esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Grid Integration  
If the hydrogen storage is depleted, or if there is a rise in local load demand, power can be imported from the grid. Conversely, if excess power is generated by the PV panels or the hydrogen fuel cell, it can be exported to the grid. Meanwhile, focus on the voltage fluctuation and cost of the system during this part. In the proposed study, uncertainty in RESs generation and electrical load is typically modeled using probabilistic techniques such as Monte Carlo simulations and optimization methods. These approaches for variability and randomness are inherent in RESs and load patterns. Load fluctuations and weather conditions can significantly affect the simulated results. In the short term, this can lead to fluctuations in the available power. This factor may have an influence on operation planning and increase the requirement for backup power sources. Over the long term, this may affect the cost-related decisions and planning of system infrastructure. Thus, effectively managing the uncertainty associated with load profiles and weather conditions is essential for establishing a successful operation and EMS. This article elaborates on robust strategies for managing RESs and ensuring grid stability. The proposed solution to address these challenges is the incorporation of a hydrogen storage system to enable backup power supply for the renewable hybrid energy systems (RHES).  
Multi-objective development of an (MG) system considering renewable energy, hydrogen storage systems, and demand response involves optimizing multiple objectives simultaneously. These objectives typically include maximizing renewable energy utilization, minimizing energy costs, and enhancing grid reliability [41]. So, how can these optimization algorithms be applied in the context of hydrogen-based microgrids? Objective function of the cost is given in (13).  
\[Objective\ _{\mathit{fcn}}=[P_{\mathsf{n}},Cost\_effectiveness] \tag{13}\]  
To calculate the current price of all system expenses counting capital, maintenance, fuel consumption, trade-off power with the grid, and CO2 emissions consequence, the Net Present Cost (NP-Cost) methodology is utilized. The objective of curtailing the total system cost can be calculated by (14).  
\[\text{NP}-\text{Cost}\ =\ \text{NP}\ -PV_{\mathit{COST}}+\ \text{NP}\ -W_{ \mathit{COST}}+\ \text{NP}\ -FC_{\mathit{COST}}+\ \text{NP}-Carbon\_{\mathit{emission}}_{\mathit{COST}} \tag{14}\]  
The precise carbon emission is measured in (kg/kWh), and to calculate the NP - \(Carbon\_emission_{\mathit{COST}}\) where \(\lambda_{\mathit{emission}}\) ($/kg) is the emission penalty calculated in (15) and (16):  
\[\text{Carbon\_emission}=[(\text{kg}/\text{kWh})\times\text{Fuel\_Cost}] \tag{15}\]  
\[\text{NP}-Carbon\_emission_{\mathit{COST}}=\sum_{t=1}^{8760}[\text{Carbon}_{ \text{emission}}\times\lambda_{\mathit{emission}}]\times\frac{1}{CR\ (IR,\ R)} \tag{16}\]  
where NP-\(PV_{\mathit{COST}}\), NP-\(W_{\mathit{COST}}\), NP-\(FC_{\mathit{COST}}\), NP-\(Carbon\_emission_{\mathit{COST}}\) are the total net current cost of solar, wind, fuel cell, and carbon emission.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 5 Learning with Errors (LWE)  
An astonishing flexible foundation for lattice-based cryptographic protocols was introduced in 2005 by computer scientist [PERSON], the learning with errors (LWE) cryptosystem [49]. [PERSON] was awarded the Godel Prize in 2018 for his work, which was considered suitable for building secure cryptosystems, even for post-quantum cryptography.  
