esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Mobile Unit (MU)
### GNSS (GPS) Subsystem Design  
In the presented solution, GNSS positioning is based on GPS satellite navigation. Each navigation device (i.e., GPS receiver) can be characterized by static and dynamic navigation parameters.  
The basic static parameter is the position, e.g., expressed in geographic latitude and longitude. The corresponding measurement uncertainty is usually expressed in meters. By analogy, accuracy is the degree of veracity, while precision is related to the degree of reproducibility. This explains why it is more convenient to use statistical analysis: histogram or cumulative histogram.  
The basic dynamic navigation parameter is speed. It has been observed that some GPS modules implement various prediction algorithms, which typically reduce the cross-tracking error. Unfortunately, in some cases (e.g., sudden stop combined with weak measurement conditions), this can result in a short non-existing trajectory (over-prediction).  
All of the above parameters have a significant influence on the MU design, i.e., a short time-to-fix allows to switch off (power down) a GPS module--which significantly reduces power consumption. Eight GPS modules were tested (all listed in Table 4).  
These receivers were selected because of the variety of options for the RF signal source: integrated or external antenna and external active or passive (custom microstripped PCB) antenna. The low price and wide availability were also important factors.  
In total, eight measurement cycles were performed, all under practical operating conditions (in the worst case, not more than half of the sky hemisphere was visible). There  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Case 1** & **Case 2** \\  & **(2 km Distance)** & **(Below 1 km Distance)** \\ \hline Transmitted power [dBm] & 26 (0.4 W) & 20 (0.1 W) \\ BS antenna gain [dB] & \(+5\) & \(+7\) \\ MU antenna gain [dB] & \(-1\) & \(-2\) \\ \(d\) [km] (2) & 2 & 0.8 \\ \(d_{f}\) [m] (3) & 300 & 400 \\ \(\Delta L\) [dB] (3) & 40 & 44 \\ \(h_{1}\) [m] (4) & 15 & 18 \\ \(h_{2}\) [m] (4) & 1.5 & 0.5 \\ \(L\) [dB] (worst case) & 137 & 133 \\ \(P_{r}\) [dBm] (1) (worst case) & \(-107\) & \(-108\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Requirements for a radio subsystem design (for both cases).  
were no high obstacles or other structures in the immediate vicinity. All measurements were made with a standard frequency of 1 Hz (once per second).
## 2 Background and Motivation  
### Background  
#### 2.1.1 Object-Based Big Data Transfer Systems (OBDTS) and Data Integrity  
Figure 1 depicts the file distribution and data transfer mechanism for the state-of-the-art and OBDTS. As shown in Figure 1, we considered two files, \(\text{File}_{a}\) and \(\text{File}_{b}\), distributed across four OSTs, \(\text{OST}_{1}\) to \(\text{OST}_{4}\), and two I/O threads, \(\text{T}_{1}\) and \(\text{T}_{2}\), for transferring the objects of these files. On initiating the data transfer, as illustrated in Figure 1b, both \(\text{T}_{1}\) and \(\text{T}_{2}\) threads contend for the \(\text{OST}_{1}\) resource due to the sequential transfer mechanism. As a result, one of \(\text{T}_{1}\) or \(\text{T}_{2}\) must yield to the other thread until it finishes its task. Similarly, later in the data transfer, the \(\text{T}_{1}\) and \(\text{T}_{2}\) threads again contend for the resource \(\text{OST}_{3}\). This kind of resource competition problem results in lower data transfer rates. In contrast, as illustrated in Figure 1c, on initiating the data transfer, OBDTS identify and avoid temporarily congested storage servers by exploiting storage architecture and employing layout- and congestion-aware scheduling algorithms. Thus, as depicted in Figure 1c, the \(\text{T}_{1}\) thread is assigned to \(\text{OST}_{3}\), and the \(\text{T}_{2}\) thread is assigned to \(\text{OST}_{4}\) to avoid \(\text{OST}_{1}\) congestion. As a result, Figure 1c reveals that objects belonging to a single logical file are transferred in a non-sequential manner. Object-based big data transfer systems (OBDTS) can achieve higher data throughput than traditional big data transfer frameworks due to their enhanced scheduling algorithms [8; 9; 10].  
Transfer efficiency and reliability are two primary requirements of big data transfer frameworks. As described, OBDTS improve data transfer rates by exploiting the storage layout architecture. However, this results in out of order object transmission of the same logical file. Therefore, simple checksum-based data integrity mechanisms result in space and computational overhead. In this study, we propose a memory-efficient Bloom-filter-based data integrity mechanism. This mechanism enables the verification of data integrity at multiple levels, including object, file, and dataset.
