esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Activation Functions
### Learning/Adaptive Activation Functions  
#### 3.4.1 SReLU  
S-shaped ReLU (SReLU) [63] is composed of three piecewise linear functions expressed by four learnable parameters (\(t^{l},t^{r},a^{l}\), and \(a^{r}\) initialized as \(d^{l}=0,\ t^{l}=0,\ t^{r}=maxInput\), a hyperparameter). This rather large set of parameters gives SReLU its high representational power. SReLU, illustrated in Figure 4, is defined as:  
\[y_{i}=f(x_{i})=\left\{\begin{array}{rcl}t^{l}+a^{l}\Big{(}x_{i}-t^{l}\Big{)},& \mid&x_{i}\leq t^{l}\\ x_{i},&\mid&t^{l}<x_{i}<t^{r}\\ t^{r}+a^{r}(x_{i}-t^{r}),&\mid&x_{i}\geq t^{r}.\end{array}\right., \tag{14}\]  
where \(a_{c}\) is a real number.  
The gradients of SeLU are:  
\[\frac{dy_{i}}{dx_{i}}=f\prime(x_{i})=\left\{\begin{array}{rcl}a^{l},&\mid&x_{ i}\leq t^{l}\\ 1,&\mid&t^{l}<x_{i}<t^{r}\\ a^{r},&\mid&x_{i}\geq t^{r}\end{array}\right., \tag{15}\]  
\[\frac{dy_{i}}{da^{l}}=\left\{\begin{array}{rcl}x_{i}-t^{l},&\mid x_{i}\leq t ^{l}\\ 0,&\mid x_{i}>t^{l},\end{array}\right. \tag{16}\]  
\[\frac{dy_{i}}{dt^{l}}=\left\{\begin{array}{rcl}1-a^{l},&\mid x_{i}\leq t^{l} \\ 0,&\mid x_{i}>t^{l},\end{array}\right. \tag{17}\]  
\[\frac{dy_{i}}{da^{r}}=\left\{\begin{array}{rcl}x_{i}-t^{r},&\mid x_{i}\geq t ^{r}\\ 0,&\mid x_{i}<t^{r},\end{array}\right. \tag{18}\]  
\[\frac{dy_{i}}{dt^{r}}=\left\{\begin{array}{rcl}1-a^{r},&\mid x_{i}\geq t^{r }\\ 0,&\mid x_{i}<t^{r}.\end{array}\right. \tag{19}\]  
Here, we use \(a^{l}=0.5,a^{r}=0.2,t^{l}=-2,t^{r}=1.5\).  
Figure 4: SReLU.
## 2 Materials and Methods
### Machine Learning Modeling Approach  
This study utilized four ML models to predict PY and IENU: Gradient Boosting (GB), Cubist (CB), RF, and Extreme Gradient Boosting (XGB). These models were chosen to leverage their collective strengths in handling the diverse and complex data necessary for accurately predicting PY. They have been widely utilized in the prediction of agricultural production, demonstrating their effectiveness and reliability.  
* GB: GB is a method of ML that addresses regression and classification challenges by creating an ensemble of weak models, typically decision trees. The approach incrementally builds the model by optimizing a differentiable loss function at each step [43]. GB has been reported as an effective model for predicting corn yields [44].
* CB: CB is a regression-based predictive model incorporating nearest-neighbor corrections to enhance its predictions [45; 46]. It constructs decision trees where the terminal leaves define rule-based \"if-then\" conditions. Each rule in the Cubist algorithm corresponds to a multiple linear regression model, improving predictive accuracy. CB has two key parameters that can be either default or fine-tuned: (1) _committees_, which determine the number of boosting iterations, and (2) _neighbors_, which specify the number of instances used to adjust rule-based predictions [47; 48].
* XGB: XGB is an optimized and scalable implementation of Gradient Boosting. It is specifically designed to improve performance and computational efficiency. It implements ML algorithms under the Gradient Boosting framework and is renowned for its performance and speed [49]. XGB includes L1 and L2 regularization to prevent overfitting. In agriculture, XGB has been successfully applied to yield prediction, demonstrating high accuracy and the ability to model complex relationships in crop data [50]. Studies have shown that XGB performs comparably to, or even outperforms, deep learning models when predicting yields using remote sensing data [51]. Additionally, XGB has explicitly been utilized to predict PY in agricultural research [52].
