esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion of Results  
One potential reason for the difference between the analytical MBE approach and numerical simulation estimates could be the simplifications and assumptions made in the analytical model. While the analytical MBE approach relies on simplified equations and assumptions to estimate hydrogen capacity, the numerical simulation captures more detailed reservoir and fluid behavior and complexities. These simplifications in the analytical model may not fully capture the dynamic interactions and complexities present in the reservoir, leading to discrepancies between the estimates obtained from the two methods.  
Additionally, uncertainties in input parameters, such as formation properties in estimating the initial gas in place or the initial conditions of the reservoir, can contribute to differences in the estimates obtained from the analytical MBE approach and numerical simulation. Variations in rock properties, fluid behavior, and reservoir heterogeneities may not be fully accounted for in the analytical model, leading to discrepancies in the estimated hydrogen capacity.  
Overall, the differences between the estimates obtained from the analytical MBE approach and numerical simulation highlight the importance of considering the limitations and uncertainties associated with each method. Careful validation and sensitivity analysis are essential to understand the reliability and accuracy of the estimates obtained from both approaches. With that being said, the comparison between the analytical approach and numerical simulation results showcased the effectiveness of the material balance equation in estimating hydrogen storage capacity in depleted gas reservoirs. The analytical method provided a practical and straightforward solution, requiring minimal input data and simplified assumptions.
###### Abstract
* We conducted comparative analyses of various algorithms under different link failure rates. Experimental results demonstrate the superior robustness of the MARL-JR algorithm when handling emergent network conditions. The remainder of this paper is organized as follows: In Section 2, we describe the system model and Multi-Agent Reinforcement Learning algorithm. In Section 3, we provide details on our MARL-JR method. We discuss the experimental results in Section 4. Finally, Section 5 concludes our work.
## 5 Conclusions  
This study performed crack detection on aluminum specimens with foreign substances on the surface using the LS-based induction thermography technique. Foreign substances on the surface were removed using the Gaussian-filtering-based FFT algorithm. When setting the Gaussian standard deviation to remove foreign substances, the shape of cracks may be removed. Therefore, the value of the Gaussian standard deviation was set to 20 to preserve the shape of the cracks. Virtual big data was secured through augmentation, and transfer learning was performed after applying ROI labeling using the temporal interpolator algorithm. After that, crack detection for each moving speed was performed using YOLOv4 deep learning, and the highest detectability was confirmed at 7 mm/s. If the moving speed of the inspection object is too fast or slow, the shape of the defect may not be clear in the thermal image due to low thermal contrast. Therefore, it is very important to set an appropriate speed when inspecting a moving object using induction thermography.  
The limitations of this study are that the detectability significantly decreases when a certain speed range is exceeded and only aluminum materials were considered. In addition, the environmental conditions of the lab are very good compared to the actual field. The objects inspected in the actual industrial field have rough surfaces and poor environmental conditions. The experiments conducted in this study are on a lab scale, which is different from the actual industrial field. Therefore, future research is essential to improve the detection speed, apply various materials, and consider the environment. In this environment, future research will be conducted to optimize algorithms that can improve detectability.  
Conceptualization, S.-J.L. and W.-T.K.; methodology, S.-J.L. and H.-K.S.; software, S.-J.L.; validation, S.-J.L. and [PERSON]-[PERSON]; formal analysis, H.-K.S.; investigation, S.-J.L.; resources, S.-J.L.; data curation, S.-J.L.; writing--original draft preparation, S.-J.L.; writing--review and editing, W.-T.K. and H.-K.S.; visualization, H.-K.S.; supervision, W.-T.K. and H.-K.S.; project administration, W.-T.K.; funding acquisition, W.-T.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National University Development Project of the Ministry of Education, Korea in 2024 and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2024-00451446).  
Figure 10: Automatic crack detection results for each moving speed.
# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 4 Dataset Details and Performance Metrics
### Parameters  
When employing neural networks, it is essential to establish several parameters, known as hyperparameters, which govern the learning process. Proper configuration of these hyperparameters is crucial for developing a neural network that is both efficient and precise during training. The key hyperparameters include:  
* Learning rate: This parameter dictates the rate at which weights are adjusted throughout the learning process. A smaller learning rate results in a more precise but slower algorithm, whereas a larger value accelerates the process at the cost of accuracy. Thus, it is vital to identify a balanced value.
