esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the context of underground coal mining, mining-induced seismicity is closely linked to the failure and movement of overburden strata, particularly the key strata that control the mechanical stability of surrounding rock masses. This study investigates these failure mechanisms as the fundamental source of mining-induced seismic events, thereby directly addressing their causes and control. This research investigates the influence of overburden strata on mining-induced earthquakes during the mining process by analyzing a single factor independently and comprehensively considering multiple factors. The main factors are mining height, advancing distance, and advancing rate. Laboratory experiments and numerical modelling are applied to investigate the mining-induced seismicity of overburdened strata and the failure process. In order to further verify the accuracy of numerical simulation results, small-scale model experiments were conducted. Based on the key strata theory, a specific project case referred to in the literature [37] is used to establish a numerical model with ABAQUS. After discussing the sensitivity of single factors, the research then goes further to analyze the interaction effect between mining height and advancing rate. By analyzing both single and multiple factors, this research aims to provide a more holistic understanding of the mechanisms underlying mining-induced seismicity and to develop strategies for mitigating the associated risks.
## 4 Discussion
HIL simulations were used in a variety of building systems applications, including HVAC, lighting, and energy management systems. One example of HIL simulation in HVAC systems is the distributed optimal HVAC systems adopting an edge computing strategy. The results showed that the IoT sensing network has the capacity to implement the distributed optimal control strategy and oversee the decomposed optimization tasks effectively [16].
## 2 Theoretical Background
### Mathematical Formalization of CKH Model
In Equation (9), \(\chi=\sum_{i=1}^{k}\chi_{i}\) and \(k\) indicate the number of backstress components that are considered. \[\sigma_{xx}=\mu\sigma_{0}+\chi \tag{9}\]  
The backstress tensor for each component in Equation (8) is multiplied by the scalar factor \(\frac{2}{3}\), and this last statement is justified by the fact that the yielding surface is translated in the deviatoric stress space, and omitting this term results in a scalar factor term in Equation (9). By substituting Equations (7) and (8) into Equation (5) and considering that, for an uniaxial loading case, \(\mathrm{d}p=|\mathrm{d}\varepsilon_{xx}^{\mathrm{pl}}|\), Equation (10) provides a general representation of the evolution of each backstress component. \[\frac{2}{3}\mathrm{d}\chi_{i} \begin{pmatrix}1&0&0\\ 0&-0.5&0\\ 0&0&-0.5\end{pmatrix} =\frac{2}{3}C_{i} \begin{pmatrix}1&0&0\\ 0&-0.5&0\\ 0&0&-0.5\end{pmatrix} \mathrm{d}\varepsilon_{xx}^{\mathrm{pl}}-\frac{2}{3} \gamma_{i}\chi_{i} \begin{pmatrix}1&0&0\\ 0&-0.5&0\\ 0&0&-0.5\end{pmatrix} |\mathrm{d}\varepsilon_{xx}^{\mathrm{pl}}| \tag{10}\]  
Rearranging Equation (10), the expression governing the dynamic of each backstress component, according to the CKH model for a uniaxial loading case, can eventually be obtained in Equation (11). \[\mathrm{d}\chi_{i}=C_{i}\mathrm{d}\varepsilon_{xx}^{\mathrm{pl}}-\gamma_{i} \chi_{i}|\mathrm{d}\varepsilon_{xx}^{\mathrm{pl}}| \tag{11}\]
## 2 Materials and Methods
### Experimental Set-up  
In our experiment, through the absorbance change spectra, we observed the aggregation state of the TPPS changes with laser irradiation. The pump-probe experimental set-up to detect the absorbance change spectra is shown in Figure 2. The stationary white light from the laser-driven Xe light source (EQ-99, Energetiq Technology, Inc., Wilmington, MA, United States) used as the probe was collimated and focused on a sample with achromatic lenses in general cases and with quartz lenses when we studied a shorter wavelength region than the F monomer peak for the results of Section 3.3. The transmitted light was detected by a spectrometer (Acton SpectraPro 300i, Acton Research Co., Acton, MA, USA) with a 128-channel lock-in detector. When the pump was intercepted at 227 Hz by a mechanical chopper, the laser light was commonly focused on the sample with the probe light. Pump-induced change in the transmitted probe light spectrum was observed primarily in the wavelength range of the B band. The change in the transmitted light intensity synchronized with the pump modulation frequency included photoluminescence from the sample, therefore the probe light was also modulated using a shutter to detect the change correlated with both pump and probe modulations