esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Clock Synchronization of Data Acquisition Based on NoC
###### Abstract  
Data acquisition based on network-on-chip (NoC) technology is a high-sampling-rate data acquisition scheme using low-sampling-rate analog-digital conversion (ADC) chips. It has the characteristics of multi-task parallel communication, being global asynchronous, local synchronous clock distribution, high throughput, low transmission latency, and strong scalability. High-speed data acquisition is realized through the combination of an on-chip network and time-interleaved data acquisition technology. In the time-interleaved sampling technique, the precision of clock synchronization directly affects the precision of sampling. Based on the proposed NOC data acquisition scheme, an improved White Rabbit clock synchronization protocol is applied to high-speed data acquisition to achieve high-precision synchronization of multi-channel time-interleaved sampling clocks. Firstly, the offset of the master clock and slave clock is determined by the PTP protocol, and the offset is corrected to achieve rough synchronization between the master clock and slave clock. Secondly, a digital dual-mixer time difference (DDMTD) is used to measure the phases of the master and slave clocks. After that, the phase of the slave clock is corrected through the dynamic phase-shift function of the clock's phase-locked loop (PLL). Finally, according to the simulation results in Modelsim, the average absolute error of a TI-ADC sampling clock can be less than 20 ps.  
data acquisition; network on chip; clock synchronization; ADC +
Footnote †: journal: Applied sciences
# An Efficient BGV-type Encryption Scheme for IoT Systems
## 2 Background Knowledge
### Security Assumption of BGV  
The security of BGV-type cryptosystems is based on the learning with errors over rings (RLWE) assumption [13; 35]. The RLWE(\(\lambda\),\(\sigma\),\(\chi\)) assumption is to distinguish two distributions (\(a\),\(a\)-\(s+\epsilon\)) and (a,b), where \(a\), \(s\), and \(b\) are randomly selected from \(R_{q}\) and \(\epsilon\) is selected from \(\chi\) referencing security parameter \(\lambda\). This assumption has been proved hard over ideal lattices in [35].
## 3 Results and Analysis
### Corrosion Properties  
Figure 14 shows that the potentiodynamic polarization curves exhibit the pitting corrosion potential of welded joint produced using different thermal frequencies. It can be seen that increasing the thermal frequency from 3 Hz to 5 Hz shifts the corrosion potential from \(-\)667 to \(-\)715 mV, and decreases the corrosion potential from 2.39 \(\times\) 10\({}^{-7}\) to 4.47 \(\times\) 10\({}^{-8}\) A/cm\({}^{2}\). According to the Figure 14, it is apparent that the corrosion properties of welded joint for 3, 4 Hz is less than that of the welded joint for 5 Hz. This might be due to the smaller grain size and the less porosity in the fusion zone, which is produced by higher thermal frequency. [PERSON] et al. [29] studied corrosion properties of aluminum alloy and reported that the porosity and crack would weaken the corrosion resistance of weld joint. The present result generally agrees with that work.
## 5 Performance Evaluation
### Multi-User  
For \(K=2\), we considered a DNN with one input layer, three hidden layers and one output layer. There were two neurons in the input layer where we input the channel parameter \(|h_{k}|,k=1,2\). The output layer consisted of three neurons that represented \((P^{*},\tau_{1}^{*},\tau_{2}^{*})\). All hidden layers contained 100 neurons. We set a batch size of 100 and used 100 training epochs. We used the ReLU activation function on the output from every hidden layer. We normalized our data by using a built-in library for prepossessing to avoid training and validation errors due to over-fitting.  
