esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quantum Key Distribution
### The BB84 QKD Protocol  
Figure 1 illustrates a notional QKD system configured to securely generate the secure shared key \(K\), which is used to encrypt/decrypt sensitive data, voice, or video communications. The QKD system consists of a sender \"[PERSON]\", a receiver \"[PERSON]\", a quantum channel (i.e., an optical fiber or direct line of sight free space path), and a classical channel (i.e., a conventional networked connection). [PERSON] is shown with a laser source configured to generate and prepare single photons, known as quantum bits or \"qubits\". The encoded photons are then transmitted over the quantum channel to [PERSON], whom measures them using specialized single photon detectors. This exchange of encoded single photons is described by the BB84 protocol.  
Figure 1: This is a Quantum Key Distribution (QKD) system context diagram. The sender “[PERSON]” and receiver “[PERSON]” generate shared secret key \(K\) for use in data encryption/decryption. Reproduced with permission from [9], Copyright IEEE, 2016.  
Table 1 describes the BB84 protocol as a prepare and measure protocol where [PERSON] encodes photons in one of four polarization states (e.g., \(\leftrightarrow,\)\(\downarrow\), \(\
earrow\) or \(\
warrow\)) according to a randomly selected bit value (0 or 1) and basis (\(\oplus\) for the pair \(\leftrightarrow\), \(\downarrow\) or \(\otimes\) for the pair \(\
earrow,\)\(\
warrow\)). Once [PERSON] randomly prepares the photons, they are sent to [PERSON] where he measures each photon using a randomly selected basis (\(\oplus\) or \(\otimes\)). If [PERSON]'s encoding and [PERSON]'s decoding bases match, the photon's bit value is read correctly with a high probability. Otherwise, a random result occurs (i.e., equal likelihood of a 0 or 1); this is due to the inherent uncertainty in the measurement of an unknown (i.e., a randomly encoded) single photon. Typically, these random results are \"sifted\" from [PERSON]'s recorded detections and do not contribute to the shared key string.  
More specifically, the security of BB84 is based on the uncertainty principle where the measurement result is random when using two conjugate bases (i.e., \(\oplus\) or \(\otimes\)) to prepare single photons [17]. For example, during an intercept-resend attack anyone attempting to listen on the quantum channel must randomly select a measurement basis and will necessarily introduce detectable errors. These unavoidable errors increase the protocol's measured Quantum Bit Error Rate (QBER)--the ratio (or percent) of errors detected with respect to the total number of matched pulse detections--and if the QBER ever exceeds the protocol's security threshold (e.g., QBER > 11% [4]), the secret key distribution process is aborted (or restarted) as it is assumed an eavesdropper is active on the quantum channel. This is because all errors on the quantum channel are attributed to adversarial interference.
## 3 Results
### Energy Savings: Heating and Cooling  
Green and high-reflectance roofs (cool roofs) have a significant effect in reducing energy consumption during cooling and heating seasons. In accordance with literature review [38, 63, 64, 65, 66, 67], from this work it emerged that cool roofs are more effective in reducing heat gain in the cooling (C) season from 15 April to 14 October, than heat loss in the heating (H) season from 15 October to 14 April (Figure 12).  
This analysis was carried out using weather data measurement recorded by Politecnico weather station (WS) for the period from 2011 to 2016. The thermal performance of a refurbished roof was compared to the typical common roof. In particular, three roof solutions were taken into account: (1) Insulated common roof, (2) insulated high-reflectance roof, and (iii) insulated green roof. The heat flux (\(Q\)) in the roof was quantified according to Equation (3). Table 7 describes the characteristics of roof solutions and the main energy efficiency results. GHG emissions were quantified using 0.210 tonCO\({}_{2}\)/MWh for natural gas and 0.46 tonCO\({}_{2}\)/MWh for electricity [68].  
Figure 11: Solar energy technology assessment (for the 2014 year): (**a**) Comparison between domestic hot water (DHW) consumption of residential sector and solar thermal (ST) production considering four collector typologies (collectors’ annual average efficiency: ST1 = 0.59, ST2 = 0.77, ST3 = 0.80, and ST = 0.79); (**b**) comparison between electrical consumption, photovoltaic (PV) production with coefficient \(K\) = 50 m\({}^{2}\)/kW (according to the Decree 28/2011), and PV max producible.  
