esa-sceva/satcom-chunk-collection huggingface dataset:
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results  
### Experimental Data  
Aiming at conducting a dynamic analysis, we performed prediction tests on heterogeneous data, including traffic flow data and weather information as follows:  
* Traffic data: PEMSD4 and PEMSD8 are two real-world datasets from the Caltrans Performance Measurement System (PeMS). The detailed information is shown in Table 1.
* Weather information: We collected weather data for the San Francisco Bay area and for San Bernardino during the periods shown, selected the visibility metric, and then integrated the data into the test as a special factor.  
In order to facilitate the test in the internal convolution network of the ACB, we preprocessed the data in advance. First, we classified the data according to whether they referred to the same intersection or the same lane. Then, we obtained the total traffic flow, average occupancy rate of lanes, and average speed every five minutes. According to the road speed limit and actual situations, we removed unreasonable data, smoothed burr points, and utilized the sliding window method with fixed length to estimate missing data. To verify the comparison of the accuracy of traffic flow prediction, we split each dataset and selected 47 days as the training set and 15 days as the test set.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Risk Parameters for the Environment Outside the Cableway
#### 2.3.2 Groundwater Level and Humidity  
Groundwater levels or humidity levels may vary at different locations in the channel, and high levels of water or humidity can increase the level of moisture in the cable channel, which can affect the insulating properties of the cables. Areas close to rivers, with rich groundwater, or with high humidity are more likely to deteriorate the insulation properties of cables and increase the risk of corrosion.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Comparison Against Related Research
#### 6.8.2 Grouping of Gestures  
Another important aspect for comparing the different implementations is how many gestures are used for calculating the performance of the classifiers. Some of the papers are not reporting their metrics for different grouping sizes of gestures, even though this is also an important metric, regarding the overall security of the continuous authentication system. Standing out from other related research, [PERSON] et al. in [1] explains the consequences of grouping gestures and proposes techniques for circumventing the impact on security, by not calculating an average score. In their work, three out of seven consecutive gestures have to be recognized as valid input, with a timeout resetting the number of valid ones. In our work, we present the performance of each trained classifier with single gestures as well as with a number of multiple combined inputs and point out the pitfalls introduced with grouping gestures.  
Figure 8: ROC multiple gestures for position, accelerometer and timestamp data with gesture lengths bigger than 50—4 hidden states and 4 mixtures.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Setup
#### 4.1.3 Cse-Cic-IDS2018 Dataset  
CSE-CIC-IDS2018 [28] provides realistic attack scenarios, including DoS, DDoS, Brute Force, Botnet, Web Attacks, and infiltration. However, certain attack categories, such as infiltration and Web Attacks, suffer from severe data imbalance, making them the key targets for augmentation.  
**Dataset Splitting:** Given the large-scale nature of CSE-CIC-IDS2018, we apply a two-stage stratified sampling strategy:First, we extract 10% of the entire dataset, ensuring that the class distribution remains proportional to the original dataset.
* Then, we allocate 70% of this subset for training and 30% for testing. This strategy maintains the dataset's class imbalance while significantly reducing computational overhead.
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
## 4 Flow Field and Pressure Fluctuation  
### Flow Field Simulation Results  
The accuracy of flow field computation is important as it determines the accuracy of subsequent noise calculations. Figure 11 presents the pressure field in the XZ plane for three flow velocities: 3 m/s, 5 m/s, and 8 m/s. Meanwhile, Figure 12 depicts the pressure field in the XY plane. The maximum pressure mainly occurs at the leading edge of the fin and rudder. The minimum pressure occurs on the rear surfaces of the rudder and fin sides. As the inlet velocity increases, the pressure on the fin and rudder gradually increases.  
Figure 13 presents the velocity field and streamline in the XY plane for three flow velocities: 3 m/s, 5 m/s, and 8 m/s. Meanwhile, Figure 14 depicts the velocity field and streamline in the XZ plane. The Reynolds number is calculated based on the maximum length of the fin and rudder. After comparison, it can be observed that there is similarity in the regions exhibiting significant velocity gradients. Apart from variations in the magnitude of the flow velocities depicted in the images, a higher flow velocity leads to a more pronounced level of turbulence in the flow field. An effect of the fin and rudder on the flow field can be observed, leading to the formation of a distinct wake region.  
