esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Communication Performance of Airborne Distributed Coherent Radar
## 2 Materials and Methods
### Performance Analysis
#### 2.2.2 The PDF of \(\sqrt{\gamma_{b}}\)  
We first calculate the PDF of \(\sqrt{P_{c}(F_{2})^{2}}\). Based on (8) and (10), we can obtain:  
\[\begin{array}{ll}f_{P_{c}(F_{2})^{2}}(r)\\ =&\int_{0}^{+\infty}\frac{1}{z}f_{F_{2}}(x)f_{(F_{2})^{2}}\big{(}\frac{r}{x} \big{)}dx\\ =&\sum\limits_{l=0}^{+\infty}\int_{0}^{+\infty}\mathcal{F}(l)x^{l-m-\frac{1}{ 2}}r^{m-1}\exp\left(-\frac{x}{2\sigma_{c}^{2}}-\frac{m\
u}{\Omega x}\right)dx \end{array} \tag{34}\]  
where  
\[\mathcal{F}(l)=\frac{\lambda_{c}^{l}\exp\left(-\frac{\lambda_{c}}{2}\right) \left(\frac{m}{\Omega}\right)^{m}}{2^{2l}+\frac{2}{2}\sigma_{c}^{2l+1}\Gamma \left(l+\frac{1}{2}\right)l!\Gamma(m)} \tag{35}\]  
We can also refer to WA 203 (15) of [33]. After some mathematical manipulation, we can obtain:  
\[f_{\sqrt{P_{c}(F_{2})^{2}}}(x)=\sum\limits_{l=0}^{+\infty}\mathcal{F}(l)2^{ \frac{9}{4}-\frac{1}{2}m+\frac{1}{2}l}\sigma_{c}^{l-m+\frac{1}{2}}\left(\frac{ \Omega}{m}\right)^{\frac{1}{2}m-\frac{1}{2}l-\frac{1}{4}}x^{m+l-\frac{1}{2}}K_{ m-l-\frac{1}{2}}\left(\sqrt{\frac{2m}{\Omega}}\frac{1}{\sigma_{c}}x\right) \tag{36}\]  
Let \(k_{0}=m\), \(m_{0}(l)=l+\frac{1}{2}\), \(\Psi_{0}=\sqrt{\frac{m}{2\Omega}\frac{1}{\sigma_{c}}}\), we can obtain:  
\[\begin{array}{ll}f_{\sqrt{P_{c}(F_{2})^{2}}}(x)\\ =&\sum\limits_{l=0}^{+\infty}\mathcal{F}(l)2^{\frac{9}{4}-\frac{1}{2}m+\frac{1 }{2}l}\sigma_{c}^{l-m+\frac{1}{2}}\left(\frac{\Omega}{m}\right)^{\frac{1}{2} m-\frac{1}{2}l-\frac{1}{4}}\left[\frac{4\Psi_{0}^{k_{0}+m_{0}(l)}}{\Gamma(k_{0}) \Gamma(m_{0}(l))}\right]^{-1}\!f_{KG}(r|m_{0}(l),k_{0},\Psi_{0})\\ =&\sum\limits_{l=0}^{+\infty}\mathcal{G}l)f_{KG}(x|m_{0}(l),k_{0},\Psi_{0}) \end{array} \tag{37}\]  
where  
\[\mathcal{G}(l)=\frac{\lambda_{c}^{l}\exp\left(-\frac{\lambda_{c}}{2}\right)}{2 ^{2}l!} \tag{38}\]  
Similar to (30), in order to turn the PDF represented by (37) into one generalized \(K\) distribution, we need to calculate the moment of \(\sqrt{P_{c}(F_{2})^{2}}\):  
\[\mu_{\sqrt{P_{c}(F_{2})^{2}}}(n^{\prime})=\sum\limits_{l=0}^{+\infty}\Psi_{0}^{ -n^{\prime}}\frac{\Gamma\left[m_{0}(l)+\frac{n^{\prime}}{2}\right]\Gamma\left[ k_{0}+\frac{n^{\prime}}{2}\right]}{\Gamma\left[m_{0}(l)\right]\Gamma\left[k_{0} \right]}f_{KG}(x|m_{0}(l),k_{0},\Psi_{0}\mathcal{G}(l)) \tag{39}\]Repeating the process in Appendix A, we can obtain:  
\[f_{\sqrt{\overline{P_{c}(\Gamma_{2})}^{2}}}(x)=f_{KG}(x|m_{\
u_{\
u}}k_{\
u},\Psi_ {\
u}) \tag{40}\]  
After further mathematical manipulation, the PDF of \(\sqrt{\gamma_{b}}\) is:  
\[f_{\sqrt{\gamma_{b}}}(x)=f_{KG}\bigg{(}x|m_{\
u_{\
u}}k_{\
u},\sqrt{N_{b}{(L_{2 })}^{\alpha}}\Psi_{\
u}\bigg{)} \tag{41}\]  
Similarly, let \(m_{2}=m_{\
u}\), \(k_{2}=k_{\
u}\), \(\Psi_{2}=\sqrt{N_{b}{(L_{2})}^{\alpha}}\Psi_{\
u}\), we can finally obtain the PDF of \(\sqrt{\gamma_{b}}\):  
\[f_{\sqrt{\gamma_{b}}}(x)=f_{KG}(x|m_{2},k_{2},\Psi_{2}) \tag{42}\]
