esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
## 2 Materials and Methods
These authors used 14 inputs: the mass of water, cement, sand, natural coarse aggregate, recycled coarse aggregate used in the mix designs, water-to-cement ratio of concrete, fineness modulus of sand, water absorption of the aggregates, saturated surface-dried (SSD) density, maximum size, and impurity content of recycled coarse aggregate, the replacement ratio of recycled coarse aggregate by volume, and the coefficient of different concrete specimens. All of these parameters were the same as those used in this study, except the impurity content of recycled coarse aggregate, the replacement ratio of recycled coarse aggregate by volume and the coefficient of different concrete specimens. The results obtained in the training set (146 sets) were: R\({}^{2}\) = 0.998; RMSE = 1.7958 and MAPE = 0.2622, while in the testing set (22 sets) they were R\({}^{2}\) = 0.9955; RMS = 3.6804 and MAPE = 1.6777. From an engineering point of view, the 14-input ANN model does not present significant differences with respect to the 11-input ANN models proposed in this study. In order to compare the results obtained with the ANN, Gaussian Process Regression, Matern 5/2 (GPR); Support-Vector Machines (SVM); and Linear Regressions (LR) have been carried out on the same data using MATLAB. The results of each of these methods appear in Table 4. where the superiority of ANN over the other methods is clearly revealed. Once it is clear that ANN provides the best results for these kinds of data, there is still a question to be answered: whether it is possible to simplify the process of obtaining compressive strength predictions while eliminating some of the input data. For that purpose, four analyses have been carried out with the same ANN: (i) substituting C and FA by their sum; (ii) eliminating SP; (iii) eliminating FM of FNA; and (iv) eliminating TM. The results of each one of these new tests are shown in Table 5.  
In all the new cases tested, the results are worse than those obtained with the complete ANN. Only in the case (iv) of eliminating the maximum particle size of coarse aggregate (TM), are the results similar to those of the complete ANN. Regardless, the difference seems not to be enough to opt for this set of data, since it does not simplify the model substantially. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **MAE** & **RMSE** & **R All** \\ \hline GPR & 2.793 & 3.5018 & 0.9487 \\ SVM & 2.8591 & 3.5099 & 0.9487 \\ LR & 3.1391 & 3.6435 & 0.9434 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of regressions methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **MAE** & **Std. Dev.** & **MAPE** & **RMSE** & **R Training** & **R All** \\ \hline i & 4.45 & 5.52 & 11.52 & 5.92 & 0.94589 & 0.91605 \\ ii & 2.04 & 2.75 & 5.32 & 2.74 & 0.98228 & 0.97994 \\ iii & 2.17 & 3.14 & 6.01 & 3.13 & 0.98347 & 0.97350 \\ iv & **1.72** & **2.38** & **4.62** & **2.37** & **0.98756** & **0.98486** \\ \hline \end{tabular}
\end{table} 
Table 5: Results of the simplified ANNs.
## 3 Results and Discussion
### Daughter Plants Classification and Stolon Morphology  
The size classification of daughter plants was not influenced by light intensity in either growing cycle (Figure 8); around 60% to 66% of daughter plants were in the small and medium size classification and 20% to 26% were in premature bud classification.  
The total number of daughter plants per stock plant per light treatment was the same between the two growing cycles (Figure 8) but the number of premature buds (PM in Figure 2) was higher in the 12 to 21 week cycle (Figure 8). The increase in premature buds from 12 to 21 weeks is associated with an increase of stolon number. Therefore, the total number of mature buds (XS, S, M, L, XL, in Figure 2) in 12 to 21 weeks would be greater than that in the 01 to 12 week cycle, if harvesting was extended for several more weeks.  
\begin{table}
\begin{tabular}{c c c} \hline
**Plant Stage** & **Percent Daughter Plant Dry Mass** & **Percent Stolon Dry Mass** \\ \hline
01–12 weeks & 63.8 \(\pm\) 2.1\% & 36.2 \(\pm\) 2.0\% \\
12–21 weeks & 55.0 \(\pm\) 1.4\% & 45.0 \(\pm\) 1.4\% \\ \(p\)-value & \(p\) \(<\) 0.0001 & \(p\) \(<\) 0.0001 \\ \hline \end{tabular}
\end{table}
Table 3: Dry mass percent distribution of stolons and daughter plants for day neutral strawberry “Albion” grown under light treatments for 01 to 12 and 12 to 21 weeks. \(p\)-value represents differences between the two growing cycles (\(p\)\(\leq\) 0.05).  
