esa-sceva/satcom-chunk-collection huggingface dataset:
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Modulation
#### 2.2.1 Linear Chirp Creation  
Initially, a reference chirp \(y_{\text{rgb}}\) is generated through  
\[y_{\text{rgb}}[n]=\begin{cases}w[n]\,\sin(\varphi[n]),&\text{for }0<n\leq N_{ \text{ref}}\\ 0,&\text{else}.\end{cases} \tag{2}\]  
For simplicity of calculation we normalize (note that implementations in Matlab often use the Nyquist frequency \(f_{\text{nyq}}=f_{\text{s}}/2\) for normalization instead) the angular frequencies  
\[\begin{split}\omega_{0}&=2\pi f_{0}/f_{\text{s}},\quad \text{and}\\ \omega_{1}&=2\pi f_{1}/f_{\text{s}}\end{split} \tag{3}\]  
with the start frequency \(f_{0}\) and the stop frequency \(f_{1}\). This allows the definition of the argument \(\varphi[n]\) for a linear chirp as  
\[\omega[n]=\omega_{0}+n\,\frac{\omega_{1}-\omega_{0}}{N_{\text{ref}}}, \tag{4}\]  
and therefore  
\[\varphi[n]=\varphi_{0}+\int_{0}^{n}\omega[v]\text{d}v. \tag{5}\]  
Figure 2: Modulator block in detail: The data input \(d\) is mapped onto \(N_{\text{lo}}\) sub-bands through a multiplexer (MUX) and modulated by the up-converted chirped symbols from the DUC. The transmission sequence \(y_{\text{rx}}\) is assembled by the CSK block, already in the transmission band. Simple arrow lines indicate vectors, double lines arrays.  
With (4) and (5), we can calculate the instantaneous phase for a linear sinusoidal chirp according to  
\[\varphi[n]=\varphi_{0}+n\,\omega_{0}+\tfrac{1}{2}n^{2}\,\frac{\omega_{1}-\omega_ {0}}{N_{\text{ref}}}. \tag{6}\]  
The modulation will become clearer if we substitute  
\[\omega_{\text{c}}=\frac{\omega_{1}+\omega_{0}}{2}, \tag{7}\]  
\[B_{\omega}=|\omega_{1}-\omega_{0}|, \tag{8}\]  
and introduce  
\[\zeta=\text{sign}(\omega_{1}-\omega_{0}). \tag{9}\]  
The instantaneous phase of the chirp from (6) then takes the form of  
\[\varphi[n]= \varphi_{0}+n\,\omega_{\text{c}}+\zeta\frac{B_{\omega}}{2}n\left( 1+\frac{n}{N_{\text{ref}}}\right), \tag{10}\]  
where each bit of the data is mapped onto the sign \(\zeta\). The resulting chirp sequence is generated in the base-band frequencies according to (2) through Algorithm A1 and up-converted to each channel through Algorithm A3. In Figure 3 (leftmost), an example for such a base-band chirp is shown and the result of the up-conversion in Figure 3 (center left).  
For a fixed transmission channel communication, the up-conversion through a DUC can be omitted and the reference chirps directly be calculated in the transmission band, but for the sake of flexibility, we added the up-conversion as a separate block. The resulting single chirp sequences \(y_{\text{ref}}\) for all \(N_{\text{lo}}\) transmission channels can be stored permanently and only requires to be recalculated, if the parameters, e.g., sampling frequency \(f_{\text{s}}\), chirp length \(N_{\text{ref}}\), side-band center frequency \(f_{\text{ch}}\) or bandwidth \(B\) change. While intuitively both chirp slope sequences may be pre-generated, we omit this redundancy on implementation as the inverse slope sign is equivalent to a time reversal of the entire chirp sequence.
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
## 6 Performance Comparison
### Comparison on the Deviatoric Plane  
Since there are no experimental data available with the same \(I_{1}\) for testing the specific performance of the models on the deviatoric plane, only the shapes of the models on the deviatoric plane are explored. Figure 11 illustrates the failure envelopes of the six models on the deviatoric plane for different \(I_{1}\) conditions under the rock parameter condition of No. 10 sandstone. It can be observed from the figure that at \(I_{1}=250\), the shape of the HHO-MLP model on the deviatoric plane resembles a straight line, akin to the shape and trend of the HB. At \(I_{1}=500\), the shape of the HHO-MLP model on the deviatoric plane transforms into a curve, which exhibits a similar overall form and trend to the ML. At \(I_{1}=750\), the shape change of the HHO-MLP model on the deviatoric plane becomes curved and the overall trend remains similar to that of the ML. At \(I_{1}=1000\), the shape of the HHO-MLP model on the deviatoric plane closely resembles an arc, mirroring the shape and trend of the DP.  
