esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The application of symbolic regression (SR) in the context of Wireless Power Transfer (WPT) systems has demonstrated significant potential as evidenced by the results presented in this study. The ability of SR to derive explicit mathematical formulas from both simulated and experimental data offers a unique advantage over traditional machine learning methods, particularly in terms of interpretability and computational efficiency. The results indicate that SR can achieve high predictive accuracy, with errors less than 1% in estimating the distance between coils using both scattering parameters (S-parameters) and impedance parameters (\(Z_{in}\)). This level of precision is comparable to that of neural networks (NNs), yet SR provides the added benefit of interpretable analytical expressions. These expressions not only allow for a deeper understanding of the underlying physical relationships but also enable real-time applications, such as dynamically adjusting coil distances to optimize power transfer efficiency. The study also highlights the versatility of SR in addressing different challenges within WPT systems. For instance, the application of SR to predict the phase-frequency relationship in a variable-frequency WPT system demonstrates that even a simple model can capture the main trends of the phase behavior. This suggests that SR can be utilized to complement or even replace existing control systems, particularly in scenarios where rapid adaptation to changing conditions is required.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Measurements?** & **Environment** & **Summary** \\ \hline
[13] & 2022 & yes & Outdoor & It shows experimental results at 28 GHz of 256 \(\times\) 16 mMIMO BS \\ \hline
[14] & 2020 & no & - & It joins user scheduling and HBF for mmWave massive MIMO. \\ \hline
[15] & 2020 & no & - & It joins multicast beamforming and antenna selection for mmWave Massive MIMO. \\ \hline
[16] & 2017 & no & - & It presents beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for mmWave/THz Massive MIMO. \\ \hline
[17] & 2019 & no & Outdoor & It applies the SD algorithm to outdoor uniform planar arrays’ (UPAs) hybrid beamforming mmW massive MIMO systems. It outperforms significantly the ZF and MMSE detectors with 16 QAM modulation over the whole range of SNR. \\ \hline
[18] & 2017 & no & Outdoor & It provides an overview of the existing multibeam antenna technologies which include the passive multibeam antennas (MBAs) based on quasi-optical components and beamforming circuits, and multibeam phased-array antennas enabled by various phase-shifting methods. \\ \hline
[19] & 2019 & no & - & It provides a hybrid precoding design for mmWave massive MIMO systems. It uses a phase pursuit technique. \\ \hline
[20] & 2015 & yes & Indoor & It presents an experimental study of 28 GHz band with 800 MHz bandwidth and beamforming based on Massive MIMO; 96 \(\times\) 8 mMIMO; 16 QAM; EIRP = 53 dBm; 1.2 Gbit/s can be achieved to 3.5 m. \\ \hline
[21] & 2019 & yes & Indoor & It presents an experimental study of 28 GHz frequency band with 500 MHz (100 MHz \(\times\) 5) bandwidth with 16 spatial-multiplexed streams. Number of BS antennas = 256. Adaptive modulation and coding (\(<\)256 QAM). EIRP is not indicated; 25 Gbit/s can be achieved to 10 m. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies that discuss mmWave mMIMO transmissions. frequency block code (SFBC) technique of [23; 24] and the above algorithm to carry out an experimental study of the benefit of applying this new algorithm when the signal-to-noise ratio (SNR) is low. The paper is organized as follows. In Section 2, the experimental setup and the channel sounder are described. The methodology is described in Section 3: the algorithms implemented and the physical layer parameters used. The SNR and the achievable throughput are presented and discussed in Section 4. Finally, the conclusions are presented in Section 5.
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Simulation
#### 2.3.1 Generation of Azimuth and Elevation Angles  
This part presents some basic but powerful data augmentation techniques. Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for analysis, without actually collecting new data.  
