esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors 2013, 13, 14301-14320; doi:10.3390/s131114301  
_Service_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Energy Efficient Stable Election-Based Routing Algorithm**  
**for Wireless Sensor Networks**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{3}\)* \({}^{1}\)**  
School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China;  
E-Mails: [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (Z.Z.)  
School of Software, Dalian University of Technology, Dalian 116620, China;  
E-Mail: [EMAIL_ADDRESS]  
Computer Engineering Department, Kyung Hee University, Suwon 449-701, Korea;  
E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-31-201-2514; Fax: +82-31-202-2520.  
_Received: 9 September 2013; in revised form: 18 October 2013 / Accepted: 18 October 2013 / Published: 24 October 2013_  
**Abstract:** Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs). However, sensor networks with one fixed sink node often suffer from a hot spots problem since nodes near sinks have more traffic burden to forward during a multi-hop transmission process. The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, _etc._ In this paper, a modified Stable Election Protocol (SEP), which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP.  
keywords: wireless sensor networks; mobile sink; clustering; multi-hop; lifetime +
Footnote †: journal: Journal of the American Statistical Association
## 5 Data Correction Module
### Detection and Removal of Problematic Observations  
Problematic observations are values that directly interfere with the quality of the data and that could generate false alarms or later reduce the identification of real anomalies, if the training of the algorithms is performed with these values. Two statistical methods were considered to perform these functions: the interquartile range (IQR) [58; 59] and standard deviation method (SDM) [56; 60].  
The use of the SDM was the first to be considered. However, the analysis carried out on data (Section 4) proved that it might not be sufficiently robust, because it is not guaranteed that meters conform to a normal distribution, as this method requires. For that reason, the adopted solution was the IQR method, as our data tend to be skewed and have fewer limitations associated with the distribution of data. The IQR method is very simple to apply as it considers values in the range \([Q_{1}-K\times IIQ,Q_{3}+K\times IIQ]\) to be \"normal\", where \(Q_{i}\) is the \(i^{\textrm{th}}\) quartile, \(K\) is an adjustment parameter (usually defined as 1.5), and \(IIQ=Q_{3}-Q_{1}\) is the interquartile range.  
As an example of this data treatment, Figure 13 presents the resultant data after removing the peak values shown in Figure 8. The data quality has significantly improved and virtually all readings that could be visually considered problematic have been removed. In this context, it was found that this IQR method must be adjusted by setting the tuning parameter \(K\) according to each meter, in order to preserve the maximum number of correct readings. After carrying out this procedure, the missing values were estimated and reinserted, as described in the following section.
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Product/Services
#### 4.2.6 Staking  
Staking in the DeFi ecosystem resembles a traditional deposit, wherein investors lock their crypto assets into smart contracts for a specific duration to earn passive income. It is closely associated with proof-of-stake (PoS) blockchain networks, where users become validators by staking a certain amount of the platform's native tokens. In PoS, validators undergo a rigorous approval process to prevent fraudulent transactions. The stake in PoS algorithms corresponds to one's wealth and represents the sum of currency locked up for a defined period. Staking allows users to participate in the consensus algorithm, supporting blockchain's