## 4 Magnetic Suspension and Positioning Test
### Servo Bandwidth  
Figure 12a shows the closed-loop frequency responses in the tip-tilt directions. The gain and phase diagrams in both directions are similar, and it is clear that there is no difference between the dynamic characteristics in each tip-tilt direction. The servo bandwidths are 1180 Hz and 1210 Hz in the \(\theta_{x}\) and \(\theta_{y}\) directions, respectively. The design target of 1000 Hz was achieved with simple PID controllers without having to use notch filters to reduce the signals induced by higher vibration modes.  
Figure 12b shows the Bode diagram for the axial direction. The system has a bandwidth of 220 Hz, which is more than sufficient for focus correction. Using the magnetic suspension, it is possible to suppress the elastic modes generated by conventional elastic guideways, and the servo bandwidth can be extended to a sufficiently high frequency with a simple control system and without complicated tuning.  
Figure 11: Long stroke positioning of the FSM with the magnetic suspension: (**a**) Inclination in the \(\theta_{x}\) direction; (**b**) inclination in the \(\theta_{y}\); (**c**) displacement in the \(Z\) direction.  
Figure 10: Frequency responses of the FSM using the magnetic suspension: (**a**) Bode plot from the VCM input current (\(I_{x}\)) for \(\theta_{x}\) and \(\theta_{y}\); (**b**) Bode plot from the VCM input current (\(I_{z}\)) for \(z\).
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Optimization Criteria
#### 4.3.4 Energy Expenditure  
Energy consumption is estimated following a method akin to that proposed by [PERSON] [42]. The power demand for a propeller-driven UAV is represented as follows:  
\[P_{r}=F_{drag}v_{a}=\sqrt{\frac{2(mg)^{3}}{\rho S}\frac{C_{D}^{2}}{C_{L}^{3}}}. \tag{21}\]  
In this model, \(m\) represents the aircraft's mass, \(g\) is the gravitational acceleration, and \(F_{drag}\) corresponds to the drag force. Coefficients \(C_{D}\) and \(C_{L}\) refer to the aerodynamic drag and lift, respectively, while \(\rho\) indicates air density and \(S\) stands for the aircraft's wing area [42; 44]. Alternatively, energy requirements can be estimated using an interpolated lookup table derived from experimental data when available. The experiments conducted in this study rely on the latter method. Consequently, the energy expenditure is expressed through the following criterion function:  
\[c_{e}=s_{fe}\Big{(}P_{ctrl}t_{max}+\int_{0}^{t_{max}}f\big{(}z(t),\theta(t),v_{ a}(t),\mathbf{E}\big{)}dt\Big{)}. \tag{22}\]  
Here, \(P_{ctrl}\) represents the constant power required by the controller and auxiliary systems, while \(f(\cdot)\) serves as an approximation function. Variables \(z(t)\), \(\theta(t)\), and \(v_{a}(t)\) correspond to altitude above ground level (AGL), pitch angle, and airspeed, respectively. The maximum flight duration is denoted by \(t_{max}\), with \(s_{fe}\) acting as a scaling parameter. The reference energy data are stored in \(\mathbf{E}\), and the estimated flight time is indicated by \(t\). In scenarios where wind effects are neglected and \(v_{a}\) remains constant, \(t_{max}\) is determined by dividing the total path length by \(v_{a}\).
## 4 Magnetic Suspension and Positioning Test
### Stiffness in the Tip-tilt and Axial Directions  
The passive stiffness of the magnetic suspension in the tip-tilt and axial directions were identified using one-degree-of-freedom mass-spring-damper models and the frequency responses to displacements in the theses directions from the input currents to the VCMs, as shown in Figure 10.  
The natural frequencies in the \(\theta_{x}\) and \(\theta_{y}\) directions were almost the same, being at 26.0 and 25.7 Hz, respectively. The natural frequency in the axial (Z) direction was 22.0 Hz. The designed mass (0.216 kg) and the inertia moment (\(1.23\times 10^{-4}\) kgm\({}^{2}\)) of the supported part, and the measured natural frequencies were used to identify the stiffness. The identified stiffness in the tip-tilt and axial directions were 3.2-3.4 Nm/rad and 4.1 N/mm, respectively. These identified values are almost the same as the design values given in Section 3.2.  
Furthermore, no resonant frequency due to elastic vibration modes can be seen up to 2 kHz. The measured interference between the tip-tilt movement shown in the gain plots is less than \(-34.4\) dB. The mirror system can be regarded as having a simple one-degree-of-freedom vibration system without any interference and can be easily controlled.  
