esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results of Wound Classification Model  
#### 4.2.1 Addressing Class Imbalance  
The dataset for post-operative wound classification suffered from a severe class imbalance problem addressed with an oversampling technique called SMOTE, as described in Section 3.3.3. Two types of alternatives were tried to equalize the class distribution, a combination of SMOTE and undersampling and the single SMOTE application. Figure 7 exhibits three subfigures showing the class distribution before and after the two addressed approaches. The rating of the oversampling approaches was performed for all models and combination of hyperparameters, where for each technique, the best hyperparameters, number of features, and model were chosen. Table 5 shows the results of the models' performance with the aforementioned oversampling techniques.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mean IoU\%** & **Dice\%** & **Precision\%** \\ \hline \(89.9\pm 0.40\) & \(94.6\pm 0.30\) & \(90.1\pm 0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of the segmentation model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & \begin{tabular}{c} **No** \\ **Augmentation** \\ \end{tabular} & \begin{tabular}{c} **Geometrical +** \\ **Color** \\ \end{tabular} & \
\begin{tabular}{c} **Color** \\ \end{tabular} &
\begin{tabular}{c} **Geometrical** \\ \end{tabular} \\ \hline Mean IoU\% & \(82.5\pm 0.04\) & \(82.5\pm 0.24\) & \(79.3\pm 0.10\) & \(80.4\pm 0.09\) \\ Dice\% & \(90.4\pm 0.02\) & \(90.4\pm 0.15\) & \(88.5\pm 0.06\) & \(89.1\pm 0.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the dataset with no augmentation and with the three different types of augmentations.
## 9 Porosity Evaluation and Mathematical Model Enhancement
### Porosity Evaluation in Composite Specimens--Destructive Test
#### 9.1.1 Specim Preparation and Test  
The composite tubes used in the \(PTF\) evaluation test of Section 8 were fractured. The fractured surfaces were photographed using a Dino-Lite AM-4013 MTL digital microscope (Taipei City, Taiwan). A quantitative analysis was then performed on MATLAB (Figure 14). Each specimen was photographed on its longitudinal section. The variation in light reflection on the broken surface caused light areas at the holes and dark areas at flat surfaces, revealing the pores' presence. Therefore, the photo was first transformed into grayscale. Subsequently, the photo was binarized at the threshold of 0.5, turning bright areas white and dark areas black. The fill percentage \"\(k\)\" was then introduced as the percentage of black over the number of pixels of the photo.  
Referring to Figure 15, zone A represents the top part of the initial molded tube specimen, zone B is the lower part, and zone C is the part fixed to the turning machine when manufacturing the small composite tubes (Figure 11c). Therefore, no specimens were manufactured from zone C. The cross-section of these tubes, enlarged 20.7 times, reveals the presence of pores highlighted in red. It is noticed that a higher concentration of pores is located in zone B, while zone A specimens exhibit fewer pores.  
Figure 14: An enlargement of the composite cross-section reveals trapped air in the thickness of the composite material. The photo is grayscaled, and its binarization allows identification of the number of bubbles and their dimensions.  
Figure 15: Typical tube specimen: zone A represents the top zone of the specimen, and zone B is the bottom zone. A cross-section of specimens increased by 20.7 times shows higher porosity in specimens taken from zone B than specimens of zone A.  
Recalling Equation (24), \(W_{comp}\), the denominator of the equation, represents the cross-section of the composite material tube. The presence of porosity reduces this section. Therefore, Equation (24) can be written as a function of the factor \"\(k\)\".  
\[W_{comp1}=\frac{\pi(R^{2}+R_{o2}^{2})}{\pi k(R^{2}-R_{o2}^{2})} \tag{32}\]  
Recalling that Equation (26) represents the pressure applied by the interference fit on the intermediate to hold it in place, this equation is then rectified with the correction factor \"\(k\)\".  
\[P_{1}=\frac{\delta_{r}}{\left(R\Big{[}\frac{1}{E_{comp}}\Big{(}-\frac{W_{comp} }{k}+v_{comp}\Big{)}+\frac{1}{E_{i}}(W_{i}-v_{i})\Big{]}\right)} \tag{33}\]  
Equation (33) shows that the pressure \(P_{1}\) < \(P\) of Equation (26). Knowing that the pressure is proportional to the force \(PTF\), we can fairly conclude that the presence of porosity in the molded composite material will negatively affect the \(PTF\) value.
