esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 4 Structure of Power QKD Network
### QKD-Based Power Dispatching Automation  
The safe and stable operation of the power dispatching automation system needs to meet the characteristics of reliability, safety, integrity, real time and consistency, etc. It is necessary to ensure the safe transmission of instructions and improve the anti-intrusion ability of the system. Traditional power dispatching longitudinal encryption and authentication device (LEAD) has the problems of slow key update and incapable of online secure distribution [31]. Precise data encryption technology based on power QKD technology can effectively prevent threats such as tampering, theft and illegal injection during the transmission of dispatching instructions. Figure 6 shows the QKD-based dispatching automation encryption transmission architecture. In this paper, QKD device is used for key negotiation to avoid key online transmission, and the key online real-time update of LEAD is realized, which solves the shortage of online distribution and update timeliness of traditional encryption key. By adding a key acquisition module, LEAD is transformed, and the method of obtaining the key by encryption device is changed to obtain the quantum key from QKD device. It realizes data encrypted transmission based on quantum key, such as telemetry, remote signaling, remote adjustment, etc. In addition, it guarantees the safe delivery of remote adjustment and remote-control information, the secure uploading of remote monitoring and remote signaling information of dispatching substations, and thus, the secure transmission of power dispatching information.
# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 4 Discussion
### Future Research Directions  
The PM framework for aviation presents numerous opportunities for future research to enhance its effectiveness, adaptability, and scalability. As advancements in technology, data analytics, and operational practices continue to shape the aviation industry, addressing current limitations and exploring innovative approaches can further optimize PM strategies and their implementation.  
One key area for future research is the integration of advanced predictive analytics and ML. Adaptive models that use real-time data and evolving operational conditions can improve the accuracy of failure predictions. Hybrid models combining physics-based simulations with data-driven approaches may provide deeper insights into complex degradation patterns, while enhanced anomaly detection methods can identify early failure indicators. Research can also focus on improving the explainability of AI models to foster trust among stakeholders and regulatory bodies.  
Sensor technology and IoT represent another promising direction. The design and deployment of low-cost, high-precision sensors tailored to various aircraft components can expand PM capabilities. Research into creating sensor networks for continuous monitoring and addressing challenges in data fusion will ensure that disparate data streams are seamlessly integrated for analysis. This will be particularly valuable for critical systems and components requiring precise condition monitoring.  
Enhancing the scalability and adaptability of the PM framework is another critical area for future research. This includes adapting the framework for diverse fleets, including legacy aircraft with limited sensor network, and incorporating dynamic weighting mechanisms that adjust criteria importance based on changing operational priorities. Modular frameworks that can be customized for different contexts, such as commercial airlines, cargo operations, and military fleets, will ensure broader applicability.  
Economic modeling and cost-benefit analysis also require attention. Future research can refine economic models to quantify direct and indirect benefits of PM, such as improved safety, reduced downtime, and extended component lifespans. Strategies to optimize PM investments for maximum return on investment will help operators and MRO providers justify the initial costs of implementation.  
Extending the PM framework beyond aviation into industries such as railways, shipping, and manufacturing offers significant potential. Research can focus on customizing the framework to align with industry-specific criteria and operational requirements while evaluating the transferability of aviation-based insights to these domains.  
Future research directions provide ample opportunities to enhance the PM framework and address its current challenges. They will contribute to more efficient, reliable, and sustainable maintenance practices, ensuring long-term operational and economic benefits for aviation and beyond.
## 3 Experimental Setup
### Wind Environment Simulation  
The wind tunnel test was carried out in an atmospheric boundary layer (ABL) wind tunnel, name as ZD-1, in Zhejiang University. ZD-1 has a testing segment with a size of \(4\times 3\times 18\) m, and a testing wind speed ranges from 3 m/s to 55 m/s. To simulate the ABL wind, several triangle spires are settled at the inflow entrance for forming vortex. In addition, rows of roughness elements are placed on the ground of the wind tunnel to simulate the turbulence induced by the terrain. An ABL wind flow that matches the characteristics of a typhoon was simulated. The power-law exponent of 0.14 was specified, and the corresponding wind profile is in a form of  
\[v=v_{10}\big{(}\frac{z}{10}\big{)}^{0.14} \tag{9}\]  
where \(v_{10}\) is the mean wind speed at the height of 10 m, and \(z\) is the height. The terrain category B in the code of China, namely \"Load Code for the Design of Building\" (GB 5009-2012) [40], was chosen for the turbulence intensity setup. For a typhoon on terrain category B, the turbulence intensity is amplified by the factor of 1.48 according to [PERSON]'s recommendation [41]. That is to say  
\[I_{T}=1.48I=0.207\Big{(}\frac{z}{10}\Big{)}^{-0.15} \tag{10}\]  
where \(I\) is the standard turbulence intensity profile for terrain category B.  
