esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Overall Process
### Network Architecture Design  
Current target detection algorithms can be divided into \"single-stage\" methods and \"two-stage\" methods. Two-stage methods generally consist of two main steps. First, image features are extracted through network architectures such as Convolutional Neural Networks (CNNs), forming Regions of Interest (ROIs). Then, additional networks are applied to these extracted areas to perform object classification and bounding box regression. Typical representatives include the R-CNN [9] (Region-based Convolutional Neural Network) series, Fast R-CNN [10], and Faster R-CNN [11]. [PERSON] et al. [12] introduced the ConvNeXt architecture combined with CBAM to improve Faster R-CNN, enhancing the model's focus on surface defects. The characteristic of two-stage algorithms is their high accuracy, as they separately process feature extraction and target detection through two independent networks. Due to the two phases, the model structure is relatively complex, but it can more accurately locate and classify targets.  
Single-stage methods detect targets directly in images through a single forward pass. These methods are more straightforward, achieving target location and classification information through dense predictions. Representative single-stage methods include YOLO [13] (You Only Look Once) and SSD [14] (Single Shot Multibox Detector). Compared with two-stage methods, single-stage methods are generally faster and suitable for real-time target detection scenarios. With only one forward pass, single-stage methods are usually simpler and have faster inference speeds, making them suitable for applications where speed is critical.  
In this paper, a feature extraction encoder based on Resnet50 is developed. It is connected to two decoders: Decoder 1, based on the Resnet50 FC part, performs the defect recognition task, while Decoder 2, based on the YOLOv5 prediction, is used for defect localization tasks. As shown in Figure 6, to enhance efficiency, Decoder 2 is controlled by Decoder 1, meaning it locates defects only after confirming their presence. Both networks share a single feature encoder to accelerate network runtime speed. Since the evaluation metrics for the defect detection system place high demands on the classification network, the choice of the encoder responsible for feature extraction is evaluated based on the performance metrics of the classification decoder's output.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 5 Conclusions  
The aim of this study was to introduce an approach for the prediction of travel time for vehicles on long-distance road segments in Thailand. The input data that we collected were truck GPS transactions and included vehicle identifiers, timestamps, latitudes, and longitudes. The raw data, together with road data from Thailand, were processed into time-series data of the average hourly travel time on each road segment in 2019, and the time-series data were split into a training set and a test set, where the sliding window size was 24. In our study, the LSTM was selected as a baseline and had an MAE of 27.12 min per 100 km. After studying the data in detail and conducting several experiments, the SA-LSTM with the Butterworth low-pass filter was adopted as our method, and this provided an error of \(\pm\)12.15 min for every 100 km, which was 55.20% better than that of the baseline. The results of our experiments suggest that having a predictive travel time using GPS data for each road is practical and is a necessary factor in the improvement of smart mobility. Thus, our work can contribute to the development of a data-driven smart city.  
However, our work is an early step in the area of travel time prediction in Thailand. In the future, other factors such as weather data and road repair schedules might be considered. In addition, a future plan, which will require more effort and a bigger budget, is to collect the GPS data of other types of vehicles such as personal cars and, indeed, the whole road network in Thailand. Further research can then enrich travel time prediction models.  
Conceptualization, R.C. and A.S.; methodology, R.C.; software, N.A., S.I. and N.B.; validation, R.C., N.A., S.I., N.B. and A.S.; formal analysis, R.C, N.A, S.I. and N.B.; investigation, R.C.; resources, R.C. and A.S.; writing--original draft preparation, R.C.; writing--review and editing, R.C.; visualization, R.C., N.A., S.I. and N.B.; supervision, R.C. and A.S.; project administration, R.C.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 5 Discussion
### RQ2: Which Facial Features for Automated Deception Detection Are Extracted?  
The feature-extraction process consists of transforming raw data into a set of features to be used as reliable indicators for detecting deceptive behaviours.  
