esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Interpretation of Assay Results from Previous Agonist Structures
Cysteines located in the N-terminus of TRPA1 have been reported to attack \(\upalpha\), \(\beta\)-unsaturated carboxylic moieties of agonists and form a covalent bond via a Michael addition [8,9]. The TRPA1 activity of compound 655 (EC\({}_{50}\) 5.0 \(\upmu\)M) was comparable with that of cinnamaldehyde (EC\({}_{50}\) 5-19 \(\upmu\)M) [9,14]. Since compounds 655 and 81 have an \(\upalpha\), \(\beta\)-unsaturated carboxyl moiety, they would activate TRPA1 through the same mechanism with cinnamaldehyde. The substituent of \(\beta\) and \(\gamma\) positions of the five-membered rings would modify their reactivity and TRPA1 activity because covalent bonds can be formed between the \(\upbeta\) position of their five-membered rings and cysteine residues of TRPA1. Compounds belonging to clusters 54, 57, 60, and 98 commonly have an ester moiety and double bonds unconjugated with ester (Figure 4d). Several compounds, including compound 522 (cluster 57, EC\({}_{50}\) 6.8 \(\upmu\)M, top four active compounds in the first assay) and compound 525 (cluster 98, EC\({}_{50}\) 7.8 \(\upmu\)M, top five active compounds in the first assay), activated TRPA1. Terpenes, such as geranyl propionate and nery1 acetate, are TRPA1 agonists with an ester moiety and double bonds unconjugated with ester as with those compounds (Figure 4d) [15]. Terpenes are representatives of non-electrophilic TRPA1 agonists, and some of them are suggested to bind to the transmembrane domain 5 of the channel [16]. Because the compounds of clusters 54, 57, 60, and 98 lack electrophilic moieties, they could activate TRPA1 through the same mechanism with terpenes.
# Minkowski Island and Crossbar Fractal Microstrip Antennas for Broadband Applications
## 5 The Double-Fractal Layers Geometry Antenna  
Taking into consideration the above investigation, the antenna with double fractal layers has been designed. The upper layer is based on a crossbar fractal, as presented in Figure 2a, while the ground has a Minkowski island fractal as is depicted in Figure 4a. The upper layer with the feeding line has an asymmetrical geometry. This feeding configuration enables an undisturbed flow of current from the feeding line to the patch. The final version of the antenna is presented in Figure 6. Double-fractal geometry was combined to obtain the optimal configuration of the antenna, the best S11 performance, and thus the largest bandwidth.  
The reflection properties of the antenna have been presented in Figure 7.  
Figure 7 demonstrates the widened bandwidth of the final version of the antenna, where the measured frequency range is even wider than the simulated one. It has achieved, at the level of \(-10\) dB, a bandwidth ranging from 4.1 to 19.4 GHz, for a total bandwidth of 15.3 GHz. The figures show good correlation between measured and simulated values, and the very wide bandwidth of the proposed configuration.  
Figure 5: Simulated values of S11 for the antenna with ground based on Minkowski fractal.  
Figure 6: Picture of the fabricated antenna. Picture of the fabricated antenna. (**a**) One side of the antenna (**b**) another side of the antenna.  
The gain (G) of the proposed antenna was measured over the working bandwidth, from 4 to 18 GHz. The antenna achieved an average measured gain of 6 dBi and a maximum value of 10.9 dBi at 15.2 GHz. For lower frequencies, the value of gain falls, e.g., G = 5.9 dBi at 10 GHz, G = 4.5 dBi at 6 GHz. The efficiency improves with the growth of frequency. The following values of efficiency were obtained: of 86.4% at 7.96 GHz, of 88.3% at 9.22 GHz and of 89.7% at 11.48 GHz.  
The radiation characteristics for proposed antenna were presented in Figure 8. Radiation patterns were measured in frequencies of 7.96, 9.22 and 11.48 GHz in the characteristic planes--\"X-Y\" and \"X-Z\". These are the exampled frequencies for the frequency range of significantly low reflection property (7.96 and 11.48 GHz) as well as for the rather worse reflection coefficient (9.22 GHz). It can be seen that proposed geometry of the antenna resulted in a radiation spread of 270\({}^{\circ}\). However, the shapes of radiation characteristics change in function of frequency. Future efforts should focus on obtaining similar shapes of radiation patterns in function of frequency to improve the efficiency of radio link.
