esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Test Setup  
### Test Site  
This study was performed at the Dongbok wind farm on Jeju Island, South Korea. The island is located off the southern part of the Korean peninsula, and the Dongbok wind farm is situated on the north-eastern part of the island, as shown in Figure 1. Fifteen 2-MW wind turbines were in operation, and wind turbines no. 1 and 15 were tested in this work. A nacelle LIDAR was installed on the nacelle of wind turbine no. 1, and a met mast was positioned at 2.5 times the rotor diameter from wind turbine no. 15. Topographical conditions were slightly complex, with a ruggedness index (RIX) of 0.2. There are two quarries to the south and north of wind turbine no. 1.  
To exclude the wind data disturbed by the wake due to blade rotation of neighbouring wind turbines and the two quarries, the measurement sectors were determined using Equations (1) and (2):  
\[\mathrm{D_{e}}=\frac{2\,\mathrm{l_{h}}\,\mathrm{l_{w}}}{\mathrm{l_{h}}+\mathrm{ l_{w}}} \tag{1}\]  
\[\begin{array}{ll}\alpha&=1.3\,\tan^{-1}\,\left(2.5\,\mathrm{D_{e}}\,/\, \mathrm{L_{e}}+0.15\right)+10\\ &=\,\,1.3\,\tan^{-1}\,\left(2.5\,\mathrm{D_{n}}/\mathrm{L_{n}}+0.15\right)+10 \end{array} \tag{2}\]  
where \(\mathrm{D_{e}}\) is the equivalent rotor diameter. \(\mathrm{l_{h}}\) and \(\mathrm{l_{w}}\) are the respective height and width of the obstacle. \(\mathrm{D_{n}}\) is the rotor diameter of a nearby wind turbine. \(\mathrm{L_{e}}\) and \(\mathrm{L_{n}}\) are the distance from the neighbouring wind turbine and obstacle, respectively.  
The calculated measurement sectors were from 223\({}^{\circ}\) to 347\({}^{\circ}\) for wind turbine no. 1 and from 333\({}^{\circ}\) to 97\({}^{\circ}\) for wind turbine no. 15, as shown in Figure 1. The 10-min average wind conditions for one year from 1 January 2017 to 31 December 2017 were measured by the nacelle LIDAR, the met mast, and the nacelle anemometers on the wind turbines, and these were analysed in this work.
# A Novel Optical-Based Methodology for Improving Nonlinear Fourier Transform
## 2 Scattering Coefficient for Nonlinear Fourier Transform
#### Numerical Method for the Continuous Scattering Coefficient
In particular, we performed it for the case when \(t=T+\frac{\Delta t}{2}\). Therefore, we can move to the second step of our algorithm, starting from this value of \(t\). Step 2. Assume that the conditions (9) and (10) are satisfied for \(|t|\geq T\), given that \(u(t,0)=0\) for \(|t|\geq T\). The operator \(\mathcal{L}\) (depending on \(u(t,0)\)) will assume its asymptotic configuration as:  
\[\mathcal{L}_{\infty}=i \begin{bmatrix}\partial_{t}&0\\ 0&-\partial_{t}\end{bmatrix}  \tag{33}\]  
and so for every \(t>T\), the following relation should hold:  
\[\phi(-t,\lambda)= \begin{pmatrix}1\\ 0\end{pmatrix} e^{i\lambda t} \tag{34}\]For \(t=T+\frac{M}{2}\), we can rewrite Equation (34) as:  
\[\phi\bigg{(}-T-\frac{\Delta t}{2},\lambda\bigg{)}=\binom{1}{0}e^{i\lambda(T+\frac {\Delta t}{2})} \tag{35}\]  
Step 3. Given that: \(\phi(t,\lambda)=b(\lambda)\varphi(t,\lambda)+a(\lambda)\overline{\varphi}(t,\lambda)\), we can substitute Equation (10) into it, to find the following relation for the same value of \(t\):  
\[\phi\bigg{(}T+\frac{\Delta t}{2},\lambda\bigg{)}=b(\lambda)\binom{0}{1}e^{i \lambda\left(T+\frac{\Delta t}{2}\right)}+a(\lambda)\binom{1}{0}e^{-i\lambda \left(T+\frac{\Delta t}{2}\right)} \tag{36}\]  
Use Equations (27), (31), (35) and (36) to find an expression for \(\phi(t,\lambda)\). Using simple algebraic steps, we can have a numerical approximation of both the continuous scattering data \(a(\lambda)\) and \(b(\lambda)\) as:  
\[a(\lambda)=S_{11}e^{2i\lambda\left(T+\frac{\Delta t}{2}\right)}=\left(\prod_{ i=M}^{-M}U(u_{i})\right)_{11}e^{2i\lambda\left(T+\frac{\Delta t}{2}\right)} \tag{37}\]  
\[b(\lambda)=S_{21}=\left(\prod_{i=M}^{-M}U(u_{i})\right)_{21} \tag{38}\]  
A summary of the main steps involved in the calculation of continuous scattering data is presented below:  
1. Determine a good approximation of \(T\) so that we can say that \(u(t,0)=0\) for \(|t|\geq T\);
