esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validations
### EKF Estimation Results  
In Section 5.2, the results of SOC estimation for lithium-ion batteries using the EKF obtained by using the algorithm introduced in Section 4 are presented, as shown in Figure 7. The data where the SOC of the battery pack is greater than 5% is truncated and compared with the SOC obtained by the ampere-hour integration method. At the same time, the error between the two is presented. The SOC estimation results of the EKF method and the ampere-hour integration method have small errors. The error graph shows that the EKF method has a large error at the beginning of discharge, but the SOC gradually converges in the subsequent discharge estimation, and the error gradually decreases, remaining within 0.05. The RSME is 0.0049, and the MAE is 0.0034. As shown in Table 1, the RSME of the GA + EKF algorithm is 0.0063, and the MAE is 0.0051. In comparison, it proves the  
Figure 6: (**a**) Cumulative error of parameter identification. (**b**) End voltage estimation results and error.  
effectiveness of SSA and EKF in SOC estimation, and that the EKF can accurately track the changes in SOC.
# An Improved Image Filtering Algorithm for Mixed Noise
###### Abstract  
In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2-7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.  
s +
Footnote †: journal: Applied Sciences  
1
## 2 Model and Process
### Grid Independence Verification  
To validate the mesh independence of the numerical model, three different mesh resolutions were employed for numerical simulation. The influence of mesh resolution was evaluated by comparing the results obtained from different grids. The flow velocity near the photovoltaic component was selected as the variable of attention. Figure 3 presents the simulation results of the three meshes. It can be observed that the impact of the mesh on the flow velocity is not significant when the grid density exceeds 1.3 million. This indicates that the mesh has met the convergence criteria. A grid resolution of 1.3 million was chosen for the numerical simulation, which meets the requirements for computational accuracy.  
Figure 1: Flowchart of the mathematical model.
# Microwave Photonic Signal Processing and Sensing Based on Optical Filtering
## 3 Optical Filtering for Single Passband Microwave Photonic Filter
A chalcogenide spiral waveguide with a total length of 11.7 cm has been utilized to obtain a microwave photonic filter with a continuous tuning range of 30 GHz without RF passband degradation [61]. The phase-modulation to intensity-modulation conversion approach [62; 63; 64; 65; 66; 67; 68; 69] is effective and can work well to achieve a single passband RF response, provided the optical notch filter does not introduce additional phase outside its notch region. Unfortunately, this is not usually the case, and in practice the extra non-zero phase introduced by the optical filter outside its notch bandwidth prevents full cancellation of the anti-phase modulation sidebands, with the consequent result that the extinction ratio of the RF filter and its shape factor are significantly degraded. To address the phase issue, use of an additional liquid-crystal-on-silicon device has been presented to achieve an out-of-band rejection ratio of above 20 dB [68]. However, this approach has limitations for integration. An integrated-based technique employing a cascaded pair of microring resonators on SOI platform has been presented to achieve a tunable single passband microwave photonic filter with improved shape factor and extinction ratio [69]. It is based on two optical notch filters that have slightly different stopbands and phase shifts, and are located on each side of the optical carrier. The structure could compensate the inherent phase of the optical filter and achieve a tunable bandwidth of the single passband in the RF filter by controlling the relative offset between the two optical filter bandwidths. Figure 2a shows the topology of the single passband microwave photonic filter, which enables wideband tunability and adjustable bandwidth. The key building block comprised a cascaded pair of all-pass ring resonators, where one was implemented with a resonance splitting effect and one was without the effect, each of which consisted of a straight bus waveguide and racetrack waveguides have a dimension of 220 nm by 450 nm as displayed in Figure 2b. The novel concept demonstrated a significantly reduced single passband bandwidth and shape factor with an improved out-of-band suppression ratio of around 20 dB, as well as a wide and shape invariant filter single passband that is continuously tunable from 6 GHz to 17 GHz, as shown in Figure 3.
## 4 Multi-Hazard Events  
### Earthquake Loads  
Once the probable earthquake events are sampled for the project lifetime, the next task is to generate the ground motion time history data to be compatible with sampled earthquake intensities. In this regard, the Uniform Hazard Spectrum (UHS) provided in the AIJ (2019) for Nagoya City is used, as shown in Figure 10.  
Figure 9: Flowchart of probable earthquake and wind events generation in the project lifetime.  
