esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations
### LTV Three-DOF Simulation Results  
The simulation results given by the proposed ERPCAWF technique are now presented. The first-order recognition results of PCA-based OMA methods are the results of most energetic mode, which corresponds to the first mode (sorted by natural frequency) in this simulation. The variation in the MAC value under different forgetting factors is shown in Figure 7.  
In Figure 7, we calculate the mean value of the MAC of four methods from 1250 to 2000 s. Then, by calculating, the mean value of the third-order MAC value results when \(\mu=\frac{i}{i+1}\) (without forgetting factor), \(\mu=0.9998\), \(\mu=0.9995\), and \(\mu=0.9990\) are 0.8511, 0.7983, 0.8347, and 0.8653, respectively. Therefore, the best identification results obtained when \(\mu=0.9990\). Furthermore, when the forgetting factor is not used, the accuracy of recognition decreases over time. With \(\mu=0.9990\), the modal natural frequencies identified by ERPCAWF, ARPCAWF, ARPCA, ERPCA, and theoretical calculations are shown in Figure 8. To better compare the frequency identification results, Figure 9 describes the relative errors of the four methods. The relative error calculation process is: calculate the absolute value of the theoretical value and the identification value, and then divide it by the theoretical value.  
Figure 7: Identified mode shape MAC value of ERPCAWF-based OMA for different forgetting factors.  
The modal shapes results of ARPCAWF-, ARPCA-, ERPCA- and ERPCAWF-based OMA are compared with the theoretical modal shapes to calculate the MAC values, which is shown in Figure 10. Figures 11 and 12 present the contribution of each PC to the variance at different randomly selected moments (112.75, 581.4, 1275.3, and 1987.25 s) for the ERPCAWF- and ARPCAWF-based techniques, respectively. In Figures 11 and 12, the blue histogram is the variance contribution rate and Y coordinate value is the cumulative variance contribution rate. The cumulative variance contribution rate indicates the proportion of all the currently selected principal components carrying information of the original data. The results given by ERPCAWF-, ERPCA-, ARPCA- and ARPCAWF-based OMA at these moments are listed in Table 2. Recall that the MAC value reflects the accuracy of modal shape identification [51]. To obtain the time and space requirements, for each algorithm, we ran 100 times and calculated the average. Table 3 compares the time and space complexities of the different algorithms.  
Figure 11: Cumulative variance contribution rate of PCs extracted by ERPCAWF-based OMA at different moments when \(\mu=0.9990\): (**a**) 112.775 s; (**b**) 581.4 s; (**c**) 1275.3 s; and (**d**) 1987.2 s.  
Figure 10: Comparison of MAC identified by different methods.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### DCC-EL Forecasting Framework
#### 2.3.2 Dilated Causal Convolutional Neural Network Model Layer  
To predict the time point \(y_{t}\) given a feature sequence \(x_{1}\ldots x_{t-1}\) and a corresponding time series \(y_{1}\ldots y_{t-1}\) using a causal convolutional neural network, it is necessary to jointly predict \(x_{1}\ldots x_{t-1}\) and \(y_{1}\ldots y_{t-1}\) to make \(y_{t}\) close to the actual value. However, for a causal convolutional neural network, each layer's output is composed of the previous layer's output and the input of the last position, which may lead to gradient vanishing or exploding. Therefore, this paper introduces the dilated convolutional neural network [33]. The dilated convolutional neural network is a deep learning model based on convolutional operations. Compared with ordinary convolution, the dilated convolutional neural network introduces a dilation rate to expand the receptive field of the convolution kernel, which is defined as follows:  
\[\left(K\otimes_{d}X\right)_{i,j}=\sum_{k=1}^{K}\sum_{l=1}^{L}X_{i-kd,j-ld}K_{k} \tag{34}\]  
Among them,\(\otimes_{d}\) represents the dilated convolution operation, \(K\) represents the convolutional kernel, \(X\) represents the input data, \(k\) represents the size of the convolutional kernel, and \(L\) represents the dilation rate.  
The formula for calculating dilated convolution is as follows:  
\[h_{i}=\sum_{j=1}^{k}x_{i+(j-1)d}w_{j}+b \tag{35}\]  
Among them, \(h_{i}\) represents the \(i\)-th output element after convolution, \(i+(j-1)d\) represents the \(i+(j-1)d\)-th input element, \(w_{j}\) represents the \(j\)-th weight of the convolution kernel, and \(b\) represents the bias term.  
