esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Changes in External Color and in Carotenoid Content  
The visual appearance of all samples, just after irradiation and at the end of every month of storage, is given in Figure 1 as photos taken in those times. Indeed, it appears clearly that browning was less pronounced on irradiated samples. This result can be explained by the inactivation, by ultraviolet radiation, of the polyphenoloxidase located in the surface tissue of the treated fruits. The intensification of the brown coloration in the irradiated samples is not visually distinguishable between the three doses in this case, but the results which follow have made it possible to demonstrate the difference.  
Changes in carotenoid levels revealed a moderate elevation in treated samples during the first two months of storage versus a slow decline in all samples during the remainder of the period (Table 1), indicating progressive degradation of these pigments. In the second month the increase was significant; it was observed for doses 1 and 6 kJ m\({}^{-2}\) with the values of 4.17 and 4.02 mg kg\({}^{-1}\) versus the control (3.45 mg kg\({}^{-1}\)). Similar results were observed in fresh-cut carrot [12]. Carotenoids are subject, indeed, to isomerization and oxidation during processing and storage of foods because they are highly unsaturated. Degradation  
Figure 1: Changes in color appearance of date, control and UV-C treated samples, during 5 months of storage at 10 \({}^{\circ}\)C (the number at the left corner of each photo indicates the storage month no.).  
of carotenoids increases with length and severity of the processing conditions, temperature, storage time, transmission of light and exposition to O\({}_{2}\)[13]. Moreover, the increase in their level may be due to their biosynthesis in response to stress caused by UV-C. It has been confirmed in several studies that the level of carotenoids increases following exposure to abiotic stress, such as drought, light, etc. The most important carotenoids identified in this cultivar of date are \(\upbeta\)-carotene and lutein [14].  
However, [PERSON] et al. [15] found that UV-C radiation significantly increased the content of carotenoids in pineapple core, which is agreement with the obtained result.
## 1 Introduction  
Broadband signal transmission is considered an indispensable technique in next-generation dependable wireless communication systems [1; 2; 3]. It is well known that both multipath fading and additive noises are major determinants that impair the system performance. In such circumstances, either coherence detection or demodulation needs to estimate channel state information (CSI) [1]. In the framework of a Gaussian noise model, some effective channel estimation techniques have been studied [4; 5; 6; 7; 8; 9; 10]. In the assumptions of the non-Gaussian impulsive noise model, however, existing estimation techniques do not perform robustly due to heavy tailed impulsive interference. Generally speaking, impulsive noise is used to generate natural or man-made electromagnetic waves that are different from the conventional Gaussian noise model [11]. For example, a second-order statistics-based least mean square error (LMS) algorithm [4] cannot be directly applied in broadband channel estimation [12]. To solve this problem, selecting a suitable noise model is necessary to devise a stable channel estimation that can combat the harmful impulsive noises.  
The aforementioned non-Gaussian noise can be modeled by the symmetric alpha-stable (S\(\alpha\)S) distribution [13]. Based on the S\(\alpha\)S noise model, several adaptive filtering based robust channel estimation techniques have been developed [14; 15; 16]. These techniques are based on the channel model assumption of dense finite impulse response (FIR), which may not suitable in broadband channel estimation because the channel vector is supported only by a few dominant coefficients [17; 18].  
Considering the sparse structure in a wireless channel, this paper proposes a kind of sparse SLMS algorithm with different sparse norm constraint functions. Specifically, we adopt five sparse constraint functions as follows: zero-attracting (ZA) [7] and reweighted ZA (RZA) [7], reweighted \(\ell_{1}\)-norm (RL1) [19], \(\ell_{p}\)-norm (LP), and \(\ell_{0}\)-norm (L0) [20], to take advantage of sparsity and to mitigate non-Gaussian noise interference. It is necessary to state that the short versions of the proposed algorithms were initially presented in a conference but we did not give a performance analysis [21]. In this paper, we first propose five sparse SLMS algorithms for channel estimation. To verify the stability of the proposed SLMS algorithms, convergence analysis is derived with respect to mean and excess mean square error (MSE) performance. Finally, numerical simulations are provided to verify the effectiveness of the proposed algorithms.  
