esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Continuous Approach: Optimal Control
#### 2.2.1 Optimality Conditions  
Let us briefly recall the necessary and sufficient optimality conditions for the optimal control problem (1). With \(z=(T,x,v)\in Z\) and \(\lambda\in\Lambda\), the Lagrangian is defined as  
\[L(z,\lambda)=T-\left\langle\lambda,c(z)\right\rangle_{\Lambda,\Lambda}. \tag{2}\]  
If \(z\) is a (local) minimizer, the necessary first order or Karush-Kuhn-Tucker (KKT) condition  
\[L^{\prime}(z,\lambda)=0 \tag{3}\]  
and the necessary second order condition  
\[\left\langle L_{zz}\zeta,\zeta\right\rangle_{Z^{*},Z}\geq 0\quad\forall\zeta\in \ker c^{\prime}(z)\]  
hold [24], since \(c^{\prime}(z)\) is surjective due to \(\|w\|_{L^{\infty}([0,1])}<\bar{v}\). Moreover, if the KKT conditions (3) are satisfied at \(z\) for some \(\lambda\) and the sufficient second order condition (SSC)  
\[\left\langle L_{zz}\zeta,\zeta\right\rangle_{Z^{*},Z}\geq\alpha\|z\|_{Z}^{2} \quad\forall\zeta\in\ker c^{\prime}(z) \tag{4}\]  
holds for some \(\alpha>0\), then \(z\) is a locally unique solution of (1) and stable under perturbations of the problem, e.g., due to sufficiently fine discretization. Let us point out that for wind fields with non-vanishing second derivative, in general there is no closed form solution of the necessary conditions (3), such that solutions must be approximated numerically.  
Approaches to computing solutions of (1) generally fall into two classes: indirect methods relying on [PERSON]'s maximum principle [15; 25] and direct methods based on discretization of the minimization problem (1) [26; 27]. Indirect methods lead to a boundary value problem for state \(x\) and adjoint state \(\lambda\) together with a pointwise optimality condition for the control \(v\), which can be solved by shooting type methods, collocation, or spectral discretization approaches [28; 29]. The discretization of direct methods, usually by collocation or spectral methods, translates the optimal control problem in a finite dimensional nonlinear program to be solved by corresponding optimization problems [16; 17; 30]. While indirect methods using multiple shooting lead to smaller problem sizes than direct methods based on collocation in particular for high accuracy requirements, the latter are widely seen as being easier to implement and use, in particular in the presence of state constraints.  
In both approaches, Newton-type methods for solving either discretized boundary value problems or nonlinear programming problems converge in general only locally towards a close-by minimizer. Thus, sufficiently good initial iterates need to be provided. The domain of convergence can be extended using line search methods or trust region methods, but without guarantee of global optimality. Special care has to be taken in the case of non-convex problems, since the Newton direction need not be a descent direction if far away from a minimizer satisfying the sufficient second order conditions. Convexification, truncation of iterative solvers [31; 32; 33], or solvers for non-convex quadratic programs can be used to address this.
## 3 Performance Analysis of SR-ODBR-DCSK System  
This section analyzes the energy efficiency and confidentiality of the SR-ODBR-DCSK system.
## Data and Methods
### Methodology
#### 3.3.3 Antecedent Precipitation Index  
The timing, quantity and effects of previous rainfall on vegetation are important because it takes time for water to percolate through soil and become available for plants. When precipitation occurs, soil moisture conditions evolve as a function of its current degree of saturation. Antecedent precipitation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Soil Texture, \%**} & \multicolumn{2}{c}{**Permanent Wilting Point, \%**} \\ \cline{3-6}
**Site** & **Field Capacity, \%** & \multicolumn{2}{c}{**(Sand/Silt/Clay)**} & \multicolumn{2}{c}{**Permanent Wilting Point, \%**} \\ \cline{3-6}  & & **0–30 cm** & **30–100 cm** & **0–30 cm** & **30–100 cm** \\ \hline Demokeya & 15 & 89/6/5 & 90/5/5 & 2 & 3 \\ Wankama Fallow & 16 & 90/5/5 & 88/5/7 & 3 & 3 \\ Dahra & 7 & 89/6/5 & 89/5/6 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Field capacity, soil texture and permanent wilting point (PWP) at the three sites. Soil texture was included with the soil moisture data provided by the International Soil Monitoring Network (ISMN) [51]. This information was verified against site descriptions provided by the principal investigator at each site. Field capacity and PWP was derived from the Africa Soil Profiles Database (v1.2) [52] based on the soil texture configuration at each site.  
