esa-sceva/satcom-chunk-collection huggingface dataset:
# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 4 Mathematical Model
### Criteria Preferences and Potentials  
All terms, relations, and notations defined below have been described previously [54]. The decision maker (inland terminal manager) subjectively defines the preference intensities as values of each replacement on a 5-point scale, where 0 means indifference; 1, weak preference; 2, strong preference; 3, distinct preference; and 4, absolute preference. The intensity of the replacement preference is a skew-symmetric function \(\mathcal{F}(a\gets b)\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **Categories** \\ \hline Container dimension \((a)\) & \(20^{\prime}\), \(30^{\prime}\), \(40^{\prime}\), or \(45^{\prime}\) \\ \hline Container occupancy \((b)\) & Full or empty \\ \hline Cargo type \((c)\) & Dangerous, perishable, other \\ \hline Container owner \((d)\) & Railway or road operator, shipping company, or customer \\ \hline Distance to container’s final destination \((e)\) & 0–50, 50–100, 100–150, or more than 150 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Storage criteria for containers in the inbound zone at the 11 European inland terminals analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension (Feet)** & **Number** & **Share (\(w\))** & **Retention (\(t\) in Days)** \\ \hline
20 & 20 & 32\% & 9.75 \\ \hline
30 & 6 & 10\% & 5.5 \\ \hline
40 & 24 & 37\% & 8 \\ \hline
45 & 13 & 21\% & 4.75 \\ \hline
**Total** & **63** & **100\%** & **Average: 7.07** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average daily number of inbound containers and their average retention time at the 11 European inland terminals, according to container dimension.  
\[\mathcal{F}(a\gets b)=-\mathcal{F}(b\gets a) \tag{2}\]  
A value \(\mathcal{F}(a\gets b)\) is a gain if criterion \(a\) prevails over criterion \(b\), which was determined based on interviews with inland terminal operators.  
All preferences together create the preference stream \(\mathcal{F}\). The stream is consistent if the following statement is valid for all three concatenated criteria \(a,\ b,c\in V\)  
\[\mathcal{F}(b\gets c)+\mathcal{F}(a\gets b)=\mathcal{F}(a\gets c). \tag{3}\]  
The preference stream \(\mathcal{F}(a\gets b)\) defines the preference relation \(\geq_{\mathcal{F}}\) on the set \(V\)  
\[a\geq_{\mathcal{F}}b\Leftrightarrow\mathcal{F}(a\gets b)\geq 0\ \to. \tag{4}\]  
Preference intensities define the potential criterion function \(X:V\to\mathbb{R}\), such that  
\[a\geq_{\mathcal{F}}b\Leftrightarrow X(a)\geq X(b). \tag{5}\]
## 2 Material and Methods
### GC-MS Instrument and Analytical Conditions  
All GC-MS analyses were performed with an Agilent GC 7890B gas chromatography coupled with an Agilent 5977B mass spectrometer detector (MSD). In the gas chromatographic system, an HP-5 MS capillary column 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m (Agilent Technologies, Santa Clara, CA, USA) was used. The GC was equipped with a split/splitless injector and an SPME inlet (Supelco, Bellefonte, PA, USA), which operated under the splitless mode during the analysis. The injection temperature of the GC inlet was 270 \({}^{\circ}\)C. Helium was used as a carrier gas at a constant flow of 1.2 mL/min. The oven temperature program was 60 \({}^{\circ}\)C for 2 min and increased at a rate of 7 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, 5 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C, and finally at a rate of 50 \({}^{\circ}\)C/min to 320 \({}^{\circ}\)C before being held for 3 min with a total run time of 45.4 min. The MSD transfer line, ion source and quad-pole temperatures were 300, 230 and 150 \({}^{\circ}\)C, respectively. Ionization energy was 70 eV, and scan acquisition mode was performed at a scan ranged from 50 to 600 _m/z_ at a scan speed of 10,000 amu/s.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 2 Methods and Data
### Distribution of Strong Motion Records in the Loess Plateau Region  
A total of 4010 strong motion records are collected, including records in the east-west, north-south, and vertical directions. Among these, 2678 are horizontal and 1332 are vertical strong motion records. Table 2 lists the distribution of the strong motion records based on the epicentral distance, with distances between 30 km and 100 km accounting for 48.9% while records closer to the epicenter are relatively few. Only 99 records are within 5 km of the epicenter, accounting for approximately 2.5%.  