* RF: RF is an ensemble learning technique that generates multiple decision trees during training. For classification tasks, it predicts the mode of the classes, and for regression tasks, it calculates the mean prediction from all the individual trees. Averaging the outcomes of several trees mitigates overfitting and enhances generalization [53]. RF has been widely used for crop prediction due to its ability to handle large datasets with many features and its resistance to overfitting [54].
# Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
## 4 Results and Discussions
### Stereo Matching  
We use Middlebury and KITTI datasets to validate typical cost aggregation approaches, namely BF [12], GF [11], NL [16], DT [20], LRNL [28], FCGF [18], and ST [17]. The input stereo images are normalized to [0, 1] at first, and then used to compute matching costs at all candidate disparities. We use the implementations in cross-scale cost aggregation [10] for BF, GF, NL, and ST, and implement FCGF by referring to the elaboration in paper [18]. Default parameters are used in these approaches to generate satisfactory results. We use 0.05 for \(\alpha\) and 10 for \(\beta\) on the Middlebury dataset and halve them on the KITTI dataset, as there is a large portion of textureless regions in the KITTI dataset.
_applied sciences_  
_Article_  
Sensitivity Improvement of an Optical Fiber Sensor Based on Surface Plasmon Resonance with Pure Higher-Order Modes  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2  
and [PERSON] 1,*  
1 School of Precision Instrument and Opto-Electronics Engineering, Key Laboratory of Micro Opto-Electronc Mechanical System Technology, Key Laboratory of Opto-Electronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China  
2 Wenzhou Safety (Emergency) Institute of Tianjin University, Tianjin University, Wenzhou 325000, China  
* Correspondence: [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (D.J.)  
**Abstract:** In this paper, we propose an approach to improve the sensitivity of an optical fiber surface plasmon resonance (SPR) sensor with a pure higher-order mode excited by a designed mode selective coupler (MSC). We calculate the proportion of the power of the higher-order mode in the cladding. Compared to the LP\({}_{01}\) mode, the power proportion of the LP\({}_{11}\) mode (LP\({}_{21}\) mode) in the cladding theoretically improves by 100% (150%). To generate a relatively pure LP\({}_{11}\) mode or LP\({}_{21}\) mode, a mode selective coupler (MSC, 430-580 nm) is designed. The coupling efficiency of the LP\({}_{01}\)-LP\({}_{11}\) mode coupler is over 80%, and that of the LP\({}_{01}\)-LP\({}_{21}\) mode coupler is over 50%. The simulation results show that the sensitivity of the LP\({}_{11}\) mode and the LP\({}_{21}\) mode increases by approximately 330% and 360%, respectively, using the intensity modulation (\(n=1.33\)-\(1.38\), 430-580 nm); the resolution of the refractive indices of our sensor, using the LP\({}_{11}\) mode (LP\({}_{21}\) mode), is \(2.6\times 10^{-4}\) RIU (\(2.4\times 10^{-4}\) RIU). The higher sensitivity and resolution of our presented fiber SPR sensor containing a visible MSC make it a promising candidate for the measurement of refractive indices.  
**Keywords:** surface plasmon resonance; higher-order modes; mode selective coupler (MSC); optical fiber sensor
## 2 Materials and Methods
### Data Analysis  
The Shapiro-Wilk test was employed to assess the normal distribution of the data. Means, standard deviations, and 95% confidence intervals (95% CIs) were calculated for all variables. Standard deviations of the High-Pass Single-Pole IIR Filter were used for hand tremor analysis. Statistical analysis was conducted using STATA statistical software version 18 (Stata-Corp LLC, College Station, TX, USA). One-way Analysis of Variance (ANOVA) was used to examine the effects of hand exercises on inter-limb (dominant vs. non-dominant) asymmetry of tremor in testing time (PRE vs. POST) in relation to sex (male vs. female). The statistical significance was set at \(p\) < 0.05. In line with similar research [48], to provide meaningful analysis for significant comparisons from small groups, [PERSON]'s effect sizes (ESs) were considered trivial for <0.20, small for an ES ranging from 0.20-0.60, moderate for an ES ranging from 0.61-1.2, large for an ES ranging from 1.21-2.0, and very large for an ES > 2.0 [49].
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 2 Testing Arrangements and Programs
### Preparation and Testing  
The testing procedure was as follows: (1) Three pore-pressure sensors (with their filters covered, to avoid particles entering) attached to a steel bar (diameter of 10 mm) were put to sit on the bottom of the pit. (2) The model pile was hoisted in the center of the pit during sand pluviation. (3) After the seabed (thickness of 0.58 m) had been formed, the water level was slowly adjusted to a height of 0.3 m above the bed surface. The soil sample and water level rested for 48 h to let the bed stabilize. (4) The wave generator was switched on according to the designed wave input and wave action was applied.  