The rest of the paper is organized as follows. Section 2 introduces an alpha-stable impulsive noise based sparse system model and traditional channel estimation technique. Based on the sparse channel model, we propose five sparse SLMS algorithms in Section 3. To verify the proposed sparse SLMS algorithms, convergence analysis is derived in Section 4. Later, computer simulations are provided to validate the effectiveness of the propose algorithms in Section 5. Finally, Section 6 concludes the paper and proposes future work.
## 2 Materials and Methods
### Resistance Element Welding (REW)  
After embedding the weld inserts in TPC, experimental investigations regarding resistance element welding were conducted using a powerGUN 2-C type C-frame welding gun by NIMAK International GmbH (Wissen, Germany). This welding gun has a servomotor drive with a projection of 700 mm and a maximum electrode force of 8.0 kN. The medium-frequency direct current welding device is equipped with a welding case of the type SK-Genius HWI436 WA by Harms & Wende GmbH & Co. KG (Hamburg, Germany), which has a constant current control and provides a maximum weld current of 65 kA. Standardised Electrode caps of type ISO 5821-A0-20-20-100 made from CuCrZr material were used.  
The identification of welding areas and the selection of process reliable welding parameters for the qualification of the developed weld inserts were carried out using the numeric simulation software SORPAS(r) 2D Welding V13.83 Enterprise Edition by SWANTEC Software and Engineering ApS (Kongens Lyngby, Denmark, 2020). In this study the welded joint is created between the isotropic metallic weld insert and a steel sheet. In this regard a two-dimensional, axisymmetric simulation model was built, as shown in Figure 4. The model was built according to the experimental setup. The thermal and the electrical properties of the metallic joining partners and the electrode caps were taken from the SORPAS database. The TPC was modelled in a simplified form as a thermal isotropic material. The electrical resistivity \(\left(16*10^{15}\ (\Omega*\mathrm{cm})\right)\) and the thermal conductivity \(\left(0.69\ (\mathrm{W/m*K})\right)\) of the TPC were adjusted according to the data sheet values. All parts were modelled as deformable bodies with heat conduction. For the mesh, quadrilateral elements (954 elements; 1108 nodes) were used. The associated results are presented in Chapter 3.2.  
\begin{table}
\begin{tabular}{c c c} \hline TPC & Unidirectional semi-finished product & Celstran® & CFR-TP PP-GF70 \\  & Fibre volume content & 45 vol.-\% E-glass \\  & Matrix & Polypropylene (PP) \\  & Laminate thickness & 2 mm \\  & Laminate structure & [(0\({}^{\circ}\)/90\({}^{\circ}\))\({}_{2}\)]s \\ Steel sheet & Material & HC340L ([19]) \\  & Sheet thickness & 1.5 mm \\ Weld insert & Material & S235 JR ([20]) \\ \hline \end{tabular}
\end{table}
Table 2: Material specification.  
Figure 3: Prototypic weld inserts for process-integrated embedding: (**a**) type A, (**b**) type B.
# The Effectiveness of Dental Protection and the Material Arrangement in Custom-Made Mouthguards
## 3 Results
### Overall Displacements
#### 3.1.2 Mouth Components
\begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Displ.**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Maxilla Bone (nm)**} & \multicolumn{2}{c}{**Soft Tissue (nm)**} \\ \cline{3-6}  & & **Mean \(\pm\) SD** & **Min/Max** & **Mean \(\pm\) SD** & **Min/Max** \\ \hline \multirow{4}{*}{x-axes} & NMG & \(-1.542\pm 22.151\) & \(-126.550/159.470\) & \(-70.270\pm 212.100\) & \(-1028.910/1319.900\) \\  & CMG & \(-1.167\pm 21.282\) & \(-119.730/152.430\) & \(-243.570\pm 651.280\) & \(-9516.100/1979.200\) \\  & FMG & \(-1.923\pm 18.284\) & \(-110.970/135.630\) & \(-422.500\pm 1244.000\) & \(-12991.00/1187.500\) \\  & HMG & \(-0.321\pm 12.988\) & \(-29.358/79.253\) & \(-443.180\pm 1781.600\) & \(-29,694.00/9175.000\) \\ \hline \multirow{4}{*}{y-axes} & NMG & \(-80.685\pm 71.675\) & \(-359.730/29.878\) & \(-147.070\pm 193.290\) & \(-1050.300/903.380\) \\  & CMG & \(-78.313\pm 68.102\) & \(-350.640/31.506\) & \(-137.380\pm 476.280\) & \(-4036.400/5212.50\) \\  & FMG & \(-67.587\pm 598.20\) & \(-318.350/27.140\) & \(-209.450\pm 927.590\) & \(-10,059.00/7843.20\) \\  & HMG & \(-52.562\pm 31.950\) & \(-158.310/17.040\) & \(-208.770\pm 1140.00\) & \(-16,598.00/12038.0\) \\ \hline \multirow{4}{*}{z-axes} & NMG & \(7.0434\pm 13.995\) & \(-34.142/55.197\) & \(0.08645\pm 182.090\) & \(-989.000/917.2100\) \\  & CMG & \(-2.9876\pm 15.787\) & \(-54.718/37.282\) & \(-75.214\pm 326.720\) & \(-5200.900/1843.900\) \\  & FMG & \(2.1887\pm 13.639\) & \(-41.207/35.577\) & \(31.325\pm 908.980\) & \(-7921.800/8249.700\) \\  & HMG & \(0.98216\pm 7.3187\) & \(-21.353/23.638\) & \(-60.009\pm 902.660\) & \(-13,011.000/8043.30\) \\ \hline \multirow{4}{*}{Magnitude} & NMG & \(85.533\pm 71.203\) & \(0/365.480\) & \(283.880\pm 247.95\) & \(2.937/1774.600\) \\  & CMG & \(83.142\pm 67.606\) & \(0/356.070\) & \(473.290\pm 785.88\) & \(3.727/11,007.00\) \\ \cline{1-1}  & FMG & \(71.876\pm 59.245\) & \(0/323.310\) & \(733.939\pm 1708.4\) & \(1.837/16,052.90\) \\ \cline{1-1}  & HMG & \(55.136\pm 38.456\) & \(0/158.760\) & \(817.070\pm 2205.5\) & \(5.178/29930.00\) \\ \hline \end{tabular}
