esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation
In [18], a dielectric transmitarray was proposed to reach a multi-focusing solution, in particular to simultaneously radiate four focused spots. The transmitarray was fed by a single feed; thus, the spots should be far from each other with a broad angular deviation to avoid interferences among them. Therefore, this solution is not suitable for closed spots. In addition, the transmitarray design was based on a subdivision of the antenna aperture in order to focus one spot per subdivision. This technique normally requires oversized apertures and obtains fixed spots, but not scanning performances. However, the proposed \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feed (\(\theta\) (deg))** & \multicolumn{2}{c}{**Simulations**} & \multicolumn{2}{c}{**Measurements**} \\ \hline  & **Maximum (mm)** & \(DoF\) (mm)** & **Maximum (mm)** & \(DoF\) (mm)** \\ \hline \(\theta=0^{\circ}\) & \((125.64,0)\) & 79.95 & \((130,0)\) & 85 \\ \(\theta=15^{\circ}\) & \((116.60,-30)\) & 76.11 & \((112.5,-32.5)\) & 75.76 \\ \(\theta=20^{\circ}\) & \((110.60,-38.89)\) & 74.20 & \((97.5,-49.5)\) & 65.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the maximum location \((z,x)\) and the depth-of-focus (\(DoF\)) between the simulations and measurements. Figure 14: Normalized measured amplitude in dB for three different feed positions. (**a**) \(\theta=0^{\circ}\), (**b**) \(\theta=15^{\circ}\), (**c**) \(\theta=20^{\circ}\), and (**d**). Comparison among the three focused spots. technique can be used to obtain either fixed spots or a scanning. Hence, this work proposes an alternative for multi-focusing within the near-field with scanning capabilities since the spot positions regard the feed placement. Additionally, the spots can be generated as close as the physical dimensions of the feeds enable with a high isolation between feeds. This approach can be applied at least in a scanning range of 40\({}^{\circ}\), keeping the focusing properties on all the spots in a broad-band solution in the Ka-band due to all the dielectric cells.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 2 Study Area and Data
### Data Sources  
#### 2.2.1 In Situ Observations  
The daily in situ observations used in this study are collected from six hydrological stations provided by the Zhejiang Provincial Hydrology Administration Center, and five meteorological stations obtained from the Zhejiang Provincial Meteorological Administration. The data period is 2004-2010. Six hydrological stations have records of daily pan evaporation, measured by E601 pan. Five meteorological stations have complete records of maximum and minimum air temperature, relative humidity, wind speed, sunshine duration, and precipitation. Since actual evaporation data are not available, the in situ observations of pan evaporation are used as the reference to evaluate the accuracy of remote sensing-based ET datasets. The in situ observations of meteorological data are used for hydrological model meteorological inputs.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 5 Experimentation and Evaluation
### Overall Evaluation of Our Methodology  
Taking the above experiments into consideration, we can reach several conclusions, vital for the improvement of the current methodology. First of all, it can be derived that it is very difficult to use data originating from other cities, or at least ones that do not share similar characteristics. In the case of the Random Forest approach, Chicago and Volos unfortunately had statistically significant differences in the majority of indices and night light emission. Thus, the models created under the assumption that we could use these data failed to perform as intended across most experiments, and even when they did, their overall precision was not reassuring.  
On the other hand, in the case of One-Class SVM, where data were collected from the case study area, the results were promising. In contrast with single urban extraction indices (e.g., EMBI case), nighttime lights seem essential in detecting abandoned or disused buildings. Using the combined approach resulted in considerably fewer False Positives or Negatives in the classification scheme of dense and coastal areas, where the model was able to detect various vacant properties. The model, while not adequate, performed better in suburban parts of the city than in other parts.  
