esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Simulation
### Simulation Results
We also got similar results when repeating the simulation with node 3 decrementing its DL counters and node 19 decrementing one of its UL counters. Thus, CENTERA can detect any node trying to manipulate its counters due to malfunctioning or due to the intention to hurt other nodes and cause them to be banned. In either case, the BS can after some checks and analysis isolate the exact misbehaving node. To further analyze the benefits of authentication in CENTERA, we take a look at two attackers on the network, nodes A and 4. It is directly noticed that the outside attackers are isolated completely from the network in both cases. The strong attacker took over node 4. It tried to impersonate other nodes, but this is impossible without the private key that is the identity of the node. So, the attacker started using node 4 to send bad packets into the network. The BS updated the routing paths of the nodes such that node 4 is totally isolated from the system. As for the attacker node A, without proper authentication, it is directly neglected by the all the nodes in the system, and if it used the same authentication technique, the BS directly updates the routing paths to neglect this outsider. We finally tried a broadcasting node, node 23 that is trying to broadcast packets through nodes 14, 22, 24, and 32, either due to a malfunction or in order to disrupt the whole network. However, it is clear, from the snippet of the topology shown in Figure 8, how CENTERA forced nodes 14, 22, and 24 to drop the packets from 23 because they are not the DL of node 23 as indicated by the BS. Only node 32 is forwarding the packets of node 23. Thus here we can directly see the first benefit of CENTERA in preventing broadcast storms that can increase the noise levels in the network and affect the network functionality and lifetime. Also for any discrepancies declared in its pcounters, node 23 is punished and isolated as the cases stated above. Thus, as deduced from the simulations, after the first time period, each node starts acting per its nature. This directly gets reflected in the routing paths that now avoid the bad nodes. The routing paths are updated to avoid the \"bad\" nodes and pass only through \"good\" nodes. So, all of the bad nodes are isolated and no other node is forwarding their packets.
# Editorial for Special Issue \"Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications\"
###### Abstract  
Global water cycle dynamics involve the exchange of water and energy matter among the atmosphere, hydrosphere, geosphere, cryosphere, and biosphere. Remote sensing provides a unique advantage of observing and acquiring complex water cycle and hydrological state variables across a wide range of spatial and temporal scales. The recent advances in remote sensing technology and numerical hydrological models alleviate our ability to observe and predict the storage, fluxes, and movement of water in time and space. Remote sensing offers unprecedented opportunities to gain a better and comprehensive understanding and mapping of water distribution and variability, in response to climate change and human activities. Besides, remote sensing data enables global and regional hydrological applications, and water resources management, motivates new theories in mapping applications and offers new ways to predict and resolve global water resources conflicts. This Special Issue encompasses a number of contributions in satellite and airborne sensors applications in hydrology, including: mapping theories and applications, i.e., [1; 2; 3], new methods to better observe hydrological component, i.e., precipitation [4; 5], precipitable water vapor (PWV) and vapor pressure deficit (VPD) [6; 7; 8], energy fluxes and evapotranspiration [9], and snowfall [10], and new methods to improve hydrological decision support system, i.e., [11]. The following section briefs the overall contributions in this Special Issue.
## 3 Results and Discussion
### Kernel Size Optimization  
In order to find the optimized convolutional kernel size of the network (other architectural elements are kept same as the TCN) for sEMG-based continuous estimation, we have explored the influence of different convolutional kernel size on the network accuracy.  
From the experimental results (as shown in Figure 5), it can be seen that when we expanded the convolutional kernel, the correlation coefficient increased until achieving the highest peak at 82.06% (where kernel size is 31), and then fell back. This could be explained as follows. In sEMG, there is a strong correlation between the points within a certain period of time, which we call atomic segments.  
