esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Construction of Moisture Content Prediction Model
#### 2.3.2 Extreme Learning Machine Model  
ELM is a neural network model that boasts advantages such as a fast training speed and small computational volume. It is particularly suitable for solving complex nonlinear problems and holds promising applications in predicting the moisture content of the corn drying process [40; 41]. The implementation steps of ELM are as follows:  
Step 1: Model initialization. Initialize model parameters, including weights \(w\), bias \(b\), and the number of neurons.  
Step 2: Forward propagation. Pass the temperature and humidity feature data of the corn drying process from the input layer to the hidden layer, and select the _sigmoid_ function as the activation function \(h(x)\) of the hidden layer output.  
Step 3: Calculate the output weights. Use the least-squares method to calculate the output weight vector matrix \(\beta\).  
Step 4: Calculate the output results. The prediction output result is calculated as  
\[y_{i}=\beta h(x_{i})=\sum_{j=1}^{L}\beta_{j}h(a_{j}\cdot x_{i},b_{j}) \tag{5}\]  
(\(a_{j}\): input weight vector; \(b_{j}\): bias parameter; \(L\): number of nodes in the implicit layer).  
The equation simplifies to  
\[Y=\beta H \tag{6}\]  
Step 5: The mathematical expression of the objective function \(G\) for the required solution is given by  
\[\begin{cases}min:G=\|\beta\|^{2}5+\theta\Sigma_{i=1}^{3}\|\Delta x_{i}\|^{2} \\ s.t.:\beta_{j}h(x_{j})=y_{i}^{T}-\Delta x_{i}^{T}\end{cases} \tag{7}\]  
(\(\Delta x_{i}\): error of output node \(i\); \(\theta\): penalty factor corresponding to \(\Delta x_{i}\)).  
Figure 4 provides a more intuitive visualization of the ELM process for predicting outgoing corn moisture content. This study constructs predictive models using the full set of temperature and humidity variables and the sensitive variables screened by UVE as inputs, respectively.
# AR in the Architecture Domain: State of the Art
## 8 Applications
### AR for the Architectural Design and Building Process
To monitor and control construction progress and perform comparisons between as-built and as-planned project data, quick access to easily understandable project information is necessary [200; 201], presenting a model that overlays progress and planned data on the user's view of the physical environment. Moreover, in recent years RPAS platforms integrated with AR visualization are applied to the monitoring and inspection of buildings [202];
* In the reading and progress of AEC work, AR allows for supplemental or predictive information at the construction site--for example, by superimposing the structural layout with the architectural and plant systems in space, reducing errors during construction. An example is described by the D4 AR project [203]. This ability to incorporate information on multiple levels can help workers to process complex information in order to document and monitor the in-progress construction [201]; it can also help workers to identify any on-site problems and avoid rework, quickly optimizing productivity;
* AR technology supports some phases of reading, construction, and assembly on-site--for example, by facilitating the reading of plans, construction details, and positioning of structural elements, minimizing errors caused by misinterpretation. [204]. Concerning distractions in on-site AR, emphasis should be placed on the careful use of this technology to avoid distractions caused by the screen and loss of awareness of the context in which one is immersed.
## 2 Methodology
### Dataset
#### 2.1.2 GPP Dataset  
Three GPP datasets were used in this study--a flux tower dataset and two GPP product datasets. The FLUXNET2015 dataset provides ecosystem-scale data on CO\({}_{2}\), water, and energy exchange between the biosphere and the atmosphere, in addition to other meteorological and biological measurements from 212 sites around the globe (over 1500 site-years, up to and including the year 2014) [61]. The FLUXNET2015 dataset can be downloaded at [[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)) (accessed on 1 January 2021); Data quality control and processing for this dataset have been improved compared to previous versions. We used the monthly GPP product (GPP_NT_CUT_REF) of the GOSIF grid cell flux sites, which has a dominant vegetation coverage type of over 60%.  
