esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis and Simulation Calculation
### Thermodynamic Model Parameter Calculation of the Nut-Rotating Ball Screw Transmission Mechanism
#### 3.1.3 Calculation of Convective Heat Transfer Coefficient  
Based on the model for calculating thermal resistance, when the size of each component is determined, its thermal resistance value will be determined by the thermal conductivity and convective heat transfer coefficient. Within a certain temperature range, the thermal conductivity coefficient can be considered a constant value, which is only related to the properties of the material. Therefore, the calculation of the convective heat transfer coefficient will be a key factor for calculating thermal resistance value and temperature distribution.  
6. Calculating the convective heat transfer coefficient of rotating components with the external environment  
\[\mathrm{Re}=\frac{\omega\times d^{2}}{v_{f}} \tag{62}\]  
\[Nu=0.133\mathrm{Re}^{2/3}\mathrm{Pr}^{1/3}\Big{(}\mathrm{Re}<4.3\times 10^{5},0.7<\mathrm{Pr}<670\Big{)} \tag{63}\]  
\[\alpha=\frac{Nu\cdot\lambda}{d} \tag{64}\]where Re indicates the Reynolds number; \(\omega\) represents the component's rotational angular velocity \((\mathrm{rad}/\mathrm{s})\); \(d\) is the diameter of the convective heat transfer part in the rotating component \((\mathrm{m})\); \(v_{f}\) is the air kinematic viscosity at 25 \({}^{\circ}\)C; \(v_{f}=15.5\times 10^{-6}\mathrm{m}^{2}/\mathrm{s}\); \(Nu\) is the Nusselt number; Pr stands for Prandtl number, while Pr of air is \(\mathrm{Pr}=0.72\); \(\alpha\) suggests convective heat transfer coefficient of the rotating component with external air \([\mathrm{W}/(\mathrm{m}^{2}\cdot\mathrm{K})]\); \(\lambda\) indicates thermal conductivity of the external air, at 25 \({}^{\circ}\)C, take \(\lambda=2.54\times 10^{-2}\mathrm{W}/(\mathrm{m}\cdot\mathrm{K})\).  
Calculating natural convection heat transfer coefficient  
The convective heat transfer coefficient between components in a stationary state and the external environment is calculated as follows:  
\[Gr=\frac{g\beta l^{3}\Delta t}{v_{f}^{2}} \tag{65}\]  
\[Nu=C(Gr\cdot\mathrm{Pr})^{n} \tag{66}\]  
\[\alpha=\frac{Nu\cdot\lambda}{l} \tag{67}\]  
where \(G_{r}\) is the Grayshev quasi number; \(g\) is the gravitational acceleration, taken as \(g=9.8\mathrm{m}/\mathrm{s}^{2}\); \(\beta\) is the coefficient of expansion of the external air, taken as \(\beta=1/273\); \(l\) is the length of heat flux \((\mathrm{m})\); \(\Delta t\) is the temperature difference between the stationary component and the external environment, taken as \(\Delta t=10\)\({}^{\circ}\)C; \(C\), \(n\) are both constants, depending on the liquid state and heat source of the fluid.  
The specific values are shown in Table 3. The convective heat transfer coefficient in actual calculation is generally taken as 3-10 times the theoretical calculation result, while in this article, the convective heat transfer coefficient is taken as 10 times the theoretical calculation value.
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
## 2 Monitoring Methods Used in Valleebre and Sample Results
### DInSAR Monitoring Using Corner Reflectors  
The Space Borne SAR monitoring of landslides has been reported in [21; 22; 23] and elsewhere. When applying DInSAR to landslide monitoring in forested areas, a condition that is difficult to fulfil is that a sufficient number of targets within the area of interest remain coherent during the observation period. One way to overcome this limitation is the deployment of artificial corner reflectors (CRs) that ensure coherent, high-quality DInSAR estimates. Using CRs, however, demands additional resources; it also prevents historical deformation studies based on archive SAR imagery.  
