esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 1 Introduction
### The Problems  
A qualitative study has shown that remote work can lead to feelings of isolation and disconnection from colleagues and the broader organization [8]. This can be particularly challenging for those who are not used to working remotely or who lack strong social connections outside of work [9]. Additionally, a study [10] found that students also have difficulties with the shift from hands-on laboratory experiences to online classes, which resulted in loss of communication among peers, which is important for understanding, engagement and persistence.  
Given the nature of video conferences, the audience, especially the meeting hosts, suffers from the stress which is coined as \"Zoom Fatigue\". According to the studies [11], it can be accounted for the fact that the audience have to turn on their cameras and have close engagement with others for long hours and as for the meeting hosts, online engagement creates social isolation in [12]. Additionally, having the audience unnecessarily turn on their video feed all the time can have an adverse effect on the engagement itself as well as on privacy concerns.  
[PERSON] [11] made theoretical arguments on four causes for Zoom fatigue: close eye contact, cognitive load due to harder non-verbal cues, self-consciousness from looking at one's self image and reduced mobility.  
Additionally, he and other researchers [13] conducted an empirical study on the scale (called Zoom Exhaustion and Fatigue Scale, ZEF) where they outlined general facts regarding Zoom Fatigue. Also, the same researchers [14] conducted an empirical test on five non-verbal causes, similar to [PERSON]'s [11] arguments. From [11; 13; 14], one can argue that video-first online conference settings significantly contributes to Zoom fatigue and the fatigue leads to negative attitudes towards online meeting, making matters worse and disrupting the engagement as a whole.  
A study in Thailand [15], for instance, revealed that teachers have difficulties with gauging student engagement. One potential solution to the stress associated with video sharing is to instead share engagement analytics such as face orientation and eye blink, which can address the causes of Zoom Fatigue by reducing the need for video sharing. For meeting hosts too, this can provide assurance of audience attentiveness, even without visual access to the attendees' faces.
## 3 Results and Discussion
### Analysis of Influencing Factors on Carbon Emission Intensity
#### 3.2.6 Relationship Between Influent Water Quality and Chemical Carbon Emission Intensity  
This study analyzed the impact of influent water quality, TN (total nitrogen) removal, and TP (total phosphorus) removal on the intensity of chemical carbon emissions (Figure 10). Carbon sources are vital for microbial synthesis of cellular materials and energy acquisition. During biological nitrogen and phosphorus removal processes, carbon sources serve as electron donors, participating in denitrification reactions to facilitate the reduction of nitrate nitrogen to nitrogen gas, thereby removing nitrate nitrogen from wastewater. Concurrently, carbon sources aid phosphorus-accumulating organisms (PAOs) in releasing and absorbing phosphorus, enhancing phosphorus removal efficiency. Scientific addition of carbon sources can optimize the microbial community structure in wastewater treatment systems, promoting the activity of denitfriying bacteria and PAOs and improving nitrogen and phosphorus removal rates. When influent carbon sources are insufficient, STPs often add carbon sources to ensure effluent TN compliance.  
Figure 10 shows that as influent BOD/TN increases, the carbon emission intensity of carbon sources decreases. The carbon emission intensity of carbon sources when BOD/TN \(<\) 2 (0.051 kgCO\({}_{2}\)-eq/m\({}^{3}\)) is significantly higher than when BOD/TN \(>\) 2. When BOD/TN \(>\) 4, the carbon emission intensity of carbon sources drops to 5.73 \(\times\) 10\({}^{-3}\) kgCO\({}_{2}\)-eq/m\({}^{3}\). Furthermore, the carbon emission intensity of carbon sources decreases with rising influent COD/TN. When COD/TN \(>\) 6, the carbon emission intensity of carbon sources declines sharply. No significant correlation was found between TN removal and the carbon emission intensity of carbon sources.  
Figure 9: Carbon emission ratio of different kinds of chemicals.  
