esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Array Design for LEO  
As far as antennas for LEO satellites are concerned, typical requirements for array gain are in the order of 35 dBi. Considering an element gain of 6 dBi with a scan loss of 3 dB at the maximum scan angle, the resulting number of elements is in the order of 48 \(\times\) 48 elements. The inter-element spacing providing a grating lobe-free condition is defined in Equation (1) [19; 20].  
\[\frac{p}{\lambda}\leq\frac{1}{sin\theta_{L,i}}\frac{1-\frac{1}{N}}{1+sin\theta _{S}} \tag{1}\]  
Figure 1: Open-ended ridged waveguide elements in square and triangular lattices.  
where \(\theta_{L,i}\) defines the lattice angle (i.e., see Figure 2, \(\theta_{L,1}=90^{\circ}\) for the square lattice and \(\theta_{L,2}=60^{\circ}\) for the triangular lattice), \(\theta_{S}\) is the maximum scan angle and \(N\) is the number of elements in a row/column.  
According to Equation (1) and considering both the highest frequency of the band (20.2 GHz) and a scan limit of \(60^{\circ}\), the period is equal to 7.8 mm (\(p=0.52\lambda\)) and 9 mm (\(p=0.61\lambda\)) for the square (Figure 2a) and triangular lattices (Figure 2b), respectively. Both arrangements are studied in this paper.  
The array period reported above have been computed considering a finite array approach, which is suitable for a future prototype. Nevertheless, the large number of elements, i.e., 48 \(\times\) 48, justifies an infinite array approach for the element design.
# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
## 2 Materials and Methods
### Performance Evaluation  
In order to evaluate the performance of the algorithm, the recognition accuracy (RA) is defined to quantify the recognition results.  
\[RA=\frac{N_{cor}}{N_{s}}, \tag{3}\]  
where \(N_{cor}\) is the number of correct locomotion modes identified in the test set, \(N_{s}\) is the total number of divided locomotion modes in the test set.  
Figure 5: Finite state machine transition diagram of locomotion modes.  
For a more precise analysis of gait phase recognition misclassification, the confusion matrix is constructed to quantify the error distribution.  
\[C=\begin{pmatrix}c_{11},c_{12},c_{13},c_{14},c_{15}\\ c_{21},c_{22},c_{23},c_{24},c_{25}\\ c_{31},c_{32},c_{33},c_{34},c_{35}\\ c_{41},c_{42},c_{43},c_{44},c_{45}\\ c_{51},c_{52},c_{53},c_{54},c_{55}\end{pmatrix}, \tag{4}\]  
where each element of the confusion matrix is defined as:  
\[c_{ij}=\frac{n_{ij}}{n_{i}}\times 100\%, \tag{5}\]  
where \(n_{ij}\) is the number of events in the \(i\)-th motion mode recognized as the \(j\)-th motion mode during classification and recognition, \(n_{i}\) is the total number of events in the \(i\)-th locomotion mode. Thus, the diagonal elements of the confusion matrix \(C\) represent the number of correct recognitions at the corresponding phase stage, whereas the non-diagonal elements represent the number of recognition errors.  
In addition to the recognition accuracy, pattern recognition algorithms must typically assess whether they were detected in time. Consequently, the recognition delay rate (RD) is defined to quantify the degree of identification delay of the algorithm.  
\[RD=\frac{T_{i}-T_{c}}{T_{gait}}\times 100\%. \tag{6}\]  
In this formula, \(T_{i}\) is the moment when the locomotion mode transition is correctly recognized for the first time, \(T_{c}\) is the transition moment and \(T_{gait}\) is the total time of a gait cycle. According to these three moments, the RD between gait mode transitions can be obtained. It is worth noting that the defined \(T_{i}\) also needs to satisfy the remaining time without error determination.  
In addition, the running time \(t\) of the algorithm is also an important indicator of the model performance. The time required for algorithm recognition can be used to evaluate the algorithm's complexity and the hardware resources it requires. Since the single recognition time is extremely small and difficult to measure, the entire test set can be used for recognition, and the cumulative recognition time can be calculated.
## 3 Results and Discussion
### PLS Regression: Carotenoid Content Prediction by IR Spectral Data
It was considered that by increasing the degree of isolation of the analyte, that is, by carrying out the extraction of total carotenoids in hexane/acetone, it would be possible to improve the predictability of the model. However, the acetone and hexane functional groups overlapped, and attenuate the carotenoids bands, as discussed above, ultimately causing very poor predictability of the corresponding regression models. Therefore, the results of the models obtained with the fresh and freeze-dried samples are presented in Table 1.  
