esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 6 Smooth Pulses
### Relations to Super-Gaussian Pulses  
The smooth rectangular pulses can be an excellent approximation for super-Gaussian ones. Let  
\[A(z=0,t)=A_{0}\exp\big{[}-(t/T)^{n}\big{]} \tag{69}\]  
be a super-Gaussian pulse. This pulse can be approximated by a smooth rectangular one (Equation (63)), \(A(z=0,t)=A_{0}[erfc((t-\theta/2)/\Delta)-erfc((t+\theta/2)/\Delta)]/2\), by choosing the following parameters:  
\[\theta=2T(\ln 2)^{1/n} \tag{70}\]  
Figure 17: Same as Figure 12, but for Equation (64) with the transition width \(\Delta=0.2\theta\).  
\[\Delta=\frac{2T}{\sqrt{\pi}n(\ln 2)^{(n-1)/n}} \tag{71}\]  
In Figure 18, such a comparison is presented for two different values of the super-Gaussian order \(n\). This figure clearly shows that the smooth rectangular is an excellent approximation to super-Gaussian, while, unlike super-Gaussian, the smooth rectangular pulse does have an exact analytical expression for its propagation in dispersive medium.
###### Abstract  
This paper proposes an efficient transmission line modulation by using the bending technique to realize low profile leaky wave antennas in the Ku-band for frequency scanning and sensor applications. The paper focuses mainly on the bending effects of the transmission line in terms of the sharpness of edges. The right-hand/left-hand transmission line can be designed in the form of zig-zag pattern with sharp corners and only the right-hand transmission line in the form of sinusoidal patterns with smooth corners. In this presentation, we demonstrate that transmission lines of this kind can be used to realize highly efficient leaky wave antennas with broadband impedance matching and high gain characteristics in the Ku-band. Dispersion analysis and ladder network analysis have been performed for investigating the performance of the proposed designs. The sharpness of the bends periodically distributed along the body of the antenna has been used to our advantage for frequency scanning in the left-hand and right-hand quadrants at different frequencies. The proposed bending technique has been proven to be instrumental in achieving the desired characteristics of low profile leaky wave antennas.  
transmission line; modes; leaky wave; scanning; dispersion; modulation +
Footnote †: journal: Accepted: 11 May 2019; Published: 16 May 2019
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 3 Introduction to Signal Quality Index for Photoplethysmographic Signals  
### Definition of Quality  
We distinguish between two types of quality for photoplethysmography, the metrological quality and the physiological quality.
## 2 Current Research on Data Acquisition, Refinement, and Quality Evaluation
### Research on Quality Assurance of Acquired Data and AI Models  
Quality assurance can be categorized into the tasks of extracting appropriate data for training to enhance the accuracy of AI models and conducting a performance evaluation of the developed AI models. [28] First, performance evaluation indicators on the classified model, such as accuracy, confusion matrix, precision, recall, F1 Score, ROC AUC, and mAP, are developed based on a method of judging the similarity between the actual and estimated data. In 2012, during which the so-called embryonic stage of AI occurred, quality assurance of training data was considered unimportant since the focus was only on the mechanical training model, but the necessity of quality evaluation rose as the amount of data continued to increase, and the importance of refinement and labeling was ascertained. If quality evaluation is included as a general business process, quality evaluation could be carried out by at least those who have knowledge on AI development, which infers that AI engineers would additionally have to bear the quality evaluation task of data before the training period. According to recent research, there are tendencies that prove the accuracy enhancement of the model only with a quality training data set, which would solve if research on training data evaluation indicators are done and the related manpower are educated from now on, but lack of AI development personnel would rise as a problem in the short term.
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
## 4 Results Analysis
### TC Activity Season  
Through the analysis and introduction of various TCs in the previous section, it is evident that there are differences in the activity ranges of these TCs. This discrepancy may arise because the activity seasons of different TCs vary, leading to distinct effects from  
Figure 5: Distribution of intensity levels of various TCs.  
