esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 5 Results and Discussion
### Results: Experiment 2  
Figure 11 shows the horizontal RMS error of the raw GPS measurements in comparison to the FAGPS compensated measurements. The first 100 s have been excluded as these represented the time taken to move from PCM113580231 to PCM113580230. The first 30 min show a consistently smaller RMS error for the compensated data. Past the 30 min mark the compensated data provides less value in terms of increasing accuracy which we propose is due to the fact that the compensated values are time dependant and having aged are no longer accurate. This aging of the compensated values has several components including atmospheric conditions, potential multi-path effects and swapping between different satellites. At this point in time a fiducial marker could be required to give a more accurate position estimation.
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
## 2 Proposed System Overview
### Proposed Mobility Model  
We considered the OBAN mobility scenarios where a patient was equipped with an ECG monitoring sensor and it was connected to an optical coordinator. The optical coordinator was composed of a white LED emitting an optical power of around 20 mW. The coordinator was fixed at the wrist and shoulder of the patient body. Due to the roughness of typical indoor environment surfaces that is characterized by a reflectivity parameter \(\rho\), it was possible to consider diffuse reflections when the beam hit the surface. Then, the reflected signals were collected at the receiver fixed at the center of the ceiling oriented towards the floor. The receiver consisted of a PD with a large field of view (FOV) of 70\({}^{\circ}\) and a minimal irradiance of 1 \(\mu\)W/cm\({}^{2}\). For the experimental setup, we considered room dimensions of 5 m \(\times\) 5 m \(\times\) 3 m.  
Figure 1: Proposed optical body area network (OBAN) system.  
Figure 2: Block diagram of the proposed system.  
Our aim is to provide mobility support via the non-directed optical links to transmit physiological signals of a moving patient. In this context, we considered three mobility scenarios. The first scenario involved the optical coordinator fixed at the shoulder of the patient body as shown in Figure 3a. The transmitter moves at an uniform distribution from 0 to 5 m along a \(x\) and \(y\) axis and the height is fixed at 1.5 m. Figure 3b describes the second scenario in which the optical coordinator was fixed at the wrist part of the body. In this scenario, the mobility was supported for all three axes along with the variations in focal angle \(\alpha_{T_{x}}\) of the LED. Figure 3c shows the third scenario. It is the transmit diversity scenario in which the patient is equipped with two transmitters, i.e., one at the wrist and the other at the shoulder part of the body to perform the transmission of the monitored health care data. The transmit diversity exploiting two optical channels was used to mitigate the adverse effect of the patient movements, thereby achieving high diversity gains.
## 3 Design of the New Sand Control Screen  
### Design Philosophy  
According to the summary of the above analysis, the new screen that is suitable for the argillaceous fine silt hydrate reservoirs should have the following characteristics or performance requirements, that is, it is not limited to the satisfaction of a single function, but rather emphasizes the integration of composite performances.  
1. High sand filtration precision: To obtain a balance between the accuracy of sand control and the fluid permeability. That is, it is suitable for the accuracy of sand control in gas hydrate reservoirs and allowing gas and water fluids to pass through, reducing the impact on productivity release.
2. Good flow capacity and permeability: There is a sufficient flow area, not easily plugged, with reduced flow resistance, and good flow-through performance, and with a certain function of unblocking and sand washing, ensuring the normal outflow of gas and water, which requires a reasonable layout of slits or pores and optimized dimensions.
3. Certain anti-plugging performance: High mud content increases the risk of plugging. The screen pipe should have a good anti-plugging performance, mainly through optimization of the filter structure and surface material, to reduce the adhesion and accumulation of mud or fine silt on the surface of the screen pipes.
4. Good strength and erosion resistance: They can not only achieve effective sand control but also have a certain capacity of resistance to erosion and deformation, and are suitable for the complex wellbore environment.
5. Beneficial to the formation and utilization of mud skin: A certain degree of highly permeable mud skin can assist in sand control, reducing the risk of sand plugging. Appropriate screen pipe medium structure can be selected to promote the formation and utilization of mud layer within a reasonable range.
6. Appropriate design of the number of filter layers: The appropriate number of filter layers can have a positive promoting effect on microscopic sand control, and the appropriate number of filter layers and filter screen combination methods can be selected through numerical modeling and experimental analysis.
