esa-sceva/satcom-chunk-collection huggingface dataset:
# BER Aided Energy and Spectral Efficiency Estimation in a Heterogeneous Network
## 2 Analysis
### Spectral and Energy Efficiency Model
Furthermore, we denote the instantaneous transmit power of the serving BS by \(P_{2}^{\text{tx}}\). Firstly, we adopt that a certain UE of an arbitrary location is being served by the micro tier \(i\), with a SINR \(\Gamma_{i}\) that is greater than the threshold \(\gamma\) with the probability \(\mathcal{P}_{i}\). Secondly, we consider that a certain UE is being served by the micro tier \(i\), whereas \(\mathcal{P}_{j}\) is the probability of the UE being served by the pico tier with appropriate \(\mathit{SINR}\). Thereby, from Equations (1) and (2), \(SE_{i}\) and \(SE_{j}\) can be found from:  
\[\mathcal{P}_{i}=\mathcal{P}\big{\{}\Gamma_{i}>\gamma\parallel\mathcal{R}_{i}=r _{i},\mathcal{R}_{j}=r_{j}\big{\}} \tag{12}\]  
\[\mathcal{P}_{j}=\mathcal{P}\big{\{}\Gamma_{j}>\gamma\parallel\mathcal{R}_{i}=r _{i},\mathcal{R}_{j}=r_{j}\big{\}} \tag{13}\]  
Integrating the (exponential) probability density functions of distances between the UE and the serving tier \(i\), as well as from the candidate-serving tier \(j\), provides \(SE_{i}\), and \(SE_{i}\), and, finally, the overall HetNet spectral efficiency, as follows:  
\[SE_{\text{TOT}}=SE_{i}+SE_{j} \tag{14}\]
## 6 Parametric Analysis
### Parametric Analysis for Soil Pressure
#### 6.2.2 Influence of Friction Angles on \(E_{a}\)  
The influences of the friction angles on the resultant force of a finite amount of soil were studied. The friction angles include the internal friction angle \(\varphi\), the friction angle between the enclosure structure and soil \(\delta_{1}\), and the friction angle between the existing subway structure and soil \(\delta_{2}\). For the friction angle between the structure and soil, \(\delta\), \(\delta_{1}=\delta_{2}=\delta\) is assumed to simplify the parameters [1]. The calculation results are shown in Figures 20 and 21.  
Figure 19: The influence of finite soil width.  
Figure 20: The influence of internal friction angle.  
Figure 20 shows that the resultant force of soil pressure is non-linearly decreasing with the increase of \(\varphi\). Furthermore, the effect is very significant. With the increase of internal friction angle, the area of the slip soil decreases, which leads to the weakening of the vertical stress of the soil, and the shear stress on the slip surface increases. Figure 21 shows that the resultant force of soil pressure is also decreasing with the increase of \(\delta\) because the increase of structure-soil friction angle \(\delta\) causes the interface friction between soil and structure.
## 3 Proposed Architecture
### Accelerated Algorithm Based on GPU  
Our approach accelerates the proposed position estimation of a moving target using GPU. The GPU is an architecture that has strength in parallel computing, and is used for high-speed computation of a large amount of data. The proposed GPU-based moving target position estimation can be divided into the operation of obtaining the TDOA measurement value from the sensor, the operation of PF processing, and the tracking of the moving target using multiple system state equations.  
The calculation of the TDOA measurements in the frequency domain uses GCC-PHAT. Therefore, we use the cuFFT library supported by CUDA for accelerating the computation of TDOA measurements using Fast fourier transform. To accelerate Fourier transform on the data using the same coefficients, the Fourier transform operations for multiple sensors are processed in parallel as shown in Figure 4 by using the batch function of cuFFT. At this time, when the number of sensors is K and the amount of data obtained from each sensor is N, the amount of data copied to the GPU device is K*N. Because the position estimation of the moving target using TDOA measurement is more accurate with the use of many sensors, the proposed accelerated TDOA measurement calculation has an advantage as the data amount increases. As mentioned before, since the proposed algorithm is scalable for number of sensors, the proposed parallel Fast fourier transform is also scalable in the number of sensors.  
Also, we propose the parallel computing of the PF using GPU as shown in Figure 5. Because the PF processing depends on the number of particles, the entire process can be accelerated by paralleling and accelerating the operation of each particle in GPU. In particular, the process of estimating the position of moving targets required the acceleration in performing the weight calculation process of each particle in parallel through the process of updating the state of each particle and calculating the measured value from the state.  
