esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preparation
#### 2.2.3 Soil, Weather and Crop Information  
The soil map for the study area was derived from Chinese Soil Science Database for primary Hengshui data at a scale of 1:1,000,000. The map data were compiled based on the soil survey and reference system of the Chinese genetic soil classification system [60; 61]. Physical and chemical data from the soil profiles were also obtained from the Chinese Soil Science Database and Soil species of Hebei province [62].  
Meteorological data were important external input data to run the CERES-Wheat model and derived from the National Meteorological Information Center, China Meteorological Administration ([[http://nmic.cn/web/index.htm](http://nmic.cn/web/index.htm)]([http://nmic.cn/web/index.htm](http://nmic.cn/web/index.htm))). The meteorological data observed from 11 meteorological stations at the county level in 2013-2014 included daily maximum and minimum air temperatures, sunshine duration and precipitation. The daily hours of sunshine required conversion into the solar radiation based on the following equations [63]:  
\[Q=\mathrm{Q}_{0}(a+bS/S_{0}) \tag{1}\]  
\[S_{0}=\frac{24}{\pi}\mathrm{arccos}(-\tan(\varphi)\tan(\delta)) \tag{2}\]  
\[a=0.10+0.24S/S_{0} \tag{3}\]  
\[b=0.38+0.08S/S_{0} \tag{4}\]  
where \(a\) and \(b\) are empirical coefficients dependent on the climate and the locality. \(S/S_{0}\) represents the ratio of actual to potential duration of sunshine. \(\varphi\) is the geographic latitude of the meteorological stations, while \(\delta\) represents the declination. \(Q\) is the actual daily radiation, and the \(Q_{0}\) is the incident radiation of the aeropause calculated using the FAO-56 guide [64]. Meteorological information was from reliable meteorological stations, monitored daily, and the study area was relative small. Consequently, the effects of meteorological elements were not considered in this experiment. To regionalize the crop model, the daily meteorological inputs for the study area were spatialized as a raster map with 1-km grid cells using ANUSPLIN software [65].  
The spatial distribution map of winter wheat in 2014 was derived from the Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China. The classification results were received based on the GF-1 images and field investigations, and the overall accuracy was higher than 90%.  
The regional statistical yields in 2014 for 11 counties were obtained from the agricultural extension stations of Hengshui.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 6 Joint Network-Channel Coding over Cooperative Link
### Joint Network-Channel Decoding  
In the decoding phase, the data from the relay node \(R\) has been received by nodes \(S\) and \(D\) as a result of the broadcast. The data received at both nodes is given below:  
\[y_{RS} =h_{RS}x_{R}+z, \tag{13}\] \[y_{RD} =h_{RD}x_{R}+z, \tag{14}\]  
where \(x_{R}\) is the modulated JNC coded data, broadcasted from the relay node, \(h\) is the channel coefficient and \(z\) accounts for AWGN. The received data is then demodulated and channel decoded at respective nodes. The estimates \(n_{RS}\) and \(n_{RD}\) contains the information of both nodes from which original data is extracted in following way:  
At node \(S\),  
\[\hat{n_{D}}=n_{S}\oplus n_{RS}, \tag{15}\]  
At node \(D\),  
\[\hat{n_{S}}=n_{D}\oplus n_{RD}. \tag{16}\]  
Here, \(\hat{n_{D}}\) specifies the copy of data \(n_{D}\) transmitted from node \(D\), whereas \(\hat{n_{S}}\) is the copy of data received at node \(D\) as a result of transmission of \(n_{S}\) from node \(S\).
# Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
## 3 Results
### Accuracy and Sensitivity Evaluation of PPI Method for Double Antibody SALIS  
In order to test the recovery of the quality control samples, three concentrations of high (41.25 pg/mL), medium (10.31 pg/mL) and low (2.58 pg/mL) samples were tested and the results are shown in Table 2. The results showed that the PPI concentration measured in 1 ug/mL of recombinant human insulin solution was 0.638 pg/mL and the recoveries obtained for the high, medium and low-quality control samples were 91.4%, 88.62% and 95.41%, respectively, which indicated that the established method was accurate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Theoretical** & **Measured** & **Sample** & \\
**Concentration** & **Concentration** & **Concentration** & **Recovery Rate (\%)** \\
**(pg/mL)** & **(pg/mL)** & **(pg/mL)** & \\ \hline
41.25 & 38.34 & & 91 \\
10.31 & 9.78 & 0.638 & 89 \\
2.58 & 3.10 & & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recoveries of PPI from the samples fortified with different amounts of PPI.  
