esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability of Supply and the Impact of Weather Exposure and Protection System Failures +
## 4 Case Study
The lines in the cut-set are adjacent at a single bus. In the top panel, the independent failure probabilities of the lines are relatively high, peaking around the tenth hour, while the dependency mode failure probability is comparably low. The dependency mode failure rate does however have a large impact on the failure probability of the cut-set due to its additive nature, as seen in the mid panel, where the failure probability of the cut-set is calculated with and without the inclusion of protection system failures. The resulting unavailability of the cut-set, illustrated in the lower panel, shows the impact of protection system failures as a short spike in unavailability compared to the case excluding protection system failures. This spike is due to the notably shorter outage durations associated with FT3 and FT4 of only thirty minutes, as compared with the permanent outage duration of independent faults for the lines at 11 hours. The unavailability only considering independent failures, with longer outage durations, is illustrated by the more rounded, dotted line. The failure rates for sets of components in different categories are summarized in Table 1. These are not necessarily cut-sets causing load curtailment for the RTS at peak load, however, but show the impact on failure rate and unavailability of the methods described in the paper on different sets of network components in different topologies. For single lines, the approximate and time-series methods yield similar results. This is expected, as the time-series method only points out when the failure occurs but does not alter the annual failure rate in any way. The inclusion of protection systems has a limited impact on the annual failure rate for individual cables and lines but a large impact for transformers. This is due to the initially low failure rate of the transformers, when paired with a comparably high failure rate of its protection systems affecting FT2 in addition to contributions from adjacent lines through FT3 and FT4. For second order sets where the components are not adjacent, the sets containing at least one component with a constant failure rate, e.g., cables, are similar across the methods. The inclusion of protection systems causes a small increase  
Figure 5: Contributions to fault type 3 (FT3) and FT4 for line 35 from adjacent lines. FT1–FT4 and adjusted failure rate for line 35. Annual values. in the failure rate but there is no effect due to weather. However, when looking at sets including two overhead transmission lines, the effect of correlated weather causes a large increase in failure rate from the approximate method to the time series method, both not including protection systems. The failure rate increases sharply for sets where components are adjacent when protection systems are included in the evaluation. More importantly, the frequency of occurrence of some states is notably greater when protection systems and weather effects are both taken into consideration.
## 2 Related Work
In [42], techniques are proposed for recognizing humans using ECG based on a combination of discrete wavelet transform, continuous wavelet transform and a novel deep learning approach called the capsule network. The accuracy on the MIT-BIH arrhythmia [5], PTB STDB and NSRDB [4], respectively, was 99.5%, 98.1%, 98.2% and 100%. Methods for feature extraction including such a Dwt wavelet ('db8', 'db3' and 'db10'), Biorthogonal wavelet 'bior2.6' and symlets wavelet'sym7' are used in [43] this work. A backpropagation (BP) neural network and radial basis function neural network are combined to serve as a classifier. The proposed method obtained an identification rate of 98.41% when using the Daubechies wavelet 'db8.' The proposed techniques were evaluated on some other 250 heartbeats after being trained on 250 heartbeats chosen at random. In those situations, the average identification accuracy for CWT and STFT is 96% and 94.72%, respectively, as achieved in [44]. Before being served into the transfer learning approaches, the Electrocardiogram signal was transformed into scalogram image data that use the continuous wavelet transform. This paper [45] evaluates AlexNet, GoogLeNet and ResNet. ResNet was 0.73-0.27% higher on PTB-ECG than AlexNet or GoogLeNet and 0.94-0.12% higher on CU-ECG than GoogLeNet or AlexNet. A pretrained network light CNN (GoogLeNet) was used to test and train the continuous wavelet transform images to extract from the segmentation process. On the PTB dataset, the method in [46] achieved 99.83% accuracy on light CNN and 99.94% accuracy on GoogLeNet. To enhance the performance of the electrocardiogram (ECG) biometric identification method, we introduce feature-level fusion using a one-dimensional convolutional recurrent neural network and discrete wavelet transform in our proposed method. Our model's performance was evaluated using combined datasets such as ECG-ID, MIT-BIH and NSRBD, all of which are publicly available on the PhysioNet website. In this study, pretrained transfer learning methods such as VGG-19, ResNet-152 and Inception version 3 are used to compare the results of a one-dimensional recurrent convolutional neural network (1D-CRNN) and discrete wavelet transform-based concatenated features. Sections 3 and 4 cover the methodology and results of the experiment.
