esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Irreversibility Analysis under Different Nozzle Geometries
#### 4.2.2 Expansion Angle of the Nozzle's Divergent Section  
As shown in Figure 12, as the nozzle's expansion angle was increased, the concentration region of the total entropy generation rate gradually moved upstream of the nozzle's divergent section and its peak value and area also increased. Therefore, the total entropy production increased from 17.55 W/K at the 0\({}^{\circ}\) nozzle expansion angle to 97.97 W/K at the 11\({}^{\circ}\) nozzle expansion angle; a nearly five-fold increase. This was due to the condensation behavior of the steam, which almost disappeared at the 0\({}^{\circ}\) nozzle expansion angle, so a small total entropy production value was generated.  
Figure 13 illustrates that the enlargement of the nozzle expansion angle changed the composition of the three modes of entropy generation: the nozzle expansion angle changed from 0 to 11\({}^{\circ}\), which resulted in an increase in the viscous dissipation and the heat transport entropy generation from 16.12 and 1.44 W/K to 24.41 and 2.58 W/K, respectively. Moreover, the increase in phase change entropy generation from 0 to 71 W/K also led to an increase in the liquid mass fraction. As already seen for the proportion of entropy production in Section 4.2.1, the largest proportion of entropy production was still generated by phase changes, followed by viscous dissipation and then heat transport.  
Figure 14 clearly shows that the curve of the mass flow rate has a steeper slope between the 0\({}^{\circ}\) and 3\({}^{\circ}\) nozzle expansion angles than that which was found between the 3\({}^{\circ}\) and 11\({}^{\circ}\) angles. This is because the steam's spontaneous condensation did not happen at the 0\({}^{\circ}\) nozzle expansion angle, which led to the generation of smaller values of the total entropy production and liquid mass fraction. Once the expansion angle was larger than 3\({}^{\circ}\), increased entropy production and liquid mass fraction usually led to more energy being converted into useless work and a reduction in the flow velocity of the steam, which directly slowed the growth rate of the mass flow rate, as depicted in Figure 14. Therefore, it is not appropriate to attempt to improve nozzle performance by expanding the expansion angle of the nozzle's divergent section. This caution is evidenced by the lower mass flow rate growth rate, the increase in the total entropy production and the nozzle outlet humidity that were found between the 3\({}^{\circ}\) and 11\({}^{\circ}\) expansion angles (see Figure 14).  
Figure 12: The total entropy generation rate and total entropy production at different expansion angles of the nozzle’s divergent section.
## 3 Results and Discussion
### Prediction Maps  
Hyperspectral imaging has the advantage of acquiring the spectral information of each pixel. By establishing models using spectral data, the model can be applied to each pixel to obtain the corresponding prediction value. Based on the prediction value of each pixel, a prediction map can be formed, and a color bar used to represent the values by colors ([PERSON] et al., 2020). In this research, identification of the geographical origins of Longjing tea was studied by using single tea leaves, and pixels within each tea leaf belonged to the same class. Thus, the prediction maps were formed using the class value of each single leaf rather than using the prediction value of each pixel [34]. The general procedure to form the prediction maps was to apply the established model to each single tea leaf to obtain the class value representing the geographical origins of the corresponding tea. The spectra extraction procedures for prediction maps were the same as those for modeling. In this study, SVM models were used to form prediction maps for one randomly selected image of each geographical origin.  
Figure 3 shows the prediction maps obtained from the hyperspectral images of Longjing tea from six different geographical origins at the two different spectral ranges. As shown in the prediction maps, the class value of each tea leaf was visualized, and the leaves with different class values (corresponding to the geographical origins) were identified visually.
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 3 Experimental Setup of A-RoF Based DAS
### Optical Link  
The RF coupler mixed the two generated waves, which were then introduced into a dual-drive Mach-Zehnder modulator (Thor Labs LNP4216) operating at 1310 nm. The combined RF signal power was set at approximately 10 dBm. The optical link consists of a single-mode fibre (SMF) with a length of 10 km, followed by the variable optical attenuator (VOA) that incorporates the optical coupler to achieve the cascade architecture [33]. Instead of using an optical coupler, a Diplexer (DPX) could also be used as DPX has an additional advantage in that it is frequency selective and will divide the signals accordingly. The optical wave was then divided into two separate routes, one towards the 5G remote antenna unit and one towards the 6G-RAU.
