esa-sceva/satcom-chunk-collection huggingface dataset:
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 2 Traffic Data Collection and Status Prediction Modeling Methods  
Establishing accurate prediction models for the highway traffic status is essential for improving traffic control, management, safety, and utilization efficiency. In this section, we first present the devices and schemes used for traffic data collection on highways, which integrate advanced electronics, communication, computing, control, and sensing technologies within ITS. Next, we introduce the Greenshields theory--an experimental data-driven framework that defines the relationships among traffic flow, density, and vehicle speed--which plays a crucial role in building precise traffic models and enhancing prediction accuracy. We then outline regression techniques used for traffic modeling. Finally, we describe the fuzzy logic approach employed in the proposed model, covering the design of membership functions, fuzzy rules, the fuzzy inference engine, and the defuzzification process.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 3 Results
### Half-Hourly GPP Responses to Diffuse and Direct APAR at FLUXNET Sites  
Figure 3 exhibits half-hourly FLUXNET GPP responses to diffuse and direct APAR at FLUXNET sites. The results in Figure 3 indicated a co-variation between GPP observations and APAR values under both diffuse and direct radiation conditions but with different rates, where GPP exhibited higher rates in diffuse radiation conditions. From the statistical results in Table 5, the slopes of the linear functions are extremely higher under diffuse radiation conditions (shown as magenta points, slopes varied from 0.097 to 0.266) than direct radiation conditions (shown as blue points, slopes varied from 0.032 to 0.221), which approximately three times the difference. Although the enhancement of R\({}^{2}\) between the diffuse APAR and GPP than direct APAR was relatively small, obvious increase of slope values in the linear fitting functions (the variation coefficient of slope is up to 203.125% in the EBF site) can totally indicate a stronger response to GPP under diffuse radiation conditions. This was consistent with the results found by [PERSON] et al. [64]. Moreover, half-hourly GPP values were higher under diffuse light conditions than direct light conditions for the same amount of radiation, which further manifested the enhancement effect of LUE under diffuse radiation conditions [26; 65].  
Figure 3: GPP responses to diffuse and direct APAR at FLUXNET sites for different vegetation types: (**a**) EBF; (**b**) ENF; (**c**) DBF; (**d**) SAV; (**e**) GRA and (**f**) CRO. Observed GPP denotes half-hourly GPP derived from FLUXNET network.
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 3 Experimental Results and Discussion
### Validation of the Hysteretic Seismic Response of RC Columns via OpenSees
Accounting for the hysteretic model of corroded steel reinforcement for pitting corrosion, the analysis demonstrated a convergence in respect to the experimental results, as reflected in Figure 21. The cyclic behavior extracted from the model was in good agreement with the experimental one; more specifically, there was an absolute match in both the anion and cation branches at drifts up to 1.5%, whereas the differences occurred for larger strain ranges in cation branch, where the value of the maximum force showed a 28% deviation from the actual recorded value. As a matter of fact, this divergence might have been due to the fact that the hysteretic model of the corroded steel reinforcement was set from the experimental results with a mass loss equal to 15%, whereas the actual recorded mass loss of steel rebars of the experimental tests varied from 14% to 20%, thereby resulting in a difference in the reduced effective cross section. As mentioned in the previous section, corrosion damage in embedded steel bars may vary resulting in different maximum pit depths and actual residual cross sections which can alter the bearing and ductility capacity among different steel bars. Taking into account the beneficial number of cycles corresponding to 70-80% of the bearing capacity, the latter cycles may be omitted; thus, the model demonstrated sufficient convergence in respect to the experimental results for its useful lifetime. Figure 21: Non-linear response of the column with pitting corrosion of steel reinforcement (model). Figure 20: Non-linear response of the column with uniform corrosion of steel reinforcement (model).
## Materials and Methods
### In-House Validation of the Analytical Outcome  
First, a pooled quality control (QC) sample was prepared by mixing equal volumes of six representative control virgin olive oil samples: two EVOO and four LOO. The latter included the following sensory defects (expressed as median of the intensity; \(M\)): rancid (\(n=2\), \(M>4\)) (LR), fusty-muddy sediment (\(n=1\), \(M>5\)) (LF) and fusty-muddy sediment + musty-humid-earthy (\(n=1\), \(M>5\) each) (LFH). Then, seven replicates of this pooled QC were analysed by the same operator with the same equipment, working with the same instrument operative conditions: (i) within the same day to assess intra-day repeatability and (ii) in different days to measure inter-day repeatability. The control samples were also analysed in duplicate.  
