esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Discussions  
By observing Figures 3 and 6, one can find that the merging threshold should be varied with various photos to obtain acceptable performance. The definition of a robust merging threshold for the VJ algorithm is complicated. Here, we propose using a small merging threshold, set to be unity, in the VJ algorithm as the first stage, so all face blocks can be thoroughly detected, despite the high false alarm rate. Hence, the face CNN is performed to exclude the segmented blocks without the human face, reducing the false alarm rate significantly.  
Compared with the state-of-the-art YOLOv4 network, the performances are comparable in accuracy rate, as shown in Table 2 and Figures 7 and 8. The red bounding box highlights the differences in Figures 7 and 8. However, our method is easy to train and implement. In addition, training the YOLOv4 network is time-consuming. On the contrary, our approach is easy to train the model. The differences between the VJ algorithm, YOLOv4 network, and the proposed app are summarized and shown in Table 4. Because of the short loading time and high accuracy, as shown in Tables 2-4, users will intuitively enjoy using the proposed app.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{3}{c}{**Time Consumption (s)**} \\ \cline{2-3}  & **YOLOv4** & **Proposed** \\ \hline Network loading & 102.78 & 0.09 \\ Recognition & 4.43 & 2.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of time consumption.  
Figure 6: An example of human face detection results using the VJ algorithm with various merging thresholds: (**a**) merging threshold = 1; (**b**) merging threshold = 2; (**c**) merging threshold = 5.  
Figure 7: Comparisons of people calculating results: (**a**) using the VJ algorithm with merging threshold = 2; (**b**) using the YOLOv4 network; (**c**) using the proposed method.  
To demonstrate the application for this study, we made a video as an example, and the hyperlink is given as [[https://youtu.be/xUoRrcGgm_w](https://youtu.be/xUoRrcGgm_w)]([https://youtu.be/xUoRrcGgm_w](https://youtu.be/xUoRrcGgm_w)) (accessed on 20 May 2022). A snapshot of the proposed app for calculating the number of people is shown in Figure 9. Some students will enter the museum. The staff feels sad that students cannot buy tickets to enter the museum quickly. Utilizing the proposed app can help her to calculate the number of people rapidly.  
Figure 8: Comparisons of people calculating results: (**a**) using the VJ algorithm with merging threshold = 5; (**b**) using the YOLOv4 network; (**c**) using the proposed method.
## 2 Principle
### Vector Coherence Factor  
According to the Euler formula, the complex signal can be given by the formula \(S=\mid h\mid(cos\varphi+i\sin\varphi)\), where \(\mid h\mid\) represents the mode length of the signal and \(i\) denotes \(i^{2}=-1\). When \(\mid h\mid\) is equal to 1, the complex signal only contains phase information. On the angular scale, the complex signal can be regarded as the unit vector \(X_{n}=(cos\varphi_{n},\sin\varphi_{n})\), which can be represented by phase angle \(\varphi_{n}\). For the unit vector sample \(\{X_{1},\,X_{2},\,\,\ldots,\,X_{N}\}\) of size \(N\), the length of its mean resultant vector can be expressed as:  
\[\left\lvert R\right\rvert=\sqrt{a^{2}+b^{2}}\in\left[0,1\right] \tag{3}\]  
where \(a\) and \(b\) respectively represent the real part and imaginary part of the resultant vector, which are expressed as:  
\[a=\frac{1}{N}\sum_{n=1}^{N}\cos\varphi_{n},\ b=\frac{1}{N}\sum_{n=1}^{N}\sin \varphi_{n} \tag{4}\]  
According to Equation (3), the length \(\mid R\mid\) of the mean resultant vector can represent the distribution consistency of the phase angle \(\varphi\). As shown in Figure 2, when \(\mid R\mid\) approaches 1, it indicates that all angles are concentrated in a specific direction. When  
Figure 1: Schematic diagram of phased array sector scan beam\(-\)forming process. (**a**) Beam steering and focusing, (**b**) signal acquisition, (**c**) coordinate transformation, and (**d**) sector scanning imaging display.  
the value of \(|R|\) is very small or even close to 0, it shows that the unit vector samples are randomly distributed, and the direction consistency of the phase angle is poor.
