esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Contextual and Game-Based Training for
## 4 Results
### The Effect of Training  
The effect of training was assessed by first examining the proportion of participants that received perfect scores. A perfect score means that the participants used all phishing identifiers or identified all emails correctly. The proportions of perfect scores are presented in Table 3.  
Table 3 suggests that participants who received training performed better than participants in the control group for the behavior variables and that the participants in the CBMT  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Group** & **Mean** & **Median** & **Normal Distribution** \\ \hline SCORE & Control (n = 11) & 8.82 & 9 & YES \\ out of 11 & CBMT (n = 14) & 10 & 10 & NO \\  & Game (n = 14) & 8.86 (9.09) & 9 (9) & NO \\  & Total (n = 39) & 9.26 & 9 & NO \\ \hline behavior\_manual & Control (n = 11) & 3 & 3 & NO \\ out of 5 & CBMT (n = 14) & 4.57 & 5 & NO \\  & Game (n = 14) & 3.64 (3.82) & 3.5 (4) & NO \\  & Total (n = 39) & 3.79 & 4 & NO \\ \hline behavior\_tracked & Control (n = 10) & 1.9 & 2 & NO \\ out of 3 & CBMT (n = 12) & 2.5 & 3 & NO \\  & Game (n = 14) & 2.29 (2.55) & 2 (3) & NO \\  & Total (n = 36) & 2.25 & 2 & NO \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data overview.  
group outperformed the other groups for the variable SCORE. The same tendency is seen in Table 2 where mean and median results for the different sample groups are presented. Table 2 suggests that participants in the group game performed slightly better than the control group while the participants in the group CBMT outperformed the other groups with a bigger margin. The exception is for the variable behavior_tracked where the groups CBMT and game performed equally when participants who reported not playing the game were omitted from the game group.  
Kruskal-Wallis H test was used to identify variables with statistically significant between-group differences. The results are presented in Table 4.  
The Kruskal-Wallis H tests suggest that at least one sample is different from the others when \(p\) < 0.05, as is the case for the variables SCORE and behavior_manual. The same is also true for the variable behavior_tracked when users who did not play the game are omitted. Pairwise Mann-Whitney U tests with Bonferroni correction was used to test what variables that were significantly different from each other. The results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Kruskal–Wallis H** & _p-Value_ \\ \hline SCORE & 13.965 (12.531) & 0.001 (0.002) \\ \hline behavior\_manual & 16.270 (15.434) & 0.000 (0.000) \\ \hline behavior\_tracked & 5.569 (7.332) & 0.062 (0.026) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kruskal–Wallis H tests.
In this case, the difference between two variables is statistically significant when \(p<0.05\). Table 5 shows that CBMT is separated from the groups game and control for the variables SCORE and behavior_manual while control and game cannot be separated. For behavior_tracked, game and CBMT cannot be separated but are both separated from control.
# An Optimal Fuzzy PID Controller Design Based on Conventional PID Control and Nonlinear Factors
## 1 Introduction
Based on our previous work, the equivalence between fuzzy PID controllers and conventional PID controllers is shown in [16]. Nonlinear factors are further proposed to represent the nonlinearity of the MFs distributed in the operating ranges. For each MF itself, it will exhibit an asymmetrical shape. In the proposed optimal fuzzy PID controller, there will be only a total of eight adjusted parameters. Moreover, if a conventional PID controller design can be obtained in advance, an equivalent FLC in the initial GA design can be used and this can potentially speed up the optimization process. [PERSON] previously researched designing optimal control systems through GA and neuro-fuzzy techniques [17; 18; 19], and the results can be utilized as a benchmark to compare with the proposed design. Furthermore, the proposed optimal fuzzy PID controller is also applied to the motor control system [20], and the simulation results indicate speed control with good performance and the capability of disturbance rejection [21]. This study presents the optimal fuzzy PID controller design (Section 2), followed by the simulation results of Matlab/Simulink for verifying the proposed design (Section 3). Finally, the concluding remarks are discussed (Section 4).
