esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
###### Abstract  
The use of smartphones for accurate navigation in underground spaces, such as subway stations, poses several challenges. This is because it is difficult to obtain a sure estimate of user location due to the radio signal interference caused by the entry and exit of trains, the infrastructure of the subway station installation, and changes in the internal facility environment. This study uses quick response markers and augmented reality to solve these difficulties using an error correction method. Specifically, a hybrid marker-based indoor positioning system (HMIPS) which provides accurate and efficient user-tracking results is proposed. The HMIPS performs hybrid localization by using marker images as well as inertial measurement unit data from smartphones. It utilizes the Viterbi tracking algorithm to solve the problem of tracking accuracy degradation that may occur when inertial sensors are used by adopting a sensor error correction technique. In addition, as an integrated system, the HMIPS provides a tool to easily carry out all the steps necessary for positioning. The results of experiments conducted in a subway station environment confirm that the HMIPS provides accurate and practical navigation services. The proposed system is expected to be useful for indoor navigation, even in poor indoor positioning environments.  
Keywords: indoor localization; pedestrian tracking; marker; augmented reality +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Environmental Signals
#### 2.4.1 Price Data  
Price data in financial markets are typically represented using OHLC data, which provide key information about the price movement of an asset over a specific time period. Each OHLC dataset summarizes the price action within that period, irrespective of the sampling frequency, and is commonly visualized using candlestick charts, as shown in Figure 1.  
Each candlestick represents the price movement for a single period, with the body showing the range between the opening (O) and closing prices (C) and the wicks indicating the highest (H) and lowest (L) prices during that period. A filled (red/black) body denotes a closing price lower than the opening, while an empty (green/white) body indicates a higher closing price.  
By analyzing OHLC data alongside candlestick patterns, traders can make informed decisions about potential entry and exit points in the market. These data are fundamental to technical analysis, providing a visual representation of price action and helping to detect market sentiment shifts. Although [39] highlights that postprocessing is required to obtain representative OHLC data for a specific aggregation period, in this study, it was used as-is so as to avoid the requirement for higher-frequency OHLC data or Limit Order Book (LOB) data to build the aggregate OHLC data and ensure that our research is easily reproducible.  
The EMH posits that markets are fully efficient, meaning that all available information is already reflected in asset prices. If the EMH holds true, no amount of technical or fundamental analysis will allow an investor to consistently achieve returns that outperform the market, as prices already represent all known information about an asset's current behavior and future potential. In this view, price alone would contain all relevant data, rendering additional signals from technical indicators redundant or unnecessary. However, critics of the EMH [33] argue that inefficiencies, such as human behavior, market psychology, or anomalies, provide opportunities to generate excess returns through the use of advanced signals beyond just price.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 4 Discussion and Main Conclusions
### Ionospheric Effects  
From Figure 3, we can see that the VTEC is perturbed around the globe, including the southern part of America during the main phase of the May 2021 geomagnetic storm. On the other hand, in Figure 4h, we can see an increment in ROTI, that starts at the polar regions, propagating later the increment toward the equator, agreeing with previous studies [77; 78] (see Figure 5). In Figure 7, it is possible to see the VTEC and DVTEC perturbations of the GNSS stations over Madrid, Spain and Santiago, Chile (Vizcacha station) for the May 2021 storm. In comparison, we also present the VTEC and DVTECperturbations on the same stations produced by the August 2019 storm. It is noticeable that TEC variations are similar between storms for the same station. In addition, the difference in the variations between the stations in southern and northern hemispheres is evident. The TEC variation seems stronger in the northern station. In Table 4, we can see that the VTEC is in percentage much higher over Madrid (Spain) than over Santiago (Chile) and Vega de Itata (Chile) for most of the storms, except for the March 2017 storm. We also calculated the ROT index for the May 2021 storm period. Five stations localized in high latitudes had values of maximum ROTI peak between 3.4 and 3.9 TECu/min, and average ROTI peak \(\sim\)1.1 TECu/min, and these values are similar to those presented by [PERSON] et al. [77] for moderate geomagnetic storms.
