Following the Glide XP ensemble docking, further structural modifications of the top hits were performed to enhance protein-hit interactions, using Core Hopping (CH) in Schrodinger [34]. Starting with the top hit molecule obtained from the multi-step docking workflow, ligand-based and receptor-based CH were performed. CH of the middle scaffold of the reference molecule was performed. To enhance the interactions exhibited in the protein-hit complexes, a minimum cut-off for the number of hydrogen bonds required was set to 5 for the receptor-based CH. Heavy-atom steric clashes with the receptor were limited, with a maximum number of clashing ligand atoms set to 2 and a clash criterion of <2.20 A. The Schrodinger CH library (named core_library_2014.1-86640.sqlite) was used for the scaffold searching. Figure 1: (**A**) Cartoon representation of the 7 protein X-ray crystal structures used for ensemble docking, superimposed on each other (each protein is shown in distinct color). (**B**) Virtual screening protocol adopted in the study.
## 4 Results and Discussion
### Supply-Side Analysis  
#### 4.2.1 Baseline Scenario  
Figure 12 shows the optimal selection of the power generation technologies for the baseline scenario with integrated demand. After the base year, the power generated from the oil power plants with imported oil has increased to meet Zambia's electricity demand. The baseline scenario results emphasize the future dominance of Oil(FO) to meet the electricity demand. The total installed capacity for Oil(FO) is projected to increase by 51% in 2035 from 110 MW in 2019. The local coal-based power plants seem to increase but remain constant throughout time. The hydro-based power plants also remain fixed during the horizon. Oil(FO) dominates more than Oil(HSD) and its contribution increases to 24.2%. Zambia recently included solar in the energy mix, so a consistent amount of solar from the base year can be seen through the horizon.
# Towards a Glass New World: The Role of Ion-Exchange in Modern Technology
## 2 Glass Strengthening: The Ion-Exchange Contribution
### Ion-Exchanged Noble Metal Nanoparticles in Glasses for Sers Applications
#### 3.2.5 Metal Thin Film Deposition for Noble Metal Ions Reduction in Ion Exchange Process
Figure 8 describes the mechanism mentioned above (Figure 8a), reporting a scanning electron microscope (SEM) cross-section image of the silver nanoparticles distribution under the sample surface (Figure 8b). Figure 7: Deposition of silver nanoparticles under the mask edge by masked ion-exchange process (cross-sectional view). In inset, the reduction mechanism of silver ions in metal nanoparticles under an aluminium mask. The main feature of this method is to offer a simple and low-cost solution for the production of homogeneous and large-scale SERS substrates. In both approaches described above, the formed silver nanoparticles are embedded within the glass. Therefore, a subsequent soft etching step in HF acid is necessary to expose them at the surface level and make these substrates functional for SERS applications. We would also like to mention the work of [PERSON] et al. for its simplicity. Their investigation, using optical and AFM (atomic force microscopy) measurements, showed a migration of silver Ag\({}^{+}\) cations from inside of the waveguide to its surface at room temperature. Once exposed to air at room temperature, the cations undergo an oxidation process changing the SERS response of the substrate [174]. The advantage of this approach is that it does not require any additional process other than that represented by the aging over time of the exchanged sample. On the other hand, the main drawbacks lie in the rather long aging times, the instability of the SERS substrate over time, and the poor adhesion of the metal nanoparticles formed on the glass surface, which makes them easily removable under the minimum mechanical action or event. SERS platforms created by ion-exchange are still in their early state, since the main scientific contributions reported so far in the literature demonstrate only proof of concepts of their operation through the use of aromatic molecules (e.g., rhodamine, etc.). However, thanks to their interesting performances both in terms of signal enhancement and detection limit [154], it is likely that soon these substrates will actually be used for the detection of biological targets, such as DNA sequences, proteins, bacteria, etc., as is happening for SERS substrates obtained with different technological approaches [97; 151].
