esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Static/Dynamic Response Characteristics of IEPCM
### Harmonic Response Analysis of IEPCM  
As is known, the harmonic frequency distributions and resonance response amplitudes of a structure can be achieved from the harmonic response analysis. In accordance with Reference [22], the harmonic response frequency range was set from 200 kHz to 500 kHz, the load sub-step was set as 30, and the harmonic response analysis of the IEPCM was carried out. The strains in the \(x\)- and \(y\)-direction on one point near the left edge on the top surface of the IEPCM were extracted and focused on, and the corresponding strain features changing with the frequency were obtained, as shown in Figure 5.  
As can be seen from Figure 5a,b, there exists a similar change trend in the strain in the \(x\)- and \(y\)-direction as the frequency increases. Before 400 kHz, the strain in both the \(x\)- or \(y\)-direction slowly climbs up, and then sharply rises up to the maximal values (1.59 \(\mu\epsilon\), 1.25 \(\mu\epsilon\)) and quickly drops down to the bottom during the 400-440 kHz range, which indicates the harmonic frequencies of the IEPCM in both directions are about 420 kHz [21]. When the frequency increases to 480 kHz, the strain in the \(x\)- and \(y\)-direction resumes to a balanced value 1.0 \(\mu\epsilon\), 0.58 \(\mu\epsilon\), respectively. It is worth noting that the resonance responses (strains in the \(x\)- or \(y\)-direction) near the harmonic frequency of 420 kHz are not to be neglected, which implies the work frequency of the IEPCM should be shifted away from 420 kHz.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 8 Applications
### Food Safety  
Food safety will prevent an increase in foodborne diseases. The attention to handling, preparing, and preserving the food in conventional methods is tedious and time-consuming. The optical biosensors provide specificity, ease, and a fast response. The greater potential of optical biosensors lies in the detection of toxic substances in food. Recently, SPR optical biosensors have been developed for the detection of pesticide residues, veterinary drugs in animal-derived products, microbial contamination and hygienic monitoring, heavy metal adulterants, and other toxic compounds [103]. The most beneficial applications in optical biosensors consist of biological analytes and the analysis of biomolecular interactions [93]. Recently, the research and development of SPR-based FOPS in the area of drug discovery have been established for food safety [104]. [PERSON] et al. simplified a protocol based on LSPR with enhanced sensitivity by embedding a gold nanoparticles thin film in the geometrical configuration of the biosensor [105]. The SPR-based biosensor's advancement towards multiplexing and coupling this advancement with biotechnology for high quality food safety assessment were reviewed [106]. Features of SPR-based optical biosensors used for pathogen detection in meat products were enumerated [107]. A new approach to blood glucose sensing with enhanced sensitivity of 0.0366 nm/(mg/dL) has been established [108]. Recently, a novel glucose fiber optic grating sensor for the applications in food safety, disease diagnosis, and clinical analysis has been reported with high sensitivity of 0.298 nm(mg/mL)\({}^{-1}\) in low analyte concentrations of 0\(\sim\)3 mg/mL [109]. These SPR-based FOPS developed for food safety and quality assurance also satisfy the quality standards of international trade. SPR-based biosensors have resulted in great attention to the development of new techniques for predicting the internal quality parameters of food materials.
# Hazard Model: Epidemic-Type Aftershock Sequence (ETAS) Model for Hungary
## 5 Parameter Downscaling Methods  
### Motivation for Downscaling  
By downscaling, we mean the adaptation of the model parameters to smaller zones within the study area, where a stand-alone ETAS parameter estimation is not feasible due to insufficient data. Due to the data quality requirements of an ETAS study, the parameterization presented in the previous section relies on the instrumental catalogue data from the period 1996-2021, which are dominated by the 2020-2021 Croatian earthquake sequences. On the other hand, the largest historical occurrences within Hungary itself date to earlier than the study period. From the insurance industry point of view, a Hungarian earthquake model needs a regional parameterization, covering the central area around Budapest, since this area has the densest accumulation of insured property in the country. Furthermore, for a full coverage of the country, the geographical coverage gap along the Romanian border would need to be filled.  