# Developing Occupancy Grid with Automotive Simulation Environment
## 2 Materials and Methods
### Occupancy Grid Framework
#### 2.4.2 Occupancy Probability Calculation
The location in the simulation environment can be obtained directly from a built-in positioning component or in the case of real-world testing using a high fidelity INS (Inertial Navigation System). Therefore, a rotation matrix \(R\) and a translation vector \(T\) can be created to convert the information from SCS to OGCS. The SCS co-variance matrix \(\Sigma_{qcs}\) supplied by the senor (6)  
\[\Sigma_{qscs}= \begin{bmatrix}\sigma_{xxgcs}^{2}&\sigma_{xygcs}\\ \sigma_{yxgcs}&\sigma_{yycs}^{2}\end{bmatrix}  \tag{6}\]  
gets rotated (7) to OGCS. \[\Sigma_{qgcs}=R\Sigma_{qscs}R^{-1} \tag{7}\]  
With all the input information, the computation of the probability for cells of the grid lying within a three-sigma ellipse (Figure 8) can be carried out by the integration of the PDF for each of the cells (10). The semi-minor and semi-major axes dimensions can be computed from the \(\Sigma_{qscs}\). Eigenvalues \(\lambda_{1}\) and \(\lambda_{2}\) and eigenvectors \(\vec{v}_{1}\) and \(\vec{v}_{2}\) are calculated for the \(\Sigma_{qscs}\). Using this information, we can get the ellipse Equation (8), where \(s\) corresponds to the ellipse size. \[\left(\frac{x}{\lambda_{1}}\right)+\left(\frac{y}{\lambda_{2}}\right)=s \tag{8}\]  
The value of \(s\) can be computed from the desired confidence level for the area we want to update. To find the orientation of the ellipse, the eigenvector \(\vec{v}_{max}\) corresponding to the maximum eigenvalue is used (9). \[\alpha_{ellipse}=atan\left(\frac{\vec{v}_{max}(x)}{\vec{v}_{max}(y)}\right) \tag{9}\]  
The ellipse obtained in these steps is used to select the cells of the grid lying inside of the ellipse in order to perform an update either by a lookup table with integration results or by numerically integrating the PDF of selected cell areas. The example of results numerical integration results is shown in Figure 9.  
\[p_{ij}=e_{p}\int_{x_{i}-\frac{1}{2}r}^{x_{i}+\frac{1}{2}r}\int_{y_{j}-\frac{1 }{2}r}^{y_{j}+\frac{1}{2}r}\mathrm{PDF}(X;\mu,\Sigma_{qgcs})\mathrm{d}y_{qgcs} \mathrm{d}x_{qgcs} \tag{10}\]
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 5 Results
Therefore, the load profile resulting from the original schedule is also smaller than the one in winter and leads to a load peak of 5.7 MW. The greedy algorithm results in a load peak of 3.55 MW, corresponding to 23 buses charging in the same time. On the other hand, the heuristic algorithm shows better results and leads to a load peak of 3.27 MW and 21 buses charging simultaneously. Compared to the original schedule in this scenario, the greedy algorithm managed to reduce the load peak for 37.7% and the heuristic algorithm for 42.6%.
## 3 Results and Discussion
### Effects of Consistency Coefficient of Drilling Fluid  
The rheology of drilling fluid will also have a significant impact on the decay behavior of the blade-induced spiral flow. Therefore, the decay law of swirl intensity of spiral flow induced by the hole cleaning device (\(\alpha=0^{\circ}\)) along the flow direction under four different fluid consistency coefficients (flow index \(n=0.7\)) is shown in Figure 8, and the decay equation is obtained by fitting and regression displayed in Table 4. The initial swirl intensity \(S_{0}\) changes a little with different consistency coefficients, and the main difference is the decay coefficient \(\beta\). In other words, the continuity distance of spiral flow is different, which indicates that the greater the consistency coefficient of drilling fluid, the faster the decay coefficient of blade induced spiral flow, the shorter the effective action distance of the hole cleaning device, and the worse the hole cleaning effect.  