Various data augmentation techniques were implemented in this study, such as adding Gaussian noise, scaling, and flipping and their combinations. Figure 5 shows some examples of these lightning data augmentation techniques. The generated \(Az\) and \(El\) angles from the hand-crafted lightning signal are assumed to be real-positive or negative values. The Gaussian noise \(\varepsilon\) is an independent and identically distributed random variable, \(N\sim(0,1)\), with zero-mean and unit-variance, which is added to the \(El\) angles; the output \(\hat{El}_{i}=El_{i}+\varepsilon_{i}\), \(\forall\tau_{di}<\tau_{transit}\) is a shifted version of the real \(El\) values constrained to the transit time between two antennae \(\tau_{transit}=\frac{d}{c}=5\times 10^{-8}\)\(s\).  
For scaling augmentation, since the simulated azimuth (\(Az\)) and elevation (\(El\)) angles fall in a relatively narrow range, the outward scaling is used as it expands the azimuth and elevation range, forcing the proposed methods to make assumptions about what lies beyond the simulated values boundary. The lightning representations can be flipped horizontally and vertically. However, in practice, lightning mapping is not preferred to be flipped vertically. A horizontal flip is equivalent to rotating the lightning curve by 180 degrees.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline Sample interval & 4 ns \\ Sampling Rate of VHF & 250 MS/s \\ No of samples & 52,000,002 \\ Number of Antennae & 3 \\ Signal band-limited & 40–80 MHz \\ Centred Frequency & 60 MHz \\ Distance between antennae & 15 m baseline \\ Picoscope & Picoscope 3000 (3405D) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters for the interferometer and the lightning flash.  
Figure 4: The procedure of simulating lightning signals.
# Fabrication and Photo-Detecting Performance of 2D ZnO Inverse Opal Films
## Materials and Methods
### Characterization  
The morphologies of the films were characterized by a field emission scanning electron microscope (SEM, Zeiss Ultra 55, Oberkochen, Germany) at 2 kV. The crystal structures of the annealed ZnO powder derived from the corresponding PS/CA-ZA dispersion were investigated via X-ray diffraction (XRD, Bruker D8 Advance) using Cu K\(\alpha\) radiation (\(\lambda\) = 0.15418 nm). High resolution transmission electron microscopy (HRTEM) images and selective area electron diffraction (SAED) patterns of the particles scraped from the the 2D ZnO IO film were obtained using a HRTEM (Tecnai G2F 20 S-Twin, FEI, Hillsboro, OR, USA) operating at 200 keV. A UV-Vis spectrophotometer (Hitachi U-4100, Hitachi, Japan) was used to record the absorbance of the 2D ZnO inverse opal films fabricated on quartz substrates. The current-voltage (I-V) characteristics and time-dependent on/off characteristics of the film devices were measured by a picoammeter (Keithley 4200-SCS, Keithley, Beaverton, OR, USA).
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 1 Introduction
OFRs have a dynamic surface area time series, especially during the crop-growing season, when farmers are irrigating their crops and may pump water from nearby streams [23; 24; 25]. There are more than 2.6 million OFRs in the USA alone, and these OFRs play a key role in surface hydrology by storing fresh water and as an essential component of global irrigation activities [26; 27; 28]. Nonetheless, OFRs can contribute to downstream water stress by decreasing stream discharge and peak flow in the watersheds where they are built [24; 29; 30]. Therefore, monitoring OFRs sub-weekly surface area changes is critical to the assessment of their seasonal and inter-annual variability, as well as to mitigation of their downstream impacts, with implications concerning how OFRs are managed and where they are built. Previous research assessed the spatial and temporal variability of OFRs by leveraging the long-term (\(\geq\)25 years) Landsat-based inundation datasets [23; 31; 32]. Nonetheless, these datasets are limited to a few annual observations--due to clouds, sensor issues, and the 16-day repeat cycle--and Landsat's spatial resolution (30 m) limits the applications of these datasets to monitor OFRs smaller than 5 ha (i.e., high surface area uncertainties \(\sim\) 20%). Aiming to overcome these limitations, other studies [33; 4; 4] have applied a multi-sensor satellite imagery approach, including sensors of higher spatial and temporal resolution (e.g., PlanetScope [3 m] and Sentinel-2 [10 m]) when compared to Landsat. However, a multi-sensor approach requires processing of satellite imagery of different spatial resolution from multiple platforms, which can be time-consuming and a limiting factor if it is necessary to process, download, and move the satellite imagery across multiple platforms [33]. In this study, we propose a novel use of the analysis ready datasets Basemap and Planet Fusion, and we aim (1) to assess the usefulness of both datasets to monitor OFRs sub-weekly surface area changes and (2) to compare the two datasets and describe their differences when used to monitor OFRs.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 3 Preliminaries
### Chaotic System and Chaotic Compressive Sensing  
Chaos, which is also called non-linear dynamics, is a seemingly irregular movement with internal randomness that occurs in a deterministic system. Chaotic systems are characteristic of internal randomness, sensitive dependence on initial conditions, boundedness, aperiodicity, and ergodicity. Tent and logistic systems are two typical chaotic systems and are defined as follows.  
