esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Datasets
### Satellite Data
#### 2.3.2. COSMO-Skymed X-Band SAR Satellite Data  
The Italian COSMO-Skymed program consists of four SAR satellites operating in X-band (wavelength \(\lambda\) = 3.1 cm) [39]. The constellation allows for a revisit time of 12 h. Available data have been acquired in Stripmap mode at HH polarization with a nominal spatial resolution of approximately 2 m.  
Data from all four satellites are available for central Yamal for the years 2013 and 2016. Revisit intervals vary with in some cases from 24 h in 2013 to 20 days in late autumn 2016 (see Table 2). The start of the thaw season is therefore well captured in both years. The first acquisition in 2013 is on day one of thaw and in 2016 on day five (see Figure 2b,c).  
All acquisitions have been made in ascending orbit between 5 and 6 a.m. local time and with an incidence angle of about 27\({}^{\circ}\) over central Yamal. As the normal mode is right looking, the region is viewed from SW. The image size is considerably smaller than for Sentinel-1 with 250 km swath width. The images center on the industry settlement of Bovanjenkovo. Its construction started in the 1980s [40]. The images do not extent to the VD long-term monitoring site but cover an area of similar land cover and geomorphology (distance of boundary to VD approximately 4 km). The southern part in direct proximity to VD has been analyzed in comparison with Sentinel-1 (overlap area shown in Figure 1).  
Further X-band data have been acquired over VD in 2014 and 2015 by TerraSAR-X (see application in [36]). They have not been considered for this study as there is both no overlap with Sentinel-1 availability and there are no acquisitions in the main year of interest, 2016.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Dataset  
Within the scope of our research, we made use of a real-world dataset known as activity recognition using ambient sensing (ARAS [[https://www.cmpe.boun.edu.tr/aras/](https://www.cmpe.boun.edu.tr/aras/)]([https://www.cmpe.boun.edu.tr/aras/](https://www.cmpe.boun.edu.tr/aras/)), accessed on 19 April 2024). The dataset consists of data recorded by twenty binary sensors strategically positioned in various locations across a two-resident residence. Each second, data were captured from two homes, called House A and House B, with two people each for thirty days. The creator labels the data that are recorded at each second based on the activity of both people in the house. On the basis of the readings obtained from the sensors, a total of 27 activities were assigned to both of the occupants. Because the activity of one resident is affected by the activity of the other resident and both cannot activate the same sensor at the same time, we took into account the sensor readings in addition to the activity of one resident to determine whether other resident's activities were abnormal.  
Abnormal activity is defined as any event that triggers an unrelated sensor. For instance, if someone is outside the home, but a sensor inside the home is activated, it is considered abnormal. The various types of activities and sensor placements are detailed in Table 2. In each house, a total of 2,592,000 normal activities for both residents were recorded. Since the dataset does not contain any anomalies, we created 50,000 anomalies [[https://github.com/Rahman-Motitur/Anomaly-Detection-in-Smart-Home](https://github.com/Rahman-Motitur/Anomaly-Detection-in-Smart-Home)]([https://github.com/[PERSON]/Anomaly-Detection-in-Smart-Home](https://github.com/[PERSON]/Anomaly-Detection-in-Smart-Home)), accessed on 19 April 2024, by considering all possible combinations of sensor values that may lead to anomalies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Activity** & **Sensor Placements** \\ \hline Other & Wardrobe \\ Going Out & Convertible Couch (Used as Bed for Resident 2) \\ Preparing Breakfast & TV Receiver \\ Having Breakfast & Couch \\ Preparing Lunch & Couch \\ Having Lunch & Chair \\ Preparing Dinner & Chair \\ Having Dinner & Fridge \\ Washing Dishes & Kitchen Drawer \\ Having Snack & Wardrobe \\ Sleeping & Bathroom Cabinet \\ Watching TV & House Door \\ Studying & Bathroom Door \\ Having Shower & Shower Cabinet Door \\ Toiletting & Hall \\ Napping & Kitchen \\ Using Internet & Tap \\ Reading Book & Water Closet \\ Laundry & Kitchen \\ Shaving & Bed \\ Brushing Teeth & \\ Talking on the Phone & \\ Listening to Music & \\ Cleaning & \\ Having Conversation & \\ Having Guest & \\ Changing Clothes & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset description. This table includes the placement of sensors (20 different locations in the home) and the list of activities that were evaluated.