The carbon emission intensity of phosphorus removal agents when influent BOD/TP < 20 (0.0141 kgCO\({}_{2}\)-eq/m\({}^{3}\)) is substantially higher than when BOD/TP > 20 (0.0183 kgCO\({}_{2}\)-eq/m\({}^{3}\)). No significant correlation was observed between TP removal and carbon emission intensity of phosphorus removal agents. Therefore, controlling the proportions of various pollutants in the influent to meet nitrogen and phosphorus removal requirements can reduce the carbon emission intensity of carbon sources and phosphorus removal agents. Specific measures include implementing rainwater-sewage diversion, improving the sewer network, and adjusting the influent C/N ratio using alternative carbon sources. Additionally, [PERSON] et al. [39] found that optimizing high-efficiency sedimentation tank operation and configuring precise dosing systems can reduce phosphorus removal agent dosage. Controlling the reflux rate, nitrate nitrogen at the anoxic tank outlet, and rationally distributing influent to pre-anoxic, anaerobic, and anoxic zones can effectively utilize influent carbon sources, reducing the need for additional carbon sources.
## 2 Methodology
### Image Processing  
Before being used as an input for ML, the images need to be processed to correct several effects. The correction processes are summarized in Figure 2 and discussed below.  
Firstly, despite the effort to keep the camera in the same position compared to the module, variations always occur, especially when considering the measurement conditions (outdoor, evening, possible wind). Furthermore, as the images are taken at an angle, they are distorted. Hence, the first step is to correct the images for the perspective distortion [31; 32; 33] using a code developed in Matlab [34]. The active module area is aligned, and the perspective is fixed following the procedure of Reference [35], as shown in Figure 2B. The cell images are then resized from \(300\times 300\) to \(100\times 100\) pixels to reduce computation time. They are then normalized using min-max scaling features to standardize the dataset for systematic analysis. Blurred images are identified using blur detection based on the modified Laplacian matrix technique described in Reference [36].  
An appropriate threshold value (0.80) is chosen, and EL images below this threshold are defined as 'blurred' and discarded. The module images are then segmented into cells [37]. The module and cell edges are computed by rotating the processed image at different angles and summing the pixel values along the x and y axes to locate the horizontal and vertical lines, as shown in Figure 2C. If distortion is identified, the images are perspective-corrected, using homography transformation [31; 32]. Note that this is a second distortion correction for the case where the correction on the module level is not sufficient. Busbars are then removed from the cell images by first locating them (similar method to  
Figure 1: EL cell images inflicted with different faults: (**A**) no failures; (**B**) finger failures (region marked with light blue); (**C**) micro-crack Mode A (black); (**D**) cracks Mode B (olive green) and Mode C (white).  
Figure 2: The image processing procedure used in this study: (**A**) acquired raw EL measurements; (**B**) the prospectively corrected normalized image; (**C**) busbar identification; (**D**) solar cell extraction; and (**E**) busbar removal.  
identifying the edges) and then adjusting pixel values to the neighboring pixels' mean, as shown in Figure 2E.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 2 Methods
### Bispectrum Analysis  
After preprocessing, the single heartbeat data is analyzed by Bispectrum.  
The HOS is one of the robust methods applied for the non-linear signal analysis. It depicts the spectra of third and higher order statistics (i.e., moments and cumulants). In this work, the third-order statistics (bispectrum) of the ECG signals are gathered [29].  
The power spectrum of stochastic process is defined as the Fourier transform of auto-correlation function. Similarly, the high-order spectrum is defined as Fourier transform of high-order moment. The high-order spectrum is proposed to offer precise signal analysis and estimation, which may contain more details than low-order spectrum.  
The minimum high-order spectrum is the bispectrum, a two-dimensional function of frequency, which is a very helpful tool to detect and quantify quadratic effects in time-series.  
Denoting \(x(n)\) a stationary, zero mean and stochastic processes with third-order cumulant defined as  
\[R_{3x}(\tau_{1},\tau_{2})=E[x(n)x(n+\tau_{1})x(n+\tau_{2})], \tag{1}\]  
where \(\tau_{1}\) and \(\tau_{2}\) denote the time shift. \(E[\cdot]\) denotes mathematical expectation.  
