esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Quantum Authentication in the MQTT Protocol
## 3 Results
### Results for Digital Signature Authentication
#### 3.1.2 Memory Usage  
Memory usage, as displayed in Figure 5, is seen to have a dramatic increase for the broker when running all three levels of the digital signature when there is only 1 GB of memory and 1 CPU core available. There is a jump from approximately 2 MB to about 7 MB, 12 MB and 10 MB for dilithium2, dilithium3 and dilithium5, respectively. The difference for other devices and security levels is much more subtle. Not considering the aforementioned circumstances, there was no increase in memory for the subscriber with dilithium2, but the Publisher used 1.84 MB for dilithium2 with the restricted test. The increase was between 0.46 MB and 0.48 MB for dilithium3. For dilithium5, there was an increase of 0.12 MB for the subscriber but a decrease in memory usage by 0.01 MB for the publisher.  
Memory usage increases slightly as the security level increases when there is 8 GB of memory available. The overall change from dilithium2 to dilithium5 is less than 0.23 MB for all three devices in the interaction. The greatest difference is seen for the publisher, with 0.23 MB (0.4 MB from dilithium2 to dilithium3 and 0.19 MB from dilithium3 to dilithium5); then the subscriber, with 0.1 MB (0.4 MB from dilithium2 to dilithium3 and 0.06 MB from dilithium3 to dilithium5); and the least difference is seen for the Broker, with 0.08 MB (0.3 MB from dilithium2 to dilithium3 and 0.05 MB from dilithium3 to dilithium5).  
Figure 4: Percentage CPU usage When running MQTT.  
The memory usage sometimes increases with the security level when there is 1 GB of memory available. However, there were a few times when it decreased. For instance, the memory usage decreased for the publisher as the security level increased (3.23 MB for dilithium2, 2.3 MB for dilithium3 and 2.02 for dilithium5).
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 8 Related Work  
This paper focuses on the addition of fault-tolerance properties in the presence of unchangeable environment actions. This problem is an instance of model repair where some existing model/program is repaired to add new properties such as safety, liveness, fault-tolerance and so forth. Model repair with respect to CTL properties was first considered in [11] and abstraction techniques for the same are presented in [12]. Previously, [PERSON] et al. [13] has considered the problem of model repair for UNITY specifications [14]. These results identify complexity results for adding properties such as invariant properties, leads-to properties and so forth. Repair of probabilistic algorithms has also been considered in the literature [15]. [PERSON] and [PERSON] [16] provide repair algorithms for cases where the new desired properties are in conflict with existing properties.  
The problem of adding fault-tolerance to an existing program has been discussed in the absence of environment actions. This work includes work on controller synthesis [17; 18; 19; 20]. A tool for automated addition of fault-tolerance to distributed programs is presented in [1]. This work utilizes BDD based techniques to enable the synthesis of programs with state space exceeding \(10^{100}\). However, this work does not include the notion of environment actions that cannot be removed. Hence, applying it in contexts where some processes/components cannot be changed will result in unacceptable solutions. Model repair for distributed programs using a two-phase lazy approach is proposed in [21].  
The work on game theory [22; 23; 24] has focused on the problem of repair with the 2-player game where the actions of the second player are not changed. However, this work does not address the issue of fault-tolerance. Also, the role of the environment in our work is more general than that in [22; 23; 24]. Specifically, in the work on game theory, it is assumed that the players play in an alternating manner. By contrast, we consider more general interaction with the environment. In [25], authors have presented an algorithm for adding recovery to component-based models. They consider the problem where we cannot add to the interface of a physical component. However, it does not consider the issue of unchangeable actions considered in our work.  
[PERSON] et al. [26; 27] focus on the notion of auditable events which captures special events for which the program must first transition to a special state called auditable state where every process is aware of the auditable events, before returning to the normal states. Thus, compared with fault-span considered in this paper (cf. Definition 14), the set of states that a program may reach other than its states includes states that can be reached by fault transitions and those that can be reached by auditable transitions. Considering the problem of auditable restoration in presence of environment actions is an interesting future work.
