esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 UAV-Based Data Collections and Processing
### The Data Sets  
In this article, we processed two data sets collected during different flights, with different passes over the test area in different days and different seasons. The first flight was in December, 2013 and the second flight was in the following May. We processed both flight data using the same methodology, comparing the results for what concerns water detection on ground. In this section, we describe the data sets used.  
Figure 4 shows the peak-to-noise-floor separation \(\alpha_{mean}\) of data set (1) and data set (2), in dB, for a subset of the visible pseudo-random noise (PRN) codes. We computed \(\alpha_{mean}\) at a rate of 20 Hz and we used a first order low pass digital Butterworth filter with a cutoff frequency of 1 Hz to smooth the measurements. The x-axis reports the time, in seconds, from the beginning of the data set. It can be noticed that \(\alpha_{mean}\) has a time-variant trend. Focusing on data set (1), some epochs are characterized by \(\alpha_{mean}\) values greater than 5 dB (e.g., 0-400 s), indicating potential signal reflections. Other epochs, such as those associated to PRN 32 in 600-820 s, have small values that hardly reach 2 dB. Variations can be observed among satellites, as noticeable for example between 900 s and 1200 s, where \(\alpha_{mean}\) values related to PRN 32 are lower than the values associated to PRN 19 and 3. The same can be observed on the plot referencing to data set (2) which, however, had higher reflection values through out the whole flight duration.  
In the next section, we reference each of these reflections to their ground reflecting points, with focus on reflections generated from water.  
Figure 3: Examples of the search space for a visible satellite when (**a**) evaluated from the direct signal, and (**b**) evaluated from the reflected signal.
###### Abstract  
In high penetrations, demand side management (DMS) applications augment a substation power transformer's load profile, which can ultimately affect the unit's capacity limits. Energy storage (ES) applications reduce the evening peaking demand, while time-of-use rates incentivize end-users to charge electric vehicles overnight. The daily load profile is further augmented by high penetrations of photovoltaic (PV) systems, which reduce the midday demand. The resulting load profile exhibits a more flattened characteristic when compared to the historical cyclic profile. Although the initial impact of PV and ES applications may reduce a unit's peak demand, long-term system planning and emergency conditions may require operation near or above the nameplate rating. Researchers have already determined that a flattened load profile excessively ages a unit's dielectrics more rapidly. The focus of this research was to identify an approach for establishing new transformer capacity limits for units serving flattened load profiles with a high harmonic content. The analysis utilizes IEEE standards C57.91 and C57.110 to develop an aging model of a 50 MVA SPX Waukesha transformer. The results establish a guideline for determining transformer capacity limits for normal operation, long-term emergency operation, and short-term emergency operation when serving systems with high penetrations of DSM applications.  
demand side management; electric vehicles; energy storage; harmonic distortion; photovoltaic; power quality; power transformer; total harmonic distortion
# Electronic Voting System Using an Enterprise Blockchain
## 4 System Proposal
### Introducing the SuffrageNet Network  
The first step in determining a network structure for an application is to list the participating organizations. Logically, an organization governs one or more nodes in the network and depends on an MSP to issue identities for its participants.  
Figure 5 shows the SuffrageNet network. This network consists of two organizations: SUFFRAGE, VALIDATOR. Each organization has at least one peer and one associated CA. In addition to the peers, the network consists of an MSP and an order service node (OSN) for each of the two organizations; the OSNs collaborate to create blocks and order them in a well-defined sequence. The ordering service--the ordering node and other orderer nodes form an ordering service--are distributed among all the organizations.  
Each organization plays a specific role in the voting process. SUFFRAGE is the only one authorized to mint the Ballots to manage identity and issue voter transactions, while VALIDATOR is the only one authorized in the validation of the entire electoral process. The SUFFRAGE organization has both SUFFRAGE.MINTER and SUFFRAGE.VOTER OUs to reflect separate lines of business (minting and voting, respectively) and implement access control on a chaincode level. When organization CA issues X.509 certificates, the certificate OU-field specifies the affiliation for that identity. Defining affiliations for an organization can be useful when an organization has too many responsibilities or specifies some complex policies to restrict access even at the chaincode level (as in this proposal). In SuffrageNet, ev  
Figure 4: The transaction flow in Hyperledger Fabric.  
ery invokes/query transaction should be signed valid certificate issued by the organization CA (see Figure 6).  
