esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Laboratory Measurements Results
#### 4.2.1 Laboratory Measurements of Magnetic Susceptibility  
After magnetic susceptibility was recorded for all measurement points on profiles SL1, KG1, and KG2 using the Kappameter KM-7 device, soft sediment samples were collected. These samples were dried and then crushed in the laboratory (detailed procedure described in Section 3). Subsequently, their magnetic susceptibility was measured using the Bartington MS2B device. Magnetic susceptibility was measured at two frequencies, lower (0.465 kHz) and higher (4.65 kHz).  
On the first profile from the Slovacka Cave (SL1), no negative values of magnetic susceptibility were recorded (see Table S4). At the lower frequency, values ranged from 6.60 to 99.40 \(\times\) 10\({}^{-5}\) SI. The lowest value was recorded on sample SL1-27, at the lowest point on the profile. The highest value of magnetic susceptibility corresponds to sample SL1-7. At the higher frequency, values increase from 6.50 \(\times\) 10\({}^{-5}\) SI on sample SL1-27 to 86.40 \(\times\) 10\({}^{-5}\) SI on sample SL1-7 (see Figure 4A).  
Figure 4: _Cont._  
The measured values of magnetic susceptibility at two different frequencies on profile KG1 in the Crnopac Cave System are shown in Table S5 (see the Supplementary Materials). At a frequency of 0.46 kHz, a range of values from 51.00 to 77.20 \(\times\) 10\({}^{-5}\) SI was obtained. The lowest value was recorded in sample KG1-4, and the highest in samples KG1-6 and KG1-7 (see Figure 4B). At the higher frequency, values ranged from 44.80 \(\times\) 10\({}^{-5}\) SI to 69.30 \(\times\) 10\({}^{-5}\) SI. The lowest value of magnetic susceptibility was recorded on sample KG1-4, and the highest on sample KG1-7.  
The values of magnetic susceptibility obtained at double frequency on samples from profile KG2 are shown in Table S6. Registered values at the lower frequency ranged from 4.50 to 21.40 \(\times\) 10\({}^{-5}\) SI, while at the higher frequency from 4.00 to 19.00 \(\times\) 10\({}^{-5}\) SI (see Figure 4C). At both frequencies, sample KG2-4 has the lowest value, and sample KG2-1 has the highest value.  
Figure 4: Magnetic susceptibility values obtained along the profiles (**A**) SL1, (**B**) KG1 and (**C**) KG2 using the [PERSON] device in the laboratory (light and dark blue), and in the field (grey). Left \(y\)-axis refers to laboratory measurements at 0.46 and 4.65 kHz frequencies, and the right \(y\)-axis refers to values measured in situ at 10 kHz. In the SL-1 profile samples SL1-1-SL1-10 have distance of 0.05 m, SL1-11-SL1-20 have 0.1 m, and SL1-21-SL1-27 have 0.2 m. Profile KG1 has samples with 0.5 m distance, while KG2 profile 0.2 m.
## 5 Limitations  
This study provides valuable insights into the observed trends in reports about AR in the construction industry. However, it is important to acknowledge the inherent limitations of this work. One limitation arises from the focus on the technological development of AR over a 10-year period, specifically examining papers published during this time that address AR for communication in construction tasks. Such limitations are expected when exploring emerging technologies that are still in their early stages of adoption. Additionally, it is important to consider that other domains may have reported diverse benefits and challenges associated with AR performance, extending beyond a simple examination of advantages and limitations. It is also worth noting that attributes mentioned 10 years ago may have since been resolved, which would make them no longer relevant to the use of modern AR technology. While reports from other fields could potentially validate or raise questions about the findings of this construction-centered study, they were intentionally excluded due to the defined scope of the paper. Therefore, while the observed trends in this study may not encompass all possible viewpoints, they effectively illustrate the general trends found within the published research on the construction industry. These trends can serve as a valuable foundation for guiding future studies and providing them with evidence derived from recent AR works in the construction domain.
## 4 Proposed Model
### Model Assumptions  
The Hub-and-Spoke model for Short Sea Shipment transportation considered in this study is described in detail with the following characteristics:  
* All passengers initiate their trips from a central port on the mainland.
* Two types of routes are considered, direct connection from a central port or transit travel through a hub node connection.
* Hub islands are served directly from a central port.
* All islands are served once.
* Several alternatives for placing hub nodes can be considered.
* Ships serving different routes can have different velocities.
* Time-varied intermediate stops are considered.  
