esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Numerical Results
### Evolution Law of the Temperature Field during Excavation Period
#### 5.3.2 Analysis of the Temperature-Field Cloud Diagram during Excavation Period  
The freezing design scheme for the main inclined shaft of the Shengfu Mine requires the middle-row freezing pipes and the plugging freezing pipes to be cut off as the excavation progresses. This operation process should be performed in conjunction with excavation and masonry while ensuring that the cutting process is unaffected by saltwater, freeze recovery serves its intended purpose, and smooth excavation progress is maintained. Figure 14 shows the overall distribution and evolution of the 3D freezing temperature field during the excavation period. Using COMSOL Multiphysics software, it can be seen that before the initial excavation, Zone 1 had a low negative temperature state. Once Zone 1 had been excavated, the excavation section was exposed, experiencing a \"heat flow erosion\" phenomenon, resulting in an increase in the temperature. Moreover, the freezing pipes on the section must be cut off during the excavation, temporarily suspending cooling at the bottom plate. Based on the excavation process and the rise in the temperature at the measuring hole on the bottom plate of the working face, the freezing device must be quickly restored for freezing. This ensures a sealed frozen wall before constructing the inner wall of the shaft and continuous and safe construction of the excavation. In these circumstances, the temperature rise in the excavation section could be limited, and the temperature of the frozen wall remained below \(-10\)\({}^{\circ}\)C. Similarly, when other areas were excavated, the temperature in the excavation section increased; nevertheless, with continuous cooling, the temperature remained stable. After completing the sixth excavation step, the well side and bottom plate were fully exposed, and their temperatures could still remain stable. Consequently, the excavation process of the open-excavation section in the construction plan was considered both reasonable and safe.  
During the soil excavation process, the frozen wall experienced thermal disturbances, leading to increased temperatures at the excavation border. However, as the frozen pipe rapidly re-established cooling, the frozen wall retained its effective thickness, thus ensuring that the overall average temperature remained stable. Consequently, there was no significant \"softening\" of the frozen wall during the excavation process. Figure 15 shows that the thickness and temperature of the frozen wall, both before and after the excavation, undergo negligible changes, maintaining an average temperature below \(-10\)\({}^{\circ}\)C.
## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.4 Data Cubes and ARD
Therefore, semantic content-based queries covering an area of interest in each temporal extent are possible, such as an observed moment in time with the maximum vegetation extent. [PERSON] et al. [208] introduced the concept of Earth system data cubes and implemented an integrating ARD with a suitable analytic interface, as a way to treat multiple data dimensions, and allow the effective application of user-defined functions to co-interpret EO data. The use of ingestion, and storage of ARD is common to each data cube conceptualization and initiative, which is a result of processing satellite imagery going from data acquisition to radiometric calibration and additional conversions to Top-of-Atmosphere (TOA) reflectance, and finally to surface reflectance [22; 209]. Unfortunately, different EODC initiatives are being developed using contrasting strategies to produce ARD. Consequently, getting uniform and consistent ARDremains a challenge [49]. The CEOS Analysis Ready Data for Land (CARD4L) Framework ([[https://ceos.org/ard](https://ceos.org/ard)]([https://ceos.org/ard](https://ceos.org/ard))) states that satellite data should meet certain requirements--including interoperability through time, space, and with other datasets--and also should allow immediate analysis with minimum effort. [PERSON] et al. [203] state the importance of gridding the data to a common equal-area projection and other processing procedures to provide adequate ARD such as Top-of-Atmosphere Reflectance (ToA), Brightness Temperature, Surface Reflectance, Provisional Surface Temperature, and Quality Assessment. Upon observation, [PERSON] et al. [210] evaluated the temporal consistency of the USGS Landsat ARD dataset and recommended several processing streamlines related to resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) and topographic corrections, before using it for SITS analysis. Applying these recommendations, [PERSON] et al. [209] explored the concept of ARD defined by CEOS to describe the 16-day GLAD ARD product ([[https://glad.umd.edu/ard/home](https://glad.umd.edu/ard/home)]([https://glad.umd.edu/ard/home](https://glad.umd.edu/ard/home))) developed to be used as input for LULC and LULCC mapping. However, despite global radiometric consistency, the product does not represent surface reflectance and is not suitable to be directly used as input for precise measurement of photosynthesis, water quality, and other variables that require actual surface reflectance, such as LULC mapping, due to the irregular frequency of clear-sky observation. Hence, they recommend considering the use of phenological and change detection metrics to overcome these issues.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions
