esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
## 5 Conclusions  
The shut-off spatial environment of ships facilitates the spread of viruses. The most critical aspect of pandemic preparation is the timely detection of close contacts on board. Proximity detection of smartphones can identify close contacts. Essential data for proximity detection include the Bluetooth RSSI signal. In this paper, a statistical analysis compares publicly available Bluetooth RSSI signal data in sitting and standing poses.  
The proximity detection error is lower than in medium, mode, minimum and maximum with the mean RSSI value. If only RSSI dispersion is considered, reducing the error is difficult to achieve. The RSSI values in the majority of cases are almost all left-skewed. The mean and skewness of RSSI are combined with other features. The current pose, either sitting or standing, of the person on board can be determined based on the mean and skewness of RSSI in the event of a close distance.  
The difference of RSSI in different poses decreases as distance increases. The feature of mean and skewness gradually disappears for longer distances. RSSIs of the two poses in various periods have different but closer fluctuation intervals. These characteristics enhance the precise detection of proximity and, under certain conditions, determine the ship passenger's pose. In order to prevent the further spread of COVID-19 between ship passengers, this method can be used to precisely identify close contacts with high-precision proximity detection.  
Conceptualization, Q.L.; methodology, Q.L.; software, Q.L.; validation, Q.L.; formal analysis, Q.L.; investigation, Q.L.; resources, Q.L.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, J.S.; visualization, Q.L.; supervision, J.S.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: 2021R11A3056125).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Case I. Application with a Frequency Modulation \(m_{f}\) = 12  
Two examples were developed to assess the algorithm's efficacy: A simple example was performed to observe the behavior and test the previously established equations. Table 1 was generated to determine the important initial parameters that do not require additional calculations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Source voltage & \(V_{dc}\) & 180 \\ Triangular amplitude & \(A_{t}\) & 1 \\ Sine amplitude & \(A_{s}\) & 0.9 \\ Carrier frequency & \(f_{c}\) & 12 Hz \\ Output frequency & \(f_{o}\) & 1 Hz \\ Number of samples & N\({}_{s}\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameters.  
Figure 6: Angle calculation algorithm.
## 3 Data and Processing
### Pixel Offset Estimation  
The Co-registration of Optically Sensed Images and Correlation (COSI-Corr) package, which was developed for co-seismic ground displacement extraction based on pixel offset analysis with optical images to derive horizontal displacement [54], was used in this study. The sub-pixel correlation algorithm performed in COSI-Corr produced a de-correlation effect between images due to rapid changes in the ice-covered lake's surface due to snow build-up and other causes [55]. Most of the images we collected were only 5 days apart in the processing strategy, so we could ignore the difference due to the solar zenith angle. Then, we selected the frequency correlator for the correlation analysis, with a matching window of 32 \(\times\) 32 pixels, a moving step length of 8, and a robustness iteration of 2 [50; 56]. The output of the COSI-Corr package includes the horizontal ground displacement of the east-west (EW) and north-south (NS) components, as well as the relative signal-to-noise ratio (SNR) in each pair of images. A mask threshold of 0.9 based on the SNR was selected for the output EW and NS displacements. Additionally, we undertook the destriping step after correlation calculations and used a filter to remove the outlier caused by small  
The displacement accuracy of pixel offset estimation mainly depends on the pixel co-registration error. Researchers [57] previously reported that uncertainty assessment in stabilized regions may not be representative of uncertainty in all regions of an image, especially in the case of inhomogeneous masses with small moving targets. However, in the absence of in situ measurements to assess the uncertainty of moving targets, stable regions would be useful. At each stage, we randomly selected five far-field reference objects without ground deformation and calculated the standard deviation and average value to estimate the accuracy of the pixel offset [56; 58; 59; 60; 61; 62]. Multiple stable regions were selected to reduce bias due to snow, ice, or cloud cover. We found that the uncertainty in our work was from approximately 0.7 (EW) to 2.3 m (NS) (Table 2). In the third stage, the standard deviation became larger because the freeze-thaw cycle caused a poor performance of POT. Additionally, the standard deviation overestimated the uncertainty because it did not consider reduced errors due to us averaging larger areas [63].  