## 3 Methodology
### Research Design  
MitM attacks encompass different types of attacks, including Address Resolution Protocol (ARP) spoofing, DNS spoofing, Secure Sockets Layer (SSL) Stripping, and session hijacking. This study focuses on DNS spoofing attacks. Monitoring multiple attacks simultaneously can strain the resources and lead to performance degradation; hence, focusing on a single attack is convenient to ensure that the proposed scheme remains effective and responsive [18]. DNS spoofing is a well-documented MitM attack, making  
Figure 1: The DNS Architecture.  
it a significant threat to network security. Failure to address the attack may result in severe consequences such as data interception, session hijacking, and unauthorized access. Machine learning enables the system to learn and make predictions. The study uses a machine learning-based approach to develop and evaluate the proposed anomaly-based detection scheme for DNS.  
The study employs the CSA for feature optimization and search space exploration, while a Decision Tree is used for intrusion detection. This study utilizes secondary data downloaded from Kaggle, which is both time- and cost-efficient, as the data are readily available and reduce the time, effort, and resources required for primary data collection. Kaggle provides access to large and diverse datasets, which improves robustness and comprehensiveness for analysis. These datasets are often well documented and curated, ensuring high quality and reliability. Utilizing secondary data also allows for benchmarking results against other studies, providing valuable context for evaluating the effectiveness of a given scheme.
## 7 Proposed Extensions to Bio-2 FA-IoD
### Enhanced Session Key Agreement with Diffie-Hellman for Perfect Forward Secrecy
#### 7.1.2 Security Implications  
This DH integration would provide **perfect forward secrecy** because the session key \(KS_{final}\) is derived from ephemeral secrets (\(x_{OD}\) and \(x_{GCS}\)) that are generated uniquely for each session and are not stored long-term. Even if an attacker compromises the long-term \(P_{ID_{O}}\) or \(RPMD_{ID_{O}}\), past session keys cannot be re-derived. The initial KS derived from \(P_{ID_{O}}\) would protect the DH public key exchange, mitigating potential man-in-the-middle attacks during the DH part of the exchange, as the 'OTAC' itself is authenticated.
## 3 Results and Discussion  
### Characterization of Functionalized Silica Particles  
Silica particles were effectively prepared using tetraethoxysilane as a precursor. TEM micrographs clearly revealed a highly regular spherical form (Figure 3a) for silica particles obtained at a low stirring speed. These particles showed a highly homogeneous diameter that varied in a narrow range (_i.e._, between 570 and 650 nm). Particles were slightly more irregular in form (Figure 3b) when prepared at a higher stirring speed. Diameter size was considerably reduced but a homogeneous distribution could still be observed (_i.e._, values were always within the 20-30 nm interval). The two kinds of silica preparations will be designated as micro (M) and nano (N) particles.  
Figure 4: Full scale XPS spectra (**a**) and details corresponding to silicon (**b**) and nitrogen (**c**) XPS signals detected in the functionalized silica nanoparticles.  
Figure 3: TEM micrographs of functionalized microspheres (**a**); and nanospheres (**b**).  
Surface functionalization by reaction with (3-aminopropyl) triethoxysilane was verified by analysis of XPS spectra (Figure 4), which allowed for determining a ratio between N and Si atoms of close to 8% (_i.e._, 8.8% and 7.3% for micro- and nanoparticles, respectively).  
Thermogravimetric analyses (Figure 5) also showed a significant weight loss of functionalized silica particles because of decomposition of grafted AMPS groups, which took place at around 500 \({}^{\circ}\)C. This decomposition step corresponded to an approximate weight loss of 9% and, logically, was not detected in non-functionalized particles. Gradual weight loss leading to a value of only 8%-9% at 590 \({}^{\circ}\)C was observed for all particles. Thus, at this temperature the total loss was 17% and 8% for functionalized and non-functionalized particles, respectively. Practically no differences were found in the TGA (Figure 5a) and DTGA (Figure 5b) curves of micro- and nanoparticles.
