esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Working Principle and Kinematic Modeling of Extension-Dominated Mixer
### Kinematic Mathematical Analysis  
The positions of key points on the selected rotor are shown in Figure 2. The X-axis is horizontal and the positive direction is to the right; The Y-axis is vertical, and the positive direction is up. A, B, C, D, E, F, and G are points on the rotor. Point A is the geometrical center of the rotor; Point B is the rotation center of the rotor, point B rotates counterclockwise around the geometric center of the stator, and the rotation radius is: \(e\) Point C is the point where the initial position of the rotor coincides with the geometrical center of the stator. Point D is the left intersection of the rotor and the X-axis direction, and point E is the right intersection of the rotor and the X-axis direction. F is the highest point in the vertical direction of the rotor, and G is the lowest point in the vertical direction of the rotor. The motion equation of point B is:  
\[\left\{ \begin{array}{l}x_{B}=e\cdot\cos(\frac{\pi}{2}+\omega t)=-e\sin \omega t\\ y_{B}=e\cdot\sin(\frac{\pi}{2}+\omega t)=e\cos\omega t\end{array} \right. \tag{3}\]  
The remaining points rotate clockwise around point B, and their equations of motion are  
\[\left\{ \begin{array}{l}x=x_{B}+L\cdot\sin(\varphi-\omega t)\\ y=y_{B}+L\cdot\cos(\varphi-\omega t)\end{array} \right. \tag{4}\]  
The distance from this point to point B is denoted as \(L\). \(\varphi\) represents the angle between the line connecting this point and point B and the horizontal coordinate. \(\omega\) signifies the angular speed of rotation and revolution of the rotor, with counterclockwise rotation being assigned positive values and clockwise rotation assigned negative values. Thus, the rotational angular speed of the rotor is represented by \(\omega\) for counterclockwise rotation and by \(-\omega\) for clockwise rotation. Thus, the equation of motion of each point can be obtained (\(e\) is the radius of revolution, \(R\) is the radius of the rotor), shown in Table 2.  
Figure 7 illustrates the velocity distribution of different points on the rotor, corresponding to different time instants as depicted in Figure 3. Each point on the rotor rotates clockwise around its center, point B, with a rotational speed equal in magnitude but opposite in direction to the rotation speed of point B around the geometric center of the stator. It is observed that all points exhibit identical velocity cycles, each having a value of \(\pi/\omega\). The rotational center, point B, maintains a constant speed of magnitude \(\omega\). Points A and C, positioned internally on the rotor, trace linear trajectories on the epitrochoid motion circle. Point A moves vertically with the rotor's geometric center, resulting in zero horizontal velocity, while point C exhibits only horizontal velocity, opposite in trend to point A. At the
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 4 Dynamic Service Placement Framework Based on Deep Reinforcement Learning
### Migration Conflicting Resolution Mechanism
#### 4.2.1 Service Placement under Migration Conflict  
We first formulate the service placement under migration conflict into a mixed-integer linear programming problem. As shown in the objective function in Equation (6), we aim to minimize the total delay of users in a time-varying period. We use \(z_{j}(t)\) to denote the total delay of user \(u_{i}\) at time slot \(t\), where \(z_{u_{i}}(t)=D_{u_{i}}^{e}(t)+D_{u_{i}}^{l}(t)+D_{u_{i}}^{u}(t)\). When service \(v_{i}\), which is serving \(u_{i}\), migrates successfully, the communication delay \(D_{u_{i}}^{l}(t)\) will decrease; otherwise, \(D_{u_{i}}^{u}(t)=0\). Here, \(\alpha(v_{i})\) is a boolean variable that represents whether service \(v_{i}\) migrates successfully. For the communication delay, the migration result produces two different values. We combine these two cases and transform the communication  
Figure 3: Services actions with migration conflict.  