Then, the bispectrum of \(x(n)\) is given by the expression  
\[B_{x}(w_{1},w_{2})=\sum_{\tau_{1}=-\infty}^{+\infty}\sum_{\tau_{2}=-\infty}^{+ \infty}R_{3x}(\tau_{1},\tau_{2})\cdot\exp(-j(\omega_{1}\tau_{1}+\omega_{2}\tau _{2})),(|\omega_{1}|,|\omega_{2}|\leq\pi), \tag{2}\]  
where \(\omega_{1}\), \(\omega_{2}\) are two independent frequencies.
## 1 Introduction
[PERSON]'s team prepared carbon nanotube buckypaper composites and characterized the self-sensing properties of bending deformations. The results showed that the resistance change rate of a carbon nanotube buckpaper sensor was consistent with that of a strain gauge [46]. The sensing mechanism of a carbon nanotube buckypaper sensor showed that the resistance increased with the increase of strain. Therefore, the circular carbon nanotube buckypaper  
Figure 2: Repair method: (**a**) patch repair, (**b**) scarf repair, (**c**) tapered scarf repair, and (**d**) stepped-lap scarf repair. sensor has the function of multi-direction strain monitoring, which can be used as an effective tool for composite-structure health monitoring. This sensor was selected in this study to qualitatively monitor the running status of the spar cap after repair, while numerical error analysis is not discussed. Aiming at the spar cap damage of blade in service, the stepped-lap scarf joint repair was selected for this study, and the basic configuration and health monitoring tests were constructed. The basic configuration test was a tensile test. The large aspect ratio specimens with multiple configuration parameters of stepped-lap scarf unidirectional tape were selected for the test. The results confirmed the sensitivity parameters and failure mode of the spar cap stepped-lap scarf joint repair. According to the damaged weak zone confirmed by the configuration test, the health monitoring test for the spar cap stepped-lap scarf joint repair was constructed by embedding carbon nanotube buckypaper sensors. Under static and fatigue loads, the sensors successfully monitored the progressive damage and the crack propagation signal of the test specimens. Based on the above two tests, important configuration parameters were confirmed for the spar cap repair scheme. The results provide an important basis for the health monitoring of blades in service after repair. To explain the tests, the key terms of the repair methods mentioned in this paper are given graphically. The stepped-lap mode of the stepped-lap scarf joint repair is divided into a single stepped-lap (Figure 3a) and double stepped-lap (Figure 3b). A double stepped-lap scarf joint repair (Figure 3c) mainly includes the parent structure, the patch, and the stepped-lap joint surface. The parent structure is the original structure before the damage occurred. The patch is a composite material structure that adhesively bonds over the damaged area, utilizing a scarf of a stepped lap. The single layer thickness of the patch is the ply thickness. The connection surface of the parent structure and patch is a stepped-lap joint surface. A stepped-lap joint surface contains an adhesive layer and two adhesive boundary layers. One of the two layers is between the adhesive and the patch, the other layer is between the adhesive and the parent structure. Adhesively bonded joints are on the edge of the stepped-lap joint surface. Rational design of the stepped-lap width and reinforcement layer can relieve the stress concentration of the adhesively bonded joint.
# Slope Stability Analysis of Open-Pit Mine Considering Weathering Effects
###### Abstract  
Weathering processes gradually alter the physical and mechanical attributes of slope materials, weakening the structural integrity and stability of slopes. This paper presents an in-depth analysis of slope stability in an open-pit mine, emphasizing the pivotal role of weathering effects in determining slope stability. To accurately capture the impact of weathering on slope stability, a comprehensive analysis model was developed, incorporating field observations, laboratory testing, and numerical simulations. The effects of weathering on the mechanical properties of black shale were studied through extensive laboratory tests. The uniaxial compressive strength, shear strength, and modulus of elasticity significantly decreased with increasing weathering, indicating a heightened vulnerability to slope failure. The correlation function between mechanical parameters and weathering time was obtained, providing the basis for evaluating the stability of mine slopes. It was found that more severe weathering conditions were strongly correlated with elevated risks of slope failure, including landslides and collapses. Based on these findings, practical recommendations are provided for slope reinforcement and management strategies, aimed at mitigating slope failure risks and ensuring the safe and efficient operation of the mine. By incorporating weathering effects into slope stability analysis, mine operators can make informed decisions that account for the dynamic nature of slope materials and their susceptibility to weathering, thereby improving overall mine performance and safety.  