via tandem lock-in detection. This procedure prevented contribution from the photoluminescence in the detected signal. A detailed description of the tandem multichannel lock-in detection method is given as an example in References [54; 55]. The irradiation laser light source was a diode-laser-pumped solid-state laser (MGL-FN-532-1W, Changchun New Industries Optoelectronics Tech. Co., Changchun, China) at 532 nm. We observed the irradiation intensity dependence of absorption change spectra by increasing the irradiation power from 173 mW to 1260 mW for the water and ethanol samples. To prevent decolorization from heating via laser absorption, the sample solution was flowed at 4 cm/s with a perista pump (SJ-1211, ATTO Co., Tokyo, Japan) in a cell constructed from two slide glasses and an inserted 300-\(\mu\)m flouroplastic spacer.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Water** & **Methanol** & **Ethanol** & **Butanol** \\ \hline Concentration (\(\mu\)mol/L) & 59 & 62 & 52 & 54 \\ The amount of NaOH (\(\mu\)L) & 200 & 200 & 400 & 400 \\ \hline \end{tabular}
\end{table}
Table 1: TPPS concentration and the amount of 0.1 mol/L NaOHaq for each solvent sample.
## 4 Discussion
Other studies have reported energy savings between 28-35% without affecting the food quality characteristics of products such as blueberries, raspberries, tomatoes, eggplant, be fruit, coffee, tobacco, tea, cocoa beans, rice, and nuts [3; 5; 11; 13; 15]. Although the proposed Models 1 and 2 are not hybrid systems at this stage, the incorporation of alternative complementation with solar panels for the digital system and solar collectors being used to reduce heat transfer areas is being considered for further versions, with the intention to reduce the energy demand of the designed equipment. A proper and simple design is fundamental in dryers for an effective utilization of thermal energy. Additionally, the equipment must be able to process differently a huge range of food materials [5]. Model 2 has demonstrated, with the CFD methods, that it will maintain the temperature in a range optimal for the drying processes of agricultural products. Each of these products has a different temperature range for drying, so it is very important that the equipment can adapt to the product selected. A control system that can allow for the selection of the product to be dried and adapt the temperature to that product must be implemented in Model 2. This must be achieved with fuzzy logic, which has been proven to adapt to different circumstances within nonlinear systems [10]. A good mechanical design and a hybrid system of heat sources, combined with a fuzzy logic controller, could result in a more efficient dryer. Additionally, a system larger than the ones previously proposed could make an impact in small-farm production and may help to reduce harvest waste. It is imperative that subsequent models retain these considerations.
## 5 Discussion
(2) Unsupervised classification currently requires substantial expert validation, which can be time-consuming and subject to human error. The integration of machine learning and artificial intelligence, particularly deep learning-based feature extraction and classification, could automate key steps in data processing and reduce the burden on human analysts. (3) While periodic data acquisition offers snapshots of SDA status, it may not capture rapid changes during critical construction phases. Incorporating real-time RS systems or low-cost ground-based sensors could facilitate continuous monitoring and higher temporal resolution, improving the responsiveness of interventions. (4) Fluctuating environmental conditions, such as weather and seasonal changes, can significantly affect data accuracy, especially for soil and water conservation measures. Robust preprocessing algorithms are needed to normalize these external factors, ensuring consistent data quality and comparability over time.
# Impact of Cryogenic Condition and Drill Diameter on Drilling Performance of CFRP
## 4 Results  
Drilling performance of CFRP material was experimentally investigated using constant cutting speed, different feed rates and drill bits (4 and 6 mm diameter) under dry and cryogenic conditions. The results of the comprehensive experimental study are given below.  
* When CFRP is exposed to excessive cold under cryogenic conditions, the temperature of the material decreases extremely. This decrease in the temperature leads to an increase in Young modulus and tensile strength and also makes the material stiffer, more brittle, and fragile. As materials are highly resistant to deformation at low temperatures, the thrust forces generated during the drilling of CFRP in cryogenic conditions are larger when compared with dry conditions.