The difficulty of solving hard lattice problems is as hard as solving the LWE problem [49] and that fact makes the LWE cryptosystem and its variants a candidate for the quantum era. Learning with errors is characterized by security and efficiency; it is a high probabilistic algorithm and has emerged as one of the most cutting-edge and innovative research subjects in both cryptography and computer science. After the development of the LWE cryptosystem, many variations and optimizations were created, as it constitutes a cornerstone of modern cryptography.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 5 Conclusions  
Significant progress has been made in monitoring global carbon emissions from biomass burning over the past few decades. Nevertheless, current research continues to encounter certain challenges, including the absence of consistent monitoring techniques, constraints in spatial and temporal resolution, and shortcomings in satellite remote sensing technology. Despite these issues, the introduction of new algorithms, the optimization of models, and the strengthening of interdisciplinary collaboration across field like ecology, meteorology, atmospheric chemistry, and remote sensing, as well as international cooperation and data sharing, are expected to result in a significant improvement in the accuracy and reliability of monitoring carbon emissions from biomass burning. Future research should further integrate multi-source data, promote innovation in satellite remote sensing technology, and optimize global carbon emission inventories to achieve more accurate assessments and predictions of global carbon cycles and climate change. These advances will not only provide more robust data support for scientific research but will also serve as crucial reference points for the development of global GHG emission reduction policies, thereby facilitating global efforts to address climate change.  
Conceptualization, G.Q. and Y.S.; formal analysis, G.Q., Y.Y. and Z.Z.; investigation, G.Q., Y.Y. and W.W.; resources, Y.S.; writing--original draft preparation, G.Q.; writing--review and editing, G.Q. and Y.S.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Key Research and Development Program of China (2023 YFB3907404), the FY-3 Lot 03 Meteorological Satellite Engineering Ground Application System Ecological Monitoring and Assessment Application Project Phase I (ZQC-R22227), the National Natural Science Foundation of China (42071398), and the Academic Innovation Project of Harbin Normal University (Grant: HSDBSCX2021-104).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## Materials and Methods
### Analysis
#### 2.3.6 Trend Analysis  
To identify years showing a trend over the time series (2009-2019) for each variable, we employed the Mann Kendall (MK) test [78; 79] and the Pettitt test [80]. MK identifies whether time series values tend to increase or decrease over time, or whether the central value in the distribution of time series data changes over time. The Pettitt test shows when this tend occurred or confirms the stationarity of the historical series. This non-parametric test uses a version of the Mann-Whitney test, from which it is verified whether two samples \(X_{1}\),..., \(X_{t}\) and \(X_{t+1}\),..., \(X_{t}\) fit to the same population [80]. The \(U_{t,T}\) statistic counted the number of times that a member of the first sample is greater than the member of the second, according to Equation (6). \(U_{t,T}\) is calculated for the values of \(1<t<T\), and \(k(t)\) is given by Equation (7).  
\[U_{t,T}=U_{t-1,T}+\sum_{j=1}^{T}sgn\left(X_{i}-X_{j}\right), \tag{6}\]  
\[k(t)=MAX_{1\leq t\leq T}|U_{t,T}|, \tag{7}\]  
where \(sgn(x)=1\) for \(x>0\); \(sgn(x)=0\) for \(x=0\); and \(sgn(x)=-1\) for \(x<0\).  
The Pettitt test locates the point at which an abrupt change occurred in the historical series, the t where the maximum of \(k(t)\) occurs, resulting in Kcrit, or critical values (Equation (8)), in the mean of a temporal series. Its significance was calculated by Equation (9).  
\[K_{crit}=\pm\sqrt{\frac{-ln\left(\frac{p}{2}\right)(T^{3}+T^{2})}{6}}, \tag{8}\]  
\[p-value\cong 2 exp\Bigg{\{}\frac{-6k(t)^{2}}{(T^{3}+T^{2})}\Bigg{\}}, \tag{9}\]  
For the application of the Mann-Kendall and Pettitt tests, the \"trend\" and \"ManKendall\" packages were used in the R version 4.1.2 [67].
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 5 Conclusions  
This paper developed a wavelet-based approach to rapidly access the bridge health conditions using the measured signals of a moving vehicle. Vehicle responses are applied to wavelet transform and the wavelet energies are then calculated. Compared to the reference, the change in wavelet energy can be used to assess the bridge conditions. In addition, a damage index based on component wavelet energy is developed to approximately identify the damage location.  
In this work, the VBI system is first derived and the solution to the vehicle response is found to contain information about the bridge's dynamic parameters. The numerical simulation is then used to demonstrate the feasibility of the proposed approach. It is confirmed that the wavelet-based method using the signal discontinuity properties is limited by road roughness, while the proposed approach is robust to the road surface profile. In the end, the effects of vehicle velocity, vehicle mass, noise signal, and different  
Figure 17: Bridge damage identification with noise signal. (**a**) wavelet energy; (**b**) damage index.  
