esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Literature  
### Query Image  
Utilizing DIR systems enables individuals to locate digital data such as pertinent documents or supplementary information. DIR systems serve as a conduit connecting printed materials to the digital realm. The demand for DIR systems is particularly pronounced in governmental organizations and businesses where documents exist in both printed and digital formats, necessitating the conversion of printed documents into digital content. In this research, we illustrate the application of DIR systems in education. These DIR systems analyze images captured by students using their mobile phones as input and then search a database of document images to discern the specific query or request made by the student. Particularly in educational settings, DIR systems prove highly beneficial when dealing with complex formulas and intricate figures, which are challenging to locate through conventional text-based searches.  
Figure 1 displays a sample set of query images. It is worth noting that the document image dataset contains images resembling these query images. These query images are integrated into the dataset once they have been answered or augmented with additional information. They are typically captured using various mobile phones equipped with different camera features, resulting in varying perspectives and lighting conditions. Some of these images may also include extraneous background details or irrelevant content.  
Additionally, it is common for these images to exhibit imperfections such as scratches. A proficient DIR system must show resilience in response to these image challenges and be able to prioritize relevant images over irrelevant ones. In essence, it should excel in discriminating between images based on their content, regardless of variations in image quality, perspective, and unwanted elements.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 2 Related Work
### Geographic Routing  
Geographic routing utilizes the geographical location information of nodes to make routing decisions, with high efficiency and low overhead, and is suitable for mobile ad hoc networks. However, the previous methods such as GPSR (greedy perimeter stateless routing) protocol [29] ignores the dynamic nature of network topology, resulting in short link lifetimes and frequent disconnections [13]. To address this issue, [PERSON] et al. [30] proposed an improved scheme based on the target communication range time, taking into account the dynamic changes of the network and traffic types. [PERSON] et al. [31] proposed a topology aware routing protocol based on Q-learning, which extends the information of two-hop neighbor nodes and improves the accuracy of routing decisions. In addition, the QLGR (Q-learning geographic routing algorithm) proposed by [PERSON] et al. [32], combined with multi-agent reinforcement learning, evaluates multidimensional information such as link quality, energy, and queue length to assess the value of neighboring nodes and reduce the risk of routing holes.  
Flying ad hoc networks are highly dynamic, energy-limited, and self-organizing. Compared with traditional ad hoc networks, their nodes move faster and their topology changes more frequently, thus creating greater challenges in designing location-based routing protocols for geography. [PERSON] et al. [33] proposed a topology-aware elastic routing strategy based on adaptive Q-learning. By analyzing the dynamic behavior of drone nodes through queuing theory, the neighbor change rate and neighbor change arrival time were derived. [PERSON] et al. [34] proposed a Q-learning-based geographic routing protocol, QGeo. This protocol broadcasts Hello packets at fixed intervals, uses Q-learning algorithm to dynamically select routing paths, and evaluates the quality of different paths through Q-values. In order to reduce the frequency of perceiving the geographical location of neighbors, some schemes adopt neighbor geographical location prediction. [PERSON] et al. [35] proposed a multi-objective optimization Q-learning routing protocol, QMR, which fully considers the high mobility of nodes and balances exploring new paths and utilizing known paths to optimize routing efficiency. [PERSON] et al. [36] proposed GPSR+, which adopts a position prediction strategy based on weighted linear regression model to estimate the future positions of adjacent nodes.
# Visual SLAM Based Spatial Recognition and Visualization Method for Mobile AR Systems
## 2 Related Works
### Markerless AR  
In the early days of AR, markers were used to connect the real world and the virtual world, and German AR company Metaio has been leading the marker-based AR market for years. Marker-based AR recognizes a marker from an image input through a camera and displays an image or object matching the marker on an image output to a display device. When detecting a marker with a simple pattern such as a QR code from the input image of the camera, the position or direction of the marker is also calculated, and the digital 3D content is visualized in the AR application to interact with the user. However, marker-based AR clearly has technical limitations, such as requiring a promise for a marker in advance and making tracking impossible when the camera leaves the marker.  