After a detailed discussion of the Vallebebre site suitability for DInSAR with CRs, [6] describe the installation, in 2006, of 7 metallic trihedral CRs (Figure 15) to be observed with the ENVISAT (C-band, with a 35-day revisiting period). The Vallcebre landslide movement  
Figure 12: Head of one of the borehole wire extensometers installed at Vallcebre. The cable for the piezometer (black) can also be appreciated.  
Figure 13: Sketch of the borehole wire extensometer. (**a**) just after installation; (**b**) after some shear displacement of the landslide (idealized). The relationship between the wire displacement at the pulley and the horizontal displacement of the landslide depends on the width of the shear zone and the borehole diameter [4]. The smaller the borehole diameter, the faster the wire response.  
is favorably oriented (towards the west) and has a gentle slope. Additionally, the deformation rates are moderate enough in order to prevent, the phase from \"rolling over\" (problem due to the ambiguous nature of the DInSAR). One corner (CR_2) was installed directly on the stable rock to be used as reference. The maximum distance between the corners was 300 m to keep the atmospheric bias negligible. The installation was done with utmost care, with the CR symmetry axis oriented towards the radar line of sight [6].  
Figure 14: Measurements at borehole S2. Above: rate of displacement derived from the wire extensometer. Below: groundwater table fluctuation measured by the piezometer. A perfect synchronism between rate and water level can be appreciated. This correlation means that groundwater has a big influence in the balance of forces controlling the landslide dynamics.  
Figure 15: Corner Reflectors (CRs) used for DInSAR monitoring at the Valleebre landslide [6]. Examples of installation on a big rock block (**left**) or directly over the terrain (**right**).  
In Figure 16a, the CRs are clearly distinguishable in an amplitude image of the landslide. Processing interferometrically four 2007 descending ENVISAT images, the map of the rate of movements was obtained (Figure 16b). As an example, Figure 17 displays the resulting displacements, which were small between the end of December 2006 and the beginning of March 2007, whereas for CR1 and CR4 there is a substantial increase in March and April associated with an abundant rainfall period.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-EM Model
#### 2.1.1 Adapted Analytical Solutions to the Vector Potential for a Straight Wire  
The governing equations for calculating eddy currents are described in Equations (2)-(4):  
\[\
abla^{2}\textbf{A}=-\mu\textbf{I}+\mu\sigma\frac{\partial\textbf{A}}{ \partial t}+\mu\varepsilon\frac{\partial^{2}\textbf{A}}{\partial t^{2}}+\mu \
abla(\frac{1}{\mu})\times(\
abla\times\textbf{A}) \tag{2}\]  
\[\textbf{E}=-j\omega\textbf{A} \tag{3}\]  
\[\textbf{J}=\sigma\textbf{E} \tag{4}\]  
where **A** is the vector potential generated by **I**, \(\omega\) and **I** are the angular frequency and the density of the applied alternating current (AC), respectively, \(\varepsilon\), \(\mu\) and \(\sigma\) are the permittivity, permeability and conductivity of the test piece respectively, and **E** and **J** are the induced electric field and eddy current density, respectively [20].  
As described in [15] by [PERSON] et al., for a small radius circular coil, the distribution of the vector potential **A** at \(z=0\) (surface of the sample) is not symmetrical with the radius due to the bent wire.  
The coil used in this work consists of straight wires; thus, the analytical solution for a straight wire is needed. The adapted solution for a straight wire has been presented in [15] by [PERSON] et al. Here is a brief introduction. A hypothesis is proposed: if the radius of the circular coil, compared with its width, is very large, a bent wire can be viewed as a straight wire, and the distribution of **A** should be symmetrical. To validate such a hypothesis, a model is built with a large-radius circular coil above the aluminium plate. The aluminium sample used has a dimension of 80 mm \(\times\) 30 mm, and the inner radius and the outer radius of the circular coil are set to 5.0395 m and 5.0405 m, respectively. At kHz, the current density applied to the circular coil is 1 A/m\({}^{2}\), and the lift-off of the coil is 1 mm. The permeability and the conductivity of the aluminium plate are 1.257 \(\times\) 10\({}^{-6}\) H/m and 3.8 \(\times\) 10\({}^{7}\) Siemens/m, respectively.  