Figures 13 and 14 show the results for three consecutive hot days (21-23 July 2015) and cold days (15-17 January 2012). From the comparison of hourly heat fluxes (W/m\({}^{2}\)) between common roof, insulated common roof, insulated high-reflectance roof, and insulated green roof, it emerged that using an insulated green roof there was less heat gain during the summer season and with an insulated roof there was less heat loss in the winter season.  
This methodology, used to evaluate the effect of green and cool roofs, will be implemented in future work, adding the effect of evapotranspiration on energy performance of a building.
## 4 Applications in Multi-Channel Speech Coding and Enhancement  
### Simulation of Microphone Array Speech Signals  
In order to better verify the accuracy of the microphone array speech signals simulated by the proposed MASS, we use the MASS to simulate the microphone array speech signals for multi-channel speech coding  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SNR [dB]**} & \multirow{2}{*}{**RT\({}_{60}\) [ms]**} & \multicolumn{2}{c}{**CNN\_SMARD**} & \multicolumn{2}{c}{**CNN\_MCS**} & \multicolumn{2}{c}{**CNN\_MASS**} \\ \cline{3-8}  & & \(\Delta\)P & \(\Delta\)S & \(\Delta\)P & \(\Delta\)S & \(\Delta\)P & \(\Delta\)S \\ \hline \multirow{3}{*}{-5} & 700 & 0.4289 & 0.2951 & 0.4345 & 0.3021 & **0.4502** & **0.3101** \\  & 500 & 0.6621 & 0.2706 & 0.6598 & 0.2989 & **0.6  
and enhancement. Table 4 shows the application conditions of the MASS. The room acoustic environment configuration used to simulate microphone array speech signals is same as Figure 3.
# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 4 Difference Image Clustering  
To improve the classification accuracy of remote sensing images, an improved fuzzy C-means clustering method (CFCM) was used based on the degree of correlation. Firstly, the correlation between image data were accurately described by the similarity between their key feature vectors. The improved weighted coefficient similarity measure takes the similarity degree between the central vertex \(\mathrm{O_{i}}\) and the neighboring vertex \(\mathrm{O_{j}}\) as the weight\((\mathrm{e_{ij}})\) of any edge \(\mathrm{e_{ij}}\) in a 3 \(\times\) 3 sliding window.  
\[\mathrm{weight}(\mathrm{e_{ij}})=\sum\limits_{k=1}^{3}\frac{1}{1+m_{i}\times \left|d_{ik}-d_{jk}\right|} \tag{14}\]  
where \(m_{i}\) is a coefficient, and the values of \(k\) and \(i\) belong to {1,2,3}. Secondly, the weight of an edge \(\mathrm{e_{ij}}\) of the center vertex \(\mathrm{O_{i}}\) of the texture information in the window was calculated.  
\[k_{ij}=\frac{\mathrm{weight}(\mathrm{e_{ij}})}{\sum\limits_{\mathrm{e_{ij}} \in E}weight(\mathrm{e_{ij}})} \tag{15}\]  
Next, the correlation degree of data objects based on pixel \(D(x,y)\) in difference images was calculated.  
\[\mathrm{Cor}(\mathrm{D_{i}})=\sum\limits_{\mathrm{e_{ij}}\in E}\left\{k_{ij} \times weight(\mathrm{e_{ij}})\right\} \tag{16}\]  
FCM clustering was then used to accurately classify the correlation degree of all data objects in the extracted differential images.  
\[D(p,q)=\left\{\begin{array}{l}w_{c},\mathrm{Cor}(\mathrm{D_{i}})>T_{1}\\ w_{n},T_{2}\leq\mathrm{Cor}(\mathrm{D_{i}})\leq T_{1}\\ w_{u},\mathrm{Cor}(\mathrm{D_{i}})<T_{2}\end{array}\right. \tag{17}\]  
where \(w_{c},w_{n},w_{u}\) denote change class, fuzzy class and invariant class, respectively. \(T_{1}\), \(T_{2}\) are thresholds. Lastly, a region growing method was used to optimize the classification results of CFCM based on the classified difference features and the appropriate threshold processing was used to extract more accurate change detection results.