## 5 Discussion
### Implications for Sustainable Land Management  
The findings obtained from this study provide valuable insights for sustainable land management and, consequently, the human life cycle. One of the most influential factors in sinkhole formation is the uncontrolled and severe consumption of groundwater resources. Agricultural activities and increased urban water consumption due to population growth directly contribute to the decline in groundwater levels in the region, expediting karst processes. The widespread cultivation of water-intensive agricultural crops such as corn, sugar beets, and alfalfa, which are not suited to the climatic conditions of the basin under study, further exacerbates this process. The re-evaluation and improvement of water management policies is critical in reducing or preventing the damage caused by geological disasters such as sinkholes. Many researchers in the literature have highlighted the importance of promoting modern irrigation methods, supporting crops with low water consumption and utilizing water resources more conscientiously [23; 41; 109]. Moreover, it is recommended to limit urbanization in sinkhole-prone areas and to regulate agricultural activities in these regions. On the other hand, incorporating field observations and temporal analyses will make the model more comprehensive for future studies. Especially, monitoring long-term groundwater level changes and investigating their impacts on sinkhole formation is believed to further enhance the predictive capacity of the models to be developed. In addition, integrating climate change scenarios into the model could assist in more accurately predicting future sinkhole formation risks. These types of research will make significant contributions to the development of sustainable land management strategies in karst regions.
###### Abstract  
A specific method for the estimation of the induced power at the tip by an active implantable medical device (AIMD) during an MRI scan is not defined in ISO/TS 10974-2018 for the cases of devices with multiple leads or the presence of abandoned and retained leads, where coupling between two leads could take place. Therefore, the aim of this work is to evaluate a numerical method, similar to Tier 3 of ISO/TS 10974-2018, for the estimation of the deposited power at the tips of two coupled implanted leads. Specifically, the proposed methodology applies the same parameters as the standard Tier 3 process. However, these parameters are calculated for each implant in the presence of its neighbors in order to include the coupling between them. Numerical electromagnetic simulations were performed, in which a pair of generic implant leads was excited and placed at various positions relative to each other. The deposited power at the tips of the pair was analyzed based on the standard Tier 3 approach, and the results show that this could overestimate the induced power by more than 6 dB. Therefore, a modified Tier 3 numerical method for the estimation of the deposited power at their tips is proposed, which can produce a reliable calculation. This method was confirmed by evaluating various configurations of the two leads excited by orthogonal electrical fields, and also its uncertainty budget was developed. Finally, the method was repeated for different properties of the generic implants and the surrounding tissue, as well as for a pair of realistic implants and in vivo implantation trajectories.  
implants with multiple leads; Tier 3; deposited power +
Footnote †: journal: Applied Physics Letters  
1
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 4 Literature Review
### IoT Location Detection Applications Based on Image Processing  
The IoT's growing breakthroughs allow for the building of truly smart settings that can provide first-rate amenities to its occupants and visitors alike. Lately, such smart settings have also been employed to reignite consumers' interest in cultural heritage by presenting them with real, interactive cultural experiences that they can engage in. In [61], an indoor site architecture has been built to enhance the user experience in a museum context. As a result, an image-processing wearable device would be useful for identifying and pinpointing the contents of an image. Therefore, the proposed system could be configured in such a way as to supply users with cultural content that is related to the artworks that they are currently viewing. A Bluetooth low energy (BLE) infrastructure was set up at the museum to get the location data. Furthermore, the system interfaces with the Cloud to preserve multimedia content generated by the user and to publish events made by the environment through social networks. These services connect with physical devices via a middleware that supports numerous protocols. Improvements have been made to  
Figure 5: IoT safety applications based on image processing: privacy-preserving outsourced computing architecture [56].  
cloud-based and IoT-connected location detection services thanks to image processing. The proposed localization mechanism proposed by [61] is shown in Figure 6.  