The performance of PLS models developed in different studies for the determination of carotenoid content in fresh or minimally processed food products, using MIR and NIR (Near Infrared) spectral data, are shown in Table 2. In comparison, the PLS model developed for freeze-dried samples had good statistical performance, with figures of merit comparable to those previously reported. In summary, the PLS model obtained with FTIR-ATR spectral data from freeze-dried samples was the most appropriate. This model allowed us to obtain TCC values comparable to those reported by the conventionally spectrophotometric UV-Vis technique, with lower time consumption and avoiding the need for extraction protocols and solvents. The comparison between both techniques is presented in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **Matrix** &
\begin{tabular}{c} **Range of Variation (\(\upmu\)g/g)** \\ **(max-min)** \\ \end{tabular} & **Analyte** & \(\mathbf{R^{2}}\) & _RMSE_ & **Source** \\ \hline MIR & Tomato juice & 92.5–15.22 & Lycopene & 0.97 & 0.4 & [11] \\ MIR & Tomato juice & 1.8–6.6 & \(\upmu\)g-carotene & 0.91 & 0.054 & [11] \\ NIR & Pumpkin & 67.1–451.2 & Total carotenoids & 0.95 & 31.7 & [30] \\ NIR & Pumpkin & 50.3–434.3 & Lutein & 0.96 & 26.8 & [30] \\ NIR & Pumpkin & 0–24 & \(\upmu\)g-carotene & 0.81 & 2.27 & [30] \\ NIR & Tomato & 26.2–6290 & Lycopene & 0.85 & 91.19 & [31] \\ NIR & Tomato & 2.3–28.3 & \(\upmu\)g-carotene & 0.80 & 0.41 & [31] \\ \hline \hline \end{tabular} \(R^{2}\), reported coefficient of determination of the models; _RMSE_, reported root mean square error.  
\end{table} 
Table 2: Comparative performance of reported methods based on MIR and NIR techniques for carotenoid determination in food samples.
## 2 Materials and Methods
### Characterization of Biosynthesisized AgNPsNPs  
The reduction of Ag\({}^{+}\) to AgNPsNPs in the aqueous solution was monitored using a UV-Visible spectroscopy (Uviline 9400, Secomam, Ales, France) within the range of 300 to 600 nm. The UV-Visible spectra of AgNO\({}_{3}\) solution (1 mM) without cell biomass was used as control. The presence of functional groups on AgNPs was determined by Fourier-transform infrared spectra (FTIR) in the range of 4000 to 400 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\) using Nicolet 6700 (Thermo Fisher Scientific, Madison, US), and the transmittance spectrum was recorded. The colloidal solution of AgNPs NPs and dried cell biomass of _L. plantarum_ TA4 (control) were used for the analysis. The hydrodynamic size and polydispersity index (PDI) of biosynthesized AgNPs were determined by dynamic light scattering (DLS) using a Nano S (Malvern Instruments, Malvern, UK). Briefly, the colloidal solution of AgNPs in cuvettes was measured, and the values were generated using the software equipped with the instrument. The morphology and size of biosynthesized AgNPs were analyzed using a high-resolution transmission electron microscope (HR-TEM). A drop of AgNPs sample was placed on a copper grid and air-dried before viewing using JEM-2100F (Jeol, Tokyo, Japan). The particle size distribution of biosynthesized AgNPs was measured using the ImageJ version 2.0 software (NIH).  
Scanning Electron Microscope (SEM) and Energy Dispersive X-ray (EDX) of L. plantarum TA4 Exposed to Ag\({}^{+}\)  
SEM-EDX analysis was performed to investigate the formation of AgNPs on the cell membrane of _L. plantarum_ TA4. Briefly, strain TA4 was grown overnight in MRS broth at 37 \({}^{\circ}\)C, and the cell biomass was collected by centrifugation at 2800\(\times\)\(g\) for 10 min. The collected cell biomass was washed with phosphate-buffered saline (PBS) three times. Subsequently, the cell biomass was added to the Ag\({}^{+}\) aqueous solution at a concentration of 2 mM and incubated in the dark at 37 \({}^{\circ}\)C for 24 h. The color change at the end of the incubation time indicated the formation of AgNPs, and the cell biomass  
Figure 1: Schematic representation for biosynthesis, characterization, and biopotential applications of biosynthesized silver nanoparticles (AgNPs).  
was collected by centrifugation and washed according to the described procedure. The biomass was subjected to sample processing for SEM. In brief, the cells were fixed in 2.5% glutaraldehyde for 4 h at 4 \({}^{\circ}\)C and washed thrice with 0.1 M sodium cacodylate buffer before being post-fixed with 1% osmium tetraoxide for 2 h at 4 \({}^{\circ}\)C. Afterwards, the cells were washed again with the buffer and dehydrated with graded acetone series (35, 50, 75, 95, and 100% with 10 min per step). The dehydrated cell was then subjected to critical point-dried before mounted on the stub and coated with gold film in the sputter coater. The SEM observation was performed using JEOL JSM-6400 equipped with an EDX spectrometer. The EDX analysis was used to determine the elemental silver present on the cell membrane.
