esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Simulation and Measured Evaluations Integrated with CubeSat
This special function enhances the reliability of the communication link between the ground station and other satellites in LEO. Moreover, early discussions have been initiated with collaborators at the European Space Agency (ESA) to explore the potential for incorporating this antenna into next-generation CubeSat missions focused on inter-satellite networking and onboard autonomous communication routing. Further, the thermal and radiation resilience of the antenna materials under extended space exposure will be investigated through accelerated aging tests and radiation hardness evaluations. For higher power use cases beyond the 2 W range, the nonlinear performance and dielectric breakdown behavior of the MTS and substrate materials will be studied, particularly under pulsed power or prolonged high-duty-cycle operation. Lastly, the integration of this antenna with onboard energy-harvesting systems or intelligent beam-forming ICs will be explored to create a fully autonomous, compact, and adaptable CubeSat transceiver platform capable of supporting diverse missions from Earth observations to deep-space relays. Beyond CubeSat applications, the compact footprint, high gain, and broadband capabilities of the proposed MTS-based leaky-wave MIMO antenna make it a strong candidate for adaptation to other space-constrained platforms, such as unmanned aerial vehicles (UAVs) and terrestrial Internet of Things (IoT) systems. For UAVs, where size, weight, and aerodynamic integration are critical, the antenna could be re-simulated using conformal or flexible substrates (e.g., polyimide or liquid crystal polymer) to integrate seamlessly with curved fuselage surfaces. Beamsteering features would enable dynamic tracking of ground control stations or mesh-connected UAVs. For terrestrial IoT deployments--especially those demanding long-range, directional, and high-throughput communication, such as smart agriculture, environmental monitoring, or infrastructure surveillance--the antenna could be retuned to operate efficiently in sub-6 GHz or unlicensed millimeter-wave bands (e.g., 5.8 GHz, 24 GHz, or 60 GHz) by adjusting the patch geometry and substrate dielectric profile. The low-power requirement, dual-port MIMO functionality, and polarization diversity further align with the energy and bandwidth constraints typical of IoT systems.
# Classification and Analysis of Malicious Code Detection Techniques Based on the APT Attack
## 5 Classification and Analysis of Malicious Code Detection Methods
### Data Analysis-Based Detection  
Data analysis-based detection detects malicious codes based on features that appear in a large amount of data. This type of detection can be sub-classified into a big data-based detection method and a machine learning-based detection method according to the data analysis method.  
To detect anomalous behaviors, big data technology is used to detect malicious activities based on massive amounts of network traffic and logs. As a result, big data technology, which is capable of collecting and analyzing a large number of logs, has been combined with security technologies in earnest. Big data technology prevents and predicts threats based on logs and traffic. Some programs that have been used for this purpose include Hadoop, Map Reduce, and R as an open-source option. This method differs from the anomalous symptom detection method, as it allows for not only effective and quick analysis through the reprocessing of logs, but also intuitive determination of the results through visualization. Detection methods using big data can be classified into real-time monitoring, intelligence on threats, behavior profiling, data and user monitoring, application monitoring, and analysis and interpretation methods. The real-time monitoring technique involves tracking attacks on system components and monitoring user activities in application programs. The intelligence on threats technique intelligently detects various threats and attack patterns to accurately recognize even small amounts of malicious traffic that has been disguised as benign traffic. Behavior profiling is used to detect anomalous symptoms by defining association rules based on well-defined abnormal conditions. The data and user monitoring technique detects when sensitive information and privileged user profiles are being accessed by monitoring user activity and data. The application monitoring technique monitors the activity of abnormal applications. The analysis and interpretation technique determines abnormal behaviors from the characteristics of information collected from various sources using machine learning, data mining, and network mining techniques [23; 29; 128; 129; 130].
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## 8 Conclusions  
In this paper, we presented a routing protocol for randomly deployed underwater network. The sensor nodes are deployed randomly to monitor the environment or to warn of natural disasters, like tsunamis. The protocol is designed to optimize the data throughput and energy consumption of the sensor nodes. This protocol does not require additional sensing devices to ascertain the location. Hence, the proposed routing protocol is based neither on the location of the nodes nor on the topology of the network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{3}{c}{**Energy Consumption (J)**} \\ \cline{2-4}  & **SPRINT** & **RECRP (Approx.)** & **SPRINT Reduction (\%)** \\ \hline
100 & 25.567 & 70 & 63\% \\
200 & 8.451 & 53 & 84\% \\
300 & 3.432 & 22 & 84\% \\
400 & 1.249 & 21.5 & 94\% \\
500 & 0.528 & 21 & 97\% \\
600 & 0.637 & 20 & 96\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: SPRINT and RECRP energy consumption comparison.  