The loss function in the dilated convolutional neural network is defined as:  
\[\mathcal{L}(\theta)=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{C}y_{ij}\log(\hat{y }_{ij}) \tag{36}\]  
Among them, \(\theta\) represents the model parameters, \(N\) represents the number of samples, \(C\) represents the number of classes, \(y_{ij}\) represents the actual label of the \(j\)-th class of the \(i\)-th sample, and \(\hat{y}_{ij}\) represents the predicted probability of the \(j\)-th class in the \(i\)-th sample.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.7 Feflow  
Feflow is an intuitive and complementary model for the flow and transport of fluids. It can simulate many phenomena related to fluid flow, groundwater ageing, contaminant and heat transport, and density-driven processes in differently saturated porous and fractured media at different spatial scales (from local to regional). Feflow uses the finite element method to solve many problems related to groundwater management, e.g., the impact of hydromechanical infrastructure, tunnelling, creation of retention zones, seepage through dams and dykes, remediation scenarios, etc. Feflow is widely used in practice and in science. Examples of its application to the reactive transport of organic material can be found in the work of Blake and Taffet [667], Kuhlers et al. [668], Soderberg [669], Innocenti et al. [301], Kouame et al. [670], and Praveeja and Sajikumar [671]. Kouame et al. investigated the flow of a hypothetical release of benzene through the simulated Abidjan aquifer. The initial concentrations of dissolved benzene of 43.12 mg/L and 14.37 mg/L were simulated to enter the aquifer injected at the Shell petrol stations. Adding the results of the vertical and horizontal transport time gives a global transport time of 38 to 47 years. The results show that there is a risk of pollution for 14 wells, and that possible air entrapment can allow the formation of pollution pools that represent a secondary source of hydrocarbons.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Magnetic Resonance Imaging (MRI)
#### 4.1.1 DL Techniques and Applications to MRI  
CNNs have been widely used in MRI analysis tasks. For instance, U-Net [6] has been used for MRI segmentation tasks, such as brain tumor segmentation [136] and prostate segmentation [137]. Similarly, residual networks (ResNets) [53] have been used for MRI reconstruction [119] and disease classification [110]. RNNs have also been used for MRI analysis, such as brain tumor segmentation [138]. GANs have also been used for MRI applications, such as image synthesis and image-to-image translation. For example, GANs have been used for the synthesis of brain MRI images [104] and for the generation of CT images from MRI images [124]. GANs have also been used for image denoising [139] and super-resolution [140] in MRI. Figure 9 shows the application of MRI brain image segmentation.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 1 Introduction
Figure 1: Overview of the contributions within the symbolic system synthesis approach presented in this paper. As a remedy, Figure 1 highlights the main contributions of our work that enable us to realize and significantly increase the applicability, scalability and robustness of SMT-based system synthesis:  
1. We present a _coordinated_ SMT-based synthesis approach that eases the task of the \(\mathcal{T}\)-solver to compute a feedback to the logic solver. This is realized by introducing a scheduling-aware binding and routing refinement in the logic solver. 2. We present how domain-specific heuristics (DSH) applied within the logic solver allow us to further increase scalability of our approach by utilizing our domain knowledge. In our experimental evaluation, we show that both our contributions on the one hand significantly increase applicability and on the other hand considerably enhance scalability of SMT-based system synthesis. Here, our instance sets leverage an automotive domain oriented application model that comprises both serial and parallel characteristics, namely two-terminal series-parallel task graphs [8]. Hence, we address an application model suitable for future parallel hardware architectures due to inclusion of the fork-join pattern. However, at the same time, we respect the more sequential processing characteristics of today's control applications with a not-negligible length of chained tasks in the series-parallel task graphs. The remainder of this paper is structured as follows: Section 2 explains the concepts and limitations of current SMT-based approaches in detail and motivates our contributions. In Section 3, we summarize related work while Section 4 introduces our platform and application model. We summarize how our approach leverages answer set programming (ASP) as logic solver in Section 5 and briefly illustrate the relevant points of our time-triggered scheduling formulation within the background theory in Section 6. Section 7 explains how we couple the logic solver and the background theory in order to derive the feedback to prune the search space of the logic solver efficiently. Our experimental results in Section 8 demonstrate that our _coordinated_ approach can improve the applicability of symbolic system synthesis considerably and that our DSH allow to increase the scalability and robustness of our approach. Section 9 finalizes this paper by proving concluding remarks.
## 8 Reflection
### Areas for Improvement  
Future advancements should focus on the following:  
* Technology Expansion: Investigating the integration of Siemens Edge and similar platforms for real-time data processing and edge computing.
