esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 10 Suggestions and Recommendations  
The following suggestions are made for improving IEMS utilizing ML:  
* Invest in reliable data gathering systems: Record current data on weather, occupancy, and other pertinent information as well as statistics on energy consumption. Integrate various data sources to raise the precision and efficiency of AIEMS.
* Create cutting-edge ML algorithms: Concentrate on creating cutting-edge ML models designed especially for energy management. To optimize energy usage and enhance control strategies, investigate deep learning techniques, reinforcement learning, and other cutting-edge methodologies.
* Adopt hybrid approaches: Integrate multiple ML techniques, including supervised and unsupervised learning, to use the benefits of each approach. Integrate data-driven methodologies with physics-based models for enhanced forecasting accuracy and superior control strategies.
* Ensure scalability and adaptability: Develop AIEMS technologies that can manage massive energy systems and adjust to changing circumstances. Create structures and techniques for processing and analyzing enormous amounts of data quickly.
* Focus on enhancing model interpretability: Make AIEMS models easier to understand and interpret. To help users and stakeholders understand and trust the system's suggestions and actions, develop techniques that offer transparent insights into the decision-making process.
* Integrate with IoT and edge computing: Investigate the connection between AIEMS and IoT hardware and edge computing innovations. Utilize real-time monitoring and control at the sensor or device level to enhance responsiveness and reduce energy usage.
* Give robustness and cybersecurity: This is a higher priority and deals with the problems they cause for AIEMS. Apply methods for anomaly detection, privacy preservation, and secure communication to safeguard delicate energy data and guarantee system dependability.
* Stress user-centric design: Take user preferences, comfort levels, and behavior patterns into account when designing and running AIEMS. Make individualized energy recommendations and involve users in energy-saving activities.
* Encourage collaboration and knowledge sharing: Promote cooperation between academics, industry professionals, and decision-makers to share knowledge, collaborate on research projects, and promote the area of AIEMS. To address difficult energy management concerns, encourage interdisciplinary research and collaborations.
* Carry out real-time evaluations: Test AIEMS solutions in real-time situations to gauge their efficiency, scalability, and efficacy. To perform field testing and receive feedback for continual development, work with building owners, operators, and energy management firms.  
Adhering to these recommendations can expedite the development and implementation of IEMS utilizing ML, leading to more economical and sustainable energy practices, alongside enhanced energy efficiency.
# The Full Lifecycle Evolution Model of Accidents: A Case Study of Underground Metal Mines in China
## 2 Methodology
### Safety Management Activity Analysis
#### 2.1.3. Accident Causation Logic  
1. Role Classification in Safety Management Personnel involved in safety management can be divided into three categories based on their interaction with other factors: managers, supervisors, and operators.
* Operators: Primarily interact with elements that may pose unsafe conditions, including equipment, materials, and the production environment. They are responsible for implementing specific safety measures.
* Supervisors: oversee the actions of operators to ensure compliance with safety regulations and operational procedures.
* Managers: ultimately responsible for production safety, managers establish and revise safety management systems in accordance with laws and policies. Supervisors can be hierarchical, with higher-level supervisors monitoring lower-level ones. The highest level of supervision lies with the managers.
2. Enterprise-Level Safety Management Logic The safety management system is developed by managers in accordance with laws, industry operating procedures, and the specific needs of the enterprise, serving as the foundation for safety management activities within the enterprise. This system directly impacts the human factors by guiding, constraining, and standardizing supervisory and enforcement behaviors. Personnel manage hazards present in the production process by adhering to established regulations, procedures, and operating standards, thereby ensuring their own safety.  
(3) Accident Causation Logic  
The energy transfer theory elucidates the physical nature of production accidents, positing that the source of these accidents lies in the release of energy from hazards present in the workplace, including environmental factors, equipment, and materials. Human errors or a failure to diligently adhere to management systems can result in these hazards being in an unsafe condition. If personnel fail to effectively control this unsafe condition, it may lead to the unintended release of energy from the hazards, ultimately resulting in production accidents. Consequently, the logical chain for tracing the origins of accidents can be expressed as follows: production accident \(\rightarrow\) failure to manage specific hazards \(\rightarrow\) human oversight \(\rightarrow\) breakdown in supervision \(\rightarrow\) gaps in the regulatory framework \(\rightarrow\) external factors (government oversight, societal supervision, or legal loopholes).