# Zinc Plating on Inkjet-Printed Ti3C2 Tx MXene: Effect of Electrolyte and PEG Additive
## 2 Materials and Methods
Zn deposit morphology was investigated through a scanning electron microscope (SEM, Zeiss EVO 50 EP) equipped with an EDX detector (x-sight detector, Oxford instruments, Abingdon, UK), and microstructure was analyzed with an X-ray diffraction (XRD) microscope (model PW1830, K\(\alpha\)1 Cu = 1.54058 A, Philips, Eindhoven, The Netherland). The crystallite size (D) of plated Zn crystals at 5 mA cm\({}^{-2}\) was calculated by means of the Scherrer equation, D = (0.94 \(\lambda\))/(\(\beta\) cos\(\theta\)), where \(\lambda\) is the X-ray wavelength (\(\lambda\) = 1.54058 A), \(\beta\) is the line broadening at half the maximum intensity (FWHM), and \(\theta\) is the Bragg angle of the [002] peak. Dynamic light scattering (DLS) was used to investigate the Ti\({}_{3}\)C\({}_{2}\) MXene nanosheet size distribution in the aqueous ink (Zeta sizer Nano, Malvern, UK) at a scattering angle of 173\({}^{\circ}\) (backscatter).
## 3 Results and Discussion
### Porosity and Density Determination  
A porosity level of approximately 40% indicates a moderately porous scaffold architecture, which is often desirable in guided bone regeneration applications. Such porosity is sufficient to allow fluid infiltration, nutrient exchange, and cell migration while preserving mechanical integrity during the early stages of tissue healing. Numerous studies have demonstrated that porous structures in the range of 30-70% can facilitate osteoblast proliferation and neovascularization, providing an optimal microenvironment for bone regeneration [36; 37]. In particular, the interconnected pore networks formed by electrospun nanofibers enable capillary flow and protein diffusion, critical for cell signaling and tissue integration [38].  
The density of the studied samples, oscillating around 40 mg/mL, reflects the lightweight nature of the fabricated scaffolds (Table 15). This value is significantly lower than that of natural cortical bone, which ranges between 1.8 and 2.0 g/cm\({}^{3}\)[39]. However, such moderate density is not inherently disadvantage. For bioresorbable membrane applications, lower density may enhance biodegradability while minimizing mechanical mismatch with surrounding soft and regenerating tissues. Additionally, lower mass-to-volume ratios may promote gradual scaffold resorption and replacement by the native bone matrix, aligning with physiological remodeling processes [40].  
The observed combination of moderate porosity and low density suggests that the scaffolds strike a functional balance between structural support and biological permissiveness. This architectural profile may contribute to improved osteointegration and cellular infiltration, particularly when combined with the biofunctional effects imparted by MgO and AuNP nanoparticles. Prior work on composite and nanostructured GBR membranes supports the notion that porosity and density are critical parameters for tuning cell-material interactions, degradation kinetics, and mechanical behavior [41; 42]. Therefore, the measured values confirm that the developed membranes exhibit physical properties aligned with established design criteria for guided bone regeneration scaffolds.
