esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis and Discussion
### Effectiveness of MDFN  
To validate the effectiveness of the proposed MDFN method, we compared it with three other feedforward neural networks: (1) Traditional Feedforward Network (FN) [59], (2) Gated Deep Feedforward Network (GDFN) [60], and (3) Multi-Scale Feedforward Network (MSFN) [42]. We conducted quantitative analysis on the dataset, and the results are presented in Table 5. GDFN introduces a gating mechanism to achieve performance advantages, but it still does not fully consider the learning of multi-scale knowledge. MSFN improves image restoration performance by integrating and fusing local features from different scales, but the impact of irrelevant information is not fully addressed.  
Our proposed MDFN, by incorporating parameter matrices and a frequency-domain combination on top of multi-scale features, achieves a superior gating-like information selection mechanism. As seen in Table 5, MDFN achieves the best performance in evaluation metrics, achieving significant numerical gains compared to MSFN.
###### Abstract  
Low-cost technology devices, such as smartphones (SPs) and smart watches (SWs), are widely used today to monitor various health effects and environmental risk factors associated with them. However, the efficacy of using these devices as monitoring tools is largely unknown. The present study attempts to narrow this knowledge gap by reviewing recent studies in which low-cost technological tools were used to monitor sleep and associated environmental risk factors. The study focuses on peer-refereed articles that appear in three major scientific databases, Web of Science, Scopus, and ScienceDirect, and were published between 2002 and 2022. Of the 15,000+ records retrieved from these databases by the systematic literature review (PRISMA) search, 15 studies were identified as the most relevant and consequently analyzed. The analysis shows that nighttime light pollution and noise are environmental factors that are most commonly monitored by low-cost technology tools (eight studies), followed by temperature monitoring (seven studies), humidity monitoring (seven studies), and CO\({}_{2}\) monitoring (four studies). In eight studies, tandems of SPs and SWs were used to monitor sleep, while in six studies, data obtained from SPs and SWs were compared with records obtained from conventional monitoring devices. In general, SP and SW measurements were found to be fairly accurate for monitoring sleep and light pollution and less accurate for monitoring noise. At the same time, no studies conducted to date and analyzed in this review demonstrated the effectiveness of SPs and SWs in monitoring ambient temperature, humidity, and air pressure. Our general conclusion is that although SPs and SWs often lack the precision of professional instruments, they can nevertheless be used for large-scale field research and citizen science initiatives, while their feasibility and effectiveness for monitoring several environmental attributes have yet to be determined.  
sleep; environmental risk factors; low-cost technology tools; smartphones (SPs); smart watches (SWs); systematic review of the literature
## 6 Results and Discussion
### Discussion  
This study extracts MFCC features from Arabic audio data and identifies the dialects using pre-trained speech classification models. The classification models include ANN, 1D CNN, and 2D CNN. The ANN model with random oversampler augmentation outperforms the 1D CNN and 2D CNN with a test accuracy of 94.33%. These models were also compared with AraBERT, a transformer-based model, which had a testing accuracy of 90.76%, lower than our proposed models. Other research studies on the identification of Arabic dialects using a variety of machine learning models--such as LSTM, KNN, CNN, and RNN-CNN--and datasets are presented in Table 6. The accuracy range varies across the studies, demonstrating how difficult it is to identify Arabic dialects. However, larger datasets and recent developments in deep learning methods have improved accuracy. The approach suggested in this study has the highest reported accuracy (95.5%) and makes use of a model with RandomOverSampler augmentation and a custom dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Dataset** & **Model** & **Accuracy** \\ \hline Automatic Arabic Dialect & & & & \\ Classification Using Deep & 2018 & Arabic Online & & \\ Learning [2] & & Commentary (AOC) & & & \\ Automatic Arabic Dialect & & & & \\ Identification Using Deep & 2022 & Linguistic Data & Multi-scale product & \\ Learning [5] & & Consortium (LDC) & (MP) & 76.00\% \\ Arabic Dialect & & & & \\ Identification Using & 2022 & ADI17 & KNN & 76.00\% \\ Different Machine Learning & & & & \\ Methods [10] & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of model performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Precision** & **Recall** & **F1-Score** \\ \hline Class 0 & 0.9468 & 0.9920 & 0.9689 \\ Class 1 & 0.7272 & 0.2758 & 0.4000 \\ accuracy & 0.9408 & 0.9408 & 0.9408 \\ macro avg & 0.8370 & 0.6339 & 0.6844 \\ weighted avg & 0.9311 & 0.9408 & 0.9282 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification report for the best model (ANN with RandomOverSampler augmentation).
