esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 2 Segmented Telescope Imaging Theory
### Derivation of WFE from Sub-Mirror Position Error
#### 2.2.3 Influence of Tip & Tilt Error Around X and Y Axis on Phase \(\phi_{\rm j}\)  
The X and Y axis of the sub-mirror intersect at the center point of the sub-mirror, \(\left(x_{\rm j},y_{\rm j}\right)\), and the center point of the sub-mirror remains unchanged when the tilt error occurs. When the sub-mirror is tilted around the X-axis \(\alpha\), around the Y-axis \(\beta\), the WFE caused by the tilt of the sub-mirror can be expressed as if only the first-order term is retained:  
\[\phi_{\rm j-Tip\&Tilt}=\frac{2\pi}{\lambda}\left[\left(y-y_{\rm j}\right) \sin\alpha+\left(x-x_{\rm j}\right)\sin\beta\right] \tag{8}\]  
From the above equation, it can be seen that the WFE formed by tilting around the X and Y axis are independent of each other, and this error is directly reflected in the wavefront tilt.  
Figure 2: Schematic diagram of sub-mirror splicing error.
## 2 Methods
### ANC Test Pre-Evaluation  
To evaluate what correspondence between defined feature and population group type arises, the built test was run in the following cases: Dataset 1 and Dataset 2 to check the results against BP groups and Dataset 1 and Dataset 3 to check against age groups. The splitting granularity of both groups was chosen to be 10 units. The overall results are presented into Tables 3 and 4 for first group's type and into Tables 5 and 6 for the second one. The test was run in a multiple cardiac cycles evaluation mode where a patient takes the predominant ANC type where the occurrence threshold rate was chosen at 50%. For example, if within a recording length for A type the occurrence was around 60%, the patient would have the respective label. For the post-systolic evaluation, the occurrence sum of N, NC, and C cannot exceed 100%. Therefore, the predominant one was taken as the final label.  
It can be observed from Tables 3 and 4 that A type occurs more frequently in prehyper and hypertensive class (130+ mmHg) with 75.36% probability in Dataset 1 and with 53.18% in Dataset 2. For N type which indicates a visible DCW in analyzed signal according to our classification, in prehypertensive and hypertensive class it occurs with a probability of 39.63% in Dataset 1 and 37.10% in Dataset 2. The class with a low presence in both datasets is C type which does not have a visible trend against BP sorting criteria. The class with a high occurrence rate is NC type which represents a barely visible DCW in the analyzed signal. It appears to be more frequent in Dataset 1 than in Dataset 2 for the same BP ranges. At this point, A and N types represent promising key features to distinguish between normotensive patients and hypertensive ones.  
By evaluating the ANC type against age sorting criteria, different trends can be observed especially for C type which lacks against BP assessment. Taking both Tables 5 and 6 into consideration for a large age spectrum, it can be observed that this type occurs frequently in children under 10 years old and again in people over 40. Thus, C type does not have a linear trend by the current sorting criteria. For A type, the occurrence happens in an older population with a slightly positive trend. Again, NC type is the predominant one among age spectrums in both datasets. The other type, N, appears only in young persons as it is highlighted in Table 5 within the 20-29 age group.  