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 2 Problem Formulation
### Model of Mechanical System  
In structural dynamics, modeling an MDOF vibrating system often relies on the assumption of viscous damping [14, 31]. This can be mathematically expressed as follows:  
\[\mathbf{M}\hat{\mathbf{Y}}(t)+\mathbf{C}\hat{\mathbf{Y}}(t)+\mathbf{K}\mathbf{Y}(t)=\mathbf{F}(t). \tag{1}\]  
In this equation, \(\mathbf{Y}=[y_{1}\quad y_{2}\quad\dots\quad y_{N}]^{T}\) represents the generalized displacement vector, with \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) denoting mass, damping, and stiffness matrices, respectively.  
Meanwhile, \(F\) represents the external force vector. To derive the matrices \(M\), \(C\), \(K\), and other related parameters, EMA investigates the system's response vector \(Y\) when it is subjected to a known and controlled excitation vector \(F\)[32]. This approach enables the determination of the system's natural frequencies, damping ratios, and mode shapes, which provide valuable insights into its structural properties and behavior.  
It is important to note that Equation (1) represents not just a single equation, but rather a set of \(N\) coupled equations, where \(N\) corresponds to the degrees of freedom (DOF) of the vibrating system. Although the complexity of this system may appear overwhelming, it can be simplified by expressing the vibrating system in terms of modal coordinates \(\mathbf{Q}=[q_{1}\quad q_{2}\quad\dots\quad q_{N}]^{T}\). This simplification incorporates the Rayleigh damping model, which assumes that damping \(C\) is linearly related to both \(M\) and \(K\)[14; 31; 33]. As a result, the originally coupled Equation (1) can be decoupled into \(N\) individual single-degree-of-freedom (SDOF) systems. Thus, in Equation (2), the impulse response of the system at any time \(t\) is defined as the summation of the responses of each mode, given by  
\[x(t)=\sum_{j=1}^{N}A_{j}e^{-\lambda_{j}t}\sin(\omega_{d_{j}}t+\Phi_{j}). \tag{2}\]  
In this context, \(A_{j}\) signifies the amplitudes, \(\lambda_{j}=\zeta_{j}\omega_{n_{j}}\) the damping coefficients, and \(\Phi_{j}\) the phase of the response at the damped frequency \(\omega_{d_{j}}=\sqrt{1-\zeta_{j}^{2}}\omega_{n_{j}}\), each pertaining to a specific mode or DOF [32]. Assuming that \(\lambda_{j}\) and \(\omega_{d_{j}}\) are known, one can further compute the damping ratio \(\zeta_{j}\) and the natural frequency \(\omega_{n_{j}}\) using the relationships shown in Equation (3):  
\[\omega_{n_{j}}=\sqrt{\lambda_{j}^{2}+\omega_{d_{j}}^{2}},\quad\zeta_{j}=\frac {\lambda_{j}}{\sqrt{\lambda_{j}^{2}+\omega_{d_{j}}^{2}}}. \tag{3}\]  
These equations demonstrate the link between the damping coefficient, the damped frequency, the natural frequency, and the damping ratio, encapsulating the dynamic behavior of each mode.
###### Abstract  
The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications.  