The first GPP product was the MODIS GPP product (MOD17A2H Version 6), which was obtained from the Distributed Active Archive Center (DAAC) of the U.S. National Aeronautics and Space Administration (NASA) ([[https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006/]([https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006/) (accessed on 1 January 2021)) [24]. It was generated using an LUE approach with a spatial resolution of 500 m and an eight-day interval. We also used the GPP product from the GLASS products suite [62]. It has a spatial resolution of 500 m and a temporal resolution of eight days. The algorithm originates from the Eddy Covariance Light Use Efficiency (EC-LUE) model, and it integrates eight LUE models that are widely used across the world [63; 64]. The GLASS data are available online at [[http://glass-product.bnu.edu.cn/](http://glass-product.bnu.edu.cn/)]([http://glass-product.bnu.edu.cn/](http://glass-product.bnu.edu.cn/)) (accessed on 1 January 2021). They have been extensively validated and are in agreement with FLUXNET observations and also been proven to be a reliable long-term estimate for global GPP [65; 66].
# Special Issue on Ultrasonic Modeling for Non-Destructive Testing
## 4 Lamb Waves in Plates  
Guided waves are increasingly employed for structural health monitoring [19] due to their ability to propagate over long distances. It has, notably, a wide spectrum of applications for different materials such as rails or composites [20] for aviation aircraft.  
[PERSON], [PERSON], and [PERSON] proposed a semi-analytical model to asymptotically derive the reflection of Lamb waves in a semi-infinite elastic waveguide. Their method deals with singularity at traction-free elastic corners with an internal angle greater than \(\pi\) by adding into the Lamb mode expansion newly considered corner modes that satisfy boundary and radiation conditions.  
[PERSON] and [PERSON] proposed to employ ultrasonic linear air-coupled arrays that are electronically readjusted to optimally excite and receive A0 and S0 guided wave modes in thin plastic films. The feasibility of such a measurement was evaluated through 2D and 3D guided waves simulations with a single transducer and a linear phased array.  
[PERSON], [PERSON], [PERSON] et al. modelled the scattering of an incident A0 guided wave mode from an impacted damaged zone (conical-shaped geometry with decayed elastic stiffness properties) in a quasi-isotropic composite plate. The simulation was experimentally validated by comparing the scattering directivity, and it enabled the geometrical characterization of the impact.  
[PERSON], [PERSON], and [PERSON] analyzed errors in the impact location by varying the number of piezoelectric sensors from four to eight and using the S0 mode in an aluminum plate. A good compromise between the number of sensors and the error in impact localization was obtained with six sensors.
# New Insights on Volatile Components of _Vanilla planifolia_ Cultivated in Taiwan
## 2 Results
### Analysis of the Volatile Components of Taiwan-Cultivated Vanilla Pods Using HS-SPME
#### 2.2.3 Microorganism Treatments during the Curing Process  
When examining the production of vanilla flavor, studies have reported that plant enzymes and microbial activities influence the aroma of natural vanilla [20]. It was found that enzymatic hydrolysis occurred on the surface of vanilla beans [21]. For example, [PERSON] et al. [22] determined that _Bacillus_ participates in the hydrolysis of glucovanillin during curing and related to vanillin formation during conventional curing. Based on these studies, we added various strains to vanilla pods during the curing process in an attempt to observe their effect on the volatile components. The added strains included three different yeasts (_D. bruxellensis_[Y1], _S. crataegensis_[Y2], and _C. cacaoi_[Y3]) and _B. subtilis_ (B), all of which were compared to the control group. A total of 35 volatile components were tentatively identified in this study. Table 4 lists the components and relative percentage of the vanilla pods supplemented with different strains. The main component of all the samples was vanillin (66-83%). When comparing the volatile components of vanilla pods in the control group and different yeast strains, we determined that the percentage of the minor components in the yeast-treated groups was higher than that in the control, particularly for benzyl alcohol and \(p\)-cresol. The percentage of the secondary components of B was relatively low, and guaiacol and benzyl alcohol were typically the most prevalent. However, 2-ethyl-1-hexanol, a compound that possesses a delicate and floral fragrance, has not been previously detected in other species [23]. When comparing vanilla pods that were treated with different strains, we observed that there was no significant aroma change among the groups. The other important ingredients, with the exception of vanillin, are selected including furfural, benzyl alcohol, \(p\)-cresol, guaiacol and \(p\)-creosol for comparison (Table 4). The results revealed that among the three groups supplemented with yeast, all minor components were the highest in Y1. This strain has received attention in the food industry and academic research in recent years due to its characteristic smell and its frequent use for fermentation of different wines [24]. Our results revealed that the fermentation of this strain causes the vanilla pods to possess a richer odor that affects the volatile components of vanilla.