The total ion chromatograms (TIC) were extracted from minute 5, including the whole \(m/\!z\) range acquired. All TIC were aligned by the lcoshift algorithm [39] in Matlab R2018b(r) (The MathWorks Inc., Natick, MA, USA) to correct the retention time shift between samples.  
The relative standard deviation percentage (RSD %) was calculated for each data point in the aligned chromatogram to assess intra- and inter-day repeatability [40], excluding those scans whose intensity was below the mean noise plus three folds the standard deviation. The mean noise was assessed within a representative range of more than 300 consecutive scans of the TIC without volatile compound peaks.  
The precision of the analytical outcome was also evaluated through a principal component analysis (PCA) (\(n=26\)) performed with the aligned volatile fingerprint of the QC (intra-day and inter-day replicates) and the control virgin olive oil samples that conformed the QC. Precision was evaluated by means of the score scatter and residual standard deviation plots [41]. The latter plot gives a distance to the model measurement, so samples below the critical value are judged as non-deviating. Here, the normalized critical value was set at 0.05 according to a significance level of 95%.
## 2 Materials and Methods
### Computational Methodology
#### 2.2.1 Experimental Database Construction  
After implementing the experimental system, the measured data were collected and arranged in a spreadsheet according to the variables of interest. In total, a database made up of 3168 data was formed from eight experimental variables. For the development of the correlational model, the following variables were considered: solar radiation, ambient temperature, relative humidity, wind velocity, input temperature dryer, output temperature dryer, and fan voltage as the independent variables, and the velocity drying as the output variable. Table 3 summarizes the ranges of the variables considered in the present study.  
Before training the ANN, the input variables were normalized because of the active functions' nonlinearity and the operating range fluctuating between 0 and 1. These are special sigmoidal functions for activating the hidden layer. This normalization allows the surface of the error function to be reduced, making training more efficient, considering the initialization of the variables to zero. Each input variable was normalized using the following equation [35]:  
\[u_{i,N}=\frac{u_{i}}{1.1\times u_{max}}, \tag{2}\]  
where \(u_{i,N}\) is the normalized variable, \(u_{i}\) is the real value of the variable before normalization, and \(u_{max}\) is the maximum value of the variable before normalization.
## 2 Proposed System Model
### FSO Channel Model  
The FSO channel gain is modeled as \(H_{2}=h_{1}h_{a}h_{p}\), where \(h_{l}\), \(h_{a}\), and \(h_{p}\) are the deterministic propagation loss, the atmospheric turbulence incurred attenuation, and the pointing error incurred attenuation, respectively. More specifically, the heat from the sun causes atmospheric turbulence and scintillation to the FSO link [4; 10]. Moreover, the atmospheric turbulence-induced fading is dominant factor that significantly impacts the performance of the transmission when the weather condition is good. On the other hand, the pointing error caused by misalignment between transmitter and receiver on physical impacted transmitter beam [6; 10]. Therefore, significant performance degradation is expected due to severe atmospheric turbulence and pointing error condition in many scenarios such as urban areas, where the FSO transceivers are placed on high-rise buildings.  
