esa-sceva/satcom-chunk-collection huggingface dataset:
# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 6 Classification/Regression
### Hidden Markov Model  
Hidden Markov Model (HMM) [96] is a probabilistic model that is able to predict a sequence of unknown variables from a set of observed variables. For instance, in an FER system that would mean predicting happiness (hidden variable) based on a smile (observed variable). The strengths of this classifier lie on the potential to model arbitrary features from the observations, on the potential to merge various HMMs to classify more data and on the incorporation of previous knowledge into the model. However, this classifier is computationally expensive and struggles with overfitting.
# An Electromagnetic Circuit Design to Improve a Multi-Stage Coil-Gun's Energy Conversion Efficiency
## 4 Finite Element Analysis
increase the solenoid coil's number of turns and lower the capacitance to match the coil gun circuit's RLC function's period. However, if a coil is manufactured using a copper wire with a large diameter, it has the advantage of being able to set a shorter RLC period in the circuit due to the low self-resistance and inductance. Thus, if the goal is to derive maximum projectile velocity from a limited number of stages in a multi-stage coil gun, it is recommended to fabricate a solenoid coil using a large diameter copper wire and construct an RLC function suitable for the circuit using a high-capacity capacitor. Notably, in this study's coil gun system using a 1.7 mm diameter copper wire, the maximum current was 1400 A. This required a high specification SCR, potentially affecting the coil gun circuit's life and durability. Due to the characteristics of the circuit using a solenoid coil, a freewheeling diode may be additionally required in the circuit for stable operation. Functionally, a freewheeling diode is used to avoid damaging the circuit by the inductor's charging current by connecting the diode with the load in parallel. Based on the analysis, we considered that it would be appropriate to configure the coil gun system using a 1.3 mm diameter copper wire. Therefore, we constructed a multi-stage coil gun system using a 1.3 mm diameter copper wire. We also determined its number of turns and capacitance through finite element analysis. We simulated the multi-stage coil gun system using the same coil gun analysis model by inputting the projectile velocity at the end of the previous stage as the initial projectile velocity of the current stage (except for a current, first stage). Table 3 shows the number of turns and capacitance of the respective solenoid coils of the 9 stages (total). We further optimized the nine-stage coil gun using the design of the experiment method. We designed the multi-stage coil gun by setting the projectile's final velocity derived in the relevant stage as the projectile's initial velocity in the next stage. Table 3 shows the velocities of the projectile calculated using simulations. The coil gun was designed to have a projectile velocity of at least 100 m/s. In the simulation, 107 m/s was finally derived from the nine-stage coil gun. Considering the errors that may have occurred during the experiment, we decided to manufacture a prototype of the nine-stage coil gun. Generally, as the stage number increases, the time for the projectile to accelerate decreases, so the period of the RLC function of the coil gun circuit also becomes shorter.
## 3 Results and Discussion  
### Steady Shear Viscosity  
Steady shear viscosity response of diluted polymer solutions before core flood at room temperature of 22 \({}^{\circ}\)C and at a specific high temperature of 55 \({}^{\circ}\)C with different brine compositions can be seen in Figure 5. As expected, a significant loss in polymer viscosity corresponds to brine higher salinity and hardness, as described in Figure 5. At a shear rate of 1 s\({}^{-1}\), Flopaam 6035 S depicted three times greater viscosity loss in reservoir brine (B2), as compared to seawater (B1), at 22 \({}^{\circ}\)C. However, viscosity loss was five times greater for the same polymer at the high temperature of 55 \({}^{\circ}\)C. Comparing Flopaam 6035 S and Hengfloc 63026, prepared in (B2) at 22 and 55 \({}^{\circ}\)C, it can be seen from Figure 6 that Hengfloc represents the higher viscosity at the lower temperature, while Flopaam holds the higher viscosity at the elevated temperature. This provides the insight of temperature sensitivity for polymer selection screening criteria at specific temperatures. Furthermore, Figures 5 and 6 show that the presence of an amount of divalent cations (B2) has a significant impact on polymer viscosity at the high temperature.  