The generalized objective function evaluates the fitness of each solution (hub node selection and line routing) in terms of a weighted sum of economic and non-economic components as follows:  
\[\text{min}(\text{F})=\text{w}_{1}\times(\text{D})+\text{w}_{2}\times(\text{PH })+\text{w}_{3}\times(\text{P}) \tag{1}\]  
where D represents the total distance traveled by all vessels, PH is the total passenger hours spent in all journeys, and P represents any desirable preferences, while \(\text{w}_{1}\), \(\text{w}_{2}\), and \(\text{w}_{3}\) are weight coefficients of the sub-objective functions. The first component closely relates to the traveling cost, while the second considers the number of passengers by destination, the average vessel speed, and the necessary time interval for passenger boarding and disembarking. These components are calculated as follows:
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 2 The Impact Factor of Solar Power Forecasting
### Optimization of Input Features  
The forecasting can be improved using the appropriate input selection, both in terms of quantity and type. The presence of large parameters can significantly imbalance the forecasting, whereas redundant or weakly correlated inputs will further complicate the computation. Hence, optimization techniques are critical to the selection of suitable input features.  
Many input optimization strategies for PV output models are described in the literature, including PSO [80], grid-search [81], fruit fly optimization algorithm (FOA) [82], firefly (FF) [8], ant colony optimization (ACO) [83], chaotic ant swarm optimization (CAS) [84], chaotic artificial bee colony algorithm [85], and immune algorithm (IA) [86]. While each algorithm has benefits and drawbacks, the most prominent and efficient technique for improving weights and inputs during forecasting is genetic algorithm (GA)-based optimization. Moreover, it is practical for pairing with ANN. [PERSON] and [PERSON] [87] improved the forecast accuracy of the back propagation neural network (BPNN) model by altering the input weights. Similarly, ref. [88] claimed that GA optimization improved their ANN for the PV power forecasting model. According to [89], PSO is a method of iterative optimization that is comparable to GA. The PSO method starts with a collection of random solutions and discovers the best values iteratively. Compared to GA, it has more convergence and does not require the modification of as many features [90; 91]. PSO is used as an alternative for GA in applications such as function optimization, feature selection and neural network training, fuzzy system control, and others; however, it tends to be dismissed in local extrema.
## 3 Methodology
### Model Evaluation  
#### 3.4.1 Frequency Ratio Analysis  
The frequency ratio (FR) method was applied to evaluate the relationship between LCFs and historical landslides. Accurate landslide susceptibility mapping (LSM) depends critically on selecting appropriate LCFs [15; 52]. The FR method was selected for its effectiveness in quantifying the correlation between LCFs and landslide occurrence. A higher FR value suggests a more pronounced positive correlation between the factor category and landslide occurrence, whereas a lower FR value implies a weaker correlation [33]. It provides a foundational analysis for subsequent machine learning models [53]. The FR analysis results served as preparatory work for Scenario 3. The expression for the frequency ratio is as follows:  
\[\text{Frequency ratio }=\frac{L_{j}}{L}/\frac{S_{j}}{S} \tag{4}\]  
Here, \(L_{j}\) represents the number of landslides occurring within a specific interval, while \(L\) denotes the total number of landslides across the entire study area. Similarly, \(S_{j}\) corresponds to the number of grid cells within the interval, and \(S\) refers to the total grid cells in the study area.
## 2 Related Work  
In literature, various researchers have employed FPGA for image processing applications [20; 21; 22; 23; 24]. The authors of [25] presented an FPGA hardware architecture for real-time disparity map computation. In [26], authors developed a real-time stereo-view image-based rendering on FPGA. Few researchers proposed FPGA accelerators for plenoptic camera based systems, but they only deal with the 2D refocusing algorithms. [PERSON] et al. [27] used FPGA for the real-time refocusing of standard plenoptic camera. Their results show that FPGA performs better than GPU and general purpose computer with respect to execution time. Similarly, authors of [28] suggested the hardware implementation of super-resolution algorithm for plenoptic camera. They used the FPGA hardware in their design and the results have shown that the respective execution time was better than general purpose processor based system implementation. [PERSON] et al. [29] have proposed the idea to use GPU to implement super-resolved depth estimation algorithm based on plenoptic camera system to improve the execution time performance. Similarly, the authors of [30] used GPUfor the real-time depth estimation using focused plenoptic camera. [PERSON] et al. [31] also used GPU to achieve the real-time performance of the algorithm, which consists of refocusing and stereo rendering. These studies suggest that the GPU can be used as a solution to improve the execution time of such algorithms. However, it is an expensive solution from perspective of power consumption. It is therefore not feasible for the target mobile application with limited available resources. One possible solution is to use a low-power mobile GPU, which has not been used for depth estimation algorithm based on plenoptic camera as per the best of authors' knowledge.  
