esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Quality of Seismological Solutions  
For the newly picked events, we used waveforms from all stations of HUSN (the backbone network in Greece) and the Corinth Rift Laboratory Network (CRLnet), which focuses on the Western Gulf of Corinth. Due to the weak magnitudes of the sequence, the majority of the picked phases were in near-local epicentral distances (i.e., a maximum limit of ~100 km). We offer the distribution of station distances with a determined P arrival (Figure A5a) and the magnitudes for the aftershock sequence (Figure A5b). Note that we did not pick any S phases without the respective P arrival.  
To estimate the focal mechanism of the mainshock, we used the ISOLA software for inverting regional recordings [33,34]. To this end, we selected an azimuthally satisfying distribution of broadband stations that belong to HUSN (Figure A6). Short-period instruments (e.g., STFN, with a corner frequency of 1 Hz) were de facto excluded, as the target frequencies range well below 0.5 Hz. In total, 13 stations were initially considered in inversion, but two were rejected. Inversion was carried out at frequencies ranging between 0.05 and 0.10 Hz; the lower boundary was selected by visually inspecting the spectral signal-to-noise ratio (SNR) at each station. For the upper limit, we tried various frequencies between 0.08 and 0.15 Hz to obtain the best inversion results.  
The inversion was carried out for multiple sources on a plane striking N111\({}^{\circ}\)E and dipping 33\({}^{\circ}\), as indicated by the rupture plane estimated through InSAR and the GI-NOA solution. We used 6 sources along-strike and 12 along-dip at 1-km intervals (total of 72 trials). This grid (Figure A6) was designed to approach the inferred length and width of the Kallithea fault. We note that the source located by Hypoinverse corresponds to source #4 (Figure A7). Solutions show spatial independence. Solutions beneath the optimal one (#6) showcase stable -WNW-ESE normal characteristics, similar to the 5 th km along-strike, down to the 9 th km along-dip. However, deeper sources exhibit a rotated strike and a stronger strike-slip component. The depth (analogous to the along-dip dimension) is strongly related to correlation. Shallower depths are accompanied by higher correlation; the selected solution stands at a high 88% correlation.  
Synthetics were calculated through ISOLA, using the discrete wavenumber method [58] and the regional velocity model of [31]. The similarity with observed signals (Figure A8) is exceptional. Low amplitude waveforms in MDRA and VILL, probably due to the proximity of the stations to the epicenter, were excluded from the inversion. Figure A7 offers variance reduction (\(VR\)) as a metric of similarity. \(VR=1-\frac{\sum r^{2}}{\sum d^{2}}\), where \(r\) the residuals between observed and synthetic amplitudes and \(d\) the actual observed data [33]. The average \(VR\) from all traces is 0.78. In combination with the high double-couple (DC) percentage contribution to the moment tensor (87%) and the high correlation (88%), the final solution can be considered very reliable. Note that we used a deviatoric-constrained inversion type.
## 3 Results
### Comparison of Results with Manual Feature Selection Methods  
In addition to the above results, we tested the prediction accuracy by inputting the RSP signal features manually selected from the literature [44] (Table 6) into the SVM. A comparison between the accuracy results of our method and manually selected features is presented in Table 7. It can be observed that the manually selected features based on experience and knowledge achieved higher accuracy owing to their higher relevance to the current emotional state of the subject.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Code** & **Feature Meaning** \\ \hline F1 & Waveform segmented mean \\ F2 & Variance of average of waveform segments \\ F3 & Waveform width: average of widths of all peaks in waveform \\ F4 & Distance between peaks within waveform, calculating the \\ F5 & number of samples between two peaks and their mean values \\ F6 & RSP frequency of whole segment \\ F6 & Power spectral density in 0.0–0.1 Hz range \\ F7 & Power spectral density in 0.1–0.2 Hz range \\ F8 & Power spectral density in 0.2–0.3 Hz range \\ F9 & Power spectral density in 0.3–0.4 Hz range \\ F10 & Power spectral density in 0.4–0.9 Hz range \\ F11 & Power spectral density in 0.9–1.5 Hz range \\ F12 & Ratio of power spectral density values in low frequency \\  & (0.1–0.5 Hz) and high frequency (0.6–1.5 Hz) ranges \\ \hline \hline \end{tabular}
\end{table}
Table 6: Manually selected specific features.  
