esa-sceva/satcom-chunk-collection huggingface dataset:
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 5 Neural Network (NN)-Based Phase-Field Brittle Fracture
### Data Collection  
To verify the performance of the NN-based model by comparing their predictions with FE formulations, the training data are exclusively collected from the finite element analysis, i.e., experimental data are not considered. In this regard, both geometries in Figures 10-12 are employed for the creation of the data set necessary for training the neural network model. Hereby, data from a specific part of the geometry are considered since the data of interest lie in the region where the crack is expected. For the elasticity part of the phase-field modeling of brittle fracture, the previously trained model is utilized. A second model is also trained solely for predicting the fracture phase-field \(d\). It suffices to state that the accuracy of the predictions (approximations) depends on the complexity of the relationship between the data set for the inputs and outputs. The trained neural network will approximate the mapping between the input and output. Unlike the elasticity model, the relationship between the input and output data in the fracture process is _highly nonlinear and complex_. To understand these relationships, a sensitivity analysis will provide a better insight into the selection of the proper choice of inputs required to accurately predict the target output.
# A Systematic Analysis of Narrowband IoT Quality of Service
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP Third Generation Partnership Project  
4G Fourth Generation of Mobile Networks  
5G Fifth Generation of Mobile Networks  
\(A\) Antenna Gain  
ACK Acknowledgment  
ASCII American Standard Code for Information Interchange (encoding)  
\(B\) Bandwidth  
BLER Block Error Rate  
BPSK Binary Phase Shift Keying  
cMTC Critical Machine Type Communication  
CL Coupling Loss  
DL Downlink  
ECL Enhanced Coverage Level  
eNodeB Evolved Node B (base station in a LTE network)  
GSM Global System for Mobile Communication  
HARQ Hybrid Automatic Repeat Request  
IM Interference Margin  
IMT-2020 International Mobile Telecommunications 2020 Specification  
IoT Internet of Things  
IP (-v4, -v6) Internet Protocol (Version 4, Version 6)  
ISM Industrial, Scientific and Medical Frequency Band  
\(k\) Boltzmann's constant  
LoRaWAN Long-Range Wide Area Network (LPWAN technology)  
LPWAN Low-Power Wide Area Network  
LTE Long-Term Evolution  
LTE Cat-M1 LTE Category M1 (LPWAN technology)  
\(\mu\) Mean value  
MAC Medium Access Control  
MCL Maximum Coupling Loss  
MCS Modulation and Coding Scheme  
mMTC Massive Machine Type Communication  
MNO Mobile Network Operator  
MTC Machine Type Communication  
N  Number of measurement samples  
\(N_{Rep}\) Number of ECL repetitions  
\(N_{RIU}\) Number of resource units  
\(N_{SF}\) Number of subframes  
NACK Negative Acknowledgment  
NB-IoT Narrowband Internet of Things  
NF Noise Figure  
NLOS Non-Line of Sight  
NPDCCH Narrowband Physical Downlink Control Channel  
NPDSCH Narrowband Physical Downlink Shared Channel  
NPUSCH Narrowband Physical Uplink Shared Channel  
NRS Narrowband Reference Symbol  
NTP Network Time Protocol  
OFDM Orthogonal Frequency Division Multiplexing  
\(P_{CMAX,c(i)}\) Cell-specific maximum transmit power on slot i
## 3 Problem Description and Formulation
### Mathematical Model
Proposition 1: _If the triangular inequality holds for the transportation unit cost (assumed in the literature and holds for dataset used in the numerical experiments), then \(s_{ij}^{kk}=0,\forall i,j\in I,\forall k\in K\)._  
This proves that the constraint \(s_{ij}^{kk}=0,\forall i,j\in I,\forall k\in K\), used in several mathematical models in recent papers in the literature, is an unnecessary constraint [27]. Proof: To express the fact that rail mode generate a scale economy (consolidation mode), the intermodal unit cost may be written as follows: \(c_{ij}^{km}=c_{ik}+\alpha c_{km}+c_{mj}\) where \(c_{ik}\) is the cost between \(i\) and \(k\), \(c_{km}\) the cost between the two rail terminals, and \(c_{mj}\) is the cost between the rail terminal \(m\) and the final receiver \(j\) with \(\alpha<1\). We denote by \(\alpha\) the discount coefficient expressing the scale economy generated by the rail mode. This parameter is supposed to be equal to 0.5 in [27]. Since \(c_{ij}^{kk}=c_{ik}+\alpha c_{kk}+c_{kj}=c_{ik}+0+c_{kj}=c_{ik}+c_{kj}\geq c_{ij}\) (the last inequality is the triangular inequality), and as we aim to minimize the total cost expressed by Equation (1), given the conservation flow constraint of Equation (2), it follows that all goods from \(i\) and destined to \(j\) will be routed directly using trucks, and then \(r_{ij}=q_{ij}\), which implies that \(\forall i,j\in I,s_{ij}^{kk}=0\)  
