esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Endogenous Security Concepts  
Endogenous security focuses on embedding safety mechanisms within system architectures to address vulnerabilities at their core, providing a robust defense against unpredictable threats. A foundational concept in this domain is Cyberspace Mimic Defense (CMD) based on Dynamic Heterogeneous Redundancy (DHR), which employs diverse component instances and dynamic switching to deter attackers [18]. Recent studies have connected structural security to resilience engineering [19] and elaborated on CMD's theoretical foundations and applications [20]. Other works have explored endogenous security in contexts such as cryptography for routers and cloud services [21], wireless communications [22], AI-driven cybersecurity [23], and industrial control systems [24]. Applications to IoT [25], blockchain [26], and Security-as-a-Service models in cloud environments [27] further demonstrate its potential.  
While endogenous security concepts show promise, challenges in scalability and implementation costs persist, especially for large-scale infrastructures. Our P-R-A-L framework builds on these ideas by translating endogenous security principles into measurable indicators. Specifically, the Destruction Resistance (R) dimension incorporates concepts like \"heterogeneous redundancy architecture\" and \"mimicry-based fault tolerance\", while the Evolutionary Learning (L) dimension includes indicators for \"dynamic reconfiguration\" and \"node-level endogenous adaptation\". By grounding these concepts in quantifiable metrics, our framework bridges the gap between theoretical endogenous security and practical resilience assessment.
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
###### Abstract  
Motivated by applications in unsourced random access, this paper develops a novel scheme for the problem of compressed sensing of binary signals. In this problem, the goal is to design a sensing matrix \(A\) and a recovery algorithm, such that the sparse binary vector \(\mathbf{x}\) can be recovered reliably from the measurements \(\mathbf{y}=A\mathbf{x}+\sigma\mathbf{z}\), where \(\mathbf{z}\) is additive white Gaussian noise. We propose to design \(A\) as a parity check matrix of a low-density parity-check code (LDPC) and to recover \(\mathbf{x}\) from the measurements \(\mathbf{y}\) using a Markov chain Monte Carlo algorithm, which runs relatively fast due to the sparse structure of \(A\). The performance of our scheme is comparable to state-of-the-art schemes, which use dense sensing matrices, while enjoying the advantages of using a sparse sensing matrix.  
unsourced random access; compressed sensing; low-density parity-check codes; glauber dynamics +
Footnote †: journal: 2021  
0021
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 3 Preliminaries
### Energy Consumption Model  
Each UAV has an initial energy \(E_{max}\) and residual energy \(E_{i}(t)\) at time \(t\), which depletes as the UAV transmits data, moves, and performs other tasks. The energy consumed during communication is calculated as follows  
\[E_{con}=E_{tx}+E_{rx}, \tag{2}\]  
where \(E_{tx}\) and \(E_{rx}\) represent the total energy a UAV expends during the transmission (\(E_{tx}\)) and reception (\(E_{rx}\)) of data packets, respectively. It is crucial to manage UAVs' energy efficiency and overall network longevity of the UAVs. The total energy consumption of all UAVs in the network is defined as follows:  
\[E_{total}=\sum_{i=1}^{N}E_{i}(t). \tag{3}\]  
The objective is to minimize \(E_{total}\) while selecting UAVs with sufficient energy to serve as CH as follows:  
\[E_{i}(t)>E_{th}\ \forall_{i}\in CH, \tag{4}\]  
where \(E_{th}\) is the threshold energy level of the node \(i\). The energy model in the HMAO framework calculates the total energy consumed in both single-path and multi-path routing, accounting for the transmission and reception costs based on distance and path selection. For single-path routing, the total energy consumption is defined as follows:  
\[E_{total\_single}=\sum_{k=1}^{n}E_{tx}(l,d_{k})+E_{rx}(l), \tag{5}\]  
where \(n\) is the number of hops, \(E_{tx}(l,d_{k})\) is the transmission energy for each hop \(k\), and \(E_{rx}(l)\) is the reception energy level. Energy consumption for multi-path is defined as follows  
\[E_{total\_multi}=\sum_{p=1}^{m}\sum_{k=1}^{np}E_{tx}\left(l,d_{k,p}\right)+E_{ rx}(l), \tag{6}\]  
where \(m\) is the number of paths, \(np\) is the number of hops in path \(p\), and \(d_{k,p}\) is the distance for each hop in each path.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Dataset Preprocessing  
First, experiments were conducted using an enterprise dataset comprising security alert logs issued by six NIDSs that were used to monitor a Class B network comprising mobile devices, servers, and personal computers with over 1000 users and 30,000 hosts. The logs contained 131.11 million security alerts issued by the NIDSs, referred to as Appliance IDs A, B, C, D, E, and F, for security and ethical reasons, over ten months from 1 January to 31 October 2017. Table 2 presents the dataset statistics.  
