esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Sintering Performance of W Skeletons  
After the sintering, the sintering performance, including the linear shrinkage (\(\Delta L/L_{0}\)) and densification parameters (\(\widehat{p}\)), was determined according to the measured mass and dimensions, as shown in Figure 5.  
Figure 4: Schematic of the infiltration apparatus.  
Figure 3: Schematic of the sintering apparatus.  
As shown in Figure 5a, both pure W and W powder with 1% Cu addition had minimal shrinkage within the temperature range of 950-1050 \({}^{\circ}\)C. The curve at 950 \({}^{\circ}\)C for a soaking time of 90 min overlapped the curve at 1000 \({}^{\circ}\)C for a soaking time of 90 min, and the maximum shrinkage rate was 0.5%. This indicated that within the temperature range of 950-1000 \({}^{\circ}\)C, with an increase in temperature and a constant soaking time, the linear shrinkage of W skeletons with different additive ratios did not vary significantly. However, as the temperature reached 1050 \({}^{\circ}\)C with a 90 min soaking time, the shrinkage sharply increased. This indicated that within the temperature range of 1000-1050 \({}^{\circ}\)C, with an increase in the temperature and a constant sintering time, the linear shrinkage of W skeletons with different additive ratios increased with temperature. Furthermore, the linear shrinkage increased with sintering time and Ni content. The W skeleton with a 1% Ni addition exhibited a maximum linear shrinkage of 2.194% when soaked at 1050 \({}^{\circ}\)C for 150 min.  
The densification parameter [24] is defined as  
\[\widetilde{\rho}=\frac{\rho_{s}-\rho_{g}}{1-\rho_{g}} \tag{1}\]  
where \(\rho_{g}\) represents the relative density of the compacted sample, \(\rho_{s}\) represents the relative density after sintering, and \(\widetilde{\rho}\) is the densification parameter.  
As shown in Figure 5b, no significant differences were observed between the densification parameters for pure W and W with a 1% Cu addition. Conversely, when Ni was present in the W powder, the densification parameter increased significantly with an increase in the Ni content. The highest densification parameter was achieved at 39.5% Ni for a soaking time of 150 min at 1050 \({}^{\circ}\)C. Pure W sintering requires temperatures as high as 2700 \({}^{\circ}\)C to achieve a similar density. Shrinkage was induced by the diffusion of W at the particle boundaries. The addition of a small amount of Ni in the W powder significantly improved the densification of W. According to the results for the sintering performance, W powder with a 1% Ni addition exhibited the highest linear shrinkage rate and densification parameter when soaked at 1050 \({}^{\circ}\)C for 150 min.
## 3 Results
### Free Sugars Intake  
The average FS intake accounted for 11.5 \(\pm\) 0.1% of TEI (58.2 \(\pm\) 0.6 g/day) of the general population (aged 3 and over); i.e., 15.3 \(\pm\) 0.1% of TEI in children and adolescents (67.1 \(\pm\) 1.0 g/day) and 10.5 \(\pm\) 0.1% in adults (56.6 + 0.8 g/day) (Table 1). The FS contribution to TEI decreased with age, thus starting from 15.1% in the 3-6 years to 8.2% in the 66-75 years groups. However, with 9.4 \(\pm\) 0.4% of TEI in participants aged 76 years and older, free sugars intake did increase compared to participants aged 50-75 years (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Population** & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Standard-Deviation**} & \multicolumn{1}{c}{**\% of Total**} \\  & Total energy intake (kcal/day) & 1760.9 & 18 & \\ \cline{2-5}  & Carbohydrates (g/day) & 212.0 & 2.3 & 48.1 \\ \cline{2-5} Children and adolescents & Total sugars (g/day) & 100.1 & 1.2 & 23.0 \\ (3–17 years) & Free sugars (g/day) & 67.1 & 1.0 & 15.3 \\ N = 1102 & Fats (g/day) & 66.7 & 0.7 & 34.0 \\ \cline{2-5}  & Proteins (g/day) & 67.9 & 0.7 & 15.6 \\ \cline{2-5}  & Alcohol (g/day) & 5.8 & 0.06 & 2.3 \\ \hline  & Total energy intake (kcal/day) & 2156.6 & 12.3 & \\ \cline{2-5}  & Carbohydrates (g/day) & 234.3 & 1.5 & 43.5 \\ \cline{2-5} Adults (\(\geq\)18 years) & Total sugars (g/day) & 96.2 & 0.9 & 17.9 \\ \cline{2-5} N = 1845 & Free sugars (g/day) & 56.6 & 0.8 & 10.5 \\ \cline{2-5} N = 1845 & Fats (g/day) & 84.9 & 0.6 & 35.4 \\ \cline{2-5}  & Proteins (g/day) & 83.1 & 0.6 & 15.5 \\ \cline{2-5}  & Alcohol (g/day) & 17.5 & 0.02 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall intake of macronutrients in children and adolescents as well as in adults.  
