esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study investigated the correlations between different CT dose indices, namely CTDI\({}_{\text{vol}}\), DLP, ED and SSDE as output variables, and patient-related parameters, namely BMI, weight and age as input variables. Simple and multiple linear regression models and statistical tests were utilized to explore potential correlations. The study also examined the correlations between the CT dose indices themselves in different CT exams.  
Using linear regression models, the study found that all CT dose indices correlated with BMI and weight in all CT exams with varying degrees. Age did not display any significant correlation with CT dose indices across all CT exams. Multiple linear regression models exhibited good relationships between input and output variables at least as much as the best respective simple linear regression models in all CT exams. Furthermore, the study investigated the relationships between the different CT indices and found that a strong linear relationship was apparent between CTDI\({}_{\text{vol}}\) and DLP compared to the relationships between each pair of the other dose indices. However, all dose indices exhibited statistically significant relationships. In conclusion, this study helps in better understanding how patient-specific parameters impact the reported CT dose indices and individuals' absorbed doses. Moreover, our observations are instrumental in the radiology field of medicine, as every radiology department is dedicated to optimizing radiation exposure while obtaining an adequate diagnosable image quality.  
Conceptualization, S.D. and E.D.; data curation, E.D.; formal analysis, M.A. and S.D.; investigation, M.A., S.E.H., S.D. and E.D.; methodology, S.D.; project administration, S.D.; resources, S.D. and E.D.; software, M.A., S.E.H. and A.C.; supervision, S.D.; validation, M.A., S.E.H. and A.C.; visualization, M.A. and S.E.H.; writing--original draft, M.A. and S.E.H.; writing--review and editing, M.A., S.D. and E.D. All authors have read and agreed to the published version of the manuscript.  
The work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah [Award #: OAPCEN-1410-E00274]. This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah.  
The retrospective study was approved by the Ethics and Research Committee of the University Hospital Sharjah on 19 June 2017 (Ref. UHS-HERC-030-07062017).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
M.A. and S.E.H. would like to thank the Biomedical Engineering Graduate Program at the American University of Sharjah (AUS) for funding their graduate studies.  
The authors declare no conflict of interest.
## 4 Design Generation and User Evaluation
### User Evaluation  
The online questionnaire assessment method is used to verify the perceived needs of the ordering interface design program, and a quantitative user perception assessment system is constructed through the questionnaire star platform [55]. The data collection process implements a multi-platform stratified sampling strategy, and the questionnaire links are accurately placed on social media (WeChat, Weibo, Xiaohongshu) and professional design communities (Zhanku, Pinterest, Behance), effectively covering the user groups aged 18-65 years old with interface cognitive abilities. After filtering using the screening criteria (no visual perception impairment, experience in operating mobile terminals), a total of 135 valid samples were obtained (the validity rate of the questionnaire was 95.5%), as shown in Figure 10. In the \"operation and function\" scheme, the scores of complexity-simplicity, discomfort-comfort, ambiguity-intuition, and distraction were relatively high, all above 8. In addition, the vocabulary scores of \"Visual and Emotional Impact \"and \"Interaction and Feedback Experience \"are clearly directional. The evaluation results show that users' satisfaction with these three design solutions is high, indicating that all of these solutions can effectively meet users' perceived needs for interface design. This result verifies the feasibility and scientificity of the key input statements, which reduces the randomness of MidJourney's output and increases the controllability of the results at the same time. This reduces the randomness of MidJourney's diagrams and increases the controllability of the results, allowing us to calculate the design elements' weighted summation formula and optimize the design based on the results of these calculations.  
Figure 10: User emotional evaluation chart. Source: own processing.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
###### Abstract  
The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was evaluated on a SARS-CoV-2 CT scan dataset, demonstrating superior performance across all metrics compared to baseline models. The model achieved an accuracy of 95.98%, precision of 96.07%, recall of 96.01%, F1-score of 95.98%, and AUC of 98.69%, outperforming existing approaches. These results indicate that ViTGNN is an effective diagnostic tool that can be applied beyond COVID-19 detection to other medical imaging tasks.  
