esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### OFDM-Based Opportunity Navigation  
OFDM has been widely adopted in LTE, 5G NR, and LEO satellites due to its spectral efficiency and multipath resilience. It is a promising modulation scheme for integrated space-air-ground communications [29; 30; 33; 34]. Therefore, OFDM-based opportunity navigation has become a prominent research domain. LTE opportunity navigation focuses on the time-of-arrival (TOA) delay estimation and interference mitigation. [PERSON] [52] pioneered theoretical performance bounds for LTE opportunity positioning through joint TOA and direction-of-arrival (DOA) estimation, while [PERSON] [53; 54] experimentally characterized the impact of fading environments and antenna ports on TOA estimation. [PERSON] [55] innovatively modeled inter-cell interference as Gaussian perturbations to quantify TOA accuracy degradation. Techniques such as multipath estimating delay lock loop (MEDLL) and synthetic aperture beamforming [56; 57; 58] have been developed to address multipath challenges. Additionally, channel fingerprint machine learning [59] and differential positioning frameworks [60] are used to achieve meter-level accuracy.  
The evolution of 5G NR has further expanded OFDM-based opportunity navigation capabilities. [PERSON] [61] explored carrier-phase-based localization by NR signals, whereas [PERSON] [62] and [PERSON] [63] achieved joint uplink time-difference-of-arrival (UTDoA) and angle-of-arrival (AoA) estimation through antenna arrays. [PERSON] [64] enhanced multipath robustness via ESPRIT-based joint delay-phase estimation. Tensor-based channel estimation [65], millimeter-wave beam fingerprinting [66], and machine learning-driven TOA estimation systems [67; 68] collectively constitute intelligent positioning architectures that enhance both accuracy and operational reliability.  
However, the CP is an essential part of OFDM signals, and its influence on navigation performance is seldom addressed in these existing studies. Furthermore, the relationship between navigation performance and OFDM configurations remains largely unexplored.
## 2 Materials and Methods
### Evaluation of the Measurements' Location  
Considering the place in which the measurements are carried out, the main features to examine are the space, temperature and relative humidity of the room and the presence of infrared radiation sources. In particular, the room should not be smaller than 6-9 m\({}^{2}\), air temperature should range between 18 and 25 \({}^{\circ}\)C, relative humidity is recommended in a range of 40%-70%, and the presence of infrared radiation sources should be prevented [24].  
The test was performed in the fencing hall Club Scherma Pisa Antonio Di Ciolo, located in Pisa, Italy. The fencing hall is characterised by a rectangular area of about 390 m\({}^{2}\), with a ridge height of 5.9 m and a volume of about 2000 m\({}^{3}\). It is a tensile structure with laminated wood beams and steel columns, covered with a white double membrane of PVC polyester fabric.  
Since body temperature is considerably influenced by environmental factors, during the test probes measuring air temperature and relative humidity were located inside and outside the fencing hall. In particular, the test was performed on Wednesdays and Thursdays (the days of the maximum occupancy of the fencing hall) of four different weeks during spring 2019, from 18:00 to 21:00, when the athletes were training. During the measurement period, the average ranges of air temperature and relative humidity stayed within the recommended ranges (t\({}_{\text{A,AV}}\) = 18.6 \({}^{\circ}\)C, RH\({}_{\text{AV}}\) = 67.8%).
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Parameter Optimization Algorithm Application  
_FILS15.4_ has several parameters whose values should be optimized. The following procedure describes the calculation of the score \(S(P)\):  
1. Calculate the Euclidean distance \(disF_{i}^{k}\) between the \(i\)-th average measured LQI and the \(k\)-th current fingerprint.
2. Find \(disF^{OK}\) that represents the minimum Euclidean distance against a fingerprint representing the correct room.
3. Find \(disF^{NG}\) that represents the minimum Euclidean distance against a fingerprint representing the incorrect room.