## 2 Background and Related Work
### Flexible Deployment
Large-scale distributed and self-adaptive systems as envisioned in forthcoming CPS and the Internet-of-Things (IoT) make a case for _distributed declarative programming_ promoting self-organization, while at the same time leaving _degrees of freedom_[59] to designers and deployment. We highlight several representative research efforts on deployment and automatic reconfiguration of systems. _Osmotic computing_[11] is an approach to opportunistic deployment of microservices on the edge-fog-cloud platform. An osmotic platform aims to reach and maintain an \"osmotic equilibrium\" between infrastructural and application requirements by automatically migrating microservices to deployment locations. However, the approach mainly targets centrally orchestrated systems. Other approaches leverage component-based, architectural descriptions to decouple application logic and deployment. For instance, _DR-BIP (Dynamically Reconfigurable Behavior Interaction Priority model)_[60] and _DReAM (Dynamically Reconfigurable Architectural Modelling)_[61] use _components_ (capturing behavior), _connectors_ (capturing interaction between components' _ports_), _maps_ (logical topologies), and _deployments_ (associating components to map locations), overall organized in _motifs_ (dynamic architectural configurations), to model and analyze dynamic architectures. These approaches have some similarity with the approach presented in this paper, but they are arguably more complex and our approach explicitly addresses self-organizing CPS.
# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
## 2 Ray Launching Technique and Simulation Scenarios
### Simulation Scenario Description  
The simulation scenario implemented for calculation by means of the in-house developed 3D RL code consists of an outdoor environment of an urban city area, placed at the old town of Pamplona (Navarre, Spain). The considered scenario consists of an outdoor environment with trees of different heights, grass, junctions of roads, cars, people and different types of buildings. This environment can be considered as really complex, in terms of radio wave propagation, because it has a broad range of elements with different material properties within it. The dimensions of the scenario are (150 m, 130 m, 30 m). Figure 3 presents a view of the real and schematic considered scenarios.  
All the material properties for all the elements within the scenarios have been considered, given the dielectric constant and the loss tangent at the frequency range of operation of the system under analysis. Taking into account that radio wave propagation in this scenario could change depending of the weather, as it is an outdoor environment, it is relevant to consider different conditions for the material properties of the vegetation. For that purpose, the values obtained in [37] for the material properties of the wood and the foliage of the vegetation have been used. A human body model [32], which take into account all the elements within the human body, and a car model [38], which also consider all the elements within a car, has also been used in the simulations. These models have been created to be  
Figure 2: Schematic representation of the principle of operation of the in-house developed 3D RL algorithm.  
implemented in the in-house 3D RL algorithm. The material parameters used in the simulation are defined in Table 1[37; 38; 39; 40].
# Interferometric Wavefront Sensing System Based on Deep Learning
## 2 Method
### Wavefront Detecting System
When the deformable mirror is subjected to external forces, the measuring wavefront reflected is no longer an ideal plane but becomes distorted with a certain aberration. At this time, the interference field recorded by the camera shows bright and dark stripes:  
\[\mathrm{I^{\prime}(x,y)}=\mathrm{I_{r}(x,y)}+\mathrm{I_{o}(x,y)}+\mathrm{I_{b} (x,y)}+2\sqrt{\mathrm{I_{r}I_{o}}}\cos[\varphi_{r}(x,y)-(\varphi_{o}(x,y)+ \Delta\varphi(x,y))]. \tag{2}\]  
Among them, \(\Delta\varphi(x,y)\) represents the phase delay caused by M3 at the point \((x,y)\) on the measuring wavefront. By applying different forces, we can get the wavefront with different distortions. After that, the four-step phase-shifting method is used to change the phase of the reference beam by \(\pi/2\) each time, so that four interference fringe patterns are obtained. The fringe patterns intensity can be expressed as:  
\[\mathrm{I_{n}(x,y)}=\mathrm{I_{r}(x,y)}+\mathrm{I_{o}(x,y)}+\mathrm{I_{b}(x,y) }+2\sqrt{\mathrm{I_{r}I_{o}}}\cos(\varphi_{r}(x,y)+\frac{2\pi n}{4}-\varphi_{o} (x,y)). \tag{3}\]  
The index \(n=1,\ 2,\ 3,\ 4\). The phase change of the reference beam is controlled by the PLS, and each movement distance is equal to \(1/8\) of the laser wavelength. Using the orthogonality of trigonometric functions, \(\varphi_{r}(x,y)-\varphi_{o}(x,y)\) can be obtained:  
\[\varphi_{r}(x,y)-\varphi_{o}(x,y)=\arctan\left[\frac{\mathrm{I_{4}(x,y)}- \mathrm{I_{2}(x,y)}}{\mathrm{I_{3}(x,y)}-\mathrm{I_{1}(x,y)}}\right]. \tag{4}\]  
When M3 deforms, we can use the same method to obtain the phase distribution of the distorted wavefront interference fringe pattern:  
\[\varphi_{r}(x,y)-(\varphi_{o}(x,y)+\Delta\varphi(x,y))=\arctan[\frac{\mathrm{I _{4}^{\prime}(x,y)}-\mathrm{I_{2}^{\prime}(x,y)}}{\mathrm{I_{3}^{\prime}(x,y)} -\mathrm{I_{1}^{\prime}(x,y)}}]. \tag{5}\]  
Equations (1)-(5) represent the process of the phase-shifting method. Since the reference beam illuminates vertically on the mirror M1, so the value of \(\varphi_{r}(x,y)\) is equal everywhere in the interference field and can be regarded as a constant. Therefore, we only need one phase-shifting measurement that can obtain the phase value \(\varphi_{o}(x,y)+\Delta\varphi(x,y)\) of the measuring beam.