Moreover, the PDF of the instantaneous SNR of the FSO communication link [9], \(\gamma_{2}\), can be given by  
\[f_{\gamma_{2}}(\gamma)=\frac{\alpha\beta\Omega}{\gamma}H_{1,3}^{3,0}\bigg{[} \frac{\gamma}{\psi}\bigg{|}\begin{array}{c}\big{(}\xi^{2}+1,r\big{)}\\ \big{(}\xi^{2},r\big{)}\big{(}\alpha,r\big{)}\big{(}\beta,r\big{)}\end{array} \bigg{]}, \tag{4}\]  
where \(H_{\beta}^{m,n}(\cdot)\) is the Fox H-function and \(\Gamma(\cdot)\) is the Gamma function. In (4), \(\Omega=\frac{\xi^{2}}{\Gamma(\alpha)\Gamma(\beta)}\), \(\psi=\frac{\gamma_{2}}{(\alpha\beta)}\), \(r=1\) represents heterodyne detection, and \(r=2\) represents direct detection. If \(\sigma_{s}\) denotes the standard deviation of the pointing error distribution, the equivalent beam-width [4] can be expressed as \(\xi=w_{e}/2\sigma_{s}\), where \(w_{e}\) is the equivalent beam radius at the receiver. Furthermore, \(\gamma_{2}\) denotes the average electrical SNR of the FSO link [3], which can be defined as \(\tilde{\gamma}_{2}=\tilde{\gamma}_{\text{FSO}}\) for \(r=1\) and for \(r=2\) we have  
\[\tilde{\gamma}_{2}=\frac{\tilde{\gamma}_{\text{FSO}}\alpha\beta\xi^{2}\big{(} \xi^{2}+1\big{)}}{\big{[}\big{(}\alpha+1\big{)}\big{(}\beta+1\big{)}\big{(} \xi^{2}+1\big{)}^{2}\big{]}}, \tag{5}\]  
where the average SNR is \(\tilde{\gamma}_{\text{FSO}}=\mathbb{E}[\gamma]\) and \(\mathbb{E}\) is the expectation operator. The fading parameter \(\alpha\) is the effective number of large-scale cells and \(\beta\) is the effective number of small-scale cells of the atmospheric conditions in the scattering process [13].
# PID Controller Parameter Tables for Time-Delayed Systems Optimized Using Hill-Climbing
## 6 Discussion and Outlook  
The application of the hill-climbing method shows rapid convergence. The parameters found minimize the quality criteria ITA, IAE and ISE. They lead to regulated systems that show very good transient behavior. Furthermore, a PID parameter table for the control of PTn systems was calculated using this method.  
The example of the PT3 system showed identical parameters for the optimal ITAE and IAE (L1) criteria. In fact, the optimal controller parameters of all criteria considered here for PTn systems of a smaller order were very close to one another. Since the quadratic error for deviations smaller than 1 is always smaller than the absolute error, a larger overshoot is permitted for the ISE (also L2) criterion than for IAE and ITAE. For this reason, the ISE generally shows a smaller attenuation of the system, because a small ripple, and also the time, are not strongly taken into account.  
The example of the PT5 system is remarkable because such controlled systems with Tg/Tu = 2.44 are difficult to control using PID controllers, and they require optimal controller parameters. This shows the advantage of these optimal parameters according to the ITAE criterion. The time weighting ensures here that the system is sufficiently well damped. For the reasons mentioned above, the ISE criterion would have significantly poorer damping, which would not be useful in practice for the control of higher-order systems.  
It is also shown that it makes sense to include the controller output limitation in the controller design. Thus, a good controller design and a good design regarding the physics are implemented in such a way that the controller output limitation is part of the system. Only in this way can the controller setup and the technical dimensioning of the system be carried out adequately.  
With these sensibly selected PID controller parameters, PTn systems, which are relatively common in practice, can be controlled with very good results. Furthermore, because of the good convergence of the hill-climbing algorithm, it would also be possible to further automate the process and apply it directly to real systems, if the systems allow this with regard to stability.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 10: PID control of the PT5 heating chamber, according to the optimal parameters for the IAE and ITAE criteria.
# Topology Comparison Study of Five-Phase Wound-Field Doubly Salient Fault Tolerant Generators
## 3 Comparative Study and Simulation of Five-Phase WFDSG
### Rotor Pole Optimization Considering the Fringing Effect
Overall, for five-phase WFDSG, considering the pole fringing effect, to get the low cogging torque, as well as the harmonic content of phase flux and EMF, the peak of each phase inductance is equal to the trough length \(\alpha_{r}=0.5\), the rotor pole equivalent width is the sum of the rotor pole actual width and four times that of the air gap. In the traditional machine design [35], if the axial length of the stator core and the rotor core are the same, and the leakage flux between the rotor pole and the stator pole only passes through the end face. And the equivalent length of the core is usually calculated as the sum of the axial length of the stator core and double air gap. For the WFDSG, the rotor poles and the stator poles are gradually approaching and meshing at both ends. Not only the side of the rotor pole has magnetic lines of force, but also the side of faces of the stator pole have magnetic lines of force, so the magnetic flux leakage between the stator pole and the rotor pole is twice that of their alignment. Figure 4: Influence of rotor arc length on WFDSGs. (**a**) Flux linkage when excitation current is 5A; (**b**) EMF when excitation current is 5A; (**c**) the cogging torque when excitation current is 1A; (**d**) harmonics of the flux linkage and (**e**) harmonics of the EMF. In summary, the rotor pole equivalent width is the sum of the rotor pole actual width and the four times air gap. The above results are consistent with the empirical formula for the equivalent axial length of the stator core in the traditional machine design.
