esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 1 Introduction
### System Context and Challenge
Regulatory frameworks governing cybersecurity in the space domain remain severely underdeveloped, particularly among commercial satellite operators. This lack of regulatory oversight becomes particularly concerning given the critical role that LSNs are expected to play in both civilian and defense communications. According to [16], the incidence of satellite system attacks increased fivefold during the period from 2009 to 2018 compared to the preceding decade (2000-2008), reflecting a significant escalation in the frequency and intensity of threat activity. Figure 1: LEO satellite network. The public disclosure of satellite deployment details (e.g., FCC technical specifications) [17] enables attackers to reconstruct network topologies with high precision. The prevailing reliance on shortest-path routing in LSNs creates predictable traffic patterns, as evidenced by routing analyses in operational constellations [18]. Such patterns have proven attractive to adversaries and have been directly exploited in real world incidents. In November 2022, Starlink suffered a DDoS attack on the layer, which caused some users' satellite services to be disrupted for a period of time [19]. In March 2025, a hacking group claimed responsibility for a cyberattack on 116 Iranian ships that resulted in a full-scale blow to the tanker's satellite communications system [20]. On the Internet, routing bottlenecks are structural weaknesses. In terrestrial Internet infrastructure, route-cost minimization heuristics led to traffic convergence on a small set of high-centrality links, inadvertently creating structural chokepoints. Similarly, in LSNs, such traffic concentration arises due to topology-aware routing optimizations that prioritize minimal hop counts or latency, often at the expense of robustness. The ICARUS [21] framework has experimentally validated that such bottlenecks can be exploited in LSNs through coordinated botnet traffic, though specific performance impacts require further empirical measurement. Attackers can use network probing tools to discover the transmission path from the source node to the destination node [22]. As depicted in Figure 2, adversaries can strategically target three critical attack surfaces in LSNs: uplink and downlink jamming through distributed botnets and ISL flooding using coordinated payloads. The differently colored arrows in the diagram represent traffic from different directions that converges at the bottleneck link. This paper presents a routing algorithm, in which we conducted statistical modeling of the bottleneck paths in LSN. We propose the K-Bottleneck Minimize routing algorithm, which aims to minimize the bottleneck weight during path generation. This algorithm  
Figure 2: An example of adversary attacking LSN. increases the cost for the attacker to launch DDoS attacks and enhances the detectability of the attacker, providing greater security for LSN.
## 3 Results and Discussion
### Spectral Characteristics and Carotenoid Content of C. vulgaris BIN Biomass Extracts  
The cultivation factors' influence on the spectral profiles of pigment extracts from the microalgae is shown in Figure 4. Two absorption bands were noted in the blue-violet (380-500 nm) and in the red (600-680 nm) spectrum range. The arm (420 nm) and the absorption maximum (664 nm) correspond to chlorophyll a. The carotenoid absorption band is in the spectral range of 420-480 nm, with absorption maxima of 440 and 470 nm [28,29]. This allows us to talk about the predominance of carotenoids in the pigments of sample 3.  
Figure 3: Additive effects on the pH dynamics of _C. vulgaris_ cultivation: 1, control (no additives); 2, added H\({}_{2}\)O\({}_{2}\); 3, added H\({}_{2}\)O\({}_{2}\) + pyridoxin. In order to compare homogeneous groups in the determination of pH, one-factor dispersing analysis and F-criteria analysis were used. A significant difference was found between the compared groups (F was significantly greater than 1), which was assessed at \(p\) < 0.05.  
Figure 2: Exponential growth phase of the _C. vulgaris BIN_ microalgae population in semi-logarithmic coordinates.  
Figure 5 shows the composition of photosynthetic pigments in the obtained samples of _C. vulgaris_ biomass. The highest content of carotenoids was observed in the biomass sample with H2O2 and pyridoxine additives, while the content of carotenoids was about 1.8 times higher than that in the control sample. A biomass sample with H2O2 showed a 0.2 mg/g reduction in carotenoids compared to the control sample. The contents of chlorophyll a and b in samples show the same logic.  