slope stability; weathering; black shale; mechanical properties +
Footnote †: journal: Applied Sciences
## 3 Database Collection and Processing  
### Experiment and Data Collection  
The database was prepared based on the results of two studies [4; 47]. A 3D representation of the collected database for CBR is shown in Figure 4. The input parameters for this study were carefully selected, to ensure the accurate prediction of the California Bearing Ratio (CBR) value for album sludge used in road construction. The database consisted of nine parameters, including the liquid limit (LL) and plasticity index (PI) of soil, the specific gravity (\(G_{\rm s}\)) of soil, the number of compaction blows, the optimum moisture content of soil, the maximum dry density of soil, the optimum moisture content of the mixture, the maximum dry density of the mixture, and the sludge content. These parameters were chosen, to consider different basic geotechnical tests, including compaction, Arterberg limits, and sludge content. The main goal of this study, was to predict the CBR value, which is crucial in the design of pavement and road construction. By selecting these input parameters, the researchers ensured that their predictions would be accurate and reliable, which is essential for the success of any road construction project.  
Figure 5 shows the effect of different input parameters, such as number of compaction blows, sludge content, maximum dry density, and optimum water content, on the CBR. According to the results in Figure 5a, with an increase in the number of compression hammer blows, the CBR increases dramatically. Figure 5b illustrates the effect of sludge content on CBR. It can be seen from Figure 5b that as the sludge content increases, CBR increases significantly at first. When the sludge content reaches around 8%, CBR begins to decrease as the sludge percentage increases. Figure 5c shows the effect of increasing the maximum dry density (MDD) of the soil on the CBR of the soil. According to the results, it can be concluded that the CBR increases as the maximum dry density of the soil increases. Figure 5d shows the effect of optimum moisture content (OMC) on CBR. The results show that the CBR decreases as the optimum water content increases.  
Figure 4: Database used for modeling based on the CBR.  
Figure 5: Effect of (**a**) number of compaction blows, (**b**) sludge content, (**c**) MDD, and (**d**) OMC on the CBR in the collected database.  
In order to provide a more detailed analysis of the database, Table 1 shows the statistical information for each variable, including the minimum, maximum, mean, and standard deviation.
# T-Splines for Isogeometric Analysis of the Large Deformation of Elastoplastic Kirchhoff-Love Shells
## 3 Formulations for the Large Deformation of Elastoplastic Kirchhoff-Love Shells
### Geometry Definition  
Let \(\xi^{\alpha}\) be the curvilinear parametric coordinates of the midsurface and \(\xi^{3}\) the through-thickness coordinate. In the following formulas, the Greek letters \((\alpha,\beta)\) take the values \(\{1,2\}\), and the Latin letters \((i,j)\) take the values \(\{1,2,3\}\). The midsurface of the shell is represented by \(\mathbf{x}_{m}(\xi^{\alpha})\); see Figure 6. In our work, the midsurface is described by the T-spline surface in Equation (5):  
\[\mathbf{x}_{m}(\xi^{\alpha})=(\mathbf{P})^{T}\mathbf{R}(\xi^{\alpha}), \tag{18}\]  
where \(\mathbf{P}\) is the control point and \(\mathbf{R}(\xi^{\alpha})\) is the blending function. The tangent vector \(\mathbf{a}_{\alpha}\) and the unit normal vector \(\mathbf{a}_{3}\) for any point in \(\mathbf{x}_{m}(\xi^{\alpha})\) can be defined as  
\[\mathbf{a}_{\alpha}=\frac{\partial\mathbf{x}_{m}}{\partial\xi^{\alpha}}= \mathbf{x}_{m,\alpha},\quad\mathbf{a}_{3}=\frac{\mathbf{a}_{1}\times\mathbf{ a}_{2}}{\left\|\mathbf{a}_{1}\times\mathbf{a}_{2}\right\|}. \tag{19}\]  
The Kirchhoff-Love theory assumes that the cross-sectional fibers remain normal to the midsurface throughout the deformation. According to the KL shell theory, any point in the shell can be expressed by the midsurface and unit normal vector as  
\[\mathbf{x}=\mathbf{x}_{m}+\xi^{3}\mathbf{a}_{3}. \tag{20}\]  
