esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 2 Data
### Dielectric Parameter Measurements  
The system for measuring dielectric characteristic values primarily consisted of the Japanese Hioki 3532-50 LCR meter (HIOKI EUROPE GmbH, Eschborn, Germany), parallel plate electrodes, a shielded box, and a computer, as shown in Figure 4. The testing probe (fixture) used was the self-contained 9140 4-terminal testing probe of this instrument. The electrodes were square copper parallel plate electrodes with a side length of 6 cm and adjustable spacing. The testing conditions were as follows: the clamping pressure of the plates was kept constant at 3.5 N, the temperature was (20 \(\pm\) 1) \({}^{\circ}\)C, and the testing voltage was a 1.0 V sinusoidal waveform. The measured results were directly inputted into the computer.  
The Japanese Hioki 3532-50 LCR meter and parallel plate electrodes were used for non-destructive testing. Thirteen points were selected within the frequency range of 100 Hz to 3.98 MHz. Each suspected diseased fruit and good fruit were individually numbered and the dielectric parameters were measured one by one, including impedance (Z), dielectric loss factor (D), series equivalent inductance (Ls), conductance (G), impedance phase angle (deg), series equivalent capacitance (Cs), parallel equival  
Figure 4: Schematic of experimental apparatus to measure dielectric property. 1. computer; 2. shielding box r; 3. two parallel iron plates; 4. sample; 5. clip test probe; 6. LCR teste.  
Figure 3: Area of spectral information extraction.  
equivalent resistance (Rp), parallel equivalent capacitance (Cp), series equivalent resistance (Rs), and admittance (B), totaling 11 impedance parameters [32]. The measurement values for each fruit were obtained via averaging two repeated measurements taken along two perpendicular directions at its equator.
## 4 Experimental Results
### Discussion  
The obtained results emphasise that the approaches based on direct whole image classification are not applicable in clinical practice. Indeed, we demonstrated how CNNs behave with more complex images, also answering the questions above. This was done by varying the number of cells in the image, so we did not even have to insert additional pathologies and change the problem from binary to multi-class. So what is expected is that by adding additional complexities present in daily laboratory testing, the failures may be even greater. Instead, it is not easy to determine why the CNNs could provide correct results in real test images, especially because the activation maps do not seem to focus on precise indicators for all images. In fact, it is very likely that in binary problems like this one, the CNNs manage to find very different indicators from those actually used by pathologists to provide the diagnosis but which would prove unusable in real cases.  
Certainly, our analysis presents some limitations since it has been performed on a single use case and on a single data set, but it can provide valuable suggestions/guidelines  
Figure 8: Visual Grad-CAM explanations for ResNet-50 exploited on different synthetic test sets. From top to bottom, examples of images belonging to _imgSynt10_, _imgSynt15_, and _imgSynt20_. Im stands for image, and S stands for set. **(a)** Im: 34, $ 10 (**b)** Im: 43, $ 10 (**c)** Im: 47, $ 10 (**d)** Im: 71, $ 10 (**e)** Im: 72, $ 10 (**f)** Im: 74, $ 10 (**g)** Im: 34, $ 15 (**h)** Im: 43, $ 15 (**i)** Im: 47, $ 15 (**j)** Im: 71, $ 15 (**k)** Im: 72, $ 15 (**l)** Im: 74, $ 15 (**m)** Im: 34, $ 20 (**n)** Im: 43, $ 20 (**o)** Im: 47, $ 20 (**p)** Im: 71, $ 20 (**q)** Im: 72, $ 20 (**r)** Im: 74, $ 20.  
for the creation of reliable CNN-based CAD systems that can be employed in clinical practice. In particular, when dealing with CNN, a preliminary analysis of the input data/images must always be performed. In particular, CNNs for image classification require fixed-size input images due to the inclusion of fully-connected layers [17], and if the input data are not processed correctly, they can be significantly altered (e.g., the cells or tissue form factor and size) or some important details can be lost (e.g., the presence of vacuoles or nucleolus).  
