esa-sceva/satcom-chunk-collection huggingface dataset:
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 3 BLE Signal Analysis
### Ceiling Gateways BLE Signal Segments
These findings highlight notable variations in signal strength across body positions when using ceiling-mounted GWs. The shoulder position consistently shows stronger signals, suggesting potential advantages for signal reception at this body location. These results emphasize the importance of considering body positioning in BLE device deployment near ceiling-mounted GWs for optimal signal performance. Analysis of signals from wall-mounted and ceiling-mounted GWs at various BLE positions reveals distinct patterns. Ceiling-mounted GWs consistently provide stronger signal reception at the shoulder position, indicating better signal strength in this area. In contrast, wall-mounted GWs exhibit greater signal variability across different body positions, with positions like the front pocket consistently showing weaker signals. Ceiling-mounted GWs, however, demonstrate more stable signal strength, particularly at the shoulder position. These findings emphasize the need to consider both gateway placement and body position when optimizing BLE signal reception in indoor environments. The choice between wall and ceiling GWs can impact signal performance, influencing the reliability of BLE-based applications.
###### Abstract  
This study used numerical simulations of CO\({}_{2}\) storage to identify the benefits of horizontal wells for geological carbon storage, such as enhancing CO\({}_{2}\) trapped in porous media due to relative permeability and capillary hysteresis. Two injection schemes were tested: one using a vertical injector and the other employing a horizontal well. The results revealed two main findings. Firstly, the horizontal injection well effectively prevented or minimized CO\({}_{2}\) penetration into the caprock across various sensitivity scenarios and over a thousand years of CO\({}_{2}\) redistribution. Secondly, horizontal wells provided a safe approach to trapping CO\({}_{2}\), increasing its entrapment as a residual phase by up to 19% within the storage site. This, in turn, reduced or prevented any unexpected events associated with CO\({}_{2}\) leakage through the caprock. Additionally, the paper proposes a practical method for designing the optimal length of a horizontal well. This method considers a combination of two parameters: the additional CO\({}_{2}\) that can be trapped using a horizontal well and the gravity number. In the case of the reservoir model of this study, a horizontal branch with a length of 2000 m was found to be the most effective design in enhancing CO\({}_{2}\) entrapment and reducing CO\({}_{2}\) buoyancy.  
CO\({}_{2}\) trapping mechanisms; CCS; horizontal wells; caprock integrity; saline aquifer
## 4 Experimental Setup
### Short Grass  
The experiment was conducted in a corridor grass area surrounded with banana and mango trees. The WSN comprised fixed (receiving node or Rx) and mobile (transmitting node or Tx) nodes (please see Figure 3 (left)). The Tx mobile node was moved every 5 m along a straight line between the Tx and Rx nodes. The fixed node used the collected RSSI via a notebook to estimate the path loss between the two nodes based on MBFLR. The grass area was 5.0 m \(\times\) 100 m with the minimum and maximum distances of 1 and 40 m, respectively. The measurements were performed in both forward and reverse directions during the months of January to June 2022. The RSSI values were collected for approximately 60 s for each measured point and repeated thrice to obtain 540 RSSI data sets for each antenna height. The antenna heights for the transmitting and receiving nodes were 0.2 m, 0.7 m, and 1.2 m above the ground. The measurements were repeated every month from no grass (\(\sim\)0 cm) to tall grass (\(\sim\)50 cm). We also considered the breakpoint distance to separate the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Value** & \multirow{2}{*}{**Unit**} \\  & **(min, max)** & \\ \hline Antenna gain (omni-directional) & 2.3 & dBi \\ \hline Frequency & 433 & MHz \\ \hline Bandwidth (BW) & 125 & kHz \\ \hline Spreading Factor (SF) & SF7 & \\ \hline Pr (d\({}_{0}\) = 1 m) (average) & \(-\)57 & dBm \\ \hline Output power & 10 & dBm \\ \hline Coding Rate (CR) & 4/5 & \\ \hline Antenna height (h\({}_{\text{tx}}\), h\({}_{\text{rx}}\)) & (0.2, 0.7, 1.2) & m \\ \hline Short grass height & (0.0, 0.3) & m \\ \hline Tall grass height & (0.3, 0.5) & m \\ \hline Breakpoint distance & 15 & m \\ \hline Small-scale distance (\(\lambda\)/4) & 0.4 & m \\ \hline Large-scale distance (Tx-Rx) & (1, 40) & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the equipment and measurement parameters.  