A multitude of facial cues has been correlated with deception in the literature, including lip pressing, eye blinking, facial rigidity, smiling, and head movement. A total of 22 papers among those surveyed (24 studies) extracted the following 10 facial features, represented in Table 6 with the respective number: (I) facial expression; (II) facial rigidity; (III) head pose; (IV) eye blink; (V) eyebrow motion; (VI) wrinkle occurrence; (VII) mouth motion; (VIII) gaze motion (IX); lip movement (X).  
More than half of the studies (15 studies; 68%) rely on the Facial Action Coding System (FACS), an anatomically based system for representing all visually discernible facial movements proposed by [PERSON] et al. [42]. The FACS provides a list of action units (AUs) that are the individual components of muscle movement extracted from facial expressions. [PERSON] et al. [57] provided a list of AUs that are the most difficult to produce deliberately and the hardest to inhibit. Therefore, the majority of the analysed studies detect the movements of these AUs as potential indicators for distinguishing liars from truth-tellers in high-stakes situations.  
A total of 18 studies (82%) rely on extracting facial expression features for detecting deceptive behaviours. Facial expressions, particularly micro-expressions (i.e., facial expressions of emotion that are expressed for 0.5 s or less), are often considered strong indicators of deception. Six basic facial expressions of emotion are defined in the literature: disgust, anger, enjoyment, fear, sadness (or distress), and surprise. In particular, the surveyed studies have detected deceptive behaviours by analysing the presence of both extreme facial movements, which can indicate high-intensity emotions that are not really being experienced, and facial rigidity (i.e., little or no facial movements) that deceivers could purposefully inhibit to appear truthful. Moreover, half of the studies (11) rely on head pose, followed by gaze (9 studies; 41%).
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 3 Experiments and Analysis
### Imaging Samples of Magnetic Materials
#### 3.1.2 Imaging an Array of Cylindrical Magnets  
In the second experiment, an array of cylindrical magnets was imaged with the magnetic field imaging system. For comparison with the test results of this system, we designed a linear array of Hall sensors, as shown in Figure 10. The array contains 12 Hall sensors with 2 mm spacing between adjacent sensors and the sensitivity has been adjusted to be consistent with the Hall elements. The detected sample consists of 16 small cylindrical magnets. The material of each small cylindrical magnet is also NdFeB, which has a diameter of 5 mm and a height of 5 mm.  
Figure 8: The software flowchart and the interactive interface of the magnetic field imaging system: (**a**) a software flowchart; (**b**) a setting page for displaying images.  
The 3D magnetic field images of the sample measured by the two Hall arrays are displayed in Figure 11a,b. The Hall sensor array loses a significant amount of magnetic field information in the detection process, resulting in a relatively ambiguous field distribution of the magnet array. In contrast, the unpackaged Hall element array with high spatial resolution captures more magnetic field details and maps a sharper and more complete magnetic field image. Then, we extracted the values of the magnetic field at the dashed position in Figure 10b for analysis, as shown in Figure 11c,d. In Figure 11c, two Hall arrays measured the magnetic field of the first row of magnets. The Hall sensor array captured only 12 points, with the magnet peaking at the poles to about 160.5 mT and \(-\)151.9 mT.  
Figure 10: The Hall sensor linear array and a measured sample: (**a**) a Hall sensor array; (**b**) an array of cylindrical magnets.  
Figure 9: The magnetic field imaging system imaged the magnetic field of four ring-shaped magnets (these magnets were also modeled by Maxwell software for magnetic field simulation): (**a**) experimental setting; (**b**) image after detection by the magnetic field imaging system; (**c**) the simulation image was given by Maxwell software.  
However, the unpackaged Hall element array is capable of capturing 96 points, with peak magnetic fields at the poles of approximately 176.2 mT and \(-\)180.2 mT. The peak magnetic field of the magnet at the two poles differs less in absolute value and the resulting field distribution is relatively symmetrical, but the Hall sensor array has difficulty in detecting this. Furthermore, the low spatial resolution of this array challenges the capture of real magnetic field peak points. Ditto for Figure 11d, which illustrates a similar phenomenon. This experiment demonstrates that a high-spatial-resolution system will capture more magnetic field information and display the magnetic field characteristics of the sample more comprehensively and completely.