The highest content of carotenoids was observed in the extracted biomass with added H2O2 and pyridoxine; the content of carotenoids was about 1.8 times higher than that in the control sample.
## 5 Discussion  
The truck haulage operations in the mine are not constant at each loading and unloading cycle and vary from time to time depending on the working conditions [19]. Therefore, the simulation results for each day were compared using data from October 2018 for long-term simulations. Time parameters were created by the Monte Carlo simulation for each loading point and the simulation parameters were set according to the date (Table 5). The truck capacity was set to 30 tons, and fifty iterations of the simulation were run. The ore production predictions achieved as a result of the simulation were expressed as P10, P50, and P90.  
The observed probability distribution model was used to predict the actual field data in the distribution of the simulation results (Figure 9); the TCT generated was higher than the actual TCT. The actual field data were not included in the distribution of the results of the simulation model that generated the TCT by providing a uniform distribution as the input to the Monte Carlo simulation. In the triangular distribution model, only one day--that is, 16 October--was included. In the simulation model that used the normal distribution, the results for three days were not included in the ore production simulation distribution. To quantify the simulation results, we calculated the root mean square error (RMSE, ton/day) of the P50 results of each simulation result and the actual data (Figure 10). The observed probability distribution model achieved the lowest value at 24.9 ton/day.  
Figure 8: Comparison of the simulation and haulage operations results (observed) obtained on 16 October 2018 for (**a**) uniform distribution, (**b**) triangular distribution, (**c**) normal distribution, and (**d**) observed probability distribution.  
Figure 10: Results of the correlation analysis between the predicted and observed ore productions for ten days in October 2018 for (**a**) uniform distribution, (**b**) triangular distribution, (**c**) normal distribution, and (**d**) observed probability distribution.  
Figure 9: Comparison of the simulation and haulage operation results (observed) for ten days (Month/Day) in October 2018 for (**a**) uniform distribution, (**b**) triangular distribution, (**c**) normal distribution, and (**d**) observed probability distribution.  
The results indicate that the case using observed probability distribution based on the big data of an ICT system showed the best performance for ore prediction. This demonstrates the value and importance of the data collected by an ICT system in the mining site from a practical point of view.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
###### Abstract  
Ambisonics has enjoyed a recent resurgence in popularity due to virtual reality applications. Low order Ambisonic reproduction is inherently inaccurate at high frequencies, which causes poor timbre and height localisation. Diffuse-Field Equalisation (DFE), the theory of removing direction-independent frequency response, is applied to binaural (over headphones) Ambisonic rendering to address high-frequency reproduction. DFE of Ambisonics is evaluated by comparing binaural Ambisonic rendering to direct convolution via head-related impulse responses (HRIRs) in three ways: spectral difference, predicted sagittal plane localisation and perceptual listening tests on timbre. Results show DFE successfully improves frequency reproduction of binaural Ambisonic rendering for the majority of sound source locations, as well as the limitations of the technique, and set the basis for further research in the field.  