A further preliminary analysis must be performed for the task at hand, both depending on the type of analysis performed and the type and quality of the used images, to design an appropriate pipeline to handle all the encountered issues. In particular, for the considered case study, which could be considered a fine-grained task, a preliminary detection or segmentation step, before classification, is essential to allow the network to understand the adequate information on individual WBC correctly. More generally, the more fine-grained the analysis step to be performed, the greater the need for intermediate processing steps between the input data and the output results.
## 3 Results and Discussion
### Silica Gel and Activated Carbon Adsorption Experiment
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbate**} & \multirow{2}{*}{**Absorbent**} & \multicolumn{5}{c}{**Langmuir**} & \multicolumn{5}{c}{**Freundlich**} \\ \cline{3-8}  & & \(\mathbf{K_{L}}\) & \(\mathbf{R_{L}}\) & \(\mathbf{q_{m}}\) & \(\boldsymbol{r^{2}}\) & \(\mathbf{K_{F}}\) & \(\mathbf{n}\) & \(\boldsymbol{r^{2}}\) \\ \hline Silica gel & TCE & 0.289 & 0.252 & 0.137 & 0.9585 & 0.033 & 1.71 & 0.9558 \\  & PCE & 0.334 & 0.208 & 0.147 & 0.9588 & 0.040 & 1.78 & 0.9527 \\ Activated carbon & TCE & 0.286 & 0.0034 & 29.118 & 0.9792 & 72.963 & 1.05 & 0.9801 \\  & PCE & 4.922 & 0.0002 & 31.746 & 0.9590 & 66.451 & 1.26 & 0.9614 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Adsorption parameters of [PERSON] and [PERSON] isotherm models. 7.296 and 6.645, respectively. Activated carbon has a higher K\({}_{\text{F}}\) value for adsorbing TCE and PCE than silica gel, indicating that activated carbon has a higher adsorption capacity than silica gel. In addition, 1/n represents the adsorption intensity, and n can also be expressed as the type of adsorption that occurs. The adsorption of TCE and PCE by silica gel has n values of 1.71 and 1.78, respectively, while the adsorption of TCE and PCE by activated carbon has n values of 1.05 and 1.26, respectively. This result shows that, whether it is silica gel or activated carbon being used for the adsorption of TCE and PCE, the n value is greater than 1; that is, the two adsorbents are beneficial for the adsorption of TCE and PCE. [PERSON] et al. [44] pointed out that this isotherm adsorption process may be affected by hydrogen bonding, and the adsorbate exhibits weak interactions, even for adsorption processes with higher adsorption energy.
## 2 Materials and Methods
### Experimental Design and Data Acquisition  
The experimental tests were conducted from 16 th to 30 th October 2019 in Xinzhou, Shanxi Province, China. The drying system and the heat exchanger were installed outdoors in order to investigate the influence of ambient conditions on the performance of the HE, and the operation data of the HE over four experimental days (19 th October, 21 th October, 23 th October and 24 th October) were recorded. In detail, during the drying process, the temperature of the ambient air (\(T_{ain}\)), the relative humidity of the ambient air (\(RH_{0}\)), the inlet (\(T_{fg,in}\)) and outlet (\(T_{fg,out}\)) temperature of the flue gas, the temperature of the exchanger tube (\(T_{tube}\)), the velocity of the flue gas (\(v_{fg}\)) and the velocity of the ambient air (\(v_{air}\)) were measured in 5 min intervals. Each point of the process was measured three times quickly, and the average value of the data was used for calculating the computational model mentioned above.  