The recommended UHS is the acceleration response spectrum of 5% damping in the engineering bedrock (\(V_{s}\) = 292 m/s) level, with the probability of exceedance of 39%, 10%, 5%, and 2% over the 50 years. In case the intensity (PGA) of the probable earthquake is not matching the intensity of the recommended UHSs, the interpolation technique is used to generate the appropriate response spectra. Figure 11 illustrates the steps required to generate spectrum-compatible ground motions using the Fourier and phase spectra, following the algorithm developed by one of the authors, STERA_WAVE [36]. For the phase spectrum, corresponding to the PGA intensity of the estimated probable earthquake in the project lifetime, the historical earthquake has been carefully selected from Table 5, which has a similar PGA. The sequence of the selected phase spectrum to generate the sampled earthquake ground motion in each case is presented in Figure 12. In the case that the probably estimated intensity is smaller than 250 gal, the uniform random phase angle is adopted, and the ground motion is generated by multiplying the envelope function proposed by [PERSON] et al. [37].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Phase Spectrum** & **Component** & **Date** & **Station** & **Recorded PGA (gal)** \\ \hline
1 & Random Phase & - & - & - & \textgreater{}250.000 \\
2 & Kumamoto & NS & 3 January 2019 & Wadamachi Eda & 261.697 \\
3 & Tottori & NS & 6 October 2000 & Yonago City & 280.233 \\
4 & Tottori & EW & 21 October 2016 & Kurayoshi City & 285.811 \\
5 & Toho-Oki & NS & 4 October 1994 & Honamachi & 454.975 \\
6 & Noto Hanto & NS & 25 March 2007 & Wajima City & 463.544 \\
7 & Miyagi & EW & 26 May 2003 & Izumimachi & 655.161 \\
8 & Iburi & NS & 6 September 2018 & Atsuma Kananuma & 662.241 \\
9 & Osaka & NS & 18 June 2018 & Hirakata City & 690.169 \\
10 & Tokachi-Oki & NS & 26 September 2003 & Makuhetsu-cho & 754.200 \\
11 & Niigata-Chuetsu & NS & 23 October 2004 & Ojiya City Castle & 779.244 \\
12 & Kushiro Oki & NS & 15 January 1993 & Kushiro & 814.906 \\
13 & Kobe & NS & 17 January 1995 & JMA & 818.066 \\
14 & Tohoku & EW & 11 March 2011 & Ofunato & 944.072 \\ \hline \end{tabular}
\end{table}
Table 5: List of the earthquake used for a phase spectrum to scale the probable earthquake events.  
Figure 10: Uniform hazard spectrum provided in AIJ (2019) for Nagoya city, Aichi, Japan.  
Figure 11: Flowchart of the STERA_Wave algorithm to generate spectrum-compatible earthquakes.
# Efficient Edge Detection Method for Focused Images
## 3 Edge Detection
### k-Means Algorithm  
The k-Means algorithm [36] is the Machine Learning method that is used for partitioning \(n\) observations into \(k\) clusters in such a way that each observation belongs to the cluster with the nearest mean. This method is also applied in image segmentation. An image is segmented into \(k\) segments assuring the smallest Mean Square Error (MSE). The examples of k-Means segmentation are presented in Figure 3 for different numbers of segments \(k\) together with the edges between segments.  
One can observe from Figure 3 that the k-Means algorithm can detect edges of any smoothness, since it is based on image segmentation and not on edge detection. However, to get satisfactory results, one first has to fix the number of segments first. However, direct application of the k-Means algorithm for edge detection in focused images is not efficient since the same drawback is presented as in the case of e.g., [PERSON]'s algorithm. Indeed, the more segments we fix, the better detection of background edges we obtain, but the more noise in the foreground appears as well (see image Figure 3d).  
Figure 1: The example of a focused image.  
Figure 2: Edge detection by the Canny method for the low and high thresholds that are equal to: (**a**) 50 and 100 (**b**) 150 and 200, respectively.