index (API) [57] has been previously used to characterize the influence of past rainfall events on vegetation [58]. The formulation of API used here follows [PERSON] [59]:  
\[API=\sum_{P\geq 1}^{n=8}P_{t}k^{-a} \tag{5}\]  
where \(API\) is the antecedent precipitation index in mm per 8 days; \(P_{t}\) is the observed precipitation amount on period \(t\); the decay constant, \(k\), ranges between 0.80 and 0.98 [60] and a value of 0.90 has been applied in this study; \(a\) is the number of antecedent days.
## 2 Basic Concept
### Fundamental Principles of the Utilized Cryptographic Framework
#### 2.1.2 Conceptual Aspects of the Elliptic Curve Discrete Logarithm Problem (ECDLP)  
The public key cryptography mechanism takes advantage of the features of mathematical problems based on the intractability to increase the strength of the cryptographic system. In protocols whose structure is based on the elliptic curve, the assumption that it is impractical to solve the discrete logarithm of a random element of the elliptic curve is known as the Elliptic Curve Discrete Logarithm Problem (ECDLP). According to the described conditions, the elliptic curve encryption system ensures that network attackers cannot calculate the result of the multiplication using the equation's coefficients, thereby guaranteeing the security features.  
An essential pragmatic issue in the ECC asymmetric encryption system is the computational problem of the discrete logarithm problem of an elliptic curve, which is ECDLP. This problem constitutes the main block of the elliptic curve mechanism, which operates based on coupling. Now, to explain the problem, consider two completely distinct points named P and Q, which are located at the top of the elliptic curve. Point Q results from the computation of scalar multiplication on point P using a specified scalar coefficient, indicated as \"k\". A noteworthy aspect of the discrete logarithm lies in the difficulty associated with reversing the computation of the resultant value, rendering it practically infeasible. Thus, even with access to points P and Q, ascertaining or deducing the value of K becomes exceedingly difficult. This difficulty in computation and the consequent impossibility of obtaining the value of K in the ECC asymmetric encryption system is recognized as ECDLP, or the Elliptic Curve Discrete Logarithm Problem [14; 15].
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
###### Abstract  
Saliinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (\(n\) = 32,000) were pre-processed by means of a Savitzky-Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.  
hyperspectral imaging; lettuce; salinity; non-destructive assessment; salinity index 1
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 5 Validation of the Theoretical Model
### Conventional Triaxial Compression Test  
As shown in Figure 3a, a DZSZ-15 multi-field coupled triaxial rock experimental apparatus was utilized at the Highway Engineering Experimental Center of Changsha University of Science and Technology to conduct conventional triaxial compression tests on argillaceous siltstone. The experimental apparatus comprises an axial compression loading system, a confining pressure loading system, a data acquisition system, and a control panel. The maximum axial load capacity is 50 kN, with an accuracy of \(\pm\)0.001 kN; the maximum confining pressure is \(\pm\)0.001 kN, with an accuracy of \(\pm\)0.0001 MPa; and the maximum measurable deformation is 25 mm, with an accuracy of \(\pm\)0.001 mm.  
As illustrated in Figure 3b, rock samples of argillaceous siltstone collected from the Zhuzhou train station in Hunan Province were processed into standard cylindrical specimens (height: 100 mm, diameter: 50 mm), ensuring that the flatness of the end  
Figure 3: Standard triaxial compression test on argillaceous siltstone: (**a**) experimental apparatus; (**b**) failure modes; (**c**) stress–strain relationships.  
surfaces was less than 0.05 cm. The samples exhibiting significant discrepancies in acoustic velocity were excluded. Conventional triaxial compression tests were then conducted at the confining pressures of 1, 2, 3, 4, and 5 MPa. During the testing process, the control panel applied the hydrostatic pressure at a rate of 0.02 MPa/s until the desired confining pressure was reached and stabilized, followed by an axial pressure application at a rate of 0.002 mm/s until failure occurred. The data acquisition system recorded data every 0.2 s, from which the principal stresses \(\sigma_{1}\), \(\sigma_{3}\) and principal strains \(\varepsilon_{1}\), \(\varepsilon_{3}\) were derived. The stress-strain curves are depicted in Figure 3c.
