esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Soil Sample Setup  
Reference soil samples were used for the experiment on dust emission from aggregates to fit the study's goal. Natural loess soil from the northern Negev Desert (Israel) was used as a bulk sample and for the preparation of the specific samples. The bulk soil was taken from the upper layer of the soil that is subjected to erosional processes in the field. The loam texture of the loess consists of \(-\)70% clay-silt fraction, and the quartz sand has a typical size range of fine-medium sand (<300 \(\upmu\)m). Three reference samples were prepared for aggregate groups at the sizes of 63-250 \(\upmu\)m, 250-500 \(\upmu\)m, and 500-1000 \(\upmu\)m, by sieving of the bulk sample. The samples were dried to an air-dry state (<1.5% gravimetric water content) in order to eliminate effect of soil water content.  
The bulk sample was tested for aggregate size distribution (ASD) to determine the mean weight diameter (MWD). The ASD was obtained using the dry sieving method. The samples were placed on a set of six sieves with diameters of 63, 125, 250, 500, 1000, and 2000 \(\upmu\)m, and were shaken at a moderate amplitude of 50 rounds per minute for 8 min on an electronic sieving apparatus with horizontal and vertical motions (RETSCH AS 300 Control, Haan city, Germany). Every size fraction was weighed separately to calculate the mean weight diameter (MWD) (mm) as follows:  
\[MWD\ =\ \sum_{i\ =\ 1}^{n}x_{i}\ w_{i} \tag{1}\]  
where \(n\) is the total number of aggregates measured, \(w_{i}\) is the fraction (%) that is the relative part of the group's size, and \(x_{i}\) is the equivalent diameter of the aggregate (mm).  
The samples of the different aggregate groups (63-250 \(\upmu\)m, 250-500 \(\upmu\)m, and 500-1000 \(\upmu\)m) were analyzed for several soil properties. Particle size distribution was obtained by the ANALYSETTE 22 MicroTec Plus (Fritsch, Idar-Oberstein, Germany) laser diffraction, which measures particles in the size range of 0.08-2000 \(\upmu\)m [29]. Replicas (100 mg) were dispersed in Na-hexametaphosphate solution (0.5%) by sonication (38 kHz). The data was calculated using the Fraunhofer diffraction model with a size resolution of 1 \(\upmu\)m using MasControl software. Soil organic carbon (SOC) content (%) was determined by the dry combustion method. A 5 g sample of crushed oven-dried (105 \({}^{\circ}\)C for 24 h) soil was placed in a combusting oven at 375 \({}^{\circ}\)C for 17 h. At this temperature, all organic carbon in the soil oxidizes, with no conflagration of mineral carbon. Carbonate (CaCO\({}_{3}\)) was determined as mass content (%) by a calcimeter. The carbonates present in a 200 mg sample were converted into CO\({}_{2}\) by adding hydrochloric acid 8% (HCl) to the sample. The calcium carbonate content can be calculated with reference to a standard sample of analytical (100%) CaCO\({}_{3}\).
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 3 Results
### Atmospheric Absorption  
We provide an estimation of the atmospheric absorption by taking into consideration surface temperature (T = 215 K) and pressure (P = 6.518 mbar) conditions for Mars. These provide the upper bound for gas absorption. The simulated atmospheric losses are computed from the Voigt line shape function using values from the HITRAN database. The pressure-broadening coefficients for each gas correspond to self-broadening. CO\({}_{2}\)-CO\({}_{2}\) collision-induced absorption [53] is also considered along with its line absorption features. Figure 3 shows the expected losses due to CO\({}_{2}\) (95.32%), H\({}_{2}\)O (200 ppmv), O\({}_{2}\) (0.13%), CO (0.08%), and O\({}_{3}\) (0.1 ppmv) assuming mixing ratios of the Martian atmosphere [50]. H\({}_{2}\)O vapor is expected to show significant absorption losses in the 1-10 THz range. A range of 0.001 to 0.01 dB/km is the optimal absorption to be detected in the occultation experiment [40]. Moreover, water vapor has a variable concentration in the Martian atmosphere [54]. The contribution of water vapor can be resolved with the help of an additional frequency channel in the occultation experiment.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Link Expiration
#### 4.3.1 Rf  
When it comes to transmitting data from source to destination, RF selects the route with the shortest \(RET\) and the least count of hops to obtain the most reliable path [34]. \(RF\) is essentially a discrepancy between the normalized values of \(RET\) and \(HC\) which can be computed using Equation (20).  
\[RF=\frac{RET}{RET_{max}}-\frac{HC}{HC_{max}} \tag{20}\]  
The calculation of \(RF\) requires the estimations of multiple necessary parameters such as \(RET\), \(HC\), \(RET_{max}\) and \(HC_{max}\); these important parameters and the method to calculate the \(RF\) are subsequently discussed.
# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## 6 Conclusions  
In this paper, we present a novel AKDNet designed to address the intricate task of 3D object part segmentation. Drawing inspiration from PointNet++ and DGCNN, we have introduced the keypoint-aware module, feature extension module, and attention-aware module to enhance segmentation performance. The keypoint-aware module proposes a keypoint extraction strategy to obtain the inner shape cue of the input 3D point cloud. The feature extension module establishes relationships among the current points to obtain more expressive feature representation. The attention-aware module calculates the contributions of the keypoint feature and the point feature to enhance the performance. To validate the effectiveness of the three proposed modules, we also conducted experiments on the task of point cloud classification. We obtained an 85.83 mIoU accuracy in the ShapeNet-Part dataset and a 93.0 accuracy in the ModelNet40 dataset. Qualitative and quantitative experimental results show that our AKDNet achieves reasonable performance improvement in 3D object part segmentation and classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Presented At** & **Overall Class Accuracy** \\ \hline DynamicScale [65] & MIPR2021 & 92.1 \\ PolyNet [66] & 3 DV2021 & 92.42 \\ ASSANet [67] & NeurIPS2021 & 92.9 \\ PointNet++ + SageMix [57] & ICLR2022 & 90.3 \\ Point-TnT [68] & ICPR2022 & 92.6 \\ Point-M2 AE-SVM [69] & NeurIPS2022 & 92.9 \\ MaskFeat3D [70] & Arxiv2023 & 91.1 \\ \hline Our AKDNet & – & 93.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results on ModelNet40.  
**Author Contributions:** Conceptualization, F.Z. and Q.Z.; methodology, F.Z. and Q.Z.; software, Q.Z., H.Z., and S.L.; validation, H.Z. and S.L.; formal analysis, F.Z. and Q.Q.; investigation, F.Z. and Y.H.; resources, F.Z. and Q.Z.; data curation, Q.Z. and N.J.; writing--original draft preparation, F.Z.; writing--review and editing, Q.Q. and Y.H.; visualization, Q.Z.; supervision, F.Z.; project administration, F.Z. and X.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research was funded by the BeiHang University Yunnan Innovation Institute Yunding Technology Plan (2021) of the Yunnan Provincial Key R&D Program (202103 AN080001-003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Acknowledgments:** The authors thank the editor and the anonymous reviewers for their valuable suggestions. The authors also thank [PERSON] for her suggestions and discussion regarding this paper.  
**Conflicts of Interest:** Author [PERSON] was employed by the company TravelSky Technology Ltd. The remaining authors 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.
## 4 Construction of Cutting Force Prediction Model Based on Response Surface Method
### Variance Analysis  
Table 7 is variance analysis of disc-cutter Nomex honeycomb composites ultrasonic vibration-assisted cutting force prediction model. As can be seen from the results of variance analysis, in the regression model of \(Fx\) and \(Fy\), \(P<0.0001\) and in regression model of \(Fz\), \(P=<0.005\), which indicating that the regression model established for \(Fx\), \(Fy\) and \(Fz\) was extremely significant. The feed speed \(v_{f}\), cutting depth \(a_{p}\) and cutting width \(a_{e}\) had a great influence on the cutting force of rotary vibration of the disc-cutter. The lack of fit of \(Fx\) (\(P=0.7098>0.05\)), the lack of fit of \(Fy\) (\(P=0.1271>0.05\)) and the lack of fit of \(Fz\) (\(P=0.0767>0.05\)), indicating that the lack of fit of regression models of \(Fx\), \(Fy\) and \(Fz\) was not significant. According to the credibility analysis of the quadratic regression equation, the coefficient of determination of the regression model of \(Fx\) was \(R^{2}=0.9510\) and the adjustment of \(R^{2}=0.9069\), indicating that the model could explain 90.69% of the experimental data; the coefficient of determination of the regression model of \(Fy\) was \(R^{2}=0.9568\), adjust \(R^{2}=0.9179\), indicating that the model could explain 91.79% of the experimental data; \(Fz\) regression model decision coefficient \(R^{2}=0.8916\), adjust \(R^{2}=0.7940\), indicating that the model could explain 79.40% of the experimental data.  
