esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### Comparison with Prior Research  
The proposed mechanism in this study builds on prior research by integrating Moving Target Defense (MTD) with a lightweight Diffie-Hellman (D-H) protocol and AES-128 encryption. This combination enables dynamic key updates after each transmission, providing robust resistance against side-channel attacks (SCAs) while maintaining low computational overhead. Unlike hardware-intensive approaches or static software solutions, the proposed method strikes a balance between security and resource efficiency, making it particularly well-suited for IoT environments.  
Table 3 offers a comprehensive comparison between the proposed mechanism and selected prior works, highlighting key differences in methodologies, features, and performance. The table demonstrates how the proposed solution surpasses traditional masking and hardware-based approaches by delivering strong SCA resistance with minimal resource consumption. Additionally, compared to existing dynamic key replacement mechanisms, the proposed method introduces a more granular, per-transmission key update process, further enhancing security without compromising low-latency performance. This comparison underscores the novelty and practicality of the proposed approach in addressing the security challenges faced by resource-constrained IoT devices.
## Materials and Methods
### GPP from SCOPE Model Simulations
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**2017**} & \multicolumn{6}{c}{**2018**} \\ \cline{2-13}  & **4/29** & **5/9** & **5/19** & **5/24** & **5/28** & **6/1** & **4/9** & **4/16** & **4/24** & **5/3** & **5/13** & **5/23** & **5/31** \\ \hline Cab & 65 & 65 & 60 & 55 & 30 & 20 & 47.5 & 47.4 & 58.4 & 63.4 & 65.1 & 59.8 & 33.6 \\ LAI & 2.10 & 2.00 & 1.91 & 1.81 & 1.52 & 0.75 & 1.09 & 1.57 & 1.71 & 2.28 & 1.77 & 1.64 & 1.03 \\ V\({}_{\text{cmax}}\) & 110 & 90 & 80 & 70 & 55 & 40 & 50 & 60 & 70 & 110 & 90 & 70 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The input chlorophyll content density (Cab) (unit: \(\upmu\)g/cm\({}^{2}\)), leaf area index (LAI), and maximum carboxylation rate (V\({}_{\text{cmax}}\)) (unit: \(\upmu\)mol/m\({}^{2}\)/s) values for the gross primary productivity (GPP) simulation of the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model during the time window of the SIF measurements in 2017 and 2018.
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 2 Mission Objectives
Such an alternative to country declarations is critical for the success of the implementation of the Paris Climate Agreement. On the technological side, the success of a Lidar mission for greenhouse gases on a microsatellite would open a path to further Lidar system developments. By offering campaign possibilities, the validation phase of MERLIN is the occasion to stimulate research and test new technologies, including ground-based or aircraft-based remotesensing approaches and to prepare future active GHG missions.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 7 Performance Evaluation
### Experimental Results  
In this part, we give the numerical findings from our experiments and use them to analyze the performance under two different scenarios. First, we compare each algorithm's objective function \(\mathbb{D}\), which represents the average of penalty points weighted by task priorities. Second, we determine their task processing characteristics by analyzing the task failure and cloud offloading ratios along with controller pool time (\(\overline{t}_{pool}\)). Lastly, we analyze their temporal results for task completion by considering the averages of the task completion (\(\overline{t}_{total}\)), transmission (\(\overline{t}_{tx}\)), processor queue (\(\overline{t}_{queue}\)), and processing (\(\overline{t}_{process}\)) time values.
# Analysis of the Propulsion Performance and Internal Flow Field of an Underwater Launcher
## 3 Numerical Analysis
### Analysis of Propulsion Performance  
The influence of jet structure on the propulsion characteristics is currently being investigated. Table 3 shows the charging parameters adopted in the calculations.  
