esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
On the basis of field data, it has been shown that the underwater bottom relief deformations during the non-linear wave transformations and wave energy exchange between first and second non-linear wave harmonics follow a specific scenario. The maximum of the second harmonic amplitude is the point near which the bottom relief deforms in different directions. The erosion of the underwater bottom occurs when the second non-linear harmonic amplitude increases. The accumulation of sandy sediments occurs when it decreases due to backward energy transfer to the first harmonic.  
The main reason for this is the specific change in the biphase during the period of energy exchange between the first and second nonlinear wave harmonics. Considering the influence of the undertow directed to the sea, one can expect that erosion will always occur on the underwater slope, where the second harmonic increases up to its maximum, since, in this case, both wave-induced transport and undertow carry sediments in one direction--off-shore. However, in those places where the second harmonic decreases due to nonlinear interactions, sediment accumulation is possible if the wave-induced transport, directed, in this case, to the shore, prevails over the undertow.  
The scenarios of non-linear wave transformations in which there is a backward energy transfer from the second non-linear harmonic to the first near the coast will contribute to the accumulation of the sediment on the coast. These scenarios are more characteristic of \"small waves\". The scenario only showing an increase in the second harmonic, which is characteristic of large storm waves and plunging breaking waves, will lead to the erosion of the underwater bottom profile.  
It was revealed that the periodic exchange of energy between harmonics during the propagation of waves above an existing underwater bar leads to a change in its symmetry--the transfer of sand material from its seaward slope to its shoreward slope.  
It has been experimentally confirmed that non-linear wave transformations can also be a mechanism of underwater bar formation and the movement of these bars towards the shore.  
The [PERSON]'s formula adequately reproduces the influence of the scenarios of non-linear wave transformations on cross-shore sediment transport.  
The features found in this study of the influence of the transformation of non-linear waves on cross-shore sediment transport and coastal morphodynamics might be useful in coastal engineering for protecting coasts from wave action.  
This research was partly funded by Russian Foundation for Basic Research and TUBITAK, project No. 20-55-46005.  
This research was performed in the framework of the state assignment, theme No. 0128-2019-0010. The author is grateful to their colleagues from the Institute of Oceanology of the Bulgarian Academy of Sciences, who carried out measurements of the underwater profile and kindly shared this data with all participants of the field experiment for scientific use.  
The author declares no conflict of interest.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis
### Scenario 3: Normal Fluctuations in Weather  
The system experienced a moderate amount of weather fluctuations, slightly more than in scenario 2. Scenario 3's monitoring period also extended longer than each of the previous two scenarios. Similar to scenario 2, the weather fluctuated multiple times during the night while during the day, it changed once from cloudy to rainy halfway during the day. The readings were most erroneous during the day when it was cloudy and much more stable when raining. Under this scenario, the system RMSE during the day was 0.33 m while during the night, the system RMSE was 0.2 m, one of the lowest across the entire testing period.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with ZF and MVC-MMSE Transmission
Figure 7: Average user spectral efficiency of ZF-VCMO, MVC-MMSE-VCMO, ZF-VCMG and MVC-MMSE-VCMG versus the maximal cluster size, with \(K=50\), \(\rho=20\) and \(\rho=200\).
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Powering and Navigation
#### 2.3.5 Helical Propellers  
Bacterial flagella consist of small, passive helical filaments that are actuated by molecular rotating motors at the base [103]. Utilizing rotary motors at the nanoscale is a challenge, but artificial helical propellers that use bacterial flagella can be driven by external magnetic fields. The speed of such propellers relies on their helical shape [104]. Rigid magnetic micro helices can be produced using a range of methods, such as self-scrolling; for example, rolling thin layers of material into different forms by regulating internal tension [105]. Glancing angle deposition (GLAD) uses a tilted base to develop billions of micro helices or nano helices [101; 102]. Another process of developing micro helices is known as direct laser writing (DLW), which involves coating the photoresist with biological helical templates [103; 104; 106]. Using these methods, micro propellers or these helical structures can be developed. Micro propellers contain ferromagnetic material and are propelled by a magnetic field; however, additional approaches are required to overcome the molecular structure of biological fluids (other than water) to enable locomotion. For example, urease enzymes can be applied on micro propellers to replicate the strategy of Helicobacter pylori [107]. Likewise, bacteria can evolve to incorporate different approaches for movement. The flexibility of the bacteria is used for locomotion, and there are two types of bacteria: monotrichous (mono-flagelled) and peritrichous (multi-flagelled). The flagellum is hooked to the shaft of the motor and aids in movement. The speed of the microrobot increases when the bundle of flagella has decreasing stiffness [108]; thereby, changing the stiffness of the flagella bundles can impact the speed of the microrobot, and these machines can replicate the swimming principle of sperm cells.