## 6 Current Problems and Research Difficulties  
The current problems and research difficulties pertinent to constructing an E2 RPs for UWSNs are taken into consideration in this section. In contrast to terrestrial networks, underwater data in UWSN systems incur significant bit error rates, multi-path dispersion, and propagation delays. Low dependability and excessive energy use during communicating data packet transmission are the results of these issues. In addition to these standard difficulties faced by UWSN networks, there are several unique problems that prohibits the lifetime of the network from being extended. These include issues with empty nodes, problem of hotspots, excessive high latency, limitation of ideal energy efficient route (E2R), limitation of privacy and security and unstable links. The difficulties and issues raised in this review should be useful to any interested research group.
## 4 Experiments and Analysis  
### Evaluation Metrics  
To validate the semantic segmentation performance of the pieces of training F1-score and IoU-score between the predicted results and the ground truth, annotations were measured as the main performance on the patchified-SUIM dataset. The IoU metric, often named the Jaccard index, is the ratio of intersection and union between the prediction and target value. The mean intersection on union (mIoU) is one of the common measures used in semantic segmentation tasks. In semantic segmentation, the intersection denotes base truth and union ratios correspond to segmentation prediction. This proportion is an intersection set referred to as TP, FP, and FN (union set).
## 2 Literature Review
### Non-Parametric Approaches  
In contrast, non-parametric models do not assume a fixed model structure, making them more adaptable to non-linear traffic patterns [37; 38; 39; 40]. Neural Networks (NNs) and Support Vector Regression (SVR) have proven highly effective in handling complex, multi-source traffic data, especially on arterial roads where traditional parametric models struggle [41; 42; 43]. NNs, for instance, have demonstrated superior performance in predicting both speed and volume by learning from large datasets and adapting to changing traffic conditions [44; 45; 46; 47; 48]. Non-parametric models are particularly well-suited for real-time applications in ITS, where conditions can change rapidly. Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN) [49; 50; 51; 52], have emerged as a leading approach for traffic forecasting. LSTMs are designed to handle temporal dependencies in data, making them highly effective for predicting traffic conditions over time [53]. These models are particularly valuable for arterial roads, where traffic can vary significantly due to external factors like weather or incidents. LSTMs have been shown to provide more accurate and reliable predictions than parametric approaches, especially when combined with real-time sensor data [54].
# An Experimentally Validated CFD Code to Design Coanda Effect Screen Structures
## 2 Materials and Methods  
### Experimental Setup  
Figure 1 shows the experimental setup with a laboratory-scale CES provided by Stocker Technik. The CES is designed with 17 triangular cross-section-shaped elements, each tilted by an angle of f = 5\({}^{\circ}\) and with a side length of e = 13.7 mm. The CES is set up at an angle of g = 40\({}^{\circ}\) with respect to the horizontal. An acceleration plate with a height of 58 mm is designed before the first rake element. The slot width between every CES element is d = 0.93 mm and the width is h = 100 mm. Every single element of the CES separates the flow into a bypass flow and a diverted flow region. Both are pumped back to the inlet by a centrifugal pump (Sewabloc F 50-215G H 132S, KSB, Frankenthal, Germany), which provides the desired flow rates of 7 m\({}^{3}\)h\({}^{-1}\), 8 m\({}^{3}\)h\({}^{-1}\), and 9 m\({}^{3}\)h\({}^{-1}\) for each experiment. At the inlet, there is a baffle plate and a reservoir with a height of a = 147 mm to homogenize the flow and provide stationary boundary conditions, which would not be created by the pump alone due to its transient flow behavior. The inlet volume flow rate is PI controlled [21] with an inductive flow measurement device (Promag W, ENDRESS + HAUSER, Reinach, Switzerland). The PI controller is designed to avoid control errors in the stationary boundary conditions. For the experimental analysis, the bypass volume flow rate was manually measured with an external reservoir with a volume of 100 L and a stopwatch. All investigations were performed at 20 \({}^{\circ}\)C, with v\({}_{\text{air}}\) = 1.7894 \(\times\) 10\({}^{-5}\) kgm\({}^{-1}\)s\({}^{-1}\), \(\rho_{\text{air}}\) = 1.225 kgm\({}^{-3}\), in water, with v\({}_{\text{water}}\) = 0.001 kgm\({}^{-1}\)s\({}^{-1}\) and \(\rho_{\text{water}}\) = 998.2 kgm\({}^{-3}\). There are no solid particles involved. Gravity g = \(-\)9.81 ms\({}^{-2}\) is in the y-direction (see Figure 1).  
