esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 5 Experimental Results and Analysis
### Simulated Data Experiment
Because the UBD algorithm reconstructs missing or corrupted pixels using the results of spectral unmixing, its performance depends largely on the estimation accuracy of the endmember and abundance coefficients in the spectral unmixing. In the field of HSI processing, spectral unmixing is still a challenging problem. The BPFA algorithm showed good image inpainting performance under various noise conditions, but its ability to remove Gaussian non-i.i.d noise was poor, which is evident in Figure 3. Figure 4 shows that with the exception of the PDE algorithm, all the other inpainting algorithms could remove Laplacian noise to some extent, but the INORSDL algorithm proposed herein  
Figure 1: 3D-cube of Pavia Centre hyperspectral data set. In addition, we also compared the performance of each inpainting algorithm with the mean peak signal-to-noise ratio (Mean-PSNR, MPSNR) and the mean structural similarity (Mean-SSIM, MSSIM), as shown in Table 1. The highest value in each row is shown in bold. As shown in the table, the INORSDL algorithm yielded the best results for the four kinds of noise conditions, and its inpainting performance was quantitatively evaluated. Figure 4: Inpainting results of band 60 of Pavia Centre dataset with Laplacian noise. (**a**) Clean image; (**b**) noisy image; (**c**) PDE; (**d**) UBD; (**e**) LLRSSTV; (**f**) BPFA; and (**g**) INORSDL. Figure 5: Inpainting results of band 60 of Pavia Centre dataset with Poisson noise. (**a**) Clean image; (**b**) noisy image; (**c**) PDE; (**d**) UBD; (**e**) LLRSSTV; (**f**) BPFA; and (**g**) INORSDL. Table 1 also shows the time required for the different algorithms when applied to different noise conditions. Compared with the BPFA, another algorithm based on dictionary learning, the algorithm of this work was faster and more efficient.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 4 Data Acquisition Tool--IT System Design
### Maintainability Analysis Module
#### 4.2.1 Data Acquisition Model  
Each maintenance task \(T_{i}\) is associated with a duration \(D_{i}\). The system records \(n\) samples of these durations [\(D_{i1}\),\(D_{i2}\),\(\cdots\),\(D_{in}\)] for analysis.  
* Probability Distribution of Task Durations: For each task \(T_{i}\), the duration \(D_{i}\) is modeled as a random variable. The probability distribution function (PDF) \(f_{Dl}(d)\) is estimated from the collected data: \[f_{D_{i}}(d)=\frac{1}{n}\sum_{j=1}^{n}K_{h}\big{(}d-D_{ij}\big{)}\] (4) where \(K_{h}\) is a kernel function (e.g., Gaussian) with bandwidth \(h\), used for nonparametric density estimation.
* Cumulative Distribution Function (CDF): The CDF \(F_{Dl}(d)\) is defined as follows: \[F_{D_{i}}(d)=\int_{-\infty}^{d}f_{D_{i}}(x)dx\] (5)
* Expected Duration \(E[D_{i}]\): The expected task duration is \[E[D_{i}]=\int_{\{-\infty\}}^{\{\infty\}}d\cdot f_{D_{i}}(d)\ dd\] (6)  
Figure 3: The concept of expanding the IT system Samanta.
## 6 Discussion
### Governance
#### 6.3.3 International Association of Classification Societies (IACS)  
In April 2022, the International Association of Classification Societies (IACS) issued Unified Requirements (UR) E26--Cyber Resilience of Ships [79] and E27--Cyber Resilience of On-board Systems and Equipment [80]. The purpose of the URs is to \"provide a minimum set of requirements\" for cyber resilience of \"the ship as a collective entity\" (E26) and for \"on-board systems and equipment\" (E27). At pages 32 (E26) and 14 (E27),respectively, the URs provide much more detailed guidance than the IMO Circulars or the ISPS, covering specific aspects of cybersecurity like firewalls, segregation, air gapping, data diodes, and maps out sub-goals for each functional element: identify, protect, detect, respond, and recover. Both URs note that they are \"to be uniformly implemented by IACS Societies on ships contracted for construction on or after 1 January 2024 and may be used for other ships as non-mandatory guidance.\" [79] Implementation of the two URs was subsequently postponed until 1 July 2024, and they have undergone \"extensive changes\" including dividing the requirements into mandatory and non-mandatory depending on the size and type of ship [81].  
