esa-sceva/satcom-chunk-collection huggingface dataset:
# Precise Channel Estimation Approach for a mmWave MIMO System
## 4 Proposed Extended ADMM (Ex-ADMM)-Based Channel Estimation Scheme
### Procedure for Updating \(\mathrm{J}^{\left(l+1\right)}\)  
To update \(\mathrm{J}^{\left(\mathrm{I}+1\right)}\), reformulate \(\mathcal{L}_{\mathrm{A}}\) to \(\mathcal{L}_{4}\), and, to obtain the closed-form solution for \(\mathrm{J}\), take all terms corresponding to \(\mathrm{J}\) in Equation (9) and differentiate with respect to \(\mathrm{J}\):  
\[\mathcal{L}_{4}\triangleq\underset{\mathrm{J}}{\mathrm{argmin}}\ \frac{1}{2}\|\mathrm{I}\|_{\mathrm{F}}^{2}+\left(\mathrm{P}_{2}^{\mathrm{H}} \!\left(\mathrm{I}-\mathrm{A}_{\mathrm{R}}\mathrm{DA}_{\mathrm{T}}^{\mathrm{H}}- \mathrm{J}\right)\right)+\frac{\beta}{2}\|\mathrm{I}\mathrm{I}-\mathrm{A}_{ \mathrm{R}}\mathrm{DA}_{\mathrm{T}}^{\mathrm{H}}-\mathrm{J}\|_{\mathrm{F}}^{2}\]  
\[\mathcal{L}_{4}=\frac{\partial\mathcal{L}_{\mathrm{A}}}{\partial\mathrm{J}}= \frac{\partial}{\partial\!\left(\frac{1}{2}\|\mathrm{I}\|_{\mathrm{F}}^{2}+ \left(\mathrm{P}_{2}^{\mathrm{H}}\!\left(\mathrm{I}-\mathrm{A}_{\mathrm{R}} \mathrm{DA}_{\mathrm{T}}^{\mathrm{H}}-\mathrm{J}\right)\right)+\frac{\beta}{2}\| \mathrm{I}\mathrm{I}-\mathrm{A}_{\mathrm{R}}\mathrm{DA}_{\mathrm{T}}^{\mathrm{H}}- \mathrm{J}\|_{\mathrm{F}}^{2}\right)\]\[\mathcal{L}_{4}=(1+\beta)J-\beta(I-A_{R}DA_{T}^{H}+\frac{P_{2}^{H}}{\beta})\]  
By setting \(\mathcal{L}_{4}\) to zero, i.e., \(\mathcal{L}_{4}=0\),  
\[J=\frac{\beta}{1+\beta}(\ I-A_{R}DA_{T}^{H}+\frac{P_{2}^{H}}{\beta}) \tag{33}\]  
Hence, for iteration \((I+1)\),  
\[J^{(I+1)}=\frac{\beta}{\beta+1}\bigg{(}I^{(I+1)}-A_{R}D^{(I+1)}A_{T}^{H}+\frac {1}{\beta}P_{2}^{(I)}\bigg{)} \tag{34}\]  
The dual variables \(P_{1}\) and \(P_{2}\) can be directly updated using the \(H\), \(I\), \(D\) and \(J\) variables.
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Experimental Results and Analysis
#### 4.4.1 Determination of Optimal Delimiters  
We used 38 candidate delimiters that do not include letters and numbers as input. The delimiter determination algorithm based on information entropy obtained the information entropy and frequency of each delimiter in different protocols. Table 3 shows partial de-limiter information entropy and frequency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Model or Version** \\ \hline CPU & Intel Core 9-12900 K (24 CPUs), \(-3.2\) GHz \\ Memory & 128 GB DDR 3600 MHz \\ Hard Disk & 2 TB SSD + 16 TB HDD \\ Graphics Card & NVIDIA GeForce RTX 3090 Ti \\ Operating System & Windows 10 Professional 22H2 \\ Python Interpreter & Conda 23.52 + Python 3.9 \\ CUDA & 12.2 \\ Pytorch & 1.12.1 + cu113 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment information table.  
