esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate geolocation and tracking of Radio-Frequency Interference (RFI) sources, which affect wireless and satellite systems such as Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom) systems, are considered to be a significant issue. Several studies connected to civil and military operations on this issue have been investigated recently. The literature review has surveyed many algorithm simulations for optimizing geolocation and target-tracking estimation. Although most of these algorithms have their own advantages, they have weaknesses, such as accuracy, mathematical complexity, difficulties in implementation, and validation in the real environment, etc. This study has been concerned with investigating the accuracy of geolocation and tracking under high speed and powerful rotation using extracted data from the Orolia Skydel simulator, which simulates the space environment involving Low Earth Orbit (LEO) satellites as sensors and Unmanned Aerial Vehicles (UAV) as RFI emitters. Various scenarios modeled using the Orolia Simulator for quasi-real dynamic trajectories of LEO satellites have been created. The assumed approaches have been verified by Cramer-Rao Lower Bound (CRLB) and Posterior CRLB (PCRLB) to determine the increase in Root Mean Square Error (RMSE) value. The simulation scenarios have been performed using the Monte Carlo iteration. Eventually, the overall achieved results of the considered approaches using data acquired from the Orolia Simulator were presented and compared with theoretical simulation.  
m 202211781. [[https://doi.org/10.3390/electronics11050781](https://doi.org/10.3390/electronics11050781)]([https://doi.org/10.3390/electronics11050781](https://doi.org/10.3390/electronics11050781))  
odeling and experimental analysis; Orolia Skydel simulator; accuracy geolocation and emitter-tracking; time difference of arrival; frequency difference of arrival; cross ambiguity function; high-degree nonlinear tracking filters
## 2 Materials and Methods
### Data and Preprocessing of the Data
#### 2.2.2 Ground Reference Data  
Ground reference data for this research came from two sources: the Annual Space-Based Crop Inventory (ACI) provided by Agriculture and Agri-Food Canada (obtained from [[https://open.canada.ca](https://open.canada.ca)]([https://open.canada.ca](https://open.canada.ca)) accessed on 13 December 2023), and the Database of Declared Agricultural Parcels and Production (BDPPAD) from La Financiere Agricole du Quebec (obtained from [[https://www.fadq.qc.ca](https://www.fadq.qc.ca)]([https://www.fadq.qc.ca](https://www.fadq.qc.ca)) accessed on 11 February 2024). ACI maps out agricultural and non-agricultural land cover across Canada, while BDPPAD offers detailed information on agricultural parcels linked with client records. The data extracted from this source encompassed 13 classes, including 5 essential crops for beekeepers (namely apples, corn, canola, soy, and small fruits such as berries, blueberries, strawberries, and raspberries) along with 8 land cover classes.  
Figure 2: Geographic location of the study area with a true-color median composite of Sentinel-2 satellite imagery generated for 1–10 April 2021.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 5 Space-Efficient Information Bottleneck Successive Cancellation List Decoder  
The computational simplicity of the information bottleneck SCL decoding comes at the cost of additional memory for storing the decoding and translation tables. In this section, we show how to reduce the memory requirement of the translation tables in an information bottleneck SCL decoder. First, the role of translation tables is discussed in detail. Then, the message alignment principle of [7] is exploited to obtain a single translation table that can be used instead of the \(N\) distinct translation tables. For the following discussion, we drop the level subscript from cluster indices at the \(n\)th level for the sake of brevity and write \(t_{i,n}\) as \(t_{i}\).
# Investigation on Unsteady Flow Characteristics of a SCO\({}_{2}\) Centrifugal Compressor
## 2 Design of a Centrifugal Compressor
### Geometry of Impeller  
The detailed geometric parameters of the impeller are determined in the design process. It is of significance to select a proper profile of the impeller. In this paper, multiple tests were conducted to ensure the high efficiency of the SCO\({}_{2}\) centrifugal compressor. To avoid the subcritical state at the inlet of the impeller, several CFD results of different impeller blade profiles were compared to select the one without transcritical phenomenon. This process guided the evolution of the impeller blade profile. We finally chose the profile of best performance after several improvements had been made.  