Figure 2 depicts the epicentral distance distribution of the strong motion records collected in the Loess Plateau region, revealing that the epicentral distance range broadens as the magnitude increases. Most strong motion records are concentrated within an epicentral distance of 200 km, accounting for approximately 91.2% of the total records.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Magnitude**} & \multicolumn{1}{c}{**Number of Seismic Events**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Horizontal Strong** \\ **Ground Motion Data** \\ \end{tabular} } \\ \hline
7 & 1 & 15 \\ \hline
6.0–6.9 & 8 & 1268 \\ \hline
5.0–5.9 & 19 & 888 \\ \hline
4.0–4.9 & 57 & 1335 \\ \hline
3.0–3.9 & 41 & 444 \\ \hline
2.0–2.9 & 8 & 60 \\ \hline Total & 134 & 4010 \\ \hline \end{tabular}
\end{table}
Table 1: Seismic event magnitude information.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Epicentral Distance**} & **0.0–5.0** & **5.1–10.0** & **10.1–30.0** & **30.0–100** & **100.1–200** & **>200** & **Subtotal** \\ \hline \multirow{4}{*}{Magnitude} & 7 & 0 & 0 & 0 & 3 & 0 & 12 & 15 \\ \cline{2-9}  & 6.0–6.9 & 0 & 12 & 84 & 807 & 189 & 176 & 1268 \\ \cline{2-9}  & 5.0–5.9 & 3 & 12 & 102 & 345 & 291 & 135 & 888 \\ \
# Balance of Antioxidants vs. Oxidants in Perinatal Asphyxia
## 5 Free Radical Biomarkers in NE
### Markers of Lipid Damage (Malondialdehydedehyde and 4-Hydroxymonenal)  
The amount of damage to lipids during periods of hypoxia/ischemia can exceed the body's ability to self-repair, leading to negative effects on the structure and function of cell membranes, loss of function, and even cell death. Once this damage begins, several products of lipid damage are released, many of which can be detected in the brain due to its high fat content.  
Malondialdehydedehyde (MDA), which is a sign of damage to n-3 and n-6 fatty acids, has been extensively studied because it is easy to measure using common spectrophotometry. Evidence is consistent that serum MDA levels are higher in severely asphyxiated compared to non-asphyxiated newborns [21; 79; 81; 83; 84]. As for predicting outcomes, higher MDA levels in the blood at 24 h are linked to more severe NE [21], with the highest levels found in infants who died or had long-term neurological problems [85; 86]. A potential issue with using MDA in the blood is that it might not be specific to the brain, as it could also reflect damage to other tissues due to widespread organ damage. Using MDA in the cerebrospinal fluid might provide a better idea of the damage to the brain in asphyxiated infants. Studies have shown that MDA levels in the cerebrospinal fluid are higher in asphyxiated compared to non-asphyxiated term infants, with a stronger link to the severity of NE [76]. This is also seen in infants who have died or have long-term neurological issues compared to those who have recovered normally [86]. However, more research is needed to confirm that MDA in the cerebrospinal fluid is a reliable indicator of brain damage in asphyxiated infants. Regarding 4-hydroxynonenal (4-HNE), a byproduct of damage to n-6 fatty acids, it was found that 4-HNE levels in the blood increased following a period of hypoxia [87]. However, evidence in this biomarker is limited because 4-HNE is difficult to measure and requires advanced liquid chromatography techniques as it is unstable [88].
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Adaptive
#### 5.1.2 Enhancing Routing Performance Using Clustering  
[PERSON] et al. (2017) propose an adaptive routing scheme that enables each node to use fuzzy logic to predict the next geographical location of the neighboring nodes based on their received signal strength in scenarios with unknown geographical locations (e.g., the GPS does not function) and select themost suitable next-hop node as part of a route in FANETs. The main objective is to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses three challenges, namely high dynamicity (X.1) of network topology, cost (X.2), as well as low residual energy (X.3) as a result of high energy consumption and computational cost since flying nodes are spaced far apart in the sky. The routing scheme is designed for both hop-by-hop (C.1.1) and source (C.1.2) routing approaches.  
