esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Discussion
### Comparative Analysis  
In Figure 15, some comparisons of the numerical and experimental results are made under single-frequency excitation.  
The frequency of the interference signal is 0.244, 0.282 and 0.343 Hz, respectively. For the time domain response, the interference signals in the simulation and experiment are primarily identical; however, there is a tiny difference in the control effect, particularly in the case of DC-6 A. For the frequency domain response, there is also a minor difference in the interference signals and control effect between the simulation and experiment.  
The following are the principal reasons for the discrepancy between the experimental and simulation results: (1) the time domain signal generated by the self-built non-contact permanent magnet vibration exciter is not satisfactorily robust, and the angle signal acquired by the angular sensor may not be able to completely capture the real-time perturbation, thereby influencing the actual output of the joint itself. (2) Due to fabrication and construction imperfections, there exists clearance among the primary parts of the experimental prototype, which inevitably impacts the joint output rotation angle.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{3}{c}{**Major Frequency**} \\ \hline  & \multirow{2}{*}{
\begin{tabular}{c} Without \\ control \\ \end{tabular} } & \multirow{2}{*}{\(-\)14.51–14.08\({}^{\circ}\)} & & 0.06 Hz & 0.13 Hz \\ \cline{3-6}  & & & Amplitude & 1.74\({}^{\circ}\) & 10.84\({}^{\circ}\) \\ \cline{3-6}
**I** & & & Amplitude & 0.29\({}^{\circ}\) & 3.19\({}^{\circ}\) \\ \cline{3-6}  & \multirow{2}{*}{\
\begin{tabular}{c} With control \\ -6 A \\ \end{tabular} } & \multirow{2}{*}{\(-\)4.58–4.75\({}^{\circ}\)} & & 83.33\(\%\) & 70.57\(\%\) \\ \cline{3-6}  & & & \(-\)15.56 dB & \(-\)10.62 dB \\ \hline  & \multirow{2}{*}{
\begin{tabular}{c} Without \\ control \\ \end{tabular} } & \multirow{2}{*}{\(-\)22.33–15.75\({}^{\circ}\)} & & 0.05 Hz & 0.09 Hz & 0.14 Hz & 0.18 Hz \\ \cline{3-6}
**II** & & & Amplitude & 3.36\({}^{\circ}\) & 10.30\({}^{\circ}\) & 3.49\({}^{\circ}\) & 2.99\({}^{\circ}\) \\ \cline{3-6}  & & & Amplitude & 0.67\({}^{\circ}\) & 2.21\({}^{\circ}\) & 0.53\({}^{\circ}\) & 0.35\({}^{\circ}\) \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \hline \multirow{4}{*}{**III**} & \multirow{4}{*}{
\begin{tabular}{c} Without \\ control \\ \end{tabular} } & \multirow{4}{*}{\(-\)29.89–27.73\({}^{\circ}\)} & & 0.06 Hz & 0.12 Hz & 0.19 Hz \\ \cline{3-6}  & & & Amplitude & 2.28\({}^{\circ}\) & 19.63\({}^{\circ}\) & 2.57\({}^{\circ}\) \\ \cline{3-6}  & & & Amplitude & 1.27\({}^{\circ}\) & 3.23\({}^{\circ}\) & 0.77\({}^{\circ}\) \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vibration control effect with passive damping.  
Figure 15: Comparative results of vibration control: (**a**) 0.244 Hz; (**b**) 0.282 Hz; and (**c**) 0.343 Hz.
### Scalability Concerns  
The SDN controller is often viewed as a central point of failure for the software-defined IIoT-Edge network. \(n\) SDN controllers are deployed to create redundancy and improve flow initialization computation rates. These SDN controllers are connected through East-West interfaces to form a cluster, as illustrated in Figure 5. In practice, the SDN controller software instances are shipped as _karaf_ containers. This software instance runs on a virtual machine with sufficient CPU and memory resources. These CPU and memory resourcesmust be constantly monitored to ensure that they do not exceed a stipulated threshold above which the performance and overall health of the SDN controller are degraded [48].  