4. Calculate \(S(P)\) by: \[S(P)=A\sum_{i=1}^{N}true(disF^{OK}-disF^{NG})+B\sum_{i=1}^{N}\frac{disF^{NG}}{ disF^{OK}}+C\sum_{k=1}^{M}\underset{b\
eq c}{min}\mid F_{b}^{k}-F_{c}^{k}\mid\] (3) where \(A\) and \(B\) represent constant coefficients (\(A=10\), \(B=1\) and \(C=1\) in this paper), \(N\) is the number of the average measured LQI for the optimization, the function \(true(x)\) returns 1 if \(x>0\) and 0 otherwise. The C-term represents the sum of the minimum Euclidean distance between two different fingerprints for the same room. It intends to generate different fingerprint values for the same room.  
Moreover, as the important parameters in \(paraOpt\), \(t_{i}=10\) for the tabu period, \(\Delta p_{i}=5\) for the detection interval, and \(\Delta p_{i}=1\) for the fingerprint are adopted.
## 5 Test Results and Interpretation
### Deflections of the Main Girders  
During the bridge loading phases, the vertical deflection of the deck was obtained through topographic measurements. The vertical displacements of some selected points (P1-P18) all along the girders are considered and reported in Figure 8 for the maximum loading phases (configurations C1 and C2). In detail, for C1 all the 18 points are considered and measured, whereas for C2 only those that were nearby the north lateral span (P9-P18) are taken into account since they experienced significant vertical displacements due the load position. As can be seen from Figure 8, the experimental vertical displacements of  
Figure 7: Refined FE model of the bridge: (**a**) solid model, (**b**) shell model.  
the deck vary as a function of the truck positions; for C1 the maximum experimental displacement (of about 27.3 cm) was measured at the mid-span of the bridge deck, in correspondence of the downstream side girder (the trucks are placed eccentrically in the transverse direction). On the contrary, the two lateral spans uplift of about 5 cm (4.7 cm at the mid-length of the south lateral span). As for C2, the maximum displacement of 8.8 cm was measured on the downstream side girder in correspondence of the mid-length of the north lateral span.  
The experimental deflection of the main girders is compared with the numerical predictions that were obtained through the refined FE model. The comparison is also shown in Figure 8. As can be observed, the numerical displacements are in very good agreement with the experimental ones and the maximum measured deflections are slightly lower than the theoretical ones, revealing a positive outcome of the proof test.  
The residual deflections that were measured in the U2 configuration after the removal of loads (i.e., after C1 and after C2) are plotted in Figure 9. They are characterized by very small values, proving that the bridge almost returned to the initial configuration. Moreover, these values are largely lower than the 15% of the maximum vertical displacement that was experienced by the loaded bridge in each load configuration, as required by the Italian Standards [30] for a positive outcome of the proof test.  
Figure 8: Girder experimental deflections and comparison with the numerical predictions.  
Figure 9: Residual deflections of the deck after the loads removal.
## 2 Materials and Methods
### FORMIND Lidar Simulator  
FORMIND is also able to carry out virtual lidar campaigns for simulated forest stands. The lidar module integrated into FORMIND allows for the simulation of lidar point clouds and waveforms like the ones provided by GEDI [21]. In the lidar module, 3D space is represented by cuboidal voxels with a width of 0.5 m and a height of 1 m. To simulate the lidar waveform over a forest area, we created a tree list consisting of the generated data of a forest model and the positions and parameters of each tree. Then, a voxel representation of the forest was created so that voxels belonging to a tree and thus providing a contribution to the lidar waveform could be distinguished from empty voxels without contribution. The reflected energy of each voxel was modeled as an exponential decay function (Beer-Lambert's law) of leaf area above the voxel, and the horizontal energy distribution within the laser beam was modelled with a Gaussian function [30]. This lidar module was already tested for tropical forests via comparison with real lidar data [21].  
Since the simulated lidar waveform had to be comparable to the GEDI waveform, the spatial scale of the GEDI lidar measurements was considered in the model. To do this, a circular footprint with a diameter of 25 m was cropped at the center of each simulated hectare in the model, reducing the simulated area from one hectare to a subplot. The energy of all voxels per 1 m height layer was summed up, and the derived waveform was finally normalized to the total sum to obtain the relative energy per height layer [19]. For each of the 1280 environmental regions, 100 waveforms (one per simulated hectare of forest) were simulated at 5-year intervals over a period of 1000 years, resulting in a total of 20,000 waveforms per environmental region.