Figure 18: Energy change ratio versus different damage levels.  
damage severity on the proposed approach are investigated. It is found that the wavelet energy change rate increases with the damage degree, and this increase is roughly linear. For bridge damage with a 60% damage level, the change in wavelet energy is over 7%. The rate of change in wavelet energy can provide a reference for quantitative assessment of damage severity. In addition, the proposed method is found to be very robust to noisy signals. A relatively heavier vehicle is recommended with the proposed approach for bridge health monitoring, due to having a greater change in wavelet energy. The higher velocity can also improve the likelihood of successful damage detection but damage location identification may be less accurate. Therefore, both lower and higher velocities are recommended to scan the test bridge. The higher velocity is used to determine whether the damage happened. The lower velocity is used to localize the damage. The result shows that the proposed approach has a great potential application in bridge SHM using indirect measurements from a passing vehicle.  
Conceptualization, C.T. and H.Z.; methodology, C.T.; software, B.Y.; validation, C.T., H.Z. and B.Y.; formal analysis, C.T.; investigation, C.T.; resources, N.U.; data curation, B.Y.; writing--original draft preparation, C.T.; writing--review and editing, H.Z. and N.U.; visualization, N.U.; supervision, N.U.; project administration, B.Y.; funding acquisition, C.T. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52008162), Key Research, The Fellowship of China Postdoctoral Science Foundation (2020M680114) and Natural Science Foundation of Hunan Province (2022 JJ40079).  
Not applicable.  
The authors declare no conflict of interest.
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Modeling Determination
#### 2.3.2 Modeling of Spatial Characteristics
The spatial distance between two observed stations differs from the mathematically defined distance. The spatial resolution of the model can be accurate to 1 degree by multiplying 1 degree in geographic coordinates. Therefore, based on the linear variation model, this paper adopts the modified Euclidean distance with a scale factor (\(SF\)) as the ionospheric distance [68]; that is, the distance between any two observed stations can be expressed as:  
\[d_{nj}=\sqrt{\left(\varphi_{n}-\varphi_{j}\right)^{2}+\left[SF\cdot\left( \lambda_{n}-\lambda_{j}\right)\right]^{2}}, \tag{9}\]  
where (\(\varphi_{n}\lambda_{n}\)), and (\(\varphi_{j}\lambda_{j}\)) represent the longitude and latitude coordinates of the \(n\)th and the \(j\)th ionospheric observed station, respectively; \(SF\) is the scale factor of correlation distance, and is related to the region located and reflects the correlation distance between ionospheric parameters in a region to some extent. Therefore, the goal of spatial reconstruction is to determine the optimal value of \(SF\) so that the reconstruction value is as close as possible to the observation value. In the following subsection, we will discuss how to determine \(SF\) through cross-validation with RRMSE as the evaluation standard and then give the determined spatial reconstruction model.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 3 Results
### Prediction Model
#### 3.3.4 Homoscedasticity  
Figure 7 shows that the residual plot is rectangular in all the axle torque prediction models, and a number of points are distributed around the center. The plots of all the regression models indicate that the residuals are all unbiased and homoscedastic, because there was no pattern. According to the information presented in a previous study, if the residual has a mean value of 0 in the thin vertical strip and the spread of the residual is the same in the thin vertical strip, the standard deviation is the same all across the plot [47]. Since the results of this study are similar, the residual mean is 0, and the standard deviation can be said to be the same across the plot in all the regression models.  
Figure 6: P–P plots of the regression-normalized residuals of the expected cumulative probability versus actual cumulative probability for each regression Model (**A–H**): Model A = axle torque as a linear function of engine torque and engine speed; Model B = axle torque as a linear function of tiling depth; Model C = axle torque as a linear function of slip ratio and travel speed; Model D = axle torque as a linear function of engine torque, engine speed and tillage depth; Model E = axle torque as a linear function of engine torque, engine speed and travel speed; Model F = axle torque as a linear function of engine speed, tillage depth and travel speed; Model G = axle torque as a linear function of engine speed, tillage depth and travel speed; and Model H = axle torque as a linear function of engine torque, tillage depth and travel speed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** \\ \hline
**D.W** & 1.340 & 2.113 & 2.084 & 2.104 & 2.077 & 2.105 & 2.124 & 2.146 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of the Durbin–Watson test for each prediction regression model.
