esa-sceva/satcom-chunk-collection huggingface dataset:
# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 1 Introduction
An AI model is then created, which analyzes and classifies the collected data and uses the results for diagnosis. Users of the application can record cough sounds, and be informed of the probability of being positive for COVID-19. Acoustic features are extracted from the cough sound data, and the model is trained based on these features. Diagnostic studies into the diagnosis of COVID-19 based on cough sounds have attempted to classify the COVID-19 cough sounds based on preliminary studies involving diagnosing asthma [12; 16],  
Figure 1: The trend of COVID-19 confirmed patients around the world [1]. Figure 2: Extent of vaccination in South Korea by type of vaccine [1]. pneumonia [17], or Alzheimer's disease [18] using cough sounds. The success of this work indicates that there are specific characteristics of cough produced by the way COVID-19 affects the respiratory system. In this paper, we propose a method of diagnosing COVID-19-infected people using cough sound data. To extract more information from the cough sound data, we created a feature set by extracting 36 audio features. As well as mel-frequency cepstral coefficients (MFCC) [19] and spectrograms, which have been widely used in the past, spectral centroids, spectral bandwidth, seven spectral contrast features, spectral flatness, spectral roll-off, and 12 chroma features were extracted from cough sound data. Then, using a model based on [13], the spectrogram image was presented as an input to ResNet-50, the feature set was presented as an input to DNN, and then the output values of each model were connected to derive a result. In comparison with other methods, the method proposed in this paper showed the highest performance, with a sensitivity of 93% and a specificity of 94%. The paper is structured as follows. Section 2 introduces related work and Section 3 describes the database and the proposed method. Section 4 describes the experiment method and compares the performance with existing methods to evaluate the proposed method. Section 5 ends with conclusions.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Neural Machine Translation (NMT) Architecture
#### 2.1.1 RNN Based Model  
For Seq2 Seq architecture with RNN models as the encoder and decoder [3, 4, 52, 53, 54, 55], the framework was introduced in the previous section. Given an input sequence \((x_{1},x_{2},\dots,x_{l})\), in the \(i\) th step, the key features of an RNN model are two functions that take element  
Figure 1: Example framework of NMT with LSTM as encoder and decoder [72].  
\(x_{i}\) and a hidden state \(h_{i-1}\) from the last step as input and respectively output \(y_{i}\) for post-processing, as well as a new hidden state \(h_{i}\) for the next step. Basically, each function can simply be a stack of FC layers with non-linear activation. As the encoder and decoder, a basic RNN can tackle input and output sequences of arbitrary length. It encodes and exploits the information from the input sequence in a hidden state. However, in a basic RNN, the hidden state has limited capacity and gradually forgets information obtained from earlier elements of the input sequence, which is known as the long-term dependency problem. For instance, if we pass a long sentence to a basic RNN encoder, the output translation may have the wrong subject. Because the subject is at the beginning of the input and its information vanishes from its hidden state after many updates. Long short-term memory (LSTM) [73] alleviates the problem. It employs the LSTM unit as the function to update the hidden state in RNN structures and introduces the cell state as another variable to store information. At step \(t\), input gate \(i_{t}\), forget gate \(f_{t}\), cell gate \(g_{t}\), and output gate \(o_{t}\) are calculated from input \(x_{t}\) and hidden state \(h_{t-1}\) by corresponding learnable linear transformations with non-linear activation functions. Then the cell state and hidden state are updated as Equations (2) and (3):  
\[c_{t} =f_{t}\odot c_{t-1}+i_{t}\odot g_{t} \tag{2}\] \[h_{t} =o_{t}\odot\tanh(c_{t}) \tag{3}\]  
where \(\odot\) represents element-wise multiplication. Notably, the hidden state \(h_{t}\) can be used by the other functions to produce output \(y_{t}\).  
