esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Micro-Tensile Bond Strength (\(\mu\)TBS) Test
#### 2.2.3 Micro-Tensile Bond Strength (\(\mu\)TBS) Test and Failure Mode Analysis  
Each specimen was sectioned perpendicular to the bonded surface to obtain dentin restoration beams with a surface area of 1 \(\times\) 1 mm\({}^{2}\) using a high-speed diamond saw (Isomet 5000; Buehler, Lake Bluff, IL, USA). The sectioned beams were divided into four subgroups; immediate, pH 4 storage, pH 7 storage, and pH 10 storage (n = 15). For the immediate group, after the section, the specimens were fixed to the \(\mu\)TBS 9 ig with a cyanoacrylate adhesive (Zapit; Dental Ventures of America, Corona, CA, USA). The \(\mu\)TBS was measured using a universal testing machine (AGS-X; Shimadzu, Kyoto, Japan) at a crosshead speed of 1 mm/min until bond failure occurred. For the pH 4-, pH 7- and pH 10-storage subgroups, the sectioned beams of each group were stored in pH 4, 7, and 10 buffer solutions: pH 4 (0.05 M potassium biphthalate aqueous solution); pH 7 (0.003 M potassium dihydrogen phosphate and 0.004 M disodium hydrogen phosphate); and pH 10 (0.006 M sodium borate decahydrate and 0.038 M sodium carbonate anhydrate aqueous solution) (Samchun Chemical, Seoul, South Korea) at 37 \({}^{\circ}\)C for 24 h. pH measurements were carried out before and after completed storage using pH meter (ORION Star A211, Thermo Scientific, Waltham, MA, USA). \(\mu\)TBS was measured using a universal testing machine (AGS-X) until bond failure occurred. The obtained data were converted to MPa using the Trapezium-X software (Shimadzu).  
The specimens were then examined under a stereo-microscope (SZN745; Sunny, Shanghai, China) at \(\times\)40 magnification to identify the location of bond failure. Failure patterns were classified into one of four modes: adhesive failure, mixed failure, cohesive failure in dentin and cohesive failure in restorative.
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 3 Experimental Results
### Results
#### 3.3.2 Sensitivity Analysis  
We next investigated the results using different models pre-trained on two types of ImageNet datasets (i.e., ImageNet-21k and ImageNet-1k). Table 3 shows the results on the TextRS dataset using three different BiT architectures (m-R50x1, m-R50x3, and m-R101x1). As can be seen the models pre-trained on ImageNet-21k yields better results compared to the model pre-trained on ImageNet-1k. In particular, the obtained R@1, R@5, and R@10 values for the text branch are 19.02%, 55.25%, and 71.72%, respectively. On the other hand, the obtained values for the image branch are 21.86%, 60.00%, and 75.58%, respectively. These results suggest that the modes pre-trained on ImageNet-21 are more suitable compared to the one pre-trained on ImageNet-1k. In terms of computation complexity and accuracy, the m-R50 x 1 seems a good a choice compared to the other models.  
Regarding Merced dataset (Table 4), we observe that for the largest model m-R101x1 it yields 23.68%, 60.38%, and 78.52%, respectively for text-to-image retrieval. While for image-to-text retrieval, the scores are 21.86%, 60.00%, and 75.58%, respectively. In comparison, using ImageNet-1k, the obtained R@1, R@5, and R@10 values are 19.71%, 56.04%, and 74.76%, respectively. Here again, the models pre-trained on ImageNet-2k exhibits a better behavior with m-R50x1 as a good compromise between accuracy and computation complexity.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained**} & \multirow{2}{*}{**Architecture**} & \multicolumn{4}{c}{**Text-to-Image**} & \multicolumn{4}{c}{**Image-to-Text**} \\ \cline{3-8}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline \multirow{3}{*}{ImageNet-21k} & m-R50x1 & 19.02 & 55.25 & 71.72 & 21.86 & 60.00 & 75.58 \\  & m-R50x3 & 18.27 & 52.23 & 68.79 & 23.25 & 60.93 & 74.41 \\  & m-R101x1 & 15.11 & 51.96 & 67.76 & 18.13 & 53.25 & 71.86 \\ \hline ImageNet-1k & s-R50x1 & 15.53 & 46.97 & 63.25 & 18.13 & 49.53 & 68.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Text-to-image and image-to-text retrieval results on the TextRS dataset.  
Figure 7: Failed scenarios of text-to-image retrieval.  
In order to assess further the proposed model, we propose to carry additional experiments by changing the size of the fully connected layer from 256 to 512. The results obtained in Tables 5 and 6 suggest that setting the number of neurons 256 yields in general better results.  