Figure 4: Quantitative curve of PPI.  
According to the method of sensitivity determination, the OD450 values of 12 blank samples, i.e., sample dilutions, were determined. The average value obtained by adding three times standard deviation was 0.172, and the minimum detection limit was calculated to be 3.06 pg/mL, meeting the requirement of PPI detection sensitivity in recombinant human insulin.
## 5 Numerical Simulation of Shear Test of Discontinuities Possessing Different Joint Wall Strengths
### Establishment of Numerical Model of Shear Tests  
A rectangular block with a size of 100 mm \(\times\) 40 mm was established, and discontinuities possessing different joint wall strengths with three types of morphology were imported into the middle of the rectangle to construct numerical models corresponding to the joint specimens tested in the laboratory. The initial rectangular block was divided into the triangular mesh element. The smaller the size of the mesh element, the more realistic the shear mechanics regularity of discontinuities. However, the computational load of the computer will also increase, which reduces the computational efficiency of the numerical model. Therefore, in order to elaborately investigate the shear mechanical properties of discontinuities with inclusion of the computational efficiency, the block region within 6 mm from the discontinuity was finely divided into grids with size 1 mm. In contrast, the grid size of the remaining block region was 1.5 mm. With reference to the laboratory shear test, the upper block of the model was set as sandstone or granite, while the lower block of the model was set as mortar. The numerical model of discontinuities possessing different joint wall strengths is shown in Figure 11. The loading scheme in numerical simulation was consistent with laboratory tests. The numerical analysis program of discontinuities was compiled with Fish, which monitored the shear stress, shear displacement and normal displacement.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Numerical** & \(a_{n}\) & \(e_{n}\) & \(a_{s}\) & \(e_{s}\) & \(R\) & \(\mathcal{O}^{i}{}_{m}\) (\({}^{\circ}\)) & \(\mathcal{O}\) (\({}^{\circ}\)) \\ \hline SCR1 & 0.815 & 0.738 & 0.574 & 0.832 & 0.0008 & 32.530 & 30.270 \\ SCR2 & 0.932 & 0.796 & 0.865 & 0.894 & 0.0023 & 45.280 & 30.270 \\ SCR3 & 1.348 & 0.951 & 1.097 & 0.875 & 0.0047 & 46.840 & 30.270 \\ GCR1 & 0.946 & 0.803 & 0.681 & 1.073 & 0.0008 & 33.190 & 33.490 \\ GCR2 & 1.245 & 0.992 & 1.157 & 0.926 & 0.0023 & 43.450 & 33.490 \\ GCR3 & 1.578 & 1.179 & 1.437 & 0.841 & 0.0047 & 44.620 & 33.490 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of joints.  
Figure 11: Numerical model of discontinuities possessing different joint wall strengths. (**a**) Two-dimensional grid model, and (**b**) numerical model with boundary conditions.
## 3 Results and Discussion
### Infrared Spectral Split Peak Fitting of Extracts
#### 3.2.5 Analysis of the Structural Parameters of the Three Extracts  
The structural parameters obtained from the split-peak fitting results were used for the semi-quantitative analysis of the different solvent tar residue extracts. The structural parameters were calculated according to Equations (1) and (2) and the results are shown in Table 6.  
\[\text{DOC}=\text{A}_{900-700}/\text{A}_{1600}, \tag{1}\]  
where DOC indicates the degree of aromatic ring condensation of the extract.  
\[\text{A(CH}_{2})/\text{A(CH}_{3})=\text{A}_{2920}/\text{A}_{2950}, \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Extract Sample** & **DOC** & **A(CH\({}_{2}\))/A(CH\({}_{3}\))** \\ \hline ACE & 0.91 & 3.00 \\ CS\({}_{2}\) & 1.07 & 2.65 \\ ACE/CS\({}_{2}\) & 0.75 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Structural parameters of the three extracts.  
Figure 5: Infrared spectra of 3000–3600 cm\({}^{-1}\) extracts. ((**a**) is the carbon disulphide fit, (**b**) is the acetone fit and (**c**) is the mixed solvent fit. The red line is the fitted curve and the blue line is the sub peak).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Peak \#**} & \multirow{2}{*}{**Position \(\sigma\) (cm\({}^{-1}\))**} & \multirow{2}{*}{**Assignment**} & \multicolumn{2}{c}{**Area Percentage (\%)**} \\ \cline{3-5}  & & & **ACE** & **CS\({}_{2}\)** & **ACE/CS\({}_{2}\)** \\ \hline
1 & 3600–3500 & OH-\(\pi\) & 8.31 & 5.83 & 8.64 \\
2 & 3500–3350 & self-associated OH & 61.62 & 67.84 & 52.23 \\
3 & 3300–3150 & ring hydroxy & 23.89 & 23.52 & 33.04 \\
4 & 3150-3000 & OH-N & 6.18 & 2.81 & 6.09 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Infrared spectra of the three extracts 3000–3600 cm\({}^{-1}\) split peak fitting ratio of each absorption peak area [15].  