* The high temperatures that occurred in the dry drilling of CFRP may cause several problems if temperatures in the cutting zone surpass the glass transition temperature of the matrix. As using a cryogenic coolant prevents temperature increase in the cutting zone and provides lubrication, less damaged, smoother, and higher quality surfaces were obtained under cryogenic conditions.
* Owing to the fact that the temperature increase in the cutting zone is small under cryogenic conditions, there is less tool wear compared to dry conditions.
* According to the results of this study, 4 mm diameter drills performed better than 6 mm diameter drills in all tested cases. Smaller diameter drill bits should thus be chosen unless there is a restriction in terms of design.
* Using cryogenic coolant is profitable when drilling CFRP since it enhances machining efficiency by reducing heat occurrence in the cutting zone during machining, rapidly transferring heat from the cutting zone and allowing high-speed machining.  
Figure 11: Drill wear for the dry and cryogenic condition tests after 324 holes drilled.  
**Acknowledgments:** This work was supported by Commission of Scientific Research Projects of Mehmet Akif Ersoy University (Project No. 0402-YL-16).  
**Author Contributions:** [PERSON] and [PERSON] designed the experiments and measured the AFM and SEM; [PERSON], [PERSON] and [PERSON] performed the experiments, analysed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology
### Dimensionality Reduction  
The abovementioned document representations and especially TF-IDF lead to high-dimensionality prediction problems [3], causing learning algorithms to run slowly or requesting huge memory resources [31].  
Therefore, we applied PCA [42], NMF [43] and LSA [44] dimensionality reduction algorithms on the datasets represented with TF-IDF features, reducing the number of features to 500. This means that the reduced representation for English is around 6.2 times smaller and for Romanian--about 8 times smaller.  
We did not apply dimensionality reduction on the Word2 Vec and Doc2 Vec data because the desired vector size of each representation is set before the feature extraction, in our case: 200.
# A Novel Approach for the Definition of an Integrated Visual Quality Index for Residential Buildings
## 3 Methods
### Thresholds  
Each metric is organized in four classes defined as follows:  
* Class I: the parameter's value is optimal for enhancing VQ;
* Class II: the parameter's value favors a good level of VQ;
* Class III: the parameter's value permits a sufficient level of VQ;
* Class IV: the parameter's value is not suitable for residential use.  
Some metrics come with a defined set of threshold values, while for others values from standards or protocols have been considered. For few of them, a proposal has been done based on simulation, literature, and experts' consultation. For upper levels of sDA, LEED v4 [33] suggests the thresholdsof 55% and 75%. The value of the limit between Classes III and IV was fixed by simulating a room with a window facing north with a glazed surface of 1/8 of the floor area in different conditions of weather and geometry. The thresholds for sDGP are defined with the index. According to UNI 12464-1, UGR ranges between 10 (imperceptible) to 34 (intolerable), where the value of 19 is typically considered the frontier between comfort and discomfort [49]. Values suggested by [PERSON] and [PERSON] [57] have been used in relation to user perception: unacceptable (Class IV), just acceptable (Class III), perceptible (Class II), and just perceptible (Class I). For values of maintained illuminance, the references have been UNI EN 12464 and UNI 10380, a repealed Italian standard which used to also take into account residential environments. The ranges were also adjusted on the basis of experience in accordance with common lighting design. It was decided to include in Class IV both poorly illuminated and over illuminated environments. The ranges of CCT were particularly difficult to define because in this field satisfaction depends on aesthetics preferences, culture, and even the time of day. It was decided that wide ranges should be kept with the only aim being to avoid lights that appear too reddish or bluish. In Tables 1 and 2 the thresholds for each parameter used in the definition of the index are listed.