In order to overcome the limitations of marker-based AR technology, SLAM technology uses various sensors instead of markers to move in an arbitrary space and search for information to estimate one's location and create a map of the space has emerged [6]. Markerless AR recognizes objects by detecting feature points of objects or scenes without prior knowledge of the environment, such as walls or intersections, and through this, 3D content is placed in a specific location. The markerless AR technology has improved the accuracy of image analysis due to the development of SLAM, a simultaneous positioning and mapping technology, allowing various sensor data to detect surroundings and synchronize the current position of the object in real time [7; 8]. Representative SLAM-based markerless AR development tools include Google's ARCore, Apple's ARKit, and Qualcomm's Vuforia. Google's ARCore can be linked to OpenGL, Unity, and Unreal, and motion tracking, plane detection, and lighting estimation are possible. Apple's ARKit is also capable of motion tracking and plane detection, and with support for Unity and Unreal, real-time video rendering is possible. Qualcomm's Vuforia can recognize a single image or multiple images and can recognize and track 3D objects.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.1 Robust Handling of Noisy Medical Images  
Biomedical images often suffer from inherent noise arising from the imaging process, acquisition artifacts, or other environmental factors. DDPM, with its denoising diffusion process, is specifically designed to handle noisy data. By modeling the sequential introduction of controlled noise during training, DDPM learns to reverse this process during inference, effectively denoising the input images [13; 43].
## 3 Results--Systematic Literature Review
### Common Classification Fields
#### 3.2.4 Research Method  
Table 2 shows the general and specific research methods of the studies reviewed. For a more linear analysis of the results, one method only was indicated for each paper, although a considerable number of studies make use of more tools or combined approaches. In those cases, the method that was deemed most representative of the study performed and which constituted the qualitative-quantitative part of the methodological process was mapped. For instance, whenever an article had one or more fuzzy aspects, it was simply classified as \"fuzzy\", without further specifications. As for the \"hypothesis testing\" method, this was indicated only in cases where no other accredited methods were identified in the paper. For example, in all articles using the SEM method, hypothesis testing is also typically reported; however, these studies were classified primarily considering the SEM method. Similarly, articles categorized under \"case study\" (belonging to the more general category of empirical methods) were also labelled as such only in cases where they did not adopt any other methods.  
Figure 4: Distribution of papers by data collection method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Research Method** & **No. of Papers** \\ \hline
**Statistical** & **47** \\ ANOVA & 2 \\ Contingency analysis and frequency analysis & 1 \\ Cronbach’s alpha & 1 \\ Descriptive statistics & 8 \\ Econometric & 1 \\ Hypothesis test & 5 \\ Keyword analysis & 1 \\ Logistic regression—R software & 1 \\ Partial Least Square (PLS) & 1 \\ PLS-SEM & 11 \\ Random forest regression & 1 \\ Regression & 3 \\ SEM & 9 \\ Descriptive statistics, bias and common \\ method variance test, multiple regression & 1 \\ analysis and mediation test & \\ Analysis with SPSS and Nvivo & 1 \\ \hline
**Decision making** & **27** \\ Best Worst Method & 1 \\ Decision-Making Trial and Evaluation & 1 \\ Laboratory (DEMATEL) & 1 \\ DEMATEL—Maximum mean de-entropy & 1 \\ (MMDE) & 1 \\ Fuzzy & 10 \\ ISM & 1 \\ ISM-Bayesian network (BN) & 1 \\ ISM-Cross-Impact Matrix Multiplication & 1 \\ Applied to Classification (MICMAC) & 1 \\ Multi-Attribute Decision Making (MADM) & 1 \\ Multi-Attribute Utility Theory (MAUT) & 1 \\ Multi-Criteria Decision Methods (MCDM) & 6 \\ SWOT analysis & 2 \\ Total Interpretive Structural Modelling (TISM) & 1 \\ + MICMAC analysis & 1 \\ \hline
**Empirical** & **14** \\ Case study & 7 \\ Framework and case study & 1 \\ Product design changes (PDC)—domain & 1 \\ modelling & 1 \\ Qualitative & 5 \\ \hline
**Literature review** & **6** \\ \hline
**Economic** & **4** \\ ABC analysis & 2 \\ Poisson pseudo-maximum likelihood (PPML) & 1 \\ Method of stochastic factor & 1 \\ economic–mathematical analysis & 1 \\ \hline
**Simulation** & **3** \\ Discrete Event Simulation (DES) & 1 \\ System dynamics approach & 1 \\ Multi-period simulation & 1 \\ \hline
**Total** & **101** \\ \hline
**T  
\end{table}
Table 2: Research methods (note: bold = sum of papers relating to each research method).
