esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 5 Performance Test
### Effect of the Wave Period  
Figure 12 shows the system's response to the wave period. As stated in Equation (13), the output power of the system decreases as the wave period increases. Generally, the energy conversion efficiency declines as the wave period increases. However, the changes in conversion efficiency and precession angular displacement curves were not very smooth due to the complexity of the system model. The precession angular displacement decreases rapidly with the wave period from 5 to 12 s, and the trend reduces gently after 12 s. The counter torque of the platform decreases as the wave period increases.  
As mentioned earlier, the energy conversion efficiency varies with the wave period. To maximize energy conversion efficiency, Figure 13 shows the efficiency relative to the wave period and flywheel rotating speed. The efficiency increases as the wave period decreases and the speed of the flywheel increases. However, if the period is short, the conversion efficiency decreases significantly at low speed, but it can be increased in phases by raising the flywheel rotating speed. For high speed, reducing the wave period does not improve efficiency significantly in the simulation situation. As for the actual system, the trade-off between energy output and power consumption when maintaining the high-speed rotation of the flywheel is also considered to determine the optimal flywheel rotating speed in various situations.
# Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
## 3 Results and Discussion
### Limitations  
The use of the random forest and Cubist approaches for the shear strength of RFM has limitations that need to be addressed in future research. First, the limitation of the random forest and Cubist approaches is that the datasets are relatively small; only 165 cases are involved in random forest and Cubist modeling. A larger dataset should be analyzed to improve the model's precision and reliability. Second, other qualitative parameters (i.e., the lithology, the Los Angeles abrasion value) that influences the shear strength of RFM and other hyperparameters of the random forest and Cubist algorithms may exist for improving the performance. Other optimization strategies such as heuristic algorithms [57; 58; 59; 60; 61; 62; 63; 64]  
Figure 7: Variable importance scores for the 13 predictors in the proposed models for shear strength of RFM: (a) Cubist model, and (b) random forests model.  
can obtain the desired results, and many heuristic/hybrid algorithms have been proved to have the potential to significantly reduce the computation time by avoiding exhaustive computation. Third, although regression problems for the shear strength of RFM potential were performed with high accuracy, binary classification problems and multi-output classification problems for the shear strength of RFM analysis have not been investigated in this study. Similar to other machine learning methods, the major disadvantages of random forest and Cubist models are that the resulting tree structure tends to be sensitive to the fitness of the dataset. Due to their \"black box\" nature, interpretation of the complex relationships between the predictive results and the predictor variables is difficult, and an overfitted condition often occurred. Last, other advanced supervised machine learning techniques that achieve excellent performance in nonlinear relationship modeling, such as support vector machines [55; 57] and gradient boosted machines [21; 31; 32], have not been investigated and compared with the shear strengths of RFM prediction. The optimal prediction result would depend on the size and quality of the dataset. Feature selection is an important step in building a regression model.
## 3 Controller Design  
### RBFNN  
The radial basis function neural network (RBFNN) has been extensively used in control system design because it can approximate any continuous nonlinear function with a compact set and arbitrary accuracy [24; 25], and can adapt and learn the dynamic properties of uncertain systems. For a continuous nonlinear function \(f(\mathbf{X})\in\mathbb{R}^{n}\), a RBFNN can approximate it on a compact as:  
\[f(\mathbf{X})=\mathbf{W}^{\rm nT}\mathbf{h}(\mathbf{X})+\mathbf{\varepsilon}, \tag{4}\]  
where \(\mathbf{X}\) is the input vector, \(\mathbf{\varepsilon}\) is the approximation error of the unknown upper bound, \(\mathbf{W}^{\rm n}\) is the ideal weight value of the network output, and \(\mathbf{h}=\left[h_{1}\;\;h_{2}\ldots h_{n}\right]^{\rm T}\) is the radial basis function vector denoted as:  
\[h_{j}(\mathbf{X})=\exp\left(-\left\|\mathbf{X}-C_{j}\right\|^{2}/(2b_{j}^{2})\right), \;\;j=1,2,\ldots,n. \tag{5}\]  
where \(h_{j}(\mathbf{X})\) is the output of the \(j\)th neuron in the hidden layer, \(C_{j}\) and \(b_{j}\) is the center and width of the Gaussian basis function of the \(j\)th neuron, and \(n\) is the number of nodes of the neural network.