# Genetic Algorithm for the Retailers' Shelf Space Allocation Profit Maximization Problem
## 4 Problem Formulation
### Product Constraints  
The following product constrains have been assumed:  
\[\sum_{i=1}^{S}x_{ij}\geq s_{j}^{\min},\ j=1,\ldots,P \tag{11}\]  
\[\sum_{j=1}^{S}x_{ij}\leq s_{j}^{\max},\ j=1,\ldots,P \tag{12}\]  
\[\sum_{i=1}^{S}\left(f_{ij}+c_{ij}+n_{ij}\right)\leq t_{j},\ j=1,\ldots,P \tag{13}\]  
\[\sum_{i=1}^{S}f_{ij}\geq f_{j}^{\min},\ j=1,\ldots,P \tag{14}\]  
\[\sum_{i=1}^{S}f_{ij}\leq f_{j}^{\max},\ j=1,\ldots,P \tag{15}\]  
\[c_{ij}\geq c_{j}^{\min},\ j=1,\ldots,P,\ i=1,\ldots,S \tag{16}\]  
\[c_{ij}\leq c_{j}^{\max}\cdot\left\lceil\frac{f_{ij}w_{j}}{h_{j}}\right\rceil,\ j=1,\ldots,P,\ i=1,\ldots,S \tag{17}\]  
\[n_{ij}\geq n_{j}^{\min},\ j=1,\ldots,P,\ i=1,\ldots,S \tag{18}\]  
\[n_{ij}\leq n_{j}^{\max}f_{ij},\ j=1,\ldots,P,\ i=1,\ldots,S \tag{19}\]  
\[c_{ij}\cdot n_{ij}=0,\ j=1,\ldots,P,\ i=1,\ldots,S \tag{20}\]  
Constraints (11) and (12) restrict the lower and upper bounds of the number of shelves on which the product should be placed. Constraint (13) ensures that the supply limit of a product is not exceeded. Constraints (14) and (15) guarantee the lower and upper bounds of product facings while (16) and (17) provide the bounds of side cappings per position. Constraint (17) ensures that the number of facings of the product is enough for the capping, i.e., the product may be capped. Constraints (18) and (19) limit the lower and upper bounds of nestings per facing position. Definition parameter (20) shows that only cappings or nestings for a product may present, but not both of them.
## 2 Methodology
### Development of Integrated Energy Consumption Prediction Model  
#### 2.2.1 CEEMDAN Signal Decomposition Method  
Empirical Mode Decomposition (EMD) is a denoising method used for nonlinear and non-smooth signals [39]. However, EMD is prone to modal aliasing, leading to inaccuracies in the extracted Intrinsic Mode Functions (IMFs) [40]. Modal aliasing is an error phenomenon in EMD decomposition where there is an overlap in certain moments or frequencies in the signal, resulting in less clear IMFs [41]. Ensemble Empirical Mode Decomposition (EEMD) is an improved adaptive denoising method based on EMD, designed to reduce modal aliasing [42]. In this study, denoising IMF components involves using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, known for its effectiveness in handling modal aliasing and adaptability to complex signals. The flow structure of this method is shown in Figure 2.  
Let \(E_{\rm i}(x)\) be the \(i\) eigenmode component obtained by EMD decomposition and the \(i\) eigenmode component obtained by CEEMDAN decomposition be \(\overline{C_{\rm i}(t)}\). \(v^{j}\) is the Gaussian white noise signal satisfying the standard normal distribution, \(j=1,2,\ldots,N\) is the number of times of adding white noise, \(\varepsilon\) is the standard table of white noise, and \(y(t)\) is the signal to be decomposed. CEEMDAN decomposition steps are as follows:  
Step 1: White Gaussian noise is added to signal \(y(t)\) to be decomposed to get a new signal \(y(t)+(-1)^{q}\varepsilon v^{j}(t)\), \(q=1,2\) and the new signal is decomposed by EMD to get the first-order intrinsic mode component \(C_{1}\).  
\[E_{\rm i}(y(t)+(-1)^{q}\varepsilon v^{j}(t))=C_{1}^{j}(t)+r^{j} \tag{7}\]  
Step 2: Take the overall average of the generated N mode components to obtain the first Intrinsic Mode Function (IMF) of the CEEMDAN decomposition.  
\[\overline{C_{1}(t)}=\frac{1}{N}\sum_{j=1}^{N}C_{1}^{j}(t) \tag{8}\]  
Step 3: Calculate the residual by removing the first mode component:  
\[r_{1}(t)=y(t)-\overline{C_{1}(t)} \tag{9}\]  
Step 4: Introduce paired positive and negative Gaussian white noise to signal \(r_{1}(t)\), perform EMD decomposition on the new signal, and obtain the first-order mode component \(D_{1}\). Consequently, the second intrinsic mode component of CEEMDAN decomposition can be obtained.  
\[\overline{C_{2}(t)}=\frac{1}{N}\sum_{j=1}^{N}D_{1}^{j}(t) \tag{10}\]  
Step 5: Calculate the residual by removing the second mode component:  
\[r_{2}(t)=r_{1}(t)-\overline{C_{2}(t)} \tag{11}\]  
Figure 2: CEEMDAN signal decomposition.  
