esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 3 SCADA Data Processing for for xLSTM
### SCADA Data Processing for Wind Power Forecasting
This non-uniform distribution corresponds to the prevailing wind patterns at the wind turbine location, which predominantly originate from these directions. In contrast, the quadrant of 90\({}^{\circ}\)-135\({}^{\circ}\) demonstrates minimal representation in the dataset with only 4795 measurements, which constitutes 9.5% of the total SCADA dataset. Statistical examination of outlier distribution in the SCADA dataset reveals a remarkably consistent proportion across all directional sectors, with outlier prevalence ranging between 6.34% and 6.36% of sector-specific measurements. This uniformity in outlier distribution suggests that data anomalies manifest independently of wind directions, pointing toward systematic instrumentation issues or processing errors rather than direction-dependent measurement inaccuracies. The categorization of data according to directional sectors facilitates the implementation of sectorally differentiated modeling approaches, enabling directionally sensitive parameter optimization and enhanced predictive accuracy for wind power forecasting applications.
## 4 Simulation and Analysis  
For a satellite positioned in either a low Earth orbit (LEO) or a geostationary orbit (GEO) with the transmitter on the ground, beam wander displacement variance of a Gaussian-beam wave (\(\Theta_{0}=1\)) is plotted as a function of beam radius at the receiver in Figure 1, based on the three-layer beam wander model Equation (19). The zenith angle is set \(\zeta=0\) deg, \(C_{n}^{2}(0)=1.7\times 10^{-15}\) m\({}^{-2/3}\) and \(h_{0}=0\) m are taken, respectively. The outer scale model is described by Equation (11) with \(\kappa_{0}(h)=2\pi/L_{0}(h)\). And the theoretical model of conventional Kolmogorov beam wander displacement variance is also plotted in Figure 1 for comparison. Here, we can see that beam wander displacement variance is greatest for smaller beams and steadily decreases with an increasing beam radius. In addition, it can be seen from Figure 1 that the result predicted by the three-layer theoretical model is smaller than one predicted by the conventional Kolmogorov theory model, this is due to weaker fluctuations of non-Kolmogorov turbulence in the free troposphere and the stratosphere than Kolmogorov one, which matches the conclusions of [PERSON] et al. Beam wander displacement variance predicted by the three-layer theoretical model decreases sharply with the beam radius, with respect to the conventional Kolmogorov model.  
Furthermore, beam wander displacement variance of a Gaussian-beam wave (\(\Theta_{0}=1\)) for the three-layer model is plotted as the function of zenith angles in Figure 2 for \(W_{0}=0.2\) m. As shown in Figure 2, beam wander displacement variance increases with the zenith angles, and increases lightly for smaller zenith angles and increases sharply for larger one.  
Finally, in order to find the influence of the variations of nominal value of refractive-index structure parameter on the ground \(C_{n}^{2}(0)\) on beam wander, beam wander displacement variance of a Gaussian-beam wave (\(\Theta_{0}=1\)) is plotted as a function of \(C_{n}^{2}(0)\) in Figure 3, based on the three-layer beam wander model Equation (19). The zenith angle is set \(\zeta=0\deg\) and \(W_{0}=0.2\) m is taken. As shown in Figure 3, beam wander displacement variance increases with \(C_{n}^{2}(0)\). Theoretically, the increase of refractive-index structure parameter indicates the enhancement of optical turbulence, which inevitably leads to the increase of beam wander.  
Figure 1: Beam wander displacement variance (solid curve) of a Gaussian-beam wave for the three-layer model plotted as a function of beam radius at the receiver for a transmitter on the ground and a satellite in space at zenith angle zero. The wavelength is \(\lambda=1.55\)\(\upmu\)m and the dashed curve corresponds to the conventional Kolmogorov model.  
Figure 2: Beam wander displacement variance of a Gaussian-beam wave for the three-layer model plotted as a function of zenith angle for a transmitter on the ground and a satellite in space.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 3 Optimization Models
### Load Balancing Only (LBO)  
The problem formulation and notations are listed below.  
Input Constants:  
\(N\) set of routers in the network;  
\(L\): set of directional links in the network;  
\(L_{L}^{out}:\) set of links leaving node \(n\);  
\(L_{L}^{in}:\) set of links entering node \(n\);  
\(C_{l}:\) the capacity of link \(l\);  
\(r_{nd}:\) demand volume from source node \(n\) to destination node \(d\).  
