esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
When it comes to computer-based emotion recognition, EEG-based methods are more accurate; however, unlike image-based methods, the subject has to wear EEG probes and other electrical equipment, as well as be in a controlled environment. On the other hand, image-based methods are more convenient to detect the emotions of participants in a video conference, even though their accuracy is lower.  
The resulting application of this research was the creation of a platform that video meeting organizers can use to monitor their audience. Architecture B is a fully automated application that Zoom meeting organizers can use to monitor a video conference (a Zoom meeting) without any hassle compared to architecture A, as shown in Figure 22. While conducting the research, two reliable SVM models were created for facial landmark-based emotion classification and attention detection. The dimension reduction method introduced in the preprocessing phase showed a significant level of improvement through a reduction of 136 features (136 dimensions) to 11 features.  
Instead of using the Gabor wavelet facial appearance information method [22] or [PERSON]'s method of calculating 21 normalized vectors for face emotion recognition [21], this research used only 11 calculated values with a relatively lower computational cost.  
This attention classification model showed 0.6770 accuracy using only 1150 images from the Helen dataset, which can be further improved with more training samples. The loosely coupled architecture design allows continuous improvement of the classification subsystem without disrupting other modules. The image quality of the captured image is a particularly important factor for classification accuracy. Therefore, future improvement of the image capturing subsystem can significantly enhance the effectiveness of the entire  
Figure 9: Attendance report generated by the system for a video conference: (**a**) theoretical report generated for a bigger audience; (**b**) actual report generated for four-participant audience.  
system. Using the Zoom video SDK or a video conferencing tool with direct participant video feed access can noticeably improve the result of the overall system.  
When using this system in schools, the attendance of students can also be achieved automatically.  
A public dataset was used in this research for training the model. For system testing, a group of students were selected after explaining the terms of use of their personal data. This experiment did not record images but instead kept timeseries data for each student with respect to their attention and emotion classification. When this system is converted to a production-level application, the EU General Data Protection Regulation (GDPR) needs to be implemented, and the terms of service should clearly explain the data collection, access, use, and processing of personal data in terms of the legal basis and purpose of use related to the retention period, data storage and transfer, prior consent from the person, and personal rights.
## 5 Conclusions  
In this work, a deep learning model based on the convolutional neural network is proposed, which is intended to accurately detect and recognize the varying size of objects from a video sequence. The paper starts with a detailed review of the deep learning algorithms and explains the architectures required for object detection and recognition. The existing models did not perform well for real-time object detection, specifically for small object detection. Therefore, to overcome this drawback, a model is proposed which can accurately detect and recognize the varying size of objects from a video sequence. A multi-scale anchor box is designed to capture the minor details of the object in the image. This improves the detection of state-of-the-art object detectors. The proposed model ensures the integrity of the feature of the large object and also preserves the full detail feature of the small objects by extracting the multi-scale feature of the image. The mean average precision of close to 84.49% and 11 FPS was achieved to detect the varying sizes of the objects for the network input size of 608 \(\times\) 608. The proposed model also works well in detecting and recognizing objects in complex backgrounds. The number of parameters and FLOPS (the number of floating-point operations) of the proposed architecture are used to determine the complexity of the convolutional neural network and resulted in 31.57 M parameters and 22.95 FLOPS, respectively.  
In future work, the proposed work can be examined on the MS-COCO dataset and can be optimized to run on edge devices in real time.  
Conceptualization, D.G. and K.K.; Methodology, D.G.; Validation, V.V., D.G. and K.K.; Formal Analysis, V.V.; Investigation, V.V. and K.K.; Resources, K.K.; Data Curation, D.G.; Writing--Original Draft Preparation, D.G. and K.K.; Writing--Review & Editing, V.V., D.G. and K.K.; Visualization, D.G. and K.K.; Supervision, V.V. and K.K.; Funding Acquisition, V.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 19: Object detection and recognition from an image (Example 3).
## 4 DT Use Cases and Application in Healthcare
### Facility and Operations Design  
The COVID-19 pandemic highlighted the critical role of accurate facility and operational design in safeguarding public health. Digital twin (DT) technology can address issues such as inaccurate predictions of bed occupancy by integrating internal and external data sources. It enables real-time tracking of patient flow within facilities and can anticipate surges in demand based on external factors. DT empowers healthcare providers to adopt a more strategic approach to capacity and resource planning, improving cost management and enhancing patient experiences. For instance, DT can streamline operations by optimizing scheduling and identifying workflow inefficiencies. Additionally, DT solutions assist healthcare organizations in fine-tuning hospital layouts, capacity planning, workflows, staffing, and care delivery systems. By combining predictive analytics with comprehensive data tracking, DT technology offers valuable insights that enhance patient care, operational efficiency, and financial outcomes [65].
