esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 4 Experimental Results
### Performance Indexes  
The number of AIs is one of factors which leads to the degradation of transformed image quality. To measure AIs, assuming the size of secret image is \(N_{1}\times N_{2}\), and the rate of AI in the color image can be calculated by:  
\[bpp=\frac{AI}{3\times N_{1}\times N_{2}}. \tag{8}\]  
The visual quality of stego-image is very important because the stego-image can be not detected by the attacker. The root mean square error (RMSE) and structural similarity index measurement (SSIM) are two objective evaluation indexes which are widely used in image quality evaluation. RMSE is represented by:  
\[RMSE=\sqrt{\sum_{c=1}^{3}\sum_{i=1}^{N_{1}}\sum_{j=1}^{N_{2}}\left(I(i,j)-I \prime(i,j)\right)^{2}}. \tag{9}\]  
where \(I(i,j)\) and \(I\prime(i,j)\) are pixels in the secret image and the stego-image, respectively. SSIM is calculated by the following formula:  
\[SSIM(x,y)=l(x,y)^{\alpha}\cdot c(x,y)^{\beta}\cdot s(x,y)^{\gamma}. \tag{10}\]  
and \(l(x,y)=\frac{2u_{x}u_{y}+C_{1}}{u_{x}{}^{2}+u_{y}{}^{2}+C_{1}}\), \(c(x,y)=\frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}{}^{2}+C_{ 2}}\), \(s(i,j)=\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}}\), \(\sigma_{xy}=\frac{1}{N-1}\sum_{i=1}^{N}\left(x-\sigma_{x}\right)\left(y- \sigma_{y}\right)\), where \(x\) and \(y\) are secret image and stego-image, respectively; \(u_{x}\), \(u_{y}\), \(\sigma_{x}{}^{2}\), \(\sigma_{y}{}^{2}\), \(\sigma_{xy}\) are the mean values, variances and covariance, respectively; \(C_{1}\), \(C_{2}\) and \(C_{3}\) are small constants for avoiding a zero denominator in Formula (11). \(\alpha\), \(\beta\) and \(\gamma\) are more than 0, and they are used to adjust the proportion of components \(l(x,y)\), \(c(x,y)\) and \(s(x,y)\). SSIM can evaluate quality of brightness, contrast and structure of nd the structure is the main influencing factor. When \(\alpha=\beta=\gamma=1\) and \(C_{3}=C_{2}/2\), SSIM is simimage, aplified as:  
\[SSIM(x,y)=\frac{\left(2u_{x}u_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2}\right)} {\left(u_{x}{}^{2}+u_{y}{}^{2}+C_{1}\right)\left(\sigma_{x}{}^{2}+\sigma_{y}{} ^{2}+C_{2}\right)}. \tag{11}\]
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology
### Dataset  
The Case Western Reserve University bearing dataset (CWRUBD) is recommended to test the proposed intelligent digital twin technique for FPRCI in the bearing. In the CWRUBD, a 2 horsepower (hp) reliance electric motor drives a shaft on which a torque transducer and encoder are mounted. Torque is applied to the shaft via a dynamometer and electronic control system. A motor is utilized to rotate the bearing at four different rotation per minute (RPM) speeds--including 1797, 1772, 1750, and 1730 RPM [38]. Vibration data was collected using accelerometers, which were attached to the housing with magnetic bases. Accelerometers were placed at the 12 o'clock position at both the drive end and fan end of the motor housing. During some experiments, an accelerometer (PCB 353B33 accelerometers-PCB Piezotronics) was attached to the motor supporting base plate as well. Vibration signals were collected using a 16-channel data acquisition module NI DAQ 6062E recorder and were post processed in a MATLAB environment. The vibration signals were collected via installed on bearing housing. A vibration sensor is used to collect the data in four different conditions including NS, RFS, IFS, and OFS, with three different crack sizes including 0.007, 0.014, and 0.021 inch. Moreover, the sampling rate of data collection is 48 kHz. The bearing type used in the CWRUBD is the 6205-2 RS JEM SKF roller bearing. Figure 2 shows the testbed for data collection which is designed by CWRUBD. Moreover, Table 1 summarizes the information of the CWRUBD according to the signal conditions, motor torque loads, and the bearing crack sizes [40].