Figure 6: PDI box plot of various TCs. The discrete hollow dots outside the box represent outliers, the upper and lower short solid lines represent the maximum and minimum PDI values of various TCs except outliers, the upper and lower borders of the blue box represent the 25% and 75% quantiles of various TCs, the solid lines inside the box represent the median PDI of various TCs, and the dots inside the box represent the average PDI value of various TCs.  
the environmental flow fields in the relevant sea areas (such as subtropical high pressure, monsoon trough, etc.). To test this hypothesis, this paper visualizes the average monthly active distribution of TCs throughout the year (calculated using monthly TC generation data from 2000 to 2022) along with the average monthly active distribution of various types of TCs annually. The specific distribution is illustrated in Figure 7.  
As illustrated in Figure 7a, TCs can form in the NWP throughout the year, though the peak period primarily spans from July to October. This seasonal peak is mainly due to the northward and intensified extension of the subtropical high during summer, which enhances convergence and upward motion in the intertropical convergence zone and the monsoon trough--conditions favorable for TC genesis. In autumn, although the influence of the subtropical high-pressure system weakens as it begins to retreat eastward and move southward, its residual influence remains considerable. Furthermore, the ocean retains substantial heat from solar radiation accumulated during summer, resulting in persistently warm sea surface temperatures in early autumn, which further promotes TC formation [21; 22; 23; 24; 25]. Figure 7b presents the seasonal activity distribution by cluster. Despite histogram distortion caused by the unequal sample sizes across clusters, the seasonal activity trends generally align with the overall pattern. The absence of Cluster 2 activity in many months is likely due to its small sample size, yet in the months where TCs do form, its seasonal curve remains consistent with the general trend. Interestingly, Cluster 1 exhibits relatively low activity in September, deviating from the typical seasonal pattern. This anomaly may be associated with anomalous environmental factors such as unusually strong and westward-extending subtropical highs, premature retreat of the SCS monsoon (resulting in insufficient moisture and energy), or enhanced vertical wind shear disrupting the vertical structure of developing TCs [26; 27].
## 5 Conclusions  
* The silicone-epoxy coatings produced in the entire tested spectral range showed similar optical properties (T, R, A) to those of glass. They can be potentially used in photovoltaic panels.
* The chemical modification with polysiloxanes resulted in an increase in surface roughness compared to the unmodified coating.
* The chemical modification with polysiloxanes contributed to an increase in hydrophobicity, with most samples achieving WCA values close to 90\({}^{\circ}\).
* The use of polysiloxanes resulted in a significant reduction in the CAH and RoA values compared to those of the unmodified coating.
* The WCA values at reduced temperatures decreased for all applied modifications compared to the WCA values determined at room temperature. In addition, it was observed that the lower the ambient temperature, the lower the WCA value.
* Double-functionalized polysiloxanes in the epoxy-silicone matrix led to a significant improvement in the anti-icing properties, a reduction in ice adhesion, and an increased  
Figure 10: Relationship of IA as a function of (**a**) WCA determined at room temperature; (**b**) RoA.  
freezing delay time of water droplets in comparison to the unmodified silicone-epoxy resin.
* The synergistic effect of the AGE and HEX functional groups at the polysiloxane core yielded better icephobic and hydrophobic results compared to those obtained with the AGE and OCT functional groups when using a silicone-epoxy matrix.
* Two relationships were observed for the modifications used. As the WCA values increased and as the RoA values decreased, the IA values were reduced.  
Conceptualization, K.Z. and B.P.; methodology, K.Z., B.S. and D.G.; validation, K.Z., B.P., M.L. and D.P.; formal analysis, K.Z.; investigation, K.Z. and D.G.; resources, D.G., M.L., B.S., R.K. and D.P.; data curation, K.Z.; writing--original draft preparation, K.Z.; writing--review and editing, K.Z. and B.P.; visualization, K.Z.; supervision, A.B. and R.E.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Centre for Research and Development (NCBR), grant number LIDER/15/0089/L-12/20/NCBR/2021.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Methodology
### FAO-56 Dual Crop Coefficient  
A land surface model is used for the daily prediction of the soil water budget. The land surface model used in this study is the FAO-56 model with the dual crop coefficient approach [40] because it has been extensively calibrated and used to predict evapotranspiration of wheat in the region of study [45; 53; 54; 55]. A brief description of the model is provided in the Appendix A. The model is run at a daily time step forced by the precipitation amounts and meteorological data including air temperature, air humidity, wind speed and solar radiation. Vegetation variables are the canopy cover fraction and the crop coefficient. The crop coefficient \(\text{K}_{\text{cb}}\) is estimated from S2 NDVI using the relationship proposed by [54]. Finally, soil parameters include wilting point and field capacity derived in this study from field measurements carried out on site [35; 45; 54].  
