esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Frequency Regulation Model of DFIG  
### Power Output Control Principle of DFIG  
By controlling the pitch angle and the converters on both sides of rotor and grid, the mechanical power output of wind turbine can be regulated through various controllers [15; 16], as shown in (1):  
\[\left\{\begin{array}{l}P_{m}=\frac{1}{2}\rho_{air}\pi R^{2}v_{w}^{3}C_{p}\\ C_{p}=c_{1}(c_{2}z-c_{3}\beta-c_{4})e^{-c_{5}z}\\ z=\frac{1}{\lambda+c_{6}\beta}-\frac{c_{7}}{1+\beta^{3}}\\ \lambda=\frac{\omega_{M}R}{v_{w}}\end{array}\right. \tag{1}\]  
where \(P_{m}\) is the active power output of DFIG, which equals to the mechanical power captured by DFIG while the loss of gearbox and transmission system is ignored; \(\rho_{air}\) is the air density, whose value is 1.225 kg/m\({}^{3}\); \(R\) is the blade radius of DFIG; \(v_{w}\) is the wind speed; \(C_{p}\) is the blade power coefficient of DFIG, whose value is related to the pitch angle \(\beta\) and the tip-speed ratio \(\lambda\); \(c_{1}-c_{7}\) are relevant parameters of DFIG characteristics [4], whose value are 0.22, 116, 0.4, 5, 12.5, 0.08, and 0.035, respectively; \(\omega_{M}\) is the angular velocity of blade, whose value is proportional to the rotor speed \(\omega_{r}\), as shown in (2):  
\[n=\frac{\omega_{r}}{\omega_{M}} \tag{2}\]  
where \(n\) is the gearbox transmission ratio of DFIG, whose value is constant for a fixed WT model.  
From Equations (1) and (2), it can be derived that \(P_{m}\) changes with wind speed \(v_{w}\), rotor speed \(\omega_{r}\), and pitch angle \(\beta\), which can be expressed as \(P_{m}(v_{w},\omega_{r},\beta)\). Therefore, in the situation of fixed WS, the active power output of DFIG can be regulated through controlling the rotor speed and pitch angle. In this way, DFIG will be able to participate in system FR. As can be derived from (1), DFIG blade power coefficient \(C_{p}\) changes with \(\beta\) and \(\lambda\), and Figure 1 shows the change rule of \(C_{p}\). It can be seen that when pitch angle \(\beta=0^{\circ}\), there is an optimal tip-speed ratio \(\lambda_{opt}\) corresponding to the maximum blade power coefficient \(C_{p,\text{max}}\), which can be reached by regulating the rotor speed to be the optimal rotor speed \(\omega_{opt}\). Moreover, the DFIG power output can be reduced by increasing the pitch angle to a certain extent.
# Swarm Intelligence Techniques for Mobile Wireless Charging
## 6 Simulation Results and Analysis
### The Packet Delivery Ratio (PDR) and the Total Packets Delivered  
The PDR is defined as the ratio of the total transmitted and received packets. The total number of packets delivered at the sink node may be lower than, or equal to, those generated at the sensor-nodes in the network. Figure 11 shows the results of the PDR, showing the high performance of the IEEABR over other SI-based protocols. Initially, at a very minimal node density, the IEEABR performed optimally by delivering 100% of all the generated packets in the network to the sink node via the MCs, which double as mobile data collectors. This is followed by the BeeSensor, which achieved a 99.91% PDR, and then FFR, with a 99.8% PDR. As the node density gradually increased, the PDR for the IEABR still remained high compared to the other protocols. Even up to the highest node density of 4000 sensor-nodes, the IEEABR still maintained a high PDR of 99.96% and  
Figure 16: Average throughput.  