Finally, we compare our results to the method based on triplet networks proposed recently in [31] (Table 7). As can be seen the proposed method yields better retrieval results in terms of R@1, R@5, and R@10 scores. For instance, our method yields on TextRS dataset scores of 19.02%, 55.25%, and 71.72% versus 14.18%, 44.18%, and 62.55% for the method based on triplet networks. Similarly, our method obtains on Merced dataset scores of 22.76%, 58.47%, and 78.38%, while the one based on triplet networks gives 18.52%, 50.12%, and 69.20%.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 2 Theoretical Background
### Shape Correspondence
#### 2.2.1 Feature Descriptor  
For establishing shape correspondence, we consider for each point on an object's surface a feature descriptor, which is a computational object that contains geometric shape information. To meet this aim, we will solve the wave or damped wave equation on the shape, respectively. Depending on the starting point \(x_{i}\) and the local geometry around \(x_{i}\)  
Figure 1: Almost isometric transformation \(T\) between two centaur shapes \(\mathcal{M}\) (shape 0) and \(\widetilde{\mathcal{M}}\) (shape 2) of the TOSCA dataset [2]. Additionally, we see the feature descriptor (function plots in the boxes) at three different points \(x_{i}\) with \(i\in\{13,221,\,2686,\,3269\}\). These are located at the left thumb, the back and the left front hoof, in this order. The plotted feature descriptors are created for illustration of the underlying idea and are not based on real calculations.  
the solutions \(u(x,t)\) of these PDEs will propagate differently. The feature descriptor of \(x_{i}\) will be the solution \(u(x,t)\) restricted to the spatial component  
\[f_{x_{i}}(t):=u(x,t)|_{x=x_{i}} \tag{3}\]  
Roughly speaking, we expect that the feature descriptors of different points differ enough to not get mixed up with each other but are quite similar for matching points on variations of the same shape (undergoing almost isometric transformations).  
To illustrate the idea about the nature of the feature descriptors we compute, we show in Figure 2 the analytical solution \(u(x,t)\) of the damped wave equation for a cosine initial condition propagating in time. The feature descriptor \(f_{x_{i}}\) is plotted as the black line in this figure. For more details, we refer to Section 5.  
In our framework, we need to compute the feature descriptor of all points on the different shapes. After that, we compare them with each other and search for the best matching ones.
# Investigation of a Precise Control Scheme for Rice Quality
###### Abstract  
Rice drying is a complex and nonlinear process, with product quality being easily influenced by numerous factors. This study aimed to investigate the characteristics causing variation in rice quality and provide novel insights for regulating and controlling rice drying operations. To this end, response surface methodology was employed to examine the hot air drying of rice. The study was centered on investigating the impact of different levels of drying temperature (35-55 \({}^{\circ}\)C), relative humidity (30-50%), initial moisture content (20-28%), air velocity (0.36-0.84 m/s), and tempering ratio (1-4) on the process. The measured parameters included the net drying time, total drying time, additional crack percentage, and head rice yield. The experimental data were analyzed using Design-Expert, and the results indicated that all the response quadratic polynomial models were statistically significant. All the linear terms had a significant impact on the response variables except for the impact of air velocity on head rice yield. Finally, process reference charts of actual drying operating conditions were established based on the regression models to provide a scientific reference for guiding the control of rice drying quality and integrating into intelligent grain drying control systems in the future.  
rice; hot air drying; milling quality; quality control; process reference chart +
Footnote †: journal: Applied Sciences
## 4 Simulation Results
### Case 1  
In Case 1, solar insolation in Figure 12a is used to simulate the C-\(u\)grid operation on a sunny day and the Figure 13 electricity profile is used as load demand. Sunrise was at about 05:53 and sandown was at about 17:55; during this period, sufficient power can be provided by the PV system for home user electricity as well as BES charging power. This is shown in Figure 14a, in the PV system power supply section. After about 17:55, the PV system is out of service, and then BES units turn to a discharging state that continues to supply electricity to home users until BES runs out at about 21:32. This is indicated in Figure 14a, in the BES power supply section. For the time before about 05:53 and after about 21:32, no power can be provided by either the PV system or BES, and home user required electricity may thus must be supplied by the utility grid, as shown in Figure 14a, the utility grid power supply section. Furthermore, the PV system provides little power at the beginning of sunrise. From about 05:53 to 07:30, home user electricity is supplied by both the utility grid and the PV system. A clearer electricity distribution for each hour for different power supply sources in the C-\(u\)grid is found in Figure 14b. Table 1 shows the quantified electricity of each hour relating to Figure 14b; for a total of 5 home users, load demand electricity of 83.63 kWh in the C-\(u\)grid is provided by the utility grid of 30.75 kWh and C-\(u\)grid of 52.88 kWh, respectively. Furthermore, overall operation of BES is presented in Figure 14c,d; the former shows the SOC variation, and the latter gives the charging and discharging power of BES.  
