esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 5 Related Works
### Low-Throughput Networks
It was found that, set to a 1% duty cycle, LoRaWAN data rate 5 (125 kHz channel, 5.47 kbps, 242-byte maximum payload) to be the most effective configuration for time-sensitive applications. [PERSON] et al. established that LoRaWAN technology can be effective within an indoor environment for IoT applications, as long as gateways and nodes are strategically placed. While experiments were conducted with the use of a single gateway \begin{table}
\begin{tabular}{c c c c} \hline
**Property/Technology** & **Sigfox** & **OnRamp** & **LoRaWAN** \\ \hline
**Network Topology:** & Star of stars & Star & Star of stars \\
**Channel Access Scheme:** & R-FDMA & RPMA & LoRa/CSS \\
**Tx Frequency Band:** & 868 MHz & 2.4 GHz & 433/868/915 MHz \\
**Channel Width per Signal:** & 100 Hz & 1 MHz & 125 KHz \\
**Link Data Rate (bps):** & 100 bps & 60–960 bps & 0.3–50 K bps \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of LTN technologies [22]. for all nodes, and noting the relatively small cost of gateways for practical implementation, one would expect that using multiple gateways within an indoor environment will decrease packet loss from nodes a further distance from the original gateway. Building upon the research found in [26], [PERSON] et al. in [27] tested both purely LoRa wireless RF and LoRaWAN technologies to compare the performance and reliability of each in a well-developed environment. This work by [PERSON] et al. is the first published research using a central business district (CBD) of a city, in this case Glasgow (UK), as a test-bed for this technology. Conducting two separate tests of both purely LoRa wireless RF and LoRaWAN networks, it was found that LoRa wireless RF communication was successful within an approximate 2 km radius from the static RF transceiver, even amongst buildings, with a healthier RSSI from line-of-sight communication. Conversely, signal was lost after passing over a large hill. On the LoRaWAN network however, performance increased significantly with a radius of more than double that tested in LoRa RF and a connection rate of 95.5%, with another 2.5% with data received but no acknowledgement. The LoRaWAN node also still transmitted in problem areas which even GPS signal was lost. It was established that both city topography and building density affected signal propagation. This will need to be accounted for in this project's testing procedures and gateway positioning. As previously discussed, this relatively new technology does not have many test-cases for large-scale deployment as yet. Significant testing will be required to be undertaken to prove the effectiveness of this technology in a Water Utility network. [PERSON] et al. concluded that a possible use case for this technology is within environmental monitoring applications, and it is envisaged that this paper will be one of the first to publish a proven use case in this field.
## 4 Methodology
### The Proposed CGFace Model
#### 4.1.3 Adam Optimization  
Adam (adaptive moment estimation) [25] is an optimization algorithm that replaces the classical stochastic gradient descent procedure to update the network weights. Besides storing an exponentially-decaying average of the past squared gradients \(v_{t}\), Adam also keeps an exponentially-decaying average of past gradients \(m_{t}\), similar to momentum.  
The decaying averages of past (\(d_{t}\)) and past squared gradients (\(p_{t}\)) at timestep \(t\) are computed as follows:  
\[d_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{8}\]  
\[p_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{9}\]  
Let \(d_{t}\) and \(p_{t}\) be the estimation of the first moment (the mean), and the second moment (the un-centered variance) of the gradients respectively, and \(g_{t}\) be the gradient of the stochastic objective, \(g_{t}^{2}\) (indicates the elementwise square \(g_{t}\odot g_{t}\)) a function of the gradients at all previous time steps. \(d_{t}\) and \(p_{t}\) were initialized as vectors of 0's, the authors in [25] noticed that they were biased towards zero, mainly during the initial time steps, and when \(\beta_{1}\) and \(\beta_{2}\) were close to 1 (the decay rates are small). The parameters \(\beta_{1}\) and \(\beta_{2}\) do not precisely define the learning rate, they decay the running average of the gradient. If they decay rapidly, then the learning rates will jump about all over the place. If they decay slowly, it will take ages for the learning rates to be learned.  
To prevent this problem, bias-corrected first (\(\hat{d}_{t}\)) and second moment (\(\hat{p}_{t}\)) estimates are calculated:  
\[\hat{d}_{t}=\frac{d_{t}}{1-\beta_{1}^{t}} \tag{10}\]  
\[\hat{p}_{t}=\frac{p_{t}}{1-\beta_{2}^{t}} \tag{11}\]  
Bias-corrected first and second-moment estimations are then used to update the parameters, which yield the Adam update rule, \(\eta\) indicates the step size parameter:  
\[\theta_{t+1}=\theta_{t}-\frac{\eta}{\sqrt{\hat{p}_{t}+\epsilon}}\hat{d}_{t} \tag{12}\]
## Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids
###### Abstract  
Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO\({}_{2}\)) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng-Robinson (PR) or Soave-Redlich-Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R\({}^{2}\) = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R\({}^{2}\) from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R\({}^{2}\) = 0.9896 and RMSE = 0.0201.  