As shown in Table 1, regular waves were used with a period of 1 s and heights of 8 cm and 10 cm. Pore pressure and pile-head displacement were measured during 640 s of wave action. In T8(s-_H8_-_D3p), a 200 s resting time was adopted before the second wave-loading stage. The 12 tests could be divided into two main groups according to Sample 1 and Sample 2 seabed, including the variation in pile diameter, pile cross-section, pile stiffness and wave height. Waves propagated in the flume with the same arrangement (Figure 1). Figure 2 shows the water-surface elevation (\(\eta\)) above the trapezoid-shaped blocks varying with loading time (\(t\)), \(H\) = 8 cm. Wave amplitude was uniform due to the incoming wave energy and wave reflection was dissipated well by the downstream wave absorber, indicating that the wave height selected in the tests was reasonable for the flume utilization and the resting time applied in the study was sufficient.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
###### Abstract  
Nowadays, technological advances provide people with more facilities and luxuries in life. Medicine is no exception; for example, different wireless sensors can be used to monitor patients' state of health. These sensors are used in the so-called Wireless Body Area Networks (WBAN), to improve the efficiency of doctor-patient activities at any time, in any body area, and anywhere. However, health data contains sensitive information that becomes a critical issue requiring special attention when transmitted within a WBAN. In other words, WBAN must be protected from malicious devices that intercept, alter or access without authorization or even deny the health information being transmitted. In this article, we present the design of a new cryptographic protocol that guarantees three security services, authentication, confidentiality, and integrity by securing sensitive information communication through a WBAN. We also consider a keyless sensors authentication method to distinguish whether or not the devices are placed on the same individual's body. A formal analysis of the protocol is carried out using cryptographic protocol verification tools to guarantee its correct construction and that it provides appropriate security.  
cryptographic protocol; medical approaches; WBAN; non-cryptographic authentication method +
Footnote †: journal: Applied Sociology  
**Citation**: [PERSON]; [PERSON]; [PERSON] Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications. _Appl. Sci._**2023**, 13, 1675. [[https://doi.org/10.3390/app13031675](https://doi.org/10.3390/app13031675)]([https://doi.org/10.3390/app13031675](https://doi.org/10.3390/app13031675))
###### Abstract  
Surface-coating polymers contribute to nanoparticle-based magnetic resonance imaging (MRI) contrast agents because they can affect the relaxometric properties of the nanoparticles. In this study, polyspartic acid (PASA)-coated ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles with an average particle diameter of 2.0 nm were synthesized using the one-pot polyol method. The synthesized nanoparticles exhibited \(r_{1}\) and \(r_{2}\) of 19.1 and \(=53.7\) s\({}^{-1}\)mM\({}^{-1}\), respectively, (\(r_{1}\) and \(r_{2}\) are longitudinal and transverse water-proton spin relaxities, respectively) at 3.0 T MR field, approximately 5 and 10 times higher than those of commercial Gd-chelate contrast agents, respectively. The T\({}_{1}\) and T\({}_{2}\) MR images could be obtained due to an appreciable \(r_{2}\)/\(r_{1}\) ratio of 2.80, indicating their potential as a dual-modal T\({}_{1}\) and T\({}_{2}\) MRI contrast agent.  