security and operation without the high computational energy requirements of traditional proof-of-work mechanisms [1, 52, 94]. DeFi has notable examples of staking platforms, including _AQRU_ and _Cardano_. _AQRU_ offers advanced order types, margin trading, and competitive fees. The platform's staking and rewards programs for selected coin/token holders provide attractive returns. Unique features such as advanced infrastructure, real-time market data, and decision-making tools enhance user analysis. _AQRU_'s advanced order types grant users greater trade control with limit orders, stop orders, and trailing stop orders. Margin trading, while potentially profitable, involves higher risks, requiring users to understand market dynamics. _AQRU_ distinguishes itself with a low fee structure, maximizing user profits with competitive trading and minimal withdrawal fees. Its staking program offers passive income opportunities by holding specific coins/tokens, yielding higher returns than traditional savings accounts. The _AQRU_ rewards program also incentivizes coin/token ownership, providing benefits like exclusive features or reduced fees. _Cardano_ is a blockchain platform that delivers scalable and reliable DApps infrastructure. _Cardano_'s staking mechanism, delegation, lets users delegate _ADA_ tokens to staking pools and earn rewards. Known for its emphasis on sustainability and security, _Cardano_ is a trusted staking platform promoting decentralization. Blockchain's unique approach involves peer-reviewed research for rigorous testing and security, setting it apart from other platforms with its scientific verification and academic oversight.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 2 Methodology
### Fault Feature Extraction Stage
#### 2.2.1 Complete Ensemble Empirical Mode Decomposition with Adaptive Noise  
Compared with the white Gaussian noise adopted in EMD and EEMD, CEEMDAN adds the specific noise to each step of its decomposition to overcome shortcomings of EMD [28, 29] and EEMD [30]. Concretely, the IMF is achieved as the difference between the current residual and its local mean. The number of CEEMDAN iterations is half that of EEMD, and it can accurately decompose the signals. The process of CEEMDAN is described as follows:  
(1) Add \(F_{k}\left(\omega^{(i)}\right)\) to the original signals \(X\):  
\[X^{(i)}=X+\beta_{k-1}F_{k}\left(\omega^{(i)}\right), \tag{1}\]  
Figure 1: Functional block diagram of the proposed method. CEEMDAN = complete ensemble empirical mode decomposition with adaptive noise; IMFs = intrinsic mode functions; ApEn = approximate entropy; UKF = unscented Kalman filter; RUL = remaining useful life.  
where \(F_{k}\left(\omega^{\left(i\right)}\right)\) is the \(k\)-th IMF processing Gaussian white noise, and \(\beta_{k-1}\) is the signal-to-noise radio for each procedure.
2. Use EMD to obtain the local mean of \(X^{\left(i\right)}\) to get the first residual \(r_{1}\): \[r_{1}=\frac{1}{N}\sum\limits_{i=1}^{N}\mathrm{H}\left(x^{\left(i\right)}\right),\] (2) where \(N\) is total number of sets, and \(\mathrm{H}\left(\bullet\right)\) is a function used to estimate the local mean of signals.
3. Calculate the first IMF: \[IMF_{1}=X-r_{1}.\] (3)
4. Decompose \(r_{1}+\beta_{1}F_{2}\left(\omega^{\left(i\right)}\right)\) and delimit the second IMF: \[IMF_{2}=r_{1}-r_{2}=r_{1}-\frac{1}{N}\sum\limits_{i=1}^{N}\mathrm{H}\left(r_ {1}+\beta_{1}F_{2}\left(\omega^{\left(i\right)}\right)\right).\] (4)
5. Calculate the \(k\)-th residual \(r_{k}\): \[r_{k}=\frac{1}{N}\sum\limits_{i=1}^{N}\mathrm{H}\left(r_{k-1}+\beta_{k-1}F_{k }\left(\omega^{\left(i\right)}\right)\right).\] (5)
6. Get \(k\)-th IMF: \[IMF_{k}=r_{k-1}-r_{k}.\] (6)
7. Repeat Equations (5) and (6) steps until \(R\) meets the terminal condition: \[R=X-\sum\limits_{k=1}^{K}IMF_{k}.\] (7)  
Therefore, \(X\) can be expressed as Equation (8), and all IMFs can be described as \(Y=\{IMF_{k}\},k\ =\ 1,2,...,K\).  
