esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 5 Experimental Results
### Experimental Analysis
#### 5.2.2 The Second Experiment: \(F_{n}\) Is a Random Value  
When \(F_{N}\) is an arbitrary value, considering the effect of the actual noise on the signal, the random value is 0.3 times that of the random function.  
The positioning errors are shown in Figure 9 in the case of the noise impact factor of a random value.  
Figure 8: Positioning errors of three positioning algorithms in the case of the noise impact factor of 0.3.  
Figure 7: Positioning errors of three positioning algorithms in the case of the noise impact factor of 0.2.  
In the case of different noise impact factors, the positioning errors of the three positioning algorithms are shown in Table 1.  
As can be seen from Table 1, when \(F_{N}\) is constant, the positioning accuracy decreases with the increase of \(F_{N}\), i.e., the noise has more and more influence on the positioning accuracy; when \(F_{N}\) is a random value, the positioning accuracy of the iterative positioning algorithm is improved by 37.71% compared with the centroid algorithm and the positioning accuracy of the iterative positioning algorithm is improved by 28.76% compared with the weighted centroid algorithm.  
The positioning errors of the three positioning algorithms for different values of noise impact factors are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Noise Impact Factor** & \multicolumn{3}{c}{**Average Positioning Error (m)**} \\ \hline  & **Centroid** & **Weighted Centroid** & **Iterative Positioning** \\ \hline \(F_{N}=0.1\) & 0.77 & 0.70 & 0.17 \\ \(F_{N}=0.2\) & 1.68 & 1.30 & 0.42 \\ \(F_{N}=0.3\) & 2.63 & 1.85 & 0.79 \\ \(F_{N}=\) random & 1.75 & 1.53 & 1.09 \\ \hline \end{tabular}
\end{table}
Table 1: Positioning errors of three positioning algorithms in the case of different noise impact factors.  
Figure 10: Positioning errors of three positioning algorithms under different noise influence factors.  
Figure 9: Positioning errors of three positioning algorithms in the case of the noise impact factor of a random value.  
As can be seen from Figure 10, the positioning error of the iterative positioning algorithm is smaller than that of the centroid positioning algorithm and the weighted centroid positioning algorithm in the case of different noise impact factors \(F_{N}\).
# Research of Tri-State Optical Signal Detectors for Ternary Optical Computers
## 2 Materials and Methods
### Theory of Three-State Optical Signal Detectors
#### 2.2.2 Data Conversion  
The polarization state of the optical processor becomes a set of raw electrical signals after the photoelectric conversion, which still needs to be transcoded to become the input of the optical encoder or the final output that is transmitted back to the electronic computer. In SD16, the input data of the tri-state optical encoder is represented by 10 for V-light, 01 for H-light, and 00 for W-light, while the decoder output of one processor bit requires 6 bits of data for V-light, H-light, and W-light.  
Since V-light and H-light do not appear simultaneously in three LCD pixels of one processor bit, we provide the tri-state light signal detector output data, tri-state light encoder input data, and the correspondence between the three states of light, as shown in Table 2.  
According to Table 2, we propose a circuit logic structure to convert the decoder output of one processor bit to the encoder input, as shown in Figure 4. In Figure 4, A-F correspond to the outputs of the tri-state optical signal detectors in Table 2, G and H are the output signals of the input data after the or gate, Z and N correspond to the inputs of the tri-state optical encoders in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Light State** & **Light Color** & \
\begin{tabular}{c} **The Input of** \\ **Encoder** \\ \end{tabular} &
\begin{tabular}{c} **The Output of Decoder** \\ \end{tabular} \\ \hline V & one white light pixel and two & 01 & 110000, 001100, 000011 \\ H & one light green light pixel & 10 & 100000, 001000, 000010 \\ W & three blue light pixels & 00 & 000000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correspondence of the light state, light color, and input and output data.  
Figure 3: The location of the light source.
