esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Online Teaching Quality Based on Facial Expression Recognition
## 2 Materials and Methods
### Face Detection Algorithm  
The face detection process is implemented using the MTCNN [18; 19; 20] algorithm. As shown in Figure 3, MTCNN consists of three cascaded convolutional neural network  
Figure 1: System composition.  
Figure 2: Schematic diagram of the FER2013 dataset.  
architectures, namely P-Net, R-Net, and O-Net. P-Net is mainly responsible for quickly obtaining the regression vector of the candidate window and bounding box of the face region; it uses the bounding box for regression, calibrates the candidate window, and then merges the highly overlapping candidate boxes through a non-maximum suppression (NMS). R-Net removes misidentified candidate boxes through a bounding box regression and NMS. The network structure of R-Net has one more fully connected layer than P-Net, which has a better effect of suppressing a false positive. O-Net has more convolutional layers than R-Net, which makes its processing results more refined. The role of O-Net is the same as that of R-Net, but this layer performs more supervision on the face area and outputs 5 landmarks at the same time; then, it generates the final bounding box and outputs the final face feature key points [21].  
The specific process of face detection by MTCNN is as follows:  
Step 1: Build an image pyramid. First, the image is scaled, rotated, and stretched at different scales, and an image pyramid is constructed to adapt to the detection of faces of different sizes.  
Step 2: Input the image pyramid constructed in the first step into P-Net. P-Net is a proposal network for the face area. After inputting the features into 3 \(\times\) 3 \(\times\) 3 convolutional layers, the face classifier determines whether the area is a human face and uses the bounding box regression and a facial key point locator to make a preliminary proposal of the face area. This part will eventually output many face areas that may have human faces, and these regions are fed into R-Net for further processing. The P-Net network will finally output a feature vector of size 1 \(\times\) 1 \(\times\) 32.  
Step 3: Input the features' output in the second step into the R-Net network; R-Net refines the input feature vector, removes the wrong box, and uses the border regression and facial key point locator again for face recognition. The bounding box regression and key point positioning of the region will finally output a more credible face region. R-Net uses a 128 fully connected layer at the end of the network to increase the accuracy of the network.  
Step 4: Input the features' output in the third step into the O-Net network. O-Net uses a 256 fully connected layer at the end of the network, which retains more image features and further increases the accuracy of the network. At the same time, face discrimination, face region border regression, and face feature positioning are performed, and, finally, the coordinates of the upper left corner and the lower right corner of the face region and the five feature points of the face region are output.  
Figure 3: MTCNN network structure.
## 3 Results and Discussion
### Environmental Impact Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S. No** & **Ref** & **Location, Country** & **CSP Technology** & **Title of Literature** & **Simulation** & **TES,** & **LCOE** \\ \hline
1 & [13] & Quetta, Pishin, Goki, & & & & & Quetta, 153 USD cells/AWh\({}_{2}\) \\  & Navasubhan, & & Techno-economic assessment of & & & & Fishin, 14.7 USD cells/AWh\({}_{2}\) \\  & Rahim Yar Khan, & & Parabolic Trough & concentrated solar thermal power & & & Goki, 26.9 USD cells/AWh\({}_{2}\) \\  & Rahim Yar Khan, & & Collection (PTC) & generation and potential barriers in & & & Nawababha, 29.3 USD cells/AWh\({}_{2}\) \\  & Pakistan & & & its deployment in Pakistan & & & Rahim Yar Khan, 27.9 USD \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline
2 & [15] & Navongo and & & Optimization and techno-economic & & & \\  & Tamale, Ghana & & & assessment of concentrated solar & & & STPP; 28.83 and 25.83 USD \\  & & & power (CSP) in South-Western & & & & \\  & & & Africa: A case study on Ghana & & & & \\ \hline
3 & [24] & Jamshoro, Pakistan & & & The performance prediction and & & \\  & & & parabolic solar & & techno-economic analyses of a & & \\  & & dish/Stuffing system & stand-alone parabolic solar & & & & \\  & & & (PSDG) & & & & \\  & & & & & Pakistan & & \\ \hline
