esa-sceva/satcom-chunk-collection huggingface dataset:
# Going Deeper into OSNR Estimation with CNN
## 5 Conclusions  
In this paper, a high-performance neural network scheme, namely, OptInception, was proposed for OSNR monitoring in OPM applications. We elaborated on the design of the scheme by reviewing structures and functions we used. Additionally, their principles or the ideas behind them decided whether and how we used them. An experiment was set up to verify the transparency of OptInception to the symbol rate, modulation format, and chromatic dispersion. The mean absolute error in the test dataset was approximately 0.125 dB, while the root-mean-square error was 0.246 dB. The kernels in trained the network were also investigated to reveal the complexity of neural networks. Finally, a learning curve was drawn to show the training extent of OptInception. A comparison in performance and complexity presented the advantage of the proposed scheme. In a nutshell, the training process and experimental results indicate that the design principles function as expected.  
**Author Contributions:** Conceptualization, F.S.; methodology, F.S.; software, F.S.; validation, F.S., LL. and J.Z.; formal analysis, J.Z.; investigation, F.S., L.L.; resources, L.L.; data curation, F.S., L.L.; writing--original draft preparation, F.S.; writing--review and editing, J.Z.; visualization, L.L.; supervision, J.Z.; project administration, L.L.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was funded by the Natural Science Foundation of China under Grant No. 51575517.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Performance**} & \multicolumn{2}{c}{**Complexity**} \\  & **MAE (dB)** & **RMSE (dB)** & **FLOPs** & **Params (Bytes)** \\ \hline OptInception & 0.148 & 0.277 & 12,482,975 & 25,001,732 \\ OptInception-Pro & 0.125 & 0.246 & 16,833,798 & 56,378,116 \\ Basic CNN scheme in [25] & 0.357 & 0.449 & 28,135,393 & 33,751,492 \\ CDF-based algorithm in [15] & 0.478 & 0.525 & 71,264 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of various state-of-the-art OSNR monitoring schemes with the proposed scheme in performance and complexity.
## 4 The Application of AWS Machine Learning (AML) for the Surface Finishing Process  
This section illustrates the example of an application of AML for the prediction of chamfer length as a result of a surface finishing process. The monitoring of the surface finishing processes in the aerospace manufacturing industry has become one of the key issues in maintaining the overall quality of the products or parts. To date, the surface quality monitoring post-machining processes, such as deburring and polishing, rely on visual inspections, surface roughness tests, and ultrasonic and laser gap gun measurements, which require a considerable amount of time and a skilled operator. This process-monitoring aims to eliminate the surface anomalies by halting production when defects (or part size deviations) are detected. In addition, the early detection of part size deviations will also reduce the production cost and increase the efficiency of the manufacturing processes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **IaaS** & **PaaS** \\ \hline Developer’s main focus & Storage, networking, computing & Building application \\ \hline Platform flexibility to & Flexible as the developer build the & Non-flexible as the developer build \\ build software & software on a platform that is on a & the software on a platform specific \\  & common infrastructure & to the provider \\ \hline Learning curve & Higher learning curve as the & Lower learning curve as the \\  & developer has to build both the & developer only has to focus on \\  & platform and the software & the software \\ \hline Scalability & Allows for more flexibility & Has a certain scalability limit \\ \hline Intercommunication & Allows for software from different & Software from different platforms \\  & platforms to communicate easily on & must communicate to each other \\  & the same infrastructure & from different infrastructures \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences in criteria between the Infrastructure as a Service (IaaS) and the Platform as a Service (PaaS).
## 3 Evaluation of Budding Rate  
### Image Stitching  
The image stitching of UAV images is much more complex than the satellite remote sensing data, and depends on the geolocation provides by itself. Currently, in order to generate an orthophoto from the UAV images, several steps need to be included: structure-from-motion (SfM), dense point cloud construction, meshing the surface, texture mesh, adding geolocation information to the textured mesh, and generation the orthophoto [10]. In the SfM process, the first step is to extract features from the images, and then match the features to create camera tracks, and finally to perform SfM construction to obtain the sparse point cloud [24]. Dense construction is the key step to obtaining a depth-map of the scenes, and these depth points will be useful for creating the mesh of the surface [25]. In this study, OpenSfM ([[https://github.com/mapillary/OpenSfM](https://github.com/mapillary/OpenSfM)]([https://github.com/mapillary/OpenSfM](https://github.com/mapillary/OpenSfM))) was selected as the basic SfM and dense construction tool after migrating to the windows platform. Next, the Poisson algorithm [26] was used to convert dense point cloud to meshing and the textured meshing was yielded by using the mvs-texturing module ([[https://github.com/nmoehrle/mvs-texturing](https://github.com/nmoehrle/mvs-texturing)]([https://github.com/nmoehrle/mvs-texturing](https://github.com/nmoehrle/mvs-texturing))) [27]. Finally, the geolocation was done, and an orthophoto was obtained. In the study, the hardware used to perform the stitching is Intel Xeon E5-2630 \(\times\) 2, Random Access Memory (RAM) of 128 GB, with a disk of 2 TB. The Python of version 3.7, was adopted as the glue language to call OpenSfM, Mvs-texturing, etc., to obtain the orthophoto image. As for this study, a total processing time of 3018 s was used.  