## 3 Results
### Classification Stage and Result Analysis  
#### 3.5.1 Classification Stage Design  
We selected the training phase data of the subjects in the dataset for experiments. There are two classification classes: EEG data frame from target subtrials and EEG data frame from non-target sub-trials. Subject VPnw and subject VPmg are excluded, which is consistent with the analysis of the original data set. In this paper, we only used one EEG data channel from Cz. The reason for this selection is that the ERP waveform is most obvious [33] in Cz. In this article, we used SVM to construct a classifier. The classification feature is the cosine similarity between the sample to be recognized and the code vector of the selected six neurons. Select the EEG samples of the subjects in the training phase and divide them into five parts for cross-validation: one is used for testing and four parts are used for training the sparse dictionary, SOM network and classifier. To balance the classes, we used classwise balanced accuracy, which is the average decision accuracy across classes (target vs. non-target).
## 2 Materials and Methods
### Performance Measurement of Leaf Segmentation  
Leaf segmentation performance was measured using the standard metrics: Recall, Precision, and F1 score. The metrics are defined using true positive (TP), false negative (FN), and false positive (FP). TP indicates that the extracted leaf pixel was correctly identified as a leaf. FN indicates that the leaf was not extracted. FP indicates that the extracted leaf pixel was wrongly identified as a leaf. Recall, Precision, and F1 score are defined as follows:  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{16}\]  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{17}\]  
\[\text{F1 score}=\frac{2(\text{Recall})(\text{Precision})}{\text{Recall}+ \text{Precision}}. \tag{18}\]  
The summation of TP and FN in (16) represents the ground-truth leaf pixels. Thus, Recall represents the portion of ground-truth leaf pixels present in the extracted leaf pixels in the segmented image [1]. The summation of TP and FP in (17) represents the extracted leaf pixels. Thus, the Precision represents the portion of extracted leaf pixels in the segmented  
Figure 5: Illustration of Triangle thresholding method [49].  
image that matches the ground-truth leaf pixels [1]. The F1 score combines Recall and Precision and represents the mean harmonic between them.  
In addition to TP, FN, and FP, the true negative (TN) is also computed to evaluate the confusion table composed of TP, FN, FP, and TN. TN indicates that the extracted non-leaf was correctly identified as a non-leaf object.  
According to (16)-(18), the F1 score does not consider TN like [PERSON]'s correlation coefficient or [PERSON]'s kappa. However, it is noted here that the F1 score is selected for measuring performance rather than [PERSON]'s correlation coefficient or [PERSON]'s kappa because our objective is to extract the leaf (foreground) from the image. Thus, we emphasize the TP more than TN. Furthermore, the F1 score is commonly used for evaluating the leaf segmentation performance, as described in Section 1.
