esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 1 Introduction  
Shipping is the primary carrier of international logistics, with maritime transport accounting for more than 80% of global trade, strongly supporting the formation of the \"world factory\" and making economic globalization possible. In this context, effectively managing port and aviation activities has become key to enhancing efficiency, reducing costs, and increasing profitability in the port and shipping logistics industry.  
However, public health events such as the spread of pandemics have not only brought unprecedented challenges to the global shipping industry, but also had a profound impact on the operation and development of cities. As centers of global economic, cultural, and social activities, cities heavily rely on the shipping system for their daily operations. The outbreak of public health events often leads to a significant reduction in shipping efficiency or even disruption, highlighting the instability and vulnerability of the shipping network.  
There has been widespread attention on how to establish effective cross-border risk warning systems within the shipping network, and improve the accuracy and relevance of risk assessments in the shipping industry.  
Current research on risk propagation in networks remains a key focus. From a methodological perspective, existing studies are predominantly conducted on idealized networks with distinct structural features, exploring risk transmission mechanisms through network attributes. This study advances the field by investigating risk propagation across interconnected shipping networks, explicitly quantifying their coupling characteristics to strengthen practical applicability.  
To address research gaps, this study develops a global air-maritime coupled network risk propagation model by integrating complex network theory with the improved SEIR-SEI model. The significance of this study lies in the following: (1) identifying limitations of dynamic propagation models in maritime networks and proposing an enhanced SEIR-SEI framework incorporating multiple influencing factors and transmission dynamics; (2) establishing a novel risk propagation model for globally interconnected air-sea transportation systems through complex network coupling, enabling systematic investigation of cross-modal risk transmission processes; (3) conducting empirical analyses on maritime networks across selected nations to reveal risk propagation patterns through dual perspectives of nodal inherent risks and transmission probabilities, demonstrating substantial practical implications; and (4) developing an integrated risk warning methodology that synthesizes intra-regional and cross-regional transmission mechanisms, providing a scientific foundation for constructing comprehensive risk prevention systems.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 5 Conclusions  
This study identifies critical factors governing air demand in spillway tunnels under free-surface flow conditions (Fr \(<\) 19): Air discharge correlates positively with (1) flow velocity, (2) relative flow depth (at constant tunnel height), and (3) air vent area and quantity. A novel design framework integrating energy-momentum equations, force balance principles, and experimental interfacial shear stress formulations was developed to optimize vent configuration. The hydraulic design of ventilation systems should prioritize limiting vent airspeed, with a recommended maximum allowable velocity of 60 m/s. If this criterion is unmet, structural adjustments--such as increasing vent area or quantity, or optimizing proximal gate placement--or operational modifications--such as reducing flow velocity or gate opening--can effectively constrain the airspeed. Notably, the model's validity under non-free-surface flow regimes (e.g., pressurized or transitional flows) and complex geometries remains unvalidated and requires further study. Future research should prioritize three objectives: (1) more comprehensive prototypic condition validation to evaluate scale effects, and (2) extension to high-Froude-number flows (Fr \(>\) 19) and mixed-regime scenarios. These advancements will strengthen the robustness of air demand predictions for real-world spillway designs while addressing the study's limitations.  
Conceptualization, H.Y. and W.W.; data curation, Q.F.; formal analysis, H.Y., W.W., and Z.T.; funding acquisition, Z.T.; investigation, H.Y.; methodology, H.Y.; project administration, Z.T.; resources, Z.T.; software, H.Y.; supervision, W.W.; validation, Q.F.; visualization, H.Y.; writing--original draft, H.Y.; writing--review and editing, Z.T. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China [51879178].  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
Figure 16: Comparison of air demand: prototype data (\(Q_{\text{at,obs}}\)) versus predicted data (\(Q_{a,\text{pred}}\)); dashed lines = \(\pm\)25%.
## 3 Results and Discussion
### Material Characterization
#### 3.1.2 TG-DSC Analysis  
To determine the organic contents of SiPMA and SiPMANs, a thermogravimetric analysis was conducted between 25 \({}^{\circ}\)C and 650 \({}^{\circ}\)C. The DSC curves revealed that the peak decomposition temperatures for SiPMA, SiPMA-DETA, SiPMA-TETA, and SiPMA-TETA were approximately 354 \({}^{\circ}\)C, 311 \({}^{\circ}\)C, 320 \({}^{\circ}\)C, and 321 \({}^{\circ}\)C, respectively (Figure 3a-d), indicating the decomposition temperatures of the organic polymers [30]. The mass losses for SiPMA-DETA, SiPMA-TETA, and SiPMA-TETA were 21.3%, 22.6%, and 24%; this indicates that 21.3%, 22.6%, and 24% of the resins were incorporated into the silica spheres, respectively.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 6 Analytical Work
### Proposal for Pull-Out Resistance  
According to the experimental and numerical results, the pull-out resistance of the notched perfobond shear connector was contributed by shear of the concrete dowel, shear of the perforating rebar, and yield of the perfobond rib. Therefore, an alternative equation, Equation (7), was proposed for estimating the pull-out resistance of the notched perfobond shear connector by combining these contributions.  