\end{table} 
Table 6: Mean and range of displacements over the maxilla bone and the soft tissue. Figure 6: Numerical variation of the average magnitude of displacement with the load intensity.
# Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification
## 2 Related Work
### Dynamic Time Warping with the Convolutional Neural Network  
The artificial neural network (ANN) is famous for its powerful feature extraction capability in the last decades. Recently, ANNs such as the RNN and CNN, have been used to learn supervised [17] or unsupervised representation [18] for time series analysis. The RNN is well-known for time series forecasting [17] with the advantage of sequential learning. Some improvements are proposed to reduce inference time [19] and predict sudden time-series changes [20]. Although, the RNN is also exploited in the TSC, the CNN achieves better performance in supervised learning on the UCR archive [21]. The CNN, such as the fully convolutional network (FCN) and residual network (ResNet) [22], have achieved strong baselines for TSC. Some attempts have been made to combine DTW and CNN to overcome the brittleness to temporal distortions in the conventional CNN. These attempts are roughly categorized into two categories. The first category, DTW, is a preprocessing method to transform the raw time series. Then, the transformed series are used as inputs to the CNN. In [8], a multimodal fusion CNN (MMF-CNN) is employed to predict a label for the multidimensional time series. The multidimensional time series are composed of the coordinate features and local distance features which are extracted by measuring the DTW similarity between the original time series and prototypes. The second category directly incorporates the DTW into the CNN and training an end-to-end classification framework. In [23], DTW is used to determine a more optimal alignment between convolutional kernels and time series. The DTWNet [7] replaces the inner product kernel with the DTW kernel against the Doppler effect and improves the capability to do feature extraction.
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 2 Materials and Methods
### Comparison of Findings with Other Studies  
Comparison of localization studies are presented in Table 12 and the comparison of positioning studies are given in Table 13. According to the Table 12, the most successful method seems to be the one proposed by [PERSON] et.al. [12]. However, when the average of accuracy values obtained as a result of the proposed method is considered, it ranks second among these studies. According to the Table 13, the proposed method HF-DT appears to be the most successful one. The results of the extra-trees are given as root mean square error; however, to compare with the values present in this study, the square  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Method** & **MSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline KNN & 326.695 & 9.212 & 0.928 \\ DT & 2.171 & 0.212 & 1.000 \\ RF & 19.486 & 2.064 & 0.996 \\ AB & 1571.732 & 32.841 & 0.655 \\ SVM & 4567.286 & 54.533 & \(-\)0.002 \\ HF-KNN & 755.945 & 12.510 & 0.951 \\ HF-DT & 3.751 & 0.287 & 1.000 \\ HF-RF & 17.743 & 2.127 & 0.999 \\ HF-AB & 977.574 & 25.440 & 0.937 \\ HF-SVM & 16,655.674 & 106.123 & \(-\)0.076 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Latitude regression results of training data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Method** & **MSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline KNN & 1306.443 & 16.713 & 0.914 \\ DT & 270.975 & 5.495 & 0.982 \\ RF & 173.116 & 5.939 & 0.989 \\ AB & 4599.668 & 59.047 & 0.696 \\ SVM & 16,257.159 & 105.057 & \(-\)0.076 \\ HF-KNN & 1284.751 & 16.602 & 0.915 \\ HF-DT & 246.582 & 5.596 & 0.984 \\ HF-RF & 201.812 & 5.646 & 0.987 \\ HF-AB & 1089.982 & 26.149 & 0.928 \\ HF-SVM & 16,243.445 & 105.003 & \(-\)0.075 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Longitude regression results of testing data.  