# Improved Autocollimator for Roll Angle Measurement with an Enlarged Measuring Range
## 2 Methods  
### Optical Configuration  
Figure 1 depicts a schematic of an improved autocollimator for roll angle measurements. A light beam from a light-emitting diode (LED) illuminates an aperture stop (AS). After passing through a beam splitter (BS) and an objective lens (OL), it becomes a collimated beam. When this beam hits the front (deposited with a beam-splitting film) of the WP, it is dissociated into two beams. One beam directly returns, while the other beam successively passes through the WP's front and back, bounces from a planar mirror (PM), and reverses through the WP's back and front. Finally, both beams converge on a photodetector (CMOS) and form two light spots, which will be sent to a computer to extract their coordinates by means of an image-processing program.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 2 Data Source and Preprocessing
### City Roads Extracted from OSM  
The city roads used in this study to test the extraction accuracy from Luojia 1-01 nighttime light data were derived from OpenStreetMap (www.openstreetmap.org/, accessed on 29 September 2021), an online map collaboration program that was established by [PERSON] in July 2004. As an open community formed by millions of cartographers, OpenStreetMap aims to provide free map data to the public with high accuracy and time-liness. The data are updated and maintained every day by over 1.5 million map editors through aerial images and high-precision GPS data. The data we downloaded from the OSM website included the road networks of Wuhan. The dataset was obtained in January 2018. The city roads extracted from OSM data are shown in Figure 2.  
Figure 1: Location of Wuhan (**a**) and its nighttime light data after preprocessing (**b**).
## 4 Discussion
### Limitations and Future Studies  
The present study has some limitations that should be acknowledged. First, the inability to assess the pectoralis minor (due to the use of surface electromyography) may have restricted data interpretation, given its role in scapular protraction, anterior tilt, and downward and internal rotation [99; 104]. Thus, future studies including this muscle are suggested. Then, scapular kinematics were only assessed for shoulder range of motions lower than 120\({}^{\circ}\). While this was necessary to reduce tool error and avoid excessive soft tissue artifacts [110], it may limit the generalizability of the findings. Moreover, the variable used to represent the scapular motion in the present study considered the entire range of motion. This approach could potentially limit the ability to detect differences between groups. Future studies may consider utilizing variables that describe scapular motion across different arcs of motion. Furthermore, the use of surface electromyography, particularly during dynamic movements, may be susceptible to movement artifacts and crosstalk [30; 42]. To mitigate these limitations, recommended practices such as skin preparation, electrode positioning, signal filters, and normalization were applied. Finally, although symptomatic subjects were selected based on the presence of chronic shoulder pain (lasting at least for 3 months), data regarding the total duration of shoulder symptoms were not collected. While some aforementioned facts may have limited the observation of more differences between groups in the present study, it is also possible that differences in the chronicity of each subject's shoulder symptoms could have contributed to the lack of differences between the groups.
# Research on the Aerodynamic-Propulsion Coupling Characteristics of a Distributed Propulsion System
## 3 Numerical Method Validation and Grid Independence Validation
### Grid Independence Verification  
The computational domain and mesh of the over-wing symmetric configuration with five distributed ducted fans are illustrated in Figure 10. The computational domain for the system is spherical, with a radius of 25 times the wing chord length. The coupled model is meshed using Pointwise version 18.4R3 and Meshing version 2022.R2 software. The ducted fan array is meshed with structured grids, while the external flow field of the wing is meshed with unstructured grids. A thorough inspection is carried out on metrics such as the element aspect ratio, skewness, and orthogonality. To verify the grid independence of the integrated model, Table 3 presents the comparison results of the lift coefficient \(C_{L}\), the drag coefficient \(C_{D}\), and the pitching moment coefficient \(C_{M}\) for four different grids at an angle of attack of \(0^{\circ}\). The height of the first layer of the four grids is 0.01 mm, with a grid growth rate of 1.2 and a total of 30 layers, satisfying y +\(\approx\) 1. It can be seen that when the number of grids increases to 10.65 million or more, the deviations among the lift coefficient, the drag coefficient, and the pitching moment coefficient are extremely small, fulfilling the requirement of grid independence. Considering both the computational accuracy and efficiency comprehensively, the total number of grids for the integrated model is selected to be approximately 10.65 million, as shown in Figure 10b.  
