esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Laaching of Untreated MSWI Bottom Ash  
The pH levels of the different fractions of untreated BA eluate were determined via the single-stage leaching method (LST EN 12457-2:2003). The pH values of the samples varied from 10.55 to 12.48. The lowest pH value (10.55) was found in the 11.2-22.4 mm fraction and the highest (12.48) in the <5.6 mm fraction. The pH of the BA mixture (<40.0 mm) was closest to the pH of the eluate of the smallest fraction (<5.6 mm). With these pH values, the solution was quite close to the equilibrium solubility of portlandite (Ca(OH)\({}_{2}\)) and calcite (CaCO\({}_{3}\)). The presence of Ca(OH)\({}_{2}\) in BA is caused by calcination reactions in the furnaces. Natural or synthetic calcite (CaCO\({}_{3}\)), gypsum (CaSO\({}_{4}\cdot\)2H\({}_{2}\)O), or anhydrite (CaSO\({}_{4}\)) generates CaO and CO\({}_{2}\) or SO\({}_{2}\). Later, most of the generated CaO is hydrolyzed as a result of quenching and forms the corresponding hydroxide [28].  
Using the atomic adsorption spectral analysis method (AAS), we determined the concentration of three metals (Cu, Zn, and Pb) in the five BA fractions (Figure 6). We found  
Figure 5: Amounts of heavy metals in different bottom ash fractions (mg kg\({}^{-1}\)): Mn; Cu; Zn; Pb; Sr.  
the highest Cu concentration (217.800 \(\pm\) 10.890 \(\upmu\)g L\({}^{-1}\)), and a high concentration of Pb (124.522 \(\pm\) 9.614 \(\upmu\)g L\({}^{-1}\)) was also found in the <40.0 mm fraction. We found the highest Pb concentration in the <5.6 mm fraction (145.625 \(\pm\) 7.281 \(\upmu\)g L\({}^{-1}\)). After analyzing the Zn concentration in the untreated BA eluates, we observed the highest zinc content occurred in the <40.0 mm fraction (239.700 \(\pm\) 11.950 \(\upmu\)g L\({}^{-1}\)).  
Chemical composition and leaching tests (Figures 5 and 6) showed that the highest metal concentrations occurred in the fraction eluates, which had the highest amounts of these metals in the solid phase of BA.
# Exploring the Impact of Virtual Reality on Painting Appreciation
## 1 Introduction  
### Virtual Reality (VR) in Art Exhibitions  
VR is regarded as a significant next-generation technological innovation and has been employed in exhibitions, military, gaming, education, and various other fields [1; 2; 3]. A market research report has noted that the global VR market (including software and hardware) was valued at approximately USD 16 billion in 2022 and is poised to grow from USD 19.15 billion in 2023 to USD 91.54 billion by 2031 [4]. Many of the current VR devices, such as Oculus Rift, Samsung Galaxy Gear VR, and HTC Vive, were launched several years ago. In 2023, Apple Inc., released a new-generation VR device called the Apple Vision Pro, which has boosted the adoption of VR.  
VR involves the use of computer simulations to generate a three-dimensional (3D) virtual world to provide users with the feeling of \"immersion\" [5]. VR display devices can be classified according to their degree of integration, from partial to complete, as desktop, projection-based, head-mounted display (HMD), and cave automatic, in that order, and their control devices also differ. For example, desktop VR only requires a keyboard and mouse, whereas projection-based VR requires a steering wheel or joystick and is often used for driving simulations. Moreover, an HMD can be equipped with a joystick and tactile gloves, whereas some devices, such as the Apple Vision Pro, can be controlled directly via gestures.  
Although VR technologies have been used in various fields, their application to artworks has led to exciting discoveries [5; 6; 7]. Over the past few years, many famous museums and art galleries, such as the Helmut Newton Foundation, Louvre, and Tate Modern, have hosted VR exhibitions, providing audiences with unique and exciting experiences [8; 9; 10]. The VR design is crucial for the success and quality of these exhibitions [11]. [PERSON], [PERSON], [PERSON], and [PERSON] [7] assessed the role of VR in inducing the intention to visit museums and found that VR offered education, entertainment, and immersion; enhanced the overall experience; and aroused the intention to visit the physical museum. [PERSON], [PERSON], [PERSON], and [PERSON] [6] showed that VR can create positive experiences that increase the intention to visit museums. [PERSON] et al. [12] stated that VR technologies are popular because they reduce costs and enhance technological availability and user acceptance.