delay into \(D^{l}_{u_{i}}(t)=\alpha(v_{i})\cdot D^{l}_{u_{i}}(t)+(1-\alpha(v_{i}))D^{l}_{u_{i} }(t)\). Thus, the total delay of user \(u_{i}\) is transformed to  
\[z_{u_{i}}(t)=D^{c}_{u_{i}}(t)+\alpha(v_{i})\cdot(D^{\prime l}_{u_{i}}(t)+D^{u}_{ u_{i}}(t))+(1-\alpha(v_{i}))D^{l}_{u_{i}}(t). \tag{10}\]  
For edge serve \(m_{j}\), the total delay of users that are served by corresponding services is \(Z^{m_{j}}_{u}(t)=\sum_{u_{i}\in\mathbb{U}(\mathbb{V}_{m_{j}})}z_{u_{i}}(t)\). When an edge server has a migration conflict, it means that multiple services choose it as a destination while its storage capacity cannot satisfy all services. The value of \(Z^{m_{j}}_{u}(t)\) is divided into two parts; one is determined by services with conflicts, which are shown as follows:  
\[Z^{m_{j}}_{u}(t)=\sum_{u_{i}\in\mathbb{U}(\mathbb{C}_{m_{j}})}z_{u_{i}}(t). \tag{11}\]  
Another one is produced by the placed services shown as follows:  
\[Z^{\prime m_{j}}_{u}(t)=\sum_{u_{i}\in\mathbb{U}(\mathbb{V}_{m_{j}}-\mathbb{C }_{m_{j}})}z_{u_{i}}(t). \tag{12}\]  
Since the first part \(Z^{\prime m_{j}}_{u}(t)\) is fixed, the optimization of \(Z^{m_{j}}_{u}(t)\) will be transformed to the optimization of \(Z^{\prime m_{j}}_{u}(t)\). Therefore, the problem of minimizing total delay for the service placement under migration conflicts in time slot \(t\) can be formulated as a mixed-integer linear programming problem as follows, which has been proven to be NP-hard [26].  
minimize \[\sum_{k=1}^{|M|}Z^{\prime m_{j}}_{u}(t)\] (13) s.t. \[Z^{\prime m_{j}}_{u}(t)\geq 0,\forall u_{i}\in U,\forall m_{j} \in M,\] (14) \[\alpha(v_{i})\in\{0,1\},\forall v_{i}\in V.\] (15)
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 3 The Crypto-Engine  
While considering the specific problem of securing the intra-vehicle network traffic, in this paper we propose a general and bus-agnostic solution to provide source authentication and encryption that is based on hybrid elliptic curve cryptography: the _Crypto-Engine_. Although being presented as a single component, the Crypto-Engine has to be actually considered as a _logical_ component in which the security services are grouped together. Indeed, the _Crypto-Engine_ appears as a distributed set of sub-components, located inside or in proximity of the positions where the specific security services are required. For example, if a communication bus has to be protected via encryption, all the end-points of the bus will include the Crypto-Engine's encryption and decryption primitives. The same applies for authentication, intrusion detection probes, etc.
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
## 1 Introduction
Furthermore, a feature speckle was extracted by considering statistical binary characteristics, and a graphical model-based scheme was developed for refining disparity maps to enhance measurement accuracy [15]. The accurate reconstruction of sharp surface edges has been achieved using an innovative edge detection algorithm with structured illumination microscopy (SIM) [16]. Nevertheless, 3D surface boundary edge detection remains a major challenge for DIC-based surface measurement. An initial concept was developed to improve the accuracy of the critical dimension by detecting surface edges [17]. However, the full algorithm for accurate edge detection has not yet been developed, nor has the feasibility of current measurement approaches in industrial applications been carefully evaluated. In view of this, a new speckle image-processing algorithm that uses a boundary subset and a corner subset for accurate reconstruction of surface boundary edges was developed, and its applicability to measuring industrial objects was assessed. Figure 1: Depth image of a square box with four line edges detected using the traditional Digital image correlation (DIC) method.