Furthermore, the SDN controller placement problem (CPP) has been of critical concern, especially in designing large-scale networks where deploying a single network is not feasible [95]. Studies solve the CPP by modeling approaches that deploy the optimal number of controllers within a network while meeting certain performance requirements. Studies by [PERSON] et al. [138], [PERSON] et al. [139], [PERSON] et al. [140], and [PERSON] et al. [141] explore heuristic and optimization algorithms to determine the ideal number of SDN controllers for a given network size and the optimal placement in proximity to the FDs and other SDN controllers.  
Furthermore, the SDN controller must manage communication with a potentially vast number of switches and other network devices, which can become a bottleneck as the network grows. Scalability concerns also extend to the software layers, where the controller's ability to process and respond to network events in real time is vital. To address this, SDN architectures may employ distributed controllers, hierarchical designs, or clustering techniques to enhance scalability [142]. On the positive side, the programability of SDN enables dynamic resource allocation and network adjustments, allowing for elastic scalability that can accommodate variable workloads and changing network topologies [143].
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 3 Feature of Quantum Stream Cipher
### Progress in Security Theory  
The BB-84 protocol is a key delivery technique for securely sharing secret key sequences (random numbers). The Y-00 protocol is a symmetric key stream cipher technique for cryptographically transmitting data. As mentioned above, both quantum cryptography techniques enhance security by preventing eavesdroppers from taking the exact signal on the communication channel. The models that explains the principle of such physical technology is called the \"basic model\". It is this basic model that can be found in textbooks for beginners.  
Let us start with a QKD, such as BB-84. If the basic model of the BB-84 protocol is implemented in a real optical fiber communication system, then it can be eavesdropped. Therefore, in order to guarantee security even in systems with noise and energy loss, a technique that combines error correction and privacy amplification (universal hashing) was proposed, and then a theoretical discussion of security assurance became possible. That is, in 2000, [PERSON], et al. [16] proposed a mathematical security theory for BB-84 on an abstract mathematical model called the Shor model, which was later improved by [PERSON] [17]. In brief, the security of the BB-84 protocol is evaluated by quantifying quantum trace distance of the two density operators to the ideal random sequence and the random sequence shared by the real system. This is the current standard theory for the security of QKD. It is very difficult to realize a real system that the quantum trace distance is sufficiently small.  
On the other hand, from the beginning, the Y-00 protocol can consider the effects of non-ideal communication systems. As mentioned at the above section, the selection of communication base of the Y-00 protocol is encrypted by conventional mathematical cipher. The Y-00 quantum ciphertext, which is an optical signal, is emitted as the transmission signal. So, the ciphertext of the mathematical symmetric key cipher that an eavesdropper needs to decipher corresponds to the Y-00 quantum ciphertext. However, since the set of ultra-multi-valued signals, which is Y-00 quantum ciphertext, are a non-orthogonal quantum state ensemble, their received signals are inaccurate due to errors caused by quantum noise. Therefore, the discussion based on the computational security of the mathematical cryptographic part of Y-00 mechanism to be attacked is replaced by the problem of combination of information theoretic analysis and computational analysis. However, we should emphasize that the discussion with infinite number or asymptotic theory are not our concern, because our concern is a physical system under practical situation. For example, if an attacker needs circuits of the number of the size of the universe to perform the brute-force attack, the system is unbreakable. Or, if an attacker needs 100 years to collect the ciphertext for trying the cryptoanalysis, it is also impractical and unbreakable.
## 2 Mathematical Model  
The mathematical model presented hereby was established based on the mass and energy equations written to describe the behavior the nanofluids circulating in the PV thermal solar collectors, driving an Organic Rankine Cycle, ORC, with a quaternary refrigerant mixture as shown in Figure 1. The solar radiation was absorbed by the PV solar panels and converted into electricity and thermal energy. The latter is dissipated and heats up the nanofluids heat transfer fluid that is used to drive the waste heat boiler of the ORC and generate vapor refrigerant. In the turbine, the thermal energy is converted into kinetic energy and produces power at the turbine shaft and the generator. The low-pressure vapor exiting the turbine is condensed in the condenser into liquid and pumped back to the waste heat boiler through the cooling/freezing coil and the regenerator, as illustrated in Figure 1. The refrigerant mixture circulates in the ORC loop is an environmentally sound quaternary mixture and composed of R512a, R125, R1234 fy, and R245 fa with a boiling temperature of \(-\)28.13 \({}^{\circ}\)F, a critical temperature of 220.67 \({}^{\circ}\)F at a critical pressure of 59.85 Psi. The thermodynamic and thermophysical properties of the refrigerant mixture were obtained at REFPROP [15]. REFPROP is a computer program distributed through the Standard Reference Data program of NIST that provides the thermophysical properties of pure fluids and mixtures over a wide range of fluid conditions, including liquid, gas, and supercritical phases. It contains critically evaluated mathematical models with the goal of representing the properties to within the uncertainty of the underlying experimental data used in their development.  