In the aforementioned RNN models, the encoded information is accumulated in one or two hidden states, and the output sequence is generated according to such hidden states that contain the information of the entire input. However, when producing some part of the output sequence, the context information of the corresponding part in the input is usually more important. For example, suppose the model is translating \"We go to the bank of river\" into Bengali. When it translates \"bank\", besides \"bank\" itself, the encoded information from \"river\" is necessary because it specifies the meaning of \"bank\", while other parts of the input are less relevant.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
## 3 Planar Detection System Using GPU-Based 3D Hough Transform
### 3D Hough Space Generation  
A planar surface in Cartesian coordinates is represented as Equation (1), where the parameters (\(\theta\), \(\varphi\), \(r\)) are shown in Figure 2a. For each point, a serial of variable pairs (\(\theta\), \(\varphi\), \(r\)) are sampled and registered into a 3D Hough space, as shown in Figure 2b.  
\[(\cos(\theta)\sin(\varphi),\sin(\theta)\sin(\varphi),\cos(\varphi),-r)(x,y,z, 1)^{T}=0 \tag{1}\]  
In the 3D Hough space generation process, the radius \(r_{t,i,j}\) is computed based on the horizontal angle \(\theta_{i}\), the vertical angle \(\varphi_{j}\), and the LiDAR point \(p_{t}\). The variables \(i\) and \(j\) are the index values of angle \(\theta\) and \(\varphi\), respectively, in the Hough space. Vector \(a_{ij}\) is a \(3\times 1\) column vector forming three sine and cosine pairs, where \(a_{i,j}=[\cos(\theta_{i})\sin(\varphi_{j}),\sin(\theta_{i})\sin(\varphi_{j}),\cos(\varphi_{j})]^{\mathrm{T}}\), \(I\in[1,I]\), \(j\in[1,J]\). The variable \(t\) is the index value of point \(p_{t}=[x_{t},y_{t},z_{t}]\), \(t\in[1,n]\). The set \(A\) is comprised by vector \(a_{i,j}\), which is defined as vector \(a_{i,j}\in A\), \(A\)=\([a_{1,1},\ldots,a_{i,j},\ldots,a_{i,J}]\), \(i\in[1,I]\), \(j\in[1,J]\).  
\[r_{t,i,j}=p_{t}a_{i,j}=\left[\begin{array}{cc}x_{t}&y_{t}&z_{t}\\ \end{array}\right]\left[\begin{array}{c}\cos(\theta_{i})\sin(\varphi_{j})\\ \sin(\theta_{i})\sin(\varphi_{j})\\ \cos(\varphi_{j})\\ \end{array}\right] \tag{2}\]  
In Cartesian coordinates, the subset point coordinate matrix \(P_{m}\) is formed by the coordinate vectors \(X_{m}\), \(Y_{m}\), and \(Z_{m}\), which consist of a series of \(x\), \(y\), \(z\) point coordinates. The subset \(P_{m}\) is defined as \(P_{m}=[p_{1},\ldots,p_{t},\ldots\ p_{n}]\), \(t\in[1,n]\). The variable \(m\) is the subset index, \(m\in[1,N]\). \(R_{m,i,j}\) is the radius set that is computed by all points in the subset \(P_{m}\) under the angel vector \(a_{i,j}\), as shown in Equation (3),  
Figure 1: Flowchart of the fast planar detection system.  
defined as \(R_{m,i,j}=[r_{1,i,j},\,\ldots,\,r_{i,j},\,\ldots,\,r_{n,i,j}]\). The \(R_{m,i,j}\) computed by \(a_{ij}\) is registered into the sub Hough space \(H_{m}\).  
\[R_{m,i,j}=P_{m}a_{i,j}=\left[\begin{array}{cc}X_{m}&Y_{m}&Z_{m}\end{array} \right]\!a_{i,j}=\left[\begin{array}{cccc}x_{m,1}&y_{m,1}&z_{m,1}\\ \ldots&\ldots&\ldots\\ x_{m,t}&y_{m,t}&z_{m,t}\\ \ldots&\ldots&\ldots\\ x_{m,n}&y_{m,n}&z_{m,n}\end{array}\right]\left[\begin{array}{c}\cos(\theta_ {i})\sin(\varphi_{j})\\ \sin(\theta_{i})\sin(\varphi_{j})\\ \cos(\varphi_{j})\end{array}\right] \tag{3}\]  
This way, Hough space generation is finished after all the point cloud subsets executing the 3 DHT. The value in the accumulated Hough space means that the mapping point counts in the corresponding cell. The corresponding cell indices in three directions (\(\theta\), \(\varphi\), \(r\)) are considered as planar surface parameters in a polar coordinate system.
