esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
The proposed framework included a two-level hierarchical dynamic decision for AGV dispatch and selection of the next station based on the minimum cost flow, a machining scheduler, and a station controller for operation control. The findings revealed the effectiveness of the dispatch rule by decreasing the average flow time. Artificial intelligence and bi-level programming could be investigated in a future study [40]. This study was conducted with online scheduling, while other studies employed offline scheduling. [PERSON] et al. studied offline AGV and machine scheduling and proposed a framework based on a disjunctive graph to model the common schedule problems and a memetic algorithm to schedule the machines and AGVs. The aim was to minimize the production time. A comparison of the results demonstrated that the proposed algorithm could provide optimal solutions to schedule machines and AGVs concurrently [41]. [PERSON] et al. investigated robot line balancing and AGV scheduling with a two-stage heuristic approach to assist the manager to develop or modify an effective robot line. In the first stage, the consecutive algorithm is employed to balance the robot assembly line, and in the second phase, Particle Swarm Optimization (PSO) was used to schedule AGVs between robot stations [42]. Digital-twin-based solutions have also been used for AGV scheduling. The battery charging problem was investigated with this method and a 1.32% energy saving was obtained [43]. [PERSON] and [PERSON] tested various vehicle circulation rules to minimize the average lead time. A simulation demonstrated the effect of the rules on system effectiveness [44]. Generally, the dispatch and routing problem tackles the problem of incapacitated AGVs. [PERSON] et al. employed the integer model to dispatch capacitated AGV systems. First, the mathematical solution was studied, then local search and random search methods were tested, and it was reported that the local search algorithm led to a better solution for the large-scale problem [45]. Although various numerical and experimental studies have previously been conducted, the problem of grouping AGV circuits and AGV assignment to parking stations to minimize the number of AGVs while meeting the demand is still being researched in the field of manufacturing. Several studies have been conducted on the design and control of AGV systems; these were mostly based on computer-assisted high-cost AGV systems. Considering low-cost AGVs, determination of the minimum fleet size by creating AGV pools via time windows and examinations of the effects of the system parameter changes on the required AGV number using ANNs if the production requirements change have not been widely investigated in the literature.
## 4 Pheromone-Based Alarm Delivery Concept
### Pheromone Distribution over the Ground Sensor Nodes  
The example presented in Figure 5 shows a regular distance distribution of the ground sensor nodes. This simplifies the alarm forwarding process, as the pheromone information is evenly distributed among them, assuming for instance that the received signal strength indication (RSSI) is used to define the pheromone level.  
However, considering a more general case, in which the sensor nodes are placed using a uniform random distribution on the ground, such a regular distance distribution of the pheromone information would hardly be achieved, which may cause problems in the alarm forwarding. The main problem then is the increased number of sensor nodes that would forward a given alarm in the trail-follow process, thus unnecessarily increasing the number of sent messages, leading to a waste of energy resources. To tackle this problem, a special region is defined in the center of the pheromone trail, which constrains the broadcast of alarm messages sent towards the UAV. To understand the reason for this, Figure 7a shows how an alarm propagates in the trail without the definition of such region.  
As shown in Figure 7a, the alarm is broadcasted towards the direction of the movement of the UAV, by flooding the nodes towards the UAV. Notice that this generates a number of redundant forwarding alarm messages that are unnecessary. Using the spatial component of the pheromone stored by the ground sensor nodes, it is possible to restrict the alarm forwarding to the nodes closer to the real path followed by the UAV. Like this, the alarm would be forwarded to the inner part of the trail, which is called the trail backbone, and, by reaching this inner part, the trail-follow can be constrained by its limits. The width of the backbone can be defined in terms of the UAVs' communication coverage  
Figure 7: Alarm forwarding inside the pheromone trail: (**a**) Trail-follow without backbone; (**b**) Trail-follow with backbone, showing that a smaller number of nodes are involved in the alarm message forwarding.  
those provided by ShoX, by an architecture with minimized core functionalities that provides more flexibility to the development of new simulations.