## 4 Discussion
### Average \(\tau_{\rm e}\)-med of the Presented Monosyllables and Responses  
We compared the average \(\tau_{\rm e}\)-meds of monosyllables and the responses of the three groups. We found that the average \(\tau_{\rm e}\)-meds of the monosyllables were significantly longer than those of the responses of each of the three groups. This indicates that monosyllables tend to be misheard as monosyllables with long \(\tau_{\rm e}\)-meds when confusion occurs. A previous study involving patients with sensorineural hearing impairment suggested that consonant confusion patterns were unidirectional and that the \(\tau_{\rm e}\)-med was an influential factor in determining the direction [7]. Specifically, consonants with a short \(\tau_{\rm e}\)-med tend to be misheard as consonants with a long \(\tau_{\rm e}\)-med when confusion occurs. However, we investigated the tendency of confusion among monosyllables rather than consonants. The tendency observed in this study corroborates the results of a previous study [7]. Furthermore, this tendency does not vary depending on speech recognition performance. The results indicate that patients with clinical sensorineural hearing impairment are likely to misinterpret monosyllables with long \(\tau_{\rm e}\)-meds when experiencing confusion, and this tendency is not influenced by speech recognition performance. Therefore, modification of the \(\tau_{\rm e}\), such as through hearing aid adjustment, would be helpful for processing speech signals. A previous study proposed a method to control the temporal fluctuations in the fundamental frequency of a voice that can be used to control the \(\tau_{\rm e}\) of a monosyllable [7]. When individuals with normal hearing heard a monosyllable whose \(\tau_{\rm e}\) was controlled longer, the stimulus was not heard as belonging to the consonant that had a long averaged \(\tau_{\rm e}\)-med [7]. However, no studies evaluated how patients with clinically sensorineural hearing impairment hear a monosyllable with a \(\tau_{\rm e}\) that was controlled. For patients who have difficulty hearing certain monosyllables, controlling the \(\tau_{\rm e}\) may be helpful when fitting hearing aids. Further studies are needed to verify whether patients with sensorineural hearing impairment find it easy to hear monosyllables with \(\tau_{\rm e}\) values that are controlled and long.  
To the best of our knowledge, the relationship between the \(\tau_{\rm e}\)-med and \"no response\" was not investigated in previous studies. In this study, the percentage of \"no response\" was significantly higher in the low-SRS group than in the other two groups. In contrast, there was no significant difference in the \(\tau_{\rm e}\)-meds of presentations for \"no response\" among the three groups. This result suggests that \"no response\" is not related to the \(\tau_{\rm e}\)-med, although the percentage of articulation and confusion were associated with the \(\tau_{\rm e}\)-med. In other words, the mechanism underlying \"no response\" may be different from that underlying confusion. Therefore, besides the \(\tau_{\rm e}\)-med, factors hitherto unknown may be related to \"no response\".
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 4 Simulation and Experimental Results
### Experimental Scene and Conditions  
The experimental scenario and a GNSS passive receiver are shown in Figure 6. The multistatic radar system is formed with multiple GNSS transmitters of opportunity, including GPS L1, GLONASS G1, and Galileo E5a and E5b. The passive receiver is installed to receive GNSS signals in the east of Portsmouth harbor in the UK, and it has two receiving channels. One receives the direct wave signal, and the other receives the target echo signal. The bandwidth of this receiver can cover the four GNSS bands which are recorded simultaneously at a sampling rate of 20 MHz. When the large target moves relatively close to the receiver, it can provide a sufficiently high SNR.  
We located a commercial ferry (the \"St. Cecilia\") whose length is 77m and beam is 17.2m, as shown in Figure 7. During the experiment, the ferry entered the port at a low speed, so we can know the time of departure and arrival of the ferry in advance. In addition, Automatic Identification System (AIS) installed on the ship can record the ferry's driving in real time, which provides us with ground reference data for multistatic positioning.  
Figure 5: Presentation diagram of Convex Hull Optimization Method.  
There are 12 visible satellites during the data acquisition period. All through the experimental recording, these satellites continuously illuminate the targets. The satellite geometry topology is shown in Figure 8.  
And information on these satellites can be found in Table 1.  
Figure 8: Geometry topology of 12 visible satellites.  
Figure 6: The experimental scene.  
Figure 7: The photo of the “St. Cecilia”.