COVID-19; SARS-CoV-2; CT scans; convolutional neural network; vision transformers; graph neural networks; feature extraction Article
## 3 Materials and Methods
### Data Extraction: LangChain Integration  
An easy method for implementing RAG is to employ LangChain (0.2.8) [31], a powerful framework that integrates external tools to create an environment. This subsection details the implementation of RAG and a data extraction technique that incorporates LangChain (0.2.8). The process involves the following steps:  
* Extract data from the SQL database: In this step, relevant data are extracted from the user information table through queries. This includes information related to documents, such as those tagged by the user.
* Chunking and embedding: The extracted data are partitioned into chunks using LangChain's integrated framework and sent to a vector database (retriever), where embeddings are created. These embeddings convert the document content into high-dimensional vectors, improving information retrieval and matching.
* Generating answers: When a user inputs a prompt into the system, the relevant context is retrieved from the stored vector database and used as input to generate the optimal answer. LangChain (0.2.8) manages the flow used in this process.  
LangChain (0.2.8) is a software development kit that simplifies the integration of LLMs and their applications and is becoming increasingly important as the use of LLMs is increasing. It can segment, combine, and filter documents. The data are collected from an established SQL database through an API, returned in the JavaScript Object Notation (JSON) format, and structured as key-value pairs, as illustrated in Figure 3. The unique identifier number corresponds to a particular SQL database table and generates user information in the form of titles and tag names. The main information used to build a personalized database is the number of entries that contain tagging information, such as keywords, set by individuals to categorize documents. We call this a \"TB Search\" and, as mentioned earlier, it is available as an Internet web service. In practice, a tagging box is implemented as a hyperlink to reference personalization information.  
Figure 4 shows that Context, which stores the contexts created or referenced by individuals and extracted from the SQL database, contains most of the document content, and based on this information, it sets the maximum length of the document to 1000 characters and splits the document for processing. The JSON-based TextSplitter directly takes the extracted data as input, selectively extracts and combines the required data, and returns  
Figure 3: User data extracted in the JavaScript Object Notation JSON format in the case of UserID as 2190.  
them in the JSON format. The split document is divided into chunks of a certain size, each designed to be processed independently. After splitting, the documents are embedded via the text-embedding-ada-002 model of the OpenAI API and stored in a vector database. These data help place the most relevant information at the front of the QA prompt after a search.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 3 The Proposed Scheme
### Login Phase  
In order to login to the \(GWN_{k}\) and access the data from the \(Sn_{j}\), the user \(U_{i}\) needs to execute the following steps:  
After inserting the SC into the card reader of a specific terminal device, \(U_{i}\) enters its identity \(id_{i}\) and password \(pw_{i}\), computes \(M_{3}^{\prime}=h(id_{i}\parallel M_{0}\parallel pw_{i})mod\ n_{0}\), and checks whether \(M_{3}^{\prime}=M_{3}\) is correct or not. If yes, the SC generates two random numbers, \(rug\) and \(ru\), and computes \(M_{2}=M_{1}\bigoplus h(id_{i}\parallel pw_{i})\), \(K_{U}=T_{ru}(x)mod\ q\), \(K_{U-G}=T_{ru}(M_{0})\ mod\ q\), \(M_{4}=h(T_{1}\parallel M_{2}\parallel rug\parallel id_{i})\), and \(M_{5}=E_{K_{U-G}}\big{(}id_{i}\parallel Sid_{j}\parallel rug\parallel M_{4} \big{)}\), where \(T_{1}\) is the current timestamp. Then, the SC sends the login request message, \(\{K_{U},\ M_{5},\ T_{1}\}\), to the \(GWN_{k}\).