In contrast, the shapes of the deviatoric plane curves of the five strength criterion models change less with \(I_{1}\). However, it is noteworthy that the curves of the HHO-MLP model on the deviatoric plane all exhibit slight undulations, unlike the remaining five strength criterion models, which do not display this phenomenon. This variation arises from the fact that the HHO-MLP model is trained using actual true triaxial test data. Natural rocks are inherently anisotropic, and even neighboring regions exhibit internal differences. Consequently, the mechanical property trends of different rocks within the same batch of tests may deviate from theoretical expectations. Most traditional strength criteria are developed based on various isotropic theories. Overall, the HHO-MLP modeldemonstrates shapes similar to the different strength models in different deviatoric planes at different \(I_{1}\) levels, and all satisfy the requirements of continuity and smoothness.
## 3 Results
### Principal Component Analysis  
PCA was used to analyse the relationships among physical characteristics and volatile compounds of Harbin red sausages smoked by different methods (Figure 4). The analysis showed that approximately 96.41% of variability was explained by two principal compo nents, indicating that the two principal components covered the vast majority of physical characteristics and the flavour information of the three groups of Harbin red sausages. The first principal component (PC1) was the most important variable in terms of differences among sausages as it accounted for 68.22% of the total variations; the second principal component (PC2) accounted for 28.19% of the total variations. The ILS sausage was located on the positive PC1 axis and positively related to the higher \(L^{*}\)-value and the great majority of flavour compounds. PC2 was positively related to hardness and chewiness and was inversely related to the moisture content. The TSS sausage was on the positive PC2 axis, whereas the ISS sausage was on the negative PC2 axis. Therefore, the TSS sausage was characterised by low moisture content and high hardness and chewiness. In summary, PC1 differentiated based on smoke generation method, whereas PC2 distinguished the sausages smoked with traditional method from the sausages smoked with industrial method.  
Regarding the contents of volatile compounds, the smoke generation methods were mainly related to some alcohols (e.g., 2-furanmethanol, 1-octanol, 1-decanol, 1-undecanol and 12-dodecanol), some aldehydes (e.g., 2-furaldehyde, nonanal, undecanal and benzeneacetaldehyde), some acids (e.g., acetic acid, hexanoic acid, octanoic acid and nonanoic acid), most phenols (e.g., p-cresol, 2-methyl-phenol, 2-methoxy-phenol, 2,6-dimethyl-phenol, 2-ethyl-phenol, 2,4-dimethyl-phenol, 2,5-dimethyl-phenol, 2-methoxy-5-methyl-phenol, 2,3-dimethyl-phenol, creosol, 3,4-dimethyl-phenol, 2,4,5-trimethyl-phenol and eugenol) and all hydrocarbons, which were located on the positive PC1 axis. These volatile compounds were more abundant in the ILS sausage. Especially, phenolic compounds are mainly responsible for the unique aroma and taste of smoked products. This may address why the ILS sausage had a more pronounced smoky aroma. Additionally, the traditional smoking method was mainly related to volatile compounds such as 1-octadecanol, (Z)-2  
Figure 4: Principal component analysis of the volatile compounds and quality characteristics of Harbin red sausages smoked with different methods. TSS: sausages smoked with traditional smouldering smoke; ISS: sausages smoked with industrial smouldering smoke; ILS: sausages smoked with industrial liquid smoke.  
decenal, (E,E)-2,4-decadienal, 2-undecenal, diallyl disulphide, 3,4,5-trimethyl-phenol and 2,6-dimethoxy-4-methylphenol, which were more abundant in the TSS sausage.
# TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning
## 3 Results and Discussion
### Parameter Optimization of Deep Learning Model  
We used ternary phase diagrams to optimize four major parameters of our deep learning algorithms, such as the normal size of images, deep learning network architectures, image styles, and training-testing dataset distributions. In the ternary phase diagram of color pictures (Figure 5A), the 128: 2: Color: 80-20 and 64: 3: Color: 80-20 showed the lowest performance score (~0.2) and the highest performance score (~0.9), respectively. For the normal size of the pictures, the performance score at the normal size of 128 was much lower than that at the normal size of 64 or 32, which may be attributed to reduced image quality due to enlargement. For the deep learning network architecture, the best performance score was obtained at around three sets of convolution and max-pooling layers. Generally, deep learning models can build better features than shallow models because of intermediate hidden layers. As shown in Figure 5A, the performance score of the shallow model with one set of convolutional and max-pooling layers was lower than that of deeper models with several sets of convolutional and max-pooling layers.  