Figure 14: _Cont._  
Figure 13: Used load electricity profile for a total of five home users. (**a**) Time-series electricity profile; (**b**) Cumulative electricity distribution for each hour.  
Figure 14: Simulation results for case 1. (**a**) Electricity response in the simulated C-ugrid; (**b**) Electricity distribution of each hour in the simulated C-ugrid; (**c**) Charging and discharging states of BES units in the simulated C-ugrid; (**d**) Input and output power of BES units in the simulated C-ugrid.
## 2 Proposed Method
### MLP-Based Weighted Ensemble Method
#### 2.2.2 Ensembling MLP Models  
The proposed ensemble method is built using the generated internal and external features and the artificial neural network (ANN) technique; the ANN implemented in this study is MLP. The neural network was first introduced by [PERSON] and [PERSON] in 1943 [19] and successfully used in many fields, such as natural language processing [20], image processing [21], recommendation systems [22], and so on.  
It involves input, hidden, and output layers. Neurons of the input layer represent input variables, and neurons of hidden and output layers receive the weighted summation of neurons in their previous layer and transform it using activation functions. It is trained by changing the weight of each neuron to minimize the difference between the target value and predicted output.  
In this study, we propose the weighted ensemble method by constructing two MLP models based on the extracted internal and external features. EF-MLP is built on the large volume of mailing and contract customer data-based external features to predict the total volume of parcel services during peak periods. IF-MLP is trained on calendar and internal volume-derived features. The structure of EF-MLP consists of two hidden layers with eight and two neurons, while two hidden layers of IF-MLP have 58 and 29 neurons. The ReLU activation function is used in all hidden layers. The results of each model are combined using Equation (3):  
\[y\_ens_{i}=(1-\alpha)\times y_{i}^{ef}+\alpha\times y_{i}^{if} \tag{3}\]  
where \(y\_ens_{i}\) is the ensembled predicted value; \(y_{i}^{ef}\) is the predicted value of the EF-MLP; \(y_{i}^{if}\) is the predicted value of the IF-MLP; \(\alpha(0\leq\alpha\leq 1)\) is the weight for IF-MLP; and \((1-\alpha)\) is the weight for EF-MLP.  
An example of the prediction process of the proposed ensemble method is demonstrated in Figure 4.  
In the prediction procedure, the predictive results of the EF-MLP and IF-MLP models are given as an input value of the ensemble model for peak-period prediction. For the final prediction, the outputs of the proposed EF-MLP and IF-MLP models are combined based on the weight value. The proposed weighted ensemble method with internal and external  
Figure 3: Architecture of the VAE model.  
factor-based derived features can prevent the prediction output from being inappropriately biased and complement for weaknesses of individual EF-MLP and IF-MLP models.
# Switchable Broadband-to-Tunable Narrowband Magnetic Probe for Near-Field Measurements
## 6 Conclusions  
A high-sensitivity broadband-to-multi-narrowband switchable magnetic probe has been designed and modeled using CST EM Microwave Studio. This work deployed a new method to achieve the benefits of bandwidth and high sensitivity. Furthermore, the achieved results are necessary to level up the performance in the compliance test by utilizing two modes; a broadband mode with good sensitivity from 1 GHz to 3.4 GHz, and another mode with highly tunable sensitivity for WiMAX, Mid-band 5G, PAN, and WLAN applications. The switching process from broadband to narrowband is achieved with a PIN diode. At the same time, a reverse biasing varactor diode sweeps the operation point between these four bands. The adopted via fence technique significantly impacts the isolation and sensitivity enhancements. Moreover, the equivalent circuit of this probe has been modeled using AWR Software and shows S-parameter results that agree with the CST EM simulation results.  
Conceptualization, A.A.A. and Z.K.; methodology, A.-A.A.; investigation, A.A.A. and Z.K.; resources, A.A.A. and Z.K.; writing--review and editing, A.A.A. and Z.K. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Bands/GHz** & **Improvement/dB** \\ \hline
[24] & Up to 6 & 5 \\
[25] & 1.57 & 8.68 \\
[26] & 1.57 & 6.6 \\
[28] & 0.89, 1.7, and 2.2 & 7–9 \\ This work & 1–3, 3.5, 4.8 and 5.2 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the sensitivity enhancement achieved in the proposed work and in the literature reviewed.  