## 6 Conclusions  
VIs and their transformations and derivatives are extremely useful tools in monitoring processes related to fPAR absorbed by vegetation. These include processes related to photosynthesis at the canopy or ecosystem scale (phenotype, primary productivity, net carbon fixation, gross primary productivity), and processes related to plant transpiration (ET, rainfall use efficiency, groundwater withdrawal). VIs are measures of green foliage density, and they must be combined with ground data or appropriately calibrated models to produce accurate information about these processes. VIs represent composite properties of f\({}_{\text{c}}\), LAI, and canopy architecture and they are only moderately useful in predicting individual canopy properties required in SVAT and SEB models. Therefore, remote sensing methods for estimating ecosystem variables should be formulated in ways that take advantage of their strengths and minimize their weaknesses. Remote sensing applications are perhaps strongest when they are used as scaling tools for observational ground data rather than as detailed physical models of the landscape.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion
### Future Works  
The first plan is to conduct clinical trials for real arrhythmia patients by using this platform. A multicenter trial is possible with the AWS-VPC interface. The VP-100 will be allocated to participants at different sites, and the acquired data will be sent to the internal network of the SNUH-CTC. By analyzing the acquired ECG and arrhythmia-detection results, the occurrences of arrhythmias and the effectiveness of their detection in this platform can be evaluated. The efficacy of the self-reporting technique can be evaluated as well. Furthermore, clinicians, nurses, and participants will be surveyed to evaluate the usability of the platform.  
The second plan is to strengthen the security measures in the platform. Currently, one-way encryption is applied in the log-in process, and a secure socket-layer certificate is applied to the data transfer between the DMZ and internal network. We plan to implement blockchain technology to enhance the security level of this platform. Probable undesired access and manipulation of participant information and medical records could be avoided, and more secure data sharing between hospitals could be feasible.
## 5 Validation and Results
### Simulation Platform  
As shown in Figure 7, a PanoSim-Apollo co-simulation framework was established for this study. In this framework, PanoSim serves as the scenario simulation platform, while the advanced autonomous driving system Apollo is the test object. PanoSim (version 5.0) and Apollo (version 7.0) run on different operating systems, Windows 11 and Ubuntu 18.04, respectively, with the two systems communicating via the UDP protocol. During the simulation, PanoSim sends scenario data to Apollo's decision-making and planning module via Ethernet. After processing the data, Apollo returns the calculated vehicle control commands (throttle, steering, and braking) to PanoSim's high-precision vehicle dynamics module to update the simulation state.  
As shown in Figure 8, PanoSim is a high-performance scenario simulator developed by our team for research on autonomous vehicles. This platform integrates an accurate vehicle dynamics model, a highly realistic driving and traffic environment simulation, versatile onboard sensor models, and a wide range of test scenarios. It provides comprehensive testing capabilities, making it an ideal tool for addressing various autonomous driving testing needs.  
Figure 8: PanoSim integrated simulation test platform.  
Figure 7: PanoSim-Apollo co-simulation framework.
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 2 Preliminaries  
### Local Differential Privacy  
Local differential privacy considers the possibility of server stealing, or leaking client's privacy, during data collection. Under this framework, each client first conducts randomized perturbation on the data, and then sends the perturbed data to the server. The server makes statistics on the collected data, to obtain effective analysis results. A formal definition of local differential privacy [12] is given below:  
**Definition 1** (_Local Differential Privacy (LDP)_).: _For a randomized algorithm \(M\), its domain of definition and range are \(D(M)\) and \(R(M)\), respectively. For any two records \(l\) and \(l^{\prime}\) in \(D(M)\), their same output of \(M\) is \(S\), where \(S\subseteq R(M)\). If the following inequality holds, then the randomized algorithm \(M\) satisfies \((\epsilon,\delta)\)-LDP,_  
\[Pr[M(l)=S]\leq e^{\epsilon}\cdot Pr[M(l^{\prime})=S]+\delta. \tag{1}\]  
In the above definition, \(Pr[M(l)=S]\) represents the probability that the output of algorithm \(M\) is \(S\), given the input \(l\), and \(Pr[M(l^{\prime})=S]\) represents the probability that the output of algorithm \(M\) is \(S\), given the input \(l^{\prime}\). The parameter \(\epsilon\) is called the _privacy budget_, and the privacy protection level of the data can be adjusted through this parameter. Theoretically, the larger the privacy budget \(\epsilon\) is, the lower the privacy protection level \(M\), can be achieved, and the higher the utility of the output is. The parameter \(\delta\), represents the tolerance of some possibility of failure. The client uses a randomized algorithm satisfying LDP to perturb the local model parameters. The algorithm applied by the client is referred to as the perturbation mechanism.  
