esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### FEM Modeling and Nonlinear Analysis  
The FEM model of the building before intervention (Figure 4) was set up by means of the SAP2000 v20.0 software according to the Italian Code NTC 2018. Beams and columns are defined as one-dimensional elements by entering all the acquired data about materials and geometries of sections. Interaction between soil and building is schematized by assigning full constraints at the bases of columns. The roof structure is schematized with linear elements able to represent a constraint between east and west facades. Beam-column nodes are considered as rigid with their own stiffness. Infill walls are schematized as distributed loads on the frame members, neglecting their contribution in terms of stiffness. This assumption is justified since the spans are filled with weak cladding walls having thickness of 10 cm only, which are also provided with some openings. Therefore, the contribution given by infill walls to the building lateral stiffness is neglected. In the nonlinear analyses, concentrated plastic hinges are assigned to all the structural members.  
The seismic vulnerability evaluation of the building is carried out using N2 method [22; 23]. This method is based on distribution of horizontal forces acting along the main building directions x and y, parallel to the long and short facades of the building, respectively. For each direction, two distributions of forces are considered:  
Horizontal forces equivalent to a uniform distribution of horizontal accelerations, that are proportional to the building seismic mass, and are herein called Accel_x and Accel_y.  
Horizontal forces proportional to the elastic modal strain of the main vibration mode, \(\mathrm{\Phi_{1x}}\) and \(\mathrm{\Phi_{1y}}\), that are herein called Modal_x and Modal_y.  
Figure 4: FEM model of the factory.  
The evaluation of dynamic properties of the building is carried out through a modal analysis on the structural model. The analysis results of the structure in terms of the force-displacement curves obtained from the SAP2000 software are represented in Figure 5.  
Ductile failure and brittle failure are represented on the curves of Figure 6 with cyan and red points, respectively.  
By representing the capacity curves on the ADRS plane and by comparing them to the elastic response spectrum of the soil, it is possible to obtain the required displacements (Figure 6). The ratio between capacity displacement and demand one is the safety index (IR), whose values referring to the two analysis directions and seismic force distributions are depicted in Table 1. In this table du,d* and du,b* are the ductile (bending moment) capacity displacement and the brittle (shear) capacity one, respectively, dr* is the demand displacement required by the earthquake, and IR,d and IR,b are the safety indices related to the ductile failure and the brittle failure, respectively. As it is observed in Table 1, safety index related to Modal_y case is less than 1, so that the most critical situation is identified precisely along this direction.  
Figure 5: Capacity curves: (a) x-direction; (b) y-direction.  
Figure 6: _Cont_.
# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 2 Methods
### Data Pre-Processing and Augmentation  
All of the pre-trained models were quite large to hold this dataset, and each model could be overfitted easily. To prevent this, some noise was added to the dataset; it is well known that by adding some noise to inputs of neural network, in some situations, this leads to significant improvement in generalizing the dataset. Moreover, adding noise acts as some sort of augmentation of the dataset. Furthermore, other augmentation techniques were also used. Since not all augmentation approaches were good for X-ray images, we processed the images in four steps. First, we resized images to \(224\times 224\times 3\), then three augmentation techniques were used, Random Horizontal Flip (to deal with the pneumonia symptoms on either side of the chest), Random Resized Crop (to get deeper relation among pixels), and finally augmenting images with a varying intensity of images.
## 5 Discussion  
### Can Deep Learning Automatically Identify Factors That Can Predict Learner Satisfaction in MOOCs?  
To answer RQ1, this study investigates the potential of context classification of course review data using deep neural networks. To achieve this, it is essential that the classifier is capable of understanding semantic content. As the semantics of natural language are complicated, alongside the high dimensionality of text representations, conventional machine learning approaches usually fail to learn deep semantic information. Thus, the present study adopts RCNN to combine the unique capabilities of the convolutional neural network (CNN) and the RNN to capture and learn the deep relationships within 99,779 helpful MOOC reviews. Before classifier training, 10,000 randomly selected sentences from these reviews were coded manually regarding the referred topics according to a coding scheme with seven categories (\"Platforms and tools\", \"Course quality\", \"Learning resources\", \"Instructor\", \"Relationship\", \"Process\", and \"Assessment\") being considered. The classification performance of the RCNN classifier indicates its capability to classify the referred topical categories mentioned by MOOC learners during their evaluation of the attended courses regarding instructional design and implementation.  