Figure 4: Flow chart of processing of lake ice displacement deformation obtained from Sentinel 2 optical images.  
Figure 3: Weather data collected from NOAA’s stations for Lake Khovsgol from 1980–2020.
## 5 Discussion
### Uncertainties and Limitations  
The spatial pattern of GPP of alpine grasslands on the TP exhibited obvious spatial heterogeneity in our study (Figure 7), consistent with other studies revealed that the GPP of Tibetan alpine grasslands showed a decreasing trend from southeast to northwest [14; 16]. In addition, the mean annual GPP of Tibetan alpine grasslands from 2003 to 2008 simulated by [14] using a modified VPM model was 312.3 gC/m\({}^{2}\)/yr, which is slightly lower than the GPP (317.1 gC/m\({}^{2}\)/yr) simulated by our study during the same period, and this small discrepancy may be attributable to the differences in the model used.  
On the other hand, some uncertainties still remain in our simulation of the carbon fluxes based on the improved Biome-BGC model. Firstly, since we used the EC-derived GPP data to calibrate the Biome-BGC model, which is essentially obtained through flux partitioning rather than direct observation, the GPP data will inevitably have errors and will further bring uncertainty to the model calibration and GPP simulation. Secondly, disturbances in human activities such as grazing and land use changes can also lead to bias in the GPP simulation. Thirdly, the NDVI dataset used to determine the phenology metrics may also bring uncertainty to the GPP simulation. Specifically, some studies reported that newly launched sensors such as MODIS have better performance than AVHRR for determining phenological parameters in alpine regions [24; 67; 68]. However, MODIS data was not available until 2000, so we chose to use a single data source to extract phenological parameters to avoid new uncertainties caused by the combination of different sensors. In addition, it is worth mentioning that [PERSON] et al. [69] have improved the quality of GIMMS NDVIg dataset by applying a local correction to the NDVI3g dataset using MODIS NDVI, which could also be considered in our future work. Finally, uncertainties may also come from the internal structure of the Biome-BGC model. Specifically, due to the widespread distribution of frozen soil on the TP, they will have an important impact on the physiological processes of alpine grasslands, which in turn will affect the carbon cycle and energy flow of alpine grassland ecosystems [25; 70]. However, the existence of frozen soil and its effects on the physiological processes have not been considered in the Biome-BGC model, so further improvements to the model structure are still needed. On the other hand, it is worth mentioning that some already improved versions of the Biome-BGC model could also be considered in our future work to reconcile the above issues. For example, [PERSON] et al. [71] obtained an improved Biome-BGC model (referred to as the Biome-BGCMuSo) by adding a multilayer soil module and a management module to the original model, which could help with the issue of frozen soil and human activities.
## Channel Model
### Bandwidth  
The use of acoustic waves reduces the available bandwidth in underwater communications. This is because the acoustic bandwidth is limited and most of the frequencies are severely attenuated by water. Consequently, increasing the bandwidth reduces the effectiveness of underwater networks, as shown in Table 1[7].  
The network convergence and range decrease as the bandwidth increases. As a result, underwater networks with a high bandwidth do not cover a significant part of the sea to deploy and are, therefore, not effective in covering the sea environment. As a result of the absorption of the radio waves in water, the acoustic waves used in underwater communications have a limited spectrum. This spectrum is also severely affected by the properties of the underwater medium. Consequently, the underwater channel is associated with low data rates. As shown in Table 1, the proposed protocol operates in the short convergence range, as the network has a depth of 1000 m.
