esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Optical fiber gratings can be appropriately packaged and integrated for the real-time monitoring of the structural health of buildings or composite-material films. In this work, a tilted fiber Bragg grating at the micron scale was proposed, designed, and optimized via simulation model analysis using OptiFDTD software. The effects of the grating inclination, grating period, and grating length on the transmission spectrum of the tilted microfiber Bragg grating (TMFBG) were studied. The transmission spectrum's responses to different refractive indices were simulated and compared. A TFBG was uniformly etched with hydrofluoric acid based on the chemical etching method, and several TMFBGs with different diameters were prepared. The refractive index-sensing characteristics of the TMFBGs with different structures were studied. It was found that the sensitivity of the etched TFBG was greatly improved from 0.964 nm/RIU to 6.368 nm/RIU for the higher-order cladding mode, and from 0.294 nm/RIU to 2.353 nm/RIU for the lower-order cladding mode, being approximately six times and eight times higher, respectively.  
microfiber sensor; building health monitoring; TFBG; fiber sensors; integrated optics Article
# Effects of Nozzle Exit Angle on the Pressure Characteristics of SRWJs Used for Deep-Hole Drilling
###### Abstract  
The self-resonating waterjet (SRWJ) has been applied in petroleum, natural gas, and mining engineering ever since its strong erosion ability in deep-hole drilling was recognized. Aiming at further improving the working efficiency of SRWJs, the effects of the exit angle of the organ-pipe nozzle on the axial pressure oscillations of the jet were experimentally studied. Six exit angles of \(\theta\) = 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\) were employed in the experiment, and the axial pressure oscillation peak (\(P_{max}\)) and amplitude (\(P_{a}\)) were used for characterizing the performance of SRWJs. It was found that the exit angle greatly affects the axial pressure oscillations, including the development trends against the standoff distance and the magnitudes of \(P_{max}\) and \(P_{a}\). Under testing with two inlet pressures, the exit angle of \(\theta\) = 0\({}^{\circ}\) always resulted in the greatest \(P_{max}\) and \(P_{a}\) within the range of the testing standoff distance. With the increase of standoff distance, both \(P_{max}\) and \(P_{a}\) first increased and then decreased when the exit angle was 0\({}^{\circ}\); while they kept decreasing when the exit angle was 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\). Moreover, the exit angles of \(\theta\) = 90\({}^{\circ}\) and 60\({}^{\circ}\), corresponding to inlet pressures of \(P_{i}\) = 10 MPa and 20 MPa, led to both the minimum magnitudes of \(P_{max}\) and \(P_{a}\) under the experimental conditions. The results also indicate that the exit angle affects the interactions between the nozzle lip and the jet and help provide information for improving the working efficiency of SRWJs in practical applications.  
Keywords:self-resonating waterjet; nozzle exit angle; pressure oscillation peak; pressure oscillation amplitude; deep-hole drilling +
Footnote †: journal: Applied Sciences
## 3 Our Approach
### Applying Tensor Decomposition with Different Rank Methods
#### 3.3.5 Layer Sensitivity Analysis  
We implemented Tensor Train decomposition on the model layers, with Figure 4 illustrating the sensitivity of AlexNet model layers to this method across both convolutional and fully connected layers before and after model fine tuning. Figure 4a,b highlight the model's enhanced robustness with depth, indicating minimal sensitivity impact when employing low ranks (e.g., 5 or 20) for deeper layers, akin to the performance of undecomposed layers. Table 2 presents a detailed analysis of layer compression at varying ranks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & _Top-1 (\%)_ & _Param Size (MB)_ & _Compression_ \\ \hline \multicolumn{4}{c}{ResNet-20} \\ \hline FP Model & 92.60\% & 1.1 & - \\ CP Model & 77.60\% & 0.368 & 2.9\(\times\) \\ Tucker Model & 91.180\% & 1 & 1.1\(\times\) \\ TT Model & 91.330\% & 0.947 & 1.16\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ResNet-20 architectures on CIFAR-10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & _Top-1 (\%) before_ & _Top-1 (\%) after_ & _Compression_ \\ \hline \multicolumn{4}{c}{Features-0} \\ \hline FP Model & 83.80\% & - & - \\
1 & 9.03\% & 31.71\% & 23.6\(\times\) \\
5 & 15.63\% & 31.29\% & 3.84\(\times\) \\
20 & 76.830\% & 71.81\% & 0.905\(\times\) \\
40 & 83.809\% & 79.90\% & 0.67\(\times\) \\
60 & 83.809\% & 79.13\% & 0.67\(\times\) \\
80 & 83.809\% & 79.59\% & 0.67\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layer sensitivity for AlexNet Model after applying Tensor Train decomposition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & _Top-1 (\%) before_ & _Top-1 (\%) after_ & _Compression_ \\ \hline \multicolumn{4}{c}{Features-3} \\ \hline FP Model & 83.809\% & - & - \\
1 & 16.25\% & 24.29\% & 422.1\(\times\) \\
5 & 21.590\% & 53.41\% & 77.  