As for the RCNN classifier's performance across different categories, variations were found. For example, the RCNN achieved a classification accuracy of higher than 70% for categories such as \"Instructor\" and \"Course quality\". In contrast, it showed a classification accuracy of lower than 50% for categories such as \"Relationship\" and \"Platforms and tools\". The sample sizes may explain the differences in classification performance across different categories. More specifically, the categories with higher classification performance tended to have a larger sample size than those with a lower performance. This means that it was difficult for the deep neural network-based classifier to capture the deep relationships from the course review content for categories with sample sizes. Another explanation lies in the easiness of topic interpretation. Take the category \"Instructor\" as an example. It is relatively easy for the classifier to discriminate because learners commonly use terms such as professor, instructor, and teacher when expressing their perception of or satisfaction towards instructors. Thus, the classifier can associate the \"Instructor\" effortlessly with the appearance of these identifiable terms within a sentence. On the contrary, it is much more difficult for the classifier to detect categories such as \"Relationship\" and \"Process\", as they are less likely to associate with identifiable terms.  
Figure 10: Averaged overall sentiment scores for each topic (calculated based on different methodologies).
## 2 Materials Used for Restoration and Conservation of Wooden Elements
### Consolidants Used for Wooden Elements
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Procedure** & **Agent** & **Characteristics** & **Application Method** & **Ref.** \\ \hline Inhibition of fungal growth & Paraloid B72 and Regalrez 1126 & Slowing of fungal growth in treated samples; short-term effect & Surface application & [14] \\ \hline Consolidant & Vegetable oil (linseed, Tung), natural resins (colophony) & Water repellent, crack-filling, non-toxic, easy to obtain and apply & Surface application & [20, 21, 22] \\ \hline Consolidant & Polymers and polymeric resins (melamine- or urea-formaldehyde), Pazloid B72, Regalrez 1162, Polyethylene glycol), Acrylic resins, Silanes, Epoxy resins & Wide-spread treatments, easy to apply, good stability; last generation treatments (acrylic or epoxy resins) & Surface application, immersion & [29, 30, 31, 32, 33, 34] \\ \hline Consolidant & Sugars and sugar alcohols (sucrose, lectilol, trehalose) & Reversible, non-toxic, increase stability upon crystallization of sugars & Immersion of the wood artifacts & [23, 24] \\ \hline Consolidant & Inorganic particles (calcium hydroxide, magnesium hydroxide, uranium dioxide, carbonate) & Ability to neutralize acids within the wood (in some callous even continuous deacidification), reduce cellulose hydrolysis; some have biocidal action & Spraying, surface applications & [26, 27, 28] \\ \hline Consolidant & Biobased solutions (keratin, cellulose, chitosan) & Natural resource, easy to apply, good compatibility; can undergo the same degradation issues as the wood & Immersion & [35, 36, 37] \\ \hline Biocidal & dideycldimethylammonium chloride, N-(3-aminopropyl)-N-dodecylpropane-1,3-dimine, hydrogen peroxide, glutaraldehyde, sodium hypochlorite, boric acid, lactic acid & Spraying 30\%, Boramon and 8\%. Bcoramo 101 effectively protected the wood against bacterial growth for 12 months; and molds for 3 months & Spraying, fogging & [17] \\ \hline Consolidant & Methacrylic-siloxane resin and microcrystalline cellulose & Increase of the dynamic moduli, systematic decrease of the thermal expansion coefficient, increase of the flexure stiffness, increase hydrophobicity & Surface application (coating) & [38] \\ \hline Consolidant & Alum & Naturally occurring, can cause acidic depolymerization of cellulose & Immersion & [25] \\ \hline Antifungal & Inorganic nanoparticles (TiO\({}_{2}\) suspension, containing silver or copper nanoparticles) & The highest antifungal efficiency was observed for suspensions containing highest level of metallic nanoparticles & Brushing & [18] \\ \hline Disinfection, consolidation & Polyester acrylate and polyester resin dissolved in styrene, by gamma curing & Changes in thermal, chemical and photochemical stability & Immersion, gamma curing & [39] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classic and modern conservation agents for wood artifacts (references presented in chronological order).