The vorticity distributions for inlet velocities of 3 m/s, 5 m/s, and 8 m/s are shown in Figure 15. It can be observed that the tip vortex is formed at the top of the fin, the horseshoe vortex is formed at the bottom of the fin, and vortex separation occurs at the tail edge of the rudder. These flow regimes are also involved in the flow around a surface-mounted finite-length airfoil [16]. At low flow velocity, the formation of a horseshoe vortex is not obvious in the figure, but an obvious horseshoe vortex is formed when the flow velocity increases. The trailing edge turbulence also becomes more severe when velocity increases. These vortices are generally considered to be the main source of flow-induced noise.
## 2 Materials and Methods
Moreover, it simplifies swift decision-making and responsive actions by allowing stakeholders to promptly address any vibration-related concerns that may arise. \[a_{w}(t_{0})=\left[\frac{1}{\tau}\int_{t_{0}-\tau}^{t_{0}}a_{w}^{2}(t)dt \right]^{\frac{1}{2}} \tag{3a}\] \[\text{MTVV}=\max[a_{w}(t_{0})] \tag{3b}\]  
where  
\(a_{w}(t)\) is the weighted value of vibration acceleration as a function of time;  
\(t_{0}\) is the observation time;  
\(\tau\) is the integration time, recommended to be \(\tau=1\) s.  
Defined as the highest value of RMS acceleration within the time interval, the MTVV method stands out for its ability to capture crucial peak vibration data that may not be as effectively captured by traditional methods [9]. While the peak transient vibration value excels in highlighting the maximum vibration intensity, it falls short in evaluating ongoing vibration levels comprehensively and may disproportionately emphasize short-lived events that have minimal lasting effects. Therefore, by incorporating the MTVV method into vibration monitoring practices, a more nuanced understanding of vibration dynamics can be achieved, enabling stakeholders to make informed decisions based on a thorough assessment of both peak and sustained vibration levels. The appropriate setting of measuring equipment is a very important element of test preparations, since it affects the measuring accuracy. Different standards (see, e.g., [13; 23; 24; 25; 26]) describe different approaches for locating the place for measurements. According to [24], for the ground-transmitted vibrations, the measurement disc should be located in the middle part of the floor within a distance of 1/3 of the length and 1/3 of the width of the floor. Moreover, the measuring points should be installed inside at least one room on the top story [13]. Another issue is choosing the appropriate accelerometers and the means of connecting them to the floor. The standard [24] describes what kind of disc should be used during the measurements of vibrations. The steel measuring disk should have a dimension of at least 30 cm, and the mass of such a disk should be of at least 30 kg. When recording measurements, it is important that the signal is recorded from 1 Hz to about 120 Hz, so that the 80 Hz frequency band can be included in the analysis after applying a low-pass filter [13]. Referring to the literature, in most of the cases, vertical direction plays a decisive role; however, it is important to record vibrations in three directions simultaneously (one vertical and two horizontal). The sensors should be attached to the disk in this way, such that the connection is stiff and does not influence the measurements [13]. In the case of the present study, three single-axis PCB M622B01 accelerometers were used for measuring purposes.
## 4 Discussion  
This work has evaluated the altimetry performance of the GEROS experiment on board the International Space Station. It is the natural continuation of the analysis carried out in [6], including:  
1. the use of the Cramer-Rao bound has been used to assess the performance of an ideal instrument performance, including now more directive antennas,
2. ionospheric effects,
3. the ISS orbit height decay rate, and
4. multi-path propagation due to reflections in the ISS structure, including diffraction in curved surfaces. It has been shown that there are up to 3 rd order rays (2 reflections), with delays spanning up to \(\sim\)150 ns and \(\sim\)300 ns for the 2 nd and 3 rd order rays. These coherent reflections can be mitigated as suggested in [18], but it will have an impact of the scatterometry observables over land and the cryosphere.  
In addition, the four possible combinations to get the dual-frequency observables needed to correct for ionospheric effects has been assessed quantitatively, concluding that, despite the cGNSS-R has a more graceful degradation because of the higher SNR, the iGNSS-R performs better for the antenna patterns considered. However, the directivity values are in the border line, and a small decrease in the directivity value, translates into a quick degradation of the performances. Assuming 1 s incoherent integration time, for a nominal instrument with errors, the altimetric rms error is smaller than 70-90 cm at nadir, and smaller than 11-17 m at the swath edge (250 km) for U\({}_{10}\)\(<\)\(\sim\)9 m/s, and for any orbital height h\({}_{\text{ISS}}\) = 330-460 km.