## 3 Problem Statement and Principal Approach  
This section presents measurement results that motivate the suggested adaptation methods. We describe the principal approach. Moreover, we introduce the information-theoretical measures that are used to analyze and optimize the threshold calibration.  
The voltage distributions of a flash memory change over the lifetime. Hence, a constant read reference voltage cannot minimize the bit error rate and the number of errors can surpass the error correction capability of the ECC decoder. To prevent this, the read reference voltage has to be adapted to the current life-cycle state of the flash to minimize the bit error rate. In this section, we discuss and analyze the statistical features that are used for the voltage adaptation.  
Similarly to the methods proposed in [32; 34], we exploit the error correction code to obtain information about the cell state. Based on the error correction code, we can determine the number of errors and the asymmetry of the error distribution, i.e., the probabilities of 1-to-0 errors and 0-to-1 errors are not equal [23; 25; 30; 42]. In a nutshell, the number of errors provides information about the distance to the optimal threshold and the asymmetry provides information about the direction, i.e, whether to increase or decrease the threshold voltage. We use information-theoretic measures to investigate the relation between the optimal read references and the statistical information obtained by counting errors.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 2 Background  
### HAWK Signature Scheme [11]  
The HAWK algorithm is a signature algorithm based on the lattice isomorphism problem (LIP) [12]. The lattice isomorphism problem is based on the problem of determining whether two lattice structures are identical using a mathematical structure called a lattice. HAWK has a similar name to FALCON. This reason is not very similar to the FALCON Signature method, but the NTRU [13] equation is solved with similar settings during the key-generation process and the FALCON code is reused when implementing HAWK, so it was named similarly. HAWK designed its algorithm with an emphasis on simplicity. Dueto this simplicity, it was designed to be free of floating point and rejection sampling. This simple design results in faster signature speeds and smaller signature sizes when compared to FALCON.  
HAWK provides two parameter sets, HAWK-512 and HAWK-1024, and satisfies the security levels of NIST-1 and NIST-5, respectively. The public key size is larger than that of FALCON, a similar scheme, but the private key and signature sizes are smaller. Detailed key size and signature size are listed in Table 1.
# An Innovative Adaptive-Dimension Accordion Honeycomb for Circumferential Deformation
## 5 Results and Discussion  
### The \(\{1,1\}\) Honeycomb Stiffness Variation Versus the Parameters  
Figure 8 shows the comparison between the theoretical predictions and the numerical simulation for the circumferential stiffness \(k^{11}\) vs. \(\phi\) and \(\theta\) while the unit cell is subjected to PD with a strain of 0.02. The ranges of the angles \(\phi\) and \(\theta\) are correspondingly 45\({}^{\circ}\)-75\({}^{\circ}\) and 5\({}^{\circ}\)-10\({}^{\circ}\), and other parameters are shown in Table 1. The stiffness \(k^{11}\) decreases with an increasing value of \(\theta\), but increases with an increasing value of \(\phi\). In Equations (1) and (2), for the lengths of the inside walls, the inside walls tend to increase the bending deformation with increasing length and decrease it with increasing angles \(\theta\) and \(\phi\), respectively. In addition, \(\theta\) results in a larger effective stiffness compared to \(\phi\).  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Value** \\ \hline \(\theta\) & 7.5\({}^{\circ}\) \\ \(\phi\) & 60\({}^{\circ}\) \\ \(p\) & 0.7 \\ \(r^{0}\) & 200 mm \\ \(t\) & 1 mm \\ \(H\) & 5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Default parameters of the unit cell.  
The theoretical stiffness \(k^{11}\) is slightly lower than the results obtained by FEM simulation, which can be considered generally consistent. This discrepancy mainly arises because FEM is a discrete model with finite degrees of freedom, where constraints are applied to the model, thereby increasing the circumferential stiffness. In contrast to the stiffness variation, the relative errors increase with \(\theta\) and decrease with \(\phi\). The maximum relative error is \(-3.61\%\) for \(\theta=10^{\circ},\phi=45^{\circ}\), while the minimum relative error is only \(-0.42\%\) for \(\theta=5^{\circ},\phi=75^{\circ}\).