In terms of distribution, 48% of adults and 87% of children and adolescents were found to have FS intakes above the 10% TEI limit defined by the WHO.
## 2 Structure of the Monitoring and Control System
### Sensor Node Design and Implementation
#### 2.2.2 Environmental Factors Sensing  
A commercial SHT15 (Sensirion, Laubisruetistrasse, Switzerland), a relative humidity and temperature integrated sensor was selected. The accuracy of humidity and temperature are 3.0% and 0.4 \({}^{\circ}\)C, respectively. The best performance is near 28 \({}^{\circ}\)C and 80% humidity, which are applicable conditions in a greenhouse. Besides the inherent accuracy of the NDIR gas sensor itself, the calibration, depending on the application environment, is able to further enhance the performance of the CO\({}_{2}\) sensor. In order to get more accurate environmental factors, the related calibrations are necessary. In addition, based on the working conditions, like voltage supply, temperature, humidity, and inherent ADC resolution, the nonlinear compensation of humidity detection and humidity compensation to temperature detection can be calibrated. Firstly, because of the inherent characteristic of the humidity sensor, the error from voltage dependence can be ignored. To compensate the non-linearity of the humidity result and obtain a full accuracy, the error from the ADC should be calculated and calibrated as:  
\[R_{\mathrm{Reading}}=c_{1}+c_{2}\times SO_{RH}+c_{3}\times SO_{RH}^{2} \tag{3}\]where \(SO_{RH}\) is a direct digital output from insider ADC, and \(RH_{\text{Reading}}\) is the calibration results. The linearization coefficients for humidity with the second-order polynomial fitting are decided according to the relationship between the standard relative humidity and the direct output result \(SO_{RH}\) from the datasheet of RH and Temperature Non-Linearity Compensation. After the related fitting and calibration, the constant variables \(c_{1}\), \(c_{2}\), and \(c_{3}\) in Equation (3) match different values corresponding to the ADC sampling bits.  
However, in the greenhouse, there is a large diurnal temperature, which indicates that the temperature coefficient of the relative humidity sensor should be considered as:  
\[\begin{array}{c}RH_{\text{Line}}=(T-25)\times(t_{1}+t_{2}\times SO_{RH})+RH _{\text{Reading}}\\ T=d_{1}+d_{2}\times T_{\text{Reading}}\end{array} \tag{4}\]  
Equation (4) was used to calibrate the temperature result according to the power supply and ADC bits corresponding to the parameters \(d_{1}\), \(d_{2}\), and \(T_{\text{Reading}}\), respectively. Then the calibrated temperature was used to calibrate the errors caused by the temperature significant difference from 25 \({}^{\circ}\)C. In addition, \(t_{1}\) and \(t_{2}\) are two constant values depending on the ADC resolution.  
BH1750 FVI is a digital ambient light sensor a peak sensitivity wavelength of 560 nm and a high resolution from 1 to 65,535 k. The measured luminance was converted by a 16-bit ADC and transmitted by I\({}^{2}\)C bus to the microcontroller. Because of the spectral responsivity of human eye, the measured ambient light can also be used to adjust the LCD power in the embedded terminal to save power.
# A Comprehensive Survey for Deep-Learning-Based
## 5 Methodologies
### Abnormality Detection with Deep Learning Methods
#### 5.3.2 Semi-Supervised Learning  
Semi-supervised learning is an effective approach when unlabeled data is used in conjunction with a smaller amount of labeled data during the training phase. It is valuable in abnormality detection since, in some cases, the labeled data for abnormal instances is absent, or the classifier has to distinguish new types of anomaly [114]. Like supervised learning, CNN and RNN networks also work well with some variations. [PERSON] et al. proposed a CNN-based method to identify an abnormal instance, with only one normal class used in training [20]. Instead of the traditional classification loss function, compactness loss and descriptiveness loss functions were proposed and applied together with a parallel architecture. The method was tested on a public abnormality detection dataset and had a better performance than some other methods. A 3D CNN network was presented to improve the localization of extreme weather events. The temporal information and unlabeled data were also taken into account to have more accurate bounding box predictions compared to the 2D CNN network [35]. In [115], an RNN-based method was proposed to classify hyperspectral images with just a small amount of labeled data, while the unlabeled training data was adopted with pseudo-labels generated by a non-parametric Bayesian clustering algorithm.  
