esa-sceva/satcom-chunk-collection huggingface dataset:
# Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
## 5 Conclusions  
This paper develops an AOA detection model for the spatially distributed sensor array and analyzes the impact of structural design on the field of view and detection accuracy. Based on it, a spatially distributed sensor array was designed, consisting of five InGaAs PIN photodiodes mounted on a 3D-printed panel with a 30\({}^{\circ}\) angle between the top panel and the side panels. A multi-sensor data fusion algorithm is proposed to mitigate measurement errors caused by the random variations in power density during the beam propagation process. The accuracy and repeatability of two-dimensional AOA detection were evaluated by indoor experiments. The results demonstrate that within a 60\({}^{\circ}\) field of view, the measurement accuracy of AOA is significantly improved, with a maximum angular error of 1.7\({}^{\circ}\) and an RMSE of 0.67\({}^{\circ}\), validating the effectiveness of the multi-sensor data fusion algorithm. Compared to the systems that detect the AOA based on light spot  
Figure 12: Two-dimensional experimental AOA results with the spatially distributed sensor array.  
position, the proposed design significantly reduces the SWaP, which has broad application prospects in future multi-satellite laser communication.  
Furthermore, we will conduct multi-environment optimization design and testing. By encoding the received beam and designing filters to improve the signal-to-noise ratio (SNR), the performance of the sensor will be further enhanced.  
Conceptualization, X.W. ([PERSON]) and L.Z.; methodology, S.Z., X.W. ([PERSON]) and L.Z.; software, S.Z. and X.W. ([PERSON]); validation, S.Z. and S.S.; formal analysis, S.Z. and S.S.; investigation, S.Z. and X.W. ([PERSON]); resources, L.Z. and X.W. ([PERSON]); data curation, S.Z., L.C. and H.D.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z., X.W. ([PERSON]), L.Z. and X.W. ([PERSON]); visualization, S.Z. and X.W. ([PERSON]); supervision, L.Z. and X.W. ([PERSON]); project administration, L.Z. and X.W. ([PERSON]); funding acquisition, L.Z. and X.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Thanks to the School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications for scientific inspiration and advice. Thanks Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, for assistance with the instrument and equipment.  
The authors declare no conflict of interest.
## 3 System Model
### User Satisfaction Index  
In this paper, the user satisfaction index is expressed as \(f\big{(}u_{q}\big{)}\) and defined as the ratio of the bandwidth actually allocated to packets to the bandwidth required by users. If the ratio is equal to 1, it means that the radio resources allocated to users can fully meet the demands of users. Otherwise, the smaller the value is, the fewer radio resources are allocated to packets, and the demands of users cannot be met.  
\[f\big{(}u_{q}\big{)}=\frac{\sum\limits_{a_{q}\in A}B_{a_{qi}}}{\sum\limits_{s_{ p}\in S}\sum\limits_{b_{p}\in BM}\sum\limits_{a_{q}\in A}w_{pjq}\times d_{a_{qi} }^{b_{pj}}}. \tag{23}\]  
where \(\sum\limits_{a_{q}^{c}\in A}B_{a_{q}^{c}}\) is the bandwidth actually allocated to all packets which are generated by the user \(u_{q}\). \(\sum\limits_{s_{p}\in S}\sum\limits_{b_{p}\in BM}\sum\limits_{a_{q}^{c}\in A }w_{pjq}\times d_{a_{qi}^{c}}^{b_{pj}}\) is the bandwidth required by the all packets which are generated by the user \(u_{q}\) in all beams of all satellites.  
The user satisfaction index of the adaptive packet segmentation scheme based on the irregular gradient algorithm is 18% higher than that without the irregular gradient algorithm.