Figure 17: Network lifetime.  
is also rated as high compared to the BeeSensor and the FFR. Figure 12 shows the total packets delivered by each of the routing protocols (which shows the actual number of packets transmitted to the sink from different sensor-nodes). The IEEABR achieved the highest PDR compared to the BeeSensor and the FFR, even at a maximum node density. This achievement is significant and critical for surveillance/monitoring and target-tracking applications in large-scale wireless sensor network environments.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method  
### Overall Framework  
The framework of dual-generator generative adversarial networks (DGGANs) is shown in Figure 2. The method is mainly divided into two stages, namely the training and testing stages. The training phase starts after obtaining the video frames split by the video training set.  
In the first step of the process, the noise generator is trained to generate pseudo-abnormal video frames from the normal video frames. This is achieved by training the generator against the discriminator so that the generated frames cannot be distinguished as anomalies by the discriminator.  
Next, the parameters of the generator are fixed, and the noise module is combined with the generator module. The generator is then confronted with the discriminator again, and the parameters of the noise module are updated to obtain the noise generator. The reconstruction generator is then trained, with the abnormal pattern learned in advance through the pseudo-abnormal frames generated by the noise generator. The generated  
Figure 2: The overall pipeline of DGGAN. In the training stage, the noise generator and the reconstruction generator are trained independently. The former generates pseudo-abnormal frames using the training data, while the latter strives to improve its capacity for reconstructing normal frames by being trained on both the training data and pseudo-abnormal frames. During the testing phase, the testing data are fed into the trained reconstruction generator, and the resulting reconstruction score is used to classify if the data are abnormal or not.  
pseudo-abnormal frames and real frames are then input into the reconstruction generator to obtain reconstructed pseudo-abnormal frames and reconstructed real frames. The constraints function is added between the reconstructed frame and the real frame, so that the difference between the reconstructed pseudo-abnormal frame and the real frame is maximized, while the difference between the reconstructed real frame and the real frame is minimized.  
Finally, the trained reconstruction generator is applied to the video frames of the testing set. The test frames are input into the reconstruction generator to obtain the reconstructed frames. The reconstruction error between the reconstructed frames and the real frames is calculated and used to classify the video frames in sequence.
## 3 Experimental Setup
At the same time, the phase difference between the reference 200 MHz signal and the 200 MHz signal received from site B is also measured by another DPD to obtain the phase difference value \(\varphi_{\rm AC}\). In order to obtain the time-frequency difference value of stations B and C, the DPD is used to measure the phase difference between the synthesized 100 MHz signal and the 100 MHz signal received from station A at station B, and the phase difference value \(\varphi_{\rm BA}\) is obtained; at the same time, at site C, the DPD is used to measure the phase difference between the synthesized 200 MHz signal and the 200 MHz signal received from site A, and the phase difference value \(\varphi_{\rm CA}\) is obtained. Through the calculation described in the previous section, we can obtain the relative phase difference between sites A and B, and between sites A and C. So far, we have realized multiple-access time and frequency transfer based on the two-way phase comparison between the master site and the two slave sites.
# Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
## 4 Simulations
### Simulation Results and Analysis
#### 4.2.1 Motion Stability  
To verify the effectiveness of our method in maintaining the robot's motion stability, in this section, we simulate a scenario of an MMSPR moving straightly on a flat surface. We apply disturbances in the turning direction to simulate posture disturbances resulting from structural errors, non-uniform mass distribution, and collisions.  
The robot's desired speed in the turning direction is established as: \(v_{yd}\) = 0 m/s. The simulation duration is 5 s, and the results are shown in Figures 7 and 8.  
Figure 7 shows the speed changes in the turning direction of the MMSPR under different control methods. By adjusting the controller parameters, ARLPID reduces the overshoot by approximately 93% compared to PID with fixed parameters, costing a shorter  
Figure 8: Trajectory of different control methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Success Rate** \\ \hline ARLPID (ours) & 100\% \\ Torque\_RL & 0\% \\ RLPID & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test success rate of different algorithms.  
