esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Derivation of the Hamiltonian Formalism
### Lagrangian for the Relative Motion under the Zonal Harmonic Perturbation
 \right]\frac{\bar{\mathbf{r}}+\rho}{\bar{r}+\rho}\] (A13)  
We can then introduce Equation (A13) into Equation (A12) and set  
\[\frac{1}{\bar{r}+\rho}=\frac{1}{\bar{r}\sqrt{1-2\frac{\rho}{\bar{r}}\cos\beta +\left(\bar{\rho}\right)^{2}}}\] (A14)  
where  
\[\cos\beta=-\frac{\rho^{\bar{\mathbf{T}}}}{\rho^{\bar{r}}}=-\frac{x}{\rho}\] (A15)  
The expression of \(V_{r}\) can be approximated using the Legendre polynomials  
\[\frac{1}{\bar{r}+\rho}=\sum_{k=0}^{\infty}\left[\frac{1}{2^{k}K!}\frac{d^{k}( \cos^{2}\beta-1)^{k}}{d(\cos\beta)^{k}}\Big{(}\frac{\rho}{\bar{r}}\Big{)}^{k}\right]\] (A16)  
As detailed in Appendix A.2, because \(\rho<<\bar{r}\), Equation (A16) can be truncated to the second order in \(\bar{\rho}\), obtaining  
\[\frac{1}{\bar{r}+\rho}=-\frac{1}{\bar{r}}\left[1+\frac{\rho}{\bar{r}}\cos\beta +\Big{(}\frac{\rho}{\bar{r}}\Big{)}^{2}\bigg{(}\frac{3}{2}\cos^{2}\beta-\frac {1}{2}\bigg{)}\right]\] (A17)  
and  
\[\cos\phi=\frac{1}{\bar{r}}\left[1+\frac{\rho}{\bar{r}}\cos\beta+\Big{(}\frac{ \rho}{\bar{r}}\Big{)}^{2}\bigg{(}\frac{3}{2}\cos^{2}\beta-\frac{1}{2}\bigg{)} \right]\left[(x+\bar{r})\sin i\sin u+y\sin i\cos u+z\cos i\right]\] (A18)Finally, introducing Equations (A17)-(A18) into Equation (A12) and subtracting \(V_{r}\) to \(K_{r}\) results in the following expression for the Lagrangian of the problem:  
\[\mathcal{L}_{r}=\mathcal{L}_{r}^{(0)}+\mathcal{L}_{r}^{(k)}\] (A19)  
with  
\[\mathcal{L}_{r}^{(0)}=\frac{\left(\dot{x}-ny\right)^{2}}{2}+\frac{\left(\dot{y }+\bar{r}n+nx\right)^{2}}{2}+\frac{z^{2}}{2}+\left(n^{2}\bar{r}^{2}\right) \left(1-\frac{x}{\bar{r}}+\frac{3}{2}\frac{x^{2}}{\bar{r}^{2}}-\frac{1}{2} \frac{\left(x^{2}+y^{2}+z^{2}\right)}{\bar{r}^{2}}\right)\] (A20)  
\[\mathcal{L}_{r}^{(k)}=\dot{x}^{(1)}(\dot{x}-ny)-\dot{y}^{(1)}(\dot{y}+\bar{r}n +nx)+\dot{z}^{(1)}\dot{z}+(\dot{x}^{(1)})^{2}+(\dot{y}^{(1)})^{2}+(\dot{z}^{(1 )})^{2}+f\left(j_{nr}^{k},\frac{\rho}{\bar{r}},\frac{\rho^{2}}{\bar{r}^{2}}\right)\] (A21)  
where \(\mathbf{v}^{(1)}=\left[ \begin{array}{cc}\dot{x}^{(1)}&\dot{y}^{(1)}&\dot{z} ^{(1)}\end{array} \right]^{T}\) and \(f\left(j_{nr}^{k},\frac{\rho}{\bar{r}},\frac{\rho^{2}}{\bar{r}^{2}}\right)\) collects the terms depending on the \(J_{n}\) coefficients, which shall be selected as detailed in the following section. The term \(\mathcal{L}^{(0)}\) of the Lagrangian corresponds to the one for the unperturbed problem and the HCW equations can be derived applying the Euler-Lagrange equation \(\frac{d}{dt}\left(\frac{\partial\mathcal{L}_{r}^{(0)}}{\partial\rho}\right)- \frac{\partial\mathcal{L}_{r}^{(0)}}{\partial\rho}=0\).