The measured wind speed profile, turbulence profile and wind power spectrum are shown in Figure 12, and they agree well with the theoretical results. In Figure 12b, Kaimal spectrum is used to generate the theoretical results [42], and has a form of  
\[S_{v}(z,n)=\frac{100z\sigma_{v}^{2}}{3v\big{(}1+50\frac{nz}{v}\big{)}^{5/3}} \tag{11}\]  
where \(n\) is the frequency, and \(\sigma_{v}^{2}\) is the variance of the fluctuating wind speed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale Factor** & **Value** \\ \hline Geometry C\({}_{\text{L}}\) & 1:30 \\ Density C\({}_{\text{PS}}\) & 2.05:1 \\ Mass C\({}_{\text{M}}\) & 1:13185 \\ Lateral bending rigidity & 1:5.8 \(\times 10^{6}\) \\ C\({}_{\text{EI}}\) & 11.2:1 \\ Frequency C\({}_{\text{F}}\) & 2.03:1 \\ Acceleration C\({}_{\text{A}}\) & 2.03:1 \\ Wind speed C\({}_{\text{V}}\) & 1:2.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scale factors of aeroelastic tower model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Prototype Frequency (Hz)** & **Scaled Model Frequency (Hz)** \\ \hline First mode in X direction & 1.879 & 20.9 \\ First mode in Y direction & 1.898 & 21.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Natural frequencies of towers.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Datasets and Assessment Metrics
#### 2.4.2 Assessment Metrics for Super-Resolved Images
These reports outline key metrics used to objectively evaluate the quality of SR images [57], SSIM [58], LPIPS [59], and NIQE [60]. In this context, let \(\mathbf{X}\in\mathbb{R}^{H\times W\times C}\) and \(\mathbf{\hat{X}}\in\mathbb{R}^{H\times W\times C}\) denote the ground truth image and the super-resolved image, respectively. \(H\), \(W\), and \(C\) denote height, width, and the number of components, respectively. The PSNR is the most commonly used statistic to assess the quality of reconstructed images, especially in SR and image compression. It calculates how similar the original and the compressed or rebuilt image are to each other. (e.g., SR, denoising, deblocking, and deblurring). Given \(\mathbf{\hat{X}}\) and \(\mathbf{X}\), the PSNR is defined as  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{L^{2}}{\text{MSE}}\right) \tag{3}\]  
where  
\[\text{MSE}=\frac{1}{HWC}\|\mathbf{X}-\mathbf{\hat{X}}\|_{2}^{2} \tag{4}\]  
Equation (4) denotes the mean squared error (MSE) between \(\mathbf{\hat{X}}\) and \(\mathbf{X}\), and \(L\) represents the maximum pixel value (i.e., 255 for 8-bit images). It can be seen from Equation (3) that the PSNR is more concerned with the proximity between corresponding pixels in \(\mathbf{\hat{X}}\) and \(\mathbf{X}\), which results in low consistency with perceptual quality in some cases. The SSIM [57] is a full-reference objective quality metric titled the Structural Similarity Index (SSIM). The authors in [57] used it to compare the structural similarity of two images. By analyzing the contrast, brightness, and structure of the original image \(\mathbf{X}\) against the rebuilt image, it measures the quality of the image. \[\text{SSIM}=\big{[}l(\mathbf{X},\mathbf{\hat{X}})\big{]}^{\alpha}\big{[}c( \mathbf{X},\mathbf{\hat{X}})\big{]}^{\beta}\big{[}s(\mathbf{X},\mathbf{\hat{ X}})\big{]}^{\gamma} \tag{5}\]  
where  
\[l(\mathbf{X},\mathbf{\hat{X}})=\frac{2\mu_{\mathbf{X}}\mu_{\mathbf{\hat{X}  
taken from neural networks that were previously trained, this method closely resembles human vision and offers a more accurate depiction of perceived image quality. The Natural Picture Quality Evaluator, often known as NIQE [60], is a completely blind picture quality evaluation metric that does not depend on particular aberrations or human opinion. It evaluates \"quality-aware\" features taken from images utilizing a multivariate Gaussian (MVG) model. These features include parameters from asymmetric and generalized Gaussian distributions. The MVG model fitted to the assessed image and the MVG model fitted to the natural image distribution are compared, and the difference between the two is used to determine the quality of the image.