## 5 Conclusions  
Exopolysaccharide biosynthesis is coordinated with the mutual activity of genes that participate in a multitude of cellular functions. Yeast presence in co-culture with lactic acid bacteria influenced metabolism and increased EPS production in a strain-dependent manner. Gene activation during the exponential phase and early stationary phase (from 6 to 12 h) was determinant for EPS production by the _L. rhamnus_ strains in this study. _L. rhamnus_ RW-9595M demonstrated an important over-expression of target genes in co-culture compared to monoculture. This transcriptomic activity seems to be related to higher EPS production under acid stress conditions. Analysis of the gene co-expression networks has permitted the detection of key genes (_wzb_) and the observation of direct correlations between the expression of genes of the EPS operons, sugar metabolism, amino acid biosynthesis, and lipid metabolism. This study contributes to a better knowledge of inter-kingdom interactions (bacteria - yeast) and the regulation of EPS biosynthesis. It also represents a step toward the development of bio-ingredients rich in polysaccharides. Cereal based products such as sourdough could benefit because the EPS produced in situ by starter or adjunct cultures of these strains can replace hydrocolloid additives. Future research must to be done to find out the influence of this EPS in water absorption of the dough, rheology and machinability, or particularly the prebiotic effects of foods containing EPS-containing ingredients.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/19/4026/s1](http://www.mdpi.com/2076-3417/9/19/4026/s1)]([http://www.mdpi.com/2076-3417/9/19/4026/s1](http://www.mdpi.com/2076-3417/9/19/4026/s1)), **Figure S1**. Change in pH of medium fermented by _L. rhamnus_ R0011, ATCC 9595 and RW-9595M in mono (solid symbols) and co-culture (open symbols) fermentation. **Figure S2**. Acetic acid produced by _Lactobacillus rhamnus_ R0011, ATCC 9595 and RW-9595M in mono (m) and co-culture (c) at 24 and 48 h of fermentation. **Table S1**. Primer set used for the qPCR and PMA-qPCR analyses for _Lactobacillus_ species. **Table S2**. Gene targets and parameters for RT-qPCR analysis by TaqMan for strains of _L. rhamnus_ and the assessment of specificity towards _L. rhamnus_ strains and _S. cerevisiae_. **Table S3**. Reference genes identified by the software geNorm for each time point of the fermentation for the three strains of _L. rhamnus_, **Table S4**. Changes in the profile of phenolic acids by _L. rhamnus_ R0011, ATCC 9595 and RW-9595M in mono and co-culture during 48 h of fermentation.  
Conceptualization, D.R. and G.L.; Methodology, A.B.; Formal Analysis, A.B., G.L. and D.R.; Resources, G.L. and D.R.; Data Curation, G.L.; Writing - Original Draft Preparation, A.B.; Writing - Review & Editing, G.L. and D.R.; Supervision, G.L.; Project Administration, G.L.; Funding Acquisition, G.L. and D.R.  
This project was funded by Novalait and the Fonds de recherche du Quebec - Nature et Technologie (FRQNT).  
[PERSON] is employed by Biena. [PERSON] and [PERSON] declare no conflict of interest.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 5 Simulation Results  
The performance of the proposed SS-FGSM is evaluated using Monte Carlo simulation over a statistical underwater channel [43] and a measured uncorrelated underwater acoustic channel adopted from an experimental data collection in the ASCOT01 experiment conducted off the coast of New England in June 2001 [17; 38]. Both channels are truncated to have an order of \(\mu=128\). First, for the statistical underwater channel, the same scenario as shown in Figure 1 was deployed where 4 \(T_{x}\) and 4 \(R_{x}\) are employed to validate the simulation with a distance of 500 m separating between \(T_{x}\) and \(R_{x}\). The depth of the \(T_{x}\) is covering 20 to 34 m and the same depth for \(R_{x}\) with DSF of \(4\times 10^{-3}\). Both simulation and the measured channels used \(f_{o}\) centered at 3550 Hz with channel bandwidth of 0.5 kHz with a frequency range of 3050-4050 Hz. Second, in the measurement channel, the source is deployed 4 m above the bottom and the depth of the bottom is 103 m, 4 \(T_{x}\) are employed for our simulation. Additionally, 4 \(R_{x}\) are deployed as a vertical vector sensor array covering a depth of 30 to 46 m. The source-receiver range was approximately 1 km. A low frequency modulated (LFM) signal is repeatedly transmitted every 120 s for a period of 160 min. By using a matched filter, the LFM received signal, estimates each channel actuary on a large time scale of 120 s. Using the estimated channel, we compute the equalizer coefficients used for channel equalization. The simulation outcomes are achieved for \(10^{6}\) symbols for trusted simulation data due to the added Gaussian noise. In the proposed SS-FGSM based on the DL detector, the training was performed with \(10^{3}\) epochs, each contains 20 batches of \(10^{3}\) data samples. The learning rate \(\alpha\) was set to be \(10^{-3}\). The number of nodes in the input layer of DNN was set to double of the length of the received signal since the real and imaginary parts of the received signal were concatenated, the number of nodes in \(\mathcal{Q}\) was set to be 128 and finally in output layer with a number of nodes equal to the size of incoming data block \(d_{i}\). The performance of the proposed scheme is compared with the conventional SMTs, such as SM [9], GSM [12], QSM [15], FGSM [14], GCIM [28], and EFGSM [20], in terms of the achievable data rate, ABER, EE, and receiver's complexity.