## 4 Results
### Raw Speaker Verification Performances  
Among all the verification tasks, all the general results are averaged by speaker. Relevant discrepancies or peculiarities among the three categories MFCC, RASTA and merged will be outlined in the Discussion. Table 3 displays the average performances of the classifiers on the raw dataset as a baseline, reporting balanced accuracy, sensitivity, and specificity results. Sensitivity, or \"class 1 recall\", details how many samples belonging to the main class (speaker) were correctly
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 4 Relationships Necessary for the Application of the Method
### EV Consumption  
#### 4.2.1 Charging Periods  
Data from 32,014 charging sessions were collected for more than 6 years, spanning from 1 June 2016 to 31 August 2022. Each charging session record contains the start and end time stamps of the session (noted \(ST\) and \(ET\), respectively), the identification number (\(ID\)) of the charging point, the badge number of the user and the energy that has been supplied to the EV. The transaction duration, \(ET\)-\(ST\), is denoted \(TD\).  
Figure 4 illustrates the distributions of the start and end times of the dataset. It is observed that the start time of the charging sessions is statistically concentrated around 8 a.m. (start of work hours), lunch time and early afternoon (after 4 p.m., when the business trips are terminated). There are also three main periods during which the majority of the charging sessions terminate. The first one is at 9 a.m. when the service cars that have been connected the day before are disconnected to be used for business trips, the second one is after lunchtime and the last period is at the end of the working day (around 5 p.m.) when employees leave the center. The average duration and energy consumption of each charging session were 11.3 h and 17.22 kWh, respectively, and a total of 551 MWh of electricity was consumed.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 7 Miniaturized Array Optimization: Results and Discussion
### Thirty-Two-Microphones, [2, 6.4] kHz, Array 25 \(\times\) 25 cm\({}^{2}\)  
The first scenario optimizes a 0.25 \(\times\) 0.25 m\({}^{2}\) 32\(-\)microphone array for a 4.4 kHz bandwidth. After 100 iterations, the cost function converges as shown in Figure 12. The corresponding irregular array geometry has an aperiodic structure with variable microphone spacing tailored for the acoustic parameters.  
We tried to reduce the planar array aperture (\(u\) and \(v\) range) to adjust the FOV (field of view) and to increase the number of iterations. We tested this full setting:  
* \(L\) = 25 cm;
* \(N^{\circ}\) of microphones = 32 mic;
* \(K\) = 31 (FIR length);
* \(u\in[-1.5;1.5]\);
* \(v\in[-1.5;1.5]\);
* \(N^{\circ}\) of iterations = 100;
* Bandwidth = [2000, 6400] Hz.  
As expected, low-frequency performance is improved relative to the bandwidth [2; 6.4] kHz 32\(-\)elements array. The directivity comparison (Figure 13a) with a 0.21 \(\times\) 0.21 m\({}^{2}\) prototype in the bandwidth [0.5, 6.4] kHz indicates better sensitivity below 4 kHz. This demonstrates the potential for substantial miniaturization through optimization over the frequency bandwidth. The 32\(-\)microphone design retains 15 dB (Figure 13b) robustness, with improved low-frequency gains offsetting minor high-frequency trade-offs. The beam patterns in Figure 14 verify directional selectivity and sidelobe suppression within the band.  
The beam patterns in Figure 14 show low sidelobes within the band. However, some aliasing emerges at higher frequencies due to the reduced aperture size. The 10 dB directivity in Figure 13a confirms directional sensitivity is retained over most of the band. Figure 13b indicates above 15 dB robustness to standard fabrication imperfections. In this first simulation, the performance predictions verify that a \(\approx\)4\(\times\) footprint reduction of the array surface from the 0.5 m _Dual Cam_ design is plausible with tolerable trade-offs.  
Figure 12: Simulation results for [2; 6.4] kHz optimization of a 32\(-\)elements 0.25 m acoustic array: (**a**) cost function convergence over 100 iterations, (**b**) optimized 32\(-\)microphone 0.25 m array layout.  
