esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
This appendix provides a brief but succinct derivation of convergence for the HSOR stage in our proposed scheme and its convergence conditions. Of course, as the HSOR stage converges, the CHSOR scheme converges as well. Considering Equation (39), we know that the spectral radius of the iteration matrix \(\rho(\mathbf{G})\) needs to be less than one (i.e., the maximum eigenvalue of the iteration matrix in the iteration equation is less than one); then, the iteration equation can be proved to converge. In Equation (38), the conformation of the HSOR iteration matrix \(\mathbf{G}_{HSOR}\) is \(\mathbf{G}_{SOR}\) multiplied by \(\mathbf{G}_{MSOR}\), and its expansion is as follows:  
\[ \begin{split}\mathbf{G}_{HSOR}&=\mathbf{G}_{SOR} \mathbf{G}_{MSOR}\\ &=(\mathbf{D}-\omega\mathbf{L})^{-1}[(1-\omega)\mathbf{D}+\omega \mathbf{U}](\mathbf{D}-\omega\mathbf{L})^{-1}[\mathbf{U}+(1-\omega)\mathbf{L} ].\end{split}  \tag{20}\]  
According to the eigenvalue theorem [60], we can obtain  
\[\mathbf{G}_{HSOR}\mathbf{x}=(\mathbf{D}-\omega\mathbf{L})^{-1}[(1-\omega) \mathbf{D}+\omega\mathbf{U}](\mathbf{D}-\omega\mathbf{L})^{-1}[\mathbf{U}+(1- \omega)\mathbf{L}]\mathbf{x}=\lambda\mathbf{x}, \tag{21}\]  
where \(\lambda\) is the eigenvalue of \(\mathbf{G}_{HSOR}\), corresponding to the eigenvector \(\mathbf{x}\). Here, we move two \((\mathbf{D}-\omega\mathbf{L})^{-1}\) from the left to the right side of the equality, yielding  
\[(\mathbf{D}-\omega\mathbf{D}+\omega\mathbf{U})(\mathbf{U}+\mathbf{L}-\omega \mathbf{L})\mathbf{x}=(\mathbf{D}-\omega\mathbf{L})(\mathbf{D}-\omega \mathbf{L})\lambda\mathbf{x}, \tag{22}\]  
and we can expand Equation (22) as follows:  
\[ \begin{split}&(\mathbf{D}\mathbf{U}+\mathbf{D}\mathbf{L}-\omega \mathbf{D}\mathbf{L}-\omega\mathbf{D}\mathbf{U}-\omega\mathbf{D}\mathbf{L}+ \omega^{2}\mathbf{D}\mathbf{L}+\omega\mathbf{U}^{2}+\omega\mathbf{U}\mathbf{L} -\omega^{2}\mathbf{U}\mathbf{L})\mathbf{x}\\ &=(\mathbf{D}^{2}-\omega\mathbf{D}\mathbf{L}-\omega\mathbf{L} \mathbf{D}+\omega^{2}\mathbf{L}^{2})\lambda\mathbf{x}.\end{split}  \tag{23}\]  
Then, we have  
\[ \begin{split}&[(1-\omega)\mathbf{D}\mathbf{U}+(1-2\omega+\omega ^{2})\mathbf{D}\mathbf{L}+\omega\mathbf{U}^{2}+(\omega-\omega^{2})\mathbf{U} \mathbf{L}]\mathbf{x}\\ &=(\lambda\mathbf{D}^{2}-\lambda\omega\mathbf{D}\mathbf{L}- \lambda\omega\mathbf{L}\mathbf{D}+\lambda\omega^{2}\mathbf{L}^{2})\mathbf{x}. \end{split}  \tag{24}\]  
Further rearranging Equation (24) yields Equation (25), as follows:  
\[ \begin{split}&[(1-\omega)\mathbf{D}\mathbf{U}+(1+\lambda\omega-2 \omega+\omega^{2})\mathbf{D}\mathbf{L}+(\omega-\omega^{2})\mathbf{U}\mathbf{L} +(\lambda\omega)\mathbf{L}\mathbf{D}+(\omega)\mathbf{U}^{2}\\ &+(-\lambda)\mathbf{D}^{2}+(-\lambda\omega^{2})\mathbf{L}^{2}] \mathbf{x}=0.\end{split}  \tag{25}\]  
Now, we multiply Equation (25) by the transpose of \(\mathbf{x}\):\[
## 3 Materials and Methods
### First-Frame Localization Model Based on Improved RFBNet
#### 3.2.1 Target Prediction Algorithm Based on Kalman Filtering
 \tag{16}\]  