The following sample from a signing certificate shows how the OUs are represented in this certificate, the affiliation is coded inside certificate subject, in OU. For example, subject for SUFFRAGE.MIITER.  
'subject':'CN=33932069-6 fce-4b16-8072-6715d591f35b,  
OU=client+OU=SUFFRAGE+OU=MIITER,O=SUFFRAGE'  
HF has a library known as client identity chaincode (CID), which was used within the suffrage chaincode to make access control decisions based on the client's identity (i.e., the invoker of the chaincode). In particular, access control decisions can be made based on any or a combination of the following information associated with the client: the client identity's MSP ID, an attribute associated with the client identity or an OU value associated with the client identity. Figure 7 shows how to check-in suffrage chaincode that the invoker of the chaincode is a dApp using an identity that belongs to the SUFFRAGE.MIITER affiliation.  
Figure 5: Proposed network structure.  
Figure 6: Invoke/Query transaction to peer via dApp.  
The proposed structure allows organizations to validate the information before committing it to the ledger, making the vote more challenging to manipulate. Likewise, it allows more flexibility in the network configuration and adapts it to various scenarios.
# Imputing Covariance for Meta-Analysis in the Presence of Interaction
## 1 Introduction  
When conducting an individual patient data (IPD) meta-analysis, researchers may need to examine interactions between two predictors [1, 2]. In a one-stage meta-analysis [3], all data are combined into a single dataset before analysis, allowing for the calculation of the covariance between predictors. However, this approach may not be feasible in situations where data cannot be consolidated into a single dataset, such as when data-sharing restrictions prevent data transfer. In such cases, a two-stage IPD meta-analysis is necessary [4].  
When interactions are present, it is crucial to calculate confidence intervals for both the combined main effect and the interaction effect [5]. Additionally, determining the overall covariance between the two coefficients for combined effects in two-stage IPD meta-analyses is vital.  
The most commonly used method for calculating the overall covariance between the main effect and the interaction effect in meta-analysis is multivariate meta-analysis [6, 7, 8]. In this paper, we propose an alternative, straightforward, and transparent method for calculating this covariance when interactions are considered in a meta-analysis. This method is applicable to both fixed-effect and random-effects meta-analysis models, providing a practical solution for researchers analyzing interactions within a meta-analytic framework.
###### Abstract  
Transfer factors (TFs) are widely used tools for assessing the uptake of radionuclides by plants. The literature contains numerous studies on TFs in tropical and temperate climates; however, the existing data on TFs in arid and semi-arid climates are very scarce. Furthermore, the current trend in nuclear energy expansion in countries with this type of climate necessitates knowledge of the mechanisms of radionuclide incorporation by plants as well as the TF values. For this reason, this work investigates the TFs of \({}^{238}\)U and \({}^{226}\)Ra in plants in a study area during the summer period under conditions equivalent to a semi-arid climate. The selected plants were _Scolymus hispanicus_ L., _Eryngium campestre_ L., _Chenopodium vularia_ L., and _Chenopodium album_ L., which were collected in the vicinity of a waste dump from an abandoned copper mine. The selected study area has radionuclide levels above the global average, in addition to heavy metals, as it is a waste dump from an abandoned copper mine. The range of transfer factors for \({}^{238}\)U varied between \(1.5\times 10^{-4}\) kg\({}^{-1}\) kg\({}^{-1}\) and \(7.8\times 10^{-3}\) kg\({}^{-1}\) kg\({}^{-1}\), while for \({}^{226}\)Ra, the range was between \(1.8\times 10^{-4}\) kg\({}^{-1}\) kg\({}^{-1}\) and \(4.0\times 10^{-2}\) kg\({}^{-1}\) kg\({}^{-1}\). The correlations found with PCA were (i) \({}^{238}\)U with Fe and Al, and (ii) \({}^{226}\)Ra with S, Ti, Ca, and Sr. A transfer model of \({}^{238}\)U and \({}^{226}\)Ra was created using multiple linear regression analysis. The model showed how \({}^{238}\)U was related to the presence of Al, while \({}^{226}\)Ra was related to Al, Fe, and Ti. The results obtained have allowed us to propose a model for the incorporation of \({}^{238}\)U and \({}^{226}\)Ra, taking into account the chemical composition of the soil. The results obtained indicate that both _Scolymus hispanicus_ L. and _Eryngium campestre_ L. could be utilized in phytoremediation for soils contaminated by natural radionuclides in semi-arid climates. The TFs, as well as the proposed model, allow us to expand the knowledge of the absorption of natural radionuclides by plants in regions with arid and semi-arid climates, which is necessary for the radiological risk assessment of future nuclear fuel cycle facilities.  