A combination of a camera and a GPS sensor has recently been deployed in agricultural monitoring. It is novel to acquire data in a big area swiftly and independently. Production of such a plant may be improved by employing a drone-based system where the IoT architecture was utilized to help retrieve information in a real-time situation. The detection and classification methods dedicated to plant (e.g., rice) diseases by applying image processing techniques have been reported in [62]. This was done to enhance the rice production process. With the use of a GPS sensor, the proposed system may reveal the location of ill rice plants in a real-time on a map. An earlier and real-time sickness detection system based on IoT architecture has been conceived to contribute to illness detection and prevention systems. Image processing in this example has garnered interest from other systems owing to its object identification abilities.  
Similarly, sensors and IoTSs are utilized to monitor environmental parameters. Drones and cloud computing are all components of this detecting system's wireless sensor technology mix. Multiple image processing processes could be integrated to detect, for example, fire occurrence. The proposed system has demonstrated that image processing can be used and exploited to increase the detection of events in places, which can lead to advancements and enhancements in IoTSs that provide greater services to our lives and our green environment [25].
## 4 Error Analysis and Uncertainties of Experimental Data  
The experimental data are always subjected to a certain level of uncertainty due to the errors in the measuring devices. Based on the ANSI/ASME standard [34], the approach proposed by [PERSON] and [PERSON] [35], the uncertainty of experimental measurements is determined by the following equations.  
\[\left(\frac{\Delta Nu}{Nu}\right)=\sqrt{\frac{1}{Nu}\left[\left(\frac{\partial }{\partial\overline{h}}(Nu)\Delta\overline{h}\right)^{2}+\left(\frac{ \partial}{\partial D_{h}}(Nu)\Delta D_{h}\right)^{2}+\left(\frac{\partial}{ \partial\overline{k}}(Nu)\Delta k\right)^{2}\right]} \tag{11}\]  
\[\left(\frac{\Delta\overline{h}}{\overline{h}}\right)=\sqrt{\frac{1}{\overline {h}}\left[\left(\frac{\partial}{\partial q}\left(\overline{h}\right)\Delta q \right)^{2}+\left(\frac{\partial}{\partial T_{w}}\left(\overline{h}\right) \Delta T_{w}\right)^{2}+\left(\frac{\partial}{\partial T_{b}}\left(\overline {h}\right)\Delta T_{b}\right)^{2}\right]} \tag{12}\]  
Reynolds number:  
\[\left(\frac{\Delta Re}{Re}\right)=\sqrt{\frac{1}{Re}\left[\left(\frac{ \partial}{\partial u}(Re)\Delta(u)\right)^{2}+\left(\frac{\partial}{\partial \rho}(Re)\Delta\rho\right)^{2}+\left(\frac{\partial}{\partial D_{h}}(Re) \Delta D_{h}\right)^{2}+\left(\frac{\partial}{\partial\mu}(Re)\Delta\mu \right)^{2}\right]} \tag{13}\]Friction factor:  
\[\left(\frac{\Delta f}{f}\right)=\sqrt{\frac{1}{f}\left[\left(\frac{\partial}{ \partial\Delta P}(f)\Delta(\Delta P)\right)^{2}+\left(\frac{\partial}{\partial L }(f)\Delta L\right)^{2}+\left(\frac{\partial}{\partial D_{h}}(f)\Delta D_{h} \right)^{2}+\left(\frac{\partial}{\partial Re}(f)\Delta Re\right)^{2}\right]} \tag{14}\]  
The greatest uncertainties for the \(Nu\), \(Re\), and \(f\) were determined as 4.12 percent, 1.85 percent, and 3.6 percent, respectively. Table 2 lists the accuracy values of measuring instruments.