Figure 4: Microfluidic hyperbolic converging–diverging geometry to simulate pore and pore-throat geometry (reproduced with permission from [9], Copyright Society of Petroleum Engineers (SPE), 2016).  
Figure 5: Steady shear viscosity of polymer solutions at 22 and 55 \({}^{\circ}\)C prepared in typical reservoir brine (B2) and synthetic seawater.
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
## 3 Results and Discussion  
### Contamination of Membranes by Fungi  
The aim of this work was to set up a new in vitro model to evaluate the protective effects of cosmetic or pharmaceutical products against fungal adhesion on nails.  
For this purpose, the first step was to test the affinity of the hood membranes, used as an in vitro model for human nails, to be contaminated by fungi. In this work, two specific and main representative fungal strains, _Trichophyton rubrum_ and _Candida albicans_, were used.  
These fungi present different growth characteristics. _C. albicans_ has a shorter generation time because it has an optimal growth period of 24 h. On the contrary, _T. rubrum_ has a longer generation time [31].  
Figure 2 shows the results of the nail fungal contamination by _T. rubrum_ and _C. albicans_, respectively, as a function of the contact time.  
As can be seen in Figure 2, the membranes were contaminated differently by the two fungal species. The contamination, in the case of _T. rubrum_, was proportional to the contact time, whereas the _C. albicans_ contamination presented a peak of growth at 5 h and then showed a modest decline toward 24 h. That could be explained by the fact that the optimal growth period of _C. albicans_ is 48 h, and after prolonged exposure, the fungal cells are stressed. Therefore, considering the initial growth period of the culture (48 h) and the subsequent contact time with the membranes (24 h) for a total of 72 h, a decline in the microbial content after the second exposure time was understandable.  
The fact that there was less growth for _T. rubrum_ depended on the nature of the fungus itself, as candida is a yeast with a faster duplication time. Therefore, it first colonizes the surface. _T. rubrum_ is a multicellular microorganism, so it has a longer reproduction time and therefore a slower colonization time. The different values depended on the microbial title of the starting culture, as can be seen from the control values of approximately 2-3 \(\times\) 10\({}^{6}\) CFU/mL (colony forming unit/mL) for _C. albicans_ and 2-3 \(\times\) 10\({}^{5}\) CFU/mL for _T. rubrum_.  
Figure 3 shows an example of one membrane section after 24 h of contact with _T. rubrum_, in which contamination is clearly evident.  
Figure 2: Fungal contamination of (**A**) _T. rubrum_ (CFU/mL) and (**B**) _C. albicans_ (CFU/mL) as a function of the contact time.  
The SEM analysis confirmed successful contamination, as is shown in Figure 4.  
Figure 4: Scanning electron microscope (SEM) images of a membrane contaminated with _T. rubrum_ at (**A**) a lower magnification and (**B**) a higher magnification, and with _C. albicans_ at (**C**) a lower magnification and (**D**) a higher magnification.  
Figure 3: Membrane section after 24 h of contamination with _T. rubrum_.
## 2 Materials and Methods
### Edge Sensor Node
#### 2.2.1 Hardware Configuration  
Arduino Nano 33 BLE serves as the main microcontroller for this project. With its compact dimensions (45 mm \(\times\) 18 mm), this device includes an embedded Bluetooth Low Energy (BLE) module, making it an ideal choice for wireless data transmission. This microcontroller is powered by an ARM Cortex-M4 CPU and operates at a clock speed of 64 MHz. Additionally, it features 256 KB of flash memory and 32 KB of SRAM, sufficient for complex data processing and analysis tasks.  