## 3 Results and Discussion
### PCA and Histogram Analysis
A histogram in Figure 5 aims to present a quantitative analysis, which relates the integrated areas of specific bands to the biocomponent contents inside the cells. The area integrals of each biocomponent (i.e., lipid, protein, and nucleic acid) were calculated from the primary spectra upon baseline correction. The histogram result shows that the mycoplasma-contaminated HepG2 cells contain lesser amounts of lipid and nucleic acid, with higher protein content than the decontaminated HepG2 cells. Figure 5: Histogram representing the mean integral areas of the respective lipid (3000–2800 cm\({}^{-1}\)), protein (1700–1450 cm\({}^{-1}\)), and nucleic acid (1280–900 cm\({}^{-1}\)) regions of mycoplasma-decontaminated and -contaminated cells. Data are presented as mean \(\pm\) SD (n = 3). \({}^{*}\), \(p<0.05\), paired \(t\)-test., \(n_{\rm contaminated}=58\) and \(n_{\rm decontaminated}=67\). The second derivative spectra of protein exhibited the highest discrimination upon mycoplasma contamination, especially at the amide I band, according to PCA. We, therefore, applied a curve-fitting analysis to determine the change of the secondary structure of protein during mycoplasma contamination. The curve-fitting analysis predicts the secondary structures presented under the amide I band (1550-1750 cm\({}^{-1}\)), i.e., as an \(\alpha\)-helix and a \(\beta\)-sheet [20]. In this study, the peaks of the secondary protein structures were simulated and categorized into 5 components consisting of a turn, an \(\alpha\)-helix, and a \(\beta\)-sheet (Figure 6). Our result showed that the percentage of \(\beta\)-sheet (1638 and 1624 cm\({}^{-1}\)) increased significantly, suggesting that not only the content of protein was altered upon mycoplasma contamination, but also the conformation.
## 5 Discussion  
The MICA encouraged the user to focus on the reading task, providing meaningful feedback through a voice, facial expression and head gestures. The feedback was based on the observation of the user during the working session through video analysis and by tracking their interactions with the device interface (touch screen). Through video analysis, the face of the user was constantly observed, and the orientation of the face and gaze of the eyes were estimated. Touch screen data were used to track the reading pace (i.e., how many pages were spanned in the last observation interval). The acquired data were then used to provide adequate and timely feedback to the user.  
The feedback could be provided according to two different paradigms: either every time a significant loss of attention was detected or at predefined milestones, such as at the end of a lesson or a page. The two paradigms produced sensibly different effects. The timely feedback prevented the user from wasting further time due to a distraction, but it may have become annoying when either distractions were detected too frequently or some behavior of the user was wrongfully detected as a loss of attention. On the other hand, cumulative feedback can be provided to make the user more responsible, such as by asking them some questions that assess the level of content comprehension achieved. In most cases, it is not necessary to analyze the provided answer, as the effort needed to answer the question will be sufficient to convince the user that some content review is needed. Currently, no definitive response from the experimental data has emerged as to which paradigm is better for different situations, and therefore, further investigations will be carried out.  
The data acquired while monitoring the user can also be exploited to assess the improvement produced by some treatment administered through the device on which the agent runs. For example, if the device is used to provide some content for cognitive rehabilitation, then the MICA can be used to motivate the user and, at the same time, collect observational data useful for evaluating the improvements introduced by the treatment or, preliminarily, to detect some cognitive problems [56]. Furthermore, the MICA is mobile-oriented, and therefore its architecture adheres to how people are most likely to consume books and other multimedia content (i.e., using mobile devices) [57].  
We organized a simple but effective experimental campaign to evaluate the proposed approach, performing 240 reading tests with 20 different subjects, totaling approximately 52 h of reading time. The reading sessions were designed to produce a sensible level of fatigue in the subjects, since each subject was asked to read 6 excerpts in sequence (with a 1-min stop between consecutive readings) for a total of approximately 78 min for each reading session. The results presented clearly show that the MICA greatly improved the visual attention of the subjects during the reading task.
# Effect of CeO2@GNPs on the Corrosion Properties of 2024 Alloy
## 3 Results  
### Material Characterization  
Figure 2 reveals the TEM and HRTEM analyses of the fabricated 0.5 wt% CeO2@-GNPs/2024 Al composites. The shape of the multilayer structure of the GNPs is clearly shown in Figure 2a,b, where the spacing of its level structures was approximately 0.33 nm. Additionally, the HRTEM image revealed the presence of some granular nanoparticles on the surface of the GNPs. The d-spacing provided by SAED were 0.16 nm and 0.31 nm, corresponding to the (111)\({}_{\text{CeO2}}\) crystal plane of the CeO2 [17]. This phenomenon suggested that the layer of CeO2 particles still remained at the GNPs surface after the hot isostatic pressing process preparation, which was useful for improving the bonding of the matrix with reinforcement as well as enhancing the mechanical properties of the prepared nanocomposites [18; 19].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Cu** & **Mg** & **Fe** & **Si** & **Zn** & **Al** \\ \hline Wt pct & 3.8–5.0 & 1.2–1.8 & 0.5 & 0.5 & 0.25 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the 2024 alloy (wt%).  