To investigate the effect of the ambient \(RH_{0}\) on the heat transfer performance of the HE, the experimental data from three experimental days (19 th October, 21 th October and 23 th October) with different \(RH_{0}\) values (80.2%, 85.3% and 89.6%) and similar \(T_{0}\) values (whichvary in the range of 8.02-8.06 \({}^{\circ}\)C) were recorded. On the other hand, to investigate the effect of the flue gas velocity \(v_{fg}\) on the heat transfer performance of the HE, another experiment was conducted on the 24 of October, when the \(RH_{0}\) was 90.05 % and \(T_{0}\) was 8.08 \({}^{\circ}\)C. Three levels of \(v_{fg}\) (3.06, 5.12 and 6.95 m\(\cdot\)s\({}^{-1}\)) were set up by adjusting the opening of the flue gas valve (as shown in Figure 1) when the dryer was fully loaded. The experimental design is listed in Table 2, and the partial experimental data and the related parameters mentioned in Section 2.4 are tabulated in Table 3.
## 4 Results and Discussion  
### Overview of the Protein Dataset  
Proteases are enzymes catalyzing the reaction of the hydrolysis of peptide bonds. The independent evolutionary origin of these enzymes [71] is reflected in their large variety of sizes, shapes, and specificity [72]. In this work, we focus on a specific superfamily, namely the chymotrypsin-related proteases. The latter share a common structure with two \(\beta\)-barrel-like domains accommodating the binding site (Figure 3); however, the size and structural completeness of the \(\beta\)-barrels and the length of the turns and loops connecting the sheets greatly vary. The result of this structural variability is a range of sequence lengths and protein sizes among the 116 proteins included in our dataset (Figure S1). The proteolytic reaction is performed by a catalytic triad of residues, located between the \(\beta\)-barrels. The type of amino acid playing the role of nucleophile in the mechanism of catalysis determines the class of proteases: in the serine proteases, the catalytic triad contains His, Asp/Glu, and Ser residues [73]; in the cysteine proteases, the triad is composed of His, Asp/Glu, and Cys or of a dyad of His and Cys residues [74].  
The classification used in the remainder of the paper is based on MEROPS, a hierarchical classification scheme for proteases [75; 76]. In the MEROPS database, chymotrypsin-related proteases constitute the PA clan, which contains 9 families of cysteine proteases (representing proteases of positive-strand RNA viruses) and 14 families of serine proteases (representing proteolytic enzymes from eukaryotes, bacteria, some DNA viruses, and eukaryotic positive-strand RNA viruses). Families are defined on the basis of sequence similarity and/or resemblance of the folds among their protein members. However, experimental structural information is available for a limited number of these families; therefore, not all of them are represented in the dataset employed in this work.
## 3 Artificial Intelligence in Medicine and Orthopedics
### Machine Learning and Deep Learning Techniques  
At the heart of AI are technologies such as machine learning and deep learning, which have shown remarkable progress in handling both structured and unstructured data [26]. Machine learning involves the development of algorithms that can learn from and make predictions based on data. It operates by identifying patterns within datasets and using these patterns to make decisions or predictions when presented with new data. This capability is particularly useful in processing structured data, such as numerical values from laboratory results, vital signs, and patient demographics [27].  
Supervised Learning Algorithms: Algorithms like decision trees, random forests, support vector machines, and gradient boosting machines (e.g., XGBoost) have been widely used in healthcare for predictive modeling [4; 5; 6]. These algorithms require labeled data to learn the mapping between input features and output targets, making them suitable for classification and regression tasks.  
Deep Learning: A subset of machine learning, deep learning utilizes artificial neural networks inspired by the human brain's structure and function. These networks consist of multiple layers that can learn representations of data with multiple levels of abstraction. Convolutional neural networks (CNNs) are particularly effective in image recognition tasks and have been applied extensively in medical imaging [25]. Recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) are used for sequential data, such as time-series analysis in patient monitoring [28].  
Natural Language Processing (NLP): NLP enables computers to interpret, understand, and generate human language in a meaningful way. In the clinical context, NLP can be used to analyze unstructured text from electronic health records (EHRs), extracting valuable information from clinical notes, discharge summaries, and patient narratives [29]. This information can then be used to identify patient cohorts, predict outcomes, or support clinical decision-making.