## 4 VSG Applications of Power Electronics-Based Units
### Auxiliary ESS for Vulnerable Local System  
Due to the stochastic generation properties of distributed energy sources (DES), systems fed by high-penetration renewable energy generation have low stability, as they lack sufficient inertia and active power damping to provide frequency support [43; 44; 45]. An energy storage system (ESS) is an important component because it smooths the renewable energy generation profile and increases its reliability. The ESS system can be implemented at a relatively large scale when coupling renewable energy generation to an existing grid. Besides, remote systems or systems in island scenarios are more vulnerable, as they have a relatively lower generation capacity and a faster and larger demand deviation. If a system fails to operate within the prescribed limits of the existing frequency magnitude or rate of change, tripping occurs, and the load loses power. Power systems with a large amount of renewable energy sources, remote systems, and islanded systems can be briefly categorized into vulnerable local systems due to their low stability. Then, the VSG-controlled ESS can be implemented as an auxiliary apparatus at the interconnection point to provide frequency support and regulate the power balance [43]. The schematic of a typical vulnerable local system with an auxiliary VSG-ESS is shown in Figure 10. The VSG-controlled energy storage system can also be implemented as an uninterruptable power supply or black-start power.  
Figure 9: Schematic diagram of double-stage photovoltaic (PV) generation with energy storage on a direct current (DC) bus.
# Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation
## 2 Point-Mass Model of UAV Dynamics
### Feedback Linearization for Variable Mass m Case  
The control signals in (5)-(7) are computed from the virtual controls \(v_{1}\), \(v_{2}\), and \(v_{3}\) for system (4). Because Expression (4) does not contain the mass \(m\) in it, the mass change is not taken into account when virtual controls are computed. In order to take into account the influence of the mass, we can make transformations in (4)-(7) by employing the following changes in variables:  
\[\begin{split} mv_{1}&=\bar{v}_{1},\\ mv_{2}&=\bar{v}_{2},\\ m(v_{3}+g)&=\bar{v}_{3}.\end{split} \tag{9}\]  
Then,  
\[\begin{split} v_{1}&=\frac{1}{m}\bar{v}_{1},\\ v_{2}&=\frac{1}{m}\bar{v}_{2},\\ v_{3}&=\frac{1}{m}\bar{v}_{3}-g.\end{split} \tag{10}\]  
After substituting (10) into (4), we have  
\[\begin{split}\bar{x}&=\frac{1}{m}\bar{v}_{1},\\ \bar{y}&=\frac{1}{m}\bar{v}_{2},\\ \bar{h}&=\frac{1}{m}\bar{v}_{3}-g,\end{split} \tag{11}\]  
where \(\bar{v}_{1},\bar{v}_{2},\bar{v}_{3}\) are the new virtual controls.  
Now, the transformations to the real control signals (5)-(7) are as follows:  
Option 1.  
\[\begin{split} F_{\text{thrust}}&=F_{\text{drag}}+ \cos\gamma(\bar{v}_{1}\cos\psi+\bar{v}_{2}\sin\psi)+\bar{v}_{3}\sin\gamma,\\ F_{\text{lift}}&=\sqrt{(\bar{v}_{3}\cos\gamma-\sin \gamma(\bar{v}_{1}\cos\psi-\bar{v}_{2}\sin\psi))^{2}+(\bar{v}_{2}\cos\psi- \bar{v}_{1}\sin\psi)^{2}},\\ \varphi&=\arctan\biggl{(}\frac{\bar{v}_{2}\cos\psi- \bar{v}_{1}\sin\psi}{\bar{v}_{3}\cos\gamma-(\bar{v}_{1}\cos\psi+\bar{v}_{2}\sin \psi)\sin\gamma}\biggr{)}.\end{split} \tag{12}\]  
Option 2.  