## 5 Discussion
### GF-5 Applicability to \(Z_{sd}\) Retrieval  
The above experiments show the possibility of retrieving water clarity using GF-5 AHSI imagery. Compared with multispectral instruments (e.g., MODIS and MERIS), AHSI data has the following advantages for water clarity monitoring: (1) its high spectral resolution and continuous spectral range provides a selection of numerous bands; and (2) its spatial resolution is higher than most other instruments used for water monitoring, such that it can be used in small or medium-sized water bodies. In addition, as AHSI has the same spatial resolution as the Landsat series, its high spectral resolution may yield \(Z_{sd}\) with higher accuracies. For example, the estimated \(Z_{sd}\) using the \(QAA_{V5}\)-based SAM on the Landsat-8 imagery had MREs of 12.86%, 28.83%, and 31.17% for the Nav Reservoir in the in situ \(Z_{sd}\) ranges of 2.29-4.80, 2.45-4.65, and 1.91-3.80 m, respectively [37]; the Ibitinga Reservoir received a MRE of 34.6% using the same method on the Landsat-8 image for a water clarity between 1.8-2.6 m [39]; and the MRE of derived \(Z_{sd}\) from the Landsat-8 data was 64% for the Boston Harbor, which had a water clarity between 0.5 and 4 m [40]. For the comparable \(Z_{sd}\) range (i.e., 0.3-4.5 m), AHSI achieved generally higher accuracy with a MRE of 25.3% despite its approximate \(R_{rs}\) values.  
However, the GF-5 satellite has a long revisit period due to its relatively narrow swath, which limits its implementation for dynamic monitoring. With planned launches of other hyperspectral satellites, a hyperspectral satellite constellation can be established to shorten the revisit time, thereby promoting the advantages of water clarity estimations using the GF-5 satellite, as well as monitoring for other water quality parameters.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 5 Results and Discussion
### Wafer Warpage under Different Processing Conditions  
#### 5.3.1 The Influence of Wafer Thickness on Warpage  
With the development of technologies such as micro substrate substrates, laser substrates, and optical components, the thickness of sapphire wafers continues to decrease. Therefore, this section explores the relationship between wafer thickness and warpage. The finite element model performs simulation calculations based on changes in wafer thickness by setting the wafer thickness as 200 \(\upmu\)m, 300 \(\upmu\)m, 400 \(\upmu\)m, 500 \(\upmu\)m, and 600 \(\upmu\)m, respectively. Figure 13 shows the relationship between different thicknesses of sapphire wafers and warpage, and the wire saw speed is 1200 m/min, the feed rate is 0.1 mm/min, and the diamond wire diameter is 0.28 mm.  
It can be seen that as the wafer thickness increases, the wafer warpage decreases, and the decreasing trend of warpage gradually slows down. The reason for this is that, on the one hand, under the same sawing process conditions, different wafer thicknesses have consistent natural convection heat dissipation capabilities, but thicker wafers have weaker thermal conductivity. On the other hand, the thinner the wafer, the lower its stiffness, and the weaker its ability to resist deformation. Temperature changes cause more pronounced  
Figure 12: Comparison between simulated and experimentally measured values of wafer warpage.  
Figure 13: The effect of sapphire wafer thickness on warpage.  
warping and deformation of the wafers. Under the trend of continuous thinning of wafers, optimizing the sawing process to improve the warpage problem of thin wafers is a challenge for diamond wire sawing processing.
## 2 Materials  
Data preparation required a point cloud processing tool. In this paper, Framework and Robust Algorithms for Models of Extreme Size (FRAMES) software was used. FRAMES is a complex environment developed by the Warsaw University of Technology using the C++ programming language. One of the advantages of this application is the ease of adding custom plugins with dedicated algorithms for 3D point cloud processing. The Microsoft Visual Studio 2017 was used as an editor and development platform.  
Machine learning-aided landmark recognition is divided into two main stages: detection and classification. The first detection stage is responsible for object prediction and region proposal. In this paper, a YOLO v.3, 106-layered convolutional neural network [20] was implemented. The consecutive classification process was covered with a ResNet-152, which is a 152-layered residual network. The implementation of skip connections enabled deeper learning without modification of the input [21]. The architecture of the ResNet-152 is presented in Figure 1.  
Both convolutional neural networks were implemented using the Python programming language [22] supported by the TensorFlow [23] library. A manual training set annotation was performed with the help of the VGG Image Annotator Tool (VIA) [24].  