Both approaches were hindered, however, since the hyperparameter tuning process was unfortunately manual and intuitive we are far from sure that we have selected the best parameters. Moreover, all models seem to be affected by the size of the fishnet which splits the area of interest. In most cases, increasing the number of available polygons improved the model's precision, but there were still various problems, such as the unwanted inclusion of roads, nearby barren land, or vegetation. Equal-sized polygons, while easier to implement, oftentimes cannot capture the characteristics of the AOI efficiently, creating 'noise' that the classifiers were sensitive to. Thankfully we can combat that by either using other shapes to split the area with or selecting the building's exact shape and creating a mask. Ref. [63] proves the ability of polygonal grids to cover the areas of interest more efficiently across regions with different topologies, and ref. [4] uses high-resolution imagery to extract individual vacant house parcels.  
Finally, there is possible bias and error introduced in the way we evaluate the performance of these algorithms in both cases. Since there was no available data for abandoned buildings, and the creation of a large dataset was a difficult process, we verified our results by either using Google Street View imagery, which can in many cases be outdated, or by visiting the sites ourselves. As crowd-sourcing platforms such as OpenStreetMaps and GeoWiki become more popular, a lot of stress can be alleviated [64; 65].
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 4 Conclusions  
This work proposes a novel model for predicting wind turbine gearbox failures using neural networks and signal processing techniques. We introduced the use of SCADA and OWT datasets for experimentation, and we compared the effectiveness of four methods--EMD-GRU, LSTM, CNN, and FFT. We analyzed the characteristics and distribution of gearbox vibration signals, summarized the causes and forms of wind turbine bearing failures, and proposed gearbox fault feature indicators. We also described the software and network structure used in the experiments, and we evaluated the performance of different algorithms using three comparison standards. We provided a detailed discussion of the data preprocessing and model training for the AW-LSTM model, and we presented a comparison of the AW-LSTM prediction results to the actual values. Our experimental results show that the proposed AW-LSTM model has higher prediction accuracy, better diagnostic capabilities and convergence speed, and stronger diagnostic adaptability than the other four methods, making it a promising approach for fast fault diagnosis in wind power generation systems. The algorithm code will be publicly available after the paper is accepted. Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.  
For this paper, during the experimental process, the SCADA data, as well as the OWT data, were individually used for fault prediction, and the generalization ability of the model has not been verified. The next step will be to select sample data from multiple wind farms for hybrid modeling training, to further improve the accuracy of similar anomalous pattern judgments, and to enhance the generalization ability of the model.  