propagation region into two zones for our measurement. Considering the first Fresnel zone, the breakpoint distance depended on the antenna heights above the ground, as shown in Equation (17):  
\[d_{bp}=\frac{4h_{t}h_{r}}{\lambda} \tag{17}\]  
where \(h_{t}\) and \(h_{r}\) are the transmitting and receiving antenna heights, respectively, and \(\lambda\) is the carrier wavelength. Figure 4 depicts the RSSI-distance relationship of the large-scale fading for the distance before and after the breakpoint. Note that \(d_{bp}\) with a 1.2 m antenna height was at an 8.3 m (0.92 dB) distance from the receiving node for modeling. The PLEs before \(d_{bp}\) were approximately 20 (19.4), while the PLEs after \(d_{bp}\) were more than 20 (25.87) for the LOS propagation theory [29].  
Figure 4: Uncertainty in observed RSSI and distance of short grass, for all antenna heights.  
Figure 3: RSSI measurement for short grass (**left**) and tall grass (**right**).
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 2 Implicit Assembly Algorithm for Overlapping Grid
### Classification of Grid Cells  
The classification of cells in each component grid \(G^{i}\) can be performed in the following four steps, as depicted in Figure 1.  
1. Classification of _internal_ and _external_ cells. The cells adjacent to the external boundaries of \(G^{i}\) are identified as \(C^{i}_{ex}\). Due to their locations, the flow-field properties of these cells have to be obtained by interpolating those on other component grids. These cells \(C^{i}_{ex}\) do not constitute the donor cells corresponding to the overlapping cells in other component grids. Consequently, all the cells \(C^{i}\) of grid \(G^{i}\) can be partitioned into the _internal_ cells \(C^{i}_{in}\) and _external_ cells \(C^{i}_{ex}\). Note that \(C^{i}_{in}\) and \(C^{i}_{ex}\) share no common cells.
2. Classification of _active_ and _inactive_ cells. If all nodes of cell \(c\in C^{i}\) are inactive (Equation (2)), then \(c\) is classified as an _inactive_ cell. As shown in Figure 1, \(C^{i}_{in}\) and \(C^{i}_{ex}\) can both have _inactive_ cells. If all nodes of \(c\in C^{i}_{in}\) are active, then \(c\) is treated as an _active_ cell; otherwise, \(c\) is identified as a _transitional_ cell. For the _active_ and _transitional_ cells in the _external_ cells, we collectively call them _external overlapping_ cells.
3. Classification of _inactive_ cells into _in-hole_ and _internal overlapping_ ones. If an arbitrary _inactive_ cell \(c_{na}\) shares at least one node with a certain _transitional_ cell, then the cell \(c_{na}\) is called an _internal overlapping_ cell. In this way, the _inactive_ cells adjacent to _transitional_ cells are redefined as overlapping cells, while the other _inactive_ ones are _in-hole_ cells.
4. Classification of \(C^{i}\) into _flow-field_, _overlapping_ and _in-hole_ cells. The _overlapping_ cells are constructed with _internal overlapping_ and _external overlapping_ cells. In order to ensure _overlapping_ cells from different grids cannot be donors to each other, we treat _transitional_ cells as _flow-field_ cells, which indicates that _flow-field_ cells consist of _active_ and _transitional_ ones.  
Figure 2 shows the grid distribution of two airfoil components that are distinguished with red and blue colors. The four graphs, respectively, represent the original grids (Figure 1(a)), the _overlapping_ cells that are involved in the interpolation (Figure 1(b)), the _in-hole_ cells surrounded by the hole boundaries (Figure 1(c)) and the final assembled overlapping grids (Figure 1(d)).
