esa-sceva/satcom-chunk-collection huggingface dataset:
# Circularly Polarized Broadband Printed Antenna for Wireless Applications
## 4 Conclusions  
In this article, a novel compact broadband CP sickle-shaped structure excited with a simply tapered feedline has been designed, fabricated and experimentally verified. The broad \(-10\) dB impedance and 3 dB AR bandwidth performance is achieved due to the sickle-shaped patch radiator and the circular slotted square ground plane. The entire functionality for distinctly designed parameters has been analyzed and maximized. Through parameter optimization, the measured 3 dB AR bandwidth is 73.33% (2.28-4.92 GHz), and the impedance bandwidth is 126.85% (2.22-9.92 GHz), which agree well with the simulation results. The realized antenna is compact with design simplicity, broadband CP property, and low cost, making it suitable for the practical integration in WLAN/WiMAX and other broadband wireless communication systems. Finally, we have scaling the dimension to check the impedance and CP property of the realized antenna.  
Data curation, M.S.; Formal analysis, M.T.I.; Funding acquisition, M.T.I.; Investigation, M.S.; Methodology, M.S. and M.T.I.; Supervision, M.T.I.; Writing--original draft, M.S.; Writing--review & editing, M.S.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & \
\begin{tabular}{c} **f\({}_{\text{c}}\) (GHz),** \\ **f\({}_{\text{c}}\)(GHz)** \\ \end{tabular} &
\begin{tabular}{c} **Electrical** \\ **Dimension (\(\boldsymbol{\lambda_{0}}\)3)** \\ \end{tabular} & **Relatives BW** & **AR Relatives BW** \\ \hline
[20] & 2012 & 2.65, 2.45 & 0.63 \(\times\) 1.45 \(\times\) 0.013 & 136 & 77 \\
[21] & 2012 & 3.02, 2.9 & 0.48 \(\times\) 0.44 \(\times\) 0.015 & 67.76 & 62.07  
**Funding:** This work was supported by the UKM under grant No. MI-2017-001.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Evaluation and Performance Results
### Vehicle Detection Result Analysis  
The accuracy of vehicle detection and classification is achieved through the proposed model that employs YOLOv5, trained with the MS COCO dataset and picture datasets. The model can identify all vehicles on the road and accurately classify them. Figures 4-6 depict the visual representation of the results obtained from the proposed algorithm for vehicle detection and the assignment of the ID to each vehicle for tracking their locations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **mAP** & **FPS** \\ \hline YOLOv5s [18] & 60.3 & 82.3 & 79.4 & 48 \\ AFFB\_YOLOv5s & [17] 63.4 & 82.9 & 80.8 & 50 \\ YOLOv5 + Deep SORT & 65.7 & 83.4 & 81.2 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons on the PASCAL VOC datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **mAP** & **FPS** \\ \hline YOLOv5s [18] & 32.5 & 57.7 & 50.6 & 45 \\ AFFB\_YOLOv5s & [17] 33.0 & 58.3 & 51.5 & 52 \\ YOLOv5 + Deep SORT & 34.7 & 59.3 & 51.7 & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons on the BDD100K validation set.  
Figure 4: (**a–d**) Results of vehicle detection using YOLOv5.  
Figure 5: (**a–c**) ID assignment to each vehicle for tracking.