Sections 5.2 and 5.3 describe an approach for adapting ETAS parameters to regional source zones, which incorporate historical earthquake data from a longer period. Therefore, we combined ETAS with standard techniques that have been used to parameterize stationary mainshock-only models. The zonation used in this study is based on the area sources of the SHARE project [26], with zone groupings for larger datasets. Figure 11 shows the map of the zones. The downscaling effort was restricted to the part of the zones within the core geographical window, defined between 45.5-49.0 N and 16.0-23.0 E.  
Figure 11: Source zones and zone groupings used in the model, based on the area sources of the SHARE project [26]. The core geographical window is also displayed—the downscaled model covers the part of the zones falling within the core window. Zones 01–02–03, Zones 04–10, and Zones 05–06 are grouped for the model. The non-contiguous zones with grey labels are grouped for the model as the residual Zone 99. Zones with empty labels are not modelled.
## 8 Discussion  
The results of this paper show that the proposed algorithm can improve the spectral efficiency of the communication system to a certain extent. It has lower BER compared to other algorithms. The goal of maximizing the analysis via spectral efficiency is achieved. The communication quality can be improved by increasing the number of RISs' reflecting elements, and the proposed algorithm has better performance in V2X communication because it takes into account the existence of the Doppler shift problem in V2X communication. The  
Figure 12: The performance of different vehicle speeds and the number of RISs’ reflecting elements.  
Figure 13: The effect of the maximum Doppler shift on the performance of communication systems.  
proposed algorithm effectively solves the problems of occlusion in LoS communication, the accurate DOA estimation to V2X communication and the difficulty in solving the rank corresponding to CP decomposition. The V2X communication performance is improved.  
V2X communication is currently very popular, and RIS-assisted V2X research is in its infancy. Compared with similar studies [28; 29; 30], our study discussed the communication scenario of massive MIMO V2X, but our study also considered the use of RIS-assisted communication, which is able to solve the problem of occlusion in the LoS path and improve the performance to some extent. Deep learning and artificial intelligence are now very popular in the communications field, and combining them with RIS-assisted V2X is also very novel. Therefore, our future research will be directed towards this area. The RIS phase shift matrix design problem can be better solved via deep learning.
## 4 Experimentation and Analysis of Results  
### Experimental Deployment  
During the experimental stage, this study utilized an Intel 5300 wireless network card (Intel, Santa Clara, CA, USA) and employed the Linux 802.11n CSI-Tool to serve as the receiver (Rx), responsible for capturing signals and initiating communication with the transmitter (Tx) at a predetermined rate. The configuration includes a single antenna on the transmission side and three antennas on the reception side, with 30 subcarriers per channel and a central frequency set to 2.4 GHz. In MATLAB 2020, the collected \".dat\" format data were processed to extract and visualize Doppler frequency shift information. The actual system setup is shown in Figure 6, which demonstrates the processing of raw gesture data and the recognition of different gestures, the red rectangular box represents the features displayed after processing the current gesture into a Doppler image. Model training was conducted in PyCharm 2023 using PyTorch 2.6.0, with the SE-VGG16 optimized via configurations detailed in Table 3. An Adam optimizer (learning rate = 0.001, betas = (0.9, 0.999)) ensures stable gradient descent by adapting learning rates for each parameter, combined with a stepwise learning rate decay strategy (initial LR = 0.001, 10\(\times\) decay every 30 epochs) to avoid local minima. A batch size of 32 balances GPU memory efficiency, and the model is trained for 100 epochs with early stopping--terminating training if validation loss stagnates for 5 consecutive epochs to prevent overfitting.  
To further mitigate overfitting, the model integrates ReLU activation functions, a Dropout rate of 0.5 (applied to fully connected layers to reduce neuron co-adaptation), and the aforementioned learning rate decay and early stopping strategies. This combined regularization approach significantly reduces the train-test accuracy gap, as quantitatively validated in Table 4, where the inclusion of early stopping decreases the accuracy discrepancy by 4.5% on average across environments.