## 5 Conclusions  
The principal objectives of this article were to perform multi-objective optimization and predict the grey relational grade (GRG) of polytetrafluoroethylene (PTFE) matrix composites via hybrid Taguchi-grey relational analysis (GRA) and support vector regression (SVR) models, respectively. The single optimization results revealed that mass loss and volume gave a similar optimal combination of parameters (L1G1 SD1 SSI), whereas the coefficient of friction and specific wear rate depicted different optimal combinations of parameter settings of L3G1 SD1 SS3 and L3G1 SD3 SSI, respectively. The analysis of variance (ANOVA) results showed that grit size was the most significant parameter influencing the tribological behaviors of polytetrafluoroethylene matrix composites. The results from the response table of the GRG revealed that the optimum parameters for the minimum tribological loss of PTFE matrix composites were the L3G1 SS3 SD3 combination. The ANOVA for the GRG indicated that grit size, with 94.56%, was the most significant parameter influencing the GRG of the PTFE matrix composites. The validation result depicted an enhancement of 52% in the GRG from 0.3988 (L2G2 SD3 SS1) for initial design parameters to 0.8311 for the optimal levels (L3G1 SS3 SD3). The prediction of the GRG using the hybrid models showed that the SVR-PSO and SVR-HHO models performed better than the single SVR model. Nevertheless, the SVR-HHO model predicted the GRG better than the SVR-PSO model, showing a lower prediction error of 1.16%, demonstrating its robustness and efficiency over the SVR-PSO hybrid artificial intelligence model with a prediction error of 5.46%.  
Conceptualization, methodology, formal analysis, writing and formal analysis, M.A.I.; Software analysis, S.I.A.; Resources, A.K.S. and M.M.; H.C. and M.A.S. supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Grateful to Kano University of Science and Technology, Wudil Kano State Nigeria, Kano State Scholarship Board, Kano State, Nigeria and Near East University, Nicosia, Cyprus.  
The authors declare no conflict of interest.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 3 Description of the System Under Study
### Scorbot Robot Structure  
The Scorbot robot, described in Figure 2, has a base that is normally fixed to a working surface for the first DoF (base), extending its work area with the two remaining links corresponding to the second (shoulder) and third (elbow) DoF, adding the incorporated end-effector with good accuracy in its displacement. However, it can be attached to a sliding base, allowing a range of work with greater spatial coverage.  
Figure 1: Scorbot robot.  
Figure 2: Scorbot robot morphology.  
Among its structural and technical characteristics, the number of axes, end-effectors, and actuators, among others, which are listed in Table 1, can be mentioned.
## 3 Results and Discussion
### Resistance to Moisture  
The test of resistance to moisture revealed the performance of the hybrid composites when exposed to different climatic conditions. The changes in thickness, mass and mechanical properties were analysed. At the end of the cycles (water immersion, freezing and drying), no visible changes were observed on the surfaces of the specimens; however, a slight increase in thickness (dT) of all composites was found, more obviously so for DP/C, at about 6%, compared to 4.8% for DP and 4% for DP/K (Figure 7).  
A slight decrease in mass of the composites (dM) was noticed, in the range of 1-2.8% (Figure 7). Similar findings were reported by [51] for hybrid composites made with kenaf and glass fibres after freeze-drying exposure.  
The increase in thickness was caused mainly by the hydrophilic character of natural fibres due to the presence of hemicellulose [52] and, in terms of other factors, by the small voids produced inside of structures because of the poor impregnation of date palm fibres into the polyester resin compared to the Kevlar composite (DP/K). Tensile forces can occur during freezing-drying in the polymer matrix, due to the expanding fibre volume in the saturated state after water immersion. These lead to some micro-cracks or voids in the structure (Figure 8d). These voids are more evident in the composites DP/C and DP which had greater decreases in mass compared to the DP/K structure.  
DP/C and DP registered decreases in mechanical properties after cyclic testing of about 25% and 6.5%, respectively, for MOE and of about 13% and 5.8%, respectively, for MOR compared to the values obtained before testing. The substantial strength losses occurred in the composite structures, induced by degradation through freeze-drying mechanisms, which cause losses in bonding at the fibre-matrix interface and via the micro-cracks produced by internal stresses [46; 51; 53]. DP/K performed better, Kevlar fibre being slightly stronger at low temperatures.  
After cyclic testing, the flexural strength and modulus of elasticity of DP/K registered slight decreases of about 1.4% and 2%, respectively (Figures 7 and 8).  
In the literature [54; 55], the effect of a freeze-thaw environment has been studied on carbon-fibre- or glass-fibre-reinforced polymer composites, reporting insignificant effects on shear, axial and flexural properties.  
Figure 7: The differences in thickness (dT) and mass (dM) after cyclic testing.
