esa-sceva/satcom-chunk-collection huggingface dataset:
# Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation
## 3 Results and Discussion
### Effect of Damage Zone Size  
In this section, the influence of the damage zone size on the sensitivity of the MSDI method was considered. The third level of damage can be described as \"large\" damage, simulated by the loss of depth which is equal to \"moderate\" damage (3 cm) but with more than one damaged element. These substantial damages were simulated as damage cases DC3 (elements E8 and F8) and DC4 (elements E8, F8, and E7). Figure 8 summarizes the results of the effect of the damage zone size. In both damage cases, the damage was accurately localized. Moreover, the method can be used to determine larger damaged zones, i.e., several adjacent damaged elements. The MSDI values kept increasing with the addition of more damage.  
Figure 7: Diagonal elements of the MAC and \(\Delta\)MAC matrices: (**a**) damage case DC1; (**b**) damage case DC2.
## 4 Results
### Computation Complexity  
The computational complexity of a model depends on several factors, including the total number of trainable parameters, training time per epoch, inference latency, memory footprint, and floating-point operations (FLOPs). As shown in Table 2, the proposed model has 484,842 parameters, which is higher than CNN, ResNet, and CLDNN but lower than SCRNN. Despite its larger parameter count, the proposed model achieves significantly fewer FLOPs (4.8 M) compared to all baselines, indicating greater computational efficiency during inference.  
In terms of training efficiency, the proposed model requires 187 seconds per epoch, which is faster than CNN and SCRNN but slower than ResNet and CLDNN. For real-time deployment, its inference time (9.85 ms) is competitive--slightly slower than CLDNN (8.42 ms) and ResNet (5.43 ms) but significantly faster than SCRNN (22.15 ms) and CNN (15.28 ms). Additionally, the proposed model maintains a moderate memory footprint (15.32 MB), striking a balance between ResNet's high memory usage (345.67 MB) and CLDNN's lower consumption (12.58 MB).  
Overall, the proposed model offers a favorable trade-off: while it has more parameters than some baselines, its low FLOPs, efficient memory usage, and reasonable inference speed make it suitable for scenarios where computational efficiency is prioritized without sacrificing performance.  
Table 3 presents the classification time required by the proposed model for varying input sizes. The results show a nearly linear relationship between the classification time and the size of the input data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **CNN** & **ResNet** & **SCRNN** & **CLDNN** & **Proposed Model** \\ \hline Total parameters & 398,122 & 91,626 & 428,554 & 239,818 & 484,842 \\ Epochs & 20 & 20 & 20 & 20 & 20 \\ Training time (s)/epoch & 564.7 & 119.4 & 196.7 & 139.9 & 187 \\ Inference time & 15.28 ms & 5.432 ms & 22.15 ms & 8.42 ms & 9.85 ms \\ Memory usage & 18.73 MB & 345.67 MB & 24.81 MB & 12.58 MB & 15.32 MB \\ FLOPs & 41,283,584 & 12,345,678 & 58,724,352 & 15,245,824 & 4,826,112 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity comparison between the proposed model and baseline models.  
Figure 10: Contribution of CNN and TCN components.
## 3 Methodology
### BATMAN Algorithm
#### 3.1.2 Penalty for Asymmetric links  
Since the wireless channel from node 1 to 2 and vice versa are not the same, a penalty is therefore introduced in BATMAN to reduce the \(TQ\) value such that the link that has low \(RQ\) values should be penalized (reduced) in the overall \(TQ\), as shown in the below equation [17].  
\[TQ_{asym}=TQ_{local}\times(1-(1-RQ)^{3}) \tag{4}\]  
The value presented herein (4) is derived analytically rather than by measuring the performance difference between \(RQ\) and \(EQ\).