## 5 Analysis of the Results
### Body Shadowing Component
In Figure 4, some examples of the lognormal probability density functions (PDFs) fitted to the empirical fading distribution for one of the measurement scenarios in NB channel were presented. The goodness-of-fit (GoF) metrics, i.e., the correlation coefficient (\(r\)) and the chi-square (\(\chi^{2}\)) test were also evaluated. The correlation coefficient should be higher than the adopted acceptability threshold \(r_{TH}=0.90\). The threshold value for the chi-square test, i.e., for the significance level \(\alpha=0.05\), is \(\chi^{2}_{th}=28.87\) for Rayleigh distribution, and \(\chi^{2}_{th}=27.58\) for the others. In order to visualize the obtained results in the best way, it was decided to present the distribution parameters, i.e., the average value \(\mu\) and standard deviation \(\sigma\) in the logarithmic scale. In Table 2, the parameters of the fitted lognormal distribution are shown. The average value oscillates around  
Figure 4: Exemplary results of the lognormal probability density functions fitted to empirical large-scale fading distributions (for DEP, HE\({}_{R}\), NB). 0 dB, which is in line with the results presented in the literature [28; 29]. It also means that the adaptive low-pass filter (Equation (4)) was correctly implemented. For the NB channel the standard deviation is in the range of [1.2; 2.7] for almost all scenarios; the lower limit was obtained for LOS and the upper one for NLOS conditions. This range is wider than it is presented in the literature [28], i.e., [1.49; 1.52]. This is caused, among others factors, by the waveguide effect, which yields to a greater spread of the results for various MN placements. All fitted distributions have passed the chi-square test. The value of the correlation coefficient in most cases is greater than the acceptance threshold. Only for the APR scenario and the MN placement on the chest (TO\({}_{\rm F}\)) can a significantly low (0.37) \(r\) parameter be observed. Therefore, this scenario was not taken into account during the analysis of the standard deviation. For the UWB channel the observed standard deviation values are in the range of [1.3; 2.8], similarly as for the NB channel; the lower values were obtained for the LOS, while the higher ones were obtained for the NLOS conditions. In the majority of the analyzed scenarios, the standard deviation is higher for the UWB channel. Although both NB and UWB bands are significantly different in terms of frequency, occupied bandwidth, resistance to multipath effects, and frequency selectivity, it can be noticed that the values of the standard deviations are proportional to each other only if the differences between particular scenarios are considered. The average value of \(\sigma\) equals 1.7 dB for NB and 2 dB for UWB channel. The obtained results show that the lognormal distribution, in general, properly represents the shadow fading component regardless of the MN placement and the propagation conditions.
## 5 LiDAR 3D Point-Cloud Acquisition Techniques
### Solid-State LiDAR Camera  
In a solid-state camera, simultaneous beams are sent from an optical light source in response to the camera pixels. The light backscattered from the object is recorded by a sensor array in matrix form. Unlike the laser scanning method, the object points in the entire FoV are measured simultaneously. The measurement is performed at the video rate, so mobile measurements can be made. The distance is measured by the pulse or phase-shift method (AMCW or FMCW). AMCW is usually used in ToF cameras [50]. The 3D flash LiDAR camera uses direct time of flight to measure.  
Solid-state LiDAR cameras have no moving parts and are therefore unaffected by vibrations. This makes them less expensive and more reliable than a typical scanning LiDAR sensor. They cannot capture panoramic data such as a scanning LiDAR. They are suitable to use on mobile platforms such as cars, mobile robots, and industrial production lines.
## 2 The Proposed SK Diagnosis Technology
### The Procedure for Extraction of Diagnostic Feature
It is proposed in [12; 13] to use the static statistical significance threshold to filter out all noise components with \(1-\alpha\) probability of not being produced by transients. This way, the filter will produce near-to-zero SK-residuals in the fault-free case, when mostly stationary noise is present, and non-zero SK-residuals in the faulty case. Sk based optimal denoising (Wiener) filtering  
Upon estimation of the filter using SK, the filtering is performed in the frequency domain as:  
\[Y_{i}(f)=\hat{W}(f)\cdot X_{i}(f) \tag{7}\]  
where \(X_{i}(f)\) is the Fourier transform of the i-th segment of the raw vibration \(x_{i}(t)\), and \(Y_{i}(f)\) is the Fourier transform of the i-th segment of the SK-filtered signal \(y_{i}(t)\), containing non-stationary impulsive components. Diagnostic feature extraction SK-filtered squared envelope  
The SK- filtered \(y_{i}(t)\) can be considered as an amplitude modulated signal, where the amplitude modulation part, also known as the envelope, reflects the degree of non-stationarity of the fault-induced transients. The envelope can be extracted, using the Hilbert transform [28], and the final diagnostic feature is defined as the squared envelope, reflecting the power of the SK-filtered signal. Angular alignment of segments by cross correlation to compensate for bearing random slip  
To clarify the impulses, which may be affected by random bearing slippage, it is proposed a novel improvement to the squared envelope diagnostic features by aligning the impulses using the cross-correlation approach via the following steps:  
1. Initial segment \(i\) is considered as a reference to align segment \(i+1\) by maximizing the cross correlation. 2. The mean of aligned segments \(i\) and \(i+1\) is taken as a new reference and used to align segment \(i+2\). 3. The mean of aligned segments \(i\), \(i+1\), and \(i+2\) are then used to define the new reference to align segment \(i+3\), and so forward. The result of the alignment process are straightened lines of impulses allowing to track these impulses, caused by the same ball/roller.