In the following sections, the different equations of mass and energy are written and presented for each definite control volume element of the nanofluids loop and ORC cycle presented. It is assumed in the model that the nanofluid is homogeneous, isotropic, incompressible, and Newtonian, that inlet velocity and inlet temperature are constant, and that the thermophysical properties of the nanofluids are constant.
###### Abstract
However, the algorithms face several common difficulties, such as fast convergence, local search concerns in the fitness function, and increased cost. Meta-heuristic algorithms are preferred when searching for the optimal solution becomes exhausting. Efficient meta-heuristic algorithms must include the solution space where there is a global optimum and develop novel and better solutions. There are meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Cuckoo Search that strive for global optimization (Exploration) and methods such as Simulated Annealing (SA) and Harmony Search Algorithm (HSA) that are restricted to local optima in the literature. For a better solution, a balance between exploration and exploitation is required. This prompted the combination of two popular meta-heuristic algorithms, MFA and AOA. This research presents a hybrid Mayfly-Aquila optimization technique for an energy-efficient clustered routing protocol in WSNs to address earlier mentioned difficulties. To minimize energy consumption, this work proposes to include energy consumption as a node choosing the path. For that, the following tasks must be prioritized:  
1. The shortest route between BS and CH because of its usage of less energy. 2. The selection of CH between clusters. MFA is a new evolving meta-heuristic that has a higher potential for finding the optimum solution than PSO and can identify more optimal solutions. Premature convergence may decrease the eventual solution's quality in some cases. Combining an algorithm with high exploration skills with another approach with strong exploitation characteristics is possible. The MFA approach successfully explores the search space, whereas the AOA algorithm utilizes and improves existing feature subsets. The following are the research's key contributions:  
* In the WSN, Mayfly is employed to determine the CH because of its excellent stability and low computing cost. Mayfly chooses the CH in this research based on many objective values, including residual energy, the distance between neighboring nodes, the distance towards the BS, density of the nodes, and node centrality. * AOA determines the shortest route between CH and BS because of its capability of delivering speed detection of solutions. To address the constraint of the unpredictable convergence time, the AOA is optimized using residual energy, the density of the node, and distance. * The network lifetime is increased due to the efficient CH selection and optimal path creation for data transmission. Furthermore, by reducing the energy usage of the nodes while transferring data packets, the total number of packets retrieved by the BS is enhanced. The organization of this paper is as follows: Section 2 illustrates the existing works, Section 3 presents the proposed hybrid MFA-AOA algorithm, Section 4 discusses the result and discussion, and the conclusion and future work are presented in Section 5.
# _applied sciences_
## 3 Results and Discussion  
### Chemistry  
Our previous studies confirm that compounds containing such groups as isoniconic, methoxycinnamic, succinic, polyamine, arylidene, amide, propagylaminoalkyl, and azepane display cytotoxic and antimicrobial activity [25; 26]. Also, we have shown that among the different types of triterpenoids [27; 28; 29], 2,3-indoole-betulinic acid glycine and \(L\)-phenylalanine amides were discovered as lead compounds with IC\({}_{50}\) values of 0.04 and 0.05 uM, being 3784- and 4730-fold more active than acarbose [30]. Triterpene type indoles were highlighted as excellent pharmacophore platforms for the development of new anti-diabetes drugs [31]. We have shown that lupane-type C2-furfurylidene, \(m\)-iodo-benzylidene, and 3-pyridinylidene derivatives exhibited significantly better activity (up to 700 times higher) than acarbose [32], as well as the chalcone derivatives of platanic acid (20-oxo-lupanes) with furfurylidene and trifluoromethylbenzylidene fragments [33].  