## 5 Potential 6G Applications in the Maritime Sector
### Intelligent Fleet Management and Route Optimization  
Modern maritime logistics involve dynamic fleet operations where vessels adjust their speed, trajectory, and fuel consumption based on sea conditions, port congestion, and energy efficiency goals [80]. With 6G, AI agents running at the network edge or onboard vessels can continuously optimize routes in real-time by integrating data from satellites, HAPs, coastal sensors, and digital twins of maritime traffic. Fleet-wide learning via federated models allows for collaborative planning (e.g., storm avoidance, fuel-efficient paths) while maintaining the privacy of individual operators. Figure 9 illustrates the conceptual schematic of the above-mentioned scenario.  
Figure 8: Satellite communication-based remote support in the onboard personnel for fault diagnosis and maintenance guidelines based on AR/VR information.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Proxy Mobile IPv6 (PMIPv6)  
PMIPv6 is a fully network-based variant of MIPv6 [14; 15]. Instead of MIPv6's mobility management signalling coming from the host itself, PMIPv6 relies on network entities to carry out the required signalling, management, and tunnelling of packets. PMIPv6 introduces the Mobility Access Gateway (MAG) and Local Mobility Anchor (LMA). The LMA has a similar function to the HA; it allocates the HoA and manages traffic ingress and egress for a PMIPv6 network.  
Within the network managed by a MAG, an MN receives an address that is mobile, the HoA. The address of the MAG is the CoA, which the LMA uses to forward packets to reach the MN. As the MN moves from one MAG to another, the MAGs notify the relevant LMA of an MN's change in PoA. The MAGs and the LMAs signal each other to confirm an MN's movement and set up tunnels and routes for correct traffic forwarding. The LMA will be the gateway to the rest of the Internet, while the MAG will provide the tunnel to the LMA.  
Unlike MIPv6, PMIPv6 does not require host modifications. However, the infrastructure is a lot more complex, and it limits a MN's movement only to specific networks that have PMIPv6 infrastructures.  
Limitations common across different proxy-based solutions still apply to PMIPv6, such as the performance bottleneck and the SPoF.  
Also, PMIPv6 does not support multihoming.
## 4 Emerging Lightweight Security Approaches
### Asymmetric Encryption--Lightweight ECC Approaches  
Elliptic Curve Cryptography (ECC) is an asymmetric encryption algorithm that uses public and private keys. It is sometimes considered a lightweight approach to data security because it provides similar security to traditional public-key algorithms (such as RSA and Diffie Hellman) but with a much smaller key size. In other words, it \"provides the highest strength per key bit\", according to [18]. For example, a 283-bit elliptic curve public key is said to provide the same level of security as a 3072-bit RSA public key or a 128-bit key in a symmetric cipher [91; 92]. Smaller key sizes allow ECC to utilize less memory, which is good for devices with limited power and storage. However, standardized ECC algorithms are computationally demanding. According to [24], they were \"not thought to be realized in constrained environments\".  
ECC keys can be much smaller than those in traditional public-key ciphers because of the elliptic curves' complex nature. The elliptic curves that have been recommended by the National Institute for Standards and Technology (NIST) follow the Weierstrass normal form [92], as in Equation (12).  
\[y^{2}=x^{3}+\mathrm{ax}+\mathrm{b} \tag{12}\]  
In the plotted elliptic curve in Figure 9, each value on the \(x\) axis corresponds to two points on the \(y\) axis [24].  
The two ECC protocols that are explored in this section are elliptic curve Diffie-Hellman (ECDH) for session key distribution and the elliptic curve digital signature algorithm (ECDSA) for authentication. Their applications are discussed, as well as two adaptations of ECC for IoT devices.