Figure 4: Parallel fast fourier transform using GPU.  
Figure 5: Particle filter (PF) acceleration in GPU.
## 4 Experimental Analysis  
We first evaluated the pseudo-range and carrier-phase measurement noise of the Swarm, GRACE, and Sentinel spaceborne GPS data. Then, the spaceborne observation data are simulated for the designed five LEO constellations, applying the real-calculated measurement noise. The BDS/LEO POD is finally evaluated with and without considering the spaceborne observation errors.
## 3 RFI Detection and Mitigation Algorithm in Synthetic Aperture Microwave Radiometry Imagery  
Before describing the proposed RFI detection and mitigation algorithm, it is important to understand the two basic modes of operation of the MIRAS instrument aboard SMOS:  
1. Dual-polarization mode: antenna switches sequentially connect all the receivers to the X-polarization antenna probe or to Y-polarization antenna probe (one receiver per dual-polarization antenna element), obtaining a BT image at X- or Y-polarization in one switching interval (1.2 s). This mode of operation corresponds to the SMOS images processed corresponding to March 3, 2010.  
2. Full-polarimetric mode: the antenna switches connect the receivers in one array arm either to the X- or Y-polarization antenna probes following a predetermined sequence. Figures 11 and 12 adapted from [12] show these sequences. What is important in this mode is that the four Stokes parameters images are obtained after four switching intervals, and due to the pulsed nature of many RFI sources (e.g., pulsed and/or rotating radars...) RFI can happen in one of the intervals, and not in the others, in an unpredictable manner. In the SMOS imagery, the effects are subtle, since in the main beam a \"hot\" spot appears, but the side lobes and tails of the impulse response are distorted, and fictitious third and fourth Stokes parameters appear. This mode of operation corresponds to the images processed from September 3, 2010.  
Figure 10: RFI in SMOS (dual-pol mode): Scenario 4: Tens of RFI sources in the whole BT image including the aliased regions in the full hexagon where the image is reconstructed.  
For these reasons, two different algorithms have had to be developed, one for each mode of operation.
## 2 Data and Methods
### Pigment Retrieval from \(R_{\text{rs}}(\lambda)\)
#### 2.5.2 \(a_{\text{Gau}}(\lambda)\) Versus Cpigs  
[PERSON] and [PERSON] [64] indicated that each Gaussian curve represents the absorption contributed by one or multiple pigments. However, attempts to obtain the concentration of a specific pigment from a single Gaussian curve is not always successful [39; 65; 66]. For a better understanding of the Gaussian curves and their relationships with Cpigs, a series of regression analyses were applied to relate \(a_{\text{Gau}}(\lambda)\) with Cpigs for data from SeaBASS. The t-statistics and \(p\)-value were calculated to test the significance of the parameters. Using \(p<0.05\) as the criteria, the significant contributors to each Gaussian peak and the corresponding R\({}^{2}\) of these parameters were obtained (see Table 3). The possible existence of other pigments that are not detectable with current HPLC techniques, such as phycoerythrin (PE) and phycocyanin (PC), likely explains the relatively lower R\({}^{2}\) values for Peaks 7, 8, 9 and 10.  
After a series of multivariable regression analyses, it was found that Cpigs could be estimated from \(a_{\text{Gau}}(\lambda)\) following the function:  
\[\log_{10}(\text{Cpigs})=a_{0}+\sum\limits_{i=1}^{n}a_{i}\log_{10}(a_{\text{ Gau}}(\lambda_{i})) \tag{11}\]  
The corresponding \(a_{\text{Gau}}(\lambda)\), parameters, and the R\({}^{2}\) value are shown in Table 4. Further, it was found that the estimated Cpigs agree with the measured values very well throughout the concentration range when the relationships were applied to the validation dataset (with data points scattered closely to the 1:1 line; Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(p\)-Value} & Peak & Peak & Peak & Peak & Peak & Peak & Peak & Peak & Peak & Peak & Peak \\  & **406** & **434** & **453** & **470** & **492** & **523** & **550** & **584** & **617** & **638** & **660** & **675** \\ \hline Chl-a & 0.01 & 0.00 & 0.00 & & & 0.01 & 0.03 & 0.00 & 0.00 & 0.00 & 0.00 \\ Chl-b & & 0.03 & & 0.00 & & & & & & & 0.00 & 0.00 \\ Chl-c & & & & & & & & & & & & 0.00 & 0.04 \\ PPC & 0.00 & & 0.00 & 0.00 & & & & & & & & \\ PSC & & & & 0.00 & 0.02 & 0.01 & & & & & & \\ R\({}^{2}\) & 0.80 & 0.87 & 0.83 & 0.87 & 0.83 & 0.78 & 0.64 & 0.68 & 0.76 & 0.73 & 0.81 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The \(p\)-value and R\({}^{2}\) from the t-statistics, with \(a_{\text{Gau}}(\lambda)\) at 12 different wavelengths: 406, 434, 453, 470, 492, 523, 550, 584, 617, 638, 660, and 675 nm, and Chl-a: chlorophyll \(a\), Chl-b: chlorophyll \(b\), Chl-c: chlorophyll \(c\), PPC: photo-protective carotenoids, PSC: photosynthetic carotenoids.  
