esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection and Pre-Processing
#### 2.2.2 Remotely Sensed Data  
In this paper, the basic remote sensing data included land use and land cover (LULC) data, slope data, precipitation data, and soil attribute data. The Globeland30 land cover product was used for pollution prediction. The land cover classes were cultivated land, forest, grassland, shrubland, wetland, water, artificial surfaces, and bare land.  
Remotely sensed data included Landsat8 data as the base data and for subsequent fractional vegetation cover (FVC) inversion. Since the spatial resolution of Landsat 8 is  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Name** & **Data Sources** & **Data Units** \\ \hline The statistical & Henan statistical yearbook & \\ yearbook data & & \\ resident population & & 10,000 person \\ irrigation areas & & 1000 hectares \\ cultivated land area & & 1000 hectares \\ agricultural fertilizer & & Ton \\ agricultural production & & 10,000 tons \\ crop planting area & & 1000 hectares \\ main agricultural products & & 10,000 tons \\ year-end inventory & & 10,000 heads \\ livestock breeding & & 10,000 heads \\ livestock product & & 10,000 heads \\ production & & 10,000 tons \\ \hline Remotely sensed data & & \\ land use and land & [[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed & \\ cover data & on 24 December 2024) & 30 m \\ DEM data & [[http://GloVis.usgsg.gov/](http://GloVis.usgsg.gov/)]([http://GloVis.usgsg.gov/](http://GloVis.usgsg.gov/)) (accessed on & \\  & 17 January 2024) & 30 m \\ \hline Hydrological data & & \\ soil attribute data & & \\ Sand & [[http://wdcrre.data.ac.cn/data/9a075](http://wdcrre.data.ac.cn/data/9a075)]([http://wdcrre.data.ac.cn/data/9a075](http://wdcrre.data.ac.cn/data/9a075)) & g/kg \\ Silt & (accessed on 17 January 2024) & g/kg \\ Clay & & g/kg \\ precipitation data & tpdc.ac.cn & 0.1 mm \\ \hline \end{tabular}
\end{table}
Table 1: The data used in this study.  
30 m, the spatial resolution of inversed FVC is also 30 m. The FVC inversion is an important module for NPSP pollution monitoring and prediction.  
The DEM data and the deduced slope data are also important. The DEM data were collected from the website [[http://GloVis.usgs.gov/](http://GloVis.usgs.gov/)]([http://GloVis.usgs.gov/](http://GloVis.usgs.gov/)) (accessed on 17 January 2024). We clipped the data with the shapefile of Henan's boundary. Then, we calculated the length and degree of the slope using the ArcGIS 10.8 band math function.
## 3 Results and Discussion  
### Fatigue Tests  
To define the concrete fatigue test settings, six specimens were broken under standard static three-point-bending stress to estimate the modulus of rupture (MOR). The results are presented in Table 3.  
Figure 6: Scheme of the three-point-bending test under static and dynamic loading and circuit setup.  
Based on the estimated MOR value, unlimited fatigue tests were performed according to three stress levels (S), as anticipated in Section 2. The number of cycles to failure (N), was recorded per each stress level, thus allowing the calibration of the S-log (N) model. Results are presented in Figure 7.  
The results were compared with previous studies [64]. As shown in Figure 8, data from nine previous studies of fatigue tests on plain concrete and eight studies on fiber-reinforced concrete were used for comparison. The results demonstrated that the fatigue life of concrete containing 0.15% wt MWCNT is longer compared to plain concrete and shorter compared to fiber-reinforced concrete with a higher dosage of fibers. Based on Figure 8, the regression model fitted on this study fatigue data could be properly validated by comparison with the other research data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Width** & **Span** & **Height** & **Failure Force** & **Bending Stress** \\  & **(mm)** & **(mm)** & **(mm)** & **(kN)** & **(MPa)** \\ \hline
1 & 71.0 & 250 & 75.5 & 5.20 & 4.82 \\
2 & 70.3 & 250 & 75.9 & 4.69 & 4.34 \\
3 & 70.6 & 250 & 75.4 & 4.85 & 4.53 \\
4 & 70.1 & 250 & 75.2 & 4.96 & 4.69 \\
5 & 70.8 & 250 & 74.9 & 4.72 & 4.46 \\
6 & 71.6 & 250 & 75.8 & 5.07 & 4.62 \\ \multicolumn{5}{c}{Average bending stress (MPa)} & & 4.58 \\ \multicolumn{5}{c}{Standard deviation of bending stress (MPa)} & & 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Flexural strength of concrete beams under the three-point bending test.  