Among all the alerts, only 310,125 were labeled as positive, while the rest were labeled as negative, yielding an imbalance rate of 0.237%. Unique alerts that contained distinct feature values were sampled from the dataset, and 416 positive and 25,971 negative samples were obtained, resulting in an imbalance rate of 1.577%. This sampled dataset can be efficiently loaded into the memory for further analysis. Figure 3 presents a sample security alert message issued by an enterprise security appliance representing an anomalous network interaction--an unusual port scan.  
Second, the experiments were conducted using the UNSW-NB15 benchmark dataset. Table 3 presents the dataset statistics. The dataset contained 257,680 records of 164,675 attacks and 93,005 normal traffic. To obtain an imbalanced dataset, we sampled 37,000 and 4000 records from the normal and attack data, respectively, which resulted in an imbalance rate of 9.756%. A sampled benchmark dataset was used to conduct the experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Appliance ID** & \begin{tabular}{c} **(\#) Total** \\ **Negative** \\ \end{tabular} & \begin{tabular}{c} **(\#) Total** \\ **Positive** \\ \end{tabular} & \begin{tabular}{c} **Imbalance** \\ **Rate (\%)** \\ \end{tabular} & \begin{tabular}{c} **(\#) Unique** \\ **Negative** \\ \end{tabular} & \
\begin{tabular}{c} **(\#) Unique** \\ **Positive** \\ \end{tabular} &
\begin{tabular}{c} **Imbalance** \\ **Rate (\%)** \\ \end{tabular} \\ \hline A & 11.841 & 16443 & 0.139 & 2581 & 90 & 3.370 \\ B & 66.209 & 3658 & 0.006 & 2018 & 22 & 1.078 \\ C & 0.558 & 6782 & 1.201 & 1581 & 195 & 10.980 \\ D & 5,112 & 1386 & 21.330 & 101 & 19 & 15.833 \\ E & 15.792 & 1783 & 0.011 & 6827 & 26 & 0.379 \\ F & 36.396 & 280073 & 0.764 & 12863 & 64 & 0.495 \\ \hline Total & 130.801 & 310125 & **0.237** & 25971 & 416 & **1.577** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the enterprise dataset.  
Figure 3: A security alert sample.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 4 Numerical Simulation of Simply Supported Beam and Space Truss
### Simply Supported Beam Model
#### 4.1.2 Virtual Mass Addition and Response Construction  
According to the theory of the additional virtual mass method, first, the position of the additional virtual mass at the substructural midpoint is typically determined. Second, the optimal additional virtual mass is selected using the calculated relative sensitivity of the structural lower-order frequencies. Consider the addition of a virtual mass to substructure 5 as an example. The relative sensitivity of the first fourth-order frequency when the substructure 5 added with each 0.1 kg virtual mass was calculated (Figure 2). Based on the relative sensitivity calculations for the first fourth-order frequencies of each substructure with respect to the additional virtual mass value, 0.3 kg virtual mass was added to each substructure of the original structure to obtain ten different virtual structures.  
Figure 1: Model of simply supported steel beam.  
