esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Security-Enhanced Authenticated Key-Agreement Protocol: SE-UAVAKA
### SE-UAVAKA Authentication Key-Agreement Phase
#### 3.4.3 Security Mechanisms and Design Rationale  
* **Elliptic-Curve Scalar Multiplication**: The proposed scheme employs scalar multiplication (\(N_{a}=(r_{a}\cdot ID_{A})\cdot P\)) to generate cryptographic parameters, establishing direct binding between \(ID_{A}\) and ephemeral value \(r_{a}\) as impersonation resistance. Without knowledge of both \(ID_{A}\) and the drone's long-term secret \(x_{a}\), adversaries cannot feasibly forge valid \(N_{a}\) parameters.
* **XOR-based Parameter Obfuscation**: The XOR operation (\(Eax=N_{a-x}\oplus(y_{a}-x_{a})\)) serves dual security purposes: (1) Obscures sensitive coordinate data from elliptic-curve operations during transmission, preventing eavesdroppers from directly reconstructing \(N_{a}\); (2) leverages \((y_{a}-x_{a})\) as session-specific secrets generated during registration to enforce dual-key decryption requirements. This design withstands known temporary parameter attacks since \(SSK_{AB}\) derivation requires both \(ID_{A}\) and \((y_{a}-x_{a})\), remaining secure even if \(r_{a}\) gets compromised.
* **Timestamp and Hash Validation**: The protocol incorporates timestamp validation (\(TS_{a}\), \(TS_{b}\)) to ensure message freshness, complemented by hash commitments \(H_{a}=H(ID_{A}||TS_{a}||N_{a})\) and \(H_{b}=H(ID_{B}||TS_{b}||N_{b})\) for parameter integrity verification. Any tampering during transmission alters the resulting hash values, triggering verification failure at the recipient side.
* **Identity-Protected Symmetric Encryption**: Anonymous authentication is achieved through session-specific symmetric encryption of identifiers (\(E_{y_{a}-x_{a}}(ID_{A})\)). Without access to the long-term secret \(H(ID_{A}||ID_{B})\)--securely stored in device memory and never exposed in communication channels--attackers cannot correlate sessions with specific drones or ground stations.
## 5 Conclusions  
Based on system modeling, this paper presents a specific algorithm for both power control and power management applied to a full DC microgrid. It is an improved power control and management system, going beyond the current state-of-the-art systems in several aspects. First and foremost, the full DC microgrid can switch freely between grid-connected mode and islanded mode, which improves the reliability compared to a microgrid with only renewable sources or with only traditional energy sources. Furthermore, the algorithm takes into consideration the sluggish dynamic of the DG and the self-discharging characteristic of the SC. In addition, this algorithm considers both the process of power control and power management. Finally, demand-side management is applied with real-time load demand power optimization.  
The simulation results obtained under MATLAB/Simulink verify the performance of the specific algorithm, which can keep the power balance with respect to the power management. The PV can operate in MPPT mode and PV power shedding mode. The BS can be the controllable source to well supply power deficiency, absorb the excess power during the on-grid operation of the full DC microgrid, as well as be recharged when the DG is turned on to reduce the usage of the DG during the off-grid operation of the full DC microgrid. The public grid can operate well under its limitation to balance the power in the full DC microgrid well. The DG can operate well as the backup source; meanwhile, the SC can operate to give good function on the power deficiency during DG start-up and be recharged in time.  
Future work will focus on the techno-economic dispatching optimization of the full DC microgrid, according to forecast data and historical data, to achieve the lowest overall cost and more reasonable energy allocation, which will lay a solid foundation for a future full DC microgrid to be more easily accepted and constructed.  
[PERSON], [PERSON] and [PERSON] designed the research question; [PERSON] and [PERSON] performed the simulations; [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper; [PERSON] and [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 17: Curves of common DC bus voltage and SOC of storage (**a**) and curves of power (**b**) during the period 22:00–24:00.