Figure 7: Effect of light intensity on stock plant stolon number of day neutral strawberry “Albion”. * represents differences between the 01 to 12 week cycle and the 12 to 21 week cycle (\(p\)\(\leq\) 0.05). Lowercase and uppercase letters represent differences between light treatments by growing cycle (\(p\)\(\leq\) 0.05).
###### Abstract  
This study investigates the absorption of X-rays in mandibular tissues by comparing real tissues with tissue-equivalent materials using the PHITS Monte Carlo simulation program. The simulation was conducted over a range of X-ray photon energies from 50 to 100 keV, with increments of 5 keV, to evaluate the dose absorbed by different tissues. Real tissues, such as the skin, parotid gland, and masset muscle, were compared with their tissue-equivalent polymeric materials, including PMMA, Parylene N, and Teflon. The results showed that the real tissues generally absorbed more X-rays than their corresponding equivalents, especially at lower energy levels. For instance, at 50 keV, differences in the absorbed doses reached up to 50% for the masseter muscle and its equivalent, while this gap narrowed at higher energies. The study highlights the limitations of current tissue-equivalent materials in accurately simulating real tissue behavior, particularly in low-energy X-ray applications. These discrepancies suggest that utilizing tissue-equivalent materials may lead to less accurate medical imaging and radiotherapy dose calculations. Future research should focus on improving tissue-equivalent materials and validating simulation results with experimental data to ensure more reliable dosimetric outcomes. This study provides a foundation for refining radiation dose calculations and improving patient safety in clinical applications involving X-rays.  
X-ray absorption; tissue-equivalent materials; PHITS simulation; dosimetry; mandibular tissues +
Footnote †: journal: Applied sciences  
0
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# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 3 Spoofing Detection Method
### Eddm-Msc-Mmp  
During the signal tracking phase, the receiver stripped the satellite signal carrier. Subsequently, in the EDDM-MSC-MMP workflow, the system initially acquired the output values of the correlator. The acquired output values were then used to construct an eye diagram, in which the height of the eye indicates the signal power. Then, the MSC-MMP algorithm was applied to extract the edges of the eye diagram. Finally, the EDDM method was employed to determine whether signal spoofing exists. In this process, EDDM identified the maximum connected domain and calculated its eye height, dynamically comparing these time-varying data with a pre-set threshold to confirm the presence of spoofing. The EDDM-MSC-MMP detection process is described in Figure 1.
## 1 Introduction
### Organization  
The remainder of this paper is organized as follows. In Section 2, we present the system model and formulate the channel allocation problem under the constraint of the re-allocation rate. In Section 3, we propose the solution framework based on the Lyapunov optimization. In Section 4, we present and discuss the simulation results. Finally, Section 5 concludes the paper.
# Integration of AI in Self-Powered IoT Sensor Systems
## 3 Domain Identification in the Literature Using IoT Sensors
Sensors must be biocompatible, non-invasive, or minimally invasive. The data requires real-time and secure transmission in accordance with the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA) regulations. Another concern is energy autonomy, especially when these devices are wearable or implantable. For smart mobility, integrated sensors at the level of road infrastructure, as well as vehicles, require portable or real-time monitoring, which faces the issue of latency that needs to be minimized as much as possible.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 5 Experiments Results
### Model Training  
The dataset is divided into training, validation, and test sets in a 7:2:1 ratio. During model training, the batch size is set to 32, and the number of training epochs is set to 300. Stochastic Gradient Descent (SGD) is used as the optimizer, with an initial learning rate of 0.01. An early stopping mechanism is applied to prevent overfitting by evaluating model performance on the validation set after each epoch. If multiple consecutive evaluations show no significant improvement, training is halted to avoid overfitting. In this case, training stopped early at the 295 th epoch. The loss variation curves are presented in Figure 18, illustrating the following from left to right: Distribution Focal Loss (DFL) improves localization precision by modeling bounding box coordinates as discrete distributions; bounding box loss evaluates the regression accuracy, often using IoU-based metrics; classification loss measures the discrepancy between predicted and true class labels, typically via cross-entropy or focal loss.  