Information entropy reflects the uncertainty of symbols in the protocol. The higher the entropy, the more difficult it is to predict the symbol, indicating that it is unsuitable as a delimiter. Conversely, lower entropy indicates greater symbol stability, making it suitable as a delimiter. For example, in BGP packets, SPACE, CR, LF, and \".\" all appear 792 times in the same position, indicating that these four symbols have high positional certainty and stability in BGP packets and can be used as delimiters for BGP packets. Similarly, in HTTP packets, \"/\" has the highest frequency, while LF, CR, SPACE, and \".\" have relatively low frequencies but similar results, indicating the high certainty of these four symbols in the HTTP protocol. Therefore, these five symbols can also be used as delimiters for HTTP packets.  
Based on the comprehensive experimental results, we have selected the following eight delimiters as the optimal set of delimiters:  
\[\text{CR LF SPACE NULL},..:/\]  
Additionally, three other groups are chosen as control groups to compare the impact of different delimiters on recognition accuracy. Group A consists of the 20 delimiters proposed in [28], Group B is composed of a random selection of 17 symbols from the ASCII table's 38 symbols, and Group C is composed of six symbols that appear together in Group A and Group B, and the optimal separator set we have selected. The specific selected delimiters are shown in Table 4.  
In summary, we designed five sets of experiments, namely the original data group, Group A, Group B, Group C, and the separator group we selected.
## 3 Results and Discussion
### Source Apportionment Results
The profile of this source included mostly Cl\({}^{-}\) with a percentage of 81.19% and earth crust metals, specifically 73.38% of Al, 50.98% of Cr and 26.57% of Fe. Such a high percentage of chlorine anions was probably due to the daily use of chlorine (along with other detergents) by the cleaning service inside the classroom. Similar research in educational buildings [120; 121] traced elevated Cl\({}^{-}\) levels in indoor dust due to cleaning products such as chlorinated detergent and chlorinated water on the floor. Nevertheless, as [PERSON] et al. [122] mentioned, the contribution of marine airborne particles from sea salt to the formation of chlorine anions (Cl\({}^{-}\)) was also possible, especially during the days that the south wind was prevailing. Furthermore, in accordance with [PERSON] et al. [123], earth crust elements were commonly traced in the dust that settled on floors, desks and other surfaces of the classroom. The contribution of the fifth factor to the total PM\({}_{10}\) was the second largest (29.89%). Lastly, the sixth factor contained airborne particles originating from the asphalt of the nearby streets. Heavy metals such as Cd and Pb demonstrated a strong contribution, with 56.43% and 37.33%, respectively. According to [PERSON] et al. [124], these particles appear from the friction of brakes, tires and other mechanical parts of vehicles passing by. However, this factor was not likely related to the second factor (Traffic NEX), as it contained a negligible amount of earth crust elements. Furthermore,the impact from small-scale industries (e.g., metallurgical activities) in the area could not be excluded. For that reason it was characterized as Traffic NEX II-Industry. This source contributed 12.86% of the total examined PM\({}_{10}\). The contribution of this factor in combination with that of the fourth factor highlight that local traffic load played a key role on the air quality levels of the school building. In conclusion, the PMF receptor model demonstrated the profiles of six main factors as PM\({}_{10}\) sources in the indoor and outdoor environment of the school for the experimental period along with the contribution of each one of them to the total particulate mass. It seems that the dominant sources were vehicles (combustion and break and tire wear) and biomass burning during the colder days. Secondly, suspension of airborne particles in classroom C3 was also an important parameter that appeared mainly due to human activity and to frequent use of cleaning products. Finally, non-negligible sources of PM\({}_{10}\) were found to be SOA, originating from primary sulfate and nitrate transformations through long distance transport mechanisms.