Proposition 2: _Let 1 be the number of rail links to be located and \(|K|=p\) the cardinal of the candidates sites set. The Incomplete ITLP modeled by the aforementioned MIP is unfeasible when \(l>\frac{p(p-1)}{2}\)._Proof. : It is an immediate result of the handshaking lemma (a classical lemma in graph theory: an incomplete graph with \(p\) vertices has at most \(\frac{p(p-1)}{2}\) edges).
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 2 Materials and Methods
### Methods  
#### 2.3.1 Data Pre-Processing and Plotting  
The Landsat-generated NDVIs were already pre-processed, and no further processing was performed. However, the oil spill data were cleaned to remove some spill points with incomplete data, such as the lack of complete GPS coordinates (longitude and latitude) and spill volume. The oil spill data were plotted in ArcGIS 10.8.2 and overlaid on the NDVI map.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Measurement Principles
#### 2.1.3 Continuous Wave Frequency Modulated (FMCW) Approach
Unfortunately, in general, a perfect linear or triangular optical frequency sweep cannot be realized by a linear modulation of the control current and, in addition, the frequency-current curve is in general nonlinear, especially close to the moment of slope change. As a consequence, deviations from the linear ramp usually occur, which, in turn, bring on relevant variations in \(f_{r}\). Moreover, the range resolution depends on the measurement accuracy of \(f_{r}\) and also on the accuracy with which the modulation slope is controlled or known [17; 26; 36]. The FMCW method is fundamentally different from the two previous approaches because of the use of the coherent (homodyne) detection scheme in the Fourier domain, rather than the incoherent intensity detection schemes described until now for time counting or phase measurement approaches [17]. FMCW has shown to be useful in outdoor environments and to have improved resolution and long-range values relative to the pulsed and AMCW approaches. Its main benefit in autonomous vehicle applications is its ability to sense \(simultanously\) the speed value, and its direction together with range. However, its coherent detection scheme poses potential problems related to practical issues like coherence length (interference in principle requires the beam to be within coherence length in the full round-trip to the target), availability of durable tunable lasers with good temperature stability, external environmental conditions, accuracy of the modulation electronics, or linearity of the intensity-voltage curve of the laser, which require advanced signal processing. Although it is not the principal approach in lidar imaging systems for autonomous vehicles, some teams are currently implementing lidar solutions based on FMCW in commercial systems due to its differential advantages, and clearly winning presence when compared to the rest of approaches we described.
## Appendix B SfM Mapping with UAS Platforms and Considerations for Snow-Covered Arctic Locations
Lower apertures are preferred (as long as there is no risk of underexposing snow-free areas of the image on overcast days) since not only do they provide sharper images and reduce optical aberration (or noise) but also enlarge the depth of field, reducing the risk of images being out of focus due to changes in the UAS elevation. High winds on low-luminance days might also compromise image quality since they require very high shutter speeds in order to cope with abrupt and fast movement. Various technical difficulties were encountered in the field, which is characteristic of mapping in high-latitude Arctic environments. GPS and compass navigation are compromised at high latitudes(due to magnetic field interference), reducing survey accuracy and increasing the risk of vehicle loss in long-range flying transects or fly away. This needs to be considered and properly pre-assessed depending on the location and survey/flight mode. It is recommended that pilots be prepared to perform the survey in manual mode. Operating UAS in the field requires many spare components. In the Arctic specific supplies are probably not available nearby, which should be considered before any field campaign. Cold temperatures experienced (\(-20\) to \(-30\)\({}^{\circ}\)C) were a great limitation for LiPo battery life, reducing available flying by 85%. Our solution consisted of keeping the batteries thermally insulated prior to the survey (e.g., in inner layers of clothing) and having them wrapped in highly insulating material during the survey. This greatly reduced the battery life loss for both set-ups.