Figure 7: Control effects of different control methods.  
convergence time. It takes only 0.02 s to converge, compared to 0.03 s required by PID. RLPID performs the best in convergence speed, requiring just 0.01 s, which is faster than PID, and its overshoot is also lower than PID. However, there is a small fluctuation of 0.06% in the robot's speed after convergence, and it does not stabilize completely. This might be attributable to using the _tanh_ function when designing the reward function of reinforcement learning. The reward value tends to saturate and stabilize when the error is small. Hence, it cannot continue to optimize. This also results in a lower success rate of RLPID in the testing environment. In comparison, ARLPID introduces a switching control strategy to eliminate static errors, which can avert speed fluctuations prominently.  
Figure 8 displays the robot's trajectory during the simulation process. The maximum offset displacement of the robot reaches 0.00048 m under the control of PID. With RLPID control, the trajectory significantly fluctuates and cannot maintain stability, showing a maximum offset displacement of 0.00052 m. However, due to variable parameters and switching control strategy, the trajectory under ARLPID control changes smoothly, with the minimum offset displacement being merely 0.00041 m.  
The simulation results show that the ARLPID method, which introduces variable controller parameters and the switching control strategy, significantly enhances control performance in the problem of the spherical robot's motion stability. This improvement is especially notable compared to the traditional PID with fixed parameters and the RLPID method, which lacks the switching control strategy.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 5 Conclusions  
This paper established a RFI impact quantitative analysis model and derived the accurate RFI power, which is suitable for both GEO-to-LEO RFI and LEO-to-GEO RFI. Based on the theoretical analysis, accurate SINR of disturbed SAR systems was obtained, allowing the impact of RFI on SAR images to be quantitatively evaluated. The theoretical analysis was validated by experiments.  
Furthermore, the SINR of images were given while the GEO SAR and the LEO SAR were demonstrated to have various bandwidths and pulse widths. It was found that the impact is more serious when the RFI source has a smaller bandwidth, and the large ratio of the pulse width of the RFI source compared to the disturbed system was also shown to seriously effect SINR. This indicates that the high-quality images of LEO SARs and GEO SARs have conflicting requirements for pulse width, which should be chosen based on the demand from designers.  
The SINR, corresponding to different bistatic configurations, was also discussed. We conclude that when the target forms a specular scattering geometry with a GEO SAR and a LEO SAR, the bistatic scattering RFI will lead to poor image quality, while the effects can be neglected in other cases. In addition, the impact of LEO-to-GEO RFI is more serious than that of GEO-to-LEO RFI in the same configuration. Furthermore, we presented the probable specular scatter for several designs of the LEO SAR constellation. The results implied the necessity to suppress the RFI between the GEO SAR and LEO SAR system due to the growing number of LEO SAR constellations and suggested that the orbit design of LEO SARs should be optimized to avoid this case as much as possible.  
In this paper, we discussed the RFI between LEO SARs and GEO SARs. In the future, this theory could be extended to the RFI between any two SAR systems, including the LEO-to-LEO RFI and GEO-to-GEO RFI. In addition, RFI suppression from both signal processing and the optimization of orbital parameters should be considered.  
[PERSON] and [PERSON] conceived and designed the methods and wrote the paper; [PERSON] performed the simulation and analyzed the data. [PERSON] and [PERSON] commented about the paper and helped editing. [PERSON] gave many suggestions. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant Nos. 61960206009, 61971039, 61971037, Distinguished Young Scholars of Chongqing (Grant No. cstc2020 jcyj-jqX0008), National Ten-thousand Talents Program \"Young top talent\" (Grant No. W03070007), and the Special Fund for Research on National Major Research Instruments (NSFC Grant Nos. 61827901, 31727901).  
The authors declare no conflict of interest.