With these two sorting criteria, BP and age group, the summary of ANC type allocation among the population is presented. The scope is to avoid biases, like the belief that N type occurs only in a young population. However, as it was shown, this parameter is not dependent exclusively on the age since it is also distributed among various systolic blood pressure values.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 2 System Architectures  
The general optical configuration of self-interference digital holography is shown in Figure 1. The light emitted from each object point is collected by a beam splitting system, in which the input wave is split into two, or more, and each wave is modulated differently. Since the waves originate from the same object point, they are mutually coherent, and hence they can produce an interference pattern on the image sensor plane. The sensor accumulates the entire interference patterns of all the object points into an incoherent hologram. A single hologram, or several acquired holograms, are fed into a digital computer. In the case of several holograms, they are superposed into a single digital hologram. Finally, the image of the object is reconstructed from the processed hologram by some digital algorithm. Next, we survey several recently proposed systems of the family of COACH methods.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 2 Related Work  
### KPI Time Series Anomaly Detection Method  
With the development of artificial intelligence technology, some artificial intelligence applications, such as intelligent operation and maintenance [1; 2; 3; 5] and intelligent transportation [10], have attracted researchers' attention. Time series anomaly detection for KPIs is a research hotspot in the field of intelligent operation and maintenance. At present, the most popular methods of time series anomaly detection for KPIs are mainly based on traditional statistics and machine learning models. The earliest KPI time series data anomaly detection methods are based on traditional statistics, and the main applications are anomaly detection based on ARIMA [11] and anomaly detection based on a Gaussian Mixture Model [12]. However, the statistical method is based on abnormal detection with certain assumptions. Although it has a degree of robustness, it is too dependent on assumptions. With the continuous development of machine learning, the KPI time series data anomaly detection method based on machine learning has increasing applicability. For example, EGADS [3] proposed by Yahoo and Opprentice [4] proposed by Tsinghua Laboratory both require a large number of anomaly label training models to achieve anomaly detection in multiple types of KPIs, but also require a large number of labels that are in line with the actual operation and maintenance situation. [PERSON] et al. [13] proposed the ROCKA method, which is based on a clustering algorithm. Although this method does not rely on a large number of labels for training, it has high computational complexity, and is too dependent on the model.  
With the continuous development of deep learning, many experts and scholars choose to use deep learning models such as RNN (Recurrent Neural Networks) and LSTM (Long Short-Term Memory) for anomaly detection [14; 15]. For example, [PERSON] et al. [16] used LSTM for anomaly detection in spatial telemetry time series data. LSTM alleviates the problem of error accumulation found in RNN, but LSTM itself needs a lot of training memory in the case of long input sequences [17]. To solve these problems, some methods [18; 19] use a Convolutional Neural Network (CNN) to replace, or work in combination with, RNN, LSTM, and other models to analyze time series data. For example, [PERSON] et al. [19] used SR and CNN for anomaly detection in time series data, but CNN could not extract the dependence between the time series. Then, [PERSON] et al. [17] proposed a Temporal Convolutional Network (TCN) model for processing temporal data, which reduces the error accumulation via extracting temporal features. [PERSON] et al. [20] used CPA-TCN to detect anomalies in time series data, which overcame the gradient explosion caused by the presence of insufficient memory in deep learning models such as RNN, and obtained dependence between time series.
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 4 Results and Discussion  
### Land Uses  
The area of each land use and its percentage proportion in the study area was estimated and illustrated in Table 2. As shown in Table 2, agricultural land use covers a significant part of the area studied. Human activities have strongly influenced the land uses of the study area. Thus, the urban and agricultural uses cover up to 92% of the total area.  
The smooth morphology supports urban development in the study area. The Ioannina city's airport is a mid-sized airport with an annual mean of 2219 flights; during 1994-2016, there were 48,732 flights [49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cd** & **Co** \\ \hline Detection limit & 0.2 & 0.3 \\ \hline \multicolumn{3}{c}{Agricultural land use (\(n=102\))} \\ \hline Mean & 1.7 & 30.8 \\ Median & 1.5 & 23.5 \\ Minimum & 0.3 & 4.0 \\ Maximum & 5.1 & 90.0 \\ \hline \multicolumn{3}{c}{Urban land use (\(n=8\))} \\ \hline Mean & 2.0 & 37.1 \\ Median & 1.7 & 35.5 \\ Minimum & 0.3 & 7.0 \\ Maximum & 3.6 & 77.0 \\ \hline \multicolumn{3}{c}{Wetlands (\(n=2\))} \\ \hline Minimum & 3.2 & 33.0 \\ Maximum & 3.7 & 35.0 \\ \hline European topsoil [58] & 0.145 & 7.0 \\ EPA Residential soil [59] & 7.1 & 2.3 \\ EPA Plant-Avian-Mammalian [59] & 0.00222 & 0.14 \\ CCME Agricultural land use [60] & 1.4 & 40 \\ CCME Residential land use [60] & 10 & 50 \\ DEC Ecological Investigation level [61] & 3 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection limits, descriptive statistics of Cd and Co concentration (in mg kg\({}^{-1}\)) in soil gathered from the Ioannina plain, and criteria obtained from the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Land Uses** & **Area (km\({}^{2}\))** & **Area (\%)** \\ \hline Urban areas & 14.8 & 15 \\ Agricultural areas & 77.8 & 77 \\ Shrub and sparsely vegetation areas & 6.6 & 6 \\ Wetlands & 1.6 & 2 \\ Total & 100.8 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The land uses of the study area, their area in km\({}^{2}\) and percentage proportion (%).