AutoEncoder (AE) is an efficient approach to learning both linear and nonlinear features from unlabeled data, so it is widely used in semi-supervised and unsupervised learning [36]. [PERSON] et al. applied AE together with a neural network to detect a dynamically targeted anomaly, and it achieved a good performance in finding anomalies from majority classes [21]. Deep AE has also been adopted in finding implausible data from electronic health records (EHR) [116; 117; 118], which can reduce the workload of doctors [37]. The method was evaluated with multivariate data from 30 clinical observations.  
Furthermore, generative adversarial networks (GAN) have been used to detect anomalies that are absent in the training phase [38]. GANs also show success in generating training data for classifiers [86]. A multi-class classifier is trained with nominal and novel data that is generated from a generator, and the generator is trained with feature-matching loss in order to cheat the classifier. In [30], the authors proposed a corrupted GAN, which does not need convergence during training and addresses the one-class classification task. The method had a better performance than a solution based on AE.
## 3 Stability Analysis of Ridge Slope
### Deformation and Failure Characteristics of Ridge Slope under Different Ridgeline Dip Angles
#### 3.1.3 Stability of Ridge Slope under Different Ridgeline Dip Angles  
Figure 7 shows the relationship between the displacement at the maximum displacement (at the Apex of the ridge slope) and the reduction coefficient at different ridgeline inclination angles.  
Figure 7a shows the relationship between the reduction coefficient and the vertex displacement of the ridge under different \(\alpha\) values when \(\beta=70^{\circ}\) and \(H=10\) m. When the reduction coefficient was less than six, no displacement of the ridge slope occurred under different ridgeline angles (Figure 7). When the reduction factor was 8, the ridge slope with \(\alpha=20^{\circ}\) exhibited a displacement inflection point, indicating that the safety factor of the slope at this time was eight. When the reduction factor gradually increased to 10, the ridge slope at other angles also displayed an inflection point. The smaller the ridgeline inclination angle of the ridge slope, the worse its stability.  
When \(\beta=60^{\circ}\), \(H=10\) m, or \(\beta=50^{\circ}\), \(H=10\) m, the displacement of the ridge slope did not change with the reduction coefficient, and when the reduction coefficient was greater than six, the displacement of the ridge slope increased uniformly with the reduction coefficient under different ridgeline inclination angles (Figure 7b,c). \(\beta\) also had a great influence on the displacement of the ridge slope (Figure 7). The influence of \(\beta\) on the ridge slope displacement was analyzed.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Results
#### 5.3.1 Impacts of Varying Data Generation Rates from Sensor Devices  
In this subsection, we study the performances of the radio resource management approaches for increasing data generation rates from the healthcare sensor devices. The rate at which data are generated (in packets per second) has a considerable impact on PAUM's scalability and efficiency. This occurs as the traffic density is determined by the network applications and their traffic injection rates. In PAUM, the term utility refers to how well priority users are able to transmit data in a good channel with mitigated interference. The graphs in Figure 4a illustrate that, as a result of the network's event-driven prioritization, the utility achieved by PAUM users remains stable while the data rate increases. It shows that the PAUM's functionality is highly appreciable by the system's users. In the case of PAUM's prioritized event scheduling, the performances of average utility outperform over \(bRRM\) and \(iRRM\) because none of the research comes up with the utility measurement by well-managed resource allocation governed by higher priority with classified events. It is obvious that the _Utility_ measurement of the PAUM is effective in terms of event prioritization as the rate of data generation increases. The graphs of Figure 4b show that as the number of packets generated per second increases, the average packet delivery ratio (PDR) slowly declines. The PAUM system achieves minimal data loss over \(bRRM\) and \(iRRM\) since it avoids congestion and allocates high-quality resources. Resource allocation is critical when it comes to assessing user usefulness. These findings support the use of PAUM for real-time healthcare applications leveraging NB-IoT. Finally, fairness ensures that resources are distributed to each user in accordance with their requirements. As illustrated in Figure 4c, the value of equitable resource sharing is maximized. It can be seen that because most schedulers focused solely on the channel conditions of UEs while allocating radio resources, their associated fairness increases as SINR values rise. However, this is not the case for the event-influenced prioritized _Multilevel Queue with Preemptive Round Robin scheduling scheme_, which maximizes the overall system utility by considering the UEs with best channel conditions along with their time slot; it automatically excludes all other UEs with relatively bad channel conditions, resulting in the highest fairness index. The PAUM outperforms \(bRRM\) and \(iRRM\) because it considers event type and priority, along with SINR value.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 5 Experiment
### Experiment Process  
The hardware resources used in the experiment were Tesla k80 GPU, Intel Xeon E5 and 2.60 GHz CPU, and the deep learning framework is PyTorch1.4. Five-fold cross-validation was used in all experiments.  