## 2 Materials and Methods
### Plant and Soil Analysis  
Physiological plant parameters (Photosynthetic rate and stomatal conductance) were measured during the growing season using an Infrared Gas Analyzer (IRGA, LCA-4, Analytical Development, Hoddesdon, UK). Chlorophyll content was determined using a SPAD-502 meter (Minolta, Osaka, Japan), using the procedure prescribed by [PERSON], et al. (2014). All of the physiological parameters were taken during heading stage 10.5. Soils were sampled immediately after each harvest. Plant sampling was performed at maturity. The samples were washed first with tap water, followed by 1% (\(v/v\)) HCl water, and then finally with distilled water. The collected samples were air-dried under the shade for 24 h, followed by drying in an oven at 65 \(\pm\) 2 \({}^{\circ}\)C until a constant weight was achieved. After oven drying, the grain yield and the dry weights of the roots, shoots and leaves were recorded.  
Saturated soil pastes were prepared to measure the pH using a calibrated pH meter (Hanna HI-83141). The soil water was extracted from the prepared soil paste using a negative pressure extraction pump for further analysis. The electrical conductivity (EC) of the water extracted from the saturated soil paste was determined using a conductivity meter (Lovelbond SensoDirect con200) after calculating the cell constant for the conductivity meter. The soil total nitrogen (N) was determined using the Kjeldahl apparatus ([PERSON], 1965). Available phosphorus (P) was extracted following the [PERSON] method devised by [PERSON], et al. (1993). Potassium (K\({}^{+}\)) was analyzed using the method described by [PERSON] ([PERSON], 1997). The Walkley-Black method was used to determine the SOM content ([PERSON], 1997). The cation exchange capacity (CEC) of the sampled soil was determined using the method described by [PERSON] (1997). The sodium adsorption ratio (SAR) was calculated with the equation below:  
\[SAR=\frac{Na}{\frac{\sqrt{Ca+Mg}}{2}}\]  
where \(SAR\) is the sodium adsorption ratio (mmol dm\({}^{-3}\))\({}^{1/2}\)), \(Na\) is the concentration of sodium ions (mmol\({}_{c}\) dm\({}^{-3}\)) and \(Ca+Mg\) is concentration of calcium plus magnesium (mmol\({}_{c}\) dm\({}^{-3}\)).
# Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine
## 4 Application Analysis  
### Selection of Main Influencing Factors  
There are many factors making a difference to the thickness of loose zone. The complex relationship between them often shows strong nonlinearity. Currently, there is no universal mathematical model. Based on the relevant literature [64; 65] and the principles of simplicity, independence and accessibility, the influence of in situ stress on the size of loose zone is reflected by roadway burial depth, and the influence of roadway span and roadway section area on the size of loose zone is reflected by the geometric size and shape of roadway. Five indexes are used to predict the thickness of loose zone.  
(1) Depth of roadway. The thickness of roadway surrounding rock loose zone increases with the roadway burial depth.  
(2) Span of roadway. The thickness of roadway surrounding rock loose zone increases with the roadway span.  
(3) Strength of surrounding rock. The difficulty of resisting failure of surrounding rock can be reflected by the surrounding rock strength, and the thickness of loose zone is inversely proportional to the surrounding rock strength. The strength of surrounding rock can be described by selected rock uniaxial compressive strength Rc.  
(4) Development degree of surrounding rock joints. Joints are small fracture structures with no significant displacement on both sides of rock mass after stress fracture. The greater the degree of joint development, the greater the thickness of loosening zone.  
(5) Sectional area of roadway. The size of roadway section also makes a difference to the thickness of loose zone which is proportional to the roadway section area.  
Figure 3: Prediction model of surrounding rock loose circle in roadway.