where \(Z_{k}\) denotes the observed position of the instantaneous punch press arm, \(H_{k}\) denotes the observation matrix, \(m_{k}\) denotes the observation noise vector satisfying the normal distribution \(N(0,M)\), and \(M\) denotes the covariance matrix of the observation noise during the movement of the punch press arm. The observation equation of the instantaneous punch press arm is as follows:  
\[Z_{k}= \begin{bmatrix}1&0&0&0\\ 0&0&1&0\end{bmatrix}\begin{bmatrix}x(k)\\ y(k)\\ v_{\kappa}(k)\\ \
u_{\gamma}(k)\end{bmatrix} +m_{k} \tag{17}\]  
The prediction equation for the state of the perforating machine arm is as follows:  
\[\hat{X}_{k}^{-}=F_{k-1}\hat{X}_{k-1} \tag{18}\]  
where \(\hat{X}_{k-1}\) denotes the instantaneous state estimate and \(\hat{X}_{k}^{-}\) denotes the instantaneous prediction of the state of the punch press arm. The prediction error covariance matrix is calculated as follows:  
\[P_{k}^{-}=F_{k-1}P_{k-1}F_{k-1}^{T}+E \tag{19}\]  
where \(P_{k-1}\) is the moment-to-moment error covariance matrix estimate and \(P_{k}^{-}\) is the moment-to-moment error covariance matrix prediction. The Kalman gain matrix is calculated as follows:  
\[K_{k}=P_{k}^{-}H_{k}^{T}(H_{k}P_{k}^{-}H_{k}^{T}+M)^{-1} \tag{20}\]  
The estimated value of the state of the punching machine arm at the moment \(k\) is calculated as follows:  
\[\hat{X}_{k}=\hat{X}_{k}^{-}+K_{k}(Z_{k}-H_{k}\hat{X}_{k}^{-}) \tag{21}\]  
The estimation error covariance matrix is calculated by the following procedure:  
\[P_{k}=(\,l-K_{k}H_{k})P_{k}^{-} \tag{22}\]  
where \(\,l\) is the unit matrix. ```
0: Bounding frame \(\hat{l}_{0}\) of the perforated robot arm labeled in frame 1. 0: Predicted perforated robot arm bounding box \(\hat{l}_{k-1}\) in frame \(k\). 1: Initialize the Kalman filter:
2: Set the initial state vector \(\mathbf{x}=[x,y,\
u_{x},\
u_{y}]\) based on the bounding box \(\hat{l}_{0}\) in frame 1. 3: Set the initial observation vector \(\mathbf{z}=[x,y]\). 4:for\(i=2,3,\dots,k\)do
5: (1) Predict the state of the perforated robot arm in frame \(i-1\):
6: Compute the predicted state \(\hat{\mathbf{x}}_{k}^{-}\) and the prediction error covariance matrix \(P_{k}^{-}\). 7: (2) Update the Kalman filter based on the observation in frame \(k\):
8: Calculate the observation \(\mathbf{z}_{k}\) and the Kalman gain \(K_{k}\). 9: (3) Refine the state and covariance estimates:
10: Compute the state estimate \(\hat{\mathbf{x}}_{k}\) and the error covariance \(P_{k}\). 11: (4) Obtain the predicted bounding box \(\hat{l}_{k}\) from the state estimate \(\hat{\mathbf{x}}_{k}\). 12:endfor
```  
**Algorithm 1****Target Prediction Algorithm Based on Kalman Filter**
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Evaluating the Classification of the Operating Condition
#### 5.2.3 Lesson Learned  
AEs can effectively recognize the operating conditions where the device is being worked on. The results registered in the experiments show high levels of accuracy and F1 score, making possible the application of the technology in real world scenarios. AEs largely overcome the competing approaches in both supervised and unsupervised scenarios. The approach is robust with respect to the window size selected.  