Table 1, shows that the time required to find the optimal solution to Equation (7) from our proposed scheme is much less than the SPCA method. Figure 7 shows the optimal value for power obtained from the DNN and SPCA methods. Figures 8 and 9 show the optimal values for uplink time allocation for \(U_{1}\) and \(U_{2}\), respectively. The MSE convergence of validation and training data, as shown in Figure 10 validates the performance of our proposed network. We achieved a low MSE in this case due to normalization. In testing, our proposed scheme obtained an MSE for optimal power of \(3.67\times 10^{-9}\). These results show that the DNN achieved the same accuracy with less complexity.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Single User** & **Multiple Users** \\ \hline  & \(K=1\) & \(K=2\) \\ \hline DNN execution time per sample & \(5.789\times 10^{-5}\) s & \(5.18\times 10^{-5}\) s \\ \hline SPCA execution time per sample & \(13.074\) s & \(26.993\) s \\ \hline MSE for optimal power & \(1.802\times 10^{-7}\) & \(3.67\times 10^{-9}\) \\ \hline MSE for time allocation & \(1.632\times 10^{-6}\) & \(5.69\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation for the deep neural network (DNN) and SPCA method. MSE: mean squared error.  
Figure 4: Optimal power samples from the DNN and SPCA.  
Figure 5: Optimal downlink time allocation samples from the DNN and SPCA.  
Figure 6: MSE convergence for training and validation data in the DNN (single user).  
Figure 8: Optimal uplink time allocation samples from the DNN and SPCA for User 1.  
Figure 7: Optimal power samples from the DNN and SPCA for \(K=2\).  
Figure 9: Optimal uplink time allocation samples from the DNN and SPCA for User 2.
## 2 Data  
### SAR Dataset  
Sentinel-1 is a C-band radar mission of the ESA in a two-satellite constellation, including Sentinel-1A and Sentinel-1B. Sentinel-1A was launched on April 2014 and Sentinel-1B was launched on April 2016. Both satellites are in the same orbital plane, but are separated by 180\({}^{\circ}\). Thus, the revisiting intervals could be reduced from 12 to 6 days. Sentinel-1's orbit is sun-synchronous and has an altitude of 693 km and an inclination of 98.18\({}^{\circ}\). From April 2016 to April 2017, Sentinel-1B did not provide SAR data covering Taiwan. This study uses 22 SAR images in the ascending track from Sentinel-1A from April 2016 to April 2017. The dates of the scenes are shown in Table 1. There are only 13 SAR images from the descending track over our study area (the coverage and the dates of the scenes are shown in Figure 1a and Table 1) and our processing results show that they produced poor qualities, and thus the images from the descending track were not used in this study (also see Section 3).  
Figure 1: (**a**) The Sentinel-1 scene in the ascending and the descending tracks covering Yunlin. The red and blue dashed rectangular frames show the coverage of Frame 74 along ascending Track 69, and the coverage of Frame 510 along descending Track 105 in the IW mode, respectively. The solid rectangular frames show the three subswaths. (**b**) The distribution of four aquifers along a West–East profile (A-A’) in the Choushui River Alluvial Fan (CRAF), modified from Reference [14]. Dashed lines B1–B3 mark the aquifer boundaries (Aquifer 1, 2, and 3 are above B1, between B1 and B2, and between B2 and B3, respectively; Aquifer 4 is below B3). The vertical axis for A-A’ (bottom right) shows the aquifer elevations in m.  
Sentinel-1's mission provided data in different modes. In this study, we used the level-1 Single Look Complex (SLC) data from the Interferometric Wide swath (IW) mode, which had three separate subswaths. In Yunlin, we used the IW1 data from Track 69 and Frame 74, as shown in Figure 1a. The solid rectangular frames in Figure 1a show the three subswaths, IW1, IW2, and IW3 in the scene.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## 3 N-Modular Redundancy and M-Partitions Approach
### Background  
#### 3.1.1 Redundancy  
This allows a system to continue working properly in case one or more components fail. This is possible due to the fact that parallel components added to the system provide an alternative path to operate. There are several approaches that improve the reliability by the replication of contents of processor components during the execution of an application without modifying the hardware: cache citeKim1999,Zhang2003,Zhang2005,Sugihara2007, instruction [30] and register contents [31,32]. In contrast, other redundancy techniques modify the hardware. Among them, the TMR [33] is a well-known fault-tolerant technique that implements triplication of modules and majority voting. This technique has been largely used to avoid errors in integrated circuits, but imposes very high hardware overhead. Nevertheless, this method becomes more attractive when implemented on multi-/many-core processor architectures.  