## 4 Experiment Testing and Discussions  
### Pressure and Bending Angle  
In order to control the bending angle of the soft actuator, some experiments were carried out to evaluate its relationship between input air pressure and bending angle. The input pressure was controlled by a pneumatic valve in the experiment, and some experimental results were obtained  
Figure 6: The algorithm of 3D shape reconstruction.  
with the pressure of 0, 10, 20, 50, 80 kPa, and each experiment was repeated ten times. An AIRTAC VALVE (GPR30008L, AirTac, Taiwan, 2016) model was used to input pressure in the experiment; the received optical signals are translated into digital signals and extracted by the computer. A LabVIEW (LabVIEW 2014) program is developed to store the data and reconstruct the bending shape of the soft actuator (LubVIEW, 2014; LubVIEW, 2015). In some robotic applications, the range of the soft surgical manipulation motion is more important than its actuation velocity, so the velocity of actuation is seldom considered. Consequently, for the sake of avoiding some dynamic oscillations, the bending motion of the soft actuator generated at a slow pressure rate of 0.2 Hz. Figure 7 shows some snapshots of the bending movement of the soft actuator, and an improved track learning detection algorithm was applied to recording the motion trajectory of the soft actuator.  
The average bending angle of the soft actuator was calculated from the same input pressure, as depicted in Figure 8. The theoretical and experimental results of the bending trajectories of the soft actuator are presents in Figure 9.  
As depicted in Figure 8, due to the radial bulging effects of the base layer and extension of the soft silicone actuator, there exists some minor error between the analytical and experiment results, and a repeatable nonlinear pattern of results possibly caused by the gravity of the actuators, and the center of gravity of soft actuator is changing in different bending state. As depicted in Figure 9, although the actual motion trajectory of the soft actuator is not smooth and has some deviations, there was only a slight deviation between the theoretical and experimental results, so the design validity and effectiveness of the soft actuator was demonstrated.
## 4 Implementation
### Objectness Clarification Process
#### 4.4.2 Bitrate  
Figure 11 shows the bitrate change of video transmission when our two methods, ROI expansion and background coloring, were applied to enhance objectness of vehicles.  
In Figure 11, the highest bitrate occurs when transmitting the original image, while the lowest bitrate was observed with the black background case without dilate operation applied. Additionally, we could confirm that applying dilate operation resulted in increased bitrates compared to cases without dilating.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 5 Biological Functions of RAC1B
### Mutual Negative Regulation of RAC1 and RAC1B  
Intriguingly, it was observed that RAC1B negatively regulates RAC1 activity. Specifically, expression of RAC1B in HeLa cells i) blocked RAC1 activation by PDGF, ii) reduced the fraction of membrane-bound RAC1 and iii) promoted an increase in RHO activity. The antagonistic relationship between RAC1 and RAC1B perturbs actin cytoskeleton dynamics and led to alterations in cell morphology and motility [59]. A likely mechanism for this antagonism is competition for activation by GEFs. Along the same lines are findings from our group that exon 3b-targeting siRNA-mediated knockdown of RAC1B in the pancreatic carcinoma cell line Panc1 resulted in upregulation of RAC1 protein levels [35]. Although the mechanistic basis for this is still elusive, it indicates that RAC1B can act to suppress expression of RAC1. A reverse interaction which impacted protein stability has been observed in that degradation of RAC1B via ubiquitination is enhanced upon coexpression of the constitutively active RAC1-Q61L mutant [16] (see Section 4.3). Mutual negative regulation of RAC1 and RAC1B may be involved in one or more of the cellular processes described in Sections 5.4-5.10
## 2 Materials and Methods
### Mold Design and Processing  
Compared with spherical elements, aspheric optical elements have a unique curvature radius to achieve improved optical performance. They can correct spherical aberration, improve imaging resolution, reduce lenses in lens modules, and miniaturize the lens. By adjusting the surface constant and aspheric coefficients, the spherical aberration caused by the spherical lens can be eliminated to the greatest extent. The profile of the aspheric lens is expressed as  
\[\begin{split} Z=\frac{cS^{2}}{1+\sqrt{1-(K+1)cS^{2}}}+AS^{4}+BS^{6 }+CS^{8}+DS^{10}+ES^{12}+FS^{14}+GS^{16}\\ c=1/r,S^{2}=x^{2}+y^{2}\end{split} \tag{1}\]  
The profile of a 10 \(\times\) 10 aspheric microlens array mold is designed by MATLAB software, along with the schematic diagram and parameter values (see Figure 1a). The aspheric coefficients are as follows: K = \(-\)0.4133338, A = 0, B = \(-\)0.0121199, C = \(-\)0.39822027, D = 1.0637157, E = \(-\)3.06126374, F = 4.7284994, G = \(-\)3.50416555, and the diameter of a single lens is 0.84 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **PMMA** & **S136H** \\ \hline Young’s Modulus (GPa) & 2.4 & 220 \\ Poisson ratio & 0.37 & 0.2 \\ Density (kg/m\({}^{3}\)) & 1185 & 7850 \\ Thermal conductivity (W/m\({}^{2}\cdot\)K) & 0.2 & 16.5 \\ Specific heat (J/kg\(\cdot\)K) & 1466 & 461 \\ Thermal expansion(K\({}^{-1}\)) & \(4.4\times 10^{-4}\) & \(11\times 10^{-6}\) \\ Glass transition temperature (\({}^{\circ}\)C) & 105 & \\ Melting temperature (\({}^{\circ}\)C) & 220 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of PMMA and S136H die steel.  