CH\({}_{2}\)/CH\({}_{3}\) characterises the length of the fatty side chains and the degree of branching in the samples. As can be seen from Table 6, the CS\({}_{2}\) extract had a larger DOC, indicating a higher degree of aromatic ring condensation and the presence of more aromatic hydrocarbons with a large number of aromatic rings, while the acetone/CS\({}_{2}\) mixed solvent extract had a smaller DOC value. Comparing the A(CH\({}_{2}\))/A(CH\({}_{3}\)) data for the three extracts shows that the acetone extract has the largest CH\({}_{2}\)/CH\({}_{3}\) index, indicating that the extract has the longest aliphatic side chain and the fewest branched chains.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 5 Conclusions  
The present study addressed precision agriculture's water scarcity and energy efficiency challenges. We proposed a novel UAV-based olive irrigation system, which leverages the combination of unmanned aerial vehicles and massive MIMO technology. This approach encompasses the integration of the IoT, drones, and sensors to optimize the irrigation process. The UAVs ensure remote sensing through onboard sensors and collect necessary data (such as soil moisture) for the irrigation process, whereas M-MIMO communication is adopted to meet the increasing requirements regarding spectral efficiency and to guarantee the energy efficiency of the studied system. The numerical results proved that the proposed UAV-based irrigation system outperforms conventional ones regarding energy efficiency, while conserving the network's spectral efficiency.  
In future work, we plan to deploy drones equipped with cameras to capture images of olive leaves to identify leaves with different types of infections, including Aculus olearius, olive peacock spot, and olive scab, from healthy leaves. Additionally, since collected data should be protected from unauthorized access or interception, we plan also to study the physical layer security issue.  
Conceptualization: A.M., A.B. and A.H.; Formal analysis: A.B. and H.B.; Investigation: A.H. and H.B.; Methodology: A.M., A.B. and M.B.A.; Validation: A.M., A.B. and A.H.; Writing--original draft: A.M.; Writing--review and editing: A.B., A.H., M.B.A. and H.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deanship of Scientific Research in cooperation with the Olive Research Center at Jouf University under Grant Number (DSR2022-RG-0163).  
The authors extend their appreciation to the Deanship of Scientific Research in cooperation with the Olive Research Center at Jouf University under Grant Number (DSR2022-RG-0163).  
The authors declare that they have no conflict of interest to report regarding the present study.
## 1 Introduction
Meanwhile, its authors also pointed out that the GMM is computationally inefficient and not satisfactory for large datasets (more than 7000 profiles) [21], which leads to a limitation of the dataset size and the dimensionality of the vertical depth layers. Therefore, efficient methods for the analysis of Argo temperature profiles need to be proposed. Currently, there is no valid mathematical model for the TA profile itself and no study has focused on automatically extracting multiple fluctuation parameters, including extreme values, depth of extreme values, and steepness in a TA profile. This study proposes a Gaussian function model that can be combined with multiple Gaussians (MG) to fit and extract the fluctuation information. The large dataset of eddy TA profiles can be effectively divided based on the physical characteristics of thermal structures, such as extreme values, depth of extremes, and steepness of the profile, with the characteristics being obtained from model parameters. The MG model presented in this paper is not limited by sample size when dealing with large datasets. It can objectively calculate the characteristics of tens of thousands of Argo profiles and efficiently perform spatial pattern analysis. Numerous eddies occur in the Northwest Pacific Ocean (NWPO), where a large number of Argo profiles have also been accumulated, and the three-dimensional structures and transports of mesoscale eddies have been comprehensively investigated using a combination of satellite data and Argo profiles [19; 20]. It is the integral contribution of eddies over thousands of kilometres in space and years in time that defines their role in the climate system [4]. Therefore, after applying the MG model to the eddy TA profiles in the region, the spatial distribution of mesoscale eddies with different types of TA profiles was analysed. This paper is organized as follows: Section 2 presents the details of the study area and the data used in this study. Section 3 presents the proposed model to fit the Argo TA eddy profile. The verification results are presented in this section, while the sensitivity analyses and comparative analyses of the parameters associated with the model have also been carried out. The application based on the model parameters, that is spatial distribution variations analysis, is shown in Section 4. Discussions and conclusions are drawn in Section 5 and Section 6.