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
## 2 Materials and Methods
### Transgenic Plants Identification and qRT-PCR Analysis  
To identify transgenic tomato plants resulting from plant transformation, we used a PCR with genomic DNA from leaves and a qRT-PCR with cDNA from both leaves and  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment Purpose** & **Gene Abbreviation** & **Gene ID** & **Primer Sequences (F-3)** \\ \hline \multirow{6}{*}{Homologous recombination clone} & \multirow{2}{*}{R\(\prime\)GGP2} & \multirow{2}{*}{evm.model.Contig360.207} & \multirow{2}{*}{F.GCCGGTCCAGCTACCATGCGTCAGACT-} \\  & & & AAGAGGGGGCGCGTCCAGCTACCATGGGT \\  & & & RTAGACATATGCGCGTACCTTCTCTCCAAGA- \\  & & & CGAGGCATCATCATCATCATCATGGGGATT \\ \cline{2-4}  & \multirow{2}{*}{R\(\prime\)DHAR} & \multirow{2}{*}{evm.model.Contig319.146} & \multirow{2}{*}{GCTCCGGCGCGTCACATACGGTCCGTGCGT-} \\  & & & TAGACATATGGGGCTACCTCTTTGGGGTACTTTTCGG \\  & & & TAGACATATGGGACCACCATGGGGTGTACTTCCGGT \\ \hline \multirow{6}{*}{PCR identification} & \multirow{2}{*}{R\(\prime\)GGP2} & \multirow{2}{*}{evm.model.Contig360.207} & \multirow{2}{*}{F.ATCCTCGAGAACACAGGCGTTTCCCCAC} \\  & & & RTATCTCGAGAACACAGGCATTCCGT \\ \cline{2-4}  & \multirow{2}{*}{R\(\prime\)DHAR} & \multirow{2}{*}{evm.model.Contig3619.146} & \multirow{2}{*}{F.ATGCCTCTCTGGGGGTCCAGCTCTCTCT} \\  & & & RCCATCATCATGCGCCCACCACCACCACCAACAT \\ \cline{2-4}  & \multirow{2}{*}{R\(\prime\)DHAR} & \multirow{2}{*}{evm.model.Contig319.146} & \multirow{2}{*}{F.ACACACCCCCAATGGTTTACAGA} \\  & & & RCCTCAGAATCCCCACCAAC  
fruits. Additionally, a qRT-PCR was utilized to analyze the gene expression related to AsA biosynthesis and metabolism in the leaves and fruits of both wild-type and transgenic plants. The genes analyzed included GPI, PMI, PMM, GMP, GME, GGP, GPP, GDH, and GLDH in the L-galactose pathway, AO and APX in the catabolism pathway, as well as DHAR and MDHAR in the recycling pathway. Previous studies [46; 47] were referenced for primer design. The _Acitin_ gene of _Solanum Lyopersicum_ (Solyc11g005330) was utilized as an internal reference gene to normalize expression data. All the primers were synthesized through Sangon Bioengineering Co., Ltd., (Shanghai, China) (Table 1). The qRT-PCR was performed on an ABI ViiA 7 DX system (Applied Biosystems, Waltham, MA, USA) using TB Green(r) Premix Ex TaqMan(tm) II kit (Takara). The qRT-PCR reaction system was 20 \(\mu\)L: SYBR Premix Ex TaqMan (2\(\times\)) 10 \(\mu\)L, 10 \(\mu\)mol/L forward and reverse primers 1 \(\mu\)L each, c-DNA 1 \(\mu\)L, ddH\({}_{2}\)O 7 \(\mu\)L. Reaction procedure was 95 \({}^{\circ}\)C 30 s; 95 \({}^{\circ}\)C 5 s, 55-60 \({}^{\circ}\)C 20 s, 40 cycles; 72 \({}^{\circ}\)C 20 s. Melting curve procedure was 95 \({}^{\circ}\)C 15 s; 55-95 \({}^{\circ}\)C 1 min in 0.3 \({}^{\circ}\)C increments; 95 \({}^{\circ}\)C 15 s. All reactions were performed in three replicates and the relative expression was calculated using the 2\({}^{-\Delta\Delta\mathrm{CT}}\) method.
