esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study  
### Case Description  
Based on Sections 2 and 3, a new model was built up to solve the low-carbon economic optimal dispatching and optimal carbon price solution problem of IPGES containing P2G considering wind power uncertainty and carbon trading. This model was developed in the most general form, while various parameters for objectives, constraints, power source structure, network parameters, characteristics of wind power and carbon trading can be set according to the different conditions. Therefore, the proposed model is suitable for most situations. With the thorough development of low-carbon electricity, power dispatching should meet the requirements for carbon emission reduction. In virtue of the proposed model, this study tries to explore how carbon trading affects the optimal dispatching solution of IPGES under wind power uncertainty, to compare the three carbon trading price mechanisms, and to solve optimal carbon price under the requirements of carbon emission reduction.  
In this study, an IPGES composed of the modified IEEE39-bus power system and the Belgian 20-node gas system was used as the research object for simulation analysis [31; 32], and its structure is shown in Figure 6. The IEEE 39-bus power network has 46 branches, four coal-fired generators (CG), three gas-fired generators (GG) and two wind power units (WT), where the capacity of wind power units accounts for 27% of the total installed capacity of 5500 MW. The modified Belgian high-calorific 20-node gas system has 24 pipelines, four natural gas wells, two natural gas storage stations and two P2 Gs. The parameters of the  
Figure 5: The flow chart of solving the bi-layer optimization model.  
power system are from [18; 32] and the parameters of the natural gas system are from [31; 32]. The electric load and gas load are shown in Appendix A Figure A2; the uncertain wind power scenarios set and typical wind power output scenarios are obtained by the method introduced in Section 4.1, as shown in Figure 5; the network structure parameters and units cost parameters of IPGES can be found in [18]; the carbon emission coefficients of the units are shown in Table A1; for other parameters in IPGES, such as natural gas well parameters, natural gas storage parameters and P2G parameters, see Tables A2-A4 in Appendix A, respectively; The carbon trading parameters are shown in Appendix A Table A5. The optimal scheduling cycle is 24 h, and the optimal step size is 1 h. The set up for the multiple cases for simulation analysis as follows.
## 4 Proposed Method
### Predicting Natural Frequency Using GCN--Step 1  
Graph convolutional neural networks (GCNs) were adopted to predict the natural frequency of panels and cavities. Figure 3 shows the GCN structure for this study. Inputs are the adjacency matrix, which defines the connectivity of nodes, and three features, which means the panel/cavity FE model's node location (_x,y,z_). For panel natural frequency prediction, skip connection was added and Relu/Elu activation functions were used alternately to improve the accuracy. Both GCN models use graph classification with a read-out layer, which uses a global mean.
## 5 Comparative Analysis of Incremental Launching Monitoring Results
### Whole-Process Expression of Guide Grider Stress  
In unfavorable working condition 2, the front end of the guide girder is cantilevered by 35.5 m, and the guide girder is in the most unfavorable condition. Therefore, the whole-process analysis of the second stage of incremental launching is carried out to analyze the change in the stress of the guide girder. Figure 14 shows the stress change diagram of the stress of the guide girder section during the second stage of incremental launching. From the diagram, the general pattern of stress in the guide girder section exhibits an initial rise followed by a decline as the incremental launching distance increases. The trend of the stress curves of measuring points D\({}_{1}\) and D\({}_{2}\) is roughly the same. With the increase in incremental launching distance, it is first pulled and then compressed. When the incremental launching distance is 20 m, it attains its peak. The maximum stress of D\({}_{1}\) is 117.4 MPa. The maximum stress of D\({}_{2}\) is 103.4 MPa, lower than the steel strength limit of 295 MPa. The trend of the stress curve of measuring points D\({}_{3}\) and D\({}_{4}\) is the same, and the whole process is tensioned. As the distance in incremental launching grows, the overall trend increases first and then decreases. When the incremental launching distance is 25 m, the stress of the measuring points is the largest, 98 MPa and 138 MPa, respectively, both less than 295 MPa.  