A small number of researchers used FPGA for the depth estimation [32], and disparity [33] calculation algorithms based on plenoptic camera. However, these algorithms do not work directly on the raw data captured by the plenoptic camera. [PERSON]'s approach uses multiple focused images separated by some distance, which can either be obtained by multiple cameras or by generating several images from one camera with different perspectives. It consequently adds an intermediate processing overhead and ultimately increases the total time of processing. To overcome this overhead, this paper presents an FPGA design that focuses on the algorithm which works directly on raw data and hence does not require intermediate processing. The raw data in this case consists of micro-images captured directly from the plenoptic camera. There are only few researchers who published algorithms which work directly on the raw images captured from a focused plenoptic camera, such as [6; 7; 34; 35]. However, there are no hardware accelerators designed for such algorithms.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 2 Communication Channel Modeling under Resonant EMI Coupling  
In this paper, the interfering potential of periodic and SS-modulated EMI generated by switching signals and coupled to a digital communication channel by a resonant coupling mechanism, as depicted in Figure 2, are compared in the framework of Shannon information theory in terms of an equivalent EMI-induced channel capacity loss [43], following the approach adopted in [37].
# Performance of CRTS-II Ballastless Track-Bridge Structural System Rebars under Fatigue Loading Test
## 4 Finite Element Analysis Based on Material Fatigue Damage Model
### Constitutive Model of Material
#### 4.1.3 The Constitutive Model of Cement Asphalt Mortar  
In this paper, the cement asphalt mortar static and fatigue viscoelastic-damage constitutive model is introduced in order to study the evolution of cement asphalt mortar fatigue damage [39].  
The stress-strain curve of the CRTS-II cement asphalt mortar material in the compression process measured by uniaxial compression test is as follows:  
\[\sigma=1.761\times 10^{-7}\varepsilon^{6.069\varepsilon^{-0.135}} \tag{25}\]  
In order to simplify the calculation, the constitutive relationship of the static force and fatigue damage is taken as \(1/10\) of the compression. Therefore, the following only briefly describes the fatigue random damage constitutive model of CA mortar material under compression. The fatigue constitutive relationship of CRTSII cement asphalt mortar under constant amplitude fatigue load in compression is as follows:  
\[\sigma=(1-D_{M,N})E_{f0}\varepsilon \tag{26}\]  
The irreversible deformation elastic damage variable \(D_{M,N}\) is determined by reference [39].  
The fatigue elastic modulus at the N-th fatigue loading cycle is as follows:  
\[E_{fN}=\frac{\Delta\varepsilon_{1}}{\Delta\varepsilon_{N}}.E_{f0} \tag{27}\]  
\[\Delta\varepsilon_{1}=\varepsilon_{max,1}^{+}-\varepsilon_{r,1}^{+} \tag{28}\]  
\[\Delta\varepsilon_{N}=\varepsilon_{max,N}^{+}-\varepsilon_{r,N}^{+} \tag{29}\]where \(\varepsilon^{+}_{max,1}\) is the total fatigue strain of the CA mortar under the first loading cycle, \(\varepsilon^{+}_{r,1}\) is the residual strain at the first loading cycle, \(\varepsilon^{+}_{max,N}\) is the total fatigue strain at the \(N\)-th load (at the time of fatigue failure), and \(\varepsilon^{+}_{r,N}\) is the residual strain at the \(N\)-th load. The above variables are determined according to reference [39].  
According to the revised multi-stage linear cumulative damage criterion, the fatigue constitutive relationship of CA mortar under variable amplitude fatigue is deduced as follows:  
\[\sigma=\prod_{i=1}^{m}\frac{\Delta\varepsilon_{1}}{\Delta\varepsilon_{N,i}}.E_ {f0}\varepsilon \tag{30}\]  
The damage variable \(D^{VF}_{CA}(N)\) of CA mortar under variable amplitude fatigue load is as follows:  
\[D^{VF}_{CA}=1-\prod_{i=1}^{m}\frac{\Delta\varepsilon_{1}}{\Delta\varepsilon_{N,i}} \tag{31}\]  
The fatigue life of CA mortar is calculated by the following formula [40]:  
\[\text{Lg}N_{f}=10.08-8.05S \tag{32}\]  
where \(S\) is the stress level--the ratio of the maximum compressive (tensile) stress to the compressive (tensile) strength.