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Simulation
#### 2.3.2 Derivation of Time Delay  
For calculating the time delay \(\tau_{d1}\) and \(\tau_{d2}\), a reverse mathematical derivation of the geometrical equations in Section 2.1.2 is followed. \(\tau_{d1}\) can be derived from the spherical Equation (3) and as follows,  
\[\tau_{d1}=\tan(Az)\tau_{d2} \tag{5}\]  
From Equations (4) and (5), the TDOA \(\tau_{d2}\) can be found by,  
\[\begin{array}{c}\cos(El)=\frac{\varsigma}{d}\sqrt{\tau_{d1}^{2}+\tau_{d2}^{ 2}}\\ \frac{d}{c}\cos(El)=\sqrt{\tau_{d1}^{2}+\tau_{d2}^{2}}\\ \frac{d^{2}}{c^{2}}\cos^{2}(El)=\tau_{d1}^{2}+\tau_{d2}^{2}\end{array} \tag{6}\]  
Substituting Equation (5) into Equation (6) we get  
\[\begin{array}{c}\frac{d^{2}}{c^{2}}\cos^{2}(El)=\left(\tan(Az)\tau_{d2} \right)^{2}+\tau_{d2}{}^{2}\\ \frac{d^{2}}{c^{2}}\cos^{2}(El)=\tan^{2}(Az)\tau_{d2}^{2}+\tau_{d2}^{2}\\ \frac{d^{2}}{c^{2}}\cos^{2}(El)=\tau_{d2}^{2}((1+\tan^{2}(Az))\\ \tau_{d2}^{2}=\frac{d^{2}\cos^{2}(El)}{c^{2}((1+\tan^{2}(Az))}\\ \tau_{d2}=\sqrt{\frac{d^{2}\cos^{2}(El)}{c^{2}((1+\tan^{2}(Az))}}\end{array} \tag{7}\]  
Consequently, substituting Equation (7) into Equation(5), we can obtain the time difference between the two antennae:  
\[\begin{array}{c}\tau_{d1}=\tan(Az)\sqrt{\frac{d^{2}\cos^{2}(El)}{c^{2}(1+ \tan^{2}(Az)}}\\ \tau_{d1}=\frac{d\,\tan(Az)\,\cos(El)}{c\sqrt{(1+\tan^{2}(Az))}}\end{array} \tag{8}\]  
Figure 5: Example of angles augmentation by (**a**) adding Gaussian noise, (**b**) scaling, (**c**) flipping, (**d**) combination of (**a–c**).
# Unsupervised Pre-Training for Voice Activation
## 3 Results and Discussion
### Training Procedure  
Our experiments followed exactly the same procedure as the TensorFlow reference for the Google Speech Commands dataset [14]. The Speech Commands Dataset was split into training, validation, and test sets, with 80% training, 10% validation, and 10% test. This resulted in roughly 80,000 examples for training and 10,000 each for validation and testing. For the Russian dataset, these numbers were roughly 320,000 and 40,000. For the Lithuanian dataset [23], we had 326 records for training, 75 for validation, and 88 for testing (we skewed the distribution to ensure more stable test results). For consistency across runs, the SHA1-hashed name of the audio file from the dataset determined the split.  
To generate the training data, we followed the Google preprocessing procedure by adding background noise to each sample with a probability of 0.7 at every epoch, where the noise was chosen randomly from the background noises provided in the dataset.  
Accuracy was our main metric of quality, which is simply measured as the fraction of classification decisions that are correct. For each input utterance, the model outputs the most likely predicted class.  
We ran an extensive random hyperparameter search [38] for all experiments in order to reliably compare audio features and architectures. We used stochastic gradient descent with initial learning rate \(L\), momentum 0.9, and mini-batch size BS (see Appendix A for the specific values of the hyperparameters). The validation metrics (cross-entropy loss and accuracy) were computed every \(S\) steps of optimization. The minimal validation accuracy was stored. If the new validation accuracy was bigger than the minimal or if the cross-entropy loss obtained a \"not a number\" value, the weights of the best (by validation accuracy) step were loaded, but the learning rate dropped by a factor of \(L^{\prime}\). The training process stopped when the learning rate drop happened the sixth time. The test accuracy was computed exactly once: on the best model by the validation accuracy at the end of the training process. We report the test accuracy in this work.  