* Batch size: This indicates the number of samples processed before the network's weights are updated.
* Epoch: This term refers to the total number of times the model will process the entire dataset during training.
* Optimization algorithm: This specifies the learning algorithm employed to train the neural model. The Adam algorithm, commonly used for training LSTMs, merges the benefits of RMSProp--akin to gradient descent--with momentum [69]. In this study, the Adam algorithm has been utilized in all ANNs.  
The selection of these hyperparameter values is influenced by computational constraints, aiming to avoid both overfitting and underfitting, two phenomena that can compromise the predictive power of the neural model.  
Extensive research has been conducted on methods for selecting hyperparameters [70]. Machine learning models may be estimated through an iterative trial-and-error approach. This methodology is often applied in detailed experiments tailored to the specific characteristics of the data. However, techniques like grid search and random search--designed for automatic optimization--quickly lose effectiveness as the number of hyperparameters increases, leading to substantial time and computational resource costs during model training.  
[PERSON] [71] provided recommendations regarding the most suitable parameter choices for making predictions with neural networks, along with a comparison of various options. The final selection will also be constrained by computational limitations while striving to prevent overfitting and underfitting, which pose risks to the predictive capabilities of the neural model. Table 2 outlines the primary hyperparameters employed in this study.  
The significance of parameter selection is further emphasized by [72], who examined the estimation of parameters under the condition that they are independent of one another. This independence is feasible only when the number of parameters is limited; otherwise, it incurs substantial computational costs. [PERSON]'s research [73] underscores the potential for minimizing errors through appropriate hyperparameter selection, exploring various strategies to enhance network performance.
## 2 Preliminaries
### Secure Comparison Protocol
#### 2.2.3 Secure Comparison Protocol  
[PERSON] et al. proposed three enhanced secure comparison protocols in [17]. Here, we recall the most efficient one that uses the packing method defined by Equation (6). There are three participants in this protocol. [PERSON] and [PERSON] who have \(\mu\)-bit integers \(a\) and \(b\), respectively, compare \(a\) and \(b\) without revealing the data through a server. The server obtains the comparison result, which is the output of the protocol. The protocol is described as follows:  
1. [PERSON] generates a secret-public key pair \((sk,pk)\) and sends \(pk\) to [PERSON] and the server.
2. [PERSON] and [PERSON] compute \[\llbracket a\rrbracket_{i}:=\mathsf{Enc}(pk,\mathsf{poly}_{i}(a^{\mathsf{b}}))\] (8) and \[\llbracket b\rrbracket_{i}:=\mathsf{Enc}(pk,\mathsf{poly}_{i}(b^{\mathsf{b}}))\] (9) for \(i=1,2\), respectively, and send the results to the server.
3. The server computes \[\llbracket d\rrbracket:=(\llbracket a\rrbracket_{1}\odot\llbracket b \rrbracket_{1})\otimes(\llbracket a\rrbracket_{2}\odot\llbracket b \rrbracket_{2}\oplus\mathsf{poly}_{1\to 3})\oplus\widetilde{\mathsf{poly}}( \mathbf{1}).\] (10)
4. The server masks \(\llbracket d\rrbracket\) in encrypted form using random polynomial \(\gamma\leftarrow\mathcal{Z}_{p}\) \[\llbracket d^{\prime}\rrbracket:=\llbracket d\rrbracket\oplus\gamma\] (11) and sends \(\llbracket d^{\prime}\rrbracket\) to [PERSON].
5. [PERSON] decrypts \(\llbracket d^{\prime}\rrbracket\) \[d^{\prime}=\mathsf{Dec}(sk,\llbracket d^{\prime}\rrbracket)\] (12) and sends \(d^{\prime}\) back to the server.
