esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 3 Proposed Disaster Response Systems for Tunnel Accidents
### Localization and Commanding for Firefighters
When it is installed inside a tunnel, the accuracy can be reduced to within 3 m due to wireless physical error and the loss of line of sight (LOS), because of obstruction by surrounding vehicles and walls. However, assuming that the length of a typical passenger vehicle is 5 m, there is no great difficulty in estimating the position of an accident vehicle. In order to obtain optimal accuracy performance, it is important to arrange the appropriate anchor nodes. In the proposed system, each anchor is arranged at intervals of 40 m per wall as shown in Figure 12. The installation height of the anchor is set to about 4 m to overcome the non-loss of line of sight (NLOS) problem caused by vehicles. The target tunnel is selected for the 1.9 km-long motor way tunnel (latitude: 35.776131, longitude: 128.497783) located in Daegu city, Korea. The tunnel consists of two lanes, the road width is 13 m and the height from the floor to the ceiling is 7.5 m. The target tunnel reflects the latest design structure of  
Figure 11: Localization with ultra-wideband (UWB). a tunnel and vehicles running at a high speed of 80 m/h or more. It is also difficult to access the inside because this tunnel has a length of more than 1 km, like in the Gottard tunnel fire case. The shape of the inside of the tunnel and the progress of the experiment are shown in Figure 13.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 5 Conclusions
### Dfe  
The feedback growing does not have any advantage in most of the cases. As well as in the FFE, the advantage manifested notably in the fiber length of 2.5 km. In other conditions, the addition led to a small performance deterioration, or it did not affect the performance at all.  
The feedback addition is supposed to improve the equalizer's performance by fully neutralizing the ISI caused by previous symbols. However, the disadvantage of this neutralizing is energy and data loss from previous samples. As the equalizer eliminates the ISI from the symbols that were decided in the detector, we profit from a better compensation; on the other hand, we lose details about other previous symbols. These data are critical to the detection reliability and noise reduction. In fact, the feedback increases the equalizer's sensitivity to the noise, stemming from the spacing of the taps, as explained in the subsection above.  
Since we saw that the FFE copes reasonably well without the feedback, as we discussed short-reach fibers, the feedback addition has no contribution to the performance, and it can actually only degrade it.  
Conceptualization, G.K.; methodology, G.K. and D.A.; validation, G.K.; investigation, D.A.; writing--original draft preparation, D.A.; writing--review and editing, G.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Organic Crystals for THz Photonics
## 2 Second-Order Organic Nonlinear Optical (NLO) Crystals
### Organic Single Crystalline Materials
The macroscopic second-order susceptibility coefficients \(\chi_{ijk}^{(2)}\) can be evaluated by the conventional Maker-fringe technique and electro-optic measurements [6; 9; 37; 38]. For these measurements, high optical quality plane-parallel samples with polished surfaces and possibly with different orientations are required and the refractive index dispersion should be also measured with a relatively high precision (see an example in Ref. [39]), which is very time-consuming. For newly developed organic NLO crystals, simpler screening methods are therefore employed to evaluate their potential for NLO applications. A relatively simple Kurtz and [PERSON] second-harmonic generation powder test can be performed to screen the new materials [40]. The test can be done qualitatively only to determine if the material is non-centrosymmetric and estimate the order of magnitude for the \(\chi^{(2)}\)-effects, but also more qualitatively including the measurements of the second-harmonic generation efficiency as a function of powder size and as a function of pump wavelength, which may indicate a possible phase-matching enhancement [40]. For highly nonlinear organic crystals that typically present a red-shifted absorption edge in the visible (above ca. 600 nm, see the following section), the coherence length for second-harmonic generation is relatively short (in the order of 1 \(\upmu\)m), much smaller than the typical powder size, which gives then a relatively good estimate for the macroscopic susceptibility based on the powder test (see, e.g., Figure 15 in Ref. [15]). Another possibility to estimate the \(\chi^{(2)}\)-potential of novel materials is to evaluate first the molecular nonlinearities \(\beta_{xyz}\) by quantum chemical calculations, determine the crystal structure by X-ray diffraction and then use the above-mentioned oriented-gas model according to Equations (7) and (8). Because of the additional influence of intermolecular interactions and local field corrections, the theoretical approximation with the effective first hyperpolarizability tensor \(\beta_{ijk}^{\text{eff}}\) only roughly explains the actually measured NLO and electro-optic coefficients but is still well suited to compare different materials and their potential for NLO applications [15]. More recently, several groups have used newly developed organic NLO crystals directly for THz-wave generation experiments and compared the generated THz fields with some standard material such as ZnTe; some examples are mentioned in Section 4.2. This method is also relatively convenient in case an appropriate THz system is available because the basic requirements for the sample quality for THz-wave generation are relatively low compared to the requirements for the optical characterization. This is because the (surface) quality requirements scale with the wavelength, which is about two orders of magnitude larger for THz compared to the optical waves.