Figure 2: \(a_{\text{Gau}}(\lambda)\) estimated pigment concentrations versus the measured concentrations from HPLC using the \(a_{\text{ph}}(\lambda)\) and HPLC from SeaBASS: **A**: chlorophyll \(a\) (Chl-a), **B**: chlorophyll b (Chl-b), **C**: chlorophyll c (Chl-c), **D**: photoprotective carotenoids (PPC), and **E**: photosynthetic carotenoids (PSC).
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 3 Results  
### Modeling Results  
Random Forests, XGBoost, and LightGBM could predict the SPAD values by capturing the color changes in different light environments (Figure 2). Among the three algorithms, Random Forests showed the highest accuracy in SPAD value prediction. Therefore, we will focus on the results from the Random Forests computations in the subsequent analyses. Figure 3 shows a scatter plot between the observed and predicted values of the best model with Random Forests for the five-fold cross-validation, and Figure 4 shows the results of the best model with Random Forests for illuminance-limited training data (LED_RES). The accuracy of both models was high, with a COR of 0.85 or higher.  
study, the model with the HSL value and illuminance as the model input (LED_RGB+ILL) using data obtained using LED light sources had the highest accuracy when COR was used as the performance measure (Figure 3j).  
In this study, the prediction accuracy was high, with a COR of 0.85 or higher even in the case of limited illuminance, and the applicability was verified under extrapolated conditions (LED_RES). In this case, the highest prediction accuracy was obtained when the HSL value was used as the model input, as in the case of data obtained under LED light sources (Figure 4b).
## 4 Experimental Results and Analysis  
We used the GIS vector maps of the country of Scotland [18] with differential scales in visualization experiments, and evaluation of security and computation time. The detailed information of the GIS vector maps is shown in Table 1. The data format of the GIS vector map was the shape-file (SHP) format [19], which is a popular geographical vector data format. The proposed algorithm was applied to the polylines and polygons of the vector maps. The backbone encryption process and the feature vertices encryption process were performed by the AES algorithm. We selected the AES algorithm because its security is higher than others. Compared with conventional approaches, the proposed algorithm is more original than previous methods because it encrypts objects based on encrypting the value of feature vertices in polylines/polygons. Consequently, it does not alter or expand the size of the encrypted file, thus preventing data loss. The GIS vector maps are completely altered after the random encryption process (see Figures 5-9).  
Figure 5: (**a**) Original railway map, and (**b**) encrypted railway map.  
Figure 6: (**a**) Original land-use map, and (**b**) encrypted land-use map.  
Figure 8: (**a**) Original nature map, and (**b**) encrypted nature map.  
Figure 7: (**a**) Original waterway map, and (**b**) encrypted waterway map.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 5 Experiments
### Experimental Results and Analysis
#### 5.2.1 Simulation with Different Motion Speeds  
The number of nodes and the running time were set to 30 and 200 s, respectively, and the speed of the nodes increased from 3 to 15 m/s. The performance of the three protocols was observed. The end-to-end delay between nodes is shown in Figure 3.  