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 2 Local Zernike Moments Transformation
### Local Zernike Moments  
Zernike moments describe the holistic characterization of images using radial polynomials at different orders and repetitions. Each of these radial polynomials used corresponds to a different characteristic of the image [57]. In problems such as face recognition, rather than the holistic characterization of images, local statistics have more importance [14]. Therefore, the local Zernike moments (LZM) [39] transformation was proposed to extract local variations by calculating these moments around each pixel on the face images.  
In the LZM transformation, \(V_{nm}(\rho,\theta)\) are used as \(k\times k\) filtering kernels:  
\[V_{nm}^{k}(i,j)=V_{nm}(\rho_{ij},\theta_{ij}). \tag{5}\]  
Because Zernike polynomials are defined in polar coordinates, the real and imaginary components of these filters can be obtained by converting the filters to cartesian form. The real and imaginary components of the filtering kernels calculated with \(n\) values up to 5 are shown in Figure 1. Using \(V_{nm}^{k}(i,j)\) filtering kernels in (1), the ZMs around each pixel are calculated as follows [39]:  
\[Z_{nm}^{k}(i,j)=\frac{2(n+1)}{\pi(k-1)^{2}}\sum_{p,q=-\frac{k-1}{2}}^{\frac{k- 1}{2}}f(i-p,j-q)V_{nm}^{k}(p,q). \tag{6}\]  
Since the imaginary component of ZMs for \(m=0\) is not available, the filters calculated using \(m\) values different from 0 are used in the LZM transformation. As a result, a different number of complex images are obtained depending on the value of \(n\) used. The number of these images can be calculated using:  
\[K(n)=\left\{\begin{array}{ll}\frac{n(n+2)}{4}&\text{if n is even,}\\ \frac{(n+1)^{2}}{4}&\text{if n is odd.}\end{array}\right. \tag{7}\]  
Figure 2 shows the real and imaginary components obtained for a face image using LZM filters given in Figure 1.  
Figure 2: Real and imaginary components obtained for a face image using theLZM filters given in Figure 1.
## 4 Experimental Validations
### Compliant Path Generator  
The design domain and boundary conditions of the compliant path generator are plotted in Figure 4. Two input forces \(F_{I1}\) and \(F_{I2}\) that are applied at input points \(I1\) and \(I2\) lead to output displacements \(u_{O1}\) and \(u_{O2}\) at the same output point \(O1(O2)\), respectively. The void areas at the right top and the right bottom corners have the same size of \(40\times 40\)
## 2 Sorbent-Based Sportive Microextraction Techniques
### Micro Extraction by Packed Sorbent Procedures (MEPS)  
Recently, [PERSON] and coworkers [39] reviewed the literature published on Micro Extraction by Packed Sorbent (MEPS) methods. This extraction procedure shows some very interesting potential benefits, such as low solvent consumption, small sample volume (10-250 \(\mu\)L), and the ability to be directly injected into the HPLC system without further treatments, with solvent volumes being compatible with several instrumental configurations and analyses. Figure 1 shows the device and the general procedure applied in MEPS extraction.  
This device is used in different fields, from biological applications to food and food supplement analyses. The main drawback is also related to its advantages. In fact, the possibility of using small sample volumes permits its application in analyses where only small volumes are available, for example plasma. In the case of higher volumes (such as in the environmental field), this device often shows its limitations.  
Nowadays, several types of packing materials are available, including:  
- Silica-based sorbents SIL (unmodified silica),  
- C2(ethyl),  
- C8 (octyl),  
- C18 (octadecyl);  
- Mixed-mode C8 and ion exchange (SCX),  
- Mixed-mode M1 (80% C8 and 20% SCX with sulfonic acid bonded silica);  
- Polystyrene-divinylbenzene (PS-DVB),  
- Porous graphitic carbon,  
- Molecular imprinted polymers (MIPs) based on different templates,  
- Metal organic framework (MOF)-based MIPs [40; 41],  
- Monoclonal antibodies (mAbs) for immunoaffinity sorbents production.  