As is shown in Figure 5, the experimental process consists of two parts: the data generation and mixing part, and the signal separation part. In the separation part, it consisted of three modules: 1-D convolutional encoding module, separation module, 1-D convolutional decoding module.
## 1 Introduction  
Recently, the application of sapphire in manufacturing light emitting diodes (LEDs) has become increasingly popular in the LEDs market ascribe to the low fabrication cost. Nonetheless, large lattice mismatch (15%) and thermal expansion coefficient mismatch (25%) between GaN (gallium nitride) films and sapphire substrates result in high dislocation densities and residual stress, which seriously decrease the internal quantum efficiency and affect the optical and electrical performance of the GaN-based LED [1, 2]. To improve the performance of the films grown on sapphire substrate, a lot of methods have been put forward, such as the use of buffer layers [3], epitaxial lateral over-growth (ELOG) technology [4], Bragg reflecting layers [5], photonic crystals [6, 7], surface coarsening [8, 9], and flip-chip technique [10], a more efficient solution provided by some researchers is using patterned sapphire substrate (PSS) [11, 12]. For instance, it includes grooved type [13], hemispherical type [14], conic type [15] and hexagonal type [16] PSS (patterned sapphire substrate). Among these patterns, grooved PSS, which is widely applied to PSS applications, exhibits more potential to improve LED efficacy along with its effectiveness to reduce dislocation density [17]. Meanwhile, there has been little study about the PSS created by femtosecond laser. Under this circumstance, we proposed a new approach to create striped grooved PSS by laser ablation. In this study, we describe the fabrication and characteristics of the c-plane GaN epitaxial layers grown on striped PSS and demonstrate the improvement in the crystallinity of striped PSS sample, compared with the traditional planar sample.
# Evaluating the Physicochemical Properties of Some Kosovo's and Imported Honey Samples
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Directive 2001/110 EU** & \\ \hline \multirow{2}{*}{**Composition Criteria**} & & **Blossom Honey** & **Honey*** \\ \cline{2-4}  & **General** & **Exception** & **General** & **Revised CODEX 2001** \\ \hline \multirow{2}{*}{Moisture \%} & \multirow{2}{*}{\(<\)20} & _Calluna and baker’s honey \(<\) 23 Baker’s from_ & \multirow{2}{*}{\(<\)20} & The same No indication for \\  & & _Calluna \(<\)_ 25 & & baker’s honey \\ \hline \multirow{2}{*}{Fructose + glucose \%} & \multirow{2}{*}{\(>\)60} & _Robina, Medicogo, Banksia, Hedystarum,_ & \multirow{2}{*}{\(>\)45} & The same \\  & & _Eucalyptus, Encrighasp,_ and & _Cittrus \(<\)_10 & \(<\)5 & The same \\  & & _Larendula \& Borgo \(<\)_ 15 & \\ \hline \multirow{2}{*}{Water-insoluble\%} & \multirow{2}{*}{\(<\)0.1} & Pressed honey \(<\) 0.5 & \(<\)0.1 & The same \\ \hline \multirow{2}{*}{Electrical conductivity mS. Cm\({}^{-1}\)} & \multirow{2}{*}{\(<\)0.8} & _Chestnut, Arbutus, Erica, Encolyptus, Tilia,_ & \multirow{2}{*}{\(<\)0.8} & The same \\  & & _Calluna, Mamuka and Melaleuca_ & & \\ \hline \multirow{2}{*}{Free acid meq. kg\({}^{-1}\)} & \multirow{2}{*}{\(<\)50} & Baker’s honey \(<\) 50 & \(<\)50 & The same \\ \hline \multirow{2}{*}{Diastase activity DN ***} & \multirow{2}{*}{\(>\)8} & Baker’s honey and honey with low & \multirow{2}{*}{\(>\)8} & Honeys with low natural \\  & & natural enzyme content \(>\) 8 when HMF is & & enzyme cintent \(>\) 3 DN \\  & & less than 15 mg\(\cdot\)kg\({}^{-1}\) & & \\ \hline \multirow{2}{*}{HMF mg\(\cdot\)kg\({}^{-1}\)**} & \multirow{2}{*}{\(<\)50} & baker’s honey Honeys of tropical climate & \multirow{2}{*}{\(<\)40} & Honeys with low natural \\  & & and blenda of these honey \(<\) 80 & & enzyme vontent \(>\) 3 DN. \\ \hline \multirow{4}{*}{Honeydew honey and blends of honeydew honey with blossom honey. *** Determined after processing and blending.} \\  & & & & Honeys of tropical climate and \\ \end{tabular}