# Size-Dependent Mechanical Behaviors of Defective FGM Nanobeam Subjected to Random Loading
## 2 Theory of FGM Timoshenko Nanobeam
### Material Properties of FGM Beam  
It is assumed that the nanobeam is made of FGM materials, where the top and bottom surfaces of the beam are ceramics and metals, respectively. Generally, the FGMs are continuously varied in the thickness (z-axis) direction. The material properties are assumed to vary, as the following simple power law:  
\[\begin{array}{l}E(z)=(E_{\rm m}-E_{\rm c})\big{(}\frac{z}{h}\big{)}^{n}+E_{ \rm c}\\ G(z)=(G_{\rm m}-G_{\rm c})\big{(}\frac{z}{h}\big{)}^{n}+G_{\rm c}\end{array} \tag{1}\]  
Figure 1: Illustration of FGM nanobeam model: (**a**) hinged-hinged nanobeam under distributed loads; (**b**) physical neutral surface and geometry middle surface.  
where \(E\) and \(G\) present the Young's and shear moduli, respectively. In Equation (1), the subscript'm' and 'c' stand for metals and ceramics, respectively. \(n\) denotes the material variation factor, which is a non-negative number. For instance, a Timoshenko nanobeam is made of FGM, as listed in Table 1. The variation the of the properties is plotted in Figure 2.
## 5 Results and Discussion
### Accuracy Results for Conformal Mapping  
In the present study, a meticulous computation and an analysis were conducted on the errors at a series of test points for complex functions. Initially, a set of test points were uniformly sampled on the unit disk, and through the inverse mapping of the complex functions, these points were correspondingly mapped to a collection on the complex plane, as illustrated in Figure 9a. Subsequently, the discrepancies between each test point and its associated point post inverse mapping were evaluated, with the maximum norm employed as the metric for error quantification.  
According to the computed outcomes, a parabolic trend in the distribution of errors between the test points and their inversely mapped counterparts was discerned, as depicted in Figure 9b. Within this error distribution, the maximum error norm was found to be 2.7280 \(\times\) 10\({}^{-11}\), signifying that the inverse mapping method exhibited exemplary performance in the majority of instances. Such a method could accurately preserve the relationship between the test points and the original points, underscoring its robustness and precision.  
Figure 8: Relationship between contact force and contact depth for different clearance values.
# Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection
## 5 Conclusions  
To classify high-resolution skin cancer images, this article proposes the use of the attention module. On the HAM10000 dataset, the model achieved better results than the state-of-the-art methods now. Another technique used to assess the efficacy of the proposed model is crossdataset validation, specifically with the ISIC-2019 dataset. These findings demonstrate the promise and utility of the deep learning architecture, which utilizes soft attention, in the field of image analysis. In addition, the soft attention method reduces the need to use external explainers such as LIME, SHAP, and GradCAM. Furthermore, it provides the precise place where the model concentrates its attention when classifying a disease, hence improving the overall performance of the primary model. Soft attention has the extra benefit of effectively managing visual noise internally. This model has the potential to be included in future dermoscopy devices, providing valuable support to dermatologists implementing this methodology to classify data from alternative medical approaches.  
Conceptualization, I.M.N., S.T., R.D. and R.M.; Methodology, I.M.N.; Software, I.M.N. and S.T.; Validation, I.M.N., S.T., R.D. and R.M.; Formal Analysis, R.D. and R.M.; Investigation, I.M.N. and S.T.; Resources, I.M.N., S.T., R.D. and R.M.; Data Curation, I.M.N., S.T., R.D. and R.M.; Writing--Original Draft Preparation, I.M.N. and S.T.; Writing--Review and Editing, R.D. and R.M.; Visualization, I.M.N. and S.T.; Supervision, R.D. and R.M.; Project Administration, I.M.N. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Two publicly available datasets were used in this research: HAM10000 and ISIC2019 [13].  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 17: Attention maps generated using InRe + SAB when trained on the HAM10000 dataset and validated on the ISIC-2019 dataset.
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Methodology
#### 2.6.2 Simulation Settings  
Each node illustrated in Figure 1 was defined with the wireless communication module LoRa SX1276 in the frequency range of 915 MHz. The node range was calculated considering antennas with a gain of 3 dBi, antenna height of 2.5 m, and transmission power of 20 dBm. In addition, the LoRa parameters are shown in Table 1.  