Impulse excitation was applied to the original structure with an action time of 2 s and a sampling frequency of 10,000 Hz to determine the acceleration response of the original structure and virtual structures. The acceleration response signal was preprocessed using an exponential window function. Based on Equation (2), the frequency-domain responses of the original and virtual structures, adding a virtual mass on substructure 5, were determined (Figure 3).
## 3 Experiments and Results
### Influence of Structural Parameters on Cavitation Performance
#### 3.1.3 Outer Diameter of Hydrodynamic Cavitation Generator  
With the tooth row number set at 3 and 10 teeth per row, the impact of the hydrodynamic cavitation generator's cylinder diameter on cavitation performance was investigated by maintaining the size of each tooth unchanged and selecting the tooth outer diameters as D100, D120, D140, D160, and D180.  
As shown in Figures 14 and 15, the volume fraction of cavitation bubbles increases with the increase in the outer diameter of the fixed gear. With the increase in the outer diameter of the fixed gear, the centrifugal force acting on the hydrodynamic cavitation generator increases [33], leading to an increase in the relative velocity between the rotated and fixed gears. This increase in relative velocity results in a higher shear force, creating more low-pressure zones and enhancing cavitation performance. It is indicated that centrifugal force can induce the formation of more aeration bubbles. Furthermore, as the outer diameter of the hydrodynamic cavitation generator increases, the circumferential speed increases when the speed is constant, which means that the shear speed increases. Consequently, a larger outer diameter of the hydrodynamic cavitation generator leads to better cavitation effects. However, it is crucial to consider that the size of mechanical equipment should not be expanded without limitations when considering energy consumption and processing convenience.  
Figure 13: Number of teeth in a single row versus cavitation bubble volume fraction.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 6 Results and Evaluation
### Network Security
#### 6.5.2 Security Evaluation  
We also discuss the best practices for evaluating the security when using the network emulation platforms in this study:  
* **Software version**: Ensure that all platform software, including any Virtual Machines (VMs) and hypervisors used, are running the latest and most-stable versions. Regularly updating software helps to address security vulnerabilities and improve overall system performance.
* **Platform security and configuration analysis**: Conduct a thorough evaluation of the platform's configuration settings, network settings, node configurations, and security features. Identify any weak or misconfigured settings that could potentially create vulnerabilities in the system. Addressing these issues will enhance the platform's overall security condition.
* **Access and identity management**: Analyze and assess the effectiveness of the access control mechanisms and identity management processes used by the platform. This includes evaluating username and password combinations, as well as integration with external authentication systems to ensure only authorized users can access the network resources.
* **Analysis of emulated network nodes**: Perform security analysis on emulated components within the platform, such as routers, switches, and VMs. Pay specific attention to the security of different node models, such as Cisco equipment, to identify potential vulnerabilities and address them proactively.  
* **Traffic analysis**: Monitor network traffic for anomalies during the testing and production phases. Identify unencrypted communication, unauthorized network traffic, and potentially malicious network activity. Timely detection and response to such incidents can prevent security breaches and data compromises.
* **Vulnerability testing**: Conduct vulnerability testing and analysis by scanning the emulated network for open ports, services, and potential weaknesses. Assess the severity of identified vulnerabilities and take appropriate measures to remediate them promptly.
* **Compliance**: Evaluate relevant industry standards and regulations that the platform should comply with, such as PCI-DSS [70], GDPR [71, 72], ISO 27000 Series [73], NIST 800 Series [74], and Network Security Design (SS-018) [75]. Ensure the platform adheres to these standards and assess any vulnerabilities that could impact compliance. Maintaining compliance helps to protect sensitive data and maintain a high level of security within the network.
## 1 Introduction  
### Research Context and Challenges  
In the context of the global digital transformation in education, the application of intelligent technologies is reshaping various aspects of education. A careful analysis of the nature of learning tasks, combined with the characteristics of these technologies, is equally applicable to physical education. Research shows that image- and video-based teaching methods significantly enhance students' athletic performance and interest in learning in university physical education programs [1]. Educators need to understand how to find, access, and utilize these educational technologies [2].  
