esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 2 Methods
### OISL-OSU Service Slicing
#### 2.2.2 RS(255,223) Interleaving and Encoding
,\quad j=0,1,\ldots,7\] (3) where \(T_{r}(z)\) is defined as \[T_{r}(z)=\sum_{k=0}^{7}z^{2^{k}},\quad z\in GF(2^{8})\] (4) Then each symbol of the RS can be expressed as \[z_{0}l_{0}+z_{1}l_{1}+\ldots+z_{7}l_{7},\quad z_{i}\in\{0,1\}\] (5)  
The dual-base method is adopted, where 8 bits represent \(\{z_{0},z_{1},\ldots,z_{7}\}\), and the transmission sequence starts with \(z_{0}\). The two bases are converted using the formulas in Equation (6) and (7), respectively:  
\[[z_{0},\ldots,z_{7}]=[u_{7},\ldots,u_{0}] \begin{bmatrix}1&0&0&0&1&1&0&1\\ 1&1&1&0&1&1&1&1\\ 1&1&1&0&1&1&0&0\\ 1&0&0&0&0&1&1&0\\ 1&1&1&1&1&0&1&0\\ 1&0&0&1&1&0&0&1\\ 1&0&1&0&1&1&1&1\\ 0&1&1&1&1&0&1&1\end{bmatrix}  \tag{6}\]  
\[[u_{7},\ldots,u_{0}]=[z_{0},\ldots,z_{7}] \begin{bmatrix}1&1&0&0&0&1&0&1\\ 0&1&0&0&0&1&0\\ 0&0&1&0&1&1&1&0\\ 1&1&1&1&1&0&0&1\\ 1&1&1&1&0&0&0&0\\ 0&1&1&1&1&0&0&1\\ 1&0&1&0&1&1&0&0\\ 1&1&0&0&1&1&0&0\end{bmatrix}  \tag{7}\]To illustrate the conversion relationship between codes, for a given information symbol \(I\),  
\[[10111001] \begin{bmatrix}1&1&0&0&0&1&0&1\\ 0&1&0&0&0&0&1&0\\ 0&0&1&0&1&1&1&0\\ 1&1&1&1&1&1&0&1\\ 1&1&1&1&0&0&0&0\\ 0&1&1&1&1&0&0&1\\ 1&0&1&0&1&1&0&0\\ 1&1&0&0&1&1&0&0\end{bmatrix} =[u_{0},u_{1},\ldots,u_{7}]=[00101010]=I^{\prime} \tag{8}\]  
Given the check symbol \(C^{\prime}\), and \([u_{0},u_{1},\ldots,u_{7}]=[01011001]\),  
\[[01011001] \begin{bmatrix}1&0&0&0&1&1&0&1\\ 1&1&1&0&1&1&1\\ 1&1&1&0&1&1&0&0\\ 1&0&0&0&0&1&1\\ 1&0&0&1&1&0&0&1\\ 1&0&1&0&1&1&1\\ 0&1&1&1&0&1&1\end{bmatrix} =[z_{0},z_{1},\ldots,z_{7}]=[11101000]=C \tag{9}\]  
For the proposed RS(255,223) encoding, the FEC overhead efficiency is defined as follows [23]:  
\[\eta_{FEC}=\frac{R_{s}}{R_{c}}\big{(}1+\alpha\cdot\log_{2}(1/BER_{target}) \big{)} \tag{10}\]  
where \(R_{s}\) is the source rate, \(R_{c}\) is the code rate (\(R_{c}=k/n\), \(k\): information bits, \(n\): coding bits), and \(BER_{target}\) is the target bit error rate (\(10^{-6}\)\(\sim\)\(10^{-15}\)), and deep space communication requires \(BER_{target}\leq 10^{-12}\). \(\alpha\) is the channel degradation factor, which characterizes the channel dynamics caused by Doppler frequency shift or atmospheric turbulence, etc., and is defined as \(\alpha=\frac{\sigma_{ch}^{2}}{\sigma_{AWCN}^{2}}\).