# Dual-Band MIMO Antenna for 5G/WLAN Mobile Terminals
## 2 Antenna Geometry  
The detailed geometry of the proposed dual-band four-element array for 5G MIMO terminal applications is shown in Figure 1. There are two kinds of printed circuit boards (PCBs) in the proposed MIMO antenna, including a main board and two side boards. The dimensions of the main board and the side boards are 150 mm \(\times\) 75 mm \(\times\) 0.8 mm and 134 mm \(\times\) 6.2 mm \(\times\) 0.8 mm, respectively. Four antenna elements are printed on the two side boards which are placed vertically to the main board. The side boards and main board are all printed on FR4 with \(\varepsilon\)r = 4.4 and tan\(\delta\) = 0.02, which are bonded to each other by metal glue. There are two clearance areas (75 mm \(\times\) 8 mm) situated at both top and bottom sides of the main board, which are reserved for 2G/3G/4G or other wireless communication systems in current mobile handsets. Figure 1b shows the detail dimensions of a single antenna element that is a dual-band antenna operating in 3.3-3.84 GHz and 4.61-5.91 GHz. As shown in Figure 1, the dual-band antenna contains an open-loop ring resonator feeding element and a T-shaped radiating element which is connected to the ground plane. The total size of a single element is 14.9 mm \(\times\) 7 mm \(\times\) 0.8 mm, which takes advantage of the resonator to reduce the size and improve the performance of the individual element.  
Figure 1: \(Cont.\)
## 4 Discussion
### Moment-Rotation Envelope Curves
#### 4.2.3 Effect of the Local Strengthening Connection Method and Anchorage Method on Joint Performance  
Figure 14b indicates that specimens SJ-5 and SJ-3 had comparable initial stiffness values. In the initial loading stage, the deformation of the thick steel tube wall was very small. Therefore, the effect of the local strengthening connection method on the joint can be neglected, and only the influence of the anchorage method needs to be considered. Thus, it can be concluded that the change in the anchorage method (with or without an anchor nut) had little influence on the initial stiffness of the joint. The flexural resistance of specimen SJ-5 was 13% less than that of specimen SJ-3, indicating that  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & \(M_{\mathbf{y}}\) & \(\theta_{\mathbf{y}}\) & \(M_{\text{max}}\) & \(\theta_{\text{max}}\) & \(M_{\mathbf{f}}\) & \(\theta_{\mathbf{f}}\) & \(K_{i}\) \\
**Number** & **(kN-m)** & **(mrad)** & **(kN-m)** & **(mrad)** & **(kN-m)** & **(mrad)** & **\(\mu_{\theta}\)** & **(kN-m/mrad)** \\ \hline SJ-1 & 136.89 & 8.89 & 178.48 & 36.79 & 151.71 & 44.53 & 5.01 & 29.80 \\ SJ-2 & 166.03 & 7.10 & 197.26 & 12.35 & 167.67 & 33.14 & 4.66 & 41.04 \\ SJ-3 & 136.1 & 7.56 & 178.65 & 27.79 & 153.66 & 38.96 & 5.15 & 35.29 \\ SJ-4 & 194.63 & 7.53 & 225.11 & 15.08 & 196.9 & 26.28 & 3.49 & 42.59 \\ SJ-5 & 124.21 & 8.16 & 155.44 & 27.87 & 147.28 & 58.08 & 7.11 & 37.30 \\ SJ-6 & 92.37 & 9.40 & 118.39 & 34.82 & 100.63 & 46.4 & 4.93 & 21.72 \\ SJ-7 & 146.12 & 8.13 & 184.56 & 34.04 & 184.56 & 34.04 & 4.19 & 33.27 \\ SJ-8 & 145.65 & 5.83 & 188.19 & 24.51 & 172.95 & 35.76 & 6.13 & 32.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: of the main results.  
Figure 15: Determination of the key parameters in the \(M\)-\(\theta\) relationship of joints.  