From the industrial point of view, the AEs allow the detection of faults regardless of whether it has already occurred in the past. In particular, the supervised implementation is used for fault diagnosis, so that it is possible to stop the machinery before severe consequences occur once the failure is classified. On the other hand, when the unsupervised implementation is used and a novel condition is detected, an alarm can be generated to suggest a double check by the machinery operator. In the meantime, the observations are automatically labeled as \"novel\" to facilitate the subsequent model re-training.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 8 Effects of Ohmic Heating on Inactivation Microorganisms in Food  
Many foodborne diseases have spread throughout the world despite the continuous development of food technology [27]. Although food products were treated at high temperatures to ensure their microbiological safety, thermal damage occurred such as changes in flavor, texture, taste, and nutrient composition. Therefore, modern studies have found that advanced thermal treatment techniques play a vital role in preventing damage to processed foods and eliminating microorganisms such as bacterial cells and spores [76]. [PERSON] et al. [77] mentioned that the influence of the electric field on biological cells was depended in particular on the size of the cell. This study evaluated the inactivation of vegetative microorganisms by ohmic heating at 12 kHz and 300 kHz compared to non-thermal effects. The inactivation of _Microbacterium lacticum_, _Escherichia coli_ and _Saccharomyces cerevisiae_ were tested using conventional and ohmic heating in the 12-kHz and 300 kHz system. The results found that the inactivation levels by kilohertz ohmic heating were not increased compared to conventional heat treatment. Ohmic heating can inactivate bacteria not only by its heating effects, but also through the non-thermal effects of the electrical current [78]. Bacterial count is one of the basic and most used methods in assessing the microbial stability of the foodstuff. Several studies have been conducted to measure the effect of ohmic heating on the total number of bacteria in juices, fruits, milk, and seafood [14; 79]. [PERSON] et al. [80] mentioned that the main reason for microbial inhibition during ohmic heating is due to the effect of heat treatment. However, other studies indicated the ability of non-thermal treatment such as chemical and mechanical effects to inhibit microbial growth [80]. The mechanism of the chemical effect is due to the formation of free radicals, oxygen, hydrogen, hydroxyl, and mineral ions that cause bacterial death, while the mechanical effect is due to the disruption of the microbial cell membrane, which leads to leakage of cell contents through electroporation. This study determined that ohmic heating had a significant effect in eliminating _Bacillus subtilis_ spores compared to conventional methods [80].
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 3 Experiments and Results
### Tomographic Accuracy Evaluation
Figure 17: Horizontal distribution of difference in water vapor density between five schemes and ERA5 data at different altitudes. Similarly, the RMS and the mean absolute error (MAE) values of the difference between the five schemes and ERA5 data, are given in Figure 18. Among the five schemes, GRECL288 presented the best accuracy. Similarly, the maximal error occurred at the bottom layer, where the distribution of observations entirely depended on the distribution of ground stations. Increasing the number of ground stations, or introducing additional observations, can fundamentally solve the problem of the poor accuracy of the tomographic bottom.
# Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples
## 4 Experiments
### Ablation Study  
In order to explore the impact of adversarial prototypes on model performance, we eliminated the adversarial prototype generator (APG) and retrained the model. In addition, we studied the effects of using only the positive adversarial prototype and using both positive and negative adversarial prototypes on segmentation performance. The results are shown in Table 3.  
The experimental results show that the mIoU value obtained by the model with the adversarial prototype generator is about 4% higher than that of the model without the adversarial prototype generator. The performance improvement of adding a negative adversarial prototype to guide segmentation is not significant, the value has only increased by 0.35%. We make the following analysis: the positive adversarial archetype can increase the attention of positive examples, while the negative adversarial archetype inhibits the attention of positive examples. In the attention layer of the decoder, there is a huge gap between the attention of the foreground pixels in the query image to the two adversarial archetypes, and the negative adversarial archetypes play a small role in the attention weight. Therefore, the negative adversarial archetype will not have much effect on the segmentation of the positive example.  
We performed ablation experiments on the encoder modifications, and the results are shown in Table 4.  
The results show that the learnable position embedding will pay more attention to the training data, and the generalization performance of new classes will not be strong when the training data are less. The proposed encoder can effectively improve the generalization performance of new classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Encoder** & **Number of Training Classes** & **mIoU** \\ \hline encoder with learnable PE & 400 & 68.21\% \\ encoder with & & \\ non-parameter PE & 400 & 76.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of our encoder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **mIoU** \\ \hline OSLSM [2] & Vgg-16 & 43.3\% \\ Guided Network [27] & Vgg-16 & 44.1\% \\ PANet [4] & Vgg-16 & 55.7\% \\ CANet [29] & Res-50 & 57.1\% \\ CWT [30] & Res-101 & 64.7\% \\ GFormer (Ours) & vit & \(\approx\)57\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with other few-shot methods for 5-shot segmentation in Pascal-5\({}^{i}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adversarial Prototype Generator** & **Neg-Adversarial Prototype** & **mIoU** \\ \hline \(\times\) & & 72.38\% \\ ✓ & \(\times\) & 76.27\% \\ ✓ & ✓ & 76.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of the adversarial prototype.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 3 Results and Discussions
### Evaluations on Real Lightning Data  
In this section, we present the experimental evaluation of the proposed CCWD-PSO method for lightning mapping on the real collected lightning data described in Section 2.2.  