## 4 Leak Detection Technologies
### Software-Based Methods
#### 4.2.2 Pressure Point Analysis  
The pressure point analysis (PPA) method operates on the premise that a pipeline's pressure decreases when a leak develops. To detect a leak, pressure measurements can be employed to monitor the rate of pressure change, identify abnormally low-pressure levels, or compare current pressure readings to a running statistical analysis created from prior data [22]. Pressure measurements are conducted using sensors that are installed in the pipeline and can range from a single measurement point to multiple pressure transducers distributed along the pipeline [59]. A leak alarm is triggered if the pressure drop exceeds a pre-defined level. The PPA technique can be utilized for pressurized pipelines ranging from 75 to 1000 mm in diameter and is unaffected by the material of the pipes or properties of the liquid. Employing this method for pressure monitoring in a network offers advantages in terms of simplicity and cost. However, it cannot detect minor leaks because they do not significantly impact the pressure. Additionally, a pressure drop in a network can be caused by events other than leaks, leading to the possibility of false alarms with the PPA technique.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 5 Results and Discussion
### Routing Overhead  
Figure 9a represents the deviation in routing overhead's value in AOMR-LM, AOMDV, SRMP and AOMDV-ER. When the node mobility increases like (0, 2.5, 5, 7.5, 10 m/s), routing overhead of AOMDV increases from 20% to 70%, AOMR-LM varies from18% to 65%, SRMP increases from 25% to 80%, and our proposed AOMD-ER only increase from 15% to 54%. The AOMD-ER shows least routing overhead among all because it uses the path which has the strong links where route failure chances is negligible.  
Figure 9b expresses the variation in routing overhead for AOMR-LM, AOMDV, SRMP and AOMDV-ER. The packet size varies, routing overhead in AOMDV increases from 35% to 60%, in AOMR-LM varies from 30% to 54%, in SRMP increases from 40% to 67%, and our proposed AOMD-ER only increases from 29% to 50%. The AOMD-ER requires the least routing overhead among all as it required less discovery process along with verification of link stability.  
In Figure 9c, the simulation time is varied, the overhead of AOMDV increases from 40% to 49%, AOMR-LM varies from 34% to 46%, SRMP increases from 44% to 62%, and AOMD-ER from 34% to 45%. The comparison shows that the performance of AOMD-ER is better than other routing protocols.
# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 2 Our Model Architecture
### Retinex Image Enhancement  
Image enhancement technology is to restore the attributes of the image itself better. Preprocessing the image input to the model through image enhancement technology can improve the model's performance. In this paper, the vehicle image of the input model is first preprocessed by the Retinex [17] algorithm to preprocess the poor image.  
Retinex theory introduces the useful property of derivatives; larger derivatives are usually due to changes in reflectivity, while smaller derivatives may come from smooth lighting. Taking advantage of this property, Retinex decomposition can be performed by classifying image gradients into reflection components R and illumination components L. Retinex achieves better performance than previous competing methods in decomposition, low-light image enhancement, and color correction. The Retinex model is a color perception simulation of the human visual system. The basic assumption of Retinex theory is that the original image \(S\) is the product of the illumination image L and the reflectance image \(R\), which can be expressed in the following form. Its physical goal is to decompose the observed image \(S\in R_{n\times m}\) to its illuminance and reflectance components, which is shown in Formula (10).  
\[S\left(x,y\right)=R\left(x,y\right)\cdot L\left(x,y\right) \tag{10}\]  
The Retinex decomposition includes illumination \(L\) and reflectance estimation R. Accurate lighting estimates should not distort the structure while keeping the space smooth. At the same time, accurate reflectivity should reveal the details of the observed scene. The Retinex algorithm can accurately estimate the specific location of the noise in the data and remove the noise to restore the image better.