# Finite-Time Set Reachability of Probabilistic Boolean Multiplex Control Networks
## 2 Preliminaries and Problem Setting
### Problem Setting
The algebraic form (4) is used to illustrate our approach and outcome in this paper. \(x(t;\eta,x_{0})\) and \(\widetilde{x}(t;\eta,x_{0})\) are used to represent the overall and global state trajectory of the system under the stochastic switching signal \(\eta(t)\), respectively. Assuming that the probability distribution of \(\sigma(t)\) is \(\Pr(\sigma(t)=i)=p_{i}^{\sigma},i\in[1,O]\), where \(0\leq p_{i}^{\sigma}\leq 1\) and \(\sum_{i=1}^{O}p_{i}^{\sigma}=1\). For convenience, let \(\mathrm{p}^{\sigma}=[p_{1}^{\sigma}\ p_{2}^{\sigma}\ \dots\ p_{O}^{\sigma}]\). The vector \(\mathrm{p}^{\sigma}\) is called a probabilistic distribution vector of \(\sigma(t)\). Then, the one-step overall transition probability matrix \(P^{x}\) and global transition probability matrix \(P^{\widetilde{x}}\) can be represented as  
\[ \begin{aligned} P^{x}=\sum_{i=1}^{O}p_{i}^{\sigma}L_{i}=L\ltimes \mathrm{p}^{\sigma},\\ P^{\widetilde{x}}=\sum_{i=1}^{O}p_{i}^{\widetilde{L}_{i}}\widetilde{L} _{i}=\widetilde{L}\ltimes\mathrm{p}^{\sigma},\end{aligned} \]  
where \(p_{ij}=[P^{x}]_{ij}=\Pr\{x(t+1)=\delta_{2^{NK}}^{i}\ |\ x(t)=\delta_{2^{NK}}^{j}\}\) and \(\widetilde{p}_{ij}=[P^{\widetilde{x}}]_{ij}=\Pr\{\widetilde{x}(t+1)=\delta_{ 2^{n}}^{i}\ |\ x(t)=\delta_{2^{NK}}^{j}\}\). **Definition 4**. : _For the PBMCs(4), assume that \(\mathcal{M}_{0}\), \(\mathcal{M}_{d}\subset\Delta_{2^{\pi k}}\), \(\widetilde{\mathcal{M}}_{d}\subset\Delta_{2^{\pi}}\) are the initial subset, overall target subset and global target subset, respectively._  
* \(\mathcal{M}_{d}\) _is said to be overall reachable with probability one from_ \(\mathcal{M}_{0}\) _on_ \([0,\gamma-1]\) _if, for any initial state_ \(x_{0}\in\mathcal{M}_{0}\)_, there exists an input sequence_ \(\{\mathbf{u}(t),t=0,1,\dots,\gamma-1\}\) _and a positive integer_ \(\xi\geq 1\) _such that_ \[\Pr\{\exists\xi\in[0:\gamma-1],s.t.\ x(\xi;\eta,x_{0})\in\mathcal{M}_{d}\}=1.\] (5)* \(\widetilde{\mathcal{M}}_{d}\) _is said to be global reachable with probability one from_ \(\mathcal{M}_{0}\) _on_ \([0,\gamma-1]\) _if, for any initial state_ \(x_{0}\in\mathcal{M}_{0}\)_, there exists an input sequence_ \(\{\mathbf{u}(t),t=0,1,\ldots,\gamma-1\}\) _and a positive integer_ \(\xi\geq 1\) _such that_ \[\Pr\{\exists\xi\in[0:\gamma-1],s.t.\ \widetilde{x}(\xi;\eta,x_{0})\in \widetilde{\mathcal{M}}_{d}\}=1.\] (6)
## 3 Datasets and Methodology
### Methodology  
#### 3.2.1 Principle of SBAS-InSAR  
To monitor ground surface deformation using the SBAS-InSAR technique, a total of \(N+1\) SAR images covering the study area are initially co-registered. Based on predefined spatial and temporal baseline thresholds, M interferometric pairs are subsequently generated [20], described as follows:  
\[\frac{N+1}{2}\leq M\leq\frac{N(N+1)}{2} \tag{1}\]  
