esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G Network Architecture Using FSO-PDM/PV-OCDMA System with Weather Performance Analysis
## 5 Simulation Results
### Effect of Clear Air on FSO-PDM/PV-OCDMA System  
As clear air has less attenuation, that is 0.14 dB/km, this leads to less effect of the information signal during its propagation. It is seen from Figure 2 (log (BER) versus different FSO ranges) that the eight channels, which are transmitted using two polarization signals (four channels on x-polarization and four channels on y-polarization), can support up to 7 km FSO link with an overall capacity of 160 Gbps and log (BER) \(<-5\). Table 3 shows the computed log (BER) for the eight channels at a 7 km propagation range.  
Figure 3 depicts the Q-factor for channels 1, 2, 3, and 4 transmitted using x-polarization and channels 5, 6, 7, and 8 transmitted using y-polarization versus the propagation range for the proposed FSO-PDM/PV-OCDMA system. At a 7 km propagation range, the Q  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol/Parameter** & **Value** \\ \hline \(t_{p}\) (dBm): CW laser source input power & 15 \\ Laser linewidth & 10 MHz \\ \(R_{b}\) (Gbps): bit rate per channel & 20 \\ Number of channels & 8 \\ \(B_{electric}\) (Hz): electrical bandwidth & 0.75 \(\times\) Bit rate \\ Divergence angle & 1 mrad \\ \(d_{r}\) (cm): receiver aperture diameter & 10 \\ \(d_{t}\) (cm): transmitter aperture diameter & 20 \\ \(R\) (A/W): PD responsivity & 1 \\ Thermal noise power density & \(10^{-22}\) W/Hz \\ \(T\) (K): receiver noise temperature & 300 \\ \(R_{L}\): receiver load resistance & 1030 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters [27; 28; 29; 30; 31].  
Figure 2: Log (BER) of FSO-PDM/PV-OCDMA system versus FSO link under clear air weather.  
factor values for channels 1 and 5, 2 and 6, 3 and 7, and 4 and 8, respectively, are 4.7 and 4.6, 4.5 and 4.4, 4.8 and 5, and 4.9 and 4.9, respectively.  
Figure 4 displays the eye diagrams for the eight channels that propagate on two different polarization signals (X at 0 degrees and Y at 90 degrees) at a 7 km propagation distance. As all channels have a wide eye-opening, they advocate successful information transmission with an overall capacity of 160 Gbps.  
Figure 4: Eye diagrams for eight channels of the FSO-PDM/PV-OCDMA system transmitted on (**a**) x-polarization and (**b**) y-polarization.  
Figure 3: Q-factor of FSO-PDM/PV-OCDMA system versus FSO link under clear air weather.
# Measures and Methods for the Evaluation of ATO Algorithms
## Literature Review
### Testing in Other Modes of Transportation
#### 2.3.4 Test Generation  
The extraction and generation of test scenarios is a major research field. In [16], a more refined taxonomy of the scenario-based approach is proposed. The important module of this strategy is the creation of a scenario database filled with scenarios either generated or more explicitly extracted from knowledge or data. Three different types of scenarios are generated. _Functional scenarios_ can be described as linguistically expressed descriptions of a scenario abstractly. Defining functional scenarios in more detail by using parameters and placing them in a physical state space they can be called _logical scenarios_. Finally, assigning one specific value to each parameter results in the definition of _concrete scenarios_ ([27]). A list of safety metrics to evaluate scenarios can be found in [22].  
Standardization of tests and even more important standardization of tools and data exchange formats is beneficial to ensure an efficient and improved test and validation phase. Two major standards, according to [16] already known to most researchers and manufacturers in the automotive field, are _OpenDRIVE_ and _OpenSCENARIO_. The first one is used to define static elements in a simulation, the latter is used to describe dynamic elements, such as vehicle manoeuvres [28]. Furthermore, the standard _ASAM XIL_ allows the communication between test automation tools and test benches as well as transferring tests between different test systems [29].  