Software, Y.Z.; Data curation, R.H.; Writing--original draft, Y.Z.; Writing--review & editing, Y.Z.; Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China: Research on a new high-efficiency on-chip integrated polarization controller: 61705127.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
## 3 An Energy-Based Model Relative to the Reversible Hydropump-Turbine System
### A Reversible Hydropump-Turbine System
#### 3.3.4 Hydropump System  
We define the state variables of the hydro pump system of Figure 1 as \(\tilde{x}=\mathrm{col}\big{(}\tilde{P}_{r},\tilde{Q},\tilde{p}_{mp}\big{)}\), and its energy (Hamiltonian) function is as follows:  
\[\tilde{H}(\tilde{x})=\frac{1}{2}C_{h}\tilde{P}_{r}^{2}+C_{h}g\rho h_{w}\tilde{ P}_{r}+\frac{1}{2}J_{a_{h}}\tilde{Q}^{2}+\frac{1}{2}\frac{\tilde{p}_{mp}^{2}}{J_{ a_{m}}}, \tag{28}\]  
with the dynamics of \(\tilde{P}_{r}\) as in (19), \(\tilde{Q}\) as in (18), and \(\tilde{p}_{mp}\) as in (20). Given the partial derivatives of (28):  
\[\
abla_{\tilde{x}}\tilde{H}(\tilde{x})=\begin{bmatrix}\
abla_{\tilde{p}_{r}} \tilde{H}(\tilde{x})\\ \
abla_{\tilde{Q}}\tilde{H}(\tilde{x})\\ \
abla_{\tilde{p}_{mp}}\tilde{H}(\tilde{x})\end{bmatrix}=\begin{bmatrix}C_{h} \tilde{P}_{r}+C_{h}g\rho h_{w}\\ J_{a_{h}}\tilde{Q}\\ \omega_{mp}\end{bmatrix}, \tag{29}\]  
we can rewrite the dynamics of (18)-(20) as follows:\[\dot{\hat{P}}_{r} =\frac{1}{C_{hI}J_{a_{h}}}\
abla_{\hat{Q}}\hat{H}(\tilde{x}), \tag{30}\] \[\dot{\hat{Q}} =-\frac{1}{C_{hI}J_{a_{h}}}\
abla_{\hat{P}_{r}}\hat{H}(\tilde{x})- \frac{R_{a_{h}}}{J_{a_{h}}^{2}}\
abla_{\hat{Q}}\hat{H}(\tilde{x})+\frac{K_{o} \omega_{m_{P}}}{J_{a_{h}}}\
abla_{\hat{p}_{m_{P}}}\hat{H}(\tilde{x}),\] (31) \[\dot{\hat{p}}_{m_{P}} =-\frac{K_{o}\omega_{m_{P}}}{J_{a_{h}}}\
abla_{\hat{Q}}\hat{H}( \tilde{x})-R_{a_{m}}\
abla_{\hat{p}_{m_{P}}}\hat{H}(\tilde{x})+\tau_{M}. \tag{32}\]  
which, in turn, are computed and simplified as follows:  
\[\Sigma_{P}\left\{\underbrace{\begin{bmatrix}\hat{P}_{r}\\ \dot{\hat{Q}}\\ \hat{p}_{m_{P}}\end{bmatrix}}_{\hat{x}}&=\underbrace{\begin{bmatrix}0&\frac{1}{ C_{h}J_{a_{h}}}&0\\ -\frac{1}{C_{h}J_{a_{h}}}&-\frac{R_{a_{h}}}{J_{a_{h}}^{2}}&\frac{K_{o}\omega_{m_{ P}}}{J_{a_{h}}}\\ 0&-\frac{K_{o}\omega_{m_{P}}}{J_{a_{h}}}&-R_{a_{m}}\end{bmatrix}}_{J(\tilde{x}) -\hat{R}(\tilde{x})}\underbrace{\begin{bmatrix}\
abla_{\hat{P}_{r}}\hat{H}\\ \
abla_{\hat{Q}}\hat{H}\\ \
abla_{\hat{p}_{m_{P}}}\hat{H}\end{bmatrix}}_{\
abla_{\hat{q}}\hat{H}(\tilde{ x})}\\ &+\underbrace{\begin{bmatrix}0\\ 0\\ 1\end{bmatrix}}_{\hat{\tilde{q}}(\tilde{x})}\underbrace{\begin{bmatrix}\tau_{M} \\ \tau_{M}\end{bmatrix}}_{\tilde{u}}\\ &\tilde{y}=\tilde{g}^{\top}(\tilde{x})\
abla_{\tilde{x}}\hat{H}(\tilde{x})= \omega_{m_{P}}\end{bmatrix} \tag{33}\]  
with \((\tilde{u},\tilde{y})=(\tau_{M},\omega_{m_{P}})\) being the system's port pair (input and output, respectively), and matrices \(\hat{J}(\tilde{x})\) and \(R(\tilde{x})\) satisfying \(\hat{J}(\tilde{x})=-\hat{J}(\tilde{x})^{\top}\), and \(\hat{R}(\tilde{x})=\hat{R}(\tilde{x})^{\top}\succeq 0\), respectively. Clearly, the power balance, \(\hat{\hat{H}}(\tilde{x})\leq 0\), for (33) holds.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 4 Discussion
### Analysis of Factor Values Providing the Best Performance  
In this subsection, we further analyze the impact of the two most relevant factors identified in the 2k factorial analysis: Density and the NRT-HMP combination. Our goal is to identify which factor values provide the best routing performance in terms of the PDR.