Figure 8: Validation of fatigue results with respect to the average results of several studies [64].  
Figure 7: (S-log N) straight line interpolating the experimental data.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 4 Baseline Algorithms for RF Fingerprinting Method
### Baseline 1: Statistical Moments Based RF Fingerprinting  
The first baseline aims to reflect the conventional RF fingerprinting approaches based on handcrafted features. It was designed for statistical moments of the SFs, similar to that in [4; 5].  
The SF extraction process was the same as that of the proposed method described in Section 3.1.  
For feature extraction, the SFs were segmented using \(N_{seg.}\). Because the RT and FT signals were too short to be segmented, segmentation was applied only to the SS signal.  
\[\mathbf{s}_{\text{SF}}=\left[\mathbf{s}_{\text{SF}|1},\mathbf{s}_{\text{SF}|2 },...,\mathbf{s}_{\text{SF}|N_{seg}}\right] \tag{24}\]  
where \(\mathbf{s}_{\text{SF}|n}\) is the \(n\)th segment of SF. For each segmented SF, a total of six sub-features were considered. The instantaneous amplitude, phase, and frequency, described in [5], were calculated as sub-features, and the time, frequency, and time-frequency axes of the spectrogram, identified as good features in [4], were applied as sub-features. Subsequently, the statistical moments (i.e., mean \(m\), variance \(\sigma^{2}\), skewness \(\gamma\), and kurtosis \(\kappa\)) and entropy \(H\) were calculated for each sub-feature. Thus, a total of 30 features were calculated and arranged in a vector form such that  
\[\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|n}}=\left[\left(m,\sigma^{2},\gamma, \kappa,H\right)_{1},\left(m,\sigma^{2},\gamma,\kappa,H\right)_{2},...,\left(m,\sigma^{2},\gamma,\kappa,H\right)_{6}\right] \tag{25}\]where \(\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|n}}\in\mathbb{R}^{1\times 30}\) is the vector form of the handcrafted features calculated from the \(n\)th segments of the SF. Finally, the composite handcrafted feature \(\mathbf{s}_{Feature}\in\mathbb{R}^{N_{\text{SF}}^{\text{data}}\times 1}\) can be defined as follows  
\[\mathbf{s}_{Feature}=\left[\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|1}}, \mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|2}},...,\mathbf{s}_{Feature|\mathbf{ s}_{\text{SF}|N_{\text{seg}}}}\right] \tag{26}\]  
where \(N_{\text{SF}}^{\text{data}}\) was the size of the statistic moments vector.  
For classification, a linear SVM from [4] was applied. Random forest or multi-class AdaBoost from [5] and linear discriminant analysis from [4] were also investigated. We compared these algorithms when applied to our FH signal dataset, and the linear SVM showed the best classification results.
_Applied_  
_sciences_  
**Impact of Blockage Ratio on Thermal Performance of Delta-Winglet Vortex Generators**  
**[PERSON] 1,2,3,* \({}^{,}\)**, [PERSON] 2,*, [PERSON] 1,3 and [PERSON] 1,3**  
1 Department of Energy and Environmental Engineering, Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan; [EMAIL_ADDRESS] (T.M.); [EMAIL_ADDRESS] (S.K.)  
2 Department of Mechanical Engineering, Faculty of Engineering, Sebelas Maret University, JI. Ir. [PERSON] 36 A, Surakarta 57126, Indonesia  
3 Thermal Science and Engineering Division, International Institute of Carbon-Neutral Energy Research (I2 CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan  
* Correspondence: 3 [EMAIL_ADDRESS] (I.Y.); [EMAIL_ADDRESS] (A.T.W.); Tel.: +81-925-837-832 (I.Y.)  
Received: 29 December 2017; Accepted: 24 January 2018; Published: 26 January 2018  
**Abstract:** The impact of double-sided delta-winglet tape (DWTs) inserts on convective heat transfer and friction behaviors in a tube was experimentally investigated. Three DWTs with ratios of winglet-height (\(b\)) to inner tube diameter (\(d_{i}\)) called blockage ratio (\(R_{b}\)) of 0.28, 0.35 and 0.42 were tested and their performance was compared to that of a longitudinal strip and plain tube under similar test flow conditions. Experiments were conducted over a wide range of flow rates, 3.35 \(\times\) 10\({}^{-5}\)-8.33 \(\times\) 10\({}^{-5}\) m\({}^{3}\)/s, which correspond to 5500 \(\leq\) Reynolds number (\(Re\)) \(\leq\) 14,500 in the 14.3 mm i.d. tube. The results revealed that using DWTs dramatically increased the Nusselt number (\(Nu\)) by as much as 364.3% and the friction factor (\(f\)) by 15.5 times compared with those of a plain tube. Thermal performance (\(\eta\)) increased with a corresponding increase in \(R_{b}\). The highest thermal performance (\(\eta\)) obtained was 1.4. Showing a notable improvement on the thermal performance of the system, DWTs are proposed as a favorable insert device.  