## 3 Numerical Model and Validation  
A numerical model is proposed that integrates a train track dynamic model with a track-soil-building finite element model to accurately predict building vibrations. This comprehensive model effectively simulates the motion of a six-vehicle metro train on the track and has been validated through measurements.
# A Compact Ultra-Thin 4 \(\times\) 4 Multiple-Input Multiple-Output Antenna
###### Abstract  
This article reported a compact ultra-thin tightly arranged 4 \(\times\) 4 multiple-input multiple-output (MIMO) antenna pair (AP) functioning in the fifth-generation (5G) n78 band (3.4-3.6 GHz) for the ultra-thin 5G mobile handset. Two APs were printed on the center of two sideboards. A T-shaped open-ended slot was utilized in the grounding plane to improve the port impedance matching and attenuate the reciprocal magnetic coupling. A minimized total volume of 145 \(\times\) 75 \(\times\) 5 mm\({}^{3}\) was obtained, and the area of each radiating unit was only 8.5 \(\times\) 4.2 mm\({}^{2}\) (0.1\(\times\) 0.05\(\times\)0.0\(\times\)0.0\(\times\)0.0 is the free-space wavelength at the frequency of 3.5 GHz). By placing two radiating elements in an exceeding closed (1 mm or 0.01167\(\times\)0) distance, the designed AP precisely resonated at 3.5 GHz, and an acceptable measured isolation performance superior to 17 dB was attained. A prototype of this presented APs system was printed and tested, and remarkable consistency was observed between the simulated and measured curves. Numerous indicators were computed to assess its MIMO performance, such as Envelope Correlation Efficiency (ECC), Diversity Gain (DG), Total Active Reflection Coefficient (TARC), and Multiplexing Efficiency (ME).  
5G; MIMO; antenna pair; mobile phone; array Article
## 3 Calculation Methodology  
### Mathematical Model  
#### 3.1.1 Continuous Phase Model  
The three-dimensional unsteady simulation was performed using ANSYS Fluent 2022 R2 (Advanced Technology Joint Stock Company, Hanoi, Vietnam). The fluid flow was solved by utilizing a locally averaged approximation of the continuity and Navier-Stokes equations [50]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{i}}(\rho u _{i}u_{j})=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{i}} \left[\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}-\frac{2}{3}\delta_{ij}\frac{\partial u_{j}}{\partial x_{i}} \right)\right]+\frac{\partial}{\partial x_{i}}\left(-\rho\overline{u_{i}^{ \prime}u_{j}^{\prime}}\right)+F \tag{2}\]  
where the indices \(i\) and \(j\) represent the directions; \(u\), \(u^{\prime}\), \(x_{i}\), \(t\), \(\rho\), \(p\), \(\delta\), and \(\mu\) are the mean and fluctuating fluid velocity, position, time, fluid density, pressure, Kronecker delta, and fluid viscosity, respectively. The coupling term \(F\) between the particle and the gas phase was approximated as the sum of the drag force on each particle within the corresponding fluid control volume. \(-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\) is the Reynolds stress term due to turbulence and was solved via the Reynolds stress model (RSM) provided in ANSYS Fluent, while turbulence modification due to the presence of particles was also considered in this work. The equations for the transport of the Reynolds stresses are expressed as follows [50]:  
\[\frac{\partial}{\partial t}\left(\rho\overline{u_{i}^{\prime}u_{j}^{\prime}} \right)+\frac{\partial}{\partial x_{k}}\left(\rho u_{k}\overline{u_{i}^{ \prime}u_{j}^{\prime}}\right)=D_{T,ij}+G_{ij}+\phi_{ij}+\varepsilon_{ij} \tag{3}\]  
where the first term on the left-hand side of the equation is the local time derivative of the Reynolds stresses; the second term is convection; \(D_{T,ij}\), \(G_{ij}\), \(\phi_{ij}\), and \(\varepsilon_{ij}\) are turbulent diffusion, buoyancy production, pressure strain, and dissipation, respectively; and \(x_{k}\) is the positional length. The RSM requires the following empirical constants: Prandtl number = 0.82; TKE Prandtl number = 1.0; TDR Prandtl number = 1.3; C\({}_{\mu}\) = 0.09; C\({}_{1}\) = 1.8; C\({}_{2}\) = 0.6; C\({}_{1^{\prime}}\) = 0.5; C\({}_{2^{\prime}}\) = 0.3; \(\delta_{e}\) = 1.0; C\({}_{e1}\) = 1.44; C\({}_{e2}\) = 1.92.