Figure 10: Computational domain and mesh of five-ducted-fan over-wing integrated model: (**a**) computational domain; (**b**) computational mesh.  
Figure 9: Variations in lift and drag with angle of attack for NACA 4415 wing: (**a**) lift; (**b**) drag.
# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
###### Abstract  
An enhanced YOLOv8-based network was developed to accurately and efficiently detect the ripeness of strawberries in complex environments. Firstly, a CA (channel attention) mechanism was integrated into the backbone and head of the YOLOv8 model to improve its ability to identify key features of strawberries. Secondly, the bilinear interpolation operator was replaced with DySample (dynamic sampling), which optimized data processing, reduced computational load, accelerated upsampling, and improved the model's sensitivity to fine strawberry details. Finally, the Wise-IoU (Wise Intersection over Union) loss function optimized the IoU (Intersection over Union) through intelligent weighting and adaptive tuning, enhancing the bounding box accuracy. The experimental results show that the improved YOLOv8-CDW model has a precision of 0.969, a recall of 0.936, and a [EMAIL_ADDRESS] of 0.975 in complex environments, which are 8.39%, 18.63%, and 12.75% better than those of the original YOLOv8, respectively. The enhanced model demonstrates higher accuracy and faster detection of strawberry ripeness, offering valuable technical support for advancing deep learning applications in smart agriculture and automated harvesting.  
YOLOv8; channel attention; DySample; detection of strawberry ripeness +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Persister Resuscitation by Amino Acids  
_X. citri_ cells were grown to a turbidity \(\sim\)0.8-1.0, and persisters were obtained by a 45 min pretreatment with tetracycline (10 \(\times\) MIC), followed by 3 h treatment with ciprofloxacin (100 \(\times\) MIC), as previously described. Cultures were serially diluted 10,000-fold (10\({}^{-4}\)) and 100 \(\mu\)L was plated on M9 solid medium supplemented with one of the following amino acids groups: #1 (Ala, Arg, Cys, Phe, Ser), #2 (Gly, His, Thr, Val, Tyr), #3 (Asn, Ile, Lys, Pro, Trp) or #4 (Asp, Glu, Gln, Leu, Met). Both M9 broth and the amino acids we used were as previously reported [42]. Plates were observed daily until day 4 when colonies could be first observed. The plates where colonies first appeared was considered as an indication of which amino acids combination were more efficient in resuscitating persistent cells. To identify which specific amino acids was the most important in resuscitating persister cells, the same experiment was repeated, under the same conditions, but now using agar plates containing individual amino acids from group 3 (Asn, Ile, Lys, Pro, Trp) and 4 (Asp, Glu, Gln, Leu, Met) [42] that presented faster-growing colonies.
# Prediction of Wind Power with Machine Learning Models
###### Abstract  
Wind power is a vital power grid component, and wind power forecasting represents a challenging task. In this study, a series of multiobjective predictive models were created utilising a range of cutting-edge machine learning (ML) methodologies, namely, artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks, and long short-term memory (LSTM) networks. In this study, two independent data sets were combined and used to predict wind power. The first data set contained internal values such as wind speed (m/s), wind direction (\({}^{\circ}\)), theoretical power (kW), and active power (kW). The second data set was external values that contained the meteorological data set, which can affect the wind power forecast. The k-nearest neighbours (kNN) algorithm completed the missing data in the data set. The results showed that the LSTM, RNN, CNN, and ANN algorithms were powerful in forecasting wind power. Furthermore, the performance of these models was evaluated by incorporating statistical indicators of performance deviation to demonstrate the efficacy of the employed methodology effectively. Moreover, the performance of these models was evaluated by incorporating statistical indicators of performance deviation, including the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) metrics to effectively demonstrate the efficacy of the employed methodology. When the metrics are examined, it can be said that ANN, RNN, CNN, and LSTM methods effectively forecast wind power. However, it can be said that the LSTM model is more successful in estimating the wind power with an R\({}^{2}\) value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614.  
artificial neural network; convolutional neural network; recurrent neural network; long short-term memory; wind power forecasting