4 & [27] & Dodoma site, & & Is Concentrated Solar Power (CSP) a & & & \\  & Sulang site & & feasible option for Sub-Saharan & & & \\  & Tanzania & & & Africa? Investigation & & & \\  & & & techno-economic feasibility of CSP & & & \\  & & & in Tanzania & & & \\ \hline
5 & [47] & Ethiopia & Central receiver & Comparative Analysis of Rankine & & & \\  & & system (CRS), linear & Cycle Linear Present Reflector and & & & \\  & & Fresnel reflector & & Solar Tower Plant Technologies & & \\  & & & (LFR) & Techno-economic Analysis for & & & \\ \hline
6 & [64] & Algiers, Algeria & & STP & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Ref** & **Location, Country** & **CSP Technology** & **Title of Literature** & **Simulation** & **TES,** & **LCOE** \\  & & & & & **Software** & **Hours** & \\ \hline
1 & [15] & Quetta, Pishin, Goki, & & & & & Quetta, 153 USD cells/AWh\({}_{2}\) \\  & Navasubhan, & & Techno-economic assessment of & & & & Fishin, 14.7 USD cells/AWh\({}_{2}\) \\  & Rahim Yar Khan, & & & concentrated solar thermal power & & & Goki, 26.9 USD cells/AWh\({}_{2}\) \\  & Rahim Yar Khan, & & Collection (PTC) & generation and potential barriers in & & \\  & Pakistan & & & & & \\  & & & & & & \\ \hline
2 & [15] & Navongo and & & Optimization and techno-economic & & & \\  & Tamale, Ghana & & & & & \\  & & & & power (CSP) in South-Western & & \\  & & & Africa: A case study on Ghana & & & \\ \hline  & & & & & & \\  & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CSP technology in other published works, for comparison.
# Influence of Partial and Total Replacement of Used Foundry Sand in Self-Compacting Concrete
###### Abstract  
In this work, the feasibility of partially and totally replacing natural sand with used foundry sand in self-compacting concrete was studied. Natural sand was replaced in 50% and 100% vol. by used foundry sand. The fresh state properties parameters analyzed in this study were slump flow, t\({}_{500}\), V-funnel, Japanese ring and L-box following EFNARC guidelines. Results indicated an improvement in the fresh state properties when used foundry sand was utilized for partial and total replacement. The mechanical properties compressive strength and splitting tensile strength were obtained and analyzed at 7 and 28 days. Regarding the compressive strength, used foundry sand enhanced compressive strength by up to 67% compared to control concrete. For splitting tensile strength, the self-compacting concrete with 50% vol. of used foundry sand displayed a slight decrease (2.8%) compared with the control concrete. SEM images showed that the concretes with used foundry sand had a less porous and more compacted matrix than the control concrete. It was concluded that the incorporation of used foundry sand in large volumes can be utilized as a sustainable alternative natural fine aggregate.  
self-compacting concrete; used foundry sand; siderurgical aggregates; eco-concrete; mechanical properties +
Footnote †: journal:
## 3 Security and Privacy of Computing Paradigms
### Cloud-Related Aspects  
The majority of today's networks and the idea of storing data remotely is greatly inclined to technologies relating to Cloud computing. One of the exceptional demands is for the Cloud to see that services are always made available consistently, the reliability is maintained, and data are supplied as demanded. As mentioned earlier, one of the prime reasons organizations or individuals are reluctant to embrace the quick movement to the Cloud model is the huge concern for information security and privacy. Some acknowledged issues tied to security and privacy in Cloud computing include confidentiality, data security, phishing, and multi-tenancy [50]. This section looks into the various threats aligned with security and privacy within the Cloud computing system and suggests some modalities for threat mitigation.  
Cloud computing users adopt different distributed Cloud models based on their specific needs, and because of this, the Cloud security and privacy threats differ according to the infrastructure hosted in the Cloud. According to the Cloud Security Alliance (CSA), major regular threats are information leakages, Denial of Service (DoS) Attack, and Advanced Persistent Threats (APT) [51].  
Adequate Cloud infrastructural security largely depends on the established protective technologies with many layers. This brings about the importance of adapting an Intrusion Detection System (IDS) specifically to trace suspected threats intelligently and intercept potential attacks over a network. Furthermore, the various events witnessed can be separated to carry out network status analysis. Resources and services of Cloud CIA are said to encounter different types of threats originating from either inside or outside intruders [52].