**Keywords:** blockage ratio; delta winglet; vortex generator; friction factor; Nusselt number
# Strengthening Mechanisms in a Medium-Carbon Steel Subjected to Thermo-Mechanical Processing
## 1 Introduction  
Low-alloy ultra-high-strength (UHS) steels with yield strengths (YS) above 1400 MPa are used as structural materials for critical applications, especially in the aerospace industry, due to sufficient ductility and good toughness [1]. Heat treatment of UHS steels includes full austenitizing followed by water quenching and final low-temperature tempering. This treatment results in the formation of a low-temperature-tempered martensitic microstructure that provides a high strength but relatively low Charpy impact toughness [1]. A thermomechanical processing known as temporning was developed by Japanese scientists to enhance the impact toughness [2; 3; 4]. This processing consists of austenitizing, water quenching, and high-temperature tempering at T \(\geq\) 500 \({}^{\circ}\)C, followed by large-strain rolling at the same temperature. Temporning increases the impact toughness by a factor of 10 along with a 30 pct increase in YS owing to the formation of a lamellar-type microstructure with the transverse crystallite dimension of \(\sim\)100 nm [2; 3; 4; 5; 6]. Numerous works have dealt with the examination of fracture mechanisms in temporned steels [2; 3; 4; 7]. However, the strengthening mechanisms responsible for the YS increment after temporning have not been studied in detail. [PERSON] et al. [8] suggested that grain boundary strengthening gives the largest contribution to the overall YS of a warm-rolled pearmitic steel plate. [PERSON] et al. [9] reported that the boundary strengthening attributed to small spacing between cementile lamellae and the dislocation strengthening associated with high dislocation density evolved during cold drawing were the major contributors to the strength of an eutetoid composition steel. In contrast, [PERSON] et al. [5] considered the dispersion strengthening and the dislocation strengthening to be the major contributors to the strength of a Fe-0.08C-1.155 Mn-1.16 Cr-0.42 Mo-0.17 Si-0.08V-0.03 Nb steel subjected to temporning.  
The aim of the present study is to evaluate and compare the strengthening mechanisms in a medium-carbon UHS steel after tempering and tempforming. Specific attention is paid to the effect of rolling on the contribution of different strengthening mechanisms to overall YS.
# Multiscale Damage Evolution Analysis of Aluminum Alloy Based on Defect Visualization
## 5 Fatigue Life Prediction  
Fatigue lives were predicted by establishing the relationship between the several important characteristic parameters: the damage Young's modulus E\({}_{\text{d}}\), the damage variable D, and the number of cycles.  
By the continuous damage mechanism (CDM) [35; 36] theory, the damage variable resulted from the initiation and growth of microvoids was often used to describe the degradation of material properties. For isotropic metal materials, the stiffness degradation represented by damage variable D is given by.  
\[D=\frac{E-E_{d}}{E} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**load Sequence** & **Stage** & **2** & **4** & **6** & **8** & **10** \\ (\(\times 10^{4}\)) & **Group** & & & & & \\ \hline Damage Young’s modulus E\({}_{\text{dt}}\) (GP\({}_{\text{a}}\)) & Group1 & 36.581 & 31.816 & 29.109 & 24.169 & 11.356 \\  & Group2 & 39.647 & 33.169 & 32.147 & 27.549 & 9.365 \\  & Group3 & 34.946 & 34.149 & 30.564 & 28.764 & 13.248 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Young’s modulus E\({}_{\text{dt}}\) of three groups of fatigue specimens after the tensile test.  
Figure 13: The verification of the fatigue damage evolution model.  
where E represents [PERSON]'s modulus of material without damage, and E\({}_{\text{d}}\) is the effective Young's modulus with damage.  
The damage variable D of each loading stage was calculated by Equation (7) based on the effective Young's modulus E\({}_{\text{d}}\) of each fatigue specimen after static tensile test in ABAQUS. Consequently, the relationship between damage parameter D and fatigue life in the fatigue process could be obtained. The variation of the damage variable D and the damage Young's modulus E\({}_{\text{d}}\) with the number of loading cycles N is, respectively, plotted in Figure 14a,b.  