Figure 14: Change in accuracy of classification results of (**a**) valence and (**b**) arousal.
## 3 Results and Discussion
### Optimization Logic
**For FFA**
* Stage 1: Fireflies=11, Iterations=25, \(\gamma_{F}=0.0018\), \(\beta_{0}=1\), \(\eta_{F}=0.05\) and \[\mathbf{\alpha_{0}}=\text{diag}\{0.5,0.5,0.5,25,0.5,0.5,0.5,0.5,15\}.\]
* Stage 2: Fireflies=11, Iterations=15, \(\gamma_{F}=0.0012\), \(\beta_{0}=1\), \(\eta_{F}=0.09\) and \[\mathbf{\alpha_{0}}=\text{diag}\{0.15,0.15,0.15,0.15,1.5,0.15,0.15,0.15,1.5\}.\]  
**For DE**  
* Stage 1: Individuals=10, Generations=25, \(Cr=0.6\), \(F_{1}=0.15\) and \[\mathbf{F_{2}}=\text{diag}\{0.5,0.5,0.5,25,0.5,0.5,0.5,0.5,15\}.\]
* Stage 2; Individuals=10, Generations=15, \(Cr=0.55\), \(F_{1}=0.12\) and \[\mathbf{F_{2}}=\text{diag}\{0.15,0.15,0.15,1.5,0.15,0.15,0.15,0.15,1.5\}.\]  
The initial conditions of the design vectors \(\mathbf{x_{i}}\) for every algorithm, for example, the initial positions of the particles in PSO, the initial population in DE and the initial positions of the fireflies in FFA, were generated as linear combinations of the heuristically tuned parameters used in the not optimized controller, which are contained in column three of Table 1. This was done in each one of the optimization stages by multiplying the value of the corresponding set of parameters by a random number \(r_{0}\in[0,1]\) drawn from a uniform distribution, for example, in the second optimization stage, for the DE algorithm, every individual of the initial population was generated by multiplying a random number \(r_{0}\) by the values of the parameters of inputs \(\hat{x}\) and \(\dot{\theta}\).
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
## 3 Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter
### Algorithm for Kernel Bandwidth Adaptation  
The innovation sequence shows the difference between the predicted measurement \(\overset{\boldsymbol{\kappa}}{\boldsymbol{\kappa}}_{k|k-1}\) and the actual measurement \(\mathbf{z}_{k}\),  
\[\boldsymbol{\upsilon}_{k}=\mathbf{z}_{k}-\overset{\boldsymbol{\kappa}}{ \boldsymbol{\kappa}}_{k|k-1}=\mathbf{z}_{k}-h(\overset{\boldsymbol{\kappa}}{ \boldsymbol{\kappa}}_{k|k-1}), \tag{39}\]  
while the weighted innovation, \(\mathbf{K}_{k}(\mathbf{z}_{k}-\overset{\boldsymbol{\kappa}}{\boldsymbol{ \kappa}}_{k|k-1})\), acts as a correction to the predicted estimation \(\overset{\boldsymbol{\kappa}}{\boldsymbol{\kappa}}_{k|k-1}\) with the estimated \(\overset{\boldsymbol{\kappa}}{\boldsymbol{\kappa}}_{k}\). The covariance matrix of the innovated sequence can be expressed as  
\[\mathbf{C}_{\
u_{k}}=E[\boldsymbol{\upsilon}_{k}\boldsymbol{\upsilon}_{k}^{ \text{T}}]=\mathbf{H}_{k}\mathbf{P}_{k|k-1}\mathbf{H}_{k}^{\text{T}}+\mathbf{ R}_{k} \tag{40}\]  
Given that the length of the window is one and the degree of divergence \(\xi\) is used as the record of the innovation covariance matrix,  
\[\xi=\boldsymbol{\upsilon}_{k}^{\text{T}}\boldsymbol{\upsilon}_{k}=tr( \boldsymbol{\upsilon}_{k}\boldsymbol{\upsilon}_{k}^{\text{T}}) \tag{41}\]  
This variable can be adjusted for the adaptive filtering or utilized to identify the divergence or outliers.  