## 4 Conclusions  
At the end of this work, we could establish the following conclusions:  
* Crystalline rods with compositions in the CaO-SiO\({}_{2}\)-MgO-P\({}_{2}\)O\({}_{5}\) system were grown via the LFZ technique at speeds of 50, 100, and 300 mm/h, and an MgO primary phase was obtained with other bioactive phases at all speed conditions.
* The surface of the crystalline rods (M50, M100, and M300) presented a response in contact with the SBF, and this response consisted of the dissolution of magnesium oxide phases and the transformation of some of the other phases. The dissolution of MgO phases generated a porous layer with interconnected pores that acted as transport channels for the liquid medium to continue MgO leaching inside the matrix.
* Samples grown at 50 and 100 mm/h after soaking in SBF for four weeks did not form HAp layer, and this favored the dissolution of the MgO phases. The release of biocompatible Mg\({}^{2+}\) ions acted as an inhibitor of hydroxyapatite crystal growth suppressing unwanted crystallization in vivo.
* The micro hardness of the uns soaked samples lies between 4.63 and 5.30 GPa, which is comparable to other bioceramics used in bone repair. Porosity formation by the leaching of magnesium oxide in SBF can reduce the hardness, bringing it closer to that of natural bone.
* As the material is able to generate a porous scaffold, in order to conclude if this new Mg-based formulation can be used as a bone replacement, cell tests and in vivo experiments are needed.  
[PERSON] and [PERSON] conceived and planned the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] carried out the experiments. [PERSON] contributed to sample preparation and characterization. All authors contributed to the interpretation of the results and provided critical feedback. [PERSON] wrote the paper with input from all authors. All authors have read and agreed to the published version of the manuscript.  
This project has been partially funded by the PIT2 program of the University of Murcia's own research plan. Fundacion Seneca grants No 20647/JL/18 and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie IF No 795630 are also acknowledged.  
Figure 8: SEM image of a sample grown at 300 mm/h after soaking in the SBF solution for 28 days: longitudinal cross section (**a**) and a detailed zone of the sample surface in contact with the fluid (**b**).  
**Acknowledgments:** The authors would like to acknowledge the use of Servicio General de Apoyo a la Investigacion-SAI, Universidad de Zaragoza.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### RNA Extraction and Gene Sequence Analysis  
Pm TPS gene sequences and corresponding gene-specific primers were derived from the previously determined _M. alternatus_ feeding transcriptome [18] (BioProject accession number PRJNA328403). Four monoterpene synthase genes, unigene 26781, unigene 36618, unigene 26791, and unigene 18558, and two sesquiterpene synthase genes, unigene 380 and unigene 12384, were selected for amplification of the coding sequence (CDS). The total RNA was extracted from the sections of _P. massoniana_ using a Miniprep RNA Purification Kit (TIANGEN, Beijing, China) and reverse-transcribed to synthesize complementary DNA (cDNA) using a 5 \(\times\) All-In-One RT Master Mix (Accurate Biology, China). The SMARTer(r) Rapid Amplification of cDNA Ends 5\({}^{\prime}\)/3\({}^{\prime}\) Kit (Clontech Laboratories, Inc., Mountain View, CA, USA) was used for 5\({}^{\prime}\) or 3\({}^{\prime}\) rapid amplification of cDNA via polymerase chain reaction to obtain the complete sequences. The amplified products were recovered using the MiniBEST Agarose Gel DNA Extraction Kit Ver.4.0 (Takara Bio Inc., Otsu, Japan). The amplified sequences were ligated to a pCE2 TA/Blunt-Zero vector (Vazyme, Nanjing, China) to identify full-length sequences. The full-length sequences of _Pm TPS (+)-a-pinene_, _Pm TPS (-)-a-pinene_, _Pm TPS (-)-\(\beta\)-pinene_, _Pm TPS limonene_, _Pm TPS selinene_, and _TPS longifolene_ were submitted to GenBank (accession numbers MN652001.1, MN652002.1, MN652003.1, MN652005.1, MN652006.1, MN652007.1). The basic local alignment search tool at the National Center for Biotechnology Information was used to identify sequences homologous to Pm TPSs ([[https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)]([https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)) Blast.cgi, accessed on 13 January 2022).