## 3 Theoretical Analysis  
In this section, the theoretical background behind the mathematical modeling of the underwater to above water radio channel is reviewed. In particular, the physical laws ruling the propagation of a generic EM signal in a lossy medium are investigated, limiting the derivation of the complex relative permittivity to the saline water case, which is a high-loss medium due to the presence of dissolved salts that increase the medium conductivity; finally, the modeling of the link budget and of the path loss is presented.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Matrix** & **Analysis** & **Internal** & **(Range of)** & **(Range of)** & **Description of the Study** \\  & & **Method** & **Standard** & **Carbon** & **LOD/** & \\  & & & & **Number of)** & **ppb(v)** & \\  & & & & **Target** & \\  & & & & **Carbonyls** & & \\ \hline [PERSON] _et al._, 2008 & Aqueous & GC-MS & butanal-d\({}_{2}\) and & C1-C12 & 0.1–55 & A SPME-GC-MS method for trace \\  & particulate & (TIC) & benzaldehyde-d\({}_{6}\) & Ethanol & 3.7 & analysis of a wide range of carbonyls \\  & matter extracts of & & & Benzaldehyde & 2.3 & was developed. Headspace and direct \\  & wood smoke and & & & 2-Furfural & 35 & extractions from liquid phase with \\  & diesel exhaust & & & Hexanal & 0.7 & on-fiber derivatization were tested. \\  & & & & & Effects of extraction time and temperature were tested. \\ \[PERSON] _et al._, 2008 & Beer & GC-MS & 2-heptanol and & C3-C10 & 0.003–310 & Two SPME-GC-MS procedures for \\  & & (TIC) & guaicol & or & Hexanal & 0.028 & analysis of volatiles in beer were \\  & & & p-fluorobenzal & Benzaldehyde & 0.078 & developed. Effects of time and temperature of PFBHA doping and derivatization, salt addition, and matrix effects were studied. \\ \[PERSON] _et al._, 2008 & Municipal & GC-MS & acetone-d\({}_{6}\) & C1 & 3.7 & A rapid and automated \\  & wastewater & (TIC/SIM) & & & & SPME-GC-MS method utilizing \\  & [32] & & & & & SPME autosampler was developed \\  & & & & & & for formaldehyde analysis. \\  & & & & & Reproducibility and background \\  & & & & & contamination were reported. \\ \[PERSON] _et al._, 2003 & Water, well water, & GC-MS- & decafluorobiphenyl & C1-C5 & 0.12–0.34 & A SPME-GC-MS method for the analysis of short chain aliphatic \\  & chlorinated tap & & & & & aldehydes was developed. Effects of time for PFBHA doping and derivatization were tested. \\  & & & & & & derivatization were tested. \\ \hline [PERSON] _et al._, 2004 & Human blood & GC-MS & None & C2-C7 & 0.44 \(\times\) 10\({}^{4}\)– \\  & & (TIC/SIM) & & & \(6.0\times 10^{-4}\) \\  & & & & & Ethanol & \(0.44\times 10^{-4}\) \\  & & & & & Hexanal & \(6.0\times 10^{-4}\) \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of studies on carbonyl compounds analysis using solid-phase microextraction (SPME) on-fiber/direct derivatization with PFBHA in liquid samples.
# ETAS Space-Time Modeling of Chile Triggered Seismicity Using Covariates: Some Preliminary Results
## 6 Discussion of Results  
The obtained results suggest that from a simple AIC point of view, models with four covariates must be preferred for both areas, as already underlined in Section 5.1. Moreover, the estimated parameters differently characterize the two areas, as shown in Tables 4 and 5 (note for instance the different sign of the variable Mt1D), reflecting the different behavior of the seismicity along the Chilean coast.  
However, we report a diagnostic residual plot to see how the four-covariates models perform well. In general, in diagnostic analysis, if the estimated model is a good approximation of the real model that describes data, then the transformed observations (moving each point on the integrated intensity function) should behave like a homogeneous Poisson process [37]. Deviations from it are caused by some characteristics of data not taken into account by the fitted model.  