## 3 Materials and Methods
### Classification Methods  
For the classification of the dataset, the following algorithms frequently used in the literature were preferred: Decision Tree (DT), Random Forest (RF), Extra Tree Classifier (EXT), Logistic Regression (LR), Naive Bayes (NB), and Multi-Layer Perceptron (MLP).
# Shape Modelling and Volume Optimisation of Salt Caverns for Energy Storage
## 2 Three-Dimensional Numerical Model and Assessment Criteria
### Constitutive Models and Mechanical Parameters  
The structural stability of caverns in the bedded salt deposit depends on the strength and deformation characteristics of the salt and nonsalt beds surrounding and overlying the avern [16]. The geomechanical model was built based on the geological characteristics of the Mechelinki salt deposit in order to study the response of the rock mass around the cavern under the bedded rock salt conditions. The salt beds are part of the Zechstein salt formation (cyclothem PZ1). In the geomechanical model, the rock salt beds were located at a depth of 960-980 m below ground level (b.g.l.). The thickness of the salt layers ranged from 160 to 190 m (Figure 2). The salt beds are underlain and overlain by anhydrite layers (the lower anhydrite being A1d and the main anhydrite being A1g) with thicknesses ranging from about 1.5 to 20.0 m. There are two anhydrite interlayers in the rock salt beds with a thickness of up to 0.5 m [37]. These interlayers were not considered in the simulations described in this paper because their stability performance would obscure the impact of shape and smoothing on the cavern's stability. Moreover, the influences of anhydrite interlayers on the cavern's stability were analysed in a previous paper [21]. The salt beds are underlain by Kupreschiefer (T1), Zechstein Limestone (C1) and Rotliegend and Silurian sediments and covered by the sediments of the PZ2 and PZ3 cyclothems as well as Triassic, Jurassic, Cretaceous, and Kenozoic strata [37]. The mechanical behaviour of the rock mass surrounding the examined caverns was simulated with the use of constitutive models and the mechanical parameters of the rock salt and the nonsalt rock. The Mohr-Coulomb elastic-plastic model was applied to describe the mechanical behaviour of the surrounding rocks and the anhydrite; furthermore, a two-component Norton Power Law with a Mohr-Coulomb plasticity criterion was used to simulate plastic yielding and creep. These models were chosen to accurately simulate the elastic-plastic response of anhydrite and the viscoelastic plastic behaviour of rock salt. The mechanical parameters of rock salt and nonsalt rock used in the numerical simulations are given in Table 2. The presented parameters were determined based on laboratory tests and validated in previous studies [21]. Two types of materials were distinguished, as required for the numerical modelling: rock salt and anhydrite. The parameters applied to the numerical calculations were based on the results of laboratory tests (Table 2). In
## 4 Proposed Work
### Unequal Cluster-Tree Topology Construction  
Table 2 details the list of notations that are used in the paper. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method/Technique** & **Concept** & **Drawback** \\ \hline CHPS [29] & Positioning of mobile nodes using PSO & High energy consumption/Convergence issues \\ \hline SRC [30] & \begin{tabular}{c} Removes the redundant nodes and use \\ Rips complex to detect coverage holes \\ \end{tabular} & \begin{tabular}{c} Affects connectivity/Highly complex for resource \\ constraint devices \\ \end{tabular} \\ \hline SA-CRP [31] & \
\begin{tabular}{c} Implements TDMA to schedule the nodes \\ into sleep and active mode \\ \end{tabular} & Control packet overhead/Increased packet loss \\ \hline MOMHR [32] &
\begin{tabular}{c} Cluster formation and routing to \\ minimize hop counts \\ \end{tabular} & High complexity/Large time consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research gaps in previous works. Figure 1: Network Architecture. In this phase, we aim to construct a new topology that supports energy efficiency with effectual data transmission. We propose a novel clustering method by considering the triangle-based partitioning of the unequal Sierpinski cluster-tree topology (UST) construction method in the IoT environment based on the formation of triangle partitions \(P=\{P1,\,P2,\,P3,\,P4\}\) to avoid the coverage hole as shown in Figure 2. The proposed USCT method constructs unequal cells by following the procedure of the Sierpinski triangle. Initially, the network is constructed as a graph N = \(V,E\) in which V denotes the sensors deployed and E = \(\{(p,q)\,/\,dist(p,q)\leq TR\}\) represents the connectivity of the nodes. In which p, q are any two sensor nodes in the network, TR denotes the range of transmission. c = \(\{p\subset V\}\) is a cluster comprising of a subset of vertices \(p\) (nodes) whose center is denoted as \(ct\). The cluster tree topology is utilized in which all the nodes are linked with each other which reduces the data loss problem in the network. The network is divided into four triangles and the remaining triangles are formed by repeating the above process to cover the entire area. By doing so, the unequal clusters are formed in which the size of the triangles nearer to the base station is larger than the size of the triangles that are situated away from the base station. Algorithm 1 describes the steps involved in the formation clusters in the network.