Figure 9: Startup response of the FSM using the magnetic suspension: (**a**) Translation in the X, Y, and Z directions; (**b**) inclination in the tip-tilt (\(\theta x\) and \(\theta y\)) directions.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### All Features Together except Hu Moments  
When combining all extracted features, except for Hu moments, we observed nearly identical results. Although the RF method showed slightly weaker performance, the GBM exhibited a slight improvement, as shown in Table 14.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**SVM** & precision & 51.11 & nan & 44.81 & 45.61 & 43.13 & 46.92 \\  & recall & 51.11 & 46.43 & 47.01 & 44.24 & 44.56 & 47.21 \\ \hline
**RF** & precision & 60.12 & 63.80 & 68.85 & 71.04 & 76.32 & 78.20 \\  & recall & 56.50 & 61.36 & 68.51 & 70.54 & 75.99 & 77.94 \\ \hline
**KNN** & precision & 47.17 & 50.11 & 51.05 & 56.90 & 59.93 & 64.53 \\  & recall & 47.85 & 50.10 & 51.01 & 57.36 & 60.12 & 64.51 \\ \hline
**ANN** & precision & 52.65 & 58.04 & 56.01 & 57.74 & 61.72 & 70.20 \\  & recall & 51.55 & 57.65 & 56.46 & 56.34 & 61.47 & 70.15 \\ \hline
**NB** & precision & 53.71 & 58.41 & 55.06 & 55.48 & 55.51 & 56.43 \\  & recall & 50.24 & 56.90 & 55.14 & 54.64 & 54.17 & 54.80 \\ \hline
**GBM** & precision & 56.68 & 61.64 & 64.24 & 69.12 & 74.47 & 77.74 \\  & recall & 54.92 & 59.83 & 62.81 & 68.48 & 74.13 & 77.45 \\ \hline \hline \end{tabular}
\end{table}
Table 11: LBP features’ results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**KNN** & precision & 24.62 & 34.60 & 37.43 & 38.37 & 40.59 & 44.83 \\  & recall & 25.99 & 32.41 & 34.55 & 37.36 & 40.24 & 44.17 \\ \hline
**ANN** & precision & 17.15 & 42.36 & 44.65 & 35.79 & 49.34 & 54.05 \\  & recall & 15.45 & 42.35 & 29.90 & 37.17 & 47.38 & 48.33 \\ \hline
**NB** & precision & 54.77 & 61.67 & 62.09 & 58.64 & 59.69 & 59.21 \\  & recall & 54.75 & 58.64 & 58.96 & 58.73 & 59.29 & 58.87 \\ \hline
**GBM** & precision & 50.28 & 60.28 & 67.56 & 73.34 & 76.17 & 78.62 \\  & recall & 50.91 & 58.67 & 65.76 & 72.79 & 75.56 & 78.43 \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Generation Traffic Tools  
To generate the training dataset comprising both normal and DDoS flows, we utilized a variety of tools. The Ping tool was used to test the reachability of a host on an IP network, while the iPerf tool served as an active measurement mechanism for determining the maximum possible bandwidth on IP networks. The SimpleHTTPServer Python Module was employed to create a primary web server with minimal effort, and the Hping3 tool allowed us to generate various types of attacks. These tools enabled us to effectively simulate a range of normal and DDoS traffic scenarios, providing a diverse and realistic dataset for training a deep learning model to detect low-rate DDoS attacks in software-defined networking (SDN) environments.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
###### Abstract  
A Global Navigation Satellite System (GNSS) is widely used today for both positioning and timing purposes. Many distinct receiver chips are available as Application-Specific Integrated Circuit (ASICs) off-the-shelf, each tailored to the requirements of various applications. These chips deliver good performance and low energy consumption but offer customers little-to-no transparency about their internal features. This prevents modification, research in GNSS processing chain enhancement (e.g., application of Approximate Computing (AxC) techniques), and design space exploration to find the optimal receiver for a use case. In this paper, we review the GNSS processing chain using SyDR, our open-source GNSS Software-Defined Radio (SDR) designed for algorithm benchmarking, and highlight the limitations of a software-only environment. In return, we propose an evolution to our system, called Hard SyDR to become closer to the hardware layer and access new Key Performance Indicator (KPI)s, such as power/energy consumption and resource utilization. We use High-Level Synthesis (HLS) and the PYNQ platform to ease our development process and provide an overview of their advantages/limitations in our project. Finally, we evaluate the foreseen developments, including how this work can serve as the foundation for an exploration of AxC techniques in future low-power GNSS receivers.  
