esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calculation of Fiber-Sprayed Concrete Stacked Beams with Flexural Stiffness Correction  
### Theory of Fiber-Sprayed Concrete Stacked Beam Flexural Stiffness Calculation  
For the fiber-sprayed concrete stacked beams, there are three existing assumptions for the calculation of the cross-sectional moment of inertia (second moment of area) of the beams, as shown in Figure 12. (1) Assuming that the whole fiber-sprayed concrete stacked beams are deformation-coordinated and the neutral axis remains in the middle position of the cross-section, the cross-sectional moment of inertia of the beams is corrected using the parallel axis theorem. (2) Assuming that the entire fiber-sprayed concrete stacked beam is deformation-coordinated and the neutral axis is at the interfacial position, the cross-sectional moment of inertia of the beam is corrected using the parallel axis theorem. (3) Assuming that the entire fiber shotcrete stacked beam is a delaminated structure without a bond, add the moments of inertia of the two parts of the section.  
The moment of inertia of the beam section for different layers of the fiber shotcrete beam for its centroid axis is calculated as follows:  
\[I_{xc}=\frac{bh^{3}}{12} \tag{5}\]  
In the formula, b and h are the width and height of the section, respectively, mm.  
If the neutral axis position is not at the center of the form, the cross-section moment of inertia needs to be corrected using the parallel axis theorem, which is given by Equation (6) as follows:  
\[I_{x}=I_{xc}+a^{2}A \tag{6}\]  
Figure 12: Moment of inertia calculation assumptions.  
In the formula, a is the neutral axis offset distance relative to the centroid axis, mm. A is the cross-sectional area, mm\({}^{2}\).  
According to the test results, the material is C35 high-performance fiber shotcrete, and the modulus of elasticity E test results are all 29 GPa. Combined with Equations (3) and (4), the flexural stiffnesses under different assumptions are calculated, as shown in Table 6.
# Environmental Risk Source Analysis and Classification of Zones: Subway Construction
## 3 Risk Source Analysis of Subway Construction  
### Introduction of SBAS-InSAR  
Small Base Line Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) is a satellite-based monitoring technique that offers all-weather, all-day, high-resolution, high-precision, wide-range, and convenient data acquisition advantages. This technology can accurately measure surface deformation at a millimeter level, with high spatial resolution and wide coverage. Given its unique benefits, it has considerable potential in monitoring land subsidence over large areas and long-time periods and has been successfully applied in various regions for surface deformation monitoring.  
Compared to traditional geodetic methods, such as leveling and topographic surveys, SBAS-InSAR technology overcomes limitations such as manual distribution, low spatial resolution, and high cost. SBAS-InSAR technology can provide a comprehensive view of the subsidence process along the subway and subsequently identify changes in ground deformation in time and space. The flow chart of SBAS-InSAR is presented in Figure 2.  
Figure 1: Study area coverage and land use data map. (**a**) Distribution of land use data in the study area in 2021; (**b**) planning map of Urumqi subway line, including Lines 1, 2, 3, 4, 5, 6, and 7.  
Figure 2: SBAS-InSAR workflow flow chart.  
Urumqi Metro Line 1 starts at Santunbei Station in Tianshan District and passes through Tianshan District, Shayibake District, Shuimogou District, and Xinshi District, with stops at Santunbei, Xinjiang University, International Bazaar, Nanhu Square (People's Government of Urumqi City), and the international airport station line in Xinshi District. The line covers a distance of 27.615 km and commenced operation on 28 June 2019. It is an underground line with 21 stations and runs roughly in a north-south direction.  
The study area focuses on surface deformation during the subway construction. This study used surface deformation driving factors during the construction of Urumqi Metro Line 1 to determine and identify environmental risk sources during subway construction. The study obtained 22 satellite images from Sentinel-1A between 2017 and 2018 to extract the deformation rate field along the 600 m range of Metro Line 1 using SBAS-InSAR technology [39]. By studying the regional differences in deformation rate during the construction of Urumqi Metro Line 1 and combining this data with the characteristic environmental risk sources of each region, the study compared the surface deformation performance of different construction areas to determine environmental risk sources of subway construction in the study area [40].