## 1 Introduction
### Determining the SIR Parameters  
The surging interest in infectious diseases modeling due to the COVID-19 pandemic has led to a different approach of compartmental modeling from the usual mathematical modeling strategy to statistical modeling strategy [22]. In the latter strategy, the model parameters are estimated instead of being specified by or adapted from certain subjective prior information as in the former strategy (usually from previous studies, previous pandemic or values estimated by WHO). The estimation of the SIR model parameters is essentially an optimization problem attempting to find a model that best fits the data. The estimated parameters are then used along with any numerical approximation methods to obtain the simulated SIR compartments' trajectories. A least squares loss function in terms of the simulated and observed trajectories, such as the Sum of Squared Error (SSE), is usually applied to quantify the discrepancy that arises from the simulation. Hence, the objective of the optimization is to minimize the loss function by estimating the parameters that lead to the best fit curve through any standard optimization techniques.  
Bayesian approach is becoming the popular optimization and estimation tool, as evidenced by the studies concerning COVID-19 pandemic. This approach is commonly employed by calibrating the available data while using Markov Chain Monte Carlo (MCMC) method with Metropolis-Hastings (MH) algorithm sampling as implemented in [6; 12; 13; 14; 18; 23] to obtain posterior estimates and credible intervals of the epidemiological parameters. Although the estimation of the model parameters is an obvious optimization problem, the metaheuristics family of optimization techniques received very little attention in epidemiological modeling. The first metaheuristic algorithm used in estimating the parameters in ODEs in general, and infectious diseases specifically, is the Genetic Algorithm (GA) (e.g., [24; 25; 26]). Recently, the Particle Swarm Optimization (PSO) algorithm is implemented in order to estimate the parameters in ODEs governing the SIR model variants, as presented in [27]. As for COVID-19 pandemic, very few studies applied metaheuristic algorithms for estimating the epidemiological model parameters, such as GA in [28], PSO in [29; 30], Stochastic Fractal Search in [31], Marine Predators Algorithm in [32], and Flower Pollination Algorithm with Salp Swarm Algorithm in [33]. In this regard, we are interested in applying a metaheuristic algorithm, namely the Harmony Search and its variants, to the optimization problem of estimating the SIR model parameters.  
This paper is organized, as follows. Section 2 details the Harmony Search algorithms that were used in this study. Section 3 presents the experimental setup of estimating the epidemiological parameters of SIR model while using Harmony Search algorithms. Section 4 provides the simulation results with detailed discussions. Finally, Section 5 provides the conclusion and possible future works of this study.
###### Abstract  
This paper presents the first try to fabricate degradable polylactic acid (PLA) biomedical stents with round edges by the multi-axis micro-milling process. Conventionally biomedical stents are produced by laser processing. Post-processing operations are usually required to handle sharp edges and thermal defects of the stent due to laser processing. A computer graphics software package was used to design the strut structures with round corners of the PLA stent. A PLA tube was first created using injection molding, and a degradable biomedical stent was then fabricated through micro-milling by using a five-axis computer numerical control (CNC) machine tool. This study investigated the error in the rotation center that can occur during five-axis micro-milling. Data obtained from experiments on center-of-rotation errors were substituted into homogeneous coordinate conversion formulas. Center-of-rotation errors in the five-axis machine tool were compensated for improving the milling precision (\(A\) and \(C\) axes) to be within 5 \(\upmu\)m. Furthermore, milling parameter optimization experiments were conducted, which determined the optimal conditions for milling PLA to be a spindle speed of 60,000 rpm, feed per tooth of 0.005 mm, and feed rate of 600 mm/min, and achieved the minimum burr 0.01 mm and the average surface roughness (Ra) 0.4 \(\upmu\)m. These optimal cutting parameters will be used in the following actual stent processing experiments. Finally, the error compensation and optimal parameters were combined in a CAM software package and layered spiral micromilling to machine the actual stent. The experimental results revealed that the combination of five-axis micro-milling led to the successful fabrication of a degradable biomedical stent (stent diameter = 6 mm, strut width = 0.3 mm, and radius at round corners = 0.1 mm). The machined actual stent had cross-section height and width errors within 0.01 mm, and arc depth of cut variation within 6 \(\upmu\)m. In addition, the PLA stent machining results indicated a rebound of approximately 33% at the strut round edge machining. This work may also open up future possibilities for complex three-dimensionally structured biomedical stents for better performance and special functionality.  
