esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.1 Geographically Weighted Regression Models
By resampling the regression coefficients to a 1 km resolution, respectively, and establishing a GWR model on the 1 km scale to estimate the precipitation from satellite data with a 1 km resolution, the following expression is obtained:  
\[\text{P}_{\text{i,d}}^{\text{1 km,SAT}}=\text{$\beta$}_{\text{i,d}}^{\text{1 km,SAT,0}}+\text{$\beta$}_{\text{i,d}}^{\text{1 km,SAT,1}}\text{CER${}_{\text{i,d}}^{\text{1 km}}$}+\text{$\beta$}_{\text{i,d}}^{\text{1 km,SAT,2}}\text{COT${}_{\text{i,d}}^{\text{1 km}}$}+\text{$\beta$}_{\text{i,d}}^{\text{1 km,SAT,3}}\text{CWP${}_{\text{i,d}}^{\text{1 km}}$} \tag{8}\]  
For the residual error generated in Equation (7) of the coarse-resolution GWR model, the IDW method is applied to interpolate it to a 1 km resolution (\(\text{\[\text{CV}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\big{(}y_{\text{i}}-\hat{y}_{ (\
eq i)}(\text{b})\big{)}^{2} \tag{6}\]0}_{\text{i,d}}^{\text{1 km,SAT}}\)) and fuse it with the 1 km resolution precipitation data estimated by Equation (8) to obtain the modified satellite precipitation data (\(\text{\[\text{CV}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\big{(}y_{\text{i}}-\hat{y}_{ (\
eq i)}(\text{b})\big{)}^{2} \tag{6}\]1}\)). \[\text{CV}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\big{(}y_{\text{i}}-\hat{y}_{ (\
eq i)}(\text{b})\big{)}^{2} \tag{6}\]2  
It is worth noting that other researchers believe that the residuals generated by the GWR correlation model are spatially correlated and can be interpolated to help explain the spatial variation of the target variable [96; 97]. In our fusion scheme, the residuals produced by the GWR model were interpolated to the downscaled precipitation resolution by employing an inverse distance weighted method and incorporated into the model estimate (residual correction) to improve prediction accuracy. In this study, after verifying the residual correction results of satellite downscaling, the data quality of IMERG and CMORPH improved to a certain extent, and ERA5 was negatively optimized. It is speculated that residual correction after precipitation downscaling is not applicable to ERA5 in this research scheme [33; 53]. Therefore, in the follow-up research, in order to ensure the downscaling accuracy, ERA5, IMERG, and CMORPH are used to employ the downscaled precipitation data before and after residual correction, respectively, as the final precipitation downscaling result.
## 2 System Synthesis
### Safety Group  
The high seas are territories where the deployment of any assets of value will need to be guarded from physical damage due to harsh conditions and theft.  
The **Collision Avoidance Subsystem** is responsible for avoiding collisions with other vessels or other obstacles, such as sea banks. This would involve the use of both Communication and Position Detection Subsystems to detect its own and the positions of vessels in the vicinity, then communicate the expected path with others, involve the control centre if required, and also adapt the course to achieve a margin of safety from collision.  
As a SailREC System has resources of value, including fuel or stored energy, machinery, and the material used for construction, it must be protected from theft by using a **Pirate Protection Subsystem**. Since the SailREC System is envisioned as autonomous, the possible solutions are a design that is hard to access by other parties and steal from, as well as a strategic deployment in areas of operation where hostile activity might be avoided.  
The **Storm Avoidance Subsystem** is responsible for adapting the course of the SailREC System in such a way to avoid storms and other extreme environmental conditions. This subsystem also decides whether an expected storm is too dangerous and should be avoided or passed through. For this task, position data, global weather data, and data from other systems of its kind will likely be used.  
The **Storm Survival Subsystem** is responsible for increasing the chance of survival if an environmental risk factor was underestimated by the Storm Avoidance Subsystem or to improve the stability if areas of extreme environmental conditions need to be passed through. Possible mechanisms may include floodable sections, increasing the draught of the system to reduce the centre of gravity, etc.
# Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
## 3 Results
### Colorimetric Results of Different Immersion Times at Different Thicknesses  
Analysis of colorimetric changes across different immersion durations at each thickness level revealed distinct trends between the two zirconia brands, as presented in Figure 2. For TT specimens, \(\Delta\)E values increased with longer immersion times at all thickness levels. Thinner specimens (1.0 mm and 1.5 mm) exhibited the greatest color deviations, particularly at 5 min, whereas thicker specimens (2.0 mm and 2.5 mm) showed smaller but still measurable increases. In contrast, DD specimens maintained relatively stable \(\Delta\)E values across all immersion times and thicknesses.  