We chose \(-\log_{10}L\) from \(U\{0,3\}\), \(\log_{2}\text{BS}\) from \(U\{4,7\}\), \(\log_{2}S\) from \(U\{3,12\}\), and \(L^{\prime}\) from \(U\{1.1,10.0\}\), where \(U\) is a uniform distribution (discrete uniform distribution in the case of \(S\) and BS).  
The code is available at [[https://github.com/kolesov93/keyword_spotter_train](https://github.com/kolesov93/keyword_spotter_train)]([https://github.com/kolesov93/keyword_spotter_train](https://github.com/kolesov93/keyword_spotter_train)).
## 3 Methodology
### Data Preprocessing for Dataset Generation  
We record the original spectral-spatial hyperspectral data cube as \(X\in\mathbb{R}^{W\times H\times D}\), where \(X\) represents the original HSI data, \(W\) is the width, \(H\) is the height, and \(D\) is the number of spectral bands. Each pixel in HSI has a pixel sequence \(Y=(y_{1},y_{2},\cdots,y_{c})\in\mathbb{R}^{1\times 1\times C}\), where \(C\) donates the land cover categories. Due to the huge computational burden caused by the high dimensionality and existing redundant data in HSI, we apply the PCA strategy to the original HSI along spectral bands for spectral feature enhancement and to reduce redundant data. The original data \(X\in\mathbb{R}^{W\times H\times D}\) are modified to \(X\in\mathbb{R}^{W\times H\times B}\), where \(B\) refers to the number of spectral bands after PCA. The PCA not only reduces the number of spectral bands from \(D\) to \(B\) but also preserves the spatial dimension information,  
Figure 9: Procedure of hyperspectral image classification based on HDCDF.  
which is of great significance for pixel classification. The data cube is divided into a number of small 3D neighboring patch sets, \(Z\in R^{P\times P\times B}\), from \(X\); the final label depends on the label of the center pixel. We set the missing adjacent pixels as zero when the core pixel is located at the edge of \(X\) so that all patches can cover the \(P\times P\) size of the space and all spectral bands of HSI. Then, the patch set is randomly split into a training set \(Z_{train}\), testing set \(Z_{test}\), and validation set \(Z_{val}\), and their respective labels are divided into \(Y_{train}\), \(Y_{test}\), and \(Y_{val}\).  
Considering the fact that training deep networks requires numerous samples while available training samples are limited, we adopt the date enhancement strategy to increase the number of training samples and extract more sufficient features, which can effectively avoid overfitting and the problem of sample imbalance, improving the generalization ability of the model HDCDF. With equal probability, patches are rotated 45 degrees, 90 degrees, or 135 degrees; vertically flipped; horizontally flipped; or kept the same. Then, the total number of training patches can be multiplied \(n\) times, compared with \(Z_{train}\), obtaining better network parameters. We represent the enhanced training samples by \(Z_{en\_train}\) and, accordingly, their corresponding new label set is \(Y_{train\_on}\).
## 5 Summary and Discussion  
We summarize the expected throughput (TP) and EE gain of LSAS for each component with relevant schemes in Figures 12 and 13. As observed, all of the mentioned technologies are important to increase EE. 'No scheme' indicates using existing BS technologies for the LSAS. As we can see, even though we can achieve a relative increase of TP as \(N_{t}\) increases using existing technologies, there is no EE gain due to the high power consumption from RF components connected to each antenna. Once we achieve low power RF components with \(P_{RF}=6\) W (\(N_{t}=600\)) and \(\eta=25\)%, there is a somewhat improvement in EE, however even though if the RS overhead reduction scheme is successfully applied, the TP/EE improvement is not so high. With RS overhead reduction and high precision channel estimation, we can achieve a relative TP of 17.6 ((bps)/(bps)) and a relative EE of 99.4 ((bps/W)/(bps/W)), when \(N_{t}=600\). This means with an \(N_{t}=600\) TX antenna LSAS, we can reduce the carbon footprint around 99.4 times compared to the current system. Maximum achievable bound indicates an ideal case with no RS overhead and perfect channel estimation. To achieve this, we need a perfect blind channel estimation scheme that is very difficult to achieve in real systems. Nevertheless, it can provide a very good reference for how much TP/EE gain can be achieved using an LSAS. As observed, once antenna and RF modules are appropriately implemented, the RS overhead and channel estimation errors are the dominant factors to overcome. The important point is that resolving only parts of issues can not increase the EE performance, that is, all the issues should be resolve together to enjoy the benefit of LSAS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(\boldsymbol{\xi}\)} & \multicolumn{2}{c}{**TP**} & \multicolumn{2}{c}{**EE**} \\ \cline{2-5}  & **ZF** & **MF** & **ZF** & **MF** \\ \hline
1 & 17.6 & 10.5 & 99.4 & 64.4 \\
0.8 & 10.0 & 7.3 & 56.3 & 44.9 \\ \hline \end{tabular}
\end{table}
Table 3: Relative TP (bps/bps) and EE ((bps/W)/(bps/W)).