biomedical stent; polylactic acid; micro-milling; five-axis machining Article
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 3 Results
### Pre-Processing
#### 3.2.3 Kinect Volume Correction  
Figure 11 shows a volume estimation error present in experiment one's data, which aligns with a subsequent review of the literature [10; 18; 19]. The 21 bunches have a mean actual volume (the reference volume) of 144.952 cm\({}^{3}\) and a mean estimated Kinect volume of 175.672 cm\({}^{3}\). Overestimation by the Kinect V1 sensor was evident from the results. Volume correction via cross-validation was therefore subsequently incorporated into the methodology, where a mean corrected volume of 144.952 cm\({}^{3}\) was achieved. Thereafter, the correction was applied to the remaining Kinect datasets.
## 3 Fracture State of Roadway Roof and Its Rock Mechanics Characteristics
### Rock Mechanics Experiment  
According to the research content, the test rock sample was taken from the ZF3806 working face of Shuilandong Coal Mine in Bin County, Shanxi Province. The rock samples were roof coal, mudstone, silty fine sandstone, and siltstone. Through on-site sampling, the rock samples were transported from the underground to the ground, and coal cores and rock cores were drilled in the laboratory with a coring machine [35; 36; 37; 38]. Then, the end was cut and polished with an end grinder. All standard specimens prepared were \(\Phi\) 50 mm \(\times\) 100 mm; the standard specimen processing equipment and process are shown in the Figure 4. Rock specimens were processed into standard specimens, and uniaxial compression tests were carried out. Because the mudstone layer was thin and fractured, no good rock specimens were obtained. It was processed into coal rock combination test pieces with some coal specimens to explore the collective failure of mudstone and coal.  
Figure 4: Processing of standard specimens.  
Figure 3: Characteristics of rock core.  
The stress-strain curve and failure of rock specimens after the uniaxial compression test are shown in Figure 5. The stress-strain curves of all specimens had evident stages of compaction, elasticity, yield, and failure. The failure stage of the coal sample was mainly brittle shear failure. The coal rock assemblage was distinguished by symbiotic double shear failure and numerous fracturing failures. The analysis showed the reason is that there were many microfissures in mudstone and coal. At the same time, the coal sample and coal rock combination exhibited expansion, indicating the roof is subject to obvious crushing and bulking deformation. Sandstone mainly showed shear and double shear failure. The uniaxial compressive strength of the coal sample was 11.42-19.17 Mpa with an average of 14.31 MPa. The uniaxial compressive strength of the coal-rock combination was 7.86-13.49 Mpa with an average of 10.85 MPa. The uniaxial compressive strength of the fine siltstone sample was 18.06-21.13 Mpa with an average of 18.97 MPa. The uniaxial compressive strength of the siltstone sample was 27.92-41.20 Mpa with an average of 35.36 MPa. The strength of the coal rock combination was the lowest and that of siltstone was the highest.  
Figure 5: Stress–strain curve and failure mode of rock uniaxial compression experiment.
## Results and Discussion
### Antimicrobial Activity  
Table 3 presents the antimicrobial activity of the selected strains with L-asparaginase and free of L-glutaminase activity. _Bacillus_ MKHJ 1-1, 13-2, and 19-2 showed broad antimicrobial spectra against target bacteria. Among the selected strains, in contrast, 2-2 did not have antimicrobial activity. Some _Bacillus_ spp. are known to play an important role that involves the inhibition of pathogens in the intestinal tract by producing enzymes that break down cell walls (such as chitinase, glucanase, and protease), lipopeptide, biosurfactants, and bacteriocin. Among them, the lipopeptides produced by _Bacillus_ spp. include surfactin, iturin, and fengycin [31]. Iturin is only detected in restricted species, such as _B. subtilis_, _B. amyloliquefaciens_, and _B. pumilus_, whereas surfactin and fengycin are found in most _Bacillus_ spp. [32]. The antimicrobial activity of the selected strains may be due to the interaction of the protease, lipopeptide, and bacteriocin. When the harmful intestinal bacteria increase, MKHJ 1-1, 13-2, and 19-2 can be expected to stabilize the intestinal flora and inhibit the harmful bacteria. Based on the results of L-asparaginase, hemolysis, \(\beta\)-glucuronidase, and antimicrobial activity, MKHJ 1-1 were finally selected.
