esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 9 Discussion
### Illumination and Limitation of Acquisition Technology  
Changes in lighting due to skin reflectance properties and due to the internal camera control can significantly affect the performance of 2D FER systems. Illumination alterations cause troubles in AUs and facial expression recognition algorithms by the production of shadows and significant 2D texture variations [157]. To overcome these difficulties due to changes in the illumination conditions, image representations that are not very sensitive to these variations exist. Some examples are edge maps, image intensity derivatives, and images convolved with 2D Gabor-like filters, but [PERSON] and [PERSON] results [158; 159] showed that none of these representations is sufficient by itself. In general, it remains an open question of whether edge maps and the others provide an illumination-insensitive image for face recognition.  
If on the one hand all or most of the 2D methods present illumination problems, on the other hand, the 3D systems are naturally robust to light variations. However, the technologies available for 3D data acquisition may be significantly affected by the difference of illumination conditions during the acquisition phase [160]. For example, artifacts, like holes or spikes, occur in oily facial regions or the proximity of eyebrows, mustache, or beard, even under ideal illumination conditions.  
This problem is even more significant when tridimensional dynamic sequences are acquired. In these cases, to overpower the ambient light, the illumination provides by 3D sensors (i.e., flash) is not enough, and conspicuous lighting equipment is necessary for obtaining a constant illumination. Hence to develop a system robust to light variations for facial expression recognition remains an important goal. For this reason, in 2011, [PERSON] et al. [161] introduced a novel 3D dataset, called Relightable Facial Expression 3D database (ITC_3 DRFE), which enables experimentations, having photometric information for studying the effect of illumination on FER. The author aimed to evaluate what kind of lighting can improve the algorithm's performances.  
Despite recent advances in 3D technologies and devices, the acquisition of facial data can be accomplished only in controlled environments, representing a common drawback of 3D recognition systems. These solutions also require that the person stay still in front of the 3D scanning device for a time range from some seconds up to a few minutes. Besides, multiple scans from slightly different acquisition view-points are typically necessary to reconstruct parts of the face that can be affected by self-occlusions from a particular view.
## 3 Results of the Model
### Comparison with State-of-the-Art Models  
The main purpose of this paper is to design a deep network model to classify the fault in transmission lines based on real, generated, and public datasets. We can see that different studies have been carried out in the literature for similar purposes. To demonstrate the superiority of the proposed method, existing state-of-the-art methods for fault classification were also compared, as shown in Table 3. The following information about some basic studies is included in the table: methods, approaches for generating the dataset, their objectives, performance criteria, and the results of the proposed models.  
The dataset is one of the important parameters we see in studies. With these datasets, analysis and evaluation of fault types are made. It is observed that in the literature, MATLAB-Simulink [17; 22; 34] or OpenDSS-G [23] software is used to generate datasets. In addition to this, the research utilize public datasets [25; 35] as well as datasets acquired with field measurements [35; 36; 37]. The models are then trained utilizing a variety of techniques and the datasets that have been provided. Using the trained models, fault detection or fault classification can be effectively accomplished. The findings, as shown in Table 3, are satisfactory, particularly when compared to the performance requirements established for fault classification.  
Figure 12: (**a**) Accuracy performance of the proposed model for the real dataset. (**b**) The loss of the proposed model for the real dataset.  
Providing real data requires businesses to have the necessary hardware and software. This increases operating expenses. Therefore, obtaining data in businesses is a very difficult process. For that reason, it is mentioned in the studies [38] that real data will be used, especially within the scope of future studies. However, in this study, fault classification was made using real data. Apart from this, a synthetic dataset was produced with the model designed in MATLAB-Simulink and public dataset was used in this study.  
Utilizing simulation tools to generate datasets results in the data values being ideal. Performing analysis with ideal data causes a disadvantage. To prevent this, realistic datasets are created by adding white noise, such as 20 dB or 30 dB, to the datasets [20].  
The real dataset, the dataset generated with Simulink, and the public dataset were used separately to train and evaluate the model that was suggested in this study, and the model was found to have a strong structure based on the results. The public dataset used in this study was also used in the reference study [25], and it was found that the outcomes of this study yielded better results because of the proposed model.  
Figure 13: (**a**) Confusion matrix of the real dataset with a classification report. (**b**) ROC curve of the real dataset.
## 2 Proposed Overall System Structure  
A universal IoT system consists of three major parts, namely, the perception layer, the network layer, and the application layer. Among them, the perception layer is the \"five senses\" of an IoT system, which receives external information; transmits the perceived information to the IoT system, mainly through the relevant sensors; and collects the data required by the IoT system. The network layer is the host that transmits the information to the cloud through the network, which is equivalent to a human body's nerves; receives the information coming from the lower layer; and transmits it to the corresponding cloud according to the demand. The purpose of the application layer is to react according to the data according to the application written after receiving various information, or to send the corresponding commands on its own initiative, which is equivalent to the brain of a human body.  