In the ternary phase diagram of the grayscale pictures (Figure 5B), the 128: 2: Grayscale: 80-20 (128 \(\times\) 128 sizes of grayscale pictures trained by two convolution and pool layers on a training-testing dataset distribution of 80-20%) had the lowest performance score (<0.2), whereas the 64: 2: Grayscale: 70-30 (64 \(\times\) 64 size grayscale pictures trained by two convolution and pool layers on a training-testing dataset distribution of 70-30%) had the highest (~0.7). Compared with the color pictures, the performance score trained by the grayscale images was much lower, which may be attributed to information loss of images during the transfer from color to grayscale images. As shown in Figure 5B, the grayscale pictures cannot improve the accuracy due to the loss of some important tick information. Therefore, we chose the 64: 3: Color: 80-20 as our optimized deep learning model in our study.  
Figure 4: Tick photo and its visualization of activations. (**A**) A photograph of a female tick that was taken using our smartphone. (**B**) Visualization of activation in the first convolution layer of 64: 2: Color: 80–20, demonstrating that the model has learned to efficiently activate against the morphologic characters of the female tick (**A**).
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Integrating Blockchain with Mec and 5G Technology in V2X Networks
#### 6.3.1 Integration of Blockchain with Cellular Technology in V2X  
The 5G and beyond technology in next-generationneration vehicular communication is envisaged to provide higher data rate, higher capacity, enhanced QoE, lower operating cost, lower delay, persistent service delivery, massive vehicular node connectivity, and security [77; 78]. The evolution of C-V2X towards 5G NR and its implementation in V2X technology was discussed in Section 3. The convergence of blockchain in next-generation vehicular communications based on 5G opens up new opportunities to empower 5G V2X networks, capabilities, applications, and services, as given in Table 8. Some research works are based on the integration of blockchain with cellular networks in V2X communications [79] and [80]. The authors in [79] presented 5G-based vehicular networks that enable technologies, like Content Centric Networking (CCN) based on blockchain for secure vehicular communications. The authors use CCN instead of TCP/IP and allowed blockchains to monitor the confidentiality of information quality, source, and authenticity of information shared among the participating vehicles. It is possible to store the record of the trusted vehicles as well as messages exchanged between the vehicles in the blockchain. The authors mentioned that 5G provides lower latency, edge connection, and higher capacity based on network slicing in the connected vehicles; however; they did not explain much about network slicing, and the lack of a standard consensus protocol for a permissioned blockchain. Similarly, [PERSON] et al. [80] proposed a primary stage for a blockchain-enabled 5G autonomous vehicular network, and its architecture offering some remarks and discussed the challenges. The authors presented secondary authentication in 5G-based vehicular networks and model inter blockchain communication. However, the authors raised concerns regarding the constraint of short propagation time required by the vehicular networks. They gave remarks that, if each transaction has short propagation delay, then the system can provide fast authentication, but at the cost of selfish mining by PoW consensus because of the huge number of orphaned blocks. On the other hand, if the block generation time is reduced, it degrades the security level, which hampers the blockchain system.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases  
Early in 2014, the LPWAN market rapidly developed thanks to the emergence of IoT. Realizing the need and potential for new communication ways, 3 GPP started a feasibility study on cellular system support for an ultra-low complexity and low throughput IoT solution referred to as cellular IoT. In May 2014, Huawei and Vodafone proposed the Narrowband Machine to Machine (NB-M2M) to 3 GPP as a study item to cope with the IoT market needs. Additional telecom industrial players got interested and later the same year Qualcomm proposed narrowband orthogonal frequency division  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Survey** & \multicolumn{2}{c}{**The Third Generation Partnership Project**} & \multicolumn{2}{c}{**Layers**} & \multicolumn{2}{c}{**Deployment Strategies**} \\ \hline
**[Ref]** & **Rel 13** & **Rel 14** & **Rel 15** & **Rel 16** & **Physical** & **Media Access Control** \\ \hline
[28] 2017 & ✓ & & & & & & \\
[29] 2017 & ✓ & ✓ & & & & & \\
[30] 2017 & ✓ & ✓ & & & & & \\
[31] 2017 & ✓ & & & & & & \\
[32] 2018 & ✓ & & & & & & \\
[33] 2019 & ✓ & ✓ & & & & & \\ \hline This survey & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarized comparison of this survey’s contribution with respect to the existing surveys.  
multiplexing (NB-OFDM). In May 2015, 3 GPP merged the two proposals (i.e., NB-M2M and NB-OFDM) and formed the Narrowband Cellular IoT (NB-CIoT). Eight months later, Ericsson proposed the Narrowband Long-Term Evolution NB-LTE. In September 2015, 3 GPP included all proposals as a work item for Release 13. The key difference between NB-CIoT and NB-LTE was the number of the reused legacy LTE network resources to support interoperability. In June 2016 NB-IoT was recognized as a new clean slate RAT. Only further improvement changes were allowed and implemented thereafter.  