==WARNING: Truncated because of repetitions==
0&if\ x<-1,\\ 1&if\ -1\geq x\leq 1,\\ 0&if\ x>1.\end{array}\right.\] (6)  
\begin{table} \
\begin{tabular}{c c c} \hline
**Rank** & _Top-1 (\%) before_ & _Top-1 (\%) after_ \\ \hline \multicolumn{3}{c}{Basic Block 3} \\ \hline FP Model & 92.6\% & - \\  
nd{tabular}\end{table}
+++  
The weight values in XNOR-net are approximated using binary filters, as shown below; by treating quantization as an optimization problem, as shown in the equation, a better scale factor can be selected.  
\[I*W\approx(I\oplus\beta)\alpha \tag{7}\]  
\[J(\beta,\alpha)=\left\|W-\alpha\beta\right\|^{2} \tag{8}\]  
where \(W\) denotes real value filters, \(B\) denotes binary filters, and \(\alpha\) denotes a positive scaling factor. The binary weight filter is the sign of the weight values after solving this optimization problem, and the scaling factor is the average of the absolute weight values.  
\[\beta^{*}=sign(W),\ \alpha^{*}=\frac{1}{n}\left\|W\right\|_{I_{1}} \tag{9}\]  
A block of XNOR-net is different from a block in a CNN, as shown in Figure 6b.
## 2 System and Channel Model
### HHT Protocol  
Similar to the HDT protocol, the total period \(T\) is divided into \(K\) periods on average, and each period is composed of two parts, which are the time \(\alpha_{01}T\) for energy harvesting and the time \((1-\alpha)_{k}T\) for packet transmission in the HHT protocol, as shown in Figure 3. As a result, the energy harvested by sensor node \(N_{k}\) is as follows:  
\[E_{k}=\alpha_{0k}TPG_{k3}\eta_{k}d_{k}^{-\lambda}. \tag{9}\]  
After harvesting energy, the sensor nodes use the harvested energy to send packets to the BS. The time spent is \((1-\alpha)_{k}T\); thus, the sending power is as follows:  
\[P_{k}=\frac{E_{k}}{(1-\alpha)_{k}T}=\frac{\alpha_{0k}PG_{k3}\eta_{k}d_{k}^{- \lambda}}{(1-\alpha)_{k}}. \tag{10}\]  
The SNR of sensor node \(N_{k}\) sending packets to the BS under the HHT protocol is as follows:  
\[\gamma_{k}^{(v)}=\frac{P_{k}G_{k1}^{(v)}d_{k}^{\lambda}}{W\mathcal{Z}_{0}}= \frac{\alpha_{0k}G_{k1}^{(v)}G_{k3}\gamma_{0}\eta_{k}d_{k}^{-2\lambda}}{(1- \alpha)_{k}}. \tag{11}\]
## 4 Suggestions for Future Trends
### Integrated  
As RFI environment is evolving temporally and varying geographically, the characterization and mitigation are not easy tasks. No single technique can address all possible RFI scenarios for SAR observations. To better improve the anti-jamming capability and prevent being RFI-affected, integrated means that the RFI mitigation should be performed by combing different aspects of techniques ranging from the transceiver system level to signal processing level.  
From the transceiver system level, the sniffing pulse can be exploited for RFI pre-detection and monitoring, with the cost of reducing the pulse repetition frequency of transmitted waveform. For electronically scanned SAR systems, the digital beamforming techniques could be employed to create antenna null steering for side-lobe RFI rejection, while not applicable to deal with main-lobe jamming. Theoretically, for forthcoming SAR constellations or distributed SAR systems, the synthetic large aperture could narrow the nulling beam width and improve the jamming rejection performance. However, due to its long distance between array elements, the far field assumption will be violated and the traditional digital beamforming is not applicable. In this case, more complex effort should be made to realize near-field beamforming and alleviate grating lobe effect [96; 97]. Future multiple-input  
Figure 11: Illustration of system architecture for future SAR systems incorporated with RFI mitigation strategy.  
and multiple-output (MIMO) SAR system could provide more waveform diversity ability to maneuver in time, frequency, space, and polarization, and thus have the potential to improve the anti-jamming capability [98].  