transfer factors; uranium; radium; natural radioactivity
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 4 Internet of Things (IoT) for Healthcare Delivery
### Communication Technologies for IoT in Healthcare  
Several wireless technologies have been applied in the interconnection of IoHT systems, as presented in Table 3. The present protocols mostly used for mapping the IoHT devices are, according to [19], wireless fidelity WiFi (use in local area networks), worldwide interoperability for microwave access WiMax (use in broadband wireless networks) global system for mobile communications (GSM) and enhanced data rates for GSM evolution (EDGE) for 2 nd generation (2G) mobile carriers networks, universal mobile telecommunications system (UMTS) and code division multiple access (CDMA) for 3 rd generation (3G) telecommunication, and long-term evolution (LTE) for 4 th generation (4G) mobile telephony [67]. Personal area networks (PAN) are generally applied in the development of IoHT. A wireless personal area network (WPAN) is regarded as a low-powered PAN for a close-range wireless network and includes technologies such as infrared (i.e., wireless optical communication based on the point-and-shoot principle), ZigBee (which provides a low-energy consumption, but also low transmission rates [68]), ultra-wideband (UWB) 3-D tracking of multiple tags for clinical usage [69] and Bluetooth and Bluetooth Low Energy (BLE) [70]. Another choice for communication in the IoHT is the application of protocols that are based on electromagnetic field communications, comprising radio-frequency identification (RFID) based solutions to tracking patient movement and location [71] and near field communication (NFC) [72].  
The unique ideology of the IoT is precisely linked to the radio frequency identification concept, which usually comprises the tags and readers for data acquisition. These are mostly used in the field of logistics [47]. The tags and readers of radio frequency identification can be either passive or active. The active component possesses an internal power source that enables wider spectrum communications and supports the passive component of the device [48]. Recently, the application of radio frequency identification in physiological parameter estimation has been considered a novel approach. This is true because of the advent of ultra-high frequency readers with advanced sensing features. This also allows a more friendly and easy approach to sending patient data across the IoT chain. Near-field communication is a non-invasive concept of communication that can detect vital signs within a short range [73]. In addition, near-field communication has proximity features with simple handling mechanisms. Thus, communication within the hospital records becomes easier. Low power area networks (6 LoWPAN) permit the Internet Protocol Version 6 (IPv6) layer to be conveyed on the WSNs to the range of a sensor node with the IoT platform [74]. Internet Protocol Version 6 presents several merits to the IoHT technology, comprising better scale, optimizing mobility, efficient management, and permitting enhanced smart object regulations.
## 4 Analysis and Result
### Nonlinear Separation with a Single SVM  
The results of a nonlinear analysis of the normal and defective specimens using the radial basis function of the kernel function as a single SVM are shown in Figure 9.  
As a result, 94 out of 111 normal data were classified as normal, but all the defect data were classified as support vectors. This indicates that they are distributed in the margin range of the boundary dividing normal and defect data. Thus, they can be defective or normal for the defect in each analysis, and it is difficult to accurately determine the data because they are separated by the boundary. Therefore, because the data classification is ambiguous, additional analysis was conducted to increase its reliability.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 1 Introduction
### Three-Dimensional Industrial Anomaly Detection  
To fill the gap in the 3D AD&S domain, the MVTec-3D AD [40] dataset along with its corresponding baseline algorithms and performance metrics were released to the public, which introduced new opportunities and challenges to industrial AD&S. In subsequent research, various novel methods have been developed and achieved notable results. These include methods based on teacher-student networks such as 3D-ST [41] and AST [42], methods leveraging the PatchCore scoring function like (back to the features) BTF [43] and M3 DM [44], and methods focused on constructing anomalies such as 3 DSR [45]. Additionally, approaches that utilize neural implicit functions and pre-trained models, exemplified by shape-guided methods [46], have been introduced. These studies primarily aim to design new methods for 3D feature extraction and feature integration.  