When the incremental launching is 30 m, the guide girder reaches the maximum cantilever state, and the stress of the four measuring points of the guide girder has a  
Figure 14: The guide girder section stress of the second stage of incremental launching.  
sudden downward trend. Meanwhile, the stress begins to decrease gradually once the leading edge of the guide girder arrives at temporary incremental launching pier L4, aligning with the findings from theoretical analysis.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
###### Abstract  
With the development of navigation satellite constellation systems, to improve navigation service and orbit determination performance, the accuracy requirements for maintaining temporal references have increased rapidly. Among the current navigation satellites, a dual one-way ranging (DOWR) approach based on intersatellite links (ISLs) is widely adopted in the BeiDou system and global positioning system (GPS) to transmit satellite time reference information. However, the accuracy of DOWR is restricted by the pseudonoise (PN) code rate. To improve the accuracy of DOWR, the PN code measurement must be replaced by the carrier phase measurement. This paper introduces an algorithm that utilizes frequency hopping to achieve carrier phase ranging. In addition to the high-precision advantages of carrier phase measurements, the anti-interference performance of the ranging signal is also improved due to the characteristics of the frequency hopping signal itself. Ultimately, at a carrier-to-noise ratio of 40 dB-Hz, the measurement accuracy is 9.54 \(\upmu\)m, while the PN code measurement accuracy in the same environment is 0.13 m. As the carrier-to-noise ratio increases, the measurement accuracy further improves.  
time reference; dual one-way ranging; carrier phase ranging; frequency hopping Article  
Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal  
1
## 3 The Proposed Method
### Convolutional Neural Network Architecture  
The second kind of neural architecture that is often used is CNN. The two main distinctions between CNN and ANN are the architecture and input data. In ANN, numerical values are employed, but in CNN, pictures are used. A collection of pixels with the dimensions w, h, and d make up an image (I). Images are sized, first by their width and height, and then by their depth. The color model used affects how deep an image seems. For example, d is equal to three in the case of the three-color RGB (Red-Green-Blue) scheme.  
The architecture of the neural model consists of fully connected layers, convolution, and pooling. Convolutional filtering layers change the picture by emphasizing and extracting certain elements, which is accomplished by the convolution operation (a star operation *) between the filter \(k\) (a matrix of size \(p\times p\)) and the image \(I_{x,y}\) (for each individual pixel in the position \((x,y)\)):  
\[k*I_{x,y}=\sum_{i=1}^{p}\sum_{j=1}^{p}k_{ij,j}.I_{x+i-1,\;y+j-1}+b_{1} \tag{5}\]  
where \(b_{1}\) is a bias. The pooling layer is used to reduce the image's file size. To analyze each pixel and its surroundings, the function \(\omega(.)\) is employed (such as minimum, maximum, or average). The pixel that performs the function is added to the reduced picture \(\omega(.)\). It can be expressed in the following way so as to maximize its utility:  
\[\omega\big{(}I_{x,y}\big{)}=\max_{i,j\in\{-1,0,1\}}I_{x-i,y-j} \tag{6}\]  
Image resizing may be calculated as \(\frac{w-k}{s+1}\times\frac{h-k}{s+1}\), where \(s\) is a kernel shift (the size of neighborhood). These two layers may be used repeatedly before the FC layer, which is the last kind of layer. This neural network has a number of hidden layers and a single output layer (similar to ANN).  
Our CNN model's structure is shown in Figure 6. As can be observed, this structure is made up of convolutional layers, two pooling layers, and a fully connected layer where classification is performed using the output from the second pooling layer. Six probability values are created for each of the six classes of generated modulation by our CNN after it analyses a scalogram picture.  
Figure 6: Structure of CNN model in this work.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
###### Abstract  
This study introduces an innovative optimization method to identify the optimal configuration of a sparse symmetric 2D array for applications in security, particularly multistatic imaging. Utilizing genetic algorithms (GAs) in a sophisticated optimization process, the research focuses on achieving the most favorable antenna distribution while mitigating the common issue of secondary lobes in sparse arrays. The main objective is to determine the ideal configuration from specific design parameters, including hardware specifications such as number of radiating elements, minimum spacing, operating frequency range, and image separation distance. The study employed a cost  
of elements, resulting in unwieldy arrays, high computational requirements, and potential impracticality for real-time applications. In contrast, random distributions offer a simpler design process and the potential for improved resolution compared to uniform meshes but lack the systematic performance optimization characteristic of our chosen methodology and occasionally yield unpredictable image quality [27].  