## 4 Summary and Conclusions  
The comparative analysis of attosecond pulse generation techniques have shown that single pass the SASE FEL scheme is suitable to produce a 0.1 nm wavelength X-ray pulse with a radiation power of up to 100 GW. The introduction of the energy modulation by a 1200 nm laser before an undulator is found to be beneficial. In our study, we employed an enhanced SASE scheme for the electron bunch parameters of the PAL-XFEL. We optimized laser, chicane and electron beam parameters to generate an isolated attosecond hard X-ray pulse. The optimization shows that 1200 nm and 7.5 fs FWHM seems to be the best laser parameters for achieving a single isolated radiation pulse inside the undulator for a given 10 GeV electron bunch of PAL-XFEL. It is shown in simulation that by changing the shape of the energy distribution of the electron bunch, a single current spike can be produced in the current profile. This single current spike profile of the electron bunch gives a single isolated attosecond pulse through the undulator. Using the PAL-XFEL parameters, we have shown that an isolated 146 attoseconds, 58 GW peak-power X-ray pulse at 0.1 nm is expected to be generated in a 34 meter-long undulator for a driving laser of 1200 nm, 7.5 fs FWHM and 0.2 mJ. This isolated attosecond hard X-ray pulse will be an excellent tool to realize electron diffraction with attosecond temporal resolution.  
Figure 11: (Color online). The GENESIS simulation for radiation power at 0.1 nm, using the three electron bunches shown in Figures 8–10. (**a**) The radiation power from the main peak and two side peaks along the undulator for 6 kA case. (**b**) The radiation pulse at z = 34 meters for the 6 kA, 12 μm-long bunch. (**c**) The radiation pulse at z = 73 meters for the 3 kA, 20 μm-long bunch. (**d**) The radiation pulse at z = 55 meters for the 4 kA, 16 μm-long bunch (here, 1 μm \(\approx\) 3.3 fs in time scale) [42].
## 2 Numerical Simulations and Wind Tunnel Tests
### Grid Independence Verification  
To verify the effect of grid size on the results of the hill-based impinging jet model, this section uses the REA model as an example for grid size verification. On the medium grid of REA, two sets of grids with different sizes were recreated: a coarse grid with a first-level height of 0.005 m and a total of 3 million grids, and a fine grid with a first-level height of 0.0005 m and a total of 8.8 million grids. Figure 4 shows the near-ground comparison of different grid sizes.  
Figure 5a depicts the vertical wind profile of horizontal velocity at various measurement points, using three distinct grid numbers. The figure reveals a high degree of conformity in wind velocity at each position under the three grid numbers. Notably, in the near-ground portions of the hillside on the windward side, findings derived from the fine and medium grids were in agreement, but those of the coarse grid deviated from those of the medium grid. Figure 5b compares the error analysis of the coarse and fine grids with that of the medium grid. Both the coarse and medium grid computations exhibited errors, notably when the wind velocity was below 4 m/s. Error rates exceeding 5% were observed at certain measurement points. On the other hand, the fine grid yielded results aligned with those of the medium grid. Similar validation outcomes were obtained using other turbulence models. Based on these results, selecting the medium grid for numerical simulation in subsequent working conditions ensures accuracy, improved computational efficiency, and reduced computational costs.  