# Characterization of the Solution of the Seismic Source Mechanism in Southeastern Sichuan
## 4 Stress Field Inversion
### Stress Field Spatial Characteristics
MSATSI also allows for precise control over model complexity through its damping parameter, enabling an optimal balance between residual minimization and solution stability, as shown by the trade-off curve in Figure 6. These features make MSATSI a suitable and effective choice for capturing finer details of stress variability in seismically active regions. Utilizing MSATSI software, we performed a 2D stress field inversion. The trade-off curve depicted in Figure 6 indicates that an optimal balance between model complexity and data fitting residuals is achieved with a damping parameter \(e\) value of 1.4. Subsequently, we obtained the spatial distribution of the stress field across the southern Sichuan Basin, as presented in Figure 7. The inversion outcomes for the maximum horizontal stress (\(\sigma_{\text{Hmax}}\)) exhibit consistency with the results from individual area analyses. In the northern segment of the study area, the stress orientation trends WNW-ESE, aligning with the principal compressive stress field of the Sichuan Basin and the velocity direction of the GNSS stations in this region. Conversely, the principal stress field in the southern segment aligns nearly east-west, displaying a counterclockwise rotation relative to the background principal compressive stress field direction. Furthermore, we identified spatial variations in the distribution of earthquakes with distinct focal mechanisms. As previously discussed in the section on focal mechanism solution classification, Region A is predominantly characterized by reverse and strike-slip earthquakes. Figure 7 elucidates that reverse earthquakes are predominantly clustered in the northern part of Region A, whereas strike-slip events are concentrated in its southeastern quadrant. Figure 6: The trade-off curve of the 2D stress field inversion of the study region. An optimal balance between model complexity and data misfit is achieved at a damping parameter \(e\) value of 1.4. Figure 7: Map of the maximum horizontal stress direction (\(\sigma_{\rm{Hmax}}\)). Blue arrows represent the directions of \(\sigma_{\rm{Hmax}}\) derived from the regional stress field inversion. The faulting regime is categorized by different colors: blue indicates thrust faulting, and green denotes strike-slip faulting. The red lines represent fault traces, and the thick black arrow shows the GNSS-derived velocity direction.
# Triple-Band Uniform Circular Array Antenna for a Multi-Functional Radar System
###### Abstract  
A phased array radar has been developed toward an effective means for integrating multiple functionalities into one radar platform. The radar system necessitates the ability to operate at multiple frequencies simultaneously. In this paper, a triple-band uniform radial sub-array using a shared aperture antenna is proposed for a multi-functional radar system. An efficient placement of different radiating elements is realized based on the radial displacement of the circular array. In addition, multi-layer feed networks are used to reduce the intricacy of integrating several feed networks into one antenna. The reasonable matching characteristics and far-field gain are acquired at the S-band, C-band, and X-band. The measured results of the prototype are presented, and the results are compared to the simulation, which showed a good agreement.  
shared aperture antenna; radial array antenna; triple-band array antenna; multi-functional radar system +
Footnote †: journal:  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-000  
the mass of the entire structure [4; 5; 8]. In addition, it is difficult to achieve the best layout of the array antenna while it satisfies the optimum spacing at its operating frequency.  
In this paper, a triple-band array antenna consisting of three different uniform radial arrays is proposed for radar applications. The use of a radial array antenna [11; 16] is advantageous in deriving the best layout of multiple array antennas. The radiating parts are placed on the top layer for realizing efficient radiation characteristics while a feed network for each frequency band is implemented on the top and bottom layer. The adoption of the common ground for antenna and feed line is greatly helpful to derive the low-profile two-layered design and reduce the interaction between different frequency bands. The antenna prototype was fabricated, and antenna characteristics were measured. The reasonable impedance matching and excellent isolation characteristics between different frequencies were obtained at 2.0/5.8/10.75 GHz. The radiation patterns at the frequencies were measured inside a far-field anechoic chamber.