The changing curves of precision, recall, mAP50 and mAP50-95 during training are shown in Figure 19. After 200 rounds, the curves have basically converged.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Category** & **Positive** & **Negative** \\ \hline True & \(TP\) & \(FN\) \\ False & \(FP\) & \(TN\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix.
###### Abstract  
Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country's commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries.  
forest fire; burned area; NBR; Sentinel-2; Sentinel-5; GEE; time series analysis +
Footnote †: journal: Geo-Informat
# Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System
## 5 Case Study
### Low-Carbon Analysis  
Figure 9 suggests that under the constraint of RPLTCTM, PIES simultaneously generates carbon emissions in both ladder 1 and ladder 2 only when the outputs of MT and GB are high while the carbon emissions of PIES in ladder 3 during the dispatching cycle are zero. In addition, PIES reasonably allocates carbon quota in periods of high carbon emissions by the system. This analysis shows that RPLTCTM effectively limits the carbon emissions of PIES.  
To verify the low-carbon nature of the collaborative carbon control strategy, this section sets up four scenarios based on the constructed model to further investigate the effects of factors such as the carbon trading mechanism, carbon capture device, and carbon quota mechanism on the operation of PIES. The specific scenarios are set up as follows:  
Scenario I: Stepped carbon trading mechanism considered; carbon capture and carbon quota not considered.  
Scenario II: Stepped carbon trading mechanism considered, carbon capture considered, and carbon quota not considered.  
Scenario III: Stepped carbon trading mechanism considered, carbon capture not considered, and carbon quota considered.  
Scenario IV: Stepped carbon trading mechanism considered; carbon capture and carbon quota both considered.  
It can be seen in Table 3 that scenario IV can better realize low-carbon economic operation of PIES by jointly considering the stepped carbon trading mechanism, carbon capture, and carbon quota mechanism. Most significantly, compared with Scenario I, which only considers the stepped carbon trading mechanism, Scenario IV can reduce PIES carbon emissions and operating costs by 45% and 3%, respectively. Meanwhile, compared with Scenario II and Scenario III, it can be noticed that the carbon capture equipment can dramatically reduce the system carbon emissions by 31.8%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Scenario I** & **Scenario II** & **Scenario III** & **Scenario IV** \\ \hline Operating & 5479.63 & 5444.77 & 5329.63 & 5294.77 \\ Costs/V & 3926.35 & 2471.82 & 3626.35 & 2171.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of the low-carbon characteristics of PIES.  
Figure 9: PIES optimal carbon emission plan.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results
### Further Optimization of the DSCNN-GRU Surrogate Model  
The previous analysis compared various coupling models, demonstrating that the DSCNN-GRU not only maintained the prediction accuracy of the proxy model but also significantly improved its efficiency. To further improve the proposed DSCNN-GRU model, the impact of network parameters was examined, including variations in the number of convolutional layers, optimizers, and activation functions. Initially, the experiment assessed three, four, and five convolutional layers to identify the optimal configuration. Subsequently, Adam, Nadam, and RMSprop were employed as optimizers to determine the most effective option. Finally, Relu, Softmax, and Tanh were evaluated as activation functions to ascertain the best choice.  