## 4 Experiment and Results Analysis
### Experiment on the Improved YOLOv8 Object Detection Algorithm
Future optimizations could focus on balancing precision and recall to further improve overall performance. To contextualize the proposed Multi-Scale Feature Consolidation (MSFConv) module, we compare it with established multi-scale feature extraction methods--Feature Pyramid Networks (FPNs) [21], HRNet [22], and [PERSON]'s SLAM-specific approach [17]--focusing on detection accuracy, computational efficiency, and real-time suitability for SLAM. Table 4 summarizes the results. YOLOv8n+MSFConv achieves a precision of 74.2%, mAP@50 of 65.1%, and mAP@50:95 of 43.5%, with 3.19 M parameters, 8.9 GFLOPs, and 233.1 FPS. YOLOv8n+FPN, with hierarchical feature fusion, improves precision to 76.1% and mAP@50 to 66.8%, but increases complexity, reducing FPS to 198.4. YOLOv8n+HRNet, maintaining high-resolution features, excels with 77.3% precision and 67.5% mAP@50, yet its 10.2 M parameters and 12.5 GFLOPs yield a 165.2 FPS. [PERSON]'s method [17], tailored for dynamic SLAM, scores 73.5% precision and 64.8% mAP@50, with 3.45 M parameters and 9.8 GFLOPs, resulting in 210.6 FPS. heavier alternatives. While FPN and HRNet offer superior accuracy, their computational cost limits practicality in dynamic SLAM. MSFConv thus provides an optimal trade-off, with the potential for future hybrid enhancements.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 4 Security Analysis
### Decoding from Two Shares

Table 3: Color probability diagram of two shares. Based on the above a posteriori probabilities, the MAP module detector decodes  
\[\hat{\mathbf{H}}(x,\;y)=\left\{ \begin{array}{ll}0,&\text{if}\;\mathbf{H}_{2}(x, \;y)\in\left\{\text{R},\;\text{B},\;\text{M}\right\},\\ 1,&\text{if}\;\mathbf{H}_{2}(x,\;y)\in\left\{\text{C},\;\text{Y}\right\}.\end{array} \right. \tag{47}\]  
The probability of module recovery error can be calculated as:  
\[\Pr\left(e_{m}\right) = \Pr\left\{\hat{\mathbf{H}}(x,\;y)\
eq\mathbf{H}(x,\;y)\right\} \tag{48}\] \[= \frac{5}{12}\Pr\left(\hat{\mathbf{H}}(x,\;y)=0|\mathbf{H}(x,\;y) =1\right)\] \[= \frac{1}{6}.\]  
Since each codeword consists of 8 modules, the probability of codeword error \(e_{c}\) is:  
\[\Pr\left(e_{c}\right)=1-\prod_{t=1}^{6}\left[1-\Pr\left(e_{m}(t)\right) \right]=1-\left(\frac{5}{6}\right)^{8}=0.767. \tag{49}\]  
Similar to Equation (31), the probability of correct decoding should be:  
\[\prod_{\ell=1}^{4}\sum_{q=1}^{8}\binom{25}{q}\Pr(e_{c})^{q}\left[1-\Pr(e_{c}) \right]^{25-q}=4.73\times 10^{-23}. \tag{50}\]  
This means that the attacker needs to try on \(10^{23}\) shares to get a correct decoding, which is almost impossible for ordinary computers nowadays. In summary, given any two shares, it is also computational infeasible to decode the secret QR code correctly.
## 3 Generalized Algorithm
### Algorithms for Calculating Aperture Impedance  
In the main flowchart, the calculation of the \(Z_{ap}\) impedance for an enclosure wall with a single aperture follows the algorithm shown in Figure 11. The choice of analytical expression is made, depending on the shape and internal filling of the aperture. In particular, the algorithm takes into account the filling of the aperture with dielectric, magnetic, or magnetodielectric (MDM) materials, as well as wire grids. It also provides a procedure for calculating \(Z_{ap}\) for an arbitrarily shaped aperture, in accordance with the model from [29].  