In fact, an N-Modular Redundancy system can be considered. An NMR consists of N identical parallel systems with the same input. It also includes a voter to obtain the system output. The voter compares the outputs of each parallel systems and applies majority criteria to select the response. In general, N is an odd integer to simplify the voting operation. However, it is possible to use an even integer depending on the characteristics of the system [34]. It is clear that the reliability of an N-modular redundancy systems depends on the voter [35]. Therefore, considering the potential errors occurring in the voter, it is also possible to implement redundant voters. The most common implementations of this kind of system are the Double Modular Redundancy (DMR), which allows error detection, and the TMR, which masks faults by detecting and correcting errors.  
Regarding the nature of the replication, there are four types of redundancy: spatial and temporal in data and in execution. This work uses:  
* Spatial redundancy: uses different physical components. It can separate identical data signals in space.
* Redundancy in data: replicates the information. It stores the data in different memory spaces.
## 1 Introduction
Therefore, the DWT is recommended for pre-processing of input data to ANFIS to diagnose SC-IM faults. The rest of the paper is organized as follows: Section 2 offers features extraction, whereas Section 3 provides an adaptive neural fuzzy inference system. In Section 4, the proposed method is discussed and outlined. The experimental setup of the SC-IM along with the control arrangements are investigated in Section 5. In Section 6, the results and their discussions are illustrated. The main conclusion points are drawn in Section 7.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 1 Introduction
Only the initial input from this optimal sequence is applied as transmission power, allowing the system to rapidly stabilize the BER at a target value during malicious jamming, thereby improving transmission reliability. Finally, a comparison is made between the proposed system, traditional adaptive power control systems, and those using integral feedback. Simulation results show that, compared to existing methods, the MPC-based anti-jamming power control responds more quickly to jamming, improving system adaptability, and enhancing the reliability of wireless communication systems in jamming environments. The main innovations of the algorithm are as follows:  
1. The MPC power controller is capable of anticipating future channel states and interference conditions, thereby enabling the formulation of power adjustment decisions in advance and, consequently, the effective mitigation of the impact of sudden anti-interference. 2. Through the application of rolling optimization and feedback correction, the MPC power controller is able to adapt continuously to channel changes and interference conditions, thus ensuring the stable transmission of the communication system. The remainder of this paper is organized as follows:  
Section 2 provides a comprehensive overview of how wireless communication systems can be modelled as linear control systems. In Section 3, MPC is introduced as a power controller in the communication feedback link to control the transmitter to transmit the optimal power and maintain the stability of BER. The algorithmic implementation of the MPC power controller, including detailed steps and a flowchart, is presented in Section 4. Section 5 reports on the simulation experiments conducted to evaluate the performance of the proposed MPC controller and compared with other control methods. Finally, Section 6 summarizes the key findings and contributions of this study. An additional Section 7 is to further explore how to maximize communication stability when the feedback link is blocked.
## 3 Geometrically Exact 3D Beam Model and Variational Form for Assigned Strains  
The nonlinear 3D beam model for large displacements and rotations used for the numerical prediction is a reformulation of that presented in [15], summarized recently in [16], in a variational form able to provide the finite kinematics of the structure from an assigned (measured) strain field.  
The parametrization of finite rotations in 3D space is a nontrivial problem, mainly because of the nonlinear character of the 3D rotation space. In order to make the numerical model as simple as possible and to optimize the computational cost, a total Lagrangian formulation based on the parameterization of 3D rotations by means of the pseudo rotation vector is used.  
This choice is convenient since it requires a minimal number of parameters, makes the modeling of boundary conditions easy, and allows for additive updates in incremental analysis. On the other hand, the direct interpolation of the total rotation vector has some drawbacks: (i) the singularity of the operators involved for rotation magnitude of \(2\pi\); (ii) a numerical non-objective discrete model.  