## 2 Theory
### Opto-Acoustic Method  
In thin-film adhesion studies, the acoustical characterization of elastic behaviors associated with a resonant frequency lower than 100 MHz is difficult. The wavelength of the probing acoustic wave must be shorter than the width of the thickness of the film and substrate. To generate 10 or more wave numbers in a 100 \(\upmu\)m-thick silicon substrate, for instance, the wavelength must be shorter than 10 \(\upmu\)m. The acoustic velocity in silicon is approximately 8 km/s. Therefore, the corresponding frequency is of the order of 8 km/s \(\div\) 10 \(\upmu\)m = 800 MHz. For smaller thicknesses, the frequency must be increased further in proportion to the reduction in the wavelength. To overcome this difficulty, we use the opto-acoustic method described here.  
Figure 1: Physical model of thin-film systems. The figures at the bottom show the membrane-like vibration of the film surface due to the weaker elastic constant of the middle part.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## The Model of Room Monitoring System
### Proposed Person Detection Scheme
#### 3.3.1 Tile Division  
First, we discuss the approach to calculating and generating tile images. A copy of a full-frame image is generated to create tile images; only the upper part of the copied image is used owing to the higher frequency of small-sized persons appearing in this area. We divide the used image into several equal-sized subimages with a fixed portion of overlapping area on either side or both sides, as shown in Figure 3a. The number of tiles is calculated as in Equation (3). Here, n is the number of tile images; \(W_{full-frame}\), the width of the full-frame image; \(W_{tile}\), the width of the tile image; and \(d_{intersect}\), the width of the overlapping areas of neighboring tile images, as shown in Figure 4. The resulting tile images are shown in Figure 3b.  
\[\begin{split} n=\begin{cases}m&if\quad l\times m+d_{intersect} \leq W_{full-frame}\\ m+1&if\quad l\times m+d_{intersect}>W_{full-frame}\end{cases}\\
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 2 Optical Packaging
### Laser-to-PIC Integration
#### 2.2.2 VCSEL Integration  
Direct integration of vertical cavity surface emitting lasers (VCSELs) is also a promising avenue for hybrid integration. Here, the significantly reduced footprint of a VCSEL chip (250 \(\times\) 250 \(\upmu\)m) over the MOB (1 \(\times\) 1 mm) allows for very high integration densities. Planar VCSEL-to-PIC integration has been demonstrated, where photoresist based 'wedges' are used to refract the VCSEL mode onto the grating-coupler with the required near-normal angle-of-incidence [43]. Alternatively, a grating-coupler can be designed to couple the VCSEL-mode into a pair of opposing waveguides, which can then be recombined into a single-channel, after phase-compensation [44]. A tilted-VCSEL approach can also be used, where the VCSEL is directly flip-chipped onto the PIC with an asymmetric distribution of solder balls [45]--see Figure 10. By controlling the contact area of the bond-pads available for solder wetting, the tilt-angle of the VCSEL can be optimized. This tilted-VCSEL approach allows for passive flip-chip alignment to the PIC for rapid assembly at commercial volumes.  
Figure 10: Tilted vertical cavity surface emitting laser (VCSEL) to Si-PIC integration; (**a**) Schematic of the tilted-VCSEL geometry, showing the VCSEL positioned above the grating-coupler on the Si-PIC, and the tilt being provided by the solder balls; (**b**) SEM (scanning electron microssoscope) image of a tilted VCSEL on the Si-PIC; (**c**) Microscope image of a tilted-VCSEL on the Si-PIC, showing the electrical-tracks and on-PIC waveguides used for routing the electrical and optical signals.  