\[\begin{split} F_{\text{thrust}}&=F_{\text{drag}}+ \bigl{(}\dot{x}\bar{v}_{1}+\dot{y}\bar{v}_{2}+\dot{h}\bar{v}_{3}\bigr{)}\,/V_{ g},\\ F_{\text{lift}}&=\frac{\bar{v}_{3}\cos\gamma-( \bar{v}_{1}\cos\psi+\bar{v}_{2}\sin\psi)\sin\gamma}{\cos\varphi},\\ \varphi&=\arctan\biggl{(}\frac{\bar{v}_{2}\cos\psi- \bar{v}_{1}\sin\psi}{\bar{v}_{3}\cos\gamma-(\bar{v}_{1}\cos\psi+\bar{v}_{2} \sin\psi))\sin\gamma}\biggr{)}.\end{split} \tag{13}\]  
Option 3.  
\[\begin{split} F_{\text{thrust}}&=F_{\text{drag}}+ \cos\gamma(\bar{v}_{1}\cos\psi+\bar{v}_{2}\sin\psi)+\bar{v}_{3}\sin\gamma,\\ F_{\text{lift}}&=\frac{\bar{v}_{3}\cos\gamma-( \bar{v}_{1}\cos\psi+\bar{v}_{2}\sin\psi)\,\sin\gamma}{\cos\varphi},\\ \varphi&=\arctan\biggl{(}\frac{\bar{v}_{2}\cos\psi- \bar{v}_{1}\sin\psi}{\bar{v}_{3}\cos\gamma-(\bar{v}_{1}\cos\psi+\bar{v}_{2} \sin\psi)\sin\gamma}\biggr{)}.\end{split} \tag{14}\]  
Note that the right-hand sides of (12)-(14) do not include the mass \(m\).  
Equation (21) describes the internal dynamics, while (22) is used for the external dynamics. Obviously, the null dynamics of system (21) are exponentially stable [49].
## 3 System Model and Problem Statement
### Observation Space  
In SAG-MCS, each UAV agent \(u\) can obtain the multi-scale observation \(\{\mathbf{o}_{t}^{u}\}_{u\in\mathcal{U}}\) at timestep \(t\) from different sources, as introduced in Section 3.1. In Figure 2, we formulate the observation space with three elements: \(\mathcal{O}\triangleq\{\mathbf{o}_{t}^{u}=(\mathcal{O}_{local}^{u}\, \mathcal{O}_{global}^{u}\,\mathcal{O}_{self}^{u})\}_{\forall u\in\mathcal{U}}\).  
(1) Local observation \(\mathcal{O}_{local}\) from embedded sensors: UAV can observe local information within a circle of radius \(R_{obs}\) in real-time, centering on itself. Let \(\mathcal{O}_{local}\triangleq\{\mathbf{o}_{l}^{u}=(\mathcal{O}_{local}^{u,1}, \mathcal{O}_{local}^{u,2},\mathcal{O}_{local}^{u,3})\}_{\forall u\in\mathcal{U}}\) denotes local observation space, which consists of three 2D vector channels. The first channel contains the data amounts and distribution of surrounding Pols. We set the data value \(d(k)\) as the corresponding pixel value if it refers to PoI \(k\), otherwise 0. The second channel contains the locations of obstacles relative to the UAV, where we set pixel value 1 for coordinates of obstacles, otherwise 0. The third channel includes the locations of other UAVs within \(R_{obs}\). In addition, we define pixel value 1 for coordinates of UAV agents as well, otherwise 0.  
(2) Global observation \(\mathcal{O}_{global}^{u}\) from satellites: Every \(n\) timesteps, satellites will capture fuzzy global observation and transmit the information to all UAVs. As shown in Figure 2, \(\mathcal{O}_{global}^{u}\) consists of three 2D channels with reduced size of \(L_{sat}\times L_{sat}(L_{sat}<L)\), which cannot provide precise locations of the environment elements globally. We define \(\mathcal{O}_{global}\triangleq\{\mathbf{o}_{g}^{u}=(\mathcal{O}_{global}^{u,1}, \mathcal{O}_{global}^{u,2},\mathcal{O}_{global}^{u,3})\}_{\forall u\in\mathcal{U}}\) in absolute positioning coordinates. The encoding method for global observation is nearly the same as local observation, except in the third channel of UAV locations, we set \(-1\) as the corresponding pixel value if it refers to the absolute location of UAV \(u\) in global map.  