Next to the efforts in standardization by the Association for Standardization of Automation and Measuring Systems (_ASAM_) the German federal ministry for economic affairs and energy launched a project called _PEGASUS_. PEGASUS aims to \"deliver[s] the standards for automated driving\", as stated on the website of the project [30]. The goal of this program was to develop a cross-manufacturer method for testing and evaluating automatic driving vehicles and their components. In [31], an overview of the established method is given.  
Within the project, the so-called _6-layer-model_ was introduced which shall enable a structured design of an ODD (defined also in SAE standard J3016) and a simpler extraction of test scenarios. By [PERSON] et al. the model was then further developed and refined. In [32], it is described how the original model was designed and what changes were performed to further improve it. By using this model a clear structure of an operational design domain shall be feasible.  
Another way to structure an ODD is given in [33]. The proposed framework for automatic driving system testable cases and scenarios is using an \"ODD classification framework with top-level categories and immediate subcategories\". A method for deriving test scenarios, namely critical corner cases, to falsify the functionality of an automatic vehicle in certain situations is also proposed by [PERSON] in [15]. This approach is also applying the layer model to retrieve test cases and set parameters for each scenario.
## 4 Global Path Planning Based on Network Analysis Method
### The Procedure of Coordinate System Conversion  
Due to the fact that the coordinate of local path planning is different from global path planning, the WGS-84 coordinate system is transferred to the local path plan coordinate, and the converting sequence is shown in Figure 8.  
The GIS system is built based on the WGS-84 coordinate system [26], so the GPS coordinate data includes latitude, longitude, and elevation. The local, fixed-tangent plane is a coordinate system whose starting point is set as the origin; its \(x\) axis is the same as the orient, its \(y\) axis is the same as the north, and its \(z\) axis is the same as the vertical direction. We switch the WGS-84 coordinate system to the local fixed tangent plane through several conversions using Equation (1). Here, \((B_{P},L_{P},H_{P})\) represents the GPS coordinate data from the GIS system, and it converts them to \((X_{P},Y_{P},Z_{P})\) by Equations (1)-(2).  
\[\left[\begin{array}{c}X_{P}\\ Y_{P}\\ Z_{P}\end{array}\right]=\left[\begin{array}{c}(R_{n}+H_{P})\cos B_{P}\cos L_{ P}\\ (R_{n}+H_{P})\cos B_{P}\sin L_{P}\\ [(R_{n}+H_{P})-e^{2}R_{n}]\sin B_{P}\end{array}\right] \tag{1}\]  
Figure 8: The converting sequence of several coordinate systems.  
Figure 7: Two special points and GPS points with the unique index in the crossroad.  
where \(R_{n}\) is radius of curvature in prime vertical of point \(P\), and \(e\) is the first eccentricity of the ellipsoid. \(a,b\) is a semi-major axis of the ellipsoid and a semi-minor axis of the ellipsoid.  
\[R_{n}=a^{2}/(a^{2}\cos^{2}B+b^{2}\sin^{2}B)^{1/2} \tag{2}\]  
\[e=(a^{2}-b^{2})^{1/2}/a \tag{3}\]  
\[\left[\begin{array}{c}x_{\rm QP}\\ y_{\rm QP}\\ z_{\rm QP}\end{array}\right]=\left[\begin{array}{ccc}-\sin L_{\rm P}&\cos L _{\rm P}&0\\ -\sin B_{\rm P}\cos L_{\rm P}&-\sin B_{\rm P}\sin L_{\rm P}&\cos B_{\rm P}\\ \cos B_{\rm P}\cos L_{\rm P}&\cos B_{\rm P}\sin L_{\rm P}&\sin B_{\rm P}\\ \end{array}\right]\left[\begin{array}{c}X_{\rm Q}-X_{\rm P}\\ Y_{\rm Q}-Y_{\rm P}\\ Z_{\rm Q}-Z_{\rm P}\end{array}\right] \tag{4}\]  
By the same way, the coordinate of Q point \((x_{\rm QP},y_{\rm QP},z_{\rm QP})\) is worked out by Equation (4), and P point is the origin point of the coordinate.  
\[\left\{\begin{array}{c}x^{\prime}=x*\cos yaw-y*\sin yaw\\ y^{\prime}=y*\cos yaw+x*\sin yaw\end{array}\right. \tag{5}\]  
In the end, the point is converted to the local path coordinate by Equation (5), _yaw_ is the car's heading angle, and they provide \((x^{\prime},y^{\prime})\) for local path planning.