The Nadam optimizer combines the principles of the Adam optimizer with Nesterov momentum. Its primary objective is to accelerate convergence and enhance training stability, making it particularly effective for handling large datasets with high similarity. In contrast, RMSprop is an adaptive learning rate method that facilitates timely adjustments to the learning rate, thereby reducing potential data loss caused by inappropriate learning rates, especially when the complexity of the water head field data in the training samples varies.  
Among the various activation functions, the Softmax function is frequently employed for multi-class classification problems. However, it is important to note that the exponential calculations inherent in the Softmax function can cause the output value to approach 1 when the input value is large and to approach 0 when the input value is small. When there is a substantial disparity between the input values, this can lead to saturation effects and potential vanishing gradient issues. Given the highly complex head field data present in the training samples, the differences between individual samples may be significant, resulting in increased training time and reduced prediction accuracy for models utilizing this function. Conversely, the Tanh (hyperbolic tangent) function is an S-shaped function that maps real number inputs to a range between \(-1\) and \(1\). It exhibits symmetry in handling both positive and negative inputs; however, the data in the training samples of this study are highly nonlinear and do not demonstrate symmetry across a wide range. Consequently, the prediction accuracy of models employing this function may not be optimal.
## 4 Materials and Methods
### Gene Expression Analyses for CCL20 and CCR6 in PBMCs  
PBMCs from 40 subjects were thawed in medium consisting of 1640 RPMI with glucose and L-glutamine (Life Technologies, Paisley, UK), augmented with 5% human AB\({}^{+}\) serum, 2 mM glutamine and 0.05% gentamicin (supplemented RPMI). Cells were washed with PBS, followed by centrifugation (8 min at 450\(\times\)\(g\)). The cell pellet was snap frozen in liquid nitrogen and kept at \(-\)80 \({}^{\circ}\)C. PBMCs from three healthy donors stimulated with CCL20 and unstimulated control cells were washed with PBS and snap frozen in liquid nitrogen, after lysis buffer treatment. RNA extraction was done in lysis buffer using the RNeasy mini kit and QiaShredder columns from Qiagen (Hilden, Germany). RNA quality control was done with NanoDrop Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) for quantity and purity. Qbit (Thermo Fisher Scientific, Waltham, MA, USA) and Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) were used to determine RNA integrity. Samples with a RIN < 4.5 were excluded from the final analysis (final \(n=38\)). Isolated RNA was stored in cryotubes in liquid nitrogen. Reverse transcription was done using the High Capacity RNA-to-CDNA kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions. PerfeCTa assay was used for CCL20 and CCR6 detection with PerfeCTa qPCR FastMix (Quantabio, Beverly, MA, USA) and TaqMan probes (CCL20: probe ID Hs01011368_m1, CCR6: probe ID Hs01890706_s1, (Applied Biosystems, Foster City, CA, USA) with the reference genes _beta actin_ (ACTB: probe ID Hs001060665_g1), _TATA box binding protein_ (TBP: probe ID Hs00427620_m1) and _eukaryotic 18S rRNA_ (18S: probe ID Hs99999901_s1). StepOnePlus Real-Time PCR System and StepOne software v. 2.1 (Applied Biosystems,Foster City, CA, USA) was used for all PCR procedures. Fold changes were calculated using the \(\Delta\Delta\)C\({}_{\rm T}\) method, where individual expression levels were relative to the mean of reference genes and further relative to the mean in the healthy control group (N).
# Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
###### Abstract  
Compressive sensing theory has addressed the limitations of traditional methods in the field of information technology, and led to a revolution. On the basis of compressive sensing theory research, this study utilized the exterior determinacy and inherent randomness of chaotic sequences, designed a pseudo-random circulant measurement matrix based on chaotic sequence. Compared with other deterministic measurement matrices, the restoration effect of the designed measurement matrix remarkably improved and showed advantages in hardware and storage. Then, this study developed a single-pixel imaging scheme that could accurately obtain color information. The proposed improved measurement matrix combined with the hardware system could accurately reconstruct color images and had good robustness according to various experimental data.  
single imaging system; compressive sensing; measurement matrix +
Footnote †: journal: Applied Sciences
# Fast and Efficient Lunar Finite Element Gravity Model
## 3 Representations of Lunar Potential Using Orthogonal Finite Elements
### Orthogonal Approximation of the Gravitational Acceleration  
Using orthogonal approximation, the lunar gravitational acceleration is estimated by transforming gravity into sample points. These sample points are located in the distribution in the least-squares approximation. In this subsection, a brief discussion is given.  
