esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The observed FR image in the neighboring view of the stereoscopic image can provide richly detailed information of the scene. Thus, the relativity between views has been utilized to strengthen the particular LR views. [PERSON] et al. [22] proposed an SR method that exploits depth information for the MR multi-view video. On the basis of the available depth maps, their approach enhanced the observed LR image by extracting the high-frequency content from the neighboring FR view. However, the acquisition of the depth maps was not discussed in their work as these are not easy to accurately estimate. In addition, [PERSON] et al. [23] employed the estimated depth map calculated in advance from the original FR stereoscopic pairs to render the LR view from other HR views. Again, the original FR stereoscopic pairs cannot be obtained at the decoder side. Therefore, all of the above methods are not fully consistent with the MR stereoscopic imaging and processing techniques. Unlike existing SR methods, which require depth maps or depth estimation, we combine the correlation between the views of the MR stereoscopic image without estimating the depth map. We propose a stereoscopic image SR method based on view incorporation and convolutional neural network (CNN). The proposed stereoscopic image SR method can be implemented in two stages. In the first stage, for establishing links between views, a view difference image is defined, and the modified CNN is created to estimate a high-precision view difference image. Then, the estimated right view image is obtained by subtracting the estimated view difference image from the observed FR left view image. In the second stage, we consider that the estimated right view image should be retained with the LR right view image with regard to the MR stereoscopic image observation model. Accordingly, we model the global reconstruction constraint for incorporating the right view by projecting the estimated right view image obtained in the first stage onto the solution space of the image observation model. The solution can be computed by iterative back projection [24]. The SR results demonstrate that the proposed SR method retained more details and well reduced ringing. In short, the contributions of this paper are outlined as follows:  
* We combine the correlation between the views of the MR stereoscopic image without estimating the depth map;
* We use the view difference image containing the image texture information, as well as the depth information of the stereoscopic pairs, as the input to the modified CNN, whose pooling layers and fully connected layers are removed according to the SR task;
* We combine the high-precision view difference image estimated by the modified CNN with the global reconstruction constraint to further improve the performance of the MR stereoscopic image SR. The remainder of this paper is organized as follows. Section 2 describes an MR stereoscopic image observation model. Then, the proposed stereoscopic image SR method is illustrated in Section 3. Experiments are given and discussed in Section 4. Section 5 presents the conclusions.
## 4 Results and Discussion
### MGF Model Validation
The analytical solution of Equation (15) for Coastal water (without and with ISI). (**a**) optimum \(h_{\gamma}(t)\) solution, (**b**) the corresponding outputs \(r_{\gamma}(t)\), (**c**) the frequency response for the three \(h_{\gamma}(t)\) solutions, (**d**) the frequency response for \(h_{c}(t)\), optimum \(h_{\gamma}(t)\), & \(r_{\gamma}(t)\), (**e**) the eye diagram for the Gaussian transmitted optical pulse \(h_{\gamma}(t)\) with pw = 0.25, and (**f**) the eye diagram for the filter output \(r_{\gamma}(t)\). Figure 5: Use Case II: \(r_{sig}(t)\) with white AGTN The analytical solution of Equation (18) for Coastal water (without and with ISI). (**a**) optimum \(h_{r}(t)\) solution, (**b**) the corresponding outputs \(r_{r}(t)\), (**c**) the frequency response for the three \(h_{r}(t)\) solutions, (**d**) the frequency response for \(h_{c}(t)\), optimum \(h_{r}(t)\), & \(r_{r}(t)\), (**e**) the eye diagram for the Gaussian transmitted optical pulse \(h_{r}(t)\) with pw = 0.25, and (**f**) the eye diagram for the filter output \(r_{r}(t)\). Figure 6: Use Case III: \(r_{\rm sig}(t)\) with colored AGTN and ISI-[Equation (22) Numerical solution for Coastal water]. (**a**) three optimum \(h_{\rm f}(t)\) solution scenarios with different \(\{N_{0}\), \(a_{0}\), \(a_{1}\}\) value sets, (**b**) the corresponding outputs \(r_{\rm f}(t)\), (**c**) the frequency response for the three \(h_{\rm f}(t)\) solutions, (**d**) the frequency response for \(h_{\rm c}(t)\), optimum \(h_{\rm f}(t)\), & \(r_{\rm f}(t)\), (**e**) the eye diagram for the transmitted optical pulse \(h_{\rm f}(t)\) with pw = 0.25, and (**f**) the eye diagram for the filter output \(r_{\rm f}(t)\).