## 4 Proposed Method
### Feature-Selection-Based AHA-AO  
In this section, the main stages of the proposed FS approach are introduced, as shown in Figure 4. In the method developed here, which is named AHA-AO, the aim is to enhance the exploitation ability of AHA by using the AO method.  
The AHA-AO begins by splitting the input dataset into training and testing sets, which represent 70% and 30% of the instances of input data. Then the initial population \(X\) is constructed by using the following formula:  
\[X_{j}=rand\times(UB-LB)+LB,\,j=1,2,...,N,\,i=1,2,...,D \tag{22}\]  
where \(N\) shows the number of solutions. \(D\) stands for the dimension of each \(X_{j}\). \(LB\) and \(UB\) refer to the boundaries of the search domain.  
Figure 4: The architecture of the proposed AHA-AO for the FS problem.  
Thereafter, the Boolean form of \(X_{i}\) is obtained using Equation (23).  
\[BX_{ij}=\left\{\begin{array}{ll}1&if\ X_{ij}>0.5\\ 0&otherwise\end{array}\right. \tag{23}\]  
Thereafter, the fitness value of \(X_{i}\) is computed according to its binary form \(BX_{i}\) and the training set by using the following formula:  
\[Fit_{i}=\lambda\times\gamma_{i}+(1-\lambda)\times\left(\frac{|BX_{i}|}{Dim} \right), \tag{24}\]  
In Equation (24), \((\frac{|BX_{i}|}{Dim})\) stands for the ratio of features selected from the training set. \(\gamma_{i}\) denotes the error classification value obtained using \(KNN\) (at \(K=5\)). In this study, the KNN algorithm is applied because it is simple and easy to implement, and it is considered more stable than other classification techniques that have few parameters. The parameter \(\lambda\) represents the weight value for \((\frac{|BX_{i}|}{Dim})\) and \(\gamma_{i}\).  
The next step is the allocation of the best fitness value \(Fit_{b}\) and its corresponding solution \(X_{b}\). After that, the current population \(X\) is updated in two stages; during the first stage (i.e., exploration), the operators of the AHA are used to update \(X\), as shown in Equations (14)-(18). However, during the second stage (i.e., exploitation), the integration of the operators of the AHA and AO is applied to update \(X\). This is achieved using replacing the exploitation phase of the AHA with the exploitation phase of AO. Therefore, the main weakness of the AHA results from this phase. This process is formulated using the Equations (4)-(9).  
If the conditions have been met, the next step in the AHA-AO is to return \(X_{new}\). Alternatively, the upgrading steps are repeated.  
Finally, the dimension of the testing set is reduced according to the binary form of \(X_{b}\), and the KNN algorithm is used to assess the quality of this dimension reduction process that is achieved by using the AHA-AO technique.
## 2 Study Area and Data
### Data Sources and Preprocessing  
The \"Zhuhai-1\" hyperspectral satellite, launched on 26 April 2018, is a commercial satellite launched and operated by Zhuhai Orbit Aerospace Science and Technology Company Limited, and its hyperspectral data contain a total of 32 bands, with spectral wavelengths ranging from 400 nm to 1000 nm. The satellite provides a spatial resolution of 10 m and a spectral resolution of 2.5 nm [12]. The data used in this study are hyperspectral remote sensing images captured by the Zhuhai-1 (OHS) hyperspectral group 02 satellite, with a data processing level of L1 (Table 2). The PIE-Hypp6.3 hyperspectral image data processing software was employed to preprocess the Zhuhai-1 hyperspectral image data, and the preprocessing process included radiometric calibration, 6S atmospheric correction, and orthometric correction.  
From September 2020 to September 2022, field research and sampling were conducted for five vegetation species in the study area. The coordinates of the corresponding points were recorded using GPS technology, and, in some instances, multispectral images of some points were captured by a UAV to assist in the selection of sample points during the process of vegetation image classification. The number of sample points obtained varied among different vegetation types based on factors such as their respective growing areas and the  
Figure 1: Location of the study area.  
level of sampling difficulty, among which there were 22 sample points for _Phragmites_, 24 for _Suaeda glauc_, 23 for _Typha orientalis Presl_, 12 for _Tamarisk_, and 17 for _Spartina alterniflora_.