Considering that the DVR protocol is proactive, a period of 5 min for sharing the routing tables was defined, with the first sharing being initiated at the start of the simulation. In the case of AODV protocol simulations, a period of 90 s was defined for the route maintenance mechanism (sending 'Hello' messages). Finally, a period of 30 s was used to transmit general messages and data packets from the sensors.  
Figure 1: Network topology considered in the simulations.
Dual-Band Passive Beam Steering Antenna Technologies for Satellite Communication and Modern Wireless Systems: A Review  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Sydney, NSW 2007, Australia; [EMAIL_ADDRESS]  
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; dushmatha.  
The necessity for ubiquitous connectivity for some of the platforms in the present-day communication landscape is clearly illustrated in Figure 1.  
Some of the typical commercial communication satellite systems include Iridium-NEXT, LeoSat, OneWeb, Starlink, O3B, and Kuiper [7; 8]. Most satellite communication networks utilize low-earth orbit (LEO) and medium-earth orbit (MEO) satellites, which are preferred over geostationary (GEO) satellites due to their proximity to Earth. This proximity results in lower power consumption and lower latency [9; 10; 11]. These LEO satellites utilize the Ku/Ka bands for full-duplex communication. Since LEO satellites are non-stationary, beam steering antennas maintain connectivity with the satellite by constantly switching beams from one satellite to another. Moreover, medium-to-high gain antennas are required since they can close the link budget with lesser transmission power, reducing the power burden on the radio frequency (RF) system [12].
## 2 Main Concepts
### Artifacts  
HDR image reconstruction, both single-frame and multi-frame, is often affected by various visual artifacts that compromise the quality of the final result. These problems can be classified into three broad groups based on their origin: motion-related artifacts (resulting from misalignments or scene changes during multi-frame capture), photometric artifacts (linked to sensor limitations and exposure differences), and model-induced structural artifacts (generated by the reconstruction algorithm itself). Understanding and mitigating these different types of artifacts are crucial for obtaining high-fidelity HDR results. The following subsections detail the most common artifacts within these categories and discuss strategies for their correction.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 2 Related Work
### Data Aggregation Based Approaches  
[PERSON] _et al._[20] have used a transmission based scheme to avoid energy holes. They have restricted the transmission of duplicate messages for any pixel in the area. Reduction in the number of packets transmitted improves network lifetime. An entropy (a measure of the degree of randomness or disorder)-based model to preserve sensor energy has been proposed by [PERSON] _et al._[8]. Sensors periodically sample the data and transmit to the sink; however, the sampling produces useful data as well as useless raw data. The entropy-based model is used to perform data aggregation by extracting high-precision data from the sensors. The entropy is determined by different probability models. They have also proposed an energy efficient clustering process of the sensor nodes in the network. At the beginning, sensors sensing the different type of data were placed within separate clusters. Then, remaining un-clustered sensors assess their deviation with respect to the clustered neighbors and eventually joins the least-deviated cluster in the network. We have also used data aggregation techniques in our model. Data aggregation is performed by cluster heads as well as by the gateway nodes.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology
### Experiment 1-Single Point Audio Sources
#### 3.4.3 Conditions  
Ambisonic B-format content audio signals were encoded using the Opus 1.2 codec with channel mapping family 2 implementation [11] at a variety of bit rates to produce a range of conditions. These signals were then rendered to a binaural format for headphone presentation using a generic head related transfer function (HRTF). For FOA examples, a Neumann KU 100 binaural dummy head (SADIE subject 2) with a cube layout was used. For 3 OA examples, the same KU 100 binaural dummy head using a 26-point Lebedev Quadrature layout was used with the angles presented in Table 2. The layout and post-processing procedure followed for both 1 st and 3 rd order HRTFs are described on the SADIE project website ([[https://www.york.ac.uk/sadie-project/GoogleVRSADIE.html](https://www.york.ac.uk/sadie-project/GoogleVRSADIE.html)]([https://www.york.ac.uk/sadie-project/GoogleVRSADIE.html](https://www.york.ac.uk/sadie-project/GoogleVRSADIE.html))). Head symmetry optimization (assuming the ears are reverse-identical filters) was applied by  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Label** & **Music Type** & **Source** \\ \hline vega & Vocals ([PERSON]) & CD \\ castanets & Castanets & EBU \\ glock & Glockenspiel & EBU \\ vegaRev & Vocals ([PERSON]) w. Reverb effect & processed CD \\ castanetsRev & Castanets w. Reverb Effect & processed EBU \\ pinkRev & Bursty Pink Noise w. Reverb Effect & synthetic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples used during single point audio listening tests (reproduced from [3]).  