The temporal averaged bypass flow rate of 8 min is evaluated and quantitatively compared to the simulations for each of the three experiments. To evaluate the fill level, a camera (Basler Ace 2412-30 us, Basler, Ahrensburg, Germany) with a lens (Basler C11-1620 1.1\", f = 16 mm, Basler Germany) is used. A coarse reference grid with a spacing of 1 mm for the small grid and 5 mm for the extended grid is inserted at the bottom border of the CES. The lens is focused on the thorn, see Figure 2. In total, three pictures are captured: (a) with the mm-grid, (b) without the mm-grid, and (c) with a thorn that shows the remaining water in the middle of the rake. Each picture has a resolution of 4096 \(\times\) 3000 pixels with a 96 dpi vertical and a 96 dpi horizontal resolution. The camera data were saved in TIFF file format and were not compressed. Furthermore, the fill level was measured with a caliper at every rake element.
# Phase-Coded and Noise-Based Brillouin Optical Correlation-Domain Analysis
## 2 Phase-Coded Brillouin Optical Correlation-Domain Analysis
### Applications of Phase-Coded Brillouin Optical Correlation-Domain Analysis  
Phase-coded B-OCDA was used in a series of experiments in which fibers under test were embedded in layers of composite materials [26]. The measurement resolution varied between 2 and 4 cm [26]. The analysis was employed in temperature and strain monitoring during and following the production of a composite material beam and in stiffness estimates of a loaded beam in a three-point bending configuration [26]. Figure 9 shows strain maps across the upper surface of a model wing of an unmanned aerial vehicle made with an outer skin of carbon composite layers. Static load was applied to one end of the wing [26]. Three fibers under test were placed underneath the outer-most structural layer of the model surface. The measured strain profile is presented alongside corresponding finite-element, structural analysis calculations [26]. Good agreement is obtained between simulation and experiment. In another recent study, phase-coded B-OCDA was used to monitor strain in composite material cylinders following impact [27; 28].  
Phase-coded B-OCDA also finds novel application in the mapping of opto-mechanical interactions along tapered glass micro-wires [29; 30]. Optical fibers that are tapered down to micron-scale diameter support a rich variety of bulk, surface, and hybrid guided acoustic modes [48; 49]. The spectrum of stimulated Brillouin scattering through multiple acoustic modes strongly depends on the geometry of the micro-fiber [48; 49]. In 2015, [PERSON] and co-authors used phase-coded B-OCDA to measure the local diameter of a 13-cm-long chalcogenide glass micro-wire with a spatial resolution of 1.3 mm [29]. The experimental uncertainty in the estimate of the local micro-wire diameter was only \(\pm 5\) nm [29]. Very recently, the same groups applied phase-coded B-OCDA to the distributed analysis of SBS through multiple acoustic modes of a silica micro-fiber [30]. The diameter of the fiber was gradually tapered over several cm down to a minimum waist of 0.9 \(\upmu\)m. Maps of SBS gain spectra as a function of position and a broad range of frequencies are shown in Figure 10. The spatial resolution of the analysis was 5 cm. Multiple surface and hybrid acoustic modes are observed [30].  
Figure 9: Strain profiles across the upper surface of a composite model wing of an unmanned aerial vehicle subject to a static loading of 550 N. (**a**) Strain map extrapolated from high-resolution phase-coded B-OCDA traces [26]. (**b**) Predicted strain map obtained by numerical, structural analysis [26]. Reproduced with permission from [26], MDPI, 2017.
## 7 Overview of the Major Applications of AI in Dermatology  
In recent years, artificial intelligence has revolutionised dermatology, providing advanced tools for the automatic detection and classification of skin lesions at the stages of dermoscopy, histopathology, and dermatoscopic photography. Technologies based on deep neural networks are increasingly used in both mobile applications for initial patient assessment and clinical decision support systems, contributing to improvements in diagnosis speeds and the optimisation of therapeutic paths.