## 2 Polarization State and Observation Analysis
### Overall Observation  
The orbits used by the spaceborne platform to observe the polarization information of the Earth are mostly sun-synchronous orbits. However, there are specific limitations in the observation of this orbit position; that is, only the information of a specific fixed area of the Earth can be observed at the same time within a day, and it is difficult to achieve continuous acquisition of the entire polarization information of the Earth. When implementing the observation of the Earth at the L1 point, the characteristics of the Earth's rotation are fully utilized, and a single satellite can achieve stable observation of the entire Earth, and polarization observation operations can be carried out on a daily time scale. TheLagrangian gravitational equilibrium point exists in the Earth-Moon system. The Earth's rotation period is fixed at 23 h, 56 min and 4 s, and its centroid position is constant and the attitude is stable in the Earth-Moon coordinate system, which makes it possible to achieve conditional stability for the satellite's orbit [4; 14; 15]. Therefore, the Earth-Moon L1 point becomes an ideal platform for Earth polarization observation. In addition, the position and optical properties of the Moon are relatively stable, and its trajectory in the sky can be accurately predicted, which makes the observation arrangement more convenient to accurately arrange the polarization observation program. The physical properties of the Moon itself change slowly in a long time scale, and the polarization characteristics of its reflected light also have high repeatability, which provides a stable reference standard for long-term and periodic calibration work. Figure 2 shows the Earth-Moon system observed from the Earth-Moon L1 point.  
Compared with the regional measurement method, the full Earth measurement can obtain complete Earth image information at the same time, which effectively guarantees the time synchronization of data in different regions. This feature is of great significance for in-depth exploration of natural phenomena with spatio-temporal correlations such as atmospheric circulation and ocean circulation on a global scale. It can accurately analyze its interaction mechanism and dynamic change law, and then significantly improve the data quality and its application value in earth science research. It provides a more reliable and effective observation basis for a comprehensive and in-depth understanding of the complex processes of the Earth's system.
## 3 Results and Discussion  
Figure 3a,b shows the cutting section of the conductive wrapped yarns where the metallic wires are confirmed to be wrapped in PSA roving regardless of the number of metallic wires. The observation indicates that the employment of the custom-made feeding device produces wrapped yarns with an intact morphology and thus solves the corrosion problem caused by the exposure of metallic wires. Figure 3c shows the morphology of the control group (PSA-SS-W1) that is not processed with waterproof 2P, whereas Figure 3d shows the morphology of the experimental group (i.e., PSA-SS-W1\({}^{\prime}\)) that undergoes superhydrophobic treatment using waterproof 2P, and the fabric is covered with a transparent breathable film. The major component of the hydrophobic agent is fluorine-containing an emulsion polymer. Polysiloxane is aligned along the same direction with hydrophobic agent-CH3  
Figure 2: The schematic diagram of the processing procedures of hydrophobic/flame retardant/ electromagnetic interference (EMI) shielding fabrics.  
being aligned outwards. Moreover, waterproof 2P is composed of silicon and oxygen atoms that interact with some atoms of the fibers to form coordination bonds and hydrogen bonds. Subsequently, the vapor and air can penetrate the fabrics instead of the water droplets. The fabrics demonstrate a good hydrophobic effect with CAs of 155\({}^{\circ}\). The microstructure of the core-spun yarns after FR testing is shown in Figure 3e-h. It can be showed that the fabrics do not demonstrate distinct shrinkage, cracking, and dripping during the combustion, after which the fabrics exhibit carbonization and have a crisp edge, retaining good fabric morphology. A film is formed on the surface of the PSA-SS-W1 fabric after hydrophobic treatment. The outermost film will destroy the flame retardant effect of the PSA-SS-W1 fabric. This is also the damaged length of the PSA-SS-W1 sample, which is higher than that of PSA-SS-W1'. Figure 3i shows that water droplets with different pH values displayed on the surface of the superhydrophobic fabric, including acid and alkali water droplets that can stand well on the fabric surface. This phenomenon shows that the superhydrophobic fabric can be used in harsh conditions such as humid, acid and alkali environments. The thermal properties of fabrics were investigated by PSA-SS-W1 and PSA-SS-W1'. The results show that the hydrophobic treatment will not destroy the stability of the thermal properties. A more detailed discussion is shown in thermogravimetric analysis.