## 5 Autonomous Navigation Technology
### Path Planning
#### 5.2.1 Global Path Planning
However, at the same time, the calculation speed is slowed down with relative low searchingefficiency. In addition, too many inflection points in the path result in the generation of some meaningless populations during iterative evolution of the algorithm, which slows down the subsequent calculation process. Thus, this method is not suitable for online path planning. 2. Neural Network Algorithm Neural networks are intelligent systems composed of many simple but highly interconnected processing elements that transmit information through dynamic responses to external inputs [93]. Neural networks have the characteristics of high fault tolerance, distributed representation, extensive parallelism and generalization. [PERSON] et al. [94] proposed a multi-level system built with a deep reinforcement policy gradient algorithm, which can collaboratively plan multi-vehicle collision-free travel paths through motion planning. [PERSON] et al. [95] proposed a multi-agent reinforcement learning algorithm to solve the problem that unmanned vehicles learn slowly or even fail to learn in a completely unknown environment. By ensuring the corresponding reward methods and completing the training process, the optimal path can be found. [PERSON] et al. [96] reported a route search strategy for unmanned vehicles that integrates the reinforcement learning algorithm and the deep learning algorithm. It determines the driving path by driving comfort constraints together with the function about reward and punishment of obstacle information and traffic regulations. 3. Ant Colony Algorithm The ant colony algorithm has good comprehensive performance and strong global optimization ability, which can complete the scraper path planning in complex mining environments, but it is easy to reach a stalemate of only local optimal. [PERSON] et al. [97] proposed global path planning for underground intelligent LHD through an improved ant colony algorithm to solve the problems of slow convergence speed and easy stagnation due to local optimum in the traditional ant colony algorithm. 4. Particle Swarm Algorithm Particle swarm optimization is also a probabilistic global path planning algorithm. Because of its multi-possibility of the iteration, it is much more possible to cover the global map during the path searching process with this algorithm. Correspondingly, the global optimal solution is easier to be obtained [98]. The particles adapt well to complex situations through the interconnection of information. Hence, this method is highly adaptable, even in a high-dimensional environment.
# Microalgae: Potential for Bioeconomy in Food Systems
## 5 Applications in Human Nutrition  
The need for mass food production and the search for sustainable processes have led to the development of new strategies for food production with low impact on the environment. The use of microalgae as a food source has been studied since the late 1980s [89]; however, only in recent years has it generated greater interest. _A. platensis_ (spirulina) is one of the most widely used microalgae. The interest in this microalga for human consumption is based on the nutritional characteristics of its biomass, which are comparable to those of meat and soybeans, as well as on its vitamin, phenolic compound, mineral, amino acid, and essential fatty acid content [90].  
In addition to basic nutritional properties, other benefits of microalgae for human health have been described [51; 91; 92]. For instance, foods supplemented with _Arthrospira_ sp., _C. vulgaris_, _A. platensis_, _H. pluvialis_, and _D. salina_ had positive effects on the anti-inflammatory activity, immune system, and properties that can regulate hyperglycemia and protect against oxidative stress [15; 91; 92; 93; 94; 95; 96].  
The biosafety and toxicity of lipid compounds extracted from _N. oculata_, _Rlodococcus apacus_, _Schizochytrium_ sp., and _Cryptheodimium colnii_ have been assessed. These compounds are used as an alternative food to human breast milk. No cytotoxic effects were observed, supporting the feasibility of using microalgae oils for human nutrition from birth to 2 years of age or more [97; 98; 99]. Similarly, the composition and use of species such as _B. braunii_, _C. vulgaris_, _H. pluvialis_, and _Isochrysis galbana_ have been studied. These species are an ideal vegetarian supplement because they contain essential amino acids, as well as omega-3 and omega-6 fatty acids [15; 100].  
Based on their high nutritional value, reduced environmental impact, and economic sustainability, microalgae have been used as functional ingredients to enhance the characteristics of foods [101], such as milk or dairy products [99] and meat products [90], or in the manufacturing of snacks [102; 103], food supplements [104; 105], and food pig ments [103; 106]. This highlights the importance of research, development, and innovation in the use of microalgae for human consumption.