## 2 Analysis of the Macroscopic FP Principle  
We used the Fraunhofer diffraction theory to verify the mathematical model of the proposed macroscopic FP method. The schematic and experimental configurations of the macroscopic FPs are shown in Figure 1. The coherent light source and camera sensor were located on opposite sides with respect to the object.  
As shown in Figure 1a, the laser point light source emits a divergent spherical wave that propagates to the focusing lens and becomes a converging spherical wave, illuminating the object imaged at the pupil plane. In this experiment, in order to effectively control the system energy, we placed a neutral density filter behind the laser point light source. The distance between the light source and the focusing lens is \(z_{1}\), and the distance between the object and the pupil plane is \(z_{2}\). The amplitude distribution of the object near the focal position of the point light source is the Fraunhofer approximation pattern, which is located at the pupil plane (entrance pupil) of the photographic lens.  
As shown in Figure 1b, the scanning path of the camera is in the red box. In addition, the light field emitting from the object propagates distance \(z_{2}\) toward the pupil plan, and the image of the object is finally imaged on the camera sensor.  
The spatial coordinates and frequency coordinates of the system are shown in Figure 1. \((x,y)\) represents the spatial coordinates corresponding to the plane where the lighting source is located, \((\zeta,\eta)\) represents the spatial coordinates corresponding to the object plane, \((u,v)\) represents the frequency coordinates corresponding to the pupil plane, and \((x^{\prime},y^{\prime})\) is the spatial coordinates corresponding to the plane of the camera sensor.  
Figure 1: A model of far-field Fourier ptychography imaging based on near-infrared laser illumination with 808 nm using camera scanning; the scanning path is shown in the red box. (**a**) Propagation from the light source to the pupil plane; (**b**) propagation from the object to the camera sensor.
## 3 System Properties for Constant Concentrations
### Special Case: \(P_{\max,1}=P_{\max,2}\)
The partial derivatives of the equations defining the dynamics at the equilibrium point are given by \[\frac{dQ}{dt}=\left[r_{Q}-\delta_{Q}\left(1+\frac{E_{\max,1}E_{*}}{1+E_{*}} \right)\right]Q\]2 \[\frac{dQ}{dt}=\left[r_{Q}-\delta_{Q}\left(1+\frac{E_{\max,1}E_{*}}{1+E_{*}} \right)\right]Q\]3 \[\frac{dQ}{dt}=\left[r_{Q}-\delta_{Q}\left(1+\frac{E_{\max,1}E_{*}}{1+E_{*}} \right)\right]Q\]4 \[\frac{dQ}{dt}=\left[r_{Q}-\delta_{Q}\left(1+\frac{E_{\max,1}E_{*}}{1+E_{*}} \right)\right]Q\]5  
Note that the equilibrium condition for \(P\) brings in \(Q_{*}\) in \(\frac{\partial f_{2}}{\partial P}_{|(Q_{*},P_{*},E_{*})}\). The characteristic polynomial for the Jacobian matrix is given by  
\[\chi(t) =\left| \begin{array}{ccc}t&0&\delta_{Q}Q_{*}\frac{E_{\max,1}}{ (1+E_{*})^{2}}\\ -k_{P}&t+k_{P}\frac{Q_{*}}{P_{*}}+r_{P}&\delta_{P}P_{*}\frac{E_{\max,2}}{(1+E _{*})^{2}}\\ 0&\delta_{E}E_{*}\frac{P_{\max,2}-P_{\max,1}}{(1+P_{*})(1+P_{*}+P_{\max,1}P_{* })}&t+s_{E}\left(1+\frac{P_{\max,1}P_{*}}{1+P_{*}}\right)\end{array} \right|\] \[=t^{3}+a_{2}t^{2}+a_{1}t+a_{0}.\]  
By the Routh-Hurwitz criterion, all eigenvalues have negative real parts if and only if \(a_{0}>0\), \(a_{1}>0\) and \(a_{1}a_{2}>a_{0}\). These coefficients are given by  
\[a_{2} =\left(k_{P}\frac{Q_{*}}{P_{*}}+r_{P}\right)+s_{E}\left(1+\frac{P _{\max,1}P_{*}}{1+P_{*}}\right)>0,\] \[a_{1} =\left(k_{P}\frac{Q_{*}}{P_{*}}+r_{P}\right)s_{E}\left(1+\frac{P _{\max,1}P_{*}}{1+P_{*}}\right)+\frac{E_{\max,2}\delta_{E}E_{*}}{(1+E_{*})^{2 }}\frac{(P_{\max,1}-P_{\max,2})\,\delta_{P}P_{*}}{(1+P_{*})\left(1+P_{*}+P_{ \max,1}P_{*}\right)}\] \[a_{0} =\delta_{Q}Q_{*}\frac{E_{\max,1}\delta_{E}E_{*}}{(1+E_{*})^{2}}k _{P}\frac{P_{\max,1}-P_{\max,2}}{(1+P_{*})\left(1+P_{*}+P_{\max,1}P_{*}\right)}.\]  
If \(P_{\max,1}<P_{\max,2}\), then \(a_{0}\) is negative and the positive equilibrium point is _unstable_, i.e., once the maximal inhibiting effect of the tumor on the effector cells is larger than the maximal stimulating effect, no steady-state positive solution exists. Note further that for \(a_{0}<0\) the characteristic polynomial \(\chi(t)=t^{3}+a_{2}t^{2}+a_{1}t+a_{0}\) has exactly one change of sign in its coefficients and thus there exists a unique positive root. So the equilibrium point has a two-dimensional stable manifold that separates the regions where \(Q\) and \(P\) diverge to infinity from the region where \(Q\) converges to \(0\). Thus we have the following result:  