Figure 9: Tyndall micro-optical bench (MOB); (**a**) Schematic of the MOB showing the laser-chip, ball lens, and micro-prism; (**b**) _COMSOL_-software thermal model of the MOB showing the elevated temperature of the laser-chip during CW operation; (**c,d**) _Zemax_ ray-tracing applied to the MOB shows how emission from the laser-chip is re-imaged onto the grating-coupler on the surface of the PIC. Unlike a fiber-mode, the re-imaged spot is elliptical, just like the mode form the laser-chip.
## 2 Literature Review and Hypothesis Development
### Moderating Impact of Novelty Seeking  
Novelty seeking is the stimulus that consumers with a high level of innovativeness desire to try unusual/novel products [37]. It can motivate consumers to try new things which they would not consume in their ordinary lives [71]. Consumers who are seeking novelty are experimental, and they try new stimulating challenges, such as purchasing new products or services that they have not experienced [39]. Consumers with high novelty seeking tend to look favorably on technology [72], so the investigation of the consumers' novelty seeking is important in regard to new technology-based products/services. [PERSON] et al. [73] discovered that consumers with high novelty seeking have favorable attitudes towards using drone delivery services when ordering food. However, [PERSON] et al. [36] identified that novelty seeking has no statistically significant effect on the overall image of robotic restaurants, whereas quality and hedonic experience seeking are important in regard to forming the overall image. Restaurants inherently provide good quality foods and services, so novelty seeking might not directly influence the evaluation of new technology-based restaurants.  
Some studies in consumer behavior focused on the concept of novelty seeking as a moderator as opposed to a predictor. For example, [PERSON] et al. [74] found that the consumers' novelty seeking significantly strengthened the effect of the neophilic tendency on food satisfaction. [PERSON] and [PERSON] [75] also identified that high-novelty seekers show a greater path coefficient of the relationship between frequency of visits and travel spending than low-novelty seekers. These types of studies demonstrated the moderating roles of novelty seeking that strengthen positive relationships in regard to consumer behavior, but there are no studies where novelty seeking weakens a negative relationship.  
Novelty seeking is also regarded as an important factor in the risk-taking behavior context [76; 77; 78]. [PERSON] [40] stated that an individual who seeks novelty is more willing to try new products/services and take risks. [PERSON] and [PERSON] [79] also support this finding by delving into the different dimensions of sensation seeking and discussing its implications in various areas of life, which include risk-taking behavior. This means that novelty seeking can weaken it despite perceiving certain behaviors as being negative due to potential risks. It can be inferred that consumers perceive the risks of ISFR, but consumers with high novelty seeking would have more favorable attitudes towards using ISFR than consumers with low novelty seeking. The current study thus proposed the hypothesis below in regard to the moderating role of novelty seeking.  
**Hypothesis 6 (H6).**_Novelty seeking weakens the negative effect of perceived risks on AT._
## Numerical Methods
### Validation of Numerical Method  
To validate the numerical technique used herein, flow simulations were performed on a two-dimensional NACA0012 airfoil, and the results were compared with those of wind tunnel tests [21]. Figure 1a shows the partial view of the computational mesh used in this analysis. The first cell size was \(1.5\times 10^{-4}\), which results in a maximum y+ value of approximately 6.0. Approximately 150,000 meshes were used, including 20 layers of viscous meshes. The analysis was performed at a Reynolds number of \(2.88\times 10^{6}\) based on the airfoil chord length, and the surface pressure coefficients were compared, as shown in Figure 1b,c. At an angle of attack of \(0^{\circ}\), the predicted pressure is slightly lower than that of the wind tunnel test, owing to the over-prediction of the flow acceleration at the leading edge. It is believed that this discrepancy originates from the assumption of a fully turbulent flow. In actual flow, the laminar flow progresses to some extent and then transitions to a turbulent flow; however, in this simulation, a fully turbulent assumption was applied without considering a transition from the laminar to the turbulent domain. Except for the local discrepancy, the trend of the pressure recovery after the maximum thickness exhibits good agreement with that of the wind tunnel test. Moreover, at the angle of attack of \(10^{\circ}\), the predicted pressure distribution shows good agreement with that of the wind tunnel test. Thus, it can be concluded that the considered CFD method is acceptable for the numerical study.  