## 4 The Design of the Proposed Filter
### The Design of the Proposed Dual Side Resonators  
In order to improve the results and to obtain a microstrip low-pass filter with smaller dimensions, two dual-side resonators were designed and loaded at the main transmission line of the filter. The structure of these resonators is depicted in Figure 9. The dimensions of the proposed dual-side resonators, which are depicted in Figure 9, are listed as follows: L\({}_{1}\) = 1.2 mm, W\({}_{1}\) = 1.56 mm, L\({}_{5}\) = 3 mm, W\({}_{5}\) = 0.1 mm, L\({}_{6}\) = 1.8 mm, W\({}_{6}\) = 1.4 mm, L\({}_{7}\) = 0.8 mm, W\({}_{7}\) = 9.9 mm, L\({}_{8}\) = 0.8 mm.  
The frequency response of proposed dual-side resonators is shown in Figure 10. As can be seen from the frequency response, these resonators create two transmission zeros with more than a 60 dB attenuation level.  
Figure 8: The S-parameter results of the proposed dual polygonal resonator, loaded with folded transmission lines.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 5 Real-Time UWB Indoor Positioning Systems (RTIPSs)
### Obstacles and Non-Line-of-Sight Obstructions
[71] proposed an efficient cooperative positioning algorithm to mitigate the environmental noise and enhance the performance and accuracy of IPSs in non-line-of-sight environments. The algorithm is based on selective tags' coordinate information sharing, and it includes three steps: initial positioning, tags classification, and precise positioning. The evaluation process is conducted in a non-line-of-sight environment, and the evaluation results show a 71.7% accuracy improvement. [PERSON] et al. [72] proposed an approach that combines Discrete Wavelet Transform (DWT) and convolutional neural networks (CNNs) to solve non-line-of-sight ultra-wideband signal recognition. The UWB signal coefficients are extracted from the Channel Impulse Response (CIR) using DWT, and then the coefficients are fed as inputs to a CNN to explore deep features to distinguish between NLoS and LoS signals. The experimental results revealed that this method achieves better accuracy compared to other approaches. [PERSON] et al. [73] proposed a technique for compensating the NLoS occlusion errors of the UWB IPS signals using a micro-electromechanical system (MEMS) inertial measurement unit (MIMU)-assisted UWB localization. The NLoS occlusions of UWB devices are identified with the channel information of the UWB transceivers and tag-anchor distance variation patterns. The localization process is implemented by fusing the UWB and IMU data with an unscented Kalman filter (UKF) framework. The data fusion of the system is adjusted according to the NLoS effect on the UWB signals. System tests have shown high efficiency in identifying and compensating for errors caused by NLoS in the used UWB IPS modules. [PERSON] et al. [74] proposed a system (UWBLoc) to mitigate the NLoS errors in IPSs using ranging residuals to characterize the influence of the environment on ranging and measuring distance and angles using the Taylor-series-based least squares (TS-LS) method. Similar approaches have been proposed in [75, 76, 77, 78].
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
###### Abstract  
In recent years, although a variety of deep learning models have been developed for magnitude estimation, the complex and variable nature of earthquakes limits the generalizability and accuracy of these models. In this study, we selected the waveform data of the Japan earthquake. We applied four deep learning techniques (MagNet combined with bidirectional long- and short-term memory network Bi-LSTM, DCRNN with deepened CNN layers, DCRNNAmp with the introduction of a global scale factor, and Exams with a multilayered CNN architecture) for real-time magnitude estimation. By comparing the estimation errors of each model in the first 3 s after the earthquake, it is found that the DCRNNAmp performs the best, with an MAE of 0.287, an RMSE of 0.397, and an R\({}^{2}\) of 0.737 in the first 3 s after the arrival of the P-wave, and the inclusion of S-wave seismic-phase information is found to significantly improve the accuracy of the magnitude estimation, which suggests that S-wave seismic-phase waveform features can enrich the model's understanding of the relationship between the seismic phases. It shows that S-wave phase waveform features can enrich the model's knowledge of the relationship between seismic fluctuations and magnitude. The epicentral distance positively correlates with the magnitude estimation, and the model can converge faster with the improved signal-to-noise ratio. Despite the shortcomings of model design and opaque internal mechanisms, this study provides important evidence for deep learning in earthquake estimation, demonstrating its potential to improve the accuracy of on-site earthquake early warning (EEW) systems. The estimation capability can be further improved by optimizing the model and exploring new features.  