To achieve LDP, a data perturbation mechanism needs to be introduced. At present, random response (RR) [20] is the mainstream perturbation mechanism of LDP. Its main idea is using the uncertainty of response to sensitive issues to protect the privacy of the original data. A Gaussian mechanism [21] is not a typical perturbation mechanism of LDP, but it is often used to realize LDP in federated learning [22]. The basic idea of the Gaussian mechanism is to blur the original data, by adding 0-mean Gaussian noise. The advantage of Gaussian noise is that the sum of two Gaussians is a Gaussian, which means the effects of the Gaussian mechanism on the statistical analysis may be easier to correct for [11].
## 3 Results and Discussion
### Analysis of Phase Content Based on Raman Spectra
#### 3.2.2 C12A7-CaS and C3A-CaS Samples  
Figures 6 and 7 show the Raman spectra for binary samples for the calcium aluminate phases of C12A7, C3A, and calcium sulphide (CaS). Figure 6 presents the samples that contain CaS and C12A7, showing that when the phase fraction of C12A7 in the sample increased, a corresponding increase in the Raman shift in the region of 517-520 cm\({}^{-1}\) was observed. This rise in intensity may be associated with the increasing phase fraction of C12A7, because the most intense Raman peak for this phase was located around 517 cm\({}^{-1}\).  
For the samples comprising C3A and CaS, an increase in the peak intensity within the Raman shift in the region of 756-765 cm\({}^{-1}\) could also be observed. This may be related to the increase in the  
Figure 8: Raman spectra for CaS (_i_), Al\({}_{2}\)O\({}_{3}\) (_v_) and binary CaS–Al\({}_{2}\)O\({}_{3}\) phase sample with CaS content, where (_ii_) 80 wt. %, (_iii_) 60 wt. %, and (_iv_) 20 wt. %.  
phase content of C3A. The peak within the Raman shift in the region of 756-766 cm\({}^{-1}\) also increased when the phase fraction of C3A in the sample increased. These phenomena can also be observed in Figure 7 for the C3A-CaS samples. Additionally, the most intense peak for C3A in this study was located at approximately 766 cm\({}^{-1}\). The comparison between the change in phase fraction for CaS and relative Raman intensity for the spectra presented in Figure 6 for C12A7-CaS and Figure 7 for C3A-CaS all showed that an increase in CaS content corresponded to an increase in the Raman shift in the region of 157-162 cm\({}^{-1}\).  
For the CaS-Al\({}_{2}\)O\({}_{3}\) samples, the Raman spectra shown in Figure 8 demonstrate that a change in the phase fraction of Al\({}_{2}\)O\({}_{3}\) resulted in a relative Raman shift in the region of 420-425 cm\({}^{-1}\). A similar trend in the effect of change on CaS for CaS-Al\({}_{2}\)O\({}_{3}\) can be observed in the Raman spectra shown in Figure 8 compared with Figures 4-7. Therefore, based on Figures 4-8, the Raman spectroscopy measurements made for the binary samples of MA-CaS, CA-CaS, CaS-C12A7, CaS-C3A, and CaS-Al\({}_{3}\)O\({}_{3}\) provided qualitative information for estimating the individual phase content for a given binary sample.