Figure 1: The location of the study area in Qitai County, Xinjiang, China (Left), the satellite image of the study area (upper right, data source from bing map), and the locations of captured images (Bottom right).
## 4 Discussion
Since these sloshing patterns are highly dissipative, accounting for their influence in the reduced-order sloshing model was found to be essential in order to model the induced damping effects at lower acceleration amplitudes (i.e., the \(A_{1}<2.5g\) region in Figure 15) appropriately. The above insights lead to the following main observations and motivate future research directions:  
* The spanwise location of the fuel influences the vertical sloshing regimes. Previous single degree-of-freedom studies indicate substantial sloshing-induced damping effects that depend on the sloshing regime [5,7]. There is thus an indication that depending on the available amounts of fuel, the net damping following the excitation similar to the step release (such as a discrete gust) may be increased by moving the liquid to the specific spanwise positions, taking into account the filling level as well. Supplementary investigations are planned in order to further assess such effects. Figure 17: Estimation of sloshing mode frequencies for the 50% filling level case for each MiniWoT tank cell, shown as a percentage of \(F1\). Values coloured by their proximity to 50%. Figure 18: Average liquid surface location in 1 DOF harmonic excitation tests [7] (**left**) and MiniWoT cells 3&4 sloshing pattern (**right**). * Liquid sloshing patterns dominated by different sloshing modes are prominent at different spanwise locations and persistent even at the lowest amplitudes of excitation. Such sloshing modes are highly dependent on the geometry of the tank and filling level, and they have previously been observed to add substantial additional damping at lower amplitudes by promoting liquid impacts with the top of the tank. Therefore, there exists the possibility that the geometry of the fuel tank could be tailored to promote such sloshing patterns and, hence, achieve an increased sloshing-induced damping.
## 2 Materials and Methods
### Curve Fitting Algorithms
#### 2.3.1 Levenberg-Marquardt Algorithm  
The Levenberg-Marquardt Algorithm [21; 22] acts more similarly to a gradient-descent method when the parameters are far from their optimal value and acts more similarly to the Gauss-Newton method when the parameters are close to their optimal value [31]. The equation for the step \(\mathbf{h}\) at the \(k\)th iteration is as follows:  
\[\Big{(}I(\mathbf{x}_{k})^{\top}J(\mathbf{x}_{k})+\lambda_{k}I\Big{)}\mathbf{h}=-J(\mathbf{x}_{ k})^{\top}f(\mathbf{x}_{k}) \tag{8}\]  
where \(J\) is the Jacobian of \(\mathbf{f}\), and \(\lambda_{k}\) is the damping parameter. It controls both the magnitude and direction of \(\mathbf{h}\), and it was chosen at each iteration. It can be shown [22] that, at each iteration, Equation (8) solves the minimization problem over a reduced set of admissible solutions, i.e., those that satisfy \(\|\mathbf{h}\|\leq R(\lambda)\), limiting the correction step within a region near \(\mathbf{x}_{k}\). The radius of the trust region \(R=R(\lambda)\) is a strictly decreasing function with \(\lim_{\lambda\to\infty}R(\lambda)=0\). When \(\lambda_{k}=0\), step \(\mathbf{h}\) is identical to that of the Gauss-Newton method and its magnitude assumes the maximum value. As \(\lambda\to\infty\), \(\mathbf{h}\) tends towards the steepest descent direction, with the magnitude tending towards 0.  
Based on the above, we infer the qualitative update rule for \(\lambda_{k+1}\): if \(\Psi(\mathbf{x}_{k}+\mathbf{h})<\Psi(\mathbf{x}_{k})\) then the quadratic approximation works well and we can extend the trust region, i.e., it will be \(\lambda_{k+1}<\lambda_{k}\). Otherwise, the step is unsuccessful, and we reduce the trust region, i.e., it will be \(\lambda_{k+1}>\lambda_{k}\); in this way, the next step tends toward the negative gradient method and a lower value of \(\Psi\) is more likely to be found.  