If the RFI signal emitted from the same direction with the SAR echoes and recorded by the receiver, the post-processing mitigation is then applied. This energy can be excised with a variety of advanced signal processing techniques as introduced in Section 3. For more general and heterogeneous environment, the RFI entering the main-lobe could be mitigated from the signal processing level by performing joint domain analysis. By jointly exploiting the characteristics among time, frequency, spatial, polarization, code, and power domain, the difference between RFI and target echoes can be characterized more accurately, and thus improve the mitigation performance by using tensor-based multilinear analysis [99].  
By integrating the methods ranging from the transceiver system level to signal processing level, it can overcome the limitations of the applicability to various RFI signal types as listed in Table 2. Each part could share the burden for improving the overall RFI mitigation performance to a higher level.
## 3 System Model
### Structure of Proposed Scheme
This means the traffic load across the CCs can be better balanced in a CR-based SA system. It is impossible for the proposed resource scheduling structure to outperform the JQS, but its complexity is lower than that of the JQS. Greater resource utilization of the proposed resource scheduling structure over the DQS is imaginable. Although it increases the complexity of the proposed resource scheduling structure, this complexity increase is moderate. The resource scheduling structure of the RT-CCCS scheme also has some advantages over SBLS. In SBLS, there are the same number of resource schedulers (RSs) and CCs, and each RS in SBLS carries out RB assignment and the user's data transmission control. In the resource scheduling structure of RT-CCCS, there are also the same number of RCs and CCs, but each RC only carries out the user's data transmission control. The function of RB assignment is implemented by the packet scheduling in the resource scheduling structure of RT-CCCS, and there is only one packet scheduling module in the structure of RT-CCCS. Hence the proposed resource scheduling structure of RT-CCCS shows lower complexity than SBLS. In other hand, when the user data is assigned to a certain CC during the burst transmission, the user data will wait for data transmission in SBLS if the certain CC is suddenly occupied by PUs in CR networks, but the user data will be switched to another idle CC in the structure of RT-CCCS instead of waiting for data transmission. Thus, the proposed structure of RT-CCCS has better delay performance than SBLS in CR networks.
## 2 Materials and Methods
### CFD Simulation
#### 2.4.4 Computational Domain and Boundary Conditions  
A two-dimensional flow domain was adopted for the analysis. This type of computational domain is often adopted for VAWT turbine flow calculations to reduce the required amount of computing resources. However, according to a study by [PERSON] et al. [35], which concerned the range of the tip speed ratio \(\lambda\) from 1.4 to 4.0 in a simulation of VAWT, the results obtained for average torque and power coefficients may be overpredicted for higher values of the tip speed ratio when using the 2D domain, and the error increases as \(\lambda\) increases. On the other hand, the tip speed ratio in our study was very small, with a value of 0.27. Therefore, the 2D domain was considered to be sufficient for the purpose of this study.  
The dimensions of the computational domain were accepted, considering findings presented in studies by [PERSON] et al. [36] and [PERSON] et al. [37]. The computational domain with boundary conditions is shown in Figure 4. The diameter of the blade's rotation was \(D=600\) mm, and the diameter of the circle circumscribed on the dual-coherent blade was \(D_{b}=263\) mm. The blade height of \(H=625\) mm was accepted. The geometry of the mast was not included due to the low impact on power loss in VAWTs [38].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Air density & \(\rho\) & 1.225 & kg/m\({}^{3}\) \\ Air viscosity & \(\mu\) & \(1.7894\times 10^{-5}\) & kg/ms \\ Atmospheric pressure & \(p_{atm}\) & 101325 & Pa \\ Wind speed & \(W\) & 5 & m/s \\ Tip speed ratio & \(\lambda\) & 0.27 & \(-\) \\ Rotor angular velocity & \(\omega_{R}\) & 4.5 & 1/s \\ Blade angular velocity & \(\omega_{B}\) & 2.25 & 1/s \\ Time step size & \(TSS\) & 1.93925\(\times 10^{-4}\) & s \\ \hline \end{tabular}
\end{table}
Table 1: Analysis parameters.  
Figure 3: Torque coefficient \(C_{m}\) depending on the number of revolutions.  
The rotating rotor domain had a diameter of \(2D\), whereas the rotating blade domains had a diameter of \(1.5D_{b}\). The distance between the inlet and the rotor axis was \(10D\), the distance between the outlet and the rotor axis was \(10D\), and the domain height was \(20D\).  
It was assumed that the blade domain's diameter was \(1.5\,D_{b}=394.5\) mm.