# New Candidate Multicomponent Chalcogenide Glasses for Supercontinuum Generation
## 5 Nonlinear Optical Property Variation  
In addition to high transparency and tailorable linear refractive index of the parent glass, the optical properties in a nonlinear regime are key attributes to SC materials, since self-phase modulation of incident light source scales with optical nonlinearity of a target material [3]. Figure 4a,b show Z-scan data for bulk glasses with 20 and 40 mol% PbSe, respectively. Each set of data includes two types of Z-scan measurements in closed and open modes where nonlinear refractive indices and multiphoton absorption coefficients are extracted from the closed and open modes, respectively. The Z-scan data in the closed mode are fitted with the following equation:  
\[T_{CA}=1+\frac{4a\Delta\varphi_{0}\Big{(}\frac{z}{z_{0}}\Big{)}}{\bigg{[}1+ \Big{(}\frac{z}{z_{0}}\Big{)}^{2}\bigg{]}\bigg{[}9+\Big{(}\frac{z}{z_{0}}\Big{)} ^{2}\bigg{]}}\]  
where \(T_{CA}\), \(\Delta\varphi_{0}\), \(z\), and \(z_{0}\) correspond to normalized transmittance in a closed-aperture mode, phase change of the laser beam due to nonlinear refraction, sample position, and Rayleigh length, respectively [78, 100, 101, 102, 103]. Values of \(\Delta\varphi_{0}\) extracted from the fitting are inserted in the following equation to extract values of nonlinear refractive indices:  
\[n_{2}=\frac{\Delta\varphi_{0}\lambda}{2\pi I_{00}L_{eff}}\]  
where \(n_{2}\), \(\lambda\), \(I_{00}\), and \(L_{eff}\) correspond to nonlinear refractive index, laser wavelength, peak intensity, and effective path length for multiphoton absorption, respectively [78, 100, 101, 102, 103]. The Z-scan data in the open mode are fitted with the following equation to extract multiphoton absorption coefficients:  
\[T_{OA(nPA)}=\frac{1}{\{1+(n-1)\alpha_{n}L_{eff}[\frac{I_{00}}{(1+\Big{(}\frac{ z}{z_{0}}\Big{)}^{2}}]^{n-1}\}^{\frac{1}{n-1}}}\]
# Altering the Supply of Shielding Gases to Fabricate Distinct Geometry in GMA Additive Manufacturing
### Methods  
The experiments were performed with the WAAM system developed at Oak Ridge National Laboratory. The system configuration has been presented in more detail elsewhere ([PERSON] et al., 2020). A ternary blend of He-Ar-CO\({}_{2}\) (90-7.5-2.5%) and a binary blend of Ar-CO\({}_{2}\) (90-10%) were used as shielding gases while fabricating two-bead walls made of martensitic stainless steel. The Ar-CO\({}_{2}\) binary blend was mixed in-house using a variable ratio gas blender, designed to create a blend of welding shielding gases, while the ternary blend was a pre-mixed, industrial-grade blend of He-Ar-CO\({}_{2}\). The flow rate of the shielding gas was 18.8 L/min. Touch sensing and commercially available through arc seam tracking (TAST) sensors were used in the custom-built G-code robotic tool path generator in order to maintain the same standoff distance.  