(3) Auxiliary observation \(\mathcal{O}_{self}^{u}\): Then we utilize information from onboard flight control computer to assist UAV to learn optimal policy. Specifically, we define \(\mathcal{O}_{self}\triangleq\left\{\mathbf{o}_{s}^{u}=\text{concatenate}\big{(}x(u),y(u),v_{x}(u),v_{y}(u),\phi(u),\{\Delta x(c),\Delta y(c)\}_{ \forall c\in\mathcal{C}}\big{)}\right\}_{\forall u\in\mathcal{U}}\). For UAV \(u\), \(\mathbf{o}_{s}^{u}\) includes its absolute position, velocity and current remaining battery, and the relative locations of all charging stations towards UAV \(u\).
# Encapsulation of \(\alpha\)-Lipoic Acid in Halloysite Nanotubes
## 3 Results
Ultraviolet irradiation testing of a hybrid based on montmorillonite and promethazine in solution indicates effective protection of this organic drug against photodestruction. There are known studies on the successful loading of nifedipine into clay minerals, which undergo photodegradation under the influence of light in other drug delivery systems and turn into compounds that have no pharmacological effect. A high photoprotective effect of photolabile nifedipine was obtained by intercalation in montmorillonite [41] and halloysite [42]. It was proven that nifedipine in the composition obtained by encapsulation into halloysite has high photostability [42]. Thus, after testing for photodestruction under conditions of irradiation for 1000 h, about 58% of nifedipine remained in the composite and only 18% in the micronized state. Figure 7: Degradation of \(\alpha\)-lipoic acid under temperature stress. Thus, the results of research in the publications indicate that organic substances intercalated in natural mineral clays have increased photostability. One of the effective clays is montmorillonite, whose protective effect is based on the intercalation of photolabile molecules in the internal cavities and the formation of certain interactions with them, including the flow of cation exchange reactions. This ensures the formation of stable hybrid materials. Halloysite, which consists of nanotubes, is an equally effective nanocarrier that also provides protection against photodegradation. The photoprotective effect of halloysite is ensured by the fact that the organic molecules intercalated in the middle of the nanotubes are protected by multilayered tubes of the mineral. The results of the research presented in this paper indicate that the obtained effect of increasing the photostability of \(\alpha\)-lipoic acid in the composition of halloysite is commensurate in terms of protection with other hybrid materials based on halloysite and montmorillonite.
# Minimizing Interference-to-Signal Ratios in Multi-Cell Telecommunication Networks
## 3 Dot-Product Clustering Algorithm for Total Interference Minimization Problem
### Phase 3: Assigning Users to BS Clusters  
Let \(\mathcal{I}:=\mathcal{I}_{b-M}=\{I_{1},\ldots,I_{M}\}\) be the final partition produced by the hierarchical clustering on the primary class. We are looking for the desired final graph partition in the form of \(\mathcal{P}=\{(I_{k},J_{k})\mid k\in[M]\}\), so it only remains to find an appropriate clustering \(\mathcal{J}=\{I_{1},\ldots,J_{M}\}\) of \(J\).  
We assign each user \(j\in J\) to the cluster \(J_{\ell}\) where \(\ell\) is defined by  
\[\ell=\operatorname*{arg\,max}_{k\in[M]}\sum_{i\in I_{k}}w_{i,j}. \tag{8}\]  
The choice described by Equation (8) is easy to compute. A high-level pseudo-code can be found in Algorithm 2. If no element of \(U\) is isolated in \(G\), then Algorithm 2 ensures that there are no isolated users in the subgraph induced by the base station of \(I_{k}\) and users \(I_{k}\). However, this is not necessarily the case for elements of \(I_{k}\). However, the definition of \(\sum^{*}\) in Equation (2) takes care of this. This completes the description of our algorithm.  