## 2 Materials and Methods
### Reference Data
#### 2.2.2 Sampling Design  
We split our reference data into three distinct datasets for training, validation and testing. The training set was used for the learning process of the classification model, while the validation set was used for estimating the importance of handcrafted features and for tuning the model's hyperparameters. The test set was used to assess the classification accuracy. To ensure independence between the training, validation and test datasets, we designed a splitting scheme that accounted for spatial autocorrelation. The issue of spatial autocorrelation can be caused by the similarities that spatially adjacent observations tend to have, which leads to inflated accuracy and overestimated model predictive power [59; 60]. Therefore, drawing random samples from the entire spatial domain would not have guaranteed independence between datasets.  
Our scheme for splitting the data into training, validation and test datasets was designed separately for cocoa and non-cocoa plots due to their disparate distribution across the study area. Since our acquired cocoa plots were distributed unevenly, we did not split them at the polygon level because groups of polygons were located close to each other (Figure 1b). Instead, we split them into spatially homogeneous groups with a minimum inter-group distance of 50 km, i.e., training, validation and test cocoa datasets were at least50 km apart from each other. In addition to that, we took care that each dataset represented different locations across the study area (Figure 1b).  
The distribution of the raw and refined cocoa datasets was identical, as both were derived from the same cocoa polygons, but the number of cocoa pixels dropped to approximately half after applying refinement. Our splitting scheme resulted in 68 cocoa polygons for training, from which 9427 raw and 4437 refined cocoa pixels were derived; 14 cocoa polygons for validation, from which 405 raw and 186 refined cocoa pixels were derived; and 11 polygons for testing, from which 723 refined cocoa pixels were derived (see Table 1). The refined test dataset was used for assessing all classification schemes to ensure a fair comparison. The number of pixels refers to a 10 \(\times\) 10 m resolution.  
Curated non-cocoa polygons were distributed more evenly across the study area compared with the cocoa dataset (see Figure A1). For this reason, we implemented a gridded block sampling design [60]. In particular, we created a grid of irregular blocks where each one incorporated a relatively consistent number of non-cocoa polygons. Spatially disjoint blocks were used to draw samples for the training, validation and test datasets to ensure diverse regional coverage for each dataset. From the respective blocks, samples were drawn randomly, yet in such proportions to achieve a total size approximately equal to that of the cocoa dataset (Table 1).
## 5 Graphene/Graphene Oxide-Based RF Gas Sensing Platforms
### Other Graphene/Graphene Oxide-Based RF Gas Sensors  
Other developed graphene and graphene derivative-based RF gas sensors are summarized in Table 4. Based on the tabulated data, the recent research on RF gas sensors with graphene and GO (rGO) has focused on the RFID system for real-time and continuous sensing platforms, such as wireless biosensing platforms. In particular, [PERSON] et al. [86], [PERSON] et al. [87, 88], and [PERSON] et al. [89] studied gas sensing applications in the UHF band due to its compatibility with the conventional RFID band. The gas sensing schemes based on an antenna [90] and a high-quality (high-Q) resonator [91] were also researched. Additionally, [PERSON] et al. studied a gas sensing platform that utilizes the non-contact near-field communication (NFC) technique in smartphones [92].