## 1 Introduction
According to the literature, the interferometer-based machine tool geometric error measurement technology can be mainly divided into two methods: laser tracker-based measurement methods and laser tracer-based measurement methods [30; 31]. Regarding laser tracker-based measurement methods, [PERSON] et al. proposed a general method based on point measurements for identifying the geometric errors of multi-axis machine tools [32]. [PERSON] et al. also used a laser tracker to propose a novel principle: a pose measurement principle to realize the precise calibration of a numerical control (NC) machine tool [33]. The time-sharing measurement principle as well as the multi-station methods were proposed to identify the relative position relationship between the rotary and linear axes of a multi-axis NC machine tool by using a laser tracker [34]. As mentioned above, the single-base station and multi-base station measurement methods of using the laser trackers for measuring the geometric errors of machine tools have been widely used. Using laser trackers for machine tool geometric error measurements has advantages, as they are low cost and easy to set up; nevertheless, the measurement accuracy is limited by the measurement principles and the encoder of the rotary axes in the laser tracker. As a result, in order to address this issue, the LaserTRACER was designed by researchers at the Physikalisch-Technische Bundesanstalt (PTB) and the National Physical Laboratory (NPL) [35; 36; 37]. Compared to the laser tracker-based measurement methods, the laser tracer-based measurement methods have more precise characteristics due to them having a measurement algorithm and mechanism. As a result, for identifying the geometric errors of the machine tools more precisely, the laser tracer-based measurement methods have become much more popular and are utilized in high-precision machine tool manufacturing [38]. Therefore, based on laser tracer-based measurement methods, this paper proposes a novel measurement method to identify the 6-DOF geometric errors of rotary tables by using LaserTRACER and reflectors. Based on the multilateration method, homogeneous transformation matrix (HTM), and least squares method, the mathematical measurement algorithm is built for calculating the 6-DOF geometric errors of rotary tables. In addition, the set-up errors of the reflector standard fixture that affect the geometric error measurement accuracy are also considered and identified. Finally, verification of the simulation and experiment is executed by using LaserTRACER and the reflectors, which are mounted on the reflector standard fixture.
## 4 Proposed Algorithm
### Mechanism of EPOA-CHS Algorithm  
In this method, CHs are selected from a pool of standard sensor nodes, and energy efficiency is given priority to extend the running time of the network. The residual energy and various distance characteristics of sensor nodes are also considered to achieve the optimal CH selection based on energy efficiency. These distance features include the average distance between nodes within each cluster and their distance from the sink node. By taking these factors into account, the algorithm aims to choose CHs that optimize both energy consumption and communication distances, thereby improving overall energy efficiency and prolonging the network's lifespan.  
Let \(f_{1}\) represent a function in which we consider two kinds of distances, the convergence distance and the mean in-cluster distance of CHs. The main goal of CH selection is to minimize the value of the function.  
The representation of the objective function \(f_{1}\) is:  
\[f_{1}=\frac{\sum_{i=1}^{K}\frac{1}{M}(\sum_{j=1}^{M}D(s_{j},CH_{i})+D(CH_{i},BS ))}{\sum_{i=1}^{K}D(CH_{i},CH_{j})} \tag{20}\]  
where \(D(s_{j},CH_{i})\) stands for the distance from a node to the cluster head \(i\).  
The distance between the cluster head \(CH_{i}\) and the base station (\(BS\)) is denoted as \(D(CH_{i},BS)\). Similarly, the distance between the two cluster heads \(CH_{i}\) and \(CH_{j}\) is represented by \(D(CH_{i},CH_{j})\) in the equation. Additionally, there are two parameters representing the total numbers: the symbol \(K\) for the nodes and the symbol \(M\) for the cluster heads. If \(s_{j}\)is a normal node, the protocol selects the initial cluster of the network based on two parameters: the energy of the node and the distance within the cluster. For advanced nodes, these two parameters are the energy of the node and its distance from the base station (\(BS\)).  
The representation of the objective function \(f_{2}\) is:  
\[f_{2}=\left\{\begin{aligned} &\alpha\cdot\left(\frac{E_{\max}-E_{r(s_{j})}}{E_{ \max}-E_{\min}}\right)+\beta\cdot\left(\frac{D_{\max}-D(s_{j},CH_{i})}{D_{\max} -D_{\min}}\right),&\text{if}\ E_{r(s_{j})}>0,\text{Normal Node}\\ &\alpha\cdot\left(\frac{E_{\max}-E_{r(s_{j})}}{E_{\max}-E_{\min} }\right)+\beta\cdot\left(\frac{D_{\max}-D(s_{j},BS)}{D_{\max}-D_{\min}} \right),&\text{if}\ E_{r(s_{j})}>0,\text{Advanced Node}\\ \end{aligned}\right. \tag{21}\]  
where \(\alpha\) and \(\beta\) are the weights, with the constraint \(\alpha+\beta=1\). The node's remaining energy is denoted as \(E_{r}(s_{j})\), where \(j\) represents the index. \(E_{max}\) and \(E_{min}\) are the maximum and minimum values at the two ends of the cluster, respectively. \(D_{max}\) and \(D_{min}\) denote their respective maximum and minimum distances, with \(D(s_{j},BS)\) representing the distance from node \(j\) to the base station.  