Figure 24: Average energy consumption.  
Figure 25: Reliable energy-efficient cross-layer routing protocol (RECRP) energy consumption.  
SPRINT is a proactive protocol to minimize the routing delay. Each node that wants to send a packet knows the forwarding node in advance. At regular intervals, when a node receives the data packet, it computes RSS, hops, and neighbors to optimize the routing path and overcome the issue of a dead node. This regular update of the routing path makes the protocol resilient and more efficient. However, computing routing path parameters upon receiving each data packet will increase the energy consumption significantly, therefore the routing path update will be carried out after certain number of data packets, depending on the arrival rate.  
Energy consumption is low due to the adaptive transmission power, which is adjusted with the help of RSS estimation. Throughput can also be increased by reducing the relay nodes between the data source node and the gateway. Data traffic among the relay nodes is distributed evenly by considering the number of neighbors at the time of forwarding node selection.  
[PERSON] developed in detail the protocol, and carried out the simulations. [PERSON] helped with the protocol mechanism, pointed out some scenarios to be considered, and helped with the structure of the paper. [PERSON] made suggestions to state of the art, and made the main revision of the manuscript. [PERSON] also gave support in the revision of the manuscript.  
This research has been funded by \"Universidad de Malaga\".  
Authors express their gratitude to the Research Institute of Oceanic Engineering (IIO-UMA) by its dedication and effort.  
The authors declare no conflict of interest.
## 1 Introduction
In such research, the intensity or power density of radiation and the duration of exposure varied widely. Many research institutions have conducted studies on the effects of the electromagnetic field on DNA damage, cell cycle arrest, reactive oxygen species formation, cellular morphology, proliferation and growth profile, immunological responses, cell death mechanisms, heat-shock protein signaling, and viability, weighing the genotoxic effect of such radiation. The assessment of genotoxicity was tested using different frequencies and doses under different irradiation conditions, which included continuous or pulsed exposures as well as amplitude- or frequency-modulated waves. Such effects have often been focused on for risk assessment. The results showed that the effects were dependent on the type of cells and the choice of endpoint. However, conflicting results have been observed more than once in the same cell types in the same assay [11;12]. New research techniques are constantly being developed that would enable a better understanding of the influence of the electromagnetic field on the behavior of cells. This paper describes an experiment carried out by monitoring the electrical parameters of a cell culture that was subjected to electromagnetic radiation generated by a mobile phone. The Electric Cell-Substrate Impedance Sensing (ECIS) method was used to measure impedance, resistance, and capacitance using a small, non-invasive alternating current (AC) that is applied across the electrode pattern at the bottom of the ECIS array. This results in potential across the electrodes which is measured by the ECIS instrument. The impedance is determined by [PERSON]'s law. The conducted analysis aimed to answer the question of whether it is possible to use this method to study the influence of electromagnetic radiation on living cells.