Figure 10: Main flowchart of the generalized algorithm for the SE evaluation.  
The expression for calculating the \(Z_{qp}\) impedance of the aperture array (i.e., perforated wall) is chosen based on the flowchart from Figure 12. When the aperture array is located outside the centre of the enclosure front wall, and when the apertures are staggered, the model from [32] is used to calculate \(Z_{qp}\). This algorithm also allows \(Z_{qp}\) to be calculated for arrays of round or square apertures, which are implemented by expression (12) from [25].  
Figure 11: Algorithm of \(Z_{qp}\) calculation for a single aperture (Flowchart 2).  
Figure 12: Algorithm of \(Z_{qp}\) calculation for an aperture array or a perforated wall (Flowchart 3).  
Figure 13 illustrates a flowchart of the algorithm for selecting an analytical expression to calculate the \(Z_{qp}\) impedance for an enclosure wall with a group of apertures. If the enclosure wall contains an ordered group of identical circular or square apertures, then \(Z_{qp}\) is calculated according to [32]. In other cases, the expression for calculating \(Z_{qp}\) is chosen according to the algorithm from Figure 11.
## 3 Related Description of IEEE Std. 802.15.6  
### UWB Phy  
The IEEE Std. 802.15.6 defines three PHY layers: narrowband (NB), UWB, and human body communications (HBC). This study focused on an impulse radio ultra-wideband PHY layer (IR-UWB-PHY) which offers high data rate transmission, low energy consumption, powerful multi-pass resolution, good coexistence with other wireless communication systems, and so on.  
The UWB PHY frame format is formed by the synchronization header (SHR), the physical layer header (PHR), and the physical layer service data unit. (PSDU), respectively, as shown in Figure 1[17]. The PSDU contains the MAC protocol data unit (MPDU) and the BCH parity bits. The PSDU can therefore be regarded as the payload. The information contained by the PHR includes the data rate of the PSDU and the length of the MAC frame body, and the SHR contains the preamble used for timing synchronization, packet detection, and other purposes, and the start-of-frame delimiter (SFD) for frame synchronization. This research mainly focused on the performance of the payload (PSDU).  
Figure 1: Ultra-wideband physical layer frame format. SFD, SHR, PHR, and PSUD are abbreviations of start-of-frame delimiter, synchronization header, physical layer header, and physical layer service data unit respectively.
# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
## 4 Experimental Results
### Social Media Dataset
The classification of media engagement, which determines the level of audience reception based on its attributes, is a topic of significant interest. To this end, the application of the TSDR algorithm to a social media dataset was carried out. Prior to the classification process, the structure of the dataset must be defined and a construction process must be implemented. In 2019, data from the social network Instagram were collected from 25 different users. For each of them, 2522 interactions were analyzed via an analysis of information from (up to) 12 of their last publications, repeating this same process for all users who interacted with them. A set of 6522 media records was built, belonging to 606 different users, enriched with information regarding their age, gender, hair color, and other cognitive data [32]. * Media width in pixels. * Media height in pixels. * Number of hashtags used. * Length (in number of characters) of the caption. * Number of males identified in the media. * Average age of males identified in the media. * Number of females identified in the media. * Average age of females identified in the media. * Gender of the owner of the media profile. * Age of the owner of the media profile. * Hair color of the owner of the media profile. The dataset employed in the present study consisted of two distinct categories: \"good engagement media\" and \"poor engagement media\". These categories were represented by 3,835 (58.8%) and 2687 (41.2%) media items, respectively. Engagement was determined as the ratio between the number of interactions (likes and comments) and the total number of followers. In the media industry, an engagement rate above 6% is commonly regarded as good, while values below that are considered poor. This classification serves as a starting point, although the engagement rate scale can be refined further [33]. The results of the original study, which utilized the same dataset as inputs for a multilayer perceptron (MLP), showed an accuracy of approximately 73% [34]. As per the results shown in Tables 5 and 6, the application of the TSDR algorithm in the present study revealed a clear improvement, yielding an average accuracy of 77% using only two dimensions against 11 from the original research. This highlights not only the established premise, but also the efficacy of the proposed algorithm. Moreover, the accuracies for all methods were higher when using the TSDR subset for classification.