The Arduino Nano 33 BLE has a wide range of I/O ports for easy integration with other components, which can be configured as digital pins, analog inputs, and PWM outputs. The microcontroller can also be programmed and powered via a USB-C connection. Its energy efficiency makes it ideal for portable and extended field operations, as it provides maximum performance with minimal power consumption. The integrated Bluetooth feature enables the device to transmit collected data to other system components or edge data collectors with low power consumption.
## 2 Results and Discussion
### Thermal Gravimetry Analysis  
Thermal gravimetry analysis (TGA) and differential thermal gravimetry (DTG) were employed to assess the thermal properties of pristine (untreated) and treated flax fiber samples. Figure 6 shows the weight loss and maximum decomposition temperature of raw and treated samples. The first phase close to 100 \({}^{\circ}\)C is associated with the drying phase of flax fibers and water removal from the surface of flax fibers. The next weight loss event was ascribed to decomposition of the hemicellulose and cellulose fractions near 200-350 \({}^{\circ}\)C [34]. In the last step, steady degradation of lignins was observed from 250 \({}^{\circ}\)C [35]. Compared to the pristine sample, the treated FFs were shifted slightly to lower temperatures in the modified Fenton (FT-15), and alkali-modified Fenton treated samples (F-T15-ALK) due to the partial oxidation (and/or removal of lignins and pectins) from the flax fiber surface. In  
Figure 5: IR spectra of raw and treated fibers. The reader is referred to Table 2 for definitions of the sample ID codes used in this figure.  
turn, such modification led to enhanced electrostatic repulsion and a reduction in linkage between the components. Moreover, it should be noted that thermal decomposition of lignin occurs at a higher temperature (ca. 350 \({}^{\circ}\)C) than the cellulosic biopolymer fraction, near 300 \({}^{\circ}\)C, which was reported by [PERSON] et al. (2018). A comparison of the DTG profile of the Fenton AOR oxidized fiber samples versus the pristine and alkali-treated fibers revealed a lower lignin content, in accordance with expectations for such oxidation processes ([PERSON] et al., 2018).
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 3 The Proposed Neural Network
### Architecture of MSA-FCN  
MSA-FCN consists of four modules, namely, the multi-head sensor data self-attention module (DMSAM_Data), the multi-head fully convolutional network (MHFCN), the multi-head spatial feature self-attention module (DMSAM_Features), and the multilayer perceptron module (MLPM), where the first three modules are constructed based on Transformer's multi-head self-attention [22] and multi-head structure [22]. The first module DMSAM_Data and the third module DMSAM_Features form the aforementioned DMSAM. The overall structure of MSA-FCN and its key modules are shown in Figure 2.  
Figure 2: MSA-FCN framework overview.  
MSA-FCN extracts degraded representations with spatial characteristics from the input and weights the important data and representation parts for RUL by considering the correlation among various sensor data points and representations, thereby achieving the corresponding RUL prediction. The forward propagation of MSA-FCN is as follows: firstly, DMSAM_Data processes training sample, learns the correlation among different sensor data points, and assigns corresponding attention weights to the important parts of the above data for RUL. Then, MHFCN processes the weighted sensor data, focusing on the correlation among different sensor data points at the same time point through convolutional operations, thereby extracting the degradation representations with spatial characteristics. Subsequently, DMSAM_Features further processes the aforementioned representations, learns the correlation among these representations, and assigns corresponding attention weights to the key parts of the above representations for RUL. Finally, MLPM integrates the weighted representations with spatial characteristics and outputs the predicted RUL value. In more detail, the key modules of MSA-FCN are illustrated in the following sections.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 1 Introduction
### Tower Companies
#### 1.1.3 RAN-Sharing Types  
Another way to classify RAN sharing is as a RAN as a service (RANaaS) and RAN services. Provisioning a RANaaS is about providing the necessary capabilities of a RAN to an operator, where it exposes the same interfaces to the surrounding environment for control and data plane. An additional interface for monitoring the capability performance is exposed to the tenant without disclosing the actual provisioning of the services as seen in Figure 4. Furthermore, each local area RAN, provided as a service, is logically independent, with separate network functions and interfaces. This service exposes a management plane interface that allows the operator to control and manage the logical nodes and links that are created by TowerCos. Thus, each RAN has its own gNBs, base stations, and interfaces that form a separate network from other tenants' RANs. From an operator's perspective,  
Figure 4: RAN as a service in ClusterRAN.  