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
## 4 Finite Element Model
### Model Validation  
The air temperature and beam temperature collected in the field test were used as boundary conditions for progressive loading, and the temperature field distribution of the beam section at different times was calculated. By comparing the measured data of temperature sensor in Tuotuhoe region with the results of corresponding nodes calculated by finite element method (Table 2), it was found that the temperature values are basically consistent, thereby determining the correctness and reliability of this model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Position**} & \multicolumn{2}{c}{**Finite element**} & \multirow{2}{*}{**Measured Data (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Error of Calculation**} \\  & & **Calculation Results (\({}^{\circ}\)C)** & & \\ \hline \multirow{2}{*}{7/31-5:00} & Internal measuring point & 6.59 & 6.60 & 0.15\% \\  & Outer measuring point & 6.13 & 6.10 & 0.49\% \\ \multirow{2}{*}{7/31-19:00} & Internal measuring point & 9.63 & 10.20 & 5.60\% \\  & Outer measuring point & 11.81 & 12.10 & 2.40\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison between finite element calculation results and measured data.  
Figure 12: Sunshine temperature field simulation model.
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 4 Conclusions  
In this paper, the prediction-based near-lossless compression technique is used to reduce the size of the hyperspectral image. The target pixel is predicted from the combination of previous pixels in the spatial and spectral bands. The coefficients are predicted using the weight matrix of the RLS filter with an in-loop quantizer. The experiments were performed on three types of CCSDS hyperspectral images' datasets, including three, five, and eight scenes, respectively. The optimal number of bands for the loop predictor was analyzed experimentally. The results indicate that the proposed scheme provides an average optimization from 5.29% to 31.19% in bit-per-pixel for different peak absolute errors and achieves a competitive reconstructed image quality compared to the state-of-the-art methods.  
In the future, we plan to develop an automated model for parallel processing of near-lossless compression schemes and feasible hardware implementation solutions for space-based platforms. This research work will further pave the way for future developments in the field of deep space exploration.  
Conceptualization, T.Z.; methodology, T.Z. and Y.D.; investigation, T.Z. and C.X.; formal analysis, C.X., L.Z. and T.Z.; writing--original draft, T.Z.; writing--review and editing, Y.D. and C.X.; funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese Academy of Sciences Project, grant number: CXJJ-20S017.  
Not applicable.  
The authors declare no conflicts of interest.
## 5 Discussion
Regarding interoperability, blockchain-based solutions can facilitate automated information sharing between actors because all actors are required to use the same data structure or standard [125; 122]. Furthermore, combining IoT technologies with blockchain includes improved traceability, enhanced collaboration between actors, operational efficiencies and integration between systems of different actors [122; 123; 125]. Regarding monetary aspects, the costs and benefits of an IoT-based real-time monitoring system can be distributed unequally along the chain. [PERSON] et al. [117] describe that the actor paying for installation and hardware costs is not necessarily the one who benefits most from the system. This can be seen as critical if missing concepts for sharing the costs and benefits along the supply chains might enhance already existing power asymmetries between actors. All these factors illustrate that the application of IoT-based sensor technology in fresh fruit and vegetables' supply chains is influenced by many aspects and faces specific challenges. Accordingly, implementation of comprehensive real-time monitoring systems including subsequent data processing in fresh fruit and vegetable supply chains is still rare. Only few studies apply an extensive system in practice [97; 103; 106; 117]. Establishing a system that is genuinely scalable and easily transferable to other fruit and vegetables or supply chains remains an unsolved challenge. Until now, solutions have to be customized to each supply chain, because generic concepts for implementation are missing. This includes guidelines on the choice of technology and knowledge of process-related, technical and actor-related requirements, as discussed above. For exploitation of the full potential of IoT-based ambient parameter monitoring systems, these knowledge gaps need to be filled. Large scale implementation of IoT-based monitoring could also facilitate the application of other quality-related technologies. Solutions are in development for directly measuring product quality parameters. These include physical and chemical characteristics such as the soluble solids content, dry matter or firmness [126]. Emerging technologies in this field are near-infrared (NIR) spectroscopy, multi- and hyperspectral imaging, freshness sensors and E-noeses which have already been studied for various products [126; 127; 128; 129; 130]. Direct measurement of quality parameters at specific processing steps can help in determining the optimum harvest time, in sorting of products to more uniform batches and in supplying additional information on ripening status and shelf life [129; 130]. Possibly, these technologies could also routinely be used after an ambient parameter monitoring system noticed a deviation, e.g., a temperature abuse, for determining certain quality parameters and making logistic decisions. Further research on integration of these technologies with an ambient parameter monitoring system could help to explore synergies and benefits regarding quality retention and food loss prevention.