Genetic algorithms (GAs) have emerged as the preferred method for array optimization due to their adaptability, robustness, and efficiency in tackling complex array design problems encountered in scientific research. GAs excel at exploring large search spaces; handling non-linear, non-convex optimization landscapes; and supporting multi-objective optimization. They are highly efficient in combinatorial search spaces and work effectively with black-box functions, making them suitable for complex optimization challenges.  
The core objective of this study is to identify the optimal antenna configuration from a set of design parameters. The structure of this paper unfolds as follows: Section 2 outlines the architecture, imaging procedure, and the evaluation function used for potential solutions. In Section 3, we introduce GAs and describe their specific application in this context. Section 4 delves into the simulation process, while Section 5 presents the results, including a compelling comparison between optimized and non-optimized approaches. Finally, in Section 6, we summarize our achievements and lay out future steps to further enhance imaging system efficiency.
## 4 Discussion
### Comparisons between Models  
A comparison between the previous New Zealand model [1], the present model developed in this paper, and the EUPTF2 model clearly suggests that the model presented here is the best, given that only a restricted set of explanatory variables are available for predictions. Estimates of TAW from the EUPTF2 model were biased with respect to measured values, but this is likely due to the restricted range of explanatory variables provided and the fact that the soil types in New Zealand do not match soil types fit to the EUPTF2 model. Other published PTFs were not included in the comparison for several reasons.  
1. Many PTFs require soil inputs such as bulk density that are not spatially available in New Zealand.
2. Some PTFs are so geographically specific that their use for New Zealand soils would be pointless [30].
3. Either the coefficients for the published model were not available for application with New Zealand data, or the software was not suitable for use in calculations of large quantities of 5-map sample data.
4. Global maps [31] in image format of the mean and coefficient of variation of saturated moisture content, field capacity, and permanent wilting point estimated using an ensemble model can be found online ([[https://doi.org/10.7910/DVN/VPIN2B](https://doi.org/10.7910/DVN/VPIN2B)]([https://doi.org/10.7910/DVN/VPIN2B](https://doi.org/10.7910/DVN/VPIN2B)), accessed on 1 May 2024). In this case, the PTF predictions are available at such a low spatial resolution (10 km postings) that a comparison with New Zealand point data was pointless. We also note that recent research is finding that local models usually perform better than global models (e.g., [32; 33; 34; 35]).
## 2 Materials and Methods
### Proposed Solution  
We trained a binary classification model using the 10 local thresholds presented in Section 2.1, local and global statistics (average, standard deviation, skewness, kurtosis,minimum and maximum values), and a global histogram computed with five bins. Four scenarios were explored, using the following feature sets:  
1. Only local thresholds;
2. Only local and global image statistics;
3. All features except the global histogram;
4. All features.  
No matter the scenario, the feature set is computed per pixel and contains pixel-specific values for local thresholds and statistics, and image-specific values for global statistics and histogram.  
The DIBCO datasets [32, 33, 34, 35, 36, 37, 38, 39, 40, 41] were used because they provide a wide variety of hard-to-binarize document images, and many other binarization algorithms in the scientific literature use at least one of them, allowing us to compare our results with those of other state-of-the-art methods. The dataset was split into ten partitions, one for each DIBCO dataset, and the models were trained using 10-fold cross-validation. This means that, for each DIBCO dataset, the model was trained on all other DIBCO datasets and validated on the respective DIBCO dataset.  
The F-measure (\(FM\)), also known as the \(F_{1}\) score, is used as a metric in many studies on image binarization and is defined as follows:  
\[FM=2 TP/(2 TP+F), \tag{5}\]  
where \(TP\) is the number of correctly classified foreground pixels and \(F\) is the number of incorrectly classified pixels. \(FM\) takes values between 0 and 1, with 1 indicating a perfect classifier.  
