esa-sceva/satcom-chunk-collection huggingface dataset:
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 3 Proposed WPE with Crossband Filtering
### WPE with Crossband Filtering  
To consider the contribution of the two nearest crossband filters, we define a prediction filter \(\tilde{\mathbf{g}}_{f}\in\mathbb{C}^{LM}\), where \(\tilde{L}=L_{\text{bb}}+2L_{\text{cb}}\), and an observation vector:  
\[\tilde{\mathbf{y}}_{f,t,\tilde{L}}=\left[\mathbf{y}_{f,t,L_{\text{bb}}}^{T}, \mathbf{y}_{f+1,t,L_{\text{cb}}}^{T},\mathbf{y}_{f-1,t,L_{\text{cb}}}^{T} \right]^{T}\in\mathbb{C}^{LM}, \tag{12}\]  
where we consider \(\mathbf{y}_{f,t,L_{\text{bb}}}\) and \(\mathbf{y}_{f\pm 1,t,L_{\text{cb}}}\) as the \"band-to-band component\" and \"crossband components\", respectively. The enhanced signal is now obtained as  
\[\tilde{z}_{f,t}=y_{f,t}-\tilde{\mathbf{g}}_{f}^{H}\tilde{\mathbf{y}}_{f,t, \tilde{L}}\in\mathbb{C}. \tag{13}\]  
The definition of \(\tilde{\mathbf{y}}_{f,t,\tilde{L}}\) in (12) forces the enhanced signal to take into account samples from the two nearest crossband frequency bins in addition to the samples from the band-to-band frequency bin. The filter coefficients \(\tilde{\mathbf{g}}_{f}\) are estimated according to the method described in Section 3.1.1 and in Algorithm 1. Here, the term \(\mathbf{g}_{f}^{H}\mathbf{y}_{f,t,\tilde{L}}\) in (11) is substituted with the term \(\tilde{\mathbf{g}}_{f}^{H}\tilde{\mathbf{y}}_{f,t,\tilde{L}}\).  
We propose two versions of the proposed WPE with crossband filtering. First, we fix \(L_{\text{bb}}=L\). The parameter \(L_{\text{cb}}\) controls this setup's tradeoff between model complexity and model accuracy. When \(L_{\text{cb}}=0\), the proposed method is equivalent to the conventional WPE, and the length of \(\tilde{\mathbf{g}}_{f}\) is equal to the length of \(\mathbf{g}_{f}\). When \(L_{\text{cb}}\) increases, the accuracy of the model approximation increases, but so does the length of \(\tilde{\mathbf{g}}_{f}\), resulting in larger computational complexity. In the second version, we fix \(\tilde{L}=L\). Here, \(L_{\text{bb}}\) decreases when \(L_{\text{cb}}\) increases, meaning that early samples from crossband components are taken into account instead of late samples from the band-to-band component. This setup reduces the accuracy of the band-to-band model approximation but maintains fixed computational complexity.
###### Abstract  
The aim of the study was to evaluate the effect of heat- (95 \({}^{\circ}\)C) and/or salt (0.1 M NaCl) treatment on the physical stability and rheological properties of oil-in-water emulsions stabilized with chickpea protein concentrates (CPCs) for various purposes. Thus, the particle size distribution (PSD), shear behavior, and long-term Turbiscan stability of the prepared emulsions were examined. The oscillatory (dynamic) measurements were also performed to obtain information on the viscoelasticity of tested fluids during thermal treatment. The obtained results indicated that the emulsion stabilized with gelling CPC (ec\({}_{\text{S}}\)) was Newtonian fluid with a homogeneous structure, but susceptible to creaming. Heat-treated e\({}_{\text{S}}\) exhibited a sol-gel transition at 86 \({}^{\circ}\)C and formed fine-stranded aggregates without affecting stability. In turn, heat-induced gelation of e\({}_{\text{S}}\) in the presence of 0.1 M NaCl resulted in the formation of an aggregated, spatial gel network, stabilization of the system, and a significant change in both shear rheological properties and PSD. Contrariwise, emulsions stabilized with standard CPC (e\({}_{\text{S}}\)) were unstable heterogeneous systems containing both fine particles \(<\) 1 \(\upmu\)m and coarse particles of about 100 \(\upmu\)m, exhibiting shear-thinning and yield stress. The heat-induced viscoelasticity of e\({}_{\text{S}}\) was reversible, while heat- and salt-treated emulsions did not form a gel.  