# Estimation of Rock Load of Multi-Arch Tunnel with Cracks Using Stress Variable Method
## 2 Estimation Methods for Rock Load  
### Empirical Methods  
[PERSON]'s rock mass classification system, i.e., rock load classification, estimates the rock load to be carried by the steel arches installed for tunnel support [10]. A shear failure plane was assumed for calculating the rock load in this method. The influence of geology on the design of tunnel steel supports was discussed, and various rock loads carried by steel sets were estimated based on the descriptive classification of rock mass classes. [PERSON] defined the rock load classification in a tabulated form, where the rock load on the tunnel steel support is a function of tunnel size and rock mass class.  
Figure 1: Mechanism of rock load development in a multi-arch tunnel.  
Moreover, the original RMR system was applied to the rock load [11] and was extended by [PERSON] [3,12] for RMR\({}_{89}\) using Equation (1). The rock load was defined as the height of the potential unstable zone above the roof line, which tends to decrease eventually.  
\[\text{Rock load}= \frac{100-RMR_{89}}{100}\gamma B \tag{1}\]  
The rock load on the pillar in a multi-arch tunnel in Japan was estimated using the relation between the overburden (H) and the combined span (D\({}_{a}\)) of the multi-arch tunnel [13,14]. As illustrated in Figure 2, the rock load was estimated to be equal to H for shallow tunnels when H was less than or equal to D\({}_{a}\) and was estimated to be equal to D\({}_{a}\) when H was greater than D\({}_{a}\), as presented in Equations (2) and (3), respectively. However, these empirical equations do not consider the rock conditions and the construction stages of the multi-arch tunnels.  
\[\text{Rock load (m)}=\text{H},\;(\text{H}\leq\text{D}_{a}) \tag{2}\]  
\[\text{Rock load (m)}=\text{D}_{a},\;(\text{H}>\text{D}_{a}) \tag{3}\]
# Effect of Boundary Conditions on the Mechanical Behavior of the Geogrid-Soil Interface
## 3 Results and Discussion
### Growth Pattern and Fitting of Pullout Resistance under Different Boundary Conditions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Boundary Conditions** & **Polynomial Fitting Equation** \\ \hline T(R)& \(y=3.456+0.08x-2.15\times 10^{-4}x^{2}\) (R\({}^{2}\) = 0.99) \\ T(F)& \(y=-3.834+0.17x-4.91\times 10^{-4}x^{2}\) (R\({}^{2}\) = 0.98) \\ T(R)& \(y=-1.906+0.085x-2.29\times 10^{-4}x^{2}\) (R\({}^{2}\) = 0.98) \\ T(F)& \(y=-0.811+0.073x-1.96\times 10^{-4}x^{2}\) (R\({}^{2}\) = 0.99) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Polynomial fitting of pullout resistance under different boundary conditions. Figure 13: (**a**) The pullout displacement versus pullout resistance under T(F)&P(F); (**b**) the growth pattern of pullout resistance under T(F)&P(F). and (T(F)&P(F)), the normal loading corresponding to the maximum pullout resistance was 185.0 and 186.0 kPa, which was both greater than 120 kPa. Hence, the _BRC_-P could be taken as 0.5. By reducing the results under (T(R)&P(R)), the maximum pullout resistance used for design was 5.45 kN under (T(R)&P(F)) and (T(F)&P(F)), which was both less than 5.981 and 5.986 kN, at the same time, the safety factor was 1.1. Thus, the _BRC_-P of the positive boundary was reliable. In addition, based on the results, it was observed that although the boundary conditions affect the pullout resistance of the geogrid-soil interface, the vertical loading corresponding to the maximum pullout resistance under different boundary conditions was about 180 kPa. Hence, it was concluded that the normal loading corresponding to the maximum pullout resistance was not related to boundary conditions.