The predicted surface soil moisture (\(SSM_{m}\)) is derived from the surface soil water depletion \(D_{\epsilon}\):  
\[SSM_{m}=\theta_{fc}-\frac{D_{\epsilon}}{Z_{e}*1000} \tag{1}\]  
where \(\theta_{fc}\) is the field capacity and \(Z_{e}\) the soil evaporation layer depth.  
The depletion \(D_{\epsilon}\) is given by the daily water balance equation of the surface soil layer for the exposed and wetted soil fraction \(f_{ew}\)[40]:  
\[D_{e,i}=D_{e,i-1}-P_{i}-R_{o,i}-\frac{I_{i}}{f_{w}}+\frac{E_{i}}{f_{ew}}+T_{ew,i}+DP_{i} \tag{2}\]  
where \(D_{e,i}\) is the depletion of the day \(i\), \(P_{i}\) the precipitation, \(R_{o,i}\) the precipitation runoff, \(I_{i}\) the irrigation, \(E_{i}\) the evaporation, \(T_{ew,i}\) the transpiration from the exposed and wetted soil fraction, \(DP_{i}\) the deep percolation and \(f_{w}\) the fraction of soil surface wetted by irrigation.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 3 Transmitter Design
### Analog Precoder Design: No CSI at Users Terminals  
In this case, the analog precoder vector is generated randomly for the \(u\)th user according to [31]  
\[\mathbf{f}_{a,u}=\left[\mathrm{e}^{\left|2\pi a_{u}^{u}\right|}\right]_{1\leq n \leq N_{tx}}, \tag{8}\]  
where \(\omega_{n}^{u}\), \(n\in\{1,\ldots,N_{tx}\}\), \(u\in\{1,\ldots,U\}\) are i.i.d. uniform random variables with support \(\omega_{n}^{u}\in[0,1]\).
## 1 Introduction  
Oropharyngeal squamous cell carcinoma (OPSCC) is a head-and-neck cancer affecting the tonsils, base of the tongue, pharyngeal walls, and soft-palate region [1]. Despite advances in OPSCC treatment, over 25% of patients relapse, 16% experience local recurrence, and 7% experience distant recurrence [2]. It is important to develop culture methods that recapitulate key events in tumorigenesis, allowing for drug screening in an in vivo-like setting.  
Tissue invasion and metastatic capacity are fundamental traits of cancer stem cells (CSCs), a heterogeneous subset of cancer cells with the ability to reproduce tumor complexity at a cellular level [1, 3]. When cultured in a hydrogel, CSCs generate 3D structures known as organoids, self-organized cellular structures that exhibit phenotypes resembling those seen in tumors [3, 4]. Basement-membrane matrices provide appropriate mechanical stimuli that allow CSCs embedded within them to aggregate, grow, and differentiate, resulting in organoids of different sizes and morphologies [4, 5] with the ability to replicate cellular, mechanical, and physical signals that take place in the tumor microenvironment [6, 7]. In OPSCC, CD44 and nerve growth-factor receptor (NGFR) are two candidate markers for identifying cancer stem cells [8, 9], as shown by a study where CD44\({}^{+}\)/NGFR\({}^{+}\) cells transferred into in vivo models recovered initial tumor heterogeneity [10]. However, loss of NGFR expression in the tonsillar crypt was reported in tonsil-cancer patients [11]. Therefore, studying the organoid-forming ability of different cancer-cell phenotypes may improve our understanding about tumor or metastasis initiation. In vivo tumor behavior can be monitored using transgenic cancer cells expressing bioluminescent or fluorescent proteins. Although useful, these methods generally lack single-cell resolution, are time-consuming, and require extensive manipulation of the model [12].  
Here, we present a quick and inexpensive method for imaging organoid formation over short time periods. Homogeneous tumor organoids could be generated by embedding OPSCC-tumor cells in a hydrogel. The motility of single cells and the optical properties of organoids could be monitored, and used to determine aggregation events and organoid growth over time. Using this setup, we confirmed the motility and capability of CD44-enriched OPSCC cells to form organoids using the HoloMonitor M4 (Phase Holographic Imaging AB (PHI), Lund, Sweden) instrument.