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 3 Materials and Methods
### Data Processing
#### 3.3.1 Wiener Filtering  
The primary objective of the Wiener filter is to reduce the error between the signal's true value and the predicted result. The Wiener filter's basic operation is shown in Figure 3. The previous signal is 0.025 times the length of the KS signal, the window length is set to 80 ms, and the shift percentage is set to 60%.  
Where \(y(n)\) is the input signal, \(s^{\prime}(n)\) is the output signal, \(s(n)\) is a pure speech signal, and \(d(n)\) is a noise signal, as in the figure above, then the filter formula will be as follows:  
\[s^{\prime}(n)=y(n)\cdot h_{n}=\sum_{m=1}^{M-1}y(n-m)\cdot h_{m} \tag{1}\]  
where, \(h_{m}\) is the filter coefficient and \(M\) is the number of filter taps. The system's filter coefficient can be calculated by minimizing the error:  
\[e(n)=s(n)-s(n) \tag{2}\]
## 3 Methods
### PS Point to Raster Conversion  
The generated 1.25 million PS points are interpolated into 20 m image elements, and since the number and density of PS points are sufficiently high, inverse distance weight (IDW) interpolation is used; the basic principle is:  
Firstly, calculate the distance from the unknown point to all known points.  
\[d_{i}=\sqrt[2]{(x-x_{i})^{2}+(y-y_{i})^{2}} \tag{2}\]  
where \(d_{i}\) is the distance from the unknown point to the \(i\)-th known point, \(x_{i}\) and \(y_{i}\) are the coordinates of the \(i\)-th known point, and \(x\) and \(y\) are the coordinates of the unknown point.  
Then, calculate the weight of unknown points based on distance.  
\[w_{i}=\frac{1/d_{i}}{\sum_{1}^{n}1/d_{i}} \tag{3}\]  
The weight from the unknown point to the \(i\)-th known point is then calculated.  
Finally, calculate the value of the interpolation point.  
\[Z_{0}=\sum_{i=1}^{i=n}w_{i}*Z(x_{i},y_{i}) \tag{4}\]  
where \(Z\) (\(x_{i}\), \(y_{i}\)) is the value of the \(i\)-th known point.  
Each interpolated point can be obtained using the above method.  
Then, the 90-period raster layers are converted into netCDF files, which improves the computational efficiency and enables clearer results. In addition, the surface deformation hazard grading index is formulated with reference to the \"Specifications for risk assessment of geological hazard\", as shown in Table 1 below. This specification was proposed by the Ministry of Natural Resources of the People's Republic of China and is applicable to the risk assessment of geological disasters in China. It stipulates that when the local surface deformation rate is less than 10 mm/yr, it is a low-risk situation of weak deformation development; when the surface deformation rate is 10-30 mm/yr, there is a moderate risk of moderate deformation and weak development; when the surface deformation rate is greater than 30 mm/yr, it is a high-risk situation for strong deformation development. The following analysis focuses on the areas where the surface deformation rate is greater than 10 mm/yr.
# Annealing Process-Induced Microstructural Variation in NiV/B4C Multilayers
## 3 Results and Discussion
### Atomic Force Microscope Analysis  
In Figure 7, comparisons of atomic force microscopy (AFM) measurements of the NiV/B4C multilayers are shown at 340 \({}^{\circ}\)C for different annealing times. To improve the accuracy of statistical information, the surfaces of all samples were measured several times at different spots. The average rms values of the NiV/B4C multilayers were rq \(\approx\) 0.124 nm with annealing time less than 120 min. The rms value for the 120 min annealing sample was rq = 0.359 nm. Significantly, after 120 min of annealing, uniform bright spots appeared on the surface of the multilayer film, which were caused by the crystallization of the multilayer. This was consistent with the XRD results above.  