Additionally, other commercial sorbents, such as new kinds of graphitic sorbent, polypyrrole/ polyamide, polyaniline nanowires, CMK-3 nanoporous materials, functionalized silica monoliths, APS (amino-propyl silane), and cyanopropyl hybrid silica have been successfully applied in MEPS devices to extract different groups of analytes, as recently reviewed [39; 42].  
To aid and improve the reproducibility during the extraction process, MEPS devices are also coupled to syringes in semi-automated and/or fully automated configurations. In fact, the main critical point during extraction relates to the reproducibility of the flow rate (generally mL s\({}^{-1}\)) used in the different steps (Figure 1). A MEPS syringe and a one-way check valve [43] was used for the realization of automated or semi-automated MEPS extraction, both for _on-line_ and _off-line_ instrument configurations.  
Table 1 presents selected MEPS applications in different fields and the performances obtained when applying this device.  
Figure 1: Device (**left**) and general procedure (**right**) applied in MEPS extraction.  
As previously mentioned, applications in environmental fields are very limited due to the large volumes that are necessary for the trace analysis of pollutants, both organic and inorganic. Additionally, applications are relatively limited for foods and food supplements due to difficult application of MEPS, which requires a longer time in the pre-analytical steps.
# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
###### Abstract  
The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model.  
correlation analysis; Copula entropy; PM2.5 concentration; forecasting model; LSTM Article  
Research on PM2.5 Concentration Prediction Based on CE-AGA-LSTM Model.
## 4 Simulations
### EDETA-e_ vs. _DBR_  
In this section, performance of EDETA-e using different scheduling and retransmission parameters will be compared with the performance of DBR using different values for the \(\delta\) and threshold parameters. The \(\delta\) parameter specifies the holding time, and the threshold parameter specifies the minimum depth difference for a packet to be retransmitted.  
We simulate DBR with two different sets of parameters: the first one with \(\delta=0.25\) and the threshold value of 40 m which according to our tests gives the best energy savings, and the second one with \(\delta~{}=~{}100\) and the threshold value of 0 m which gives the best performance in terms of packet delivery ratio.  
Both protocols use the same MAC layer (ALOHA protocol) and the same modem parameters, although DBR transmission speed is increased to 1,000 bps since this protocol does not need the usage  
Figure 11: DAck _vs._ DCsma2.5 _vs._ DnoAck2 energy comparison. (**a**) Leaf nodes energy consumption; (**b**) CH nodes energy consumption.  
of CDMA spreading codes. In both protocols, each node sends one byte of data plus its corresponding header every 250 s.  
Each scenario has been simulated several times in order to achieve a confidence interval of \(\pm 3\%\) with a confidence level of 95%. All simulations were seeded using the number 1310572618 and each repetition was done advancing the run number [5].  
Figure 12 shows the average end-to-end delay for EDETA-e with the delay aware schedule and without acknowledgement. It also shows the parameters set for energy saving and packet delivery ratio performance of DBR. As it can be seen, the average end-to-end delay for EDETA-e is higher than the delay for the two types of DBR.  
However, in terms of lost data due to collisions, EDETA-e experiences no collisions since it is a fully scheduled algorithm. DBR, on the other hand, presents high packet loss as depicted by Figure 13.  
Figure 12: End-to-end delay.  
Figure 13: DBR lost data ratio.  
The packet loss ratio of DBR in these simulations is higher than the one presented in the original paper because in the original paper authors use a CSMA-like MAC with backoff mechanism which increases the packet delay and indirectly modifies the packet holding time that is a key parameter of the protocol.  
Finally the simulation end time for the three alternatives is shown in Figure 14 using a logarithmic scale. As it can be seen, there are no significant differences between the two DBR alternatives. This is because nodes using DBR never use a low-power radio mode. On the other hand, using EDETA-e the last node dies more than 100 times later.