Combining the above analysis of variance and model significance tests, it is shown that the predicted values of \(Fx\), \(Fy\) and \(Fz\) fit well with the experimental values; the regression effect of the model was great, and it had a high credibility. Therefore, the regression model of the disc-cutter rotary-vibration-cutting force of the honeycomb-core material established by the response surface method was effective, and it could be used to analyze and predict the cutting force.
## 2 Materials and Methods
### Antimicrobial Activity  
#### 2.5.1 GF-EO Evaluation  
Qualitative evaluations were conducted on nutrient agar plates, following the methodology of [PERSON] et al. [37]. The pathogenic microorganisms used in this study included Gram+ bacteria (_Bacillus cereus_ (ATCC 6629), _Microoccus leuttus_ (ATCC 10240), _Staphylococcus aureus_ (ATCC 6538), and _Staphylococcus epidermidis_ (ATCC 12228)), Gram- bacteria (_Escherichia coli_ (ATCC 25922), _Salmonella enterica_ (ATCC 255566), and _Pseudomonas aeruginosa_ (ATCC 27853)), and the pathogenic yeast _Candida albicans_ (ATCC 10231). These microorganisms were obtained from fresh overnight broth cultures grown in a nutrient broth medium and incubated at 37 \({}^{\circ}\)C [38]. The inoculum size of this pathogenic strain was prepared and adjusted to approximately 0.5 McFarland standard (\(1.5\times 10^{8}\) CFU /mL) [39]. Subsequently, 25.0 \(\upmu\)L of the microorganism inoculum was added to each plate containing 20.0 mL of sterile nutrient agar medium. Then, in the cooled and solidified agar medium, a sample of GF-EO (10 \(\upmu\)g/mL) was added to a 0.6 cm well previously created in the agar plate using a 6.0 cm corn borer, following the well diffusion method. These plates were then refrigerated for one hour to enhance the sample diffusion, followed by incubation at 37 \({}^{\circ}\)C for 24 h. The zones of inhibition were measured in millimeters (mm).
# Optimization Design for Sparse Planar Array in Satellite Communications
## 5 Summary and Outlook
### Future Research Directions  
On the basis of the above research content, three future research directions and two important challenges are proposed for optimizing sparse planar arrays in satellite communication scenarios.
# Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring
## 2 Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring
### Typical Communication Scenarios of PLC Visual Monitoring  
In a typical communication process, the PLC programming software tool on a supervisory computer sends a request of reading data, and then remote PLCs return data from memory in a specified frame as shown in Figure 2. This communication process takes place in different scenarios. For example, in most cases, real-time data should be refreshed from remote PLCs to the current monitoring view regularly. When the user scrolls the monitoring view, it is expected that the real-time monitoring data should be updated immediately.  
In typical industry applications, the user program of PLCs usually includes a number of program blocks, each of which is formed by a set of subnetworks. When online debugging the variables of the program, loading the required data for visualization rather than all the real-time data of program blocks can satisfy the monitoring requirements and also decrease the communication load. For example, the data updating process of scrolling a monitoring view can be described as (1) generating a table of variables according to the current monitoring screen, and (2) generating monitoring data tables from multiple memory addresses in remote PLCs.  
As illustrated in Figure 3, the monitoring data should be updated from data Table 1 in Figure 2(c) to Table 2 in Figure 2(c), when the monitoring screen is scrolled from view 1 in Figure 2(a) to view 2 in Figure 2(b). Since the monitoring data or so-called variables are often distributed in different internal memory areas in a field PLC, there are different strategies to load monitoring data from the discrete memory addresses and pack one or multiple data packets for transmission. Furthermore, it is essential to optimize the size of data packets so as to improve communication effectiveness and efficiency.