6. The server unmasks \(d\) from \(d^{\prime}\) as follows: \[d=d^{\prime}-\gamma=\sum_{i=0}^{\mu-1}d_{i}x^{i\mu}+\text{other terms of degree},\] (13) and then verifies whether any \(i\mu\)-th term for \(i=0,\ldots,\mu-1\) is \(0\). If so \(a<b\); otherwise, \(a\geq b\).
## 2 Preliminaries
### Join Types and Implementations in Conventional Databases  
In a relational database, join queries can be expressed on various types of join predicates. The most widely used join type is a _theta-join_, \(R\bowtie_{A\theta B}S\), in which the join predicate is \(R\). \(A\)\(\theta\)\(S\). \(B\) where \(\theta\) is one of the following comparison operators: \(<\), \(>\), \(\leq\), \(\geq\), \(=\), and \(\
eq\). When \(\theta\) is \(=\), it is called an _equi-join_.  
The three major implementations for these join types in the conventional databases are _nested-loop join_, _hash join_, and _sort-merge join_[10]. In this section, we briefly review each implementation. They are adapted to in-network processing of joins in WSNs [2; 3; 4; 5; 8; 11; 12] as described in Section 4.  
**Nested-Loop Join.** Given a theta-join \(R\bowtie_{A\theta B}S\), it matches \(R\) and \(S\) tuples that satisfy the join predicate out of the Cartesian product of \(R\) and \(S\) through nested loops. In the _outer_ loop, the tuples of \(R\) are scanned. For each tuple of \(R\), the tuples of \(S\) are scanned in the _inner_ loop. \(R\) is called an _outer relation_ and \(S\) an _inner relation_. If there is an index on \(S\).\(A\), the tuple matching in the inner loop can use it. Such an implementation is called an _indexed nested-loop join_.  
**Hash Join.** Given an equi-join \(R\bowtie_{A=B}S\), it first scans \(R\) (\(S\)) and partitions it to \(k\) buckets \(R_{1}\),..., \(R_{k}\) (\(S_{1}\),..., \(S_{k}\)) using a hash function on the joining attribute \(A\) (\(B\)). Then, it carries out the matching of \(R\) and \(S\) tuples that satisfy the join predicate only between bucket pairs \(R_{i}\) and \(S_{i}\), \(i=1\),..., \(k\).  
**Sort-Merge Join.** Given an equi-join query \(R\bowtie_{A=B}S\), it first sorts the tuples of \(R\) (\(S\)) on the joining attribute \(A\) (\(B\)). Then, it carries out the matching of \(R\) and \(S\) tuples that satisfy the join predicate by scanning \(R\) and \(S\) in one pass each.
## 6 Simulation Results and Discussion
### Mean Acquisition Time Using Hexagon Scan  
Figure 11(a),b display the mean acquisition time when the scanning method follows a hexagonal path. Similar to a spiral scan, the path begins at the center (high probability) and extends outward to the low-probability region. The mean acquisition time \(E[T_{\mathrm{h}}]\) values  
Figure 11: Mean acquisition time analysis for square spiral scan path. (**a**) Mean acquisition time vs. uncertainty scan area size for square spiral scan path. (**b**) Mean acquisition time vs. receiver satellite position error for square spiral scan path.  
Figure 10: Mean acquisition time analysis for raster scan path. (**a**) Mean acquisition time vs. uncertainty scan area size for raster scan path. (**b**) Mean acquisition time vs. receiver satellite position error for raster scan path.  
are [0.13, 0.43, 1.58] seconds for \(\sigma\) values of [10, 20, 40] urad, for various FOU values. The time required to scan the area increased as the standard deviation increased, as seen in Figure 11(b), as expected due to the target satellite position's probability distribution.
# SMCSPO-Based Robust Control of AUV in Underwater Environments including Disturbances
## 5 Conclusions  
In this study, an SMCSPO controller is designed for the robust control of an underwater vehicle in an underwater disturbance environment. To evaluate the robustness of the SMCSPO control, a PID controller is also designed based on a linearized underwater vehicle model. The simulation results confirmed that the control results using the SMCSPO controller are overall better than the PID control result, especially in an underwater environment with small disturbances. When the vehicle is controlled in an environment with high disturbances, SMCSPO is not significantly better than PID due to the angle limitation of the control fins. The SMCSPO controller performed well when the vehicle could be controlled within the angle limit of the control fins. In addition, by estimating the nonlinear dynamics terms of the underwater vehicle using SPO, the gain of the SMC controller is reduced, and the vehicle could be controlled stably without chattering by SMCSPO.  