## 4 Results
### Anomaly Detection Performance
#### 4.3.2 DVS Camera Anomaly Detection  
The DVS camera anomaly detection model showed different characteristics compared to the RGB model, reflecting the unique properties of event-based vision. Figure 15 illustrates the anomaly scores for different operational states in Episode 5.  
Figure 14: RGB camera anomaly scores across different operational states in Episode 1. Note the clear separation between normal operation and RGB noise attack conditions.  
Figure 15: DVS camera anomaly scores across different operational states in Episode 5. Note the elevated scores during attack conditions without defenses.  
Statistical analysis of the DVS anomaly detection results revealed:
* **Normal Operation**: Mean anomaly score of 0.608308 with a standard deviation of 0.121466, establishing a baseline that reflects the inherent variability of event-based vision data.
* **DVS Noise Attacks**: Mean anomaly scores of 0.55 (with defense) and 0.75 (without defense). Interestingly, the score with defense is slightly lower than normal operation, while the score without defense is significantly higher.
* It is important to note that our DVS anomaly detection model is specifically designed to detect anomalies in DVS data only and does not process data from other sensors. Additionally, DVS cameras are currently used for research purposes only and are not integrated into the vehicle's driving decision model.  
These results highlight the importance of sensor-specific anomaly detection in autonomous vehicles. The DVS anomaly detection model successfully identifies abnormal patterns in DVS data, while other sensor-specific models (such as the RGB anomaly detection model) are responsible for detecting attacks on their respective sensors. Each detection system is specialized and optimized for its particular sensor type, emphasizing the need for a multi-layered security approach.
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 2 Related Work
### Three-Dimensional Tumor Reconstruction  
Recent advancements in the medical field have led to the development of 3D models, significantly enhancing patient treatment strategies [48; 49]. A prime example of this application is in liver resection surgeries, where 3D models are crucial in providing surgeons with a detailed understanding of liver anatomy [50]. Furthermore, the creation of 3D brain models using magnetic resonance imaging (MRI) has been investigated [51]. The predominant emphasis in current research revolves around the 3D reconstruction of lung tumors [52; 53]. [PERSON] et al. [21] proposed a method for segmenting lung cancer tumors and reconstructing 3D CT images. Their approach used snake optimization for lung segmentation, Gustafson and Kessel (GK) clustering [54] for tumor detection, and a statistical technique for 3D reconstruction. [PERSON] et al. [22], and [PERSON] et al. [55] employed a similar method for lung and tumor detection but with including a GAN-based approach for reconstructing 3D images. However, the accuracy of snake optimization heavily relies on the initial positioning of the contour [56]. If the initial contour placement is correct or deviates from the actual lung boundaries, it can lead to accurate segmentation results. Snake optimization may face challenges when segmenting complex lung structures, such as irregular shapes, nodules, or lesions, as these variations can make it difficult for the contour to adapt and accurately capture their boundaries. Similarly, GK clustering, like other clustering algorithms, is sensitive to the initial selection of cluster centroids. Choosing appropriate initial centroids is crucial to obtain accurate tumor detection. Poorly chosen centroids can cause suboptimal clustering outcomes. GK clustering may also struggle to handle cases where tumors overlap or exhibit heterogeneous characteristics, making it challenging to differentiate between different tumor regions and assign data points accurately to their respective clusters [52].