## 3. Results and Discussion
### Volume-Fixed Near-Equal Optimal Bale Dimensions Design
#### 3.2.1 Algorithm 2  
Assume film parameters \(b_{f}\), \(v_{f}\), \(\varepsilon_{lf}\), overlap ratio \(k_{f}\), Equation (20), number of global film layers \(p_{l}\), the order of near-equilibrium \(\varepsilon_{0}\), and bale volume \(V_{b0}\) are given. Take minimal \(\delta_{min}\), \(k_{fb,min}\) and maximal \(\delta_{max}\), \(k_{fb,max}\) values of the overlaps \(\delta\), \(k_{fb}\) to guarantee the appropriate tightness of the wrappings.  
1. Determine the set \(\mathcal{P}_{2}\) of all integer \(p_{b}\) for which there exists \(2\leq q\leq 5\) satisfying applicability condition, Equation (21), and the set of overlap ratios \(\mathcal{K}_{2}(p_{b})\) defined by the inequalities expressed by Equations (29) and (30) is nonempty.
2. If the set \(\mathcal{P}_{2}\) is empty, then the solution to Problem 2 does not exist--go to step 3. Otherwise, go to step 4.
3. Change the lower \(k_{fb,min}\), \(\delta_{min}\) or upper \(k_{fb,max}\), \(\delta_{max}\) bounds of wrapping parameters, or the order \(\varepsilon_{0}\), or the film width \(b_{f}\) and go to step 1.
4. Solve in \(p_{b}^{*}\) the integer programming task expressed by Equation (36) for \(p_{b}\in\mathcal{P}_{2}\).
5. For the best \(p_{b}^{*}\) choose practically reasonable \(k_{fb}\in\mathcal{K}_{2}\big{(}p_{b}^{*}\big{)}\), compute the overlap \(\delta=\delta\big{(}p_{b}^{*}\big{)}\) according to Equation (34) and optimal \(D_{b}^{*}\), \(H_{b}^{*}\) using Equations (5) and (6). The minimal film consumption is equal to \(FC\big{(}p_{b}^{*}\big{)}\) computed in step 4.
## 1 Introduction  
The experience of some developed Western countries with high reliance on the car shows that car-based transport causes traffic congestion [1, 2] and a range of environmental problems [3]. Metro transport is considered to be a better way of addressing the issues caused by high levels of car dependency [4, 5, 6] because it helps to reduce car dependency and congestion [7, 8, 9], improve road safety [10], and reduce social exclusion [11]. Therefore, urban policymakers highly value the construction and operation of metro transport [6, 12], especially in developing countries [13]. The planning and construction of metro transport in China are rapidly growing. The 14 th Five-Year Plan for the Development of a Modern Comprehensive Transport System issued by the State Council of the People's Republic of China states that the total operational mileage of metro transport in the country will reach 10,000 km by 2025 [14]. However, mega-cities like Beijing have already experienced excessive ridership at some metro stations during peak morning hours. The urban metro operator in Beijing has had to limit the ridership to ensure the safety and comfort of metro operations [15]. Still, the measures to restrict the ridership have greatly affected the efficiency of residents' travel. How to ensure that metro transport can meet the commuting needs of urban residents has become a widespread concern [16]. So, determining the factors influencing metro ridership is very important for the planning and operation of metro transport [2; 4; 17].  
Linear models are widely used by scholars. However, linear models usually assume a linear function to fit the data. In real life, the influence of built environment factors may have a threshold effect. Therefore, using a linear model can cause errors in the results [18; 19; 20]. To date, Gradient Boosting Regression Tree (GBRT) models have been used to investigate the nonlinear influence of the built environment factor on metro ridership [3; 21]. But, GBRT models suffer from over-fitting [19]. Daily and weekly ridership are widely focused and fewer have looked separately at peak-hour inbound or outbound ridership during the day. In addition, most existing studies have used experience, the TOD theory [22], or borrowed from others [23; 24] to identify the pedestrian catchment areas (PCA) at metro stations, where most use a uniform size PCA of metro stations to calculate built environment factors.  