There are three main parts in this system: the sensor part, the microcontroller part, and the information management system part [23; 24; 25]. The system structure is shown in Figure 1.  
Figure 1: System structure.  
Elevators may experience various failures during their everyday usage, such as component wear causing changes in the original precision of the parts, foreign objects obstructing elevator door components, and parts becoming loose due to external force impacts. There are many types of elevator failures, including elevator personnel entrapment accidents, elevator shearing accidents, elevator collision accidents, and elevator fall accidents. To prevent such mechanical failures, it is necessary for companies to focus on monitoring data, such as elevator door switches, elevator posture, and elevator height. This system specifically focuses on monitoring elevator posture data and elevator height data. The system is highly integrated with a six-axis acceleration gyroscope sensor, a barometric pressure sensor, and an STM32 minimum system to obtain the vibration signal of an elevator car, as well as its height signal, and the edge calculation processing is carried out by the microcontroller on the collected data so as to derive the relevant state information of the elevator car, which completes the collection of the relevant state information and ultimately realizes the diagnosis of the elevator's abnormal behavior. After completing the diagnosis, the basic information of the elevator and the diagnostic results are transferred to the database [26] through NB-IoT technology, in which time stamps are automatically added to the data for the subsequent visualization screen to retrieve the data according to the time. After completing the data transfer, the data visualization screen reads the data in the database, updates the data on the screen in real-time, and triggers an alarm automatically when there is a fault message. When there is a need to view historical data, the relevant data can also be retrieved and displayed on the visualization screen.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 4 Experiments
### Alignment of Eigenvalues  
According to Equation (3), square root normalization produces the normalized bilinear representation matrix \(\mathbf{A}^{1/2}\) by square-rooting the eigenvalues of the matrix \(\mathbf{A}\). Our approach, RegCov, adds regularization terms in the objective function Equation (9) and encourages the network without the normalization to output the normalized bilinear representation. To verify, Figure 4 plots the distribution of the largest 25 eigenvalues in the matrices \(\mathbf{A}\), \(\mathbf{A}^{1/2}\) and the one made by our approach. For each dataset, we randomly selected 256 samples, calculated the eigenvalues of each sample, sorted them in descending order, and produced average eigenvalues across the samples. Compared to the one before square root normalization (red curves), the square root normalization considerably changes the distribution of the largest 25 eigenvalues (blue curves), making it more uniform. For our approach, RegCov succeeds in making the spectrum (green curves) close to the normalized one (blue curves). Especially for the large eigenvalues, the green and blue curves are almost overlapping. And good normalization on the principal eigenvalues is key to enhancing bilinear representation [23; 46]. Finally, on the sample-wise level, our method can effectively reduce the distance to the eigenvalues after square root normalization, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**State** & **ImageNet1K** & **Aircraft** & **Bird** & **Car** \\ \hline Before & 927.5 & 0.8 & 36.7 & 6.2 \\ RegCov & 10.0 & 0.4 & 2.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(L2\)-norm distance to the eigenvalues after square root normalization. Sample-wise \(L2\)-norm distance is calculated and then averaged across samples.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Backbone** & **Accuracy (\%)** & \(\kappa\) \\ \hline F-CNN [1] & & 97.62 \(\pm\) 0.75 & 0.976 \\ B-CNN [13] & ResNet-34 & 98.14 \(\pm\) 0.48 & 0.981 \\ IB-CNN [72] & & 98.67 \(\pm\) 0.39 & 0.986 \\ RegCov & & **98.81 \(\pm\) 0.34** & **0.988** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of our method RegCov with the state-of-the-art bilinear pooling methods bilinear on the UCM dataset [71].
###### Abstract  
The nitrogen nutrition index (NNI) is a significant agronomic statistic used to assess the nitrogen nutrition status of crops. The use of remote sensing to invert it is crucial for accurately diagnosing and managing nitrogen nutrition in crops during critical periods. This study utilizes the UHD185 airborne hyperspectral imager and the ASD Field Spec3 portable spectrometer to acquire hyperspectral remote sensing data and agronomic parameters of the winter wheat canopy during the nodulation and flowering stages. The objective is to estimate the NNI of winter wheat through a winter wheat nitrogen gradient experiment conducted in Leling, Shandong Province. The ASD spectral reflectance data of the winter wheat canopy were selected as the reference standard and compared with the UHD185 hyperspectral data obtained from an unmanned aerial vehicle (UAV). The comparison focused on analyzing the trends in the spectral curve changes and the spectral correlation between the two datasets. The findings indicated a strong agreement between the UHD185 hyperspectral data and the spectral data obtained by ASD in the range of 450-830 nm. A spectrum index was developed to estimate the nitrogen nutritional index utilizing the bands within this range. The linear model, based on the first-order derivative ratio spectral index (RSI) (FD\({}_{666}\), FD\({}_{826}\)), demonstrated the highest accuracy in estimating the nitrogen nutrient index in winter wheat. The model yielded \(R^{2}\) values of 0.85 and 0.75, respectively, and may be represented by the equation \(y=-2.0655x+0.156\). The results serve as a benchmark for future utilization of the UHD185 hyperspectral data in estimating agronomic characteristics of winter wheat.  