The Matlab implementation has been used, specifically the lscurvefit function with the Levenberg-Marquardt option [32].
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation: Comparison with Laboratory Data
#### 5.1.3 Results and Interpretation  
Figure 17 presents the Bland-Altman plots for pH, TDSs, and turbidity, comparing system and laboratory measurements.  
* pH Analysis:The mean difference (\(\mu_{D}\)) is close to 0.00, with LOA within \(\pm\)0.2 pH, indicating minimal systematic bias and high consistency across the measurement range.
* TDS Analysis: The system measurements show a slightly positive bias, with values 5-10 ppm higher than laboratory readings, particularly at higher concentrations (>800 ppm). The LOA extends to \(\pm\)50 ppm, suggesting minor discrepancies due to sensor non-linearity at elevated TDS levels.
* Turbidity Analysis: The mean difference is 0.1 NTU, with LOA at \(\pm\)0.8 NTU, consistent with WHO drinking water standards (<5 NTU). However, higher variance near 0 NTU indicates that measurements in ultra-low-turbidity conditions may be affected by sensor noise or light scattering effects.  
The overall results suggest that the system performs reliably within the expected tolerances for drinking water monitoring, with differences remaining within the 95% confidence limits.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 2 System Overview
### Server Back-End
#### 2.4.2 Android Mobile Application  
In addition to the Web application, a mobile Android application has also been developed. While the Web interface is more suitable for the deep analysis of the data, the Android interface provides real-time notifications of the predefined alarms (see Figure 12). The Android application visualizes the data measurements in real time as well as displays present the weather observations and weather forecasts obtained from the weather service weatherbit.
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 1 Introduction
Apart from the sub-6-GHz spectrum, 5G smartphones are also expected to support the MM-Wave spectrum [37]. Compact antennas arranged as an array can be employed in different portions of a smartphone PCB to form linear phased arrays with high gain and directional radiation beams [38; 39; 40]. In contrast to conventional antennas, such as patch, slot, or monopole antennas, end-fire antennas are more suitable to achieve the required radiation coverage [41; 42]. Phased array antennas with high performance are highly desirable for MM-Wave 5G communications as they can increase the radiation and the connectivity of the system [43; 44; 45]. In addition to the proposed MIMO antenna, a new and compact phased array millimeter-wave (MM-Wave) antenna with broad bandwidth and end-fire radiation is introduced for 28 GHz applications. Its configuration is composed of eight loop dipole resonators with pairs of directors arranged in a linear form which can be easily integrated into the smartphone antenna system. The following sections present the design details, single-element performance, characteristics of the tri-band MIMO antenna, and the 28 GHz phased array.
## 2 Materials and Methods
### Statistical Evaluation of Satellite Precipitation Products
#### 2.5.2 Categorical Statistical Indices  
To evaluate the precipitation detection capability of IMERG V05 and 3B42 V7 products, four categorical statistical indices were calculated to assess the ability of PPSs. The most common measures, counting Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and Frequency Bias Index (FBI) are used in this study. The values of all categorical statistical measures are between 0 and 1.  
The POD indicated the ratio of the number of precipitation events correctly detected by satellites among all real precipitation events. The FAR is the ratio of false alarming precipitation events to the total number of detected precipitation events. The FBI repre  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistical Index** & **Units** & **Reference Values** & **Equation** & **Reference** \\ \hline
**Correlation Coefficient (CC)** & Ratio & 1 & CC = \(\frac{\sum_{i=1}^{\infty}(\text{Pi}-\overline{\text{P}})(\text{Si}-\overline{5})}{ \sqrt{\sum_{i=1}^{\infty}(\text{Pi}-\overline{\text{P}})}^{2}\sum_{i=1}^{ \infty}(\text{Si}-\overline{5})^{2}}\) & \\
**Root Mean Square Error (RMSE)** & mm & 0 & RMSE = \(\frac{\sum_{i=1}^{\infty}(\text{Pi}-\overline{\text{Si}})^{2}}{\sum_{i=1}^{ \infty}\text{N}}\) & [40] \\
**Bias** & mm & 0 & Bias = \(\frac{\sum_{i=1}^{\infty}(\text{Pi}-\overline{\text{Si}})}{\sum_{i=1}^{ \infty}\text{N}}\) & \\ \hline
**Probability of Detection (POD)** & Ratio & 1 & POD = \(\frac{\text{a}}{\text{a}+\text{c}}\) & \\
**False Alarm Ratio (FAR)** & Ratio & 0 & FAR = \(\frac{\text{b}}{\text{a}+\text{b}}\) & \\
**Critical Success Index (CSI)** & Ratio & 1 & CSI = \(\frac{\text{a}+\text{b}+\text{c}}{\text{a}+\text{b}+\text{c}}\) & \\
**Frequency Bias Index (FBI)** & Ratio & 1 & FBI = \(\frac{\text{a}+\text{b}}{\text{a}+\text{c}}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical metrics for evaluating IMERG V5 and 3B42 V7 products.  