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 3 Materials and Methods
### DualCT Module
#### 3.2.3 Residual Mask Attention  
In the imaging process of the CASSI system, the original scene is first modulated through an aperture mask, and the aperture mask is usually set to a random matrix with elements 0 and 1. The positions of element 1 in the mask allow the light reflected from the target in the scene to pass through, so these positions have high information fidelity. Based on this imaging mode, the literature [25] designed a mask guidance mechanism to enhance the quality of reconstructed images. Inspired by [25], we designed a residual mask attention mechanism and encapsulated it into the DualCT module. This mask attention can guide the network to focus on spatial regions with high-fidelity spectral representations. As shown in Figure 5, in accordance with the CASSI imaging mechanism, we first perform the reverse dispersion on the aperture mask \(M^{*}\):  
\[M_{s}(x,y,n_{\lambda})=M^{*}(x,y+d(\lambda_{n}-\lambda_{c})), \tag{12}\]  
where \(M_{s}\in R^{H\times(W+d(N_{\lambda}-1))\times N_{\lambda}}\) represents the shifted version of \(M^{*}\). Then \(M_{s}\) undergoes a sequence of operations to obtain the two-dimensional weight map. These operations include two consecutive 3 \(\times\) 3 convolutional layers followed by GELU activation, a 5 \(\times\) 5  
Figure 4: Spectral Transformer.  
convolutional layer and a sigmoid activation function. With the residual link, we can obtain the spatially enhanced mask \(M_{s}^{{}^{\prime}}\in R^{H\times(W+d(N_{\lambda}-1))\times C}\), and it is calculated as follows:  
\[M_{s}^{{}^{\prime}}=M_{s}\odot(1+\sigma(\varphi\beta^{2}M_{s}))\,, \tag{13}\]  
where \(\sigma(\cdot)\) represents the sigmoid activation function, \(\beta(\cdot)\) represents the conv \(3\times 3\) convolution operation and GELU operation, and \(\varphi(\cdot)\) represents the mapping function of the \(5\times 5\) deep convolution layer. We perform the reverse dispersion process and shift \(M_{s}^{{}^{\prime}}\) backward to obtain the mask attention map \(M^{{}^{\prime}}\in R^{H\times W\times C}\):  
\[M^{{}^{\prime}}(x,y,n_{\lambda})=M_{s}^{{}^{\prime}}(x,y-d(\lambda_{n}-\lambda _{c}),n_{\lambda})\,, \tag{14}\]  
where the spectral bands are indexed as \(n_{\lambda}\in[1,\dots,C]\). The obtained mask attention map \(M^{{}^{\prime}}\) adequately indicates the spatial positions with high-fidelity spectral information. Therefore, we integrate the mask attention \(M^{{}^{\prime}}\) into the DualCT module and enhance feature maps in a residual formulation:  
\[\hat{X}^{l+1}=X^{l+1}\odot M^{{}^{\prime}}+\hat{X}^{l}, \tag{15}\]  
where \(X^{l+1}=CONV(LN(\hat{X}^{l}))\) represents the deep feature map in the DualCT module, \(\hat{X}^{l}\) is the output feature of the parallel complementary fusion structure, and \(\hat{X}^{l+1}\) represents the output feature of the DualCT module. The mask attention is helpful for recovering spatial-spectral structures and improving reconstruction performance. Ablation experiments in the next section verify the effectiveness of mask attention.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 4 Experimental Setup
### Noise Estimator  
In this work, we utilize the WADA SNR estimator, an algorithm initially proposed by [28]. This algorithm provides a direct mapping from a signal to an estimated SNR value. It assumes that the amplitude distribution of clean speech can be approximated via the Gamma distribution with a shaping parameter of 0.4 and that the signal is corrupted with additive noise with Gaussian distribution. Even with these assumptions, experiments from the original authors show that the algorithm performs well when tested for three different types of noise: additive white Gaussian noise, musical segments from the DARPA HUB 4 Broadcast News database, and noise from a single interfering speaker. The noise signals were artificially added to the speech signals at different SNRs ranging from \(-10\) dB to 30 dB. We chose to use this algorithm due to its computational efficiency and ease of use.