\[X=R+\sum\limits_{k=1}^{K}IMF_{k}. \tag{8}\]
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 2 PRAN-Net Fabric Defect Detection Method Based on Faster R-CNN
### Priori Anchor Generation
#### 2.2.1 Location Prediction  
The first step is to predict the location of the priori anchor in each feature map by the location prediction network \(N_{l}\) as shown in Figure 3. The semantic segmentation is made to get the defective score map by a \(1\times 1\) convolutional layer. Then, the defective scores are transformed by an element-wised sigmoid function to get the defective probability map with the same size as the feature map. The pixels whose value are larger than the set threshold \(\Delta L\) are considered as the defect pixels. We determine the defect sub-regions by gathering the defect pixels within the set distance between the adjacent pixels. The GT box, presented by \(gt=(x_{g},y_{g},w_{g},h_{g})\) is the labeled defect in the original fabric image, where \(x_{g}\) and \(y_{g}\) are the center coordinates of the GT, \(w_{g}\) and \(h_{g}\) are the width and height of the GT, respectively. The GT box is mapped to each feature map as GT', presented by \(gt^{\prime}=(x^{\prime}_{g},y^{\prime}_{g},w^{\prime}_{g},h^{\prime}_{g})\), where \((x^{\prime}_{g},y^{\prime}_{g})\) and \((w^{\prime}_{g},h^{\prime}_{g})\) are the center coordinates and the shape. If \((x^{\prime}_{g},y^{\prime}_{g})\) is in one of the sub-regions, the enclosing rectangle of this sub-region is taken as the candidate anchor, which is described as \(a_{cam}=\{(x_{0},y_{0},w_{0},h_{0})|w_{0}>0,h_{0}>0\}\), where \((x_{0},y_{0})\) are the center coordinates, \(w_{0}\) and \(h_{0}\) are the width and height of the candidate anchor. We introduce GHM-C loss \(L_{loc}\)[26] as the priori anchor location loss.
## 6 Discussion
### Resolution of Sensors  
It can be seen from the experimental results of six groups that the proposed EAEND model (10) can work effectively under different spatial resolutions, and can adapt to the change in observation scale well. In several groups of experiments from Section 4.3, optical remote sensing images of the Arctic sea ice with spatial resolutions of 10 m, 50 m, 5 m, and 16 m are selected as experimental materials. From the perspective of spatial resolution, several identification methods used for comparison (e.g., MNI model (27), NRI model (28), ETZNN model (29), SVM, Otsu thresholding, and MD) have better identification effects under different spatial resolutions. Therefore, the spatial resolution has little influence on several algorithms, indicating that several identification algorithms are universal. Further, when sensors with a spatial resolution of 16.5 m and 5 m, respectively, are used to observe the Arctic sea ice in the same region, more image details are presented with a spatial resolution of 5 m. However, this may also lead to \"overfitting\" in classification, that is, the identification algorithm may pay too much attention to noise or unimportant details in the image, increasing the difficulty of identification. However, the image with a spatial resolution of 16.5 m shows less feature details, and some thin and broken sea ice may fuse with the surrounding environment in the pixel, which makes it difficult for the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MetricsMethod** & **EAEND** & **SVM** & **Otsu Thresholding** & **MD** \\ \hline OA & **0.980968** & 0.809322 & 0.922221 & 0.965818 \\ AA & **0.987546** & 0.797767 & 0.949104 & 0.976625 \\ PP & **1.000000** & 0.969362 & **1.000000** & 0.999060 \\ Kappa & **0.948641** & 0.529627 & 0.806326 & 0.909534 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Identification metrics and results of four methods for the Arctic sea ice from HJ-2A CCD3.  
Figure 8: Identification results of four algorithm-assisted CEM method (1) for the Arctic sea ice from HJ-2A CCD3. (**a**) Original image. (**b**) Ground truth. (**c–f**) Identification results of EAEND model (10), SVM method, Otsu thresholding method, MD method.  
classification algorithm to accurately distinguish these features, and it is therefore prone to the phenomenon of missing identification, as shown in Figure 7f.