benchmarking; computational complexity; field-programmable gate array (FPGA); global navigation satellite system (GNSS); open-source software +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Effects of Wind and Earthquake Excitations on Wind Turbine Tower
### Wind Load Acting on the Tower  
According to the load code for the design of building structure GB50009-2012 [28], the characteristic value of the wind load can be calculated with the following equations:  
\[\omega_{k}=\beta_{z}h_{s}h_{z}\omega_{0} \tag{8}\]  
\[\beta_{z}=1+2\phi_{f}I_{10}B_{z}\sqrt{1+R_{f}^{2}} \tag{9}\]  
\[R_{f}=\sqrt{\frac{\pi}{6\zeta_{1}}\frac{x_{1}^{2}}{\left(1+x_{1}^{2}\right)^{ 4/3}}} \tag{10}\]  
\[x_{1}=\frac{30f_{1}}{\sqrt{k_{w}\omega_{0}}} \tag{11}\]  
\[B_{z}=k_{fr}H^{a1}\rho_{x}\rho_{z}\frac{\phi_{1}(z)}{\mu_{z}} \tag{12}\]  
\[\rho_{z}=\frac{10\sqrt{H+60e^{-H/60}-60}}{H} \tag{13}\]  
where \(\omega_{0}\) is the basic wind speed at a height of 10 m, \(\beta_{z}\) is the wind-induced vibration factor, \(\mu_{s}\) is the wind load shape coefficient and \(\mu_{z}\) is the wind pressure height coefficient, \(g_{f}\) equals to 2.5, \(I_{10}\) is the nominal turbulence intensity, \(B_{z}\) is background component of fluctuating wind load, \(R_{f}\) is the resonance component of the fluctuating wind load, \(\zeta_{1}\) is the damping ratio and is equal to 0.03 in this paper, \(f_{1}\) is the first-order natural frequency,  
Figure 1: Computational locations for \(V_{1}\) and \(V_{2}\).  
\(k_{w}\) is the surface roughness correction coefficient and is equal to 1.0 in this paper, \(H\) is the height of the tower, \(\rho_{x}\) is the horizontal correlation coefficient and equals 1.0 due to the small width of the windward side of the tower, \(\rho_{z}\) is the vertical correlation coefficient, and the \(k_{fr}\), \(a_{1}\), \(\phi_{1}(z)\) can be determined according to GB 50009-2012 [28].  
The lateral static force is applied along the height of the tower as a distributed load. The force of the tower section at height \(i\) owing to the wind can be calculated as [28]:  
\[F_{i}=\omega_{k}A_{i} \tag{14}\]  
where \(F_{i}\) is the wind force of the tower section at height \(i\) and \(A_{i}\) is the wind pressure area of the section.
## 3 Experimental Setup  
### Radar System Equipment  
In this study, we utilized the ADAR6902 from Analog Devices as the millimeter-wave FMCW radar transceiver MMIC. This device features an all-digital PLL based on a Rotary Traveling Wave Oscillator (RTWO) architecture. The RTWO circuit facilitates lower phase noise [17], which is crucial for precise clocking, while simultaneously supporting ultra-high frequencies and broader bandwidths, despite the inherent design complexities. Using this MMIC, we developed a millimeter-wave FMCW radar sensor system named \"miRRadar CbM\", which features a narrow field of view (FoV) of \(\pm 3^{\circ}\) to accurately detect specific machine vibrations.  
To achieve precise sensing, the system employs a single transmitter and receiver in a 1T1R RF configuration, although the MMIC itself supports three transmitters and four receivers. Figure 4 shows the system block diagram of the sensor. In Figure 5, the left side (a) shows the hardware, and the right side (b) displays the software GUI (version no. 2021_0611_2150 Ver0.1) used for measurement. Table 1 presents the sensor system specifications.  
Figure 4: Sensor system block diagram.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 7 Laser Scanners Technique
Although technology has proven to be beneficial for SHM, the limitations of technology prevent it from being more widely implemented in civil engineering. The great limitation of the method is the laser methods cannot detect the internal cracks and damages in concrete [75], and laser methods need manual processing of a large amount of data, which requires time and training. As laser beams may not be applied in parallel because of mistakes in installation, corrections for the laser alignment algorithm (in the deep learning approaches) is required to increase the accuracy of the cracks detecting [77]. On the other hand, these limitations were decreased. With the recent developments in AI/ML and high-performance and supercomputers, these limitations can potentially be addressed [69]. A combination of acoustic and laser techniques enables reliable measurements of internal defects.