## 3 Proposed Methods
### RM Method  
Reference Model (RM) technique is a linear method that approaches the behavior of any system of \"\(n\)\" order to the desired behavior of a reference system of first or second order. If the reference behavior is that of a first order, the desired settling time \(T_{s}\) is fixed and the value of \(\tau\) is deduced, such that \(T_{s}=3\tau\). The dominant pole is then to place at \(-1/\tau\) and the (\(n\)\(-\)\(1\)) other poles on left of \(-1/\tau\) in the complex plane [14,15]. If the reference behavior is that of a second order, with imposed values for damping \(\zeta\) and natural pulsation \(\omega_{n}\), the two dominant poles are to place at \(-\zeta\omega_{n}\pm j\omega_{n}\sqrt{(}1-\zeta^{2})\) and the (\(n\)\(-\)\(2\)) other poles on left of \(-\zeta\omega_{n}\) in the complex plane [14,15].  
The closed loop transfer function \(H(p)\), of the system including the controller and its denominator \(D_{H}(p)\) are computed. By taking into account some desired specifications (\(T_{s}\), \(\tau\) or \(\zeta\), \(\omega_{n}\)), the characteristic polynomial \(\mathrm{R}_{s}(p)\) of the reference system is evaluated.  
\[\mathrm{R}_{s}(P)=\left\{\begin{array}{cc}\Big{(}p+\frac{1}{\tau}\Big{)} \Big{(}p+\frac{\alpha}{\tau}\Big{)}^{n-1}&if\ a\ first\ order\ behavior\ desired\\ \Big{(}p^{2}+2\zeta\omega_{n}p+\omega_{n}{}^{2}\Big{)}\left(p+\alpha\zeta\omega _{n}\right)^{n-2}&else\end{array}\right. \tag{18}\]  
The parameter \"\(\alpha\)\" in (18) is made much greater than \(1\) to ensure the dominance of the poles \(-1/\tau\) or \(-\zeta\omega_{n}\pm j\omega_{n}\sqrt{(}1-\zeta^{2})\). By equating the coefficients of \(D_{H}(p)\) and \(D_{s}(p)\), the controller parameters can be extracted [14,15].
## 3 MATLAB and ADAMS Co-Simulation
### DDPG Reinforcement Learning Algorithm  
The DDPG algorithm is an upgraded version of the Actor-Critic (AC) algorithm shown in Figure 4. It integrates the advantages of DQN and overcomes the shortcomings of the Actor-Critic algorithm. It uses a replay memory buffer and soft-update target networks to realize stable and robust control in large-scale states or continuous action space. In addition, it adopts the mini-batch method to eliminate the correlation between samples. Through batch normalization to gain hyper-parameters, the agent is enabled to generalize across the environments with different scales of state values. The following paragraphs briefly describe the DDPG algorithm process according to the control flow presented in Figure 4. Details were previously presented elsewhere [24,32]. It is worth noting that the DDPG algorithm adopts the AC structure which includes a policy-based neural network (Policy-net) system and a value-based neural network (Q-net) system. Each consists of an online network and a target network. The two target networks are updated using the same software approach, while the two online networks are different: the online Q-net updates the parameters by minimizing the mean squared error (MSE) as the loss function (LF) to obtain the maximum value of Q, whereas the online Policy-net updates the network parameters by training the mini-batch data to achieve an unbiased estimate of the DPG according to the Monte Carlo method.  
Here, the LF can be expressed as follows:  
\[LF=\frac{1}{N}\sum_{i}\left[\underbrace{r_{i}+\gamma Q^{\prime}\left(S_{i+1}, \underbrace{u^{\prime}\left(S_{i+1}|\theta^{\prime}\right)}_{Target\text{ policy-net. 5-$\to$6}}\right)\left|\theta\mathcal{O}^{\prime}\right)}_{TargetQ-net:\ 6\to 7} -\underbrace{Q\left(S_{i},a_{i}|\theta^{\text{Q}}\right)}_{ OnlineQ-net:\ 6\to 7}\right]^{2} \tag{15}\]  
Figure 4: The block diagram of the DDPG algorithm.  