Based on tent system Equation (4), we can get a chaotic sequence \(z_{l}^{{}^{\prime}},l=1,2,3\cdots\),  
\[z_{l+1}^{{}^{\prime}}=\left\{\begin{array}{cc}z_{l}^{{}^{\prime}}/b,&0<z_{l }^{{}^{\prime}}<b\\ (1-z_{l}^{{}^{\prime}})/(1-b),b<z_{l}^{{}^{\prime}}<1\end{array}\right. \tag{4}\]  
where \(b,0<b<1\) is the chaotic parameter and \(z_{0}^{{}^{\prime}}\) is an initial value.  
Simultaneously, another chaotic sequence \(z_{l},l=1,2,3\cdots\), with the chaotic parameter \(\mu\) and an initial value \(z_{0}\) as inputs, could be generated by logistic system Equation (5),  
\[z_{l+1}=\mu z_{l}(1-z_{l}),\mu\in(0,4] \tag{5}\]  
As we noted above, the compressive sensing measurement matrices should be chosen specifically, in order to guarantee the quality of reconstruction. [PERSON] et al. presented that chaotic matrices could satisfy RIP and perform as well as Gaussian random matrices and Bernoulli random matrices when they act as compressive sensing measurement matrices ([PERSON] et al., 2017).
## 7 Risk Analysis
### Camera
#### 7.7.2 Denial of Service  
1. **Component** _Security risk_: Denial of service attacks targeting the camera can disrupt crash data collection and transmission, leading to delayed or missed crash notifications. This compromises the emergency response, hampers insurance claims processing, and undermines the overall effectiveness of the ACN system. _Security measures_: intrusion prevention systems (IPS) can be deployed to track network traffic and detect patterns indicative of DoS attacks. The system can automatically block or mitigate traffic from suspected attackers, ensuring the camera's availability and data integrity.
2. **Ports** _Security risk_: Denial of service attacks targeting the port connecting the ACN camera disrupt data transmission, leading to delayed crash notifications and compromised emergency response efforts. _Security measures_: The implementation of rate limiting on incoming connections to the port can prevent overload on the port with excessive requests and reduce the risk of DoS attacks causing disruptions in data transmission.
# DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization
## 3 The Proposed Work
### Parameter Setting  
Parameter tuning plays a key role in the performance of optimization algorithms and machine optimization algorithms. The hyperparameters listed in Table 1 for the three classifiers used in this study, XGBoost, KNN, and RF, were tuned using Gaussian Optimization. To evaluate the impact of hyperparameter tuning, the classification accuracies achieved before and after optimization were compared. As illustrated in Figure 5, tuning the parameters led to a noticeable improvement in performance across both binary and multiclass classification tasks. The results clearly demonstrate that the use of optimized parameters enhances the effectiveness of the classifiers. For the CNN-based component, we adopted the same model architecture and parameter settings as used in [31]. Moreover, the parameters for the ACO were manually configured based on preliminary testing and prior experience to ensure effective convergence. The number of ants was set to 30, the pheromone evaporation rate was assigned a value of 0.5, and the pheromone importance factor was set to 1. Additionally, the number of iterations was fixed at 500 to provide sufficient exploration of the search space while maintaining a reasonable computational cost.