The distribution of the magnitude of **A** based on the adapted solution is shown in Figure 2. The red marker denotes the maximum magnitude of the vector potential. The distribution of the magnitude of **A** is symmetrical with the radius of 5.04 m, where the coil is located. The result verifies the hypothesis that, when the radius of the circular coil is very large, a bent wire serves as a straight wire.  
Figure 2: The magnitude distribution of A under a large-radius circular coil. Reproduced with permission from [15], IEEE, 2016.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 7 Conclusions
### Summarizing and Featuring the Protocol  
The QKD procedure presented is intended to generate sensitive secret information to transmit it to a second party but is still assembled by post-measurement during the process, instead of like previous QKD protocols, such as the BB84, where any sensible information is transmitted by a quantum channel directly, being affordable for eavesdroppers at all times. Thus, the protocol presented also considers the action of a possible eavesdropper performing an individual attack under time uncertainty. In fact, with the correct prescriptions, [PERSON] can guarantee with the desired success threshold on her post-measurement, a faithful reproduction of the BB84 protocol. Nevertheless, the control complexity is increased, together with implementations of double teleportation and quantum processing, the success for the eavesdropper becomes notably reduced if the attack is performed before the assembling. QBER remains in the typical range and it could be configurable on the election region of the parameters. Thus, while the eavesdropper has in the BB84 protocol a theoretical 50% chance of success on the useful key, in the present protocol, the probability of success drops down to as low as between 7% and 17%, thus improving the security.  
Due to the processing complexity involved, aspects regarding the decoherence demand attention. There is not a unique procedure to quantify the loss of fidelity for a trend of gates, mainly because it depends on their architecture, specific physical realization and, inclusively, on the input states being considered. Despite this, for technologies other than light (which reaches large decoherence times), such as NISQ ones, currently there is a good fidelity performance of around 0.9 for the range of tens of gates. Then, despite the reports stating an increase of QBER due to decoherence, it still could be controlled by reducing the operation times of the gates as in the NISQ technologies.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 2 Electrical Connection Faults
### Bonding Wires  
Next, consider the effects of bonding wires on circuit performance. The connection between two solder pads that need to be electrically connected usually consists of multiple bonding wires in parallel to improve the power and reliability of the bonding area. When current flows through the bonding wires, an inductance effect is produced. Accordingly,  
Figure 3: The normal and fault values of the connection resistance of connectors in the experimental testing and model simulation.  
Figure 2: Equivalent connection impedance network of a contact surface between connector pin and receptacle.  
the effects of bonding wires on circuit performance can be modeled as equivalent to an inductance, as shown in Figure 4.  
When circuits work in extreme environments, some bonding wires may fail, and the equivalent inductance \(L_{2}\) in the bonding area increases. Since the inductive reactance of the equivalent inductance increases with frequency, the influence of bonding wire faults on signal transmission is mainly reflected in the high-frequency band. In consideration of the higher standards for some circuits in engineering applications, the maximum normal equivalent connection inductance used in the simulation model is 1 nH, which is smaller than the 1.6 nH obtained from experimental testing. In addition, the maximum fault connection inductance of bonding wire in the simulation model is 3 nH, which is equal to that obtained from experiment, as shown in Figure 5 [14].
## 6 Experiments on Shooting Mission Planning
### Integration with Real Drones  
After validating the system in simulation, we integrated our tools with custom-built drones to run media production on real outdoor events. Figure 13 shows one of our drones, based on an hexarotor platform (120 cm of diagonal distance) with a Pixhawk 2.1 autopilot running PX4 [24]. The payload consists of an Intel NUC computer, a Here + RTK GNSS, a communication board, and a gimbal with a Blackmagic Micro Cinema camera.  
The MULTIDRONE consortium ran some experimental media production in a countryside area near Bothkamp, in the north of Germany. We tested our system in mock-up, outdoor scenarios, implementing missions (e.g., shooting cyclists and rowers) with one to three drones and different shot types. The objective was to demonstrate the integration of the whole system and to explore the artistic possibilities of our tools for media production.  