It could be seen from Figure 14a,b that the damage variable D increased with the increase of the number of loading cycles N, and the damage Young's modulus E\({}_{\text{d}}\) decreased gradually. However, the damage Young's modulus E\({}_{\text{d}}\) decreased drastically if the damage degree D exceeded 0.1, resulting in a sharp degradation of material properties. Moreover, Figure 14b shows that the damage parameter D of three groups of curves increased slowly in the early stage, which is the stable initiation stage of voids, and accounted for 10% to 80% of total life. After that, the damage parameter D increased rapidly in the unstable propagation stage of voids. However, no matter which damage stage the material was in, fatigue life could also be predicted conveniently based on the calculated damage variable D by the proposed multiscale damage evolution model for nondestructive detection.
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 3 Proposed Method
### Loss Function  
To predict the rPPG task and HR task, we have designed specific loss functions for each.  
For the prediction of the rPPG signal, our objective is to ensure similarity between the predicted rPPG signal and the true rPPG signal. Therefore, we choose the negative Pearson correlation coefficient \(L_{np}\) as the loss function for rPPG estimation:  
\[L_{np}=1-\rho=1-\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y}) }}{\sqrt{\sum_{i=1}^{n}{(x_{i}-\overline{x})}^{2}{\sum_{i=1}^{n}{(y_{i}- \overline{y})}^{2}}}} \tag{14}\]  
For the HR prediction, our goal is to minimize the difference between the predicted HR and the true HR. Therefore, we use the L1 loss function \(L_{I1}\) as the HR estimation loss function:  
\[L_{I1}=\frac{1}{n}\sum_{i=1}^{n}{|y_{i}-f(x_{i})|} \tag{15}\]  
For multi-task prediction, we need to design a multi-task loss function that weights the relative contributions of each task equally, ensuring that the model learns all tasks equally rather than favoring easier tasks. The overall loss function \(L\) is composed of \(L_{np}\) and \(L_{I1}\), weighted by a parameter \(\alpha\), defined as:  
\[L=(1-\alpha)L_{np}+\alpha L_{I1} \tag{16}\]
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 3 Arabic OCR Challenges  
### OCR Challenges [37; 38; 39]  
Many challenges can occur in the OCR in images from which text is extracted, such as the following:  
Figure 3: Different levels of segmentation from words to isolated characters.  
Figure 2: Binarization exemplified.  
1. Scene complexity: The segregation of text from non-text can be difficult in cases in which the rate of noise is high or when there are strokes similar to text, but which are part of the background and are not texts themselves;
2. Conditions of uneven lighting: Due to the presence of shadows, pictures taken of any document can have the problem of uneven light distribution, in which part of the image has more light while the other is quite dark. Thus, scanned images are preferred, since they eliminate such unevenness;
3. Skewness (rotation): The text has skewed lines and deviates from the distinctive orientation. This can be a consequence of manually taking pictures;
4. Blurring and degradation: The grade of the image can be that of low quality. A remedy for this problem is to sharpen the characters;
5. Aspect ratios: Different aspect ratios can require different search procedures to detect the text;
6. Tilting (perspective distortion): The page itself may not appear in a correct, parallel perspective. The taking of photographs of texts by non-experts without the use of a scanner can account for such non-parallelism, making the task difficult. A simple solution is to use scanners or to try to take photographs for each page while perceiving the perspectives. An example is shown in Figure 4;
7. Warping: This problem is related to the text itself. The page may be perfectly parallel at the time at which the picture is taken; however, the nature of the text and how it is written are warped, which is not usually the case in writing;
8. Fonts: Printed fonts and styles vary from those that are handwritten. The same writer can write the same passage in the same space in a different way each time. On the other hand, printed text is easier because the differences tend to be less frequent from one print to another;
9. Multilingual and multi-font environments: Different languages feature different complexities. Some languages use cursive scripts with connected characters, as in the Arabic language. Moreover, recent individual pages can contain multiple languages, while old writings can have multiple fonts within the same page if they are written by different writers.  