Decision Variables:  
\(u:\) utilization on the most congested link;  
\(x_{dl}:\) total traffic with destination \(d\) carried on link \(l\).  
Problem (LBO):  
\(min\ u\)  
Subject to:  
\(\sum\limits_{l\in L_{n}^{out}}x_{dl}-\sum\limits_{l\in L_{n}^{in}}x_{dl}\ =r_{nd}\) \(\forall\ d\in N\), \(n\in N\backslash\{d\}\) (1)  
\(\sum\limits_{l\in L_{n}^{out}}x_{dl}\ =\ 0\) \(\forall\ d\in N\) (2)  
\(\sum\limits_{l\in L_{n}^{out}}x_{dl}\ =\ \sum\limits_{n\in N}r_{nd}\) \(\forall\ d\in N\) (3)  
\(\sum\limits_{d\in N}x_{d}\leq\ uC_{l}\) \(\forall\ l\in L\) (4)  
\(x_{dl}\ \geq 0\) \(\forall\ d\in N\), \(l\in L\) (5)  
The Objective Function (OF) of this ILP is to minimize the link utilization on the most congested link. The first constraint (1) is the flow conservation law. Constraint (2) states that the total outgoing flow from the destination node must be zero, which means there is no flow leaving the destination node. Constraint (3) is the flow conservation law at the destination node \(d\). Constraint (4) indicates that the sum of all the flows on each link must be less than the upper bound of the capacity.
## 5 Conclusions  
A series of wind tunnel tests on 1:150 scaled-down isolated and group-arranged quasy side cranes models is conducted using the HFFB tests. CFD simulations of the corresponding cases were conducted for verification. Under different wind incidence azimuths, the force and moment coefficients along or vertical to the rail of the whole crane body or isolated members were obtained. Based on the tests and simulation, the wind force time histories were generated by WAWS, and the wind-induced responses were analyzed according to the finite element models. The main findings of the study are summarized as follows:  
Figure 23: IF for peak deformation of the 119# node of boom-down crane.  
Figure 22: IF for peak deformation of boom-up 3# crane.  
Based on the results of HFFB, the wind load spectra were deduced. WAWS was applied to generate fluctuating wind forces. Combined with the mean wind loads obtained by CFD simulation, the wind-force time history of each member was generated. The proposed analysis method was first applied on the quayside container cranes;
2. The most unfavorable azimuth of the boom-up and the boom-down crane is \(75^{\circ}/105^{\circ}\) and \(60^{\circ}/120^{\circ}\), respectively. In some cases, the fluctuating wind loads can increase by up to \(16\%\), while the wind loads of most unfavorable cases will not change greatly. The minimum IF is \(0.6\) and \(0.7\) for boom-down and boom-up crane, respectively;
3. The interference effect is thought as the combined effects of both shielding effect, which reduce the wind load, and the amplification of turbulence. The shielding effect always exerts a greater influence on the boom-down cranes with less center spacing and becomes more dominant when the wind direction approaches \(90^{\circ}\);
4. Similar to the wind load patterns, the IE mainly reduced the wind response. The minimum interference factor of peak nodal deformation is \(0.59\) and \(0.70\) for the boom-down and boom-up crane respectively. The group arrangement is suggested when suffering storms or typhoons. Furthermore, the current codes could cover the amplification in the wind-induced response of the IE.  
Conceptualization, Y.W.; methodology, Y.S.; validation, Y.S. and X.W.; resources, N.S. and S.P.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, Y.S., X.W. and N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52178132, 52202415) and National Key R&D Program of China (Grant No.2019 YFD1101004).  
Not applicable.  
The authors declare no conflict of interest.
# Image De-Quantization Using Plate Bending Model
## 8 Conclusions and Future Directions  
Our proposed method based on the bending of deformable plates gives an intuitive way to handle non-continuous regions, and allows the incorporation of extra information into the model while ensuring a high-order signal approximation. To our knowledge, this is the first algorithm with such properties for the de-quantization problem.  
Several questions remain open for future research. Mask generation requires the separation of foreground-background objects. It is a research area on its own, and low-bit-depth input makes it even more challenging.  