Figure 6: Comparison between shell sizes of \((4\times 4)\) and \((6\times 6)\) square degrees.  
Let gravity data \(U(r,\lambda,\phi)\) transform to \(U(\xi_{i},\zeta_{j},\eta_{k})\) with sample points located as a cosine distribution. For the three components of gravity, the approximation is as follows:  
\[\begin{split} G_{x}(\xi_{i},\zeta_{j},\eta_{k})& \cong\sum_{\kappa=0}^{N_{x}}\sum_{\beta=0}^{N_{y}}a_{\alpha\beta}(\xi_{i}) \phi_{\alpha\beta}(\zeta_{j},\eta_{k})=\Psi_{x}^{\mathsf{T}}(\zeta_{j},\eta_{k} )A_{x}(\xi_{i})\\ G_{y}(\xi_{i},\zeta_{j},\eta_{k})&\cong\sum_{\kappa=0 }^{N_{x}}\sum_{\beta=0}^{N_{y}}A_{\alpha\beta}(\xi_{i})\phi_{\alpha\beta}(\zeta _{j},\eta_{k})=\Psi_{y}^{\mathsf{T}}(\zeta_{j},\eta_{k})a_{y}(\xi_{i})\\ G_{z}(\xi_{i},\zeta_{j},\eta_{k})&\cong\sum_{\kappa=0 }^{N_{x}}\sum_{\beta=0}^{N_{y}}a_{\alpha\beta}(\xi_{i})\phi_{\alpha\beta}( \zeta_{j},\eta_{k})=\Psi_{z}^{\mathsf{T}}(\zeta_{j},\eta_{k})A_{z}(\xi_{i}) \end{split} \tag{31}\]  
where the indices \((i,j,k)\) range from 0 to \((M_{r},M_{\lambda},M_{\phi})\), and \(\Psi_{x,y,z}\) are the basis functions (which are transposed) of the acceleration components.  
Furthermore, Equation (32) can be expressed more compactly if the same basis function for the acceleration components uses \(\Psi=\Psi_{x}=\Psi_{y}=\Psi_{z}\), which provides us with the following relation: \(\Psi=\Psi_{x}\otimes\Psi_{y}\otimes\Psi_{z}\). We then utilize the tensor product of \(\Psi(\eta_{j},\zeta_{k})\) and the identity matrix, \(I_{n}\), twice to \"undo\" the transpose acceleration components. This creates the finalized gravitational acceleration approximation equation:  
\[G(\xi_{i},\zeta_{j},\eta_{k})=\begin{bmatrix}G_{x}(\xi_{i},\zeta_{j},\eta_{k} )\\ G_{y}(\xi_{i},\zeta_{j},\eta_{k})\\ G_{z}(\xi_{i},\zeta_{j},\eta_{k})\end{bmatrix}=[(\Psi(\zeta_{j},\eta_{k}) \otimes I_{n})\otimes I_{n}]\begin{bmatrix}A_{x}(\xi_{i})\\ A_{y}(\xi_{i})\\ A_{z}(\xi_{i})\end{bmatrix} \tag{32}\]  
where \(\begin{bmatrix}A_{x}(\xi_{i})\\ A_{y}(\xi_{i})\\ A_{z}(\xi_{i})\end{bmatrix}\) are the gravitational acceleration components in Cartesian representation. To use the FEM for computing gravitational acceleration, the maximum polynomial order is employed, ensuring consistency levels as compared to the overall model. In particular, at larger radii above the lunar surface, the computational speed decreases by an order of magnitude when only non-zero terms are included.