Keywords:chickpea protein; protein-stabilized oil-in-water emulsion; emulsion stability; emulsion rheology +
Footnote †: journal: Applied Sciences
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Main-Parsing Module
#### 4.2.3 Basic Path Set Extraction
The basic block types and corresponding stack operations are listed in Table 2, where \"value\" represents the type value of the basic block, and the values in \"Push\" and \"Pop\" columns represent the number of the corresponding operations that should be performed. Subsequently, the parsing process will parse each line of a basic block, extract the field's variable name, offset, data type, endian, and the bit-level information of length if it finds a buffer reading function, and maintains this information in a list. Next, the parsing process will extract the field's identifier, offset, length, dependencies, variable name, and endian information by tracing the generation process of the protocol tree, and then complete the field's variable name and bit-level information of length based on the matching of variable name with the fields extracted from the buffer reading function. Finally, the parsing process will complete the field's name, data type, and optional value information by matching the field's identifier with standard fields. Figure 5: Sample code for basic block partitioning.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown
### Geometric Dependence
Figure 4: Breakdown probability plots for three SW cavities with different aperture radii \"\(a\)\" [31; 79]: \(a/\lambda=0.105\), \(a/\lambda=0.143\), and \(a/\lambda=0.215\). Cavities with \(a/\lambda=0.143\) and \(a/\lambda=0.105\) are powered by a shaped rf pulse with charging time of \(\sim\)170 ns and flat top of 200 ns, and for structure with \(a/\lambda=0.215\) the flat top is 150 ns. Breakdown rate is vs. (**a**) accelerating gradient; (**b**) pulse heating; (**c**) pulse heating multiplied by \(P_{max}/Max(H^{2})\). Figure 3: X-band structures with different ratios of peak Poynting vector \(P_{max}\) to peak \(H^{2}\)[79]. All the structures have the same \(a\) = 3.75 mm aperture radius. The distribution of \(H^{2}\) and magnitude of Poynting vector are shown above and below, respectively: (**a**,**d**) for on-axis coupled structure with elliptical iris, max. \(H^{2}=0.44\) kA\({}^{2}\)/mm\({}^{2}\), \(P_{max}\) =173 W/\(\upmu\)m\({}^{2}\), ratio is 390 Ohm; (**b**,**e**) for on-axis coupled structure with optimized shape, max. \(H^{2}=0.38\) kA\({}^{2}\)/mm\({}^{2}\), \(P_{max}=240\) W/\(\upmu\)m\({}^{2}\), ratio is 632 Ohm; (**c**,**f**) for side-coupled structure with elliptical iris, max. \(H^{2}=0.64\) kA\({}^{2}\)/mm\({}^{2}\), \(P_{max}=193\) W/\(\upmu\)m\({}^{2}\), ratio is 301 Ohm. The fields are normalized to rf power losses of 10 MW in the cavity.
# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 2 Materials and Methods
### Datasets and Preprocessing of Images  
Chest-14 data [16; 17; 18] and clinical images were used for this study. Only the patients' positions were evaluated using the clinical data of Otaru General Hospital. Other datasets were constructed using Chest-14. To focus on adult patients, data of children aged <15 years from Chest-14 were carefully excluded. Inappropriate images were also excluded from Chest-14 based on the visual review by the four radiological technologists. All images were confirmed as having no subjects with left-right organ reversal by two radiological technologists. All images were converted into a 224-square matrix in Joint Photographic Experts Group (JPEG) format to apply CNNs.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Performance Comparison between the HSN and FC-AE-1553
#### 5.2.2 Results and Discussion
Overall, the average latency of the hybrid network was still significantly lower than the latency of the original network.
## 3 Cartographer Algorithm Improvement  
Based on Google's open-source Cartographer algorithm, this study improves and optimizes its front-end part to enhance the SLAM system's performance in environment modeling and robot self-localization. In the front-end optimization, the focus is on processing point-cloud data and introducing the secondary filtering method of radius filtering after the voxel filter.  