## 4 Evaluation Results and Discussion
### Experimental Setup
#### 4.1.3 Deployment of Resources  
To emulate realistic circumstances, the involved SN resources were randomly and partially committed, simulating already-deployed VNs of served VNRs. Both reserved and available remaining CPU and BW resources are illustrated in Figure 5 and also documented in Table 8.  
The SN and VNR components of the adopted experimental setup were initialized via the ALEVIN tool, which provides the ability to assign resources to all networking elements. To avoid any restrictions due to different measurement units, the networking and computational metrics were scaled to BW units and CPU cycles, respectively. Initially, 100 CPU cycles for each SN node and 100 BW units for every link were allocated. Table 8 also documents the pre-configured PDV and RTT values, essential for the \(PJ\) and \(L\) computation, interchangeably. Finally, all computational and networking demands of the VNRs were randomly set with an interval of (10, 40).  
Figure 5: The initialization parameters of the experimental SN (\(V=\{A,B,C,D,E,F,G,H\}\)).  
Figure 6: The initialization parameters of the experimental VNRs (\(V_{1}^{{}^{\prime}}=\{a,b,c\}\) & \(V_{2}^{{}^{\prime}}=\{d,e,f\}\)).
## 1 Introduction  
The rise in the use of real-world data and electronic health records (EHRs) has made large-scale longitudinal health studies [1, 2] more cost-effective. Longitudinal data provide a more accurate account of patients' health history over time, facilitate the study of temporal associations and patterns [3], and enhance prediction model performance [4]. Unlike classical statistical methods, machine learning techniques are ideal for harnessing the potential of EHRs for clinical predictions due to their ability to handle numerous variables with non-linear relationships [5] and challenges associated with EHRs like missing values, heterogeneity, and temporal sparsity.  
Healthcare workers usually prefer transparent models over \"black-box\" ones, seeking clarity in informed decision-making [6]. While machine learning-based models lack natural explainability, recent approaches aim to identify feature contribution to the outcome [7], improving interpretability [6]: attention mechanisms [8, 9, 10], gradient-based methods [11], and model-agnostic techniques (i.e., LIME [12], SHAP [13]). These contributions can be calculated in different ways (e.g., feature or time dimension) to obtain more complete information [14, 15, 16]. Attention mechanisms can also potentially enhance model performance [17]. Some machine learning techniques can analyse EHRs with attention mechanisms to support decision-making [5], most being based on sequential deep learning architectures [14, 16, 18, 19, 20]. Benchmarking studies usually assess only discriminability [14, 21, 22], but attention weights' clinical relevance also requires scrutiny, ensuring meaningful explanations align with clinical insights. As noted in a previous systematic review [18], there is a lack of guidelines in how to run benchmarking studies on prediction models in healthcare, hampering comparability between models, especially considering interpretability. Recently developed reporting guidelines for prediction models in healthcare with AI such as TRIPOD-AI [23] do not define how to perform these comparisons either.  
In this study, several attention-based deep learning architectures handling longitudinal EHR data were designed with increasing degrees of complexity and compared, aiming to test whether improved discriminability necessarily implies improved interpretability. To do so, we designed multiple architectures with attention mechanisms at different levels and analysed their impact not only in terms of discriminability but also in the distribution of attention weights. The evaluation was performed on all-cause mortality with real-world EHRs.