In particular, Figure 14 depicts some snapshots from an experiment where two drones have to shoot a rowing race in a lake. In that experiment, the original shooting mission consists of a lateral shot that runs concurrently with a fly-through followed by a static panoramic. The first shot is performed by one of the drones, which flies parallel to the boats over the yard. The other two shots are carried out by the other drone, which goes over the lake coming through the trees, and then keeps static following the rowers with the camera. In this case, one of the boats carries a GNSS receiver sending their position to the computers on board the drones, so that the gimbal cameras can track them all time as shooting targets. A video showing the whole shooting mission is available online ([[https://www.youtube.com/watch?v=COay0](https://www.youtube.com/watch?v=COay0) hZsMzk&feature=youtu.be]([https://www.youtube.com/watch?v=COay0](https://www.youtube.com/watch?v=COay0) hZsMzk&feature=youtu.be)).  
Figure 14: Experimental media production of a rowing race with two drones. The images are taken from the cameras on board the drones: the drone taking the lateral shot from the yard (**left**), and the drone taking the static shot after the fly-through (**right**).  
Figure 13: One of the drones used for autonomous media production.
## 4 Results and Discussion  
### Performance Metrics  
The performance metrics are as follows:  
Alive nodes: The number of alive nodes in a network is defined by the number of nodes that are alive. When a network contains a large number of active nodes, the network performance improves.  
Average energy consumption: During each iteration, it specifies how much energy each node uses on average  
Total packets transferred to BS: The overall data packets sent to BS are proportional to the number of alive nodes and their remaining energy. When there are a lot of alive nodes, BS obtains a lot of packets.  
Throughput: Over WSN, the number of bits delivered to BS is referred to as throughput. Bits per second are used to measure throughput.  
Packet drops ratio: It refers to the amount of data lost from the source to BS during transmission.  
Routing overhead: It is described as the ratio of the total number of packets obtained by BS to the total number of packets generated.
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 4 Statistical Analysis of the Occurrence of Plasma Bubbles
### Statistical Analysis of the Seasonal Occurrence of Plasma Bubbles  
Considering the seasonal occurrence of EPBs, based on the airglow data from autumn 2014 to summer 2015, we made statistics on the seasonal occurrence of EPBs. For the seasonal statistics of airglow data, we set March to May as spring, June to August as summer, September to November as autumn, and December to February as winter. Figure 9 shows the statistical results of the seasonal occurrence rate of EPBs from September 2014 to August 2015.  
Figure 9 presents the pie chart of seasonal statistics of EPB occurrence rate. From Figure 9, EPBs have the highest occurrence rate in autumn, reaching 32%, followed by spring, with an occurrence rate of 24%. Next is winter, the EPB occurrence rate is 11%, and the lowest in summer, with 5%. According to Figure 9, we can conclude that EPBs tend to occur more frequently in spring and autumn, with a high occurrence rate, while EPBs can be less observed in summer and winter.  
Through the statistical analysis of airglow data from autumn 2014 to summer 2015, it can be extrapolated that plasma bubbles will mainly occur in spring and autumn for a given year. Plasma bubbles are the main cause of scintillations in the ionosphere [21; 22], GPS scintillation data can be used to analyze and study the variation and occurrence of plasma bubbles [11]. The appearance and development of plasma bubbles can be closely related to ionospheric scintillations [23]; thus, ionospheric scintillations also appear more likely in spring and autumn.
## 3 Hotline-Based Reliable WSN Topology  
We assume a WSN with two-level heterogeneity. At the first level, we have resource-constrained sensor nodes which are deployed densely on a two-dimensional grid. All of the first level nodes have the same resources. At the second level, we have sensor gateways that operate as cluster heads to regulate the flow of traffic and to manage sensor motes deployed in the given geographical region. The gateways are not resource-constrained and their density is many orders of magnitude lesser than the density of the sensor nodes. Gateways are connected to each other in a bus topology. In the traditional topology, the bus is constructed using long-haul wireless links. Under the proposed hotline-based topology, the gateways are connected via highly reliable links, e.g., Ethernet cables or point-to-point wireless links. We assume a fixed network topology where both gateway nodes and the sensor nodes are static.  