\[T_{u}=\gamma_{w}\Big{[}C_{1}\Big{(}d_{p}^{2}-d_{r}^{2}\Big{)}f_{c}+C_{2}d_{r} ^{2}f_{ny}+C_{3}d_{p}t_{p}f_{sy}\Big{]} \tag{7}\]  
Figure 20: Influence of steel strength. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
where \(T_{u}\) is the pull-out resistance per hole (N); \(d_{p}\) is the hole diameter (mm); \(d_{r}\) is the diameter of the rebar in the hole (mm); \(f_{c}\) is the concrete compressive strength (MPa); \(f_{ry}\) is the yield strength of the rebar (MPa); \(t_{p}\) is the thickness of the perfobond rib (mm); \(f_{sy}\) is the yield strength of the structural steel (MPa); \(\gamma_{w}\) is the influence factor of the notch; and \(C_{1}\), \(C_{2}\), and \(C_{3}\) are fitting coefficients.  
Nonlinear regression analysis was performed on a total of 39 experimental and numerical results. The best fittings of the coefficients in Equation (7) were derived as \(C_{1}=0.95\), \(C_{2}=0.45\), and \(C_{3}=0.18\). Therefore, the pull-out resistance of notched perfobond shear connectors could be evaluated by using the final proposal as Equation (8).  
\[\begin{split} T_{u}&=\gamma_{w}\Big{[}0.95\Big{(}d_ {p}^{2}-d_{r}^{2}\Big{)}f_{c}+0.45d_{r}^{2}f_{ry}+0.18d_{p}t_{p}f_{sy}\Big{]}\\ &\text{with }\gamma_{w}=\begin{cases}1&0\leq c_{w}/d_{p}\leq 0.75 \\ 4\big{(}1-c_{w}/d_{p}\big{)}&0.75<c_{w}/d_{p}\leq 1\end{cases}\end{split} \tag{8}\]
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 4 Energy Storage Demand Simulation
### Energy Storage Demand Simulation  
After performing the above estimations, the future annual renewable energy power generation can be determined, thus establishing the required energy storage power and capacity under a no-curtailment scenario. Figure 4 shows the power generation simulation during the Spring Festival of 2027 in Taiwan. The yellow curve represents the total load curve, approximately 23,065 MW. The green curve represents the total load minus wind power generation. The blue curve is the net load, approximately 16,565 MW. The red line represents the system's minimum generation limit, about 5000 MW. When solar power is at full capacity at noon, the net load will fall below the minimum disconnection limit of the traditional generators in the power system. Since traditional generators cannot be shut down or disconnected at this time, the excess electricity represented by the blue area can be considered the required capacity for energy storage. At night, when solar PV does not generate electricity, the electricity stored during the day can be used to meet the load demand during night time.  
Simulations from 2026 to 2030 show that assuming the power system's minimum generation to be 30% of the maximum net load provides the required annual energy storage capacity. As shown in Figure 5, with the development of renewable energy and the growth of load, the gap between the total load and the net load increases, and thus, the required capacity for energy storage also increases. Using extreme generation data for solar PV and wind power, along with the lowest load data of the Spring Festival within a year, the energy storage capacity shown in Figure 5 represents the maximum required for that year under a no-curtailment scenario.  
The above power generation scheduling yields the annual energy storage capacity requirement. As shown in Figure 6, the required storage capacity increases from 3 GWh in 2026 to 43 GWh in 2030, indicating a 40 GWh increase over this five-year period.  
Figure 4: Power generation simulation during the Spring Festival of 2027 in Taiwan.  
Figure 5: (a) Net electricity load and energy storage requirements in Taiwan during the Spring Festival from 2026 to 2030. (b) Net electricity load and energy storage requirements on winter Sundays in Taiwan from 2026 to 2030.