and the DPG can be written as shown in Equation (16):  
\[\
abla_{\theta^{n}}\mu|S_{i}\approx\frac{1}{N}\sum_{i}\underbrace{\
abla_{a} \underbrace{Q\Big{(}S,a|\theta^{Q}\Big{)}}_{\begin{subarray}{c}S=S_{i},a=\mu(S_ {i})\\ OnlineQ-net:\ 9\to 10\to 11\end{subarray}}\
abla_{\theta^{n}}\underbrace{\mu(S|\theta^{n}) |_{S_{i}}}_{\begin{subarray}{c}S_{i}\\ OnlinePolicy-net:\ 9\to 11\end{subarray}}}_{\begin{subarray}{c}PPG:\ 11\to 12 \end{subarray}}\underbrace{\mu(S|\theta^{n})|_{S_{i}}}_{\begin{subarray}{c}S_{i }\\ OnlinePolicy-net:\ 9\to 11\end{subarray}}} \tag{16}\]  
Thus, according to the subscript in Equations (15) and (16) and the structure in Figure 4, the calculation process of the DDPG algorithm can be clearly understood. In this paper, the adaptive moment estimation (Adam) optimizer used in the online neural networks of DDPG enables faster convergence to the global optimum.
# Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors
###### Abstract  
Quantification of the concentration of particular cellular metabolites reports on the actual utilization of metabolic pathways in physiological and pathological conditions. Metabolite concentration also constitutes the readout for screening cell factories in metabolic engineering. However, there are no direct approaches that allow for real-time assessment of the levels of intracellular metabolites in single cells. In recent years, the modular architecture of natural bacterial RNA riboswitches has inspired the design of genetically encoded synthetic RNA devices that convert the intracellular concentration of a metabolite into a quantitative fluorescent signal. These so-called RNA-based sensors are composed of a metabolite-binding RNA aptamer as the sensor domain, connected through an actuator segment to a signal-generating reporter domain. However, at present, the variety of available RNA-based sensors for intracellular metabolites is still very limited. Here, we go through natural mechanisms for metabolite sensing and regulation in cells across all kingdoms, focusing on those mediated by riboswitches. We review the design principles underlying currently developed RNA-based sensors and discuss the challenges that hindered the development of novel sensors and recent strategies to address them. We finish by introducing the current and potential applicability of synthetic RNA-based sensors for intracellular metabolites.  
biosensor; RNA-based sensor; aptamer; metabolism; metabolite; fructose-1,6-bisphosphate; metabolic flux; ribozyme; riboswitch; spinach 2023
## 5 Conclusions  
In this paper, a prediction model (wPSO-BP) and a multi-objective optimization algorithm (NSGA-III-LBWO) are proposed, and the blade inlet and outlet angle, volute tongue radius on the front disc side, and front disc side blade chamfer angle of the multi-blade centrifugal fan are optimized based on the wPSO-BP prediction model and NSGA-III-LBWO, which improves the comprehensive performance of the fan. The conclusions can be drawn as follows:  
Figure 22: Comparison of noise at far-field monitoring point before and after optimization.  
A prediction model between design variables and optimization objectives called wPSO-BP is proposed and compared with the BP prediction model. The result indicates that wPSO-BP has a better effect.
* A Logistic chaotic map is used for population initialization in beluga whale optimization (LBWO), and a multi-objective optimization algorithm is proposed based on NSGA-III and LBWO (NSGA-III-LBWO).
* Based on the wPSO-BP prediction model and NSGA-III-LBWO, the prototype fan is optimized, and the result shows that the aerodynamic and noise performance of the fan improved, which provides a certain reference value for the optimization of multi-blade centrifugal fans.  
Conceptualization, W.J. and J.L.; methodology, W.J. and J.W.; software, W.J. and R.X.; validation, J.L., M.Z. and Q.H.; formal analysis, W.J. and J.W.; investigation, R.X. and M.Z.; resources, Q.H. and M.Z.; data curation, [PERSON] and J.W.; writing--original draft preparation, W.J. and J.W.; writing--review and editing, J.L. and Q.H.; visualization, W.J.; supervision, J.L. and Q.H.; project administration, J.L.; funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Innovation Driven Development Special Fund Project, grant number AA22068060-6, and the Guangxi Science and Technology Major Special Project, grant number AA23062073-3.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The computation was completed on the HPC Platform of Huazhong University of Science and Technology.  