## 3 System Design
### Architecture and Basic Ideas of TinyONet
Additionally, TinyONet should provide the node-centric communication for an application which needs to communicate directly to a physical sensor. For these diverse communication methods, _SyncSlice_ is designed to provide both synchronous access and the node-centric communication. When these kinds of communication methods are requested, _SliceManager_ spawns a _SyncSlice_ to handle these requests. Unlike ordinary _Slice_, _SyncSlice_ makes a session that promptly invokes a response from physical sensors.
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 1 Introduction  
Digital watermarking and steganography are part of the field of data hiding where some piece of information is stored in digital objects for various purposes ranging from simple information saving to copyright protection, from integrity protection or origin authentication to covert communication [1]. The digital objects involved in data hiding may be of any type, like images [2; 3; 4], audio files [5; 6], movies [7; 8; 9], 3D models [10; 11; 12], neural networks [13; 14; 15; 16], and textual data [17; 18; 19].  
In the field of data hiding in textual data, [PERSON]. [20] proposes a method to embed information into binary strings encoded in sequences built using a printable alphabet: there, the authors focus on two encodings, namely Base45 [21] and Base85 [22], that have spare configurations that can be used to save one bit of information. An improvement for the Base45 encoding is presented in [23] where bits are reversibly embedded by exploiting the most probable printable sequences depending on the type of binary data of the cover object.  
In [24], several applications for the data hiding potentiality of those unused sequences are suggested: for example, digital signatures, MACs, CRCs, and other security information.  
In this work, the frequency distribution of the input binary sequences for a file type is computed on a large data sample and used to optimize the number of bits embedded in every printable sequence, thus improving the performance of the method described in [23]. Note that the proposed approach can be applied to any encoding method pertaining to the framework defined in [20], the only limit being the size of the unused sequences as it will be defined. In the Base45 case here studied, for example, our approach improves the average payload obtained in [23] by up to 53%.  
The remainder of the paper is organized as follows: Section 2 introduces some notation to be used throughout the paper, whilst Section 3 recalls some works related to the presented encoding improvement. Section 4 gives a brief description of the Base45 printable encoding of binary data and Section 5 presents the original data embedding algorithm and the following improvement, then proposes an optimization based on the input data frequency distribution. The performance of the method is shown in Section 6 and conclusions are drawn in the last Section 7. Finally, the Appendix A presents a possible compressionmethod to efficiently save the table recording the optimal assignment of bit configurations to the most frequent pairs of octets.
## 3 Defocus Effect Calibration and Compensation
### Defocused BFP Ellipsometric Error Calibration  
Vibrational defocus causes BFP images to blur and distort and results in abnormal optical ellipsometric responses, e.g., \(\Psi\) and \(\Delta\). As shown in Figure 3, the errors of \(\Psi\) and \(\Delta\) increase as the defocus distance increases regularly. Obviously, it is crucially important to correct this error once a sample is defocused. The ellipsometric parameter errors have been investigated by [PERSON] et al. [22; 33], following which a Jones matrix of \(J_{C}=\left[\tan\delta_{\Psi}\mathrm{e}^{i\delta\Delta},0;0,1\right]\) was used to model the ellipsometric parameter errors resulting from the defocus from an objective lens in this study. In the coaxial path system, the objective polarization errors are considered twice, corresponding to emission and reflection through the objective lens. Thus, a complete error model from a measured sample can be described as \(J_{C}=J_{C}J_{S}J_{C}=\left[\tan^{2}\delta_{\Psi}\tan\Psi\mathrm{e}^{i\left( \Delta+2\delta_{\Delta}\right)},0;0,1\right]\), where \(\tan^{2}\delta_{\Psi}\) and \(2\delta_{\Delta}\) are the ellipsometric compensation's amplitude and phase parameters, respectively. The calibration of \(\tan^{2}\delta_{\Psi}\) and \(2\delta_{\Delta}\) can be achieved using some reference materials through Equation (7) based on the least square error fitting:  
\[\mathrm{LSE}=\frac{1}{N\!-\!1}\!\sum_{i=1}^{N}\!\left(\left|\frac{\tan(\Psi_{ \mathrm{exp}}(\theta_{i}))}{\tan(\Psi_{the}(\theta_{i}))}-\tan^{2}\delta_{\Psi }\right)\right|^{2}+\left|\left(\Delta_{\mathrm{exp}}(\theta_{i})-\Delta_{the }(\theta_{i})\right)-2\delta  
By observing a reference material at different defocus positions \(x_{i}\) and fitting through Equation (7), a two-dimensional compensation look-up table of \(\Psi\) and \(\Delta\) could be made on a \(x_{i}\)-\(\theta(R)\) domain. In the actual measurement, the ellipsometric compensations \(\tan^{2}\delta_{\Psi}\) and \(2\delta_{\Delta}\) are found from the look-up table by determining the defocus position. Film parameters can then be calculated through LM fitting using the merit function in Equation (7).
# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient
## 2 Principle of Aeromagnetic Compensation
### Tolles-Lawson Model
The interference field generated by the platform can be expressed as follows:  
\[H_{d}(t)=H_{PERM}(t)+H_{IND}(t)+H_{EDDY}(t)=\sum_{i=1}^{18}A_{i}(t)c_{i}. \tag{6}\]  
The corresponding matrix notation of Equation (6) can be expressed as follows:  
\[\mathbf{H}_{d}=\mathbf{A}\mathbf{C}, \tag{7}\]  
where \(\mathbf{H}_{d}\) and \(\mathbf{C}\) are both column vectors constituted by \(H_{d}(t)\) and \(c_{i}\) (\(i\) = 1, 2,..., 18), and \(\mathbf{A}\) is the feature matrix that can be expressed as follows:  
\[\mathbf{A}=\left[ \begin{array}{cccc}A_{1}(1)&A_{2}(1)&\cdots&A_{18}(1)\\ A_{1}(2)&A_{2}(2)&\cdots&A_{18}(2)\\ \vdots&\vdots&\ddots&\cdots\\ A_{1}(n)&A_{2}(n)&\cdots&A_{18}(n)\end{array} \right]. \tag{8}\]  
The compensation coefficients can be obtained by the least square algorithm [15; 30], as given by:  
\[\mathbf{C}=\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}\mathbf{A}^{T}\mathbf{H}_{d}. \tag{9}\]  
Under the Tolles-Lawson model, the compensation coefficients are only related to the platform and the local background geomagnetic field. Thus, the compensation coefficients do not change unless the airplane platform system has changed. The time-varying \(A_{i}(t)\) (\(i\) = 1, 2,..., 18) can be obtained from the direction cosines \(u_{i}\) (\(i\) = 1, 2, 3), their time derivatives \(u_{i}^{\prime}\) (\(i\) = 1, 2, 3), and the magnitude of the geomagnetic field. It can be clearly demonstrated that the interference field generated by the platform would be well eliminated if the accurate compensation coefficients can be obtained.
## 2 Radio Wave Propagation Measurements in a Corn Field  
### Measurement Environment  
We conducted measurements in a corn field in an agricultural area (Figure 1) near Dabravata Village, Yablanitsa Municipality, Bulgaria, in the summer of 2024 at the corn reproductive (R3) stage. It is important to note that, from emergence (VE) to physiological maturity (R6), corn growth is divided into two key stages--the vegetative (V) and reproductive (R) stages. The corn plant reaches full height, and all leaves emerge at the end of the vegetative growth stage. The reproductive stages begin at silking (R1) and at the end at maturity or \"black layer\" (R6). Plants at R2 have reached their maximum height, and the kernel size is rapidly increasing. At stages R3, R4, R5, and R6, the kernels'moisture content is approximately 80%, 70%, 55%, and 30-35%, respectively [35, 36, 37]. We chose to observe the radio wave propagation at stage R3 (known as the milk growth stage due to the milky white fluid in the kernel) because, at this stage, the leaves and stalk almost do not change, the kernels contain approximately 80% moisture, and starch starts to accumulate within them [35, 36, 37]. Moreover, in stage R3, we expect the highest signal attenuation due to the highest density of leaf and kernel moisture in this stage. In the chosen agricultural field for the experiment, the corn was planted in rows with a row spacing of 0.70 \(\pm\) 0.05 m. The distance between individual corn plants within each row was 0.20 \(\pm\) 0.03 m. The average number of plants per hectare was 70,000. The average height of the corn plants was 2.6 \(\pm\) 0.2 m, and the average leaf number was 12 \(\pm\) 1. We chose to study radio wave propagation in the corn field because corn is one of the most widely produced crops globally [38, 39], making it an excellent choice for implementing smart agriculture.