## 3 Results and Discussion
### Variations of PNA Signals with Bias
In contrast, interferences represent the sum of two phase-shifted monochromatic signals with similar amplitudes. Although the amplitude in log scale near the minimum may look as a resonance, in linear scale, it looks like a sinus function (Figure 1c, inset) with a minimum that will actually reach the residual thermal noise signal if the amplitudes of the two shifted signals are exactly the same. The perturbation of the signal on one branch by the change of the impedance-induced wave reflection unbalances the interference which results in a variation of the amplitude and phase of the measured signal. However, qualitatively, this mechanism presents some analogy with the Q-factor variations observed by [PERSON] et al. in their resonator and attributed to specific bias induced loss, hence conductivity variations. Their observed frequency shift is attributed to permittivity variations induced by charge trapping. The existing analytical models supporting SMM systems augmented with a Mach-Zehnder interferometer-based detection unit [15], once applied to the above results displayed in Figure S3b, may attribute PNA amplitude and phase signals to capacitance and conductivity. Quantitative evaluation of the variations of these parameters usually lie in the order of the aF and the \(\upmu\)S, respectively [15]. More precise determination, however, must precisely consider the operating setpoint (f\({}_{\text{OP}}\)) regarding the interferometric profiles (f\({}_{0}\)) to fix the sensitivity of the measurement. Figure S3b shows two sets of time profiles captured successively applying a \(-1\) V step bias. As the interferometric drift further moves away the operating setpoint compared to f\({}_{0}\), the mean PNA amplitude (phase) increased (decreased) in the second profile while the variations of the PNA phase with bias are also less pronounced. This must also be taken into account in interpreting Figure S3c, for which the significant increase in phase variations with bias is a combination of the drift (opposite to that of Figure S3c since here f\({}_{0}\) < f\({}_{\text{OP}}\)) and the injection of carriers the P3 HT.
## Materials and Methods
### Membrane Performance Evaluation
#### 2.5.3 Mixed-Gas Separation  
Mixed gas permeation tests were performed on the in-house built separation unit at SINTEF (Norway) to investigate the influences of the process parameters (feed pressure, temperature and relative humidity) [61]. This setup provides a high degree of automation, enabling continuous data collection (temperature, pressure, gas flow rate, composition and humidity) during operation under process conditions close to real flue gas treatment. The membranes with an active area of 19.63 cm\({}^{2}\), were tested using synthetic flue gas: mixture of 15 mol % CO\({}_{2}\) and 85 mol % N\({}_{2}\) with and without 90% of relative humidity (RH). To verify the reproducibility of the results obtained, at least three replicas of TFC membranes were tested under the same conditions. The results are presented as average values with standard deviations indicated by error bars. The permeate gas was continuously withdrawn with He as sweep gas, and sent to a gas chromatograph and a flow meter for the permeate composition and flow rate measurements, respectively. The sweep gas stream was additionally humidified in the experiments with wet feed gas. The online composition analysis of the permeate and feed streams was performed by a micro-gas chromatograph (GC, Agilent, USA). A detailed explanation of the methodology is reported in the electronic supporting information.