polyaspartic acid; gadolinium oxide nanoparticle; magnetic resonance imaging; T\({}_{1}\) and T\({}_{2}\) dual-modal imaging +
Footnote †: journal: Applied Sciences
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 4 Experimental Analysis
### Fault Diagnosis Results and Comparative Analysis

Table 7: The average recognition precision and standard deviation of the compared method. Figure 16: Comparison of experimental results. Figure 17: Average recognition precision of different training and test sample set ratios. as a training set and input into the fuzzy neural network for training. The recognition results of five tests obtained through training are shown in Figure 18. It can be seen from Figure 18 that the recognition accuracy of the fuzzy neural network is ideal, and the average recognition accuracy is 99.2%, which proves that the fuzzy neural network can identify rotor faults in the case of small samples. (4) To study the influence of different classifiers on recognition precision and calculation time, and to verify the advantages, the sample set extracted from the symmetrical polar coordinate image of the reconstructed signal based on VMD is identified five times by four classifiers: support vector machine (SVM), BP neural network, Random Forest and fuzzy neural network, and record the average recognition accuracy precision and calculation times (this time includes the time used to construct the feature sample set). The results are shown in Table 8. Where, the penalty factor C of SVM is 1, and the width coefficient \(\sigma=1.58\); for the BP neural network, the neural network structure is 6-14-5, the maximum number of iterations is set to 1000, the initial network learning rate is 0.01, and the system error is 0.001; for Random Forest, the decision tree is 500. This proposed method shows the highest recognition precision. Besides, compared with BP neural network, its computing efficiency has been slightly improved.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 4 Results and Discussion
### Effect of Stabilizers on CBR Values  
CBR tests were carried out on untreated and treated soils, in accordance with ASTM D-1883. The tests, which included stabilizer contents up to 12%, involved a 4 day soaking period and a 7 day curing period, with the results displayed in Figure 12.  
Figure 13 illustrates that the CBR of treated soil increases with molasses content up to 8%, after which it declines as the molasses content continues to rise. The highest CBR  
Figure 12: CBR combine graph of Stress versus Penetration of native and stabilized soil.  
value is observed with a combination of 12% molasses and 12% cement. These findings align with those reported by [PERSON] [35]. The increase in CBR values for soils treated with molasses is attributed to cation exchange reactions, as well as the flocculation and agglomeration effects. [PERSON] et al. [36] explained that the decrease in soil pH is due to calcium and magnesium cations from cane molasses replacing weaker monovalent sodium ions at the clay particle surfaces. The decline in CBR values when molasses content exceeds a certain threshold may be due to the coating of individual soil particles with molasses [37].  
The results indicate that the CBR value of soils treated with cement increases as the cement content rises. This improvement may be attributed to the formation of secondary cementitious materials, which are produced by the reaction between free lime Ca(OH)\({}_{2}\) released during cement hydration and the pozzolanic reaction between the lime and soil.  
The significant increase in CBR value can be attributed to the interactions between molasses, cement, clay soil, and water. As discussed earlier, the cation exchange reaction and the adhesive properties of molasses contribute to the enhanced load-bearing capacity of the soil. Additionally, the hydration of cement and the pozzolanic reaction further improve the soil-cement mixture [38]. Soils stabilized with S\({}_{12,4}\), S\({}_{12,8}\), and S\({}_{12,12}\) after 7 days of curing and 4 days of soaking, achieving CBR values of 10.2%, 11.3%, and 12%, respectively. The peak California Bearing Ratio (CBR) values recorded at 12% indicate a slight compliance with the Engineering Research Association (ERA) specification, which mandates a minimum CBR value of 10% after a 7 day curing period for effective sub-grade soil stabilization. All the stages of sample preparation for CBR testing are presented in Figure 14.  
Figure 13: CBR values comparison of native soil with stabilized soil samples.
## 2 Materials and Methods
### GC-MS Analysis of Volatile Aroma Compounds  
A gas-chromatograph with a mass-selective detector 5977B (GC-MSD; Agilent Technologies, Inc., Santa Clara, CA, USA) with an HP-5 MS column (length 30 m, inner diameter 0.25 mm and film 0.25 \(\mu\); Agilent Technologies, Inc.) with helium as the carrier gas (flow rate of 1 mL min\({}^{-1}\)) and an integrated autosampler system PAL3 (CTC Analytics AG, Zwingen, Switzerland) was used to evaluate the aroma compounds present in each of the wine samples (done in triplicates). A total of 5 mL of each wine sample was placed into a 20-mL vial and assessed using the headspace method with a solid-phase microextraction (SPME) divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS) 1.1 mm grey fiber (Agilent Technologies, Inc.). A blank was used at the start to ensure no carryover effects from any previous analyses. Further details about the method used were described by [PERSON] et al. [30]. Furthermore, the National Institute of Standards and Technology (NIST; National Institute of Standards and Technology, Gaithersburg, MD, USA) library was used to identify the compounds observed, and only compounds with greater than 70% certainty were used.