For the STMCCEKF based on adaptive kernel bandwidth mechanism, the kernel size is modified in the following way in the measurement-specific processing for outliers:  
\[\sigma_{j,k}=\lambda_{j,k}\cdot\sigma_{\text{max}},\text{ for }j=1,2,\ldots,m \tag{42}\]  
where \(\lambda_{j,k}\) denotes the adaptive factor for the \(j\)th measurement element at time step \(k\), \(\mathbf{z}_{j,k}\), with the maximum kernel size \(\sigma_{\text{max}}=12\).  
Further, \(\lambda_{j,k}\) must be suitably adjusted for varying noise intensities. The correlation between the innovation factor and its covariance matrix \(\mathbf{C}_{\
u_{k}}\) is the first point to consider in identifying the presence of measurement outliers, and \(\alpha_{j,k}\) can be defined as  
\[\alpha_{j,k}=\frac{\mathbf{C}_{\
u_{j,k}}}{\boldsymbol{\upsilon}_{j,k}^{2}}= \frac{\mathbf{H}_{k}\mathbf{P}_{j|k|k-1}\mathbf{H}_{k}^{\text{T}}+\mathbf{R}_ {k}}{\boldsymbol{\upsilon}_{j,k}^{2}} \tag{43}\]  
where \(\mathbf{C}_{\
u_{j,k}}\) is the \(j\)th diagonal element of the innovation covariance matrix, with \(\boldsymbol{\upsilon}_{j,k}\) being the innovation term \(\boldsymbol{\upsilon}_{k}\) for the \(j\)th element, and \(\boldsymbol{\upsilon}_{j,k}=\mathbf{z}_{j,k}-\overset{\boldsymbol{\kappa}}{ \boldsymbol{\kappa}}_{j,k|k-1}\). The \(\lambda_{j,k}\) is obtained by using the relation  
\[\lambda_{j,k}=1-\exp\left(-\alpha_{j,k}\right) \tag{44}\]  
See reference [13] for further details.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 3 Deep-Coupled Navigation System Design
### Design of the Integrated Navigation Filter
#### 3.4.2 Measurement Model
\begin{array}{l}\alpha_{1}=\frac{\left(\delta\rho_{\rm L2 CM}^{j} \right)^{2}}{\left(\delta\dot{\rho}_{\rm L1 CA}^{j}\right)^{2}+\left(\delta\dot {\rho}_{\rm L2 CM}^{j}\right)},\alpha_{2}=\frac{\left(\delta\dot{\rho}_{\rm L 1 CA}^{j}\right)^{2}}{\left(\delta\dot{\rho}_{\rm L1 CA}^{j}\right)^{2}+\left( \delta\dot{\rho}_{\rm L2 CM}^{j}\right)^{2}}\\ \delta\dot{\rho}_{\rm L1 CA}^{j}=-\delta\dot{f}_{\rm L1 CA}^{j}\cdot c/f_{\rm L1 CA0}\delta\dot{\rho}_{\rm L2 CM}^{j}=-\delta\dot{f}_{\rm L2 CM}^{j}\cdot c/f_{\rm L2 CM0} \end{array} \right.\]  
Therefore, the measurement of the integrated filter can be written as  
\[Z_{\rm nav}=\big{[}\delta\rho_{1},\delta\rho_{2},\cdots,\delta\rho_{\rm n}, \delta\dot{\rho}_{1},\delta\dot{\rho}_{2},\cdots,\delta\dot{\rho}_{\rm n} \big{]}^{T}. \tag{27}\]  
The observation matrix given below in Equation (28) is linearized at each measurement epoch to accommodate the error measurements from each channel:  
\[\mathbf{H}_{\rm nav}=\left[ \begin{array}{cccccc}0_{1\times 6}&e_{x}^{1}&e_{y}^{1} &e_{z}^{1}&0_{1\times 6}&1&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0_{1\times 6}&e_{x}^{n}&e_{y}^{n}&e_{z}^{n}&0_{1\times 6}&1&0\\ 0_{1\times 3}&s_{x}^{1}&s_{y}^{1}&s_{z}^{1}&0_{1\times 9}&0&1\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0_{1\times 3}&s_{x}^{n}&s_{y}^{n}&s_{z}^{n}&0_{1\times 9}&0&1\end{array} \right] \tag{28}\]  
where \([s_{x}^{j},s_{y}^{j},s_{z}^{j}]=\mathbf{C}_{\varepsilon}^{G}\cdot[e_{x}^{j},e_{y} ^{j},e_{z}^{j}]\) and \(\mathbf{C}_{\varepsilon}^{G}\) denotes the transformation matrix from \(\varepsilon\) to \(G\) frame.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
#### 3.1.1 Remote Care: Surgery and Diagnosis
The model collected 3842 vaginal delivery cases performed in 2017 at the Beijing Hospital of  