# Observations about Propagation, Attenuation, and Radiation of EMI from HVDC Converter Stations
### Previous Research
Several measurement results are given for points near HVDC converter stations, but no modeling was performed. In two papers that appeared in 2007, and another in 2009, [PERSON] and various colleagues [15; 16; 17] described a complete circuit of a converter station including wideband equivalent circuits for components such as the smoothing reactor and parasitic elements. The entire circuit was evaluated using lumped circuit theory. Hence, the results are not likely to be valid at higher frequencies, since at 30 MHz the wavelength is 10 m and distances are no longer small compared to a wavelength. No attempt was made to model the shielding of the valve hall. Similarly, no attempt was made to model the valve hall shielding in a 2016 paper by [PERSON], [PERSON], and [PERSON] [18] or in the 2020 work of [PERSON], [PERSON], and [PERSON] [19]. In a 2019 ABB review article, it was reported that \"ABB's smart simulation models, or digital twins, reproduce the entire converter stations including valves, valve hall, converter reactors, wall bushings, converter transformers, high frequency (HF) filters and the entire wiring in the AC- and DC-yards\" [20]. While details of the modeling are not given in this article, some information about their models has been published in [2; 21; 22]. Their simulations were performed in the CST Studio Suite using the transmission line matrix (TLM) time-domain solver with broadband circuit models for the smoothing reactors and transformers. It was noted in [2] that the highest frequency of interest was 10 MHz. However, while the comparison between the simulation and measurements reported in their Figure 17 was generally good, there are significant differences at some frequencies below 10 MHz and the measurements in the range above 10 MHz suggest that some attention being paid to modeling in this range is warranted. Further, in [20] it is noted that bushings, doors, vents, and possible deficiencies in wall construction will reduce shielding efficiency because they create openings in the Faraday cage. These issues will be addressed in this paper. Most recently, [PERSON] et al. developed a model for calculating the electromagnetic radiation of the converter valve based on the method of moments [23]. This model has been used to study the interaction between the converter valve and its operating system. It does not report any calculations of the EMI outside the station.
## 6 Simulation
### Delay Caused by Relay Nodes without Concurrent Traffic  
The second scenario, considers \(d_{a}=d_{min}\) and all relay nodes delaying the stream fragments (\(\mathbf{V}^{j}\)) per \(d_{a}/10000\) or \(0.01\%\) of the \(d_{a}\). In this scenario, the application cannot send \(\mathbf{V}\), because the relay nodes eventually can be executing another task, consequently, the deadline \(d_{a}\) cannot be met. We change the number of nodes (\(128\), \(256\), \(512\), and \(1024\)) and \(|\mathbf{V}|\) (\(256\), \(512\), \(1024\), and \(2048\)). The objective of this scenarios is to identify the appropriate strategy when relay nodes have other high priority tasks.  
Figure 8 shows the delay results when we change the number of nodes with \(|\mathbf{V}|=2048\). In contrast to prior scenario, \(d_{a}\) is always met. The complex formulation, in a more realistic scenario, presents a better time usage, _i.e._, the delay is closer to deadline values. This occurs because the \(\mathbf{\Psi}\)-reduction estimation in sample case [Equation 7] is related to \(d_{a}\), _i.e._, if \(d_{a}\geq d_{min}\) reduction cannot be efficient, when we consider other delay aspects like concurrent traffic. Another important observation is that more data is received in the complex formulation strategy. When the number of nodes is \(1024\), nearly \(20\%\) of data are deliver, in the complex formulation, against \(18\%\), in the simplified one. This fact indicates that, considering only the deadline achievement, the complex formulation is more appropriate to more realistic scenarios.  