### Reversibility  
The reversibility of an RDH scheme is that the original image can be recovered losslessly from the stego image. In such case, the scheme is reversible. As shown in Section 3.3, our method is a reversible one. To verify whether the recovered image \(\mathbf{O}^{\prime}\) is identical to the original image \(\mathbf{O}\), we adopted the correlation coefficient \(r\) and MSE (mean squared error). The correlation coefficient is widely used in statistical analysis of the correlation between two variables:  
\[r(\mathbf{O}^{\prime},\mathbf{O})=\frac{\sum\limits_{m}\sum\limits_{n}(O^{\prime}_{m,n} -\bar{\mathbf{O}^{\prime}})(O_{m,n}-\bar{\mathbf{O}})}{\sqrt{(\sum\limits_{m}\sum \limits_{n}(O^{\prime}_{m,n}-\bar{\mathbf{O}^{\prime}})^{2})(\sum\limits_{m}\sum \limits_{n}(O_{m,n}-\bar{\mathbf{O}})^{2})}} \tag{17}\]  
where \(O^{\prime}_{m,n}\) and \(O_{m,n}\) are pixel values at the \((m,n)\) position of recovered image \(\mathbf{O}^{\prime}\) and original image \(\mathbf{O}\), respectively. \(\bar{\mathbf{O}^{\prime}}\) and \(\bar{\mathbf{O}}\) are the mean values of \(\mathbf{O}^{\prime}\) and \(\mathbf{O}\), respectively. The value of \(r\) is limited in the range \([-1,1]\). The closer the value is to 1, the more similar the two images are to each other. Therefore, the value of \(r\) is supposed to be 1 if the two images are identical. The MSE between two images \(\mathbf{O}^{\prime}\) and \(\mathbf{O}\) is defined as  
\[MSE(\mathbf{O}^{\prime},\mathbf{O})=\frac{1}{N_{1}N_{2}}\sum\limits_{m=1}^{N_{1}}\sum \limits_{n=1}^{N_{2}}(O^{\prime}_{m,n}-O_{m,n})^{2} \tag{18}\]  
which is zero if \(\mathbf{O}^{\prime}\) and \(\mathbf{O}\) are identical. The correlation coefficients of original images and recovered images are all 1 s and all the MSE values are 0 s. That is to say, our scheme is reversible.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 4 Experiment Studies
### Experimental Setup and Data Description  
The rolling bearing dataset generated by the Institute of Design Science and Basic Components at Xi'an Jiaotong University (XJTU) [27], is analyzed. The experimental setup is shown in Figure 2. The bearing testbed consists of an alternating current (AC) induction motor, a motor speed controller, a support shaft, two support bearings (heavy duty roller bearings), a hydraulic loading system, and so on. Fifteen rolling bearings of the type LDK UER204 were carried out in different degradation tests under three operating conditions. There exist five types of fault conditions with these tested bearings, which are inner race fault, outer race fault, cage fault, and two mixed faults. In order to collect the degradation signals, two PCB 352C33 accelerometers are placed on the horizontal and vertical directions of housing of the bearings, and signals are collected at a sampling frequency of 25.6 kHz. A total of 32768 run-to-failure sampling points are recorded every minute.  
Figure 1: Data-to-image conversion method.  
In each run-to-failure testing experiment, the accelerated deterioration test of the bearing was close to stop when the amplitude of the vibration signal was higher than 20 g. Based on this, the subsequent degradation vibration signals are selected as the complete fault signals when the acceleration exceeds 20 g for the first time. Figure 3 shows the result of complete fault signal selection of fifteen degradation tests. In this study, due to the load acts on horizontal direction, the horizontal monitored signals of the tested bearings are selected to evaluate the performance of the proposed method.  
The types of bearing condition are only considered in this study, and the impact of different operating conditions was ignored. Therefore, the datasets of the different operating conditions of bearings with the same condition went into one dataset. Figure 4 shows the vibration signals of the six types of condition signals.  
To increase the number of samples, the samples are resampled by using a sliding window from vibration signals, where each sample contains 1225 data points. There are six datasets, and each dataset contains 1200 samples. The details of the datasets are shown in Table 1. For each dataset, 660 samples are selected for the training dataset, 180 samples are randomly selected to compose the validation dataset, and 360 samples are used for the testing dataset.  
Figure 2: Experimental setup.  
Figure 4: Six fault types of vibration signals. (**a**) normal signals, (**b**) inner race fault signals, (**c**) outer race fault signals, (**d**) cage fault signals, (**e**) inner and outer race fault signals, (**f**) inner race, ball, cage and outer race fault signals.  
Figure 3: The selection of fault vibration signals. The (**a**) to (**o**) represent the vibration signals for the fifteen degradation experiments.