# Influence of Quality of Mounting Process of RF Transistors on Their Thermal Parameters and Lifetime
## 1 Introduction
The influence of the size of the void spaces in the soldered joint on the temperature distribution on the surface of these transistors is also assessed. Based on the performed measurements, changes in the lifetime of transistors depending on the size of voids are estimated. The obtained values of thermal parameters are compared with the data declared by the manufacturer. The main goal is to investigate how the voids influence thermal resistance and the lifetime of an RF transistor. In contrast to the studies given in the literature, we used both: the measurement methods based on infrared measurements with X-ray imaging and the statistical analysis. The investigated power transistors are described in Section 2. The soldering process is described in Section 3. The applied method and the measuring system are described in Section 4. The results of measurements are presented and discussed in Section 5. The influence of thermal parameters on the lifetime is analyzed in Section 6.
# Lung Field Segmentation in Chest X-rays:
## 2 Materials and Methods
### Lung Region Approximation: A Robust Method  
A series of trivial preprocessing steps were taken to reduce irrelevant information as well as to improve the separability of the region of interest. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique achieves locally significant contrast enhancement. Furthermore, the glenohumeral joint area introduces many challenges during segmentation but its location and size are generally invariable; thus, a straightforward cropping strategy can produce satisfactory elimination results. Afterwards, in order to separate the left and right parts, an automatic symmetry line detection algorithm is employed [26]. This is essentially achieved by comparing features on the image to those in the reflected version of the image. From now on, the segmentation algorithm is applied on each part separately.  
The algorithm employs bone structure information inside the lung area. This structural information can help us to better approximate the pixel intensity distribution of the lungs, as described in Figure 1. Based on this distribution, we are able to select a universal upper intensity threshold to initially filter out the background (Figure 2). Moreover, as will be shown in the Results section, abnormal patterns can already be identified by comparing individual CXRs with this distribution.  
We found that the Hough Line Transform succeeds in partially identifying the ribs, despite their predominantly curvy shape. This is sufficient for our particular segmentation problem. The two ends of every identified line are included in an initial set of bone points. Afterwards, these points are filtered by reviewing the likelihood of belonging to the rib cage--more specifically in a pattern of almost equally spaced parallel lines (which is usually the case inside a lung field). The third column of Figure 2 shows the distribution of the most probable rib cage points, after the filtering process. Based on their location, it should be straightforward to select only one of the lung contours for further processing (last column of Figure 2). This method is robust, as is also proven during the evaluation process.  
Figure 1: Cumulative probability distribution of the intensities of automatically selected most probable bone points inside the lung areas, after aggregating over all Chest X-ray (CXR) images in the dataset. A few points were not taken into account: those around the symmetry line that crosses the sternum, and around the cropped areas around the glenohumeral joints. This plot can be indicative of an appropriate upper intensity limit for an initial thresholding approach. In our specific case, we decided to drop high-intensity values with a total probability of less than 0.25.  
An important erroneous case is visible in the last row of Figure 2. Our segmentor identified the biggest part of the actual lung region but failed to separate it from neighboring parts, such as the areas around the glenohumeral joint and the sternum. The method for dealing with this problem is presented in the next subsection.