## 4 Results and Discussions  
The performance of the hybrid filter in MG based power system is analyzed in MATLAB/SIMULINK tool using the AMDWPT technique. The BESS is connected with WT and PV to a supply system at POI and is charged/discharged through a BDC. The line voltages (\(V_{LL}\)) are considered to be 440 V, the non-linear RL loads are considered to be 50 \(\Omega\) and 0.05 mH. The interfacing inductor (\(L_{f}\)) is considered to be 4 Mh, it is calculated as per the Equation (A11) shown in Appendix A.6. The rating of solar PV (\(P_{PV}\)) is taken for 5 kW. The solar irradiations current is considered to be 5.98 A. The voltage across the DC link bus (\(V_{dc}\)) is considered to be 1000 V and the DC link capacitor (\(C_{d}\)) is taken at 200 \(\upmu\)F. The resistance, inductance, and capacitance value of BESS are given as1 Mh, 10k\(\Omega\), and 4560 F respectively. The voltage (\(V_{bat}\)) and current (\(I_{bat}\)) of battery are given as 240V, 56 Ah. The wind speed (\(V_{w}\)) is considered around 11 m/s, mechanical power (\(P_{m}\)) developed in the WT is taken around 15 kW, and the turbine radius (\(R\)) is taken at 5.5 m. The performance of the system is investigated in different cases. Simulation outputs are acquired in various instances, such as wind variation, solar irradiation,and load perturbations with an altered state of battery charge (SOC). The MG system employs solar PV, WT, and BESS. Therefore, the operation of the hybrid filter depends upon the individual characteristic components of the MG. In this work, three different cases have been analyzed to test the efficiency of the proposed hybrid filter with AMDWPT, which is presented in the subsequent subsections.
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
###### Abstract  
The growing population of space debris poses significant risks to operational satellites and future space missions, necessitating innovative and efficient tracking solutions. Ground-based radar for space surveillance has been a central area of research since the early Space Age, with recent advancements emphasizing the use of bistatic radar systems that incorporate sensitive radio telescopes as receivers. This approach offers a cost-effective and scalable solution for monitoring space debris. Preliminary observations demonstrated the viability of employing radio telescopes in bistatic configurations for effective debris tracking. This review provides a comprehensive analysis of experiments utilizing radio telescopes as bistatic receivers, highlighting key advancements, challenges, and potential applications in space surveillance systems. By detailing the progress in this field, this study underscores the critical role of bistatic radar systems in mitigating the growing threat of space debris.  
radio telescope; bistatic radar; debris tracking; space surveillance and tracking; passive radar 2025252900
## 3 Description of the Firework Routing Protocol
### Mobility Tolerant Firework Routing (MTFR)  
#### 3.2.1 Basic Principle  
In our method, MTFR [2], potential-based routing and limited broadcast are combined. Packets that the source node sends are started as being transmitted along the steepest temperature gradient path in the beginning and middle and then are broadcast to the neighbors, which are closer to the destination. Hence, the robustness against node mobility is achieved with a small transmission workload. We introduce two additional parameters, firework threshold and firework hop limit, to react faster in situations when the destination node becomes unreachable. We assume that if the destination node moves away from its last position, it will be most likely somewhere near its last location. Since the temperature can be regarded as a relative distance of a node to the destination, the firework threshold is used to limit the broadcast to only those nodes that are in the vicinity of the destination's previous location. If the temperature of a node lies below the firework threshold, the node only forwards all data packets it receives to the neighboring node with the highest temperature, just like in regular PBR. On the other hand, if the temperature is above the threshold, it broadcasts the packets to all of its neighbors. In order to limit the unnecessary overhead, broadcasts are only replicated up to the firework hop limit.  
Figure 2 shows the basic procedure in MTFR when a disconnection of the destination occurs. Let us assume that the destination node moves in the direction of the arrow and becomes unreachable over the current path, due to leaving the wireless transmission range of node \(i\) (Figure 2(a)). Instead of involving all nodes in the network in broadcasting, only those with temperatures above the firework threshold (dashed line) are included (Figure 2(b)). Finally, a new route to the destination node via node \(j\) is established, and the route with reachability is sustained (Figure 2(c)). Although we only talked so far about the last hop to the destination node being lost, our method can equally handle the situation if an intermediate node leaves the current path. In those cases, the data replication of MTFR will also contribute to higher packet reachability for intermediate nodes to forward data packets to the next hop. As stated above, our MTFR can provide a higher reachability without recalculation of potentials on all nodes to cope with sudden changes in network topology.