## 3 Results and Discussion
### Evaluation of Models  
#### 3.2.1 Comparison between Machine Learning Models and the Equation of State  
To make comparisons among machine learning models and the theoretical model, we should first compare the RMSEs of different models (Table 5). Results show that the best machine learning models we have chosen in the four experimental groups are all apparently more precise than those results calculated by the [PERSON] and [PERSON] equation, with lower RMSEs. The predicted values in the testing sets are generally highly close to their actual values in all the four machine learning models (Figure 5). It should be noted that experimental results provided by [PERSON] _et al._[1] are generally more precise than the other two groups of experimental results [24; 25], according to the generalized Tait equation [1; 49]. Additionally, the testing RMSE of the SVM for the data provided by [PERSON] _et al._[1] is the lowest during Table 5. One possible reason is that data provided by [PERSON] _et al._[1] may have less experimental errors  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **RMSE (for Testing)** & **Training Time** & **Prediction Accuracy** \\ \hline Linear Regression & 96.42 & 0:00:01 & 93.0\% \\ SVM & 15.79 & 0:00:02 & 99.2\% \\ GRNN & 92.33 & 0:00:02 & 93.0\% \\ MLFN 2 Nodes & 39.70 & 0:06:50 & 96.1\% \\ MLFN 3 Nodes & 25.03 & 0:08:36 & 97.7\% \\ MLFN 4 Nodes & 22.65 & 0:10:06 & 99.2\% \\ MLFN 5 Nodes & 73.84 & 0:13:49 & 93.0\% \\ MLFN 6 Nodes & 23.64 & 0:17:26 & 99.2\% \\ MLFN 7 Nodes & 65.74 & 0:14:39 & 93.8\% \\ MLFN 8 Nodes & 55.32 & 0:16:18 & 93.8\% \\... &... &... &... \\ MLFN 29 Nodes & 164.54 & 0:52:29 & 89.1\% \\ MLFN 30 Nodes & 136.96 & 0:37:38 & 89.8\% \\ MLFN 31 Nodes & 168.13 & 0:41:35 & 89.1\% \\ MLFN 32 Nodes & 88.25 & 0:50:43 & 93.0\% \\ MLFN 33 Nodes & 143.65 & 2:30:12 & 89.8\% \\ MLFN 34 Nodes & 163.78 & 1:00:17 & 89.1\% \\ MLFN 35 Nodes & 166.92 & 0:44:16 & 89.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction models using experimental data by all the three experiment reports [1; 24; 25].  
due to a well-developed measurement method, leading to better training effects, which indicates that data provided by [PERSON] _et al._[1] is a good sample for training in practical predictions.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Compressive Elastic Modulus (GPa)**} & **Compressive Strength (MPa)** \\ \hline HA0 & 0.13 \(\pm\) 0.04 \({}^{\text{a,b}}\) & 6.18 \(\pm\) 0.28 \({}^{\text{a}}\) \\ HA30 & 0.05 \(\pm\) 0.02 \({}^{\text{b}}\) & 1.73 \(\pm\) 0.22 \({}^{\text{b}}\) \\ HA60 & 0.19 \(\pm\) 0.08 \({}^{\text{a}}\) & 3.23 \(\pm\) 0.75 \({}^{\text{b}}\) \\ Cancellous bone & 0.12–1.1 [7] & 1–12 [7; 30] \\ \hline \hline \end{tabular} \({}^{\text{a,b}}\) Values with different superscripts in the same parameter are significantly different (\(p<\) 0.01).  
\end{table} 
Table 5: Mechanical properties of the HA scaffolds and human cancellous bone (\(n=5\)). that HA60 had higher compressive strength than HA30. Furthermore, the tendencies of the rate of increase for the density and the linear shrinkage were similar; thus, it was expected that the walls between the pore channels rigidly supported the internal structure of the scaffolds. [PERSON] et al. suggested that the distance between the pores (i.e., the wall thickness of the pore channel) is one of the variables for calculating the porosity [38]. In other studies, a thicker scaffold wall led to better mechanical properties [40; 41; 42]. However, in the present study, the wall thickness and compressive strength were inversely proportional, and wall thickness and porosity tended to be proportional. This shows that it is important to identify the complex mechanism depending on the phenomenon occurring in the manufacturing process, rather than simply comparing the results after the production of the scaffolds. It is necessary to identify the correlation between the changes during the process (e.g., the rate of linear shrinkage, the rate of density increase, and the physical and mechanical characteristics of the scaffolds) and to apply it for manufacturing an optimal artificial bone scaffold to be implanted into the human body.