## 4 SF-PPP Performance Analysis of Android Mobile Phones
### GNSS Data Quality Analysis of Mobile Devices
The P40-INT dataset showed larger RMS values over all elevation bins for both the pseudorange and the carrier phase than the P40-EXT case, which should be attributed to the fact that the linearly polarized built-in antenna of P40-INT is more vulnerable to environmental interference leading to the larger observation noise. The pseudorange noise of P40-INT varied irregularly with the satellite elevation increment; however, it was more stable for P40-EXT. When the C/N0 value was lower than 25 dB-Hz, the pseudorange noise gradually reduced from 4 m to 2 m, showing good correlations, while the carrier-phase observations could not be tracked for both P40-INT and P40-EXT under such a C/N0 condition. However, when C/N0 increased to the range of 25-30 dB-Hz, the pseudorange noise from both P40-INT and P40-EXT manifested a dramatic increment to 5-6 m. To investigate such abnormal behavior, the recorded signal strength flags were checked, and it was found they were jumping between 1 and 4 constantly when C/N0 dropped below 30 dB-Hz, indicating an unstable tracking status. It was suspected that there were tracking problems owing to the P40 chips for C/N0 falling below 30 dB-Hz. Thus, in our subsequent SF-PPP processing, these measurements were discarded (the data volume for C/N0 below 30 dB-Hz was only less than 5%). For C/N0 values higher than 30 dB-Hz, both the pseudorange and carrier phase noise showed a gradual decrease, and their triple-difference RMS values were at the level of 1 m and 0.1 cycles when C/N0 reached 48-50 dB-Hz, respectively. The correlations between the P40-INT pseudorange and carrier-phase noise with respect to C/N0 were much stronger than to the elevations; thus, constructing a C/N0-dependent weight model could be more appropriate in P40-INT positioning than an elevation-dependent one. Figure 5: Pseudorange noise and C/N0 (left panel) and carrier-phase noise (right panel) of the observations on G01 L1 frequency (**a**), C36 B1I frequency (**b**), C36 B1C frequency (**c**), and E21 E1 frequency (**d**) for different smartphone antenna devices. The GNSS measurement noises are derived from the above triple-difference RMS values by a normalization factor of \(1/\sqrt{20}\)[3]. The P40 observation noise of different constellations is then calculated and shown in Table 3. The BDS BIC code noise was the smallest for both P40-EXT and P40-INT with values of 0.28 m and 0.41 m, respectively, while the GPS L1 was the largest with values of 0.56 m and 0.99 m, respectively. The BDS B1I and Galileo E1 showed very similar code noise levels at 0.40 m and 0.60 m, respectively. The noise level for different systems might be related to their C/N0 levels, as indicated in Figure 5. The average pseudorange noise values were 0.40 m and 0.67 m for P40-EXT and P40-INT, respectively, indicating about 70% improvement using an external geodetic antenna. For the carrier phase observations, the noise level of different GNSS systems were quite close to each other and their RMS values were 0.02 cycle and 0.03 cycle for P40-EXT and P40-INT, respectively.
## 4 Discussion
(**d–f**) Results plotted as functions of wind speed for incidence angles of 30\({}^{\circ}\), 40\({}^{\circ}\), and 50\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Wave**}} & \multicolumn{11}{c}{\multirow{2}{*}{**Wind Speed (m/s)**}} & \multicolumn{11}{c}{\multirow{2}{*}{**Wind Speed (m/s)**}} & \multicolumn{11}{c}{\multirow{2}{*}{**Wind Speed (m/s)**}} & \multicolumn{11}{c}{\multirow{2}{*}{**Wind Speed (m/s)**}} & \multicolumn{11}{c}{\multirow{2}{*}{**Wind Speed (m/s)**}} \\ \cline{2-2} \cline{11-17}  & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** \\ \hline A & 0.80 & 1.00 & 1.40 & 1.80 & 2.40 & 3.40 & 5.00 & 8.00 & 29.00 & 25.00 & 9.00 & 5.00 & 4.00 & 3.00 & 2.00 \\ AEH18 & 0.80 & 1.00 & 1.40 & 1.80 & 2.40 & 3.40 & 5.00 & 8.00 & 29.00 & 25.00 & 9.00 & 5.00 & 4.00 & 3.00 & 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The \(\zeta\) values at different wind speeds under incidence angle of 30\({}^{\circ}\). Figure 12 shows the upwind-downwind asymmetry predicted by the modified TSM with A + Gaussian and AH18 + Gaussian input, and their comparison with CMOD5.n as functions of wind speed at incidence angles of 30\({}^{\circ}\), 40\({}^{\circ}\), and 50\({}^{\circ}\) at high wind speeds of 16-30 m/s. The results show that, compared with CMOD5.n, the upwind-downwind difference can be well predicted by the modified TSM at high wind speeds under different incidence angles, except that the prediction of CMOD5.n gradually decreases with wind speed at the wind speeds of 16-19 m/s under incidence angle of 40\({}^{\circ}\), while the results of modified model show an increasing trend. Although the upwind-downwind difference predicted by the modified model is highly consistent with CMOD5.n, the predictions of modified TSM in upwind and downwind directions gradually deviate from the results of CMOD5.n and the measurements with wind speed increasing. This may be because the wind wave spectra only work well at low and moderate wind speeds, and are no longer accurate at high wind speeds.
## 2 Neural network input: profiles from radio occultation data  
First, we have collected 445 FORMOSAT-3/COSMIC radio occultation events provided by CDAAC [7], covering the inter-tropical land area during the period from July 17 to August 18, 2006. The FORMOSAT-3/COSMIC is a joint Taiwan-U.S. mission of six micro-satellites, launched on April 2006, with onboard receivers registering phase and amplitude of radio waves at the two GPS carrier frequencies (1575.42 and 1227.6 MHz).  