7. Integration of screen functions with simple structure, stable and reliable structure, convenient for well site operation, and with safe operation.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 3 The Calculation of Measurement Vector for Heading Estimation
### Heading Estimation Based on Gyroscope  
The relative heading can be calculated using the raw outputs of a gyroscope, and the absolute heading can be obtained when the initial heading is known. The quaternion heading can be directly derived from the outputs of the gyroscope [25].  
\[\overset{\bullet}{q}=\frac{1}{2}M(\omega)q=\frac{1}{2}\left[\begin{array}{cccc} 0&-\omega_{x}&-\omega_{y}&-\omega_{z}\\ \omega_{x}&0&\omega_{z}&\omega_{y}\\ \omega_{y}&-\omega_{z}&0&\omega_{x}\\ \omega_{z}&\omega_{y}&-\omega_{x}&0\end{array}\right]\left[\begin{array}{c}q _{1}\\ q_{2}\\ q_{3}\\ q_{4}\end{array}\right] \tag{29}\]  
where \(\overset{\bullet}{q}\) is the differentiation of the quaternion, \(\omega\) is the angular rate measured by gyroscope, and \(M(\bullet)\) is the mathematical function of \(\omega\).  
The quaternion-based update with the angular rate can be obtained from the discrete form of Equation (29),  
\[q_{k}=\left[\cos\left[\frac{\eta}{2}\right]I+\frac{1}{2}\frac{\sin\left[\frac {\eta}{2}\right]}{\frac{\eta}{2}}M[\omega]dt\right]q_{k-1} \tag{30}\]  
where \(\eta=\sqrt{\left(\omega_{x}dt\right)^{2}+\left(\omega_{y}dt\right)^{2}+\left( \omega_{z}dt\right)^{2}}\), \(dt\) is the sampling interval, and \(I\) is unit vector.  
In this paper, the interconversion between quaternion and the Euler angle is required. Their relationship can be expressed as follows.  
\[\left[\begin{array}{c}\theta_{gyro}\\ \gamma_{gyro}\\ \psi_{gyro}\end{array}\right]=\left[\begin{array}{c}\arcsin[2[q_{3}q_{4}+q _{1}q_{2}]]\\ \arctan[-[2[q_{2}q_{4}-q_{1}q_{3}]],q_{1}^{2}-q_{2}^{2}-q_{3}^{2}+q_{4}^{2}]\\ \arctan[-[2[q_{2}q_{3}-q_{1}q_{4}]],q_{1}^{2}-q_{2}^{2}+q_{3}^{2}-q_{4}^{2}] \end{array}\right] \tag{31}\]
# Biogas Production Prediction Based on Feature Selection and Ensemble Learning
## 2 Materials and Methods
### Biogas Prediction Based on Heterogeneous Ensemble Learning
#### 2.2.3 Construction of the Heterogeneous Ensemble Learning Model  
For the regression problem, the prediction performance of the learner is the starting point, and the selection of a model with a strong learning ability helps to improve the overall prediction effect of the model. LSTM, SVR (support vector regression), RF, XGBoost, LightGBM, and CatBoost (gradient boosting and categorical features) are chosen as the first layer learners in the stacking model. Among them, the single learner LSTM has a mature theory and good practice, and is widely used. SVR is an important application branch of SVM, which uses a support vector in performing the fitting and a Lagrange multiplier style to analyze the data regression. RF uses the ensemble learning method of bagging. XGBoost, LightGBM, and CatBoost use the ensemble learning method of boosting. These methods have found wide application and practice in various fields. In order to prevent the model from overfitting on the training set, the selection of the second layer stacking  
Figure 3: Two-layer stacking ensemble learning model.  
model should not be too complicated, and a model with a strong generalization ability and simplicity should be selected. In summary, LSTM, SVR, RF, XGBoost, LightGBM, and CatBoost are initially selected as primary learners, and XGBoost, LightGBM, and CatBoost as meta-learners. The biogas prediction model based on model fusion stacking ensemble learning is shown in Figure 4.  
Further, different models are built from different data perspectives and the algorithmic principles of the models themselves. For the first layer of primary learners, while considering the prediction performance, in order to obtain a better fusion model effect and give full play to the advantages of different models, the complementary characteristics between the different models need to be considered, that is, there needs to be certain differences between models. The stacking model relies on the differences between sub models to maximize the advantages of the integration algorithm [24].