First, by applying the voxel filter, we effectively reduce the volume of the point-cloud data while retaining the critical feature information. However, after the voxel filtering process, the point-cloud data still has some diffusivity. To solve this problem, this paper adopts the secondary filtering method of radius filtering. The filtering effect is further improved by setting the threshold radius to filter out the point clouds that do not satisfy the conditions.  
In the implementation process, the PCL library converts the point-cloud data to the format for the filtering operation. After converting the point-cloud data to the data format in the PCL library, the data can be processed using the rich filtering methods provided. After filtering, the data are converted back to the point-cloud data type required by the Cartographer algorithm to ensure compatibility with the subsequent SLAM algorithm.  
In addition, this paper also preprocesses the IMU data and uses a multi-sensor-fusion method to fuse the processed IMU data with the point-cloud data. By combining the techniques of filter improvement and IMU data preprocessing, the accuracy and stability of the SLAM system for environment modeling and robot self-localization are improved.  
Through these improvements and optimizations, this study aims to advance the performance of SLAM algorithms in practical applications and provide more accurate and reliable solutions for research and development in areas such as map construction and robot localization.  
Figure 4 shows the flow chart of the improved algorithm.
## 5 Conclusions  
In summary, this study provides a theoretical analysis of the influence of typical oceanic environmental parameters on the microwave scattering coefficient, clearly identifying the \(SATD\) as a key parameter affecting the scattering coefficient besides the wind field. Based on the scatterometer, radiometer data, and ECMWF reanalysis data, the response relationships between the \(SSTA\) and \(SCA\), as well as between the \(SATDA\) and \(SCA\) were quantitatively characterized. It was found that the coupling coefficient \(k_{sstda}\) between the \(SATDA\) and \(SCA\) is larger and exhibits a seasonal variation, with higher values in summer and lower values in winter. Further quantitative analysis was conducted to examine the correlation between \(RMMSATD\), \(RMMAT\), \(RMMSST\), or \(RMMS\) and \(k_{sstda}\). The results indicate that \(RMMSATD\) exhibits the most significant correlation with \(k_{sstda}\).  
To verify the impact of \(SATD\) on the accuracy of the scattering coefficient evaluated by the GMF, the evaluated scattering coefficients with and without considering the effect of \(SATD\) have been compared with the scattering coefficients measured by the QSCAT. Thevalues of the _BIAS_ and the _RMSE_ indicate that the accuracy of the scattering coefficient evaluated by the GMFs can be improved by introducing the impact of _SATD_. It should be noted that this study only focused on the effects of several typical marine atmospheric parameters on the scattering coefficient. In future studies, the effects of the other marine environmental parameters on the scattering coefficient should also be discussed in depth.  
Conceptualization, Y.J., Y.Z. and Y.W.; methodology, Y.J., Y.Z. and Y.W.; validation, Y.Z. and Y.W.; software, Y.J., Y.Z., Y.W. and F.S.; formal analysis, Y.Z. and Y.W.; investigation, Y.J., Y.Z., F.S. and D.S.; writing--original draft preparation, Y.J.; writing--review and editing, Y.J., Y.Z., Y.W., F.S. and D.S.; funding acquisition, Y.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant no. 42376176; the Natural Science Foundation of Shandong Province under grant no. ZR2021 MD023; the Laoshan Laboratory Science and Technology Innovation Projects, grant number LSKJ202201302; and the Postdoctoral Fellowship Program (Grade C) of the China Postdoctoral Science Foundation, grant no. GZC20232027.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
## 2 Geological Setting and Petroleum Background  
### Geological Setting  
The Wuerhe area is located in Karamay City, Xinjiang Uygur Autonomous Region. It is located in the Wuxia fault zone of the northwest margin of the Junggar Basin, adjacent to the Mahu oil-rich depression in the South (Figure 1).  
The elastic sediments in the basin were mainly deposited after the Carboniferous [8]. The vertical sequence of strata encountered during drilling primarily includes the Tugulu group of the Cretaceous system; the Qigu, Sangonghe and Badaowan Formations of the Jurassic system; the Baijiantan, Karamay, and Baikouquan Formations of the Triassic system; and the lower Wuerhe, Xiazjie, and Fengcheng Formations of the Permian system. Among them, Cretaceous and Jurassic, Jurassic and Triassic, and Triassic and Permian are regional unconformity contacts (Figure 2).  