## 2 Data and Methods
### Best Track Data of Typhoons  
Information regarding the occurrence and movement of typhoons from 1992 to 2016 in the study area was obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) version 3 release 10 (v03r10) [49]. The number of typhoons during the study period was calculated in bins of \(2^{\circ}\times 2^{\circ}\) and used as an index to evaluate whether extreme conditions such as typhoons were sufficiently represented in the EVA results.
# Comparison and Identification of the Aroma-Active Compounds in the Root of _Angelica dahurica_
###### Abstract  
Solid-phase microextraction (SPME), purge and trap (P&T), stir bar sportive extraction (SBSE), and dynamic headspace sampling (DHS) were applied to extract, separate and analyze the volatile compounds in the roots of Hangbaizhi, Qibaizhi, and Bobaizhi and the GC-O-MS/MS (AEDA) was utilized for the quantification of key aroma compounds. Totals of 52, 54, and 43 aroma-active compounds extracted from the three samples by the four extraction methods were identified. Among these methods, the SPME effectively extracted the aroma compounds from the _A. dahurica_. Thus, using the SPME methods for quantitative analysis based on external standards and subsequent dilution analyses, totals of 20, 21, and 17 aroma compounds were detected in the three samples by the sniffing test, and sensory evaluations indicated that the aromas of _A. dahurica_ included herb, spice, and woody. Finally, principal component analysis (PCA) showed that the three kinds _A. dahurica_ formed three separate groups, and partial least squares discriminant analysis (PLS-DA) showed that caryophyllene, (\(-\))-\(\beta\)-elemene, nonanal, and \(\beta\)-pinene played an important role in the classification of _A. dahurica_.  
**Keywords:**_Angelica dahurica_ root; aroma-active compounds; solid-phase microextraction (SPME); gas chromatography-olfactometry-mass spectrometry (GC-O-MS); aroma extract dilution analysis (AEDA)
# Digital Twin and Internet of Things--Current Standards Landscape
## 3 Standards for Digital Twins and the Internet of Things
### Web of Things  
The goal of the W3C Web of Things (WoT) Working Group is to counter the fragmentation of the IoT by providing so-called building blocks that should complete and enhance already existing standards. The most important of those building blocks is the WoT ThingDescription (WoT TD or only TD) [55], which became an official W3C Recommendation in April 2020. The TD provides a meta-model to describe existing resources in the IoT, e.g., a DT or a part of a DT, called Thing. As Figure 8 shows, a Thing comprises a set of properties (which can be read-only or read/write), actions, i.e., methods that can be invoked on the Thing, and events. The type system is based on JSON with additional support for adding constraints, e.g., regular expression for strings, based on JSON Schema. TDs use URI-based identifiers and can be serialized as either JSON-LD, JSON, or RDF. The standard does not contain any kind of (cross-)domain model and therefore neither supports geo-spatial, temporal nor historical data. One highlight of the TD is that by being grounded in Semantic Web technologies, is it easily possible to add semantic information to every aspect of a TD.  
A serialized TD can be published and a consumer will be able to access the described resource. Resource discovery, i.e., how these TDs can be found, is still work in progress but is expected to be published in Q1 2021 [56]. Not yet standardized, there are also at least two existing implementations ([[https://github.com/thingweb/thingweb-directory](https://github.com/thingweb/thingweb-directory)]([https://github.com/thingweb/thingweb-directory](https://github.com/thingweb/thingweb-directory))) ([[https://github.com/linksmart/thing-directory](https://github.com/linksmart/thing-directory)]([https://github.com/linksmart/thing-directory](https://github.com/linksmart/thing-directory))) providing sophisticated search capabilities, e.g., via DNS-Based Service Discovery [57], CoRE Resource Directory [58], SPARQL queries or JSON-LD framing [59].  
Unlike other standards, the TD does not define any API that has to be implemented by any resource. Instead, it defines a meta-model to describe existing APIs including e.g., protocol, payload format, security, in a machine-readable way. This is a unique and promising approach at closing the gap between existing (and future) standards and enabling interoperability.  
Figure 7: Class diagram depicting the data model of the SensorThings API Part 1 and 2 in version 1.0 (based on Figure 2 from [53] and Figure 1 from [54]).