## 2 Results
### Chemical Composition of Essential Oil
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Compound** & **Rt (min.)** & **RI** & **\%Conc.** \\ \hline
42 & Biphenyl & 36.65 & 1391.00 & 2.40 \\ \hline
43 & 2-Dodecanone & 36.74 & 1393.00 & 0.50 \\ \hline
44 & \(\beta\)-Elemene & 37.28 & 1406.00 & 0.30 \\ \hline
45 & 2-Undecyl acetate & 38.25 & 1428.00 & 1.40 \\ \hline
46 & (E)-\(\beta\)-caryophyllene & 38.80 & 1442.00 & 0.70 \\ \hline
47 & Dictamol & 38.96 & 1445.00 & 0.20 \\ \hline
48 & (E)-\(\beta\)-farnesene & 39.56 & 1460.00 & 0.30 \\ \hline
49 & (E)-Ehyl cinnamate & 40.01 & 1470.00 & 0.30 \\ \hline
50 & \(\alpha\)-curcumene & 40.91 & 1492.00 & 0.30 \\ \hline
51 & 2-Tridecanone & 41.04 & 1495.00 & 0.70 \\ \hline
52 & Germacrene D & 41.33 & 1502.00 & 0.20 \\ \hline
53 & \(\beta\)-Selinene & 41.63 & 1509.00 & 0.10 \\ \hline
54 & \(\alpha\)-Selinene & 41.95 & 1517.00 & 0.10 \\ \hline
55 & \(\beta\)-Sesquiphellandrene & 42.68 & 1535.00 & 0.30 \\ \hline
56 & \(\delta\)-Cadinene & 42.81 & 1539.00 & 0.10 \\ \hline
57 & (E)-\(\alpha\)-Bisabolene & 43.31 & 1551.00 & 0.20 \\ \hline
58 & Elemol & 43.83 & 1564.00 & 0.20 \\ \hline
59 & (E)-Nerolidol & 43.99 & 1568.00 & 0.10 \\ \hline
60 & _cis_-Davanone & 44.99 & 1593.00 & 0.20 \\ \hline
61 & Dulcinyl & 45.45 & 1605.00 & 0.80 \\ \hline
62 & Dill apiole & 46.52 & 1633.00 & 0.60 \\ \hline
63 & \(\beta\)-Eudesmol & 48.13 & 1675.00 & 0.10 \\ \hline
64 & 7-Methoxycoumarin & 50.60 & 1742.00 & 0.10 \\ \hline
65 & Psoralen & 54.43 & 1851.00 & 3.20 \\ \hline
66 & Methyl palmitate & 56.89 & 1923.00 & 0.10 \\ \hline
67 & Moskachane D & 61.33 & 2043.00 & 0.10 \\ \hline
68 & Bergaptene & 62.91 & 2081.00 & 0.10 \\ \hline  & Cyclic Monoterpenes & & 9.30 \\ \hline  & Acyclic (Hydrocarbon) Monoterpenes & & 0.60 \\ \hline  & Cyclic Sesquiterpenes & & 3.10 \\ \hline  & Acyclic (Hydrocarbon) Sesquiterpenes & & 0.40 \\ \hline  & Cyclic Diterpenes & & 0.10 \\ \hline  & Other Chemical Classes & 84.20 \\ \hline  & Total \% & 97.70 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._predominantly contains aliphatic ketones and some minor aliphatic alcohols; however, they generally lack terpene components. Our findings revealed that the long-chain aliphatic ketones are the class of the most abundant component and account for approximately half of the total oil content (63.80%). 2-Undecanone represented 37.30% of total oil yield and, followed by 2-nonanone (20.00%), was a major compound. Camphor and limonene (2.70% and 2.60%, respectively) dominated the monoterenes and occupied 9.90% of the total oil yield. Sesquiterpenes and diterpenes were the other terpenoid chemical classes identified in the oil extracted from the selected plant growing in Saudi Arabia, with percentages of 3.50% and 0.10%, respectively.
# Toward Defect Engineering Strategies to Optimize Energy and Electronic Materials
## 3 Defect Engineering Oxygen Diffusion in Ceria
### Doping and Strain Strategies
For example, MD calculations were previously employed to study the effect of uniaxial strain on the oxygen ion diffusivity as a function of temperature and rare-earth dopant (Yb, Er, Ho, Dy, Gd, Sm, Nd and La) using the well-established Cooper-Rushton-Grimes potential model that appropriately describes the thermal expansion of doped ceria [26]. This MD study is consistent with an increase of the oxygen diffusion coefficient due to strain, but not of the same order of magnitude as the study of [PERSON] et al. [32] inferred. Given the flexibility of MD calculations to change conditions, it was shown that the increase in temperature will result in the decrease in the difference between the diffusivities between the dopants [26]. This in essence illustrates the diminishing impact of the association between the dopants and the oxygen vacancies [26]. Therefore, at temperatures above 1000 K, the activation energy of oxygen migration will be primarily represented by the energy barrier required for diffusion, whereas the association energy of oxygen vacancy-dopant clusters will have a smaller contribution [35; 36; 37]. A very interesting feature that was demonstrated by the MD calculations for undoped CeO\({}_{2}\) is that there is a limited increase of the oxygen diffusion coefficient for strains below 3% but there is a 15-fold increase in the oxygen diffusion coefficient as the strain increased from 3.0% to 5.0% (refer to Figure 4) [26]. This in essence demonstrates that the strain not only impacts the association of dopants with the oxygen vacancies, but also leads to changes in the oxygen diffusivity because of the changes in the lattice.