## 3 Improved Multi Mobile Agent Data Fusion Scheme
### Multi Mobile Agent Framework
#### 3.1.3 Application Information Fusion Framework for Sensor Networks  
Mobile agent frameworks is very useful for collaborative information processing. They promote the integration of the hardware agent of the physical environment and the application layer of the software agent. The two main characteristics are the agent's communication and mobility.  
The Interoperability agent model [14] shows an information network architecture for business processes. The most important part is the middleware, which provides shared information based on data from RFI technology. In our sensor network framework, we extend the model, and the extended model has the following software components replacing the RFI middleware:  
* Sensor Agent (SA): part of the sensor node, and can access the data source of the sensor node. It manages sensor nodes to make information collected by the sensor node available for specific applications.
* Agent Management System (AMS): manages the agent, including registration, authentication, security, and mobility. Extensions of the software can support clustering and cooperation. It also registers Sensor Agents
* Directory Facilitator (DF): delivers query services for the upper application, and finds suitable partners for other agents in a session.
* Agent Communication Channel (ACC): a message routing agent that sends the request message to the sender and supports local and remote communication.
* Data Manager (DM): main task is to manage and tap the local sensing data.
* Application Agent (AA): a static or dynamic proxy that communicates, moves, collaborates, analyzes, adapts, learns, and performs other application specific tasks.  
Table 2 shows the data structure of the application agent. It consists of four components: ID number to distinguish it from other agents, Data space to store the results of intermediate integration; Service classes to distinguish between integration algorithms; and Migration route of the mobile agent.  
In the above six types of agents, sensing nodes may include SA, AMS, ACC, DF and AA. Regional Management Stations may include ACC, AMS, DM and AA. They support local and remote communication and use wireless transmission to communicate between nodes. Software agents communicate with each other from point to point for local and remote communication, and use wireless transmission as a mechanism for communication between nodes.
# Analytical Electromechanical Modeling of Nanoscale Flexoelectric Energy Harvesting
## 5 Conclusions  
The flexoelectric energy harvesting based on the Euler-Bernoulli beam model has been investigated in this paper. Using the electric Gibbs free energy and the generalized Hamilton's principle for the flexoelectric body, the electromechanically-coupled dynamics and electrical circuit equations of the flexoelectric cantilever beams have been developed. The mode-superposition method was used to obtain the closed-form analytical expressions of the electrical and mechanical responses in the modal space. The numerical results indicate that the frequency of the peak vibration response exhibits a shift from low frequency to high frequency with increasing load resistance in the small scale. The peak voltage and power density with finite values of the loading resistance depend on the external excitations. Interestingly, there is an intrinsic length scale for the optimal power density, which depends on the material properties. Different from piezoelectricity, flexoelectric energy harvesting has an enhancement of the electromechanical coupling with decreasing size and thus could be more effective in micro- or nanoscale electromechanical systems.  
Conceptualization, R.H. and Z.Z.; Methodology, Y.S., X.L., and Z.Z.; Investigation and data analysis, Y.S., X.L., and Z.Z.; Writing--original-draft, Y.S. and X.L.; Writing--review and editing, Y.S., R.H., and Z.Z.  
This research was funded by the National Natural Science Foundation of China (Grant No. 11572271), Young Teachers Education and Research Projects of Fujian Province (Grant No. JAT170918), Youth Foundation of Chengyi University College, Jimei University (No. CK17002), and Scientific and Technological Innovation Platform of Fujian Province (2006L2003).  
The authors declare no conflict of interest.
# Emotional Dynamics in Social Interactions: A Multi-Strategy Synergy Model
## 6 Case Study Application
### Emotional Cycle Simulation  
Using the basic parameters in Table 2 and the strategy parameters in Table 5, we further optimized the parameter settings by subdividing the natural emotional decay rate \(\gamma\) into weekday (\(\gamma_{\mathrm{w}}=0.3\)) and weekend (\(\gamma_{\mathrm{r}}=1\)) components. The initialization of the emotional state on Monday morning was set to \(E(0)=0.2\) to simulate two-week emotional dynamics. Time units were measured in days, with a simulation step of \(\Delta t=0.5\) h. The fourth-order Runge-Kutta method was used to solve the differential equation system (19).  