of the given values are converted into the MSE metrics [11]. Carrying out a literature review, it was found that the only study to perform both localization and positioning procedures was conducted by [PERSON] et.al. Also, according to the findings, it was seen that the proposed method provides higher performance values [14].
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 3 Renewable Generation and Load Probabilistic Forecasting
### Solar Probabilistic Forecasting
[29] aimed to make short-horizon forecasts based on multi-step-ahead (e.g., six forecasts with 10-min time slots), considering 11 data-driven machine-learning and parametric methods, including least angle regression, least angle regression with elastic-net regularization, lasso regression, generalized linear models, generalized linear model with elastic-net regularization, Bayesian generalized linear model, gradient boosting machines, linear regression, boosted generalized linear model, multivariate adaptive regression splines, and projection pursuit regression. These models tried to probabilistically predict solar generation. The paper also discussed how the so-called \"reconciliation techniques\" proposed by [37] can improve the probabilistic forecasts. Additionally, ref. [30] compared three parametric probabilistic forecasting models for solar irradiance. The first model considers the use of Beta distribution with several shape factors, the second model utilizes a generalized triangular distribution and the third one combines multiple probabilistic forecasting models to construct the probability distribution of solar irradiance. Regarding non-parametric approaches, ref. [31] compared two 1-to-6-h-ahead probabilistic models for predicting global horizontal irradiance. The first model directly produces a set of quantiles for each time slot using regression methods \"linear model in quantile regression (LMQR)\", \"quantile regression forest (QRF)\", or \"gradient boosting machine (GBM)\". The second model, however, consists of two stages. The first stage deploys the recursive least square autoregressive and moving average (ARMArls) method to make a point forecast for the irradiance. The outputs are used in the next step to estimate quantiles for each time horizon using the same methods. As mentioned earlier, the reviewed model tried to first forecast irradiance and then predict PV generation accordingly, using a physical model and relationships. These models are often called white-box models. By contrast, non-physical or so-called \"black-box\" forecasting approaches that predict PV generation are purely based on historical data and do not deal with the physical process and the meteorological data. For instance, ref. [32] developed a probabilistic forecasting model using ANN and an analog ensemble to produce 72-h forecasts of PV power. Additionally, ref. [33] utilized a more complicated approach, long short-term memory (LSTM), to probabilistically predict solar power and compared it with simpler models. Similar to NWP models, although physical models may be more accurate in day-ahead and long-term horizons, data-driven black-box models work better over short-term horizons such as intra-hour ones [38; 39]. Figure 4 summarizes the probabilistic forecasting models for PV generation considering the forecasting horizon.
## 3 Experiments and Analysis of Results
### Analysis of Prediction Results
#### 3.2.2 Comparative Analysis of Prediction Results  
In addition, we used machine learning methods such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Artificial Neural Networks (ANN), and K-Nearest Neighbors (KNN) to compare model prediction effects, and the model comparison results are shown in Tables 6 and 7. Experimental results show that the error between the true and predicted values of our proposed method was smaller compared to the prediction method of thermal dissatisfaction rate without the inclusion of thermal discomfort body language expression.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input**} & \multicolumn{8}{c}{**Algorithms**} \\ \hline
**Indoor Parameters** & **Individual Parameters** & **Discomfort Expression** & **KNN** & **SVM** & **RF** & **DT** & **ANN** & **Ours** \\ \hline \(\surd\) & \(\surd\) & \(\times\) & 0.0986 & 0.2000 & 0.0971 & 0.0619 & 0.1501 & - \\ \(\surd\) & \(\surd\) & \(\surd\) & - & - & - & - & - & 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean Absolute Error of the model.  