Figure 2: (**a**) TEM image of the graphene; (**b**) TEM image of the graphene with a CeO2 nanoparticle layer; (**c**) Selected area electron diffraction of CeO2 nanoparticle layer.  
(SAED) pattern of Fe-Al phase particles
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 3 IoHT Security Landscape
### Security Vulnerabilities in IoHT Implementations
#### 3.1.1 IoT Device's Operating System  
Due to the specialized requirements of IoHT devices and limitations of existing operating systems, specialized operating systems such as RIOT, Contiki, FreeRTOS, and TinyOS have been developed for IoT devices [64; 65]. The constrained computational power, memory, and limited power of IoT devices make them vulnerable to the system and network attacks. Furthermore, constrained resources do not allow the implementation of complex encryption and authentication schemes on these devices as they may significantly consume the computational resources and cause a long delay, resulting in the degraded performance of the regular operation of these devices which is critical, especially for real-time IoT devices. This scenario facilitates attackers in using memory vulnerabilities and compromises the security of such devices [66]. With inadequate resources on lightweight IoT devices, it becomes challenging to implement strong encryption and authentication schemes.
## 4 Results and Discussion
### Evaluation of the Trap Dynamic Sealing Capacity  
#### 4.4.1 Evaluation of Dynamic Sealing of the Cap Rock  
Based on the 3D stress model, combined with the scheme and results of the pilot test, the dynamic sealing performance of the cap rock is usually described using the shearing safety index during the process of dynamic stress field changes.  
\[\chi=1-\frac{(\sigma_{H}+\sigma_{h})/2}{c\cos\phi+(\sigma_{H}+\sigma_{h})\sin \phi/2}=1-\frac{\tau}{\tau^{*}} \tag{5}\]  
where \(\chi\) is the shearing safety index of the cap rock; \(c\) is the cohesion of the cap rock; \(\phi\) is the internal friction angle of the cap rock; \(\tau\) is the current shearing stress of the cap rock, Pa; \(\tau^{*}\) is the maximum shearing stress of the cap rock, Pa.  
The shearing safety index represents the degree to which the cap rock deviates from the critical shearing failure state of the underground storage in a depleted gas reserve at any given moment. When the index is between 0 and 1, it indicates that the cap rock has not yet experienced shear failure, and the larger the value of the shearing safety index of the cap rock, the smaller the risk of shear failure. When the index is equal to 0, it means that the cap rock of the gas storage has reached the critical shear failure state and is no longer able to continue dynamically storing natural gas in the reservoir layer.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Fault** & **Density** & **Fault** & **Fault** & **Clay** & **Vertical** & **Angle** & **Fault** & **Clay** & **Normal** & **Pressure of the** \\
**Name** & **(g/cm3)** & **Depth** & **(m)** & **Thickness** & **Content** & **Throw (m)** & **(\({}^{\circ}\))** & **Content** & **Fault** & **Fault** \\ \hline F2 & 2.561 & 2818.2 & 58 & 0.45 & 31.11 & 71 & 0.84 & 33.03 & 5.13 \\ \hline F5 & 2.573 & 2811.8 & 49.3 & 0.60 & 31.66 & 80 & 0.93 & 35.51 & 4.83 \\ \hline F11 & 2.953 & 2809 & 39.4 & 0.55 & 25.86 & 68 & 0.84 & 45.09 &During the dynamic process of changes in the stress field of the underground gas storage in the depleted gas reserve due to natural gas injection and extraction, the stress state with the minimum shearing safety index of the cap rock was selected for analysis. At this time, the distribution of the shearing safety index of the cap rock is shown in Figure 22, with the global shearing safety index ranging from 0.7 to 0.9, far exceeding the critical value required for integrity (considering safety factors, generally around 0.2). From a planar distribution perspective, in the vicinity of faults and locally complex structural areas, due to the concentration or sudden changes in stress, the changes in the shearing safety index are more noticeable, but the cap rock can still maintain a relatively high shearing safety index. This indicates that the underground gas storage in the depleted gas reservoir can still maintain a strong dynamic sealing of the cap rock.