For the \(i\)-\(th\) interferometric pair (\(i\in 1\), 2,..., \(M\)), let the acquisition times of the master and slave images be \(t_{a}\) and \(t_{b}\), respectively, where \(t_{b}>t_{a}\)[22]. After removing non-deformation phase components, the residual interferometric phase can be expressed as  
\[\Delta\varphi_{j}^{(t_{a}t_{b})}=\varphi^{t_{b}}-\varphi^{t_{a}} \tag{2}\]  
The deformation phases of the \(M\) interferometric pairs can be organized into the following matrix form [21]:  
\[\Delta\varphi=[\Delta\varphi_{1},\Delta\varphi_{2},\Delta\varphi_{3},\ldots, \Delta\varphi_{M}]^{T} \tag{3}\]  
Each interferogram provides one observation equation. By combining Equations (2) and (3), a system of \(M\) observation equations with \(N\) unknown deformation parameters can be formulated as [23]  
\[A\varphi=\Delta\varphi \tag{4}\]  
Here, \(A\) is an \(M\times N\) coefficient matrix. If the rank of \(A\) (\(r\) (\(A\))) is greater than \(N\), the system can be solved using the least squares method [23]:  
\[\varphi=\left(A^{T}A\right)^{-1}A^{T}\Delta\varphi \tag{5}\]  
In practice, however, \(r\) (\(A\)) is often less than \(N\), making the inverse of \(A^{T}A\) unavailable [23]. To address this, singular value decomposition (\(SVD\)) is applied to decompose \(A\) as  
\[A=USV^{T} \tag{6}\]  
In this decomposition, \(U\) is an \(M\times M\) orthogonal matrix composed of the eigenvectors of \(AA^{T}\); \(S\) is an M-order diagonal matrix; and \(V\) is an \(N\times M\) orthogonal matrix formed from the eigenvectors of \(A^{T}A\).  
\[A^{+}=VS^{+}U^{T} \tag{7}\]  
\[\varphi=A^{+}\Delta\varphi \tag{8}\]  
In Equations (7) and (8), \(U^{T}\) denotes the transpose of \(U\), and \(A^{+}\) and \(S^{+}\) represent the Moore-Penrose pseudoinverses of matrices \(A\) and \(S\), respectively. Once the timeseries deformation \(\varphi\) is obtained, the corresponding deformation rate can be calculated by dividing the deformation by the time interval between observations.
## 3 Results
### Spatial Distribution of Soil Organic Matter Content  
The study area is primarily mountainous, with large undulations and complex terrain, resulting in differences in soil water and heat conditions and vegetation types between different slope positions and orientations, thereby exacerbating the spatial heterogeneity of SOM within cultivated land. To predict the spatial distribution of SOM within the cultivated land of the study area, environmental features across the entire region were used as model inputs. The trained GPTransformer, 1D-CNN, and GPTransCNN models were employed to generate corresponding SOM spatial distribution maps. As shown in Figure 8, SOM concentrations are predominantly in the range of 20-30 g/kg, with higher values mainly distributed in the northeastern and southwestern parts. This spatial differentiation pattern is likely driven by the combined influence of topography, land use types, and human activities.  