In the MVTec-3D AD dataset, it was observed that BTF achieved significant performance in terms of AS but lagged in AD performance. Upon further investigation, it was found that methods such as ReConPatch, BTF, and M3 DM perform anomaly scoring calculations based on the PatchCore method. These algorithms differ significantly from the initially proposed image domain anomaly scoring algorithm based on \(k\)NN [34] and the traditional \(k\)NN classification algorithm [47]. The papers lacked specific discussion when introducing these differences. In this paper, the distance measures involved in these algorithms are compared and analyzed, and their advantages and disadvantages are pointed out. According to these advantages and disadvantages, combined with our improvement of the existing MBBM method, we propose a method that uses different anomaly scores in the AD and AS phases, respectively.
## 5 Evaluation
### Results and Discussion
#### 5.2.3 Network Lifetime  
In this experiment, we measure life time of the network under consideration. We calculate network lifetime based on how much a node is used for communication. We analyze this performance metric on the basis of an energy parameter (an arbitrary threshold value). We measure and compare the algorithms depending upon the time when 50% of nodes reach this threshold value. Figure 6 shows a comparative analysis. Network employing hierarchical cooperative clustering has a longer life time as compared to CMIMO and SISO (When comparing the algorithms, distribution of nodes and packets generation is the same). An improvement in energy conservation at the communicating clusters elongated network lifetime manifolds. There is initially an increase in network lifetime with an increase in grid length because the intercluster distance increases, which makes MIMO a more favorable communication mode. Nevertheless, when grid length is stretched beyond 500 m, the total number of hops also increases, which results in a decrease in network lifetime for our algorithm as well as CMIMO.  
Figure 4: Energy (J) comparison for three algorithms.  
Figure 5: Energy (J) consumption per bit.  
Figure 7 shows a comparison of the time taken for networks employing HMIMO and CMIMO to reach an energy threshold value. The graph is plotted for intercluster range as the percentage of grid size versus time. In this experiment grid size is set to 1,000 with CCS in the centre of the square grid. The two dotted lines are the trend lines for HMIMO and CMIMO. For shorter distances, SISO mode of communication is prevalent in both the algorithms. As the intercluster distance increases, clusterheads switch to MIMO mode of communication which is depicted in the graph by an increase in network lifetime. MIMO outperforms SISO for a certain distance range. When grid size is approaching 1,000, there is drop in network life time for both CMIMO and our algorithm.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 2 Wireless Communications Technologies for m-Health
### Comparison of Wireless Communication Technologies  
Table 1 gives some distinguishing features and important comparisons between OWC and RF technologies. OWC compared to RF technologies does not constitute or suffer from electromagnetic interference and offers a huge unregulated bandwidth. OWC is relatively safe from potential hackers, as the optical radiation is very well confined and does not penetrate walls, compared to RF, where the data security is poor. This advantage present in VLC is in turn a disadvantage from the point of view of mobility and coverage, as RF has a wide coverage and therefore allows mobility for users.
## 3 Validation Study Based on Hemisphere Model  
### Hemisphere Modeling  
To evaluate the performance of the proposed surface registration algorithm, a hypothetical hemisphere model [26] was created using MATLAB R2018b (MathWorks, Inc., Natick, MA, USA). The surface points of the hemisphere were constructed so that the distribution of points was uniform (Figure 3A). In order to uniformly distribute the points on the surface of the hemisphere, the outer area of a hemisphere with a radius (\(r\)) of 1 was divided by the number of points \(N\). A square was obtained, with area \(A\) and side length \(d\).  
\[A=\frac{4\pi r^{2}}{N} \tag{5}\]  
The unit angle \(M_{\theta}\) was calculated by dividing a semicircle with the arc \(\pi\) belonging to the hemisphere by the length (\(d\)) of one side of the square (\(A\)) and rounding up the result.  
\[M_{\theta}=round\Big{(}\frac{\pi}{d}\Big{)} \tag{6}\]  
Figure 3: Hemisphere model (**A–C**).  