## 2 Project Overview
### Construction of the MS Monitoring System  
MS events refer to rock fractures created by an overload or the growth of existing fractures. When fractures occur, the elastic waves are captured by sensors, and data, including the occurrence time, location, and strength, can be collected by sensors. The damage to the rocks can be well reflected by the MS data. The evolution of rock fractures is tracked by the MS monitoring in real time, and the technical support for assessing rock stability during the excavation of underground caverns is provided by the MS monitoring.  
The studies are based on the Shuangjiangkou hydropower station. The Canadian ESG MS monitoring system was arranged in the underground powerhouse of the station, and its network topology is shown in Figure 3. Elastic waves released by MS events in the rock mass were captured by the preburied sensors and transmitted to the signal collector Paladin through a cable. The original waveform file, which includes rich fracture information, was transmitted to a computer equipped with a Hyperion system through an optical fiber. It was created and forwarded to the camp office via a wireless network. Then, the data were sent back to the data analysis center in Chengdu, where the signal was processed to locate microseismic events. The MS monitoring system was primarily intended to monitor the plant. Ten acceleration sensors (S1-S10 in Figure 3) with a single-sensor monitoring range of 150 m and a sensitivity of 30 V/g were deployed on the top floor of the drainage gallery. After the floors were excavated downward, some sensors were moved to the intermediate and bottom floors of the drainage gallery according to the construction progress. A single Paladin data acquisition substation can be connected to a maximum of six sensors. Therefore, two Paladin substations with synchronized times were used to connect the ten sensors. The positioning accuracy of the MS monitoring was closely related to the set wave velocity of the rock mass. According to the field blast and the positioning test, when the wave velocity is set at 5800 m/s, a minimum positioning error of 5.79 m may be attained. Other methods for performance and waveform analysis using the MS monitoring system can be found in some previous work, e.g., [28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stress Classification** & **Maximum Principal Stress** & **Rock Strength–Stress Ratio** \\  & **Level \(\sigma_{\text{m}}\) (MPa)** & **R\({}_{\text{b}}\)/\(\sigma_{\text{m}}\)** \\ \hline Very high & \(\sigma_{\text{m}}\geq 40\) & \(<\)2 \\ High & \(20\leq\sigma_{\text{m}}<40\) & 2–4 \\ Moderate & \(10\leq\sigma_{\text{m}}<20\) & 4–7 \\ Low & \(\sigma_{\text{m}}<10\) & \(>\)7 \\ \hline \hline \end{tabular} Note: R\({}_{\text{b}}\) in the table is the saturated uniaxial compressive strength of the rock (MPa), and \(\sigma_{\text{m}}\) is the maximum principal stress (MPa).  
\end{table}
Table 2: Geostress classification.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Features Extraction  
The maximum value in each wave cycle is the R wave. The time interval between two consecutive R waves is called the \(RR\) interval [28]. \(RR\) intervals can be calculated to find the heart rate, for which the unit is beats per minute (BPM). AF detection can be recognized through \(RR\) irregularities [29]. Previous research works have detected important characteristics of ECG signals to identify AF patients, including the coefficient of variance (\(CV\)) [8], the root means square of the successive differences (RMSSD) [9], and the median absolute deviation (\(MAD\)) [10]. A new measurement, called the square of the average variation (\(SAV\)), is introduced in this research. These indices are used to compare the performance of AF diagnoses.
## 4 The Integration of the Streamed and Crack Growth Signals  
Signal is defined as the crack growth signal of the UIC fatigue test and noise is defined as the streamed AE signal of the NAVAIR gearbox test. AE signals with different signal to noise ratios (\(SNR\)s) are generated by embedding the crack signal into the streamed signal. The SNRs are obtained by calculating the square of the voltage amplitude ratio [17]:  
\[SNR=\left(\frac{A_{signal}}{A_{noise}}\right)^{2} \tag{1}\]  
where \(A\) is the root mean square (RMS) voltage amplitude. The amplitude of the crack signal is adjusted with respect to the amplitude of the streamed signal in order to have \(SNR\)s in the range of 1.0 to 7.5. Signal amplitude, \(A_{signal}\) in Equation (1), represents the adjusted amplitude of the crack signal, noise amplitude \(A_{noise}\), represents the actual amplitude of the streamed signal. The methodology to combine the signals is illustrated in Figure 8 for the \(SNR\) of 7.5.  