Figure 8: Comparison of multi-method prediction results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input**} & \multicolumn{8}{c}{**Algorithms**} \\ \hline
**Indoor Parameters** & **Individual Parameters** & **Discomfort Expression** & **KNN** & **SVM** & **RF** & **DT** & **ANN** & **Ours** \\ \hline \(\surd\) & \(\surd\) & \(\times\) & 0.1217 & 0.2121 & 0.0760 & 0.1063 & 0.1730 & - \\ \(\surd\) & \(\surd\) & \(\surd\) & - & - & - & - & - & 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Root Mean Square Error of the model.
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 5 Applications of the Proposed Method
### Results of Component Importance Analysis of the Two Buildings  
The calculations of the component importance analysis of the buildings were achieved by a commercial finite element software ANSYS (17.0). The information of models was from the in-situ investigation works, including the structural arrangements, sectional dimensions of the components, and strengths of the materials, etc. The results of component importance analysis for the buildings are illustrated in Figures 10 and 11.  
It is clear that for the two typical historical RC buildings, the Critical Components are the columns on the ground floors around the central area on the plan view. The results were obtained in line with the engineering experience. Accordingly for a building structure, often the columns on the ground are more important than beams and columns on higher stories, and the inner columns or middle columns as more distributed loads and interconnected components are more important than side columns [30]. Nevertheless, the structures of pseudo-traditional style roofs contribute differences on the structure from the common reinforced concrete frame structure. It can be noticed that the importance of some structural components of the roof structures ranks next to the critical columns for the Songfengge Building and the Main Hall of the Tomb of Yu, which are in the Important Component rank. Although they are not Critical Components, they should also be investigated carefully on-site, and in the subsequent works for loading capacity analysis and conservation design, they should all be given priority.  
Figure 10: (**a**) The results of component importance analysis for the Songfengge Building; (**b**) the critical components of the Songfengge Building.  
Figure 9: The picture of the Main Hall of the Tomb of Yu.
## 2 Data and Methods  
### Data Sources  
The remote sensing data of Shanghai from 1990 to 2018 used in this paper are Landsat TM/ETM image data with a spatial resolution of 30m from the Geospatial Data Cloud ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 6 January 2022)) of the Chinese Academy of Sciences (CAS). After the remote sensing image is processed by radiation correction, geometric correction, image enhancement, image mosaic, clipping, etc., the land use change characteristics are determined according to the land use classification system of the CAS. To unify the study area and eliminate the influence of borders and marine waters, the Shanghai administrative map in 2018 was used as a standard division scope to clip the following seven years of remote sensing images: 1990, 1995, 2000, 2005, 2010,2015, and 2018 (Table 1). In this study, we mainly used remote sensing images in summer of each year. Since the remote sensing images of April, May, June, and August of 2005 have high cloud cover, and the remote sensing image data of September is not available, we choose the remote sensing image of October of 2005, which had better data availability but ignored the production cycle/seasonal pattern of crops.  
The social and economic data and agricultural statistical data used in this paper come from the following sources: The data of agricultural scale operation households in Shanghai suburbs are from \"Shanghai Agricultural Census (1997, 2007, 2017)\", the data of specialized farmer cooperatives in Shanghai suburbs are from \"Shanghai Statistical Yearbook (2014-2020)\", and the data of family farms in Shanghai suburbs are from \"Shanghai Suburbs Statistical Yearbook (2014-2020)\". Data of cultivated land scale in Shanghai suburbs come from \"Shanghai Suburban Development Report (1991, 2001, 2011, 2020)\". The driving factors of cultivated land scale change mainly include economic development, industrial structure adjustment, urbanization, and agricultural science and technology. The data are all from the \"Shanghai Statistical Yearbook (1991-2019)\".
## 6 Open Challenges, Future Prospects and Research Directions
### Open Challenges  
Despite the significant advancements in FSO technology, several open challenges remain that require further research. These include the development of more robust mitigation techniques for AT, the integration of FSO with emerging technologies like Light Fidelity (Li-Fi), and the exploration of new materials and devices for FSO systems. This section identifies the key areas where further future research is needed to fully realize the potential of FSO communication in next-generation networks.