GPS radio signals passing through the atmosphere are refracted due to the vertical refractive profile: the overall effect of the atmosphere can be characterized by a total bending angle \(\alpha\), an asymptotic impact parameter \(a\) and a tangent radius \(r_{p}\)[2]. With the assumption of local spherical symmetry, the refraction index profile \(n\) can be retrieved from measurements of \(\alpha\) as a function of \(a\) during an occultation by using an Abel transformation as in [8]:  
\[n(r_{p})=exp\left(\frac{1}{\pi}\int\limits_{a_{r_{p}}}^{\infty}\frac{\alpha(a )}{\sqrt{a^{2}-a_{r_{p}}^{2}}}da\right) \tag{1}\]  
where \(a_{rp}\)= \(n(r_{p})\,\tau_{p}\) is the impact parameter for the ray whose tangent radius is \(r_{p}\). The refractivity profile is then \(N\)=(\(n\)-1)\(\cdot\)10\({}^{6}\).  
As a second step, we have computed the refractivity as in eq. (1) using the impact parameters and the bending angles profiles provided by CDAAC, vertically spaced from 4 m to 50 m in the low and high atmosphere, respectively.  
The distribution of the considered GPS-RO events is shown in Figure 1. The FORMOSAT-3/COSMIC GPS occultations and the corresponding ECMWF observations have been co-located, in time and space, with a maximum time difference of 1 hour and a maximum geographical coordinate distance of 0.5\({}^{\circ}\).  
Figure 1: Distribution of FORMOSAT-3/COSMIC GPS occultation employed in the neural networks.  
In order to train and test the neural networks we have created two uniform databases (desert and vegetation) of refractivity profiles sampled at the same altitude intervals for each RO observation. As a result, the desert database contains 98 profiles with 56 fixed altitude levels, representing the atmosphere from 1.1 to 12.1 km, i.e. the troposphere, and the vegetation database contains 347 profiles with 54 fixed altitude levels, representing the atmosphere from 1.4 to 12 km. Each profile is characterized by the vertical resolution of 200 m.  
We have chosen RO with the minimum altitude of 1.1 km and 1.4 km over desert and vegetation area respectively as a trade-off between the requirement of a adequate number of observations to train the networks and the need of tropospheric profile estimation as much as possible close to the surface.
# Robust Cross-Validation of Predictive Models Used in Credit Default Risk
## 3 Model Validation Methodologies  
Model validation has several applications according to the purpose of the overall analysis [10,21]. On one hand, the validation of models to select the best among them is called \"model selection\". On the other hand, model validation with the aim of estimating what the performance might be on the underlying population is often called \"model assessment\".  
To carry out an evaluation of models with certain reliability, some requirements must be fulfilled by the data samples available for both the training and testing to be performed during the validation process. Mainly, the data must be highly representative of the underlying population, as otherwise there will always be an indeterminate bias in the estimated expected model performance in the rest of the population.  
In real use cases, the lack of representativeness of the data will usually not allow a trustworthy assessment of the predictive models. So, the uncertainty of the evaluation should also be considered [7,13,14], although it is difficult to evaluate [3]. For this reason, model validation is usually more reliable for model selection purposes.  
At the same time, the selection of models can be executed within different scopes that have their own particularities. One may be the comparison of previously trained models to pick which one should be used in a production environment with future data samples. Another framework is the model training process itself [10,21] since many iterations that compare eligible intermediate models are performed before coming up with the final model. In this case, resource consumption during the validation process becomes critical, and this workload could make the use of certain sophisticated methodologies that are computationally intensive unfeasible.  
In this research, the validation scope is model selection among a predefined set of models with different combinations of explanatory variables, ranging from a few variables to all variables available in the data sample. The more covariates used by the model, the more complex it is.  
There is a trade-off between model complexity and model robustness: too many variables will achieve more precision on the training data but less precision when making predictions on new data [22]. This potential loss of model performance is due to over-fitting to the training data in such a way that they do not correctly reflect the true patterns present in the rest of the population.  
Therefore, it is critical to consider this circumstance in the validation process and avoid overestimating the performance of the model by finding a balance between model accuracy and model complexity. This will allow us to select the model with the best expected performance in new samples taken from the underlying population. The different approaches to solving this issue will be described next.