The positions of the embedded crack signal are randomly selected. A total of 150 partial signals are produced by decomposing the 150 ms long streamed signal with 1 ms window size. A random number is selected in the range of 1 to 150 using the Matlab software (MATLAB 8.5, The MathWorks, Inc, Natick, MA, USA, 2015), and the crack signal is embedded into the position of the selected random number. The streamed signals with different embedded crack signals and \(SNR\)s are compared in Figure 9. As expected from the AE features listed in Table 2, higher frequency components (>200 kHz) are dominant in the streamed signals, while the embedded crack signals influence the lower frequency components (<200 kHz). The embedded signal becomes more difficult to identify in both time and frequency domains with the decrease of \(SNR\). It is important to note that the crack growth releases wideband signal; therefore, it is not expected to have a complete overlap of the frequency spectra of the crack signal and the background noise signal.  
Figure 7: The statistical analysis of streamed signal without crack emission (**a–c**) the AE power, peak frequency and frequency centroid of each window, and (**d–f**) normal distribution of AE power, peak frequency and frequency centroid obtained from the windowed streamed signal.
## 2 Food Security: A Global Challenge in View of Soil Pollutants and Heavy Metal Toxicants
### Smart Farming Initiatives Is the Need of the Hour
Researchers expect to grow and harvest an entire hectare of land barley without humans ever entering the field as a proof of concept for autonomous farming technology. Companies like Aerobotics [62] already have commercialized software that analyzes infrared photos taken by drones to detect unhealthy vegetation, similar to a real-life game of SIM farming in which the farmer receives a warning on their computer when a disturbed area is detected. Machine learning is constantly improving the system's ability to distinguish between crop varieties and weeds that pose a threat to the farmer. Furthermore, various pilots are hired by Micro Aerial Vehicle (MAV)-based companies to fly light aircraft equipped with multispectral cameras on data-gathering missions over vast farms around the country [63]. Planet Labs has a fleet of CubSat satellites that take weekly photographs of entire farms from space to aid in crop monitoring [64]. To cope with this new wave of data, other companies are developing analytics tools to act as farm management systems, enabling farmers to work on a variety of land sizes. Farmers' business networks can now integrate data from several farms into one large pool, giving their members access to macro-level insights that were previously only accessible to corporate mega farms.
## 2 Methodology
### Weighting Functions
All three sets of weighting functions have very similar values beyond 370 m. This clearly suggests that the present technique will fail to profile turbulence for \(z>370\) m.  
By sampling the weighting functions along the path and generating a matrix \(\mathbf{M}\), where the rows are formed from the individual sampled weighting functions, and the columns then correspond to the range where these functions are sampled, \(C_{n}^{2}\) along the path can be estimated:  
\[C_{n,est}^{2}=\mathbf{M}^{+}V, \tag{14}\]  
where \(\mathbf{M}^{+}\) is the pseudo-inverse of \(\mathbf{M}\) and \(V\) is a set of measured differential tilt variances. Equation (14) can be rewritten as:  
\[C_{n,est}^{2}=\mathbf{M}^{+}\mathbf{M}C_{n}^{2} \tag{15}\]  
Equation (15) shows a relationship between the estimated and actual values of \(C_{n}^{2}\) along the path. The matrix \(\mathbf{M}^{+}\mathbf{M}\) is in essence the influence function matrix, or the impulse response matrix. Each column of the matrix describes the response of the estimation method to a turbulence impulse at that range. It also describes how turbulence estimated at a particular location is influenced by turbulence elsewhere along the path. Figure 3 shows these influence functions at 5 different locations for the mixed path experiment. Near the apertures (Figure 3a), the profiling resolution is satisfactory, and improves between 120-220 m as the influence function starts getting narrower (Figure 3b). The resolution again starts degrading as we move closer and closer to the source end (Figure 3c). The nature of the influence functions at and beyond 370 m (Figure 3d,e) clearly suggest that the present technique fails to profile turbulence here. Figure 2: Path weighting functions of differential tilt variance for 15 different source separations in the long-range experiment. The weighting functions are for the imaging path geometries shown in Figure 1. (**a**) Sensing paths cross; (**b**) Sensing paths do not cross; (**c**) Sensing at a single aperture.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 7 Performance Analysis  
In this section, we compare the proposed technique with existing techniques in terms of the dataset, and the methodology they used to detect the network intrusion. We also reviewed the technique to detect the limitations of the literature. A detailed comparison with existing techniques is presented in Table 5. Most of the authors used the NSL-KDD, KDD99, UNSW-NB15, and AWIS dataset, while the approaches used were the Sparse autoencoder, conjugate gradient algorithm (CGA), deep belief networks, DNN, CNN, RNN, and LSTM [48, 49, 50, 51, 52, 53, 54]. Most of the research conducted used limited data, while, in [49], the performance could be improved by improving the sampling methodology. There are different types of networks attacks, and no study caters to all of them; most research focuses on the detection of binary classification while some research considers multiclass classification within a limited scope. From Table 5, it is evident that our proposed approach achieves significant results as compared to existing techniques.  