The late Hercynian tectonic movement formed the basic outline of the Junggar Basin, including large uplift, depression, and large-scale fault zone in Permian. The Karamay-Wuerhe fault zone was formed in this period. Early strong tectonic activity accompanied by a volcanic eruption resulted in the deposition of extremely thick sandy conglomerate and volcanic rock of the Jiamuhe Formation. During the sedimentary period of the Fengcheng Formation, volcanic activity decreased gradually, the climate became dry, and evaporation began, forming an alkaline lake sedimentary environment. The Fengcheng Formation, composed of high-quality source rocks, was formed by the lithologic combination of marginal conglomerate, middle dolomite, and local volcanic rocks [9].  
Figure 1: Structural location map of the research area, northwestern Junggar Basin.  
The intensity and scope of the Triassic Indosinian tectonic movement was smaller than that of the previous Permian movement. It inherited the previous tectonic movement and formed some nose structures and reverse faults [8]. The whole Triassic is a normal cycle terrigenous coarse elastic deposit with lithology from coarse to fine and water body from shallow to deep. From bottom to top, the lithologic combination of sandstone and  
Figure 2: Comprehensive stratigraphic column of Wuerhe area, showing the stratigraphic conditions.  
mudstone interbedding was formed. Under the influence of the Late Triassic tectonic movement, the strata were uplifted, and the lake water retreated.  
The intensity of the Yanshanian movement in Jurassic was much weaker than that of the Carboniferous-Permian movement and only inherited activity in the Wuxia fault zone. In the late Yanshanian movement, the main faults and folds in the Wuxia fault zone stopped, and the tectonic movement was mainly characterized by intermittent subsidence. The Cretaceous and Later Cenozoic strata were filled steadily and overlapped with low angle from southeast to northwest [10]. On the underlying paleotectonic background, the stratum was thick in the south and thin in the north, which gradually thinned out.
## 3 Conclusions  
In this work, we review and analyze different family of deep blue TADF emitters. A wide range of theoretical molecular engineering and synthetic strategies are currently deployed to establish guideline to produce the deep blue TADF emitters. The Golden rules for the design of TADF deep blue emitters are listed below.  
* DFT calculations proved to be useful to investigate the photophysical properties of the materials prior to its synthesis.
* The maximum decrease of the \(\pi\)-conjugation through the whole molecule by introducing a large twist angle between the electron-donating and withdrawing parts is an effective way to reduce the electronic communication between the two parts and \(\Delta E_{\rm ST}\). However, the donor should be in proximity of the acceptor to avoid a complete isolation of the electro-donating and the accepting groups. A careful selection of the \(\pi\)-bridge connector introduced between the electron donor/acceptor moieties is of crucial importance. This has been exemplified throughout this review and unequal performances were observed for materials simply differing by the substitution pattern.
* A small value of \(\Delta E_{\rm ST}\) optimizes the rate constant of the reverse intersystem crossing by the small energy gap.
* Elongation of the \(\pi\)-conjugated system of both the donor and/or the acceptor maximizes the oscillator strength, and thus increases the PLQY.  
Figure 10: TADF emitters examined for the device lifetime.  
To address the excited states annihilation issue, lifetimes of the delayed component should be shortened.  
However, despite these well-established molecular design rules, the number of deep blue emitters satisfying standard blue color coordinate are still very limited. As illustrated through the review, the OLED device engineering is also as important as the emitter design in order to have efficient stable deep blue OLEDs for real applications. Recently, a great deal of attention is now paid to the molecular alignment of the emitter within the emissive layer inn order to optimize the EL performance. The light outcoupling efficiency is also another crucial important parameter that should/will be more systematically investigated in the Future.  
All authors equally contributed to the writing of the present manuscript.  
The authors declare no conflict of interest.