## 2 Materials and Methods
### Field Measurements
#### 2.2.3 Meteorological, Tidal, and Physiological Measurements  
Meteorological factors were observed using a Biomet system (7900-101, LI-COR Inc., Lincoln, NE, USA). The system consisted of the EC system and the following sensors: a quantum sensor (LI-190 SZ, LI-COR Inc., Lincoln, NE, USA) measuring photosynthetically active radiation (PAR) at a height of 6 m and a temperature and humidity probe (HMP155, Vaisala, Helsinki, Finland) measuring relative humidity (RH) and air temperature (T\({}_{\text{a}}\)). Soil temperature and moisture sensors (Stevens Hydra Probe II, Stevens Water Monitoring Systems, Inc., Portland, OR, USA) were placed at soil depths of 10 cm. In addition, we used the tidewater level (TWL) recorded in 2019 at the tide station Zhongjun near the observation tower (Figure 1). For consistency with other data, we interpolated the TWLs to a half-hour interval. Salinity was measured by a WQM-X Water Quality Monitor (Sea-Bird Electronics Inc., Bellevue, WA, USA) mounted on a buoy in the South Passage of the Changjiang Estuary, China (Figure 1).  
Leaf area index (LAI, m\({}^{2}\) m\({}^{-2}\)) was determined using an LAI-2200c plant canopy analyzer (LI-COR Inc., Lincoln, NE, USA). An MC-100 chlorophyll concentration meter (MC-100 chlorophyll Concentration Meter, Apogee Instruments Inc., Logan, UT, USA) was used to measure leaf chlorophyll content (C\({}_{\text{ab}}\), \(\mu\)g cm\({}^{-2}\)), which had been calibrated. LAI and C\({}_{\text{ab}}\) were measured monthly.
## 3 Detection of Damage Occurrence
### Frequency Domain Methods
[79] proposed to detect structure damage employing transmissibility functions compressed by principal component analysis. Some other output-only methods in the frequency domain are developed based on auto/cross-spectral densities, such as the peak picking method, the frequency domain decomposition techniques (FDD) and the enhanced technique [80]. In addition, [PERSON] et al. [81] proposed a frequency contour lines method to identify damage in plate-type structure and the Fourier spectrum-based strain energy damage detection method for beam-like structures in noisy conditions [82]. [PERSON] and [PERSON] [83] combined the joint approximate diagonalization (JAD) technique and gapped smoothing method (GSM) to form a sensitive and robust damage index in frequency domain.
# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
## 3 Simulations and Damage Classification
### Model Validation and Dynamic Simulation  
To evaluate the impact on dynamic performance, the catenary is integrated with the pantograph at a speed of 250 km/h. A 10-span catenary with nominal dropper conditions is simulated to validate against [28], which is presented in Table 2. The simulation framework has also been validated in [36] but, for the sake of brevity, the details are not presented here.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Component** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{FE catenary} & Span structure & Span length (m) & 60 \\  & & Dropper position (m) & [5; 10.5; 17; 23.5; 30; 36.5; 43; 49; 55] \\  & \multirow{2}{*}{Dropper} & Elasticity in extension (N/m) & \(1\times 10^{5}\) \\  & & Elasticity in compression (N/m) & \(0\) \\  & & Steady arm stiffness (N/m) & \(300\) \\  & & Bending stiffness (Nm\({}^{2}\)) & \(195\) \\  & & Density (kg/m) & \(1.35\) \\  & & Tension (N) & \(2\times 10^{4}\) \\  & & Suspension stiffness (N/m) & \(5\times 10^{4}\) \\  & & Bending stiffness (Nm\({}^{2}\)) & \(131.7\) \\  & & Density (kg/m) & \(1.07\) \\  & & Tension (N) & \(1.6\times 10^{4}\) \\  & & alpha (s\(-\)1) (proportional index to mass) & \(0.0125\) \\  & & beta (s) (proportional index to stiffness) & \(1\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 1: FE catenary with pantograph mode.  