## 3 Results and Discussion
### Results of Parametric Study
#### 3.2.4 Optimal Parameters  
As calibrated previously, the final ANN model exhibits (see Figure 9):  
* 15 neurons in the hidden layer;
* A rectilin activation function;
* A LM training algorithm;
* Mean square error as a cost function.
# A Systematic Review of the Role of BIM in Building Sustainability Assessment Methods
## 2 Building Sustainability Assessment Methods
### SBTool  
The international initiative for a Sustainable Built Environment (iiSBE) developed the Sustainable Building Tool (SBTool). This method is considered one of the most comprehensive of all the BSAmethods and has the flexibility to be adjusted to the local conditions of each region [2; 3]. This feature allows to compare the sustainability level of buildings from different countries.  
SBTool has influenced the national rating systems of Austria, Spain, Japan and South Korea. Custom versions have been introduced in Italy, Czech Republic and Portugal [33]. It can be adapted to assess the sustainability level of different type of buildings, such as houses, offices, schools or medical facilities, and already has versions for urban neighbourhoods.  
SBTool has a set of parameters with different weights according to the national standards and practices. Each parameter is classified with a qualitative \"score\" that results from the comparison between two benchmarks: best and conventional practice. After weighing all the parameters, a final sustainable classification is given to the building. The parameter weights and the benchmarks must reflect a country's characteristics and specific factors [2]. The system covers a wide range of sustainable building issues. The scope can be modified to be as narrow or as broad as desired, from more than 100 criteria to half a dozen. Parameter weights can also be adjusted to region-specific and site-specific factors.
# Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance
## 3 Fuzzy Positioning
### Image Pre-Processing
Based on the identified irregular quadrilateral corner points, the quadrilateral edge lengths of the original pixel coordinates can be obtained, and the approximate edge lengths of the target rectangle can be calculated by averaging the de-pairing edges. The interpolation algorithm [16] is based on a known discrete sequence, using the interpolation function to generate a continuous function, and resampling the new function to obtain a new discrete sequence, with the following Equation (5). \[i(x,y)\ =\ \sum_{n}\sum_{m}\mathrm{i}(m,n)\varphi_{i}(x-m,y-n) \tag{5}\]
# Face Keypoint Detection Method Based on Blaze_ghost Network
## 4 Experiment and Result Analysis  
### Dataset  
The WFLW [19] dataset is primarily a facial keypoint localization dataset used for training facial alignment algorithms. As shown in Figure 1, it contains rich attribute annotations, such as occlusion, poses, makeup, lighting, blur, and expressions, enabling comprehensive analysis of existing algorithms. Compared to previous datasets, the WFLW dataset exhibits significant variations in facial expressions, poses, and occlusion, allowing for the evaluation of robustness in these aspects. The WFLW dataset consists of 10,000 images, each accompanied by an annotation file. These images cover a wide range of features, expressions, and poses from faces of different ages, races, and genders. The annotation information in the dataset includes the bounding box of the face and the coordinates of 98 keypoints, which mark important facial locations, such as the eyes, nose, and mouth.  
The common facial landmark localization methods include 5-point, 68-point, and 98-point localization. The 5-point localization can only be used for facial contour localization; the 98-point localization contains too much facial information, which can cause a large computational load and low recognition efficiency. The 68-point localization can accurately describe the facial contour and local features, such as the eyes and mouth, while reducing the computation time and improving the recognition efficiency. Therefore, in this paper, we processed the WFLW dataset for the training and testing of 68-keypoint localization. As shown in Figure 2, in 68-point localization there are 6 keypoints in the left eye (37-42), 6 keypoints in the right eye (43-48), and 20 keypoints in the mouth (49-68), while the rest are facial contour keypoints.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
###### Abstract  
Since the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep learning technology and communication physical layer technology is the future research hotspot. The purpose of this research paper is to summarize the related algorithms of the combination of Automatic Modulation Recognition (AMR) technology and deep learning technology in the communication physical layer. In order to elicit the advantages of the modulation recognition algorithm based on deep learning, this paper firstly introduces the traditional AMR method, and then summarizes the advantages and disadvantages of the traditional algorithm. Then, the application of the deep learning algorithm in AMR is described, and the identification method based on a typical deep learning network is emphatically described. Afterwards, the existing Deep Learning (DL) modulation identification algorithm in a small sample environment is summarized. Finally, DL modulation is discussed, identifying field challenges, and future research directions.  