Keywords:CO\({}_{2}\) solubility; ionic liquids; carbon dioxide; multilayer perceptron; gene expression programming; prediction; equation of state; machine learning +
Footnote †: journal:
## 3 Field Testing
### Results of the Ratio of Power Spectral Density (RPSD)  
From Equations (19)-(25), the estimation of **RPSD** is related to how many natural frequencies are considered during the calculation. Figure 8 shows an example of the estimated density distribution when considering different numbers of natural frequencies. For example, Num = 10 means that the first ten orders of natural frequencies are considered to calculate and estimate the density distribution.  
Figure 8: Estimated density distribution of **RPSD** considering different numbers of natural frequencies (J2, the second test).  
Figure 7: Measured deflection-based LTE of different joints on two dates.  
From Figure 8, it is found that the more natural frequencies is considered, the lower deviation of the estimated density distribution is, as well as the more stable the maximum will be. This is due to the different numbers of samples for density estimation. Measurement errors will influence the estimation results easily if there are only a few samples. If more natural frequencies are considered, more samples will come into the estimation and can reduce the influence of measurement errors.  
However, more samples bring a heavier burden of calculation. Figure 9 illustrates the calculated **RPSD** maximum (**RPSD\({}_{\text{m}}\)**) of different joints when different numbers of natural frequencies are considered. It is found that the calculation results fluctuate a lot when considering less than four natural frequencies and become stable when more than eight natural frequencies are considered. This phenomenon exists in most joints and both two tests, which proves the influence of natural frequency. Therefore, to find a balance between accuracy and effectiveness, the first nine natural frequencies were selected to calculate **RPSD\({}_{\text{m}}\)**.  
Figure 10 illustrates the estimated density distribution of **RPSD** from different joints. Each subfigure shows the results of two tests. The left side is the first test, and the right side is the second test. The maximum of the **RPSD**'s density distribution is marked by dark blue points. It is found that:  
Figure 9: Calculated **RPSD\({}_{\text{m}}\)** of different joints considering different numbers of natural frequencies.  
(1) There is a shift in the maximum of **RPSD**'s density distribution in J1, J4, J5, J8, J9, and J11. The deflection-based LTE (DLTE) of these six joints also changes by more than 5% according to Table 1. It suggests the maximum density distribution, as the representative value of **RPSD** estimation, can reflect the change in DLTE sensitively.  
(2) J8 and J10 just have a slight change in DLTE, as well as the maximum of **RPSD**'s density distribution. However, their deviation of **RPSD** changes a lot. This is because that the deterioration of the joint is usually caused by local damage or defects. After deterioration, the load transfer efficiency is not the same along the length of the joint.
# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
## 1 Introduction  
One of the most important research directions of Machine Learning (ML) is Feature Optimization (FO) (collectively grouped as Feature Engineering (FE), Feature Selection (FS), and Filtering) [1]. For FS, a saying \"Less is More\" becomes the essence of this research. Dimensionality Reduction [2] has become a focus in the ML process to avoid unnecessary computing power/cost, overlearning, and predictive errors. In this regard, redundant features which may have similar predictive value to other feature(s), may be excluded without negatively affecting the learning process. Similarly, the irrelevant features should be excluded as well. FS and FE not only focuses on extracting a subset from the optimal featureset but also building new feature sets previously overlooked by ML techniques. This also includes reducing the higher dimensions into lower ones to extract the feature's value. Latest research has shown noteworthy progress in FE. In [3], the authors reviewed the latest progress in FS and associated algorithms. Out of a few, principal component analysis (PCA) [4] and Karhunen Loeve expansion [5] are widely used with eigen-values and eigen-vectors of the data covariance matrix for FO. The squared error is calculated as well in the mapping of orthonormal transformation to reduce general errors. Another approach is Bayes error probability [6] to evaluate a feature set. However, Bayes errors are generally unknown. Discriminant analysis are also used in FE. Hence, in the line with the latest progress and related study (See Section 2), the work proposed in this paper uses ML and mathematical techniques, such as statistical pattern classification [7], Orthonormalization [8], Probability theory [9], Jacobian [7], Laplacian [3], and Lagrangian distribution [10] to build the mathematical constructs and underlying algorithms (1 and 2). To advance such developments, a unique engineering of the features is proposed where the classifier learns to group an optimum set of features without consuming excessive computing power, regardless of the anatomy of the underlying datasets and predictive goals. This work also effectively addresses the known challenges of ML process such as overfitting, underfitting, predictive errors, poor generalization, and low accuracy.
# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Scheduler
#### 3.2.1 Pre-Defined Scheduler
MPTCP tackles the HoL blocking problem by penalizing the utilization of longer paths and increasing the receiving buffer sizes. However, this is considered to be suboptimal. Therefore, the BLEST scheduler minimizes the HoL blocking by reducing the number of unnecessary retransmissions. Another approach to mitigate bufferbloat in heterogeneous networks is to ensure in-order packet arrivals. WestwoodSCTP [33] sorts each transmission path, and transmits the packets in descending order of rank. However, it still cannot reach the goal when significant different latencies exist among different paths. To overcome this significant latency gap, [PERSON] et al. [74] proposed the FPS mechanism, which not only estimates the arrival time of packets on the transmitting path, but also estimates the data delivery time on the other paths, realizing the data arrivals at the same time on all paths. However, FPS only considers RTT as an estimation metric and does not consider lossy networks. Instead of only utilizing RTT, [PERSON] et al. [75] proposed DPSAF, dynamic packet scheduling, and adjusting with feedback. DPSAF consider the loss rate in packet scheduling. DPSAF is able to conduct fine-grained scheduling based on the amount of estimated data which is obtained by applying maximum likelihood estimation on all transmitting path and therefore is capable of responding quickly to network changes due to frequent feedback receptions. [PERSON] et al. [76] proposed a packet scheduling mechanism, called F2P-DPS in lossynetworks. In addition to RTT and loss rate, it also takes congestion window size (cwnd) into consideration. F2P-DPS models the TCP's behavior to calculate the latency on the scheduling path. Meanwhile, the states of other transmitting paths will also be estimated. F2P-DPS outperforms FPS in throughput and in-order packets arrival. Since delay is more critical for small size flows, in order to complete the transmission as quickly as possible, [PERSON] et al. [77] proposed a scheduler which stops the slow path temporarily if there are large delay gaps between different transmission paths. A generic modular scheduler framework was designed in [78]. This scheduler is able to change the data distribution over the subflows easily. The framework is utilized for testing different schedulers of multipath TCP. In [79], [PERSON] et al. proposed a delay-aware based path scheduling scheme on asymmetric paths for multipath transmissions. The proposed scheme improves the link utilization rate and in-order packets arrival rate in the receiver buffer. However, the scheme currently only supports single TCP flow, and further studies are required to support multiple flows and assure fairness among flows.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.2 Constraints  
Each sensor position \(\mathbf{x}_{j}\) must be located within the designated sensor domain \(\mathbf{\Omega}_{s}\), which is a subset of three-dimensional space \(\mathbb{R}^{3}\). In other words, the position of each sensor is constrained by  
\[\mathbf{x}_{j}\in\mathbf{\Omega}_{s}\subseteq\mathbb{R}^{3} \tag{2}\]  
The orientation of each sensor is defined by the azimuth angle \(\left(\theta_{j}\right)\) and elevation angle \(\left(\phi_{j}\right)\), where the limits are expressed as follows:  
\[\begin{array}{l}\theta_{j}=\left[0,\pi\right)\\ \phi_{j}=\left[0,\pi\right)\end{array} \tag{3}\]  
The total number of sensors \(\left(m\right)\) must be less than the number of load cases \(\left(n\right)\). This constraint is applied to avoid redundant or overlapping data.  
\[m<n \tag{4}\]
## 5 Conclusions  
This article considered the problem of analysis of the electromagnetic compatibility in a ship's environment. This problem is important to consider and has an impact on people's safety and the ship's operation. Currently, techniques and ship technologies are characterized by the use of complex systems and electronic devices, including radio communication and radio navigation systems. The electronic systems and devices for automatic control of the ship's functions are particularly sensitive in this respect. Usually, they operate at relatively low energy levels and are sensitive to electromagnetic disturbances.  
In this paper, the problem concerning EM interactions between electrical and electronic devices in a naval ship's environment are analyzed. These devices radiate EM fields that have a mutual influence on their operation, thereby disturbing their operation properties. This problem is important to consider because it is vital that these devices work effectively. The described method has important utility for analyzing EMC conditions when designing a ship's internal structure, the deployment of energetic equipment and radio and navigation devices, and electronic elements of the ship's control systems.  