The impact of the fifth dropper's damage on the contact force performance is presented in Figure 3. It can be seen that if the damage is minor, the difference in contact force can be negligible. On the contrary, if the fifth dropper fully malfunctions, its impact on force can propagate to the whole span and distort contact behaviour.
## 3 Results  
Preliminary Evaluation of the Impact of Growing Area on Hop Samples by Means of Vibrational Spectroscopy  
FTIR spectroscopy is a technique widely used in the determination of the authenticity and geographical traceability of agrifood products [21] but, as far as we know, it has never been applied in authenticating the geographical origin of hops. Besides, [PERSON] et al. [22], for the first time, evaluated the potential of near infrared spectroscopy in the classification of hop samples according to genotype and origin. Here, to screen for differences or similarities quickly and easily in the overall fingerprinting of Cascade hop samples from the two different geographic regions considered in the brewing trials, the FTIR spectra of hops collected over three consecutive years were evaluated by PCA. The results highlighted spectral differences between hop samples analyzed, mainly in the region of the molecular signature (below 1800 cm\({}^{-1}\); Figure 2). On the basis of eigenvalues > 1 ([PERSON]'s criterion) and of the scree plot (not shown), two principal components (PCs), accounting for about 84% of the data matrix variance, were considered significant and allowed us to group the samples according to their different growing area along PC1 (Figure 3a). Based on the loading plot (Figure 3b), the variables contributing the most to the separation on PC1 and PC2 were absorbance values at \(\lambda\): 2964, 2888, 1724 and 986 cm\({}^{-1}\). The results point out that FTIR screening, combined with chemometric analysis, can differentiate the samples analyzed in relation to their geographical origin, suggesting a _terroir_ effect on the molecular fingerprint of the hop samples coming from the two investigated farms. These findings agree with literature studies [6; 8; 9]. Based on this experimental evidence, we wanted to investigate if these differences were also transferable to beers produced with these hops. To this end, we conducted the brewing tests on the 2018 hop harvest, reporting below the results obtained on both hop cones and derived beers.  
Figure 2: FT-IR raw spectra of hop samples from different growing area, collecting over three consecutive years: blue line: harvest 2016; red line: harvest 2017; green line: harvest 2018. (**a**) CAS_L2 Cascade hop samples from Latium; (**b**) CAS_T: Cascade hop samples from Tuscany.
## 3 Results and Discussion
### SEM and EDX Analysis  
Figure 7a shows the morphology of PP-25G, where the glass fiber can be clearly seen embedded in the polymer matrix. It is noticeable throughout every sample that holes can be seen uniformly distributed, which would suggest that the glass fibers are well dispersed in the polymer matrix. Material from the polymer matrix can be seen on the surface of the glass fibers after the fracture in Figure 7c \(\times\)1000, evidence of good adhesion between the fiber and the polymer matrix. Studies by [PERSON] et al. (2018) and [PERSON] et al. (2018) found that the amount of residual polymer still attached to the fibers can indicate the state of fiber-matrix adhesion. The addition of elastomer seems to prevent the pullout of the glass fibers (more organic material present on the fibers), the glass fibers being better anchored in the polymer matrix, as seen in Figure 7c, which correlates with previous analysis from this study that showed a strong interaction between PP, maleic anhydride and SEBS (2018). It can also be pointed out that the shape of the holes left by pulling out the glass fibers do not have perfectly round shapes. This indicates that the organic material from the matrix remains attached to the fibers when pulled out. With the addition of ash powder, agglomerates with dimensions of 45-50 \(\upmu\)m can be seen in Figure 7b,d. However, it should be noted that the ash particles are well embedded in the polymer matrix, as no separation can be seen and the pullout effect is decreased compared to PP-25G (percentage of holes reduced and the irregular shape of the holes). EDX analysis (Table 7) confirms that the agglomerates are made of ash powder particles, as their elemental composition coincides with the chemical oxidic composition of the ash powder ([PERSON] et al., 2018).  