###### Abstract
The support pressure, support height, and support stiffness of the FSS are easily affected by the ambient temperature and working medium, and the influence relationship is time-varying and nonlinear. This leads to poor stability of the FSS, which in turn affects the accuracy of the optical mirror. Therefore, it is necessary to study and analyze the changes in performance parameters of the FSS at different temperatures and air contents of the working medium. According to the structural parameters of the FSS, a nonlinear mathematical model of the FSS is established. This model can reflect the influence of ambient temperature and air content in the working medium on the FSS. Through the simulation of the mathematical model, the mapping relationship between the influencing factors and affected factors is analyzed. Finally, combined with relevant experimental analysis, the validity of the established model is verified. It provides a theoretical basis for improving the accuracy of the FSS and provides a research foundation for the application of the FSS in the processing of large optical mirrors. The established mathematical model provides a theoretical basis for the subsequent modal analysis of the FSS and provides an experimental basis for the design of the controller and the study of error compensation control strategies. The mathematical model-simulation-experiment method used in this study examines the relationship between the performance parameters of the support system and the air content in the working medium, thereby providing a theoretical framework for the analysis of time-varying uncertainty, nonlinearity, and external interference research.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 5 Security Analysis
### Ciphertext Only Attack  
In this case, the attacker accesses some stego signals, but he ignores the value of the key. He tries to reveal the right sequence by analysing, for example, the spectrum of the stego signal. Since all the scrambled signals look like noise, and follow the behaviour of a super-Gaussian signal, their spectrum does not give traces of the original secret message. Then, our system cannot be broken with this kind of attack.
## 7 Positioning Performance of LEO NR Signal  
This section first simulates the motion trajectory of LEO satellites to generate the wireless channel characteristics. Next, the positioning performance of the ZPC receiver will be discussed, covering static and dynamic scenarios as well as the impacts of various error sources.
## 3 Method
### Pre-Processing  
Noise in a time series is brought about by cloud contamination and other factors such as snow or device malfunction. Pre-processing is necessary to reduce this noise because it may conceal actual trends in a time series. We employed the QA data to remove low quality observations by retaining the time points in a time series flagged as \"good quality\" (values computed from a day with high quality without the nadir view requirement; [17]). In addition to the QA data, a test for sudden drops was conducted to  
Figure 2: Flow chart of the proposed method for detecting change date in a time series.  
retain good quality values throughout the time series [21]. The application of this test ensures data quality, especially when QA information is imperfect or missing. The test searches forward and rejects points that have both low value and follow a value, which is greater than 20% of the difference between the low value and its previous high value. This technique is designed to record the process of transitioning from cloudy to clear sky conditions [21]. It can preserve the values recording the gradual process of re-growth from fire or drought, which were not represented by sudden falls and rises (sudden drops) in NDVI. By utilizing the QA data and implementing the test for sudden drops, the observation points contaminated by various noises were detected and discarded from the time series. The values at those points were then filled by implementing a simple three-point moving average interpolation (Figure 3a-c).  
Next, we utilized a wavelet decomposition technique to reduce high frequency or random noise and smooth the time series while preserving local details [22]. A four-level Haar wavelet decomposition [23] was applied to decompose the time series into four components, each with a set of approximation parameters at different levels. The time series was smoothed by being reconstructed from the low frequency component at the fourth level. Figure 3d shows the reconstructed time series, which greatly reduced noise due to cloudy conditions or random noise compared with the original time series (Figure 3a).  
Figure 3: Pre-processing procedures implemented on a NDVI time series: (**a**) original time series (the red circles mark the time points that are filtered based on QA data); (**b**) time series retained after filtering based on QA data; (**c**) time series with linear interpolation on filtered points; and (**d**) time series attained after wavelet decomposition.
## 8 Reliability and Accuracy Analysis  
Time-series predictions are commonly evaluated by looking at the \"residuals\", as shown above, which are typically measured as the difference between actual values and predictions, often using the root mean squared error (RMSE).