## 4 Model-Based Calibration  
Calibration proceeds on the basis of residual errors identified in the reference target responses. It entails introducing corrections to sensor parameters that aim to minimize these errors. External calibration thus becomes a rigorous optimization process: explicit models link sensor properties to the expected target response. Appropriate sensor parameter corrections are then the result of error minimization by model inversion.  
To make this inversion robust to outliers caused by RFI, system noise and other transient effects, model inversion uses L1-norm error minimization [25] in favor of least-squares approaches throughout. Given an error functional \(E_{pq}^{s}(az)\) that measures some model mismatch over azimuth, L1-norm optimization yields a set of model parameters \(\mathcal{O}=\{o_{1},o_{2},\dots\}\) that are the solution to  
\[\operatorname*{arg\,min}_{\mathcal{O}}\left(\sum_{az,pq,s}\left|E_{pq}^{s}(az) \right|\right), \tag{20}\]  
where errors are summed over all target responses in all data channels of all acquisitions.  
To ensure that the optimization problem is convex, the following sections uses a closely related objective function  
\[\operatorname*{arg\,min}_{\mathcal{O}}\left(\sum_{az,pq,s}\Psi\left(E_{pq}^{s} (az)^{2}\right)\right)\text{ with }\Psi(\epsilon)=\sqrt{e+\epsilon}, \tag{21}\]  
where \(\epsilon\) is introduced only to ensure numerical stability and is set to \(1\times 10^{-8}\). The convexity of this function guarantees convergence to a global minimum. The minimisation of (21) is achieved by setting the derivative of the total error with respect to each model parameter \(o_{i}\in O\) to zero, such that  
\[\forall o_{i}\in\mathcal{O}:\ 0=\sum_{az,pq,s}\frac{\partial\Psi\left(E_{pq}^{s} (az)^{2}\right)}{\partial o_{i}}=\sum_{az,pq,s}\Psi\ E_{pq}^{s}(az)\ \frac{\partial E_{pq}^{s}(az)}{ \partial o_{i}}, \tag{22}\]  
where \(\Psi=\left(E_{pq}^{s}(az)^{2}+\epsilon\right)^{-\frac{1}{2}}\).  
Figure 4: The results of phase and delay analysis for the target shown in Figure 2, imaged at X-band with a bandwidth of 384 MHz. The two plots in the top row show the original phase in the raw data (left) and the wrapped residual phase \(\delta\phi_{pq}^{s}(az)\), obtained after the expected target phase has been removed from the raw response (right). The plot in the bottom row illustrates the consistency of the residual delay \(\delta\tau_{pq}^{s}(az)\) and the absolute residual phase \(\delta\chi_{pq}^{s}(az)\).  
While this system of equations is clearly non-linear, it can be solved using a fixed point iteration. In each iteration, \(\Psi\) is pre-computed on the basis of the currently available parameter estimates \(\hat{\mathcal{O}}\). \(\Psi\) is then treated as a constant in the solution of (22) and new, refined model parameter estimates are obtained. This process is repeated for a fixed number of iterations (e.g., 10) or until the model parameters have converged.
## 4 Proposed Approach and Experiments
### Experiment 4: Extracting Features Using Single GAT  
In Experiment 4, GAT is used solely as a feature extractor, and the resulting embeddings are given as input to CNN-GRU-3 and ensemble learning models. This experiment aims to analyze the effectiveness of GAT as a feature extractor and its classification success when combined with other classifiers.