**Theorem 1**. : _If \(P_{\max,1}<P_{\max,2}\), then the positive equilibrium point \((Q_{*},P_{*},E_{*})\) is unstable with a two-dimensional stable manifold in parameter space._  
If \(P_{\max,1}=P_{\max,2}\), then the equilibrium point has the eigenvalue \(0\) and two negative eigenvalues. Thus there exists a one-dimensional center manifold which in this case consists of all equilibria, namely the equilibrium manifold \(M\) defined earlier. For \(P_{\max,1}>P_{\max,2}\), the coefficients \(a_{0}\), \(a_{1}\) and \(a_{2}\) are all positive.
# Interconnected Government Services: An Approach toward Smart Government
## 6 Conclusions  
In the last few years, we have encountered the intensive implementation of ICT technologies in public administrations, which aim to improve interactions with citizens and between the administration bodies. In order to make them more effective, democratic, and transparent, many governments transform their e-government systems into smart governments through the open-data concept. Something more than introducing the open-data concept into the e-government system is needed to solve numerous problems, such as interoperability and data privacy (practical experience has shown that the very concept of open data leads to concerns about the privacy of potential users).  
Interoperability between different public authorities within the administration (at all levels) is a significant problem. It requires the upgrading of the e-government system to provide efficient collaboration within public administration. The new information system model proposed in this paper enables easy and safe data transfer among public authorities. Using advanced ICT technology (e.g., database virtualization and mediator-wrapper technology) decouples data processing from data storage and enables collaboration between different databases without additional costs (databases remain in their locations). Further, this model implies building a collaboration layer with numerous service functions (instances). The system can determine which databases store the requested data and enable simultaneous data processing on multiple database nodes. This way, the IGS application can communicate with a single instance, despite multiple database nodes acting over the data. This results in better problem-solving and the optimal utilization of resources.  
We have proved the effectiveness of the proposed model by (Di)graph theory in the example of a specific public administration service. We have modeled the current state and compared it with the proposed model. As a measure of similarity, we have used the time required to collect documents (a critical measurement of citizens' service satisfaction).  
Testing has shown that the same model can be used elsewhere in public administration without jeopardizing the individual's privacy.  
Z.B.--Conceptualization, data curation, software, investigation, methodology, and writing--original draft; D.K.--resources, project administration, and funding acquisition; P.D.B.--conceptualization, software, methodology, and writing--original draft; I.M.J.--data curation, formal analysis, and validation; J.S.--investigation, writing--review and editing; and V.S.--investigation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
The research presented in this paper was supported by the European Union's Horizon 2020 research and innovation program under Grant Agreement number 856967 and by grants of the Ministry of Education, Science, and Technological Development of the Republic Serbia (III44003), (III47005).  
Not applicable.  
Not additional data is provided.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Hydrogeological Study
#### 2.3.1 Conceptual Hydrogeological Functioning Model  
The hydrogeological conceptual model of the Autzagane aquifer was intended to convey the essence of the fundamental principles and basic functionality of the system, which it represents, and was defined by its nature, recharge and discharge mechanisms, and the groundwater flow scheme within it. The aquifer was divided into several hydrogeological sectors based mainly on the identified zones of groundwater discharge during the inventory of water points, although hydraulic continuity could exist between adjacent sectors. The hydrogeological sector of the Autzagane aquifer that is most directly related to the Urdinbide tunnel is the Ategorri sector.  
The recharge of the Autzagane aquifer occurs through the infiltration of precipitation. The development of a significant layer of surface alteration and the moderately sloping terrain in the area promote a high infiltration coefficient in relation to the total usable rainfall (precipitation minus evapotranspiration). The average precipitation in the area is 1550 mm/year.  