eurmabake early warning (EEW); magnitude estimation; real-time estimation; deep learning Article
## 2 Datasets and Methods  
### The BEPS Model  
The BEPS model's GPP and NPP simulation results were used in this study. The BEPS is based on the ecosystem process and combines satellite remote sensing data to simulate the global carbon cycle in terrestrial ecosystems [18]. The BEPS model stratifies canopiesinto sunlit and shaded leaves, simulates photosynthesis by [PERSON]'s biogeochemical model [20; 21], and then calculates canopy productivity by the sum of sunlit and shaded leaves [18; 20]. The BEPS model was originally developed to estimate the carbon budget over Canadian landmass and the NPP in Quebec, but it has been validated with many experimental results and widely used to estimate regional and globe's terrestrial ecosystem carbon flux, such as in China [10; 22], East Asia [23], North America [24], Europe [25], and across the globe [19].  
\[GPP=A_{sun}LAI_{sun}+A_{shade}LAI_{shade}, \tag{1}\]  
\[LAI_{sun}=2 cos\theta\bigg{[}1-\exp\bigg{(}-\frac{0.5\Omega LAI}{cos\theta} \bigg{)}\bigg{]}, \tag{2}\]  
\[LAI_{shade}=LAI-LAI_{sun}, \tag{3}\]  
\[NPP=GPP-R_{a}, \tag{4}\]  
where \(A_{sun}\) and \(A_{shade}\) are the rate of photosynthesis by sunlit and shaded leaves, respectively. \(LAI_{sun}\) and \(LAI_{shade}\) are the LAI of sunlit and shaded leaves, respectively. \(\Omega\) is the foliage clumping index. \(\theta\) is the solar zenith angle. \(R_{a}\) is autotrophic respiration.
## 2 Data and Methods
### Theoretical Background of Coastal Effects
The profiles in green, blue and red, respectively, refer to neutral, stable and unstable boundary layers. The curves are obtained using different values for the normalised Obhukov length L' = L/z\({}_{sea}\) in combination with the stability functions in Equations (8) and (9). One can see that the acceleration effect is strongest in unstable conditions. It is also interesting to see that for stable conditions, the wind speed starts to drop at a certain land distance after reaching a maximum. These simulations are supposed to give a very simplifying qualitative impression about the basic mechanisms and are not perfectly expected to match observations. An important conclusion from the study presented in Taylor [9] is that under certain simplifying assumptions, the downstream wind speed profiles scale with the upstream wind speed and also with the equilibrium wind speed that is finally obtained far offshore. This means that it makes sense to look at relative wind speed changes in relation to the land or ocean conditions. In the following analysis of SAR-derived wind speed transects, we will make use of this property. Figure 3: (**a**) Profiles of normalised wind speeds with \(z_{land}/z_{sea}\)=55 at different normalised downstream distances for neutral conditions. (**b**) Associated normalised stress profiles. (**c**) Downstream transects of normalised wind speeds at two normalised heights \(z/z_{sea}=10^{5}\) (solid lines) and \(z/z_{sea}=10^{6}\) (dashed lines). Colors refer to different stabilities of the boundary layer with neutral in green, stable in blue and unstable in red. (**d**) Sea surface roughness length as a function of 10 m wind speed for a fully developed sea state according to [PERSON].