The interior ballistics results with three different jet structures are shown in Table 4. It can be seen that when projectile A is utilized, the maximum pressure is 338.9 MPa and the muzzle velocity is 729.6 m/s. The maximum pressure and muzzle velocity are reduced to 325.0 MPa and 688.2 m/s, respectively, when the diameter of the central nozzle is enlarged (projectile B). The overall nozzle area of projectile C is the same as that of projectile B. While projectile C is adopted, the maximum pressure and muzzle velocity are 353.2 MPa and 775.1 m/s, respectively.  
Figure 5 shows the distribution curves of the characteristic parameters of interior ballistics (chamber pressure, projectile velocity, and warhead pressure) under the above calculation conditions. As can be observed from Figure 5a,b, when projectile C is used, the rise rate of chamber pressure is the largest, and when projectiles A and B are used, the chamber pressure rise rate decreases in turn. This is because the propellant gas behind the projectile is continuously ejected to the projectile front through the jet structure. Compared with projectiles A and C, projectile B is more unfavorable to the storage of propellant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Projectile Type** & **x/m** & **\(\omega\)/g** & **\(p_{\text{m}}\)/MPa** & **v/m\(\cdot\)s\({}^{-1}\)** & **\(t\)/ms** \\ \hline
1 & A & 1.028 & 17 & 338.9 & 729.6 & 2.37 \\
2 & B & 1.028 & 17 & 325.0 & 688.2 & 2.42 \\
3 & C & 1.028 & 17 & 353.2 & 775.1 & 2.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Interior ballistics results with three jet structures.  
Figure 4: Chamber pressure of the gas-curtain launcher.  
gas behind the projectile. From Figure 5c,d, it can be found that the ascending rates of projectile velocity from large to small are in the following order: projectile C, projectile A, and projectile B. On the one hand, this is due to the influence of chamber pressure. On the other hand, the force areas of the three gas-curtain projectiles are different. From large to small, projectile C has the largest force area, the second is projectile A, and the smallest is projectile B. From Figure 5e,f, it can be found that projectile B can obtain the lowest head pressure, then projectile A, and finally projectile C. This is also because compared with projectiles A and C, projectile B is more conducive to the expansion of the propellant gas jet near the nozzle exit. It should be noticed that when projectile B is employed, the pressure at the projectile head rises dramatically later in the propulsion phase, which is due to the insufficient expansion of the gas-curtain.  
It is obvious that jet structure within a gas-curtain projectile has an important influence on the drag reduction and propulsion performance of an underwater launcher.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 5 Experimental Analysis
### The Effect of CNN Kernel Size  
The effect of CNN kernel size was first studied. The experimental results are shown in Figure 4. It can be seen that the kernel size of the CNN layer is positively related to the accuracy; that is, the larger the CNN kernel size, the better the result.  
However, with the large kernel size, the training time will be increased, and the verification set will become unstable and unable to converge, so the best CNN kernel size is set to 4 \(\times\) 4, as shown in Table 1.  
Figure 3: The pruning model structure.
## 4 Conclusions  
In conclusion, the waveguide effect is demonstrated in yttria-stabilized zirconia crystal by 4.5 MeV oxygen ion irradiation with different fluences at room temperature. The mode-line spectrum is studied, and the results show that a single mode structure is formed at a fluence of 1.5 \(\times\) 10\({}^{15}\) ions/cm\({}^{2}\). The propagation profiles of the TE-polarized light were investigated in the visible band, and the results have good agreement with the results simulated by the beam propagation method. The Rutherford backscattering/channeling and the high-resolution X-ray diffraction spectra demonstrated that lattice damage occurred during the oxygen ion irradiation. The fabricated yttria-stabilized zirconia waveguide can effectively support the monomode transmission in the near-surface region of the crystal, which is of great significance for the application of integrated optical and optoelectronic devices.  
[PERSON] originally conceived the idea and wrote the manuscript. [PERSON] and [PERSON] carried out the experiments. [PERSON] and [PERSON] carried out the simulations. 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 Nos. 11905159) and the Natural Science Foundation of Tianjin City (Grant Nos. 19 JCYBJC17000).  