Current challenges in tactical teaching for university basketball include the complexity of teaching strategies and the need for effective training methods. Virtual reality technology can help students better understand and execute complex tactics by creating immersive training environments. This technology not only simulates dynamic confrontation scenarios in real games but also analyzes players' running trajectories and tactical execution using multi-camera tracking systems, thereby improving the effectiveness of training [3]. Furthermore, the Tactical Game Approach (TGA) is proven to enhance students' physical fitness and game performance [4]. Therefore, designing specialized tactical knowledge and cognitive skill training programs for basketball players may be beneficial [5].  
Although the applications of digital twin technology and federated learning frameworks have been widely studied in industrial and privacy protection fields, there is still a significant research gap in their use within physical education. In the industrial sector, digital twin technology is primarily applied to optimize production processes and improve efficiency by creating virtual models of physical objects [6]. Meanwhile, federated learning frameworks are widely used for privacy protection, especially in the processing of medical data, by allowing model training without sharing data and, thus, safeguarding patient privacy [7; 8].  
In physical education, digital twin technology can be employed to simulate athletes' training processes, provide personalized training plans, and monitor their performance in real time. Similarly, federated learning frameworks hold potential in physical education, particularly for providing personalized learning experiences while ensuring privacy protection [9]. This study aims to address three core issues: how to achieve high-fidelity dynamic tactical simulations; how to ensure cross-institutional data collaboration while protecting privacy; and how to establish a multidimensional competency evaluation framework.
## 3 Full Characterization of the V2G
### Techno-Economic Dimension  
The techno-economic dimension is composed of attributes that characterize the types of electric energy storage considering technical and financial factors.  
It is formed by three main attributes: Reliability, Technical Facility and Costs, which are divided into sub-attributes (refer to Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Scenario** & **Uncontrolled Charging** & **Smart Charging** \\ \hline IRENA, **2019**[10] & 50\% penetration in an isolated system with a 27\% solar share & 9\% increase in peak load & 5\% increase in peak load (V2G) and down to 0\% curtailment \\ \hline RMI, **2016**[11] & 23\% penetration US (California, Hawaii, Minnesota, New York, Texas) & 11\% increase in Peak Load & 1.3\% increase in peak load (V2G) \\ \hline Taljegard, **2017**[12] & 100\% penetration in Denmark, Germany, Norway and Sweden & 20\% increase in peak load & 7\% decrease in peak load (V2G) \\ \hline McKenzie, **2016**[13] & 50\% Penetration in the Island of Oahu, Hawaii, US 23\% VRE share & 10 \(\times\) 23\% VRE curtailment without Evs & 8–13\% VRE curtailment with smart charging Evs \\ \hline Chen and Wu, **2018**[14] & 1 Million Evs in the Guanzhou region, China & 15\% increase in peak load & 43–50\% reduction in valley/peak difference \\ \hline \hline \end{tabular} Source: the authors, assembly based on [10].  
\end{table}
Table 1: Studies on the impacts of smart charging technologies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Recharge Times** & \\ \hline Emergency Cable & Wall Box & Fast Charge \\ \hline
6.6 kW & 6.6 kW & 50 kW \\ \hline
40 kW 100\% & 40 kW 100\% & up to 80\% \\ \hline up to 20 h & up to 8 h & up to 40 min \\ \hline Standard Connection & Standard Connection & Fast Charge Connection \\ \hline \hline \end{tabular} Source: The authors, assembly based on [10].  
\end{table}
Table 2: Electric vehicle recharge times.