The ML-NET binary classification experiment was executed with a memory limit of 28 GB and a time limit of 48 h on a system with an Intel 14600K processor and 32 GB of RAM. \(FM\) was used as the optimizing metric, and CFT was used as the search strategy. The 10 \(FM\) values obtained from the 10-fold cross-validation were aggregated into an average value, \(\mu_{FM}\), and a standard deviation, \(\sigma_{FM}\). The best model was considered to be the model with the highest value for \(\mu_{FM}-\sigma_{FM}\), thus encouraging the robustness of the selected model. For example, a model that achieved 95% \(FM\) on 9 partitions and 80% on the 10 th would be considered worse than a model that achieved 90% on all 10 partitions, because a robust model would achieve consistent results on any dataset.
## 2 System Model and Problem Formulation
### Problem Formulation  
It is expected to maximize the total secure rate by optimzing channel allocation from UAVs to HAP, users' power, and UAVs' 3D position. The optimization problem is formulated as  
\[\mathrm{P1}:\max_{\mathbf{A},\mathbf{P},\mathbf{H},\mathbf{Q}} \sum_{m=1}^{M}\sum_{k=1}^{K}\sum_{p=1}^{N_{p}}a_{m,k}R_{p}^{\mathrm{ sec}},\] (7) s.t. \[\sum_{m=1}^{M}\sum_{k=1}^{K}a_{m,k}\leqslant 1, \tag{8}\] \[a_{m,k}\in\{0,1\},\forall m,\forall k,\] (9) \[0\leqslant P_{a}\leqslant P_{\max},\forall n\in\mathrm{N},\] (10) \[0\leqslant P_{d}\leqslant P_{\max},\forall n\in\mathrm{N},\] (11) \[h_{\min}\leqslant h_{u}\leqslant h_{\max},\forall m\in\mathrm{M}, \tag{12}\]  
where \(\mathbf{A}=\left\{a_{m,k}\right\}_{\forall m,\forall k}\mathbf{P}=\left\{P_{a /d}\right\}_{a,d\in N_{p}}\mathbf{H}=\left\{h_{u}\right\}_{u\in M}\), and \(\mathbf{Q}=\left\{x_{u},y_{u}\right\}_{u\in M}\). In \(\mathrm{P1}\), \(a_{m,k}\) is a binary variable. If \(\mathrm{UAV}_{m}\) is served by HAP in channel \(k\), \(a_{m,k}=1\), otherwise \(a_{m,k}=0\). In addition, \(P_{\max}\) is the maximum transmit power of the users, and \(h_{\min}\) and \(h_{\max}\) are the altitude constraint of UAVs.
## 2 Background
### Asset Health Index
While SHM technologies have achieved significant progress, such as process automation and data filtering to mitigate environmental effects, a fundamental disconnect persists. This disconnect arises from the focus on monitoring and damage detection rather than adopting a broader perspective that treats infrastructure as integrated digital assets. This approach entails not only data collection and analysis but also the inclusion of digital models to enable comprehensive maintenance management within a digitized environment. Integrating SHM-generated data with tools such as digital twins facilitates not only real-time structural monitoring but also predictive maintenance strategies, resource optimization, and data-driven decision-making. In this context, SHM advancements bridge the gap between existing monitoring technologies and the need for holistic, digitized management. This work aims to serve as a bridge, addressing the challenges of digital transformation in infrastructure maintenance by proposing an approach rooted in a holistic and structured vision of assets to optimize SHM integration into sustainable infrastructure management. To enhance clarity and facilitate understanding, we have categorized the references discussed in this section into four main groups: conceptual foundations of digitization and digitalization, reference architectures, advanced models integrating IoT and AHI, and practical applications through case studies. This classification highlights the contributionsof the existing literature in relation to the proposed framework. Table 1 summarizes these categories and their key characteristics.