Figure 8: Delay considering the delay caused by relay nodes without concurrent traffic.  
more appropriate when we have large scale networks. The partial conclusion, considering more realistic real-time scenarios, is that the complex formulation is more appropriate, because the deadlines are met in all cases and the data representativeness is kept.
## 2 Methods Used
### Validation Methods  
#### 2.6.1 Receiver Operating Characteristic (ROC) Curve  
In the ROC curve, the proportion of pixels correctly predicted by the presence or absence of groundwater potential on the horizontal axis (true positive or 1-specificity) and the proportion of pixels that are incorrectly predicted is plotted on the vertical axis (false positive or sensitivity) [46; 47; 48; 49; 50; 51]. The area under this curve is called the AUC, and the model with the highest AUC has the highest relative performance [52; 53; 54; 55; 56; 57; 58; 59; 60; 61]. The AUC values equal to 0.5 indicate random prediction for a model [62; 63; 64; 65; 66]. The AUC values range from 0 to 1. Greater values of the AUC indicate a higher prediction efficiency of a model [67; 68; 69; 70; 71; 72].
## 2 Methodology
### Interpolation Model
#### 2.1.1 Dim  
DIM can be expressed as a linear combination of a set of reference station corrections, and the combination coefficient is the reciprocal of the distance. When there are \(n\) reference stations, the mathematical model is as follows [5]:  
\[A_{u}=\sum_{i=1}^{n}\alpha_{i}\cdot A_{i} \tag{1}\]  
where \(A_{u}\) represent the atmospheric delay interpolations of the user, \(i\) represents the reference station, \(u\) represents the user station, \(\alpha=\left[p_{1}/p\quad p_{2}/p\quad\cdots\quad p_{n}/p\right]\) represents the interpolation coefficients, and \(p_{i}\) is the reciprocal of the distance between a reference station and a user station and \(p=\sum_{i=1}^{n}p_{i}\). DIM is related to the geometric distribution of user stations and reference stations, as well as the geometric relationship between reference stations and satellites, and has nothing to do with interpolation.
## 4 WirelessHART Scheduling Schemes
### The Zhang Policy  
The work in [82] establishes the lower bound for the convergecast time, establishes a lower bound for the number of channels for time-optimal convergecast and proposes two scheduling policies based on the optimal scheduling for line topologies proposed in [83]. It is important to note that no actual algorithm was proposed and that no redundancy technique was adopted. The proposed policies apply to multi-line topologies like the one presented in Figure 12. It is worth noting that the main issue addressed by this proposal is the fact that the last hop to the gateway is a limitation on the system, since all of the packets must pass through this hop, and thus, the half duplex radio of the gateway limits the flow to one packet per slot time.  
The first policy guarantees a time optimal convergecast, sustaining the established lower bound for the size of superframe needed to allocate all transmissions. However, this policy is not efficient in terms of channel usage. In order to achieve something near-optimal in that way, the second proposed policy uses the established lower bound to the number of channels, although this bound is not always achievable. The second policy (time- and channel-optimal convergecast policy) is a heuristic approach that is based on two steps that should be applied to each time slot.  
The first step is called connectivity keeping and traverses the levels of the topology from the gateway to the leaf nodes and prioritizes the scheduling of links that avoid the so-called schedule holes. The  
Figure 12: Multi-line topology example.  
schedule holes are those that have a slot time where the gateway does not receive a packet, while the network still has packets; in other words, the transmission opportunities to the gateway are underused. This situation causes delays for all of the network schedule.  
If the number of devices scheduled in connectivity keeping is not the maximum that this slot supports, the policy called deadline-based scheduling should be applied. This policy organizes the devices that can be scheduled for transmission in that slot time and sorts them in non-decreasing order, giving scheduling priority to those with tighter deadlines. The hop count is used as a secondary sorting parameter.  
A heuristic algorithm based on the presented time- and channel-optimal convergecast policy was implemented in a MATLAB(r) simulation and in a Contiki OS Java (COOJA) simulator [84]. For the MATLAB(r) simulation, the proposal obtained a schedule that used at most one slot time more than the established lower bound schedule length. Of the 10,000 runs, only two did not reach the lower bound for the convergecast time. The test on the COOJA simulator showed that the scheme can collect data from 34 devices divided into six lines (the lines have, respectively, 10, 8, 6, 4, 4 and 2 devices) in 0.4 s for a 125-byte payload.