Regarding the discharge, the Autzagane aquifer is crossed by the heads of several streams that run in a NNE-SSW direction. These small valleys locally represent the axes of the lower relative elevation of the portion of the aquifer situated on both sides of them, and, therefore, constitute the discharge zones, through springs or diffuse discharges to the streams, of these sectors. The presence of luititic and marly layers that separate the permeable sandstone layers of the aquifer causes the discharge of each sector to occur not at a single point corresponding to the intersection of the aquifer roof with the stream (as would be the case in a homogeneous aquifer) but at a series of springs along the stream related to each permeable level separated by less permeable intercalations.  
This phenomenon was evident in the Ategorri sector, where there were four stepped springs in its headwaters (Ategorri-1 to 4). According to the data provided by the study's forometric monitoring network, the subterranean discharge in the Ategorri stream system  
Figure 7: Inventory of main water points close to the south portal of the Urdinbide tunnel (4 groundwater intakes, 3 surface water intakes, 1 distribution chamber, and 1 reservoir).  
would be around 4 L/s, of which 1.4 L/s were used for the partial supply of the municipality of Amorebieta-Etcano through the capture of the headwater springs of the Ategorri stream.  
The circulation of groundwater from the recharge zones of the aquifer (the interflives) to the discharge lines (the streams) occurred mainly through the permeable sandstone levels in the direction of the layers. Most of the groundwater flow occurred under confined conditions. The groundwater in the massif had a medium mineralization, and its hydrochemical facies was calcium bicarbonate [1].
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 2 Related Work
### Trajectory Prediction
On this basis, different from single-trajectory prediction, STA-LSTM [32] and O-LSTM models take into account the interaction between research objects in a certain space-time region and the impact of environmental information to different degrees, and perform well on ETH and UCY data sets. [PERSON] and [PERSON] [33] combined the characteristics of the two networks and proposed a CNN-LSTM-SE model for ship trajectory prediction, which performed well on several indexes. However, when using a pure CNN model or a fusion of CNN and LSTM for temporal prediction of non-Euclidean spatial data such as vessel trajectories, the model is unable to adequately capture the dependencies between trajectory features and mine the temporal patterns of trajectories. Aiming at the characteristics of trajectory data, a graph-based neural network, which learns and trains on the graph structure, is capable of mining the potential feature relationships and temporal patterns of non-Euclidean data such as trajectories. The Spatio-Temporal Graph Convolutional Networks (STGCN) proposed by [PERSON] et al. [34] are suitable for non-linear and complex non-Euclidean spatial data, such as traffic flow, and can effectively capture spatio-temporal correlation. [PERSON] et al. [35] proposed Attention Based Spatial-Temporal Graph Convolutional Networks (ASTGCN), which impose an attention mechanism in the temporal and spatial dimensions and achieve better results in capturing the spatio-temporal dependence in traffic flow data. It is worth noting that no matter whether STGCN, ASTGCN, or some other graph-based time series prediction models, they all need the dependency between multiple variables as prior knowledge, and this prior input will greatly affect the subsequent prediction results. To solve this problem, [PERSON] et al. [36] proposed a Spectral Temporal Graph Neural Network (StemGNN) for multivariate temporal prediction. The model learns the implicit correlation between variables through the structure of its latent correlation layer, and then inputs the learned adjacency matrix into the model for time domain, space domain to frequency domain transformation, and graph convolution operations, and finally returns to the original domain and outputs the prediction results. The model can capture the spatiotemporal dependence in multivariate time series without prior output. Therefore,this study takes the correlation matrix extracted from the potential correlation layer of StemGNN after training as the prior input of ASTGCN and then experiments with ship trajectory prediction.
## 2 Related Work
### Summary and Conclusion  
In summary, symbol detection in engineering domains is a long-standing research area that is continuously adopting the latest technology to accomplish the task. Currently, Deep Learning models are mainly used for recognizing symbols in technical sketches or drawings. However, the availability of data is a major challenge, as the Deep Learning based models require a lot of labelled data for training. The problem becomes even more apparent when it comes to domain-specific tasks like the detection of principle sketches, where open-source datasets are not available and scraping cannot be used. Thus, the following research question emerges: how can the detection of symbols in principle sketches from the mechanical engineering domain be achieved and has the data generation or model architecture a greater influence on the detection?  
In order to answer this question in detail, a general framework for the solution of symbol recognition is presented first. The individual steps are then explained in more detail. Finally, the procedure is analyzed and evaluated with unknown datasets.