Colorimetric Changes and Lightness Ratio (L*) of Zirconia Specimens After 3 Months of Coloring Solution Aging  
After 3 months of continuous use, significant alterations in the optical properties of the coloring solutions were observed, impacting the colorimetric outcomes of zirconia specimens. Color differences (\(\Delta\)E) increased notably, particularly in specimens immersed in aged liquids (Figure 3), and these changes were further supported by variations in the surface brightness (L*) of the zirconia (Figure 4).  
In TT specimens, \(\Delta\)E values increased markedly after immersion in aged solutions, especially in thicker specimens (2.0 mm and 2.5 mm). Complementary analysis of the L* ratio at 2.5 mm thickness revealed a substantial increase in lightness after 3 months across all immersion times. In contrast, DD specimens demonstrated superior optical stability. \(\Delta\)E values remained low across thicknesses and immersion times, even after 3 months of solution use. The L* ratio analysis further confirmed that the surface brightness of DD specimens remained relatively unchanged after immersion in aged solutions, indicating minimal optical alteration.  
Figure 2: Color differences (\(\Delta\)E) of TT (**a**) and DD (**b**) zirconia specimens evaluated at multiple immersion times across a range of thicknesses, analyzed using L*a*b* and LCH methods. TT: TT ONE Multilayer; DD: cubeX\({}^{2}\) ML.
# Genetic Programming to Remove Impulse Noise in Color Images
## 1 Introduction  
Recently, the notion of color has played a relevant role in a large number of computer vision applications. Color information provides features that are invariant to scale, translation, and rotation changes, which are suitable for image segmentation [1], image classification [2, 3], or image retrieval [4, 5]. One of the most critical tasks in computer vision applications is image denoising, which involves recovering an image from a degraded noisy version. Various types of noise can affect digital color images, particularly impulse noise [6].  
Impulse noise in digital images is a random variation in the intensity of pixels caused by short-duration pulses of high energy. This type of noise can significantly degrade the quality of images and poses various challenges in real-world applications. For example, the impulse noise in dashboard camera footage under low-light conditions [7] can lead to the misinterpretation of videos, making it challenging to accurately identify vehicles involved in incidents. Additionally, impulse noise is a kind of ordinary noise in medical imaging (X-rays, MRIs, and CT scans) [8] that can result in the misinterpretation or disappearance of critical details important in diagnosis.  
Impulse noise commonly occurs during the acquisition or transmission of an image caused by imperfections on the device lens, malfunctioning camera photosensors, the agingof the storage material, errors during the compression process, and the electronic instability of the image signal [9]. Addressing impulse noise in real-world applications often involves the use of various image processing techniques to restore the integrity of images. Impulse noise affects color digital images in such a way that the perturbed pixels differ significantly from their local neighborhood in the image domain.  
Nature-inspired optimization algorithms have been widely applied in the image processing literature to address various challenges, including optimizing image quality evaluation [10], feature selection [11], and image reconstruction [12]. Of particular note is genetic programming [13], an evolutionary computing technique based on the principle of natural selection, which offers a flexible and adaptive methodology for addressing image processing problems. Like other evolutionary algorithms, the main idea is to transform a population of individuals (programs) by applying natural genetic operations such as reproduction, mutation, and selection [14]. The adaptability and robustness of genetic programming make it well suited for addressing the challenges associated with impulse noise removal, particularly for color digital images.
## 5 Conclusions  
This study proposed a novel isolation system for underground structures by employing the incorporation of a negative-stiffness amplification system (NSAS) and an isolator. Considering the energy-dissipation-enhancement effect, extensive parametric and comparative analysis was conducted to examine the effectiveness and design of the NSAS. The conclusions are summarized as follows:  
1. The system is effective in the vibration control of underground structures, and it exhibited a significant energy-dissipation-enhancement effect in which the series connection of the negative and positive stiffnesses amplified dashpot deformation for enhanced energy-dissipation capacity and efficiency.
2. In comparison to a conventional isolator with the same damping coefficient, the NSAS isolation system provides underground structures with a multiperformance effect. Particularly, the simultaneous relative displacement of the central column and shear force responses can be mitigated more effectively by the NSAS.
3. Benefiting from the energy-dissipation-enhancement effect, more vibration energy can be dissipated by the NSAS, thereby reducing the energy-dissipation burden of the central column. Under seismic excitation with different intensity levels, the enhanced energy-dissipation capacity  
Figure 16: Histogram of energy proportions for (**a**) uncontrolled US, (**b**) US-CIS and (**c**) US-NSAS isolation system.  
Figure 17: Hysteresis curves of dashpots in CIS and NSAS isolation system.  
of the NSAS isolation system held true, which demonstrated the robustness of the proposed system for the multilevel safety of underground structures.