It can be seen from the figure that as the nodes move faster, the end-to-end delay is higher in AODV and AODV-UU. Obviously, the two protocols are too sensitive to the movement of the node. The average end-to-end delay of the AODV-EOCW routing protocol also increases significantly, but it still has obvious advantages compared with the other two protocols. The authors believe that this is because the AODV and AODV-UU protocols choose the communication link with the minimum number of hops; as the nodes move faster, the broken links cause AODV and AODV-UU to rebroadcast packets to find new routes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wireless module & Ieee80211 ScalarRadioMedium \\ Motion module & MassMobility \\ Energy module & StateBasedEpEnergyConsumer \\ Visualization module & IntegratedCanvasVisualizer \\ Node’s initial energy & 1 J \(\sim\)5 J \\ Shutdown threshold & 0.1 J \\ Antenna’s transmission power consumption & 3 mW \\ Node’s range of motion & 1000 m \(\times\) 1000 m \\ Receive buffer size & 17 \\ Limit of retransmission times & 7 \\ Direction of movement & Random \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experiment parameters.  
In terms of the message delivery rate, the results are shown in Figure 4. It can be seen from the figure that although the message delivery rates of all three routing protocols show a downward trend as the nodes move faster and faster, AODV-EOCW still exhibits a far better performance than the other two protocols. The main reason for the low message delivery rate of the other two routing protocols is that only routing hops are used as routing metrics. Therefore, it is very easy to cause some routing nodes to be congested and the packet to be lost. Clearly, the better delivery rates of AODV-EOCW are due to the multimetric routing protocol design.  
Finally, survival rate experiments of the same experiment setup are shown in Figure 5. It can be seen that as the nodes move faster, the survival rate of the nodes decreases. The rapid movement of nodes leads to a rapid change in the network topology. The network is prone to link breakages in this situation. Thus, the node sends more control packets to sense the network's status and also consumes more energy. The experimental results show that the AODV-EOCW routing protocol has obvious advantages.  
Figure 4: Comparison of the average message delivery rates.  
Figure 3: Comparison of the average end-to-end delay.
## 2 Bearing Failure Modes
The advantages of vibration analysis methods are high sensitivity, strong real-time performance, and non-contact, usually with low cost and easy implementation, suitable for online monitoring and early fault detection. However, in complex environments, signals often contain noise and require complex signal processing methods to improve accuracy. Figure 3: Principle of vibration analysis: The vibration analysis process generally includes steps such as data acquisition, signal preprocessing, feature extraction, and fault diagnosis. First, the bearing’s vibration signals are collected using sensors, followed by filtering and denoising to improve analysis accuracy. Then, key features are extracted using time domain, frequency domain, and time–frequency analysis methods to determine whether faults exist and identify their types. Also, diagnostic accuracy may be affected by the vibration of other equipment, necessitating dependence on empirical judgment and professional knowledge.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 4 Temperature Compensation
### Temperature Compensation of Through-Cabin Coaxial Cables  
The internal calibrator and the antenna calibration networks are connected through coaxial cables passing through the cabin, with coaxial cables length of approximately 4.5 m. The large cable length generally affects the temperature measurement accuracy. However, due to the average power of the signal transmitted in the coaxial cables being below 0.2 W, and the longest imaging duration for a single startup being 15 min, it is analyzed that the maximum temperature rise of the cables is less than 5 \({}^{\circ}\)C. Based on this, the temperature compensation error of the through-cabin coaxial cables can be calculated, as shown in Figure 11.  
The cable temperature is measured during in-orbit operation and compensated during ground processing. If the temperature measurement accuracy is 10 \({}^{\circ}\)C, the amplitude error of the through-cabin coaxial cables after temperature compensation is less than 0.04 dB, and the phase error is less than 0.4\({}^{\circ}\). The two-way amplitude error is less than 0.08 dB, and the phase error is less than 0.8\({}^{\circ}\).  
Figure 11: Temperature characteristics of the through-cabin coaxial cables under temperature conditions of \(-\)5 \({}^{\circ}\)C\(\rightarrow\)75 \({}^{\circ}\)C. (**a**) Phase characteristics of the through-cabin coaxial cables. (**b**) Amplitude characteristics of the through-cabin coaxial cables.  
Figure 10: Temperature compensation test results of the internal calibrator under temperature conditions of \(-\)10 \({}^{\circ}\)C\(\rightarrow\)20 \({}^{\circ}\)C. (**a**) Phase compensation curve of the internal calibrator. (**b**) Amplitude compensation curve of the internal calibrator. (**c**) Phase compensation error of the internal calibrator. (**d**) Amplitude compensation error of the internal calibrator.