## 1 Introduction
The polarimetric SAR (PolSAR) can provide a potential way to overcome wake detection in a high sea state. Using the polarimetric whitening filter (PWF) and polarimetric detection optimization filter (PDOF), we have proven that the polarimetric enhancement methods can obviously enhance the wake detection performance of small surface vehicles. However, the application of PolSAR data in marine monitoring is very limited [12]. Since conventional wake feature detection and enhancement techniques are implemented in the image domain, the final detection and enhancement performance are heavily constrained by the quality of the original SAR images [13]. In recent years, with the rapid development of artificial intelligence technology, methods based on DL have gradually been applied to wake detection. In 2019, [PERSON] and [PERSON] first proposed a convolutional neural network (CNN) for ship wake detection in TerraSAR-X images. However, this method was limited to estimating ship velocities and required the wake signatures to be spatially adjacent to the parent ship (i.e., the ship generating the wake) [14]. Later in 2021, [PERSON] et al. presented a dataset including more than 250 wake chips. Several object detectors based on CNNs were tested and assessed for their effectiveness in detecting wake in SAR images. The results demonstrated that wake detection is very different from ship detection [15]. In 2022, [PERSON] provided the datasetsof SAR images, including both simulated SAR images and real SAR images, and proved the feasibility of performing DL technology on the simulated datasets [16]. Utilizing DL algorithms, such as YOLO5, wake samples from SAR images are learned and trained automatically to extract features, enabling the automatic detection and tracking of ship wakes. Radon transforms are then used for analyzing and extracting ship motion information, and validation with real SAR wake data has shown improvements in wake detection [17]. However, deep learning methods face challenges, such as insufficient SAR wake samples and noise interference during the process of extracting motion information, particularly in the selection and extraction of peak points in the transformed domain [18]. Despite these advancements, existing methods remain limited by the insufficient suppression of sea clutter and dependency on post-imaging processing. The classification and introduction of spaceborne SAR wake enhancement research methods in recent years are shown in Table 1. Our work addresses these gaps by integrating wake enhancement directly into the SAR imaging pipeline, leveraging sub-aperture sequences to achieve real-time noise reduction. \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref No.
## 4 Results
### Invention Classification
#### 4.2.3 Fuel Management  
Patents in this category address the complex challenge of monitoring, controlling, and optimizing hydrogen fuel systems in aviation environments characterized by fluctuating demands, variable altitudes, and safety-critical operations. These inventions integrate sensors, control systems, and hybrid fuel cell configurations to manage tank pressure, thermal loads, water recovery, and reactant supply. Real-time fuel tracking, in-flight fuel characterization, and fuel-switching methods enable adaptive propulsion based on composition, emissions, or sustainability goals. Several systems recover or recycle unused hydrogen, boil-off gas, or residual heat to improve onboard energy use and minimize waste. The predictive and autonomous control of compressors, airflows, and oxidant mixing ensures stable fuel cell performance without reliance on batteries. Some of these patents overlap with safety enhancements through active hydrogen dilution, inert gas generation, and onboard air separation for combustion optimization or shutdown protocols. Collectively, these patents form a robust foundation for intelligent hydrogen fuel management in aircraft.  
The following are two examples of patents in this category that illustrate how fuel management innovations often link energy efficiency with environmental and operational control. [PERSON] et al. (2024) proposed a closed-loop water management system within a hydrogen steam-injected turbine engine ([PERSON] et al., 2024). It monitors and regulates water pressure, condensation, and evaporation in real time, supporting both fuel efficiency and propulsion system health. [PERSON] et al. (2024) expanded the scope by integrating fuel and air conditioning via a turbo-compressor and a dual-purpose heat exchanger that simultaneously warms hydrogen and regulates cabin air ([PERSON] et al., 2024). The system aligns with onboard thermal loads while maintaining consistent fuel flow. These specific patents show how advanced control systems and multi-functional hardware can harmonize propulsion, cabin comfort, and fuel utilization.
## 4 Discussion
### Limitations of the Study  
This study has several limitations that should be acknowledged and discussed in more depth. Firstly, the study was conducted with tennis practitioners, which may limit the generalizability of the findings to other sports or health conditions. The specific demands and skills required in tennis may influence the interaction between mental fatigue and physical performance differently compared to other sports. Future studies should aim to replicate the study with participants from various sports to examine the applicability of the results across different athletic populations.  
Secondly, due to limitations with the Polar Team software, only the RMSSD metric of heart rate variability (HRV) was reported in this study. HRV offers valuable insights into autonomic modulation, and exploring a wider range of HRV metrics (such as time domain, frequency domain, and non-linear metrics) would provide a more comprehensive understanding of the impact of HIIT with a cognitive task on autonomic regulation. Therefore, future studies should strive to include a broader set of HRV metrics to gain a more detailed assessment of the autonomic responses.  
Another limitation is the lack of cognitive performance data during the incongruent Stroop task. Monitoring cognitive performance would have provided valuable information regarding the impact of mental fatigue on cognitive functioning, the level of engagement of participants with the task, the temporal evolution of cognitive performance during the training session, and potential differences between groups (e.g., the Stroop task performed in the first session versus the second session). Incorporating cognitive performance measures in future studies would enhance our understanding of the cognitive effects of mental fatigue during HIIT sessions.  