## 2 Related Work  
### Research Related to Technology-Organization-Environment (TOE) Theory  
The TOE framework, proposed by [PERSON], [PERSON], and [PERSON] in 1990, is designed to analyze the key determinants influencing the adoption and development of innovations [28]. This framework examines innovation adoption from three dimensions: technology, organization, and environment [29], and provides the systematic supervision of each component [30]. As a widely recognized organizational theory, TOE posits that the adoption of new technologies is influenced by the combined effects of technological, organizational, and environmental factors. Technological factors primarily encompass the inherent characteristics of the technology itself and its compatibility with the organization [31]. Organizational factors refer to internal elements within an organization, including managerial support, organizational size, readiness, communication processes, and resource availability. Additionally, the organizational context, another critical component of the TOE framework, encompasses factors such as organizational size, structure, culture, and resources [30,32]. Organizational culture, particularly the cultivation of a technology-friendly environment, the promotion of teacher training, and the capability to integrate AI-driven tools into curricula, plays a significant role in technology adoption [33, 34, 35]. Studies indicate that organizational culture impacts technology adoption across various contexts, particularly in educational institutions [36, 35, 37]. Environmental factors pertain to the external environment in which an organization operates, primarily including government policies, cultural considerations, and external pressures [28,30]. Moreover, this theoretical model has been applied in various fields to study the factors influencing the adoption of information technology innovations. In the field of education, researchers have also conducted empirical studies based on the TOE framework to examine the factors affecting the adoption and diffusion of cloud computing technologies. For instance, [PERSON] et al., using the TOE-DOI framework, analyzed the differential impacts of technological, organizational, environmental, and sociocultural factors on the adoption of cloud computing in the higher education sector [38]. Similarly, [PERSON] et al. integrated four theoretical models (TOE, TAM, DOI, and HOT-fit) to explore the effects of factors such as relative advantage, compatibility, reliability, security, organizational size, and managerial support on the adoption of cloud computing technology within India's school education system [39].
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 4 Experiments
### Experiment Analysis
The research findings indicate that the algorithm proposed in this paper exhibits rapid and efficient capabilities in optimal path search. Diverging from ant colony algorithms, this method outperforms the basic ant colony algorithm, particle swarm algorithm, and genetic algorithm, reducing path lengths by 31% and 10%, respectively. Moreover, it achieves the fastest convergence rate while enhancing global optimization capabilities. In summary, the algorithm presented in this paper offers unique advantages in terms of minimum path length, iteration count, and turning point count. Figure 9: Minimum path length and number of iterations planned by different algorithms.
## 2 Acquisition, Pointing, and Tracking in Inter-Satellite Laser Links
### Acquisition Schemes
#### 2.1.1 Stare/Stare  
In this method, the transmitter's beam divergence is large enough to cover the entire uncertainty area, and the receiver satellite's FOV is similarly large enough to view the full uncertainty area. Acquisition can occur instantaneously, with the probability determined by the product of the detection probability and the area coverage. This method is suitable for close-range acquisition but is not feasible for long distances due to power constraints.  
where \(x\) represents the pointing error in the elevation direction, \(\mu_{x}\) is the mean, and \(\sigma_{x}\) is the standard deviation. Similarly, the PDF for the elevation direction error can be written as  
\[f(y)=\frac{1}{\sqrt{2\pi}\sigma_{y}}\exp\left(-\frac{(y-\mu_{y})^{2}}{2\sigma_{ y}^{2}}\right), \tag{6}\]  
where \(y\) represents the pointing error in the elevation direction, \(\mu_{y}\) is the mean, and \(\sigma_{y}\) is the standard deviation.  
The radial error for the LOS link can be defined as  
\[r=\sqrt{x^{2}+y^{2}}, \tag{7}\]  
with the assumption that  
\[\sigma_{x}=\sigma_{y}=\sigma_{r}, \tag{8}\]  
and that the elevation and azimuth pointing errors are independent and identically distributed with zero mean and no bias. Under these conditions, the radial pointing error can be modeled using the Rayleigh distribution:  
\[f(r)=\frac{r}{\sigma^{2}}\exp\left(-\frac{r^{2}}{2\sigma^{2}}\right), \tag{9}\]  
and the joint PDF can be written as  
\[f(x,y)=f(x)f(y)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{x^{2}+y^{2}}{2\sigma ^{2}}\right). \tag{10}\]
## 7 Complexity
### Clustering Property
#### 7.2.1 Safety Property  
The safety property ensures that network nodes are grouped into clusters and each cluster has only one CH to avoid overlapping between clusters.  
**Lemma 9**: _The safety property is verified._  
Proof.: Each ordinary node elects the node with the highest weight among its multi-hop neighbors as its cluster head and the variable _Mych_ holds only one value based on \(CRL\) list. Thus, a node can only belong to one cluster at a time and is covered by a unique CH. As a result, the safety property is satisfied.
# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 4 Taxonomy  
### Hardware  
#### 4.1.1 UAVs  
Initially, in terms of vehicle type, 17 types of UAVs were mentioned specifically. The types of UAVs were three: drones, fixed-wing and single-rotor. It should be noted here that the drones category includes all vehicles that have more than one propeller mounted horizontally to the main body of the vehicle. More specifically, there were ten applications with drones: seven with quadcopters [19; 20; 21; 22; 23; 24; 25], three applications with hexacopter [26; 15; 27] and one with octacopter [28]. In addition, there were four applications with fixed-wing UAVs [29; 30; 31; 32], while with single-rotor, it was limited to one application [33]. Finally, one application [34] was implemented with 2 UAVs, one fixed wing and one quadcopter.  