# Mobile-Application Loading-Animation Design and Implementation Optimization
## 2 Materials and Methods
### Experiment I--Perception Measure
#### 2.1.4 Results with Discussion
The results (Table 3) of the multivariate tests of within-subject effects showed statistically significant differences in wait-time-animation evaluation (in general) between the eight proposed designs (F = 2.763, \(p<0.001\); Wilk's \(\Lambda\) = 0.693; partial \(\eta^{2}\) = 0.09). Table 3 contains the results of the tests of within-subject contrasts. As is evident from the table, participants significantly differed in responses for all four variables of wait-time-animation evaluation (perceived duration, perceived aesthetics, and two semantic differential dimensions: affective and utilitarian), depending on the design of each wait-time animation. The estimated marginal mean values of the four variables of wait-time-animation evaluation for each of the eight proposed designs (throbber, identity animation, progress bar, attribute animation, circular indicator, percentage spinner, activity animation, and text animation) are given in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Perceived**} & \multicolumn{3}{c}{**Perceived**} & \multicolumn{3}{c}{**Perceived**} & \multicolumn{3}{c}{**Perceived**} & \multicolumn{3}{c}{**Affective/ Emotional**} & \multicolumn{3}{c}{**Functional-Utilitarian Factors**} \\ \cline{3-10} \multicolumn{1}{c}{**Loading**} & \multicolumn{3}{c}{**Duration**} & \multicolumn{1}{c}{**Passage of Time**} & \multicolumn{1}{c}{**Duration**} & \multicolumn{1}{c}{**Interest**} & \multicolumn{1}{c}{**Aesthetics**} & \multicolumn{1}{c}{**Emotional Factors**} & \multicolumn{1}{c}{**Utilitarian Factors**} \\ \cline{2-10} \multicolumn{1}{c}{**Animation**} & \multicolumn{3}{c}{**Likert Scale**} & \multicolumn{1}{c}{**Likert Scale**} & \multicolumn{1}{c}{**Likert scale**} & \multicolumn{1}{c}{**Likert scale**} & \multicolumn{1}{c}{**Likert scale**} & \multicolumn{1}{c}{**Likert scale**} \\ \multicolumn{1}{c}{} & \multicolumn{3}{c}{**ms**} & \multicolumn{1}{c}{**1—Slow, 1—Slow, 5—Fast**} & \multicolumn{1}{c}{**1—Long-Short**} & \multicolumn{1}{c}{**1—Boring, 5—Interesting**} & \multicolumn{1}{c}{**1—Low, 5—High**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Mean**} \\ \hline
1. & Throbber & 4310 & 3.40 & 3.27 & 3.53 & 3.87 & 3.65 & 3.64 \\ \hline
2. & Identity & 4280 & 4.10 & 3.93 & 4.37 & 4.42 & 3.98 & 4.02 \\ \hline
3. & Progress & 5150 & 2.90 & 2.80 & 3.20 & 3.04 & 3.40 & 3.43 \\ \hline
4. & Attribute & 4460 & 3.60 & 3.87 & 3.87 & 3.8 These results confirm the positive effects of semantic meaning, anticipation, and stimulus novelty on subjective duration. Repetitive and abstract loading-animation designs were perceived to last longer and evaluated as less aesthetically pleasing. Since the semantic differential score differences did not follow the same pattern exactly, we can conclude that the differences between the subjective durations of repeated and novel items were related to accuracy of discrimination [53].
## 3 Results and Discussion
### Mechanical Properties
#### 3.2.3 Hardness  
The Vicker's hardness results obtained for the samples are shown in Figure 4a,b. There is a continuous increase in hardness with an increase in cowpea husk fibre content. Expansion in hardness of composites might be ascribed to the solidifying impact of the filler particles as composites become stiffer and thus harder as reinforcement increases, as shown in Figure 3[70]. This is consistent with previous studies [37; 71; 72]. All the composites recorded hardness values higher than the 100% unsaturated polyester similar to the works of [PERSON], et al. [73] and [PERSON] et al. [74] Addition of 3% clay into the composite increased the [PERSON]'s hardness values of all the sample because clay enhanced the interfacial adhesion between the unsaturated polyester and CPH; therefore, prompting less micro-voids and filler-polyester debonding in the interphase region [20; 75]. The maximum hardness value of 55.87 and 69.7 HV at 18% CPH composition was exhibited by mono-reinforced and hybrid composites, respectively.  