Not applicable.  
Figure 6: Electronic energy loss (blue star, divided by 10) and nuclear energy loss (red ball) of 4.5 \(\,\mathrm{\,K}\mathrm{\,e}\mathrm{V}\) oxygen ion irradiation.
## 3 Advances in Control Strategies
### Adaptive and Robust Control  
Adaptive control remains a cornerstone for industrial robots operating under uncertainty, with recent work extending classical schemes toward data-driven and resilience-aware formulations. [PERSON] et al. [33] developed a generative adversarial network (GAN)-assisted MPC for weld-pool imaging that improved weld precision by 15% versus a conventional PID controller on the same hardware under dynamic conditions. [PERSON] et al. [34] proposed a deception-resilient MPC that reduced tracking errors by 12% relative to a classical teleoperation baseline. [PERSON] et al. [35] introduced an adaptive sliding-mode controller that achieved a 10% vibration reduction against a fixed-gain SMC. [PERSON] et al. [72] provided a critical commentary on reinforcement learning (RL) for autonomous process control, outlining pathways to improve robustness in disturbance-prone manipulators by addressing current RL limitations. [PERSON] and [PERSON] [66] reported a 15% stability gain relative to Z-V controllers in legged robotic platforms, a result that, while outside the canonical manipulator domains, highlights transferable adaptive strategies for complex contact dynamics. For fault-tolerant applications, [PERSON] et al. [65] reported a 15% reduction in computational overheads versus LSTM-only predictors, and [PERSON] et al. [73] achieved a 12% latency reduction relative to traditional safety-envelope methods. Collectively, these advances strengthen stability and precision in industrial tasks, such as welding, assembly, and material handling (representative bio-inspired/human-centric adaptive exemplars are summarized in Appendix A, Table A3).  
Nonlinear and Resilient Control Approaches  
Managing nonlinear dynamics has become central to adaptive industrial control. [PERSON] et al. [65] proposed an adaptive continual-learning approach for nonstationary time-series prediction that improved the forecasting accuracy by 20% in industrial datasets. [PERSON] et al. [74] developed an AI-driven nonlinear controller for soft continuum robots, enhancing flexibility by 18% in fluidic environments and demonstrating 15% lower energy use in sustained trials; improvements that increase resilience in complex, unpredictable tasks [66; 73]. [PERSON] et al. [73] introduced advanced safety-oriented speed-control strategies (wheelchair testbed; methods that are transferable to collaborative mobile platforms) that reduced response latency by 12% [34; 74]. [PERSON] et al. [35] demonstrated a 10% vibration reduction under nonlinear disturbance, which complements these results, underscoring the role of adaptive compensation in disturbance rejection [65]. Together, these nonlinear techniques extend adaptive control into high-variability operating regimens aligned with the Industry 4.0/5.0 requirements.
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
## 4 Verification of Developed Impact Formula  
### Comparison between Test Results and Predictions  
There are many impact formulae for assessing the penetration depth, some of which have similar formulations or require further research [5; 7; 10; 51]. Therefore, some representative formulae, such as the modified NDRC, ACE, Hughes, Haldar, UKAEA, UMIST, and Li-Chen impact formulae, are used to compare the degree of accuracy with which the proposed formula in this study predicts the penetration depth. Regarding the evaluation of the proposed formula, 402 experimental results obtained from various experimental programs are used. The impact test data obtained by [PERSON] et al. [24; 52; 53], [PERSON] et al. [54; 55], [PERSON] et al. [56], [PERSON] et al. [57], [PERSON] et al. [58], [PERSON] and [PERSON] [59], and [PERSON] [60] are summarized in Table 1. The total number of specimens is 402; the projectile diameter (\(d_{p}\)) ranges from 20 to 76 mm; the projectile length (\(l_{p}\)) ranges from 24 to 531 mm; the projectile mass  
Figure 9: Idealized spalled cone.  