The meridian plane of the impeller is presented in Figure 1a below, while the 3D impeller model is shown in Figure 1b. In Figure 1a, the blue arrow stands for low pressure and low temperature while the red arrow is just the reverse.  
The impeller adopts twisted blades, which reveal preferable performance in most centrifugal compressors. The profile of hub and shroud as well as the blade lean angle distributions along hub to shroud are shown in Figure 2, respectively. The blade was modeled by stretching from hub profile to shroud profile, according to the blade lean angle distributions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{performance estimates} & input power (MW) & 5 \\  & mass flow rate (kg\(\cdot\)s\({}^{-1}\)) & 273.1 \\  & rotational speed (rpm) & 15,000 \\  & isentropic efficiency (\%) & 76.3 \\ \hline \multirow{4}{*}{state parameters} & temperature of inlet (K) & 306.7 \\  & pressure of inlet (MPa) & 8.8 \\  & temperature of outlet (K) & 327.5 \\  & pressure of outlet (MPa) & 18.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance estimates and state parameters of the designed compressor.  
Figure 1: Geometry of impeller: (**a**) meridian plane; (**b**) 3D model.  
Leading edge (LE) and trailing edge (TE) of the blades adopt ellipse type and circular type, respectively. The LE elliptic ratio is 2.0. These types can effectively reduce the impact at LE and TE, thus decreasing the aerodynamic loss.  
The specific geometric parameters of the impeller meridian plane and the twisted blades are given in Table 2. To guarantee the sealing performance, a shrouded impeller was adopted.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 2 Materials and Methods
### Random Forests Combined with Simulated Annealing
#### 2.1.2 Simulated Annealing  
The accuracy of RF predictions is highly dependent on the values of the hyperparameters used. It is not practical to test a large number of hyperparameter sets to determine the best one. A satisfactory model can be obtained by manually adjusting hyperparameters, but it may not necessarily be the best-performing forest. Simulated annealing (SA) presents a framework for optimization aimed at searching for the best global hyperparameters. Introduced by [PERSON] in 1983 [38], SA is an extension of the method proposed by [PERSON] in 1953 [39]. SA is particularly useful for analyzing cases with multiple parameters to optimize [40].  
Simulated annealing serves as a stochastic optimization approach suitable for problems with extensive parameter spaces. This method begins at a point in the parameter space with an initial \"current\" set of parameters and then proceeds with a series of steps. Each step results in a proposed set of parameters slightly different from the current one. More details can be found in [39]. This method aims to freely explore the parameter space in initial iterations, increasing the probability of approaching the global optimum. With decreasing temperature, the algorithm narrows its exploration, ultimately moving towards a nearby local mode.