Calculating the partial derivative of the shell expression, we can acquire the covariant base vectors:  
\[\mathbf{g}_{\alpha}=\frac{\partial\mathbf{x}}{\partial\xi^{\alpha}}=\mathbf{a} _{\alpha}+\xi^{3}\mathbf{a}_{3,\alpha},\quad\mathbf{g}_{3}=\mathbf{a}_{3}. \tag{21}\]  
The metric coefficients are defined as  
\[\mathbf{g}_{\alpha\beta}=\mathbf{a}_{\alpha\beta}-2\xi^{3}\mathbf{b}_{\alpha \beta},\quad\mathbf{g}_{\alpha 3}=0,\quad\mathbf{g}_{33}=\mathbf{a}_{33}=1, \tag{22}\]where the covariant metric coefficient \(a_{\alpha\beta}\) and the covariant curvature coefficient \(b_{\alpha\beta}\) are given as  
\[a_{\alpha\beta}=\mathbf{a}_{\alpha}\cdot\mathbf{a}_{\beta},\quad b_{\alpha\beta} =\mathbf{a}_{\alpha,\beta}\cdot\mathbf{a}_{3}. \tag{23}\]  
Contravariant base vector \(\mathbf{g}^{i}\) can be computed as \(\mathbf{g}^{\alpha}=g^{\alpha\beta}\mathbf{g}_{\beta},\mathbf{g}^{3}=\mathbf{ g}_{3}\), where \(g^{\alpha\beta}=\left(g_{\alpha\beta}\right)^{-1}\). It should be noted that in the above equations, we ignore the deformation in the thickness direction, which needs to be corrected in the next section.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 4 The Proposed Protocol
### The Integration in Delay-Tolerant Applications  
Our protocol is convenient for vehicular delay-tolerant applications. The key benefit that it offers to these applications is the low-latency response, because vehicles do not have to query the cloud server by themselves. Instead of querying the cloud directly, we propose to allow vehicles to query the edge first. This way, unnecessary delays are avoided. In addition, connectivity is enhanced since vehicles with no internet connection can seek traffic information by querying the nearest edge. The edge can connect to the Internet if necessary for providing appropriate responses to requesting vehicles. Since user data can be produced and consumed locally and anonymously without the need to store it or upload it to the cloud, our proposed methodology provides an enhanced privacy for drivers on the road.  
An interested vehicle states its preferences for a data collection service in advance, such that it can request for traffic information on demand. The proposed integration of our protocol in delay-tolerant applications is shown in Figure 4. As the figure shows, a vehicle seeking traffic information broadcasts a query to the nearest edge node, which searches for the requested data in its local data store first. If the edge node can provide an answer, it would directly send a reply message to the requesting vehicle. Otherwise, it connects to the cloud server, receives a reply and then responds to the requesting vehicle.  
Figure 4: Integrating our Protocol in Delay-tolerant Applications.  
If the requesting vehicle is unable to communicate with a network edge, it would create a route request (RREQ) message and broadcasts it to its one-hop neighbors. RREQ is then routed to the nearest edge by utilizing a routing protocol in a V2V communication. If the answer is available, the edge node reachable via VANET will respond with the corresponding data in a form of a route reply (RREP). RREP is routed back to the requesting vehicle via VANET. An edge node that is indirectly communicated can connect to the cloud server when an answer to the query of the requesting vehicle is not available.  
In the case of sparsely connected VANET where a network edge is unreachable, we propose to utilize a dynamic vehicular cloud as described in [39]. When the RREQ is received by vehicles at the desired destination, vehicles subscribed to the same service will cooperate in providing an answer to the requesting vehicle. Whenever an internet access is required, vehicles with no internet connection can borrow these services from vehicles with an internet access via VANET. This methodology provides a generalized step-by-step procedure to request and receive traffic information on demand in a VEC environment. It merges both paradigms: VANET and MEC for a maximized connectivity. Further works would include comprehensive specifications of a VEC method for travel convenience delay-tolerant applications.