## 1 Introduction
[PERSON] et al. [23] studied the rheological behavior of CSA cement paste for various supplementary cementitious materials. According to their report, silica fume (SF) significantly reduced the maximum shear stress and plastic viscosity of the CSA cement paste and increased the yield stress. The combination of fly ash and slag mitigated the negative effects of the increase in the yield stress of the paste due to SF. The plastic viscosity of the CSA cement paste increased significantly with decreasing water/cement ratio (\(w/c\)). A study on the tendency of compressive strength in the 1-year-long term of CSA cement mixed with OPC, pulverized fuel ash (PFA), and ground granulated blast furnace slag (GGBS) was also reported [24]. Their findings showed that increasing the CSA cement content with the same total binder content increased the early age strength. However, when only CSA cement and PFA or GGBS were combined, the compressive strength decreased significantly at later ages in the absence of OPC. In a study, the authors performed experiments in which CSA was applied to alkali-activated fly ash cement, and the resulting properties were compared with those of portland cement [25]. Recently, there have also been studies using CSAes. [PERSON] and [PERSON] [26] stated that a high \(w/b\) of about 0.8 is required for the complete hydration reaction needed by CSAe to form ettrinigite. [PERSON] and [PERSON] [9] investigated the effect of CSAes on the drying shrinkage of high-strength fiber-reinforced cementitious composites prepared with various \(w/b\) values. The result indicated that a higher \(w/b\) was more effective in compensating for the shrinkage of the mixtures caused by the mixing of CSA. [PERSON] et al. [27] presented a study on the hydration properties of cement mixed with CSAe and red mud. CSA clinker or CSA cement was studied for its mechanical properties, hydration reactions and mechanisms, expansion/shrinkage properties, and microstructures, with focus on studies mixed with OPC. The purpose of this study is to perform a basic experiment about the development of a low-carbon, eco-friendly cement that comprises a mixture of CSAe and slag and does not use OPC. CSAe and SF were substituted for slag mass. For CSAe, the substitution percentages were 10%, 20%, and 30%, and those for SF were 0%, 5%, and 10%. Nine mixtures were tested. The compressive strength, X-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetry (TG)/derivative TG (DTG), mercury intrusion porosimetry (MIP), and dry shrinkage up to 91 d were measured. The cement to be developed must exhibit the mechanical performance required for structural use. The mechanical properties, hydration products, and drying shrinkage were analyzed according to the mixing ratio of CSAe, slag, and SF. The findings can be used as data for the development of cement that can replace OPC.
## 2 Study Area  
The Fraser River Delta is situated along the Pacific coast of Canada (Figure 1). It is the largest river in western Canada, and it has been prograding into the Strait of Georgia (SoG), a forearc basin, for about 10,000 years, following the end of the last glaciation [10; 11; 12]. This modern delta sits on top of Pleistocene marine- and glacial-origin sand, silt, and, to a lesser extent, gravel deposits [10]. Due to its undammed nature, the Fraser River Delta represents a natural river system, with the exception of some relatively recent modifications along its river (e.g., replacement of dykes).  
The Fraser River has a dynamic nature, with significant long-term and short-term changes in river discharge, resulting in variations in its degradation rate over time [13]. Between 10,000 BP and 5000 BP, the delta prograded approximately 13 km from the river  
Figure 1: (**A**) Schematic map of the Fraser River Delta. The gray scale is the multibeam-derived bathymetric map provided by the Geological Survey of Canada, Pacific Division (contour interval: 50 m). (**B**) Three-dimensional block diagram of the Fraser River Delta. The yellow line represents low-tide sea level.  
mouth, and another \(\sim\)9 km in the last 5000 years, based on terrestrial organic matter distribution, radiocarbon age dating, and marine shell distribution collected from bore hole data [13; 14; 15]. The river discharge also dramatically fluctuates. The mean river discharge rate is approximately 3400 m\({}^{3}\)/s, while minimum discharge is 1000 m\({}^{3}\)/s and maximum discharge can reach 15,200 m\({}^{3}\)/s during snowmelt-induced freshet [16; 17; 18]. Fluctuations in the river discharge also result in variations in transported sediment load. The river annually transports about 17 million tonnes/year of sediment. Under ambient conditions, the river predominantly transports mud-size particles (i.e., silt and clay) into the SoG, while during the freshet, half of this material is sand-sized [19; 20].  