2. Descretize the interval \((-T,T)\) in \(2M+1\) samples \(t_{n}=n\Delta t=n\frac{T}{M}\), with \(n=-M,-M+1,\ldots M-1,M\);
3. Find \(\prod_{i=M}^{-M}U(u_{i},\lambda)\) for the necessary \(\lambda\);
4. Use Equations (37) and (38) to find the continuous scattering data for the chosen \(\lambda\);
## 3 Technical and Operational Challenges
### Technical and Maintainability Challenges  
Low-code platforms, by design, promote rapid application assembly through visual modeling and pre-built components. However, this speed often comes at the expense of sound architectural practices, leading to the accumulation of technical debt. As [PERSON] et al. observe, \"higher productivity and reduced costs of low-code development could be achieved at the expense of quality management practices established for software engineering\" [65]. In this context, shortcuts--such as bypassing rigorous design reviews or deferring refactoring--become entrenched, mirroring [PERSON]'s original metaphor that \"shipping first-time code is like going into debt... The danger occurs when the debt is not repaid\". Without frequent audits and disciplined repayment strategies, these expedient choices incur \"interest\" in the form of escalating maintenance effort, brittle integrations, and hidden defects that can jeopardize long-term system health.  
When compared to traditional hand-coded systems, low-code solutions often exhibit lower flexibility and robustness as they scale, with direct implications for maintainability. [PERSON]'s analysis [66] highlights that, while low-code platforms \"enhance development efficiency by reducing coding time and supporting agile practices, they may limit flexibility and robustness in large-scale implementations, posing risks to long-term quality and maintainability\". Similarly, a recent head-to-head benchmarking study finds that, although low-code environments enforce consistent design patterns--thereby reducing entropy--they impose \"vendor lock-in and limited customization,\" making core behavior modifications cumbersome and driving teams back to traditional coding to address complex requirements [67]. As a result, organizations must weigh the short-term gains in speed against potential increases in effort and cost during later phases of maintenance and evolution.  
Despite these technical and operational challenges, low-code platforms have demonstrated substantial efficiency gains and cost reductions in diverse sectors. Before exploring future trends, we highlight representative cases of such measurable impacts. We present empirical evidence showcasing the tangible benefits of low-code adoption across different organizational contexts. Table 2 summarizes real-world case studies where low-code platforms have significantly improved development efficiency, reduced costs, and enabled broader participation from non-technical staff. These cases span multiple sectors--including education, automotive, public services, and government IT--demonstrating the versatility and economic potential of low-code solutions. The metrics provided serve as practical benchmarks for organizations evaluating the return on investment (ROI) of low-code initiatives.
# An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges
## 5 Experimental Results and Analysis
### Comparative Experiments  
To verify its performance, the proposed model was compared with traditional shallow NNs like the backpropagation neural network(BPNN) and support vector machine (SVM) and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges.  
With relatively few layers, the shallow NNs are good at solving simple problems. However, the functions of the shallow NNs do not have enough expression ability to tackle complex real-world problems. By contrast, deep CNNs provide excellent tools for complex problems, but their robustness varies from problem to problem.  
Therefore, the above contrastive models and our model were trained and tested at the same ratio between training and test samples. All the models were tested under the framework of Matlab2018a. The results of these models are compared in Table 1.  