# Effect of Micro-Silica Addition into Electric Arc Furnace Steel Slag Eco-Efficient Concrete
## 1 Introduction
EAFS and cements used in the construction industry contain natural radioactive isotopes, such as radium (\({}^{226}\)Ra), thorium (\({}^{232}\)Th), potassium (\({}^{40}\)K) and \({}^{238}\)U [21; 22], and their specific activity may exceed the average specific activity. Micro silica fume or silica fume is an amorphous grey material with pozzolanic properties, which can reduce permeability and increase concrete strength. Micro silicon powder is a by-product of electric arc furnace in the production of ferrosilicon alloy. The material is highly pozzolanic, with more than 90% amorphous silica, in the form of amorphous particles, with an average diameter of 0.1 microns on average. Addition of micro silica to the concrete mix causes its activated Si\(\alpha\) to be mixed with the free solution of calcium hydroxide Ca(OH)\({}_{2}\) present in the capillaries of the concrete and to produce insoluble calcium silicate crystals. Electrical, flexural and tensile strength of concrete increase the resistance of concrete to erosion, reduce permeability and prevent the penetration of chlorine ions, sulphates and other harmful chemicals into the concrete. Moreover, due to the particle shape of the pozzolanic material, less water is required to achieve stronger concrete, so that the required water is reduced by 5% to 10% more than concrete made of ordinary OPC, and this will reduce the permeability and reliability of concrete hardened in front of water and increases the strength of the concrete against the attack of sulphates. Due to the fact that EAFS and Micro-silica are both by-products, the production of eco-concrete with these materials is very important. In this regard, many studies have been done on the effects of EAFS on the structure and behaviour of the concrete [11]. [PERSON] et al. showed that EAFS aggregates are suitable for making concrete in confined areas [23]. [PERSON] el al showed the use of EAFS aggregates in concrete can increase compressive strength by 20% compared to conventional concrete and can be used as coarse and fine aggregates in concrete [24]. The main original contribution of this research on the behaviour of different coarse grain and fine grain percentages of EAFS has been produced individually with the participation of a different percentage of micro-silica and its effect on compressive strength and flexural strength of concrete.
## 3 Framework Design and Deployment
### Data and Data Preprocessing
This decryption process involves using a private key \(sk\), where \(x=\text{Decrypt}(E(x),sk)\) reveals the encrypted model weights or performance metrics to verified users only. This decryption step is exclusively performed by entities with explicit authorization, preserving the confidentiality of the data throughout the FL pipeline. By incorporating Homomorphic Encryption and blockchain-based smart contracts, the data preprocessing pipeline ensures that privacy, security, and data integrity are maintained. At the same time, these privacy-preserving mechanisms enable sophisticated machine learning analysis on sensitive data, contributing to a secure and resilient framework in IIoT applications.
# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 5 Conclusions  
IoT technology and sensor networks are widely utilized for monitoring, data collection, and analysis of smart environments using the wireless communication system. However,  
Figure 10: The performance evaluation of the proposed algorithm compared to QL-MAC and CTEER for computational complexity.  
due to the constraints of resources of the nodes, most of the solutions are unable to balance the routing load on the selected routes and incur rapid data losses in the presence of security attacks. In this paper, we presented a D2D multi-criteria reinforcement learning algorithm with secured IoT infrastructure for smart cities. It offers a more authentic and verified solution for directly connected devices and increases the trustworthiness of transmission. Using multi-criteria reinforcement learning, the proposed algorithm offers intelligent methods for sensing the coverage area and efficiently distributing the energy load between mobile devices. The proposed algorithm can be used for smart buildings to interconnect various operations and for security surveillance using mobile IoT devices and sensors technologies. Our proposed algorithm makes it possible to gather the real-time data from the smart building and timely transmit the data towards network applications for further analysis and appropriate actions.  
However, the proposed algorithm still suffers from link disruption with the high exchange of control packets, and thus in the future, we aim to utilize the deep learning model and real-time dataset to train the network nodes and cope with network anomalies. Additionally, we would like to introduce the concept of multi-clouds in the proposed algorithm for high scalability and parallel processing.  
Conceptualization, K.H. and A.R.; methodology, K.H. and A.R.; software, T.S. and S.A.B.; validation, J.L. and T.S.; formal analysis, A.R., J.L., and K.H.; investigation, J.L. and S.A.B.; resources, J.L. and A.R.; data curation, A.R. and T.S.; writing--original draft preparation, K.H. and A.R.; writing--review and editing, T.S. and J.L.; visualization, S.A.B. and T.S.; supervision, J.L. and T.S.; project administration, J.L. and A.R.; funding acquisition, J.L. and A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are available in the manuscript.  
This research was technically supported by the Artificial Intelligence and Data Analytics Lab (AIDA), CCIS Prince Sultan University, Riyadh, Saudi Arabia. All authors are thankful for the technical support.  