## 3 System Model
### Equations of the UAVs Ellipsoidal Movement  
On the horizontal plane, an ellipse's equation, according to [41], can be presented as  
\[\frac{(x-C_{x})^{2}}{R_{x}^{2}}+\frac{(y-C_{y})^{2}}{R_{y}^{2}}=1, \tag{2}\]  
where the ellipse's center is located at \((C_{x},C_{y})\); \(R_{x}\) and \(R_{y}\), respectively, are the radii of the \(X\) and \(Y\) axes. If the ellipse rotates at a particular angle, \(\theta\), the equation can be written as  
\[\begin{split}&\frac{((x-C_{x})\cos(\theta)+(y-C_{y})\sin(\theta))^{2} }{R_{x}^{2}}\\ &+\frac{((x-C_{x})\sin(\theta)-(y-C_{y})\cos(\theta))^{2}}{R_{y}^ {2}}=1.\end{split} \tag{3}\]  
Equation (3) can be written as a parametric equation of a rotated ellipse, as follows:  
\[\begin{split}& x(\delta)=C_{x}+R_{x}\cos(\delta)\cos(\theta)-R_{y} \sin(\delta)\sin(\theta),\\ & y(\delta)=C_{y}+R_{x}\cos(\delta)\sin(\theta)+R_{y}\sin(\delta )\cos(\theta).\end{split} \tag{4}\]  
This parametric equation is used to calculate the UAV's X and Y location points on the rotated ellipsoidal path, using parameter \(\delta\). Specifically, for any given time \(t\), \(\delta(t)=\frac{2\pi}{T}(t)\) calculates the UAV's position along the ellipsoidal path [42], where \(2\pi\) and \(T\) represent the complete circumference of the ellipse and the total time for the UAV to complete one cycle over the ellipse, respectively.  
In our considered scenario, we assume the \(j\)th UAV-MEC server following an ellipsoidal trajectory on the horizontal plane with major radius \(R_{x,j}\) and minor radius \(R_{y,j}\), as well as rotation angle \(\theta_{j}\) (Figure 1). The center position of this ellipsoidal trajectory is represented by \((C_{x,j},C_{y,j})\). The UAV-MEC server maintains a constant altitude \(h\) while flying, similar to [25; 43]. Within cycle period \(T\), the UAV-MEC server completes one cycle of this ellipsoidal trajectory, in order to assist the associated MDs. We partition cycle period \(T\) into \(P\) equally sized time intervals. Thus, in each time slot \(t\), the coordinates of the \(j\)th UAV-MEC server are denoted \([x_{j}(t),y_{j}(t),h]\), where \(x_{j}(t),y_{j}(t)\), and \(h\) represent the \(X,Y\), and \(Z\) coordinates, respectively, of UAV-MEC server \(j\) in time slot \(t\) along the ellipsoidal path. By using Equation (4), the \(X,Y\) location of the \(j\)th UAV-MEC server in time slot \(t\) can be written as  
\[\begin{split} x_{j}(t)&=C_{x,j}+R_{x,j}\cos\biggl{(} \frac{2\pi}{T}t\biggr{)}\cos(\theta)\\ &-R_{y,j}\sin\biggl{(}\frac{2\pi}{T}t\biggr{)}\sin(\theta),\\ y_{j}(t)&=C_{y,j}+R_{x,j}\cos\biggl{(}\frac{2\pi}{ T}t\biggr{)}\sin(\theta)\\ &+R_{y,j}\sin\biggl{(}\frac{2\pi}{T}t\biggr{)}\cos(\theta).\end{split} \tag{5}\]
## 4 Design of Scaled Prototype Antenna  
A prototype antenna has been designed and manufactured at 3.8 GHz center band by scaling down the model designed for Ka band. That frequency has been chosen to allow for easy and cheap realization in our laboratory. Before realization, the scaled model was numerically analyzed to determine its radiating and impedance characteristics. Similar to the previous model, we use Rogers RT6002 for the dielectric 1 and the RO4003 for the dielectrics 2 and 3, while the fixed design parameters are now scaled by the ratio between the center-band frequencies.  