S136H tool steel was used as the mold material, and single-point precision turning was prone to tool wear when carbon-based metals were machined. Therefore, we used Toshiba ultra-precision processing machine UVM-450C (Tokyo, Japan) to micro-mill the mold surface and process the microstructure on the mold surface. Then, the work employed optical mold polishing center with grinding and optical polishing technology to machine the mold, thereby improving the mold end surface's smoothness and reducing the surface roughness.  
Figure 1 shows the shape design, manufacturing, and characterization process of the microlens array mold. The mold surface was characterized to verify the mold processing accuracy (See Figure 1c). The overall three-dimensional surface captured by a laser confocal microscope was used to measure multiple locations' local surface quality under a white light interferometer. Through the surface measurement of the white light interferometer, the measurement results indicate that the surface average Sa was 5.62 nm.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 5 Discussion
### About the Technical Aspects
This ensures that the code is not overwritten during the back-end maintenance process. Through careful balancing of the transpiled core and customized components, the system can effectively leverage the advantages of the design model while accommodating the unique requirements of each target language. This approach promotes modularity and flexibility, enabling developers to maintain and enhance the back-end efficiently. By separating the core logic and personalized code, developers can preserve their respective functionalities and facilitate collaboration between transpiled and native components. Debugging presents several challenges in the context of multi-programming-language software [58]. As the transpilation process involves translating code from a source language to various target languages, debugging becomes more complex due to potential mismatches between the original source code and transpiled code in different languages. One significant challenge is the potential loss of source-level debugging capabilities when working with transpiled code. Debuggers are often language-specific and rely on source-level information for effective debugging. However, when debugging transpiled code developers may not have access to the same level of source-level information, making it more challenging to accurately track and diagnose issues. Furthermore, the presence of language-specific constructs and nuances across different target languages can introduce additional complexities into the debugging process. Debugging in one target language may require a different approach or set of tools compared to another, potentially leading to inconsistencies and difficulties in reproducing and resolving bugs. To overcome these challenges, it is essential to establish robust debugging practices specific to transpilation processes. This may involve implementing language-specific debugging tools or techniques that can bridge the gap between the original source code and transpiled code in different languages. Additionally, thorough testing and validation of the transpiled code in each target language can help to identify and address potential debugging issues early in the development cycle. By acknowledging and addressing these challenges, developers can mitigate the complexities associated with debugging in a multi-language transpilation scenario, ensuring the stability and reliability of the software throughout its lifecycle.
# The Predictive Grey Forecasting Approach for Measuring Tax Collection
## 2 Literature Review and Conceptual Framework
### Trends in Direct Tax Collection  
India's direct tax collection has improved due to tax compliance enhancements resulting from tax policy reforms, lower tax rates, and improved tax administration ([PERSON], 2005). Over the period covering the fiscal years of 2000-01 to 2022-23), several trends and patterns were identified in India's direct tax income (Figure 1). Direct tax collection has increased steadily over the years, with very minor changes. Total tax revenue rose from INR 68,305 corres in 2000-01 to INR 16,63,686 crores in 2022-23, suggesting a significant growth in tax receipts over time. Throughout the period, corporate tax income increased steadily. The total amount increased from INR 35,696 crores in 2000-01 to INR 8,25,834 crores in 2022-23. This demonstrates firms' growing contribution to the country's overall tax income. Personal income tax revenue has also steadily risen, reflecting the rise in individual income levels. Personal income tax receipts increased from INR 31,764 crores in 2000-01 to INR 8,33,307 crores in 2022-23, demonstrating significant growth. Other direct taxes, albeit relatively small compared to corporate and personal income taxes, have undergone notable variations. However, their contribution to total tax revenue has remained relatively low. There have been situations where tax revenue has fluctuated significantly. For example, tax collections increased substantially during the fiscal years of 2006-07 and 2007-08, reflecting the period's substantial economic expansion. In contrast, the growth rate has slowed in some years (Government of India, CBDT, 2022).  