During the neighbor and route discoveries stage, each node sends control messages that contain node identity (M.5) (i.e., node addresses) to form a neighbor set comprised of two-hop neighboring nodes as well as to predict the geographical locations of one-hop neighboring nodes based on received signal strength, which indicates the distance between nodes, using fuzzy logic [127]. During the route selection stage, in order to reduce energy consumption, the underlying network is clustered based on a) residual energy (M.4), whereby nodes with higher residual energy are selected as CHs, and b) geographical location (M.3), whereby nodes near a CH joins its cluster. The CH collects data from CMs and then aggregates and forwards the data. Selecting CHs with high residual energy and node density reduces energy consumption and, hence, prolongs route lifetime. During the route maintenance stage, which is required when neighboring nodes move out of a node's transmission range, the node initiates the route discovery mechanism (or the first stage).  
The routing scheme is compared with a state-of-the-art scheme which is an energy-efficient multi-hop hierarchical routing protocol that optimizes clustering process based on an energy-efficient strategy. Random direction mobility model (A.3) is used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., higher throughput) and to reduce energy consumption (P.2).
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 4 Discussion
### Estimation of the Practical Solar Resource to VIPV in Other Regions  
Assuming that the density and height distribution of the shading objects are the same to that of Miyazaki, Japan, namely the average shading height is about 15.5\({}^{\circ}\), it may be possible to anticipate the energy yield of the VIPV in various area in the world; this is useful to anticipate the merits of the introduction of solar-driven vehicles.  
Figure 19 indicates the map of the practical solar resource on the car roof normalized to the GHI, affected by climate conditions. They are calculated using a model in Sections 2.2 and 2.5, using METPV-ASIA solar irradiance database. Due to the shading impact and curved surface, the practical solar resource for the car roof is less than the typical installation. The rough value may be 3/4 of the GHI.  
Figure 19: Map of the effective solar irradiance for the car roof: (**a**) curve-correction factor in a typical car roof; (**b**) effective solar resource to the car roof normalized to GHI, including the loss by the curved surface; (**c**) correlations between latitude (related to the sun height) and the curve correction factor; (**d**) correlations between latitude (related to sun height) and the effective solar resource to the car roof normalized to GHI, including the loss by the curved surface.  
Generally speaking, both the shading impact and curve impact increases with the decrease of the sun height, namely, increasing the latitude. However, both curve-correction factor and effective solar resource to the car roof normalize to GHI (including the loss by the curved surface), do not show a strong correlation to latitude (thus sun height), and unlike other typical solar resource parameters, is affected by local meteorological conditions. Also, note that both the curve-correction factor and the effective solar resource relative to GHI are strongly affected by the distribution (both special and height) of shading objects.
## 5 Limitations  
This study has several limitations. No correction for multiple comparisons (e.g., Bonferroni adjustment) was applied in this study. While this allowed for a broader exploratory analysis, it increased the chance of Type I error, which should be addressed in future research with stricter statistical controls. Even though the group-level classification was performed with high accuracy, the use of ML models created in this study cannot be implemented for personalized prescriptions due to the limited number of players. Another key limitation of this study is the use of BMI to assess body composition, which may not accurately reflect muscle mass and body fat distribution in athletic populations. Future research should consider more precise methods, such as DXA or BIA, for a more detailed analysis. Additionally, this study is limited by the age differences among groups and the absence of randomized group assignment. While all players had completed their biological development, these factors may have introduced bias and should be controlled in future research.
# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
## 4 Training and Analysis of Cutting Force Estimation  
The training results of 200 epochs for cutting force estimation using the proposed model and dataset are shown in Figure 13. In the figure, the blue line represents the root mean square error of cutting force (\(RMSE_{F}\)) calculated between the cutting forces obtained from the dynamometer (training dataset) and those estimated by the LSTM-based algorithm [27]. The red line represents the \(RMSE_{F}\), calculated using the validation dataset. The definition of \(RMSE_{F}\) is provided in Equation (9):  
\[RMSE_{F}=\sqrt{\sum_{i}^{n}\frac{\left(F_{xi}-\hat{F}_{xi}\right)^{2}+\left(F_ {yi}-\hat{F}_{yi}\right)^{2}}{n}} \tag{9}\]  
where \(n\) and \(i\) represent the total number of datasets and the index of each dataset, respectively. \(F_{xi}\) and \(F_{yi}\) are the actual cutting forces for the \(i\)-th dataset, while \(\hat{F}_{xi}\) and \(\hat{F}_{yi}\) represent the cutting forces estimated by the LSTM-based learning algorithm.  