## 5 Simulation and Evaluation Results
### Privacy Protection Effectiveness Evaluation
#### 5.2.3 Evaluation Results  
To present the performance of our privacy protection scheme, we evaluate the variability in task size and task jitter over 30,000 time slots. As shown in Figures 9 and 10, five groups with different redundancy rates (i.e., \(\omega=0.05,0.10,0.15,0.20\), and 0.25, respectively) are implemented to compare with the original real tasks. It can be noticed that after adding redundancy packets with different \(\omega\), compared to the original real tasks, all the task sizes and task jitters are changed to a certain extent. Specifically, the overall envelope shapes are shown to be quite different from the original. And the spikes' location, as well as their corresponding peak values, are also significantly altered. These demonstrate that our  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **Group 5** \\ \hline \(\omega\) & 0.05 & 0.10 & 0.15 & 0.20 & 0.25 \\ \((\mu,\sigma)\) & \((0.05L_{r},\frac{0.05L_{r}}{10})\) & \((0.1L_{r},\frac{0.1L_{r}}{10})\) & \((0.15L_{r},\frac{0.15L_{r}}{10})\) & \((0.2L_{r},\frac{0.2L_{r}}{10})\) & \((0.25 Lprivacy protection scheme is able to produce noticeable perturbations in both the packet size and packet arrival interval, thus effectively misleading the adversary.  
We also notice an increasing trend in task size and task jitter with adding redundant dummy tasks from Figures 9 and 10, which will cause additional overheads in energy and latency. Thus, a further evaluation of the privacy protection cost is conducted.  
As shown in Figure 11, we evaluate the average energy consumption over 30,000 time slots for five redundancy rates \(\omega\). It can be observed that as \(\omega\) rises, both the transmission energy consumption (including trans-UT-SAT and trans-SAT-TC) and computational energy consumption (computing energy) tend to increase gradually, whether the task is offloaded to the satellite or to the TC. Despite this, we still believe that this is a tolerable price to pay for privacy protection since the increase in total energy consumption after adding dummy tasks is only moderate compared to the original real tasks.  
Figure 10: Effectiveness of perturbing task arrive jitter at different redundancy rates \(\omega\).  
Figure 9: Effectiveness of perturbing task size at different redundancy rates \(\omega\).  
As shown in Figure 12, we evaluate each average latency consumption over 30,000 time slots for five redundancy rates \(\omega\). Similar to the latency overhead, although each average transmission latency consumption (including trans-UT-SAT and trans-SAT-TC) and computation latency consumption (computing latency) tends to increase as \(\omega\) rises, the average total latency overhead is not significantly increased compared to the original real task. We therefore consider the latency overhead caused by the privacy preservation also to be within a tolerable limit.
## 2 Materials and Methods
### Procedures for Collecting and Analyzing Sampled Sediments  
Samples of sedimentary material from the bottom of the waters of Cartagena Bay were collected at 12 collection points in the regions of Bocagrande, Isla Tierra Bomba, and Playa Blanca using the Draga Van Veen collector. The samples were obtained at an approximate distance of 200 m from the bank, with a minimum spacing of 1000 m between each point [36; 37], during January (summer season) and October (winter season) of 2020 and 2021. Cartagena Bay (Figure 1) is highly frequented annually by tourists worldwide [38]. Four collections were carried out at each of the 12 points, totaling 48 collections throughout 2020 and 2021.  
Samples of sedimented material totaling 100 mL in volume were added to Niskin bottles and stored in Styrofoam boxes, following the collection standard determined by [PERSON] et al. [38]. Samples were submitted for analysis to the Analytical and Environmental Chemistry laboratory at Universidad de la Costa in Colombia. Before carrying out the analyses, all laboratory equipment, together with the glassware used, went through a manual washing process using Milli-Q water filtered by an ultrapure organic solvent and inorganic membrane (Whatman Anodisc at 20 nm), intending to avoid contamination by external impurities concerning the sampled (homogenized) material [39]. After drying the samples, the material was subjected to HR-TEM (high-resolution transmission electron microscopy) (New York, USA) and FE-SEM (field emission scanning electron microscopy) (New York, USA) analyses (Figure 2), which enabled the quantitative identification of the types of major chemical elements present in the sampled material [36].  