* System Interoperability: Exploring hybrid systems that combine multiple architectures to enhance flexibility and adaptability.
* Machine Learning and AI: Developing predictive models for fault detection and process optimization, leveraging Python-based ML/AI frameworks.
* Human Factors and Knowledge Transfer: Investigating psychological barriers to knowledge sharing and technology acceptance, addressing concerns such as job security and resistance to automation.
# Partitioning Strategies for Parallel Computation of Flexible Skylines
## 1 Introduction  
In the age of information, the sheer volume of data generated every second is staggering. As businesses, researchers, and individuals alike deal with these enormous datasets, the main challenge besides storing the data is extracting meaningful insights from them. This is where efficient techniques for data retrieval become invaluable. With the growth of big data, efficiently finding interesting data within large datasets has become essential. Skyline queries are a method for selecting a subset of data by returning tuples that are not dominated by any other tuple. A tuple \(t\) dominates another tuple \(s\) if \(t\) is not worse than \(s\) in any attribute and is strictly better in at least one. Top-\(k\) queries, on the other hand, reduce multi-objective problems to single-objective ones using a scoring function that incorporates parameters like weights to reflect user preferences for different attributes. While skylines provide a global view of potentially interesting data, they do not consider user preferences and may return too many tuples, making it difficult for users to make decisions.  
To address these issues, _Flexible Skylines_ combine the concepts of skyline and top-\(k\) queries by applying constraints to attributes in order to specify preferences and give different importance to each. The concept of \(\mathcal{F}\)-dominance is the key idea to extend dominance: a tuple \(t\)\(\mathcal{F}\)-dominates another tuple \(s\) if \(t\) is always better than or equal to \(s\) according to all scoring functions in \(\mathcal{F}\). Flexible skylines identify a subset of the skyline and exist in two flavors: the non-dominated flexible skyline (ND), which returns the subset of non-\(\mathcal{F}\)-dominated tuples, and the potentially optimal flexible skyline (PO), which returns a subset of ND representing all tuples that are top-1 with respect to a scoring function in \(\mathcal{F}\). Although, as mentioned, the cardinality of interesting tuples is typically smaller than in the case of skylines, flexible skylines are still computationally intensive operators.  
In order to tackle the challenges of scalability in the face of very large datasets, we propose a parallel scheme that partitions the data and then treats every partition in parallel.  
Each partition processes a part of the dataset and returns a so-called \"local\" result, which is then merged in a sequential phase to find the \"global\" result.  
Other works have attempted the adoption of partitioning strategies for computing skylines, but this is the first attempt for flexible skylines, which have inherent difficulties of their own, which include possibly increased load imbalance and inter-partition dependencies.  
We also assess the effect of an initial filtering phase to decrease the load on the parallel part, as well as methods for eliminating the sequential phase entirely. The algorithms are implemented in PySpark, a parallel environment based on Spark, and tested on virtual machines on a datacloud with up to 30 cores.
## 5 Conclusions
In such conditions, one sensor can only communicate within the programmed time slot. The Listen Before Talk contention-based protocol verifies the degree of channel occupancy multiple times within the same scheduled time slot [41]. By integrating such a mechanism, the decrease in the latency level associated with the LoRa communication is possible alongside with the decrease in the number of collisions. Another goal is the improvement of the LoRa communication protocol, so that it becomes suitable for real-time environments and time-sensitive applications. One other method of nearing the LoRa communication protocol to the real-time concept involves the introduction of the ability of self-management and grouping of the devices, so that they themselves can determine their position on the communication time slots, as part of an architecture that is based on time-slotted communications. The use of a single slot dedicated to each frame is suggested in Reference [42] as a solution for global synchronization and for the management of acknowledgements. The experimental results show that the implementation of such mechanisms leads to a packet delivery rate of over 99%, with an added plus of energy efficiency, even in the case of distant sensors. The novel LoRa traffic generator that has been developed is a useful and reliable instrument for the analysis of scalability and performance of LoRa communications. The traffic generator can be integrated into many applications, having immense potential. Thus, different manufacturers can use it in assessing the real throughput of their gateway architecture. Moreover, LoRa network operators can easily field-test their network distribution on a large-scale geographical area, reducing the initial deployment costs. The ability to further test and develop new communication protocols makes the LoRa traffic generator an asset that can provide a contribution to large-scale LoRa networks research. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and V.P. analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript. This work was partially supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0776/No. 36 PCCDI/15.03.2018, within PNCDI III and in part by the project \"Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control,\" contract No. 671/09.04.2015, Sectoral Operational Program for Increase of the Economic Competitiveness co-funded by the European Regional Development Fund. The authors declare no conflict of interest.