The SoG is a restricted basin, with complex hydrodynamic processes transporting sediments. These processes include tidal currents, waves, and deep-water renewals [17; 21; 22; 23]. Waves are the dominant processes in the upper 10-15 m of the water column [17]. Once the river flow enters into the SoG, suspended sediments are either transported offshore via the river's jet plume (during freshet), or waves transport sediments southward due to the dominant strong wind direction [24], where the sediment settles in the southern parts of the SoG. In the deeper parts of the SoG (\(>\)15 m depth), tidal processes dominate along with deep-water renewals as a subordinate process. Both processes cause net northward sediment transport on and across the delta front and prodelta [21; 23; 25; 26].
# Kinematics and Performance Analysis of the 2T1R/1T2R Reconfigurable Decoupled Parallel Mechanism
## 3 Kinematic Analysis
### Motion Decoupling Analysis  
When the mechanism is in 2T1R motion mode, we take the derivative of Equations (27)-(29) on \(t\) and organize it to obtain  
\[\mathbf{J_{q}}\begin{bmatrix}\dot{\theta}_{12}\\ \dot{\theta}_{21}\\ \dot{\alpha}_{31}\end{bmatrix}=\mathbf{G}\begin{bmatrix}\dot{Z}\\ \dot{\alpha}\\ \dot{X}\end{bmatrix} \tag{32}\]  
where \(\mathbf{J_{q}}\) is the input Jacobian matrix, and \(\mathbf{G}\) is the output Jacobian matrix  
\[\mathbf{J_{q}}=\begin{bmatrix}-\sin\theta_{12}&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix} \tag{33}\]  
\[\mathbf{G}=\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix} \tag{34}\]  
If the determinant of \(\mathbf{G}\) is not 0, then Equation (32) can be written as  
\[\begin{bmatrix}\dot{Z}\\ \dot{\alpha}\\ \dot{X}\end{bmatrix}=\mathbf{f}\begin{bmatrix}\dot{\theta}_{12}\\ \dot{\theta}_{21}\\ \dot{a}_{31}\end{bmatrix}=\mathbf{G}^{-1}\mathbf{J_{q}}\begin{bmatrix}\dot{\theta}_{ 12}\\ \dot{\theta}_{21}\\ \dot{a}_{31}\end{bmatrix}=\begin{bmatrix}-\sin\theta_{12}&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}\dot{\theta}_{12}\\ \dot{\theta}_{21}\\ \dot{a}_{31}\end{bmatrix} \tag{35}\]  
When the mechanism is in 2T1R motion mode, \(\mathbf{J}\) is a diagonal matrix with a different diagonal element. It can be seen from [28] that the mechanism is a decoupled mechanism in the whole workspace.  
When the mechanism is in 1T2R motion mode, we take the derivative of Equations (28), (30) and (31) on \(t\) and organize it to obtain  
\[\mathbf{J_{q}}\begin{bmatrix}\dot{\theta}_{12}\\ \dot{\theta}_{21}\\ \dot{\theta}_{31}\end{bmatrix}=\mathbf{G}\begin{bmatrix}\dot{Z}\\ \dot{\alpha}\\ \dot{\beta}\end{bmatrix} \tag{36}\]  
where  
\[\mathbf{J_{q}}=\begin{bmatrix}-\sin\theta_{12}&0&-\sin\theta_{31}\\ 0&1&0\\ 0&0&1\end{bmatrix} \tag{37}\]  
\[\mathbf{G}=\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix} \tag{38}\]If the determinant of \(\mathbf{G}\) is not 0, then Equation (36) can be written as  
\[\begin{bmatrix}\dot{Z}\\ \dot{\alpha}\\ \dot{\beta}\end{bmatrix}=\mathbf{J}\begin{bmatrix}\dot{\theta}_{12}\\ \dot{\theta}_{21}\\ \dot{\theta}_{31}\end{bmatrix}=\mathbf{G}^{-1}\mathbf{I}_{\mathbf{q}}\begin{bmatrix}\dot{ \theta}_{12}\\ \dot{\theta}_{21}\\ \dot{\theta}_{31}\end{bmatrix}=\begin{bmatrix}-\sin\theta_{12}&0&-\sin \theta_{31}\\ 0&1&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}\dot{\theta}_{12}\\ \dot{\theta}_{21}\\ \dot{\theta}_{31}\end{bmatrix} \tag{39}\]  
When the mechanism is in 1T2R motion mode, \(\mathbf{J}\) is a triangular matrix. It can be seen from [34] that the mechanism is a partially decoupled parallel mechanism.
