esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The EDBA Simulation Program and Results of the \(Rh\) Optimization
### The Optimization of the Rh Decision Criterion  
For the efficiency of the bandwidth utilization for the dedicated wavelength, an especially important and crucial parameter is the \(Rh\) decision criterion. In a previous Section 4.1, the value \(Rh\) = 1 is used for keeping more than 50% efficiency at the bandwidth allocation utilization of wavelengths based on Equation (1). In our work, the \(Rh\) decision criterion is optimized for various numbers of ONU units that can be incorporated into the NG-PON2 network. After multiple runs of the EDBA simulation program, where a number of utilized ONU units is changing and requested bandwidths are stochastically generated based on the real data traffic loading, a number of allocated wavelengths for each cycle is stored, and a median of this number is found. The median is an indicator for a determined number of ONU units, where a value of the \(Rh\) decision criterion is optimized and simultaneously Equation (1) is allowed. In Figure 8, optimized \(Rh\) decision criterion values are displayed for diverse number of ONU units.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 3 Current Closure Planning Practices in Poland  
In Poland, the national company Polish Railways is the owner of the whole railway infrastructure. Closures are planned due to either maintenance or investment works. All maintenance activities and investment works are scheduled by local departments and corresponding investment sections, even though the execution is outsourced. A closure planning is performed in three-time horizons, i.e.,  
* long-term planning--performed once a year; there is no initial timetable and only long term closures are known in advance and may be taken into consideration, the aim is to prepare an initial closure plan and a timetable; both may be slightly modified later during the year;
* periodic planning--carried out every two months; the initial closure plan and a timetable which were prepared during long-term planning are given, but there is also number of new closure requests which have been appeared meantime; such closures impose changes in closure plan and timetable, so the aim is to obtain new closure plan and modified timetable; however, the new timetable cannot differ much from the initial one; and
* weekly planning--performed once a week, the valid closure plan and timetable obtained during periodic planning are given, but some new, short-term closure requests may appear, and they can be added to the closure plan if only no changes in closure plan and timetable are required.  
For each mentioned time horizon, closure plans are made according to the hierarchical organizational structure of the Polish Railways. Firstly, local departments prepare closures plans, and they are coordinated between neighboring local departments. Then, the plans are verified, corrected, and coordinated similarly at the regional level. The precise rules of the closure planning are defined in the particular instruction Ir-19 [60; 61]. From the Polish Railways' point of view, the most crucial is the periodic planning, since there are only 48 h to prepare final closure plan for the next two months having only limited changes in the initial timetable. Our approach is, first of all, designed just for such planning process.
## 3 Results
### Mesh-Based Convergence Modeling  
The optical scanning model of the vase demonstrated excellent external surface texture and color information; however, the internal structure could not be digitized. Meanwhile, X-ray CT modeled the entire internal and external geometries but exhibited limitations in terms of representing the unique surface texture and color of the vase. Hence, mesh-based convergence modeling was performed to create a 3D model of the vase that precisely exhibited the external surface and internal structure. The workflow of mesh-based convergence modeling is illustrated in Figure 10.  
During mesh-based convergence modeling, the external geometry of the optical scanning model was utilized as much as possible and the unsc  
Figure 10: Workflow of 3D convergence modeling using mesh data obtained from optical scanning and X-ray CT.  
Figure 9: Deviation analysis and RMS deviation visualization after aligning the mesh models of the optical scanning and X-ray CT mesh models.  
supplemented using the X-ray CT model to minimize data interference caused by the overlapping of the two models. Consequently, a high-quality converged model was generated by removing data with low precision and dimensional accuracy. The external geometry of the X-ray CT model was removed from the overlapping areas of the aligned mesh models.  