## 4 Results of the Shaking Table Tests
### Dynamic Longitudinal Strains of the Cross Station  
The time histories and peak tensile values of the dynamic longitudinal strains recorded at interchange station in _section 3_, _section 5_ and _section 6_ are presented in Figure 11. To eliminate the spurious frequency components caused by the sensor [35], the presented strain data were processed by a band-pass filter using a zero-phase, eighth-order Butterworth filter. The applied filter introduced no phase shifts. The characteristics of the filter for each case are summarized in Table 5. The bandwidth was determined to be wide enough to conserve the main frequency characteristics of the strain response.  
Figure 11a,b compares the time histories of the dynamic, incremental longitudinal strains in section 3, section 5 and section 6 during the sinusoidal case with a frequency of 10 Hz. In general, the time histories of the dynamic strain pairs recorded at the two sides of the station were found to be 180 degrees out of phase, indicating the deflection of the two-story structure along the longitudinal axis during transversal seismic excitation. As expected, the dynamic increment of the longitudinal strains was conspicuously higher in section 6, which decreased in _section 5_, _section 3_ and also _section 1_ (which was not shown here). Figure 11c extracted the maximum tensile strains at different locations of the three cross-sections from the time histories. In general, the strain gauges in _section 6_ picked up larger strains compared with the other sections at their corresponding locations. As an example, the maximum tensile strain of S6-2X was 12.4 \(\mu\)e, while it was 4.5 \(\mu\)e and 4.4 \(\mu\)e in _section 5_ and _section 3_, respectively. In _section 6_, the peak dynamic longitudinal strains recorded at the elevation of the bottom slab were generally higher than those recorded at the elevation of the top slab.  
Figure 10: Transverse acceleration response recorded in _section iii_ of the single station and _section 1_, _section 3_ and _section 5_ of the interchange station during the sinusoidal excitation, with a dominant frequency of 10 Hz. The locations of the accelerometers are also illustrated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Earthquake** & \begin{tabular}{c} **Low-Frequency** \\ **Cutoff (Hz)** \\ \end{tabular} & \
\begin{tabular}{c} **High-Frequency** \\ **Cutoff (Hz)** \\ \end{tabular} &
\begin{tabular}{c} **Frequency Range of Significant** \\ **Acceleration Response (Hz)** \\ \end{tabular} \\ \hline Sin-2 Hz & 0.5 & 45 & 1.5–2.5 \\ Sin-4 Hz & 1 & 45 & 2.0–7.0 \\ Sin-8 Hz & 1 & 45 & 5.5–12.0 \\ Sin-10 Hz & 2 & 45 & 6.0–14.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of the filters used in data processing.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Routes Design Based on Radar Geographic Information  
#### 2.3.1 Determination of the Radar Far-Field Distance  
The electromagnetic field is divided into the near field and the far field according to the area of the induction field and radiation field. The correspondence between the electric field and the magnetic field within the near field is more complicated, and the electric field intensity is affected by the change in the target distance. The electric and magnetic field intensity in the far field is inversely proportional to the distance from the center of the antenna. And it has a fixed conversion relationship which is different from the near field [18]. It is commonly assumed that electromagnetic waves in the far field are plane waves, which enables the acquisition of a stable radar cross-section of the target. In fact, electromagnetic waves are actively propagating into space as spherical waves. As the radar system used in this paper has a parabolic antenna, the far-field distance of the antenna is given by the following equation.  
\[R=\frac{2D^{2}}{\lambda} \tag{7}\]  
where \(R\) is the radar near- and far-field demarcation point (unit: m), and \(D\) is the antenna diameter (unit: m). The diameter of the X-band radar antenna used in the experiment is 2.4 m, the frequency is 9.4 GHz, and the wavelength is about 3.2 cm, so the far-field distance of the radar is 360 m.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Band** & **S** & **C** & **X** \\ \hline Range resolution (m) & 250 & 150 & 75 \\ \hline \end{tabular}
\end{table}
Table 1: Radar range resolution in different bands.