In this regard, this section presents the main NB-IoT design changes from Release 13 until today that enabled the massive IoT connections with the corresponding solutions to respond to the adopted NB-IoT objectives. The enhancement features are classified following the objectives that are presented in the releases which would make it easier for the readers to refer back to the official 3 GPP documents [8; 9; 34; 35; 36; 37; 38].
# The Biological Assessment and Rehabilitation of the World's Rivers: An Overview
## 3 River Rehabilitation
### Oceania  
Numerous rivers in southern and eastern Australia and New Zealand were severely affected by human activities before bioassessment programs were underway. Examples are: extensive sands deposited in lowland river reaches throughout south-eastern Australia from the late 1800s [395], flow alteration from water extractions and dam-building starting in the mid-20 th century [396], and the spread of European carp [397] and other pest fish.  
Rehabilitation policies and actions in Australia are often driven by either a response to the conservation status of local biota (e.g., the endangered Macquarie Perch, Table S2) [398] or to legislation, regulations, and plans aimed at limiting or reversing environmental degradation caused by excessive nutrients, sediments, salinity, organic carbon and cyanobacteria or to restore flows. Many jurisdictions have environmental flow objectives that arise from the Water Resources Act 2007 [399], which are also a policy driver for rehabilitation.  
The factors driving river rehabilitation can be numerous. For example, the Murray Darling Basin Plan was built on the National River Health Initiative and other programs such as the Sustainable Rivers Audit (SRA) [322] and the Land and Water Resources Audit (LWRA) [313]. The culmination of increasingly evident water issues (emphasized by drought) and associated bioassessment programs indicated the basin was in poor ecological condition.  
In New Zealand, there is no national legislation requiring river rehabilitation, although the National Policy Statement on Freshwater Management requires Councils to maintain or improve water quality in all waterways. There are limited examples of rehabilitation of waterways being undertaken by the Government (e.g., Project River Recovery, Tui Mine Remediation Project), some by Government-Industry partnerships (e.g., Living Water--a Department of Conservation and FONTERRA partnership in four catchments). However, most projects are undertaken by community organizations or individuals. Ecological monitoring is sporadic, often short-term, and measures aiming at ecological recovery are rare. Often the aims are either not clearly defined or may consist of conflicting or unrealistic goals. Riparian planting dominates rehabilitation projects, increasingly fish passage barriers are being targeted, but dam removal has not yet started in NZ.
# Measurement of High Numerical Aperture Cylindrical Surface with Iterative Stitching Algorithm
## 4 Experimental Demonstration
Compared with the number of fringes in the pattern in Figure 6a, the value of the position error is near the magnitude of the contour of the lens. The position errors of the first few sub-apertures are quite large, which is consistent with the result in Figure 7c. Because the smallest reading value of the tilt stage was 1\({}^{\circ}\) and the resolution was 18\({}^{\circ}\), the precision angles of manual adjustment could not be obtained. However, through the simulation according to the fringe number before manual adjustment, the range of the manually tilt angle was about \(\pm\)10\({}^{\prime}\), and the horizontally motion was about \(\pm\)200 \(\upmu\)m. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(Err_{i}^{\text{(3)}}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) & \(\mathbf{6}\) & \(\mathbf{7}\) & \(\mathbf{8}\) \\ \hline \(\Delta z\) (\(\upmu\)m) & \(1.2137\) & \(-0.5741\) & \(0.8844\) & \(0.7786\) & \(0.7942\) & \(0.7232\) & \(0.5038\) & \(0.4332\) \\ \(\Delta y\) (\(\upmu\)m) & \(-4.4061\) & \(2.7769\) & \(-0.1865\) & \(0.0128\) & \(0.0205\) & \(0.0574\) & \(0.1129\) & \(0.0850\) \\ \(\Delta\gamma\) (\(\%\)) & \(-14\) & \(-16\) & \(-0.7\) & \(-0.5\) & \(-0.27\) & \(0\) & \(0.2\) & \(0.3\) \\ \(\Delta\beta\) (\(\%\)) & \(-3.51\) & \(-2.27\) & \(3.71\) & \(0\) & \(-7.01\) & \(-3.92\) & \(-6.39\) & \(-5.57\) \\ \hline \(Err_{i}^{\text{(3)}}\) & \(\mathbf{9}\) & \(\mathbf{10}\) & \(\mathbf{11}\) & \(\mathbf{12}\) & \(\mathbf{13}\) & & & \\ \hline \(\Delta z\) (\(\upmu\)m) & \(0\) & \(0.4166\) & \(-0.6006\) & \(0.5141\) & \(0.5827\) & & & \\ \(\Delta y\) (\(\upmu\)m) & \(0\) & \(0.1540\) & \(-0.5952\) & \(0.3576\) & \(0.3520\) & & & \\ \(\Delta\gamma\) (\(\%\)) & \(0\) & \(-1.6\) & \(-4.4\) & \(-0.3\) & \(-0.3\) & & & \\ \(\Delta\beta\) (\(\%\)) & \(0\) & \(-4.1\) & \(4.6\) & \(-0.8\) & \(-0.9\) & & & \\ \hline \end{tabular}