4. The objective of this study was to develop a novel isolation system, the NSAS, for the vibration mitigation of underground structures. In future studies, the parameter design of NSAS for underground structures should be detailed. Correspondingly, the design method and the energy-dissipation-enhancement effect of NSAS should be further verified by experiment analysis.  
The proposed NSAS isolation system may bring some difficulties in manufacturing and installation in underground structures. In future studies, an experimental study of the NSAS isolation system for the seismic-vibration control of underground structures should be undertaken.  
Conceptualization: Q.C. and Z.Z.; methodology: Q.C., Y.W., and Z.Z.; software: Y.W. and Z.Z.; validation: Q.C., Z.Z., and Y.W.; formal analysis: Z.Z. and Y.W.; investigation: Q.C., Y.W., and Z.Z.; data curation: Y.W. and Z.Z.; writing--original draft preparation: Q.C., Y.W., and Z.Z.; writing--review and editing: Q.C., Y.W., Z.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (grant number 51778489), the National Science and Technology Pillar Program of China (grant No. 2015 BAK17B04), and the Basic Research Project of State Key Laboratory of Ministry of Science and Technology (grant number SLDRCE19A-02)[PERSON]  
The authors declare no conflict of interest.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Binary Target Detection: Experimental Results  
The test results are split into the received target echo and its associated MF response. We use a flat plate reflector to produce a single reflection observed at the receiver. This reflector choice is made purposefully to verify that the theoretical fast-time behavior of the target echo matches the work of [PERSON] in [49].
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 1 Introduction
The multi-temporal consistency of observed and BRDF-normalized surface reflectances and Normalized Difference Vegetation Index (NDVI) is then assessed at global scale over a large set of sites, spanning a wide range of biomes and environmental conditions. The statistical analysis is aggregated per land cover class and angular configuration (near-nadir, forward and backward scattering) to characterize the orbital drift effect for different surface types and sun-viewing geometries. The paper is structured as follows. In Section 2, the background and the rationale of the study are illustrated, with emphasis on the motivations for the chosen twofold approach. Section 3 describes the used methods and input data and Section 4 presents the results, which are then discussed in Section 5. Conclusion are presented in Section 6.
## 3 Results and Discussion
### Bending Test
#### 3.2.3 Mechanism Analysis  
The content of CNF in CNFPUC was located in the seepage zone, thus the CNF overlapped with each other to form a three-dimensional grape-like network conductivity pathway. With the increase of strain, the interfacial degumming phenomenon and partial fiber pullout appeared between CNF and the polyurethane matrix, which destroyed the lap-over network between CNFs. At this time, the resistivity increased gradually. When the strain was close to the limit, the microcracks developed into main cracks, and the CNF fibers had few lap connections, thus the resistivity rose sharply until fracture. Through the change of resistivity in the bending process of CNFPUC, the three failure processes of elastic deformation, stable crack propagation, and unstable propagation were reflected [23].  
Figure 6: Flexural failure. (**a**) Bottom transverse fracture; (**b**) CNFPUC is separated from the concrete.  
Figure 7: \(\rho\)-\(\varepsilon\) curve.
## 3 Results and Discussion
### Subsumption Checking
Moreover, from the abovementioned instances from the experiment, we could see that rules must be analyzed not only based on the number of items contained in the rule's antecedent or consequent, but also considering the interestingness measure of each rule before deciding which rule is to be considered as anomalous and discarded. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule ID** & **Antecedent** & **Consequent** & **CF** \\ \hline
33 & SBP: Normal & FG: Normal & 0.298 & sub-antecedent \\ \hline
**46** & **[TC: High Cholesterol A** & **FG: Normal** & **0.484** & **sub-antecedent** \\  & **SBP: Normal**] & & & \\ \hline
277 & [BMI: Overweight \(\wedge\) SBP: Normal] & FG: Normal & 0.119 & sub-antecedent \\ \hline
**307** & **[TC: High Cholesterol A** & **BMI: Overweight \(\wedge\) SBP: Normal]** & **FG: Normal** & **0.542** & **sub-antecedent** \\ \hline
837 & [TC: High Cholesterol \(\wedge\) BMI: Overweight \(\wedge\) SBP: Normal \(\wedge\) A: Middle Aged] & FG: Normal & 0.431 & proper rule \\ \hline Notes: & & & \\ \(A\) & : Age & & \\ \(SBP\) & : Systolic Blood Pressure & & \\ \(BMI\) & : Body Mass Index & & \\ \(FG\) & : Fasting Glucose & & \\ TC & : Total Cholesterol & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Example of subsumption rules (proper rule and its sub-antecedents) (2). Figure 5: Screenshot of command window MATLAB after subsumption checking.