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 5 Cryptanalysis
### Parameter Space  
In order to analyze the security of our system, the parameter space that, in our system, is given by \(\gamma\), \(x_{0}\), and the initial state of the LFSR, \(y_{0}\) should be considered. First, all of the initial parameters should generate sequences with random-like behavior. In our case, the usage of a chaotic system, such as the Skew Tent Map (STM), which is chaotic for any initial parameters chosen in a continuum range \(\gamma\), \(x_{0}\in(0,1)\), and the inclusion of an LFSR, which increases the period length of the sequences, guarantees that our system meets this requirement.  
On the other hand, the key space size \(\kappa\) should be big enough to prevent brute-force attacks. Assuming a 64-bit precision and a 61 st order LFSR, the total key space size obtained from counting all possible combinations of values of \(\gamma\), \(x_{0}\), and \(y_{0}\) is: \(\kappa=2^{64+64+61}=2^{189}\). Although some popular lightweight encryption systems such as [31] use 80-bit key sizes, some guidelines recommend that, in order to prevent brute-force attacks in the following years, the key space size should be greater than \(\kappa=2^{112}\)[32; 33]. Therefore, our key space can be considered to be big enough.
# Physiological Control Law for Rotary Blood Pumps with Full-State Feedback Method
###### Abstract  
One concern about pulsatile rotary blood pumps is their physiological controller reactions when \"venous return\" changes. When a patient rises from a supine to a standing position, the blood volume in the leg veins is raised, owing to vasodilation, thus venous returns to the right atrium, and consequently, the left atrium is reduced. In this work, a physiological control law using a full-state feedback control method was developed in order to drive mechanical circulatory support. This strategy was used as a validated state-space pump model, to implement the controller and regulate the desired reference flow. The control law was assessed using a software model of the hemodynamical cardiovascular system interacting with the left ventricular assist device in different physiological conditions ranging from rest to exercise scenarios. Under these scenarios, heart failure disease was simulated by changing the hemodynamic parameters of the total blood volume, heart rate, cardiac contractility, and systemic peripheral resistance. The results were numerically observed during the postural changes. The rate of change in the physiological variables showed that the proposed control law can regulate the desired reference pump flow with minimal error within the acceptable clinical range in order to prevent suction and over perfusion.  
heart failure; cardiac assist devices; full state feedback; cardiovascular system; pulsatility index +
Footnote †: journal: Applied Soft. 2019, 9, 4593; doi:10.3390/app9214593
# ICN-Based Enhanced Content Delivery for CDN
## 4 Problem Formulation
### Model Description
Note that \(|s|\geq 1\), which means there is generally more than one available replica for content. Equation (5) means that if there is no available content in the ICN in-network cache, the source will be assigned to origin server, and the latency is denoted as \(l^{\prime k}_{1}\) in such a case. \(l^{k}_{2}\) in Equation (6) denotes the latency to deliver content from the edge server to the user. In (7), we use the standard flow conservation constraint of the MCF problem, i.e., for nodes that are neither the source nor destination, the inflow is equal to the outflow. This means our model does not support multicast routing, even though multicast is another attractive enhancement technique of ICN; we intend to explore this further in our future work. Constraint (8) ensures that each link chosen by demand \(k\) fulfills its demanded transmission rate of \(\omega^{k}\). Constraint (9) ensures that the traffic on link \((i,j)\) does not surpass its capacity. Content cached on each router and the CDN edge server should not surpass its capacity, and only one replica of the content is cached per server/router as enforced by constraint (10)-(13). Finally, we add to the model the binary constraint (14) on \(y^{k}_{n}\) and (15) on \(x^{k}_{r}\).
## 3 Test Results Analysis
### Stress-Strain Analysis under Different Apertures  
The peak strength, stress-strain, and elastic strain energy curves of the test blocks with different borehole diameters after uniaxial compression (the test blocks without holes and the test blocks with borehole diameters of 6, 10, 15, 20, 25, and 40 mm are replaced by numbers 0, 6, 10, 15, 20, 25, and 40 in Figure 2b) are shown in Figure 2. From Figure 2a and Table 2, it can be seen that compared with the complete test block, with the increase in the diameter of the borehole test block, the peak strength and strain value decrease to varying degrees.  