Figure 1: _Cont._
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 2 Materials and Methods
### Device Classification Techniques  
As mentioned previously, the ResNet neural network architecture was used for classification. A diagram of this approach is shown in Figure 1. ResNet is a deep learning architecture that uses residual connections in a cascaded network to allow for efficient training of large, deep convolutional networks [8]. Additional fundamentals of deep learning can be found in research from [PERSON] et al. [9]. Various layers in the ResNet architecture give a variety of benefits to this application. For instance, the convolutional layers provide a translational invariance to the classification process, which allows the features of interest to drift in the domain in which they are processed without classification performance degrading. The translational invariance is valuable for UCE data obtained via NILM because many of the devices (especially COTS devices) are stable in their operation only to within some commercially acceptable tolerance and are likely to drift in their characteristics over time (this is especially evident for UCE characteristics in the frequency domain position, which became of particular interest in this investigation).  
In these deep learning architectures, features are not chosen by the programmer. The features are learned by the neural network based upon the nature of the input and the structure of its architecture. Certain aspects of a feature may be seen by examining the input and output of each layer of the network. Some tools and methods allow a certain level of explainability; development of these methods has mainly focused on imaging applications such as image classification. Examples of the types of methods include DeepLIFT [10] and Integrated Gradients [11], which assume differentiability of the model and propagate the prediction to features using gradients. Perturbation-based methods, such as LIME [12], are also available. LIME is applied to a subset of the devices in this dataset in [13] via formation of a spectrogram with FFT and subsequent analysis. The methods utilized in [13] are validated for this application by human inspection of the very clean and undisturbed data set that was collected. The methods described in this paper could be loosely grouped into the perturbation-based methods approach for explainability.  
Understanding the details of the features important to the ResNet model when using a complicated and obscured data set described in the prior section is challenging. Certain levels of understanding can be drawn regarding the features of most value and the effects of noise on the classification performance of the ResNet model by using a methodical process of data augmentation and experimentation to infer something about what the model is doing.  
A few variations in the training process and validation sets were used, and these are described in detail in Section 3.1.
## 3 Method Descriptions
### System Hardware  
A prototype system was developed to test the deep learning model in real-time. The system's hardware components include a urinary flow meter, a Kistler triaxial accelerometer (8688A50), a signal extractor (NI 9234), and a mobile user device that displays real-time feedback information. The system usage procedure is described as follows: When a patient urinates, the impacting urine creates vibrations on the accelerometer, which are first converted into vibration signals registered by the accelerometer and then into digital signals by the signal extractor. The signals are transmitted wirelessly to the mobile device, and the results are tabulated and displayed.  
Figure 2: The components and architecture of the prototype system.
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 4 Model Validation
### Creating Validation Stimuli
#### 4.1.1 Selecting Microphones  
Ten studio microphones were selected that weren't used in the generation of the training dataset and that varied in terms of their sensitivity, self-noise, transient response, distortion, diaphragm size, transduction type, directivity, and frequency response: Sony C800; AKG D12; Schoeps CMC-6U with 2H capsule; Countryman B3; Neumann U87 (cardioid); Royer R-121; Sony F730; DPA 4015; Sony ECM670; and Hebden HS 3000 (hyper-cardioid). Eight independent experts were then asked to suggest microphones representing each extreme of the scale for the perceptual attributes of brightness, noise level, harshness, clarity, and piercing (the five timbral attributes contributing most to perceptual differences between microphones [2]). Analysis of these responses led to the addition of the HS3000 (omnidirectional) and AKG C1000S microphones.  
In the microphone set used to generate the brightness model training data, two MEMS microphones were included. For consistency, three new MEMS microphones were included in the set used to generate the validation data: WM7132; WM7138; and WM7331. Two of each of these microphones were included in the validation microphone array: one with the soundhole facing the source (face-on, more typical studio microphone orientation); and a second with the soundhole perpendicular to the source (side-on, more typical MEMS microphone orientation).