Furthermore, the relatively small sample size of 32 tennis players is another potential limitation. It is important to consider whether this sample adequately represents the larger population of tennis players and if it limits the generalizability of the findings. A larger sample size would increase the statistical power and improve the robustness of the results, allowing for more reliable conclusions to be drawn. Future studies should aim to include larger and more diverse samples to strengthen the validity and generalizability of the findings.
###### Abstract  
The dual-active-bridge (DAB) converter has emerged as a promising topology for renewable energy applications and microgrid systems due to its high power density and bidirectional energy-transfer capability. Enhancing the overall efficiency and reliability of DAB converters requires the simultaneous realization of zero-voltage switching (ZVS) across all switches and the minimization of current stress over wide load and voltage ranges--two objectives that are often in conflict. Conventional modulation strategies with limited degrees of freedom fail to meet these dual goals effectively. To address this challenge, this paper introduces an enhanced integrated optimization strategy based on triple phase shift (EIOS-TPS). This approach formulates the power transmission requirement as an equality constraint and incorporates ZVS and mode boundary conditions as inequalities, resulting in a comprehensive optimization framework. Optimal phase-shift parameters are obtained using the Karush-Kuhn-Tucker (KKT) conditions. To mitigate zero-current switching (ZCS) under a light load and achieve full-range ZVS with reduced current stress, a modulation factor \(\lambda\) is introduced, enabling a globally optimized control trajectory. An experimental 1176 W prototype is developed to validate the proposed method, which achieves full-range ZVS while maintaining low current stress. In the low-power region, it improves efficiency by up to 2.2% in buck mode and 2.0% in boost mode compared with traditional control strategies, reaching a peak efficiency of 96.5%.  
dual-active-bridge (DAB) converter; zero-voltage switching (ZVS); minimize current stress (MCS); regulatory factor \(\lambda\); full load range +
Footnote †: journal: Applied sciences  
Article  
An Enhanced Integrated Optimization Strategy for Wide ZVS Operation and Reduced Current Stress Across the Full Load Range in DAB Converters  
[PERSON]  
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (Y.Z.); [PERSON]IL_ADDRESS] (D.Z.)
## 6 Application of the Method on Two Earthquakes in Greece
### Stations in Zakynthos Area  
On 25 October 2018, at 22:54 UTC, a Mw 6.8 earthquake occurred in the sea, south-west of Zakynthos Island (Lat 37.34\({}^{\circ}\), Lon 20.51\({}^{\circ}\); see Figure 4) [14]. It caused some damage to the island, but there were no casualties. There was large-scale monitoring of the event, and [PERSON] et al. [12] indicate that this event ruptured the Hellenic megathrust.  
Co-Seismic Displacements at the Three Closest Stations  
Data from three permanent GNSS stations, not far from the epicentre of the earthquake, were available to analyse the surface shifts caused by the earthquake. These are the PYRG (Pyrgos), STRF (Strofades), and ZAKY (Zakynthos) stations. Raw GNSS data sets were available 5 (or 6) days before and five days after the earthquake. Data processing, leading to the acquisition of observation station coordinates, was performed using the PPP method, described in Section 3.  
Time series analyses for all three stations was performed using the anharmonic spectral analysis method (Section 4). From the analysis of the time series during the earthquake (see series of Figure 5), it can be seen that in all cases, station offsets occurred during  
Figure 3: The systematic shift of EYPA station—coordinate differences expressed as the difference between the mean values before and after 18 January 2010. The error belt is from the PPP analysis.  
the earthquake. Therefore, the appropriate series before and after the earthquake were analysed for co-seismic displacement. In Figure 5, these lines are highlighted in red.
## 3 Methods and Materials
### Application Logic Unit Layer
#### 3.4.3 Robot Movement Control System  
The _Robot Movement Control System_ is responsible for controlling the movement of the robotic arm end-effector to reach the desired object position. The information on the latter is inherited by the _KCF_, which tracks it frame by frame.  
The arm movement is managed through the _FSM_ shown in Figure 6. The _FSM_ states are:  
* **IDLE**: Before receiving a user command, the system remains in the _IDLE_ state. It corresponds to the initial position of the robotic arm. When a new command is received--i.e., a new target position--the system transits to the _XY Kalman_ state.
* **XY Kalman**: In this state, the robotic arm is forced to align the target position (X,Y) with respect a predefined point \((K_{x},K_{y})\), called _Camera Features_. A square of side of \(25\times 25\) pixels is defined around the target position. When the difference between the (X,Y) coordinates of the object and \((K_{x},K_{y})\) coordinates of the fixed point is enclosed in the square, the end-effector is considered aligned. Thus, the system transits to the _APPROACHING Kalman_ state.