As shown in Figure 6, we define the atomic segment as the shortest time sequence in which sEMG can express effective information. When the convolutional kernel is smaller than the atomic segment, such as 1 \(\times\) 3 in TCN, it is difficult to obtain sEMG's information. This is caused by the fine-grained, long sequence information contained in the sEMG, and a shallow network with a small convolutional kernel cannot obtain enough temporal features. Similar situations have appeared in image segmentation. [PERSON] et al. (2019) found that a large convolutional kernel has better performance for image segmentation (pixel classification). This further proves that a large convolutional kernel is helpful for maintaining the underlying details.  
Figure 5: The relationship between kernel size and PCC.  
On the other hand, if the convolutional kernel is too large (such as larger than 31 in Figure 5), it will contain non-strongly correlated and redundant information, which will not help improve the network accuracy but will increase the number of network parameters. Therefore, when setting the convolutional kernel size equal to or slightly larger than the length of the sEMG's atomic segment, the network will maximize the network precision rate while maintaining a minimum network parameter size.
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 6 Conclusions and Future Work  
### Conclusions  
This article proposes a new hybrid hierarchical RSU deployment algorithm for road-side unit deployment. The algorithm introduces a novel hybrid RSU deployment model that combines the traditional RSU deployment strategy with the strict selection rules on candidate t-RSUs chosen from multiple parking cars using an online algorithm. The concept of combining the candidate parking cars as temporary RSUs is first proposed, which contributes to saving RSU resources and improving network connectivity and vehicle coverage. The driving vehicles in the corresponding RSU coverage section are divided into clusters and summarized. The simulation results demonstrate that the hybrid deployment strategy, thanks to the use of t-RSUs, significantly outperforms the other two conventional deployment strategies (i.e., RandDeploy and MVCD algorithms) in terms of vehicle coverage ratio and point-to-point connectivity within given costs. For instance, when the transmission range is 100 m, the vehicle coverage ratio in the proposed model improves by 69.81% and 92.5% compared to the RandDeploy algorithm at median and high vehicle densities, respectively. Regarding the connectivity ratio, even at low density in a 100-m transmission range, the proposed deployment scheme achieves a connectivity ratio of over 50% with the assistance of the t-RSUs, demonstrating the best performance among the three deployment methods. The proposed scheme provides a new solution and idea for the future deployment of RSU in urban traffic environments.
## 4 Service Scheduling Algorithms
### Algorithm Analysis  
Upon receiving updated information regarding the compute cluster services, the network control plane initiates the TOPSIS scheduling algorithm to update the candidate nodes in the data plane's state domain. When a request arrives at the data plane, the subsequent matching and selection process are then executed.  
The time complexity of the TOPSIS algorithm is primarily determined by the number of service information items in the control plane and the number of evaluation criteria. Given \(m\) service information items and \(n\) evaluation criteria under a certain delay level and success rate classification, the time complexity for constructing the evaluation matrix is \(\mathcal{O}(m*n)\). During the ideal solution selection phase, the algorithm requires finding the maximum and minimum values from the weighted normalized values of each criterion, with a complexity of \(O(n)\). In the stage where the distances between each candidate and both the ideal and anti-ideal solutions are calculated, since it is necessary to compute the distance for all criteria of each candidate, the complexity is \(O(m*n)\). When calculating the overall closeness of each candidate, the algorithm's complexity is \(O(m)\). Thus, the overall time complexity of the TOPSIS algorithm is \(O(m*n)\).  
However, it is worth noting that this study significantly reduces the value of \(m\) by dividing network nodes into layers and domains and classifying services according to delay level and success rate level. In addition, the value range of the evaluation criterion \(n\) is predictably small and limited. Therefore, it can be said that the mechanism proposed in this paper has good scalability.