The dimensions of the base plate were 152 \(\times\) 304 \(\times\) 6 mm, and the diameter of the wire was 0.9 mm. The wall geometry CAD model had dimensions of 150 \(\times\) 250 \(\times\) 10 mm, which was sliced to generate 41 layers. Each layer consisted of two continuous weld beads with a center-to-center distance of 3.2 mm. The tool path was reversed for each alternating layer. The wire feed speed (\(\sim\)5.7 m/min) and travel speed (\(\sim\)6.7 mm/sec) were set to identical values for both shielding gases. The torch angle was set to 90\({}^{\circ}\) and the contact tip-to-work distance was set at 12 mm. The surface tension transfer (STT\({}^{\circledR}\)) waveform control technology was used for both cases. Two wall geometries were printed, each time using one of the two blends. The arc profile was measured with a Phantom VEO-E-310 L high-speed camera with a 105 mm F2.8 Micro Nikon 2160 lens, and a bandpass filter, a neutral density filter, and an ultraviolet filter. The wall structures were scanned with a three-dimensional geometry scanner and compared with the original CAD geometry.
# Optimization of Composite Material Repair Patch Shape Based on Strength Analysis
## 6 Conclusions  
This paper introduces the strength theory of composite laminated panels. Based on this, the ultimate strength and failure modes of healthy laminated panels that applied the Tsai-Hill strength criterion are presented.  
Then, uniaxial tensile tests and the DIC method were conducted on laminated panels repaired using rounded square and circular patches. The experimental values were used to quantitatively evaluate the repair quality of the panels and the influence of different patch shapes on the post-repair strength of the panels.  
Then, by comparing the average displacements of the two types of repaired specimens, it is indicated that the average repair quality of specimens with square patches is significantly better than that of specimens with circular patches. Although the quality stability of specimens with circular patches is poorer, the overall strength restoration with circular patches is better.  
During the repair process, for the same damage under equivalent conditions, the type of structural component should be considered first, followed by determining the primary loads it transmits, and finally, in combination with the shape and location of the damage, minimizing material to be removed. The principle is to prioritize whether the repair primarily guarantees stability or needs to withstand larger and more complex loads, then decide the shape of the opening and patch to be used. If it is a non-primary load-bearing component with a relatively simple load, the repair quality stability being the main concern, it is recommended to use square openings and patches. If it is a primary load-bearing component with complex force transmission and higher load levels, it is recommended to use circular openings and patches, while strictly controlling the repair process.  
Figure 52: The method for optimizing the selection of patch shape for composite material repairs is based on strength analysis.  
**Author Contributions:** Conceptualization, R.X. and F.W.; methodology, R.X. and F.W.; software, R.X. and Y.Y.; validation, R.X. and Y.Y.; writing--original draft preparation, R.X.; writing--review and editing, R.X. and Y.Y.; visualization, R.X. and Y.Y.; supervision, F.W.; project administration, F.W.; funding acquisition, F.W. and G.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Boea Wisdom (Hangzhou) Network Technology Co., Ltd. The remaining authors and [PERSON] declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
In Wireless Sensor Networks (WSNs), unlicensed users, that is, sensor nodes, have excessively exploited the unlicensed radio spectrum. Through Cognitive Radio (CR), licensed radio spectra, which are owned by licensed users, can be partly or entirely shared with unlicensed users. This paper proposes a strategic bargaining spectrum-sharing scheme, considering a CR-based heterogeneous WSN (HWSN). The sensors of HWSNs are discrepant and exist in different wireless environments, which leads to various signal-to-noise ratios (SNRs) for the same or different licensed users. Unlicensed users bargain with licensed users regarding the spectrum price. In each round of bargaining, licensed users are allowed to adaptively adjust their spectrum price to the best for maximizing their profits. Then, each unlicensed user makes their best response and informs licensed users of \"bargaining\" and \"warning\". Through finite rounds of bargaining, this scheme can obtain a Nash bargaining solution (NBS), which makes all licensed and unlicensed users reach an agreement. The simulation results demonstrate that the proposed scheme can quickly find a NBS and all players in the game prefer to be honest. The proposed scheme outperforms existing schemes, within a certain range, in terms of fairness and trade success probability.  
wireless sensor networks; cognitive radio; strategic bargaining; spectrum sharing; Nash bargaining solution +
Footnote †: journal: Sensors  
0000-0002-4000-0002-9999-4000][PERSON]