Figure 6: Remote surgery assisted by a teleoperated robot. Gynecology and Obstetrics. This dataset enabled the development of the machine learning prediction model, collecting 23 PPH-relevant features from each patient. [PERSON] et al. [29] presented a proposal for the telesurgery architecture. It consists of three domains: master, slave, and network. The system consists of a surgical arm, which is located in the slave domain; in the master domain, the surgeon is in charge of remotely manipulating the robotic arm, through a human system interface (HSI); and the network domain is used as the communication channel. [PERSON] and [PERSON] [36] proposed a tactile robotic telesurgery architecture. This system consists of a master-slave console. The master console houses the surgeon, who through commands, remotely controls the robotic arm located in the operating room (slave console). In addition, this architecture has two links, a direct link and a feedback link. The direct link is used to control the movement of the robotic arm, and the feedback link allows monitoring the patient's vital signs. [PERSON] et al. [45] presented a model for the diagnosis of diseases based on the deep belief network (DBN) using big data and deep learning (DL) techniques. This model consists of an acquisition stage in which physiological data are collected through body sensors and wearable devices. The generated data are stored in the cloud and managed through a Hadoop echo system. Subsequently, the information was analyzed and classified by DBN, allowing the diagnosis of diseases to be made based on patients' medical data. [PERSON] and [PERSON] [34] conducted an analysis of remote-surgery systems, detailing that it reduces the time that the surgeon spends to enter an operating room in a traditional surgery. In addition, it eliminates the need for the doctor's presence in the operating room and allows more experienced surgeons to provide support to less experienced ones. Finally, [PERSON] [39] is one of the authors who emphasized the use of remote systems for COVID-19 pandemic implications. He mentioned that through technologically supported systems, remote care and prevention services can be provided. He highlighted the use of AI as a prediction and detection system during the spread of the virus. These systems use X-ray images, and their use in learning algorithms helps specialists when making decisions.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 5 Experiments and Results  
In GNSS denied areas, the vehicle position is computed directly from its inertial and odometry data. If the elapsed time without having GNSS data is not significantly high, the positioning information will remain accurate enough, as the inertial and odometry data alone can provide accurate positioning estimates over short periods. However, these data have a tendency to increase their error with time without an upper limit. Meaning that, if GNSS signals are not available over long time periods the vehicle position will accumulate a high positioning error. For avoiding this behaviour, as this research work proposes, UWB ranging estimates can be used as a complementary source of information for correcting the computed position over time from the inertial and odometry data of the vehicle.  