As shown in Table 1, deep NNs clearly outperformed shallow ones in the detection of surface defects on cement concrete bridges. Among the deep NNs, our model achieved better detection results than AlexNet, GoogLeNet, and ResNet. Hence, our model is highly suitable and robust for detecting the surface defects on cement concrete bridges.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 6 Smart Contract Specification
### Main Smart Contract Functions
#### 6.2.3 Publish Function  
Once the sender receives the acceptance event from the recipient, they must provide the decryption key to the recipient. To accomplish this, the sender calls the _publish_ function (see Listing 5), passing as parameters the delivery identifier (_id_) and the cryptographic data required for recovering the decryption key (_k_). The smart contract employs the _onlySender_ modifier, which verifies that the blockchain address used to sign the function call matches the address provided by the recipient. Additionally, the contract checks that the status of the delivery associated with the provided identifier is accepted (modifier \(validStatus\)) and that the deadline has not yet expired (modifier \(deadline\)).  
Finally, the function emits an event indicating that \(A\) has published the decryption key data and provides these data.  
```
1 functionpublish(bytes32_id,bytesmemory_k)
2 onlySender(_id)
3 validStatus(_id,State.accepted)
4 deadline(docDelivery[_id].td)
5 public{
6 docDelivery[_id].status=State.delivered;
7 emitResult(msg.sender,_id,uint(State.delivered),_k);
8}
```  
Listing 5: Fair delivery of documents: _publish_ function
###### Abstract  
To recommend the excavation procedures and design parameters for underground powerhouses, excavation procedures of fifty-one underground powerhouses in China were summarized and analyzed based on in situ stress conditions. Firstly, the complex stress environment in China was introduced and fifty-one underground powerhouses with their engineering scale, size, lithology, rock classification and in situ stress level were listed in detail. Subsequently, to evaluate the influence of in situ stress levels on excavation procedure design, the correlation between excavation procedures and in situ stress level in three main excavation zones were analyzed accordingly. Moreover, to provide the excavation design recommendations, the strength-stress ratio (SSR) was promoted to analyze and recommend the design parameters, and the blasting excavation design based on the stress transient unloading control was also supplemented. The results show that excavation procedures have different priorities under different in situ stress levels, and the design parameters show an obvious relationship with in situ stress levels. Moreover, the excavation procedure parameters are suggested to adjust accordingly under different SSR. The discussion of influencing factors and specification ensures its rationality and accuracy. It is believed that the summary and recommendations can provide a good reference for excavation procedure optimization of underground powerhouse under high in situ stress.  
underground powerhouse; excavation procedure; high in situ stress; strength-stress ratio +
Footnote †: journal: Applied Sciences
# Identification of Tire Model Parameters with Artificial Neural Networks
## 3 Methodology and Results
### Ann Validation vs. Real Test Data
The results are shown in Figure 13 and Table 3.  
Figure 12: Comparison of real bench data point vs. predicted and optimized curves. From the observation of Figure 13, it is noticed that there is a very good match for \(B\), \(D\)-\(F_{z}\) and \(f_{x}\) parameters. \(C\) and \(E\) also show a high similarity in general, but some isolated predictions with higher differences were also observed. It seems that the most difficult parameter to predict in this case corresponds to \(f_{y}\). The ANN, for an error prediction range below 5%, is able to identify correctly more than 85% of the test curves for the parameters \(B\), \(C\), \(E\), \(D\)-\(F_{z}\) and \(f_{x}\). The parameter \(f_{y}\) identifies in that range up to 42.86% of the curves. If the prediction error range is allowed in 10%, then the \(f_{y}\) parameter identifies correctly 81% of the curves. On the other hand, the parameter \(D\)-\(F_{z}\) is predicted adequately in 100% of cases. Therefore, it can be concluded that prediction of the real parameters of the real test bench curves is, for most of the parameters, quite good, as the success percentage is over 85% of the cases. It should \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Error Range** & **B (\%)** & **\(C\) (\%)** & **\(E\) (\%)** & **\(D^{*}F_{z}\) (\%)** & \(f_{y}\) (\%)** & \(f_{x}\) (\%)** \\ \hline \(<\)5\% & 85.71 & 90.48 & 85.71 & 100 & 42.86 & 90.48 \\ \hline
5–10\% & 9.52 & 0 & 9.52 & 0 & 38.10 & 9.52 \\ \hline
10–15\% & 4.76 & 4.76 & 0 & 0 & 9.52 & 0 \\ \hline
15–20\% & 0 & 4.76 & 4.76 & 0 & 4.76 & 0 \\ \hline \(>\)20\% & 0 & 0 & 0 & 0 & 4.76 & 0 \\ \hline \end{tabular}
\end{table} 
Table 3: Range of values for MF model parameters. Figure 13: Comparison of real bench data point vs predicted and optimized curves. be noted however that the \(f_{y}\) parameter can only be predicted within the 5% range only in 42.86% of the cases. Besides, it should be noted that in all the 21 real cases it was possible to carry out the optimum adjustment of the curves using as initial iteration parameters the ones provided by the ANN prediction (Step 10). Therefore, it seems clear that the results predicted by the ANN, despite errors in the input data, can be used, at least as initial iteration parameters for finer posterior curve fitting if needed.