ambisonics; binaural; equalisation Article
## 1 Introduction
Therefore, it does not appear to be suitable for computing the resilience of more complex systems such as a transportation network. On the contrary, quantitative methods are more appropriate. Quantitative resilience analyses are often carried out by using analytical or simulation models. However, traffic simulation models, by identifying the vulnerable components of a transport system and comparing different scenarios, appear to be better since they may represent a more useful tool for supporting the decisions made by road and/or tunnel management agencies. There are various studies, which by using traffic simulation models, have investigated the resilience of a transportation network from different perspectives. However, as far as the authors of the present paper are aware, the resilience of road tunnels, as a part of a transportation network, has been scarcely studied. In particular, it has not been sufficiently investigated in relation to different possible strategies for containing the delay time, after the occurrence of a traffic accident or fire in a tunnel and its following functionality recovery. This represents a gap of knowledge that the present paper will attempt to fill. Given the limited body of research dedicated to road tunnel resilience, the goal of our paper is: (i) to perform a traffic macro-simulation model to investigate the issue in greater detail; (ii) to identify a more appropriate strategy for containing the delay time in order to improve tunnel resilience. In this respect, an existing twin-tube motorway tunnel has been investigated. In the light of the above considerations, it is to be stressed that the paper is primary concerned with delay time-resilience relations during an incidental event occurring in a tube of a twin-tube motorway tunnel. Its scope is to describe the concept of resilience for road tunnels and to give additional points of view on certain relevant aspects, as well as to recommend more appropriate recovery strategies in order to improve the tunnel resilience, which is based on the methodology developed and the knowledge of findings achieved. In addition, it allows to collect further information on this topic for the management of the tunnel as a system. The manuscript is organized as follows: the next section contains a literature review concerning studies on the resilience of transportation networks. Then, a traffic simulation model is developed and implemented. Subsequently, the results are presented and discussed by comparing different scenarios, and a proposal is presented to improve the resilience of the tunnel investigated. Finally, comments for practical applications, conclusions, and further developments of the research are made.
## 5 Analysis Results
### Effect of Drag Spring Stiffness  
In order to explore the quantitative effect, the models with drag spring stiffness S as 40 N/m, 4.0 N/m, 0.4 N/m, \(4\times 10^{-4}\) N/m, and \(4\times 10^{-7}\) N/m are computed to investigate the influence of different levels of drag spring stiffness on the vibration natural frequencies of reflector structure. The analysis results are shown in Table 6.  
It is obviously stated that the natural frequencies enlarge with the increase of drag spring stiffness. It is worth noting that there is no difference of natural frequencies between Model 5 and 6. It means  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **Model 3** & **Model 4** & **Model 1** & **Model 5** & **Model 6** \\
**Order** & **S = 40 N/m** & **S = 4.0 N/m** & **S = 4 \(\times 10^{-4}\) N/m** & **S = \(4\times 10^{-7}\) N/m** \\ \hline
1 & 0.9256 & 0.8290 & 0.7847 & 0.7496 & 0.7494 \\
2 & 1.0139 & 0.9009 & 0.8301 & 0.7500 & 0.7495 \\
3 & 1.6613 & 1.0934 & 0.8533 & 0.7705 & 0.7704 \\
4 & 1.6889 & 1.1296 & 0.8819 & 0.7820 & 0.7818 \\
5 & 2.4792 & 1.3610 & 0.9268 & 0.7990 & 0.7989 \\
6 & 2.5731 & 1.5566 & 0.9858 & 0.8090 & 0.8087 \\
7 & 3.1160 & 1.6749 & 1.0077 & 0.8121 & 0.8120 \\
8 & 3.2852 & 1.8324 & 1.0359 & 0.8650 & 0.8648 \\
9 & 3.4908 & 1.8618 & 1.0896 & 0.8749 & 0.8744 \\
10 & 3.7181 & 2.0415 & 1.1364 & 0.8816 & 0.8815 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Natural frequencies (Hz) of Mode 3, 4, 1, 5, and 6.  
Figure 8: First order mode shapes of Model 1 and 2 in 3D visualization.  
that the natural frequencies of Model 6 with drag spring stiffness S = 4 \(\times\) 10\({}^{-7}\) N/m can be treated as the lower limit of this PDSR with drag springs. This lower limit of structural dynamic property is better than the model without drag springs. It is inferred that the existence of drag springs with pre-tensioned force 3.5 N can substantially change the dynamic property of this reflector structure even though the stiffness tends to zero.  
When the stiffness becomes larger, the dispersion of first ten natural frequencies also gets bigger, especially in Model 3. It is indicated that the excessive stiffness of drag springs may apparently affect the stiffness distribution of petals and drag springs. The proportion of drag spring and petal stiffness is the important factor to decide the degree of collaborative work in adjacent petals as shown in Figure 7a. Thus, the moderate stiffness as S = 0.4 N/m in Model 1 is feasible for improve the dynamic property of this reflector structure.