acoustic emission; adaptive control; CNC; milling; wood Article
## 2 Thermal Barrier Coating
### Thermal Properties of Thermal Barrier Coatings
#### 2.4.3 Thermal Conductivity Dependence on Various Deposition Method  
In the APS method, the powder has a layered structure in which layers are stacked in the form of flat agglomerates on the superalloys, and contains several pores, which causes the thermal conductivity of the TBC to be 0.8-1.1 W/m-K depending on the temperature mainly owing to the pores in the structure. LPPS is a method of depositing high temperature plasma, similar to APS, at a low pressure in the chamber during deposition. The reason for maintaining a low-pressure environment is to increase the mechanical strength of the TBC by minimizing the air layer mixed with the pores. The thermal conductivity of TBCs deposited by the LPPS method is 2-6 W/m-K, depending on the temperature. This thermal conductivity is higher than that achieved by the APS method because the air layer inside the TBC is minimized through the deposition at low pressure, thereby reducing the porosity and density. Compared to the APS method, the LPPS method has an advantage as it achieves a higher mechanical strength. However, the process is difficult and results in a slightly higher thermal conductivity than that of the APS method, which is a disadvantage when considering the thermal barrier. In the plasma spraymethod, the parameters that can be controlled during deposition vary with plasma gas, plasma power, nozzle diameter, the grain size, the distance between the spray and the base material, the temperature of the base material, and the roughness of the superalloy. The grain size is directly related to thermal conductivity. The changes in thermal conductivity according to the grain size have been studied using simulations [79; 80]. As the grain size of the TBC decreases and becomes smaller than the mean free path of the phonon, the thermal conductivity decreases because scattering occurs.  
The EB-PVD method vaporizes the deposition material using an electron beam in a vacuum chamber. TBCs formed using the EB-PVD method are deposited on superalloys with a columnar structure. The thermal conductivity of TBCs deposited using this method is 1.5-1.9 W/m-K, depending on the temperature. If only thermal conductivity is considered, the APS method with a layered structure achieves a lower thermal conductivity than EB-PVD method with a columnar structure, thereby improving the thermal barrier performance. However, because the EB-PVD method achieves a very good mechanical strength due to the internal structure of the TBC, many studies have been conducted on methods to lower the thermal conductivity using EB-PVD.
# Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
###### Abstract  
The diagnosis of abnormal transformer oil temperature is of great significance to guarantee the normal operation of the transformer. Due to concept drift, the oil temperature abnormal diagnosis of the oil-immersed main power transformer is usually unstable via the classic data mining method. Thus, this paper proposes an adaptive abnormal oil temperature diagnosis method (AAOTD) of the transformer based on concept drift. First, the bagging ensemble learning method was used to predict the oil temperature. Then, abnormal diagnosis was performed based on the difference between the predicted oil temperature and the actual measured oil temperature. At the same time, based on the concept drift detection strategy and Adaboost ensemble learning methods, adaptive update of the base classifier in the abnormal diagnosis model was realized. Experiments validated that the algorithm proposed in this paper can significantly reduce the influence of concept drift and has higher oil temperature prediction accuracy. Furthermore, since this method only utilizes the existing power grid data resources to realize abnormal oil temperature diagnosis without extra monitoring equipment, it is an economic and efficient solution for practical scenarios in the electric power industry.  
transformer; oil temperature; ensemble learning; abnormal diagnosis; concept drift
## 4 Model Validation  
First, the effective thermal conductivity of the aggregate, FAM, and asphalt concrete were measured experimentally. As mentioned in the previous section, asphalt concrete is regarded as a composite material. Asphalt concrete with an asphalt content of 4.76% and a porosity of 6% was prepared in the laboratory. In this study, the asphalt concrete and FAM have different aggregate gradations and these are listed in Table 1.  
When determining the thermal conductivity of solids, the transient plane source technique could be utilized [25]. The transient plane source approach was used to assess the thermal characteristics in order to confirm the validity of model.  