sents the fraction of falsely detected precipitation events (false alarm) compared to the total number of detected precipitation events, and indicates whether the dataset tends to overestimate (FBI \(>\) 1) or underestimate (FBI \(<\) 1) precipitation events. The CSI reported the number of correct predictions of a rain event divided by the total number of successes, false alarms, and failures.
# Local Convergence Study for an Iterative Scheme with a High Order of Convergence
## 2 Local Convergence Analysis  
In this section, we provide the local convergence analysis for scheme (1). Let \(\overline{B}(x^{*},r)\) and \(B(x^{*},r)\) represent the closed and open balls, respectively, in Banach space \(X\), centered in \(x^{*}\) (the solution) with radius \(r>0\). In order to develop a local convergence study, certain conditions are applied on operators \((\mathcal{T},\,\mathcal{T}^{\prime})\) in \(x^{*}\), which is presumed to exist. Hence, we consider, for real numbers \(L_{0}>0,\,L>0\) and for all \(x,\,y\in D\), the conditions listed below:  
\[(C_{1}) \mathcal{T}(x^{*})=0,\quad\mathcal{T}^{\prime}(x^{*})^{-1}\in \mathcal{L}(Y,X)\] \[(C_{2}) \left\|\mathcal{T}^{\prime}(x^{*})^{-1}\big{(}\mathcal{T}^{ \prime}(x)-\mathcal{T}^{\prime}(x^{*})\big{)}\right\|\leq\,L_{0}\|x-x^{*}\|\] \[(C_{3}) \left\|\mathcal{T}^{\prime}(x^{*})^{-1}\big{(}\mathcal{T}^{ \prime}(x)-\mathcal{T}^{\prime}(y)\big{)}\right\|\leq L\|x-y\|\] \[(C_{4}) \left\|\mathcal{T}^{\prime}(x^{*})^{-1}\mathcal{T}^{\prime}(x) \right\|\leq M,\,\,\forall x\in D\text{ for }M>1\text{ real}.\]  
[PERSON] et al., in [11], examined the scheme (1), applying complex dynamics techniques. However, they did not provide a theoretical analysis of local convergence, which we consider essential for the reasons previously outlined. In Section 2.1, we perform this study.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 2 Background and Algorithm Overview
### Overview of GNSS-R Open Loop Tracker Algorithm
#### 2.4.1 Defining Reflection Geometry  
Initially, it is required to establish the reflection geometry as illustrated in Figure 1 and define unique path segments used in subsequent derivations. This includes estimating the positions of the transmitter and receiver and the location of the surface specular point. The algorithm for estimating the surface reflection point is derived in detail in Section 3. An alternative specular point estimation algorithm, developed for use with the CYGNSS end-to-end simulator was previously developed by [PERSON] and can also be used with comparable performance [24].  
Note that the specular point solver solution is iterative based on an initial (rough) guess of the specular point location. The convergence criteria for the solver is discussed in Section 3.1. Additionally, the speed of convergence of the specular point solver can be optimized by varying a linear scale factor. How this gain can be optimized is discussed in Section 6 for the case of CYGNSS GNSS-R observations.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
## 4 Maximizing the Minimal Residual Energy
### m-Hop Routing  
Based on this reasoning, the protocol can easily be extended to m-hop routing as well. In this case, we assume that packets are forwarded to the BS via an m-hop path and the sender node is node \(l_{0}=s\) and the sink node is the BS denoted by \(BS=m+1\). The path containing the sender and the relay nodes is denoted by \(\Re=\{l_{0},\ldots,l_{m}\}\).  
Then, there are \(m+1\) components changing in vector \(c(k+l)\) compared to \(c(k)\), given as \(c_{l}(k+1)=c_{l}(k)-g_{l_{j},l_{j+1}},j=0,\ldots,m\). Similar to the previous constraint, if we want to ensure that the packet sent by node \(s\) reaches the \(BS\) with a given probability \((1-\varepsilon)\), then the reliability constraint can be expressed as \(\Pi_{j=0}^{m}P_{l_{j}l_{j+1}}=1-\varepsilon\).  