Figure 7: SEM surface morphology (100\(\times\)) and (1000\(\times\)) of (**a**) PP-25G; (**b**) PP-25G-C composite; (**c**) PP-25G-E composite; (**d**) PP-25G-C-E composite.
## 4 Damage Detection Using TRM
### Truncation Window Determination  
The inevitability that both the direct waves and echoes from boundary will focus at the trigger point of data acquisition can breed confusion of damage detection. The practicable approach of solving this problem is to extract the defect scattered signal through a properly designed truncation window. \(V_{s_{L(0,1)}}(t)\), the signal of mode \(L(0,1)\) received by the sensor at 790 kHz, will last emerge in time domain within the direct guided waves due to the lowest group velocity shown in the Figure 4, and hence \(T_{wl}\), the right side of \(V_{s_{L(0,1)}}(t)\) are taken as the left side of the truncation window. Notably, because \(V_{s_{L(0,1)}}(t)\) can be conveniently simulated depending on the equation  
\[V_{s_{L(0,1)}}(t)=\sum_{n}V_{A}(\omega_{n})\exp[-ik(\omega_{n})L]\exp(i\omega_{ n}t) \tag{14}\]  
then \(T_{wl}\) can be predetermined consequently. Where, \(n\) is the number of sampling points of frequency, and \(L\) is the length of the rod. In terms of the right side of the truncation window \(T_{wr}\), it should be set as the time when the first boundary reflection of the fastest mode \(L(0,2)\) arrives. The arriving time \(T_{L(0,2)}\) of direct mode \(L(0,2)\) can be clearly identified, so the time of its first boundary reflection is easily deduced as \(3T_{L(0,2)}\). The designed truncation window is shown in Figure 6.  
Figure 4: Group velocity dispersion curves of longitudinal modes for a 7 mm diameter galvanized steel wire.  
Figure 5: Response signal due to the 12-cycle tone burst excitation at 790 kHz.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 8 Applications  
FOPS-based SPR sensors have brought several advantages, namely, minimum measurement of sample volumes, possibility of multi-sensing, geometric feasibility, miniaturization of the sensing device, and immunity to the electric signal noise of the sensing system [94]. The development and investigation of the biosensors have been reviewed and reported [38; 45; 95]. SPR-based FOPS have  
Figure 8: The detection mechanism based on FOPS for antibody-antigen binding process [92].  
applications in biology and medicine. They can generally be classified into two groups, chemical sensors and biological sensors. The chemical sensors are \"miniaturized analytical devices that can deliver real-time and on-line information in the presence of specific compounds or ions in complex samples,\" as defined by [PERSON] [96]. A biosensor is an analytical device that converts the analyte into an electrical signal through the bio-recognition system with a transducer [97]. A range of biosensors for the detection of different biological samples are reviewed and tabulated in Table 1. It provides details of the sensor configuration with an application and the specific technique used for binding the functional materials, sample range used, and sensor performances. It includes both SPR and LSPR implementations in the reviewed sensor configurations. The SPR-based sensor devices operate in the visible region. Recently, an ITO-based SPR sensor has been established to operate in the near-infrared region. To model a sensor device in the near-infrared regime, the nanoparticle coating is incorporated in the sensor configuration, which further improves the optical properties [98].
## 2 Numerical Modelling
### Ballistic Pendulum and Sandwich Structure  
The numerical model reproduces the experimental set-up, as shown in Figure 2. The ballistic pendulum, similar to the one used by [PERSON] et al. [19], consists on an IPN 270 beam of length  
Figure 1: Experimental test apparatus (adapted from [10]) (IPN:European Standard I Beams).  