Both these drawbacks are not so significant for this case study since rotations and displacements can be large but their magnitude has to be considered far from the singularity threshold; the objectivity error, which is a discretization error, is very small when a quadratic interpolation is used and for the number of finite elements usually adopted and its effect is practically negligible for the technical application under consideration. Therefore, the use of the total Lagrangian approach with a total rotation vector seems to be the most attractive choice for its simplicity and efficiency.  
However, for arbitrarily large rotations, the singularity can be avoided using the incremental rotation vector [17; 18; 19] or using the corotational strategy with the exact strain measure recently proposed in [16], which also eliminates the objectivity and path-dependence issues of the former.  
Other beam models could be also employed, like for instance a 3D Kirchhoff beam [20]. However, a higher continuity of the approximation functions is required in the Kirchhoff model.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 2 Effective Filtering Approach (EFA)
### Forwarding Unicast Frames  
1. First frame When the first frame is sent from the source DANH to the destination DANH, it is forwarded and circulated in all rings except the destination DANH ring, as shown in Figure 2.  
When the destination DANH receives the first sent frame, it sends a control message called the locking message back to the source DANH, as shown in Figure 3. Upon receiving the control message, trunk QuadBoxes check whether the QuadBox rings to which they are connecting are passive QuadBox rings. For the communication session between source DANH 1 and destination DANH 10, QuadBox ring 3 is a passive QuadBox ring, whereas QuadBox rings 1 and 2 are active QuadBox rings.  
Figure 2: Flooding the first frame to discover nondestination DANH rings in EFA. EFA: effectively filtering approach.  
2. \(k\)th frame (\(k\geq 2\))  
From the second unicast frame of the communication session, EFA does not forward the frame to nondestination DANH rings and passive QuadBox rings, as shown in Figure 4. In addition, EFA uses QR for removing the circulated traffic from the rings.
# Weight Effects on Vertical Transverse Vibration of a Beam with a Nonlinear Energy Sink
## 5 The Effects of Weight on the Beam's Vibration
### The Free Vibration Simulated by the Runge-Kutta Method  
For the free vibration, the influence of weight is studied. The initial dimensionless values are set as follows:  
\[\begin{array}{l}q_{1}=0,\;\dot{q}_{1}=3.87\times 10^{-5},\;q_{2}=0,\;\dot{q}_{ 2}=0,\\ q_{3}=0,\;\dot{q}_{3}=0,\;q_{4}=0,\;\dot{q}_{4}=0,\\ v=0,\;\dot{v}=0.\end{array} \tag{25}\]  
Using the Runge-Kutta method to numerically integrate Equation (23) under the initial conditions, as in Equation (25), the generalized coordinates of the beam \(q_{i}(t)(i=1,2,3,4)\) can be obtained. In the first four terms, the truncation coefficients \(\phi_{i}(x_{c})(i=1,2,3,4)\) and \(q_{i}(t)(i=1,2,3,4)\) can be multiplied to obtained the displacements of the beam and nonlinear energy sink.  
In the case that the weight is neglected, the Runge-Kutta method and the fast Fourier transform are used for Equation (24) to investigate the time responses, as above. As shown in Figure 8, the time histories were obtained. The red lines represent the case considering weight, and the blue lines represent the difference between the displacements when considering and neglecting weight. Compared with the displacement at the one-fourth point, the displacement at the one-eighth point of the beam decays and reaches stability faster. The errors in the figures are defined by subtracting the displacement value when neglecting the weight from the displacement value when considering the weight.  
All the frequency components of the beam are obtained based on the fast Fourier transform. The numerical and analytical solutions are compared to investigate the differences between the cases where the weights are offset with static deflection and the cases where they are not. Figure 9 depicts the amplitude-frequency diagrams at the one-fourth point and one-eighth point of the beam when the effects of weight are considered and neglected. The details of the amplitude-frequency diagrams are shown in Table 3. Accounting for the nonlinear energy sink weight introduces half super-harmonic responses in addition to the quantitative differences in the first primary harmonic response.