## 4 Experimental Results
### Simulation Settings
All experiments were conducted in five independent trials on a computational server with Intel Xeon W2295 CPU, NVIDIA GeForce RTX 3090 GPU, 64 GB memory, and Ubuntu 20.04 OS (Santa Clara, CA, USA). The experimental results are detailed in the following order. The learning capability of all compared methods in OpenAI robot arm scenarios is evaluated in Section 4.2, where the learning curves are shown in Figure 3, and the average rewards are compared in Table 3. The corresponding results of the Panda robot arm scenarios are demonstrated in Figure 4 and Table 4. Figures 5-7 illustrate the control behaviors of DDPG-DMP and the baseline in OpenAI and Panda robot arm scenarios as a case study. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **DDPG-DMP** & **DDPG** & **TD3** & **SAC** & **NDP** \\ \hline PandaReach-v2 & \(0.99\pm 0.00\) & \(0.41\pm 0.24\) & \(0.99\pm 0.00\) & \(0.99\pm 0.01\) & \(0.00\pm 0.00\) \\ \hline PandaReachJoints-v2 & \(0.99\pm 0.01\) & \(0.00\pm 0.00\) & \(0.92\pm 0.01\) & \(0.85\pm 0.41\) & \(0.00\pm 0.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Success rates over two Panda simulation control scenarios. Figure 4: Learning curves over two Panda robot arm simulation scenarios. Curves are uniformly smoothed for visual clarity. The shaded region represents the corresponding standard deviation. Figure 5: Screenshots of robot arm in the Pusher-v0 task using trained DDPG-DMP and DDPG agents during one complete rollout. The subfigures indicate the robot’s state at steps \(0,25,50,75,\) and \(100\) from left to right. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Reacher-v1** & **Pusher-v0** & **Thrower-v0** & **PandaReach-v1** & **PandaReachJoints-v1** \\ \hline Networks Structure & \((256,256)\) & \((256,256)\) & \((256,256)\) & \((256,256)\) & \((256,256)\) \\ \hline Batch Size (\(J\)) & \(256\) & \(256\) & \(256\) & \(256\) & \(256\) \\ \hline Discount Factor (\(\gamma\)) & \(0.99\) & \(0.99\) & \(0.99\) & \(0.95\) & \(0.95\) \\ \hline Steps per Update (\(K\)) & \(5\) & \(5\) & \(5\) & \(5\) & \(5\) \\ \hline Memory Buffer Size & \(10^{6}\) & \(10^{6}\) & \(10^{6}\) & \(10^{6}\) & \(10^{5}\) & \(10^{5}\) \\ \hline Warmup Steps & \(10^{4}\) & \(10^{4}\) & \(10^{4}\) & \(2000\) & \(2000\) \\ \hline Control Modes & Torque & Torque & Torque & End-Effector Position & Joint Angle \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 3 Problem Statement
### Irradiation  
The available power from the solar panels in Equation (1) is obtained through the irradiance model developed in [19]. The model provides the flux of irradiation \(G_{t}^{\prime\prime}\) (W/m\({}^{2}\)) obtained from a combination of orbital parameters and spin of the CubeSat (also known as attitude), which is here combined with the solar panel's area (\(A_{w}\)) of each side (\(w\)) and the efficiency (\(\eta\)) of the cells. The standard CubeSat has six sides, and for this reason, the summation is from one to six.  
\[r_{t}=\sum_{w=1}^{6}\eta A_{w}G_{t}^{\prime\prime} \tag{1}\]
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
###### Abstract  
With the rapid proliferation of the Internet of Things (IoT) in recent years, the number of IoT devices has surged exponentially. These devices collect and transmit vast amounts of data, including sensitive information. Encrypting data is a crucial means to prevent unauthorized access and potential misuse. However, the traditional cryptographic schemes offering robust security demand substantial device resources and are unsuitable for lightweight deployments, particularly in resource-constrained IoT devices. On the other hand, with the automotive industry making strides in autonomous driving, self-driving vehicles are beginning to integrate into people's daily lives. Ensuring the security of autonomous driving systems, particularly in preventing hacker infiltrations, is a paramount challenge currently facing the industry. An emerging lightweight sequence cipher--aiming to strike a balance between security and resource efficiency--has been proposed in this paper based on S-box substitution and arithmetic addition. The designed security threshold is 280. It has been verified that with a slight performance disadvantage, it can reduce memory usage while ensuring the security threshold. The key stream generated by this structure exhibits excellent pseudo-randomness.  