# Design of a Solenoid Actuator for a Cylinder Valve in a Fuel Cell Vehicle
## 3 Design of Double Plunger Type Solenoid Actuator
### Optimal Design of Solenoid Actuator
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbols** & **Values** \\ \hline Distance between magnetic poles & \(x_{1}\) & 0.9 mm \\ \hline Magnetic pole area & \(5_{10}\) & 57.0 mm\({}^{2}\) \\ \hline Inner diameter of coil bobbin & \(B_{i}\) & 16.7 mm \\ \hline Height of coil bobbin & \(h\) & 12–40 mm \\ \hline Width of coil bobbin & \(w\) & 3–5 mm \\ \hline Bare copper wire diameter & \(N_{d}\) & 0.3, 0.32, 0.35, 0.37, 0.4, 0.45, 0.5, 0.55 mm \\ \hline Enameled wire diameter & \(C_{d}\) & 0.36, 0.38, 0.41, 0.43, 0.46, 0.52, 0.57, 0.62 mm \\ \hline Enameled wire resistance per unit length & \(\rho\) & 245.6, 215.9, 180.5, 161.5, 138.2, 109.2, 87.81, \\  & & 72.57 \(\Omega\)/km \\ \hline Initial state duty ratio & \(D_{\text{ri}}\) & 100\% \\ \hline Steady state duty ratio & \(D_{\text{rs}}\) & 40\% \\ \hline Voltage & \(V_{\text{max}}\) & 12 V \\ \hline Permeability of free space & \(\mu_{0}\) & \(4\pi\times 10^{-7}\) H/m \\ \hline Permeability of STS430 FR & \(\mu_{1}\) & 0.003407 H/m \\ \hline Permeability of SUM24L & \(\mu_{2}\) & 0.00117 H/m \\ \hline Saturation temperature & \(T_{s}\) & \textless{100 }C \\ \hline Slope of heat dissipation coefficient & \(a\) & 0.039 \\ \hline Intercept of heat dissipation coefficient & \(b\) & 8.83 \\ \hline Allowance coefficient & \(c\) & 1.2 \\ \hline Required attraction force for opening & \(F_{R}\) & \textgreater{}15.2 N \\ (Saturation temperature) & \(F_{R}\) & \textgreater{}15.2 N \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Design parameters of solenoid actuator.
###### Abstract  
When discussing Cultural Heritage (CH), the risk of causing damage is inherently linked to the artifact itself due to several factors: age, perishable materials, manufacturing techniques, and, at times, inadequate preservation conditions or previous interventions. Thorough study and diagnostics are essential before any intervention, whether for preventive, routine maintenance or major restoration. Given the symbolic, socio-cultural, and economic value of CH artifacts, non-invasive (NI), non-destructive (ND), or As Low As Reasonably Achievable (ALARA) approaches--capable of delivering efficient and long-lasting results--are preferred whenever possible. Electromagnetic (EM) techniques are unrivaled in this context. Over the past 20 years, radiography, tomography, fluorescence, spectroscopy, and ionizing radiation have seen increasing and successful applications in CH monitoring and preservation. This has led to the frequent customization of standard instruments to meet specific diagnostic needs. Simultaneously, the integration of terahertz (THz) technology has emerged as a promising advancement, enhancing capabilities in artifact analysis. Furthermore, Artificial Intelligence (AI), particularly its subsets--Machine Learning (ML) and Deep Learning (DL)--is playing an increasingly vital role in data interpretation and in optimizing conservation strategies. This paper provides a comprehensive and practical review of the key achievements in the application of EM techniques to CH over the past two decades. It focuses on identifying established best practices, outlining emerging needs, and highlighting unresolved challenges, offering a forward-looking perspective for the future development and application of these technologies in preserving tangible cultural heritage for generations to come.  