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 2: OCSVM + Deep Autoencoder (dAE)
#### 4.3.6 Output and Classification  
During the detection phase, the dAE handles new incoming data, which could comprise both normal and anomalous samples. When a new instance is input to the dAE, it undergoes encoding followed by decoding using the learned functions e and d. If the instance is normal, the reconstruction error will be minimal, reflecting the model's familiarity with normal data pattern. In contrast, anomalous data points will not align well with the learned patterns of the dAE. When an anomalous sample undergoes encoding and decoding, the reconstruction error will be notably higher due to the model's lack of training on these patterns. The elevated reconstruction error serves as a signal or indicator of an anomaly occurring. The data points characterized by high reconstruction errors are identified as abnormal and categorized under \"zDATAAb2\".  
The output \"zDATAAb2\" from the dAE algorithm represents the classified attack data. The algorithm efficiently reduces the mean squared error, thereby improving its capability to accurately classify normal and anomalous traffic.
# Simulation, Fabrication and Testing of UAV Composite Landing Gear
## 3 Results and Discussion
### Mechanical Testing of the Landing Gear  
#### 3.2.1 Three-Point Bending Testing of Landing Gear Models  
A comparative study of the bending behaviors of five different composite materials was carried out in order to establish the material influence on the landing gear strength. The data obtained during the three-point bending tests were determined, and the load-displacement curves for the landing gears made of PLA GF, PP GF30, PET CF15, PAHT CF15 and CFRP are shown in Figure 5a.  
After analyzing the aspect of the load-displacement curves, a difference was found between the mechanical behavior of the analyzed materials. All four FFF-manufactured landing gears exhibit three different phases:  
* In the first phase, a linear elastic behavior was observed: the increase in force is matched by a minor displacement;
* In the second phase, the force has a small increase, while the displacement values increase significantly.  
The third phase comprises the final range of the curve where the sudden rupture of the landing gear occurs.  
For the CFRP material is found the lack of the second phase, demonstrating that the components of this material remain solid until the moment of breakage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** \\ \hline PLA GF & 4000 & 0.25 \\ PP GF30 & 3507 & 0.35 \\ PET CF15 & 5452 & 0.3 \\ PAHT CF15 & 8886 & 0.3 \\ CFRP & 121,000 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material characteristics used in FEA simulation.  
After comparing the results obtained from the three-point bending tests, it is found that FFF-manufactured landing gear has similar bending strengths (except for PET CF15, which has a slightly better strength) but is clearly inferior to CFRP. The results shown in Figure 5b represent the values of the bending strength for all of the components tested during three-point bending. After examining the results of the three-point bending test, the following conclusions were drawn:  
* All four FFF-manufactured landing gears have bending strength ranges between 88 MPa (PAHT CF15) and 118 MPa (PET CF15). Among them, PET CF15 has the best properties, with a bending strength 25% higher than PAHT CF15.
* With a bending strength of 1455 MPa (12.3 times more than PETCF 15), CFRP has the best stiffness of all the materials used to build the landing gear.  
In Figure 5c, stiffness was determined as the ratio between the force applied to the displacement of the landing gear models. It can be observed that the CFRP landing gear presented the highest value (approximately 47 N/mm), and among the landing gear models manufactured by the FFF process, the PET CF15 model had the highest stiffness (approximately 12 N/mm).  