Combined with the pre-peak cumulative strain energy curve in Figure 2b, it can be seen that as the borehole diameter increases, the peak strength is similar to the strain image at the peak stress and shows a decreasing trend. The elastic strain energy stored before the peak of the test block decreases with the increase in the borehole diameter. When the borehole diameter is 6 mm and 10 mm, the peak strength of the test block decreases slightly; when the borehole diameter increases to 15 mm, the peak strength of the test block suddenly decreases greatly, and the reduction percentage is more than three times that of the former. This is because the optimal pressure relief critical value is achieved when the borehole diameter is 15 mm, resulting in a sharp drop in the strength of the test block. However, when it increases to 20 and 25 mm, the peak strength and pre-peak accumulated strain energy tend to be stable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Borehole** & **Reduction Percentage of** & **Percentage Reduction in Strain\%** \\
**Diameter/mm** & **Peak Strength/\%** & **Percentage Reduction in Strain\%** \\ \hline
6 & 6.15 & 32.31 \\
10 & 6.90 & 36.72 \\
15 & 21.02 & 38.03 \\
20 & 23.70 & 48.44 \\
25 & 25.10 & 49.00 \\
40 & 32.17 & 55.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak intensity and strain value decrease by percentage at peak intensity.  
Figure 2: (**a**) is the peak strength of the test block under different pore sizes, and (**b**) is the stress–strain and pre-peak cumulative strain energy curve.
# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 4 Experiment and Analysis  
### Dataset and Experimental Environment  
In this section, we describe the brain stroke precursor detection and disease prediction experiments that were conducted with older people on the previously described machine-learning models with raw value, power value, and relative value. We collected data on 48 stroke patients and 75 control patients in 2017, as well as 13 stroke patients and 137 control groups in 2018. For our experiments, all 61 stroke patients and a randomized selection of 61 patients from the control group were selected to balance the data from the stroke and control groups. In total, five scenario-specific measurement protocols were implemented, including walking, sleeping, moving objects, standing, and standing from a chair to stimulate the daily activities of an elderly person. The experiment was designed so that all subjects would go through one practice round before executing the measurement protocol according to each scenario. Despite the initial trial run, the first measured and collected values were not used as experimental data, because they could reflect human noise values stemming from the subject's tension and discomfort. The last fifth measurement protocol was also not reflected in the experimental data, because repeated experiments and fatigue were likely to be reflected in the data.  
For the machine-learning-based experiments, we conducted stroke disease prediction experiments and analyze the data by extracting power and relative values based on raw values in the form of brain waves. We also experimented with the optimal set of attributes chosen using CFS (Correction Feature Selection) by power and relative values. The optimal subset of attributes from the power value is: Fz(\(\theta\)), Fz(\(\theta\)_to\(\beta\)), Fz(\(IDAR\)), Fz(\(RRI\)), T7(\(RRI\)), C1(\(DAR\)), Oz(\(IDAR\)), T8(\(RRI\)), and the optimal set of attributes from the relative values  
Figure 4: Overall structure of ML-based elderly stroke disease prediction module using real-time EEG vital signals.  
is: Fz(\(\alpha\)), Fz(\(\beta\)), Fz(\(\gamma\)), Fz(\(IDAR\)), T7(\(\theta\)), T7(\(\alpha\)), T7(\(\gamma\)), C1(\(\theta\)), Oz(\(\beta\)), Oz(\(\gamma\)), C2(\(\beta\)), C2(\(\gamma\)), C2(\(Low\_\beta\)), T8(\(\theta\)), and T8(\(\gamma\)). This combination of 23 attributes was used, and the algorithms of machine learning used in the experiment were Random forest, C4.5 decision tree, C5.0 decision tree, naive Bayes, MLP (multi-layer perceptron), logistic regression, two-class SVM (support vector machine), C&RT (classification and regression tree), and QUEST (quick unbiased efficient statistical tree).