When testing asphalt concrete and FAM for their effective thermal conductivity, six cylindrical samples were created in the laboratory with diameters of 100 mm and 50 mm in height. We clamped the sensor with two identical samples, and used a thermal conductivity tester to measure the sample under test, as shown in Figure 7. After the measurement was completed, the sample was removed and the sensor was left to cool down naturally for 20 min. After that, the sample was rotated 180\({}^{\circ}\), and measured again. Asphalt concrete, FAM, and the aggregate were measured 6 times, respectively, and the average of the 6 measurements was thought to be the value of the thermal conductivity of every material. According to the results of the experiment, the average effective thermal conductivity of asphalt concrete, FAM, and the aggregate were 2.127 Wm\({}^{-1}\)K\({}^{-1}\), 1.423 Wm\({}^{-1}\)K\({}^{-1}\), and 2.855 Wm\({}^{-1}\)K\({}^{-1}\), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sieve Size (mm)**} & \multicolumn{2}{c}{**Passing Percentage (\%)**} \\ \cline{2-3}  & **Asphalt Concrete** & **FAM** \\ \hline
16 & 100 & 100 \\
13.2 & 96.0 & 100 \\
9.5 & 82.1 & 100 \\
4.75 & 52.2 & 100 \\
2.36 & 30.9 & 100 \\
1.18 & 23.0 & 74.4 \\
0.6 & 16.9 & 54.7 \\
0.3 & 11.0 & 35.6 \\
0.15 & 8.4 & 27.2 \\
0.075 & 6.8 & 22.0 \\ \By using the gradation in Table 1, five random three-dimensional meso-structure models of asphalt concrete were formed. These models were marked as A1\(\sim\)A5, respectively. The effective thermal conductivity of three-dimensional meso-structure models were calculated through Equation (2). Thermal conductivity is seen in Figure 8 as 2.1744 Wm\({}^{-1}\)K\({}^{-1}\) on average. The average discrepancy between the experimental and numerical results was 2.34 percent. This shows that the model and calculation method of thermal conductivity used in this paper are reliable.
# A Realistic Approach of the Maximum Work Extraction from Solar Thermal Collectors
## 1 Introduction  
Solar energy is a promising renewable energy source that is able to substitute the use of fossil fuel to a large degree [1; 2]. Recently, more and more studies have been carried out in order to optimize the operation of solar thermal systems, and especially the concentrating solar power plants [3; 4; 5]. The most important parameter in the solar Plants is the operating temperature, because this parameter influences the collector efficiency and the thermodynamic cycle efficiency. Higher temperature levels make the solar collector have greater thermal losses, but the efficiency of the coupled thermodynamic cycle is greater. Moreover, the selection of the solar collector kind is dependent on the operating temperature levels. Flat plate collectors are used in low-temperature applications (up to 90 \({}^{\circ}\)C) for domestic hot water production, space heating, etc. [6]. Evacuated tube collectors are usually selected for applications as solar coolers, with industrial heat in the low-medium temperature range up to 150 \({}^{\circ}\)C [7]. At higher temperature levels, concentrating collectors are the only possible choice. The parabolic trough collector is the most mature technology for the temperature range from 200 \({}^{\circ}\)C to 400 \({}^{\circ}\)C [8; 9; 10], while for higher temperature levels concentrating collectors with dishes are preferred [11]. Fresnel collectors [12] and solar towers are also promising concentrating technologies for medium (up to 400 \({}^{\circ}\)C) and high temperatures (up to 600-700 \({}^{\circ}\)C), respectively.  