Regarding the fitness function:  
\[f=\omega\times f_{1}+(1-\omega)\times f_{2}. \tag{22}\]  
\(w\) is the weight factor.
## 6 Conclusions  
An EFCLC has been proposed and investigated in this study to reduce the DCCB requirement in terms of fault interruption capability and breaking speed. The EFCLC mainly consists of the energy dissipation resistor and FCL reactor. These devices are plugged into the fault loop after fault detection to limit the fault current rising speed and fault current level. Thus, while the DCCB operates, the fault current level is restrained, and the energy stored in the smoothing reactor is also reduced. Consequently, the fault current extinguishment stage can be accelerated given the increased energy dissipation resistor and decreased energy storage. Furthermore, the FCL reactor can be bypassed to accelerate the decaying of the freewheeling current. Moreover, the AC feeding current is eliminated via the bidirectional parallel-connected thyristor, whose feasibility and effectiveness have been verified in  
\begin{table}
\begin{tabular}{c c c} \hline
**Term** & **Requirement of Fault Current Interruption** & **Fault Interruption Time** \\ \hline Without EFCLC & 12.4 kA & 34.8 ms \\ With EFCLC & 5.1 kA & 13.6 ms \\ Improvement & 41.1\% & 39.1\% \\ \hline \end{tabular}
\end{table}
Table 4: Effectiveness verification of enhance fault current limiting circuit.  
existing works. Thus, this study focused on the methods for limiting fault current caused by capacitor discharging in the FCLC design. The proposed EFCLC has been verified to limit fault current under an expected level with a proper parameter setting on components. However, the performance tradeoff and additional cost of the EFCLC must be considered in terms of the cooling system, surge arrester, and overvoltage protection. The development of the EFCLC in the multi-terminal HVDC system, the related fault protection scheme, and the FRT strategy will be investigated in future research.  
All the authors have contributed to this paper in different aspects. [PERSON] proposed the original concept and wrote the original draft. [PERSON] and [PERSON] acted as supervisor and gave suggestions on paper improvement. [PERSON], [PERSON] and [PERSON] helped with software simulation and methodology improvement. Last but not least, [PERSON] contributed to paper editing and reviewing.  
This work was supported in part by the National Key Research and Development Project (Grant No. 2018 YFB0904600).  
The authors declare no conflict of interest.
###### Abstract
The mission is designed to support three core areas: maritime surveillance (ice, wind, oil pollution and ship detection), disaster management (mitigation, warning, response and recovery), and ecosystem monitoring (forestry, agriculture, wetlands and coastal change monitoring) [16]. The new polarization configuration under circular-transmit-linear-receive (CTLR) offers advanced capabilities for land and water monitoring thanks to wider swath ranges (up to 500 km). Preliminary studies have shown that the CP configuration has great potential for applications related to sea ice classification [17; 18], wetlands mapping [19], and the mapping and monitoring of coastal zones [20] among others. This study will begin to evaluate simulated RCM data as a replacement for RADARSAT-2. This paper presents a multi-sensor approach proposed by CHS in the context of shoreline extraction and an evaluation of the rate of change on the Mackenzie River. The study begins by examining the use of HH and HV polarizations for land and water classification in RADARSAT-2 imagery, as well as RR (right circular transmit and right circular receive) and RH (right circular transmit and linear horizontal receive) polarizations in simulated RADARSAT Constellation Mission (RCM) under a CSA Data Utilization Application Plan (DUAP) project. Shoreline changes and shoals were then identified using RADARSAT-2 and medium resolution sensors such as Landsat and Sentinel-2. Due to the high percentage of cloud cover during the low water period (October-December), RADARSAT-2 was the main sensor used to obtain unobstructed views of the Mackenzie River, allowing the low water line to be extracted. A rate of change evaluation was conducted using historical Landsat data from the 1970s to today in conjunction with modern Sentinel-2 imagery. A change of approximately 20 m per year was observed in areas of maximum change. The results found indicate the need to regularly update charts located in dynamic areas.