Figure 3: Loss graph of proposed deep learning model with respect to epochs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Dataset** & **Methodology** & **Results** & **Limitations** \\ \hline [PERSON] et al. [48] & NSL-KDD & Sparse Taught Learning with Sparse autoencoder & Binary Classification, 85.44\% Precision, 95.95\% Recall, 90.4\% F-measure, 88.39\% Accuracy & Implementation of an efficient NIDS is required to handle a multi-class problem \\ \hline [PERSON] et al. [49] & KDD-Cup1999 & Conjugate Gradient algorithm (CGA) & Reported accuracy of 93.2\% and 54.13\% for binary and multi-class classification, respectively & The use of the sampling method can improve the performance of the proposed technique.  
\begin{tabular}{c c c} \hline \hline  & RF Classification, Nonsymmetric deep autoencoder (NDAE), DL Stacked NDAEs & Accuracy of 89.22\%, Precision of 92.97\% Recall of 89.22\%, and F-Score of 90.76\%. & This method does not apply to handling zero-day attacks. \\ \hline [PERSON] et al. [51] & AWID, NSL-KDD & Adversarial environment reinforcement learning & Reported accuracy of 80.16\%, precision of 79.74\%, Recall of 80.16\%, F-Score of 79.40\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with Existing Techniques.  
By performing extensive experiments, we determined the optimal parameters for our proposed model: it took 3.06 s per epoch for training and validation. The proposed model was trained for 60 epochs due to early stopping and it took approximately 3 min in total to train the model.
## 4 Implementation and Experimental Setup
### Experimental Setup  
The core system components include Ubuntu 18.04.1 as an operating system (LTS version), 32 Gb memory, and Nvidia GeForce 1080 as a graphics processing unit (GPU). In addition to the core system component, we used python language for development along with Tensorflow API.
## 2 System Architecture and Components
### Energy Storage System Model
* If (SOC of \(ESS<80\%\)) \(\beta=1\) [charge ESS by charging power of ESS] else (SOC of \(ESS>80\%\)) \(\beta=0\) if (SOC of \(ESS<20\%\)) \(\beta=0\) [charge ESS by charging power of ESS] else (SOC of \(ESS>20\%\)) \(\beta=-1\)  
**Step 3**: -Check Status of PV output. * Check PV output at current _ts_, if \(PV\) output is available \(\gamma=1\), else \(\gamma=0\),  
**Step 4**: -Check New Arrival of EVs and Priority Determination. * Check newly arrived EVs in current _ts_. * Create EV set then enroll new arrival EVs in EV set. * Get EV information such as arrival and departure time, SOC, battery capacity and trip distance. * Compute priority for EVs according to EV information. * Descending sort EVs according to priority. * Choose EV with highest priority from sorted EV set. * Check the current SOC and decide participation with user's preference (charge or discharge). * Allocate numbers of charging/discharging timeslots into the building load profile. **Step 5**: -Update load profile  
* The load profile is updated. * If _ts_ number is greater than 95, finish programming unless _ts_ increased by one then go to step two.