## 4 Discussion  
### Discussion of the Results of the Analysis for a Variable Weight of the Satellite  
The analysis of Table 7 and the parameters presented there reveals the following: specific impulse is a measure of propulsion efficiency. Higher \(\mathrm{I_{sp}}\) values mean that the propulsion is more efficient, i.e., that it consumes less fuel to achieve the same change in velocity. For heavier satellites, the specific impulses are usually larger in order to maintain the appropriate propulsion efficiency and not to waste fuel. Heavier satellites also often use more advanced propulsion technologies (e.g., engines with a higher \(\mathrm{I_{sp}}\)) to compensate for their larger weight. The values of specific impulse were adopted so as to reflect the improvement of efficiency as the weight of the satellite increases. For a satellite weighing 8 kg, \(\mathrm{I_{sp}}\) of 180 s was adopted, while for a satellite of the weight of 18 kg, the specific impulse is 230 s. The range of 180-230 s is a realistic range of specific impulse for various types of satellites with chemical or hybrid propulsion systems (depending on the technology used) and the given weight. The specific impulses of engines used in small satellites, such as CubeSats, range from approx. 150 s even to 300 s for more advanced propulsion systems. The specific impulses in actual satellites from the Nano and CubeSat classes depend on the type of propulsion. Chemical monopropellants (such as those based on hydrazine) offer values of specific impulse in the range of 220-250 s, which are compatible with the values for small satellites of the weight of approx. 15 kg. Green monopropellants offer values in the range of 220-230 s, similar to those of AF-M315E. Cold gas propulsion systems for smaller satellites generate specific impulse in the range of 110-170 s, depending on the system and type of gas used. For larger satellites (closer to 18 kg), electric propulsion (e.g., \"hall thrusters\") may generate significantly higher specific impulse, even up to 800-3000 s, but the specific impulse adopted for these types of satellites was smaller than 300 s. The thrust force is the direct force generated by the propulsion system to change the velocity of the satellite. Swath width is the size of the area that may be captured by the telescope onboard the satellite platform during observations of the ground. The quality of sensors and of the optical systems on the satellite improve with the increasing weight. Heavier satellites may often carry more advanced sensors, which increases the swath width. Heavier satellites offer the possibility to install more advanced optical systems that increase the field of vision of the satellite, improving its capacity to observe large areas on the surface of the planet. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{ \begin{tabular}{c} **Initial** \\ **Weight of of** \\ **Satellite** \\ **m\({}_{\mathrm{p}}\) [kg]** \\ \end{tabular}
# Efficient Preamble Design Technique for Millimeter-Wave Cellular Systems with Beamforming
## 3 Efficient CBID Detection Technique for mmWave Cellular Systems
The detection of the CID, BID, and the residual STO using the received signal can be performed by correlating the received signal in Equation (11) with the preamble sequence in the frequency domain as follows:  
\[\left\{\hat{c}=N_{v}\hat{g}+\hat{v},\hat{b},\hat{\chi}=2\hat{u}_{\hat{l}}+\hat {l},\hat{i}\right\}=\underset{g,v,b,l,u_{\hat{l}},\hat{i}}{\text{arg max}}\left\{\Lambda_{l,u_{\hat{l}},\hat{i}}^{g,v,b}\right\} \tag{12}\]  
\[\Lambda_{l,u_{\hat{l}},\hat{i}}^{g,v,b}=\left|\sum_{q=0}^{1}\Omega_{s=\left(q +l\right)_{\forall\text{N}_{0}},\hat{i}}^{g,q}\left(\widehat{d}_{s=\left(q+l \right)_{\forall\text{N}_{0}}}^{g,b}+u_{\hat{l}}+l\left(q+l\right)_{\forall \text{N}_{0}}\right)\right|^{2} \tag{13}\]  
\[\Omega_{s,\hat{i}}^{g,q}\left(\left(n\right)_{\forall\text{N}_{0}}\right)= \sum_{m=0}^{L-1}\widetilde{Y}_{i}^{q}\left(m\right)\left(Z_{s}^{S}\left(m \right)\right)^{*}e^{j2\pi mn/N_{0}} \tag{14}\]  
where \(\widetilde{Y}_{i}^{q}\left(m\right)=Y_{i}^{q}\left(\left(m+N_{0}/2-\left[L/2 \right]\right)_{\forall\text{N}_{0}}\right)\)  
\[Y_{i}^{q}\left(\left(k\right)_{\forall\text{N}_{0}}\right)=\sum_{n=0}^{N_{0}- 1}y_{i}\left(2n+q\right)e^{-j2\pi kn/N_{0}}\]  
Here, \(\hat{g}\), \(\hat{v}\), \(\hat{c}\), and \(\hat{i}\) denote the detected GID, SID, CID, and Rx beam index, respectively. \(\hat{\chi}\), \(\hat{l}\), and \(\hat{u}_{\hat{l}}\), denote the estimated parameters for the residual STO. \(Y_{i}^{q}\left(\left(k\right)_{\forall\text{N}_{0}}\right)\) represents the frequency domain version of \(y_{i}\left(2n+q\right)\). Here, \(y_{i}\left(2n+q\right)\) denotes the decimated version of the received signal in the time domain with \(q\in\left\{0,\ 1\right\}\). Because \(r_{s}^{S}\in\left\{1,2,\cdots,N_{r}\right\}\) is always satisfied for the root indices in \(\left\{r_{0}^{S},r_{1}^{S}\right\}\) selected by Equation (7), the IFFT operation required for the CBID detection needs to be performed \(N_{r}\) times for each \(\widetilde{Y}_{i}^{0}\left(m\right)\) and \(\widetilde{Y}_{i}^{1}\left(m\right)\). Therefore, the computational complexity for the CBID detection decreases as \(N_{r}\) decreases.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 2 Related Work
### Quantum Key Distribution
#### 2.2.1 Possible Technical Limitations in QKD Deployment Scenarios  
When deploying low-cost QKD; the following limitations can affect the performance of the security framework.  