Figure 4: (a) Coarse grid; (b) medium grid; (c) fine grid.
## 5 Discussion
### Limited Bandwidth  
Effectively managing bandwidth is essential to optimise the performance and energy efficiency of IoT networks [48]. Bandwidth limitations are common in IoT environments, as these systems often rely on low-power, low-bandwidth communication protocols designed to conserve energy and extend device lifetimes. However, limited bandwidth can restrict the data transmission rates, impacting the ability of IoT networks to support high-throughput applications and affecting the overall network efficiency.  
Effective bandwidth management is essential to prevent bottlenecks that can hinder data flow, cause packet loss, and increase retransmissions--all of which lead to higher power consumption and slower processing speeds. In typical IoT networks, the available bandwidth is around 200 kbit/s, significantly lower than traditional network standards. This constraint means that even moderate data traffic can lead to congestion and degrade network performance if not managed effectively. To accommodate this limitation, IoT systems must prioritise bandwidth optimisation techniques to ensure smooth communication, particularly in dense or high-traffic environments.
# Drone Arc Routing Problems and Metaheuristic Solution Approach
## 5 Computational Results
### Computational Results for CARP
As a result, it was found that the proposed algorithm obtained the same results as the HMA for small-sized problems, approximately 0.9% away from the HMA for medium-sized problems, and approximately 3% away from the HMA for large-sized problems on average. Therefore, it is clear that the proposed algorithm performs very close to the algorithms that provide the optimal/the best known results for the CARP, demonstrating that it is a highly competitive algorithm. In addition, high quality solution was obtained for the Hefei-4 test problem with a total of 1212 edges, 485 required edges, and 727 non-required edges in about 2.5 h with a deviation of 4% from the best known result provided by [PERSON] et al. [41]. Therefore, when comparing the proposed solution approach with the UHGS algorithm, used by [PERSON] et al. [41] and proposed by [PERSON] [42], for the Hefei-4 problem, it is seen that the result obtained with the proposed approach is only 4% away from the best known result. This test problem is quite large-sized, and problem-specific heuristic algorithms are usually used to solve such challenging problems in the literature. For this reason, the suggested algorithm is successful and has the ability to reach high-quality solutions. The performances of neighbor search operators were analyzed using the bmcv-d18 test instance, which is the large-sized test problem for which the optimal solution was obtained. As a result of the analysis, it is seen that the proposed algorithm improves the initial solution value 63 times in total and reaches the optimal solution. When the performances of the neighbor search operators were examined, \(k\)-route shorten improved the current solution 20 times, edge postpone 17 times, and edge direction and smart edge direction change 11 times, respectively. Therefore, it is seen that \(k\)-route shorten, edge postpone, edge direction, and smart edge direction change operators have high performance and make a great contribution to reaching high-quality solutions to the CARP.
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 2 Related Studies
### Hierarchical Routing  
A large number of routing protocols have been proposed in WSN because of their inherent characteristics, as well as their application and architectural requirements. These routing protocols can be categorized based on the network structure, protocol operation, and path base. The selection of an appropriate hierarchical routing algorithm should consider various factors like network size, topology, traffic patterns, and energy constraints. These are essential factors that need to be explicitly considered while choosing an algorithm for any application in WSN.  