Figure 24: S21-parameter simulation results for different biasing voltages: (**a**) Isolation in terms of the S21-parameter without using the via fence technique; (**b**) Sensitivity enhancement resulting from using the via fence technique.  
**Funding:** This research was supported by the Ministry of Education, Youth and Sports of the Czech Republic under the project OP VVV Electrical Engineering Technologies with High-Level of Embedded Intelligence CZ.02.1.01/0.0/0.0/18_069/0009855 and by the project SGS-2021-005: Research, development and implementation of modern electronic and information systems.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Experiments and Methods
### Verification Test of Xanthium sibiricum's Corrosion Inhibition Effect on Reinforcing Steel
#### 2.1.4 Reinforcing Steel Mass Loss Test and Corrosion Area Test  
1. Reinforcing Steel Mass Loss Method: After cutting the concrete specimens, the reinforcing steel samples were obtained and initially weighed, with their mass recorded as m\({}_{1}\). The samples were then immersed in a 10% ammonium citrate solution to remove corrosion products. After this treatment, they were weighed again, and their mass was recorded as m\({}_{2}\). The following formula was used to calculate the mass loss ratio of the reinforcing steel: \[R=\frac{m_{1}-m_{2}}{m_{1}}\times 100\%\] (1) where \(R\) represents the rebar weight loss rate (%), \(m_{1}\) represents the initial quality of steel bars (g), \(m_{2}\) represents the quality of steel bars after rust removal (g).
2. Corrosion Area Method: After cutting the concrete specimens, the reinforcing steel was extracted. Transparent sulfuric acid test paper was used to trace the corroded areas on the surface, as shown in Figure 3. Relevant analytical tools were employed to calculate the corroded area. The following formula was used to determine the corrosion area ratio of the reinforcing steel: \[P=\frac{s}{s_{0}}\times 100\%\] (2) where \(P\) represents the corrosion area rate of steel bars (%), \(s\) represents the corrosion area of steel bars (mm\({}^{2}\)), and \(s_{0}\) represents the total surface area of steel bars (mm\({}^{2}\)).
## 3 Results
### Comparison of UV, US, and UV+US Pasteurization Applications with Conventional Pasteurization  
The quality parameters of samples that are untreated and treated with conventional pasteurization, UV, US, and UV+US pasteurization processes are given in Table 7. These values represent the measurements at the optimum point obtained from the separate optimization process. In the conventional method, pasteurization was carried out at 72 \({}^{\circ}\)C-15 s. The US, UV, and combined US+UV treatments were applied at 50 \({}^{\circ}\)C. For each technique, we used the previously-obtained optimum conditions mentioned above.  
Conventional and UV+US pasteurization processes completely exterminated the yeast and mold populations (\(p<0.05\)). DPPH radical scavenging activities of PJ treated with UV+US pasteurization process compared to conventional method, the UV + US method retained more DPPH activity (\(p<0.05\)). The anthocyanin values of unprocessed and pasteurized samples were in the range of 17.95-25.1 mg/L. The lowest anthocyanin value was obtained from samples applied conventional pasteurization process. The best pasteurization method for saving anthocyanin content in the samples was found to be the US treatment (\(p<0.05\)). The HPLC TPC of unprocessed and pasteurized samples differed from 701-1141 ug/g. The TPC amount of unprocessed and pasteurized samples ranged from 889 to 1273 mg GAE/L. The conventional process and UV treatment reduced the TPC amount according to HPLC TPC and TPC analysis results. On the other hand, UV and UV+US treatment increased the amount of phenolic compounds. Turbidity for unprocessed and pasteurized samples was determined in the range of 4.02-4.84 NTU. The lowest and highest value for turbidity were obtained from the UV and conventional methods, respectively. The color a* values of samples did not vary significantly after all  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Run** \\ **Order** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{1}\)** \\ **X\({}_{2}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{3}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{4}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{5}\)** \\ **(mg GAE /L)** \\ \end{tabular} & \begin{tabular}{c} **TPC** \\ **(mg GAE /L)** \\ \end{tabular} & \begin{tabular}{c} **Turbidity** \\ \end{tabular} & \begin{tabular}{c} \({}^{\circ}\)**Brix** \\ \end{tabular} & \
\begin{tabular}{c} **Colar a* \\ **(log CFU/mL)** \\ \end{tabular} &
\begin{tabular}{c} **Yeast and** \\ **Mold** \\ **(log CFU/mL)** \\ \end{tabular} \\ \hline
**1** & 40 & 5 & ON & 1 LAMP & 3.5 & 1051 \(\pm\) 3.09 a  & 14 \(\pm\) 0.0 a  & 3.51 \(\pm\) 0.09 a [FOOTNOTE:a]Footnote a: \(\pm\) 0.02 a [FOOTNOTE:a]Footnote a: \(\pm\) 0.  