## 3 Results
### Simulation Results at FSC  
The simulation results based on FSC age data for the 140 FSC show very low R\({}^{2}\) and high RMSE compared with the ACS and H data estimated from UAV lidar. However, for the FSC planted after 2015, the simulation results are quite well-matched (Figure 7), with RMSE of H and ACS of 2.91 m and 14.22 Mg/ha, respectively. Obviously, for the FSC planted before 2015, there is no significant relationship between the simulated results and validation data, due to the unknown harvest information and inaccurate forest age data, and the RMSE of H and ACS are 14.06 m and 80.78 Mg/ha, respectively. This suggests that accurate and timely updating of forest age is critical for the model simulation of eucalyptus plantations. The accuracies of simulated H and ACS using forest age data from Landsat significantly increase for the 140 FSC compared to the results based on FSC age data (Figure 8). The forest age data from Landsat are very effective for driving the 3-PG model. Both the simulated H and ACS show high R\({}^{2}\) and low RRMSE, but the accuracy is not so good as the SP level.  
Figure 6: Validation of simulated diameter at breast height (**a**), height (**b**), and aboveground carbon stock (**c**) based on remote sensing stand age data at SP scale.  
Figure 7: Validation of simulated height (**a**) and aboveground carbon stocks (**b**) based on FSC forest age data.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
This work proves that the understanding and use of the ordinary and extraordinary waves [11] from the F2 layer of the ionosphere [12] lead to an improvement in the NVIS channel's performance. Moreover, applying single input multiple output (SIMO) [13] techniques to circular polarized antennas [14] will allow the system to receive both characteristic waves simultaneously, thus further increasing the channel's yield. [PERSON] in [15] and [PERSON] in [13] measured the correlation between the ordinary and extraordinary waves; the results stated an isolation of at least 13 dB. Our work not only studies the behavior of the characteristic waves but also demonstrates that this channel de-correlation can lead to an improvement in the ionospheric link. [PERSON] in [16] studied the NVIS channel with a \(2\times 2\) MIMO link. An improvement of the communication's capacity is achieved, as it focused on the throughput improvement compared to a SISO environment. In contrast to this, our work focuses on the economic cost of the overall system and the transmitting power, which are aspects not studied in [13]. Reference [13] also showed how the capacity of the ionospheric channel is improved by using both waves, but all the tests were realized with a 500 W power amplifier. Although it proves that using both waves improves the performance of the channel, is not tested in a data transmission protocol for a low power consumption platform. Finally, our work proposes a comparison with different diversity combining algorithms (selection combining and equal-gain combining), merging both waves to improve the performance and the robustness of the NVIS channel. This article is organized as follows. In Section 2, we explain the bases of the NVIS ordinary and extraordinary waves and their use on polarization techniques. In Section 3, we describe the developed system used to perform the tests. In Section 4, we detail the frame design and the tests performed. In Section 5, we present the results obtained of the tests. Finally, Section 6 contains the conclusions achieved.
# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 2 Background and Related Work
### SSD Internals and NAND Flash Memory
For example, if the overprovisioning is 10%, the actual physical capacity of the SSD might be 100 GB, but only 90 GB of storage space is exposed to the file system. Therefore, even if 10% of the blocks become bad, the SSD can still operate with the remaining 90 GB of functional blocks. However, if the bad blocks exceed 10%, the available storage space falls below what is exposed to the file system, causing the entire SSD to become inoperable. To maximize the lifespan of an SSD, it is necessary to delay the occurrence of bad blocks as much as possible, which requires achieving wear leveling that maintains an even distribution of accumulated erase counts across all blocks. While increasing the level of overprovisioning can further extend the SSD's lifespan, it also increases the actual physical capacity required beyond what is exposed to the file system, leading to higher manufacturing costs for the SSD.