Figure 4: Example of tilting.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Chemical Sensors  
For municipalities, water quality detection and monitoring systems have to be reliable, autonomous, and flexible. For example, in some countries fluoride is a priority chemical constituent to be monitored, since its consumption at early ages can induce fluorosis, which affects the teeth and bones of children. There are several commercially available sensor products for fluoride monitoring including the Hach Sension+ Fluoride Ion Electrode, and various in-line sensors for larger-scale water distribution systems such as the Rosemount Fluoride Monitoring System [33], and the ATI Fluoride Monitor [34]. These technologies can even sense moderate levels of chronic exposure to fluoride concentrations (above 1.5 milligrams per liter (mg/liter) of water--i.e., the World Health Organization (WHO) guideline value for fluoride in water), which can help prevent risk of disease.  
In Bangladesh, where arsenic is a widespread contaminant impacting health (causing the deaths of an estimated 43,000 people each year), researchers and practitioners are developing and testing biosensors that can be rapidly deployed to indicate arsenic contamination in drinking water [35].  
Similarly, there are several off-the-shelf sensor-based technologies for monitoring arsenic contamination, a priority chemical for SDG 6 monitoring. These include the Palintest Digital Arsenic Test Kit [36] and the IPI Digital Arsenic Detector [37]. However, there remains a need for additional technology development in this area, as was highlighted by the recent United States Environmental Protection Agency (US EPA) Arsenic Sensor Prize Competition [38].
## 4 Evaluation Models
### Empirical Models  
We utilize the following three empirical models in our study: Okomura-Hata, COST-231 Hata, and ECC-33.  
The Okumura-Hata model is frequently employed for path loss prediction. A basic formulation of the Okumura-Hata model, in general form, is presented in Equation (1) [3]:  
\[PL=A+B\times log10(f)-C+(D\times log10(h))+E\times(log10(h))^{2}+F\times log10(d) \tag{1}\]  
where \(A\), \(B\), \(C\), \(D\), \(E\), and \(F\) are model-specific constants that can be tailored based on specific environmental factors as well as the frequency of operation and the receiver height. \(f\) is the frequency of the signal in MHz. \(h\) is the height of the transmitting antenna in meters. \(d\) is the distance between the transmitter and receiver in kilometers. The application of Okumura-Hata includes urban and suburban as well as open rural areas in the frequency bands from 150 MHz to 1500 MHz.  
(2) COST-231 Hata Model  
The COST-231 Hata Model is an extension of the Okumura-Hata model to the frequency bands of 1500 MHz \(\leq\)\(f\)\(\leq\) 2000 MHz [3]. The proposed model for path loss is designed for mobile communication networks in urban areas and is presented in Equation (2):  
\[PL=46.33+33.9 logf-13.82 log(h_{tx})+C_{m}-a(h_{rx})+(44.99-6.55 log(h_{tx}))log(d) \tag{2}\]  
where \(f\), \(d\), \(h_{tx}\), \(h_{rx}\), and \(C_{m}\) are the frequency in MHz, distance from the transmitting station to the receiving antenna, transmitting antenna height, receiver antenna height, and an environment-specific constant term. The correction factor is as follows:  
\[a(h_{rx})=(1.1 log10(f)-0.7)\ h_{rx}-(1.56 log10(f)-0.8)\quad for\ 1500\ MHz\leq f\leq 2000\ MHz. \tag{3}\]  
(3) ECC-33 Model  
The ECC-33 path loss model, which is applicable for the 700-3000 MHz frequency band, is developed by the Electronic Communication Committee (ECC). The model is based on a modification of [PERSON]'s original measurements to more accurately represent a fixed wireless access (FWA) system. The path loss equation is given as [26]:  
\[PL=Af_{s}+Ab_{m}-G_{tx}-G_{rx} \tag{4}\]  
\(Af_{s}\), \(Ab_{m}\), \(G_{t}\), and \(G_{r}\) represent free space attenuation, medial PL, transmitter antenna height gain, and receiver antenna gain factor. The functions are separately defined:  
\[Af_{s}=92.4+20 log(d)+20\ log\ (f) \tag{5}\]  
\[Ab_{m}= 20.41+9.83\ log(d)+7.849\ log(f)\ +9.56\ (log(f))^{2} \tag{6}\]  
\[G_{tx}= log(\frac{h_{tx}}{200})(13.958+5.8(log(f))^{2}) \tag{7}\] \[G_{rx}= (42.57+13.7 log(f))\ (log(h_{tx})-0.585) \tag{8}\]Note that the specific version of the Okumura-Hata, COST231 Hata, and ECC-33 models is presented in [10]. The three aforementioned models are used by us as a basis to evaluate the results of our study. However, in our study, we do not replicate the results corresponding to each of these models. We only use the spreadsheet containing the real measured PL data versus the PL data prognosis by the three models in [10]'s Supplementary Material to compute accuracy metrics in our result section.