# Simplified Calculation Model for Contact Resistance Based on Fractal Rough Surfaces Method
## Modeling
### Contact Resistance Model  
According to the classical shrinkage resistance theory [26], the shrinkage resistance \(R_{c}\) of a sinusoidal asperity with base diameter \(l_{max}\) and contact spot radius \(r_{i}\) can be approximated as  
\[R_{c}=\left[\frac{\rho}{2r_{i}}\right]\left[\overline{R_{co}}+s_{F}\left(\frac{ d}{r_{i}}\right)\right] \tag{13}\]  
where  
\[s_{F}=\frac{4}{\pi}\left(\frac{2r_{i}}{l_{\max}}\right)\left[1-\frac{2r_{i}}{l_ {\max}}\right] \tag{14}\]  
\[\overline{R_{co}}=1-1.41581\left(\frac{2r_{i}}{l_{\max}}\right)+0.063\left( \frac{2r_{i}}{l_{\max}}\right)^{2}+0.15261\left(\frac{2r_{i}}{l_{\max}}\right) ^{3}+0.19998\left(\frac{2r_{i}}{l_{\max}}\right)^{4} \tag{15}\]  
where: \(\rho\) is the equivalent rough surface substrate material resistivity, \(\rho=\rho_{1}+\rho_{2},\rho_{1}\) and \(\rho_{2}\) are the two original contact material resistivity; \(s_{F}\) is the contractional slope; \(\overline{R_{co}}\) is the classical contraction resistance fitting formula.  
When the electrical contact surface is exposed to high humidity and severe pollution for a long time, the asperities may be covered with a contaminated film layer, such as sulfide film, oxide film, and other surface films. The presence of the contaminated film layer will further affect the current distribution in the contraction region, generating another additional resistance, called the film resistance \(R_{f}\)  
\[R_{f}=\rho_{f}\frac{d_{c}}{\pi r_{i}^{2}} \tag{16}\]  
where: \(\rho_{f}\) is the resistivity of the contaminated film layer; \(d_{c}\) is the thickness of the contaminated film layer.  
The contraction resistance and film resistance on the asperity are connected in series within the circuit, whereas the asperities of different scales are connected in parallel on the plane. The equivalent circuit model for the contact resistance is shown in Figure 6, which has a total contraction resistance of \(R\)  
\[R=\frac{1}{\sum\limits_{i=1}^{n^{\prime}}\frac{1}{k_{i}}} \tag{17}\]  
where \(R_{i}\) is the contact resistance of sinusoidal asperities of different scales, satisfying \(R_{i}=R_{ci}+R_{fi}\).  
Figure 6: Contact resistance equivalent circuit model.
## 3 Results and Discussion
### UV-Visible Studies  
The UV-visible absorption spectrum of the ZnO-NPs synthesized with and without _C. chinense_ fruit extract is shown in Figures 5a and 6a, respectively. The presence of peaks at around 302 nm and 481 confirmed the formation of ZnO-NPs synthesized with and without _C. chinense_ fruit extract. According to [PERSON] et al. [53], the peaks could be attributed to the intrinsic band-gap absorption of ZnO due to the electron transitions from the valence  
Figure 4: (**a**) SEM, (**b**) TEM images, and (**c**) particle size distribution histogram inset of ZnO-NPs synthesized without _C. chinense_ fruit extract.  
Figure 3: (**a**) Elemental mapping of ZnO, (**b**) Zn, and (**c**) O elements of ZnO-NPs biosynthesized using extract of _C. chinense_ fruits.  
band to conduction band (O\({}_{2}\)p \(\rightarrow\) Zn\({}_{3}\)d). The band gap energy was estimated using [PERSON]'s plot equation (Equation (3)) for ZnO-NPs.  
\[[\alpha\mathrm{hv}=\mathrm{C(hv}-\mathrm{Eg})]^{m} \tag{3}\]  
where \(\alpha=\) absorbance coefficient, C = constant, h = [PERSON]'s constant, \(\
u=\) photon frequency, Eg = optical band gap, and m = 1/2 for direct band gap semiconductors. The calculated band gap energy for the ZnO-NPs synthesized with and without fruit extract was observed to be 3.76 eV (Figure 5b) and 3.49 eV (Figure 6b), respectively. These bandgap energies are higher than that of the bulk (3.4 eV), and higher than those reported in the literature [46; 54; 55; 56]. The high band gap energy suggested that the ZnO-NPs synthesized using \(C\). _dcinense_ extract have more energy required to excite an electron from the valence band to the conduction band [57]. According to [PERSON] and [PERSON] [58], this variation might be due to various factors such as grain size, surface roughness, and lattice strain.