1200 mm, denoted by 1 in Figure 2. A balancing mass of 163.5 kg is attached at the back and a 20 mm thick steel end plate at the front, denoted by 2 in the figure. This structure is connected to a 15 mm thick plate (3) and to the specimen support frame (5) through a set of threaded bars and nuts (4). The specimens are \(320\times 320\) [mm\({}^{2}\)] sandwich panels of different thickness, which are fixed to the pendulum in the support frame, leaving an exposed area of \(300\times 300\) [mm\({}^{2}\)]. A Composition C-4 high explosive charge of mass \(m=30\) g is fixed and aligned with the centre of the sandwich panel, at a stand-off distance \(d=300\) mm. This corresponds to a scaled distance \(Z=d/m^{1/3}=0.965\) m/kg\({}^{1/3}\). The pendulum is suspended on four steel cables (6), although only two are shown in Figure 2 due to symmetry. The balancing mass is modelled as a lumped mass.  
No plastic deformation is observed after the experimental tests in any of the steel components of the ballistic pendulum. Consequently, each individual part is modelled as linear-elastic with density \(\rho=7850\) kg/m\({}^{3}\), Young's modulus \(E=210\) GPa, and Poisson ratio \(\
u=0.3\). The model parts are connected using a tied contact algorithm. Hexahedral solid elements with reduced integration are used throughout the whole model, leading to a total of up to 107,402 elements and approximately 400k degrees-of-freedom. Table 1 lists the numbers of finite elements and degrees-of-freedom for each individual part in the ballistic pendulum model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Part** & **Elements** & **Degrees-of-Freedom** & **Number of Parts** \\ \hline IPN 270 Beam (1) & 7184 & 41,382 & 1 \\ End plate (2) & 2888 & 12,330 & 1 \\ Support plate (3) & 5235 & 22,236 & 1 \\ Threaded bars (4) & 1520 & 6318 & 2 \\ Double frame (5) & 3960 & 16,800 & 2 \\ Supports & 1080 & 4380 & 2 \\ Nuts & 1160 & 5220 & 4 \\ \hline Specim (range) & 3,375–28,125 & 12,288–277,248 & 3 \\ \hline
**Total (range)** & **26,402–107,402** & **133,872–398,832** & **16** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the finite element discretisation of the four-cable ballistic pendulum and specimen.  
Figure 2: Numerical model of the 4-cable ballistic pendulum subjected to blast: **(1)** IPN 270 beam, **(2)** 20 mm end plate, **(3)** 15 mm support plate, **(4)** threaded connecting bars, **(5)** specimen support double frame, and **(6)** ballistic pendulum support cables.
## 3 LPS transport to the Outer Membrane
### Regulation of Lpt Complex Assembly and LPS Transport Process
When an OM stress occurs, the OMPs cannot be properly folded and accumulate into the periplasm. The \(C\)-terminal residues of unfolded OMPs interact with the protease DegS activating the cleavage of RseA [105]. Following the proteolytic cleavage of the periplasmic region of RseA by DegS, the multipass IM protease RseP cleaves the transmembrane region of RseA thus freeing \(\sigma^{\mathrm{E}}\)[100]. However, RseA cleavage by activated DegS is prevented by RseB binding to RseA [103, 104, 106]. A recent work by [PERSON] and co-workers demonstrated that the cell senses defects in LPS transport through LPS binding by RseB in the periplasm and that LPS-RseB interaction results in RseA-RseB, dissociation allowing RseA cleavage by DegS with consequent \(\sigma^{\mathrm{E}}\) activation [99]. This two-signals regulation appears a strategy adopted by the cell to ensure a precise homeostatic control of the barrier function of the OM and reflects the strict connection between the biogenesis of its different components, as defects in LPS biogenesis are expected to generate problems with OMPs assembly and _vice versa_.