Figure 3: ClusterRAN system model.  
the RAN provided as a service looks similar to one that would have been built originally by the operator. An alternative would be to provide the RAN as tenant-specific hardware and software; however, for resource efficiency reasons, a pooling of resources between tenants would be preferred, with reduced CAPEX for TowerCos.
## 2 Related Works
### Magnetic Field Approximation  
Conventional magnetic field approximation methods include the analytical, finite difference (FD), and finite element (FE) methods. The FE method is the most prevalent and general tool, with a settled sequence of operations when analyzing different problems, among these methods. However, developing a fast FE solver for analyzing electric machines is not easy. Conventional FE simulation is a very high-computational-complexity task, especially when the number of meshes is enormous and the computation time can be very long. Moreover, due to the algorithm's parallelism limitations, FE methods are not well accelerated, although they can be accelerated using domain decomposition methods. On the other hand, the analytical method is very fast in solving the motor performance parameters, but the accuracy is low due to the negligence of some conditions.  
In recent years, machine learning-based field approximation development has shown a promising solution to this problem. Machine learning can extract the critical features and rules from the given simulation or experimental data, especially in data-driven process control, robotics control, and computational fluid dynamics [16; 17; 18; 19; 20; 21]. Concerning the training method, most studies are based on supervised training with parameters obtaining more high-dimensional data, such as a magnetic field's 2-D or 3-D flux density distribution in the real world [16; 22; 23; 24; 25]. Furthermore, if simulated data are used, it also costs considerable computation time for the simulation.  
Regarding network architectures, generative AI is the most promising technique for field approximation, which can generate new data based on conditional inputs and random noise. There are many innovative combinations of layers used for generative AI, such as fully connected networks (FCN), recurrent neural networks (RNN), convolutional networks (CNN), and generative adversarial networks (GAN) [16; 26; 27].  
These preliminary studies show that neural networks can learn the relationship between structural geometry and magnetic field distribution. However, some critical challenges to the approach should be noted [28]. For example, generating or collecting enough data for training the network is difficult. Although the existing data generated from optimization using FE simulation can be applied for training the network, the preprocessing is complex and time-consuming since the well-organized training sample needs a structured mesh to interpolate the FE solution. In this regard, GAN-based technology can reduce the number of training samples and obtain better generalization capabilities among many neural networks [22].  
However, it is still unclear how to effectively generalize the field-based methods to a more complex electromagnetic model. In this regard, several critical challenges in Section 6 are investigated.
## 1 Introduction  
Optical Coherence Tomography (OCT) is the only clinical equipment that allows the in-vivo visualization of individual layers of the retina [1]. This plays an important role in the diagnosis and monitoring of various ocular and neurological diseases [2, 3, 4, 5]. Different types of OCT are available commercially and each type comes with its own automated segmentation algorithm [6]. The objective and quantitative information provided by the OCT is valuable in the diagnosis and follow up of patients with glaucoma [7], macular diseases [4, 8], and neurological disorders [9, 10]. The segmentation algorithms available for the clinical OCT instruments provide data, mainly the total retinal thickness and individual layer thickness of ganglion cell layer (GCL) and retinal nerve fiber layer (RNFL).  