To evaluate the cutting force estimation performance of the proposed LSTM architecture, a comparative analysis was conducted by replacing the LSTM layer with a fully dense layer while maintaining the overall model structure. The alternative model was trained using the same dataset to assess its effectiveness in capturing cutting force  
Figure 12: LSTM architecture for cutting force estimation.  
Figure 13: Performance of the learning model during training: \(RMSE_{F}\) in training and validation datasets.  
patterns. Figure 14a,b illustrate the estimation performance for \(F_{x}\) and \(F_{y}\), respectively. The LSTM-based model demonstrates a strong correlation with the actual cutting forces measured by the dynamometer, exhibiting minimal error and closely following the real force patterns. In contrast, the fully dense layer-based model exhibits significant oscillations in its estimations, indicating instability in force prediction.  
This discrepancy is further evident in the cutting force correlation estimation performance, as shown in Figure 15a. The fully dense model struggles to capture the variations in force correlation, whereas the LSTM-based model accurately follows the real correlation trend. Notably, in Figure 15b, the correlation coefficient experiences a noticeable decline around the 17 th layer. While the fully dense model fails to detect this critical change, the LSTM-based model successfully tracks the actual trend, highlighting its superior ability to model the temporal dependencies in cutting force variations.  
Figure 14: Estimated force components using validation data: (**a**) estimated \(\hat{F}_{x,avg}\) using validation data; (**b**) estimated \(\hat{F}_{y,avg}\) using validation data.  
Figure 15: Correlation results estimated using the trained model: (**a**) correlation results for the entire pass; (**b**) correlation results for 420–600 passes.
# 2 \(\times\) 2 Matrices: Manifolds, Realizations, Applications
## 3 Canonical Integral Transforms and the Universal \(2\times 2\) Matrix Covers
### Transforms of Functions and of Operators  
All \(2\times 2\) matrices of unit determinant, such as the displacement and lens transformations (11) and (12), will now act on phase space Schrodinger operators of position and momentum, \(\widehat{q}\) and \(\widehat{p}=-\mathrm{i}\partial_{q}\), which in turn are applied to a space of functions, \(f(q)\in\mathcal{L}^{2}(\mathsf{R})\), that stand for wavefield (or'signal') amplitudes on a one-dimensional screen. These functions are assumed to belong for convenience to the Hilbert space of square-integrable functions over the real line, under the usual inner product \((f_{1},f_{2}):=\int_{\mathsf{R}}\mathrm{d}q\,f_{1}(q)^{*}f_{2}(q)<\infty\).  
In this context, the well-known [18] Fourier transform \(\mathcal{F}\) that maps functions \(\mathcal{F}:f(q)\mapsto\widetilde{f}(q)\), will map \(\widehat{q}f(q)=  
The appearance of the inverse matrix is mandated because we need the canonical transforms to form a group, so that the application of one transform \(\mathcal{C}_{\mathbf{M}_{1}}\) followed by another \(\mathcal{C}_{\mathbf{M}_{2}}\), compose as:  
\[\begin{split}\mathcal{C}_{\mathbf{M}_{2}}\mathcal{C}_{\mathbf{M}_{ 1}}\!\left(\frac{\widehat{q}}{\widehat{p}}\right)\!\mathcal{C}_{\mathbf{M}_{ 1}}^{-1}\mathcal{C}_{\mathbf{M}_{2}}^{-1}&=\mathcal{C}_{ \mathbf{M}_{2}}\mathbf{M}_{1}^{-1}\!\left(\frac{\widehat{q}}{\widehat{p}} \right)\!\mathcal{C}_{\mathbf{M}_{2}}^{-1}&=\mathbf{M}_{1}^{-1} \mathcal{C}_{\mathbf{M}_{2}}\!\left(\frac{\widehat{q}}{\widehat{p}}\right)\! \mathcal{C}_{\mathbf{M}_{2}}^{-1}\\ &=\mathbf{M}_{1}^{-1}\mathbf{M}_{2}^{-1}\!\left(\frac{\widehat{q }}{\widehat{p}}\right)&=(\mathbf{M}_{2}\mathbf{M}_{1})^{-1}\! \left(\frac{\widehat{q}}{\widehat{p}}\right)&=\mathcal{C}_{ \mathbf{M}_{2}\mathbf{M}_{1}}\!\left(\frac{\widehat{q}}{\widehat{p}}\right) \!\mathcal{C}_{\mathbf{M}_{2}\mathbf{M}_{1}}^{-1}.\end{split} \tag{23}\]  