## 4 Model Development
### Developing ANN Model and Generating New Datasets
of the input variables in predicting the RSI values. It should be mentioned that the method considers the absolute values of the weights and does not present the relational directions of variables with respect to the output variable [53]. However, many researchers have adopted it widely due to its accuracy in evaluating the contributions of predictor variables in the network [24, 54, 55, 56, 57, 58, 59]. The relative importance results are shown in Figure 5. It is noticed that AC% and Va% are the most essential variables in predicting the RSI, with 35.6% and 33.5% contribution rates, respectively. F% has a moderate contribution rate of 22.4% compared to Ab%, which has the lowest rate of 8.5%. The connection weight method by [PERSON] and [PERSON] [60] was adopted to assess the impact of the predictor variables on the predicted RSI value, as illustrated in Figure 6. The results revealed that AC positively impacts the RSI value, while the other variables (F, Ab, and Va) do the opposite. These results, however, prove that the generated ANN model effectively considers the significance of each input variable while predicting the RSI. Consequently, the ANN model with two hidden neurons was utilized to generate new datasets of various combinations of variables. First, the input variables were kept constant at their original mean values. The new values of each input variable were then interchanged at a regular interval and within their upper and lower limits, while keeping the values of the rest of the variables constant at their average. This approach was repeated independently for all input variables. The new combinations of datasets were then utilized as predictor data points to predict their corresponding output values of RSI using the ANN model. Finally, the obtained datasets were combined with the raw dataset to be used again for developing a simple empirical equation of RSI using the GEP software. Figure 5: Contributions of variables to RSI. Figure 6: Impact of input variables on RSI.
## 3 Experimental Approach
The DMT model is a variation of the Hertz model that also takes into account adhesion forces [44], according to Equation (3):  
\[F=\frac{4}{3}E^{*}\sqrt{R\left(d-d_{0}\right)^{3}}+F_{adh} \tag{3}\]  
where \(F\) is the applied force, \(R\) is the tip radius, \(d_{0}\) is the initial probe-surface contact point, \((d-d_{0})\) is the deformation value at a given force, \(F_{adh}\) the maximum adhesion force and \(E^{*}\) is the effective elastic modulus. \(R\) was set as the curvature radius of the probe, estimated from calibration measurements performed using a calibration grating TGT1 from NT-MDT. The effective elastic modulus \(E^{*}\) is defined as:  
\[\frac{1}{E^{*}}=\left[\frac{1-v_{sample}^{2}}{E_{sample}}+\frac{1-v_{tip}^{2} }{E_{tip}}\right] \tag{4}\]  
\(E_{sample}\), \(\upsilon_{sample}\) and \(E_{tip}\), \(\upsilon_{tip}\) being the [PERSON]'s modulus and the Poisson ratio of the sample and the tip, respectively. For the diamond-coated probes used in this study, constant values \(E_{tip}=1140\) GPa and \(\upsilon_{tip}=0.07\) have been used [45]. The [PERSON]'s modulus of the sample \(E_{sample}\) is derived from the fitting analysis performed at low loading force stages of the retraction curve (see Figure 1), that correspond to the elastic regime. The DMT  
Figure 1: A typical force–distance curve measured on PEN during approach (green line) and retract (red line) of AFM probe to and from the sample surface. model assumes a full elastic response from the sample as well as any Hertz theory-based model [38; 42; 43; 44]. The following experimental procedure was set up to perform the nanoindentation measurements. First, the sample surface was scanned in AFM contact mode to acquire topographic images over an area of 5 \(\times\) 5 \(\upmu\)m\({}^{2}\). To avoid damages and alteration of the investigated surface during the scanning, a set-point value for the approach of the probe to the sample was chosen to have a low magnitude of interaction between the tip and the surface. Thereafter, four different force-distance curves were recorded in force spectroscopy mode over a 2 \(\times\) 2 grid of points whose center corresponds to the center of a preselected 1 \(\times\) 1 \(\upmu\)m\({}^{2}\) area inside the 5 \(\times\) 5 \(\upmu\)m\({}^{2}\) area scanned in contact mode. Finally, the same area was scanned in AFM semi-contact mode to locate the exact position of the four indents, which are then imaged over 1 \(\times\) 1 \(\upmu\)m\({}^{2}\) areas to acquire images with the highest resolution. The value of projected area \(A_{P}\) for any of the intends is calculated from the semi-contact AFM images using the indentation analysis function of the free SPM data analysis software Gwyddion [38; 46]. The procedure was then repeated in different areas of the sample, to obtain at least 20 different force-distance curves and intends, and hence at least 20 values of hardness and Young's modulus for any sample.