The SOM spatial distribution maps predicted by different models exhibit certain differences in spatial patterns. From the perspective of the overall distribution pattern, GPTransformer focuses on extracting global features, effectively capturing the overall spatial trends and macro-level distribution patterns of SOM across the study area. This model demonstrates particular strengths in responding to large-scale topographic variation and the overarching influence of climatic factors. However, its limited capacity to capture local details during the modeling process results in relatively smooth spatial predictions, lacking the expression of fine-scale spatial variability. In contrast, 1D-CNN focuses on extracting local features and is particularly sensitive to the spatial variability of environmental covariates at a small scale, demonstrating stronger capability in detail recognition. The predicted spatial distribution maps of SOM generated by 1D-CNN clearly reveal more refined spatial variations, effectively reflecting the spatial heterogeneity of SOM under varying microtopographic conditions. GPTransCNN not only retains the global trend modeling capability of GPTransformer but also enhances the local feature extraction capability through 1D-CNN. Its prediction results are able to delineate the spatial boundaries between high- and low-SOM regions, while simultaneously capturing the effects of topographic undulations and climatic variations in SOM distribution, demonstrating the optimal spatial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline GPTransformer & 0.3227 & 6.5835 & 5.0062 \\
1D-CNN & 0.3998 & 6.2091 & 4.4844 \\ GPTransCNN & 0.4260 & 6.0631 & 4.4334 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ten-fold cross-validation.  
Figure 8: Spatial distribution of soil organic matter.  
prediction ability. After further analysis of the enlarged view of the local area in the upper right corner of Figure 8, the same conclusion can be reached.
## 2 Mathematical Model of PMSM  
It is assumed that the magnetic circuit of the permanent magnet synchronous motor is not saturated, the hysteresis and eddy current loss are ignored, and the distribution of the magnetic field is assumed to be a sinusoidal space. In this case, the ideal mathematical model of the surface mount permanent magnet synchronous motor is expressed in the \(d\)-\(q\) coordinates, as follows [27]:  
\[p\begin{bmatrix}i_{d}\\ i_{q}\\ \omega\end{bmatrix}=\begin{bmatrix}-\frac{R_{s}}{L_{d}}&n_{p}\omega&0\\ -n_{p}\omega&-\frac{R_{s}}{L_{d}}&-\frac{n_{p}\psi_{f}}{L_{q}}\\ 0&\frac{3n_{p}\psi_{f}}{2J}&-\frac{B}{J}\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\\ \omega\end{bmatrix}+\begin{bmatrix}\frac{u_{d}}{L_{d}}\\ \frac{u_{s}}{L_{q}}\\ -\frac{T_{l}}{J}\end{bmatrix} \tag{1}\]  
where \(R_{s}\) represents the stator resistance, \(u_{d}\) and \(u_{q}\) presents the stator voltage of the \(d\)-\(q\) axis, \(i_{d}\) and \(i_{q}\) represents the stator current of the \(d\)-\(q\) axis, and \(L_{d}\) and \(L_{q}\) represents the synchronous inductance of the \(d\)-\(q\) axis. The research object of this article is a surface mount permanent magnet synchronous motor, so \(L_{d}=L_{q}=L\). \(n_{p}\) represents the number of pole pairs of the permanent magnet synchronous motor, \(\omega\) represents the rotor angular velocity of the motor, \(\Psi_{f}\) represents the permanent magnet flux linkage, \(T_{L}\) represents the load torque, \(B\) represents the viscous friction coefficient, and \(J\) represents the rotor inertia.  
The block diagram of the permanent magnet synchronous motor servo speed control system is shown in Figure 1. The entire servo speed control system consists of a permanent magnet synchronous motor with load, a space vector pulse width modulation module (SVPWM), a voltage source inverter, and two controllers based on a field-oriented strategy. Among them, the d-axis controller adopts the \(i_{d}=0\) control strategy. As this article mainly studies the speed control of permanent magnet synchronous motors, the \(d\)-axis controller is designed as a PI controller, and the PI controller is used to stabilize the \(d\)-axis current. The \(q\)-axis controller is a back-step sliding mode controller. The disturbance observer is used to feed the observed mismatch disturbance to the controller.  