* **APPROACHING Kalman**: In this state, the robotic arm approaches the object by moving along a direction orthogonal to the camera plane axis. If the movement leads to a significant misalignment on the (X,Y) plane (greater than 35 pixels), the systems transits back to the _XY Kalman_ state. The information provided by the _Proximity Node_ is used to monitor the distance between the object and the end-effector. When the end-effector reaches a fixed distance, the system transits to the _STOP_ state.
* **STOP**: The _STOP_ state is used by the system to perform simple movements that depend on the task required. For instance, in the case of a pressure of a key, the manipulator touches the button. After the completion of the task, the system comes back to the _IDLE_ state.  
In the states _APPROACHING Kalman_ and _XY Kalman_, a _Kalman filter_ is used to make the object's position estimation more robust by disturbance such as vibrations induced by the arm movements.  
Figure 6: Finite State Machine for the control of the robotic arm end-effector movement.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 3 System Model and Mechanism
### Problem Description
Channel coding fundamentally entails computing parity bits from information bits using a corresponding generator matrix, thereby facilitating error recovery through receiver-side correlation. Viterbi decoding principles maximize the probability of information bits given the posterior distribution of received symbols. Hence, from both technical and semantic perspectives, an ideal communication system should possess the following characteristics:  
* \(S\) should be as short as possible, but cannot reach the entropy bound. The process from \(X\) to \(S\) is an encoding process that retains a certain amount of information redundancy. * The information redundancy inherent in \(S\) can be effectively exploited, allowing the design of algorithms to implement Equation (8). DeepSC [5] employs a Transformer-based architecture for joint semantic encoding and decoding. The encoder maps input text \(X\) to a fixed-length semantic vector \(S\in\mathbb{R}^{d}\), while the decoder reconstructs \(\hat{X}\) from noisy channel outputs. Its training objective is to minimize semantic loss (e.g., sentence similarity) rather than bit-level errors, enabling robustness in low-SNR regimes but limiting error-free transmission at high SNRs.
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods
### CoVe-Pol Model for Right Circular-Vertical (RV) Polarization
#### 2.2.2 Determination of the Coefficients for CoVe-Pol Model
Figure 7: Comparisons of: (a) RV-polarized NRCSs between RADARSAT Constellation Mission (RCM) simulated data and results computed by the new compact RV polarization GMF; and (b) SAR-retrieved wind speeds from CoVe-Pol model and data measured by buoys.
## 5 Security Analysis
### Checkups Station Impersonation Attack  
If an attacker tries to impersonate \(C_{j}\) by transmitting a request message \(\{ID_{i}M_{0^{\prime}}M_{1}\}\) to \(U_{i}\) and obtains message \(\{M_{4}M_{5^{\prime}},M_{6}\}\), where \(M_{0}=\mathrm{h}(R_{U}\oplus UA_{i}\oplus UC_{i})\), \(M_{1}=R_{U}\oplus UA_{i}\oplus E_{i}\), \(M_{4}=R_{R}\oplus R_{U}\oplus\mathrm{h}(X_{RCCj}\oplus X_{5})\), \(M_{5}=\mathrm{h}(SK\oplus M_{4})\), and \(M_{6}=R_{C}\oplus\mathrm{h}(ID_{i}\oplus X_{s})\). In order to compute \(M_{4}\) and \(M_{5^{\prime}}\) the attacker must find \(R_{U}\) and \(R_{R}\) using a shared key \(X_{RCCj^{\prime}}\), where \((R_{U}\oplus R_{R})=M_{4}\oplus h(X_{RCCj}\oplus X_{5})\). In communication, if \(C_{j}\) is illegal, it will get the wrong value, and at that time, RCRS will immediately recognize and terminate this illegal authentication phase. From the above description, we confirm that the proposed TCVK scheme can resist the checkups station impersonation attack.