## 4 Evaluation Results and Discussion
Thus, the weights and activations which correspond to two XAC values being 128 each is the worst case for power consumption. Under this worst case, we evaluated the energy dissipation and frequency across the operating voltage as shown in Figure 22. The energy consumption and maximum frequency increase with the supply voltage. At 1.0 V and room temperature, the CIM macro takes 3.42 ns and consumes 20.8 pJ for two 128-input XAC operations, reaching an efficiency of 24.6 TOPS/W. Using the evaluation results in Figures 19 and 22, we compared the proposed CIM architecture with conventional digital computing in Figure 23. For two 128-input XAC operations at 1.0 V supply, it was estimated that the proposed CIM macro obtains at least 11.9\(\times\) higher energy efficiency, improves the delay by at least 38.8\(\times\), and thereby achieves at least 461\(\times\) better energy-delay product compared with the conventional 8-bit wide digital hardware, which reads 256 weights byte-by-byte; receives 256 activations, also byte-by-byte; and eventually accumulates two XAC values over 32 cycles. Figure 21: Comparison between CIM and conventional digital computing for a two-word RCA operation (\(\mathrm{V_{DD}=1.0\) V and T = 25 °C}). Figure 20: Maximum frequency and energy dissipation in the logic–arithmetic mode (T = 25 °C). In Table 1, the performance of this design has been summarized in comparison with prior GCDRAM-based CIM units. Compared to the previous analog CIM macros [54; 55; 56; 57], which support only the MAC or MAV operation with a certain amount of computation errors, the digital CIM architectures (this work and [61]) are configurable between different operating modes and supports data storage as well as versatile CIM operations without accuracy degradation even under PVT variation. This design supports the logic, arithmetic, and XAC computations, while the work in [61] offers the MAC computation based on an embedded DRAM look-up table. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & [54] & [55] & [56] & [57] & [61] & **This Work** \\ \hline Technology & 65 nm & 65 nm & 28 nm & 65 nm & 28 nm & 45 nm \\ \hline Cell structure & 3T1C & 4T2C & 2T1C & 2T1C & 3T & 3T \\ \hline Bit cell area (1) & 389 F2 & 256 F2 & 413 F2 & N/A & 185 F2 & 127 F2 \\ \hline Array size (bit) & 4 \(\times\) 64 \(\times\) 32 & 128 \(\times\) 128 & 144 \(\times\) 128 & 32-k & 256 \(\times\) 16 \(\times\) 10 & 2 \(\times\) 128 \(\times\) 128 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison with prior GCDRAM-based CIM units. Figure 23: Comparison between CIM and conventional digital computing for two 128-input XAC operations (V\({}_{\text{DD}}\) = 1.0 V and T = 25 \({}^{\circ}\)C). Figure 22: Maximum frequency and energy consumption in the convolution mode (T = 25 \({}^{\circ}\)C).
## 2 The Proposed Multi-Histogram Equalization Scheme  
### Global Histogram Equalization (GHE)  
For an input image \(X\) with \(L\) discrete gray levels, the probability density function \(p(x_{k})\) is defined as:  
\[p(x_{k})=\frac{n^{k}}{n}\quad\text{ for }\quad k=0,1,2,...,L-1 \tag{1}\]  
where \(x_{k}\) represents a specific intensity in the input image and \(n^{k}\) represents the number of times that the grey level \(x_{k}\) appears in the input image. Additionally, \(n\) is the total number of pixels contained in the input image \(X\). Based on the probability density function, the cumulative density function \(c(x_{k})\) is defined as:  
\[c(x_{k})=\sum_{j=0}^{k}p(x_{j})\quad\text{ for }\quad k=0,1,2,...,L-1 \tag{2}\]  
GHE is a scheme that enhances the input image \(X\) by using the cumulative density function as a transform function. Let us define a transform function \(f(x_{k})\) based on the cumulative density function as:  
Figure 1: The original grayscale images and their histograms.  
Figure 2: The output images and their histograms (global histogram equalization (GHE)).  
Figure 3: The output images and their histograms (brightness preserving bi-histogram equalization (BPBHE)).  
Figure 4: The output images and their histograms (multi-HE).  
To evaluate the performance of the proposed method, we chose a widely used metric, _i.e.,_ the absolute mean brightness error (AMBE). The AMBE was utilized to investigate whether the proposed method successfully maintains the input mean brightness. The AMBE is defined as Equation (20).  
\[AMBE=\left|X_{m}-Y_{m}\right| \tag{20}\]  
The variable \(X_{m}\) represents the mean brightness of the output image, and the variable \(Y_{m}\) represents the mean brightness of the original image. A good enhancement method that is able to preserve the mean brightness will give a small value of AMBE. The AMBE values performed by GHE, BPBHE and multi-HE are tabulated in Table 1. The average AMBE values taken from the test images are tabulated in Table 2.