## 2 Materials and Methods
### Anttimicrobial Activity  
The in vitro antimicrobial activity of _E. planum_, _K. elatine_, and _L. flos-cuculi_ cell biomass extracts was assessed against _Staphylococcus aureus_ ATCC 25923, _S. epidermidis_ ATCC 35984, _Pseudomonas aeruginosa_ ATCC 27853, and _Candida albicans_ ATCC 10231. Each bacterium was inoculated into 4-5 mL of Mueller-Hinton broth (MHB; bacteria) or RPMI 1640 medium (RPMI; fungi) and incubated overnight at 36 \({}^{\circ}\)C until it reached approximately 1.5 \(\times\) 10\({}^{8}\) CFU/mL. A dilution of the inoculum suspension was prepared by adding 100 \(\upmu\)L of inoculum into 19.9 mL fresh MHB so that the final concentration of bacteria was 5 \(\times\) 10\({}^{6}\) CFU/mL. Similarly, before the antifungal assay, _C. albicans_ inoculum was diluted in RPMI to obtain 2.5 \(\times\) 10\({}^{6}\) CFU/mL.  
The antimicrobial activity was studied using the microbth dilution method by EUCAST (European Committee on Antimicrobial Susceptibility Testing) guidelines. The studied extracts were first diluted in water and sterilized using a Millex(r)-GV 0.22 \(\upmu\)m filter unit (Millipore, Carrigtwohill, Ireland). Twofold serial dilutions of the extracts were performed, yielding a concentration range starting at 100 mg/mL. Aliquots of 100 \(\upmu\)L of fresh MHB/RPMI and 90 \(\upmu\)L of each plant extract dilution were distributed in 96-well plates. Each well was added to 10 \(\upmu\)L of the bacteria/fungi suspension. Growth control and media sterility control were performed simultaneously. The plates were then incubated for 24 h at 36 \({}^{\circ}\)C. The minimum inhibitory concentration (MIC) was determined as the lowest concentration at which observable bacteria/fungi growth is inhibited. Gentamycin and Nystatin were used for antibacterial and antifungal positive control, respectively.  
Minimum bactericidal concentration (MBC) and minimum fungicidal concentration (MFC) assays were performed to determine the lowest concentration of plant extracts, which resulted in the death of 99.9% of the inoculum. The assay was performed after the MIC test and data collection. From each well of the 96-well plates, 10 \(\upmu\)L was taken and inoculated onto tryptic soy agar (bacteria) or Sabouraud dextrose agar (fungi) for further microbial observation. The plates were incubated at 36 \({}^{\circ}\)C overnight. All tests were duplicated, and the antimicrobial activity was represented as the mean values.
# Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device
## 3 Results and Discussion  
### Optimal Design Result  
During the optimization, the value of \(F_{r}\) for \(P\) converged after 21 iterations. The process of optimizing the ratio R using the SQP algorithm is shown in Figure 8, and the process of optimizing each design variable is shown in Figure 9. From Figure 8, it can be seen that the ratio R has an extremely large value in the first three iterations but converges stably thereafter. However, as shown in Figure 9, all parameters selected for the optimization converged after the 17 th iteration. Thus, the optimization process was interrupted after the 21 st iteration.  
The final derived optimal design variable values are listed in Table 3. In Table 3, it can be seen that the optimized lengths of Link1 and Link3 are increased compared to the initial design length, while the optimized length of Link2 is considerably decreased. In addition, the angle \(\alpha\) between Link2 and Link3 increased slightly, and the horizontal distance \(k\) between points \(A\) and \(C\) also increased. Each design variable satisfies all the previously defined inequality constraint Equations (9)-(11).  
Figure 8: Objective function with respect to sequential quadratic programming iteration number.  
Figure 7: Optimization flow chart.  
If the ratio \(R\) of the operating force \(F_{r}\) to the vertical force \(P\) is calculated using the initial design variable values, it is 0.683, and the ratio \(R\) calculated through the optimized design variables in Table 3 is about 0.082. It can be confirmed that the optimized operating force is reduced by about 87.1% from the initial operating force.  
In addition, the height of the initial scaffold (127.31 mm) is out of the range of the set constraints. This value is judged to be unsuitable for pulling out the pipe because it reduces the operating range of the device. On the other hand, the height of the optimized footrest is 150 mm, which has a sufficient operating range. It is sufficient to apply force with the foot without the risk of injury based on the body of an elderly woman. In addition, the overall length and height are 300 mm, which is suitable for removing pipes while moving between crops.