The length \(\pi\) of the semicircle with a radius (\(r\)) of 1 was divided by the unit angle \(M_{\theta}\) to calculate the gap \(d_{\theta}\) between circles with a constant latitude, and the width of the square \(A\) was divided by \(d_{\theta}\) to calculate the gap (\(d_{\varphi}\)) between points with constant hardness.  
\[d_{\theta}=\frac{\pi}{M_{\theta}}\quad and\quad d_{\varphi}=\frac{A}{d_{ \theta}} \tag{7}\]  
The set of points was placed at constant intervals (\(d_{\varphi}\)) in circles with constant latitudes and \(d_{\varphi}\) values, using the previously calculated values of \(d_{\theta}\) and \(d_{\varphi}\). The radius of the hemisphere was set to 100 mm considering the size of the human face, and it was modeled for eight cases, ranging from 1527 points to 12,100 points, to evaluate the effectiveness of the number of medical image point clouds. The point cloud in the patient space represented the patient's face traced in a left-to-right motion (Figure 3B). The point sets were constructed in the same form as the hemisphere model, and the number of point clusters was limited to 200.  
The six target positions (3 rows \(\times\) 2 columns) were located inside the hemisphere model to evaluate the TRE. The interval between targets was set at 50 mm. The coordinates of each target were [0,0,25], [0,\(-\)50,25], [0,\(-\)100,25], [0,0,\(-\)25], [0,\(-\)50,\(-\)25], and [0,\(-\)100,\(-\)25].
## 3 Numerical Simulation
### Simulation of Diagonal Slice Spectrum  
The diagonal slice spectrum can identify the secondary phase coupling relationship in the signal, and suppress the independent frequency components effectively. To illustrate and verify its effectiveness, the following simulated signals are used in the simulation analysis:  
\[x(t)=\sum_{i=1}^{6}\cos(2\pi f_{i}t+\varphi_{i}) \tag{17}\]  
where \(f_{1}=15\) Hz, \(f_{2}=22\) Hz, \(f_{3}=45\) Hz, \(f_{4}=38\) Hz, \(f_{5}=58\) Hz, and \(f_{6}=f_{4}+f_{5}=96\) Hz. \(\varphi_{i}\) is an independent random variable uniformly distributed in interval \([0,2\pi]\), and \(\varphi_{4}+\varphi_{5}=\varphi_{6}\), thus \(f_{4},f_{5},f_{6}\) satisfy the quadratic phase coupling relationship. Although \(\varphi_{1}+\varphi_{2}=\varphi_{3},f_{1}+f_{2}\
eq f_{3}\), so \(f_{1},f_{2},f_{3}\) do not satisfy the condition of quadratic phase coupling.  
The sampling frequency of the experiment is \(f\)s = 1000 Hz, sampling points \(N=5000\). Figure 11 is the diagonal slice spectrum of the original signal, and the obvious spectral lines only appear at \(f_{4},f_{5},f_{6}\), which shows that the diagonal slice spectrum can identify the frequency components with secondary phase coupling and eliminate frequency components without secondary phase coupling. A certain level of Gauss white noise is added to the original signal, and SNR = \(-6\) dB. In this case, the diagonal slice spectrum of the noisy signal is shown in Figure 12, and due to the interference of strong noise, there are many frequency components that do not have a secondary phase coupling relationship. Figures 13 and 14 are diagonal slice spectrums after using standard local projection for noise reduction and multiscale local projection, respectively. In Figure 13, the frequency components of \(f_{4},f_{5},f_{6}\) are extracted, while still interfered by other frequency components. In Figure 14, the obvious spectral lines only appear at \(f_{4},f_{5},f_{6}\). Therefore, the proposed method based on the multiscale local projection algorithm and diagonal slice spectrum can achieve better results.