For the deep-learning purposes, a dataset containing 220 3D body scans with the known locations of the body's landmarks was used \(-\)200 for training (148 male and 52 female) and 20 (10 male and 10 female) for validation and testing. Volunteers' age ranged from 22 to 55 years old, weight from 55 to 105 kg, and height from 150 to 205 cm. In order to ensure satisfying results despite using datasets of significantly limited quantity, transfer learning [25] was applied using the Microsoft COCO dataset [26]. The transferred knowledge pertained to the capability of object segmentation from the background and shape recognition. This method utilized precalculated weights for learning time reduction and accuracy enhancement. All participants were asked to take standing poses with arms and legs spread [27]. Scans were captured using the multi-directional structured light method [28]. The data were acquired in the form of a 3D point cloud in Euclidian space. A marked sample scan is shown in Figure 2.  
Figure 1: Architecture diagram of the ResNet-152 convolutional neural network (CNN) used.  
Implemented neural networks were trained to detect and classify 22 standardized landmarks [29; 30; 31]. Abbreviations, names and norm references of utilized characteristic points are presented in Table 1.
# A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?
## 3 Canada's Contact Tracing Application  
A few months after the beginning of the pandemic, most provinces in Canada acknowledged that the virus was here to stay. The first two years, however, have been a very different story, as each month garnered a new spike in COVID-19 cases and further restrictions to prevent its spread. One of these prevention methods was CTAs, which, although not widely adopted within Canada, did exist to lessen the spread of COVID-19. We now go into more detail on the methodology behind the Canadian CTA and its resourcefulness during the pandemic.  
Originally, contact tracing was conducted by a team of public health experts, tracking individuals that have been infected by the virus or have been in contact with those that have been infected. Automated CTAs attempt to replicate this by using geolocation to track an individual's whereabouts while they were within public spaces while infected and using notifications to alert others to avoid these spaces. CTAs replace the intensive manual labor required to track millions of people. To accomplish this, however, they require the active participation of the majority of the population to effectively track those infected with the virus.  
\"COVID Alert\" is Canada's contact tracing application that the federal government of Canada launched on 31 July 2020. Participation was voluntary, and the collection of personal data was governed by Canada's Privacy Act [19]. The app leverages Bluetooth technology and employs Apple\(\backslash\)Google API, which is a decentralized architecture. Each user receives and broadcasts randomly generated IDs from and to the other users within the user's vicinity. Users who test positive for COVID-19 can anonymously notify other users of possible exposure.  
The app collects and stores randomly generated IDs on users' phones for 14 days. In addition, IDs of other app users' phones within a certain distance will also be stored to identify potentially infected users in the future. These random IDs are stored only on users' phones and not with any governmental authorities. Moreover, the app does not collect or store data about a user's identity, location, name, address, phone contact, or health information. All random IDs collected by COVID Alert are deleted after 14 days. In addition, users can delete the app from their phones and wait for 14 days until the data be automatically deleted. Users can also choose to delete the Exposure Logs manually [20].  
Moreover, the user's consent is required to disclose information about a positive COVID-19 case. Once the consent is collected, the COVID Alert app immediately notifies potentially exposed users. In other words, the app sends out alerts to users that have spent a minimum of fifteen minutes within a two-meter distance of an infected user.  
However, COVID Alert has been downloaded about 6.86 million times and reported about 57 thousand cases [1], which are about 18 percent of Canada's population and less than 2 percent of total positive COVID-19 cases in Canada.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
###### Abstract  
The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability.  
blockchain; satellite networks; image storage model; verifiability 2020 12 2861 1  
[PERSON], [PERSON], [PERSON], [PERSON], P. SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks. _Mathematics_**2024**, _12_, 2861. [[https://doi.org/10.3390/math12182861](https://doi.org/10.3390/math12182861)]([https://doi.org/10.3390/math12182861](https://doi.org/10.3390/math12182861))  
1
# Secure Voting Website Using Ethereum and Smart Contracts
## 4 Proposed System
### Data Flow  
The data flow in a blockchain voting system is described as follows:  
* User Registration: The first step in the data flow of a blockchain voting system is the user registration process. Users must register by providing basic information such as their name, address, and date of birth. This information is stored in the user database to verify the user's identity during voting.
* Election Creation: Once the users have registered, the administrator can create an election by setting the start and end dates, the list of candidates contesting the election, and other relevant details. This information is stored in the election database.
* Voting: The user logs into the voting application and selects the candidate during the voting process. The vote is then encrypted using the user's private key and stored in the blockchain. This ensures that the vote is secure and cannot be tampered with.