Implementing the URs in practice may present a challenge for ship owners, particularly smaller operators with limited cyber knowledge, as expressed by one interviewee:  
You get a new system from a supplier and it's put onboard and then you need to make sure that it becomes cyber secure when it's connected to all the other systems onboard. That's an issue especially on older ships where many ship owners do not exactly know how to handle that and because they do not have the knowledge about that system and about the technical aspects of the other systems and how they can make them cyber secure. [74]  
It should be clarified here, that the mandatory aspects of UR E26 and E27 only apply to newly built ships, and as such will not cause regulatory deficiencies in existing ships. However, they provide a useful guide of where maritime cybersecurity regulation is heading; that is, a requirement that Original Equipment Manufacturers (OEMs) provide devices where cybersecurity is an inherent aspect of their design, and that, when installed alongside other ship systems, the cybersecurity of the _whole_ is assured.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 3 Numerical Experiments
### Computational Procedures  
The performance of our hybrid framework is assessed using a dataset comprised of 3026 PSC inspection records. Rather than splitting the dataset into large and uneven subsets where the representation of exterior data points might be skewed, we opt for smaller and more manageable batches. This not only provides a more balanced proportion of interior and exterior points but also facilitates multiple comparative validations to confirm the effectiveness of our hybrid model M3.  
To achieve this, the dataset is randomly divided into 30 batches, each containing 100 records. Each batch is then randomized and further divided into three subsets: a training set \(D_{\text{train}}\), a validation set \(D_{\text{valid}}\), and a test set \(D_{\text{test}}\), encompassing 60%, 20%, and 20% of the records, respectively. This translates to 60, 20, and 20 records per subset.  
For each data batch, firstly, \(D_{\text{train}}\) is used to train two models: the \(k\)NN model \(f_{1}^{\text{kNN}}\) and the linear regression model \(f_{1}^{\text{LR}}\). The optimal parameter \(k^{*}\) for the \(k\)NN model is determined with the corresponding minimum MSE on \(D_{\text{valid}}\) from \(\mathcal{K}=\{1,2,\ldots,10\}\). Then, we use \(D_{\text{valid}}\) to determine the optimal parameter \(h^{*}\) for the hybrid prediction model \(f_{1}^{\text{hybrid}}\). Specifically, for each data point \(\left(x_{j}^{\text{v}},y_{j}^{\text{v}}\right)\) (\(j\in\{1,\ldots,|D_{\text{valid}}|\}\)) in \(D_{\text{valid}}\), we obtain \(f_{1}^{\text{kNN}}\Big{(}x_{j}^{\text{v}}\Big{)}\) and \(f_{1}^{\text{LR}}\Big{(}x_{j}^{\text{v}}\Big{)}\) by using \(k\)NN and linear regression models. Then, we solve the Optimization Model M0 using Gurobi to obtain \(d^{*}\Big{(}x_{j}^{\text{v}}\Big{)}\) and calculate  
\[d_{\max}=\max_{x_{j}^{\text{v}}\in D_{\text{valid}}}\Big{\{}d^{*}\Big{(}x_{j}^ {\text{v}}\Big{)}\Big{\}}.\]  
We denote by \(H\) the result of retaining \(d_{\max}\) to one decimal place, and \(\mathcal{H}=\{0.1,0.2,\ldots,H\}\) is the tuning range for the hyperparameter \(h\). Then, we are able to calculate \(f_{1}^{\text{hybrid}}\Big{(}x_{j}^{\text{v}}\Big{)}\) by choosing a candidate \(h\) from \(\mathcal{H}\), and select \(h^{*}\) that minimizes the MSE of \(f_{1}^{\text{hybrid}}\) on \(D_{\text{valid}}\) as the optimal hyperparameter of the hybrid framework. Finally, we concatenate \(D_{\text{train}}\) and \(D_{\text{valid}}\) to retrain the \(k\)NN model \(f_{2}^{\text{kNN}}\), the linear regression model \(f_{2}^{\text{LR}}\), and our hybrid prediction model \(f_{2}^{\text{hybrid}}\). For each data point in \(D_{\text{test}}\), we calculate the MSEs of \(f_{2}^{\text{kNN}},f_{2}^{\text{LR}}\), and \(f_{2}^{\text{hybrid}}\). Algorithm 1 describes the detailed computational procedures of the experiment.
# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods
### Machine Learning Classification Algorithms
#### 2.3.5 XGBoost  
The XGBoost algorithm, which stands for eXtreme Gradient Boosting, is a well-known and robust machine learning algorithm that excels in a variety of applications, especially supervised learning issues like regression and classification. It is a member of the boosting ensemble methods family in the context of ensemble learning. XGBoost works by aggregating the predictions from several different models usually decision trees to generate a final prediction that is more reliable and accurate. A series of decision trees are constructed iteratively, with each new tree fixing the mistakes made by the preceding ones. This iterative procedure continues until a predetermined stopping point is reached, like attaining a certain number of trees or performing well enough on the validation data. XGBoost stands out for its distinct optimization objective and regularization strategies. In order to prevent overfitting, it uses a gradient-boosting framework that penalizes model complexity while minimizing a particular loss function. This is accomplished by incorporating regularization terms into the objective function, which regulates the complexity of both the individual trees and the ensemble as a whole. More details about the XGBoost algorithm can be found in [26].
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 6 Conclusions  
This paper addresses the issue of amplitude-frequency-phase distortion in sub-carriers in DSDT systems during satellite cross-transponder transmission, along with the high synchronization accuracy requirements. It combines the decision feedback algorithm in equalization and introduces the error vector of each sub-carrier based on the least squares criterion. A data assistant-free algorithm for inter-subcarrier error synchronization tracking and adaptive compensation has been proposed. Experimental simulation analysis and measurements based on the NI-USRP wireless transceiver platform demonstrate that the error feedback mechanism of multiple sub-carriers has significant performance advantages over traditional decision feedback equalization of the combined signal, thus effectively correcting amplitude, frequency, and phase errors. Compared to the blind estimation algorithm in [12], the proposed algorithm avoids the contradiction between FFT points and estimation accuracy, thus resulting in improved estimation accuracy. By combining it with pilot synchronization, the algorithm can further track and compensate for subtle residual errors after synchronization without introducing additional pilot overhead. This approach maximizes the demodulation performance of the DSDT system and performs well in practical engineering applications. The simulations and measurements in this paper have been conducted under limited error conditions, but the analysis of the algorithm's effective tracking range for maximum errors is lacking. Further exploration of this issue will be undertaken in future research.  
Methodology, C.W.; formal analysis, G.W.; data curation, Z.L.; writing--original draft preparation, C.W.; writing--review and editing, L.Z.; visualization, C.Y.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Laboratory of Wireless Communications Foundation under Grant IFN20230209, and the Ningbo Key R&D Program under Grant No.2023Z231, the Zhejiang Province Postdoctoral Research Funding Project under Grant ZJ2023008, the China Postdoctoral Science Foundation under Grant No.2023M740741, and the Ningbo Natural Science Foundation under Grant 2023J. Meanwhile, it is also supported in part by the Fund of Key Laboratory of Cognitive Radio and Information Processing through the Ministry of Education under Grant CRKL200106.  
The data that support the findings of this study are available from thecorresponding author on reasonable request.  
The authors declare no conflicts of interest.  
Figure 17: Measured bit error rate of DSDT.
## 4 Result Experiment
### Case Study: Estimation of Remaining Lifetime Using ACI
#### 4.4.3 Step 3: Estimation of Remaining Lifetime Until a Target ACI Level  
* To estimate the remaining lifetime of the cable \"A\" specified operating conditions, the time required for the ACI to decrease from its current value to a predefined threshold is calculated.