Composition then follows matrix multiplication, left to right, as \(\mathcal{C}_{\mathbf{M}_{2}}\mathcal{C}_{\mathbf{M}_{1}}=\pm\mathcal{C}_{ \mathbf{M}_{2}\mathbf{M}_{1}}\); the double sign appears because the bilateral action \(\mathcal{C}\cdots\mathcal{C}^{-1}\) is quadratic. It also follows that \(\mathcal{C}_{\mathbf{M}}^{-1}=\pm\mathcal{C}_{\mathbf{M}^{-1}}\), and that the identity canonical transform operator \(\mathcal{I}=\pm\mathcal{C}_{\mathbf{1}}\), corresponding to two 'unit' transformations that commute between themselves and all other \(\mathcal{C}_{\mathbf{M}}\)'s--the first and second metaplectic units. Thus we find a fundamental upgrading of classical to quantum formalism that occurs between geometric and wave optics.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 3 Materials and Methods
### Training and Evaluation  
The model was trained using the Adam optimizer with an initial learning rate of 0.001. The learning rate was maintained constantly throughout the training process, which lasted for 500 epochs. To effectively capture the complex-valued nature of the eddy current signals, the loss function employed was the complex-valued mean squared error (CMSE), defined as  
\[L_{CMSE}=\frac{1}{N}\sum_{i=1}^{N}\Bigl{[}\left(\Re(\hat{z}_{i})-\Re(z_{i}) \right)^{2}+\left(\Im(\hat{z}_{i})-\Im(z_{i})\right)^{2}\Bigr{]} \tag{8}\]  
where \(\hat{z}_{i}\) and \(z_{i}\) represent the predicted and true complex values, respectively, and \(\Re(\cdot)\) and \(\Im(\cdot)\) denote the real and imaginary parts of the complex numbers.  
This loss function enabled the model to simultaneously learn the amplitude and phase features of the signals, thereby improving its representation capability. For model evaluation, several classification metrics derived from the confusion matrix were utilized, including accuracy, precision, recall, and F1-score. Accuracy quantifies the overall correctness of model predictions; precision measures the proportion of true positive predictions among all positive predictions, reflecting the reliability of the model; recall assesses the coverage of actual positive samples correctly identified by the model; and F1-score, the harmonic mean of precision and recall, provides a balanced evaluation especially in cases of class imbalance. These metrics are widely adopted in defect detection tasks to comprehensively assess models' performance.
## 2 Material and Modelling
### Finite Element Modelling  
The CODAM2 material model presented in previous section is applied to simulate progressive damage in compact tension (CT) and open-hole tension (OHT) tests shown in Figures 2 and 3, respectively. The FE simulation of the CT tests serves as calibration to determine an optimal set of material input parameters where details are presented in Section 3. Furthermore, the simulation of OHT tests in Figure 3 will validate the calibrated properties in Section 4.  
Each FE model features a mesh that incorporates only one shell element through its thickness. In the expected fracture process zone of CT and OHT models, the in-plane element size measures 1 mm \(\times\) 1 mm. In cases where alternative element sizes are utilised, [PERSON]'s crack-band scaling [30], as detailed in Equation (4), can be used to adjust the damage properties to ensure that the fracture energy of the material remains constant, regardless of the finite element size.  
For the simulation of CT tests, a displacement is prescribed to the rigid loading pins (grey) in opposite vertical directions shown in Figure 2. The simulation of OHT tests uses fully constrained nodes at one edge of the test sample in Figure 3 while a prescribed displacement is applied to the opposite edge. Multiple OHT geometries with varying hole diameters \(D\) will be virtually assessed as part of the validation in Section 4.  
The modelling of the quasi-isotropic \([90/45/0/-45]_{s}\) been veneer laminates consisting of only one through-thickness shell element leads to highly efficient computation times. The simulations of the CT and OHT tests only take 2-3 min and 1-2 min, respectively, on a conventional computer with four CPUs. Note that this is faster than the corresponding physical tests.