## 4 Experiments and Results
### Utility Evaluation
By reducing the manual effort required and improving accuracy, these models can streamline compliance workflows while minimizing the risk of errors that could lead to regulatory penalties. However, latency and contextual understanding remain areas for further refinement to fully align with operational needs. Building on these results, the following discussion explores their broader implications, focusing on the research questions and practical applications for GDPR compliance.
## 5 Explanation of the Prediction Results
### Personal Explanation  
To give an individual perspective on the prediction model, the explanation module explains the patient-specific analysis. Visualization of the personal explanation was implemented using LIME in the interpretML library [48]. Figure 7a,b present two examples of the visualization of the personal explanation of high risk using a local surrogate analysis to explain the impact of the predictors. In both cases, sex, frequency of smoking, and smoking duration affected the prediction of developing gastric cancer, consistent with the risk factors identified in Section 5.1 through Chi-squared tests. The important features  
Figure 5: (a) Distribution of the probability of the three groups; (b) 3D scatterplot.  
Figure 6: Age at diagnosis of each group.  
affecting the prediction of gastritis patients differed from patient to patient. As shown in Figure 7a, in addition to smoking habits, sex and income were identified as risk factors to progression from atrophic gastritis to gastric cancer; whereas in case of Figure 7b, family history of cancer, alcohol habit, and exercise were identified as risk factors.  
Personal explanation was performed on 59 patients with a probability for progression to gastric cancer of 0.97 which is the highest probability. The risk factor that appeared in the most patients was age at diagnosis, and was identified as an important predictor in 36 patients. The next most frequent risk factor was smoking duration, which was found in 20 patients. The experimental results showed that to prevent development of gastric cancer, individual analysis and treatment are essential.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
###### Abstract
This work aims at using the strengths of simulation modelling along with the domain knowledge expertise of mariners. Figure 1 shows a schematic block diagram of the simulation tool. The simulation tool is implemented using a combination of discrete-event and agent-based simulation, along with process flow modelling. This choice of modelling techniques allows for the visualization of the simulation processes and results, the incorporation of stochastic elements, and a relatively fast execution time (~5 min per simulated operating month). The behavior of individual entities of the FSWNS, such as individual merchant vessels, icebreakers, and ports, are modelled using an agent-based framework, including details of their decision-making logic. Each entity (vessel or icebreaker) has several decisions  
Figure 1: Block diagram of the simulation tool. to make during the winter, reacting to the changing parameters. The decision-making logic for each entity involves a set of rules in the if-then-else format, where entities change their speed, direction, and mode of operation based on system parameter values. Additionally, for the icebreakers, there is an optimization logic for deciding on the best course of action in assisting vessels. This article is organized as follows. In Section 2, prior work in this field is reviewed and comparisons are made between the different aspects of each piece of research. In Section 3, first, the parameters of the system are introduced and categorized. Next, the tool schematic is presented, and the different components of the decision-support tool are highlighted. Finally, the simulation principle is described.