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Determination of Zeta Potential
At the same time, the presence of the electrolytes with an opposite charge to the droplet surface charge in the continuous phase can neutralize the droplet surface potential, leading to the flocculation of the droplets and destabilization of the emulsion [149]. It is worth noting that zeta potential can also impact the molecular mass distributions of nanoemulsions--those with higher zeta potentials are electrically stabilized and display better physical stability by resisting coagulation or flocculation.
# Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis
## 2 Theoretical Basis  
MFDFA is a process to remove the influence of the non-stationary trend of the time series, can finely characterize its fractal structure, this section on how to improve the MFDFA is analyzed, and according to the relationship between the generalized Hurst index, the mass index and the multi-fractal spectrum to extract the five multi-fractal spectrum characteristic parameters that can describe the dynamic behavior of the time series, in the face of the complexity of the actual operation of the centrifugal pump, multiple sets of sensor data are selected for feature extraction. Different BP neural network diagnostic results are obtained, and then on the basis of D-S evidence theory research, the information fusion of different diagnostic results is carried out, and the basic probability allocation function that can reflect the objective reality is constructed, thereby improving the fault diagnosis effect of the centrifugal pump.
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 3 Mission Design
### Concept of Operations  
Resurrect operations are highly autonomous, utilizing onboard optical navigation to fully eliminate radiometric interference and allow the use of non-DSN ground stations like APL's Satellite Communications Facility for data downlink and FRE beacon reception. The spacecraft determines its orbit and trajectory from images taken by its navigation camera of Earth, the Moon, and a variety of asteroids while making its way to the vicinity of L3. This data is fed through a Kalman filter inside the commercialized AutoNav Mark 4 software onboard, solving for the orbit and maneuver modifications to keep its burn on track with  
Figure 6: Candidate instrumentation for the SEP suite on SRM. **Left**: energy and ion species coverage of the EPD sensors. **Right**: Pictures of the flight units of STEP (upper left ©A&A), EPT-HET (upper right ©A&A), and SIS (bottom ©A&A) (2018).  
its desired trajectory. Uplink is needed only occasionally, on the order of once per month, to load new background sequences for science gathering and housekeeping.  
Upon reaching the vicinity of L3, the spacecraft autonomously completes a pop-up maneuver to place it in the desired location to use its helioimager at a nominal cadence of 30 min and continue transmitting the RF beacon to Earth. The autonomous sequences run the instruments according to a standard plan with observation modes and transmit data to Earth, eliminating the need for most uplink activities. Spacecraft station-keeping is performed using AutoNav Mark 4 software, which also performs onboard navigation relative to the nearby Spitzer Space Telescope.  
The Resurrector's second mission of reactivating Spitzer by providing a crosslink and an Earth relay as the telescope comes out of solar conjunction occurs simultaneously with the space weather science operations.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 3 Radiation-Hardened SRAM Cell Design
### Radiation-Hardened-by-Design 10 T and 14 T SRAM Cell (RHBD-10 T and RHBD-14 T)
This results in the data flip from \"1\" to \"0.\"  
Another approach can be applied to resolve the weak write ability problem of RHBD-10 T. Figure 5c shows the structure of the radiation-hardened polar design of the SRAM cell (RHPD-12 T) [17]. RHPD-12 T is the polarity-reversed version of RHBD-10 T, while two additional transistors--N7 and N8--are applied to enable direct access to the internal storage nodes. In this manner, the complete tolerance to SEU can be maintained while the write failure problem can be significantly resolved. However, during the read operation in which S0 or S1 stores a weak \"1,\" the driving strengths of N3 and N4 are weakened. As a result, the read stability margin is significantly reduced, which means that the data flip probability during the read operation becomes impractically high in the low-voltage region.