# An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems
## 3 Simulation Experiment and Result Analysis  
### Model Construction  
To evaluate the efficacy of the proposed improved synchronous control architecture, a six-axis motor synchronous control model is developed within the MATLAB/Simulink (R2021a) environment. The six AC permanent magnet synchronous motors (PMSMs) share identical parameters as delineated in Table 1. The simulation model, depicted in Figure 4, encompasses six single-axis control modules and synchronous compensator modules. Each single-axis control module comprises controllers and motors, as illustrated in Figure 5, where the PMSM adopts a vector control strategy with \(i_{d}=0\). Within the controller part, based on relevant studies [27; 28; 29] and combined with several simulation results, the parameters \(K_{P}\), \(K_{I}\), and \(K_{D}\) are configured as 2.5, 0.3, and 0, respectively. The fuzzy theory domain of deviation \(e\) and the deviation rate of change \(ec\) are specified as [-10; 10], while the fuzzy theory domain of \(\Delta K_{P}\) and \(\Delta K_{I}\) are set to [-1; 1]. Furthermore, the quantization factors \(\lambda e\), \(\lambda ec\), \(\lambda_{p}\), and \(\lambda_{I}\) are established at 0.01, 0.0001, 7, and 3.5, respectively. In the synchronous compensator module, without synchronization control, deviation coupling control and improved deviation coupling control are separately implemented. The efficacy of these strategies is compared by real-time monitoring of speed variations across each axis.  
Figure 3: Fuzzy PID controller structure.  
Figure 4: Six-axis motor synchronous control model.  
Figure 5: Single-axis control module.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Rated torque \(T\) & 15 Nm \\ Rated speed \(v\) & 3000 rpm \\ Pole logarithm \(P_{n}\) & 4 \\ Moment of inertia \(J\) & 0.003 kg\(\cdot\)m\({}^{2}\) \\ Stator resistance \(R\) & 0.958 \(\Omega\) \\ Magnetic chain amplitude \(\psi_{J}\) & 0.1827 Wb \\ Friction coefficient \(B\) & 0.003 N\(\cdot\)m\(\cdot\)s \\ Cross-axis inductance \(L_{d}\) & 0.00525 H \\ Straight-axis inductance \(L_{d}\) & 0.012 H \\ \hline \hline \end{tabular}
\end{table}
Table 1: Motor parameters in the simulation.  
The initial system speed is configured at 1000 rpm, with a simulation duration of 0.1 s and a sampling time of 0.00001 s utilizing fixed steps. Initially, all six motors commence operation under no load conditions. At 0.05 s, a load of 13 Nm is applied to motor 1 and 10 Nm to motor 2, while the remaining motors remain unloaded. By introducing loads to axes 1 and 2, motor 1 experiences multiple disturbances, thereby ensuring that the simulation conditions are in line with the actual situation and effectively demonstrates the efficacy of the synchronous control strategy.
## 2 Methods and Procedure Analysis
### Measurement Set Up and Procedure Analysis  
This section describes and illustrates the experimental set up and analysis procedure for all the experiments. The vibration data recording was carried out at our laboratory's test rig for industrial innovation technology and robotics. One motor (WEG 2 hp), one impact hammer (PCB 086c03), one force sensor, and three piezoelectric accelerometers (PCB 353B17) were also used to conduct the experiments. All the vibration signals were simultaneously digitized by using a NI-DAQ 9/75. Experimental data were generated from a steel bar, gearbox, and induction motor. To have enough analysis data and reduce measurement variabilities, each test was measured three times for 5 min. The sampling frequency for all the experiments was about 25.6 kHz.  