The application implemented via single-rotor UAV (Helicopter) is one of the oldest applications of the review (2011). In addition, two applications with fixed-wing UAVs are also two of the oldest implementations (2011, 2012). The two newer applications with fixed-wing UAVs (2019, 2020) were implemented through these vehicles, as their mission was to supervise a large area that required great autonomy of vehicles. The applications with octacopter and hexacopter were part of a stereovision system. The rest of the applications were implemented via quadcopters.  
The results of the research lead to the conclusion that the implementations of the last decade regarding UAVs and Computer Vision in the field of fire detection are implemented in the largest percentage (70.58%) with drones. In Figure 7, the tree-map for UAVs types used in the reviewed applications is shown.  
Compared to other types of UAVs, multicopters are capable of being firmly above a point in hover mode. This capability provides a 360\({}^{\circ}\) visual contact from the camera. Other  
Figure 5: Burnt areas by forest fires in Canada from 2000 to 2019.  
Figure 6: Fire Detection using UAV trendline.  
than that, they do not require a runway to take off and land. A disadvantage of this type of UAVs is their reduced autonomy, compared to fixed wing that have a long duration time [4].
## 4 Tectonic Evolution and Sedimentary Filling of the Basin
### Basin Sedimentary Filling
The Niuttating Formation developed with thick layers of black carbonaceous shales throughout the region, marking the first set of hydrocarbon source rocks in southern China. From the Late Ordovician to the Silurian, the Caledonian orogen intensified, leading to the emergence of several uplifts in the central and upper Yangtze regions, such as the Jiangnan, Qianzhong, and Chuanzhong Uplifts. Surrounded by these uplifts, the central Yangtze area formed a foreland basin (Figure 7). During the Early Silurian period, the Sichuan Basin in South China extensively developed deep-to-shallow shelf sedimentary environments. Controlled by sedimentary facies, the region deposited thick, dark, organic-rich mud shales [65]. The lithology of the deep water shelf subfacies mainly consists of radiolarian-rich carbonaceous graptolite shales, while the shallow-water shelf subfacies primarily comprise carbonaceous siltstone mudstones, graptolite shales, and silty mudstones. Following the Middle Silurian, the Caledonian orogen intensified, causing uplift and erosion in the central and upper Yangtze regions, leading to sedimentary discontinuity [66]. From the Devonian to the Early Carboniferous, most of the central Yangtze area was uplifted due to tectonic movements. From the Late Carboniferous to the Early Triassic, the central and upper Yangtze regions were under the extensional background of the expansion of the Paleo-Tethys Ocean. By the end of the Middle Triassic, the Yangtze Plate and the North China Plate converged and sutured, completing the north-south amalgamation of the Chinese mainland. After the Late Triassic, the central and upper Yangtze regions entered a phase dominated by foreland basins. By the end of the Middle Jurassic, the Xuefeng Uplift further squeezed and uplifted, causing significant subsidence to its northwest side. The sedimentary center shifted westward to the central Sichuan area, solidifying the Sichuan Basin's basic structure and stably depositing continental molasse. Moreover, uplift and erosion occurred in the eastern Chongqing and western Hunan-Hubei regions, with only lacustrine clastic rocks deposited in the Yuanma Basin. From the perspective of sedimentary infilling, the Silurian period in the North Africa-Middle East-South China region exhibited similar sea level changes. Overall, they manifested a single primary cycle of sea level change. Accompanied by the rise and fall in sea levels during the Silurian sedimentary infilling process in the basin, extensive marine and deep-to-shallow shelf sedimentary environments developed within it. In the deep water shelf environment, planktonic organisms, primarily bacterioplankton and algae, thrived. The oxygen-rich photic zone in shallow waters was conducive to the proliferation of planktonic algae, ensuring high paleoproductivity. This environment guaranteed the development of high-quality hydrocarbon source rocks for oil and gas combinations. Figure 7: Basin structure and filling stratigraphy in the middle and upper Yangzi regions of South China.