Author [PERSON] was employed by Wuhan Second Ship Design and Research Institute. Author [PERSON] was employed by Valeo Automotive Air Conditioning Hubei Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
### Contributions of the Paper  
It is the opinion of the authors that the manuscript that is presented here has made several contributions to the existing Sate of the Art on how to use UAVs in a secure framework when monitoring livestock. These contributions are as follows:  
1. A study on the State of the Art regarding the existing solutions for UAV usage in livestock monitoring oriented to Smart Farming. In this way, not only a collection of related works about this topic has been made available, but also the main open issues that have been found are present as well.
2. A specific list of requirements, both for hardware and software components, with regard to how to deploy a UAV in a secure manner whenever it is needed to do so. Therefore, UAVs can either be built from scratch with the specific purpose of cattle monitoring or purchased with the requirements needed to perform monitoring and surveying missions satisfactorily. It must be born in mind that there are several aspects like communications bandwidth, operational distance or data transmission rates that have to be specified for the better performance of a UAV in this application domain.
3. A security threat analysis on how to create a secure framework for UAV livestock monitoring applications. As mentioned, cybersecurity must be considered from the very beginning when deploying a system in the application domain of Smart Farming. Combining the security analysis with the requirements one, an accurate and effective livestock monitoring UAV can be developed.
4. An actual implementation and testing of the proposed framework under the formulated UAV requirements. Thus, the theoretical concepts that are described previously can be put to practice and assessed under a real-world scenario. Not only hardware components for the UAV have been considered, but also the software components (blockchain, IPFS) and protocols (TLS, HTTPS) that are decisively assisting the security capabilities.
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## 4 Discussion
### Potential Application of the Research Results in Urban Design
#### 4.2.2 Geometrical Configuration and Purpose of an Urban Open Space  
The three case sites speak also about the challenges and opportunities an urban designer encounters when dealing with soundscape intervention tasks in urban open spaces of different sizes and shapes.  
The almost 1000 m long seaside promenade, location A, allowed for the largest distance (360 m) between the measurement points. During the quieter periods of the day, the Sea Organ can be heard at that distance (350-400 m, as observed by authors), but this was not the case during the measurement session in question.  
Perhaps the most challenging was the location B as it was impossible to find a measurement point where the sound art installation was not perceptible while playing. However, the principle of controlling the distance between the sound source and the measurement point was followed also at that location.  
Larger urban open spaces offer the opportunity of including a spatial sequence characterized by the high value of the variance in the presence of the integrally designed sound source. This can contribute to a richer spatial experience and more positive assessment of the overall soundscape. The small difference in the distance between the measurement points in Maksimir indicates that even in the case of smaller urban open space, significant effect can be achieved if the added sound can be contained within a certain part of the space--i.e., the pavilion footprint--which then makes the experience less expected and the contrast higher. In the Maksimir case, it is achieved by acoustically limiting the experience of echo to the space between the two reflecting surfaces.  
The experiment on the location C can be considered a successful example of a simulated soundwalk experience since the distinct change in the acoustic characteristics (pronounced flutter echo effect in the very pavilion) was statistically significant, and the particular experience fits the goal of what using the park should be--leisure, fun, and entertainment. Moreover, this speaks in favor of valuing'small acoustic imperfections' such as accidental flutter echo or unusual frequency response occurring in urban spaces, as advocated by [PERSON] [93].
## 3 4-SSB System Design
### The Concept of Application of 4-SSB into OFDM M-QAM 4-SSB Uncoded System Using the Shadow Equalizer
#### 4.3.1 System Model  
The proposed system model is depicted in Figure 3. The system has two major devices in a typical communication system: transmitter and receiver. Suppose that the traditional MIMO system has \(N_{T}\) transmitter antennas and \(N_{R}\) receiver antennas and the number of receiver antennas generate more than that of transmitter antennas i.e., \(N_{R}>N_{T}\). Firstly, the transmission scheme is fed by the independent data binary value \(\{1,0\}\) with equal probability. Each binary in the uncodec system then will be mapped using two parallel QAM Gray codec symbols. For M-QAM signals, the complex value is represented in the real and imaginary part of the symbol which takes value in the general set of two M-QAM proceeded by 4-SSB modulatio \(\left\{\pm 1,\pm 3,\ldots,\pm\left(\sqrt{\mathrm{M}}-1\right)\right\}\) to produce one profile called 4-SSB M-QAM with the same bandwidth as of two symbols of QAM.  