## 3 Fundamentals
### The Influence of the Degree of Dispersing of the AAC in the Materials  
3.3.1. Factors #1 and #2: Molecular Structure and Charge of Polar Groups of a Surfactant  
The subject of research was a concrete based on an AAC with \(\mathrm{M_{b}}\) = 1.2 (Mix 10) (reference composition, see Table 2). The criteria for evaluating the effectiveness of a surfactant was the consistency (slump) and consistency retention time of fresh concrete, as well as the compressive strength of concrete.  
The rheological properties of the fresh AAC concrete (Figure 8b) were significantly more influenced by the molecular structure of the surfactant (1% by mass) compared to those of the analog, which was based on OPC cement (Figure 8a). In the presence of an anionic high-molecular-weight plasticizer (NaLST), the action effect of cationic and nonionic surfactants (BC-50, Triameen Y12D 30, OS-20, PEG 400) increased. On the contrary, the effectiveness of anionic surfactants (PCE) decreased.  
Figure 7: The consistency (flow) of fresh mortar based on the AAC with \(\mathrm{M_{b}}\) = 1.2 (Mix 2 (green) and Mix 3 (light blue)) vs. the type of surfactant and the anion of the alkaline component.  
At low values of the AAC basicity resulting from high contents of GBFS, cationic surfactants exhibited a higher adsorption ability compared to that of anionic surfactants.  
The decreased effectiveness of polyesters (Figure 9b) compared to that of the OPC-based analog (Figure 9a) resulted in low strength gains at different stages of hardening. A conclusion about the high effectiveness of nonionic surfactants like, for example, PEG, used in AAC in conjunction with NaLST, is supported by the absence of the early-age strength decrease and the higher strength of concrete at an age of 28 days compared to those of the reference compositions.  
Figure 8: The consistency of fresh concrete vs. the type of surfactant (without admixture (reference)—blue, Muraplast FK 59—red, BC-50—green, Triameen Y12D 30—violet, OS-20—light blue, and PEG 400—orange), the type of cement (strength class 32.5 N): CEM II/A-S (**a**) and AAC with \(\mathrm{M_{b}}=1.2\) (Mix 9) (**b**), and the time after mixing.  
With the modification of the AAC concrete by cationic surfactants, the early strength and strength at 28 days of the AAC concrete, not depending upon the AAC composition, decreased as a result of foam formation (Figure 9). The addition of a defoaming agent increased the consistency of fresh concrete and prevented the strength from decreasing [90].  
A conclusion can be drawn that the use of cationic surfactants in the concrete mixes with low AAC basicity values is more effective compared to nonionic and anionic surfactants.
## 3 Material and Methods
### Data Pre-Processing
#### 3.7.1 Feature Scaling  
The feature scaling operation is applied to transform features on the same scale. Most machine learning models require data to be on the same scale except for a few, for example, Decision Tree and Random Forest. The UNSW-NB15 dataset features have high variance; therefore, they were transformed before being supplied to the machine learning model.  
Standardization: Features were transformed by using the standardization technique. The standardization method transforms data around mean 0 and standard deviation 1. This method does not bind data within specified limits. Equation (7) shows a standardization formula where x represents an observation, \(\mu\) represents the mean of the observations, and \(\sigma\) is the standard deviation.  
\[\mathrm{x^{\prime}}=\frac{\mathrm{x-\,\mu}}{\sigma} \tag{7}\]
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband
### Two Distinct Time Delays  
Latency incurred in the RX DBB originates from data propagation time delays and finite throughput values of individual components. The former represents the time a data unit, e.g., a bit, has to spend in the component before exiting it, while the latter stands for the elapsed time between two consecutive data units entering the component. The finite throughput delay is only applied to data arriving at a busy component, where the waiting time is a multiple of the throughput delay and the number of queued data elements. Only data propagation delays apply for data passing through otherwise idle components. For example, the first data element in a stream. Figure 4 illustrates both delays on a simplified example. The input data units can be symbols, bits, or data blocks, depending on the assessed component. A component's throughput must reflect the rate of incoming data to avoid becoming a bottleneck, while data propagation delays will add latency to the entire stream of data. The work at hand models individual components as black boxes, with the finite throughput and data propagation delay influencing the flow of data through it. The interplay of multiple components and their time delay performance figures yields the total latency incurred in the RX DBB.  