# Metrology for Measuring Bumps in a Protection Layer Based on Phase Shifting Fringe Projection
## 3 Theoretical Model of Fringe Projection Profilometry
### Phase-Height Conversion  
In order to introduce the three-step phase-shifting image into the camera, the ratio of \(d_{D}\)/\(dc\) is \(1/3\):  
\[d_{D}=\frac{1}{3}d_{c}=\frac{1}{3}d_{i}\times n\cos\theta_{c} \tag{8}\]  
where \(d_{D}\) is the selected pixel row spacing (from center to center) within the camera ROI, which corresponds to one-third of a collecting fringe pitch. Thus, the three selected rows with equal spacing \(d_{D}\) within ROI can simultaneously acquire three fringe images with \(120^{\circ}\) phase shifts. A fringe originally positioned at \(P_{1}\) is displaced to \(P_{2}\) when a curved profile is measured, as shown in Figure 4. To simplify the derivation, we considered the problem as one-dimensional, where the same result holds for both the \(x\) and \(y\) axes. The displacement caused by the shift in the fringe position can be given by  
\[u(x)=h(x)(\tan\theta_{i}+\tan\theta_{c}) \tag{9}\]  
where \(h\) is the height of \(P_{2}\) above the reference plane. This gives the modulation factor \(M(x)\):  
\[M(x)=\frac{u(x)}{d_{i}}=\frac{h(x)(\tan\theta_{i}+\tan\theta_{c})}{\frac{d}{m \cos\theta_{i}}}=\frac{m\times h(x)}{d}\frac{\sin(\theta_{i}+\theta_{c})}{ \cos\theta_{c}} \tag{10}\]  
The phase value \(\varphi(x,y)\) acquired in the image plane is given by  
\[\phi(x)=\frac{u(x)}{d_{i}}\times 2\pi=\frac{2\pi\times m\times h(x)}{d}\frac{ \sin(\theta_{i}+\theta_{c})}{\cos\theta_{c}} \tag{11}\]  
Thus, the height-to-phase conversion equation gives  
\[h(x)=\frac{d}{2\pi m}\frac{\cos\theta_{c}}{\sin(\theta_{i}+\theta_{c})}\phi(x) \tag{12}\]
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 2 Biology and Physiology of FNDC5/Irisin
### Distribution and Production of FNDC5/Irisin  
Irisin can be found in nearly all organs and tissues. In rats, immunohistochemistry has detected irisin in the kidney, brain, heart, muscle, liver, skin, retina, thyroid, and pineal gland [15; 25; 26; 27; 28; 29], meanwhile its expression has already been observed in the cerebrospinal fluid, liver, pancreas, stomach, serum, saliva, and urine of humans [30; 31; 32; 33]. Additionally, irisin is also detected in human breast and ovarian cancer cells by using flow cytometry and laser confocal microscopy [28]. Different tissues of humans show differing levels of irisin, among which the skeletal muscle has the highest expression levels of irisin, followed by other tissues including muscle, the tongue, heart, rectum, etc.; irisin expression is 100-200 times lower in adipose tissue than it is in muscle tissue [34; 35; 36].  
Irisin secretion is influenced by a variety of stimuli, such as exercise, diet, cold, pharmacological agents, and pathological conditions [37], among which exercise is the most strong stimulating factor for the synthesis and secretion of irisin. Irisin expression was dramatically elevated in muscles of diabetic rats after a 12-week exercise regimen [38], and a prolonged exercise protocol (90-min running at an intensity of 60% VO\({}_{\text{2 max}}\)) increased serum irisin concentrations of men and women by 20.4% and 24.6%, respectively [39]. Although irisin is mainly secreted by skeletal muscle, white adipose tissue (WAT) is also a major producer, accounting for about 30% of the total circulating levels of irisin [12]. Similar to skeletal muscle, the secretion of irisin in WAT significantly increases in response to exercise training [12]. It is implied that irisin is both a myokine and an adipokine.
## 4 Experimental Results
### Comparison of Machine Learning Model Results  
Table 7 shows the performance of comparative ML models such as RF, SVM, GBM, XGBoost, and LightGBM. As mentioned in Section 3.2, each model is a final prediction model selected by tuning hyperparameters through grid search and \(k\)-fold cross-validation. According to the analysis shown in Table 7, it may be observed that LightGBM exhibited the best performance among comparative ML models, with an accuracy of 0.8384. In addition, the F1-score and FPR outperformed other ML models in most aspects. However, owing to the imbalance between classes, the overall F1-score generally showed low performance. In addition, the FNR, which is actually a positive ratio but was predicted to be negative, was mostly high. As a result, ML models tend to focus more on predicting outbound telemarketing failure (class 0) than predicting outbound telemarketing success (class 1).