Figure 1: Collection of direct tax and its constituents. Source: Author calculation using Central Board of Direct Tax (CBDT) data.  
Thus, we identified a rising trend in direct tax revenue in India over the years, owing to increased corporate and personal income tax collections. This reflects the country's enhanced tax compliance and broadening tax base. The government requires a sustained increase in direct tax collection to fund public services and development efforts.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## The Model of Room Monitoring System
### Proposed Person Detection Scheme
#### 3.3.2. Object Detection and a Person Class Filter  
This section presents the object detection and the person class filter on each tile image and the full-frame one. We apply the Mask R-CNN scheme to detect objects on each tile image as well as the full-frame one. Additionally, a filter that aims to obtain objects from the person class is used in the output layer. After initial detection in the tile images and full-frame image, the bounding boxes in all images are merged to output a final detection image, as described in the next section.  
The Mask R-CNN scheme proposed by [PERSON] et al. [21] aims to generate bounding boxes and object instance segmentation in the image. Figure 5 shows a Mask R-CNN framework for instance segmentation. Basically, the Mask R-CNN scheme can be implemented with different. Therefore, this study uses a Mask R-CNN scheme based on ResNet-101-FPN [41], including a feature pyramid network (FPN) [42] and a ResNet-101 [43] backbone.  
The Mask R-CNN model trained on the COCO dataset [44] has the capacity to detect 80 object classes, including a boat, cat, stop sign, person, and truck. Only persons are considered in this study; therefore, we use a filter in the output layer to obtain objects belonging to the person class [6], as shown in Figure 3c.
# Influence of Material-Dependent Damping on Brake Squeal in a Specific Disc Brake System
## 1 Introduction
Despite the capabilities of modern FE tools to model viscous damping in CEVA, the damping is still not commonly included in industrial computational models for squeal prediction. This may be mainly due to two factors: The first is deliberately neglecting dissipation-induced instability by anticipating purely stabilizing damping, so that undamped models appear to be on the safe side of the instability prediction. This may stem from the lack of studies that objectively quantify the effect of damping on the dynamic instability of disc brakes. The second reason is the complicated quantification and estimation of damping properties of given mechanical systems, which can be seen from a variety of measured data in various studies [16; 21; 22; 23; 24]. Some uncertain guesses are required to set the proper damping properties of computational models. Systems such as disc brake exhibit very close eigenfrequencies, and proper system identification tools for damping estimation should be chosen [25]. Based on the above information, the basic questions that we aimed to answer were:  
* Can material-dependent (nonproportional) damping have a significant influence on the stability of specific disc brake structures? * Can material-dependent damping be directly applied to computational finite element models (FEMs) to streamline the instability prediction process? These questions were answered by numerical analyses of damping destabilization effects on simplified as well as the complex FE models of a disc brake. The modal properties of the FE model were experimentally verified and damping properties of the disc structure were estimated from the experiment.
# Investigation on the Effect of Femtosecond Laser Induced Spin Transfer Torque of GdFeCo Alloy
## 3 Results and Discussions
Figure 6: Comparison of normalized magnetization changes between experimental results and simulation results. **(a)** The experiment results performed by a single pulse of 60 fs and a wavelength of 800 nm in Gd\({}_{25}\)Fe\({}_{66,6}\)Co\({}_{9.4}\) alloy, and the laser power density is 4.4 mJ/cm\({}^{2}\); **(b,c)** the simulation results considering spin transfer torque effect. We put forward a new assumption to the AOS phenomenon occurring in GdFeCo amorphous alloy thin films: when the femtosecond laser single pulse acts on the recording medium, the electronic temperature rises rapidly, thermal perturbation makes the rare earth sublattice and transition metal sublattice demagnetization occurs quickly; however, the demagnetization rate of transition metal element sublattice is faster than that of rare earth element. Therefore, when Fe sublattice spins after complete demagnetization, Gd sublattice spins are still in a state of magnetization. The electronic temperature then drops rapidly below the Curie temperature, and the thermal disturbance does not play a critical role. At the same time, the superdiffusive spin current or the spin current caused by temperature gradient exceeds the threshold value, causing the spin transfer torque effect, which only works on the Fe sublattice. The effect of spin transfer torque causes the angular momentum change of Fe sublattice, and the magnetic moments of Fe atom process occurs, and stochastic switching takes place on the magnetization of Fe. The magnetization of Fe sublattice reverses the magnetization of Gd sublattice due to exchange interaction. Finally, two sublattices maintain a stable antiferromagnetic coupling state, and the whole process of AOS is completed. This switching occurred on a subpicosecond time scale is nonlinear and it can be accomplished in a certain laser power density range. Due to it not involving the circular polarization of the laser pulses, the assumption is not only to adapt to describe the HI-AOS in RE-TM alloy materials, but is also to explain the HD-AOS in the same medium.