```
functionDP-SimilarityClustering(\(G,W,M\)) \(I,J\leftarrow\) index sets of \(B\) and \(U\) \(\{I_{1},\ldots,I_{M}\}\leftarrow\)DPH-clustering(\(I,W,M\)) \(J_{1},\ldots,J_{M}\leftarrow\) empty clusters for all\(j\in J\)do \(\ell\leftarrow\operatorname*{arg\,max}_{k\in[M]}\sum_{i\in I_{k}}w_{i,j}\)  add \(j\) to \(J_{\ell}\) endfor return\(\{(I_{k},J_{k})\mid I_{k}\
eq\emptyset\}\) endfunction
```  
**Algorithm 2** Dot-product hierarchical clustering on \(I\) then assigning each element of \(J\) to the best cluster.  
Lemma 2: _Let the time complexity of the matrix multiplication \(W\cdot W^{T}\) be \(f(W)\). Then DP-Similarity-Clustering runs in \(f(W)+\mathcal{O}(b\cdot((b-M)\log b+u))\) time._  
Proof: Follows from Lemma 1 and a cursory analysis of Algorithm 2.
## 4 Testbed Model and Use Case  
### Testbed Model  
The emulated software-hardware electrical grid testbed is set up with an OP5600 real-time simulator (Opal-RT Corporate, Montreal, QC, Canada)/expansion box with relays/meters in the loop. The real-time simulator is connected to a host computer that is used as an interface to set the test scenarios based on running different temporary electrical faults and selecting the duration of the simulations around 50 s. The emulated software-hardware electrical grid reflects a real-world grid condition because it uses relays and meters in the loop and the simulations are performed with a time step of 50 us to solve electrical system models and reproduce the nature of electrical events. The testbed model is shown in Figure 11. It is formed by the substation grid that is connected to the IB-PV farm. The meters and relays that are in the red dashed area have different sample rates. In the testbed model, data from the relays and meters are recorded at same time. Therefore, the recorded events from the meters and relays are collected at the same time by using the event signal system.  
From the saved relay and meter events, the data (i.e., phase current and voltage signals) are analyzed, and the envelope- and ML-based electrical fault type detection algorithms for the electrical distribution grid with different sample rate relays/meters located at different sites from the IB-PV-array farm are assessed in detail. The test scenarios are based on assessing these algorithms for different temporary fault types at the distribution line of utility \(A\) as shown in Figure 11. These temporary electrical faults have a duration of 0.5 s (30 cycles), and the testbed is prepared for the relays that do not trip the breakers for recording and saving the whole fault states in all protective relays and meters.  
Figure 10: The illustration of designed convolution neural network (CNN) model.
## 2 Method
### Data Analysis
#### 2.5.2 P300 Analysis  
EEG Data Processing  
EEG data were processed using MATLAB R2020a (MathWorks, Natick, MA, USA). Following an initial review, two participants were excluded due to poor data quality--one from the full penetration condition and one from the partial penetration condition--resulting in 22 participants included in the final analysis.  
For these remaining EEG data, noisy segments were removed. Then, a Finite Impulse Response (FIR) band-pass filter (low cutoff: 1 Hz; high cutoff: 30 Hz) was applied. Artifacts were identified via extended Infomax Independent Component Analysis (ICA). Independent components associated with typical artifacts (e.g., muscle, eye, and cardiac activity) were excluded based on standardized criteria, with thresholds ranging from 0.9 to 1. The EEG data were then re-referenced, binned, and segmented into epochs spanning 200 ms pre-stimulus to 800 ms post-stimulus onset. Baseline correction was performed by subtracting the mean voltage of the 200 ms pre-stimulus interval. Epochs exhibiting anomalies--defined as values exceeding two standard deviations from the mean based on probability and kurtosis--were discarded. For each participant, ERPs were averaged across all remaining epochs, and the P300 amplitude was extracted for each eHMI information content condition under both partial and full penetration conditions.