## 1 Introduction
### Viscoelastic Phase Properties
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phase** & **Bulk Modulus** & **Shear Modulus** & **Mass Densities** & **Reference** \\  & **(GPa)** & **(GPa)** & **(g cm\({}^{-3}\))** & \\ \hline Air & \(k_{\text{air}}=0\) & \(\mu_{\text{air}}=0\) & \(\rho_{\text{air}}=0\) & \\ Water & \(k_{\text{water}}=0\) & \(\mu_{\text{water}}=0\) & \(\rho_{\text{water}}=1.00\) & \\ Hydrates & \(k_{\text{hyd}}=18.69\) & \(\mu_{\text{hyd}}=11.76\) & \(\rho_{\text{hyd}}=2.073\) & [23; 29; 43] \\ Cement & \(k_{\text{cem}}=116.70\) & \(\mu_{\text{cem}}=53.80\) & \(\rho_{\text{cem}}=3.15\) & [33; 44] \\ Polymer P1 & \(k_{\text{P1}}=3.97\) & \(\mu_{\text{P1}}=0.85\) & \(\rho_{\text{P1}}=1.03\) & [8; 9] \\ Polymer P2 & \(k_{\text{P2}}=4.02\) & \(\mu_{\text{P2}}=0.86\) & \(\rho_{\text{P2}}=1.02\) & [8; 9] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Elastic properties and mass densities of the material phases in polymer-modified cement pastes. using a power-law [45; 46; 47]. This is the motivation to introduce the creep tensor function of the polymers, \(\mathbb{J}_{\text{pol}}(t-\tau)\), as:  
\[\mathbb{J}_{\text{pol}}(t-\tau)=\frac{1}{3k_{\text{pol}}}\mathbb{I}^{\text{vol} }+\frac{1}{2}\left[\frac{1}{\mu_{\text{pol}}}+\frac{1}{\mu_{c,\text{pol}}} \left(\frac{t-\tau}{t_{\text{ref}}}\right)^{\beta_{\text{pol}}}\right]\mathbb{ I}^{\text{dev}}, \tag{7}\]  
where \(k_{\text{pol}}\) and \(\mu_{\text{pol}}\) denote the elastic bulk and shear moduli of the polymers and \(\mu_{c,\text{pol}}\) and \(\beta_{\text{pol}}\) denote their shear creep modulus and the power-law exponent, respectively. While the elastic stiffness properties of the polymers can be readily identified, e.g., by ultrasonic testing [9], direct experimental characterization of the polymer creep parameters \(\mu_{c,\text{pol}}\) and \(\beta_{\text{pol}}\) is out of reach. As a remedy, they are identified from macroscopic creep tests by means of \"downscaling\". In this context, scale-transition relations linking the microscopic viscoelastic phase behavior to the macroscopic creep behavior are required.
## 4 Study on the Blast Resistance of Ultra-Wide Concrete Twin-Edge Box Girders Coated with Polyurea
### Parametric Analysis
#### 4.2.3 Analysis of the Impact of Different Repeated Explosion Patterns
The local strain gradually diffused outward and diminished. Due to the short distance from the T-beam center and the quarter-span points, a high-strain region connected the undamaged areas between the two perforations during simultaneous detonation. In specimen PPCG, the top plate under each explosion center was not fully perforated, and concrete spalling occurred on the top surface. Minor polyurea delamination and slight perforations were observed at the central and quarter-span points. The results indicate that simultaneous detonation at multiple points caused damage along the transverse axis of the bridge, reducing its overall strength and stability and significantly affecting its traffic capacity. However, after coating with polyurea, the overall blast resistance of the box girder was greatly enhanced, and it retained a certain level of traffic capacity. Figure 40: Damage pattern of ultra-wide box girder coated with polyurea under the charge of 500 kg with simultaneous multi-point explosions. Figure 39: Finite-element model of ultra-wide box girder subjected to simultaneous multi-point explosions. In summary, a comparative analysis was conducted on polyurea-coated box girders under three blast scenarios: single-point repeated detonation, multiple-point detonations, and simultaneous detonations. In terms of the extent of damage to the box girder, repair time, and traffic capacity, it was concluded that multiple-point repeated detonation (i.e., sequential detonation from the bridge surface to the interior of the box) caused the most severe damage. Nonetheless, after polyurea coating, the overall blast resistance improved significantly, with perforations occurring only in the top plate and no significant damage elsewhere, resulting in a relatively shorter repair time. Simultaneous detonation at multiple points caused several damage locations along the transverse axis, reducing the bridge's overall strength and stability while severely affecting traffic, but the polyurea coating substantially improved the blast resistance of the box girder, which still retained some traffic capacity.
# Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
## 3 Proposed Method  
In Section 3, we will describe the proposed method. The method utilizes hierarchical clustering of latent vectors obtained from a pre-trained autoencoder to establish clustering criteria based on the presence of two types of actual abnormal data. This approach effectively reduces the number of unnecessary models and training iterations. The encoder of the autoencoder is trained to accurately represent the features of the training data in the latent space. Consequently, when obtaining latent vectors of data using a pre-trained autoencoder, data with similar characteristics to the training data exhibit shorter distances in the latent space, while data with different characteristics exhibit longer distances. To leverage these characteristics, we propose a method that involves pre-training an autoencoder model with normal data containing abnormal data, performing hierarchical clustering using the obtained latent vectors, and grouping and managing data with similar characteristics. By employing hierarchical clustering, we use the distance between the latent vector of the training data and the latent vector of abnormal data as the threshold for grouping. If the latent vector of normal data from untrained products falls within this threshold, they are grouped. However, if it exceeds the threshold, individual models are trained for anomaly detection. Figure 1 shows the flowchart of the proposed method. Pre-training of the autoencoder is described in detail in Section 3.1 and hierarchical clustering in Section 3.2.
## 4 Experiment Evaluations
### Romo_SODsda Effectiveness  
#### 4.1.1 Datasets and Experiment Setup  
Datasets: we considered nine real-world datasets from the work of [14]. Meanwhile, in the unconstrained preprocessing stage, regular mining tool RMiner [39] was used for mining roles. The results including the number of roles and execution time are shown in Table 6.  
Experiment setup: constraints were constructed synthetically. According to the length of constraint enforcement, we studied four different kinds of \(k\)-\(n\) RSSOD: 2-2 RSSOD, 2-3 RSSOD, 3-5 RSSOD, and 5-10 RSSOD, where \(n\) permissions were randomly chosen from the set of all permissions. According to the amount of constraint enforcement, we chose 30 and 50 as the number of each kind of \(k\)-\(n\) RSSOD, respectively.  
However, after analyzing these datasets, we found that the Domino, Firewall1, Firewall2, and Healthcare datasets could not be used to represent the performance of role-mining algorithms with RSSOD constraints. Some user sets in these datasets violate the constraints, and they have all permissions assigned to them. Thus, valid RSSOD constraints cannot be generated from them.
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 5 Real-World Use Case  
In this section, a real-world use case is presented to further explain the benefits of our proposal in a light traffic UAV-WSN. For assessing the performance of CWC in real applications, we constructed the wireless network, UAV control system and carried out an experiment to compare our approach and other traditional schemes (FCM).  
Figure 6 shows a prototype platform of UAV-WSN for detecting the multiple emerging events, and this platform includes a variety of physical equipment, such as a UAV-sink node, sensor nodes, group leaders, etc. System configuration parameters are as follows. The antenna height of sensor nodes is 0.5 m; the transmission power is 1 dBm; the communication rate was 250 kbps; the node omni-directional antenna gain is 3 dBi. The wireless sensors are powered by batteries and equipped with CC2530 along with GPS module to get the position. WSN uses ZigBee as the wireless communication protocol, with a radio frequency of 2.4 GHz. The cluster heads are charged with photovoltaic panels. The UAV (a quadrotor) is controlled by an MCU stm32f103.  
As mentioned above, sensor nodes are randomly placed in the surveillance area. After obtaining the communication quality index and its coordinate, the ground WSN is established. An auxiliary computer is connected to the WSN base station. As shown in Figure 6, the UAV followed this scheduled sequence of testing and completes the WSN data collection accordingly. The Figure 6b also compares the flying path generated using traditional means (FMC) and our approach.  
Figure 5: Comparison of Total Flying Scores. (**a**) Flying Scores in different cluster number; (**b**) Flying Scores in different event number.  
Figure 6: Multi-motor UAV sink. Right Flight path of UAV. (**a**) Implement scene; (**b**) UAV-Sink flying path in different ways. Note that, in Figure 6b, the yellow and green line indicate flying path using traditional means and our proposal, respectively. Red circles stand for emerging events.  
It is obvious that the cluster 3 could sense two emerging events, and the value of event correlation coefficient of cluster 3 is greater than the other clusters. So, UAV firstly collected the data of this cluster. After three rounds of experiments, we got the average assessment metrics of different criteria. The lengths flying time of CWC and traditional strategies are 5 min and 12 min respectively. Meanwhile, the collected data integrity is 99% and 45%, which is more than two times higher. Therefore, we draw the conclusion with confidence that the data communication quality between WSN and UAV is improved while the flying time and energy consumption of the whole cooperative system is obviously reduced. We make the system more efficient in terms of energy consumption and task response.