###### Abstract  
A new technique to reduce flicker noise generated in light-fidelity (Li-Fi) transmission links based on the white light-emitting diode (LED) is proposed. Here, flicker noise with a frequency of 120 Hz, which is twice the frequency of AC power (60 Hz), is generated. The proposed technique is implemented in the receiver of the Li-Fi link. It can reduce flicker noise regardless of various digital modulation formats. In addition, there is no need to change the structure of the electrical circuit driving the LED to reduce the flicker noise. As a result, the non-return to-zero-on-off-keying (NRZ-OOK) signal waveform is tilted according to the flicker noise waveform. We implement the derivative equalization with a pseudo-flicker weight function to reduce the flicker noise. The derivative value of the NRZ-OOK signal mixed with flicker noise becomes larger than that without the flicker noise. In the proposed technique, the derivative value between adjacent sampling points is suppressed below the preset thresholds when it is greater than the preset threshold. Furthermore, a pseudo-flicker weight function is applied to accelerate the flicker noise reduction. As a result, using the proposed technique, a 2 dB signal-to-noise ratio (SNR) gain is obtained based on the bit error rate (BER) threshold (\(3.5\times 10^{-5}\)) corresponding to 10% flicker modulation, which is known to have no serious effect on human health. This means that it is possible to implement a Li-Fi transmission link based on an illumination environment with a flicker modulation reduced from 10% to 7%.  
derivative equalization; flicker noise; Li-Fi; pseudo-flicker weight function; white light emitting diode +
Footnote †: journal: Sensors
## 2 Femoroacetabular Impingement in Human Hip Joint  
The morphologies that result in mechanical FAI can be Cam (an enlarged femoral head deformity), Pancer (an acetabular over-coverage), or a combination of both. These deformities, which appear as bumps, are characterized by a greater localized head radius. A variety of techniques have been used to quantify them. These include the alpha angle. Figure 1 shows the three types of FAI, in which the incongruuity between the head of the femur and the acetabulum is visible [15; 16]. Figure 1a shows the healthy hip joint, in which the alpha angle can be seen. The Cam-type deformity (Figure 1b) is identified by a decreased headneck offset. This leaves a pronounced anterolateral bump without a con-cavity at the femoral head and neck [17; 18; 19; 20]. This has been ascribed to a number of factors. These include adverse hip trauma and loading [14; 17; 20], excessive athletic activity [14; 21; 22] and contact sports [23; 24; 25] before skeletal maturation. Persons who have a larger Cam deformity (i.e., greater alpha angle) [26], face a greater risk of anterosuperior femoral head obstruction with the acetabulum. This can occur during motions that combine hip flexion, rotation [9; 20; 21; 27; 28; 29; 30] and squatting [9; 11; 29; 31]. The result can be early adult cartilage degeneration [3; 15; 28; 32]. The early stages of FAI are seldom painful. Consequently, it may not be recognized during its preliminary asymptomatic settling-in phase [33; 34; 35; 36]. However, early diagnosis of FAI and treatment are important to minimize the risk of severe irreversible damage to the hip cartilage, and osteoarthritis (OA). Pancer-type describes the condition in which the socket or acetabulum for the ball or femoral head is over-covered (Figure 1c). This over-coverage typically appears along the front-top rim of the socket (acetabulum). Pincers occur more frequently in women who are in in their 30s and 40s and are athletically active [18; 37]. Finally, Figure 1d shows the FAI mixed-type [38; 39].  
Figure 1: Types of hip joint with femoroacetabular impingement: (**a**) healthy hip joint, (**b**) Cam-type, (**c**) Pancer-type and (**d**) mixed-type [7; 25].
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
###### Abstract  
Fluid dynamic simulations using the FENE-P model of polymer physics are compared to those of an incompressible Newtonian fluid base case in order to understand the role of elasticity in the formation of vortices in a 90\({}^{\circ}\) bend narrow channel. The analysis bridges the flow behavior of a purely elastic fluid and that of a Newtonian fluid. We evaluated how four dimensionless numbers--Reynolds number (Re), Weissenberg number (Wi), viscosity ratio (\(\beta\)), and elasticity number (El)--affect the formation of vortices. It is shown that increasing Re and Wi, or lowering \(\beta\) will cause vortices to grow in size. Two phase space diagrams, \(\beta\) vs. El and \(\beta\) vs. Re, were created to show the range of values where inertial and elastic vortices form. Both diagrams have three zones. Depending on the polymer viscosity ratio and the elasticity number, the vortices form either upstream of the bend (elasticity driven) or form downstream of the bend (inertia driven), are suppressed. Our predictions are in good agreement with previous experimental and numerical works.  
elasticity; polymer; vortex; bend; flow +
Footnote †: journal: Physics Letters A