###### Abstract
However, small nanoparticles' loading within the chaotic coils exhibits a considerable improvement in Nusselt number. Comparison between vertically and horizontally helical coiled heat exchangers, in terms of pressure loss and thermal energy exchange, was carried out by [PERSON] et al. [28]. The difference in heat transfer enhancement between vertical and horizontal arrangements was negligeable. Moreover, regardless of these arrangements, a better improvement in Nusselt number is achieved with higher nanoparticles loading and turbulent flow regime. Numerous researches have confirmed the advantages of using nanofluids in heat exchangers [29; 30; 31]. Some recent papers dealing with the effect of using nanofluids on the performances of helical coil heat exchanger can be found in the literature. [PERSON] and [PERSON] [32] studied the heat transfer and exergy production of a helical coil heat exchanger working with water ethylene glycol mixture diamond nanofluid. It was found that compared to the pure fluid the use of nanofluid leads to enhancements of the heat transfer rate and exergy efficiency by 36.05% and 36.48%, respectively. Based on numerical simulations and artificial neural network modeling, [PERSON] et al. [33] investigated the effect of varying the coil pitch on the performances of a 3D helically coiled shell and tube heat exchanger working with a hybrid nanofluid. The authors mentioned that the combined variations of coil pitch and nanoparticles volume fraction can be used to optimize the heat transfer. Similar conclusions on the enhancement of the heat transfer in coiled heat exchangers by using nanofluids were presented in the recently published works of [PERSON] et al. [34], [PERSON] et al. [35], and [PERSON] et al. [36]. According to the above summary of previous research, it is worth noticing that most investigations are devoted to the use of mono nanofluids, and it seems to be a shortage of hybrid nanofluids application in helical coil heat exchangers. From this perspective, the present work exhibits an experimental analysis of the thermal performance enhancement of an economical and cost-effective vertical helical coil heat exchanger using copper oxide-graphene (80-20%) hybrid nanofluid.
# A Method for Modeling and Testing Near-Real-Time System Scenarios
## 4 Modeling the System Scenarios with Finite-State Machines
### Detailing Scenarios with UML Sequence Diagrams  
Any scenario written in the form of a sequence of UML state machine states can be detailed as a list of consecutive sequence diagrams, where at least one sequence can be represented for each state. Sequence diagrams model in detail the processing paths of a system implementing a specific scenario.  
Figure 5 shows a model in the form of a sequence diagram of one of the system functions described by the state (U(WP), P(CO)).  
Figure 5: Sequence diagram for state (\(U(WP),P(CO)\)). A control scenario involving setting the WAY-POINT flight mode is shown. The diagram shows the analytical classes modeling the UAV control system (EFIS), the control message transformation module compliant with STANAG 4586 (VSM), and the UAV flight computer.  
In each state, the classes responsible for processing in the system have been distinguished. In the case of a ground station in the system, communication with the UAV is carried out by the VSM (vehicle-specific module) and EFIS (electronic flight instrument system) objects. From the platform side, communication is carried out by the flight computer. It is worth noting here that the VSM facility is an element described in the standard for the construction of an unmanned platform management system. Its task is to transform messages described in the standards into control messages understandable for a specific type of UAV. Therefore, the VSM is usually the most thoroughly tested element of the system.  
The flight computer verifies the possibility of changing the flight mode (e.g., by determining the position of the UAV in the context of the planned flight route). After checking the feasibility of setting the flight mode, this computer generates a message to the autopilot controllers. After changing the flight mode, the flight computer sends the status to the EFIS. The status is presented to the pilot.