## 5 Conclusions  
Chemical vapor deposited graphene layers were successfully transferred into Si/SiO\({}_{2}\) substrate. After that some samples were treated with Al\({}_{2}\)O\({}_{3}\) in ALD process, where 5 cycles of TMA and H\({}_{2}\)O were applied at room temperature. After deposition the small grains of Al\({}_{2}\)O\({}_{3}\) were visible in SEM images. According to the micro-Raman analysis, after the Al\({}_{2}\)O\({}_{3}\) deposition process, the G- and 2D-bands of graphene slightly broadened but the overall quality did not change (D-band was mostly absent). Despite this, the process slightly decreased the graphene quality and increased the count of defects. The micro-Raman probing allowed one to conclude that after five ALD cycles on graphene the (averaged) FWHM values for 2D-bands (\(\Gamma_{\text{2D}}\)) changed from 34 up to 37.5 cm\({}^{-1}\), but those five cycles increased the elastic properties of the sample. [PERSON]'s modulus increased from 93 and hardness from 9.5 GPa up to 110 GPa and 10 GPa, respectively.  
Figure 8: (**a**) Average elastic modulus of nanostructure Si/SiO\({}_{2}\)/G and (**b**) Si/SiO\({}_{2}\)/G/Al\({}_{2}\)O\({}_{3}\) with different load forces.  
Our measurements revealed that the elastic modulus of a coated CVD graphene is better with that of pristine graphene. Comparing to the approaches for measuring the mechanical property by using different load forces on the samples Si/SiO\({}_{2}\)/G and Si/SiO\({}_{2}\)/G/Al\({}_{2}\)O\({}_{3}\), the improvement in [PERSON]'s modulus, 96 versus 125 GPa (at max. load 10 mN) was achieved. This study establishes that large-area CVD-graphene, being coated with thin film made by ALD, is a strengthened material, that would be suitable for flexible electronics and components of nanodevices.  
Conceptualization, A.T.; methodology, T.K. and T.J.; validation, A.T. and H.-M.P.; investigation, H.-M.P. and T.K.; resources, A.T.; writing--original draft preparation, A.T.; writing--review and editing, H.-M.P., T.K. and T.J.; visualization, T.K.; project administration, A.T.; funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
The present study was partially funded by the European Regional Development Fund project \"Emerging orders in quantum and nanomaterials\" (TK134) and Estonian Research Agency (PRG4).  
Not applicable.  
This work was supported by European Regional Development Fund project \"Centre of nanomaterials technologies and research\" (NAMUR+, Project No. 2014-2020.4.01.16-0123). The authors are grateful to [PERSON] in assistance for deposition of films.  
The authors declare no conflict of interest.
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 3 Notation and Terminology for Geometric Algebra
: _A vector-valued signal \(\mathbf{x}(t)\) in a finite \(N\)-dimensional space can always be decomposed into the projections with respect to the TOPs_  
\[\mathbf{x}(t)=\sum_{k=1}^{M}\mathbf{x}_{\|\mathbf{j}_{k}}(t) \tag{35}\]  
_where \(\mathbf{j}_{k}\) is provided in (33) and \(M\) is given by (34)._  
**Definition 17** (**Polar Decomposition of a VV Signal Decomposed into TOPs). : _A vector-valued signal \(\mathbf{x}(t)\) in \(N\)-dimensional space can always be decomposed in polar form relative to an Euclidean orthonormal basis vectors \(\mathbf{e}_{2i-1}\) with \(M\) number of totally orthogonal planes_  
\[\mathbf{x}(t)=\sum_{i=1}^{M}x_{i}(t)X_{i}^{-\frac{1}{2}}(t)\mathbf{e}_{2i-1}X_ {i}^{\frac{1}{2}}(t) \tag{36}\]  
_where \(M\) is given by (34)._  
**Remark 8**. : _In traditional LTI system theory, the real axis serves as a reference for measuring angles. To align with this convention, we adopt \(\mathbf{e}_{1}\) as the reference direction while using the polar decomposition of an arbitrary VV signal in (30). However, when we decompose the VV signal into TOPs, a reference vector (direction) must be specified for each TOP, as given in (36)._