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 5 Results  
In our evaluation, we aimed to determine which factors impacted the capacity aggregation of our multi-access system, for different CCA combinations. We pick up from the results in Section 1, where aggregation varied from nearly optimal to non-existent. In particular, in our evaluation, we aimed to answer the following questions:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Key** & **RTT** & **Path 1 RTT** & **Path 2 RTT** \\ \hline Short RTT & SHORT & 40 & 20 & 40 \\ Long RTT & LONG & 80 & 40 & 60 \\ Proxy near user & NEAR & n & 0.2n & 0.2n + 20 \\ Proxy far from user & DIST & n & 0.8n & 0.8n + 20 \\ No asymmetry & ASYM0 & n & 0.5n & 0.5n \\ Large asymmetry & ASYM40 & n & 0.5n & 0.5n + 40 \\ Excessive asymmetry & ASYM200 & n & 0.5n & 0.5n + 200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenarios used in the evaluation.  
* How does the proxy deployment impact the performance of the multipath framework (cf. Section 4.1)?
* What is the impact of bottleneck buffer sizes for different congestion controls (cf. Sections 4.2 and 4.3)?
* How do different nested congestion control algorithm combinations impact download completion times (cf. Section 4.4)?  
To present our results, we plotted examples of achievable throughput over time, with the per-path throughput stacked in graphical plots. Additionally, we plotted the average throughput for multiple tests using bar diagrams. For a more comprehensive presentation, we used contour plots to illustrate the impact of the proxy placement and end-to-end RTT. This involves varying both the end-to-end RTT and the percentage of that RTT within the tunnel itself. For instance, if the end-to-end RTT was 40 ms and the tunnel RTT accounted for 25%, then the tunnel RTT was 10 ms and the RTT from proxy to server was 30 ms. We organized the results in batches of 400 tests, where both the end-to-end RTT and the tunnel RTT were randomly selected. The former ranged from 0 to 100 ms, and the latter was determined as a percentage of the end-to-end RTT, ranging from 0 to 100. To distribute the points more evenly, any point too close to a previous point was discarded and regenerated. The same distribution of points was used for all batches. To convey the information effectively, we used color coding. A deep blue color indicates good aggregation, with throughput reaching 95 Mbps--double the single-path throughput. A deep red color indicates no aggregation at all, with throughput being as low as 40 Mbps--less than the single-path throughput.  
In Sections 4.1-4.3, we look primarily at one single combination of tunnel and end-to-end CCA, namely, the combination most impacted by the observed phenomenon. Finally, in Section 4.4, we present the findings from the original set of tests, which prompted the deeper evaluation presented in Sections 4.1-4.3.
## 6 Discussion and Conclusions  
In this study, the characteristics of echo envelope fluctuation intensity were studied, two features of which were characterized. The data processing results of the simulation and scaled benchmark tests are consistent. The echo envelope fluctuation intensity and its STD features strongly depend on the carrier frequency: when the carrier frequency increases, the echo envelope fluctuation intensity increases correspondingly. The magnitude of the echo fluctuation intensity and its STD vary with azimuth, which is minimum on the beam, and maximum on the keel-line. This is because the time difference between the echo highlights in the keel-line is greater than that in the beam direction, causing a larger magnitude fluctuation of the echo envelope. The magnitude of the fluctuation intensity changes as the pulse length changes--when the pulse length increases, the magnitude of the echo fluctuation intensity decreases. The fluctuation intensity and its STD could be used to characterize the fluctuation in the echo envelope magnitude, but the sensitivity of the standard deviation of the fluctuation intensity is relatively low, compared to that of the fluctuation intensity, especially for a narrowband signal.  
Based on these echo envelope fluctuation characteristics, we propose a novel method for discriminating between a real underwater target echo and synthetic echo. The feasibility of the method was verified through a sea experiment.  
It should be noted that the waveform structure of the synthetic echo becomes more like the real target echo when the number of highlights simulated by the echo decoy increases. The performance of this proposed method for a more complex synthetic echo will be studied in further work.  
All authors contributed significantly to the work presented in this manuscript. [PERSON] proposed the novel method and wrote the paper; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] contributed with valuable discussions and scientific advice.  
This research was funded in part by the Foundation of Science and Technology on Underwater Test and Control Laboratory under grant no. 9140C260201130C26096, and the APC was funded by that too.  
The authors are grateful to [PERSON] from Shanghai Jiao Tong University for the acoustic scattering program code based on the Planar Element Method that originally developed by [PERSON].  
The authors declare no conflict of interest.
## 4 Conclusions  
In conclusion, this paper proves an effective method to interconnect metal oxide nanowires using femtosecond laser irradiation of a metal nano-solder. The nanowire interconnection state is controlled by the laser fluence and irradiation time. The results indicate that a femtosecond laser fluence of 55.25 mJ/cm\({}^{2}\) and an irradiation time of 60 s are favorable for nanowire soldering. It was found that the current increased significantly after soldering through the electrical test, confirming that the ZnO nanowires and Ag solder successfully achieve a better interconnection. Under UV light irradiation, the resistance was reduced to approximately one-quarter of the resistance without light. The device's response time to 350 nm UV light detection was 8 s, and its recovery time was 10 s, showing its potential as a UV detector. The effect of femtosecond lasers with different laser wavelengths and pulse duration on nanowires interconnection requires further exploration. It is expected to interconnect more difficult-to-melt metal oxide nanowires by soldering and develop more metal oxide nanowire functional devices in future.  