###### Abstract
In this model, the uncertainty model of day-ahead price demand response virtual units and intraday IDR virtual units are established, and the operation cost and slope climbing cost of thermal power units in deep peak regulation are concerned. Considering the wind and light output fluctuation on the power generation side and the uncertainty of demand response on the load side, a low-carbon economy optimization scheduling model is set for the multi-energy system, which is calculated by the improved bat algorithm and two-point estimation method. The effectiveness and rationality of this two-stage optimal scheduling method are analyzed by the example results. The main contributions of this paper are as follows:  
(1) Under the background of \"three types two nets\" strategy and low-carbon electricity, large-scale renewable energy is integrated into the power grid and the peak-valley difference of system load increases. In a multi-energy scheduling system of wind-solar-thermal plants and BESS, the unit output adjustment can be reduced by making full use of many complementary energy features on multiple time scale, and it is beneficial to realize the optimal low-carbon economic dispatch. (2) Considering the source-load interaction in multiple time scales, the user-side demand response is divided into the day-ahead price-type response virtual unit and intraday incentive response virtual unit. In addition, they are expressed with triangular membership functions and uniform distribution. The fluctuation of wind and solar on the power generation side is described by a probability density function. Then the influence of generation-load prediction error on a multi-energy collaborative scheduling system can be analyzed, and the optimal scheduling plan can be determined. (3) Due to the high proportion of renewable energy integrated into the grid, the volatility and intermittency of new energy sources increase the peak load regulation pressure of the power system. To keep the system running safely and stably, thermal power units frequently climb hills and are often in deep peak regulation state, which will increase coal consumption. These effects on the optimal scheduling model cannot be ignored. (4) The genetic algorithm is introduced into bat algorithm to make bats possess the genetic characteristics of the genetic algorithm so that the solution accuracy and global search ability of the algorithm are improved, and this new bat algorithm is applied to solve the optimal scheduling plan in the wind-PV-thermal-storage joint scheduling system. The remaining of this paper is organized as follows: Section 2 describes the scheduling mode of the wind-PV-thermal-storage system when demand response is considered. Section 3 describes the uncertainty model of generation and load. Section 4 describes a two-stage optimal scheduling model for the wind-PV-thermal-storage system and its solutions. Section 5 describes a specific example study and the analysis of the results. Conclusions are drawn in Section 6.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
###### Abstract
Panel-level monitoring can be instrumental in assessing efficiency, productivity, and profitability of solar installation as a whole. This can also help in the timely replacement of under-performing modules to keep the energy output and profit margins above a threshold. For example, as reported in [10], an investment of only USD 30 in an IoT-based monitoring system for a 4 KW solar plant for timely solar panel cleaning resulted in a return of USD 1200 per year. A typical IoT-based solar monitoring system comprises numerous interacting, geographically spread nodes that sense operational (meteorological and electro-physical) parameters of a solar plant. These sensed data are then transmitted wirelessly to servers (located locally or on a cloud platform) either periodically or upon the occurrence of a certain event, allowing stockholders to perform predictive diagnostics and efficient management of solar plants. It is therefore imperative to devise and formulate cost-efficient, reliable, and scalable IoT architectural paradigms. In this paper, we present a review and comparative analysis of different IoT architectural paradigms which are employed for solar plant monitoring in the existing literature. Underlying technologies such as compute boards, sensors, communication, and cloud platforms employed have been analyzed. Two commonly employed IoT architectures (centralized and distributed computing) are explored and their associated challenges are presented in detail. This work highlights the shortcomings of current approaches and provides guidelines for further efficiency enhancements. This enhancement is proposed through the recommendation of employing an edge computing paradigm that is implemented through commonly used, readily available, cheaper, and open-source hardware and software. The proposed solution is IEC61724 standard-compliant with the ability to monitor performance, fault detection, and reporting at the module level. This solution is suitable for both small- and large-scale solar plant monitoring, with the added advantages of low cost, reliability, scalability, and security. The overall architecture of this review paper is based on comparative analysis and proposed methods in the literature, IoT architectural paradigms, primary technologies, active architectures, and AI-based techniques, as shown in Figure 1.  