## 4 Results and Discussions
(**a**) 10 Gbaud for B2B; (**b**) after 80 km SSMF transmission. Figure 6: Constellation of 10 Gbaud QAM16 after 80 km SSMF transmission. (**a**) RSB, LP = 5 dBm; (**b**) RSB, LP = 11 dBm; (**c**) RSB, LP = 14 dBm; (**d**) LSB, LP = 5 dBm; (**e**) LSB, LP = 11 dBm; (**f**) LSB, LP = 14 dBm. Similar performance can be observed for SSB signals at the 28 Gbaud data rate. The BER curves are shown in Figure 7. As for the B2B case in Figure 7a, BERs are maintained at almost the same level, since the CSPR is large enough with the absence of nonlinear distortion. The increased BER can be found in Figure 7b when the launch power exceeds 7 dBm, which is the same as that of the 10 Gbaud signals. This is also reasonable, since the threshold of the SBS should be the same for different data rates, which is one more proof of the SBS nonlinear distortion. The mitigation of the SBS nonlinear distortion can also be achieved by using the OBPF filtering with reduced BER for both LSB and RSB. However, there is no significant difference between LSB and RSB. We believe that this is due to the bandwidth limitation of the signal channel for 28 Gbaud, which to a certain extent erases their performance difference. This can also be observed from the performance of the constellations, as shown in Figure 8, in which the noise difference between LSB and RSB at 14 dBm is much smaller for 28 Gbaud than that for 10 Gbaud. Figure 8: Constellation of 28 Gbaud QAM16 at 80 km SSMF transmission. **(a)** RSB, LP = 5 dBm; **(b)** RSB, LP = 11 dBm; **(c)** RSB, LP = 14 dBm; **(d)** LSB, LP = 5 dBm; **(e)** LSB, LP = 11 dBm; **(f)** LSB, LP = 14 dBm. Figure 7: BER versus launch power. **(a)** 28 Gbaud for B2B; **(b)** after 80 km SSMF transmission. On the whole, SBS has different impacts on signals with varying data rates, and some of the impacts of the nonlinear distortion by SBS as CSPR increased can be alleviated by utilizing OBPF to filter out the component of SBS. The SBS has a greater overall impact on the higher data rate transmission systems than it does on the lower data rate systems when OBPF can be applied to filter out peak of SBS. For the higher rate transmission system, the impact of SBS on the RSB signal is greater than that on the LSB signal under the same experimental conditions.
# A Field Investigation on Vortex-Induced Vibrations of Stay Cables in a Cable-Stayed Bridge
## 4 Correlation between Cable Response and Wind Characteristics
### Vibration Amplitude Versus Wind Characteristics
The additional data of September and October of 2010 was employed to validate the presented relationship between the vibration amplitude and the mean wind speed, as shown in Figure 10. It is suggested that the maximum vibration amplitude at different mean wind speeds can be reflected by the presented relationship. Figure 10: Validation of the presented relationship between the vibration amplitude and the mean wind speed. Figure 9: Vibration amplitude versus 10 min mean speed in April, July, and August. To reveal the correlation between the cable vibration and the velocity, the mean wind speed was often changed into the reduced velocity, i.e., \(V_{r}=U/(f_{n}D)\), where \(f_{n}\) is the \(n\)th modal frequency and \(D\) is the diameter of the cable. As a multimode vibration in the present study, the reduced velocity \(V_{r}\) was calculated based on the dominant mode frequency that had the maximum vibration amplitude in all vibration participative modes. Figure 11a shows the correlation of the 10 min acceleration RMSs and the corresponding 10 min mean reduced velocity. Next, the relationship of the dimensionless vibration amplitude with the reduced velocity is obtained, as shown in Figure 11b. The results indicate that the large vibration amplitudes are mainly located in the region near the reduced velocity of five. This phenomenon is similar to the feature of a vortex-induced vibration of a stayed cable observed by [PERSON] and [PERSON] [6]. Figure 12 shows the relationship of the RMS of the acceleration of the CAC20 cable and the corresponding mean wind direction in April, July, and August. It is found that the large amplitudes mainly happen at the wind direction nearly perpendicular to the bridge. The large amplitudes are more concentrated, with a wind direction near 90 degrees (deg), and are distributed wider when the wind direction nears 270 degrees. The reason is believed to be that the wind direction is mainly concentrated in the direction perpendicular to the bridge axis when the wind blows from the south; and the wind direction covers more angles when the wind comes from the north, as shown in Figure 7.  