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
###### Abstract
The implementation of software on devices with very limited resources is not as fast. Another 64-bit block cipher is PICCOLO [15], also very compact when implemented in hardware. It has two substitution box layers and is considered one of the competitive ultra-lightweight block ciphers for RFID applications. Because it uses a substitution-permutation network, it is not as fast when implemented in software. PRESENT [16] uses a 4 by 4-bit substitution box, keeping in mind that the implementation of such a substitution box is typically much more compact than that of an 8-bit version. PRESENT is also optimized for small hardware implementations, making it less ideal for software running on microcontrollers. CHASKEY [17] is an ARX cipher 2.1x faster than SIMON and SPECK, and up to 8.3\(\times\) faster than AES, according to the Fair Evaluation of Lightweight Cryptographic Systems (FELICS) project [18]. The 12-round version is standardized as ISO/IEC 29192-6:2019. CHASKEY has a 128-bit block using 32-bit words for 32-bit microcontrollers. Although it is fast, the block size is too large for some applications. For example, when 1-2 bytes need to be encrypted, CHASKEY will always encrypt a block of 16 bytes. The remainder of this paper is organized as follows. Section 1.1 \"Motivation and strategy\" explains the reasoning and the strategy applied to our work. A point-by-point enumeration of contributions is presented in Section 1.2 \"Contributions\". Section 2 \"Methodology\" describes the methodology used to implement and test the cipher. Section 3 \"Encryption Evaluation\" presents the theoretical setup for evaluating the security of the encryption system. Section 4 \"Results\" contains the results obtained. In Section 5 \"Discussion\", the implications of the findings are discussed and further analyzed, and Section 6 \"Conclusions\" contains the final observations that are drawn with a perspective on future improvements.
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 3 Real-Time Surge Model of Turboshaft Engine
### Surge Dynamic Process Simulation at Multiple Operating Points  
In order to verify the ability of the turboshaft engine surge model to simulate the surge at multiple operating points, set the model to simulate the surge process of accelerating from 85% Ng speed to 100% Ng speed:  
Step 1. Give the model input and make the engine operate stably at 85% Ng speed.  
(Altitude: \(H\) = 3 km, Mach number: \(Ma\) = 0.6, Fuel: \(W_{f}\) = 0.07, \(B\) = 0.6, \(\gamma_{T}\) = 0.65)  
Step 2. Decrease the throttle valve opening \(\gamma_{T}\) to 0.55 to make the model go into surge at \(t\) = 5 s.  
Step 3. Increase the throttle valve opening \(\gamma_{T}\) to 0.65 to make the model out of the surge at \(t\) = 10 s.  
Step 4. Increase the \(W_{f}\) to 0.092556 at \(t\) = 20 s to make the engine accelerate to 100% Ng speed.  
Step 5. Increase the \(B\) parameter to 2, and decrease the throttle valve opening to 0.55 at \(t\) = 35 s, which makes the model into deep surge again.  
Step 6. Increase the throttle valve opening to 0.8 finally and the engine returns to the stable state.  
After performing the above steps, the process of the whole engine's surge, acceleration, deep surge, and recovery of stability was successfully simulated. The changes of Np, Ng, compressor outlet pressure, mass flow, and EGT during the entire simulation process are shown in Figure 13 and the engine's operating trajectory is shown in Figure 14. Firstly, the engine operates stably at 85% Ng speed, corresponding to operating point A (OP-A). A classical surge occurs when \(t\) = 5 s and the operating point trajectory presents a small limit cycle. After exiting the surge, the engine accelerated from OP-A to OP-B at 100% Ng speed, and then a deep surge occurs at \(t\) = 35 s. The deep surge corresponds to a larger limit cycle, which
## 2 Proposed Method  
The proposed methodology comprises three integral components: optimal distortion search, feature distortion space construction, and dual-path classification strategy. The optimal distortion search is employed to acquire samples with more discernible features, while the construction of the distortion magnitude space provides feature references for the optimization search. Ultimately, the dual-path classification strategy is employed to manage a more effective classification of augmented data.