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 4 Learning Physical Properties
### Coefficient of Restitution  
A coefficient of restitution is associated with type of material of two objects involved in a collision. During the scattering process, after an object is hit by the manipulator, it collides with other objects in  
Figure 6: Setup, objects, and observed parameters from collision of two objects. (**a**) Setup and object involved in scattering application. (**b**) Observed displacements. (**c**) Observed velocities. (**d**) Observed accelerations.  
Figure 7: Physical properties. Average value and standard deviation of coefficient of friction of four objects shown in Figure 5a.  
the environment. Furthermore, the impulse applied by the manipulator is propagated to all objects involved in the collision. To model all collisions, the value of \(e\) (coefficient of restitution) between the manipulator and each object and between each object and all other objects should be known. The colliding velocity of the robot manipulator is known. Furthermore, by using the observable parameters (velocity of the object, before and after collision) from the video images, the value of (\(e\)) between manipulator and each object is calculated from the following relation:  
\[{{\mathbf{v}_{a}}^{\prime}}-{\mathbf{v}_{a}}=-\frac{{\mathbf{F}_{ext}}}{{{m}_{a}}}=-(1+e){ \mathbf{v}_{I}}. \tag{31}\]  
Next, the value of coefficient of restitution (\(e\)) between objects is calculated. The velocities of objects before and after collision was observed from videos by colliding each object with all other objects (one by one) as shown in Figure 6c. Based on these observed velocities, the coefficient of restitution between objects A and B is calculated as follows:  
\[e=\frac{{{({\mathbf{v}^{\prime}}_{a}}-{\mathbf{v}^{\prime}}_{b})}}{{{({\mathbf{v}_{b}}-{ \mathbf{v}_{a}})}}}. \tag{32}\]  
Similarly, the value of (\(e\)) is estimated among other objects and. For estimation of (\(e\)), five videos of each scenario were recorded. The average values of estimated coefficient of restitution with standard deviation are shown in Figure 8.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Confidence Intervals of Probabilities for Individual Observations Using ik-NN  
The following procedure can be used to assess the uncertainty of the prediction of the damage and non-damage of individual stands or forest areas. The k-NN estimation and its improved version ik-NN produce probabilities for the predicted category on stand \(p\). These probabilities can be calculated using the weights \(w_{i,p}\) (Equation (4)) as follows  
\[\widetilde{prob(k)}_{p}=P(\text{category}(p)=k)=\sum_{i\in I_{p}}w_{i,p}Ind_{( cat(i)=k)}\;, \tag{12}\]  
where \(k\) is the mode category based on the largest sum of the weights \(w_{i,p}\) by categories on stand \(i\in I_{p}\) and \(Ind_{cat(i)=k}\) is an indicator function of the category in stand \(i\). The confidence intervals for the probabilities of the mode for the individual stands were calculated using a linear model  
\[\widetilde{prob(k)}_{p}=a+\sum_{l=1}^{n_{f}}b_{l}f_{l,p}+c\cdot k+\varepsilon, \tag{13}\]  
where \(f_{l,p}\) are the SAR features (Equation (5)); \(k\) is the predicted damage category, a categorical variable (factor); \(a\), \(b\) and \(c\) are the regression coefficients to be estimated (\(b\) being a vector); and \(\varepsilon\) is a normally distributed random error.  
The confidence intervals of the predictions were calculated in a normal way using the estimator  
\[\hat{V}_{f}=s^{2}\;\mathbf{x}_{0}\;(\mathbf{X}^{\prime}\mathbf{X})^{-1}\;\mathbf{x}_{0}^{{}^{ \prime}}+s^{2} \tag{14}\]  
for the variance for the individual prediction with a predictor vector of \(\mathbf{x}_{0}\), residual sum of \(s^{2}\) and design matrix \(\mathbf{X}\) consisting of the feature vectors \(f\) and predicted categories \(k\).
# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 1 Introduction
Electricity price is forecasted according to user electricity behavior and electricity price strategy [24]. This study shows that deep mining of the smart meter user electricity behaviors helps to improve the accuracy of load forecasting; The date type is usually introduced while making load forecasting, therefore the date type should also be considered regarding clustering loads. User electricity loads are clustered and classified to determine load forecasting. However, clustering does not calculate typical day load curves respectively according to different date types; the analysis of user electricity behavior is not accurate enough and the data used is not smart meter data, which affects the accuracy of load forecasting [25; 26]. The parallel K-means algorithm is firstly adopted to cluster the daily load and then the random vector functional-link net is used to make load forecasting for every cluster [27]. User electricity loads are also clustered and classified for load forecasting in [28; 29; 30; 31]. However, the chosen typical day load curves in clustering do not distinguish date type and average day load; typical day load curves are not calculated according to different date types, which causes the analysis of user electricity behaviors to be not accurate enough and affects the accuracy of load forecasting. According to the above analysis, in this paper, first the average load data of the same type of date within one month are calculated according to different date types (ordinary workdays, day before holidays, holidays) and the typical day load curves of users are obtained. Second, similarity between user electricity behaviors under different date types are considered to cluster users, which classifies the users with similar electricity behaviors into the same cluster, and the users from various clusters have many differences in their electricity behaviors. Finally, the load forecasting model based on OS-ELM is adopted for different user clusters for load forecasting and aggregated to get the system load. In order to prove the validity of the proposed method, we perform simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation.
## 2 Monostatic-Bistatic Geometry for Stereo-Radargrammetric Reconstruction
### Monostatic-Bistatic Observation Strategies
As shown in Figure 4, a regular trend in SNR ratio is derived for the same-side geometry, as the bistatic sensor approaches the target. The opposite-side geometry, on the contrary, exhibits a significant increase of bistatic SNR, however this is just a geometrical effect consequent to the worsening of the bistatic ground range resolution and therefore it can not be exploited. Of course the possibility to achieve high-quality relief reconstruction by stereo-radargrammetry relies on the availability of medium-to-high resolution bistatic SAR images. Figure 3: Bistatic time interval required to receive the same monostatic swath width (40 km) for different observation strategies as a function of the baseline (platforms operating at 620 km altitude in parallel trajectories). Figure 2: (a) Bistatic-to-monostatic ground range resolution ratio, (b) bistatic-to-monostatic azimuth resolution ratio, (c) bistatic angle, as a function of baseline (platforms operating at 620 km altitude in parallel trajectories).
###### Abstract  
Change detection (CD) in high-resolution remote sensing imagery remains challenging due to the complex nature of objects and varying spectral characteristics across different times and locations. Convolutional neural networks (CNNs) have shown promising performance in CD tasks by extracting meaningful semantic features. However, traditional 2D-CNNs may struggle to accurately integrate deep features from multi-temporal images, limiting their ability to improve CD accuracy. This study proposes a Multi-level Feature Cross-Fusion (MFCF) network with 3D-CNNs for remote sensing image change detection. The network aims to effectively extract and fuse deep features from multi-temporal images to identify surface changes. To bridge the semantic gap between high-level and low-level features, a MFCF module is introduced. A channel attention mechanism (CAM) is also integrated to enhance model performance, interpretability, and generalization capabilities. The proposed methodology is validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate superior performance compared to the current state-of-the-art in evaluation metrics including recall, F1 score, and IOU. The MFCF network, which combines 3D-CNNs and a CAM, effectively utilizes multi-temporal information and deep feature fusion, resulting in precise and reliable change detection in remote sensing imagery. This study significantly contributes to the advancement of change detection methods, facilitating more efficient management and decision making across various domains such as urban planning, natural resource management, and environmental monitoring.  