Figure 6 shows the emotional state trajectories, along with the variations in intensity of the strategy. The upper panel displays \(E(t)\) trends during the weekdays/weekends, using a solid blue line for emotional states and an orange dashed line for circadian rhythms. Different fluctuation patterns emerged between weekdays and weekends, linked to differing natural decay rate parameters. Circadian rhythms significantly influenced emotional dynamics, particularly on weekdays, where rhythmic oscillations induced small-amplitude emotional fluctuations. Alternating background shadings visually highlight temporal emotional shifts by marking weekday-weekend transitions.  
The lower panel displays the activation patterns of the three strategies: green for social comparison discrepancy (\(S_{1}\)), yellow for strategic self-positioning (\(S_{2}\)), and red for emotional resonance enhancement (\(S_{3}\)). Contrary to expectations, the social comparison strategy maintained a high activation on weekends. This phenomenon probably stems from amplified emotional dynamics \(E(t)\) during weekends, which increase the emotional gap  
Figure 6: Two-week emotional simulation: The blue color indicates working days, whereas the pink color represents weekends.  
\(\left(1-\dfrac{E(t)}{E_{\text{high}}}\right)\) and sustain \(S_{1}\) intensity. The strategic self-positioning mechanism exhibited periodic activation patterns on weekdays, with a period \(T\) dictating emotional fluctuation frequencies. Both the self-positioning and resonance strategies showed reduced overall intensity on weekends, due to diminished social interactions with various peer groups.
## 2 Research Method and Material
### Statistical Analysis  
The statistical analyses were conducted using S-Plus (V 6.1, TIBCO Software Inc., Palo Alto, CA, USA) and Microsoft Excel (2010). A significance level of 0.05 was set. Descriptive statistics, such as mean, standard deviation, skewness, kurtosis, and median values, were calculated using both Excel (2010) and S-Plus (6.2). The results from both software packages were consistent across all descriptive statistical measures. Linear regression analyses, normality tests using the Kolmogorov-Smirnov (K-S) method [20; 21], \(t\)-tests, and ANOVA analyses were performed using S-Plus.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Soil Properties** & **pH** & **EC (\(\upmu\)S/cm)** & **OM (\%)** & **Clay (\%)** & **Silt (\%)** & **Sand (\%)** \\ \hline Mean & 7.20 & 164.5 & 7.67 & 34.95 & 17.63 & 47.42 \\ SD & 0.62 & 82.7 & 3.52 & 14.48 & 10.15 & 18.73 \\ Min & 5.20 & 57.7 & 3.23 & 11.20 & 5.03 & 4.74 \\ Max & 7.84 & 433.0 & 19.38 & 66.37 & 59.05 & 83.77 \\ \hline \end{tabular}
\end{table}
Table 1: Soil properties of the samples from the tested sites.  
\begin{table}
\begin{tabular}{l c} \hline
**Radiological Hazard Indices** & **Limit and Average Values** \\ \hline (1) H\({}_{\rm ex}\) = C\({}_{\rm K}\)/4810 + C\({}_{\rm Ra}\)/370 + C\({}_{\rm Th}\)/259 & 1.0 \\ (2) Ra\({}_{\rm eq}\) (Bq/kg) = 0.077C\({}_{\rm K}\) + C\({}_{\rm Ra}\) + 1.43C\({}_{\rm Th}\) & 370 Bq/kg \\ (3) D\({}_{\rm Rex}\) (nGy/h) = 0.0417C\({}_{\rm K}\) + 0.462C\({}_{\rm Ra}\) + 0.604C\({}_{\rm Th}\) & 59 nGy/h \\ (4) AED\({}_{\rm ex}\) (\(\upmu\)Sv/y) = D\({}_{\rm Rex}\)\(\times\) 8760 (n/y) \(\times\) 0.2 \(\times\) 0.7(Sv/Gy) \(\times\) 10\({}^{-3}\) & 70 \(\upmu\)Sv/y \\ \hline \end{tabular}
\end{table}
Table 2: Equations for the calculation of radiological hazard indices and their corresponding permissible and global average values.