Thus, to study the inhibitory activity of \(\alpha\)-glucosidase, a series of oleanolic acid derivatives with these pharmacophore groups was obtained.  
Triterpene platforms such as oleanolic acid **1**, erythroidoid (ED) **2**, 2,3-indoole- and C2-benzylidene derivatives of 3-oxo-OA were successfully used for the synthesis of the desired compounds **4-18** and **19-28** (Schemes 1-3). Scheme 1 outlines the synthesis of compounds **4-10** by \(\mathcal{O}\)-acylation and \(N\)-amidation of hydroxy and carboxy groups at C3 and C28 positions of OA **1** and ED **2**. The reaction of nicotinic, isonicotin, and methoxycinnamic acid acyl chlorides or succinic anhydride with **1** or **2** led to mono- and bis-esters **4-8**. Amides **9** and **10** were obtained from 3-acetoxyoleanolic acid by the reaction of **1** with spermidine or diethyltriamine by the acyl chloride method in the presence of Et3N.  
**Scheme 2.** Synthesis of 2,3-indolo-oleanolic acid derivatives **12-18**. Reagents and conditions: (i) 1. (COCl)\({}_{2}\), CH\({}_{2}\)Cl\({}_{2}\), 25 \({}^{\circ}\)C; 2. corresponding amine, Et\({}_{3}\)N, CH\({}_{2}\)Cl\({}_{2}\); 25 \({}^{\circ}\)C; (ii) corresponding amine, PFA, NaOAc, CuI, 1,4-dioxane, 60 \({}^{\circ}\)C; (iii) LiAlH\({}_{4}\), THF, reflux.  
Compounds **12-18** were prepared from 2,3-indolo-oleanolic acid **3**[15] (Scheme 2). First, the coupling of **3** with secondary amines by the acyl chloride method led to amides **12-14**. The Mannich reaction of alkylynamide **11** with amines in the presence of a catalytic amount of copper iodide and sodium acetate produced propargylaminoalkyl derivatives **15-17**.  
Scheme 3 describes the Claisen-Schmidt condensation of 3-oxo-OA amide **19** and **20** derivatives with 2- or 4-pyridincarboxaldehyde or furfural to afford C2-substituted compounds **23-28**. Additionally, two other A-ring modified azepano- **29** and seco- **30** derivatives were synthesized [22].
###### Abstract  
Civil engineering structures with damage, defects, or subsurface utilities create a high-contrast exploration environment. These anomalies of interest exhibit different electromagnetic properties from the surrounding medium, and ground-penetrating radar (GPR) has the potential to accurately locate and map their three-dimensional (3D) distributions. However, full-waveform inversion (FWI) for GPR data struggles to simultaneously reconstruct high-resolution 3D images of both permittivity and conductivity models. Considering the magnitude and sensitivity disparities of the model parameters in the inversion of GPR data, this study proposes a 3D dual-parameter FWI algorithm for GPR with a composite model constraint strategy. It balances the gradient updates of permittivity and conductivity models through performing total variation (TV) regularization and minimum support gradient (MSG) regularization on different parameters in the inversion process. Numerical experiments show that TV regularization can optimize permittivity reconstruction, while MSG regularization is more suitable for conductivity inversion. The TV+MSG composite model constraint strategy improves the accuracy and stability of dual-parameter inversion, providing a robust solution for the 3D imaging of subsurface anomalies with high-contrast features. These outcomes offer researchers theoretical insights and a valuable reference when investigating scenarios with high-contrast environments.  