w +
Footnote †: journal: Applied sciences
###### Abstract  
With the advent of nano-technology, medical sensors and devices are becoming highly miniaturized. Consequently, the number of sensors and medical devices being implanted to accurately monitor and diagnose a disease is increasing. By measuring the symptoms and controlling a medical device as close as possible to the source, these implantable devices are able to save lives. A wireless link between medical sensors and implantable medical devices is essential in the case of closed-loop medical devices, in which symptoms of the diseases are monitored by sensors that are not placed in close proximity of the therapeutic device. Medium Access Control (MAC) is crucial to make it possible for several medical devices to communicate using a shared wireless medium in such a way that minimum delay, maximum throughput, and increased network life-time are guaranteed. To guarantee this Quality of Service (QoS), the MAC protocols control the main sources of limited resource wastage, namely the idle-listening, packet collisions, over-hearing, and packet loss. Traditional MAC protocols designed for body sensor networks are not directly applicable to Implantable Body Sensor Networks (IBSN) because of the dynamic nature of the radio channel within the human body and the strict QoS requirements of IBSN applications. Although numerous MAC protocols are available in the literature, the majority of them are designed for Body Sensor Network (BSN) and Wireless Sensor Network (WSN). To the best of our knowledge, there is so far no research paper that explores the impact of these MAC protocols specifically for IBSN. MAC protocols designed for implantable devices are still in their infancy and one of their most challenging objectives is to be ultra-low-power. One of the technological solutions to achieve this objective so is to integrate the concept of Wake-up radio (WuR) into the MAC design. In this survey, we present a taxonomy of MAC protocols based on their use of WuR technology and identify their bottlenecks to be used in IBSN applications. Furthermore, we present a number of open research challenges and requirements for designing an energy-efficient and reliable wireless communication protocol for IBSN.  
wake-up radio; Implantable Body Sensor Networks; Medium Access Control; closed loop medical devices +
Footnote †: journal: Sensors
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
#### 3.4.3 Statistical Analyses  
A third methodological category relies on classical statistical methods, which often focus on describing, correlating, or regressing measured audio variables against educational outcomes. Unlike machine learning or deep learning models, these approaches typically avoid predictive modeling. Instead, they aim to uncover interpretable relationships between acoustic or discourse-related metrics and constructs such as student engagement or teacher-student dynamics.  
Several studies exemplify this approach. For instance, [PERSON] and [PERSON] [57] analyzes the talk-time of teaching assistants and relates it to collaborative group performance using basic correlation measures. The same authors [56] similarly links instructor talk patterns to student responses through correlation coefficients. Others apply regression-based methods: [PERSON] et al. [58] models how real-time measures of teacher talk-time predict engagement levels. Experimental comparisons also appear, such as in [63], where an \"engagement index\" derived from acoustic and visual cues is compared statistically across baseline and intervention conditions. Studies like [37] use descriptive and correlational statistics to examine the structure of instructional discourse, while [PERSON] [96] interprets teacher-to-student talk ratios as indicators of classroom authority and control. Along similar lines, France [97] analyzes how teachers value dialogue and how they implement it in classroom practice, drawing on statistical analyses to explore its perceived importance and actual use.  
These methods offer a key advantage: immediate interpretability. Coefficients, effect sizes, and p-values provide clear, direct evidence of how specific features relate to educational variables. This transparency makes statistical analysis particularly useful when communicating findings to educators and stakeholders who may lack technical expertise.  
However, this interpretability often comes at the expense of modeling complexity. Statistical approaches may overlook nuanced patterns or interactions that more sophisticated machine learning techniques can detect. Nonetheless, their use remains widespread, especially in studies prioritizing theoretical validation or descriptive insight over predictive accuracy.