## 3 Materials and Methods
### Classification Algorithms
#### 3.5.1 Support Vector Machine (SVM)  
This algorithm projects the input space to a higher-dimensional space to separate nonlinear data. The fundamental feature of SVM is that the separation margin of the data becomes as wide as possible [22].  
SVM employs kernel methods, which can be of various types, such as linear, polynomial, and Gaussian. SVM aims to choose an optimal separating hyperplane that maximizes the distance between two data points of different classes [22].  
The SVM machine learning method is chosen due to its high generalization and classification ability. A training set \(\left(x_{j},y_{j}\right)\), \(1\leq j\leq N\), \(x_{j}\) denotes the feature vectors extracted from the EEG signals, \(y_{j}\) denote the corresponding emotion labels, and \(N\) is the number of data. The SVM decision function is calculated as follows:  
\[f(x)=\sum_{i}^{N}\alpha_{i}\ y_{i}k(S_{i},x)+b \tag{33}\]  
where \(x\) is the input vector (in this case, the feature vector extracted from EEG signals), \(k\) is the kernel function, \(S_{i}\) denotes support vectors, \(\alpha_{i}\) are the weights, and \(b\) is the bias.
# Vibration Reduction of a Timoshenko Beam with Multiple Parallel Nonlinear Energy Sinks
###### Abstract  
A nonlinear energy sink is a promising device to reduce structural vibrations. In this work, the vibration reduction performances of multiple parallel nonlinear energy sinks attached to a short beam are investigated based on the Timoshenko beam theory. The dynamic equations of a vibration reduction system subjected to a harmonic excitation are established. The frequency responses are analyzed based on Galerkin discretization and the harmonic balance method, and the accuracy is verified by the Runge-Kutta method. An optimization method based on the genetic algorithm is proposed for the number, location, and cubic stiffness of the nonlinear energy sinks. The study reveals that, with the same total mass, multiple parallel nonlinear energy sinks can achieve a larger vibration reduction ratio than a single nonlinear energy sink. The parameter influences of the nonlinear energy sinks are revealed, and unstable responses with large cubic stiffness are presented. The optimal locations of the multiple parallel nonlinear energy sinks are related to low-order modal shapes. A larger reduction ratio on the resonant amplitude can be achieved compared to a uniform distribution of the nonlinear energy sinks. The optimal locations and cubic stiffness are related to the number of nonlinear energy sinks. In the studied case, the optimal number of nonlinear energy sinks was two.  
vibration reduction; nonlinear energy sink; Timoshenko beam theory; harmonic balance method; optimization Article
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 3 The Proposed EEMIP Method
### Design Requirements  
We have stated several requirements, which the proposed solution should meet:  
* _Using end devices with sensors_--the end devices can have different sensors, producing different amount of data. A single end device can have multiple sensors.
* _Using central device for data collection_--the central device (coordinator) collects data from end devices and can send them for further analysis or usage to an external device (e.g., via Internet connection).
* _Using energy-efficient communication_--an energy-efficient communication is required for devices with a limited power supply. Therefore, the network nodes use the proposed method for communication.
* _Using priority for data_--more important data are sent more frequently than less important data and are preferred in a case of congestion.