Recent developments in instrumentation and image analysis algorithms allow further segmentation of individual layers, which is of great use in different areas in ophthalmology and neurology. Changes in inner retinal layers such as the GCL and RNFL are shown to be a hallmark in the diagnosis of glaucoma [2, 11]. The thickness of the inner retinal layers is reported to change in different neurologic disorders including multiple sclerosis [9, 12] and Parkinson's disease [13, 14]. There are other retinal disorders like central serous chori-oretinopathy, age-related macular degeneration, and inherited retinal dystrophies that cause changes in outer retinal layers [15, 16, 17, 18, 19]. Most clinical OCTs report volumetric information of individual retinal layer thickness of specific layers like RNFL and GCL [20, 21, 22]. Due to the increased use of OCT in the clinical practice for the evaluation of the individual layer thickness, it is important to have reliable inbuilt segmentation software.  
The reliability of the OCT's inbuilt segmentation algorithms for retinal thickness measurement and individual layer thickness measurement of RNFL and GCL have been widely studied in the past [20; 21; 22; 23]. The thickness measurement in these layers has been shown to be clinically acceptable in general. The individual retinal layer thickness assessed using external segmentation algorithms has also been reported to be reliable [24; 25]. As the field is developing, new OCT instruments with better imaging resolution and improved segmentation algorithms are developed continuously, which allows assessing individual layer thickness in a larger retinal area. As these instruments are made available for clinical use, there is an on-going need to assess the precision of the new OCTs and the new algorithms in both healthy eyes and in eyes with different pathologies. This study evaluates the repeatability of the OCT-HS100 (Canon, Tokyo, Japan) for the macular volumetric thickness measurements of eight different retinal layers.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
### Motivation and Contribution  
To solve the problem of low energy harvesting at the user end caused by path loss and \"multiplicative fading\" of P-RIS defects, this paper proposes a scheme to maximize the sum of energy harvesting at the user end under multi-antenna and multi-user scenarios for RIS-assisted multi-user MISO-SWIPT system. This ensures that the sum of the energy harvested by the user is maximized.  
The main contributions of this paper are summarised as follows:  
1. A communication scenario for transmitting information from multiple antennae to multiple users is considered, and a problem of jointly optimizing the BS beam direction vector, RIS phase shift matrix, signal amplitude enhancement coefficients, and power splitting factors to maximize the sum of energy harvesting at the user end is constructed under the constraints of maximum transmit power at the base station, power distribution ratio, and minimum signal to noise ratio at the user end. The problem is strongly coupled due to the entanglement of variables and requires further transformations.
2. To transform this non-convex problem into a standard convex problem, this paper uses the overall BCD algorithm to split the optimization problem into two optimization subproblems. First, we fix \(\Theta\) and P to solve \(\mathrm{w}_{k},\rho_{k},t_{k}\). The optimization sub-objective is power reduced using Taylor series expansion, and then the non-convex problem is converted into a standard convex problem using semi-definite relaxation (SDR) and successive convex approximation (SCA). The optimal solution to the subproblem is obtained by continuous iterative optimization. Next, we fix \(\mathrm{w}_{k},\rho_{k},t_{k}\) to solve \(\Theta\) and P. After transforming the objective function into a convex problem via slack variables, the convex problem can be solved via semi-definite programming. The penalty-based technique is then used to ensure that the obtained solution satisfies the rank one constraint. However, the convex difference function obtained is not a standard convex problem. We use successive convex approximations and Taylor series expansions to make it a standard convex problem. Then, iterative optimization is performed to obtain the optimal solution. Finally, the optimal solution of the initial optimization problem can be obtained by alternating the optimal solutions of the two optimization subproblems with iterative optimization.
3. Three benchmark scenarios were considered based on the optimization problem, and the simulation results demonstrate that the total sum of energy harvested at the user end is maximized with the assistance of A-RIS. Compared with the three benchmark scenarios, the total energy harvested by the user is significantly improved, and the algorithms remain converged after several iterations. In addition, this paper also demonstrates that the dynamic power splitting algorithm can better improve the sum of energy harvested by the user compared to the average power splitting algorithm.