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 4 Data Integration
To refine model parameters and better align prediction with measurements, Data Assimilation (DA) techniques have been used [137]. DA enables automated calibration of models, as well as enhancing forecast capabilities of biogeochemical and hydrological models and improving ecosystem state assessments [138]. Among the most common DA methods, the Ensemble Steady State Kalman Filter (EnSSKF) [139] and the Ensemble Kalman Filter (EnKF) [140] have been widely and successfully used to improve the prediction of lake temperature [122], suspended particulate matter concentrations [141], water levels and currents [142], and algae and algal bloom dynamics [143; 144] through the integration of remotely sensed and in situ data. Data integration techniques are variable and site specific depending at the phenomena at hand scale, this is beyond the scope of this paper and thus not addressed. To promote and facilitate the use of DA methodologies, pre-fabricated algorithms and toolkits have been developed and made available from several working groups. These toolkits include the Parallel Data Assimilation Framework ([[http://pdaf.awi.de/trac/wiki](http://pdaf.awi.de/trac/wiki)]([http://pdaf.awi.de/trac/wiki](http://pdaf.awi.de/trac/wiki)), accessed on July 2021), the Data Assimilation Research Testbed (DART, [[https://www.image.ucaredu/DAReS/DART/](https://www.image.ucaredu/DAReS/DART/)]([https://www.image.ucaredu/DAReS/DART/](https://www.image.ucaredu/DAReS/DART/)), accessed on July 2021), and the OpenDA ([[https://www.openda.org/](https://www.openda.org/)]([https://www.openda.org/](https://www.openda.org/)), accessed on July 2021), which was used successfully for the automatic calibration of a water level forecasting model [145] and current and salinity profiles [146] and for the improvement of forecast accuracy of a water quality model [147; 148]. With such a reservoir of ready-to-apply methodologies, the effort in using these techniques and applying them to current modeling is reduced. Nevertheless, the accurate calibration of system models requires high resolution description of the model inputs. These are not always readily available for many environmental systems because of the limitations of current measurement technologies. This remains a major challenge for DA applications in water quality management that can be overcome utilizing DF techniques to integrate, fuse, and harmonize in situ, remote sensing, volunteer monitoring, and modeled data.
# Post-Occupancy Evaluation in High-Rise Apartent Buildings in Vietnam
## 3 Results and Discussion
### Electricity Energy Consumption and Weather  
Figure 7 presents the monthly electricity consumption of the investigated apartments alongside the corresponding monthly average outdoor temperature. Since electricity consumption data are derived from monthly energy bills, it is impossible to distinguish between different end uses precisely. However, by analyzing the weather data, it can be inferred that the peak in electricity consumption between May and September is primarily driven by AC usage for space cooling. The increased variation in electricity consumption during this period reflects differences in AC usage across apartments. Additionally, a slight rise in electricity consumption in December may be attributed to domestic hot water systems or electric heating in some apartments.
## 3 The Present: 2010-2015
### Applications of UPS and UBPS  
The UPS and the UBPS, both in pulsed and harmonic version, have been applied to a range of nondestructive testing (NDT) applications involving polymeric, metallic and fiber-reinforced materials.
## 4 System Architecture
### Data Server  
As shown in Figure 5, the data server is the central hub of the SCADA system, responsible for storing, processing, and analyzing the data collected from RTUs [18].  
Figure 5: Diagram of Information Flow in the Proposed SCADA System.  
Data Storage: The server stores historical data, allowing operators to analyze trends and generate reports [8]. Data is stored automatically by polling executed periodically every with intervals of 5 min. The stored data includes a time stamp alongside the information characterizing each of the 160 nodes in the system: water pressure, pump state, and water flow.
* Data Processing: Advanced analytics tools are used to process the data in real-time, generating insights and alerts for operators [13].
* System Integration: The server integrates data from multiple sources, including RTUs, GIS, and external databases, providing a comprehensive view of the system [16].
* Redundancy and Backup: To ensure data integrity, the server is equipped with redundant storage and backup systems, protecting against data loss [19].
# Signal Separation Based on Knowledge Representation
## 1 Introduction
The complexity arises from the need to balance accuracy, interpretability, and computational efficiency, making it essential to carefully evaluate the suitability of a given representation for the task at hand. Based on the ideas of previous work, this paper proposes a more general signal separation method based on knowledge representation. The knowledge representation framework proposed in this paper can represent knowledge according to the application scenario, including observed knowledge representation, physical knowledge representation, and data-driven knowledge representation, making the knowledge-guided neural network signal separation method more general. Within the framework of knowledge representation, signal separation is achieved by representing the separation targets with knowledge and implementing signal separation guided by knowledge-based branch autoencoder networks. First, corresponding knowledge representation is obtained under the proposed knowledge representation framework based on the observed mixed signals. Second, the number of branches of the autoencoder is determined based on the number of separation target signals. Then, a branch autoencoder network is constructed using the results of knowledge representation, with branches achieving the separation of target sub-signals under the guidance of knowledge. Finally, a self-encoding network architecture is constructed with comprehensive constraints of observation signal reconstruction error and knowledge-guided error. This method can effectively address different application scenarios, providing new solutions for signal separation. The innovations of this paper are mainly as follows:  
(1) A more comprehensive knowledge representation framework, which includes observed knowledge representation, physical knowledge representation, and data-driven knowledge representation, provides new insights for knowledge-guided neural networks. (2) A more general signal separation method that can be applied to different types of signals, including seismic signals, image signals, etc. (3) The proposed method achieves signal separation via a knowledge-guided branch autoencoder.