* Key Rate: Achieving a high key rate can be challenging while deploying the security framework in a low-cost hardware setting. For high volumes of network traffic, QKD needs to improve the development of secure key rate generation. One of the approaches is to increase the performance of the detectors. A detector with high efficiency and short resolving time can increase the key rate [29]. [PERSON] et al. [29]'s research shows that a QKD can be performed on an optical fiber at a data rate of 1 Mbps over a distance of 50 km.
* Distance: QKD protocols provided only point-to-point communication between the RTU and MTU using a quantum channel with noise from channel imperfections. The distance between the RTU and MTU can be increased by increasing the length of the quantum channel. However, the distance between the two units is directly proportional to the noise in the channel. Thus, increasing the point-to-point distance in QKD protocols is not practically feasible. In a QKD, it is crucial to reduce the channel noise as much as possible to improve the key rate and to estimate the noise value accurately. Moreover, the low noise of single-photon detectors plays a crucial role in tolerating losses. For example, a superconducting nanowire single-photon detector in a sub-zero temperature can tolerate a loss of 72 dB, which is equivalent to around 360 km of standard single-mode fiber [29].
# Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
## 5 Conclusions  
This paper proposes an improved least squares phase unwrapping method based on the Chebyshev filter. The accuracy and robustness of the method were verified by comparing the simulated phase and the real InSAR phase. Simulation data indicate that the proposed method's RMSE is 94.53%, 31.50%, and 15.11% lower than that of LS, PCUA, and RPUA, respectively. The proposed method has a faster unwrapping speed compared with PCUA and RPUA, which is improved by 55.29% and 10.91%. The robustness of the proposed method was validated through its performance in phase unwrapping under varying phase gradients and phase noise. Under the real InSAR phase unwrapping, the phase unwrapping RMSE of the proposed method is reduced by 63.91%, 35.38%, and 54.39% compared to LS, PCUA, and RPUA. The phase unwrapping time is reduced by 62.86% and 11.64% compared to PCUA and RPUA. Based on the analysis of the above results, the proposed method has the most accurate unwrapping effect and best robustness compared to LS, PCUA, and RPUA.  
However, in the simulated and real phase unwrapping results obtained, there is still some incompletely unwrapped phase, and the proposed method speed is slower than the LS phase unwrapping speed. In the future, the speed and efficiency of the phase unwrapping can be improved by adding more appropriate filters to divide the phase of the steep gradient into more flat gradient phases or adding Chebyshev filters to other phase unwrapping methods to improve the phase unwrapping effect of other methods.  