ground-penetrating radar; three-dimensional; full-waveform inversion; composite model constraint +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Ventilation System Model
For application in the time discrete model, Equation (5) is transformed to a time-dependent function. It is an ODE of the type \(\frac{dy}{dt}=b-ay\) with the general solution \(y=\frac{b+c^{-ct}}{a}\). In this case, \(a=\frac{Q_{ven}}{V_{s}}\) and \(b=\frac{Q_{max}C_{ven}+G_{s}}{V_{s}}\), so the time-dependent CO\({}_{2}\) development is given by Equation (6), where the constant \(c\) can be determined using initial values at the start of the time step. Figure 2: Components connections, and step simulation order for a model with one ventilation system servicing two building spaces. \[C_{s}=\frac{\frac{Q_{\text{core}}C_{\text{core}}+G_{\text{s}}}{V_{s}}+ce^{-\frac{ Q_{\text{core}}}{V_{s}}t}}{\frac{Q_{\text{core}}}{V_{s}}} \tag{6}\]  
The CO2 generation in a building space is calculated as follows:  
\[G_{s}=K_{gen}P_{s} \tag{7}\]  
where \(P_{s}\) in the number of occupants and \(K_{gen}\) is the CO2 generation per occupant. For this study, \(K_{gen}=15\) L/h is assumed, since this was found to be the average human generation during office activities in an experiment by [PERSON] et al. [18]. The CO2 sensors are assumed to accurately measure the concentration of CO2 in their respective space. The output signal of the sensor model is thus equal to the actual CO2 concentration in the space. For each building space, a CO2 controller is used to regulate the opening position of ventilation dampers. One of two models are used for the CO2 controller components depending on the desired type of control. The simplest is a rule-based model, where one of several rules is chosen based on CO2 concentration and the corresponding opening degree is used for the dampers. The other model utilizes model predictive control (MPC) to optimize damper control within a chosen prediction horizon. The optimization is multi-objective with the following objective function:  
\[F_{flow}=\frac{Q_{fan}}{Q_{fan,max}} \tag{1}\]0  
where \(Cost\) is the electricity cost, \(AirKPI\) is a Key Performance Indicator (KPI) for indoor air quality (lower is better) and \(Emission\) is the CO2 emission attributed to the consumed electricity. Each objective has an adjustable weighting (\(w_{1}\), \(w_{2}\) and \(w_{3}\)) and are evaluated for each simulation time step \(i\) within the prediction horizon. For every step, the optimization is constrained by the component equations presented in this section as well as fan flow limits (\(0\leq Q_{fan}\leq Q_{fan,max}\)). The developed model needs an input for occupancy in each space at each time step of a simulation. Additionally, predictions for occupancy, electricity price and CO2 emission from electricity consumption are needed to use the MPC model for the CO2 controller.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 5 Discussion
### Computational Complexity and Parameter Amount Analysis  
This section further analyzes the computational complexity and parameter amount of the five reconstruction methods. These five methods are all run on NVIDIA GTX 3090 GPU. Table 5 displays the FLOPS and the number of parameters for each reconstruction method. Due to the introduction of bidirectional interactions between the CNN branch and the Transformer branch in the DualCT module, the FLOPS value of our network becomes higher. On the other hand, the DualCT module integrates the advantages of CNN and the Transformer, bringing effective reconstruction performance improvements compared to only using the Transformer or CNN networks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Input** & **Mask Attention** & **PSNR** & **SSIM** \\ \hline A & \(H_{0}\) & & 34.75 & 0.943 \\ B & \(H_{0}\bigodot M^{*}\) & & 35.08 & 0.946 \\ C & \(H_{0}\) & ✓ & 35.80 & 0.955 \\ D & \(H_{0}\bigodot M^{*}\) & ✓ & 35.62 & 0.951 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments of mask attention in DualCT module. (✓ indicates using this module).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parallel Branches**} & \multicolumn{3}{c}{**Interaction**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\ \cline{2-3} \cline{5-5}  & **Channel** & & & 33.88 & 0.931 \\ ✓ & & & & 34.38 & 0.939 \\ ✓ & ✓ & & 34.61 & 0.941 \\ ✓ & & ✓ & 34.75 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments of parallel branches and bidirectional cross-linking interactions in the DualCT module. (✓ indicates using this module).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameter Amount (M)** & **FLOPS (G)** \\ \hline TSA-Net & 44.25 & 110.06 \\ SRN & 1.25 & 81.84 \\ GST & 1.27 & 82.87 \\ MST-S & 0.93 & 12.96 \\ Ours & 0.92 & 122.27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of computational complexity and parameter quantity.