## 4 The Application of the ACCLN to Power Cable Fault  
### The Experimental Platform  
Block diagram of the data acquisition system is shown in Figure 6. The system is based on the PCI-6221 data acquisition card to complete the data acquisition. Cable fault data is acquired by a signal conditioning circuit, then, the data converts into a digital signal by the acquisition card. Furthermore, the data is processed by computer, or sent to the data to monitoring room and realized by remote monitoring and control.  
Table 1 shows 54 data, which contains training samples and test samples. There are two kinds of cable faults types and a normal condition, interphase short circuit, three phase short circuit, and normal condition, respectively. In this paper, those data are applied to test the IPSO-SVM method and the ACCLN method. The steps of IPSO-SVM method are introduced, the training results and test results are shown as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault types** & **Training samples** & **Test samples** & **Total sample number** \\ \hline interphase short circuit & 9 & 7 & 16 \\ three phase short circuit & 8 & 5 & 13 \\ normal condition & 15 & 10 & 25 \\ Total & 32 & 22 & 54 \\ \hline \end{tabular}
\end{table}
Table 1: Cable fault sample data.  
Figure 6: The experimental platform.
## 1 Introduction
The development of such laser sources will bring us closer to the realization of compact energetic proton, electron, or X-ray sources that can potentially revolutionize medical and industrial science and technology. In this paper, we describe a temporally and spatially high quality petawatt-class OPCPA/Ti:sapphire hybrid laser system (which we have named J-KAREN). The \(\sim\)10\({}^{12}\) contrast has been achieved using our J-KAREN laser system [11]. Injecting \"cleaned\" seed pulses into the OPCPA preamplifier at higher energy (microjoules) allows the pump fluence to be lowered while still reaching the same broadband output energy. This low-gain OPCPA operation reduces the parametric fluorescence, thereby enhancing the contrast. The introduction of an extra saturable absorber after the OPCPA preamplifier results in further contrast improvement [50] without significant parameter changes in the system. Moreover, an actively controlled acousto-optic programmable dispersive filter (AOPDF) is used to precompensate the spectral phase changes due to propagation, to enhance the contrast around the main pulse [51], and to precisely control dispersion to obtain the amplified pulse duration of shorter than 30 fs. High contrast of 1.4 \(\times\) 10\({}^{12}\) has been obtained at 70 TW without a plasma mirror. The uncompressed pulse energy of 28 J is achieved, indicating the potential for reaching the peak powers near 600 TW. Because state-of-the-art PW-class CPA laser facilities use Ti:sapphire amplifiers, temporal contrast is in the approximate range, 10\({}^{6}\)-10\({}^{10}\). Specifically, the use of double CPA with an OPCPA preamplifier and Ti:sapphire main amplifier leads to extremely high temporal contrast in the petawatt-class power range. An efficient beam homogenization scheme using DOEs produces a broadband amplified flat-topped output with a 70% filling factor. To the best of our knowledge, this is the first petawatt-class hybrid laser system which combines both Ti:sapphire CPA and OPCPA techniques, and the first to evaluate the filling factor values for the spatial profile for both pump and amplified broadband beams with DOEs. The performance of this high intensity laser system will enable many high field applications. We discuss applications with J-KAREN laser system in relativistic dominated regime of laser-matter interactions such as laser accelerated ions and electrons, XUV electromagnetic wave generation, hard X-ray generation, soft x-ray harmonic comb generation, radiation biology, and prospects of high field science experiments. We also describe compact, high efficiency diode-pumped laser systems such as Yb:YAG laser CPA laser and new Nd-doped ceramic laser.
# Fatigue Crack Propagation under Christmas Tree Load Pattern
## 3 Results
### CTOD Plots  
The analysis of Crack Tip Open Displacement (CTOD) plots to understand what is happening at the crack tip is very interesting. Therefore, for each loading spectrum presented in Figure 2, the CTOD curve was evaluated at the node immediately behind the crack tip (i.e., 8 \(\upmu\)m behind) for the crack length \(a\) = 15.30 mm. Figure 4a presents the evolution of the CTOD, comparing the four load patterns. The low-frequency pattern produces an open loop, with similar values of elastic and plastic CTOD, indicated by \(\Delta\delta_{\text{e}}\) and \(\Delta\delta_{\text{p}}\), respectively. The minimum load is 6 N, but the crack only opens above 20 N due to plasticity-induced crack closure.  