(\(M_{p}\)) ranges from 15 g to approximately 13,000 g (13 kg); the impact velocity (\(V_{imp}\)) ranges from 91 to 1358 m/s; the concrete compressive strength (\(f^{\prime}_{c}\)) ranges from 26 to 237 MPa; the concrete slab thickness (\(h\)) ranges from 10 to 2740 mm; \(\rho_{steel}\) ranges from 0% to 0.75%; and the steel fiber volume fraction (\(V_{f}\)) ranges from 0.5% to 2.33%. Of the considerable experimental data, only general-impact experiment data are used, where the general-impact experiment involves a solid projectile colliding with a concrete panel, which is reinforced with reinforcing bars at the top and/or bottom. Therefore, only the experimental results corresponding to these conditions are used. Some experimental results having special conditions, such as multiple reinforcing layers, steel plates, and an inside-hollow projectile, are excluded. As the concrete failure mechanism is very complex, it is difficult to conclude that a single parameter is the only factor affecting the impact resistance.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 2 Related Works
### Beaconless Geographic Routing  
With the aim to overcome the weakness of conventional geographic routing schemes in dynamic network topology scenarios, the stateless routing protocols [6; 15; 16; 18; 21] have been proposed, such as contention-based [15], beaconless routing [16], etc. The beaconless routing schemes (BLR) are fully reactive, wherein every node transmits packets without the aid of beacons and neighbor information maintenance. A node will broadcast the packet it wants to transmit to its neighbors. The most suitable neighbor to be a relay node and further forward the packet is determined by a contention mechanism. The contention mechanism regulation of every neighbor determines a proper delay for further forwarding the packet based on the information of how well it is suited as the next-hop relay. The forwarding decision is determined based on the actual topology when forwarding packets. Therefore, beaconless routing schemes are robust to topology changes. However, the acknowledgment collision, which is one of the core issues that must be addressed, has a significant impact on the robustness of volunteer forwarding, latency and energy efficiency [22]. In order to avoid the acknowledgment collision among multiple potential forwarder candidates, contention timers are set to decide when to answer the packet holder. However, it is still not shown how to make PFs autonomously determine their acknowledgment precedence, while excessive message collisions and hop delay are controlled. To solve this problem, some protocols limit the selection of PFs within a so-called forwarding area with some typical shape, such as sector, Reuleaux triangle and circle. There are two major principles to partition forwarding areas: (1) every pair of PFs can hear each other's replies in the same area; (2) the discrete dynamic forwarding delay (the waiting time before answering the packet holder) can be decreased while avoiding collisions.
## 3 Results and Discussion
### Parameter Sensitivity Analysis  
Computational models have, in recent time, paved the way for providing optimal solution to a design problem. Its reliability is dependent on the features of the selected model parameters that add uncertainty to the model output. However, model parameter uncertainty on the output can be evaluated using a structural optimisation design method called the sensitivity analysis (SA). Global sensitivity analysis (GSA) is a class of SA that provides valuable global insight on how the model output variance is dependent on the uncertainty of a particular model parameter by allowing more than one factor to vary at the same time [47; 48].  
In this study, the GSA was applied using the easy GSA MATLAB solution. The study by [PERSON] et al. (2019) contains a detailed development with research data for easy GSA. The Sobol first and total order indices were used to estimate model parameter sensitivity to output variance. The Sobol index is a sensitivity index that decomposes the output variance and estimate the importance of a single or specific set of variables in the uncertainty of the model output [47]. The first order accounts for individual effects on the variance of the dependent variable. On the other hand, the total indices account for the overall dependent variable effect on the model output variance and also inter-variable interactions [48].