## 2 Concept of d-MTFDI System
### Vertical Distribution of TFDIs  
Figure 4 demonstrates the vertical distribution of a d-MTFDI system subjected to wind load \(P(t)\). The system consists of \(R\) TFDIs; for the \(i\)th TFDI, the MOS are located on the \(a_{i}\)th floor, and the corresponding inerters are placed on the \(b_{i}\)th floor. This arrangement results in cables crossing \(p_{i}\) floors (\(p_{i}=\left\lvert a_{i}-b_{i}\right\rvert\)) to connect the MOS and inerters, with the number of crossing floors \(p_{i}\) denoted as the inerter topology. Generally, the TFDIs can be installed on any floor, allowing for variable inerter topology \(p_{i}\) among different TFDIs. In general, the d-MTFDI system inherently has the potential to control multi-mode responses, especially for the three-dimensional WIV response and seismic response, once letting different TFDIs tune to the corresponding modes. But for the WIV response of high-rise buildings, the crosswind response is dominant, thus reducing the basic modal response is the target of this paper.  
Figure 3: Configuration of a TFDI: (**a**) cross-section view; (**b**) plan view.
## 3 Results and Discussion
### Raman Spectroscopy of Carbon Product  
The crystalline quality of the carbon nanostructures can be determined by assessing the shape evolution of the Raman spectra of the corresponding samples [24]. The position, width and relative intensity of bands are modified according to the carbon forms [25]. The Raman spectra of different samples are shown in Figure 6. All the spectra of the carbon nanostructures consist of two characteristic peaks, however, the positions of the D and G bands both shift to a higher wavelength, with a decreased intensity of the G band. The Raman bands of Figure 6a are at 1365 cm\({}^{-1}\) (D band) and 1590 cm\({}^{-1}\) (G band). The position of the G band in Figure 6c moves to 1677 cm\({}^{-1}\), revealing the existence of \"C=N\" bands besides \"C=C\" bands. It is also consistent with the result of the EDX spectrum in Figure 3f. The ratio of I\({}_{\rm D}\)/I\({}_{\rm G}\) is derived from the quantitative measurements of the Raman spectra of each sample. The I\({}_{\rm D}\)/I\({}_{\rm G}\) value for the carbon products on 500 nm Cu and 500 nm Cu-Cr in NH\({}_{3}\) were calculated to be 1.2 and 1.32, respectively, suggesting these two types of CNLs are rich in defects.  
Figure 5: SEM images of carbon nanostructures on Cu-Cr film of different thicknesses by the CVD method in the presence of NH\({}_{3}\) gas. (**a**) 100 nm; (**b**) 500 nm.  
Figure 6: Raman spectra of carbon products synthesis using different reducing gases. (**a**) CNTs on 100 nm Cu in H\({}_{2}\); (**b**) CNTs on 100 nm Cu in NH\({}_{3}\); (**c**,**d**) carbon products on 100 nm Cu and Cu-Cr in NH\({}_{3}\).
## 4 Discussion
### Anchorage and Breakage of Root System  
The anchoring capacity and wind resistance of the root system depend on soil properties, the morphological characteristics of the root system, and the weight of the root-soil plate. In the pull-out test, when the plant was turned downhill with a winch, roots growing uphill resisted uprooting (or breaking), while roots growing downhill resisted bending [81]. During the overturning process, the taproots were wholly pulled out, which may be related to the increase in soil water content and humidity after applying a water retention agent [82]. The upslope embedded lateral roots were fractured, and most of the fine tips remained in the soil, which was consistent with the results of [PERSON]'s study [66]. The downslope lateral roots almost cracked due to resistance to bending, which agrees with [PERSON]'s study [81].  