Evidently, the results obtained at the end of the numerical analysis are similar to those of the Ka-band. As shown in Figure 6, the matching bandwidth is about 50% (2.8-4.71 GHz) for both polarizations, with a return loss of \(\geq\)10 dB. The H/V isolation is greater than 30 dB over the entire matching band.  
Figure 5: H and V polarization return loss and H/V isolation at the end of the optimization.  
Figure 6: Scaled prototype: H and V polarization return loss and H/V isolation at the end of the optimization.  
The prototype PCBs were manufactured with a CNC milling machine and are shown in Figure 7. Some polylactic acid (PLA) supports, necessary to give mechanical strength to the antenna and to allow adequate spacing between dielectrics, were fabricated with a 3D printer and are shown in the Figure 7. The lid was obtained by inserting a FR4 dielectric with a single metal face facing the feed lines.
## 3 Experimental Tests
### Model Description  
#### 3.2.1 Model Properties  
This study takes the high-speed train CRH380A as example, the parameters are illustrated in Table 2, and the structural parameters for the viaduct are illustrated in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter, Symbol (Unit)** & **Value** \\ \hline Vehicle’s weight under rated load (kg) & 42,934 \\ Bogie weight (kg) & 3300 \\ Wheelset weight (kg) & 1780 \\ Rotary of inertia for vehicle’s nod (kg m\({}^{2}\)) & \(1.712\times 10^{6}\) \\ Rotary of inertia for bogie’s nod (kg m\({}^{2}\)) & 1807 \\ Vertical stiffness of primary suspension (N m\({}^{-1}\)) & \(1.176\times 10^{6}\) \\ Damping of primary suspension (N s/m) & \(1.0\times 10^{4}\) \\ Stiffness of secondary suspension (N m\({}^{-1}\)) & \(2.4\times 10^{5}\) \\ Damping of secondary suspension (N s/m) & \(2.0\times 10^{4}\) \\ Length of train (m) & 25 \\ Fixed distance of carriage (m) & 17.5 \\ Fixed distance between axles (m) & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters of CRH380A high-speed train.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Symbol, Unit** & **Value** \\ \hline \multirow{8}{*}{Rail} & Stiffness (N/m\({}^{2}\)) & \(2.059\times 10^{11}\) \\  & Sectional inertia moment (m\({}^{4}\)) & \(3.217\times 10^{-5}\) \\  & Density (kg/m\({}^{3}\)) & 7850 \\  & Section area (m\({}^{2}\)) & \(7.745\times 10^{-3}\) \\  & Shear modulus (N/m\({}^{2}\)) & \(7.7\times 10^{10}\) \\  & Section coefficient (\(\kappa\)) & 0.45 \\  & Loss factor (\(\eta_{\rm L}\)) & 0.01 \\ \hline \multirow{2}{*}{Fastener} & Loss factor & 0.25 \\  & Distance between fasteners (m) & 0.625 \\ \hline \multirow{4}{*}{Slab} & Stiffness (N/m\({}^{2}\)) & \(3.6\times 10^{10}\) \\  & Sectional inertia moment (m\({}^{4}\)) & \(1.7\times 10^{-3}\) \\  & Density (kg/m\({}^{3}\)) & 