## 2 Data and Methodology
### Interaction between Wind Direction and Building Orientation
#### 2.2.1 The Importance of the Skew Angle and a Small-Building Experiment
Figure 5 shows the behavior of the small turbine for different skew angles (0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\)), with the 0\({}^{\circ}\) being the angle corresponding to perpendicular wind to the facade (pink color). The skew angle deviation, surprisingly, does not affect the measured \(rpm\); they even increase for higher skew deviations from the perpendicular incidence. Figure 4: Scheme of the skew angle for our small scale experiment and the horizontal axis turbine with the incoming wind field (vectors above) in the wind tunnel. 0\({}^{\circ}\) corresponds to wind direction perpendicular to the facade. A hypothesis on this anti-intuitive behavior can be related to the helical rotative wake or vortex prolonged along the horizontal axis, the energy of which is not dissipated and is captured by the turbine. This idea should be validated by more detailed CDF simulations than previous works [23], but other authors have also observed also this behavior of the Savonius and other VAWTs' power production with respect to the skew angle even with higher \(C_{p,max}\) values for non-skewed cases [24, 25, 26].
# Multivariate Analysis of Laser-Induced Tissue Ablation: Ex Vivo Liver Testing
## 2 Methods
As clinical laser systems can deliver power levels ranging from 120 to 200 W, the three values (120, 160, and 200 W) were selected for P [12]. Based upon previous studies and clinical conditions, the three values for TS and BS were chosen, respectively [3, 9, 16]. According to Figure 1a, the three levels for each parameter included low, middle, and high values. Based upon TS, the total irradiation times were determined as 10, 2, and 1.1 s for 1, 5, and 9 mm/s, respectively. Each condition was independently tested five times (N = 5) by following the BBD plans as shown in Figure 1a. Figure 2 illustrates post-experimental analysis on ablated bovine tissues. Each specimen was vertically cross-sectioned along \(y\)-axis by 1-mm thickness (dt). A total of 10 cross-sections were prepared, and a digital camera was used to photograph all the cross-sections (i.e., \(y\)-\(z\) plane). Image J (National Institute of Health, Bethesda, MD, USA) was then implemented to detect the boundary of each ablated crater in the acquired image and to estimate the detected area (A\({}_{i}\)). Ten quantified areas (i.e., A\({}_{1}\)-A\({}_{10}\)) were integrated in terms of the cross-section thickness (dt) to estimate ablation volume (mm\({}^{3}\)) for each condition. The corresponding ablation rate (AR; mm\({}^{3}\)/s) was also calculated by dividing the ablation volume by a total irradiation time. Minitab (Minitab 17, State Collage, PA, USA) was also used to implement a response surface method and to assess the effect of all the parameters on ablation efficiency for eventually determining the optimal therapeutic conditions. For statistical analysis, analysis of variance (ANOVA) was performed, and \(p\) < 0.05 was considered significant. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Low Value (\(-\)1)** & **Middle Value (0)** & **High Value (+1)** \\ \hline Power (P) & 120 W & 160 W & 200 W \\ Treatment Speed (TS) & 1 mm/s & 5 mm/s & 9 mm/s \\ Beam Size (BS) & 0.4 mm\({}^{2}\) & 1.1 mm\({}^{2}\) & 1.8 mm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three-level values of each parameter for experimental matrix. Figure 2: Schematic illustration of volume quantification on ablated tissue (dt: tissue thickness, A: ablated area, and V: estimated ablation volume).
## 3 Related Literature
### Stream Processing  
In the context of big data stream processing, two major goals can be discerned: deriving insights or statistics to help humans make informed decisions [14; 34] and anomaly detection about the process generating the data stream [8; 11; 17]. The latter is a common theme in data stream processing that lacks the volume to be seen as big data, with use cases including computer network monitoring [12], water analytics [32], or Heating, Ventilation, and Air Conditioning (HVAC) fault detection [35]. Many of these approaches extract statistical features, typically over predefined time windows, and use clustering, correlations, or other well-known machine learning methods to find outliers [12; 35; 36]. Many of these operations are well supported within the aforementioned streaming frameworks, resulting in many works using them for anomaly detection [7; 8; 12; 27].  
Pattern-based approaches such as the Matrix Profile [37] work by comparing series subsequences rather than using statistical features. The Matrix Profile can be used to find unique subsequences (discords) which can be considered anomalous, as well as matching [37] or repeated [38] patterns (motifs).  
Semantic streams can be processed using expressive reasoning techniques. These RDF Stream Processors infer additional facts from the data using background data and domain context. These derived facts can then be used to inform users or trigger other actions. Stream reasoning is a broad research domain and more details can be found in the survey by [PERSON] et al. [39].  
We did not encounter literature describing the combination of data-driven and semantic methods, as we do in this work. The system described in this work utilizes classical machine learning methods, pattern-based methods, and semantic methods. First, a random forest classifier is used to detect faulty installations by comparing configured versus detected room types in a classical, batch-based manner. Two components use pattern-based techniques in a streaming fashion to detect anomalies and repeated events respectively. Finally, a semantic reasoning component evaluates the metrics for expert-defined patterns using reasoning over time windows. We emphasize that the contribution of this work lies in describing the way these fundamentally different techniques are brought together in the platform, rather than the details or innovations of these techniques by themselves.