Figure 11: PID control simulation results with large disturbances (variance of magnitude = 10).  
Figure 12: SMCSPO control simulation results with large disturbances (variance of magnitude = 10).  
**Author Contributions:** Conceptualization, H.-J.C. and J.-H.H.; methodology, M.C.L. and J.-S.W.; software, H.-H.K.; validation, H.-H.K. and M.C.L.; data curation, H.-H.K.; writing--original draft preparation, H.-H.K.; writing--review and editing, M.C.L.; supervision, H.-J.C. and [PERSON]-[PERSON]; project administration, H.-J.C. and [PERSON]-[PERSON]; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Technology Innovation Program (10073147, Development of Robot Manipulation Technology by Using Artificial Intelligence) funded By the Ministry of Trade, Industry, and Energy (MOTIE, Korea), and the Agency for Defense Development, South Korea, under Grant UD150005 DD, the Ministry of Trade, Industry, and Energy.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in follow link. [[https://drive.google.com/file/d/119](https://drive.google.com/file/d/119) CYthn4 ihxf-pmt5 vJRJCCm1 gLGwVvK/view?usp=sharing]([https://drive.google.com/file/d/119](https://drive.google.com/file/d/119) CYthn4 ihxf-pmt5 vJRJCCm1 gLGwVvK/view?usp=sharing) (accessed on 18 November 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Discussion  
Salt stress is well known as one of the most devastating abiotic stress factors in agriculture all over the world. Numerous studies in the past have demonstrated that NO was involved in salt tolerance, acting as ROS scavenger, signaling molecule, and interacting with other molecules [20; 47]. Application of SNP has significantly mitigated the effect of salt stress on seedling growth inhibition by increasing the expression of antioxidant-related genes in different rice, especially the salt sensitive cultivar, Ediget [48]. Furthermore, the ROS-dependent lipid peroxidation and protein oxidation by ROS induced by salt stress is effectively inhibited by NO [49]. In soybean, H2O2 was increased dramatically under long-term salinity stress, which was reduced to the basal level by exogenous NO [50]. Germination, the most important stage of the plant life cycle, is severely repressed by abiotic stresses [51]. Salt stress greatly inhibits seed germination, resulting in a reduction in germination rate and a delay in seedling establishment [52; 53], which is also alleviated by application of SNP to the germinating seed under salt stress [24]. It is worth mentioning that the most optimal concentration of SNP with the highest mitigation effect is 50 \(\upmu\)M for seed of pakchoi (_Brassica chinensis_ L) [24], which is similar to our result in which 100 \(\upmu\)M SNP has the highest alleviation effect of seed germination under salt stress (Figure 1b), suggesting that NO mitigate harmful effect of stress on seed germination in a complicated pattern.
## 5 Conclusions  
* The SIT-enhanced PESTonFARM model reflects the influence of landscape structure on the behaviour of both wild female flies and released sterile males, and enables quantitative assessment of the effectiveness of different SIT scenarios.
* The model is sensitive enough to distinguish and quantify the effects of rather small spatial changes in local topography and fruit structure on medfly development and the effects of different SIT implementation approaches.
* The model simulates the development of both flies and fruits according to local annual weather patterns and can therefore generate locally relevant information on the optimal timing and spatial focus of local SIT operations.
* The use of the model enables informed decision-making and design of SIT implementation scenarios according to local conditions.  
**Author Contributions:** [PERSON] conceived the study, designed and developed SIT-related software modules of the PESTonFARM model, performed the simulations and wrote the first draft; [PERSON] substantially contributed to carrying out the literature review and developing, writing and editing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the European Union's Research and Innovation Program: Horizon project REACT (grant agreement No 101059523).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The presented studies did not involve the collection of any empirical datasets that could potentially be reused and should therefore be made available. The article is based solely on computer simulations of the performance of hypothetical SIT scenarios implemented on \"virtual\" farms located in northern Greece, and the relevant model-generated results are presented in tables, graphs or text. Upon request, for non-commercial use, inSilico-IPM may provide \"background\" numerical data for the graphs presented in the article.  