Cultural Heritage; X-ray; radiography; tomography; multispectral imaging; spectroscopy; X-ray fluorescence; Raman spectroscopy; THz imaging; AI +
Footnote †: journal: Applied sciences
# Floquet Modification of the Bandgaps and Energy Spectrum in Flat-Band Pseudospin-1 Dirac Materials
## 4 Results and Discussion
This is exactly what we see in Figure 6. However, we also note that the obtained elliptical dispersions significantly depend on the phase \(\phi\). Both the shape and size of the constant-energy cut of the electron dressed state energy dispersions are changed more significantly for larger values of \(\phi\) or relative hopping parameter \(\alpha\). The way in which the energy dispersions of the dressed states are formed in a Lieb lattice is drastically different from that for the gapped \(\alpha-\mathcal{T}_{3}\) materials. First, we have fewer parameters to consider here. The bandgap and the exact locations of the energy subbands of a non-irradiated lattice are fixed, and the main question here is whether once the dressing field is imposed, the flat band would remain flat, like in a dice lattice, or receive a finite curvature, just as we observed for the general \(\alpha-\mathcal{T}_{3}\) model with \(0<\alpha<1\). Our numerical results, presented in Figures 7 and 8, clearly demonstrate that the flat band receives a finite dispersion and its convex-concave type of curvature does not remain the same. Therefore, for any finite value of the coupling parameter \(\lambda_{0}>0\), the subbands cannot be described by the parabolic dispersions obtained using a constant-mass approximation. Moreover, for a large \(\lambda=0.9\), the former-flat band's location is such that its energy could only decrease for larger values of the wave vector \(k\) (see Figure 8). Figure 5: (Color online) The \(k_{x}\)-dependence of the energy dispersions \(\epsilon_{d}(\lambda_{0}|k_{x},k_{y}=0)\) for \(\alpha-\mathcal{T}_{3}\) materials. Each panel describes a material with bandgap \(\Delta_{0}=1.0\), electron-light coupling parameter \(\lambda_{0}=0.5\), and a specific phase \(\phi\) according to our labeling (**a–c**). Figure 6: (Color online) Angular dependence of the constant energy cut \(\mathcal{E}=2.0\) of the dispersions \(\epsilon_{d}(\lambda_{0}|k_{x},k_{y}=0)\) of \(\alpha-\mathcal{T}_{3}\) materials in the presence of an elliptically polarized dressing field. The two curves in each panel correspond to \(\beta=1\)—an isotropic circularly polarized light (red curve), and strongly anisotropic elliptically polarized irradiation with \(\beta=0.2\) (green curve). Panel (**a**) is related to phase \(\phi=\pi/8\), while plot (**b**) to a dice lattice with \(\phi=\pi/4\). We considered a finite bandgap \(\Delta_{0}=1.0\) and coupling parameter \(\lambda_{0}=0.9\) for (**a**,**b**)  
The degeneracy at \(\mathbf{k}=\mathbf{0}\) is clearly lifted, and we observe three inequivalent bandgaps between the valence, flat, and conduction bands. The locations of the valence and conduction bands also change. Each of the two gaps is decreased, and the \(k\)-dependence becomes more significant. The situation becomes very different for a large \(\lambda=0.9\), for which we find that all three subbands demonstrate low dispersions (reduced dependence on \(k\)), while each of the gaps is significantly modified.
## 1 Introduction
### Objectives  
In this paper, we integrate remote sensing techniques of classification and error detection with logistic regression into an innovative workflow. Although we categorize multiple primary land covers, the key objective is the development of a classification protocol to effectively capture cropland in a savanna landscape. The workflow combines statistical clustering, supervised classification, proportional sampling, and targeted error detection with a probabilistic reclassification technique (logit models), incorporating elements of phenology and physically-based fraction estimates of land cover. While the set of analytical techniques utilized here has been part of the existing toolkit of remote sensing researchers, the innovation in this approach is the combination of sequence and strict application of techniques. We demonstrate this methodology with results derived from a land cover classification of the smallholder-dominated, semi-arid province of Southern Zambia. Land cover is classified into five primary categories over a study area covered by three adjacent Landsat 5 Thematic Mapper (TM) footprints, producing an overall accuracy of 88.18% and reducing errors of omission and commission within the cropland class to 12% and 10% respectively.  
Policy analysts and practitioners note that satellite-derived measures of cropland in Africa, with minimum accuracies ranging between 80 and 85 percent, could fill in statistical data gaps and provide valuable data for designing development programs and targeting donor investment, as well as \"monitoring dynamics of agriculture extent and output and evaluating food security in the continent\" [18, 19]. The classification protocol we present in this paper is a practical, flexible, and effective approach to cropland mapping in a smallholder savanna environment. It has the potential to provide a valuable accounting and decision-making tool, essential for the sustainable agricultural development, food security, and ecosystem service provisioning of savanna range countries in Africa.