the local strengthening connection method and anchorage method had a certain weakening effect on the flexural resistance.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 2 Results
### Hormonal Differences between Normal and Slow Ripening Phenotypes
#### 2.3.2 Geranyligeranyl Diphosphate Biosynthesis, Precursor of ABA and GA Production
DXS, 1-deoxy-D-xylulose-5-phosphate synthase; DXR, 1-deoxy-D-xylulose-5-phosphate reductase; MDS, 2-C-methyl-D-erythriold 2,4-cyclodiphosphate synthase; HDS, hydroxymethylbutenyl 4-diphosphate synthase; HDR, hydroxymethylbutenyl 4-diphosphate reductase; DMAPP, dimethylallyl pyrophosphate; GPP, geranyl diphosphate; GPPS, GPP synthase; APS, \(\alpha\)-pinene synthase; FPP, famesyl diphosphate; FPPS, FPP synthase; AFS, \(\alpha\)-farresene synthase; GGPP, geranylgeranyl diphosphate; GGPPS, GGPP synthase; PSY, phytoene synthase; PDS, phytoene dehydrogenase; ZDS, \(\zeta\)-carotene desaturase; LCYB, lycopene- \(\beta\)-cyclase; CHYB, \(\beta\)-carotene hydroxylase; ZEP, zeaxanthin epoxidase; NCED, 9-cis-epoxycarotenoid dioxygenase; KS, ent-kaurene synthase; KO, ent-kaurene oxidase; KAO, ent-kaureneoic acid oxidase; GA20 ox, gibberellin 20-oxidase; GA3 ox, gibberellin 3-\(\beta\)-dioxygenase; GA2 ox, gibberellin 2-\(\beta\)-dioxygenase; Met, methionine; SAM, S-adenosylmethionine; ACC, 1-aminocyclopropane-1-carboxylic acid; ACS, ACC synthase; ACO, ACC oxidase; LOX, lipoxygenase; AOS, allen oxide synthase; AOC, allen oxide cyclase; OPDA, 12-oxo-phytodienoic acid; OPR, 12-oxophytodioneate reductase; JAR1, jasmonic acid-amino synthetase. ABA biosynthesis pathway was observed in NR fruit, suggesting a lower ABA production on SR individuals only in T7, as previously mentioned for the GGPP. Even though we did not observe the exact behavior of GGPP and ABA biosynthesis in GA production (Figure 3, orange section), a significant difference was observed in the expression profile of one _GA3 ox_ in T2 with a lower expression level in the SR siblings. The candidate genes _NAC072_ (Figure 1B) and AFS (Figure 3 yellow section) had same point of highest expression. This enzyme produces bioactive GA and is a critical enzyme in GA activity and regulation, suggesting lower bioactive GA production in SR siblings. In summary, SR individuals presented lower production of ABA and GA during ripening, mainly because there was a decrease in GGPP production (a precursor of both hormones) in T7. In addition, the lower GGPP production in T7 was accompanied by a lower transcript abundance of ABA biosynthetic-related genes. Moreover, a lower _GA3 ox_ expression level was identified in T2, suggesting lower bioactive GA activity in early fruit developmental stages and a more direct relationship between _NAC072_, _AFS_ and _GA3 ox_.
## 2 Pattern-Recognition-Based Ensemble Data Imputation Framework
### Module 2. Ensemble Imputation Method  
After deciding the validation dataset in Module 1, Module 2 applies the ensemble method to find the data imputation method with the best validation accuracy among a pool of imputation methods for each sensor using the validation dataset. First, it creates a pool of data imputation methods including statistical and model-based methods, for example simple (e.g., nearest available value), linear (e.g., linear interpolation), nonlinear (e.g., quadratic interpolation), piecewise (e.g., piecewise polynomial interpolation), and machine-learning-based interpolation (e.g., support vector machine). Since it is an ensemble method whose performance depends on the method with the best performance, the more diversified and more imputation methods are in the pool, the better performance this ensemble method can achieve. Then, it tests each imputation method using the validation dataset generated in Module 1 and selects the method with the highest validation accuracy from the data imputation method pool for each sensor. The validation accuracy is quantified by some accuracy and error metrics (such as coefficient of variation of root mean squared error and r-squared) between the imputed value and the truth value of the validation data. Finally, all missing data are imputed by the selected data imputation method (blue square shown in Figure 1).  
The developed framework can work if there is archiving system to store and manage history data. There is no additional cost on installation. The only cost to apply this framework is the hardware cost for computation. The output of the framework is post-processed and repaired missing data. It can be used for both online and offline missing data imputation, but, in this paper, only an offline process is considered. To demonstrate the application and the effectiveness of the framework, it is applied to the sensors from a real campus building in the next section.
###### Abstract  
This paper proposes a dual-hop mixed radio frequency-free-space optical (RF-FSO) cooperative system with amplify-and-forward (AF) relaying. The RF link is subject to Rayleigh fading and the FSO link is assumed to follow \(\mathcal{F}\)-distributed fading with pointing error. The different types of detection, including intensity modulation with direct detection (IM/DD) and coherent heterodyne detection (HD), are considered. The closed-form expressions for the cumulative distribution function (CDF), the moment generating function (MGF), and the moments of end-to-end signal-to-noise ratio (SNR) are derived. Furthermore, closed-form expressions for the outage probability, average bit error rate (BER), and ergodic capacity are presented. Asymptotic outage probability expressions are derived to obtain additional insights into the system performance. It is shown that the HD technique exhibits better performance than an IM/DD technique. The system performance is deteriorated by atmospheric turbulence and pointing error. Finally, analytical results are confirmed by Monte Carlo simulations.  
\(\mathcal{F}\)-distributed fading; mixed RF-FSO; AF relaying; outage probability; BER; ergodic capacity