The X-ray CT model of the interior and the optical scanning model of the exterior--with removed overlapping areas--were converted into a single mesh model through merging. Then, through mesh defect optimization, a 3D converged model with an average point spacing of 0.45 mm and 2,149,778 polygons was obtained (Figure 11). The surface area and volume of the converged model were 149,341 and 402,377 mm\({}^{3}\), respectively, which were 0.9% and 1.8% greater than those of the X-ray CT model, respectively. This is believed to be a result of incorporating the volumetric characteristics of the optical scanning model, which had a large external shape.
## 4 Experimental Results  
As described in Section 3.2, we resampled the smoothed GM images to 2 mm, 4 mm, 8 mm, and 16 mm isotropic spatial resolutions. This procedure generated 238,955, 29,852, 3747, and 467 voxel intensities for each individual that were used as raw features. According to the proposed MFS, we sorted the features on the basis of their classification accuracy. An example of the sorted voxels for a spatial resolution of 2 mm is shown in Figure 1.  
After sorting of the features (i.e., voxel intensities), the most informative features are selected on the basis of the proposed MFS as part of the CAD framework for the four pairs of clinical groups (i.e., AD/HC, pMCI/sMCI, pMCI/HC, and sMCI/AD). The classification performance obtained through 10-fold cross-validation without feature selection (i.e., all feature vectors) and through the proposed MFS approach followed by different isotropic spatial resolutions are presented in Table 1.  
As can be seen in Table 1, the performance of the proposed CAD system followed by the MFS method was superior to the use of all features for all pairs of clinical groups for different spatial resolutions. Figures 2 and 3 show the receiver operating characteristic (ROC) curves and the classification performance of the proposed CAD framework for the four pairs of clinical groups with respect to different spatial resolutions. As can be seen in Figures 2 and 3 as well as in Table 1, the performance of the proposed CAD framework was clearly improved with a decrease in the spatial resolution. One reason for this finding may be because a lower spatial resolution (i.e., 2 mm) may contribute to a more robust tracing of atrophic regions in AD. For example, in the case of AD/HC, the AUC was increased from 0.82 to 0.91 by reducing the spatial resolution from 16 mm to 2 mm. As can be seen in Figures 2 and 3, the optimum performance of the proposed CAD framework was achieved using  
a 2-mm spatial resolution versus the other spatial resolutions (i.e., 4 mm, 8 mm, and 16 mm). The ROC curves through the proposed CAD framework for the four pairs of clinical groups with a 2-mm spatial resolution are shown in Figure 4.
## 3 Proposed Approach
### Backbone Architectures
#### 3.2.2 Swin Transformer  
Swin Transformer (Figure 3) arranges images in a hierarchical manner, gradually reducing spatial dimensions at each stage. Rather than computing attention across the entire image simultaneously, Swin segments the image into non-overlapping local windows, which keeps attention computations manageable. A key innovation is the shifted window mechanism: between consecutive transformer blocks, the window partitions shift. This shift causes patches that were previously confined to one window to be included in a neighboring  
Figure 2: Structure of ViT.  
window in the next layer, encouraging cross-window interactions. Consequently, the model captures global context over multiple layers without the prohibitive cost of full-image self-attention in every stage [18].  
In this study, the combination of YOLOv10 and Swin Transformer is referred to as YOLOv10-Swin Transformer. Specifically, Swin Transformer replaces the traditional backbone in YOLOv10, providing the detection head with multi-scale feature representations that facilitate the localization and classification of heavy equipment (e.g., loaders and trucks). Because Swin Transformer's architecture is hierarchical, each level down-samples the spatial resolution--akin to a CNN's \"feature pyramid\". This yields multi-scale representations suitable for tasks such as object detection or segmentation, where objects of different sizes and aspects must be identified accurately. By restricting self-attention computations to small windows, Swin's attention complexity scales linearly--not quadratically--with image size, making it feasible for higher-resolution inputs. This design significantly boosts efficiency compared to naive global attention mechanisms, balancing performance and computation [25].  