# Macro-Meso Mechanical Behavior of Loose Sand under Multi-Directional Cyclic Simple Shear Tests
## 1 Introduction
Although bi-directional simple shear tests are useful in studying the effect of static shear stress and shear path on liquefaction resistance, limited by the testing device, the horizontal stress of the specimen is hard to determine. As a result, it is difficult to analyze the stress state of the specimen and failure mechanics. With the aid of computational simulation, it is possible to study the particle movement and soil fabric changes during liquefaction, and better understand the mechanics of liquefaction [16]. DEM simulations showed that the liquefaction of a specimen under a bi-directional shear path took fewer cycle numbers to liquefy. With the increase in the angle between the consolidation shear stress and the cyclic shear stress, the liquefaction resistance decreased [17]. The liquefaction resistance of samples under the figure-8-shaped shear path and the circular shear path was significantly lower than that under the uni-directional shear path [18; 19]. From a meso-mechanical aspect, [PERSON] et al. [20] found that the liquefaction resistance of samples under bi-directional shear paths is generally lower than that of samples under uni-directional shear paths, and it concluded that fabric anisotropy plays an important role in bi-directional cyclic simple shear tests. [PERSON] et al. [21] found that samples under uni-directional shear and bi-directional shear have similar meso-structures, but the samples are more prone to liquefaction under bi-directional shear. The above studies concluded that there is an influence of sand density and cyclic loading on sand liquefaction; however, the influence of consolidation shear stress direction and the cyclic shear path on loose sand is still not clear. Loose sand has a low shear strength, and even negligible changes in stress state can affect its liquefaction characteristics. Therefore, it is necessary to conduct systematic research on the liquefaction characteristics of loose sand. In this study, loose sand samples under different complex shear paths and directions of consolidation shear stress were simulated using bi-directional simple shear tests. Their liquefaction characteristics and their corresponding meso-mechanisms were analyzed. This study provides a theoretical basis for the development of the liquefaction constitutive model of sand.
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 5 Dynamic Characteristics of the ORC Engine  
In real-life applications, the working conditions of the ORC engine vary dynamically. The performance variation of the system can be predicted with advanced transient modelling or by integrating time constants in quasi-steady models. Experimental time-resolved data and step-response characteristics are key for developing dynamic system models.  
The time response of the expander power output to rising or falling steps in pump rotating velocity is investigated for a heat-source temperature set and held constant at 120 \({}^{\circ}\)C and for an expander resisitve load of 25 \(\Omega\). Once a steady operation is attained--with \(\omega_{\text{p}}\,=1100\) RPM--the data collection is initiated before increasing the pump velocity up to 2000 RPM and later decreased back to the initial value. The rising-step response is reported in Figure 8a using non-dimensional parameters, as defined in Equation (13). The rising (or falling) time, \(\tau\), that is the time taken for the response to rise (or fall) from 10% to 90% of the steady-state response, is thus determined from the analysis of the transient response. The dynamic test is repeated with various steps, namely between 1100 and 1500, and between 1110 and 2500 RPM. The influence of the step amplitude on the rising and falling times is reported in Figure 8b, using the expander power step amplitude as a reference, \(\Delta\dot{W}_{\text{exp}}\), defined as:  
\[\Delta\dot{W}_{\text{exp}}\,=\,\left|\dot{W}_{\text{exp,fin}}\,-\,\dot{W}_{ \text{exp,init}}\right|\quad. \tag{14}\]  
Figure 7: **(a)** Expander isentropic efficiency, \(\eta_{\text{exp,is}}\) expressed in %, mapped against the scroll spindle speed, \(\omega_{\text{exp}}\) and the pressure ratio, \(r_{P}\). Performance map obtained from cubic-spline interpolation of experimental data, indicated as grey dots on the map, obtained for heat-source temperatures ranging from 100 to 140 \({}^{\circ}\)C. **(b)** Expander filling factor as a function of the expander spindle speed.  
All system response time to rising steps in the pump velocity are higher than the corresponding falling steps, by 7% for the largest step tested and by up to 30% for the smallest. In other words, the ORC system reacts more rapidly to a step down in power than to a step up, as long as only the pump speed is varied.  
For both rising and falling steps, it is also observed that the response time decreases with increasing step amplitude. This denotes that the ORC-engine inertia has a more significant impact for smaller power steps. Care must then be taken for predicting the dynamic behaviour of ORC engines using quasi-steady models that estimate the time-varying performance based on steady-state results.