Figure 2: Methodological procedures and equipment used for the laboratory analysis in the following steps: Step 1—The separation of the sampled sediment in an aqueous medium; Step 2—Conditioning the samples for 48 h; Step 3—The ultrafiltration of samples (Whatman Anodisc membrane, with 20 nm pores); Step 4—Using FE-SEM and HR-TEM.  
High-resolution use of HR-TEM (JEOL-2010F, 200 keV) coupled to a ray scanning detector in an Oxford energy dispersive pattern (STEM) and EDS (energy dispersive X-ray microanalysis) was utilized to determine the chemical elements present in particles <100 nm [36]. The crystalline phases in the sedimented material were determined using XRD, allocated to a Siemens diffractometer (BRUKER AXS) (model D-5000 (\(\theta\)-\(\theta\))). The XRD analysis was performed based on reported previous works [1; 36]. [PERSON] et al. [1] emphasize that identifying the type of chemical elements present in a sampled material, together with a satellite image analysis, allows for an understanding of where the displacement of sediments occurs in the water. Water movement through the action of currents is then capable of dispersing chemical elements over large regions.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 1 Introduction
Hence, the study area's existing towers were analyzed to determine the best locations for tower placement [24]. These prior studies use a GIS environment to evaluate [25] the need for acceptable signal strength by locating many cell phone towers, but they do not focus on the best position for cellular phone towers or how the signal can cover every receiver position while retaining optimal strength [26]. To understand how the signal reaches the user's location, the initial step is to understand the propagation of the signal [27; 28; 29]. Cell phones use radio waves to communicate. In this study, it has been made clear that this signal transmission follows two paths: one is the line of sight (direct path), and the other is non-line of sight (obstructed by buildings, barriers, etc.) [30; 31], as shown in Figure 2a. In means of propagation, it is analog to sound transmission. While propagating from cell phone tower to cell phone, user signal suffers different losses [32]. These losses are due to fading and multiple propagations [33]. From the studies, it has been concluded that the range of the excellent signal is between 0 and \(-60\) dB m and for the poor signal is above \(-100\) dB m [34], as shown in Figure 2b. The goal of the suggested research is to figure out where the best cell phone tower placement is. Users play a vital part in determining path loss and attenuation in the strength of the signal, thus efforts have been made here to evaluate them and then utilize them to determine the ideal transmission route from cell phone towers to cell phones users to ascertain the optimal cell phone tower location [35].
## 2 Related Work
### Non-Terrestrial Network Overview
\begin{table}
\begin{tabular}{c c} \hline \hline
**NTN Architecture** & \\ \hline Transparent satellite & \\ Regenerative satellite—gNB processed payload & \\ Multi-connectivity involving transparent NTN-based NG-RAN and cellular NG-RAN & \\ Multi-connectivity between two transparent NTN-based NG-RAN & \\ Regenerative satellite—gNB-DU processed payload & \\ Multi-connectivity involving regenerative NTN-based NG-RAN (gNB-DU) and cellular NG-RAN & \\ Multi-connectivity between two generative NTN-based NG-RAN (gNB on board) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Types of NTN architectures. Figure 3: Difference in received signal attenuation between TN and NTN. Non-Terrestrial Network, the signal strength difference between the UE at the cell center of the NTN gNB and the UE at the cell edge is not clearly evident. Therefore, using signal strength as a handover triggering method in NTN may lead to inaccuracies. To address this issue, the corresponding TR 38.821 suggests various handover triggering methods available in NTN, which go beyond relying solely on signal reception sensitivity. Table 8 presents the proposed handover triggering methods in NTN. TR 36.763 describes the IoT NTN scenario. Among the scenarios of 3 GPP TR 38.821 Rel 16, a transparent payload scenario is assumed. The difference from the scenario of 3 GPP TR 38.821 Rel 16 is that the Medium Earth Orbit (MEO) scenario is added. MEO is a satellite located between the LEO altitude range and the GEO altitude range [32]. IoT NTN must be able to support EPC and 5 GC. And it is assumed that the UE can support Global Navigations Satellite System (GNSS). Table 9 summarizes the contents of the delay issue in LEO and GEO. IoT NTN considers the MEO scenario. Therefore, Table 9 summarizes the MEO delay and related parameters. IoT NTN assumes NB-IoT and eMTC support. In the case of NB-IoT, if it is out of cell coverage provided by NTN, it is treated as Radio Link Failure and the UE attempts RRC connection for re-establishment. In the case of eTMC, there are issues such as delay for handover signaling processing, measurement validity, frequent handover, dynamic neighbor cell list, handover of a large number of UE, and impact of propagation delay difference in measurements such as NR NTN.