Conceptualization, Y.G.; methodology, Y.G., G.W. and L.W.; validation, S.Z., Y.D. and L.Y.; formal analysis, Y.G.; investigation, Y.G. and Y.D.; resources, Y.D. and L.Y.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.D. and L.Y.; supervision, L.Y.; project administration, Y.D. and L.Y.; funding acquisition, L.Y. and Y.D. 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, Grant number 52105437.  
Not applicable.  
Figure 6: The photoelectric properties test of the ZnO-Ag-ZnO interconnection structure. (**a**) Schematic of the device based on the ZnO-Ag-ZnO interconnect structure; (**b**) I-V curve of photocurrent and dark current of the device; (**c**) photoresponse curve of the interconnection device; (**d**) the response and recovery time of a single response curve.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Thanks to [PERSON] for offering valuable guidance during the writing of this manuscript. Thanks to [PERSON] for his help on the experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 1 Introduction
### Overview of Our Contributions  
In this paper, we introduce a dynamic and efficient QRA scheme called _QRTP_ (QoS-aware resource Reallocation based on Traffic Prediction in SDN), in which we guarantee delay and bandwidth. It proactively decreases congestion by minimizing total packet loss. The proposed approach not only uses the current flow matrix but also adopts a predicted flow matrix to conquer the dynamic and burst nature of network traffic. Our contributions are as follows:  
1. To the best of our knowledge, there is no related work that considers traffic prediction in resource reallocation. This is the first work that exploits predicted flow matrix to dynamically reschedule the network. In addition, this is the first work that studies the impact of flow traffic estimation on network resource reallocation. The simulation results show significant reduction in the total packet loss along with improvement in the network throughput.
2. The proposed approach can support different traffic classes with various QoS requirements in flow level granularity. To do this, delay and bandwidth are aimed to be fulfilled while packet loss is minimized for current and predicted traffic load.
3. Two schemes are proposed for solving the introduced QRTP problem: (I) an optimal solution which is time consuming; and (II) a relaxed but fast one.
4. The proposed schemes are compared with the performance perspective. In this way, we use a real network traffic and topology for experiments.
5. Our formulation makes a trade-off between performance and computational complexity. In this way, it reschedules the network with flow granularity which can be the size of the exchanged information of \"a special application\" or \"all communications from one data center to another one\".
## 2 Theory and Method
### Measuring Eccentricity of the Earth Orbit  
According to the observation results, a GMR can respond to the variation of the solar radiation arising from the Earth-Sun distance, and, therefore, we propose a new method to calculate the eccentricity of the earth's orbit by using the observed solar microwave radiation T\({}_{\rm B}\) with a GMR when the earth is near the perihelion and aphelion. This method is simple and does not require complicated observations and mathematical calculations.  
For an elliptic orbit, the eccentricity \(e\) is given by:  
\[e=\frac{c}{a}, \tag{13}\]  
where \(a\) is the length of the semi-major axis, \(c\) is the semi-distance between the two foci. When the earth is at the perihelion and aphelion, the distance between earth and sun is \(a-c\) and \(a+c\), respectively. According to Equations (11) and (12), one has  
\[\frac{\Delta T_{sun}^{p}}{\Delta T_{sun}^{a}}=\frac{(a+c)^{2}}{(a-c)^{2}}= \frac{(1+e)^{2}}{(1-e)^{2}}, \tag{14}\]  
where \(\Delta T_{sun}^{p}\) and \(\Delta T_{sun}^{a}\) are the T\({}_{\rm B}\) increment when the earth is at the perihelion and aphelion. Let \(M=\frac{\Delta T_{sun}^{p}}{\Delta T_{sun}^{a}}\), and one has  
\[e=\frac{\sqrt{M}-1}{\sqrt{M}+1}, \tag{15}\]  
and  
\[Q=\left(\frac{a+c}{a-c}-1\right)\times 100\%=\left(\sqrt{M}-1\right)\times 1 00\%, \tag{16}\]  
\[R=\left(1-\frac{1}{M}\right)\times 100\% \tag{17}\]  
where \(Q\) represents the percentage of the variation of the Earth-Sun distance, and \(R\) represents the percentage of the variation of the solar radiation intensity.