Figure 1: PMSM servo speed control system.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Damson
#### 3.3.4 Measurement Techniques: Waveforms  
There are defined 3 main modes of operation: (a) Delay Doppler (DD) measurement mode, (b) Continuous Waveform (CW) measurement mode and (c) Time of Flight (TOF) measurement mode. The DD uses pulse compression sounding waveform to characterise the propagation path in terms of multipath delay, Doppler shift and Doppler spread measuring the channel scattering function. For instance, the waveforms used in Reference [35], which is one of the first DAMSON work published, is a 13 chips Barker sequence modulated at 2400 bps onto a bi-phase PSK carrier. Each code has a length of 5 ms, and is repeated 128 times every 15 ms providing and integration time of 1.92 s and a frequency resolution of 0.52 Hz. The range of the frequency Doppler is \(\pm\)33 Hz and the processing gain due to the integration is <32 dB. However, the particular waveform in use and their characteristics can be configurable (an example of features and configuration parameters can be seen in Table 1), as well as a sample of its output in Figure 6. The CW measurement mode is a very sensitive detector because this mode uses a single frequency with a long integration time and low receiver bandwidth. It can be used for noise measurement as pre-scanning system. It also allows the receiver to automatically adjust its gain, whenever necessary, and determine large frequency shifts. Finally, the TOF measurement mode is similar to DD measurement mode with lower pulse repetition frequency. Consequently, the TOF is only able to measure the time of flight and high values of multipath delays due to it shorter integration time [35]. The DAMSON system is described in more detail in References [34; 35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Delay** & **Number** & **Doppler** & **Doppler** & **Integration** \\
**Range** & **of Frames** & **Range** & **Resolution** & **Period** \\
**(ms)** & & **(Hz)** & **(Hz)** & **(s)** \\ \hline
5 & 64 & \(\pm\)100 & 3.125 & 0.32 \\
5 & 128 & \(\pm\)100 & 1.563 & 0.64 \\
5 & 256 & \(\pm\)100 & 0.782 & 1.28 \\
5 & 512
## 4 Understanding Centralised Identity Systems
### SAML: Security Assertion Markup Language  
SAML is an open standard for sharing authentication and authorization information between identity providers (IDPs) and service providers (SPs). When used with single sign-on (SSO), SAML allows users to authenticate once and then access all trustworthyapplications within a domain without having to log in again. Once the user is validated, XML-based assertion tokens are generated, containing authentication information that enables SPs to grant access to resources. This protocol is crucial in contexts where secure and seamless access to remote services is required [36].  
Once validated by the IDP, the SP uses the SAML assertion token, which typically contains authentication statements, attribute statements, and authorization decision statements. This token then provides access to services for the user [37].
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 3 Results and Discussion
### Decision Analysis
Moreover, the use of biochar as soil amendment is generally a highly challenging issue due to the very strict regulations for the biochar contents of major and trace elements, feedstock origin, the choice of its conversion technology, and many other obstacles [83], which may suggest some biochar as being merely a waste or hazardous material rather than having soil amendment purposes. [PERSON] et al. [84] investigate the response of ancient and modern wheat varieties to biochar application as soil amendment. The authors observed that the addition of biochar affects soil properties as follows: it increases the content organic matter, pH, and the content of phosphorus (P), potassium (K), and sulfur (S) in the soils as well as the shoot and root biomass of wheat. However, biochar can improve soil health and crop productivity, depending on the rate of wood biochar and fertilizers addition, but these parameters should be studied with respect to the properties of the soil, the agricultural plants, the biochar origin, and production technology. Table S3, Supplementary material, shows results from the currently performed decision support analysis, aiming to estimate the biochar potential application for CO\({}_{2}\) sequestration and soil amendment processes. The grape pomace expressed the highest potential for application in CO\({}_{2}\) sequestration and/or soil amendment processes, dominating over a large number of events. Although the metric distance was worse than for cherry stones, grape pomace indicated good balance between all variables. Thus, grape pomace was considered as the most advantageous for this application, followed by peach stones and cherry stones. Table S4, Supplementary material, reports the results from the decision support analysis for supercapacitor process application. This analysis showed solutions that are very similar to the ones encountered in CO\({}_{2}\) sequestration analysis, where the salt content was used instead of pH. Consequently, grape pomace was again the most suitable for this application, followed by peach stones and cherry stones. The results revealed that no solution is completely dominant because no non-dominated solutions were identified. All solutions accounted for 0% of full domination in all parameters in each application. The complexity of the decision is in part in such event, and that is why weak domination can be enlightened. The metric distance resumes equilibrium of all variables and a perfect solution does not exist. Generally, peach stones, cherry stones, and grape pomace achieved the most promising results for all applications in comparison to colza, softwood, or sunflower husks.