Figure 11: Vibration response versus reduced velocity in April. Figure 12: Vibration amplitude versus yaw angle in April. Figure 13 shows the relationship of the RMS of the acceleration of the CAC20 cable and the corresponding turbulence intensity in three months. The large vibration amplitudes mainly occur when the turbulence intensity is less than 20%.
## 1 Introduction
### Contribution of This Study  
This study addresses the critical challenges in rotational speed detection through developing an electrical signature analysis methodology that innovatively leverages vehicle power system characteristics. Unlike conventional contact-type approaches--such as centrifugal tachometers requiring intrusive crankshaft coupling [14] or magneto-sensitive sensors vulnerable to electromagnetic interference in hybrid powertrains [13]--the proposed system non-intrusively acquires electrical signals through the cigarette lighter interface. By employing an external independent power supply, it eliminates parasitic loads on the alternator that distort rotational dynamics, overcoming the limitations of OBD-dependent tools using onboard power. Spectral analysis extracts the AC signal's fundamental frequency to determine the alternator speed, with engine speed being derived from predefined correlations. A phase-difference compensation algorithm, integrating sinusoidal least-squares fitting, optimizes phase estimation at the sampling points, achieving \(\pm\)0.3% accuracy under transient conditions--surpassing alternator W-terminal methods (\(\pm\)1.2% error [24]) and circumventing the alignment constraints of laser tachometry [20]. This approach enables plug-and-play operation with real-time capabilities, outperforming vibration analysis techniques that are constrained by the computational latency in field diagnostics.  
This work makes three primary contributions to engine diagnostics: First, it establishes a novel electrical signature analysis paradigm for rotation speed detection, eliminating the need for additional sensors. Second, the proposed phase-difference compensation algorithm effectively mitigates frequency estimation errors caused by non-stationary operating conditions. Third, the developed embedded system prototype demonstrates practical feasibility for field applications through optimized computational efficiency.  
The subsequent sections are organized to comprehensively present the technical architecture: In Section 2, the system design framework is presented in a systematic manner in terms of materials and principles. In Section 2.1, the hardware configuration and software information of the system is detailed, and in Section 2.2, the overall system framework is presented in terms of principles. This section also details the RPM calculation methodology, including mathematical formulas and calculation procedures. Section 3 provides comprehensive experimental validation through the execution of engine simulator benchmark tests and real vehicle tests under controlled conditions. In Section 4, the discussion focuses on two key aspects: the simulation experiments and the real-vehicle testdata. The former is examined in order to propose future research directions. In conclusion, Section 5 offers a synopsis of the proposed measurement scheme and its significance.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 5 The Calibration of Extrinsic Parameters
### Calibration of the Rotation Matrix
#### 5.1.1 Rotation Vector Solution  
The rotation vector is consistent with the direction vector of the rotation axes. Based on the homography between the camera's optical center coordinate system and the calibration plate's corner coordinate system, we can calibrate the posture relationship between the camera and the calibration plate to calculate their respective rotation vectors. Taking horizontal rotation as an example, as shown in Figure 5, in a minimal zoom state with the optical center at position \(O_{1}\), we can use [PERSON]'s method to find the rotation matrix, \(\mathbf{R}_{1}\), and the translation vector, \(\mathbf{T}_{1}\), between the camera coordinate system, \(O_{1}-xyz\), and the planar pattern corner coordinate system, \(B-xyz\). Then, keeping the position of the checkerboard planar pattern fixed, we can rotate the camera \(\alpha\) degrees counterclockwise around the rotation axis, \(\mathbf{v}\), in a horizontal direction to position \(O_{2}\). The rotation matrix and translation vector in this position are \(\mathbf{R}_{2}\) and \(\mathbf{T}_{2}\), respectively.  