We now introduce the important components of our proposed hotline-based reliable WSN topology. Different mechanisms, like dividing the network into clusters, gateway association/dissociation and node-node, node-gateway and gateway-gateway communication will be described in detail in the following sub-sections. The role of multiple gateways can be made useful in WSNs if they are connected by a bus. Gateways are therefore being used to provide an alternative and reliable path for packet routing.  
In traditional WSN routing the expected path of end-to-end communication would include multiple hops. Packet delivery is dependent on several intermediate relaying sensor nodes. These sensor nodes forward the packets until they reach their destination. WSNs have inherently high link error rates. With multi-hop routing, the cumulative probability of error increases exponentially with the increasing number of hops [26]. Thus the chances of failure are likely to increase with every additional hop. Reliability is therefore a main concern for mission-critical WSN deployments. Our suggested topology addresses the reliability issue by using the concept of hotlines between gateways. Hotlines reduce the number of average hops between a source and destination and provide better reliability as compared to traditional WSN routing.  
Figure 1 shows an example of our hotline assisted topology where four gateways are connected to each other as a bus network. This bus network is not only connecting gateways to each other but also connecting gateways to outer world (Internet or IPv6 domain). We would describe the topology in detail in the following sections.
# On the Optimum Linear Soft Fusion of Classifiers
## 3 The Improvement Factor
### Fusion of Equally Operated Classifiers (EOCs)
For example, if \(\rho=1\Rightarrow I_{f}=1\), that is, when the scores are fully correlated, the set of individual classifiers actually behaves like a single classifier. Equation (14) is also consistent when fusion is not implemented, i.e., \(D=1\Rightarrow I_{f}=1\). We showed that, in the EOC case, the parameters affecting the improvement factor, \(I_{f}\), are the number of classifiers, \(D\); the pairwise correlation, \(\rho\); and the bias, \(b\). This is illustrated in Figure 2. Thus, in Figure 2a, we show \(I_{f}\) as a function of \(\rho\) for different values of \(D\); bias \(b\) was assumed to be \(0\). We see that \(I_{f}\) increases with \(D\), but decreases with \(\rho\) from a maximum value of \(I_{f}=D\) for \(\rho=0\) to a minimum value of \(I_{f}=1\) for \(\rho=1\). Similarly, in Figure 2b, we show \(I_{f}\) as a function of \(b\) for different values of \(D\); correlation \(\rho\) was assumed to be 0. Again, \(I_{f}\) increases with \(D\), but, as expected, decreases for an increasing bias. Let us finally compute the optimum coefficient vector, \(\mathbf{w}\), corresponding to the EOCs. \[ \begin{array}{l}\mathbf{w}_{LLMSE}=\frac{\mathbf{M}_{s}^{-1}\mathbf{1}}{ \mathbf{1}^{T}\mathbf{M}_{s}^{-1}\mathbf{1}}=\frac{(1-\rho)v+D\big{(}\rho v+b^ {2}\big{)}}{D}\frac{1}{(1-\rho)v}\Big{(}\mathbf{1}-\frac{\rho v+b^{2}}{(1-\rho )v+D(\rho v+b^{2})}\mathbf{1}\mathbf{1}^{T}\Big{)}\mathbf{1}=\\ =\frac{(1-\rho)v+D\big{(}\rho v+b^{2}\big{)}}{D}\frac{1}{(1-\rho)v}\bigg{(}1- \frac{\big{(}\rho v+b^{2}\big{)}D}{(1-\rho)v+D(\rho v+b^{2})}\bigg{)}\mathbf{1 }=\frac{1}{D}\frac{1}{(1-\rho)v}((1-\rho)v)\mathbf{1}=\frac{1}{D}\mathbf{1} \end{array} . \tag{16}\]  
Thus, as expected, the mean of the scores is the _LLMSE_ solution in the case of EOCs, always achieving an improvement factor of \(1\leq I_{f}\leq D\).