* Vote Counting: The votes are counted once the voting period ends. The smart contract deployed on the blockchain tallies the votes and declares the winner. The results are stored in the blockchain and can be viewed by anyone.
* Verification: The blockchain voting system allows voters to verify that their vote was recorded correctly. The user can use their private key to decrypt their vote and verify that it was registered correctly. This ensures the integrity of the voting process.  
The data flow in a blockchain voting system is designed to be transparent, secure, and tamper-proof. Blockchain technology ensures that the voting process is fast and cannot be manipulated by any individual or group. The system's transparency allows for easy verification of the results, which helps build trust in voting [8]. Overall, the data flow of a blockchain voting system is a critical component of the system design. It ensures that the system is secure, transparent, and tamper-proof, which are essential for maintaining the integrity of the voting process.
# The Wettability and Numerical Model of Different Silicon Microstructural Surfaces
## 4 Analysis of the Numerical Results
### The Simplified Waveform of the Microstructural Surface  
Figure 14 presents the relationship between the simulated contact angles of the three different waveforms with the area ratio, in which the black dots represent the experimental results. The simulated contact angles of the three kinds of wave surfaces are well in agreement with the contact angle of the experimental measurement, that is, the macroscopic contact angle increased following an increase of the area ratio. As shown in the experimental results, the sample surfaces were easily covered by Si\({}_{\mathrm{x}}\)O\({}_{\mathrm{y}}\)F\({}_{\mathrm{z}}\), while the ratio of the gas mixture was one-to-one. At the same time, the chemical characteristics of the surface changed, which resulted in an augmented contact angle trend. When gas mixing ratio increased, corrosion dominated and the surface of the samples were covered by Si\({}_{\mathrm{x}}\)O\({}_{\mathrm{y}}\)F\({}_{\mathrm{z}}\). Consequently, the size of the contact angle was dependent on the morphology of the samples. In this work, local surfaces in the simulation were regarded as the initial surface and the effects to the local contact angle were neglected. Therefore, the experimental results of sample 2 were not considered in the preparation of the numerical simulations.  
The simulation results indicated that the variation of contact angle on the surface of the different waveforms varied greatly with the area ratio. In particular, the contact angle of the rectangle surface (w = \(\lambda/2\)) was the largest, followed by the contact angle of the trapezium surface (w = \(\lambda/4\)), and lastly by the contact angle of the triangle surface (w = 0). The experimental contact angle was in good agreement with the surface morphology of w = 0, because the triangular waveform was closer to the actual surface topography of the sample as compared to the other two kinds of morphology and can best represent the characteristics of the real sample surface.
## 4 Energy Consumption Model
### Photovoltaic Cell  
Energy harvesting was achieved by using a photovoltaic cell (PVC) that converts light radiation into electrical energy. The CTT-1000a contains an 80 mm \(\times\) 40 mm panel with a nominal 5 V, 150 mA source. Sourced current from the PVC was modeled using a second-order polynomial curve fit, of the form \(I(k)=ak^{2}+bk+c\), where the coefficients were found by analyzing a moving average of 31 days of PVC current data from a field-deployed CTT-1000a tracking device. Empirical data averages of 10.5 h for sunlight duration, and a maximum current draw of 10 mA, were used with the MATLAB function polyfit to generate coefficients for \(a\), \(b\), and \(c\). Equation (1) shows modeled current-generated \(I(k)\) based on 10.5 h day using the values of sunrise at 6:00 a.m., sunset at 16:30 p.m., and a maximum current draw of 10 mA. The PVC model included the ability to adjust noise for simulation of intermittent cloud cover or activity outside of direct sunlight and maximum intensity for different seasons and parts of the world.  
\[I(k)=-2.7e^{-8}k^{2}+2.21e^{-3}k-35.1 \tag{1}\]  
A noise component similar to the device used in [7] of the sourced PVC current was introduced to model factors such as cloud cover, partial PVC obstruction, or lack of sunlight due to foliage. Equation (2) shows noise current \(I_{Noise}\), between a lower \(I_{Min}\) and upper \(I_{Max}\) bound, where these limits represent \(\pm\)20% of \(I(k)\) from Equation (1), which is similar to literature describing modeling of energy-harvesting PVCs [37].  
\[I_{Noise}=I_{Min}+(I_{Max}-I_{Min})\times Rand(0,1) \tag{2}\]  
PVC current generation was modeled for a period of 24 h at a time step of 1 s as seen in Figure 3 to examine energy harvesting in detail for a single day. Energy harvested by the model was zero outside of daylight hours.  
Figure 3: Photovoltaic cell (PVC) model with noise for current generated from solar radiation.