## 4 Simulation Results
### Control Strategy about Micro-CHP
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **GB** & \(\mathcal{S}_{chp,1}\) & \(\mathcal{S}_{chp,2}\) & \(\mathcal{S}_{chp,d}\) \\ \hline Total initial investment & 10,467.29 & 22,429.91 & 22,429.91 & 23,551.4 \\ Annual gas consumption costs & 3403.49 & 6851.42 & 6875.56 & 6670.36 \\ Annual operating profit from & – & 4657.87 & 6404.63 & 6466.74 \\ electricity sales & 1373.83 & 1177.57 & 1177.57 & 1236.45 \\ Annual investment costs & 3403.49 & 2193.55 & 470.93 & 203.62 \\ CSR\% & – & 40.11 & 70.16 & 73.94 \\ payback & – & 9.42 & 4.13 & 4.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The economic assessment index for three strategies and traditional system. As shown in Figure 8, \(S_{chp2}\) and \(S_{chpd}\) have similar NPV. Over time, \(S_{chp4}\) has a greater NPV than \(S_{chp2}\). Moreover, Table 5 shows the payback of micro-CHP is approximately 4 years, which is similar results of literature [13]. Compared with the traditional system, the savings of the three strategies increased by \(USD4657.87\), \(USD6404.63\), and \(USD6466.74\) respectively, which means that the annual energy operating cost is reduced by \(USD2050.64\), \(USD3622.29\), and \(USD3686.02\) respectively. The DHW system with electrothermal tube (\(S_{chp4}\)) both power and natural gas while other systems only consume natural gas. Moreover, the CSR of \(S_{chp4}\) is also higher than other systems. Detailed energy cost and saving of CHP were calculated according to the local electricity and gas price. Gas consumed almost the same cost between \(S_{chp,1}\) and \(S_{chp,2}\), as shown in Figure 9. The total annual expense of \(S_{chp4}\) is lowest, since this strategy costs lower and has similar savings in Z0. Meanwhile, there are also noticeable differences between \(S_{chp,1}\) and \(S_{chp,2}\). Compared with \(S_{chp,1}\), the annual total cost of \(S_{chp,2}\) has been reduced by nearly \(USD1722.64\). The payback of \(S_{chp,1}\), \(S_{chp,2}\), \(S_{chp,d}\) are 9.42, 4.13, and 4.23 respectively, as shown in Table 5. The \(S_{chp,d}\) presents less annual gas consumption by electrothermal tube. The total cost of \(S_{chp,d}\) is lowest, as shown in Figure 9, but it has longer payback than \(S_{chp,2}\) due to the influence of the initial investment. Compared with \(S_{chp,2}\), the annual savings of \(S_{chp,d}\) is almost equal to the annual investment, but it consumes less natural gas, so it has an excellent environmental protection performance. In terms of the savings, the system with electrothermal tubes is higher than other systems. Since CHP with electrothermal tube could further decrease the cost of grid import, the power provided a promising cost-saving potential that may enable a net profit (negative value in Figure 9) for the prosumer when connected with power grid. To sum up, under the condition of meeting the same heat load, the economic benefits of \(S_{chp_{d}}\) are the strongest.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### Empowering SON for 5G with Big Data and New ML Algorithms  
As we move towards 5G, the amount of training data needed for algorithms increases exponentially; thus, we conclude in dig data structures that must be handled. New ML algorithms must be developed so that SON systems are able to have an end-to-end vision of the network and conflicts among different SON applications handling the same or similar network parameters are resolved. The performance of ML algorithms depends on the representations that it learns to output. This is the point where recent approaches in deep learning such as \"Representation learning\" provide an efficient solution by transforming input data and defining if input training data such as specific network parameters represent a specific set mapped directly to the output. The core idea of representation learning is that the same representation may be useful for multiple tasks as an output while training sequences are classified in a more efficient manner as far as the output is concerned in this way [102].  