## 3 Results and Discussion
### Simulated Vehicle Experiments
#### 3.1.2 Percent Transfer to Uncontaminated Surfaces  
Figure 11 shows the average percent transfer of Sr-85-contaminated particles from the NSCs and from the surfaces that were stabilized with CaCl\({}_{2}\), Phos-Chek MVP-Fx, and Soil\({}_{2}\)O treated paves to paves that were previously uncontaminated. Table 5 shows the \(p\)-values from the \(t\)-tests performed on the data. The \(p\)-values indicate the probability that the groupings of data were from the same population, i.e., statistically similar.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Surface** & **Stab. Tech.** & **CaCl\({}_{2}\)** & **MVP-Fx** & **Soil\({}_{2}\)O** \\ \cline{2-5}  & Control & 0.0172 & -0.0001 & 0.1764 \\ \cline{2-5} Asphal & CaCl\({}_{2}\) & & 0.2535 & 0.1655 \\ \cline{2-5}  & MVP-Fx & & & 0.0475 \\ \hline \multirow{3}{*}{Concrete} & Control & 0.0255 & 0.0394 & 0.2081 \\ \cline{2-5}  & CaCl\({}_{2}\) & & 0.0133 & 0.0010 \\ \cline{2-5}  & MVP-Fx & & & 0.0322 \\ \hline \multirow{3}{*}{Sand} & Control & 0.0017 & 0.0023 & 0.0251 \\ \cline{2-5}  & CaCl\({}_{2}\) & & 0.6876 & 0.0101 \\ \cline{1-1} \cline{2-5}  & MVP-Fx & & & 0.0256 \\ \hline \end{tabular}
\end{table}
Table 4: Statistical results (\(p\)-values) for simulated vehicle experiments percent removal. Gray shading indicates a significant difference (\(p\) < 0.05).  
Figure 10: Percent Removal of Sr-85 Contaminated Particles for Vehicle Experiments.  
In general, the potential for transfer, indicated by the control %T, was similar for the three surfaces, as the average %T values ranged from approximately 6% to 9%. Interpreting the efficacy values in the figure in combination with the \(p\)-values from the table reveals the following for specific surfaces.  
_Asphalt._ CaCl\({}_{2}\) (1 \(\pm\) 1 %T), MVP-Fx (0.2 \(\pm\) 0.6 %T), and Soil\({}_{2}\)O (5 \(\pm\) 2 %T) resulted in significantly decreased %T compared to the NSC (8 \(\pm\) 1 %T); the statistical power of the CaCl\({}_{2}\) and MVP-Fx (\(p\) < 0.0001) were much higher than Soil\({}_{2}\)O (\(p\) = 0.04). CaCl\({}_{2}\) (1 \(\pm\) 1 %T) and MVP-Fx (0.2 \(\pm\) 0.6 %T) generated %T values not significantly different from one another, and both produced %T values that were significantly less than Soil\({}_{2}\)O (5 \(\pm\) 2 %T).  
_Concrete._ CaCl\({}_{2}\) (1 \(\pm\) 1 %T) and MVP-Fx (1 \(\pm\) 1 %T) resulted in significantly decreased %R compared to the NSC (6 \(\pm\) 2 %T), while Soil\({}_{2}\)O (4 \(\pm\) 1 %T) did not. CaCl\({}_{2}\) (1 \(\pm\) 1 %T) and MVP-Fx (1 \(\pm\) 1 %T) generated %T values that were not significantly different from one another, and both produced %T values that were significantly less than Soil\({}_{2}\)O (4 \(\pm\) 1 %T).  
_Sand._ CaCl\({}_{2}\) (3 \(\pm\) 2 %T) and MVP-Fx (3 \(\pm\) 1 %T) resulted in significantly decreased %R compared to the NSC (9 \(\pm\) 3 %T), while Soil\({}_{2}\)O (6 \(\pm\) 2 %T) did not. CaCl\({}_{2}\) (3 \(\pm\) 2 %T) and MVP-Fx (3 \(\pm\) 1 %T) generated %T values that were not significantly different from one another, and both produced %T values that were significantly less than Soil\({}_{2}\)O (6 \(\pm\) 2 %T).
## 3 Methods
### Data Processing
#### 3.1.2 Nighttime Light Data Processing  
Although obtaining nighttime light data is relatively convenient, accurate reflection of human activity requires data correction and extraction. The following operations were carried out in this study.  