To realize a feasible approach in MIMO, we translate the system transmitter into \(N_{T}\,\times\,1\) vector \(x_{4-SSB,n}[i]=\left[x_{4-SSB,1}x_{4-SSB,2}\ldots x_{4-SSB,N_{T}}\right]^{T}\). This sequence of symbols is transmitted over a flat fading channel. The 4-SSB M-QAM is first demodulated by the OFDM modulation then follows 4-SSB demodulation process and sampled in the receiver with \(N_{R}\) antennas. The received signal after demodulation process is collected in \(N_{R}\,\times\,1\,r[i]\,=\,\left[r_{1}[i],\,r_{2}[i],\ldots,r_{N_{r}}[i]\right] ^{T}\) and can be expressed as:  
\[r[i]=\,Hx_{4-SSB,n}[i]+\,v[i]. \tag{26}\]  
After the signal is sampled. The signal can be represented in the discrete-time equivalent channel which is affected by additive, zero-mean, circularly symmetric white Gaussian noise \(v[i]\) with total variance \(\sigma_{v}^{2}\). The receiver signal mathematical model is:  
\[r_{n}=\,\sum_{l=0}^{L}h_{l}x_{4-SSB,n-1}\,+\,v_{n}, \tag{27}\]  
where the set \(\{h_{l}\}\) denotes the \(L+1\) coefficient of the discrete time equivalent model. In MIMO, the element \(h_{nR,nT}\) of the \(N_{R}\,\times\,N_{T}\) channel matrix \(H\) denotes a complex channel gained from the \(n_{T}^{th}\) transmitted antenna and \(n_{R}^{th}\) with receiver antenna. For simplicity, we consider the \(H\) complexity corresponds to the optimized ordering. The arrangement is denoted as norm function \(\|H_{1}\|,\|H_{2}\|,\ldots,\|H_{N_{T}}\|\) where \((H_{n})\) represents the \(n\)th column of \(H\).
## 3 Image-Motion Compensation Technique of the Secondary Mirror Motion  
### Image-Motion Compensation Scheme  
In order to achieve high-resolution scanning imaging, aerial cameras must select an appropriate image-motion compensation scheme to overcome the forward and scanning image motions introduced by their own imaging mechanisms. Currently, there are various forms of image-motion compensation schemes commonly used in aerial cameras, which can be broadly categorized as optical image-motion compensation, mechanical image-motion compensation, electronic image-motion compensation, and digital image-motion compensation.  
For the current mainstream long focal length, common aperture, and multi-band aerial cameras, the optimal approach is to implement optical image-motion compensation, which adopts the multi-dimensional movement of an optical element to achieve the comprehensive image-motion compensation of multi-band cameras. In addition, during the selection process of the optical element for the image-motion compensation, two points should be emphasized: one is the high efficiency of image-motion compensation, and the other is that the motion of the optical element has the least influence on the image quality. Also worth comprehensive consideration is that the secondary mirror in the catadioptric optical system is used as the optical element for image-motion compensation, and the purpose of comprehensive image-motion compensation is achieved through its multi-dimensional movement. The principle and flow chart of image-motion compensation are shown in Figures 1 and 2 respectively.  
Figure 1: Principle of image-motion compensation.  
Figure 2: Flow chart of image-motion compensation.
# Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon
###### Abstract  
This study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material's density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with \(R^{2}\) values that exceeded 0.95 and minimal root-mean-square errors. The results reveal that the temperature and pressure significantly influenced the thermodynamic properties of the a-Si, while the cooling rate had a minor effect. The models generated isobaric and isothermal curves, which offered deeper insights into the thermodynamic behavior of the a-Si and complemented traditional MD simulations by providing a more efficient means to explore thermodynamic states. This work highlights the potential of machine learning to accelerate the study of materials by enabling faster exploration of thermodynamic behavior and the generation of additional data. This approach enhances the understanding of the equation of state of a-Si and opens new avenues for applying this hybrid modeling technique to other materials.  
thermodynamic properties; machine learning; statistics; molecular dynamics Article