The authors of [10] note that existing SON systems lack end-to-end network knowledge, and thus they proposed a big data framework (BSON) as a holistic approach including, apart from the already used network KPIs and performance counters, user/subscriber level data and user application-based data (social media and smartphone sensors).  
The authors of [103] proposed an application-characteristics-driven SON system (APP-SON) already applied in a tier-1 operator for next-generation 5G networks. By combining a Hungarian Algorithm Assisted Clustering (HAAC) approach and a deep learning assisted regression algorithm, cell application characteristics were identified and used for SON optimization, leading to a better classification of useful KPIs per cell and a better QoE [103]. The experimental results prove that cell traffic can be profiled according to the applications used per user case. As a next step, weather conditions affecting the propagation environment shall be included.  
In our opinion, the prementioned proposals could further evolve by combining information coming from geo-location systems already deployed in network operators [104].
# Unveiling the Role of Minerals and Trace Elements of Thermal Waters in Skin Health
## 2 Minerals, Trace Elements, and Other Compounds of TSW in Skin Health
### Trace Elements
#### 2.3.3 Manganese  
The role of manganese superoxide dismutase on the skin is very well known to exert antioxidant activities, and it is important in the defense against UV-induced photoaging [87]. A serum containing a manganese tripeptide-1 was applied topically in patients suffering from photoaging, showing an overall improvement in hyperpigmentation [88], and chitosan nanovesicles loaded with bioactive manganese were effective in protecting against infections and promoting the wound healing process [89].  
[PERSON] et al. [51] studied the effects of Saint Gervais boron- and manganese-rich TSW on wound healing. They incubated keratinocytes for 24 h with boron salts at concentrations of between 0.5 and 10 mg/mL or manganese salts at concentrations of between 0.1 and 1.5 mg/mL. The results showed that these salts accelerated wound closure compared with the control medium, but they did not increase keratinocyte proliferation, so the authors suggested that they could promote keratinocyte migration.
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
###### Abstract  
In this paper, the effects of mixture fillers on the thermal conductivity of a coating are studied, and the optimal addition proportion was obtained. Then, the corrosion behavior of the modified waterborne polyurethane coating with high thermal conductivity is investigated by salt spray experiments, pull-off tests, EIS and SKP measurements. The result shows that the coating with 2 wt % composite fillers exhibits the most excellent thermal conductivity (1.222 W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\)). In terms of corrosion resistance, there is an absence of obvious corrosion phenomenon for the modified coating, and the polarization resistance is still as high as 1.31 \(\times\) 10\({}^{7}\)\(\Omega\)\(\cdot\)cm\({}^{2}\) at the final stage, which indicates that the incorporation of 2 wt % composite fillers also markedly enhances the corrosion resistance property of waterborne polyurethane coating. This is mainly attributed to the more compact structure and more excellent shielding action to the electrolyte. The surface Kelvin potential results further confirm the advantageous effects of mixture fillers on the corrosion protective performance. The corresponding protective mechanism of the composite coating is also proposed.  
Keywords:waterborne polyurethane coating; corrosion; high thermal conductivity coating; SKP +
Footnote †: journal: Applied Sciences
###### Abstract  
The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA's characteristics will be key to developing preventive strategies. Many lives could be saved if SCA's early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 uW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSR modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician's computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of \(\leq\)0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.  
ECG; wearable; wireless; low-power; transceiver; CNN 2023232270. [[https://doi.org/10.3390/s23042270](https://doi.org/10.3390/s23042270)]([https://doi.org/10.3390/s23042270](https://doi.org/10.3390/s23042270))  
10 November 2022  
6 February 2023
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
### Contribution  
This study makes significant contributions to the field of transformer diagnostics by addressing gaps in fault detection methodologies.  
* Develops a machine learning-based framework for classifying transformer faults, including winding deformation, partial discharges, and insulation breakdown;
* Incorporates numerical indices and statistical features, enhancing classification accuracy across fault types.
* Validates fault detection methods using both simulated and real-world FRA data, ensuring practical applicability.