# Estimation of Stride Length, Foot Clearance, and Foot Progression Angle Using UWB Sensors
## 2 Materials and Methods  
### Hardware Description  
As shown in Figure 2a, the equipment used in this experiment is a UWB system, comprising UWB sensors attached to each shoe to measure the distance (collected on a personal computer (PC)), an MCS that measured the location data of the reflection marker attached to the shoe, and a device that synchronized the data of the two systems.  
The UWB system included a UWB sensor (model: DWM1000, Qorvo, Greensboro, NC, USA) and an Arduino Nano. The UWB sensor was installed on an in-house printed circuit board (PCB) and fabricated to have a total size of 43 mm (width) \(\times\) 20 mm (length) \(\times\) 15 mm (height) and a data acquisition speed of 100 Hz. The trilateration method involves attaching three anchors and a tag to a shoe, as shown in Figure 2b, using eight UWB sensors (four in each shoe). The UWB sensors were placed between the two shoes facing each other so that wireless signals could be sent and received without interruption. As illustrated in Figure 2c, the tag on each foot measures the distance between the three  
Figure 1: Schematic illustration of the UWB sensor system to analyze gait characteristics.  
anchors located on the opposite foot. The data were transmitted to and stored on a PC connected to a wire and used in the analysis. Gait data were measured using the MCS (Vicon Nexus MX, Oxford Metric, Oxford, UK) installed in the gait experiment space. The data measured by MCS were considered as the gold standard and were applied to evaluate the trajectory estimated by the UWB system. A reflection marker for motion capture was attached to the PCB for the UWB sensors, as shown in Figure 2a,b. The location of the reflective marker was placed at a site that does not affect the antenna signal of the UWB sensor. Meanwhile, a push switch (5 V) for triggers was attached to the right-foot tag, and its signal was used to synchronize the UWB data obtained from the gait experiment with the motion capture data before comparing them (Figure 2a).  
The gait experiment proceeded with the participant walking on a treadmill (M-gait, Motek, Amsterdam, The Netherlands) wearing shoes equipped with UWB sensors and MCS markers.
## 3 Optimization Strategy  
As already mentioned in the introduction, because of the serious mode-switching phenomenon of local mode, it becomes especially complicated to determine the optimum when taking the local frequency constraint into consideration. The typical optimization process is not suitable for optimization with a local frequency constraint. Accordingly, we proposed an approximate method using the information of nodes displacements in local areas to be optimized, which avoids using the order of the local mode and its identification.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 9 Limitation and Future Work  
An important limitation of this work is that it only considers a semi-honest, non-colluding security model, which assumes a relatively weak adversarial capability. We plan to extend the scheme to stronger security models with more powerful adversaries. For example, to achieve malicious security, the RSS-based distance computation component could adopt an error-detecting skill based on message authentication codes, such as the one  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Offline Phase**} & \multicolumn{2}{c}{**Online Phase**} \\ \cline{2-5}  & **Runtime (ms)** & **Comm. (MB)** & **Runtime (ms)** & **Comm. (KB)** \\ \hline \(n=241\), \(m=505\) & & & & \\ \hline FAPRIL [14] & 2500 & 9.8 & 96 & 129 \\ \(\text{Silcom}_{3R}\) & 621.3 & 87.4 & 16 + 221 & **7.2** \\ \(\text{Silcom}_{3R}^{8t}\) & 451.8 & 87.4 & 16 + 54 & **7.2** \\ \(\text{Silcom}_{trd}\), \(m^{\prime}=20\) & 57.9 & **3.7** & 16 + 23 & 8.2 \\ \(\text{Silcom}_{trd}^{8t}\), \(m^{\prime}=20\) & **43.3** & **3.7** & **16 + 7** & 8.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between different schemes. In online phase runtime, 16 refers to RTT for client’s query. In \(\text{Silcom}_{3R}^{8t}\) and \(\text{Silcom}_{trd}^{8t}\), superscript 8t refers to 8 threads. Bold values indicate the optimal results.  
Figure 16: Comparison of online communication with FAPRIL.  
proposed by [PERSON] et al. [62], while the FSS-based computation could leverage the design by [PERSON] et al. [46]. Naturally, providing malicious security typically incurs significant additional computational and communication overhead. How to further optimize performance under this stronger security setting is a meaningful direction for future research.