Using biharmonic, Laplace, or other differential equations assumes an energy-like functional to be minimized. These are derived from physical assumptions such as minimal surface or minimal bending energy. The biharmonic equation is one of the simplest models in elasticity, with location-independent \"stiffness\". Total curvature minimization was mentioned as an alternative approach. More advanced physical models [9] (e.g., location-dependent elastic properties) could take further parameters into account. In general, elastic problems are most frequently solved with finite element methods. Solving these advanced models would require more computational power, and choosing the local parameters might be challenging.  
If more advanced models are not derived from physical analogies, then machine learning could be used to learn signal properties. Normal images and their simulated down-quantized version could be used as training samples, similar to the super-resolution problem where this approach is already used [21], or to detect false contours [14] for later processing.  
Finally, temporal consistency and high-throughput processing become interesting research questions when de-quantization is applied to video sources [22; 23].  
Conceptualization, D.V.; Methodology, D.V.; Supervision, A.C.T.M., A.S.-P., D.W. and M.P.; Writing--original draft, [PERSON]; Writing--review & editing, D.V., A.C.T.M., A.S.-P., D.W. and M.P.  
This research has been funded by the Research Council of Norway through project No. 221073 \"HyPerCept--Colour and quality in higher dimensions\".  
The authors would like to thank [PERSON] and [PERSON] for their feedbacks and comments.  
The authors declare no conflict of interest.
In Section 3, a methodology for the generation and evaluation of atmospheric turbulence and cloud attenuation data is provided. The simulations of the RAOB-based scintillations are hereby explained in detail. The performance of the proposed high-end APD-TIA photodetector under test is evaluated in Section 4 at maximum data throughput using a facilitated prototype with Mach-Zehnder modulator (MZM). In Section 5, the full-scale evaluation of the built SatCom prototype in the presence of atmospheric turbulence is presented based on the number of high-quality measurement results. Finally, Section 6 provides conclusions.
## 6 Discussion
The proposed architecture has been designed to be implemented in medium-sized cities (100,000 to 1,000,000 inhabitants) that have a public transport system similar to the \"collective\" (buses of 12 to 20 passengers) and road infrastructure shared with private vehicles. It is important to consider these design aspects and the limitations (or restrictions) of the system, if this proposed architecture is going to be implemented in a similar city. For example, the geographical characteristics of the city must allow the correct location of the gateway devices to provide an optimal power level for transmission and an adequate LoS. Regarding GNSS, tests showed that this type of technology is adequate to continuously determine the location of the vehicle with an error between 1 and 5 m, however, due to high power consumption and a long time to connect to satellites, a study of the different GNSS devices should be conducted to find the most suitable one. The results obtained with the performed experiments show that the developed prototype meets certain requirements set out for the FMCS, however, its scope in functionality must be increased and tests with a greater number of nodes in an urban environment as close as possible to the real environment must be conducted for a more complete validation. Finally, this proposal could be taken into account by local and national authorities related to the Strategic Public Transport Systems (SPTS) that are currently being implemented in eight cities in Colombia. The majority of these projects have not yet developed their technological components (FMCS among them), therefore, new requirements could still be included in the analysis of this system, related to requirements identified for FMCS, the ITS architecture, and the developed prototype. Considering these aspects, important benefits could be obtained in terms of system efficiency, standardization, and savings in implementation and operation costs.
# Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome--A Narrative Review
## 6 Future Directions
### The Role of Artificial Intelligence in Assessing CVD Risk in DM  
The applications of artificial intelligence in the medical field are becoming more and more extensive, being able nowadays to generate various models of cardiovascular risk assessment with both therapeutic and prognostic roles [205; 206]. Imaging assessment of the ulcerative lesion is essential in order to identify an associated infection [207] and to provide accurate epidemiological data on the prevalence of DM [208]. Clinical studies in the literature have demonstrated that diabetic foot infections act as a cardiovascular marker based on a directly proportional correlation between the severity of infection and atherosclerotic lesions [75; 209].  