* Cable \"A\", which currently exhibits a tensile strength of 18 MPa, corresponds to an ACI of 0.652, as calculated using Equation (7). Assuming that the cable will be operated under a constant temperature of 30 \({}^{\circ}\)C, the remaining lifetime t until the ACI decreases to 0.5 can be estimated. Using Equations (8) and (9), the tensile strength corresponding to an ACI of 0.5 is calculated to be z = 16.72 MPa. \[z=ACI_{\text{\emph{Tensile strength}}}(z_{0}-z_{\text{\emph{Minimum}}})+z_{ \text{\emph{Minimum}}}\] (8) \[16.72\text{\,Mpa}=0.5\text{-}8.44+12.5\] (9)
* Using Equation (3), the time required for cable \"A\" with an initial ACI of 0.652 to reach an ACI of 0.5 under an operating temperature of 30 \({}^{\circ}\)C can be calculated using Equation (10). \[16.72=-0.46 exp(\frac{30}{84.52}+\frac{t_{\text{\emph{Tensile}}}}{256.43})+21.4,\,t_{\text{\emph{time}}}\approx 5020\text{\,hours})\] (10)  
The proposed aging index model provides a quantitative approach for assessing the aging state of cables as a function of operating temperature and time. By applying this model, it is possible not only to evaluate the current condition of the insulation based on mechanical properties but also to predict the remaining lifetime until a predefined ACI threshold is reached. This approach offers a valuable tool for condition-based maintenance and lifecycle management of power cables.
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 7 Overall Assessment  
The AC system efficiency is almost constant throughout the day; however, with solar PV panels installed, there are periods when D and I category loads are completely supplied by solar or battery and solar/battery combined. Within these periods, the grid has to supply only \"A\" category loads, which inside the building, require no converter stage, and in-building \"conversion\" efficiency is almost 100%. The dominant element that defines the system's efficiency is the transformer efficiency; other parameters that fractionally affect the system's efficiency are conductor losses and power factor. There is a tiny fall of the efficiency for the AC system during the 6 th hour, which is due to the reduced loading within the building. The combined effect of 21 buildings per transformer poses a fall in transformer operating efficiency because the transformer is operating at a reduced efficiency to some extent.  
Interpretation of results reveals that the AC system efficiency remains almost constant throughout the day, although the DC system efficiency changes with time. Due to the addition of a solar PV system, one may expect a rise in the DC efficiency, but that is not true! There are even instances when the system's efficiency is poor (as discussed for the 11 th and 12 th hour). The converters' loading is the most critical factor in defining the system's efficiency. While the converters are loaded close to their rating in those cases, the operating efficiency and in turn the system's efficiency are better than the case when converters are lightly loaded.
_Applied_  
_sciences_  
Design for Visual Quality Enhancement of Artificial Infrastructure Facilities: An Application to Electricity Pylons  
[PERSON] 1\({}^{\text{(\textregistered)}}\), [PERSON] 2,*\({}^{\text{(\textregistered)}}\), [PERSON] 1 and [PERSON] 2  
1 Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67040 L'Aquila, Italy; [EMAIL_ADDRESS] (L.D.A.); [EMAIL_ADDRESS] (P.D.S.)  
2 Department of Engineering \"[PERSON]\", University of Modena, 41125 Modena, Italy; [EMAIL_ADDRESS]  
Received: 31 December 2019; Accepted: 25 January 2020; Published: 7 February 2020  
Featured Application: A systematic method to design new tower-like structures with an enhanced visual impact quality is proposed. The method is applied to the design of electricity pylons, which are very critical structures due to their visual impact on landscape and on citizens' perception. During all the design process--from conceptual to detailed design phases--regulatory, industrial feasibility, safety, and social components are considered key requirements. The method can be easily applied to other pylons, at the service of renewable energy production and distribution, and telecommunications.  