Figure 3: IEEE 802.11 ad PHY receiver digital baseband. Above each component lies its input buffer with the corresponding base unit size noted on its left. Solid connections show data propagation paths, while dashed lines correspond to flag indicators. The processing operations contributing to latency beyond the finite symbol rate are written in bold. Those with additional control over the inflicted latency are colored in blue.
###### Abstract  
This paper presents a novel tunable multi-frequency hybrid energy harvester (HEH). It consists of a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMEH), which are coupled with magnetic interaction. An electromechanical coupling model was developed and numerically simulated. The effects of magnetic force, mass ratio, stiffness ratio, and mechanical damping ratios on the output power were investigated. A prototype was fabricated and characterized by experiments. The measured first peak power increases by 16.7% and 833.3% compared with that of the multi-frequency EMEH and the multi-frequency PEH, respectively. It is 2.36 times more than the combined output power of the linear PEH and linear EMEH at 22.6 Hz. The half-power bandwidth for the first peak power is also broadened. Numerical results agree well with the experimental data. It is indicated that magnetic interaction can tune the resonant frequencies. Both magnetic coupling configuration and hybrid conversion mechanism contribute to enhancing the output power and widening the operation bandwidth. The magnitude and direction of magnetic force have significant effects on the performance of the HEH. This proposed HEH is an effective approach to improve the generating performance of the micro-scale energy harvesting devices in low-frequency range.  
Keywords: piezoelectric and electromagnetic; hybrid energy harvester; magnetic interaction; multi-frequency; low-frequency vibration +
Footnote †: journal: Applied Sciences
## 5 Graphene/Graphene Oxide-Based RF Gas Sensing Platforms  
### Graphene Oxide-Based RF Humidity Sensors  
As previously mentioned, because GO is a hydrophilic material, it sensitively responds to the humidity of the environment. Thus, it can be used as a sensing material for a humidity sensor. For example, the electric property of GO material was used by coating a printed graphene antenna with a GO layer in a battery-free wireless RFID humidity sensor, and pristine GO was used, which is a relatively good insulator at room temperature and low  
Figure 9: Biotransferrable graphene wireless biosensor. (**a**) Graphene is printed onto bioresorbable silk, and contacts are formed containing a wireless coil; (**b**) bittransfer of the nanosensing architecture onto the surface of a tooth; (**c**) magnified schematic of the sensing element, illustrating wireless readout; and (**d**) binding of pathogenic bacteria by peptides self-assembled on the graphene nano-transducer (reprinted with permission from [79]).  
humidity. On the other hand, the ionic conductivity at high humidity due to the intercalated water increases, and consequently, GO becomes poorly conductive [80].  
In the GO-based humidity sensor (Figure 10a), the sensor with a GO layer sensitively responds to the humidity change, whereas the reference sensor without the GO layer does not. The different responses of these two resonators can only be caused by the change in the GO electrical properties due to water uptake [81]. In the resonator with the GO layer, it can be observed that the resonance shifts to a lower frequency and its fractional bandwidth increases as the humidity rises [82; 83]. This is because both the real (\(\epsilon^{\prime}\)) and the imaginary (\(\epsilon^{\prime}\)) parts of the relative permittivity of GO increase as the GO absorbs more water.  
Based on the measured results, the resonant frequency and the backscattering phase of the GO/graphene antenna sensitively change with different humidity concentrations (Figure 10b), and the RFID reader can recognize the frequency shift and phase variation. [PERSON] et al. showed the possibility that this sensing scheme can achieve battery-free wireless monitoring of the local humidity with digital identification attached to any location or item.