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 5 Aging Resistance
### Asphalt Binder
Then, the variation of the rutting parameter (G*/sin5) was also smaller in the case of the modified binders. The modified binders also showed higher retained penetration and lower increment in softening point. Other studies [111; 112] evaluating aging through the variation of rheological parameters, obtained similar conclusions. [PERSON], [PERSON] and [PERSON] [113] modified a 35/50 asphalt binder with 2%, 4%, and 6% of cloisite-30B (organically modified nanoclay). The authors evaluated fatigue of the PAV aged binders using the linear amplitude sweep (LAS) test and found the number of load cycles to failure increasing with the increasing of nanoclay dosage. Similar results were presented by [PERSON] and [PERSON] [114], that modified a PG 58-22 with 2% and 6% of cloisite-30B. In addition to the traditional methods, that essentially reproduce well the aging caused by temperature and oxidation mechanisms, several researchers are developing alternative laboratory aging simulations to address other effects. Environmental conditions, such as, solar radiation, particularly the ultraviolet (UV), and moisture damage also have a significant role in the change of binder morphology during the aging process [115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128]. [PERSON] et al. [110] concluded that the modification of an 60/80 asphalt binder with 6% organically modified montmorillonite enhanced the resistance to UV aging. After UV aging, the modified binder presented lower VAI and SPI than the control binder. Other studies presented similar conclusions [129; 130]. [PERSON] et al. [131] tested the recovered binders of TEAGE aged (under UV radiation and watering/drying cycles) [132] asphalt mixture specimens. The mixtures were modified with three nanomaterials independently, 4% nanosilica, 4% nanozion, and 4% nanoclay bentonite (hydrophilic clay). The results obtained for RP were 55%, 76%, 55%, and 54% for the control, 4% nanosilica, 4% nanozion, and 4% nanoclay, respectively. The results obtained for ISP were 14.5 \({}^{\circ}\)C, 12.2 \({}^{\circ}\)C, 14.3 \({}^{\circ}\)C, and 13.5 \({}^{\circ}\)C for the control, 4% nanosilica, 4% nanozion, and 4% nanoclay, respectively. All the modifications presented lower ISP, although, the results of nanozion modification are similar to those of control, indicating no enhancement in aging resistance. Regarding RP, the control, 4% nanozion, and 4% nanoclay also presented very similar results. Other the other hand, 4% nanosilica modification presented a clear indication of enhanced aging resistance according both parameters.
## 4 Discussion  
The point source method is a formula for calculating the acoustic field of a planar source placed on an infinitely large rigid baffle and is one of the numerical methods of the Rayleigh integral. Any arbitrarily shaped radiating surface can be calculated by the point source method. Most planar radiating surfaces satisfy the condition that the radiating surface size is much larger than the wavelength. So, the point source method can be used to calculate the acoustic field. In this paper, the ALUT method is proposed for accelerating point source method calculations. The ALUT method only pre-calculates the distance between the sampling point in the radiation plane and the spatial sampling point which is used repeatedly in the calculation. Thus, the ALUT method has the same scope of application as the point source method and does not have conditional constraints on the amplitude and phase of the points on the sound source plane. Not all ultrasound transducers are composed of homogeneous materials, resulting in different amplitudes and phases at the sampling points on the source plane. The ALUT method can achieve fast calculations for ultrasonic transducers composed of non-homogeneous media. Moreover, the laser vibrometer can help to obtain the amplitude and phase of the sound source surface. The ASLUT method improves on the ALUT method by combining adjacent discrete points, thus reducing the number of calculations. Sampling points located in the same sub-region must have the same amplitude and phase. If the amplitude and phase of any adjacent points are different, the ASLUT method will take the same computational speed as the ALUT method. The ultrasonic transducer is a typical example of a planar radiating surface. The piezoelectric crystals in the ultrasonic transducer vibrate synchronously under the control of an AC voltage. Therefore, the amplitude and phase of any point on the ultrasonic transducer can be considered to be the same. The ASLUT method is able to significantly reduce the calculation time.  
Figure 10: (**a**) The optical path of digital holographic interference (**b**) The simulation results of acoustic field calculated by ASLUT method (**c**) The measurement results of digital holographic interference experiments.
## 6 IoRT Systems Based on Hybrid NTN Networks
### Enhancement of Edge Computing Capabilities  
The authors in [132] propose an MEC-enabled NTN for wide-area, time-sensitive IoT scenarios in remote regions. The proposed system utilizes a hierarchical architecture comprising GEO satellites, UAVs, HAPs, and LAPs, with a focus on a UAV swarm equipped with an MEC server and a satellite that relays data to the cloud server via a gateway. The NTN employs a cell-free architecture to address the complex propagation environment between communication and MEC and simultaneously coordinates resources to accommodate on-demand coverage and the uneven distribution of IoT devices. The problem is modeled using large-scale CSI to minimize computing latency in the NTN, while a power allocation and data stream scheduling system is also introduced.  
Table 13 summarizes the research work on IoRT systems based on hybrid NTN networks for enhancement of edge computing capabilities. MEC-enabled framework along with large-scale CSI are employed to optimize latency and coverage for IoT devices.