## 4 Experimental Study  
### The Industrial Case Study  
The industrial case study has been developed in collaboration with CNH Industrial (CNHi), a global leader in the design and development of a wide range of vehicles, from agricultural to construction machinery, industrial and commercial vehicles, buses and special vehicles, as well as the relative engines and transmissions. In particular, the case study in the tractor field focuses on a set of maintenance tasks to be carried out on tractors during their life. This case is particularly interesting since it involves both product and process design; indeed, the final task sequence and working environment result from the combination of the product design, affecting the set of actions for product disassembly and assembly to intervene during the maintenance, and the process design, linked to the ideal sequence that users have to follow to accomplish each specific task. Moreover, tractor maintenance can be both physically and mentally demanding, depending on the tractor layout and task complexity. In addition, serviceability has a crucial role in the daily use of the tractor and highly influences the product sales. For these reasons, the company decided to push the design for serviceability of its products and further investigate how maintenance activities are accomplished in order to enhance the product quality. The selected case study addresses one of the most frequent and time-consuming maintenance tasks: the engine oil filter replacement. This activity is usually carried out by the farmer or the tractor owner, and rarely by the CNHi service workers, and is very frequent since this is required after approximately 600 working hours, which corresponds to about 75 working days (considering a working day of 8 h). The workflow consists of disassembling of a set of machine parts to access the filter, the replacement of the exhausted oil filter with a new one, and the reassembly of the product parts. Contextually with the engine oil filter replacement, the temperature sensor of the pre-fuel filter is usually controlled and eventually replaced, as also suggested by the tractor maintenance handbook. The current process for the engine oil filter replacement presents some critical issues, as reported by users over the years, mainly related to the oil filter accessibility. Indeed, the filter is actually positioned beyond the power steering tubes and other components that are hard to remove, meaning that the entire process is quite challenging if one is to avoid any damage to the tractor components during the disassembly and reassembly sequence. However, CNHi engineers and designers do not have exact knowledge of the users' workload in order to guide the product redesign. The current task sequence for the engine oil filter replacement is shown in Table 1. For each subtask, the last column indicates if any tool is required. If the subtask requires no tools, it means that it can be executed using bare hands. Figure 2 represents the location where this task is executed on the tractor, using a virtual representation of the entire product and a virtual mannequin.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 4 Experiment and Evaluation
### Experimental Result and Evaluation
#### 4.2.1 Evaluation of Defensive Performance
It also executes learning to enhance the accuracy. Hence, our experiments confirmed that DiNo-Net maximizes the denoising effect, effectively learning adversarial robustness while reducing the decrease in non-attacked accuracy. We measured the stability of learning for the ablation study. Figure 6 shows the Hessian trace of the loss function, derived from its second derivative. The Hessian trace effectively captures the curvature, and a lower value indicates more stable learning. As seen in Figure 6, our method demonstrates the most stable performance compared to all other baseline models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Defense Model** & **Attack** & **F1 Score** & **Accuracy** & \