remote sensing image; change detection; 3D convolutional neural network; feature cross-fusion; artificial intelligence  
1
## 4 Conclusions  
In summary, Fe\({}_{3}\)O\({}_{4}\)/ZnO nanocomposites were synthesized via a chemical method of coating magnetic cores (Fe\({}_{3}\)O\({}_{4}\)) with ZnO by co-precipitation of Fe\({}_{3}\)O\({}_{4}\) with zinc acetate in a basic medium of ammonium hydroxide, and the morphology and the microwave absorption properties were investigated in detail. It is suggested that the amount of ammonium hydroxide plays a key role in controlling the morphologies of the composites, and the SEM and TEM results further confirmed that ZnO shell generated chemical bonds with the Fe\({}_{3}\)O\({}_{4}\) NPs. Owing to the core-shell structure, an efficient complementary balance was achieved between dielectric loss and magnetic loss. Moreover, the enhanced microwave absorption properties benefitted from the core-shell structure, which induces intensified interfacial polarization. Specifically, the minimum RL value of \(-\)50.79 dB can be achieved at 4.38 GHz when the thickness is 4.5 mm. The mechanism of designing neoteric structures with magnetic and dielectric materials in order to broaden the effective absorption bandwidth would open up a promising domain in designing composites with high EM absorption performance. As a result, our Fe\({}_{3}\)O\({}_{4}\)/ZnO nanocomposites are expected to form a novel platform for advancing EMW absorbers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **wt (\%)** & \multicolumn{2}{c}{**Optimum**} & **Minimum RL** & **Ref.** \\  & & **Frequency (GHz)** & **Value (dB)** & **Ref.** \\ \hline SnO\({}_{2}\)/Fe\({}_{3}\)O\({}_{4}\)/MWCNTs & 70 & 10.90 & \(-\)42.00 & [36] \\ Fe\({}_{3}\)O\({}_{4}\)/SiO\({}_{2}\)/rGO & 20 & 9.70 & \(-\)26.60 & [37] \\ Fe\({}_{2}\)O\({}_{4}\)/MnO\({}_{2}\) & 40 & 16.80 & \(-\)41.50 & [38] \\ Fe\({}_{3}\)O\({}_{4}\)@C & 66.7 & 16.20 & \(-\)22.60 & [39] \\ FePc-Fe\({}_{3}\)O\({}_{4}\)-BF & 75 & 5.90 & \(-\)31.10 & [40] \\ Fe\({}_{3}\)O\({}_{4}\)/ZnO & 50 & 4.38 & \(-\)50.79 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 1: EMW absorption performances of typical Fe\({}_{3}\)O\({}_{4}\)-based composites reported in this work and recent literature.  
Figure 9: Dielectric loss tangent (**a**) and magnetic loss tangent (**b**) of the Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)/ZnO (sample B), respectively.
###### Abstract  
In this study, we evaluated the response spectra of 24 earthquake series, which includes 15 from the Kumamoto earthquake series and 9 from the Pohang earthquake series, and explored the effects of earthquake magnitude on the resonance frequencies of structures and buildings. Furthermore, the observations of this study were compared with the design response spectra, Regulatory Guide 1.60 (The United States Nuclear Regulatory Commission, 1973) for Korean nuclear power plants, and with the Korean Building Code (MOLIT, 2016, hereinafter referred to as KBC 2016) for general structures and buildings. The response spectra, after normalization with reference to the peak ground acceleration (PGA), were derived using a total of 423 horizontal and vertical accelerations. It was observed that the shapes of the horizontal and vertical response spectra were strongly dependent on the magnitude of the earthquake and the resonance frequency. Given the strong dependence of the response on the magnitude, it is suggested to consider magnitude \(>\) ML \(\sim\) 6.0 when establishing design response spectra. Compared to inland areas, a fairly higher amplitude of response at significantly lower frequency ranges could be attributed to the local geological environment of Jeju Island, which was formed by a surface volcano eruption and the distribution of unconsolidated Pleistocene marine sediments in the Jeju area. It is necessary to study the characteristic influence of layers with low shear wave velocity distributed in the Jeju region on seismic responses more rigorously while considering the frequency band and amplitudes at the surface of Jeju. The resonance frequencies of general low-rise and mid-rise buildings by the brief formula and those by design response spectra both suggested by KBC 2016 were overlapped, and these indicated that the seismic hazard could be much higher on general buildings in the Jeju region than in inland areas. Lastly, it is necessary to make the design standard criteria for Reg. Guide 1.60 and KBC 2016 more conservative in the lower frequency range of higher than 0.6 Hz and 2.0-6.0 Hz, respectively, which is significantly lower than those of the inland area, and to establish improved design response spectra with site-specific seismic design standards by referencing large amounts of qualitative data from the region around the Korean Peninsula.  
Gyeongju earthquake; horizontal response spectrum; KBC 2016; Regulatory Guide 1.60; resonance frequency; vertical response spectrum +
Footnote †: journal: Applied Sciences