Figure 13: (**a**) Directivity and (**b**) white noise gain metrics confirm reasonable performance across the [2; 6.4] kHz band from the 0.25 m array.
## 4 Performance Analysis
### C. Average Ergodic Channel Capacity
\(E\left[h_{f,i}\right]=\int_{0}^{1}h_{f,i}f_{h_{f,i}}\left(h_{f,i}\right)dh_{ f,i}=\left(\frac{z_{i}}{1+z_{i}}\right)^{k_{i}}\); \(E\left[h_{p,i}\right]=\int_{0}^{A_{\text{mod},i}}h_{p,i}f_{h_{p,i}}(h_{p,i})dh _{p,i}=\frac{A_{\text{mod},i}\sigma_{\text{mod},i}^{2}}{1+z_{\text{mod},i}^{2 }}\); \(h_{a}\) is normalized, \(E[h_{a,i}]=1\). Simplifying Equation (50), the following relationship can be obtained:  
\[\frac{P_{t}^{2}}{\sigma_{n}^{2}}=\left[\frac{1+\epsilon_{\text{mod},i}^{2}}{A _{\text{mod},i}\epsilon_{\text{mod},i}^{2}}\left(\frac{z_{i}+1}{z_{i}}\right) ^{k_{i}}\right]^{2}u_{i}=B^{2}u_{i} \tag{51}\]  
Putting the above equation into Equation (18), we can obtain the following relationship between \(\gamma_{i}\) and \(u_{i}\):  
\[\gamma_{i}=\left|h_{i}\right|^{2}P_{t}^{2}/\sigma_{n}^{2}=B^{2}u_{i}h_{i}^{2} \tag{52}\]  
According to Equation (52), we can use the average SNR to derive the end-to-end link ABER. The specific derivation process of ABER is similar to Section 4, and we will not go into details here. We directly give the closed expression of the \(N\)-hop AF FSOC link ABER represented by the average SNR:  
Figure 8: Average bit error rate (ABER) for \(N\)-hop FSOC system with different modulation orders under light fog. \[P_{b}=\frac{\delta}{2\Gamma(p)}\prod_{i=1}^{N}\frac{\varepsilon_{\text{mod},i}^{2} z_{i}^{k_{i}}}{\Gamma(\alpha_{i})\Gamma(\beta_{i})}\sum_{l=1}^{n}I_{2N+2,4N+1}^{4N,2} \left[\frac{N}{q_{i}}\prod_{i=1}^{N}\psi_{i}\left(\frac{\alpha_{i}\beta_{i}}{A_{ \text{mod},i}B\sqrt{u_{i}}}\right)^{\frac{2l_{i}}{N}}\right](1,1,1),(1-p,1,1), J_{1} \tag{53}\]  
The closed expression of single-hop FSOC link ABER represented by average SNR is as follows:  
Figure 3:0  
Figure 9 demonstrates the relationship between ABER and average SNR of each hop in \(N\)-hop FSOC system, which considers CSI-assisted relays and light fog weather. Figure 9 shows the same conclusion as mentioned above: as the number of hops or the average SNR of each hop increases, the ABER of the end-to-end link shows a downward trend, and the system performance improves. Figure 10 shows the relationship between average ergodic channel capacity and transmitted power of the \(N\)-hop FSOC system under LF and MF conditions, which considers CSI-assisted relays. As we expected, the average ergodic channel capacity of the \(N\)-hop FSOC system increases with the number of hops, and the average ergodic channel capacity of the LF channel is larger than that of the MF channel. Figure 3:1 Average bit error rate (ABER) versus average SNR of each hop for \(N\)-hop FSOC system under light fog.
## 3 Design
### Optical Design of MEPI
#### 3.2.2 Result of Design  
The system consists of a pre-common-aperture objective lens group, an aperture-divided imaging lens group, and a post-polarization component. The polarization information of each channel light is different, and the wavelength is the same (the influence of the polarization element on the optical system can be regarded as a parallel glass plate, so no polarization element is added in the optical design). In the design process, through Table 3, we first determine the focal length of the front and rear systems, which are 1033 mm and 235 mm, respectively. For these two systems, we adopt an independent design strategy and use ray tracing to optimize their optical structures. In the combination process, the combined system is constrained and optimized based on the known parameters to ensure that the system meets the design requirements. Finally, a four-channel optical layout with an F-number of 7.6 is obtained, as shown in Figure 7.  