###### Abstract  
Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed \(t\)-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.  
rainfall-runoff modeling; equilibrium optimizer; extreme learning machine; deep neural network; discrete wavelet transform +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Preliminary Study of CFPS
#### 2.3.4 Molecular Structural Characteristics  
* Determination by HPLC-RID  
The molecular weight of CFPS was firstly estimated by HPSEC (HPLC 1100 series, Agilent, Palo Alto, CA, USA) coupled to a refractive index detector (RIDG1362, Agilent). Two columns (TSKgel PWXL Type Guard column 5000 and 3000, Tosoh Bioscience GMBH, Stuttgart, Germany) were eluted with sodium nitrate (NaNO\({}_{3}\), 0.1 M) at 50\({}^{\circ}\)C and 1 mL/min, using pullulan standards as standards for calibration (from 1.3 to 800 kDa, 10 g/L). NaNO\({}_{3}\) (0.1 M) was used to solubilize CFPS (10 g/L) for 24 h at room temperature under magnetic stirring (350 rpm) before injection (20 \(\upmu\)L). The number (\(M_{n}\)) and weight (\(M_{w}\)) average molecular weights and the polydispersity index (D) were calculated with Equations (1)-(3):  
\[M_{n} = \frac{\sum N_{i}M_{i}}{\sum N_{i}} \tag{1}\] \[M_{w} = \frac{\sum N_{i}M_{i}{}^{2}}{\sum N_{i}M_{i}}\] (2) \[= \frac{M_{w}}{M_{n}} \tag{3}\]  
where \(M_{i}\) and \(N_{i}\) are the molecular weight and number of moles of polymer species, respectively.  
* Determination by SEC-MALS  
Other molecular characteristics of the CPFS fraction were estimated in the dilute regime by HPSEC (HELEOS II, Wyatt Technology Corp) coupled to three detectors: (i) a He-Ne laser at 690 nm (HELEOS II, Wyatt Technology Corp, Santa Barbara, CA, USA); (ii) a multi-angle laser-light scattering (MALS) filled with a K5 cell (50 L); (iii) a differential refractive index (RID 10A, Shimadzu, Japan); and a viscosimeter (ViscostarII, Wyatt Technology Corp., Goleta, CA, USA). Two columns (OHPAK SB-G guard column, OHPAKSB806 and 804 HQ columns (Shodex)) were eluted with LiNO\({}_{3}\) 0.1 M at 0.5 mL/min. CFPS was solubilized at 1 g/L in 0.1 M LiNO\({}_{3}\) solution for 72 h under stirring (200 rpm) at room temperature, filtered (0.45 \(\upmu\)m), and then injected through a 100 \(\upmu\)L full loop.
## 2 Materials and Methods
### Instrumentation  
The QCL spectra were acquired in back reflection using a MIR predispersive spectrometer (LaserScan(tm), Block Engineering, Marlborough, MA, USA). The spectrometer was equipped with three laser diodes with tuning ranges from 990 to 1111 cm\({}^{-1}\), 1111 to 1178 cm\({}^{-1}\), and 1178 to 1600 cm\({}^{-1}\). The scan time was approximately 0.5 s per diode for a total scan time of 1.5 s. The average power typically varied between 0.5 mW and 10 mW across the 600 cm\({}^{-1}\) tuning range with 100:1 transverse electromagnetic polarization (TEM\({}_{\text{oo}}\)) and a beam divergence of \(<\)2.5 mrad on the _x_-axis and \(<\) 5 mrad on the _y_-axis. A 3 in. diameter ZnSe lens was used to focus the MIR beam, to collect the reflected light, and to focus the light onto a thermoelectrically cooled mercury-cadmium-telluride (MCT) detector. The wavelength accuracy and precision were nominally 0.5 cm\({}^{-1}\) and 0.2 cm\({}^{-1}\), respectively. The spectroscopic system was designed to work best at a target distance of 15 \(\pm\) 3 cm, with each diode producing an elliptical beam spot of 4 \(\times\) 2 mm (see [PERSON], et al. [54]).