## 3 Results and Discussion
### Passivation Effects of Different Sterilization Treatments on the Enzyme Activity  
The inactivation of POD and PPO is necessary for improving the color retention and shelf life of fruit and vegetable juices [27]. As shown in Figure 3, the POD22 and PPO activities of CK were 101.02 and 218.17 U/(mL-min), respectively. After CTS, TS, and TSN treatment, these activities were reduced to 52.38, 74.36, 66.70, and 28.51, 59.21, and 47.70 U/(mL-min), respectively. This suggested that CTS still showed the strongest enzyme inactivation effect, while the two US-combined sterilization treatments (TS and TSN) also showed satisfactory enzyme inactivation effects. In addition, it was also found that the combined TS and nisin (TSN) treatment could significantly enhance the enzyme passivation effect. However, the mechanism of synergistic enzyme passivation by nisin still requires further study.  
The enzymes in fruit juice are mostly macromolecular proteins. Ultrasound mainly destroys the molecular structure through powerful shock waves or jets [10]. When combined with Nisin, Nisin can form pores in the cell membrane, destroying the power of protons and pH balance to make an impact [24; 35]. The effects of ultrasonic treatment on important nutrients mainly include (i) the cell walls and vacuoles of plant tissues are destroyed or colloidal particles around cavitation collapse [10; 14] and capillary effect [36], so that soluble nutrients are released; (ii) destroying the molecular structure of nutrients [10; 11]. In the whole process, the former is much greater than the latter so that nutritionally important compounds can be retained to the maximum or even increased [14; 15; 16; 17; 37].  
In general, the CTS, TS, and TSN treatments can effectively inactivate the POD and PPO activity in grape juice and improve the color retention and shelf life of grape juice, and TSN treatment is significantly better than TS treatment (\(p<0.05\)).  
Figure 3: Passivation effects of different sterilization treatments on the enzyme activity of grape juice.
## 3 Results and Discussion
### Experimental Results  
In Experiment #1, we tested both full ResNet-50 architectures (corresponding to two branches of the feature extraction stage) separately. The concatenation of features and SVM or RF classification were omitted. The results of Experiment #1 are presented in Table 2. The best results were achieved for the intraventricular hemorrhage, whereas the worst performance was observed in the case of epidural hemorrhage. Neither branch outperformed the other in all cases.  
In Experiment #2, the model described in Section 2.2 was prepared and validated with the concatenation of double-branch features and classification employing both the SVM and RF. Table 3 shows the obtained metrics for each ICH subtype and classifier. Similarly to Experiment #1, the best performance was observed in intraventricular hemorrhage, whereas the poorest outcomes were noted in epidural hemorrhage. RF produced higher quality measures in most categories except EDH. Moreover, in terms of accuracy and F1 score, the RF classifier outperformed any ResNet model from Experiment #1.
# Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink
## Appendix C Details of \(\langle\beta_{a}^{2}\rangle\) in Uplink Case  
In uplink, the variance of \(\beta_{a}\) follows  
\[\langle\beta_{a}^{2}\rangle\cong 2.91(\mu_{1u}+0.62\mu_{2u}\Lambda^{11/6}) \sec(\zeta)\times(2a)^{-1/3},\] (A18)  
where the factors \(\mu_{1u}\) and \(\mu_{2u}\) can be expressed as  
\[\mu_{1u}=\int_{0}^{\bar{h}}C_{n}^{2}(\Theta+\overline{\Theta}\cdot\frac{h}{H} )^{5/3}\mathrm{d}h,\] (A19)  
\[\mu_{2u}=\int_{0}^{\bar{h}}C_{n}^{2}(1-\frac{h}{H})^{5/3}\mathrm{d}h.\] (A20)  
\(\Lambda\), \(\Theta\) and \(\overline{\Theta}\) are the output-plane beam parameters, which describe the signal beam received at a distance \(L=H\sec(\zeta)\) from the transmitter [32]. The output-plane beam parameters can be derived from the input-plane beam parameters, which is given by  
\[\Theta_{0}=1-\frac{L}{F_{0}},\] (A21)  
\[\Lambda_{0}=\frac{2L}{kW_{0}},\] (A22)  
where \(F_{0}\) is the phase front radius of curvature at the transmitter output aperture. In this paper, we consider the collimated beam case so that the \(F_{0}=\infty\). Then, the output-plane beam parameters can be expressed as \[\Theta=\frac{\Theta_{0}}{\Theta_{0}^{2}+\Lambda_{0}^{2}}=1+\frac{L}{F}, \tag{111}\] \[\overline{\Theta}=1-\Theta,\] (112) \[\Lambda=\frac{\Lambda_{0}}{\Theta_{0}^{2}+\Lambda_{0}^{2}}=\frac{2 L}{kW^{2}}, \tag{113}\]  
where \(W\) denotes the diffractive beam radius at the receiver, and \(F\) is the corresponding phase front radius of curvature.
## 4 Experimental Results  
In this study, we focus on the defect inspection of texture patterns. Therefore, we select the Nanofiber image database [32] and carpet and grid images in the MVTec database [14] to conduct experiments. The nanofiber image database provides fewer normal images for training and testing. Therefore, data augmentation is needed to increase the normal images. The MVTec database contains mainly 5 types of texture images and 10 types of object images. In this study, we select two types of textures: carpet and grid to conduct experiments. The details of these databases are described in Tables 1-3. In this section, loss function analysis for normal area reconstruction and defective area repair, weighting analysis between SSIM and DEC loss functions, accuracy analysis for classifying normal and defect test images, subjective evaluation of defective area detection, and the comparison between our proposed method and the SOTA methods in [14] are conducted to verify the performance of the proposed method. Our system uses an NVIDIA TITAN Xp GPU, and the computing efficiency is about 32 FPS.  
their accuracy for reconstructing normal areas and repairing defective areas. Tables 4 and 5 are the simulation results of Simple and Inception Autoencoders for normal area reconstruction. The experimental results show that whether using SSIM or MSE loss function for training, in most cases, as long as the same function is used for testing, there will be good performance. In addition, the data in Tables 4 and 5 show that the Inception Autoencoder has better reconstruction accuracy on normal area, that is, it has a better capability to preserve detailed textures.  
Figures 14-16 show the subjective evaluation of the reconstruction quality using SSIM and MSE as the training and testing functions on the normal images. In Figure 14, it can be clearly seen that the reconstruction quality of SSIM is better than that of MSE for nanofiber images. In Figures 15 and 16, the texture of the carpet and grid images is relatively simple, and the
## 2 Proposed STPA Configuration for JCAS, Its Design Pipeline, and Performance
### JCAS Scenarios and Performance Metrics  
The performance metric for communication is spectral efficiency, which represents the number of bits transmitted per second in a specific bandwidth [30], and for sensing, the performance metric is angular resolution, which determines the smallest angle between two targets that are distinguishable for the radar [31]. The communication and sensing performances, as shown in Figure 4a-c, include cases without and with overlapping beams when the UE blocks the sensing link and also when the sensing target blocks the communication link. The overlapping occurs when:  
\[(u_{c}-u_{s})^{2}+(v_{c}-v_{s})^{2}<r_{b}^{2} \tag{4}\]  
where \((u_{c},v_{c})\) and \((u_{s},v_{s})\) are the angles of communication and sensing beams, respectively, and \(r_{b}\) is the radius of the beamwidth in \((u,v)\)-space. The user element may demonstrate some backscattering in reality, and a comprehensive study including the radar-cross-section of a user in a JCAS scenario is presented in Ref. [32], where the communication user is also the target for sensing. In pursuit of simplicity, we suppose that the user occupies only one range-angle bin and has a negligible reflection of electromagnetic waves. Additionally, we assume that the energy transmitted in the communication beam of the BS reaches the user after propogation loss and is captured by the user.