* _Using time intervals for data_--based on data priority, different time intervals are assigned to devices and each device communicate in a dedicated time slot.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 7 Conclusions
PAntOR-MI is a multi-interface variant, which parallelizes the sending of broadcast messages by an interface through threads. Finally, various simulations have been performed in NS-3 in order to validate the earlier proposals. The simulation results show that: (i) the AntOR-DLR protocol presented better throughput and a better delivered data packet ratio than its predecessor AntHocNet, as well as a small increase of average end-to- end delay and overhead, while these two latest metrics tend to equalize in dense networks; (ii) the AntOR-DLR protocol improves AntOR-DNR; (iii) the AntOR-RDLR protocol improves its predecessor AntOR-DLR; (iv) the AntOR-UDLR protocol also improves its predecessor AntOR-DLR in all metrics with the exception of the overload, in which case, both protocols presented similar values; (v) the AntOR-v2 protocol improves AODV in all of the analyzed metrics, with the exception of the overload, which is slightly superior; this difference becomes undetectable in dense networks; (vi) HACOR protocol improves AODV and OLSR in the throughput and the delivered data packet ratio, showing intermediate values for these two protocols in the other considered metrics; (vii) the PAntOR protocol improves AntOR-DNR; (viii) the PAntOR-MI protocol improves its predecessor in very dynamic environments; and (ix) all specified protocols behave stably in all of the simulations carried out. The previous results allow us to conclude that the family of specified sequential protocols (of which HACOR is its greatest exponent) improves the scalability of its predecessor AntHocNet, a protocol that is also better for most environments than the standards of reactive routing (AODV) and proactive routing (OLSR), and the considered parallel approaches for this type of ACO routing algorithms can further enhance the benefits of this family. This work was funded by the European Commission Horizon 2020 5G-PPP Programme under Grant Agreement Number H2020-ICT-2014-2/671672--SELFNET (Framework for Self-Organized Network Management in Virtualized and Software-Defined Networks). The authors have contributed equally to the work reported. The authors declare no conflict of interest.
## 2 Materials and Methods
### Case Study
#### 2.5.2 Tangjiashan Landslide Dam  
On 12 May 2008, a seismic event with a magnitude of 8.0 occurred in Wenchuan County, located in the southwestern part of Sichuan Province. This event triggered a landslide in Tangjiashan, resulting in the obstruction of a river and the formation of a barrier lake. The Tangjiashar Barrier Lake experienced a breach at 6:00 on 10 June 2008, as the water level hit 742.5 m and the reservoir capacity surpassed 230 million m\({}^{3}\)[20]. This work focuses on simulating the flow processes of the breach and the hydrodynamic process of the upstream and downstream areas inside a rectangular area extending 20 km upstream and 33 km downstream from the barrier dam. The terrain's digital elevation model (DEM) has a resolution of 30 m, as shown in Figure 8. The downstream border represents the unrestricted flow out of the system, while the other limits are closed, meaning they do not allow any flow. The simulation lasts for a duration of 20 h, and there are a total of 793,000 grids. The failure of the Tangjiashar barrier dam resulted in an incomplete breach of the dam. At a height of 69 m above the dam's base, the progress of the breach is halted due to the presence of substantial rocks within the dam, obstructing the further deepening of the breach. Hence, the longitudinal incision of the fracture terminates at a height of 69 m above the base of the dam in this computation. Simultaneously, during the risk mitigation process, a discharge channel in the shape of an inverted trapezoid has been excavated on the right dam shoulder of the Tangjiashan dam. The channel has a bottom width of 8 m, a depth of 13 m, and a side slope ratio of 1:1.5 (vertical/horizontal). This channel is intended to assist in safe discharge. To accurately replicate the discharge process, the model selects the unidirectional erosion development mode. To simplify the calculation and analysis of the irregularly shaped Tangjiashar Barrier Lake, we assume that it can be represented by trapezoids in both the horizontal and vertical directions. The dam has a height of 103 m and a top width of 300 m. The initial breach has a width of 8 m and a depth of 13 m. The model parameters are determined based on [PERSON]'s parameters [57; 58; 59], while the input data pertaining to dam features and soil conditions are presented in Table 2.