# First Observations of Cirrus Clouds Using the UZ Mie Lidar over uMhalthuze City, South Africa
## 5 Conclusions  
This study presents the characterization of cirrus clouds observed on 10 and 11 April 2019 using ground-based UZ lidar for the first time. The optical properties of cirrus clouds observed by the UZ lidar are also validated by using CALIPSO lidar closest overpass over the study area. This study reveals that a dense cirrus cloud with an average thickness of \(\sim\)1.5 km at a height range of 9.5-12 km was observed by the UZ lidar on 10 and 11 April 2019. The availability of the closest overpass of CALISPO on 11 April 2019 allowed for the inter-comparison between the UZ lidar and the CALIPSO observations. A strong total backscatter coefficient peak at a height range of 8.5-12.5 km due to what was classified as a cirrus cloud was observed on 11 April 2019. Overall, there is a good agreement between the UZ lidar and CALIPSO instruments for cirrus cloud layer detection. It should also be mentioned here that the UZ lidar is able to detect aerosols at the lower troposphere, a feature that is also apparent in the CALISPO observations. A study on the long-term characterization of cirrus clouds and the frequency occurrence, as well as aerosols, will follow as soon as enough UZ lidar data are available.  
Conceptualization, N.M. and L.S.; methodology, N.M. and L.S.; software, N.M.; validation, N.M. and L.S.; formal analysis, N.M. and L.S.; investigation, N.M.; writing--original draft preparation, L.S.; writing--review and editing, N.M. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the University of Zululand.  
Not applicable.  
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. CALIPSO dataset can be accessed on [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 2 February 2022).  
This project is sponsored by the CSIR NLC Rental Pool Programme. Authors acknowledge the French South-African PROTEA programme and the CNRS-NRF LIA ARSAIO (Atmospheric Research in Southern Africa and Indian Ocean), for supporting research activities,  
Figure 5: Height-time of (**a**) total backscatter coefficients, (**b**) cloud sub-type, (**c**) extinction coefficient for 11 April 2019. (**d**) Altitude vs ice and water content and temperature for 11 April 2019.  
and the National Research Foundation (NRF) of South Africa. A special thanks to [PERSON] for his assistance during the first phase of the operation of the instrument. We thank the Atmospheric Science Data Center at NASA Langley Research Center for archiving and hosting CALIPSO data. The authors declare no conflict of interest.
properties of douchi depend on its compounds, which include organic acids, fatty acids and amino acids. [PERSON] et al. [10] reported that glutamic acid was high in both the natural and artificial types of Yongchuan douchi, which contributes to its umami taste, and the amount of bitter amino acids including isoleucine, leucine and arginine were higher in artificial Yongchuan douchi. [PERSON] et al. [11] isolated and identified 131 kinds of volatile compounds from three kinds of commercial tempeh by simultaneous distillation and extraction with gas chromatography\(-\)mass spectrometry (SDE-GC-MS), mainly esters, acids and alcohols. In addition, only a small part of the volatile compounds detected by the GC-MS contributed to food flavor, namely the aroma-active compounds. Gas chromatography-olfactometry detection (GC-O) is a method for detecting the active substances of food flavor with the human nose as a detector emerging in recent years [12], which has been widely used in the analysis of food aroma. Researchers have previously studied the aroma active substances of fermented soybean products (such as soy sauce [13; 14], fermented bean red curd [15; 16] and soybean paste [17; 18]). [PERSON] et al. [19] used solid-phase microextraction (SPME) and GC-MS/O and found that 10 compounds, including 2-methylbutyratealdehyde, 2,6-dimethylpyazine, 1-octene-3-ol, phenylethanolamine and phenyl ethyl butyrate were the key aroma-active substances in Liuyang douchi. [PERSON] et al. [20] detected 21 aroma-active substances in tempeh, among which 2-acetyl-1-pyrroline, 2-ethyl-3,5-dimethylpyrazine, dimethyltrisulfide, 3-methithiopropanal and other substances showed relatively high Odor Active Values (OAVs). However, the deep research on flavor analysis and the mechanisms of volatile compound formation of douchi is rare.  