Owing to its local-to-global progression and window-shifting strategy, Swin Transformer often outperforms standard CNN backbones in detection tasks while remaining computationally efficient enough for practical, large-scale use. Its effectiveness stems from integrating the benefits of hierarchical feature extraction with the global modeling capabilities of transformers.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 3 Virulence Mechanisms
### Virulence Factors
#### 3.1.3 Competition for Resources and Iron  
Iron acquisition has been related to virulence in a variety of bacterial pathogens [126]. In _Riemerella antipestifer_, an infection that affects poultry, siderophore-interacting protein (SIP) was identified as necessary not only for iron acquisition but also for optimal virulence. A deletion of the _sip_ gene resulted in decreased biofilm formation and a reduction in adherence to and invasion of Vero cells [127].
## 4 Experiments and Discussion  
### Numerical Simulation  
To check the feasibility of the system, a numerical simulation was conducted in Matlab. The rain-flow counting method with the four peak-valley counting principle [28] is used in the simulation. The original principle is reprogrammed to remove the invalid amplitudes. Figure 5 shows a group of random signals obtained from the simulation. A total of 637 peak-valley values were detected. Table 1 and Figure 6 show the results from cycle counting. It is noted in Table 1 that the intervals of amplitude and mean values can be set randomly. To get reliable structural fatigue properties, both the theoretical and experimental analysis are expected. The data in Table 1 serves as the basic inputs for counting, and its combination with the material's S-N curve and Miner cumulative damage principle generates the fatigue damage caused by the signals.  
Figure 4: **(a)** The installation of hardware integration; **(b)** Photo of the system.  
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Table 5 showed the test results of the five specimens. Along with the decrease of stress amplitude, the fatigue life of the specimen apparently increases. The cycle counting of the system is well consistent with the cycles of fatigue loading, and the systematic error is less than 4%, which is caused by omitting cycles during the test. However, it exceeds the accuracy requirement in engineering community. Figure 14 shows the comparison between the test results and the aluminum's S-N curve. Good stability, high accuracy, quick processing, and real-time capability of the fatigue monitoring system was observed during the entire test process. Moreover, the test results also showed that the experimental method is feasible for studying the fatigue properties of 6061-T6 aluminum alloy.
+++
## 2 Emerging Applications of 6G  
The massive growth of emerging technologies is one of the key drivers behind the evolution of communication networks and their capabilities [7]. For example, with the introduction of novel applications such as the Tactile Internet that require ultra-low latency and high reliability, communication networks have undergone both architectural and algorithm-based improvements. These changes include the introduction of mobile edge computing, machine-learning based resource allocation, and software-defined networking (SDN).  
Emerging technologies continue to grow across diverse fields and facilitate a variety of services that benefit all kinds of end users [8, 9]. The suitability of indoor wireless networks in delivering 6G applications depends on the QoS requirements of the applications. For example, e-Health applications such as telesurgery require a reliable network connection with latency in the millisecond range, while applications such as augmented reality/virtual reality (AR/VR) require high bandwidths. As such, before we dive into the specifics of indoor wireless networks, it is important to understand the requirements of some of the key emerging applications to analyse how suitable these indoor wireless networks are in catering to future 6G applications. Therefore, in the following subsections, we discuss and comparatively analyse the requirements of some of the most popular emerging applications.
## 2 Method
### Model Development and Validation
#### 2.2.1 Model Description  
The engine model developed for this application uses the GT-Power software stack. The modeling of the turbocharger in this tool is implemented using compressor and turbine templates. These templates are called map-based 0D elements, with the flexibility for choosing between open/closed compressor maps. Hence, from the modeling perspective, it would be easy to identify the differences, guaranteeing complete control regarding the rest of the simulation.  