Figure 9 shows the time-averaged, annular cuttings concentration contour under four different fluid consistency coefficients. The erosion phenomenon of cuttings bed gradually weakens with the increase in fluid consistency coefficient. The blade-induced spiral flow will decay rapidly in the drilling fluid with a significant consistency coefficient, making it difficult for the drilling fluid to obtain the ability for suspended cuttings continuously. For low viscosity drilling fluid, the spiral flow induced by the rotation of the hole cleaning device can have a smaller decay coefficient; that is, there is a longer swirl duration. Therefore, using a V-shaped hole cleaning device may not achieve a good hole cleaning effect when selecting drilling fluid with a significant consistency coefficient in horizontal well drilling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Consistency** \\ **Coefficient** \\ **Pa.s\({}^{\text{n}}\)** \\ \end{tabular} & \begin{tabular}{c} **Decay Equation** \\ **Intensity \(S_{0}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Initial Swirl** \\ **Coefficient \(\beta\)** \\ \end{tabular} &
\begin{tabular}{c} **Decay** \\ **Coefficient \(\beta\)** \\ \end{tabular} & \(\mathbf{R^{2}}\) \\ \hline
0.01 & \(S_{n}=0.254e^{-0.018L/D_{\text{x}}}\) & 0.254 & 0.018 & 0.992 \\
0.05 & \(S_{n}=0.248e^{-0.051L/D_{\text{x}}}\) & 0.248 & 0.051 & 0.999 \\
0.10 & \(S_{n}=0.246e^{-0.082L/D_{\text{x}}}\) & 0.246 & 0.082 & 0.999 \\
0.50 & \(S_{n}=0.280e^{-0.241L/D_{\text{x}}}\) & 0.280 & 0.241 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Decay equation of spiral flow induced by the hole cleaning device (\(\alpha=0^{\circ}\)) under different fluid consistency coefficients (numerical results).  
Figure 8: Decay law of swirl intensity of spiral flow induced by the hole cleaning device (\(\alpha=0^{\circ}\)) along the flow direction under different fluid consistency coefficients (numerical results).
## 3 LBMR: Load Balancing Multipath Routing
### Route Maintenance Mechanism  
RM is used to react to broken links. The network topology of medical monitoring applications may change due to node failure or newly joining nodes. Once any node fails to operate normally, RM can quickly adapt sensor nodes to dynamic flow change and mal-function links locally. As mentioned above, a load estimation message carries a routing flag, which is used to confirm whether the load estimation sender S has routes to the gateway or not. When the routing flag is 1, it indicates that S has at least one route in the routing table to the gateway. If S has no route to the gateway, Routing Flag is set 0 to inform others not to use S as the upper-layer node towards the gateway. Then RM mechanism allows lower-layer neighbors of node S to recalculate their best upper-layer nodes toward the gateway without broadcasting route error messages. Suppose node Y does not receive a load estimation from upper-layer neighbor [PERSON] for a period of time, [PERSON] will remove X from both its neighbor table and routing table. If [PERSON]'s routing table has no upper-layer nodes to the gateway after removing X, the following actions start:  
1. [PERSON] will set routing flag to be 0 and layer value to be 255 and notice its neighbors by its next load estimation message.
2. Y's lower-layer neighbor Z removes Y from its own routing table and ignores Y's new load estimation messages.
3. From neighbors' load estimation messages, Y adds neighbors with the lowest layer value as upper-layer nodes into its routing table and thus Y can be connected to the gateway again.