The CTOD curves of the Christmas Tree patterns contain a kind of knurling due to the small cycles embedded into the high cyclic loading. Since each unloading stage of the Christmas Tree (15-9) pattern is steeper in comparison with the Christmas Tree (9-3) pattern, the knurling on the CTOD curves is also steeper. All the small cycles are linear, without any signal of plastic deformation, and the slope of all of them is the same, corresponding to the elastic behavior of a crack with \(a\) = 15.30 mm. Globally, the CTOD predicted for both Christmas Tree loading spectrums is similar to the one obtained for the low-frequency pattern, which agrees with the predicted FCG rate (see Figure 3). There is only a small downward movement of the loop with the imposition of the small cycles, which means that the crack opening load is higher in the Christmas Tree (15-9) pattern.  
Figure 3: Evolution of the predicted FCG rate for the different loading spectrums.  
On the other hand, in the case of the high-frequency loading pattern, the elastic CTOD is about 16 times higher than the plastic CTOD (\(\Delta\delta_{\rm e}=0.64\) um and \(\Delta\delta_{\rm p}=0.04\) um). This explains the nearly closed CTOD loop. This small value of plastic CTOD, i.e., of small crack tip plastic deformation, leads to a lower value of the predicted FCG rate, as shown in Figure 3.  
Figure 4b details the variation in CTOD with applied force in the case of Christmas Tree load patterns. The increase in load from the local minimum (point 1) produces elastic deformation up to point 2, which corresponds to the previous local maximum load. Above point 2, the material starts suffering from plastic deformation, which increases up to the new local maximum load (point 3). The subsequent unloading (3-4) only produces elastic deformation. The plastic deformation occurs in the loading and starts after point 5, increasing up to the new local maximum (6).
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 4 Vehicular Ad-Hoc Networks (VANETs)  
VANETs are an emerging sub-class of mobile ad-hoc networks capable of spontaneous creation of a network of mobile devices/vehicles [123]. VANETs can be used for vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication [44; 124]. The main purpose of such technology is to generate security on the roads; for example, during hazardous conditions such as accidents and traffic jam the vehicles can communicate with each other and the network to share vital information [125; 126]. The main components of VANET technology are:  
1. On-board unit (OBU): It is a GPS-based tracking device embedded in every vehicle to communicate with each other and with roadside unit (RSU) [124; 126]. To retrieve the vital information, the OBU is equipped with many electronic components such as resource command processor (RCP), sensor devices and user interfaces. Its main goal is to communicate between different RSUs and OBUs via a wireless link [44].
2. Roadside Unit (RSU): RSU is a computing unit fixed at specific location on roads, parking areas and intersections [127]. Its main goal is to provide connectivity between autonomous vehicle and the infrastructure and also assists in vehicle localisation [44; 127]. It can also be used to connect vehicle with other RSUs using different network topologies [44]. They have also been powered using ambient energy sources such as solar power [128].
3. Trusted Authority (TA): It is an authority which manages the entire process for VANETs, so that only valid RSUs and vehicle OBUs can register and communicate [129]. It provides security by verifying the OBU ID and authenticates the vehicle. It also detects malicious messages or suspicious behaviour [44]. VANETs have some unique properties which are very different from other ad-hoc technologies.
4. VANETs have very low discovery latency and as a result the vehicles, even at high speeds, connect to the RSU quickly and rarely face network outage [130; 131].  
* The OBUs can move with predictable and regular path. It can help to detect the actual trajectory of the vehicle at any point of time [131]. The RSUs in VANETs can localise the vehicle and also log the path of the vehicle and also predict its trajectory to avoid any hazard.
* The vehicle sensors and other nodes do not face any energy restrictions because they can extract energy from the vehicle engine.
* The use of multicast broadcasting in VANETs allows the different vehicles to communicate with each other simultaneously [132].  
Vehicular communication, utilising VANETs, includes V2V communication, V2I communication and V2X communication, as illustrated in Figure 5. The details are given below.
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
###### Abstract  
Retailers depend on accurate forecasts of product sales at the Store \(\times\) SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model's parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast trailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.  
retailing; forecasting; promotions; seasonality; shrinkage; principal components analysis Article
**Keywords:** Global Navigation Satellite System (GNSS); GNSS-integrated water vapour; convective available potential energy (CAPE); extreme rainfall; TRMM
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology
### Evaluation Criteria
#### 3.2.4 Reconstruction Error  
To measure the reconstruction accuracy (low-to-high-dimensionality mapping) of the model, we measure the input/output error for each pair-wise combination of dimensionality reduction and reconstruction techniques by computing the mean absolute error between predicted and actual parameter values. This is done using \(K\)-fold cross validation with \(k=20\) iterations, and a test partition size of 20% (160 training examples). As some of the dimensionality reduction techniques are unable to embed new information into the reduced-dimensionality space, the first part of the test process (_i.e._, the prediction of new low-dimensional values as implemented in [26]) was omitted, and only regression and interpolation techniques were evaluated.