###### Abstract  
Here, we report on adding an important dimension to the fundamental understanding of the evolution of the thin film micro structure evolution. Thin films have gained broad attention in their applications for electro-optical devices, solar-cell technology, as well storage devices. Deep insights into fundamental functionalities can be realized via studying crystallization microstructure and formation processes of polycrystalline or epitaxial thin films. Besides the fundamental aspects, it is industrially important to minimize cost which intrinsically requires lower energy consumption at increasing performance which requires new approaches to thin film growth in general. Here, we present a state of the art sputtering technique that allows for time-resolved in situ studies of such thin film growth with a special focus on the crystallization via small angle scattering and X-ray diffraction. Focusing on the crystallization of the example material of BaTiO\({}_{3}\), we demonstrate how a prototypical thin film forms and how detailed all phases of the structural evolution can be identified. The technique is shaped to enable a versatile approach for understanding and ultimately controlling a broad variety of growth processes, and more over it demonstrate how to in situ investigate the influence of single high temperature sputtering parameters on the film quality. It is shown that the whole evolution from nucleation, diffusion adsorption and grain growth to the crystallization can be observed during all stages of thin film growth as well as quantitatively as qualitatively. This can be used to optimize thin-film quality, efficiency and performance.  
GIAXS; GIXRPD; XRR; in situ; synchrotron radiation; oxid thin films; BaTiO\({}_{3}\); crystallization; sputter deposition Article  
In Situ X-ray Measurements to Follow the Crystallization of BaTiO\({}_{3}\) Thin Films during RF-Magnetron Sputter Deposition  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; [PERSON], [PERSON], [PERSON], [PERSON].  
sputtering unit [8], and the investigation techniques in situ grazing incidence small angle X-ray scattering (GISAXS) and in situ grazing incidence X-ray powder diffraction (GIXRPD) simultaneously, as well as in situ X-ray reflectivity (XRR) measurements [9; 10].  
In this work, we follow the crystallization process in situ during the high temperature deposition above the crystallization energy. We deposit BaTiO\({}_{3}\) at 600 \({}^{\circ}\)C and followed the film evolution from early formation to polycrystalline thin film of around 40 nm.
## 1 Introduction
They concluded that vertical transmit and vertical receive (VV) and vertical transmit and horizontal receive (VH) polarizations individually and combined were able to provide an accuracy of above 90% over North Dakota. All authors mention the problem of \"Big Data\" when dealing with Sentinel-1 multi-temporal images due to the increase in dimensionality. Therefore, processing multi-temporal satellite data requires more computational resources. [PERSON] and [PERSON] [19] provide a detailed review on the applications of C-band polarimetric SAR for agricultural applications. In this study, we used multi-temporal images of Sentinel-1 to develop a framework to map smallholder maize farms using well-known machine learning algorithms (Support Vector Machine--SVM and Extreme Gradient Boosting--Xgboost) under a complex environment. The strengths of these algorithms are that: (1) the SVM algorithm can handle high dimensional data using a few training samples [13]. (2) The Xgboost algorithm runs at an improved computational speed, which is advantageous when processing multi-temporal images for the maize planting season [23]. (3) Additionally, both algorithms have a good feature identification capacity and are non-parametric [13; 24; 25]. The two-stage image fusion approach was applied. Firstly, pixel-level fusion was done; the purpose of this first stage is to reduce computational demands on the system by reducing the dimensions of the datasets using Principal Component Analysis (PCA). Secondly, model-level fusion was done; this second stage uses sufficient principal components for all the reduced polarizations as input into the classifying algorithms. Generally, this approach has been used mainly in hyperspectral remote sensing image classification or change detection analysis [26; 27]. It has not yet been applied to Sentinel-1 to map smallholder maize farms and estimation of their production areas. The approach was tested on a rural community in Makhuduthamaga, Limpopo province of South Africa. This region is dominated by smallholder maize farms and most farmers farm for subsistence.