Thus, in the case of m-hop routing, we have to solve the following constrained optimization problem:  
\[l_{1 opt},l_{2 opt},\ldots,l_{m-opt}:\max_{l_{2},l_{3},\ldots,l_{m} \in\mathbb{0},\ldots,l}\left(c_{l_{j}}(k)-\left(-d_{l_{j}l_{j+1}}\frac{\theta \sigma_{Z}^{2}}{\ln\left(P_{l_{j},l_{j+1}}\right)}\right)\right) \tag{10}\]  
subject to the constraint \(\Pi_{j=0}^{m}P_{l_{j}l_{j+1}}=1-\varepsilon\).  
Solution  
Let us assume that we have chosen a path \(\Re=\{l_{0},\ldots,l_{m}\}\). Along with this path, the reliability constraint \(\Pi_{j=0}^{m}P_{l_{j}l_{j+1}}=1-\varepsilon\) can alternatively be given as \(\sum_{j=0}^{m}\ln\left(P_{l_{j}l_{j+1}}\right)=\ln(1-\varepsilon)\).  
From Equation (1) it becomes:  
\[\sum_{j=0}^{m}\ln(P_{l_{j}l_{j+1}})=-\sum_{j=0}^{m}\frac{d_{sl_{j} }^{\alpha}}{g_{sl_{j}}}\theta\sigma_{Z}^{2}=ln(1-\varepsilon). \tag{11}\]  
The minimum residual energy will be maximum if the residual energy at all the relay nodes are equal to each other, i.e., \(c_{l_{j}}(k+1)=\ c_{l_{j+1}}(k+1),\ j=0,\ldots,m\). So, from Equations (2) and (3):  
\[c_{l_{j}}(k)-g_{l_{j}l_{j+1}}=c_{l_{j+1}}(k)-g_{l_{j+1},l_{j+2}}. \tag{12}\]  
The \(m+1\) equations above yield a solution to \(g_{opt}=\left(g_{l_{0}l_{,opt}},\ldots,g_{l_{m}BS,opt}\right)\). We can calculate \(g_{opt}\) for all possible m-hop paths by fixing the path \(\Re=\{l_{0},\ldots,l_{L}\}\). Then the optimal path \(\Re_{\text{opt}}=\left\{l_{0},l_{1,opt}\ldots,l_{m,opt}\right\}\) can be sought by solving the following problem:  
\[\Re_{\text{opt}}=\left\{l_{0},l_{1,opt}\ldots,l_{m,opt}\right\} :\max_{\Re}c_{l_{0}}(k)-g_{l_{0}l_{1}},opt. \tag{13}\]  
This requires the solution of (11) \(\sum_{L=1}^{m}\frac{(N-1-)!}{L!(N-1-L)!}\) times and then \(\sum_{1}^{m}\frac{(N-1)!}{L!(N-1-L)!}\) comparisons of the value \(c_{l_{0}}(k)-g_{l_{0}l_{1},opt}\). We will refer to this method of routing the packets to the BS as the maximum of minimum residual energy protocol (MMREP).
## 3 Application of Model-Agnostic Meta-Learning (MAML) Model
### MAML Training and Test Results
As the R-squared value approaches 1.0, indicating better prediction results, it is evident that, with increasing training epochs, both during the training and testing processes, the predicted results converge to higher values, around 0.8. As depicted in Figure 0(a), the convergence speed during the training process is notably rapid. In the initial iterations, the predicted values quickly approach the true value, reaching an R-squared value of approximately 0.8 at around 300 iterations. Subsequently, the convergence rate slows down, with a slight increase in the R-squared value, nearing stability around 1000 iterations. The subsequent trend suggests that the value will eventually approach 1.0; however, this would require significantly increased costs. In Figure 0(b), the evolution curve of R-squared values during the testing process and with increasing iterations is illustrated. Figure 1: \(R^{2}\) of different numbers of repeated tests of MAML. It is noticeable that there is a notable distinction between the testing and training processes. While some individual tests still exhibit a rapid convergence speed consistent with the training process and their R-squared values are initially high, the convergence speed of tests 1, 2, and 3 significantly decreases. Although the results of these tests converge by the 1000 th iteration, their R-squared values slightly decrease, stabilizing between 0.75 and 0.82. This occurrence is likely due to the disparity in the amount of data used between the training and testing processes. Therefore, the final convergence results may be biased, but the primary objective of achieving rapid convergence while maintaining prediction accuracy on a small-sample database has been accomplished. Hence, the MAML prediction approach proves suitable for forecasting the interfacial bond performance between CFRP and concrete amidst varied environmental influences. Moreover, given the limited sample size of Meta2 tasks and the favorable prediction outcomes achieved by MAML, this method is also applicable to tasks with scant samples.