Therefore, our study has three major purposes: (1) the optimal metro station PCA for the different zoning of Beijing's metro stations was determined; (2) to investigate the nonlinear influence of the built environment of two zones on the ridership of the metro stations; (3) and to investigate the spatial heterogeneity of the nonlinear influence.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 5 Resolver Centrality Analysis
### Resoler Pool
Among the 119/24 prefixes of Google's resolver pool, we discover 172.217.33.0/24 contains 20 resolvers servicing 906 K FDNSes, whereas 74.125.44.0/24 encompasses 45 resolvers supporting merely 89 FDNSes, as marked in the two green lines in Figure 7. Only 802 in 2274 discovered resolvers in Google's resolver pool fall in the most centralized indirect resolvers. To explore the internal structure of resolver pools, we partition the resolver pool of Google into 17 sub-pools predicated upon the intersection of shared FDNSes among different sub-pools. Different sub-pools signify that they serve a different set of FDNSes. Figure 8 shows the Sankey diagram of these sub-pools and the geographic location of \begin{table}
\begin{tabular}{l r r r r} \hline \hline
**DNS SP** & **Resolver /24 \#** & **Resolver \#** & **Resolver per /24 \#** & **FDNS \#** \\ \hline Google & 110 & 2274 & 20.67 & **4,272,808** \\
114 DNS & 155 & 625 & **4.03** & 2,877,600 \\ Level3 & 74 & 937 & 12.66 & 306,539 \\ Cloudflare & **263** & **8593** & **32.67** & 284,188 \\ OpenDNS & 42 & 419 & 9.98 & 257,962 \\ Total & 644 & 12,848 & 19.95 & 7,646,226 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top five resolver pools. Figure 7: iRDNS distribution and backed FDNS of Google Public DNS. resolvers in each sub-pool. We discerned that sub-pool 1 possesses the most expansive scope and span, encompassing resolvers situated across 8 regions, including Australia, Belgium, Germany, Japan, and the United States. It serves more than 2.8 M FDNSes, reaching 65.6% of all 4.27 M FDNSes served by Google Public DNS. It may be the core infrastructure of Google Public DNS and thus accordingly exhibits evident centrality. Interestingly, 6 discrete sub-pools (sub-pools 5, 6, 10, 14, 16, and 17) reside exclusively within the US; nevertheless, the FDNSes they service reveal negligible intersections. This could be attributable to Google implementing a more granular access policy for domestic users within the US.
# Computational Aspects of \(L_{0}\) Linking in the Rasch Model
## 3 Numerical Illustration
### Results  
Figure 2 displays the absolute bias, the SD, and the RMSE of the estimated mean \(\hat{\mu}\) for \(I=20\) items as a function of the DIF effect \(\kappa\) and the sample size \(N\). The tuning parameter \(\varepsilon\) is displayed on a logarithmic scale on the \(x\)-axis. The bias increased with increasing values of \(\varepsilon\) in the loss function \(\rho_{\varepsilon}\). In contrast, the SD decreased with increasing values of \(\varepsilon\). The RMSE reflects the bias-variance trade-off of the linking parameter estimate. There was an optimal \(\varepsilon\) parameter that minimizes the RMSE. This optimal \(\varepsilon\) parameter decreased with increasing sample size \(N\). Moreover, the optimal \(\varepsilon\) parameter was larger for \(\kappa=0.8\) than for \(\kappa=0.4\).  
Figure 2: Numerical Illustration: Absolute bias, standard deviation (SD), and root mean square error (RMSE) of the estimated mean \(\hat{\mu}\) using the ratio loss function for \(I=20\) items as a function of sample size \(N\) and DIF effect size \(\kappa\). The minimum RMSE values are depicted with a triangle. The \(\varepsilon\) parameter on the x-axis is displayed on a logarithmic scale.  