The authors declare no conflict of interest.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 2 Design Methodology
### EBG-Based Antenna  
The proposed dual band textile antenna was placed over two different 5 \(\times\) 5 EBG ground planes as shown in Figure 6. In the case of the mushroom-like EBG's ground plane, the overall dimension of the antenna was 176.5 mm \(\times\) 176.5 mm, whereas, in the case of the EBG ground plane with plus-shaped slots was 136 mm \(\times\) 136 mm.  
Figure 4: A 5\(\times\)5 array of (**a**) the mushroom EBG and (**b**) the EBG with plus-shaped slots with suspended strips.  
Figure 5: Reflection and transmission coefficients of 5 \(\times\) 5 EBG array. (**a**) Mushroom-like EBG unit; (**b**) EBG with plus-shaped slots.  
This means that the antenna with second ground plane is about 22.94% more compact in size than the mushroom-like EBG-based antenna.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption
### Multi-User Spatial Files  
The second set of sensitive spatial files is _multi-user_ files. These files are shared between multiple users, for instance, during projects, via shared folders from within an organisation's file servers. In our scheme, these files are kept in separate \"project\" folders within the DBFS. Designing an encryption scheme for multi-user files is a relatively challenging task, as it requires maintaining a list of files to which not only one particular spatial DBFS user should have access but also other privileged users. It is also necessary to make sure that files are not encrypted while users who should have access to them have active sessions in the DBFS. Figure 4 demonstrates this aspect, where, upon logoff, not all of a GIS user's ([PERSON]'s) multi-user files are encrypted since another privileged user ([PERSON]) has an active session. The full user interaction workflow is included as part of Supplementary Materials. Not all files that users are granted access to using ECMSDK ACM need encryption. Therefore, the multi-user encryption scheme is implemented independently of the user's access privileges on ECMSDK files.  
The scheme is implemented using a catalogue that is used to determine the spatial users and the corresponding multi-user files that should be decrypted for their use and encrypted after their use. The catalogue is implemented using a database table, and the specification of multi-user GIS files and granting privileges to users in this catalogue is to be handled by the administrator. All users and the directories containing the files (for example, \"_0_:_home|scott|historical_places.SHP\"_) that need to be decrypted for useand encrypted back afterwards are stored in tables (see Supplementary Materials). Since single-user files might be made multi-user as soon as someone else needs to collaborate on a project, to allow multi-user access, these files can be moved into the project folders, and their access settings can be changed (using ACLs) correspondingly.
## 2 Background
### Machine Vision Capabilities  
Machine vision applications in agriculture has been one of the most studied topics and, generally speaking, one of the most successful and promising fields in the smart agriculture domain [8; 9; 10]. Among the pure CV methods in agricultural contexts, the most relevant case for our investigation is the application to viticulture (devised in Section 4). On the other hand, noticeable exploratory analyses have been conducted on wheat farming [11]-- to provide real-time spatial information of the crop and weeds, where conventional methods work undesirably in terms of segmentation accuracy or execution speed--and on the ryegrass location [12]--where a geometric approach has been shown to be effective in the determination of inter-row ryegrass weeds in a wheat field.  
Many studies in the field consider more refined types of vision challenges other than simple detection or spatial distribution tasks. Particularly relevant to the present research is the automatic determination of the maturation levels of the same fruits, which has been faced by using image analysis techniques as a general problem [13] and with respect to specific fruits such as the persimmon [14].
# Multi-Objective Optimization Study of Annular Fluid Flow Structure in Cordless Core Drilling Tools
## 2 Structural Design and Principles of Hydraulic Lifting Cordless Coring Drilling Tool
### Orthogonal Test Design  
Through the orthogonal test, the primary and secondary factors affecting the flow field of the drilling tool annulus under the effect of the factors are analyzed. The factors considered were the number of spearhead inlet ports (A), the diameter of spearhead inlet ports (B), the number of spool outlet ports (C), the diameter of outlet ports (D), and the distance between the centers of the two circular holes in the elongated outlet port (E). The optimal combination of the parameters A, B, C, D, and E was determined to optimize the structure of the spearhead and spool. A prediction model is established to predict the spool back pressure and drilling fluid flow rate under different structural parameters. Multi-objective optimization is carried out at the same time to complete the optimization of the ratio of fluid back pressure to spring force pressure and the drilling fluid flow rate.  
The test indexes are the characteristic quantities used to evaluate the results of the orthogonal tests, which are selected according to different test purposes. There is at least one test index for each test. The ratio of fluid back pressure to spring force pressure (Y1) and the drilling fluid flow rate (Y2) are used as experimental indicators. Test factors refer to the factors that may affect the test indexes. Factors such as the number of inlet ports of spearhead A, the diameter of the inlet ports of spearhead B, the number of spool outlets C, the diameter of the outlet ports D, and the center distance between the two outlet ports of the oblong holes E are selected as test factors. Considering the overall size of the drilling tools, the installation location, and the difficulty of achieving a comprehensive analysis of a number of factors, the orthogonal test factors and their levels are formulated as shown in Table 1.  