Figure 3 displays the routing protocol for the WSN and in above diagram the hierarchical routing algorithm in WSNs is essential for reducing energy consumption and improving the transmission efficiency of large-scale deployments as well. Hierarchical algorithms arrange the nodes in a hierarchy and then partition the network into clusters or partitions. In the cluster, most packets are forwarded through direct communication with CH or trusted intermediate nodes if it is not possible to communicate directly. Inter-cluster Communication means communication between clusters. When communicating outside the cluster, CH relays packets to other local CHs or a base station. This will allow routing decisions based on Trust to firmly find the paths for both trustworthiness and energy efficiency. Nodes whose trust levels fall below the minimum Threshold due to malicious actions or inconsistent behavior can be isolated from critical network operations or removed from the network to maintain overall security and efficiency. By systematically managing Trust and continuously monitoring the behavior of both existing and new cluster members, a WSN can effectively safeguard against malicious nodes (anomaly) and ensure reliable and secure network operation. Here are a few hierarchical routing algorithms that can be found in virtually all WSNs.  
LEACH [29] (Low-Energy Adaptive Clustering Hierarchy) is a system in which nodes select the CH based on the probability threshold. Therefore, CHs aggregate data from their member nodes and transmit this to a sink node. LEACH is scalable and very easy to deploy, but it may need more energy consumption across nodes, especially near the sink. Power Efficient Gathering in Sensor Information Systems (PEGASIS) [30] is a token-passing mechanism in which each sensor node receives from and transmits to the nearest sensor node, and CH, having a token, will transmit it to BS; by doing so, it conserves energy. The token is rotated to make sure all sensor nodes participate in routing. PEGASIS is sensitive to node failure, especially CH. In this protocol, nodes closer to the sink node may consume more energy due to a more significant increase in transmission. Ad-hoc On-Demand Distance Vector Routing (AODV) [31] is not required in this global occurrence of advertising at specific intervals. Overall bandwidth is less required by each sensor node in this routing protocol. Apart from these, researchers have proposed many more routing algorithms.
## 7 Conclusions  
Reduction of intra-channel nonlinearities in low-cost direct-detected optical Fast-OFDM was numerically demonstrated, for the first time, using a low-complex version of an ANN nonlinear equalizer. In contrast to conventional ANN designs for coherent optical OFDM that employs two real-valued activation functions (\"split\" complex function) to process in-phase and quadrature components separately, here, only amplitude data were processed, thus significantly reducing the computational complexity of the equalizer (e.g., far less neurons were adopted). ANN-NLE proved to be a robust nonlinearity DSP technique for up to 19.37-Gb/s 16-ASKdirect-detected optical Fast-OFDM signals. It was shown that an 80 km extension in transmission-reach was feasible when using ANN compared to the benchmark Volterra-based NLE for 9.69-Gb/s 4-ASK modulated Fast-OFDM sub-carriers. This occurred because ANN can tackle stochastic nonlinear impairments such as parametric noise amplification. Finally, the dynamic parameters requirements of the sub-ranging quantizers were relaxed (DAC/ADC limitations) compared to linear equalization, namely the clipping ratio and quantization bits by 2 dB and 2 bits, respectively moreover 1 dB and 1 bit when compared with the IVSTF NLE performance. Regarding the computational complexity analysis, the ANN-NLE over-performs the back propagation equalizers such as the IVSTF due to the use of low complexity neurons where the IVSTF uses a large number of FFT/IFFT blocks as a part of the equalization process.  
Finally, it is worth mentioning that the proposed nonlinear equalizer could be implemented in both state-art-of-the-art optical modems and future super-high-speed optical communication systems, supporting \(>\) 40-Gb/s per wavelength. More importantly, due to the low complexity of both Fast-OFDM based DSP and ANN-NLE, the proposed hybrid solution should be more practical for real-time signal processing than benchmark approaches (e.g., conventional optical OFDM and IVSTF or DBP).  
Figure 8: Computational complexity comparison between ANN-NLE and IVSTF-NLE versus the number of sub carriers, whereas the M-ASK modulation formats (M = 4, 8, and 16) corresponds to the ANN-NLE FLOPS and the 1 span, 10 span and 20 spans corresponds for the IVSTF-NLE FLOPS.  
**Acknowledgments:** I also thank [PERSON] from Dublin City University for his valuable contributions on the simulation setup and editing.  
**Conflicts of Interest:** The authors declare no conflict of interest.