* Demonstrates frequency-independent fault classification accuracy, reducing reliance on specific frequency bands.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 3 Lasso with CNC Sparse Regularization
### ADMM Algorithm
\tag{29}\]  
Step 3. update \(u^{k+1}:\)  
\[u^{k+1}=u^{k}+(\beta^{k+1}-z^{k+1}). \tag{30}\]  
Finally, the ADMM algorithm for the Lasso model with convex non-convex sparse regularization can be derived by integrating Equations (23), (29), and (30). This derivation is presented as Algorithm 1. ```
0:\(y,X,\beta^{0},z^{0},u^{0},B,\lambda,\rho,\alpha\). 0:\(\beta\). while \"stopping criterion is not met\" do \(\beta^{k+1}=(\frac{1}{n}X^{\top}X+\rho I)^{-1}(y^{\top}X+u^{k}+\rho z^{k})\); \(v^{k+1}=\mathrm{prox}_{\|\cdot\|_{1}}(v^{k}+B(z^{k}-v^{k})\); \(s^{k+1}=(1-\alpha\rho+\alpha\lambda B^{\top}B)z^{k}+\alpha\rho\delta^{k+1}- \alpha u^{k}-\alpha\lambda B^{\top}Bv^{k+1}\); \(z^{k+1}=\mathrm{prox}_{\alpha\|\cdot\|_{1}}(s^{k+1})\); \(u^{k+1}=u^{k}+(\beta^{k+1}-z^{k+1})\). endwhile
```  
**Algorithm 1** ADMM for solving (14)  
Furthermore, we provide the following convergence guarantee for Algorithm 1. **Theorem 4**. : _Through proper selection of the penalty parameter \(\rho\), the primal residual \(r^{(k)}=\|\beta-z^{(k)}\|_{2}\), and the dual residual \(s^{(k+1)}=\|\rho(z^{(m+1)}-z^{(m)})\|_{2}\), Algorithm 1 satisfies \(\lim_{k\to\infty}r^{(k)}=0\) and \(\lim_{k\to\infty}s^{(k)}=0\)._  
The proof of Theorem 4 is given in Appendix A.4. Theorem 4 illustrates that Algorithm 1 ultimately converges to satisfy both primal and dual feasibility conditions. Additionally, it confirms the equivalence between the augmented Lagrangian formulation (21) with constant \(z\) and \(u\) values and the original Lasso problem (14). During each iteration of Algorithm 1, a stationary point \(\beta\) is generated for the augmented Lagrangian formulation (21) when \(z\) and \(u\) are fixed, which indicates that \(\beta^{*}\) also serves as a stationary point for (14).
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 5 Proposed Method
### ED-MORFO-Based CH Selection
After selecting the CH, the clusters form according to distance and energy. While in the process of routing, existing routing algorithms have fitness functions and minimization processesbased on the distances to the CH only. However, in this proposed ED-MORFO, along with the distances, additional fitness function parameters such as queue length and link quality are included, to minimize the energy consumption and extend the network lifetime. When the routing is recognized by means of ED-MORFO, the transmission of the data packet is more efficient. To generate/transmit data over the network, the queue length utilized in routing is employed. The link quality is then utilized to determine the route's value, based on the transmitted and received packets.
## 4 Optimization Approach  
### Multi-Objective Particle Swarm Optimization (MOPSO)  
Particle Swarm Optimization (PSO), first introduced in [35], accounts for a population-based optimization algorithm inspired by the social behavior of animals. The baseline PSO algorithm combines simplicity with fine search capabilities: A population (swarm) of n particles is initialized at random positions \(\overset{\rightarrow}{x}\) within a search space of dimension D, assigned with random velocities \(\overset{\rightarrow}{u}\in R^{D}\). At the end of each step of the PSO algorithm, positions of all \(n\) particles are updated, using the following set of equations:  
For particle \(i\), step \(j\) and search variable \(k\):  
\[v_{ijk}=a_{k}*c_{1}*\left(x_{gbest_{jk}}-x_{ijk}\right)+b_{k}*c_{2}*\left(x_{ pbest_{ijk}}-x_{ijk}\right)+w*v_{ij-1,k} \tag{29}\]  
\[x_{ij+1k}=x_{ijk}+v_{ijk} \tag{30}\]  
where \(\overset{\rightarrow}{x}_{gbest}\) stands for the position of the global best, namely, the best-so-far solution discovered by the swarm, \(\overset{\rightarrow}{x}_{gbest}\) stands for the position of the particle's personal best which represents the best-so-far solution discovered by the particle itself; \(c_{1},\ c_{2},\ w\) are constants (named social factor, cognitive factor and inertia weight respectively); \(a_{k},\ b_{k}\) are random numbers uniformly distributed in [0,1]; \(k=1,\ \ldots,\ D\) where \(D\) is the number of search variables.  