## 3 Results and Discussion
### Simulation of Thermal Profiles Using FSC
#### 3.2.3 Correction for Drift Due to Sample Changes  
For consecutively executed measurements, a correction factor for drift attributed to sample changes was established starting from melting enthalpies normalized to sample mass produced by isothermal crystallization measurements. Melting enthalpies were again obtained by integrating the peak area between the heat flow signal normalized to the heating rate and the baseline fitted onto the heat flow signal. By plotting the melting enthalpies acquired from isothermal crystallization at 125 \({}^{\circ}\)C versus the respective sequence number of the measurement in the array of successively performed measurements, a linear trend can be distinguished. Figure 6 illustrates this trend with an example for isothermal crystallization data obtained during FSC measurements corresponding to Conditions 2,  
Figure 5: Heat flow signals normalized by respective heating rates from FSC [m]/\({}^{\circ}\)C] and DSC [J/\({}^{\circ}\)C] for LMWPA with baselines fitted for determination of FSC sample chip mass.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Material** & \(\mathbf{m_{DSC}}\) [mg] & \(\Delta\mathbf{H_{m,DSC}}\) [J] & \(\Delta\mathbf{H_{m,FSC}}\) [mJ] & \(\mathbf{m_{FSC}}\) [ng] \\ \hline HMWPA & 8.39 & 0.37 & \(1.53\cdot 10^{-3}\) & 98.86 \\ LMWPA & 8.28 & 0.34 & \(4.00\cdot 10^{-3}\) & 33.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the determination of the FSC sample masses for HMWPA and LMWPA.  
3 and 5, which form one measurement sequence. The opposite of the slope of the linear fit can thus be utilized to correct for the, in this case, upwards data drift inherent to less stable samples such as PA. The product of the correction factor and the respective sequence number of the measurement should then be added to the melting enthalpy value normalized to the sample mass. Three distinct measurement sequences were performed to assess the development of crystallinity for the different printing conditions using FSC. The corresponding correction factors are listed in Table 4.
## 2 Methods and Tools
### Satellite Images Time Series Analysis
Modern platforms like Google Earth and Bing Maps further complement this approach with their VHR mosaics, enabling precise tracking of urban and environmental changes [22; 25]. GIS tools integrate these diverse datasets, supporting spatial analyses and visualizations that reveal long-term patterns. For example, combining Corona's historical benchmarks with Sentinel-2's frequent updates in GIS provides a comprehensive view of shoreline evolution. Such diachronic analyses are essential for understanding the drivers of environmental change, mitigating hazards, and promoting sustainable management of the Dead Sea region [25].
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
## 4 Evaluation and Analyses
### Detection of Different Concentrations of Alcohol  
The dielectric constant of different liquids was quite different. Even for the same liquid, different concentrations had a certain effect on the dielectric constant. To verify this, we selected 50% alcohol, 75% alcohol, and 95% alcohol to carry out the experiment. Due to the different physical materials of the container, the WCI amplitude of each subcarrier was also affected by different containers in the same band environment. In addition, the amplitude of the WCI varied with the same kind of container at different wavelengths.  
Figure 10 shows the WCI amplitudes of 30 subcarriers corresponding to different concentrations of alcohol in the C-band where the paper cup, plastic bottle, and glass bottle were detected. We can see that when the object is in the C-band environment, various containers performed differently when identifying different concentrations of alcohol, but they still accurately identified different concentrations of alcohol. This experiment verifies that the types of containers will not affect the system's identification of liquids, and further illustrates the reliability of our system in detecting and identifying different liquids. The conclusion is also applicable to the experimental measurement in the S-band environment.  
Figure 11 shows the detection results of different concentrations of alcohol in C-band and S-band environments. From the figure, we can see that the detection accuracy of the C-band is higher than that of the S-band for the detection of alcohol with different concentrations. In the C-band frequency analysis experiment, the accuracy of the system for the detection of different concentrations of alcohol reached more than 91%, and that of S-band electromagnetic waves was up to 89%. Therefore, the C-band electromagnetic wave is superior to the S-band electromagnetic wave in the accurate detection of different concentrations of the same liquid.  
Figure 10: The wireless channel information (WCI) amplitudes of 30 subcarriers corresponding to different concentrations of alcohol at the C-band by using different containers. **(a)** Using the paper cup; **(b)** Using the plastic bottle; **(c)** Using the glass bottle.  
Figure 9: The KNN algorithm Classification results of Step 2 at the C-band and the S-band.  