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 3 Results
### The AERQ Visualization Tools  
The primary components of the AERQ visualization tools are the maps, which occupy the entire window area. The application uses OpenStreetMap as a background layer--a globally updated, open-source map maintained by a community of volunteers [37]. Over this base map, additional GIS layers are included (e.g., heatmaps and streets) to provide an effective, layered representation of data.  
Heatmaps, generated as GeoTIFF files by a post-processing pipeline following the AERMOD job, represent simulation results aggregated by hourly means. For an efficient web-based display, we employ Mod_tile (vers. 0.61) and Renderd software (vers. 0.61), forming a tile server architecture similar to that of OpenStreetMap (vers. 2023-05-25). This setup transmits only the tiles within the visible viewport, optimizing bandwidth and performance. Users can manually adjust the color scale to visualize heatmaps and select color schemes that best highlight data ranges for their analysis. This feature was implemented by modifying the open-source rendering code, enabling flexible color scaling.  
Additionally, a tile cache ensures that rendering is only performed for tiles not already stored, reducing server load and speeding up map interactions. Tiles are organized in the XYZ format, indexed by column (X), row (Y), and zoom level (Z), which provides efficient grid-based access [38].  
Vector layers, such as streets and stations, are handled using the WKT (Well-Known Text) format. This format supports efficient front-end processing and integration, making it easy to visualize and interact with spatial data directly within the application.  
Finally, MapWall--built on OpenLayers--supports the creation of responsive, interactive \"slippy maps\" for smooth panning and zooming. Menus allow users to filter and select heatmaps based on specific simulation parameters, including simulation type, year, month, and hour, enhancing data exploration by allowing precise temporal and spatial control.  
Some of our map tools include a split-view option ( enabling users to display and compare two maps side-by-side--useful for examining variations across different times, zones, or scenarios. Additionally, chart widgets display data trends with options to download the data in CSV format or as PNG images, supporting further analysis and data sharing. Each chart is also dynamic, allowing users to to toggle data options on and off and zoom into specific areas for closer examination.  
* The Front End (Figure 5) provides user interfaces for viewing and analyzing data, including the following:  
* Input Data: Displays the primary factors driving the model (e.g., all emission sources).
* Air Quality Results (Figure 6): Presents average seasonal high-resolution maps of air quality at the hourly time step.  
Figure 5: AERQ entry point.  
* Air Quality by Wind Direction: Analyzes pollutant distribution for each scenario under varying wind conditions.
## 5 Attacks and Countermeasures
### Attacking P2P
#### 5.1.1 Attacks on the P2P Routing Algorithm  
The DHT lookup algorithm has to rely on the benign, cooperative behavior of the nodes that form the P2P network. Due to this, nodes that manipulate the lookup process have a serious influence on the network.  
During a _Storage and Retrieval Attack_[22], a malicious nodes denies the access to objects maintained by itself. As it is hard to distinguish between unavailable and denied objects during a lookup, most P2P systems are not inherently able to detect this attack. The efficiency of this attack depends directly on the fraction of malicious nodes, since objects are equally distributed to nodes.  
[PERSON] and [PERSON] [22] introduced the _Incorrect Lookup Routing_ attack. Malicious nodes drop or redirect incoming route requests instead of forwarding them to the correct destination. This is a simple and straight forward attack that affects the routing of lookup requests in DHTs. During the lookup process, a request is sent from the source to a node that is closer to the destination regarding the node-ID. Whenever a node receives a request, it checks whether it is the destination or whether it has to forward the request to the next overlay hop closer to the destination. For this recursive routing, [PERSON] _et al_. introduced an analytical model that describes the effects of malicious nodes that drop packets (as shown in Equation 1). The packet delivery ratio (\(\sigma\)) is based on the average number of hops (\(h\)) per request and the fraction of malicious nodes (\(f\)). While dropping lookup requests affects the routing process strongly, redirecting requests is a more subtle attack and may affect even DHTs protected by countermeasures.  
\[\sigma=(1-f)^{h} \tag{1}\]