We can calculate the rotation matrix of the optical center transformation from \(O_{1}\) to \(O_{2}\):  
\[\mathbf{R}_{\mathrm{p-\alpha}}=\mathbf{R}_{2}\times\mathbf{R}_{1}^{-1} \tag{12}\]  
According to [PERSON] formula, the rotation matrix can be converted into a rotation vector and a rotation angle:  
\[\sin(\alpha)\mathbf{v}^{\mathrm{A}}=\frac{\mathbf{R}_{\mathrm{p-\alpha}}- \mathbf{R}_{\mathrm{v-\alpha}}^{\mathrm{T}}}{2} \tag{13}\]  
In Expression (13), \(\mathbf{v}^{\mathrm{A}}\) is the antisymmetric matrix of the rotation vector, \(\mathbf{v}\). In experiments, we can take the average value of multiple measurements to eliminate the resulting error.  
Similarly, we can determine the pitch rotation axis (\(\mathbf{h}\)) shown in Figure 5b.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 5 Simulation Experiments
The simulation results were shown in Figures 14-16 without the consideration of the TCR boresight, and the image quality analysis parameters and point target energy are shown in Table 2. We can conclude that the main lobe of the TCR image became wider as the bistatic angle increased, which degrades the resolution of the image (i.e., IRW). Otherwise, the point target energy drops quickly with the bistatic angle increasing. Therefore, the increase of bistatic angles will deteriorate the quality of the bistatic SAR image and the point target energy. Figure 14: The simulation results for the \(2^{\circ}\) bistatic angle without the consideration of the TCR optimum boresight. (**a**) The point target image after upsampling 48 times; (**b**) the azimuth profile based on the results of the electromagnetic simulation. Figure 15: The simulation results for \(6^{\circ}\) bistatic angle without the consideration of the TCR optimum boresight. (**a**) The point target image after upsampling 48 times; (**b**) the azimuth profile based on the results of the electromagnetic simulation. As shown in Figures 17-19, the simulation results were obtained as the change of TCR boresight is considered, and image quality analysis parameters and point target energy of the TCR are shown in Table 3. As long as the bistatic angle is less than 20\({}^{\circ}\), the image quality was good, and the image quality analysis parameters could meet the general image quality requirements. Otherwise, comparing the simulation results for the 2\({}^{\circ}\) bistatic angle in Figures 14 and 17, it can be seen that whether the TCR boresight is considered or not has little effect on imaging when the bistatic angle is small (less than 6\({}^{\circ}\)). In addition, if the bistatic angle is larger than 6\({}^{\circ}\), taking the TCR optimum boresight into account can improve the imaging quality and point target energy by comparing the parameters in Tables 2 and 3.
# Reutilization of Recycled CDW Sand in Mortars, Paving Blocks, and Structural Concrete
###### Abstract  
Reusing construction and demolition waste (CDW) as recycled aggregate reduces environmental impact and enhances resource efficiency. While previous research has mainly focused on the use of recycled aggregates (RAs) in concrete, this study evaluates the use of CDW-derived sand in mortars, paving blocks, and structural concrete. Natural and CDW aggregates were characterized, and samples were prepared with two types of Portland cement, replacing up to 100% of the natural limestone sand. Tests were conducted to assess workability, density, strength, and durability. CDW aggregates, primarily composed of limestone and ceramics, reduced sample density as their content increased. Workability improved in the mortars and concrete with higher CDW contents, peaking at 20% CDW in paving blocks. Although the permeability of concrete increased with CDW content, the developed recycled aggregate concrete (RAC) met structural code requirements for all the exposure classes. Despite the decline in strength with higher CDW content, the paving blocks maintained a relative tensile splitting strength above 80%, and the relative compressive strength of the mortars cured for 28 days exceeded 70%. The RAC compressive strength remained within the required range for reinforced concrete (>25-30 MPa). These results validate the feasibility of using CDW-derived sand in various sustainable construction applications with minimal strength loss. Furthermore, they contribute to the development of standardized guidelines for RAs in non-structural applications, fostering broader industry adoption and environmental benefits.  
construction and demolition waste; recycled aggregates; paving blocks; recycled aggregate mortars; recycled aggregate concrete; physico-mechanical properties 1