Table 4 shows the eccentric distance of each channel in the four systems. The eccentric distance is defined as the distance deviation between the optical axis of the subsystem and the central axis of the main system along the x-axis or y-axis direction, which determines the spatial distribution of the subsystem. Since the MEPI requires imaging in the spectral range of 400-885 nm, the lens material must be able to transmit the corresponding band. We continuously simulated different groups of materials in ZEMAX and finally obtained the optimal solution through hammering and global optimization. Tables 5 and 6 show the relevant data of the optical lens of the MEPI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel** & **X-Axis Eccentric Distance** & **Y-Axis Eccentric Distance** \\ \hline
1 st subsystem & 15 & 0 \\
2 nd subsystem & 0 & \(-\)15 \\
3 rd subsystem & \(-\)15 & 0 \\
4 th subsystem & 0 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Eccentric distance of aperture group.  
Figure 7: MEPI optical system.
# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## Security Algorithms with Lightweight Security
### System Topology
#### 5.1.3 Primary and Secondary Users (mHealth Nodes)  
Both primary and secondary nodes are connected to their respective base stations. The secondary user uses a sensing algorithm to detect the presence of a primary user's transmission. If the transmission is free, it sends an encrypted message to SUBS. After the message has been decoded and detached from the signature, SUBS sends the SU request message to the PUBS through an established control channel. This flow is depicted in Figure 6.  
Figure 5: Security system architecture. Adapted from Reference [22].  
In this system architecture, member nodes are initially registered as authenticated users using the digital signature to their respective network. Nodes communicate with each other and the base stations using encryption techniques to ensure secure communication in CRNs.
## Appendix A Assessment of Individual OIs
### Assessment Method  
The goal of individually studying these trajectory-related OIs is to assess the respective climate mitigation potential in terms of ATR20, and the effects on non-climate KPIs such as fuel consumption, flight time, and DOC. The assessment approach is displayed in Figure 11.  
Based on the defined reference missions, OI implementation scenarios representing the trajectory-based changes to these missions are defined, i.e., reduced cruise altitudes, adjusted speeds, or improved lateral routings. The respective trajectories are then re-modeled by applying either DLR's Trajectory Calculation Module (TCM) as well as ITU's Trajectory Generation Tool (TGT), or in the case of trajectory optimization, DLR's Trajectory Optimization Module (TOM) and ITU's Trajectory Optimization Tool (TOT) [17; 46; 47; 82; 83; 84]. All above-mentioned tools rely on BADA4 aircraft performance data [85] and incorporate mission-specific wind data from weather forecast models. Results are combined with emission indices from the ICAO Engine emission data bank [86] to estimain accordance with fuel-flow correlation methods [87; 88]. To incorporate economic effects on the stakeholders, an assessment of DOC is conducted based on fuel consumption and flight time with a model according to [PERSON] and Scholz [58; 59]. To compute ATR20, an updated version of aCCFs is applied [67; 68].  
Obviously, the definition of the reference case as well as the implementation scenario of the individual OIs vary for the different measures:  
* An implementation of the OI LOSL was modeled by considering combinations of altitude shifts of 2000, 4000, and 6000 ft and speed reductions of 0, 5, and 10% in comparison to the reference case. The optimized set-up is selected based on minimum ATR20. The reference case was defined by a sample of flights on four representative days performed with two selected representative wide-body aircraft types on missions from or to the ECAC area.
* The OIs FREE and WIND have been implemented by focusing on one high-complexity en-route airspace in the ECAC area, namely EDUU, and using the flight plans and weather information on a representative winter day in 2018. Three case studies of OI implementation are evaluated to explore the mitigation potentials of the concept with different objective functions (i.e., the shortest path for FREE, the weighted sum of travel duration and fuel consumption, or prioritizing decreasing NO\({}_{x}\) emissions by also considering fuel consumption and travel duration for WIND).
* The CLIM OI applies aCCFs to describe the climate impact of CO\({}_{2}\) and non-CO\({}_{2}\) effects in a set of trajectory optimization experiments. Therefore, the climate mitigation potential with respect to ATR20 was determined by avoiding climate-sensitive regions associated with different fuel penalties. Four city pairs with departures from the ECAC area were selected for the representative winter day.