Figure 3: Single point audio source localization: (**a**) fixed position (azimuth 60\({}^{\circ}\), elevation 60\({}^{\circ}\)), (**b**) audio source moving horizontally above the listener’s head, (**c**) audio source moving up in elevation on the left hand side, then down on the right hand side. Reproduced from [3].  
inverting the L/R HRTFs around the head to save on computation. Negligible differences were found between the original HRTFs and these symmetrical versions using this technique as outlined in [29].  
Rendered audio content signals (i.e., test samples) were created for a range of conditions. The original uncompressed audio, 3 OA, serves as both the \"Reference\" condition and the hidden reference for this MUSHRA test. Third-order Ambisonics (3 OA) audio encoded with 512 and 256 kbps serve as conditions 3 OA512 and 3 OA256, respectively. First-order Ambisonics audio (FOA) encoded at 128 and 64 kbps serve as conditions FOA128 and FOA64, respectively. Finally, condition FOA32 was used as the hidden anchor for testing and represents first-order Ambisonic audio encoded at 32 kbps. Details of encoding schemes and bit rates used in Experiment 1 can be found in Table 3.
## 3 Numerical and Experiment Results of Metamaterials
### Straight Beam with Cylindrical Metamaterials  
To show the characteristics of the band gap of the cylinder shape metamaterials, the cylindrical metamaterials made of aluminum are simulated by changing their dimensions. Although straight bar can be useful in supporting structure, the transmission vibrations need to be reduced. Therefore, the present metamaterials can be employed. In Figures 4-6, the characteristics of the present metamaterials are investigated. The boundary conditions applied to the interpretation are shown in Figure 4a and the displacements at some frequencies are shown in Figure 4b. Figure 5 shows the dispersion relation of the unit cell which are then matched with the frequency responses in Figure 4c. Figure 6 shows the ratio of output acceleration (Z\({}_{1}\)) to input acceleration (Z\({}_{0}\)), with some different unit cells to investigate the effect of geometric variables in the radius (\(r_{p}\)) of cylinder, the height (\(r_{h}\)) of cylinder, the distance (s) between cylinder, and the number (\(n\)) of cylinders. As the metamaterials based on the resonances of the cylinder structures, the low-frequency band gaps of the cylindrical metamaterials can be observed by increasing the mass of the cylinder or by increasing the radius and the height of the unit structure. On the contrary, the high-frequency band gaps can be obtained by decreasing the length of the distance (s) in Figure 6c. Figure 6a shows the frequency response with the three different radii of the mass part. As expected, the starting frequency of the band gap becomes higher by decreasing the radius. With 0.03 m for the radius, the band gap occurs at around 8700 Hz. The frequency range of the band gap is observed from 6000 Hz to 19,000 Hz with 0.04 m for the radius, and from 4500 Hz to 13,500 Hz with 0.05 m for the radius. Figure 6b shows the frequency response functions with the different heights. With 0.03 m for the radius, the band gap is formed at 9700 Hz and it is increased to 8500 Hz and 7700 Hz with 0.04 m and 0.05 m as their respective radii. Compared with the effect of the radius, the effect of the height of the cylinder is relatively small. As the longitudinal stiffness is inversely proportional to the length of the beam, the range of the band gap frequency becomes lower with a longer length(s) in Figure 6c. This is partially due to the mass effects of the cylinder. The band gap appears at about 7600 Hz and 8800 Hz with 0.06 m and 0.04 m for the distances, respectively. It is also observed that the band gap becomes wider. The above studies show that, by controlling the geometries, the frequency range of the band gap can be controlled. The periodic rod structures can be applied for various engineering applications. For example, a rod structure can stop oscillating in a specific frequency region. Therefore, it can be applied to create a kind of supporting structure for vibrating buildings.