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 2 BLE Mesh Architecture and Message Routing Protocols
### Message Routing Protocols  
The BLE mesh protocol relies on the flooding approach that causes network overheads and delays because of continuous broadcasting. Therefore, this section gives a brief overview of three popular routing-based messaging paradigms adopted by different multihop transmission protocols to enable readers to better understand the implementation of the proposed O-AODV protocol:  
* Reactive (on-demand) protocols Messages received by the reactive forwarding protocol provide information about the destination nodes. Every transmission table entry is only active for a limited amount of time. The item will be erased if no traffic is encountered for a certain destination within the specified time frame. A process for new route discovery will be launched upon receiving the request from the sender node. Examples of such protocols include ad hoc AODV [4] and Dynamic Source control Routing (DSR) [5];
* Proactive (table driven) protocols For all nodes, whether active or not, explicit forwarding table entries are maintained by proactive forwarding protocols. The Bellman-Ford algorithm is used to keep feasible pathways to important nodes, allowing data to be delivered to the destination as soon as possible. The examples of this kind of protocol include: Babel, Optimized Link State Routing (OLSR) [10], Destination Sequenced Distance Vector (DSDV) [11], and others;
* Cluster-based protocols Scatternet is a kind of cluster-based transmission protocol in BLE 4.1 to support multihop communications. The network nodes are divided into multiple overlapping disjoint clusters [12]. Each cluster is led by the cluster head, who maintains memberships in a cluster and is then utilized to find the path that connects the clusters. The clustering of nodes decreases the flood during the path discovery procedure. In addition, the protocol monitors any single directional links for transmission between clusters and intracluster. Protocols of this type include the Two-Tier Data Dissemination Protocol (TTDD) [13], ring routing [14], Energy-Efficient Secured Ring Routing (E2 SR2) [15], and others.  
In a BLE mesh network with identical battery-operated nodes, a flat topology would provide the most flexibility to support multihop communications among the nodes. Reactive protocols such as AODV compute routes on-demand, eliminating the need for periodic  
Figure 1: BLE stack and mesh architecture [3].  
updates [4] and reducing message flooding. Furthermore, because BLE nodes are typically resource constrained, the reactive protocol is best suited because it requires less storage than proactive and cluster-based protocols.
## 4 Conclusions  
FDM is widely embraced in prototyping due to its cost-effectiveness, speed, and versatility in creating intricate and functional prototypes using various materials. Concurrently, the growing emphasis on sustainable practices and environmental consciousness highlights considerations for utilizing biodegradable polymers and reducing their overall usage while optimizing the production of polymer-based products. This study delved into the impact fracture resistance of FDM models constructed from Polylactic Acid (PLA), specifically exploring the effects of infill density (50% and 100% infill) and sample thickness (2 mm, 3 mm, and 5 mm). In-depth analyses, including optical microscopy, FTIR spectroscopy, and SEM examinations of both the PLA filament and fractured FDM PLA surfaces, were conducted to elucidate the influence of process parameters on impact damage and failure mechanisms.  
The results of the impact fracture resistance test indicate that a 100% infill profile with a 2 mm thickness should be avoided due to significant data dispersion, suggesting unpredictable behavior under impact. For 5 mm thickness, the 100% infill profile showed a significantly higher median compared to the 50% infill profile, demonstrating superior durability. Optimal impact strength was observed in samples with a 3 mm thickness, implying that material savings can be achieved with a 50% infill without compromising mechanical properties. The SEM analysis of the PLA filament showed a brittle fracture, inorganic additives, and air voids from extrusion, while the FDM models with 100% infill showed triangular voids and micro voids at inter-layer boundaries. FTIR spectroscopy showed no alterations in PLA filament surface chemistry during FDM printing, except for molecular reorientations and decreased crystallinity, which is in line with previous research.  
Future research will be directed towards investigating the impact of exposing FDM samples to UV radiation and various chemicals to explore their stability under different conditions.  
Conceptualization, D.B. and K.I.I.; methodology, D.B.; software, K.I.I.; validation, D.B. and K.I.I.; formal analysis, D.B., K.I.I. and M.V.; investigation, D.B., K.I.I., and T.C.; resources, D.B.; data curation, K.I.I. and D.B.; writing--original draft preparation, D.B., K.I.I. and M.V.; writing--review and editing, T.C.; visualization, D.B. and K.I.I.; supervision, D.B.; funding acquisition, D.B. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the University of Zagreb.  
Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline
**Sample** & **Crystallinity Index** \\ \hline filament & 1.23 \\
100\% infill 5 mm & 0.69 \\
100\% infill 3 mm & 0.97 \\
100\% infill 2 mm & 0.57 \\ \hline \end{tabular}
\end{table}
Table 4: Crystallinity index of PLA filament and printed FDM samples with 100% infill.