Step 6: Repeat the above steps until the obtained residual signal becomes a monotonic function and further decomposition is not possible, indicating the end of the algorithm. At this point, the number of intrinsic mode components obtained is k, and the original signal \(y(t)\) is decomposed into the following:  
\[y(t)=\sum_{k=1}^{K}\overline{C_{k}(t)}+r_{k}(t) \tag{12}\]
## 3 Results and Discussion
### Batter Analysis
#### 3.1.4 Evaluation of Thermal Properties  
DSC is a powerful technique for evaluating the thermal and gelatinization properties of batter systems. Gelatinization can be described as a series of heat-induced changes in the starch granules. These events include water absorption by starch granules, swelling, and, finally, leaching out of starch components [31]. The gelatinization process is often influenced by the source of starch, amount of water, pH, sugar, and fat in the batter. It is interesting to observe that many studies in literature focus on the thermal properties of different types of flour and starch but are limited to the thermal study of batter. Figure 3b represents the endothermic heat flow of the batter samples. An endothermic transition was observed in the temperature range of 100-120 \({}^{\circ}\)C (Table 2) for all the batter samples [32]. This peak (T\({}_{\rm m}\)) has been previously reported for the dissociation of amylose-lipid complexes. These complexes are formed between amylose and lipid, creating amorphous or highly crystalline structures. In most food products, the amylose-lipid complex causes a reduction in the solubility of starch, hinders starch gelatinization and retards enzymatic digestion [33]. The control, Sa-C, and SaB-C showed nearly similar values of T\({}_{\rm m}\). However, the values were increased in Sp-C and SpB-C from the control. The increased T\({}_{\rm m}\) indicates the better thermal stability of starch-lipid complexes in Sp-C and SpB-C. This could be due to the presence of the hydrophilic emulsifier (i.e., SP) containing oleogels in these samples, which binds efficiently with the starch molecules [34]. Further, the enthalpy (\(\Delta\)H) of the corresponding endothermic peak is shown in Figure 3b. The enthalpy quantifies the extent of dissociation of amylose-lipid complexes. Sa-C showed the highest \(\Delta\)H values suggesting prominent melting of amylose-lipid complexes in this case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Peak Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Area under the Peak**} \\  & & **\(\Delta\)H (J/g)** \\ \hline Control & 115.20 & 71.65 \\ Sa-C & 116.20 & 88.05 \\ SaB-C & 114.70 & 68.43 \\ Sp-C & 119.37 & 80.97 \\ SpB-C & 119.76 & 74.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters obtained from DSC curve.  
Figure 3: (a) Line plot of electrical impedance spectroscopy of cake batter; (b) DSC study of batter: Endothermic heat flow curve.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 3 Method  
As shown in Figure 2, our proposed method can be divided into five modules: attention-based feature extraction, foreground/background segmentation, foreground depth prediction, background depth prediction, and depth map stitching. Firstly, ResNet101 [48] is applied to extract feature maps from RGB images. Then, attention mechanisms are used to extract meaningful features \(f_{a_{i}}\) from the feature maps at different scales, where \(i=1\) to 4. Secondly, the features are used to predict background depth maps via a multi-scale depth decoding scheme. Thirdly, to enhance the performance of foreground depth map prediction, RGB images of different scales are segmented into image regions and objects using mask region-based convolutional neural networks (Mask R-CNNs) [49] and foreground regions and objects are extracted, which are denoted as \(f_{s_{i}}\). Fourthly, \(f_{a_{i}}\) and \(f_{s_{i}}\) are then used to predict foreground depth maps. Finally, the depth maps from the background and foreground depth prediction models are stitched together using a stitching scheme to fuse the two maps into final depth maps \(d\).