\end{table} 
Table 2: The adjustment errors of each sub-aperture for three iterations. Figure 8: (a,b) The change of the relative motion errors of two sub-apertures in 10 iterations. Figure 7: (a,b) Stitching result without iteration and the mismatch map; (c,d) comparison with the proposed method with three iterations. Finally, to verify the robustness of the algorithm, the lens was measured eight times, with or without the iteration to compare the residual errors (Figure 10). The PV value and the RMS value of the residual error solved by iteration method were, as a rule, kept within a stable range, which further verifies the high reliability of the measurement. From the experiment, the stitching result obtained by our algorithm has advantages in comparison with the traditional technique. Among the next practical steps for improving the proposed method include slight modifications and adaptation for characterization of complex structured materials and surfaces [17; 18] as well as for inline quality control in a product line [19], which is often highly required in mass production of optical components.
## 4 Analyses and Discussions
### Aerodynamic Loads of Airfoil under Coupled Condition
#### 4.2.2 Case 2  
The dynamic stall under the coupled condition with k = 0.05 and \(\alpha=10.0^{\lx@math@degree}+6.0^{\lx@math@degree}\sin(\omega t+\pi)\) is presented in this case. The comparisons of aerodynamic loads are shown in Figure 13. It is illustrated that the dynamic stall characteristics of the OA209 airfoil is obviously alleviated under the OFV condition (i.e., the hysteresis loop area of the dynamic stall is decreased). Meanwhile, this tendency is enhanced by increasing the oscillating velocity (i.e., the stall AoA of \(C_{l}\) is postponed from \(14.4^{\lx@math@degree}\) (\(\lambda=0.0\)) to \(15.6^{\lx@math@degree}\) (\(\lambda=0.5\))). Moreover, the peaks of \(C_{m}\) and \(C_{d}\) under the condition of \(\lambda=0.5\) are \(-0.002\) and \(0.089\), which are much smaller than the peaks of \(C_{m}\) and \(C_{d}\) at \(\lambda=0.0\)(\(-0.302\) and \(0.356\), respectively). This variation is obviously different from case 1 (i.e., the deep dynamic stall condition), since the adverse pressure gradient caused by AoA increment would be inhibited due to the effect of freestream Mach number decrement at a larger angle of attack. As a result, the LEV generation would be restricted under the light dynamic stall condition. Even more, the LEV would be eliminated under the large oscillating velocity condition. As shown in Figure 14, it can be seen that there are three LEVs shed under the SFV condition. However, there are no LEVs shed from the leading edge of the airfoil under the condition of \(\lambda=0.5\). It also can be noticed that the pressure coefficient variation (normalized by \(\rho_{\infty}{a_{\infty}}^{2}/2\)) near the leading edge of airfoil under the OFV condition is milder compared with those under the SFV condition. For the case of \(\lambda=0.5\), the maximum negative pressure is \(-0.98\), which is smaller than the maximum negative pressure (\(-1.08\)) at \(\lambda=0.0\).  
By comparing the simulated results in these two cases, it is indicated that the dynamic stall characteristics of airfoil are associated with the pressure near the leading edge of airfoil, which would induce airflow separation and form a LEV. If the maximum pressure exceeds the critical value of leading edge separation under dynamic stall conditions, the unsteady characteristics would be enhanced with increased oscillating velocity. Otherwise, the dynamic stall characteristics would be inhibited, because the increment of adverse pressure gradient due to the increase of AoA would be restricted by decreasing freestream velocity.