## 4 Discussion
Finally, our study showed a tendency to improve HbA1c levels, with a reduction in 4 out of 6 patients without getting worse in any subject. Similarly, a slight improvement has also been observed in other studies with HIIT in people with T1D [10]. Although the number of patients included in our study was limited, this preliminary finding is important to confirm that HIIT with elastic bands is safe by keeping blood glucose levels in a safe range. It also showed that it is effective by positively influencing HbA1c levels, and thus it can be recommended to people with T1D. The current study has several strengths. It was the first study in examining the influence of HIIT with elastic bands in glucose control using primarily isCGM as data source and CBG and HbA1c as secondary measurements. Therefore, the impact of HIIT on glucose metabolism was evaluated from different perspectives. Second, based on the results of this study, we are the first to recommend a simple and reproducible HIIT program to be used at home, and more specifically for patients with T1D, by establishing both guidelines for safely performing the exercise program through CBG monitoring and others in relation to carbohydrate adjustments and insulin reductions to prevent hypoglycemia and hyperglycemia. Third, we evaluated and confirmed that patients followed a balanced diet before the intervention. Therefore, dietary modifications were not necessary, thus preventing changes in dietary management that could potentially affect the results of this study. However, this study also has some limitations. As a pilot study, the few number of patients included limits to the extrapolation of the main results, and only men were included. Future studies should examine the influence of simpler and achievable HIIT programs in larger populations. As also observed in most studies with subjects with T1D, we observed important inter- and intra-individual variabilities. Finally, only a single type of HIIT exercise was evaluated, limiting the comparison of our findings with other HIIT programs or types of exercise.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 6 Performance Evaluation
Measurement results of the phased antenna array radiation patterns are shown in Figure 15. For clarity, in each of Figure 15a-d, the main lobe orientation angle, \(\varphi_{0}\) is specified. Generally, in the analysis of simulation results, the HPBW of a main lobe can be determined by estimating the value of the central angle, whose segments are on either side of the maximum point of the lobe and intersect the auxiliary circle corresponding to the gain of \(-3\) dB which has a constant diameter for any orientation angle of the lobe, because the software automatically normalizes the radiation patterns to 0 dB, regardless of the beam steering angle. In real measurements, the auxiliary circles for HPBW determination were depicted with dotted black lines and corresponded to 3 dB lower gain values than those corresponding to a maximum value of the main lobes for each beam steering angle. Figure 15: Experimental results depicting the radiation patterns of the proposed phased antenna array system for azimuthal beam steering angles of 0\({}^{\circ}\) (**a)**, 45\({}^{\circ}\) (**b)**, 150\({}^{\circ}\) (**c)** and 210\({}^{\circ}\) (**d). The circle within each diagram represents the antenna array with its four elements, numbered from 1 to 4. After estimating the HPBW in simulations, a constant value of 80\({}^{\circ}\) was observed. Measurement results presented in Figure 15 indicated a minimum HPBW of 76\({}^{\circ}\) corresponding to \(\varphi_{0}=0^{\circ}\), a maximum of 87\({}^{\circ}\) for \(\varphi_{0}=210^{\circ}\), and for the other orientation angles, it was close to 78\({}^{\circ}\). Following these, it can be observed that there is a good agreement between the simulation and measurement results of HPBW. However, regarding measurement results, some differences can be observed in the HPBW for various orientation angles, primarily due to technical imperfections and the indoor measurement environment. It is expected that in LEO satellite reception in the outdoor environment, the differences between the HPBW values for different beam steering angles will be less than or equal to 80\({}^{\circ}\). With all these differences, for an antenna array designed to receive LEO satellites in the 435-438 MHz frequency band, even an HPBW between 62\({}^{\circ}\) and 87\({}^{\circ}\) may be adequate because it can provide relatively wide coverage of the passage area of satellites that move rapidly in their orbits. At the same time, the possibility of electronically controlled beam steering of the antenna array can be useful in tracking the satellites we want to receive signals from while providing a better SNR (Signal-to-Noise Ratio) than in the usual case of omnidirectional antennas for reception of LEO satellites and also a more stable and robust connection (good connection in the presence of various obstacles or under the interference effects) with satellites during their passes.
## 3 Results and Discussion
### Best-Performing Strategies  
This section presents (see Figure 3), in a simple way, the strategies that achieve the best performance regarding the expected availability, number of stoppages, and mean failure risk. It is possible to observe that for availability and stoppages, the strategy that best performs for both cases is the 10% scenario. This is consistent with the main objective of the algorithm, which is to minimise the number of scheduled stoppages to directly increase availability. On the contrary, given that the algorithm has no regard for the risk assumed,  
Figure 2: Overview section.  
the lowest risk is achieved in the 6% tolerance scenario, suggesting that this strategy may favour the advancement of more activities or the delay of activities with low risk. This confirms that through assertive scheduling using tolerances, it is possible to decrease risk while achieving increased availability.