# An Improved PIN Diode Model Design for a Tunable Frequency Selective Absorber
## 1 Introduction
This paper addresses these challenges by proposing a broadband, high-absorption tunable absorber designed specifically for X-band applications. By utilizing an improved PIN diode model in conjunction with a voltage-controlled bias network, the proposed structure enables continuous tuning of the absorption band while also providing the ability to switch between a reflective and an absorptive state. Through detailed simulations, it was shown that the absorber achieves reflectivity below \(-15\) dB over a frequency range of \(8\) GHz to \(12\) GHz, with a \(1\) GHz bandwidth, and it maintains reflectivity above \(-0.5\) dB across the entire X-band. These simulation results were further validated through experimental testing, demonstrating the effectiveness of the design in achieving both broadband absorption and high efficiency. By enabling continuous tuning and switchable modes, this design represents a significant advancement in the development of tunable absorbers for radar and stealth applications.
## 3 Determination of Parameters for Asphalt Concrete Material
### Strain Rate Effect  
The DIF curve for AC under different strain rates was obtained using servo hydraulic fast loading tests and the split Hopkinson pressure bar (SHPB) test in the current study. The strain rate produced by the servo hydraulic machine was approximately \(10^{-5}\) to 1 s\({}^{-1}\), and the higher strain rate loading was obtained through SHPB testing. The detailed setup and procedure for SHPB and the hydraulic test for AC can be referred to Wu [20]. The DIF value for compressive (\(DIF_{c}\)) and tensile (\(DIF_{t}\)) strength obtained from the test are given as:  
\[DIF_{c}=\frac{f_{dc}}{f_{sc}}=3.18+1.098\log_{10}(\overset{\bullet}{\epsilon}) +0.1397{\log_{10}}^{2}(\overset{\bullet}{\epsilon})\quad\text{for}\quad \overset{\bullet}{\epsilon}\leq 100\text{s}^{-1}\]  
\[DIF_{c}=\frac{f_{dc}}{f_{sc}}=21.39\log_{10}(\overset{\bullet}{\epsilon})-36.7 6\quad\text{for}\quad 100\text{s}^{-1}<\overset{\bullet}{\epsilon}\leq 200 \text{s}^{-1} \tag{19}\]  
\[DIF_{t}=\frac{f_{dt}}{f_{st}}=1.86+0.1432\log_{10}(\overset{\bullet}{\epsilon}) \quad\text{for}\quad\overset{\bullet}{\epsilon}\leq 15\text{s}^{-1}\]  
\[DIF_{t}=\frac{f_{dt}}{f_{st}}=6.06\log_{10}(\overset{\bullet}{\epsilon})-5.02 4\quad\text{for}\quad 15\text{s}^{-1}\leq\overset{\bullet}{\epsilon}\leq 100 \text{s}^{-1} \tag{20}\]  
However, for the compressive DIF curve, a numerical modelling of the SHPB test adopting this DIF curve found that the initial segment of this curve matched the experimental results very well, while the numerical model results for strain rate larger than 100 s\({}^{-1}\) seemed to overestimate the stress. This can be due to the \"double counting\" of the inertia effect in the numerical modelling when the strain rate exceeded 100 s\({}^{-1}\). Hence, in the current model, the second segment in compressive DIF (Equation (19)) is ignored when the strain rate exceeds 100 s\({}^{-1}\). Beyond this, the DIF is assumed to remain a constant value. For the tensile DIF curves, in the macro-level numerical model, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline \(K_{IC}\) & MPa\(\cdot\)mm \({}^{1/2}\) & 12.2 \\ \(\
u\) & - & 0.35 \\ \(E\) & MPa & 598 \\ \(G_{f}\) & MPa\(\cdot\)mm & 0.221 \\ \(w_{c}\) & mm & 40 \\ \(G_{f}/w_{c}\) & - & 0.00554 \\ \(f_{t}\) & MPa & 0.7 \\ \(b_{2}\) & - & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters from the single-edge notched beam test (SNB) and single element simulation.  
KCC material model cannot capture the aggregate interlocking that propagates the micro-cracking and energy dissipation beyond the localization zone [20; 21; 22]. Therefore, the above tensile DIF curve (Equation (20)) with two branches is used in the numerical model. The tensile and compressive DIF curves of asphalt concrete used in numerical model are summarized in Figure 6.