Figure 6: XRD patterns of the NiV/B4C multilayers annealed under different conditions.  
Figure 7: AFM data of the multilayers annealed at different times: (**a**) as-deposited, (**b**) annealing at 340 \({}^{\circ}\)C for 10 min, (**c**) annealing at 340 \({}^{\circ}\)C for 30 min, (**d**) annealing at 340 \({}^{\circ}\)C for 60 min, (**e**) annealing at 340 \({}^{\circ}\)C for 120 min.
# Investigation of a Solar-Driven Organic Rankine Cycle with Reheating
## 3 Results and Discussion
### Thermodynamic Analysis of the ORCs in the Steady-State Analysis
This fact makes the expanders of the reheating system achieve high performance, and thus the reheating system has higher performance for higher saturation temperatures, a result in accordance with the efficiency of a thermodynamic cycle according to the \"Carnot Cycle\" behavior. It is useful to state that lower condenser temperature increases the efficiency of the reheat cycle, while for the simple cycle, there is a different behavior. More specifically, in low saturation temperatures, both cycles have higher performances for lower condenser temperatures, while for high temperatures, the low condenser temperature reduces the isentropic efficiency and consequently the global system efficiency a lot. The aforementioned analysis indicates that the system with one expander faces critical problems when it has to operate with high-pressure ratios and is thus a limitation of this configuration. Special expander designs are needed in every case in order to achieve suitable efficiency. Figure 4 exhibits the ratio of the ORC thermodynamic efficiencies for the reheating system to the simple system. It was observed that for all the examined operating scenarios, this ratio takes values over 100% and thus the reheating case is a more efficient choice than the conventional simple cycle. The increase of this ratio is more intense in high saturation temperatures and also in lower condenser temperature levels. These results are in accordance with the aforementioned analysis of Figure 3.
## 4 Discussion  
Selecting an appropriate distance and illumination for using the NIR camera is essential and has to be fixed throughout the experiment. As used by [14], the distance from level water to the camera was 140 cm, whereas [17] used a distance of 150 cm. For the experiment, 150 cm was the distance, and the tank used had a depth of 50 cm; in this case, the system can only capture fish on the water's surface. Another limitation is density, but if the system has the required flow water speed, this is not a limitation. The NIR camera has a harmless wavelength between 700 to 1400 nm and can penetrate biological tissues. Additionally, this is used in different fields, for example, to monitor leaf area [26], for iris recognition [27], automobile tasks [28], fire detection [29], detect chemical compounds [30], and finger veins recognition [31].  
As previously mentioned, the Haar classifier is considered in machine learning tools; unlike other tools, such as convolutional works, big data analytics, or the internet of things, the Haar classifier just needs a one pc. For the other tools, an internet connection or cloud space is required. In the experiment, a pass band filter was used to reduce the wavelength, get focus in a specific wavelength, and help to have less range of experimentation.  
The Haar classifiers are mainly used for face detection, but some applications are used for different challenges, such as automotive applications [32; 33], agriculture [34], and cataract detection [35]. Generally, the Haar classifier can be adapted for every application, such as in this work. However, the essential part is the training part, and if this training is complex, the Haar classifier can be used for any application. Regarding this work, the training part can be more complicated, but this implies the waste of resources because, for aquaculture applications, the environment has fish and is less likely to have negative images considered as no fish.  
The mathematical models were an essential part of this experiment. They helped to obtain a relation between length and weight. Models in aquaculture are used for feeding control [36], production in general [37], harvest [38], energy calculation [39], and monitoring culture variables [40], and the use of these models helps the aquaculture system to have a precision culture. However, the most crucial aspect of aquaculture is growth, such as the model used in this experiment. The mathematical model used in the experiment had good results, as shown in Figures 16 and 17.