Evaluation of the Lightning Event Extraction Method  
A total of three fast-electric fields related to FA and VHF signals are successfully identified by the lightning event extraction method. In this study, the fast-electric field polarity of positive NBEs flashes was identified based on the atmospheric electricity sign convention. Figure 8a shows the interferometer data for NBE1 that consist of VHF and fast-electric field waveforms, including the non-lightning parts. On the other hand, Figure 8b shows the proposed lightning event extraction method that determines the starting and ending points of the extracted fast-electric field (i.e., the first point VHF and the last point VHF signals).  
Figure 9 illustrates two other examples of the lightning event extraction method performed on two samples of the lightning (NBE2, and NBE3) flashes. The lightning event extraction method efficiently discards the noisy segments of the fast- electric field and VHF signal. Moreover, this method will potentially reduce the computation time and thus  
Figure 8: Interferometer data NBE1: (**a**) original ITF data; (**b**) lightning event extraction.  
allows for faster signal processing of lightning events; it is also capable of reducing the lightning flash identification.
###### Abstract  
The aim of this study is to assess the environmental impact of storage systems integrated with energy plants powered by renewable sources. Stationary storage systems proved to be a valid solution for regulating networks, supporting frequency, and managing peaks in electricity supply and demand. Recently, their coupling with renewable energy sources has been considered a strategic means of exploiting their high potential since it permits them to overcome their intrinsic uncertainty. Therefore, the storage systems integration with distributed generation can improve the performance of the networks and decrease the costs associated with energy production. However, a question remains regarding the overall environmental sustainability of the final energy production. Focusing on electrochemical accumulators, the problems mainly concern the use of heavy metals and/or impacting chemical components of storage at the center of environmental hazard debates. In this paper, an environmental assessment from a life-cycle perspective of the hybrid energy systems powered by fossil and renewable sources located on two non-interconnected minor islands is presented. Existing configurations are compared with new ones obtained with the addition of batteries for the exploitation of renewable energy. The results show that, for batteries, the assembly phase, including raw material extraction, transport, and assembly, accounts for about 40% of the total, while the remaining part is related to end-of-life processes. The reuse and recycling of the materials have a positive effect on overall impacts. The results also show that the overall impact is strongly related to the actual energy mix of the place where batteries are installed, even if it is usually lower than that of the solution without the batteries. The importance of a proper definition of the functional unit in the analysis is also emphasized in this work.  
life-cycle assessment; renewable energy storage; energy storage; batteries; distributed energy systems +
Footnote †: journal: Applied Sciences  
Article Environmental Impact of Energy Systems Integrated with Electrochemical Accumulators and Powered by Renewable Energy Sources in a Life-Cycle Perspective  
[PERSON], [PERSON], [PERSON], and [PERSON]
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 2 Materials and Methods  
### Patients and Aortic Tissue Samples  
Aortic tissue samples were obtained from a population of AAA patients who consented to participate in the study between 2016 and 2020. The research protocol, approved by the University of Calgary Conjoint Health Research Ethics Board (Ethics ID: REB15-0777) included pre-operative electrocardiography-gated dynamic computed tomography (CT) imaging and open surgical aneurysm repair with complete aortic resection that allowed for the collection of specimens from different regions (i.e., aneurysm sac, aneurysm neck, anterior, posterior, left, right) of individual aortas. A sampling grid with 24 regions (or patches) was utilized in the systematic harvesting of aortic samples. Patches were defined perpendicularly to the lumen centerline of each aortic geometry reconstructed from the first phase of the dynamic CT scans (Figure 1a). Specifically, the 24 regions resulted from the subdivision of each aortic geometry into 6 regions along the length of the vessel ('1'--neck, '6'--bottom; average patch length along the centerline was 20 \(\pm\) 4 mm) and 4 regions along the circumference ('L' and 'R'--left and right sides of the aneurysm, 'P' and 'A'--anterior and posterior sides of the aneurysm with respect to the geometry centerline). Both the lumen and outer aortic wall were reconstructed by means of image processing and semi-automatic, threshold-based image segmentation with the imaging software Simpleware ScanIP (Synopsys Inc., Mountain View, CA, USA). The use of a sampling grid was central to the study as it facilitated tracking of specimen locations and regional analysis of both ex vivo and in vivo properties.  