sequence cipher; S-box substitution; pseudo-randomness; IoT devices +
Footnote †: journal: _Journal of the Royal Society Interface_  
sequence ciphers aim to achieve simplicity and efficiency in design, they still maintain a high level of security. They undergo thorough evaluation and analysis to defend against common attack methods and provide robust encryption protection to meet the security requirements of resource-constrained environments.
## 2 Design and Fabrication
### Design and Fabrication of the Micro Sensor
The silicon diaphragm has dimensions of 900 \(\times\) 450 \(\times\) 50/40 \(\upmu\)m. Four boron-doped piezoresistors (250 \(\times\) 20 \(\upmu\)m) are arranged in parallel on the diaphragm along the \(<\)110\(>\) direction and connected with aluminum wire to form a Wheatstone bridge. The silicon piezoresistive pressure sensor is fabricated with the micromachining technology and assembled using the anodic bonding technology. As illustrated in Figure 3, the substrate of the pressure sensor is an n-type (100) SOI wafer with a resistivity of 1-10 \(\Omega\cdot\)cm. The thicknesses of the silicon device layer, the buried oxide layer, and the handle layer of the SOI are 50/40, 0.5, and 280 \(\upmu\)m, respectively. The main processes follow: Firstly, the device layer is patterned by photoresist and doped by boron implantation with a doping concentration of 4 \(\times\) 10\({}^{14}\) atoms/cm\({}^{2}\) to form piezoresistors (steps 1 and 2). The doped region is then covered by a polyimide film, exposing only the alignment marks. By using the deep reactive ion etching (DRIE) technique, the alignment marks are transferred to the silicon substrate (step 3). Secondly, the contact areas between the Al wires and the piezoresistors are exposed by patterned photoresist and doped by heavy boron implantation with a doping concentration of 2 \(\times\) 10\({}^{15}\) atoms/cm\({}^{2}\) to form the ohmic contact (step 4). Subsequently, a layer of 500 nm-thick silicon oxide is deposited in the PECVD system to form the bottom passivation layer, while the ohmic contact areas are exposed by RIE (steps 5 and 6). Then, a layer of 1 \(\upmu\)m-thick Al is sputtering deposited and patterned by wet etching to form interconnects and four output pads of the Wheatstone bridge (step 7). Figure 2: Schematic diagrams of the silicon piezoresistive pressure sensor: (**a**) top view and (**b**) cross-section view of the pressure sensor; (**c**) dimensional parameters; (**d**) Wheatstone full-bridge of the pressure sensor. After that, a layer of 500 nm-thick silicon oxide is deposited in the PECVD system to form the top passivation layer with the output pads exposed by RIE (steps 8 and 9). In order to prevent the piezoresistors from breakage by the voltage applied during anodic bonding, the passivation layers on the edge of the SOI are also etched to expose the device layer and another 1 \(\upmu\)m-thick Al layer is sputtered and patterned to connect the device layer and handle layer. Next, the pressure sensor's sensing diaphragm is patterned by double-sided alignment lithography and etched by DRIE and RIE (steps 10, 11, and 12). After polishing by CMP, the handle layer of SOI is bonded to Pyrex7740 glass by anodic bonding with voltage of 1600 V, current of 20 mA, temperature of 300 \({}^{\circ}\)C, pressure of 1600 N, and time of 20 min (step 13). Finally, the sensor dies are released from the SOI wafer by using a dicing saw (step 14).