The conclusion of the three-point bending study is that the landing gear made of CFRP has the best mechanical performance among all the analyzed materials.
# Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
## 4 Optimised Design
### Globally Optimised Design  
The importance coefficient of each member was factored into the evaluation of the structure's global resistance to progressive collapse. Specifically, the mean of the importance coefficients of all members (\(\overline{\rho}\)) was used as an index to globally optimise the cable dome design for optimal resistance to progressive collapse and thus explore the optimal topological form of the structure:  
\[\overline{\rho}=\frac{1}{n}\sum_{i=1}^{n}\rho_{i}  
the area affected by each cable-truss frame and each member increased with the global performance index increasing from 1.04 in Dome 1 to 1.20 in Dome 6 and 1.46 in Dome 5, indicating a gradual decrease in the structure's resistance to progressive collapse. On the contrary, the global performance index reduced, the area affected by each cable-truss frame and each member reduced, and the global resistance to progressive collapse increased with the increase in cable-truss frames.  
(3) More discussions: First, the study analysed only the transition from a Geiger arrangement to a Levy arrangement and how the change in the number of cable-truss frames affected the structure's global resistance to progressive collapse. In general, the increase in structural redundancy, the decrease in the area affected by the members, and the decrease in the importance of each member all contributed to an increase in resistance. However, a more scientific and precise design for topological optimisation falls outside the scope of this study. Second, global optimisation based on changes in the structure's topology and number of cable-truss frames decreased the importance coefficient of the OHC (which had the largest importance coefficient of all members) from 0.5355 in Dome 1 to 0.5083 in Dome 4. Therefore, changing a structure's topological form and increasing its redundancy enhance its global resistance to progressive collapse and reduce the importance of its members.  
Figure 8: Global performance index under different forms.  
Figure 7: Different forms of cable dome structures.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(s\) & source node, \\ \(t\) & sink node, \\ \(H\) & hop count between the source node and the sink node, \\ \(f\) & link failure rate, \\ \(\delta\) & node density, \\ \(R\) & transmission range, \\ \(|uv|\) & the distance between nodes \(u\) and \(v\), \\ \(K\) & the total number of cooperative nodes at each hop, \\ \(k\) & the number of awake cooperative nodes at each hop, \\ \(RCR\) & reliability-aware cooperative routing, \\ \(p\) & RCR construction cost per time, \\ \(T_{epoch}\) & RCR construction refreshing interval, \\ \(\mu\) & duty cycle of sensors, \\ \(P_{k}\) & the probability with \(k\) number of \\  & awake cooperative nodes among RCR, \\ \(m_{h}^{i}\) & reference node at hop \(i\), or \(C_{m_{i}}^{0}\) \\ \(c_{m_{i}}\) & \(\{c_{m_{i}}^{1},c_{m_{i}}^{2},...,c_{m_{i}}^{k-1}\}\), \\  & the \(k-1\) cooperative neighbor nodes of \(c_{m_{i}}^{0}\), \\ \(R_{RCR}\) & cooperative nodes search region, \\ \(R_{mt}\) & the disk centered at sink \(t\) with radius \(r_{t}(m)\) \\  & where \(|mt|-R<r_{t}(m)<|mt|\), \\ \(R_{mf}\) & the disk centered at the middle node \\  & between \(m\) and \(f_{m}\) with radius \(\frac{R}{2}\), \\ \(n_{m}\) & the total number of neighbor nodes of m, \\ \(V_{RCR}(i)\) & \(\{m_{h^{i}}^{i},C_{m_{i}}^{1},C_{m_{i}}^{2},...,C_{m_{i}}^{k-1}\}\), \\  & the cooperative nodes at hop \(i\), \\ \(L_{QE}\) & energy level of sensor node, \\ \(W_{k}(i)\) & given \(k\) available cooperative nodes, \\  & successful delivery ration at hop \(i\), \\ \(W\) & end-to-end (ETE) reliability \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation. RCR, reliability-aware cooperative routing. This paper is arranged as follows. The related works are presented in Section 2. The network model for analysis of the RCR scheme is given in Section 3. The on-demand ETE reliability model, which implies the key design of RCR, is introduced in Section 4. In Section 5, the RCR algorithm is presented in detail. The performance evaluation of RCR is provided in Section 6. Finally, the work is concluded in Section 7.