Conceptualization, G.L.; Methodology, G.L.; Investigation, W.L.; Writing--original draft, G.L.; Writing--review & editing, Y.L.; Project administration, Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Smart Healthcare Network Management: A Comprehensive Review
## 3 Smart Healthcare Remote Monitoring Contexts
### Dominant Contexts (Typical)  
These contexts refer to locations where patients spend the majority of their time, including homes, hospitals, and nursing homes. Such environments are characterized by a fixed IoT infrastructure. According to [PERSON] et al. [PERSON] et al. (2018), a sensor network deployed in a hospital--where the infrastructure is stable and continuously powered--differs significantly from a network designed for home-based patient monitoring, where the patient is in a remote setting. Home-based monitoring requires a more pervasive and unobtrusive approach to effectively manage patient care.  
Various studies have explored patient monitoring systems in hospital settings [PERSON] et al. (2018) and personal monitoring at home [PERSON] et al. (2018). In most IoT-driven patient monitoring solutions, a gateway serves as an intermediary between the sensor network and the Internet. This gateway typically handles essential tasks such as protocol translation between Internet-based systems and sensor networks.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Slopes and Landslides Monitoring  
Landslides represent one of the major natural hazards, causing loss of life and enormous damage to buildings and infrastructures worldwide. According to [PERSON] et al. [162], between  
Figure 12: **Left**: Geotextiles (geogrids and rope-like textiles) manufactured by the Saxon Textile Research Institute with integrated polymer optical fibres (POFs). **Right**: \(\
u\)-OTDR traces of a 50 m long POF fibre with a 1.4 m-long portion strained up to 16%. Reproduced with permission from [152].  
2004 and 2010, approx. 30,000 casualties occurred around the world due to approx. 2600 non-seismically-triggered landslides.  
In the last decades, significant efforts have been devoted to the understanding of underlying mechanisms responsible for landslides [163; 164]. The main aim was to monitor the triggering phenomena and identify precursors of instability for the implementation of effective early warning systems. Among the triggering factors, water filtration and excavation or erosion determine an increase of shear stresses and pore water pressures, reducing the overall resistance of the soil. This mechanism leads to an anomalous strain field in the failure surface and triggers relative movements of slope portions. In turn, significant shear strains localised near the failure surface appear: in the beginning, they grow rather slowly but their rate soon increases rapidly until the stability is definitively compromised. Therefore, strain and displacement (at the surface and underground) are recognised among the physical parameters to be of paramount importance and more directly correlated to landslide occurrence [165]. Other environmental factors (e.g., rainfall, temperature, and soil moisture) and geotechnical parameters (e.g., pore water pressure) are also the matter of traditional surveys in landslide monitoring. The measurement of all these parameters enables the correlation of ground movements with their triggering mechanisms and supports the definition of causality pattern in the events.
###### Abstract  
This study presents a quantitative electrophysiological method to directly measure the passive transport of ammonium ions through bacterial outer membrane porins. Using a zero-current reversal potential assay in planar lipid bilayers under defined bi-ionic gradients, this study evaluates the permeability of ammonium salts through two general diffusion porins: Omp-Pst2 from _Providencia stuartii_ and OmpF from _Escherichia coli_. Under matched ionic conditions, Omp-Pst2 exhibited significantly higher ammonium flux--approximately 6000 ions per second per monomer at a 1 uM gradient--compared to ~4000 ions per second for OmpF. Importantly, the identity of the accompanying anion (chloride vs. sulfate) modulated both the ion selectivity and flux rate, highlighting the influence of counterion interactions on porin-mediated transport. These findings underscore how structural differences between porins--such as pore geometry and charge distribution--govern ion permeability. The method applied here provides a robust framework for quantifying nutrient flux at the single-channel level and offers novel insights into how Gram-negative bacteria may adapt their membrane transport mechanisms under nitrogen-limited conditions. This work not only enhances our understanding of outer membrane permeability to small ions like ammonium, but also has implications for antimicrobial strategy development and biotechnological applications in nitrogen assimilation.  
ammonium transport; bacterial porins; Omp-Pst2; OmpF; electrophysiology; planar lipid bilayer; passive diffusion; Gram-negative bacteria; nutrient uptake; antimicrobial strategy