## 4 Layered Models
### Communication Layer  
The second layer can be broadly characterized as a connectivity layer that supports previously delineated communication frameworks, particularly the V2X. Its fundamental role revolves around the establishment and sustenance of wireless links with established networks like Wi-Fi, Bluetooth, or 5G.  
This functionality is incorporated into the data layer in the framework proposed by [51], where wireless communication nodes are endowed with additional resources for storage and computation. These resources are somewhat abstracted as fog nodes, thereby facilitating the decentralized provisioning of services. In a different approach outlined in [45], a hybrid cloud/edge architecture was employed for communication utilizing pertinent wireless technologies. The rationale behind adopting this architecture is the imperative need for real-time data exchange and processing among intelligent devices. Leveraging the edge computing paradigm allows models built on this architecture to handle time-sensitive data from moving vehicles, thus progressively offloading other data to the cloud for comprehensive computation and analysis.  
Figure 2: Architecture of IoV model merging sensing and communication layer [44].  
In the work presented by [46], the layer employs conventional routing protocols that are contingent on the geographical positioning of nodes within the network, the topology, and vehicle clustering. Additionally, it incorporates graph and path planning algorithms for the discovery of the most efficient routing paths.
## 5 Discussion  
### Theoretical Comparison of the Employed Methods  
Traditional methods of cumulative frequency and Avg\(\pm\)k*Std methods depend on the amount of data. The former is distribution-free, whereas the latter depends on a normal or log-normal distribution. In theory, depending on the data amount limits comparisons between geochemical maps of different areas or the same area with different data amounts.  
In addition, the 0.1 value of \(\Delta\)lgSn (or difference) is acceptable and meets the requirement of analytical qualities [10; 11; 12; 13] for standard samples in labs, indicating that the \(\Delta\)lgSn between two neighbor contour levels in geochemical maps should be no less than 0.1. However, the \(\Delta\)lgSn between neighbor levels in geochemical maps may be less than 0.1 when the two traditional methods of cumulative frequency and Avg\(\pm\)k*Std methods are used. In the Gejiu area, the minimum of \(\Delta\)lgSn in the cumulative frequency method is only 0.02 and the minimum of \(\Delta\)lgSn in the Avg\(\pm\)k*Std method is 0.09 depending on the standard deviation value of 0.36 for lgSn data without outliers.  
Compared to the cumulative frequency and Avg\(\pm\)k*Std methods, the traditional logarithmic interval method and the presented fixed-value method overcome the theoretical limitation of data amount dependence and meet the implicit requirement of the \(\Delta\)lgSn between neighbor levels no less than 0.1. However, level values in the logarithmic interval method depend on the data's median value and do not have specific geochemical meanings for specific elements such as Sn. The presented fixed-value method's values for Sn geochemical maps were set to the statistical parameters of Sn concentration data derived from the RGNR and NMPRGS projects in China [6; 7].  
With respect to the classified levels and their colors, the aforementioned four methods were all designed as 19 levels. In the three traditional methods, low and high values were represented by colors ranging from blue to red. The presented fixed-value method, on the other hand, ranged from blue to red and corresponded to low backgrounds and high anomaly values, and from gray to black based on Sn concentrations in placers and ones in hard rocks. The color tones in the newly presented method with six color tones (correspond  
Figure 6: Sn geochemical map in the Gejiu area based on the fixed-value method. Color information is shown in Table 3. The geological legends are the same as those illustrated in Figure 1.  
ing to low background, high background, low anomaly, high anomaly, mineralization in placer type, and mineralization in hard-rock type) are superior to traditional methods with three color tones (from blue to yellow, then to red, corresponding to low, medium, and high values).
## 4 Results and Discussion
### Limit on the Time Resolution
It should be noted that improving the time resolution by a factor 10-100 should be technically feasible which would result in an equal reduction in required dose for the same SNR and hence is the single most important parameter to improve when trace element analysis is required through coincidence measurement. Figure 9: The SNR gain as function of mask radius for different incoming currents. The SNR gain is defined as the ratio of the SNR with the SNR when the mask radius is zero.