For the combustion profile, a simple, non-predictive approach was considered by imposing the burnt rate as a function of the crank angle or time [s]. The Wiebe function defines this heat release [28]. The function uses the parameters such as the Wiebe exponent, start of combustion, and combustion duration to replicate the experimental in-cylinder pressure profile. Furthermore, the combustion processes obtained are used to train an artificial neural network (ANN) to overcome the limitation of predicting the combustion  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Speed** & **Configuration** & **Time to 90\%** & **Work in 4s** \\
**(RPM)** & **(s)** & **(k)** \\ \hline
1000 & Open & 1 & 78.05 \\
1000 & Closed & 0.9 & 79.22 \\
1500 & Open & 3.6 & 193.67 \\
1500 & Closed & 3.6 & 193.91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of experimental transient operation performance.  
profile during the later prospective analysis. The ANN uses parameters such as engine speed, trapped mass, in-cylinder pressure, and temperature at inlet valve closer crank angle position as inputs to predict the 50% burnt rate, burning duration from 10% to 90%, and the Wiebe exponent. This information is provided to attain the heat release rate (HRR) for the extended boost study. Detailed information on the model procedure development and robustness can be read in [29]. The information on the air-to-fuel ratio is directly available from the data acquired from the test bench, which is used in the modelled injector settings to provide fuel mass in order to attain desired peak in-cylinder pressures.  
To obtain the experiment's intake temperature, a dedicated WCAC was implemented, using the experimental inlet and outlet temperature of the cooler and guaranteeing a good prediction of the intake manifold by adjusting a heat transfer multiplier. In addition, heat transfer effects through the exhaust manifold were fitted, in order to obtain accurate temperature predictions. For this purpose, a set of heat transfer multipliers had to be used, again, following the proposed methodology in [29]. Once these multipliers were identified, they were held constant throughout the simulation assessment. Further explanation of the procedure is detailed in [30] to understand the number of variables considered during the fitting process.
## 4 Experiments and Results
### Results of Autoencoder-Based Denoising
#### 4.4.2 Performance Metrics  
When reconstructing received noisy spectrograms, the objective function is, of course, to minimize the reconstruction error. The performance metrics of our DAE is the accuracy, defined by \(1-Err\), with  
\[Err=\frac{1}{|\mathcal{S}|}\sum_{\ell=1}^{|\mathcal{S}|}L(x_{\ell},z_{\ell}) \tag{7}\]  
where the index \(\ell\) runs over the set of training samples \(\mathcal{S}\) and where \(L(x,z)\) is the MSE loss function.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.5 Fuzzy Rule System
[7] proposed the Fuzzy-QPPTW flight taxi route assignment method, which is based on the fuzzy system method and time window algorithm to estimate the taxi time and its uncertainty, so as to combine the taxi route assignment with the taxi time prediction.
## 5 Conclusions  
A novel homogenization method has been developed whereby FEA can be utilized to determine effective elastic properties of 3D printed infills. By discretizing the infill's unit cell and applying periodic boundaries, a prediction of the bulk elastic properties is made for a given relative density.  
This method has been applied to the gyroid infill by performing a full suite of simulations. This allowed for generation of predictive data for the effective elastic properties at the full range of relative densities, using various base-materials. The FEA predictions have been verified using compression experiments whose results correspond well to the predicted behavior. At 50% nominal density, the error in longitudinal modulus is only 4% of neat modulus, which is less than the variation within the test results (5%). Finally, the FEA predictions have been used in order to inform semi-empirical equations, which can be used directly in designing parts using gyroid infill. These equations match the FEA predictions do a high degree of accuracy (0.44% error for the longitudinal modulus).  
It is clear that a method of predicting homogeneous bulk properties of infills is an important component in optimal design using AM technology. The method presented here will likely be very useful in designing components using any of the complex 3D infills.  