As can be seen from Table 2, wireless sensing corresponding the 5G frequency band had excellent detection results for the detection and identification of suspicious liquids, no matter which container was selected. Moreover, our method had better performance and robustness in detecting different concentrations of alcohol, and had more subtle differences in dielectric constant than existing Wi-Fi technologies.
## 2 Methods and Materials
### The Hyperparameter Combinations  
In this study, various machine learning models were employed, each with a range of hyperparameter combinations. For neural networks (NNs), the activation functions used were \"tanh\" and \"relu,\" with hidden layer sizes of 5, 10, 20, and 50. The learning rates tested were 0.01, 0.1, and 0.2, and the \"adam\" solver was utilised. For Random Forests (RFs), the number of estimators was set at 50, 100, and 200, using \"absolute error\" as the criterion and a maximum depth of 10.  
The k-Nearest Neighbours (KNN) model was evaluated with distance metrics corresponding to p values of 1, 1.5, 2, and 3, and the number of neighbours considered was 5, 10, 25, 50, 100, and 150. Both \"uniform\" and \"distance\" weight functions were applied. For XGBoost, hyperparameters included lambda values of 1 and 10, with 100, 300, and 500 estimators. The learning rates were set at 0.01 and 0.05, and maximum depths of 5, 10, and 20 were explored.  
In the LightGBM framework, the number of leaves was tested at 31, 50, and 100, with estimators ranging from 100 to 500. The learning rates were 0.01, 0.1, and 0.2, and the maximum depths were set at 5, 10, and 20. The Support Vector Machine (SVM) model used a linear kernel, with C values of 0.1 and 1 and epsilon values of 0.01 and 0.1. Lastly, for Decision Trees (DTs), the minimum number of samples required to split an internal node was tested at 2, 5, 10, 20, and 50.  
This exploration of hyperparameters allowed for a thorough evaluation of each model's performance across a wide range of configurations.
## 2 Methods
### Scale Adaptive Wavelet Packet Transform
#### 2.3.3 Performance Analysis of Adaptive Wavelet Packet Decomposition for Noisy PD Signal  
Different levels of white noises are mixed with the simulated PD signals to generate noisy signals with SNR of 0 dB, \(-\)2.5 dB, and \(-\)13 dB, the decomposition results using the adaptive wavelet packet decomposition mentioned above are shown in Figure 3.  
As shown in Figure 3, under different levels of white noise interference, the proposed scale adaptive wavelet packet decomposition achieves good balance of noise suppression and waveform distortion by selecting appropriate scale. In order to explain the results quantitatively, further calculation of NCC and SNR are shown in Table 2.  
Figure 3: Adaptive wavelet packet decomposition results of different noisy signals. (**a**) Noisy signal with 0 dB. (**b**) Noisy signal with \(-\)2.5 dB. (**c**) Noisy signal with \(-\)13 dB.  
As shown in Table 2, the adaptive wavelet packet decomposition can suppress white noise effectively in the case of small energy loss, at the same time, the waveform distortion between the extracted PD signal and the simulated real signal waveform is quite small.