## 3 Model Building and Validation
### Different Sublevel Heights of the Lower-Layer Filling Method  
The actual sublevel height of 15 m was maintained for the sublevel saving method without the sill pillar for the numerical simulation of mining. The mining sublevel height of the lower-layer filling method was set to 15 m and 20 m, and the fault's dynamic response change was analyzed, as shown in Figure 10.  
Figure 9: Dynamic response of faults under different segmentation heights in the sublevel method. (**a**) Sublevel height of 10 m. (**b**) Sublevel height of 15 m. (**c**) Sublevel height of 20 m.  
From the analysis of the above diagrams, it can be seen that the displacement range of the fault plane near the mining side was mainly distributed in the sublevel caving method without the sill pillar and in the filling method. The vertical displacement of the fault surface using the disintegration method was the largest, and it gradually decreased in the surrounding area. The maximum vertical displacement of the fault contact surface on the mining side was 1.4352 m when the sublevel height of the filling method was 15 m. The maximum vertical displacement of the fault surface was 1.4504 m when the sublevel height of the filling method was 20 m. With an increase in the sublevel height of the filling method, the vertical displacement of the fault surface also increased; the range of the change in the positive stresses was consistent with the trend in the above analysis. Through the comparison of shear stress and displacement, it can be determined that the collapse method has a greater influence on the stability of the fault than the filling method in the case of changing the sublevel height only.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 4 Results and Discussion
### Results for Only Voice Streams
#### 4.1.5 Summarized Results for the Voice-stream study  
Figure 17 (green area) represents the total traffic that Asterisk had to process during all attacks. The peaks represent the periods during which Asterisk was being attacked. As can be seen, the amount of traffic processed during each of the attacks was huge.  
Figure 16: Instantaneous values of the jitter for the voice stream between VM_4 and Asterisk (**a**) and VM_3 between and Asterisk (**b**) during the TCP FIN attack.  
Figure 15: Summarized results for the main parameters of the voice stream between VM_3 and the Asterisk (**a**) and between VM_4 and the Asterisk (**b**) during the TCP FIN attack.  
Figure 18 presents the number of different TCP packets for the whole study period. As can be seen from the graphs, TCP SYN and the TCPSYNACK packets were present during the TCP SYN attack. TCP FIN was present only during the TCP FIN attack. TCP RST packets were generated during all attacks except the TCP FIN attack.  
The following results show how the round-trip delay (RTD) between the different VMs and Asterisk changed during the whole study period. The results were obtained using the Colasoft Ping Tool (version 2.0). The purpose of these graphs is to show if and how the different TCP DoS attacks affected accessibility to Asterisk. The x-axis represents time, and the y-axis represents the value of the delay in ms. Figure 19 shows how the RTD changed between VM_1 and Asterisk for the whole study period. As can be seen from the graph, the values of the delay were far below the value of 150 ms in one direction.  
Figure 17: Traffic processed by Asterisk during the whole study period.  
Figure 18: Number of different TCP packets for the whole study period during the voice-stream study.  
Figure 20 presents how the RTD changed between VM_2 and Asterisk for the whole study period.  
Figure 21 presents how the RTD changed between VM_3 and Asterisk for the whole study period. Figure 22 presents how the RTD changed between VM_4 and Asterisk for the whole study period. Figure 22 presents how the RTD changed between VM_4 and Asterisk for the whole study period.  
Figure 19: RTD between VM_1 and Asterisk.  
Figure 20: RTD between VM_2 and Asterisk.  
As can be seen from the four graphs, the round-trip delay varied but did not reach even half of the maximum allowable value of 150 ms per direction.