Unlike inertial and odometry data, the UWB ranging estimates have a bounded error over time, indicating that their error does not increase with time. Therefore, the main goal of the performed tests is to analyse how the accuracy and reliability of the vehicle position is enhanced by including an alternative positioning system based on UWB ranging estimates.  
As mentioned in Section 3.3, the simulation platform has been used to characterize the EKF algorithm and test the solution at its initial stage. This platform presents some limitations when trying to replicate a real scenario. By using the simulator, it has not been possible to take into account the fact that the UWB tag has an external antenna physically separated from the module. Furthermore, the different errors that are introduced by the simulator on the positioning data do not correspond with the ones observed in a real case scenario, being these errors a relatively simple approximation of the real ones. Regarding the UWB system, the used simulator does not reflect the distance error highly related to the spatial location of the UWB nodes. Furthermore, this platform is not capable of emulating the reflections that an UWB system may undergo in an outdoor environment. Due to these limitations, the main idea when testing the developed algorithm with the simulation platform has been to prove its correct performance during its initial development phase.  
After this initial validation of the solution performance at a simulation level, different real tests have been carried out to validate the final proposed solution in a real scenario. The main idea behind these tests is to verify the positioning accuracy and continuity of the developed UWB solution in a simpler scenario (see Section 5.2) than the one used in the PRoPART use case. The results of these tests are presented in Section 5.3.
## 3 Numerical Model  
### Geometry  
For the purpose of this numerical study, a 2D sub-domain representing half of a finned tube (and half of the distance between two finned tubes) was modelled in SolidWorks, as shown in Figure 3. The 2D geometry was then imported into COMSOL Multiphysics 6.0 for simulation with appropriate symmetry and insulated boundary conditions.  
For the simulation to be representative of the experimental setup and experimental results, particularly in terms of the total mass of PCM in the system, it was assumed that the domain would be 30 cm deep (in the third dimension into the page), while the PCM domain was calculated to be 12.8525 cm high when having a fin spacing of 2.5 cm for 12 fins in the system for the system containing 10 kg of PCM.  
Specific to the finned-tube geometry, shown in Figure 2, the thickness of the thin extending aluminum fin was 0.5 mm. Overall, the fin length and PCM depth were kept constant, while the fin spacing was varied to keep the mass of PCM constant in the model. This required calculations to slightly adjust the fin spacing, changing from the theoretical 2.5 cm, 3.75 cm, and 7.5 cm to 2.5 cm, 3.691 cm, and 7.263 cm for 12, 8, and 4 fins, respectively. This change yielded a negligible change in the behavior of the system when compared.  
Therefore, the geometry used is shown with the boundary definitions in Figure 3. In the COMSOL model, the entire rectangular domain is meant to be rotated 90 degrees clockwise so that the insulation boundaries (red) are on the top and the bottom; it is presented in this orientation in this paper for space constraints. The symmetry line of the fin and the edge of the PCM domain are defined with a symmetry condition (green), reducing the simulation domain size, and hence the simulation time and file size. The HTF surface (blue) is defined through a convective heat flux using [PERSON]'s law of cooling. The convection coefficient is determined through empirical correlations detailed in Section 3.4, and the HTF temperature is a function of time coming from the experimental inlet temperature data that were ramped up or down at the start of experimental runs, as mentioned previously.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Property** & **Value** \\ \hline \(c_{p,s}\) @ 20 \({}^{\circ}\)C & 1.95 \(\pm\) 0.03 kJ/kg\(\cdot\)K \\ \(c_{p,1}\) @ 45 \({}^{\circ}\)C & 2.4 \(\pm\) 0.2 kJ/kg\(\cdot\)K \\ \(k_{6}\) @ 30\({}^{\circ}\)C & 0.160 \(\pm\) 0.004 W/m\(\cdot\)K \\ \(k_{l}\) @ 50 \({}^{\circ}\)C & 0.150 \(\pm\) 0.004 W/m\(\cdot\)K \\ \(\rho_{s}\) @ 22 \({}^{\circ}\)C & 930 \(\pm\) 20 kg/m\({}^{3}\) \\ \(\rho_{l}\) @ 50 \({}^{\circ}\)C & 885 \(\pm\) 20 kg/m\({}^{3}\) \\ \(T_{m}\) & 43.3 \(\pm\) 1.5 \({}^{\circ}\)C \\ \(L\) & 184 \(\pm\) 9 kJ/kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermophysical properties of dodecanoic acid [32].  