2750 \\  & Section area (m\({}^{2}\)) & 0.51 \\  & Loss factor & 0.1 \\ \hline \multirow{2}{*}{CA mortar} & Equivalent stiffness (N/m) & \(0.78\times 10^{9}\) \\  & Loss factor & 0.2 \\ \hline \multirow{4}{*}{Vaiduct} & Length (m) & 32 \\  & Stiffness (N/m\({}^{2}\)) & \(3.45\times 10^{10}\) \\ \cline{1-1}  & Sectional inertia moment (m\({}^{4}\)) & 5.757 \\ \cline{1-1}  & Density (kg/m\({}^{3}\)) & 2650 \\ \cline{1-1}  & Section area (m\({}^{2}\)) & 4.416 \\ \cline{1-1}  & Loss factor & 0.1 \\ \hline \multirow{2}{*}{Bearing for the viaduct} & Stiffness (MN/m) & \(6\times 10^{9}\) \\ \cline{1-1}  & Distance between bearings (m) & 32 \\ \cline{1-1}  & Loss factor & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic parameters of the slab-rail-viaduct system.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 3 Approximating Stationary Kernels
### Learning Locally Stationary Kernel, GaBaSR
#### 3.2.2 Learning the Gaussian Mixture  
In order to learn the parameters \(Z_{i},\mu_{k}\), and \(\Sigma_{k}\) of the Gaussian mixture, we take the following steps:  
1. **First sample \(Z_{i}\)**: \(Z_{i}\) indicates the component of the Gaussian Mixture from which the random frequency \(\mathbf{\omega}_{i}\) is drawn. For \(i=1,2,...,M\) do: 1. The element \(\mathbf{\omega}_{i}\) belongs to class \(k=1,2,...,K\) with probability: \[p(z_{i}=k|\mathbf{Z}_{-i},\alpha,\mathbf{\mu}_{k},\Lambda_{k})\propto\frac{N_{-ik}}{N -1+\alpha}|\Lambda_{k}|^{1/2}e^{-1/2(\mathbf{\omega}_{i}-\mathbf{\mu}_{k})}\Gamma_{ \Lambda_{k}(\mathbf{\omega}_{i}-\mathbf{\mu}_{k})})\]2. The element \(\mathbf{\omega}_{i}\) belongs to an unrepresented class, with probability: \[p(z_{i}=k^{\prime}|\mathbf{\alpha},\mathbf{\mu}_{k^{\prime}},\Lambda_{k^{\prime}})\propto \frac{\alpha}{N-1+\alpha}|\Lambda_{k^{\prime}}|^{1/2}e^{-1/2(\mathbf{\omega}_{i}- \mathbf{\mu}_{k^{\prime}})^{T}\Lambda_{k}(\mathbf{\omega}_{i}-\mathbf{\mu}_{k^{\prime}}))},\] where the parameters \(\mathbf{\mu}_{k^{\prime}}\) and \(\Lambda_{k^{\prime}}\) are sampled from their priors, \[\mathbf{\mu}_{k}\sim \mathcal{N}(\mathbf{\lambda},R^{-1}),\] \[\Lambda_{k}\sim \mathcal{W}(\beta,W^{-1}),\] where \(\mathbf{\lambda},R^{-1},\beta\) and \(W^{-1}\) are vague/non-informative priors.
2. **Second sample \(\mathbf{\mu}_{k}\) and \(\Sigma_{k}\)**: For \(k=1,2,...,K\), sample \(\mathbf{\mu}_{k}\) and \(\Sigma_{k}\) from: \[\mathbf{\mu}_{k}\sim \mathcal{N}\Big{(}(N_{k}\mathbf{\omega}_{k}\Lambda_{j}+\mathbf{\lambda}R )(N_{k}\Lambda_{k}+R)^{-1},(N_{k}\Lambda_{k}+R)^{-1}\Big{)},\] \[\Lambda_{k}\sim \mathcal{W}\Bigg{(}\beta+N_{k}+D-1,\Bigg{(}(\beta W)^{-1}+\sum_ {i=1}^{N}\Big{(}\delta(z_{i}=k)(\mathbf{\omega}_{i}-\mathbf{\mu}_{k})(\mathbf{\omega}_{i}- \mathbf{\mu}_{k})^{T}\Big{)}\Bigg{)}^{-1}\Bigg{)}.\]