4. Once Y has new upper-level nodes to the gateway, Y sets its own routing flag back to 1. [PERSON] can be added to others' routing table, and its load estimation is considered by others again.  
\begin{table}
\begin{tabular}{l} \hline Estimated\_Load\({}_{0}\) = SampleLoad \\ // For time slot i, \\ If(SampleLoad == 0) \{ \\ Estimated\_Load\({}_{i}\) = Estimated\_Load\({}_{i-1}/2\); \\ \} Else \\ Estimated\_Load\({}_{i}\) = \\ \((1-\alpha)\) *Estimated\_Load\({}_{i-1}+\alpha\times\) SampleLoad; \\ \} \\ \hline \end{tabular}
\end{table}
Table 2: Load Estimation Algorithm (LEA).  
The beauty of LBMR is that each node only updates its own status in load estimation messages. LBMR nodes periodically check load estimation for each surrounding node's status and react to link changes quickly. Once some particular LBMR node fails, no RERR messages, designed in AODV and AOMDV, are sent. Extra RERR messages increase routing overhead and link recovery time.
## 3 Results and Discussion
### Freeze-thaw Damage Prediction Model Based on Grey Model by GM (1,N)  
In order to illustrate the multi-variable grey model GM (1,N) in this study, the cases for damage characteristics of asphalt mixtures under the actions of 0\(\sim\)15 freeze-thaw cycles have been investigated to validate the feasibility and accuracy of GM (1,N), based on the experimental data of damage characteristics under limited freeze-thaw cycles. Through different combinations of the damage degrees of _VA_, _STS_, and _ITSM_, three GM (1,2) could be established, at the same time, the GM (1,3) could be established according to the damage degrees of _VA_, _STS_, and _ITSM_.
## 5 Proposed Coexistence Zone within WiMAX Frame Structure  
There are already several zones in the WiMAX system, as shown in Figure 6(a). The IEEE802.16e specification provides detailed explanations of these zones [17]. They may exist in the WiMAX frame structure to support some particular functions for a WiMAX system. The DL PUSC (Partial Usage of Sub-Channel) zone for FCH and DL-MAP is mandatory, and the UL PUSC is the default. We proposed a new zone, termed the coexistence zone, as shown in Figure 6(b).  
Figure 6: WiMAX zones. (**a**) Conventional WiMAX zones; (**b**) WiMAX zones, including the proposed coexistence zone.  
Figure 7 shows the coexistence zone within the WiMAX frame structure. During the coexistence zone, the WiMAX system does not transmit any signal, because no OFDM symbols and subcarriers should be used for communication. We temporarily call the coexistence zone the WiFi zone, for the coexistence of WiFi and WiMAX systems, because the coexistence zone is used for WiFi systems in this paper. The PSM and the AM of WiFi STAs are alternated, based on the original PS-Request frame transmitted by a WiFi AP, as shown in Figure 7. The WiMAX system pauses its signal transmission during the WiFi zone. Thus, either the WiFi AP or its WiFi STAs can send frames without interference from the WiMAX system according to the CSMA/CA procedure of the WiFi system. The WiFi AP sends the original PS-Request frame to all its WiFi STAs to protect the WiMAX UL and DL transmission ON time from signals from WiFi STAs.
## 2 Methods
### Gaussian Process with a Tapering Covariance Function for Local Variability  
#### 2.3.1 Formulation of the Gaussian Process Model  
Local variability of a temperature field can be affected by many intrinsic factors that cannot be fully captured by physics-specific models. For example, excessive moisture of grain leads to a quick local temperature increment, which cannot be directly characterized by conventional physical models. As shown in Figure 3, although the dynamics model is with continuous variables that consider the spatiotemporal correlation, the local variability in spatial and temporal domain caused by intrinsic factors substantially exists and exhibits with various correlation patterns. Given that the thermodynamic model is able to capture nonstationary profiles of a temperature field across the spatial and temporal domains, the local variability is assumed to be stationary. Without loss of generality, we adopt a spatiotemporal Gaussian process model to describe the spatiotemporaldependence structures of local variability using the deviance of the sensing observations from the global temperature profile in the following:  
\[\delta(\mathbf{s},t)\sim N(\mathbf{0},\Sigma_{\delta}), \tag{5}\]  
where \(\Sigma_{\delta}\) is the covariance matrix of the Gaussian process.  