## 1 Introduction
However, the MIMU installation angles were not considered in the motion constraint model and the accuracy needed to be improved. [PERSON] used a high-precision INS/odometer combination method to solve the problem of train positioning in tunnels, and achieved amazing positioning accuracy by modeling and analyzing the influence of turning on the odometer measurement [9]. However, the system was expensive and [PERSON] did not attempt to achieve the accuracy that low-precision MEMS inertial devices can attain. In the field of track inspection, where millimeter-level accuracy is required, [PERSON] designed a track trolley based on high-precision laser INS/GNSS integrated navigation [18], and used motion constraints to improve the accuracy of the system during GNSS signal outage. Since the installation angles were not considered, the improvement of system accuracy was limited. [PERSON] considered the installation angles in a motion constraint model for the above system [19], but did not mention how to obtain them. [PERSON] used the method of similar rotation to obtain these installation angles through data post-processing [20], but the installation angles were not used for motion constraints. Obtaining accurate MIMU installation angles is key to improving the effect of motion constraints [10; 21]. In this paper, our first aim was to improve the traditional motion constraint method on the basis of train motion analysis without adding additional sensors, which constrain the lateral and up speeds of the train, and thus to improve the motion constraint suppression effect on the error associated with a pure inertial navigation system (P-INS). Our second aim was to use the speed measured by the odometer installed on the train wheel to constrain the forward speed of the train on the basis of the motion constraint, so as to provide the constraint on the three-dimensional speed of the train. However, accurate estimation of the installation angles of the MIMU and the influence of the train's maneuvering turn on the three-dimensional motion constraints were key challenges. Our main contributions are summarized as follows:1. A method for high-speed train positioning in tunnels based on integrated MIMU /odometer/MC navigation is proposed;
2. A motion constraint model is established with the installation angles of MIMU, and the influence of turning on motion constraint is analyzed;
3. The installation angles of MIMU are estimated online when the GNSS signal is good and the use condition of motion constraints is met;
4. The estimated installation angles and the forward speed measured by the odometer are applied to suppress the INS error in tunnel navigation. Based on this method, high-speed train navigation tests were carried out in both a real tunnel environment and a case study using an artificially disconnected GNSS signal. The remainder of this paper is organized as follows: Section 2 introduces the motion constraints and the odometer, Section 3 describes the filter model and algorithm flow, Section 4 describes the experimental system and equipment, Section 5 discusses the results of the test, and the conclusions are given in Section 6.
# Accumulation of Vitamin C in Yeast under Pulsed Electric Field (PEF) Conditions
## 2 Materials and Methods
### Optimizing the Pulsed Electric Field Process for Vitamin C Accumulation in Yeast  
The process parameters were optimized according to the OFAT method (one-factor-at-a-time). The optimized parameters were selected on the basis of the previous studies by [PERSON] and [PERSON] [17]. The following parameters were optimized: voltage (300-2000 V), pulse width (10-100 \(\upmu\)s), treatment time (5-20 min), and cultivation time after which PEF was applied (8-24 h). The cultures of _S. cerevisiae_ were grown in flasks under continuous agitation and treated with PEF (pulse frequency of 1 Hz) using an ECM 830 unipolar square wave generator (BTX Harvard Apparatus, Holliston, MA, USA). The description of a treatment chamber and operation of the ECM 830 electroparo is presented in [PERSON] et al. [11]. Briefly, a 100 mL culture was placed in a PEF treatment chamber consisting of a glass beaker (300 mL) and four parallel stainless-steel electrodes mounted on a removable lid. The electrodes were spaced 5.1 mm apart, and each had an area of 4 cm\({}^{2}\). During the delivery of the pulses, the solution was stirred with a rotating magnet (100 rpm) to prevent cell sedimentation. After covering the beaker with a lid, the electrodes dipped approximately 7.4 mm into the solution.  
The initial pulsed electric field parameters were: voltage 1500 V, pulse width 10 \(\upmu\)m, treatment time 10 min, and cells were treated after 20 h of cultivation. Then, at each stage, one of the parameters was optimized according to a scheme presented in Figure 1. After each stage of the experiment, the cells were centrifuged, washed several times with deionized water, and then prepared for the determination of vitamin C by HPLC (as described in Section 2.4).  
Figure 1: Scheme of optimization of the PEF parameters.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 7 Analysis from Recorded IQ Signal and Discussion  
It is difficult to calculate the exact difference from the spectrum display of the SDR# because the CW beacon keeps keying and the signal changes its frequency every second with a Doppler shift. In addition, the distance from the ground station to the satellite changes every second, so the free space path loss also changes. Therefore, in this chapter the recorded IQ signal will be analyzed considering the change in distance.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Advantages of C-RAN
#### 2.5.4 Cost Reduction  
MNOs deploy more base stations to process the ever-increasing amount of mobile traffic. This results in higher network power consumption, eventually resulting in higher cost. The centralization of the BBU pool enables the efficient utilization of BBUs and reduces the CAPEX for deploying base stations in different areas and the OPEX for the operation, maintenance, and upgrade of the base stations. The electricity cost required for a traditional RAN can also be reduced by deploying a C-RAN because BBUs are absent in the cell site. Moreover, a few BBUs can be switched off during a period of low traffic load, which would result in power savings. A C-RAN also reduces the requirement of cooling resources, which accounts for 46% of the power consumption of cell sites in the traditional RAN [4].