## 6 Experiments
### Competitor Setup  
To demonstrate the superiority and efficiency of our proposed RA-FS approach, we compare it with other methods for energy-efficient DL job processing. We define the competitors of our proposed approach as follows:  
(1) MIN Frequency Setting (MIN): This approach sets the core and memory frequency values of all GPU devices to their minimum levels, irrespective of the DNN model training performance requirements and renewable energy generation. It may result in undesirable energy consumption due to the extended training time, even though it significantly compromises the performance of DNN model training.  
(2) MAX Frequency Setting (MAX): In this approach, the core and memory frequency values of all GPU devices are set to their maximum fixed values. Unlike the MIN approach, which prioritizes energy efficiency, this method aims to achieve the highest possible performance in DNN model training, but it may result in undesirably high energy consumption of high-performance GPU device like RTX4090.  
(3) Energy Optimal Frequency Setting (eOPT): In this approach, the core and memory frequency values of all GPU devices are set to their energy-optimal fixed values. We pre-determined the optimal frequency settings for each pair of deployed GPU devices and DNN models based on the raw data derived from Figure 5. For example, on Node1 (RTX3060), we set (core: 1020 Mhz, memory: 5000 Mhz) as the optimal frequency value for all DL models: SqueezeNet, PreActResNet18, and SEResNet50. This approach achieves the minimum energy consumption but does not guarantee the satisfaction of deadlines for each DL job request.  
(4) DeepPower-Controller (DP-CTR) [29]: This approach provides real-time power control for cost-efficient DL job processing in GPU-based clusters. Utilizing the concept of model predictive control, it adjusts core and memory frequency values in response to dynamic renewable generation and electricity prices. However, this approach naively minimizes DNN model training time without a defined performance requirement, as in our DJR, potentially leading to inefficient power usage in meeting the performance requirements of diverse DL job requests.  
(5) Energy Budget-based Frequency Scaling and Job Allocation (EB-FJ) [30]: This approach involves assigning a limited energy budget and aims to find the optimal frequency setting and DL job allocation plan within this budget. The authors introduce a scheduler called PowerFlow, which minimizes the average Job Completion Time without exceeding a predefined energy budget. Like the DP-CTR approach, it does not account for explicit deadlines of assigned DL jobs, failing to achieve optimal energy budget distribution for  
Figure 5: Energy Consumption [Wh] for SqueezeNet, PreActResNet18, and SEResNet50 on Node1 (RTX3060), Node2 (RTX3090), and Node3 (RTX4090).  
each job. For a fair comparison, we set the energy budget for this approach to match the total energy consumption of our proposed RA-FS approach.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Experimental Procedure  
Figure 2 shows a flow chart of the experimental steps used in this experiment to apply the MOPSO algorithm to the design of IIR filters. This experimental step can be divided into parameter setting, MOPSO optimization, and filter design.  
1. Parameter Setting: In the initial basic parameter settings, we first need to set the filter type to be optimized and the filter type, such as low-pass, high-pass, band-pass, and other filter types. The initial filter parameter settings also include filter sampling. Frequency, passband frequency range, stopband frequency range, and frequency response; MOPSO initial parameter settings include objective function, number of iterations, number of particles, weights, and other related parameters; this study uses Butterworth filter, and two types of Chebyshev Type I filters as prototypes, and the design parameters of these two types of low-pass filters, high-pass filters, and band-pass filters are optimized.
2. MOPSO Optimization: The MOPSO optimization algorithm is used to form a Pareto boundary for each set of iterated solutions and find a set of relatively optimal trade-off solutions through Pareto efficiency. All relevant constraints are defined according to the type of filter to be designed. Each candidate solution should be a vector containing the values of all objective functions. An external archive is used to store the Pareto optimal solution set (Pareto front). The solution of the current particle is compared with the solution in the Pareto Archive, and the archive is updated to keep it containing only non-dominated solutions (Pareto optimal solutions) until the stopping condition is met (for example, the maximum number of iterations is reached, or the solution no longer changes significantly in the external archive). Return to the parameter-setting part if the ideal filtering effect is not achieved and readjust parameters.
3. Design Filter: The obtained optimal design parameter solution is used to design a filter and draw the filter amplitude, phase, pole, and zero-point diagrams; then, a section of white noise is randomly generated and filtered through the designed filter. Moreover, the signal-to-noise ratio (SNR) before and after filtering is calculated to confirm whether the designed filter achieves the filtering effect.