## 3 Results and Discussion
### Gas Diffusion Test Results  
The results of the gas diffusion test of the compacted clay amended by attapulgite or diatomite are shown in Figure 7. The oxygen concentration in the diffusion chamber increased with the time of all dosages. At which time the oxygen concentrations inside and outside the chamber were the same, it reached equilibrium. Figure 7 presents that the \(D_{\theta}\) decreased with the increase of attapulgite dosage and its gas barrier performance gradually improved. With the increase in dosage of diatomite, the diffusion rate of oxygen decreased. It shows that diatomite has a good effect on gas barrier performance. It also indicates that diatomite could reduce the \(D_{\theta}\) of clay better.  
Figure 6: The moisture retention percent with different ratios of attapulgite to diatomite.  
The results of the gas diffusion test with different ratios of dual-additives are shown in Figure 8. The initial moisture content was 30% and the dosage of dual-additives was 5% of all the specimens. Among that, the \(D_{\theta}\) of the amended clay was the lowest at \(4.4\times 10^{-7}\) m\({}^{2}\)/s when the ratio was 1. After considering the economic cost, the dosage of diatomite should be reduced as much as possible. Therefore, it is suggested that the mass ratio of attapulgite to diatomite be 4, the dosage 5%, and the \(D_{\theta}\) of the amended compacted clay \(8.5\times 10^{-7}\) m\({}^{2}\)/s, reduced by about 42% compared with the unamended clay.  
Comparing the gas diffusion test results of the clay amended by attapulgite and diatomite, it can be obtained that the effect of diatomite on the gas barrier performance of the clay is more obvious compared with that of attapulgite, which can be attributed to those crystal structures. Nevertheless, the attapulgite crystals are chain structures or fibrous structures [48], meaning the gas could still migrate through the gap of fibrous or chain structure between the crystals after mixing and compaction. While diatomite, as a porous  
Figure 8: The effect of ratios of additives on the gas diffusion coefficient.  
Figure 7: The gas diffusion coefficient with different dosages of attapulgite and diatomite.  
layered siliceous rock of biological genesis, is composed of diatom wall shells, which are distributed with microporous structure [49; 50; 51]. There are few and complex channels for gas migration owing to its wall shells. Therefore, diatomite could be screened as one of the additives to improve the gas barrier performance of amended compacted clay.  
In conclusion, it is apparent that the optimal dosage of dual-additives was determined to be 5% and the mass ratio of attapulgite to diatomite 4, based on the experimental results of moisture retention percent and gas diffusion coefficient. In addition, the economic cost is also a factor to be considered.
# Fault-Tolerant Control of Multi-Joint Robot Based on Fractional-Order Sliding Mode
###### Abstract  
In this paper, the problem of fault-tolerant control of actuators for multi-joint robots is studied. Aiming at the jitter problem in the design of fault-tolerant control law for conventional sliding mode controllers (SMC), a controller design method based on fractional-order sliding mode (FSMC) theory is proposed. At first, the mathematical model of the multi-joint robot is established and the fractional-order sliding mode surface is constructed according to the mathematical model. Then, the robust control law is designed based on the Lyapunov function. Finally, the experiments are carried out. Compared with the conventional sliding mode control, the experimental results show that the multi-joint robot is more stable under the control of fractional-order sliding mode, and it can achieve almost no jitter while tracking the reference. The steady-state error for joint1 and joint2 could reach 0.073 radians under the control of SMC, while it is 0.015 radians under the control of FSMC. The steady-state error data indicate that the fluctuation amplitude under FSMC is five times smaller than SMC for the end part of the multi-joint robot under actuator gain faults. The regulation time for joint1 and joint2 is about 0.11 s under the control of SMC, and it is around 0.04 s for FSMC. The regulation time is reduced to one of three or four. These data show the effectiveness of the FSMC proposed in this paper.  
fractional-order sliding mode; fault tolerance control; multi-joint robot 202212211908 10.3990/11/20221211908