Machine Learning (ML) and Deep Learning (DL) algorithms have various applications in cardiology and diabetology [210; 211], having both diagnostic and prognostic roles by identifying predictors associated with cardiovascular risk [212; 213; 214]. Technologies using artificial intelligence are based on the analysis of complex databases, and current data on diabetes-related cardiovascular risk assessment show non-linear connections between input predictors and the obtained risk. The advantage of using the aforementioned algorithms is obtaining intrinsic relationships between several predictors used simultaneously [215; 216; 217]. To date, several algorithms have been developed to assess associated cardiovascular risk [218, 219, 220], one such example being the one proposed by [PERSON] et al. [221] which, using carotid vascular Doppler assessment and the presence of traditional cardiovascular risk factors, can assess associated cardiovascular risk with superior accuracy to traditional methods of calculation. Based on the socio-economic importance of the complications associated with DFS, artificial intelligence algorithms have been developed to screen diabetic patients to identify risk factors for the development of ulcers using different optical sensors [222, 223, 224, 225].
## 3 Reliability Analysis Method Based on PSO-BPNN
### PSO-BPNN Method
\[\mathbf{x}_{i}^{t+1}=\mathbf{x}_{i}^{t}+\mathbf{v}_{i}^{t+1} \tag{10}\] \[\mathbf{v}_{i}^{t+1}=w\mathbf{v}_{i}^{t}+c_{1}r_{1}\big{(}P_{\text{best}} ^{t}-\mathbf{x}_{i}^{t}\big{)}+c_{2}r_{2}\big{(}G_{\text{best}}^{t}-\mathbf{x}_{i}^{t} \big{)} \tag{11}\]  
where \(w\mathbf{v}_{i}^{t}\) is the inertial term of particle motion; \(w\) is inertia weight; \(c_{1}r_{1}(P_{\text{best}}^{t}-\mathbf{x}_{i}^{t})\) is the individual confidence term; \(c_{1}\) is the acceleration factor, generally non-negative constant; \(P_{\text{best}}^{t}\) is the individual extreme point; \(r_{1}\) is a random number, where the value range is \([0,1]\); \(c_{2}r_{2}(G_{\text{best}}^{t}-\mathbf{x}_{i}^{t})\) is the group extreme point; \(c_{2}\) is the acceleration factor, generally non-negative constant; \(r_{2}\) is a random number, where the value range is \([0,1]\). The steps of the PSO-BPNN method are outlined as follows:  
1. Generate the initial population adaptively based on random variables. 2. Obtain the objective function. It is the functional equation fitted by the BPNN. 3. Determine the fitness function based on the minimum value of the search objective and calculate the fitness result, as shown in Equation (9). 4. Update the position, velocity, and inertia weight of the particles, and obtain the fitness function value through each iteration. 5. End when the result meets the convergence condition; if the convergence condition is not met, repeat steps 3 and 4 until the condition is satisfied. To ensure that the output of the BPNN accurately approximates the behavioral characteristics of the target function, this paper employs the PSO algorithm to perform a global optimization of the network's weight and bias parameters. Given that the PSO algorithm significantly enhances the stability and convergence performance of neural networks in practical applications, a surrogate relationship is established between the trained neural network and the target limit state function, as expressed in Equation (12). \[\overline{g}(\mathbf{X})\approx g(\mathbf{X}) \tag{12}\]  
where \(\overline{g}(\mathbf{X})\) represents the limit state function approximated by the neural network, which has been optimized using the PSO algorithm, and \(g(\mathbf{X})\) denotes the target limit state function.