## 5 Conclusions  
In this paper a geographic distance routing protocol for vehicular traffic environments called SLD-GEDIR has been proposed and simulated using NS-2. The performance of the protocol has been evaluated in terms of end-to-end delay, throughput, and one-hop link disconnection, and compared with the state-of-the-art protocols. An analysis of simulation results leads to the following conclusions: the impact of speed and density of vehicles on SLD-GEDIR in terms of end-to-end delay is nominal. SLD-GEDIR has lower end-to-end delay as compared to the state-of-the-art protocols. The throughput of SLD-GEDIR decreases linearly with increasing speed and decreases gradually with increasing density of vehicles. However, the throughput of the state-of-the-art protocols decreases sharply as the speed and density of the vehicles increases. Further, it was found that one-hop link disconnections in SLD-GEDIR are lower as compared to the state-of-the-art protocols. Moreover, the impact of vehicle density and speed on SLD-GEDIR is insignificant in terms of one-hop link disconnection. From the analysis of the results obtained through simulation it is clear that SLD-GEDIR is more suitable for vehicular traffic environments as compared to the state-of-the-art protocols. In the future research, authors will explore the idea of using Particle Swarm Optimization (PSO)-based NHV selection and Cache Agent (CA)-based forwarding in SLD-GEDIR.
## 3 Results and Discussion
### Evaluation of the Infusions of Herbal Mixes  
#### 3.2.1 Choice of the Mixture Ratios and Phytochemical Analysis of the Infusions  
Herbal mixtures constitute an important part of ethnopharmacological research. Thus, numerous herbal blends have been shown, alone or in combination, to be able to improve health [24]. _C. operculatus_ and _M. arensis_ could be considered a potentially interesting combination of individual herbs. Therefore, we tested the potential health benefits of a herbal blend comprising the dried leaves of _C. operculatus_ and _M. arvensis_.  
Before oven drying, the leaves of _C. operculatus_ and _M. arensis_ were preincubated in a stainless-steel jar at 60 \({}^{\circ}\)C for 2 h. This incubation process corresponds to a blanching process. Blanching was used as a preliminary step of drying to obtain a high-quality dried herbal drink. It inhibits enzymatic activities. This thermal incubation process also negatively affects the chlorophyll content [25]. However, it leads to lower yields of secondary metabolites in the herb [26]. Furthermore, if many herbal drinks are found to have health benefits for their consumption, they (preferentially) need to have (at least a rather) good taste. From that point of view, our incubated herbal leaves were more preferred in aroma than the nonincubated leaves (data not shown). The incubation process also yielded better results in terms of the taste and aroma of _C. operculatus_ leaves compared with nonincubated leaves. The aroma of herbal plants is due to the presence of volatile compounds that easily stimulate the sense of smell [27].  
The yields of the extracted flavonoids and terpenoids from hot-water-infused mixtures of _C. operculatus_ and _M. arensis_ in various proportions (Mix 1 to 5 corresponding to _C. operculatus_ ratios of 100/90/70/50/0%, respectively) were measured (Table 4). The total concentrations of each family of compounds considerably varied between the different proportions of the herbal blends. The values of flavonoids range from 0.76 mg QE/g to 1.39 mg QE/g. All the mixtures have lower total flavonoid content when compared with the _C. operculatus_ leaf infusions. This decrease in the flavonoid content of the infusions prepared from the herb mixture could be partially attributed to the addition of _M. arensis_, which has a lower concentration of flavonoid compounds. Nevertheless, the different levels did not directly change as the ratio changed. Thus, the mixed infusions of the two plant species did not react as simple mixtures. Other explanations should be explored to explain the difference between the theoretical values of pure mixtures and the measured results obtained with the real extracts.  
The values for terpenoids range from 12.8 mg SE/g to 26.36 mg SE/g. The terpenoid content is high in mint leaves. On the other hand, the leaves of _C. operculatus_ also contain terpenoids. Therefore, the terpenoid content is high in all the infused mixtures with various proportions.