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 4 Scan-To-Model Matching with POU Implicit Surface Representation
### Finding Feature Point Correspondence  
In the current scan, each point is labeled according to the type of its corresponding voxel. During the matching process, we construct the correspondences between voxels in the map and the current feature points with the guidance of labels and Euclidean distance. Since such correspondences generating mechanism narrows down the potential candidates for correspondences, it can help to improve the matching accuracy and efficiency. The detailed correspondence construction process is given below.  
For each current edge point in feature set \(F_{e}\), we search for \(n_{e}\) voxels of  
\[M_{e}^{k}=\left\{F_{e}^{k-n},\ldots,F_{e}^{k-2},F_{e}^{k-1}\right\} \tag{6}\]  
inside a sphere space whose radius is \(r_{e}\). For each corresponding voxel, we find an edge line as the correspondence of the current edge point. After finding all the correspondence edge lines, we can get the projection points \(S_{e}\) of the current edge point on the corresponded edge lines. Then we use linear quantity \(c\) to determine whether \(S_{e}\) can be represented as an edge line. If \(S_{e}\) can be represented as an edge line, it is then regarded as the correspondence of the current edge point. After the edge  
Figure 2: (**a**) is the result of ground segmentation. In (**a**), the red points are labeled as ground points and the green points are labeled as non-ground points. (**b–d**) are the ground planar features, non-ground edge features and non-ground planar features.  
feature correspondences are obtained, we compute the distance from an edge feature point to its correspondence. The procedure of finding an edge line and the distance computation can be found in [6].  
For each current planar point in feature sets \(F_{p}\) and \(F_{gp}\), we respectively find \(n_{p}\) and \(n_{gp}\) voxels of  
\[M_{p}^{k}=\left\{F_{p}^{k-n},\ldots,F_{p}^{k-2},F_{p}^{k-1}\right\} \tag{7}\]  
and  
\[M_{gp}^{k}=\left\{F_{gp}^{k-n},\ldots,F_{gp}^{k-2},F_{gp}^{k-1}\right\} \tag{8}\]  
inside a sphere space whose radius is \(r_{p}\) as the corresponding voxels. Then, we use the corresponding voxels to construct the POU implicit surface representation of the model, and compute the distance from a planar feature point to its implicit surface representation of the model. The details of building the POU implicit surface representation and computing the distance from a planar feature point to its implicit surface representation of the model will be discussed below.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 2 Modelling Approach
### Simulation
#### 2.1.1 PV Model  
Global hourly irradiation data over the tilted surface G\((h,1)\) (W/m\({}^{2}\)) can be imported from the measured data or downloaded from the PVGIS [35], Renewables Ninja [36], and NASA [37] databases. The uncertainty is considered in the irradiance for each hour (h) of each year (y) of the system lifetime G\((h,y)\), which is obtained by multiplying G\((h,1)\) by a random number \(N(0,\sigma_{C})\) that follows a normal distribution with mean 0 and standard deviation \(\sigma_{G}\). Given the hourly irradiance, the irradiance in 15 min time steps G(t) (W/m\({}^{2}\)) is calculated using a first-order autoregressive function.  
The AC PV generator output at time \(t\) (year \(y\)), \(P_{PV}(t)\) (W), represents the minimum of the output of the PV (considering the inverter efficiency, \(\eta_{inv\_PV}\)) and the PV inverter-rated power (nominal AC output power, \(P_{PV\_N\_AC}\)).  
\[\begin{array}{c}P_{PV}(t)=\min\left(\eta_{inv\_PV}P_{PV\_N\_DC}(1-f_{red\_PV} )^{y-1}G(t)/G_{ref}f_{PV\_loss}\right.\\ \left.\left[1+\alpha/100\left(T_{c}(t)-T_{c\_ref}\right)\right],P_{PV\_N\_AC} \right),\end{array} \tag{3}\]  
where \(P_{PV\_N\_DC}\), \(f_{red\_PV}\), \(f_{PV\_loss}\), \(\alpha\)), \(G_{ref}\) and \(T_{c\_ref}\), and \(T_{c}(t)\) (\({}^{\circ}\)C) represent the rated DC peak power of the PV generator, PV annual reduction factor due to degradation, efficiency factor (dirt, wire losses...), power temperature coefficient (%/\({}^{\circ}\)C), irradiance and cell temperature under standard test conditions (1000 W/m\({}^{2}\) and 25 \({}^{\circ}\)C), respectively, and cell temperature during time \(t\), respectively, which is expressed as  
\[T_{c}(t)=T_{a}(t)+\frac{\text{NOCT}-20}{800}G(t) \tag{4}\]  
where \(T_{a}(t)\) (\({}^{\circ}\)C) represents the ambient temperature and _NOCT_ (\({}^{\circ}\)C) represents the nominal operation cell temperature.