The rest of the paper is organized as follows. Section 2 details performance parameters affecting solar plant efficiency. In Section 3, the related literature has been explored. Section 4 presents a novel cloud-based architecture, leveraged by the introduction of an edge computing paradigm, with recommendations on the most ideal practices (hardware, software, and communication technologies) for the deployment of a smart solar plant. In Section 5, we presented different approaches and techniques for monitoring, analysis, and management of solar PV systems from the perspective of artificial intelligence (AI). Section 7 draws the conclusions.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 2 Diabetic Retinopathy
### Role of FA in Diabetic Retinopathy  
FA has been the imaging modality of choice for diagnosis and monitoring in DR. Investigators from the Early Treatment Diabetic Retinopathy Study (ETDRS) have historically classified diabetic retinopathy using FA [22; 23]. Their protocol consists of stereoscopic FA of two 30-degree fields extending horizontally from 25 degrees nasal to the disc to 20 degrees temporal to the macula. The foveal avascular zone (FAZ), capillary loss, capillary dilatation, arteriolar, and retinal pigment epithelium abnormalities are assessed in the early-mid phase of the FA and fluorescein leakage, source of leakage and cystoid changes are graded in the late phase [23]. These FA findings are also compared to standard reference photographs [23].  
Recent improvements in FA imaging include ultra-wide-field imaging and video angiography. However as mentioned above, FA is time-consuming and has drawbacks. In addition, FA is not depth-resolved and hence is unable to differentiate the superficial, intermediate, and deep capillary plexi of the retina well [4; 12].
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 3 Models and Assumptions
### Network Model  
The network model of UWSNs is illustrated in Figure 2. Several sensor nodes are clustered and distributed randomly in the underwater environment. These sensor nodes are anchored to the ocean bottom and construct a quasi-stationary architecture [26]. Acoustic communications are used for sensing and forwarding data to the sink node. Nodes far away from the sink node can use multi-hop communication to transmit data. The sink node, usually located in the center among these sensor nodes, collects data from those sensor nodes via horizontal acoustic links. Then data are stored and further forwarded to the shore-based stations or moving platforms by vertical acoustic links. We assume that the packets sent by the sensor nodes are delivered successfully if they reach the sink node.  
Figure 2: Network model of underwater sensor networks (UWSNs).
## 5 Performance Evaluation
### Simulation Results
#### 5.2.6 Changes in Duty Cycle vs. Number of Forwarders  
The number of forwarders per packet transmission with increasing duty cycles at different node densities is shown in Figure 12. As the duty cycle increased, the number of forwarders also increased. Accordingly, the probability of multiple forwarder nodes simultaneously waking up also increased. As expected, the number of forwarders increased with an increase in density. At a lower duty cycle, the ORW and ORR protocols performed better. However, as the duty cycle increased, FCM-OR outperformed the existing protocols, and the difference between the performances of the protocols became evident.
## 2 Related Work  
Unlike land-based traffic, maritime traffic enjoys greater freedom due to the vast expanse of water. However, it is not entirely unstructured, especially for Class A AIS vessels, which are typically engaged in transportation. When designing routes, navigators refer to various nautical resources such as world ocean routes, route design charts, routing guides, and port guides [9]. They also consider geographical features and navigation rules for specific areas. Different vessels may choose different routes based on these factors, and there are certain patterns to these choices. Learning vessel behavior within fairways is beneficial not only for predicting maritime traffic but also for visualizing maritime traffic, detecting anomalies, providing collision warnings, designing routes, and supporting other decision-making systems.