1. Radiometric Correction  
Since the acquired NPP-VIIRS data is a pre-processed annual composite product (Annual VNL V2.1), there is no need for additional radiometric correction. However, the obtained Loujia-01 data is recorded as the gray value of the pixel, lacking a direct physical interpretation. Therefore, it needs to be corrected using the radiance conversion formula to address radiometric distortion errors.  
\[L=DN^{\frac{3}{2}}\times 10^{-10}, \tag{3}\]  
where \(L\) represents the radiance value after absolute radiometric correction, measured in W/(m\({}^{2}\cdot\)sr\(\cdot\)um).  
2. Background Noise and Abnormal Value Correction  
Nighttime light anomalies refer to pixels with abnormally high brightness values. In this study, NPP-VIIRS data and Loujia-01 data were processed using threshold extraction and median filtering. Threshold extraction is using nighttime light image pixel values collected in the most economically developed region of the study area as the maximum DN value; other bigger pixel values are anomalies caused by phenomena like fishing lights. Additionally, zero is set as the minimum value in the study area, and the negative values are removed. Since Chengdu city is the most economically developed city in Sichuan,we extract the maximum value of nighttime light data from Chengdu as the maximum threshold. Median filtering is a common denoising technique in image processing. It replaces the pixel value with the median value of the adjacent pixels. The method is achieved through template traversal and is effective in removing isolated noise points from images.  
3. Stable Light Source Extraction  
Due to the lack of filtering and removal of volcanic activity-related background noise in NPP-VIIRS data, Loujia-01 data presents a more stable signal. Given that stable light sources between neighboring years do not exhibit significant variations, the calibrated 2019 Loujia-01 nighttime light data was used as a reference standard to correct the unstable light sources in the 2020 NPP-VIIRS data. Based on the assumption that both the 2019 Loujia-01 and 2020 NPP-VIIRS data share the same light generation areas, a mask is created by extracting pixels with non-zero DN values from Loujia-01. The data outside this mask in the NPP-VIIRS dataset is set to 0, effectively treating it as unstable nighttime light [50].
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
###### Abstract
Several publications regarding paper feature deterioration issues, comparing natural deterioration with that eventually induced by radiation exposure for study or treatment purposes [15; 16; 17; 18; 19; 20] or in any case investigating the effects of radiation on paper durability [21]. Studies have been conducted on the effect of gamma radiation on wood structures [22; 23; 24; 25], parchment [26; 27; 28; 29], textiles [30; 31], and a rare example for the effect of irradiation on canvas oil paintings [32]. A particularly relevant case in the field of CH conservation is represented by mummies, and [PERSON] mummy, commonly known as The Mummy, probably represents the best example [8; 14; 33; 34; 35] of the relevance that radiation technologies may have in CH preservation. As a conclusion to this Introduction, we would like to point out some crucial issues that emerged from the literature reviewed:  
* There is distrust towards irradiation techniques applied to CH conservation, and in some countries, there is a prohibition on their use;
* The coexistence in some artifacts of different organic materials makes conservation procedures a real technical challenge;
* As for the EM techniques applied to diagnosis, even for those applied to conservation techniques, there are no real standards or best practices. Based on all this, the aim of our work is to provide a useful tool to introduce the reader to the deterioration processes that have the greatest impact on works of art, and to the state of the art in the EM techniques used for their conservation according to a review of the most relevant publications in the field, pointing out advantages and disadvantages with respect to traditional methods and assessing the risks in their application to CH artifacts. The paper is organized as follows: Section 2 is dedicated to the general principles of EMtechniques, their comparison with traditional techniques, main aspect of dosimetry in the context of CH, hence the potential risks associated with an improper control of the fundamental parameters involved in the irradiation process. Section 3 provides the reader with the EM techniques for restoration that have been proven to be the most efficient, yet safe, providing detailed irradiation parameters and results of the case studies present in the literature reviewed. Section 4 provides the reader with an overview of the most common types of degradation processes on different types of artifacts, namely organic and inorganic. Section 5 is dedicated to conclusions and future perspectives.