## 3 System Model and Data Preprocessing
### Data Preprocessing  
The dataset utilized in the experiment comprises twenty modulation schemes, namely BPSK, QPSK, 8 PSK, 16 QAM, 32 QAM, 64 QAM, 128 QAM, 256 QAM, GFSK, CPFSK, PAM4, B-FM, DSB-AM, SSB-AM, APSK, OQPSK, 2 ASK, 4 ASK, 2 FSK, and 4 FSK. Each modulation scheme contains 1000 samples. The dimension of each sample is (4096 \(\times\) 2). These samples belong to the IQ sequence signal. Given the outstanding achievements of deep learning in the image recognition field, numerous research methodologies have adapted signal recognition methods for image recognition problems. Our manuscript utilizes the short  
Figure 2: Semi-supervised-learning-based AMC system model.  
time Fourier transform (STFT) [44] to transform diverse modulated signals into the format of time-frequency images. The operating principle of the short-time Fourier transform involves separating the input signal into multiple overlapping windows. The data within each window are referred to as a frame, and the Fourier transform is applied to data within each frame to obtain information in the frequency domain. The output is a two-dimensional matrix where the horizontal axis represents time and the vertical axis represents frequency. From the time-frequency images, one can discern the energy distribution of the signal at different intervals of time and frequencies. We obtain time-frequency signal images; the dimension of each time-frequency image is (64,64,3), where 64, 64, and 3 refer to the length, width, and number of channels of the time-frequency image, respectively. The processed signal samples can be directly input into the convolution neural network. The data preprocessing process is illustrated in Figure 3.
# Automated Generation of Daily Evacuation Paths in 4D BIM
## 4 Framework and Algorithms for Automated Generation of Evacuation Paths in 4D BIM
### Proposed Framework
To account for possible spatial conflicts and situations not reflected in 4D BIM, it is desired for multiple crews and managers to review and select evacuation paths together as part of their pre-task planning. **Step 5: Distribute selected paths to crews:** After the evacuation paths are selected, the information can be communicated with related work crews in various channels, including printed papers or hand-held computers. Using wireless communication, any changes regarding evaluation paths or other safety relevant issues can be immediately distributed to all the work crews and field managers.
## 2 Materials and Methods  
### Sample Collection  
Surface snow samples were collected in the coastal area of Victoria Land (Antarctica) near Mario Zucchelli Station, one of the Italian research bases in Antarctica, located in Terra Nova Bay, along the coast of the Northern Foothills, north-east of Gerlache Inlet. The sampling coordinates are reported as follows: sample A was collected from the Icaro Camp meteorological station area (74 deg42'37'' S, 164 deg07''05'' E), sample B near Enigma Lake (74 deg42'38'' S, 164 deg01'10'' E), sample C from Browning Pass (74 deg36'55'' S, 163 deg56'29'' E), sample D near Cape Phillips (73 deg03'41'' S, 169 deg36'16'' E), and sample E from Adelie Cove (74 deg45' 40'' S 169 deg36'16'' E) (Figure 1). All the samples were obtained during the 2020-2021 austral summer and kindly provided by the National Antarctic Research Program (PNRA) \"Emerging COntaminants in Antarctic Snow: sources and TRAnsport ECO:ASTRA\". Environmental variables related to the sampling campaign have been reported in previous studies [34; 35]. Snow samples were manually sampled after the removal of the uppermost superficial layer (0.3-0.8 cm) using pre-cleaned premium-grade polystyrene accuvettes to prevent contamination, with a maximum depth of 12 cm [6]. The samples were collected using both wide-mouthed glass and acid-pre-cleaned HDPE bottles (500 mL volume). All the glass bottles were rinsed twice with deionized water and dried prior to sampling. The operators wore clean-room clothes and approached the sampling areas upwind from the dominant wind direction. To prevent sample contamination, motorized activity was interrupted about 300 m from the collecting site. All the samples were frozen immediately after collection and maintained at \(-20\)degC until analysis.  
All the glassware and materials used for sampling, filtering, or analysis were thoroughly cleaned following the procedure proposed by [PERSON] et al. [6], which required consecutive cleaning steps using 10% \(v/v\) analytical-grade nitric acid (nitric acid 65% Merk,  
Figure 1: Sampling sites (A–E) of surface snow in Antarctica.  
Milan, Italy), 1% \(\
u/\
u\) supra-grade nitric acid (suprapure nitric acid 65%, Supelco, Bellefonte, PA, USA), and ultrapure water (Milli-Q water).
# Advanced Deep Learning Methods to Generate and Discriminate Fake Images of Egyptian Monuments
###### Abstract  
Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language-Image Pre-training (SigLIP) as a unique image-text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Frechet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities.  
generative adversarial networks (GANs); StyleGAN; Egyptian monuments; differential evolution; SigLIP; deep learning +
Footnote †: journal: Applied Sciences