(1) Background: The visual impact of artificial infrastructures on natural landscapes generates a common negative perception in public opinion. However, as in the case of electrical energy, the increasing demand for power supply and its need for capillary distribution require the installation of new lines, commonly overhead lines with tall tower-like pylons. In most countries, this situation is faced with many attempts of solutions, as participatory workshops and design contests. Nevertheless, the solutions are usually not further developed into real structures due to many limitations (e.g., regulatory, safety, lack of feasibility). (2) Methods: This paper presents a systematic method for the design of tower-like pylons (e.g., electric ones) able to improve the visual quality on the landscape areas in which they will be installed. The method identifies a design strategy that advantageously exploits the inevitable visual impact of pylons on the landscape by integrating the symbolic morphology and the topologically optimized pylon structure from the earliest design phases. (3) Results: The resulting structure is designed in three steps. First, a concept is morphologically developed by integrating symbolic references to the landscape, environment, or cultural society. Second, the concept is topologically optimized, by reducing the structural weight and its visual impact, and respecting regulatory requirements. Third, the resulting structure is engineered and embodied into an industrially feasible layout. (4) Conclusions: The method is able to develop an original, brand new tower-like pylon integrating all the types of requirements, such as regulatory, industrial feasibility, and social components' needs. The resulting electricity pylon presents an enhanced visual quality according to the citizens' feedback.  
integrated design method; electricity pylon; visual impact improvement; topology optimization
## 4 Results
### Comparison  
The stated aim of this study is to find an approach that produces good results with a high degree of accuracy, even with highly varying test runs. Both methods have undeniable advantages for this purpose.  
In addition to the results shown, the duration of the calculation is an important criterion for evaluating the PdM methods. For this purpose, a timer was integrated into the scripts for training and evaluation to output the calculation times. Table 5 shows the calculation duration for the empirical probability of the OSVM of the respective tests. To better understand the influence on this, various factors are investigated. On the one hand, the effect of an already pretrained model is examined, which shows how long the evaluation, including the training process, takes. In addition, the weighting of the number of runs and features tested is also analyzed. To do this, the calculation for one run is shown, and the calculation time for all runs of the respective test is given in brackets. The table shows that, for the OSVM, a pretrained model reduces the calculation time slightly, especially when only a few runs are evaluated, as in this case, for example, which had only one run. There is almost no difference between the number of features used.  
Equivalent to Table 5, a runtime calculation was made for the DL method. As can be seen in Table 6, the times for a single run and for all runs of a test are determined with and without a pretrained model. Despite the complexity of the RNN structure, the statement on the condition of the DUT of a single run is calculated in less than a second. In direct comparison, it is therefore significantly faster than the OSVM. When it comes to calculating the data for an entire test, evaluation with a pretrained LSTM network is up to twenty times faster.  
Figure 13: Result series of the LSTM network. All tests were taught on the model of the first DUT. First DUT (**a**), fourth DUT (**b**), seventh DUT (**c**), and ninth DUT (**d**).  
It is difficult to determine the accuracy of OSVM in detecting anomalies, as it uses semi-supervised learning where no data are labeled as \"abnormal\" and therefore cannot be used to validate the results. The anomalies detected in the tests can be marked for subsequent evaluation. Figure 4 shows that normal data can be clearly distinguished from abnormal data. Other anomalies, such as damaged components of the test bench, also resulted in a detected anomaly. Of the nine test runs carried out, it can generally be said that no anomaly remained undetected.  
The validation accuracy and the loss for the trained LSTM networks for the respective tests are determined in Table 7. To calculate the accuracy, the trained model is applied to its own mixed data set, which contains a label of the actual class. The loss is formed by cross entropy of the predicted and the actual class and should be minimized in the training process. Both the high accuracy of over 99% and the low loss show that the trained models work with great precision.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 2 Materials and Methods
### Machine Learning Models  
To model and predict the quality of water in the urban lake, we employed a combination of multiple machine learning (ML) algorithms. The choice of these models was based on their proven effectiveness in dealing with similar high-dimensional, non-linear, and complex datasets. The chosen models include Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Random Forest (RF). Each model provides unique strengths and is capable of capturing different aspects of the relationships in the data [12; 32].