The table shows that there are five structural parameters to be analyzed. Among these, two parameters have two levels each, one parameter has six levels, and two parameters have nine levels each. Therefore, 81 experiments are required for the simulation. According to the number of factors and their decided levels, the SPSS software (SPSS2022) is used to generate a hybrid orthogonal test table. According to the test table, the simulation test is carried out in FLUENT fluid simulation software (Fluent 2022R1). The test program and results are shown in Appendix A.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline A & 3 & 4 & & & & & & & \\ B & 16 & 16.5 & 17 & 17.5 & 18 & 18.5 & 19 & 19.5 & 20 \\ C & 2 & 3 & & & & & & & \\ D & 16 & 16.5 & 17 & 17.5 & 18 & 18.5 & 19 & 19.5 & 20 \\ E & 10 & 12 & 14 & 16 & 18 & 20 & & & \\ \hline \end{tabular}
\end{table}
Table 1: Factors and level setting of mixed orthogonal test table.
## 2 Conventional Direction Detection Technology  
There are many direction detection technologies, and some of them can be applied to wireless relay systems, albeit with some issues. As for the direction detection of ground stations, there are two methods: one without markers and the other with markers.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation  
### Goals, Metrics, and Methodology  
#### 5.1.1 Goals of Experiments  
Our main goal in this section is to show that our hybrid trust model that considers consecutive drops detects insider packet drop attacks more _quickly_ and _accurately_ than the beta trust model, which does not consider consecutive drops in the packet routing domain. In addition, since a routing algorithm must deliver packets to the destination _reliably_ and _energy efficiently_ even under insider packet drop attacks, we show how our hybrid trust model improves both the reliability and energy efficiency of a routing algorithm versus the  
Figure 3: Hybrid trust model.  
beta trust model. We discuss reliability and energy efficiency in more detail in Section 5.1.4. We conduct extensive simulations based on the OPNET Modeler.
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.3.4 Access and Security  
Confidentiality and varying levels of access among different user groups was a key issue raised during consultation. EASY should provide a fine-grained approach to securing access for different user groups. Some functions such as the base map viewing and query functions would be available to all registered users of the EASY system, whereas other more specialised extension programs would have access configured by the program manager targeting specific groups of landholders.
## 3 Dataset Construction  
### Channel Design and Dataset Splitting  
In this work, the channel datasets were generated using professional PCB design software. As shown in Figure 5, Altium Designer 20.0.13 and Allegro 17.4 (EDA software) were primarily utilized to create the layouts for high-speed differential lines. Subsequently, the PCB layout files were imported into Advanced Design System 2021 (ADS) or HFSS 2023 R1 for electromagnetic (EM) simulation. Our design conforms to the specifications for USB end-to-end differential transmission lines, including parameters such as line width and length, the characteristic impedance of transmission channels, and differential line spacing.  
The differentiated characteristics of channel data, as presented in Table 1, primarily include parameters such as material, trace dimensions, and PCB type. The board characteristics encompass both numerical features--such as permittivity (_Er_), the dissipation factor (_Df_), and board thickness--and categorical features such as the PCB type. _Er_ consists of the sheet permittivity's real part and the dielectric loss tangent angle (_TanD_). _Df_ is derived from the Djordjevic model, which is defined by parameters including the low and high frequencies of _TanD_ and relative high-frequency permittivity. In this study, _Df_ is quantified according to the numerical values of _TanD_ and its corresponding high and low frequencies. The PCB type is categorized into either the stripline type, represented by a numerical value of 1, or the microstrip type, represented by 0.  
These trace features are quantified based on specific parameters such as the trace length, width, thickness, conductivity, profile, and differential line spacing. The profile is characterized by the geometric shape of the trace, such as the number of corners. The other characteristics are directly represented by their corresp  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Variable** & **Input** & **Output** \\ \hline _Er_ & PCB sheet permittivity real part and TanD \\ \hline _Df_ & PCB sheet dissipation factor \\ \hline \multirow{4}{*}{Trace} & PCB trace width \\ \cline{2-3}  & Differential line space & COM values \\ \cline{2-3}  & PCB trace thickness & Equalizer parameters \\ \cline{2-3}  & PCB trace conductivity \\ \cline{2-3}  & PCB trace structure (profile) \\ \hline _Zd_ & Differential impedance \\ \hline PCB Type & Stripline is 1 and microstrip is 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel feature parameters and quantitative methods.  
Figure 5: Channel fabrication and simulation process.