## 2 Materials and Methods
### Study Variables  
Following the ISAK considerations and protocols [5], we decided to measure the following: weight (kg), height (cm) in standing and sitting position, armspan (cm), perimeter (cm) of the contracted arm, waist, hip, thigh, and calf, size (cm) of styloid diameter of the wrist and bicondylar of the femur, and the fold (mm) of biceps, triceps, male pectoral, subscapular, abdominal, suprailiac, thigh, and calf. With all these measures we calculated the following [18; 19; 20]:  
\[\text{Body Mass Index }(\text{BMI})=\frac{\text{weight}}{\text{height}^{2}}\]  
\[\text{Ponderal Index }(\text{PI})=\frac{\text{height}}{\sqrt[3]{\text{weight}}}.100\]  
\[\text{Cormic Index }(\text{CI})=\frac{\text{sitting height}}{\text{standing height}}.100\]  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Total** & **Male** & **Female** \\ \hline Football & 12.0\% (69) & 14.4\% (52) & 7.9\% (17) \\ Walking & 11.1\% (64) & 8.9\% (32) & 15.0\% (32) \\ Basketball & 9.2\% (33) & 7.5\% (27) & 12.1\% (26) \\ Crossfit & 8.5\% (49) & 10.6\% (38) & 5.1\% (11) \\ Running & 8.5\% (49) & 11.1\% (40) & 4.2\% (9) \\ Handball & 8.4\% (49) & 7.2\% (26) & 10.3\% (2) \\ Volleyball & 7.0\% (40) & 4.4\% (16) & 11.2\% (24) \\ Gym & 5.4\% (31) & 4.4\% (16) & 7.0\% (15) \\ Airsoft & 3.1\% (18) & 5.0\% (18) & 0.0\% (0) \\ Rugby & 4.4\% (25) & 6.9\% (25) & 0.0\% (0) \\ Canoe & 3.0\% (17) & 4.4\% (16) & 0.5\% (1) \\ American football & 2.6\% (15) & 4.2\% (15) & 0.0\% (0) \\ Cycling & 2.1\% (12) & 3.3\% (12) & 0.0\% (0) \\ Swimming & 1.0\% (0) & 0.0\% (0) & 2.8\% (6) \\ Capoetia & 0.9\% (5) & 0.8\% (0) & 0.9\% (2) \\ Sedentary lifestyle & 12.7\% (73) & 6.7\% (20) & 22.9\% (49) \\ \hline  & 574 (100\%) & 360 (100\%) & 214 (100\%) \\ \hline \end{tabular}
\end{table}
Table 1: Categories of subjects studied according to sex.  
F-Bone mass: composed of several diameters (bicondylar of the femur and bistyloid of the wrist) in addition to the percentage of bone tissue, as they all relate to the bone tissue of the organism.  
F-Skinkfolds: composed of several skinfolds, namely, the thigh, calf, and biceps.  
F-Robustness: composed of several measurements, namely, standing height, sitting height, and armspan.  
In all multivariate procedures, only one missing data item excludes the subject from the analysis. That is why, in our research, we went from a total sample of 574 participants to one of 375, which divided groups based on their physical activity:  
- Sedentary = composed of 24 individuals of the 73 initial participants.  
- Amateur athletes = composed of 351 individuals of the 501 initial participants. Additionally, we divided this group into another two based on which body area they use:  
- Upper and lower body = composed of 135 individuals of the 194 initial participants.  
- Mainly lower body = composed of 216 individuals of the 307 initial participants.
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 3 Dynamic Model Setup
### Tuning and Validation of the Standalone Propeller Model  
To ensure consistency in the aerodynamic modeling of the propeller, a comparison was conducted based on the two different time-domain analysis approaches. This was achieved by examining the performance curves for the standalone propeller without the presence of the wing. The thrust and torque coefficients with reference to the advance ratio for the simulations conducted using DUST and MBDyn are presented in Figure 4, considering a collective pitch angle of 38.2\({}^{\circ}\). The aerodynamic model in MBDyn was tuned with a linear tip loss function to consider the three-dimensional effects on the blades and with an empirical correction factor (\(K_{\text{f}}=1.3\)) on the dynamic inflow model to include the power losses. These modifications were required to match the thrust and torque curves obtained in DUST. As observed in Figure 4, both curves exhibit a perfect match, indicating a consistent aerodynamic modeling approach.  
Additionally, the whirl flutter stability map for the standalone propeller was created in SDBox for a free stream velocity of 133 m s\({}^{-1}\) and windmilling condition (see Figure 5). As expected, the largest required stiffness is obtained at isotropic conditions, equal to 188,000 N m rad\({}^{-1}\). The same conditions were analyzed using both time-domain approaches, i.e., hybrid and fully coupled. The time histories were post-processed using the matrix pencil estimation (MPE) to extract eigenfrequencies and damping ratios [32]. Results are summarized in Table 7, further supporting the aerodynamic consistency between the two proposed analysis approaches.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Name** & **Requirement** & **Rot. Speed, rpm** \\ \hline Windmilling & \(Q=0\) N m & 2500 \\ Thrusting & \(T=750\) N & 2500 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Operational conditions under investigation.  
Figure 4: Thrust coefficient **(left)** and torque coefficient **(right)** vs. advance ratio (J) for a collective pitch of 38.2\({}^{\circ}\).