pasteurization methods (\(p>0.05\)). As for \({}^{\circ}\)Brix value, no significant effect of the UV, US, and UV+US treatment on PJ samples was observed in comparison to PJ applied conventional pasteurization.
## 3 Results and Discussions
### Loss of Flow Accident
#### 3.2.2 Protected Loss of Flow  
The calculation results of the protected loss of flow are presented in Figure 11, where the primary pump trips at 5 s, and the shutdown signal occurs at 12 s. It can be seen that the reactor power and the fuel temperature of both ADS and CNES drop rapidly and then reach the shutdown level after the protection system is activated. As shown in Figure 11a, the reactor power of CNES declines faster than that of ADS. During this process, the maximum temperatures of the fuel center and the cladding surface in the ADS and CNES reactors are lower than the security limit. These simulation results indicate that the CNES system could ensure reactor shutdown by inserting safety control rod. It also implies that ADS could quickly restore to safety by cutting off the proton beam under loss of flow accident.  
Figure 10: Transient responses of unprotected loss of flow between the ADS core and the CNES core.
# Computationally Efficient PM Power Loss Mapping for
## 5 Modification in the Full Operational Envelope
### The Influence of Temperature on the Remanence of PMs  
With the increase in temperature, the magnetic performance loss of a PM includes reversible loss and irreversible loss. When the temperature reaches the Curie temperature, the magnetic performance disappears. The relationship between the residual magnetic flux density of the PM and temperature changes is shown in the formula in Equation (9). The curve of the residual magnetic flux density of the PM with temperature is shown in Figure 5.  
\[B_{r}=B_{r0}\Big{(}1+\alpha_{1}(T-T_{0})+\alpha_{2}(T-T_{0})^{2}\Big{)}\quad 0 \ ^{\circ}\mathrm{C}\leq T\leq 120\ ^{\circ}\mathrm{C} \tag{9}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the temperature coefficients of the residual magnetic flux of the permanent magnet, \(T_{0}\) is the reference temperature, and \(B_{r0}\) is the residual magnetic flux density when the temperature is \(T_{0}\) (\({}^{\circ}\mathrm{C}\)).  
According to [14], the PM loss caused by the slotting effect is proportional to the square of the residual magnetic flux density of the PM. The PM loss caused by the armature reaction has nothing to do with the residual magnetic flux density of the PM. Therefore, only the coefficients related to the slotting effect in the fast calculation method of PM loss need to be corrected to obtain the following formula:  
\[\begin{split} P_{PM-SE}\big{(}n^{2},i_{d},B_{r}\big{)}=\Big{(} ci_{d}\Big{(}\frac{B_{r}}{B_{r0}}\Big{)}+d\Big{(}\frac{B_{r}}{B_{r0}}\Big{)}^{2} \Big{)}\Big{(}\frac{n}{n_{w}}\Big{)}^{2}\\ =\bigg{(}ci_{d}\Big{(}1+\alpha_{1}(T-T_{0})+\alpha_{2}(T-T_{0})^ {2}\Big{)}+d\Big{(}1+\alpha_{1}(T-T_{0})+\alpha_{2}(T-T_{0})^{2}\Big{)}^{2} \Big{)}\Big{(}\frac{n}{n_{w}}\Big{)}^{2}\end{split} \tag{10}\]  
where \(\alpha_{B_{r}}\) is the temperature coefficient of the residual magnetic flux of the PM.  
Figure 6 shows the relationship between the PM loss caused by the slotting effect and the operating temperature calculated by the fitting formula in Equation (10) and the finite element calculation. The conductivity change caused by temperature change is not considered here, that is, only the influence of temperature change on the residual magnetic flux density is considered. In the finite element analysis, the residual magnetic flux density and coercive force are changed to keep the relative permeability constant. It can be seen from the figure that the difference between the calculation results of the finite element and analytical methods is small; the temperature increases, and the PM loss decreases.  
Figure 5: The residual magnetic flux density of a PM varies with temperature (\(\alpha_{1}=-0.1\%\mathrm{K}^{-1},\alpha_{2}=0\)).