# Evacuation Management System for Major Disasters
## 2 Mathematical Model  
The particular characteristics of disasters and the factors that shape evacuation evolution have to be considered in order to create appropriate models. In fact, disasters have common attributes that can be mathematically redefined to better understand the proposed models.  
* A disaster is denoted as \(D_{i}=\{E_{i},\ A_{i}\}\) where \(i\in\mathbb{N}\) is the number of active hazards at the same time.
* \(E_{i}=\big{\{}e_{1},\ e_{2},\ \ldots,\ e_{j}\big{\}},\ j\in\ \mathbb{N}\). is the set of active evacuations taking place during the disaster \(D_{i}\).  
* \(A_{i}=\{a_{1},\ a_{2},\ \ldots,\ a_{k}\},\ k\in\mathbb{N}\) is the set of damaged assets or impassable areas resulting from the disaster \(D_{i}\).
* Elements contained in \(E_{i}\) and \(A_{i}\) are also defined as a set of geographical coordinates \(e_{j},a_{k}=\{(\phi_{1},\ \theta_{1}),\ (\phi_{2},\ \theta_{2}),\ \ldots,\ (\phi_{l},\ \theta_{l})\}\) where \(l\in\ \mathbb{N}\) is the number or coordinates used to define the evacuation/damaged area.  
A real-time stochastic approach was used to predict mass evacuations involving two stages: (1) pedestrians respond (or not) and move on foot towards vehicles and (2) the use vehicles (private and/or public vehicles) through the road network. A staged controlflow evacuation strategy was selected as appropriate based on the study conducted by [PERSON] et al. [28]. The staged methodology divides the evacuation area into small zones that are sequentially evacuated according to the proximity of the hazard. The contraflow methodology optimizes traffic flow by simultaneously making use of lanes in the same direction (higher speeds and less congestion).
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 4 Evaluation
### Accuracy Measurement  
#### 4.3.1 Accuracy of Topology  
After obtaining the estimated hops from each node to the station, cost parameters are calculated with cost Equation (3) and node couples are established according to them within the whole network. While calculating parameters, we found that the valuation range of cost parameters that are obtained using the estimated node hops is always self-contradictory, (for example, the valuation range of \(\gamma\) at the \(i\)th node couple captured is \((\gamma_{i\max},\gamma_{i\min})\), the valuation range obtained at the \(j\)th node captured is \((\gamma_{i\max},\gamma_{i\min})\), but \((\gamma_{i\max},\gamma_{i\min})\). For this reason, we introduced one valuation compensation method. We thought, although the valuation range of cost parameters cannot be determined due to the error in estimating node hops, the general proportion of the upper and lower limits will not be changed. Therefore, we no longer used the method of calculating the valuation range of \(\gamma\) using each node couple before calculating the minimum range while calculating cost parameters, but did calculate the valuation range of \(\gamma\) for each code. As only the unilateral range of \(\gamma\) can be calculated with single nodes, the other side is made up with the maximum and minimum thresholds, the absolute values of which are the same. At last, all valuation ranges are averaged to obtain the valuation range \((\gamma_{i\max},\gamma_{i\min})\) of \(\gamma\), and then the median \(\gamma_{\text{mid}}\) is calculated and \(e^{\gamma_{\text{mid}}}\) is taken as the cost parameter. According to the simulation results, over 50% correction rate can be achieved by rebuilding topology structure for the first time. After rebuilding the topology for the first time, the results of the first formation is compared with the node couples acquired, node couples that are different from correction and acquisition results are corrected, and the minimum hops that each node can obtain are adjusted again for the corrected results. Besides, costs are calculated and node couples are selected again according to new hops. 255 node pairs detected by the platform are compared with the actual node pairs, and the correct ratio between the total number of node pairs and the total number of node pairs is used as the reference of accuracy. After three times of iteration, the accuracy rate of the rebuilt topology is more than 95% (the comparison of rebuilt topology and original topology is shown in Figures 11 and 12; the accuracy of topology reconstruction is shown in Figure 13).  
Figure 11: Part (**a**) is the original topology of the target network of GEM routing protocol, and Part (**b**) is the topology rebuilt by the platform.