Figure 4: (**a**) Graph of wt% of mono-reinforced and (**b**) hybrid composite against hardness.
# Camera-Sourced Heart Rate Synchronicity: A Measure of Immersion in Audiovisual Experiences
## 3 Methodology
### Recording Heart Rate
As our stimulus has a slow-moving narrative spread across almost 17 min, a window length of 60 s and a stride of 1 s is deemed viable. This balances the necessary window length for accurate camera method heart rate measurements with the consideration of appropriate frequency content for assessing ISC. Notably, [PERSON] et al. suggest that an 8 s window is not long enough to capture heart rate data of appropriate accuracy for psychophysiological research [68]. [PERSON] et al. showed a low mean absolute error (MAE) of 2.68 BPM when implementing a window length of 60 s, with similar compression to the camera used in this study [61]. With the _Polar H10_[65] ECG data, the instantaneous heart rate is obtained from the time between each heart beat (RR intervals). This series of heart rate measurements is then processed with the same window length and stride as the camera-measured data. Notably, each 1 s output sample is calculated using the combined past and subsequent 30 s of input data. This produces an equivalent ground-truth ECG HR signal and subsequent ECG ISC-HR feature for comparison with the camera-sourced data. The code and visualisations of this process can be found in the project _GitHub_ repository [67]. Bright and consistent room illumination is crucial for accurate video-based heart rate measurement [68]; therefore, lighting conditions were standardised across all trials. Based on previous research and the lighting control system available, lighting similar to a well-lit domestic setting was recreated [68]. Brightest and darkest measurements were recorded at the listening position corresponding to the screen when turned off and when displaying a white screen. Overhead lighting, incident upon the viewer's face, was measured at 357 and 373 lux. Frontal lighting was measured at 121 and 163 lux, incident on the viewer's face. Notably, research by [PERSON] et al. also suggests that darker room illumination influences a greater feeling of media engagement and appreciation [69]. Hence, this further motivates the necessity for maintaining these constant lighting conditions across all trials.
# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## 3 Results and Discussion
### Green Rooibos and Green Honeybush Blend
To confirm the presence of green honeycomb and green rooibos in the tea bag blend using UHPLC-MS, chromatographic profiles of the raw materials and the tea bag were determined and compared using the same method. Figure 6a is the chromatogram of green rooibos where the marker compound aspalathin with a retention time (\(Rt\)) of 5.43 min was identified in the positive ion mode. The green honeycomb chromatogram also displayed the marker compounds mangiferin (\(Rt\) = 3.34 min) and hesperidin (\(Rt\) = 8.02), in the positive ion mode (Figure 6b). The presence of both raw materials in the tea bag blend was confirmed by the occurrence of all three marker compounds aspalathin (\(Rt\) = 5.43 min), mangiferin (\(Rt\) = 3.34 min) and hesperidin (\(Rt\) = 8.02 min) in the chromatographic profile of the tea bag blend (Figure 6c). When comparing the three chromatograms, it is clear that the tea bag blend is similar to green honeybush, which may be due to the occurrence of this species/tea in higher proportions (65%) compared to green rooibos (35%). The UHPLC-MS results support the HSI findings showing the presence of both species. Figure 5: (a) Partial least squares-discriminant analysis \(\mathbf{Y}\)-image showing classification of the raw materials in the calibration set and (b) the prediction set comprising tea bags colored according to the predicted classes.
## 2 System Configuration and Methods of Analysis
### Innovative Methods and Experimental Setup  
In this research, an algorithm is proposed to process IR images of solar modules in Matlab environment. The following two conditions must be confirmed before applying this method. First, ensure that irradiance is I \(\geq\) 700 W/m\({}^{2}\) before carrying out the investigation [23; 24]. Second, it is necessary to measure the intensity of solar radiation and set a temperature meter on the solar module before performing an inspection. This is because the environmental temperature will affect the temperature on the solar module, and thus causing deviations of infrared display temperature. Therefore, drone inspection must be conducted under the conditions mentioned above. Its purpose is to ensure that the temperature reading is correct when performing MATLAB image analysis.