# A High Resolution Vernier Digital-to-Time Converter Implemented with 65 nm FPGA
###### Abstract  
In this paper, a digital-to-time converter (DTC) based on the three delay lines (3D) Vernier principle is proposed and implemented with field programmable gate arrays (FPGAs). Based on the 3D Vernier principle, the DTC is realized by three period approximate phase locked loops (PLLs). The theoretical fine resolution of the proposed DTC is improved by calculating the period difference two times. The achieved resolution of the proposed DTC is 203 fs realized with an Altera Stratix III FPGA chip, which is about tenfold higher than traditional FPGA-DTC implemented with the same series FPGAs. The worst absolute differential nonlinearity (DNL) and integral nonlinearity (INL) are verified smaller than 0.88 least significant bit (LSB) and 4.4 LSB, respectively. By optimized computation logic, there are only 448 adaptive look-up-tables (ALUTs), 237 registers and three phase locked loops (PLLs) utilized for circuit implementation. Experimental results prove that the proposed DTC features high resolution with low cost.  
digital-to-time converter (DTC); three delay lines Vernier principle; field programmable gate arrays (FPGA), phase lock loop (PLL) +
Footnote †: journal: Applied Sciences
## 3 Inversion Algorithm of Atmospheric Temperature in MLT
### Peak Relative Intensity Ratio Method
The accuracy of retrieved temperature based on the least squares method is the second, and the mean absolute bias within the range of 80-120 km is 6.5 K. The retrieved temperature based on peak relative intensity ratio method is the lowest, and the mean absolute bias in the range of 80-120 km is 16.7 K. The reason is that because the least squares method is a statistical method, it is limited to a simple linear problem and only quadratic terms and other simple non-linear forms, thus limiting the accuracy of inversion. Through the study of the principle of the relative peak intensity method, we found the inversion of temperature is directly related to the intensity of the six central spectral lines in each field of view, because the retrieval of temperature comes from the fitting line from the interaction between the intensity of the six central spectral lines and the molecular spectral parameters. However, each line-of-sight cuts through multiple height layers in limb observation; the total observed radiance at a single tangent height includes radiance from other tangent heights, so it can be considered that when this method is used in the limb mode, there is an inherent error. Therefore, the optimal estimation algorithm combined with Tikhonov regularization is finally matched in the following research.
## 2 Measurement Procedure
### Data Processing Procedure
#### 2.2.1 Hologram Reconstruction  
As long as the holograms have been captured, the numerical reconstruction should be performed to obtain its object waves. The recorded holograms are expressed as:  
\[I(x,y)=\left(O+R\right)^{2}=|O|^{2}+|R|^{2}+O^{\prime}R+OR^{*} \tag{1}\]  
where \(O\) is the object wave and \(R\) is the reference wave, * represent the complex conjugate operation.  
We use the angular spectrum method [14] for numerical reconstruction. The object wave at a propagation distance d is expressed as:  
\[U(x,y)=IFFT[FFT[I(x,y)]\exp(ikd\sqrt{1-\left(\lambda f_{x}\right)^{2}-\left( \lambda f_{y}\right)^{2}})W(f_{x},f_{y})S(f_{x},f_{y})]*\Gamma^{H}_{RCH} \tag{2}\]  
Figure 3: The algorithm flow chart of the data processing.  
Figure 2: Schematic diagram of beam collection at multiple rotation angle. (**a**) the rotation angle is 0\({}^{\circ}\) around the R_X -axis, 0\({}^{\circ}\) around the R_Y-axis. (**b**) the rotation angle is 15\({}^{\circ}\) around the R_X-axis, 0\({}^{\circ}\) around the R_Y-axis. (**c**) the rotation angle is -15\({}^{\circ}\) around the R_X-axis, 0\({}^{\circ}\) around the R_Y-axis.  
where FFT and IFFT represent fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT), respectively, and \(f_{x}\) and \(f_{y}\) is spatial frequencies in x and y direction, respectively. In order to get rid of the zero-order term and the twin image, a proper spectrum window \(W(f_{x},f_{y})\) is used to implement filter in the spatial frequency domain. Due to the existence of one-order phase aberration, this region is shifted into the center of the spectrum by a translation function \(S(f_{x},f_{y})\). What is more, the optical element of this configuration can produce the high-order phase aberration, the calibration method called reference conjugated hologram [15] is used for removing the influence of phase aberration. In this method, the compensation term \(\Gamma_{RCH}^{H}\) can obtain by recording a blank hologram without sample.