## 4 Experiment
### Depth Estimation Accuracy Analysis  
#### 4.2.1 Qualitative Experimental Comparison and Analysis  
To verify the algorithm's scene depth recovery effect, this study conducted a qualitative analysis of depth estimation results generated by three depth estimation algorithms in preset indoor and outdoor scenes.  
Figures 12-14 show the depth estimation performance of three algorithms on indoor and outdoor scene datasets, specifically including (1) Overall Effect: The BLADE algorithm estimated extremely sparse depth information in R32-Indoor and HR260-Outdoor, failing to effectively recover object shapes, as shown in the fifth column of Figures 12 and 14. It was inadequate in maintaining depth information completeness and struggled to recover depth information at object edges. In R12-Indoor, BLADE's depth results had the least noise, with a relatively complete overall structure and clear object edge information. The Zeller algorithm recovered relatively complete depth information across three datasets. However, depth recovery at object edges remained poor, producing ghosting effects, as shown in the fourth column of Figures 12 and 14  
Figure 14: Depth estimation results for HR260-Outdoor. The first column shows the original light field images, the second column displays laser scanning point clouds, the third to fifth columns represent depth values generated by Our, Zeller, and BLADE, respectively, with Our and Zeller’s depth values shown in grayscale images, and BLADE’s depth values displayed as RGB images.  
Figure 13: Depth estimation results for R12-Indoor. The first column shows the original light field images, the second column displays laser scanning point clouds, the third to fifth columns represent depth values generated by Our, Zeller, and BLADE, respectively, with Our and Zeller’s depth values shown in grayscale images, and BLADE’s depth values displayed as RGB images.  
(2) Local Detail Recovery: In texture-rich areas such as doll eyes, calibration board corners, and basketballs, Our algorithm clearly showed doll eye contours, letters on shoe boxes, and basketball line textures, as shown in the third column of Figures 13 and 14. In contrast, the other two algorithms failed to clearly display these details, as seen in the fourth and fifth columns of Figures 13 and 14. Additionally, compared to the other two algorithms, Our algorithm had fewer noise points in weak texture areas like running tracks and ground surfaces, as shown in the third column of Figure 14. This is because Our algorithm introduced prior planar information, enabling more effective recovery of weak texture depth information in object surface planar regions.
## 1 Introduction  
The third molar is the most commonly impacted tooth. Several causes can lead to impaction of the mandibular third molars. Eruption can be interfered by local physical barriers such as adjacent teeth, dense bones, excessive soft tissue, and lesions [1]. The prevalence of impacted third molars has been reported in the range of 30.3% to 68.6% [2; 3; 4; 5; 6]. The extraction of the mandibular third molar is one of the most common surgeries in oral and maxillofacial surgery [7; 8]. Complications that can occur when extracting the mandibular third molar include damage to the adjacent teeth, swelling, bleeding, infection, and nerve damage. Among the potential complications associated with removal of the third molar, inferior alveolar nerve (IAN) injury is a representative complication. Several studies have reported an incidence of IAN injury of 0.26-8.4% [9; 10; 11; 12; 13; 14; 15]. Various factors such as the age of the patient, the experience of the surgeon, and the extent and location of the impaction are discussed as the risk factors of the nerve injury [15; 16; 17].  
The most important risk factor for IAN injury is the correlation between the anatomical position of the third molar and the proximity of the IAN [12; 18]. Panoramic radiographs are widely used for initial examination to assess the third molar. However, it is not possible to accurately determine the buccal and lingual positioning of the third molar and nerves on a two-dimensional panoramic radiograph. Therefore, it is not easy to assess the possibility of nerve injury from panoramic radiographs [19]. There have been many studies regarding the correlation between the mandibular third molar and the IAN, and the signs that can predict the possibility of IAN injury using a two-dimensional panoramic radiograph [20]. Previous studies have reported a correlation between some panoramic signals and nerve injury, but the results are not unified [21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. In order to overcome the limitations of panoramic radiographs, the use of cone beam computed tomography (CBCT) has recently increased. CBCT is very useful for the evaluation of impacted mandibular third molars [31] and the proximity of the IAN, and to improve the risk assessment prior to surgery [32].  
Panoramic signals of IAN contacts have shown frequently, however, the occurrence of IAN injury is rare and it is very hard to predict nerve injury. The purpose of this study was to analyze the incidence and risk factors of possible IAN injury after extraction of the mandibular third molars. CBCT and panoramic radiography was used to evaluate the factors that may have affected inferior alveolar nerve injury.