Figure 2 illustrates the gearbox used for the experiments, and it was a two-stage compound reverted gear train to be in-line. The spur gear E\({}_{1}\) had 17, E\({}_{2}\) had 40, E\({}_{3}\) had 13, and E\({}_{4}\) had 27 teeth, respectively.  
Figure 3 denotes the measurement set up for EMA for an induction motor and gearbox. The tests were conducted using the impedance hammer, force sensor, and three accelerometers, which were mounted firmly on the steel bar, induction motor, and gearbox, respectively. It can be observed that the measured objects were suspended using soft springs, as shown in Figure 3. An impulse force hammer is a hammer equipped with a piezoelectric force sensor,  
Figure 2: Experimental set up for gearbox’s measurements: **(a,b)** show the set of the spur gears, (c) denotes the gearbox’s top view, and (d) shows the gear box and mounted accelerometers.  
and it is used in structural and modal analysis to stimulate a structure. Then, the EMA measured data will be used for validation proposes with OMA data.  
The second set of measurements were carried out when the machines were running in normal conditions. Figure 4 illustrates OMA's measurement set up for the induction motor, with mounted accelerometers. To measure the gearbox, the induction motor was connected to gearbox's shaft for the generation of rotational speed. The gearbox's measurement set up shows in Figure 5. The measurements were done with three speeds at 1800, 2400, and 3000 rpm, respectively. Signals were collected from the steel bar, induction motor, and gearbox.  
Figure 4: OMA’s measurement set up for the induction motor with mounted accelerometers.  
Figure 3: Experimental set up for an induction (**a**) motor and (**b**) gearbox, placed for a free–free condition and excited by an impedance hammer.  
Figure 6 illustrates the typical measured signals from the force impedance hammer and vibration response in the time-domain.
## 4 Results and Discussion
### Estimation of Elapsed Time since the Last Earthquake (t) and Average Recurrence Interval (R)
#### 4.2.1 Maqin Segment  
The tectonic geomorphology preserved in the segment at the surface, such as earthquake swells and fault scarps, reflects that the segment had multiple active episodes during the Holocene Epoch. Previous research utilized methods such as excavating trenches and using natural outcrops to identify ancient earthquake events along this segment during different stages (Figure 8). Various researchers [17; 24; 25; 26] determined different periods and times of ancient earthquakes, as shown in Figure 8. [PERSON] (2009) established that the most recent age of the buried ancient surface is 358-430 Cal a BP from the footwall (Tc3) of a secondary bench fault profile east of Gequ River, the sampling location of which is an ancient plant layer [18]. However, no records of a relevant earthquake were found after referring to historical documents covering nearly 400 years. Therefore, this age still requires further investigation. It is found from Figure 8 that when the age of an ancient earthquake is 977-1090 Cal a BP, the conclusions of researchers are essentially consistent [17; 24] and agree with the time of an earthquake recorded in one passage of the 'Epic of King Gesar' historical literature. It is speculated that this age is relatively reliable, and it can be used as the latest elapsed time for the occurrence of an earthquake in this segment. Finally, by comparing and analyzing the main research results, ancient earthquake events of the Maqin segment can be mainly divided into six events: the first is 977-1090 Cal a BP; the second is 2000 \(\pm\) 300 Cal a BP; the third is 3342-3454 Cal a BP; the fourth is 6600 \(\pm\) 700 Cal a BP; the fifth is 7971-8050 Cal a BP; and the sixth is 10,000 \(\pm\) 200 Cal aBP. The calculated average recurrence period of ancient earthquakes is approximately 1765 a.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Name of Segment** & **Length of Segment (km)** & **Boundary Mark of Segment Division (with West Segment)** & **Type of Rupture** & **Equation of Statistical Rupture** & **Estimate of Relationship** & **Estimate of the Magnitude (Mtw)** & **Estimate of the Magnitude (Ms)** \\ \hline Maqin segment & 132 & Animaqing Mountain uplift & Single-segment rupture & & 7.5 & 7.7 \\ \hline Maqin segment & 180 & Triple junction (fault intersection decomposition zone) & Single-segment rupture & \(M\)\(\underline{s}\) = 1.412 + 0.845\(M\underline{w}\) & 7.6 & 7.8 \\ \hline Maqin–Maqu segment & 312 & Animaqing Mountain uplift & Two-segment joint rupture & & 7.9 & 8.1 \\ \hline \end{tabular}
\end{table}
Table 1: Estimate of the magnitude of Maqin and Maqu Segment for single or joint rupture.