The anchorage of moderately weathered rocky slopes is mostly determined by embedded secondary lateral roots [41]. When the soil moisture content is sufficient, the water retention agent's water absorption characteristics change the soil structure, limiting the delivery of soil moisture to the root system and thereby inhibiting root growth. As a result, the quantity of secondary lateral roots trapped in fractures diminishes, soil-root cohesiveness drops, and anchorage strength falls. According to [PERSON]'s research, the more embedded the lateral roots are, the larger the root anchoring capacity [83]. According to [PERSON]'s research, the wider the root system's diameter is, the deeper the root is buried, the larger the soil-root contact area is, and the stronger the root anchorage force is [31].  
The root-soil plate of a plant is a composite structure composed of tree roots and surrounding soil, and its anchoring capacity is directly related to plant growth and slope stability [84]. Additionally, the moisture content of the soil beneath the root-soil plate has a substantial effect on root anchoring and maximum overturning moment. According to [PERSON]'s winch tests, water content below the root-soil interface greatly inhibits plant anchoring [85]. Increased soil moisture content following application of a water retention agent results in an increase in soil pore water pressure. Hydraulic fracture occurs as a result of the plant self-loading. Water is injected into this fractured split, minimizing friction between the root and the soil. The root-soil board is prone to bulge when the winch is pushed [86]. Due to friction between the root-soil plate and the soil, the root-soil plate separates from the soil. At this point, the plant topples over due to the combined action of gravity and horizontal external force, and it is readily uprooted.
## 4 Dual-Band Passive Metasurface Based Beam Steering Techniques
### Transmitarrays-Based Beam Steering  
Recently, another class of antennas known as transmitarrays has gained significant research focus, enabling the design of highly directive antennas with beam steering capabilities. They incorporate passive metasurfaces with properties similar to those of a lens but are planar and fabricated using printed circuit board (PCB) technology. Compared to lens antennas, they are lightweight, conformable, and reconfigurable (in beam shape, direction, and polarization) [58]. Similarly to a RA, in TAs, a feed source is placed at the focal point of the metasurface. Dual-band beam steering TAs are reported in [32; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69]. In most steering mechanisms, in-plane feed translation or feed rotation is used to steer the beam, requiring additional feed displacement arrangements [32; 67; 33; 68]. In some TAs, the rotation of the dual-band metasurface is used [59]. In [32], an optimal approach for designing a dual-frequency band TA is presented, exhibiting a measured gain of 24 dBi at 20 GHz and 27 dBi at 30 GHz. The consistent beam direction in both frequency bands with steering confirms the full-duplex operation in the dual-frequency bands. In [68], the dual-band TA operating at Ka-band, with a gain of 28.3 dBi and 28.4 dBi at the two frequency bands of 20 GHz and 30 GHz, is presented. Another dual-band beam steering TA using planar metasurfaces is presented in [59]. The metasurface comprised the double square loop resonant elements in the multi-layer unit cell, providing a steering range of around \(\pm 50^{\circ}\), with the peak gain of 19.8 dBi at 14 GHz and 16.7 dBi at 8 GHz, respectively. The concept of beam steering with independent phase control using a folded TA is presented in [60]. The unit cell provides independent phase control for the dual frequency bands utilizing polarization conversion from linear to different circular polarization (RHCP and LHCP). Several types of multilayer dielectric, composite, and metallic phase-shifting surfaces or metasurfaces have been reported in the literature with linear- and circular-polarization-compatible unit cells.
## 3 Results
### Distribution Uniformity  
The computed distribution uniformities (_DUs_) also showed big variations among the Agri-Parks (Figure 4). Nevertheless, all the _DUs_ were greater than 50%. The Agri-Park-level _DU_ performance decreased in the order Rooiwal > Tarlton > Westonaria > Soshanguve with _DU_ values of 95, 86, 67, and 60%, respectively. Nevertheless, these values were generally lower than expected for pressure-compensated drip irrigation systems. Comparing these values with the standard _DU_ for drip irrigation of 85% [24], indicates that only Rooiwal and Tarlton were performing at the acceptable level, while Westonaria and Soshanguve performed poorly. Noteworthy is that the value of 86% for Tarlton can be a cause for concern as it is just above the acceptable limit.