# Cosmeceutical Potentials of _Equisetum debile_ Roxb. ex Vaucher Extracts
###### Abstract  
Trends in skin and hair treatments focus on natural products due to undesired effects of chemically synthetic ingredients. This study aims to investigate the cosmeceutical effects of _Equisetum debile_ (horsetail) extracts relating to anti-hyperpigmentation via tyrosinase, anti-wrinkle formation via matrix metalloproteinases (MMPs), and anti-androgenic alopecia via 5\(\alpha\)-reductase. Ethanolic extracts were sequentially partitioned into semi-purified fractions hexane, dichloromethane, ethyl acetate, methanol, and methanol insoluble residue. The ethyl acetate fraction possessed the highest total phenolic content (39.24 \(\pm\) 0.72 mg gallic acid/g), the strongest anti-tyrosine activities (583.33 \(\pm\) 23.59 mg kojic acid/g), and potent collagenase inhibitions (IC\({}_{50}\) MMP-1 and MMP-2 of 0.82 \(\pm\) 0.09 and 0.94 \(\pm\) 0.11 mg/mL, respectively). All extracts showed considerable inhibitions of 5\(\alpha\)-reductase ranging from 44.59 \(\pm\) 0.40 to 83.07 \(\pm\) 3.46% with the strongest activity from the dichloromethane fraction (1.48 \(\pm\) 0.06 mg finasteride/g). In conclusion, _E. debile_ extracts exhibit cosmeceutical potentials. This study suggests that the _E. debile_ ethyl acetate fraction could be used as a promising ingredient to organically treat hyperpigmentation and delay the skin aging process. In addition, compared to the current recommended intake of finasteride (1 mg/day) for androgenic alopecia, the dichloromethane fraction is proposed as an alternative source to naturally remediate hair loss.  
natural product; _Equisetum_; horsetail; tyrosinase; collagenase; 5\(\alpha\)-reductase; hair; skin; whitening; wrinkle
## 3 Results and Discussion
### Dried Juice and Pomace
#### 3.3.3 Water Content and Water Activity of Dried Juice and Pomace  
The stability of powder is strongly impacted by water activity and moisture content [44]. In the case of powders obtained by spray drying of juice, an increase in water content was observed with more intensive exposure of beetroots to the electric field. The subsequent variants (DASDJC0, DASDJC1, DASDJC2) reached higher values than the others, amounting to 9.9, 10.9, and 11.3%, respectively (Table 4). From a statistical point of view, powders DASDJC0 (without PEF treatment) and DASDJC2 (44 pulses with intensity E = 1.07 kV/cm) differed from each other, while powder DASDJC1 (3 pulses with intensity E = 1.07 kV/cm and the total energy of 0.5 kJ) was similar in terms of water content both to the variant without PEF pretreatment and to sample DASDJC2 obtained from the beetroots with the total given energy of 4 kJ/kg.  
Figure 2: Particle size distribution and cumulative particle size distribution of the spray-dried juices from the non-PEF treated beetroots (DASDJC), low PEF-treated beetroots (DASDJC1), and high PEF-treated beetroots (DASDJC2). Note: \({}^{\text{a,b}}\)—the same symbols in the columns indicate homogeneous groups (for each group of materials separately).  
The freeze-dried pomace was characterized by a water content ranging from 5.8% for FDP2 to 7.4% for FDP0. In the case of pomace, no statistically significant differences were found. All the variants were classified into one homogeneous group.  
The water content of beetroot powders dried using a foam mat drying was 8.27% [45], while beetroot powder obtained by the sublimation method with a simultaneous micromization process contained about 10% water [46]. [PERSON] and [PERSON] [47] obtained spray-dried beetroot powders based on maltodextrin, with a moisture content of 0.98-1.25%.  
Analyzing the water activity, it was noticed that samples DASDJC0 and DASDJC2 belonged to the same homogeneous group; the results were at the level of 0.23 and 0.24. The highest water activity coefficient was observed in sample DASDJC1, for which the result was 0.26. The use of PEF treatment (for DASDJC2) had no significant effect on the water activity compared to the control sample DASDJC0 (without treatment).  
Much greater differences in water activity were observed in the beetroot pomace. In the tested samples, the value of 0.080 was obtained by sample FDP1, which was the highest value. For FDP0, the value was 0.030, for FDP2--0.040. Each of the samples was classified into a different homogeneous group. The pomace achieved visibly lower water activity values compared to the spray-dried juice powders. Comparing the results of the pomace and spray-dried juice powders, it can be seen that the water activity values of the pomace were almost 10 times lower than those of the spray-dried juice powders. This can probably be explained by the differences in drying, as well as by the different drying materials (pomace, juice) with different chemical composition (juice--more low-molecular-weight sugars; pomace--more high-molecular-weight compounds, such as fiber).