**Acknowledgments:** The authors would like to express their gratitude to the interviewed SIT experts for sharing their opinions and estimates regarding the expected performance parameters of sterile males in field conditions, and to the reviewers whose constructive comments and suggestions contributed significantly to improving the article.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company inSilico-IPM. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The developed software is the property of inSilico-IPM and is used in the company's research and development activities.
## 1 Introduction
Active suppression, which removes loop signal disturbances from the received signal by injecting a suppression waveform, has been extensively studied in the literature. The suppression waveforms can be injected not only over the transmission line, as in [12; 13], but also over the air using an additional antenna, as in [14]. Note that neither active nor passive cancellation is able to completely suppress loop interference, which means that IIR channels will always exist. Therefore, an OFDM technique for the IIR channel (IIR-OFDM) was proposed recently, which carries the dedicated guard interval to maintain the circular convolution of the received signal and the IIR channel coefficients. At the receiver, the loop of an IIR channel can be converted to the frequency domain, and single-tap equalization can still be used to equalize loop interference, like classical OFDM. In this paper, we describe how to build the IIR system based on the channel with loops and derive the properties of the dedicated guard interval in a general multi-order IIR channel, which is different from the classical CP obtained by replicating the samples at the tail end of the signal. In particular, we address two special models for first-order and delay IIR channels. It is demonstrated that the two special models' GI composition and power characteristics are similar. Moreover, the complexity of GI depends not only on the maximum delay of the loop, but also on the number of loops. The remainder of this paper is composed as follows. In Section 2, we describe the IIR-OFDM system in detail, including how to model the IIR channel, design the guard interval, and demodulate the received signal. Next, we derive the guard interval characteristics of IIR-OFDM under different IIR channel models in Section 3. Finally, the IIR-OFDM is verified by different computer simulations in Section 4.
## 5 Conclusions
Inter-frequency phase bias of the BD3 satellites is also evaluated by GFIFP combination. The results show that obvious variation can be found in the GFIFP series and the amplitude is about 2 cm. Similar results are also found in the GPS BLOCK IIF satellites, but the amplitude of GPS is 5 cm. Besides, the GFIFP combination series of the Galileo is a zero mean distribution. In addition, a multi-GNSS POD in the study is performed using about a hundred global-distributed IGS and iGMAS stations. Three-day arc length and B1I-B3I IF combination are used and the 48-h orbit overlap results show that the accuracies of the orbit are about 5.0 cm in the along-track, 2.2 cm in the cross-track, and 1.5 cm and 5.7 cm in the radial and 3D directions, respectively. Considering that the four satellites of BD3 can be tracked by ILRS, the RMS values of the SLR residuals for C20, C21, C29, and C30 are 5.1 cm, 6.4 cm, 4.7 cm and 4.3 cm respectively. The atomic clock's frequency stability is evaluated as well, using the clock bias parameters calculated in POD. The results of overlapping Allan Deviations show that the frequency stability of BD3 is approximately \(2.43\times 10^{-14}\) at an interval of 10,000 s and \(2.51\times 10^{-15}\) at an interval of 86,400 s, which is a little better than that of the GPS BLOCK IIF satellites but worse than that of the Galileo FOC satellites. According to the study above, compared with the BD2 satellites, the performance of the BD3 FOC in-orbit satellites has been significantly enhanced in the signal performance and atomic clock's frequency stability. Thus, better performance can be expected with the full constellation operation by the end of 2020. [PERSON] and [PERSON] provided the initial idea of the study; [PERSON] performed the data analysis and wrote the article; [PERSON] helped with data organization and analysis; [PERSON] and [PERSON] supervised the experiments. This research was funded by National Natural Science Foundation of China (Grant Nos. 41574030, 41774035). The authors are very grateful to IGS, iGMAS and ILRS for data supply. The authors declare no conflict of interest.