The following is a list of the contributions by the various authors: Conceptualization, R.A.L.-A., S.V. and P.B.; methodology, P.B.; software, P.B.; formal analysis, P.B.; investigation, P.B.; resources, R.A.L.-A. and S.V.; data curation, P.B.; writing--original draft preparation, P.B.; writing--review and editing, R.A.L.-A., S.V. and P.B.; visualization, P.B.; supervision, R.A.L.-A. and S.V.; project administration, R.A.L.-A. and S.V.; funding acquisition, R.A.L.-A. All authors have read and agreed to the published version of the manuscript.  
This research was funded under the U.S. Army Natick Soldier Systems Center, and has been approved for public release--DEVCOM SC PR2021-30271.  
Not applicable.  
This research was done in partnership with the University of Maine Advanced Structures and Composites Center. Special thanks to the [PERSON], [PERSON], and the Additive Manufacturing Team!  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AM Additive Manufacturing  
FEA Finite Element Analysis  
RVE Representative Volume Element  
DIC Digital Image Correlation
## 4 The WADGPS Master Station Processes
### GPS Observations Modeling  
Each RS uses a dual-frequency receiver to receive code and carrier phase observations at the L1 and L2 frequencies. These raw observations are sent to the WADGPS master station to process the corrections for common errors and the corresponding confidences [3, 4]. The common errors include the ionospheric delay and satellites ephemeris and clock errors. The observations are expressed as follows [1, 15]:  
\[PR_{L1}=\rho_{i}^{\ j}+b_{i}-B^{j}+I_{i}^{\ j}+T_{i}^{j}+\
u^{j}_{\ i,L1} \tag{6}\]  
\[PR_{L2}=\rho_{i}^{\ j}+b_{i}-B^{j}+\gamma I_{i}^{\ j}+T_{i}^{j}+\
u^{j}_{\ i,L2} \tag{7}\]  
\[\phi_{L1}=\rho_{i}^{\ j}+b_{i}-B^{j}-I_{i}^{\ j}+T_{i}^{\ j}+N_{L1}\lambda_{L1 }+\epsilon^{j}_{\ i,L1} \tag{8}\]  
\[\phi_{L2}=\rho_{i}^{\ j}+b_{i}-B^{j}-\gamma I_{i}^{\ j}+T_{i}^{\ j}+N_{L2} \lambda_{L2}+\epsilon^{j}_{\ i,L2} \tag{9}\]  
\[\gamma=\left(\frac{L_{1}}{L_{2}}\right)^{2}=\left(\frac{1575.42}{1227.6} \right)^{2}=1.647 \tag{10}\]  
where:  
\(PR\) is the pseudorange and the subscripts \(L1\) and \(L2\) indicates L1 and L2 frequencies, respectively,  
\(\rho_{i}^{\ j}\) is the geometric range from satellite \(j\) to user \(i\),  
\(\phi\) is the carrier phase and the subscripts \(L1\) and \(L2\) indicates L1 and L2 frequencies, respectively,  
\(b\) is the receiver clock bias,  
\(B\) is the satellite clock error,  
\(N\lambda\) is the integer ambiguities and the subscripts \(L1\) and \(L2\) indicates L1 and L2 frequencies, respectively,  
\(I\) is the ionospheric delay at L1 frequency,  
\(T\) is the tropospheric delay,  
\(v\) is the pseudorange measurements noise and the subscripts \(L1\) and \(L2\) indicates L1 and L2 frequencies, respectively, and  
\(e\) is the carrier phase measurements noise and the subscripts \(L1\) and \(L2\) indicates L1 and L2 frequencies, respectively.  
The differences in these observation equations are the ionospheric delays. The pseudorange (code phase) measurement is delayed and the carrier phase is advanced, and this is the reason of the sign difference of \(I\) in Equations (6-7) and Equations (8-9). This delay is inversely proportional to the signal frequency [1]. In Equations (7) and (9), \(\gamma\) equals 1.647. Thus, the ionospheric delay on L2 is 1.647 times larger than that on L1. Additionally, the carrier phase observations also suffer from integer ambiguity (\(N\lambda\)) [1].