Yongchuan douchi with unique flavor is the representative of Mucor type douchi, which has strong and lasting aroma, a dark color and a soft texture favored by many people [21]. At the present stage, the aroma research of Yongchuan douchi is still carried out by single extraction method and GC-MS [10], and the research on its aroma-active substances is rarely reported. Therefore, the characterization of the aroma-active compounds of Yongchuan douchi is necessary. In this study, modern separation and extraction methods with sensory-directed flavor analysis was applied, such as gas chromatography-olfactometry (GC-O), gas chromatography\(-\)mass spectrometry (GC-MS), aroma extract dilution analysis (AEDA), odor activity value (OAV) calculation and aroma recombination and omission [22]. This research will produce insights into learning about the key aroma compounds in Yongchuan douchi, and help to study and adjust the formation pathways during Yongchuan douchi processing.
## 5 Conclusions  
To investigate the characteristics of temperature, CO concentration, and human eye visibility at characteristic heights, a series of numerical simulations were conducted considering the radius of curvature and longitudinal ventilation velocity. The impact on personal evacuation was designed based on the results. The major conclusions are as follows:  
Under the influence of longitudinal ventilation, fire smoke in curved tunnels does not directly disperse toward the tunnel exit. Instead, smoke reflects between the inner and outer sidewalls of the tunnel. Consequently, multiple high-temperature zones are formed on both sides of the tunnel near the source of the fire. The location of high-temperature zones changes with variations in the tunnel curvature radius, resulting in different characteristics of smoke distribution within the tunnel.  
When the radius of curvature of the tunnel is less than 1000 m, there is a more pronounced disparity in smoke distribution characteristics between curved and straight tunnels. As the radius of curvature of the tunnel exceeds 1000 m, smoke distribution within curved tunnels gradually aligns with that within straight tunnels.  
Because of the distinctive smoke distribution characteristics in curved tunnels, a comparison of fire source locations reveals that when the fire source vehicle is positioned in the inner lane of the tunnel, it poses a higher risk for personal evacuation. Consequently, through an analysis and comparison of temperature, visibility, and CO concentration distribution on both the inner and outer side walls across different curvature radii of tunnel curves, it is determined that the adit for people passing should be located on the inner side of the tunnel.  
For personal evacuation within the core area of the fire source, considering the impact of high-temperature smoke distribution characteristics resulting from longitudinal ventilation in curved tunnels, we recommend initially conducting evacuations along the tunnel's central line. Subsequently, individuals should be guided along the inner pedestrian lane toward the adit for people passing away from the core area of the fire source.  
Conceptualization, Y.C. and Z.L.; methodology, Y.C.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Z.L.; resources, Z.L.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Z.L.; visualization, Y.C.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41461016 and grant number 41761015.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Test Methods  
### Materials  
70# Base bitumen (SK Corporation, Seoul, South Korea), basalt aggregates (Jinshan Songhe Aggregates Company, Yidu, China), limestone filler (Malignag Aggregates, Jinmen, China), steel slag and steel fibers were used in this research to produce different asphalt mixtures. The basic properties of the bitumen tested according to Chinese Standard test method of bitumen (JTG E20-2011) are shown in Table 1. The specific gravity of coarse and fine aggregates used in this paper is 2.951 g/cm\({}^{3}\) and 2.877 g/cm\({}^{3}\) respectively.  