The covariance matrix of the Gaussian Process is obtained from a covariance function describing the spatial and temporal correlations. Because various thermal transmission effects exist in different directions of the granary, there are various spatial correlation patterns of local variability in different directions of the granary. Temporal evolution also exists among the time domain. Therefore, we use an anisotropic spatiotemporal covariance function to characterize spatiotemporal correlation between two different spatiotemporal locations as follows:  
\[C(\mathbf{h};\boldsymbol{\theta})=\sigma^{2}\exp\left(-3\left(\frac{\delta x} {a_{x}}\right)^{2}+\left(\frac{\delta y}{a_{y}}\right)^{2}+\left(\frac{\delta z }{a_{z}}\right)^{2}\right)\right)(1-\varphi)^{\delta t}, \tag{6}\]  
where \(\mathbf{h}=\left\{\delta x,\ \delta y,\ \delta z,\ \delta t\right\}\) is the distance metric of two locations in the spatial and temporal domains, respectively, and \(\boldsymbol{\theta}=\left\{\sigma^{2},a_{x},a_{y},a_{z},\ \varphi\right\}\) is the set of parameters in the covariance function. In the spatial domain, \(a_{x}\), \(a_{y}\), and \(a_{z}\) are the parameters corresponding to the distance in \(x\), \(y\), and \(z\) directions respectively, which capture anisotropic spatial correlation structures of a granary; in the temporal domain, a decay parameter \(\varphi\) corresponding to the time dependence effect is used to capture the temporal correlation of the local variability.
# Evaluating the Effect of Road Surface Potholes Using a Microscopic Traffic Model
## 1 Introduction
In [32], Median U-turn (MUT) intersections were shown to improve the operation and safety of vehicles, pedestrians, and cyclists. In [33], route diversion through congestion pricing strategies using a microscopic traffic model was shown to reduce travel time by 10-16%. Carbon dioxide (CO\({}_{2}\)) emissions were predicted in [34] using a microscopic transportation emission model. The results obtained can help in developing environmental transportation policies. The influence of road surface conditions on traffic is a crucial factor in traffic modeling. In [35], a model was proposed to examine the impact of road surface conditions on traffic flow. The results obtained reveal that a well-maintained road surface improves both traffic flow and speed [1]. However, this model does not characterize the quantitative effect of road surface conditions, in particular, the combined effect [35]. Explicit expressions were given in [36] to characterize the effect of road surface conditions on traffic flow. In addition, a macroscopic traffic model was developed in [1] which considers potholes. Similar models have been developed which consider road surface conditions but to date, there are no microscopic traffic models. In this paper, a new microscopic traffic model is presented which characterizes the effect of potholes on traffic behavior. The ID model does not consider road surface conditions and employs a constant acceleration exponent, so it can result in unrealistic traffic behavior. The performance of the proposed and ID models is evaluated on a single-lane circular road of length 1 km. A platoon of 31 vehicles is considered for 200 s. The results obtained indicate that the proposed model accurately captures the impact of real-world factors such as potholes on traffic. The remainder of this paper is organized as follows. Section 2 presents the model formulation. The performance is evaluated in Sections 3 and 4 provide some concluding remarks.
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
###### Abstract  
The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD9 GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (\(\mu\)m) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD9 GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 \(\mathrm{g}\mathrm{m}^{3}\), respectively. Computed LST values from MOD9 GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system.  
IoT stations; remote sensing images; smart environmental management 202315245. [[https://doi.org/10.3390/f15070245](https://doi.org/10.3390/f15070245)]([https://doi.org/10.3390/f15070245](https://doi.org/10.3390/f15070245))  
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