Figure 1: (**a**) Methodology summary and demonstration of the sampling grid defining the circumferential regions (RP—right posterior, LP—left posterior, RA—right anterior, LA—left anterior) and axial regions (1 to 6) of an aneurysm. (**b**) Biaxial testing setup and representative specimens: normal-sized specimen with four hooks per side and smaller specimen with two hooks per side. The latter is shown to fail due to tissue fragility. (**c**) A 3D representation of the biaxial data for a displacement-controlled test with different loading ratios. The surface interpolation is used to determine the equi-biaxial stretch-stress state. Stress and stretch are defined in the circumferential direction (11) and the longitudinal direction (22). (**d**) A 2D representation of the equi-biaxial stretch state and determination of all relevant mechanical properties—the low and high tangential modulus (LTM and HTM), the transition zone onset and end (TZo and TZe).
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 3 LDPC Decoding Algorithms  
The original LDPC decoding algorithm was the sum-product algorithm (SPA) [16]. The SPA can almost achieve the theoretical decoding ability, but it is difficult to implement because of the excessively complex calculations. Therefore, alternative algorithms have been published, such as the NMSA and OMSA. Both algorithms use approximate values adjusted by a normalized or offset coefficient to simplify the overall operation complexity and maintain the approximate decoding capability of the SPA. It is noteworthy that the NMSA was widely used for a decade in the hardware implementations of LDPC decoders. Furthermore, additional discussions and research on simplifying the hardware complexity of the NMSA have been published, such as the NPMSA, approximate extrinsic minima (rExMin) algorithm [17], second minimum approximation min-sum algorithm (SMA-MSA) [18], and single variable weight min-sum algorithm (svwMSA) [19]. These algorithms are dedicated to alleviating the problems of high hardware complexity and area cost, which are caused by the operations of comparators. Although these studies successfully alleviated the above problems, the decoding capability was weakened. Below, we introduce the details of the algorithms and their differences.
## 3 Experiment and Results
### Evaluation Metrics and Determination of Parameters  
We use two different evaluation metrics to measure the effectiveness of various models for Chl-a time series prediction, including the root mean squared error (RMSE) and the mean absolute error (MAE). The smaller RMSE or MAE of the model, the better its performance. The RMSE function and the MAE function are defined as follows:  
\[RMSE(Y,Y^{\prime})=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{t}^{i}-\hat{y}_{t}^{i})^{ 2}}, \tag{16}\]  
\[MAE(Y,Y^{\prime})=\frac{1}{N}\sum_{i=1}^{N}\biggl{|}y_{t}^{i}-\hat{y}_{t}^{i} \biggr{|}, \tag{17}\]  
In our experiments, we have three hyperparameters, including the length of window size \(T\), the number of hidden states in encoder as \(m\), and the number of hidden states in decoder as \(p\). During the training phase, we set 128 as the size of the minibatch. Firstly, we chose a proper value for the number of hidden states in encoder \(m\) and in decoder \(p\) from [32,64,128,256]. We discovered that the best performance occurred when \(m=p=64\) from Table 3. As for the size of window size, we set \(T\in\{6,12,18,24\}\).
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Background
At one end of the spectrum are simple, low-compute thresholding approaches; however, their accuracies may be lower compared to more complex learning models (e.g., CNN's), which require greater computation resources and specialized hardware. Whilst both approaches are viable, the relative strengths and weaknesses should be evaluated. Models that rely upon linear distributions are often simpler and generalize well, and therefore, do not require high quality training labels. Whilst that, in itself, is a useful attribute, it can also become problematic when the observed phenomena does not always adhere to that assumption. Land-water classification has some non-linear exceptions such as cloud, shadows, and snow. A more complex non-linear model can often overcome these issues when provided with good training data, or alternatively a logic-based system can be integrated into the decision-making process of a simpler model, whereby the problematic areas are removed through specific thresholds or basic decision trees. We chose to use a simple logistic regression model, in combination with logic-based masking, to ensure sufficient accuracy, whilst maintaining computational efficiency and simplicity that facilitates analysis and understanding at scale.