Table 1 presents the optimal \(\varepsilon\) values that minimize the RMSE of the estimated mean \(\hat{\mu}\) under a ratio loss function. These values are reported for different DIF effect sizes \(\kappa\), numbers of items \(I\), and sample sizes \(N\). As the sample size \(N\) increased, the optimal \(\varepsilon\) generally decreased. This pattern was more pronounced for small item numbers (e.g., \(I=10\)), where \(\varepsilon\) dropped sharply with increasing \(N\). Larger item numbers \(I\) were associated with smaller optimal \(\varepsilon\) values, suggesting that when more items were available, the best trade-off under the ratio loss function occurred at a lower \(\varepsilon\) value. This effect was particularly evident for \(\varepsilon=0.4\), where \(\varepsilon\) decreased substantially as \(I\) increased from 10 to 40. When the DIF effect size \(\kappa\) increased from 0.4 to 0.8, the optimal \(\varepsilon\) values tended to be slightly higher for the same \(I\) and \(N\). This suggests that stronger DIF effects required a higher \(\varepsilon\) to achieve the lowest RMSE, likely due to greater bias introduced by DIF at the smaller effect size \(\kappa=0.4\).  
Overall, the results highlight a trade-off between sample size, the number of items, and DIF effect size in determining the optimal correction for minimizing the RMSE.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
###### Abstract  
Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images.  
image fusion; Planet; Worldview; pansharpening; forward prediction; spatiotemporal
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 11 Conclusions and Recommendations for Future Tandem Phases  
In view of this experience, we provide a list of recommendations for the implementation of tandem phases for the constellation of earth observation satellites. These conclusions are particularly relevant for future Copernicus missions.  
1. A tandem phase should be implemented for satellites operating in constellations whenever possible. This recommendation applies to all types of earth observation missions, as the benefits are found to be equally important for all Sentinel-3 instruments.
2. The duration of the tandem phase must be adapted to the needs of the particular mission. Altimetry missions require a relatively long tandem phase covering several observation cycles. For Sentinel-3C, at least 4 cycles of tandem operations in SAR open-loop mode are considered necessary to reduce the uncertainty on the bias on the global mean sea level to an acceptable level. For the optical mission, the duration shall include enough time for the instrument to reach a stabilized state after launch (radiometrically and geometrically).
3. A time-lag of 30 s between the two satellites is considered appropriate for all analyses. Altimetry studies would probably benefit from several phases with different time lags, in order to assess the impact of different oceanic effects on measurements. Measurements acquired during the post-tandem drift phase can be used to analyze the impact of viewing geometry for optical instruments.
4. A data reprocessing campaign at the end of the commissioning should be planned in order to obtain the best possible data from the commissioning period, corrected for, e.g., inaccuracies in pre-launch characterization, post-launch effects, etc.
5. Whenever possible, inter-satellite differences should be compared to uncertainty assessed from models and budgets.  
**Author Contributions:** Methodology and investigations: N.L., S.E.H., D.S., M.M., J.M., E.W., M.H., C.B., T.M., B.P., M.R., and P.R.; Data curation: S.C. and T.M.; Writing--original draft preparation: S.C.; Writing--review and editing: All; Supervision: C.D. and F.B.; Project administration: S.C.; Project concept and funding acquisition, C.D.; Formal analysis: A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been performed under the European Space Agency Science and Society Contract 4000124211/18/I-EF.  
**Acknowledgments:** Support from EUMETSAT for tandem data collection is acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Data Analysis  
### Experimental Process  
First, the acoustic signals obtained from the distillation column of Hebei University of Technology were used to establish a characteristic acoustic parameter database and verify the acoustic recognition method. The following steps were performed, as shown in Figure 3.  
Figure 2: The prediction probability corresponding to different \(k\) values.  
1. For acoustic signals, 100 acoustic signals in two operating states (normal and flooding) were recorded, respectively.
2. MATLAB was used to convert all analog signals into digital signals to achieve A/D conversion.
3. The characteristic information of the acoustic signal was extracted from the time domain and frequency domain.
4. The characteristic parameter data of 100 acoustic signals were divided into a training set and test set. The ratio of training set to test set was 7:3. Each group included the characteristic value of the signal and the corresponding operation state. The training set was used as the signal feature database.
5. The k-NN was constructed by using the signal characteristic database of three flow states. Then, the characteristic parameters of the test set were input into the algorithm to predict the operation state of the distillation column. The precision of k-NN was calculated according to the matching degree between the predicted state and the actual state.