# Implementation of a Demand-Side Management Solution for South Korea's Demand Response Program
## 2 Materials and Methods
### Development of Demand-Side Management Solution (DSMS)
 \tag{2}\]  
\(DRBP_{i.m}\) Demand response basic payment by monthly (KRW)  
\(ORC_{i.m}\) Obligation reduction capacity (MW)  
\(BP_{m}\) Basic price by monthly (KRW/kW)  
\(IBPC_{i.m}\) Initial basic penalty charge (KRW)  
\(TDRBP_{i}\) Total basic settlement money during the contract period (KRW)  
\(MRT\) Maximum reduction time (Max 60 h)  
\(DRD_{i.t}\) Dispatch reduction deficiency (kWh)  
\(RSO_{i,t}\) Reduction ordered by system operator (MWh)  
\(DR_{i,t}\) Dispatched reduction (kWh)  
\(DF_{i,t}\) Dispatch flag (1 for active, 0 for non-active)  
\(BPC_{i.m}\) Basic penalty charge by monthly (KRW)  
\(FDRBP_{i.m}\) Final demand response basic payment by monthly (KRW)  
To verify the DSMS functional test, the sampled data is divided into the following three categories: (1) large amount, (2) medium amount, and (3) small amount. Table 5 shows sampled data. Baseline load, peak, average power consumption, and CBL are calculated based on the customer's power usage which is monitored and recorded from an electricity smart meter. Figures 6-8 show the data on 26 June 2017. The large amount, N company, is a chemical company located in the southwestern area of South Korea. The power consumption pattern of N company is a typical factory type. Figure 6 illustrates the power consumption pattern baseline load and peak. The medium amount, a provincial government building, is located in the central area of South Korea. The CBL of this customer is 1938.56 kW at 13:00-14:00 on 26 June 2017, as shown in Figure 7. Lastly, the small amount, G store, is a retail store located in the southeastern area of South Korea. Figure 8 shows the RRMSE value as 9.815%, less than 30%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Amount** & **Large** & **Medium** & **Small** \\ \hline Name & N Company & Provincial Government & G store \\ \hline Type & Factory & Building & Retail \\ \hline Peak (kW) & 24,561 & 2002 & 57 \\ \hline Average power consumption (kW) & 22,306 & 1534 & 52 \\ \hline Contracted capacity (kW) & 10,000 & 1000 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The data for functional verification of demand-side management solution (DSMS). Figure 8: G store power RRMSE. Figure 6: N company power consumption and baseline load. Figure 7: Provincial government building CBL calculation.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Mann-Kendall Test and Sen's Slope Estimator  
The Mann-Kendall test is used to determine whether a time series has a monotonic upward or downward trend. It does not require that the data be normally distributed or linear. It does require that there is no autocorrelation. The null hypothesis for this test is that there is no monotonic trend in the series. The alternate hypothesis is that a trend exists. This trend can be positive, negative, or non-null. The Mann-Kendall test with n sets of a time series can be expressed as follows:  
\[S=\sum_{i=1}^{n-1}\left[\sum_{j=i+1}^{n}sign\big{(}x_{j}-x_{i}\big{)}\right] \tag{10}\]  
If \(S>0\), the later observations in the time series tend to be larger than those that appear earlier in the time series, and vice versa. It has been documented that when \(n\geq 8\), the statistic \(S\) is approximately distributed with the mean \(\text{E}(S)=0\). The variance statistic is given as:  
\[\text{Var}(S)=\frac{n(n-1)(2n+5)-\sum_{i=1}^{m}t_{i}(i-1)(2i+5)}{18} \tag{11}\]  
where \(t_{i}\) is the number of ties up to sample \(i\), the Mann-Kendall test statistics \(Z_{c}\) is defined as follows:  
\[Z_{c}=\left\{\begin{array}{c}\frac{S-1}{\sqrt{\text{Var}(S)}},\ S>0\\ 0,\ S=0\\ \frac{S+1}{\sqrt{\text{Var}(S)}},\ S<0\end{array}\right. \tag{12}\]  
\(Z_{c}\) follows a standard normal distribution here. When \(Z_{c}>0\), the original time series signifies an upward trend, and vice versa. Suppose we want to test the null hypothesis \(H_{0}\): There is no monotonic trend in a time series, versus the alternative hypothesis \(H_{1}\): There is a statistically significate downward or upward trend. Given a confidence level \(\alpha\), \(H_{0}\) would be rejected if \(|Z_{c}|>Z(1-\alpha/2)\), which means the time series would be supposed to experience a statistically significant trend, where \(Z(1-\alpha/2)\) is the corresponding value of \(p=\alpha/2\) following the standard normal distribution. On the contrary, \(H_{1}\) would be rejected if \(|Z_{c}|<Z(1-\alpha/2)\), which means there is no monotonic trend in the time series.  
In addition, the magnitude of a time series trend can be evaluated by a simple non-parametric procedure developed by [PERSON]. The trend is calculated by:  
\[\beta=\text{Median}\bigg{(}\frac{x_{j}-x_{i}}{j-i}\bigg{)},\ j>i \tag{13}\]  
where \(\beta\) is Sen's slope estimate. \(\beta>0\) indicates an upward trend in a time series, \(\beta<0\) significates a downward trend.