## 2 Experimental Section
### Radiochemistry
#### 2.2.1.2 Radiosynthesis of [\({}^{11}\)C]**2a**  
Approximately 1.2 mg of Precursor **4** was placed in the reaction vessel, and 0.20 mL of DMF was added, followed by 3.0 \(\upmu\)L of 5 N NaOH. The mixture was thoroughly mixed on a vortex for 30 s. A stream of [\({}^{11}\)C]CH\({}_{3}\)I in helium was bubbled for 3 min into the reaction vessel. The sealed vessel was heated at 90 \({}^{\circ}\)C for 5 min, at which point the vessel was removed from heat and 20 \(\upmu\)L 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) in 50 \(\upmu\)L DMF was added via syringe. The reaction mixture was heated at 90 \({}^{\circ}\)C for 7 min (Scheme 2); then, the reaction was quenched by adding 1.7 mL of the HPLC mobile phase, which was composed of acetonitrile/0.1 M ammonium formate buffer (60/40, _v/v_) and pH = 4.5. The diluted solution was purified by high performance liquidchromatography (HPLC) by injection on a Phenomenex Luna C18 reverse phase column (9.4 \(\times\) 250 mm) using the mobile phase described above. The radiolabeled product was eluted using a flow rate of 4.0 mL/min, and the UV wavelength was set as 254 nm. Under these conditions, the retention time of Precursor **4** was \(\sim\)7 min; the retention time of [\({}^{11}\)C]2a was \(\sim\)16 min. [\({}^{11}\)C]**2a** was collected in a vial containing 50 mL Milli-Q water, which was then passed through a Sep-Pak Plus C18 cartridge (Waters, Milford, MA, USA). The trapped product was eluted with ethanol (0.6 mL) followed by 5.4 mL 0.9% saline. After sterile filtration, the final product was ready for quality control (QC) analysis and animal studies. QC was performed on a Phenomenex Prodigy C18 reverse phase analytic HPLC column (250 mm \(\times\) 4.6 mm, 5 \(\upmu\)A) and UV detection at a 254 nm wavelength. The mobile phase was acetonitrile/0.1 M ammonium formate buffer (80/20, _v/v_) using a 1.5 mL/min flow rate. Under these conditions, the retention time of [\({}^{11}\)C]**2a** was 4.82 min. The radioactive dose was authenticated by co-injection with the cold standard Compound **2a**. Radiochemical purity was \(>\)99%; the chemical purity was \(>\)95%; the labeling yield was 35%-45% (\(n\) = 4, decay corrected to EOB), and the specific activity was \(>\)363 GBq/\(\upmu\)mol (decay corrected to EOB, \(n\) = 4).
# Model for Quantifying the Quality of Secure Service
## 1 Introduction  
Communication networks rely on a series of wired or wireless channels between intermediate nodes. In addition to noise, these channels may be affected by any combination of three malicious attack vectors: Denial of Service (DoS), data injection, or eavesdropping. A DoS attack may involve cutting a wire or overpowering a particular frequency (jamming). A data injection, or spoofing attack, involves the adversary sending fabricated data that takes the place of actual data. Finally, and the most difficult to discover, is an eavesdropping attack, which involves an adversary intercepting and extracting useful information from the channel. Managing those threats requires an ability to accurately gauge the likelihood or severity of the threat, and adapt the security features available in the system to meet it.  
This paper describes a mathematical model for quantifying the Quality of Secure Service (QoSS) deployed in static communications networks. Just as Quality of Service (QoSS) metrics describe measurable aspects of the available network, QoSS describes, in measurable and repeatable terms, the security available to an end-user, facilitating meaningful comparisons.  
Even when security is momentarily adequate in a communication system, security mechanisms tend to be static, implemented at installation or while running [1], and cannot be adjusted dynamically based on changing environmental conditions or adversarial capability. This document illustrates the mathematical framework and analysis to define the design requirements for networks and provides a foundation for subsequent work analyzing dynamic network security performance in the presence of varied environmentalcharacteristics1. The final model demonstrates the probability of data surviving intact against multiple forms of adversarial actions.
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
## 3 Results and Discussion
### Effect of Point-Cloud-Registration Algorithm  
For most of the system running time, simply using SVD to solve for correspondence points in the point clouds can achieve the desired effect. However, due to factors such as noise or the small number of corresponding points, the transformation solution sometimes falls into a local minimum and the two groups of point clouds after rigid-body transformation have obvious angle differences, suggesting poor stability of the algorithm.  
Figure 14a shows the effect of using the traditional SVD algorithm to solve the transformation. It is not difficult to see that the registration of the two groups of point clouds has not achieved the expected effect, and there is an obvious angle difference between the planes where they are located. After applying the improvements proposed in this paper, it is clearly found from Figure 14b that the matching effect is better at this time. The two groups of point clouds have a small error between points, and their plane normal vectors are also almost parallel.  
During the measurement of the pipe section illustrated in Figure 11, three sets of binocular cameras generate three feature point clouds that were not given in the same coordinate system. To reduce the error generated in the point-cloud-registration process, the three sets of point clouds are all converted to the coordinate system based on the second set of point clouds, and the integration result is shown in Figure 15. Figure 15a shows the three sets of original point clouds before point-cloud registration. After point-cloud registration, the semicircular arc shown in Figure 15b is generated, which proves the effectiveness of the point-cloud-registration algorithm.  
Figure 14: (**a**) Estimation results by SVD; (**b**) estimation results by the improved algorithm.  
Figure 15: (**a**) Raw point clouds; (**b**) point clouds after registration.