The objective of this study is to present a model for the prediction of the maximum work production from a solar thermal collector by taking into account the main thermal and optical losses that cannot be neglected in real systems. In every case, the optimum operating temperature of the collector is determined, and then the thermal efficiency of the collector is calculated. This temperature level is the one that maximizes the work production of the coupled Carnot engine. In the literature,this quantity is generally expressed by using the exergy parameter, but this parameter does not take into account the thermal and optical losses of the solar collector. The present model takes into consideration parameters such as the collector optical efficiency and the receiver emittance in a way that corresponds to the real systems. The results of this work can be used for the accurate design of the solar thermal systems and for the determination of the realistic optimum operating temperature levels for every kind of solar collector using its basic design parameters.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Database** & **LIVE** & \multicolumn{6}{c}{**TID2013**} & \multicolumn{6}{c}{**KADID-10k**} \\ \hline \hline
**Subsets** & & **Noise+SC** & **Noises** & **ImProc** & **RS** & **Blur** & **Compr** & **Noises** & **RS** & **RS2** \\ \hline
**Distortions \#** & **All** & **1-7,19** & \begin{tabular}{c} **1,2** \\ **4-7,19** \\ \end{tabular} & \begin{tabular}{c} **8-11,21** \\ **5 metrics** \\ \end{tabular} & \begin{tabular}{c} **1-11,19,21** \\ **5 metrics** \\ \end{tabular} & \begin{tabular}{c} **1-3** \\ **9,10** \\ \end{tabular} & \begin{tabular}{c} **9,10** \\ **11-15** \\ \end{tabular} & \
\begin{tabular}{c} **11-3** \\ **1-3,9-15** \\ \end{tabular} &
\begin{tabular}{c} **1,9-15** \\ **5,9-15** \\ \end{tabular} \\ \hline \multicolumn{10}{c}{**5 metrics**} \\ \hline SISBLIM\_WFB & 0.8233 & 0.1272 & 0.2102 & 0.6823 & 0.3789 & 0.8625 & 0.7083 & 0.5265 & 0.6590 & 0.6204 \\ IL-NIQE & 0.8537 & 0.6763 & 0.6092 & 0.7770 & 0.7221 & 0.8766 & 0.7916 & **0.6207** & **0.7794** & **0.7474** \\ HOSA & 0.9430 & 0.3693 & 0.5469 & 0.7987 & 0.5690 & 0.8846 & **0.8545** & 0.3206 & 0.6540 & 0.5886 \\ CORNIA & **0.9550** & 0.0978 & 0.1253 & 0.8771 & 0.4735 & **0.8908** & 0.8438 & 0.3628 & 0.6623 & 0.5884 \\ MSGF & 0.9166 & 0.1851 & 0.3399 & 0.4845 & 0.3085 & 0.8107 & 0.8214 & 0.2552 & 0.4840 & 0.4020 \\ NN metric & 0.8921 & **0.7880** & **0.7641** & 0.8459 & **0.8208** & 0.8492 & 0.7742 & 0.4502 & 0.7135 & 0.6750 \\ \hline \multicolumn{10}{c}{**10 metrics**} \\ \hline MLV & 0.2326 & 0.1162 & 0.1588 & 0.5124 & 0.1755 & 0.8489 & 0.2757 & 0.2213 & 0.4017 & 0.2799 \\ SISBLIM\_WFB & 0.8233 & 0.1272 & 0.2102 & 0.6823 & 0.3789 & 0.8625 & 0.7083 & 0.5265 & 0.6590 & 0.6204 \\ TCLT & **0.9950** & 0.0395 & 0.1487 & 0.7099 & 0.3151 & 0.7683 & 0.7086 & 0.1131 & 0.4622 & 0.3941 \\ IL-NIQE & 0.8537 & 0.6763 & 0.6092 & 0.7770 & 0.7221 & 0.8766 & 0.7916 & 0.6207 & 0.7794 & 0.7474 \\ HOSA & 0.9430 & 0.3693 & 0.5469 & 0.7987 & 0.5690 & 0.8846 & **0.8545** & 0.3206 & 0.6540 & 0.5886 \\ CORNIA & 0.9550 & 0.0978 & 0.1253 & 0.8771 & 0.4735 & 0.8908 & 0.8438 & 0.3628 & 0.6623 & 0.5884 \\ MSGF & 0.9166 & 0.1851 & 0.3399 & 0.4845 & 0.3085 & 0.8107 & 0.8214 & 0.2552 & 0.4840 & 0.4020 \\ PSI & 0.1638 & 0.3090 & 0.4925 & 0.3596 & 0.0105 & 0.8771 & 0.1072 & 0.0839 & 0.2445 & 0.0468 \\ NMC & 0.3607 & 0.2344 & 0.2865 & 0.3606 & 0.0485 & 0.6812 & 0.3087 & 0.0906 & 0.2171 & 0.0520 \\ BLUR & 0.3361 & 0.4312 & 0.6477 & 0.3554 & 0.0393 & 0.8510 & 0.2328 & 0.2521 & 0.1965 & 0.0205 \\ NN metric & 0.8342 & **0.8484** & **0.8362** & **0.9051** & **0.8813** & **0.8954** & 0.8203 & **0.6217** & **0.7964** & **0.7648** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the verification of the universality of the proposed NN-based metric: SROCC values obtained for LIVE database and various subsets from the TID2013 and KADID-10k databases; the highest SROCC correlations are marked with bold fonts. Figure 4: _Cont_.
## 2 Data Overview and Pre-Processing  
The collected radar image data cannot be directly used for rainfall detection and should be pre-processed. In addition, the co-channel interference is produced in most of the raw radar images, which seriously affects the accuracy of rainfall detection. Therefore, it is necessary to suppress the co-channel interference before detecting rainfall.