## 3 System Development
### System Requirements  
The system processes signals from the upper L-band from all constellations, as demonstrated by the S\({}_{21}\), or transmission loss, measurement results presented in Figure 2, where each of the 16 channels operates from 1.45 GHz to 1.75 GHz. The choice of this band was due to project requirements aiming for the development of antenna arrays for the L1/E1 band. However, the FPGA platform can operate with other frequency bands, and even perform dual-band operation, with new software and calibration. At its output it produces an analog signal like a regular antenna to facilitate seamless integration with commercial receivers.  
By ensuring negligible impact on positioning, time delays are carefully managed. The control unit, integrated with a receiver, supplies the positioning solution to an autonomous vehicle's decision unit, meeting Society of Automotive Engineers' standards with a total delay of not less than 100 ms [33]. Efforts are made to minimize signal delays introduced by the control unit, particularly crucial when the vehicle is in motion at highway speeds.  
The feedback interface from the receiver utilizes the NMEA 0183 specification [34], the most common among commercial receivers. This interface facilitates effective communication between the control unit and the receiver.  
A noteworthy feature is the system's ability to support multiple beam implementations, allowing for the testing and implementation of various beamforming algorithms without significant reconfiguration. The system is equipped for live testing of diverse beamforming algorithms without extensive reconfiguration.  
Designed with scalability in mind, the system accommodates varying numbers of antennas and beams, ensuring adaptability to systems with a larger number of antennas  
Figure 1: Block diagram of the SDA system.  
Figure 2: Measured S\({}_{21}\) parameters of the FPGA’s 16 channels; a demonstration that the board’s current configuration can process signals in the 1.45–1.7 GHz range.  
and beams. Additionally, the implementation is crafted for reusability, making it applicable in platforms with a feasible cost for integration into production vehicles.
## 1 Introduction
We extended the lumped equivalent circuit-based thermal model by adding an external power source and two additional resistive elements reflecting the degree of heat transfer from the excitation coil to the sample and its surroundings. To the best of the authors' knowledge, this aspect has not been addressed in previous publications on modeling non-adiabatic calorimeters for ferrofluid measurements. In addition to the expansion of the non-adiabatic calorimeter model, we increased the number of temperature curves recorded at three different points of the measurement system, using a total of three independent fiber-optic thermometer probes: two for monitoring the temperature changes at the two nodes of the system corresponding to the sample and its closest surroundings, and an additional one to track the temperature change in the carcass heated by the resistive power loss generated by the induction coil, which caused an undesired temperature increase in the sample and its surroundings. The result was an increased quantity of calibration data for the global optimization system, leading to a more reliable estimation of the multi-parameter RC model. To the authors' knowledge, the use of multi-probe measurements in a non-adiabatic calorimeter system for ferrofluid heating power measurements has not been encountered in the literature. We show that this thermal model enables the proper calibration of power measurements using an AC magnetometer. The parameter values of the presented model were identified by a numerical global optimization algorithm that fits temperature-time curves to measurement data. For this procedure, calibration measurements were performed with fiber-optic probes and two adjustable power sources, namely a heating resistor and excitation coil, which were both controlled by the independent DC voltage source. Another approach to the linear calibration of a quasi-adiabatic calorimeter used to perform SAR measurements of nanoparticles using a piece of copper wire was presented in [45]. The authors estimated the power dissipation in the sample by theoretically calculating the eddy currents generated in the thin copper wire subjected to an alternating magnetic field. A new idea presented in our paper is the use of direct current instead of alternative current in the excitation coil during the calibration. The direct current driven through the excitation coil allowed us to determine the influence of external heating on the examined sample without inducing any excess thermal power from eddy currents in the heating resistor wires. The calibration results for the proposed expanded model with three-point temperature measurement were compared with the calibration and verification results for the standard model with a single heat loss parameter, corresponding to the corrected-slope method.