## Appendix A  
Figure 14 shows the computing (CPU) time required for the classical and structural engineering LSFs (Figure 15a) and for the coastal engineering LSF (Figure 15b). The CPU times include computing of MCS and probabilities of failure, fitting of the normality polynomials and obtaining of the reliability parameters by multi-linear regression. The employed processor is an Intel(R) Core(TM) i7-9750H CPU @ 2.60 GHz with RAM of 16.0 GB and operating system of 64 bits.  
It is highlighted that CPU times are for 1000 sets of \(n_{sim}\) and for 100 sets of \(n_{sim}\) (as indicated in the horizontal axes of the figure) for the classical and RCB LFSs and overtopping LSF, respectively. It can be observed in Figure 15 that the CPU times are significantly larger for the coastal engineering application, as shown by the different ranges used in the vertical and horizontal axes and by the fact that only 100 sets of \(n_{sim}\) are used for this case (compared to 1000 sets for the others, as mentioned before). This significant larger computing time is imposed by the joint distribution of wave heights and periods and the Longuet-Higgins distribution used to represent them, which must be solved numerically and for which a different sampling technique is required, as indicated in the main body of this article.  
Figure 14 could assist the readers to establish feasible simulation schemes. It was noticed that efficient computing time is obtained if 10,000 simulations at a time are considered for the overtopping LSF. See for instance that for \(n_{sim}=100\) in Figure 14 (which translates into \(100\times 100=10\),000 MCS), a reasonable CPU time is required; in fact, it is shown in Figure 14 that over this threshold the CPU time starts to increase to a much faster rate. Therefore, once the random numbers are simulated (this is not a problem in terms of CPU times for millions of random numbers), a programming scheme dividing the computing in 10,000 MCS can be used to improve the efficiency (e.g., subdividing the tasks within the same program, running several windows simultaneously and/or using several computers).
# Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review
###### Abstract  
This review paper summarizes the recent and past experimental findings to evaluate the damage characteristics of carbonate rocks subjected to thermal treatment (20-1500 \({}^{\circ}\)C). The outcomes of published studies show that the degree of thermal damage in the post-heated carbonate rocks is attributed to their rock fabric, microstructural patterns, mineral composition, texture, grain cementations, particle orientations, and grain contact surface area. The expressive variations in the engineering properties of these rocks subjected to the temperature (>500 \({}^{\circ}\)C) are the results of chemical processes (hydration, dehydration, deionization, melting, mineral phase transformation, etc.), intercrystalline and intergranular thermal cracking, the separation between cemented particles, removal of bonding agents, and internal defects. Thermally deteriorated carbonate rocks experience a significant reduction in their fracture toughness, static-dynamic strength, static-dynamic elastic moduli, wave velocities, and thermal transport properties, whereas their porous network properties appreciate with the temperature. The stress-strain curves illustrate that post-heated carbonate rocks show britteness below a temperature of 400 \({}^{\circ}\)C, brittle-ductile transformation at a temperature range of 400 to 500 \({}^{\circ}\)C, and ductile behavior beyond this critical temperature. The aspects discussed in this review comprehensively describe the damage mechanism of thermally exploited carbonate rocks that can be used as a reference in rock mass classification, sub-surface investigation, and geotechnical site characterization.  
m meral dilatancy; thermal cracking; static-dynamic compression; brittle-ductile transition; thermal transport properties +
Footnote †: journal: Applied Sciences
## 4 Methodology and Testing Process  
The experimental process was carried out in three steps: dataset processing, model training, and model testing and generation, as shown in Figure 2.  
The dataset processing involved manually labeling the collected floor plans to create functional space segmentation masks, thereby establishing the dataset. Unlike the application of GAN models, the SD is a text-prompt-guided image generation model. Therefore, we used a tagging tool [30] to attach corresponding descriptive text to each image, generating a corresponding.txt file. The image files and.txt files together form the final training dataset. This experiment relied on the Google Colab platform, utilizing the interactive computing environment Jupyter Notebook for model training and dataset debugging. Model testing and generation involved hyperparameter tuning and image output. This step was performed on the Stable Diffusion WebUI platform, a Python web UI framework suitable for showcasing deep learning tasks. It incorporates various common functionalities of the SD model and supports extensions such as ControlNet and LoRA [25; 26; 27; 28].  
Figure 2: Methodological framework of the experiment.