Figure 3: Model geometry and boundary conditions.
## 2 Literature Review
### Technology Adoption Theories
#### 2.4.1 TOE Framework  
TOE framework can define the innovation process within an enterprise context because TOE considers three aspects of an enterprise, namely technology, organization, and environment, that affect the adoption of emerging technologies [43]. In this framework, technology refers to the internal and external technical knowledge of an organization, as well as the mechanization that may influence the adoption decision. Besides, the characteristics of the company, including its particular communications channels and resources, are under the organization aspects, while the external forces such as competition and the regulatory and market conditions sit within the environment aspect [43; 85; 86].
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 3 Investigation of All-Bands BadNets Attack in Remote Sensing Dataset
### Dataset  
We evaluated both all-bands and sub-band BadNets backdoor attacks on the remote sensing imagery dataset EuroSat [51]. This dataset comprises 13 bands, as detailed in Table 1, including aerosol, blue, green, red, red edge 1, red edge 2, red edge 3, NIR, red edge 4, water vapor, cirrus, SWIR-1, and SWIR-2. The spatial resolution and central wavelength for each band are also provided in Table 1. EuroSat encompasses ten classes, including annual crops, permanent crops (e.g., fruit orchards, vineyards, or olive groves), pastures, herbaceous vegetation, sea and lake, river, highways, residential buildings, and industrial buildings. Each class consists of approximately 2000 to 3000 images, amounting to around 27,000 images in total, with each image measuring 64 \(\times\) 64 pixels. We conducted extensive experiments on the EuroSat data and also carried out additional brief experiments on the same series of datasets, with results presented in the Appendix A.  
Figure 1: BadNets backdoor attack in remote sensing imagery. The EuroSat dataset [51] is used for illustration. (**a**) BadNets backdoor planting. The target label is chosen as the ‘River’ class (label 8), and the backdoor trigger is defined as a white square pattern located at the bottom right corner of the image. The attacker first poisons some samples from each of the classes in the training data such as ‘highway’ and ‘industrial buildings’ by embedding the trigger and labeling the poisoned samples as ‘River’. The attacker then uses the poisoned samples together with clean samples to train the model and plant the BadNets backdoor. (**b**) Model inference. The backdoored model performs well on clean data samples while misclassifying any sample as long as the trigger is presented. Note that the trigger is embedded in all bands of the remote sensing imagery.
## 2 Materials and Methods
### Datasets
#### 2.2.2 Evapotranspiration and Soil Moisture Datasets  
The Evapotranspirationpiration (ET) data were obtained from the Global land surface satellite (GLASS) products actual evapotranspiration dataset [4; 24], publicly accessible at the University of Maryland GLASS Data Center ([[http://www.glass.umd.edu/](http://www.glass.umd.edu/)]([http://www.glass.umd.edu/](http://www.glass.umd.edu/)), accessed on 19 July 2022). The GLASS ET product was characterized by a long time series (1981-2018), spatiotemporal resolution was 5 km and 8 days ([[http://www.glass.umd.edu/](http://www.glass.umd.edu/)]([http://www.glass.umd.edu/](http://www.glass.umd.edu/)), accessed on 19 July 2022), giving it an advantage over existing ET products [4; 28]. The soil moisture (SM) information was obtained from the climate reanalysis database [37; 38], and is open for freely downloadable access to a large amount of climate data ([[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/)), accessed on 19 July 2023).