To analyze and solve the problem, a new computational model in the form of a matrix equation set is proposed. In the model, the ship's internal structure (under the main deck) and the locations of the sources of disturbing signals are taken into account. The ship's topological structure and its EMC topological model are described. Classification of the disturbing signals in a real naval ship scenario is shown.  
An example application of the matrix description and verification of the measurements in an in situ naval ship scenario are also presented. This verification confirms the usefulness of the proposed model in the design and exploitation of naval ship operations. For example, this helps to determine  
Figure 5: The comparison of the calculated and measured values of the disturbing signals at the equipment under test (EuT).  
the kind of shielding planes (walls) that can be applied to effectively protect electrical and electronic ship devices against EM disturbances. It can be applied to the modernization of a ship structure's exploitation to obtain better EMC conditions, and also to improve the safe functioning of electrical and electronic equipment.  
Moreover, the described topological matrix EMC model can be used in simulation investigations of extraneous EM disturbances.  
All authors contributed to developing the measurement equipment, designing and performing measurements, data analysis, scientific discussions, and writing the paper.  
This research received no external funding.  
This work was performed with financial support from the Faculty of Electronics, Telecommunications, and Informatics of Gdansk University of Technology and the R&D Marine Technology Centre in Gdynia, Poland.  
The authors have no conflict of interest to declare.
## 3 Results and Discussion
### Nanoparticle Tracking Analysis (NTA) and Zeta Potential  
The NTA analysis revealed an average diameter of 57.2 \(\pm\) 5.7 nm for AgNPs synthesized using latex extracts (Table 2). Although this value may seem large, it is relatively smaller in comparison to studies where the size of particles ranged from 32 nm to 220 nm in diameter [38; 39]. Furthermore, it was observed that the average diameter of particles observed using NTA analysis was much larger in comparison to HRTEM studies. It is suggested that the various techniques involved before or during analyses may influence the particle size differently. The measurement of particle size using NTA analyses in a hydrodynamic environment is often preferred since it is closely associated with an in vivo system, unlike HRTEM samples, which are in a dehydrated or powdered state [51; 52].  
The zeta potential value is often used to measure the surface charges of particles to ensure their stability within colloidal solutions [39]. The zeta potential value herein was poor (12.3 \(\pm\) 1.2 mV), with the preferred range being greater than 25 mV or less than \(-\)30 mV [27]. These results indicated that the latex AgNPs of _T. ventricosa_ displayed poor stability, low mobility, and reduced electrostatic repulsion. [PERSON] et al. [39] observed a zeta potential of \(-\)19.3 mV, thus suggesting moderate stability of the NPs, whereas [PERSON] et al. [38] revealed a zeta potential of \(-\)25.2 mV. [PERSON] and [PERSON] [53] suggested that proteins, polyphenols, and carbohydrates largely influence the synthesis of NPs, which may affect the zeta potential.
## 2 Materials and Methods
### Observation Network and Geodetic Measurements
Each point was measured twice and its \(x\) and \(y\) coordinates were determined for the WGS 84 rotation ellipsoid as the average value of the two measures. Then, the coordinates obtained this way were transferred to the PL 2000 reference system applicable in Poland. The error (\(m\)) in the position of the point along the \(x\) axis was \(m_{x}=\pm 0.035\ [m]\), and along the \(y\) axis it was \(m_{y}=\pm 0.020\ [m]\). Because of the required small error in measuring the heights of the ground points, altitude measurements were conducted using a Carl Zeiss Jena Ni007 (Koni007) precise leveler (Carl Zeiss, Jena, Germany) with a reading accuracy of 0.0001 m (Figure 7b) on 4 March 2019 (first measurement) and on 17 August 2019 (last measurement). There were differences determined in the altitudes of neighboring points (\(\Delta l_{i,i+1}\)), which eliminated the need to refer measurements to the reference points of known altitudes. Changes in the height differences were observed between the initial (\(s\)) and final (\(f\)) measuring cycles. The error in determining the relative altitude difference was \(m_{\Delta h}=\pm 0.001\ [m]\). In addition to the situational and altitude surveys, double measurements of the horizontal distances between neighboring, observation points (\(l_{i,i+1}\)) were also carried out to determine the values of the inclinations occurring in individual sections (\(T_{i,i+1}\)). They were measured using the Leica Geosystems D510 electronic distance meter with a reading accuracy of 0.001 m (Figure 7c). The error in determining the length between subsequent points was \(ml_{i,i+1}=\pm 0.0015\ [m]\).