automatic modulation recognition; deep learning; wireless communication; intelligent communication; intelligent signal processing +
Footnote †: journal: Electronics Letters  
1
## 3 Nanofluid Thermal Conductivity Models
### Aggregation Model  
Due to the stochastic motion of nanoparticles within a nanofluid, constant aggregation and dispersion occur. Particle aggregation is the clustering of particles which are dispersed within a liquid medium [69]. These clusters form irregularly and randomly within the mixture (see Figure 29).  
The formation of aggregate clusters leads to reduced Brownian particle velocity within the nanofluid. Nanoparticle clusters having larger sizes impede movement due to increased fluid resistance exerted on the clusters from the surrounding base fluid. More importantly, the clusters reduce the stability of the nanofluid suspension, which can lead to settling of the nanoparticles. However, the nanoparticle clusters can be beneficial by increasing the contact between nanoparticles, thus leading to increase in thermal conductivity [77]. Different nanoparticle shapes and clusters were considered by [PERSON] and [PERSON] [78] to determine the thermal conductivity enhancement. The first model they developed was specifically for dilute and homogeneously spread nanoparticles, and was considered to be overly simplified. The second model developed took into consideration the formation of complex microstructures which yielded more accurate results over the simplified model. [PERSON] et al. [77] specifically created models considering the aggregation and clustering of nanoparticles. Their equations were based on the effective medium theory, along with the  
Figure 29: An illustration of randomly formed nanoparticle clusters due to particle aggregation. The white arrows show the heat percolation paths through the formed nanoparticle clusters.  
particle concentrations, the developed equation reduces to the effective medium theory. With higher particle concentration, the model relies on the Bruggerman model.
## 4 Conclusions  
This paper proposes a hologram-classifier on the digital hologram obtained by an off-axis interferometer from biological specimen tissues. The method does not require an intact sample with a complete outline shape of the specimens or the organs to understand the objects' identities and achieves a high success rate of 99.60% on the out-training test set, and is able to adapt for optical aberrations from lenses and mirrors without prior background compensations. The result demonstrates that the hologram-classifier EDL-IOHC is robust under noisy and imperfect holography optical processes, and the off-axis interferometer is effective for scanning the microstructure of the tissues from the specimens. The simplicity of direct application in recognition of biological specimen tissue by EDL-IOHC, without any changes in architectural or hyperparameters, verified the generality of EDL-IOHC. In passing, more potential applications such as pollutant plastics, defective glasses, or identification of infected red blood cells from normal cells will be feasible by using EDL-IHC with a similar interferometer. Intuitively the biological specimen tissue hologram looks noisy and challenging. However, it is numerically not more challenging than the occluded simple digit patterns with speckle noise tested in the EDL-IOHC. Furthermore, for potential applications that require more compact size and lower cost but could accept lower performance, a smaller on-axis inline interferometer could also be considered. The method demonstrates a new paradigm in object identification by ensemble deep learning through the EDL-IOHC wavefront recognition technique.  
Conceptualization, project administration, and writing-original draft preparation, H.L. and Y.Z.; software, investigation and methodology, H.L.; validation, visualization and writing--review and editing, Y.Z., P.B. and H.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data that support the findings of this study are openly available in github at [[https://github.com/lamhoson/Hologram-Dataset](https://github.com/lamhoson/Hologram-Dataset)]([https://github.com/lamhoson/Hologram-Dataset](https://github.com/lamhoson/Hologram-Dataset)) (accessed on 2 December 2021).  
We acknowledge the support provided by the staff of the Imaging Systems Laboratory at the University of Hong Kong.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **Success Rate** \\ \hline Test set & 99.60\% \\ Complete set (both out- and in-training sets) & 99.82\% \\ \hline \end{tabular}
\end{table}
Table 3: Success rates for classifying the out-training test set and the complete dataset by the EDL-IOHC hologram-classifier.