Steel slag and steel fibers were used as conductive materials in this paper. Steel slag was used to replace coarse aggregates partially. The steel slag used was produced by Wuhan Iron industry (Wuhan, China). The test method of coarse aggregate was used to evaluate the properties of steel slag. The specific gravity of steel slag was 3.396 g/cm\({}^{3}\), and the water absorption was 3.26%. The Los Angeles abrasion and crush values of steel slag were 12.6% and 13.5% respectively. Chinese standard test method for effect of water on bituminous coated aggregate using boiling water (80 \({}^{\circ}\)C and 30 min, similar to AASHTO Guide) was used to determine the adhesion property between asphalt and steel slag, and the adhesion grade of asphalt and steel slag was 5, which is the best adhesion grade in the Chinese specification. Steel fiber used in this paper was produced by Shanghai Odika metal materials company (Shanghai, China). The equivalent diameter of steel fibers ranged from 70 \(\upmu\)m to 130 \(\upmu\)m and the average length was 4.2 mm. The recommended steel fiber content 6.0% (by volume of bitumen) was used in this research [19].  
\begin{table}
\begin{tabular}{c c c} \hline
**Properties** & **Results** & **Technique Index** \\ \hline Penetration (25 \({}^{\circ}\)C, 100 g, 5 s) (0.1 mm) & 62.3 & 60–80 \\ Softening point (ring \& ball) (\({}^{\circ}\)C) & 47.8 & 44–54 \\ Ductility (5 cm/min, 15 \({}^{\circ}\)C) (cm) & \textgreater{}100 & \textgreater{}100 \\ Density (g/cm\({}^{3}\)) & 1.034 & - \\ \hline \end{tabular}
\end{table}
Table 1: Properties of 70# base bitumen.
## 3 Experimental Results
### Networking Training  
All the subnetworks including the GCM-based residual subnetwork, ISMP, IRS, and SARDS were comprised of convolution and ReLU layers. Hereinafter, a pair comprising convolution and ReLU layers is known as a convolution block. In the subnetworks, \(m\) filters measuring \(5\times 5\times c\) were used in the convolutional layers. Here, \(c\) represents the number of input channels. Table 1 shows the number of filters and channels used in the convolutional layers. In the input layer of the SARDS, \(c\) was set to 3 because three input channels (the base layer, the Laplacian map, and the halftoned image) were input to the input layer. The filters were initialized using a random number generator. The number of convolution blocks used in the GCM-based residual subnetwork, IRS, and SARDS was set to 16. The number of convolution blocks used in the ISMP except the IRS was 6. In other words, ISMP uses six more convolution blocks than IRS. To update the convolution filters, the mini-batch gradient descent algorithm [43] was used. The epoch number was 200, and the batch size was 64. Each epoch involved 1000 backpropagation iterations. The learning rate began at \(10^{-5}\) and decreased linearly every 50 epochs to \(10^{-6}\). All loss functions were modeled by the \(l_{2}\) norm.
# Feasibility of LoRa for Smart Home Indoor Localization
## 4 Discussion
### Localization  
In our work, we discuss 2D localization with LoRa. In some scenarios (e.g., multi-floor buildings), it might be of interest to estimate the elevation of tags. In such cases, we need to install more LoRa nodes with different elevations to be able to localize tags in 3D.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Threat Model and Assumptions
The NOC provides a central authentication service. It comprises a sufficiently large server with a fail-over configuration and able to maintain the state of all of the devices on the network. Given the nature of the service requirement of the smart meters in the smart grid,all authentication attempts, except the one at start-up upon installation, must be approved before the NOC sends an authentication response to the node requesting authentication. * The NOC maintains a history of the metadata (originator-ID, timestamp, group-ID) over a sufficiently long period to derive statistics, such as message arrival epochs, message arrival times, inter-message times, message size, and activity profiles so that it knows when it can expect the next packet from a specific ID. Such a history is essential to detect malicious attack traffic since our scheme does not require the devices on the network to be time synchronized.