As with most similar algorithms, a variety of multi-objective variants of PSO have been proposed expanding the method's capabilities to handle multiple objectives in a single optimization run [36]. Among these, the Multi-Objective Particle Swarm Optimization (MOPSO) introduced in [21] represents one of the most popular approaches and has been adopted for this study. The method retains the basic features of PSO, its principal difference with the latter lying in the selection of the global best: Instead of a single position in search space, the global best is chosen from an external repository containing the members of the updated Pareto front by means of a roulette wheel selection scheme weighted in accordance with the local density of the front. The procedure comprises the following steps:  
1. The objective space is divided into \(N\) hypercubes and the number of non-dominated solutions contained into each hypercube is calculated.
2. Each non-empty hypercube \(i\) is assigned with a fitness values \(f_{i}\) inversely proportional to the number \(n_{i}\) of non-dominated solutions it contains, through the formula: \[f_{i}=\left\{\begin{array}{rl}&0,\ n_{i}=0\\ &10/n_{i},\ n_{i}>0\end{array}\right.\] (31)
3. Using fitness values \(f_{i}\), a roulette wheel selection is conducted to select the hypercube from which the global best will be taken. The probability \(p_{i}\) of hypercube \(i\) being selected is: \[p_{i}=\frac{f_{i}}{\sum_{j=1}^{N}f_{j}}\] (32)
4. The global best position is picked at random from the solutions contained within the chosen hypercube.
## 4 Comparison Results
### Accuracy Comparison
The voltage dataset measured from TC5 was used for the MSE feature calculation, and the scale had the value of 1, 2, and 3, respectively. It can be seen from Figure 15a that no linear relationships between the MSE and the cycling time existed when the scale was larger than one. As a result, as seen in Figure 15b, the slope of the first-order polynomial function was negative, which will lead to a large estimation error. The estimation results in Figure 16 indicate that the percentage error of the MSE-based method decreased with the reduction in the scale. Table 3 shows more clearly that the MSE-based method with scale 1 had the highest accuracy. In this case, MPE and RMSPE were 1.67% and 1.95%, respectively. It can be concluded that MSE feature is not suitable for battery capacity estimation. Figure 14: The diagram of the mean percentage error and root-mean-squared percentage error considering the state of charge level and current direction (minus one indicates the charging direction and plus one indicates the discharging direction). (**a**) Approximate entropy-based method; (**b**) Sample entropy-based method. \[MPE=\frac{1}{N_{T}}{\sum_{i=1}^{N_{T}}{PE_{i}}} \tag{20}\]0 Estimation results under six test conditions. (**a**) Approximate entropy-based method; (**b**) Sample entropy-based method. The experimental results showed that the combined action of the SOC level and the current direction plays a main role in the capacity estimation accuracy. Only if the battery SOC enters into the polarization zone (SOC larger than 0.8 or smaller than 0.1) will a high accuracy of the capacity estimation be achieved. In addition, both AE-based and SE-based methods showed very similar estimation errors. In this work, MSE was not suitable as a feature because large scales lead to information loss and eventually causes a drop in the estimated accuracy; nevertheless, higher voltage sampling (e.g., 100 ms) could improve the accuracy of this method. [PERSON] conceived this paper, analyzed the data, and also wrote this paper; [PERSON]-[PERSON] designed and performed the experiments; [PERSON] provided guidance in programming and paper revision; [PERSON] and [PERSON] supervised the findings of this work; [PERSON]-[PERSON], [PERSON], and [PERSON] revised the paper and provided some valuable suggestions. This research received no external funding. The authors declare no conflicts of interest.