# Antimicrobial Activity of L-Lysine and Poly-L-Lysine with Pulsed Electric Fields
## 4 Discussion
PEF studies have a wide range of applications for microbial inactivation in liquid and semisolid products (milk, milk products, egg products, juice, etc.) [8,10,52]. It is known that PEF treatment at 35 kV/cm and 90 \(\upmu\)s can decrease by 5.15 log numbers of _E. coli_ colonies. Although, PEF treatment at 20 kV/cm with 60 pulses provided nearly 2 log reduction in viable cell counts of _S. aureus_[53,54]. Therefore, modulation of the inactivation efficiency based on PEF parameters is possible and higher inactivation rates can be achieved. Also, in this study, we used Lys and PLL concentrations, which cause less than <0.25 log reduction in all microorganisms when applied without PEF. Therefore, for further studies higher concentrations of Lys and PLL can be used. Generally, PLL has higher cationic behavior than Lys due to the higher number of repetitive L-Lys residues, which can vary from 1000 to 2000. This could be the reason for PLL's higher antimicrobial activity, which was also observed by combining with pulsed electric field treatment. Additionally, the use of PEF with PLL or Lys could induce generation of negative curvature and may simplify interactions at the surface of the membrane. During this process, cationic amino groups induce negative curvature wrapping of anionic membranes and lead to micellization/vesiculation, which disrupts membrane integrity causing the thinning of membranes.
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 3 Experiments
### Quantitative Evaluations  
#### 3.2.1 KolektorSDD2  
Figure 7 presents a comparison of the performance of loss functions conditioned on different networks trained on KolektorSDD2. The balanced loss significantly outperforms other loss functions in all networks, surpassing the second best by 18% at most and 10% at least. The experiment also reveals that region-based loss (Dice loss and Lovasz loss) is also advantageous over other pixel-based loss functions, because the pixel label imbalance problem is mitigated in Dice loss by Dice coefficient computation. However, our balanced loss succeeds to ease label imbalance in a pixel-based manner, retaining the room for solving other imbalance problems. Featuring online hard example mining, focal loss has some improvement on the basic cross-entropy loss. However, focusing on hard examples, which are possible noisy or invaluable examples, does not turn out to be a good strategy in KolektorSDD2. These findings can be consistently observed across all networks. Figure 8 presents visualized semantic segmentation results of KolektorSDD2 defect examples.
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 1 Introduction  
The main question and objective of this study is to deeply explore the application potential of digital twin technology in building energy management. Especially in the context of Building Information Modeling (BIM) and Digital Twins (DT), how to improve energy efficiency and achieve sustainable building energy management by monitoring, optimizing, and predicting building energy consumption in real time. This is also the literature gap that this study aims to address. The motivation behind the research is to drive improvements in energy efficiency and sustainability in buildings. Advances in building energy analytics and machine learning technologies are expected to improve forecasting accuracy, optimize energy efficiency, and improve the management processes to achieve sustainable development goals.  
The application and importance of digital twin technology in the field of building energy management have been widely recognized. This innovative technology provides managers with an in-depth understanding of building energy consumption patterns by monitoring the energy usage of building facilities in real time, such as lighting systems,power consumption, and HVAC usage, to achieve energy use improvement and improvement optimization.  
The innovation of this study lies in the in-depth exploration of the application potential of digital twin technology in building energy management. The unique contribution of this study to the field of investigation is that it not only helps us to better understand and manage energy use in buildings, driving energy efficiency, and environmental goals but also significantly improves the economics of buildings. Therefore, digital twin technology should be fully utilized and developed to achieve higher efficiency and environmental protection in building energy management.
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 2 Materials and Methods
### Mid-Range Wireless Link Formulas  
This subsection presents appropriate methods for calculating the mid-range wireless link and important features of the different techniques. Three different methods used in the evaluation include (i) asymptotic Friis formula, (ii) Chu formula, and (iii) integral coupling formula. The first two formulas based on the far-field gain are advantageous in providing an almost instant calculation. However, the drawbacks include the limitation of the possible scenarios and the slight loss of the effective range, compared to other methods. The integral coupling formula utilizes the FFT of the far-field data with magnitude and phase. Even though relatively complex far-field data are required, the applications of the formula, including various case studies, are very wide. The three different methods are all accurate and time-efficient, even when not taking the recent advancement of computing capabilities into consideration.