\end{table} 
Table 1: Compositional criteria of honey. Kosovo aims to support the competitiveness of the agro-food sector in line with EU veterinary, phytosanitary, and food safety standards. Thus, the physicochemical and microbiological properties of certain food products are addressed through different assessments [22; 23]. As far as we are aware, there are no data about the general physicochemical properties of honey from Kosovo. Despite this fact, there are few studies, which show features, such as the concentrations of selected elements for soil, honey and bee pollen, and radioactivity level in Kosovo's honey as well as the phenolic and flavonoid content of honey from Kosovo [24; 25; 26]. This study aimed to assess the physicochemical properties of several honey samples of different floral origins from Kosovo, including some samples of imported commercial honey available in Kosovo markets.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 5 Results and Analysis
### Network Complexity Analysis  
Table 2 shows the number of network parameters, running time and FLOPs of different methods, where FLOPs are calculated on \(256\times 256\) image blocks, the running time is calculated on the average processing time over 100 blur images, and the deblurring performance of each method on the RealBlur-J dataset is listed for comparison at the same time. Note that all experiments are executed on an NVIDIA GeForce GTX 2080 Ti GPU. As shown in Table 2, IDR\({}^{2}\)ENet has 13.4 M parameters and 317.91 G FLOPs during training, while it has 7.5 M counts and 169.78 G FLOPs during testing, which has a smaller memory footprint because no reblurring process is involved during testing. Compared with most methods, IDR\({}^{2}\)ENet has a greater advantage in terms of the number of parameters and FLOPs because it does not involve iterations and other complicated designs. Although the FLOPs of MIMO-UNet+ [41] are slightly smaller than those of this paper with a difference of 15.54 G (compared to the FLOPs during testing), it still has twice the number of parameters of IDR\({}^{2}\)ENet. Lightweight MIMO-Wnet [43] has smaller FLOPs by improving a lightweight structure of MIMO-UNet [41]. Although its deblurring effect is somewhat improved compared to the latter, it is still lower than IDR\({}^{2}\)ENet's performance. For the running time, IDR\({}^{2}\)ENet requires less time than any other network, as shown in Table 2. In general, compared with other methods, IDR\({}^{2}\)ENet ensures excellent deblurring performance while keeping the network complexity to a smaller level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FLOPs**} & \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**RealBlur-J**} \\ \cline{3-4}  & & & & **PSNR (dB)** & **SSIM** \\ \hline DMPHN [40] & 21.7 M & 678.56 G & 0.034 s & 28.42 & 0.86 \\ \hline DeblurGAN-v2 [16] & 60.9 M & 411.34 G & 0.082 s & 28.7 & 0.867 \\ \hline DBGAN [31] & 11.6 M & 660.20 G & 0.084 s & 24.93 & 0.745 \\ \hline MIMO-Unet+ [41] & 16.1 M & 154.24 G & 0.032 s & 27.63 & 0.837 \\ \hline MPRNet [42] & 20.1 M & 760.11 G & 0.077 s & 28.7 & 0.876 \\ \hline Lightweight MIMO-WNet [43] & 14.1 M & 138.81 G & 0.028 s & 28.52 & 0.865 \\ \hline
**IDR\({}^{2}\)ENet (Ours) Training** & **13.4 M** & **317.91 G** & - & - & - \\ \hline
**IDR\({}^{2}\)ENet (Ours) Testing** & **7.5 M** & **169.78 G** & **0.012 s** & **28.81** & **0.876** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of network complexity of different methods.  
Figure 12: **Deblurring performances of IDR\({}^{2}\)ENet on low-contrast images from RealBlur-J.** From left to right: (**a**) the low-contrast blur image, and (**b**) the corresponding deblurred image of IDR\({}^{2}\)ENet.