\begin{tabular}{c} **Attack Success Rate (\%, \(\downarrow\))** \\ \end{tabular} &
\begin{tabular}{c} **Defense Success Rate (\%, \(\downarrow\))** \\ \end{tabular} \\ \hline \hline \multirow{8}{*}{ResNet18 [52]} & - & False & 0.917 (0.010) & 0.918 (0.010) & \multirow{8}{*}{66.5} & \multirow{2}{*}{33.5} \\ \cline{2-5}  & True & 0.254 (0.019) & 0.253 (0.023) & & & \\ \cline{2-5}  & \multirow{2}{*}{DN [45]} & False & 0.819 (0.07) & 0.819 (0.007) & \multirow{2}{*}{68.3} & \multirow{2}{*}{31.7} \\ \cline{2-5}  & & True & 0.142 (0.016) & 0.136 (0.017) & & \\ \cline{2-5}  & \multirow{2}{*}{ADT [20]} & False & 0.766 (0.005) & 0.767 (0.004) & \multirow{2}{*}{28.2} & \multirow{2}{*}{71.8} \\ \cline{2-5}  & & True & 0.484 (0.008) & 0.484 (0.004) & & \\ \cline{2-5}  & \multirow{2}{*}{DN + ADT [48]} & False & 0.769 (0.004) & 0.770 (0.004) & & \\ \cline{2-5}  & \multirow{2}{*}{KD [41]} & True & 0.488 (0.007) & 0.491 (0.006) & & \\ \cline{2-5}  & \multirow{2}{*}{KD [41]} & False & 0.848 (0.004) & 0.848 (0.004) & & \\ \cline{2-5}  & & True & 0.567 (0.009) & 0.564 (0.009) & & \\ \hline \multirow{2}{*}{**DiNo-Net (Proposed)**} & **KD + DN** & False & **0.847 (0.002)** & **0.847 (0.002)** & \multirow{2}{*}{**27.3**} & \multirow{2}{*}{**72.7**} \\ \cline{2-5}  & \multirow{2}{*}{KD + DN} & True & **0.579 (0.004)** & **0.575 (0.004)** & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performances of defensive models (DN: denoising, ADT: adversarial training, KD: knowledge distillation).
# Research on Key Technologies of Quantum-Safe Metro-Optimized Optical Transport Networks
## 3 Results
The performance of the running key rates in the DS-QKD system is illustrated in Figure (a)a, where data were collected at six different transmission distances: 0, 20, 40, 60, 80, and 100 km. Specifically, the secret key rates were 14,125 bps, 12,511 bps, 7962 bps, 4468 bps, 2679 bps, and 797 bps, respectively. Additionally, the bit error rate (BER) performance in the back-to-back (B2B) scenario is presented in Figure (b)b. The BER and average received optical power (\(\mathrm{P_{R}}\)) were measured over a transmission distance of 100 km. These measurements were conducted at a full throughput of 10.709 Gbps at the network-to-network interface (NNI), achieving error-free operation (with the bit error rate satisfying forward error correction (FEC) requirements). For the B2B scenario, Figure (b)b shows the results with and without scrambling using dashed lines. Compared to the unscrambled OSU optical signal, the introduction of scrambling triggered by QS-QKD led to a slight degradation in BER performance. At \(BER=10^{-9}\), the \(\mathrm{P_{R}}\) values for the unscrambled and scrambled OSU optical signals were approximately \(-21.31\) dBm and \(-20.40\) dBm, respectively, resulting in an optical power loss of about 0.91 dB for the scrambled signal. After 100 km of transmission, as indicated by the solid lines in Figure (b)b, similar BER performance was observed. At \(BER=10^{-9}\), the \(\mathrm{P_{R}}\) values for the unscrambled and scrambled OSU optical signals were approximately \(-19.64\) dBm and \(-17.87\) dBm, respectively, leading to an optical power loss of about 1.77 dB for the scrambled signal. Figure 8: Eye diagram of OSU optical signal scrambling and descrambling. Figure 7: Eye diagram of OSU electrical signal. These findings demonstrate a real-time optical signal temporal scrambling/descrambling method, which enhances the physical-layer security of OSUs in M-OTNs. Cryptographic keys are exchanged between transceivers and receivers via DS-QKD over a 100 km range. An eavesdropper without the scrambling strategy and synchronization key cannot correctly extract the digital features of the OSU optical signal, rendering it indistinguishable from noise and making interception and subsequent analysis nearly impossible. Synchronous scrambling and descrambling using the correct quantum key can achieve optical physical-layer security, albeit with a slight attenuation in optical power.