Therefore, in Figure 9 we report the transformed time residual plots for the Northern area, on the left, and for the Southern area, on the right. In both plots, we draw in red the theoretical line (if the model is estimated correctly), in black the line for the transformed residuals obtained fitting an ETAS model with four covariates, and finally in green the line of the transformed residuals fitting the classical ETAS model. From the left panel of Figure 9 it is clear that the four-covariates model can describe the triggering behaviors in the Northern area. Nevertheless, the black and green lines on the right panel of Figure 9 related to the South area are very close to each other, showing that probably the fitted four-covariates ETAS model does not fully describe the complex triggering mechanism of the Southern area.
## 4 Multi-Threaded Control with C/S Architecture Incorporating AES Encryption and TLS Protocol
### Security Analysis
#### 4.3.3 Application Layer Security Analysis  
Prevent database leakage: If database administrators or malicious insiders access the database and steal users' personal information, payment information, etc., then AES can be used to encrypt sensitive data stored in the database. Even if the database is accessed without authorization, plaintext data cannot be easily obtained.  
Consequently, this paper enhanced the security protection of the network layer, transport layer, and application layer through the combination of TLS and AES.
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
###### Abstract  
A problem with synthetic aperture radars (SARs) is that due to the poor penetrating action of electromagnetic waves within solid bodies, the ability to see through distributed targets is precluded. In this context, indeed, imaging is only possible for targets distributed on the scene surface. This work describes an imaging method based on the analysis of micro-motions present in volcanoes and generated by the Earth's underground heat. Processing the coherent vibrational information embedded in a single SAR image, in the single-look-complex configuration, the sound information is exploited, penetrating tomographic imaging over a depth of about 3 km from the Earth's surface. Measurement results are calculated by processing a single-look-complex image from the COSMO-SkyMed Second Generation satellite constellation of Vesuvius. Tomographic maps reveal the presence of the magma chamber, together with the main and the secondary volcanic conduits. This technique certainly paves the way for completely new exploitation of SAR images to scan inside the Earth's surface.  
synthetic aperture radar; Doppler frequencies; multi-chromatic analysis; micro-motion; volcanoes; seismic images 2022
## 3 Results and Discussion
### Anti-Proliferative Activities  
#### 3.2.1 Cell Proliferation Testing by MTT Assay  
Antiproliferative activities of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{\rm x}\)Eu\({}_{\rm x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs on cancerous cells were done by the MTT assay. After 48 h of treatment, the cytotoxic effects of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{\rm x}\)Eu\({}_{\rm x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs were observed and we found that Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{\rm x}\)Eu\({}_{\rm x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs showed inhibitory action on HCT-116 cancerous cells. The inhibitory concentration (IC\({}_{50}\)) values of different compounds were calculated as depicted in Table 1. We also examined the effects of NPs on normal cells (HEK-293) to check whether they produced any cytotoxic effects. We found that NPs did not produce any significant cytotoxic effects on HEK-293 cells after 48 h of treatment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{x}\) & **IC\({}_{\mathbf{50}}\) (HCT-116) (ug/mL)** & **IC\({}_{\mathbf{50}}\) (HEK-293)** \\ \hline
0.01 & 0.75 \(\mu\)g/mL & No inhibition \\
0.02 & 0.85 \(\mu\)g/mL & No inhibition \\
0.03 & 2.25 \(\mu\)g/mL & No inhibition \\ \hline
0.04 & 0.88 \(\mu\)g/mL & No inhibition \\
0.05 & 0.79 \(\mu\)g/mL & No inhibition \\ \hline \hline \end{tabular} Note: IC\({}_{50}\) Value [ug/mL] = Inhibitory concentration (IC).  
\end{table}
Table 1: Effect of Mn\({}_{0.5}\)Zn\({}_{0.5}\)SmEuFe\({}_{1.8-2}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs on cancerous cells **human colon carcinoma cells** (HCT-116) and normal cells **human embryonic kidney cells** (HEK-293).  
2.2 Nuclear Disintegration by Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2\times}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs Treatment  
The cancer cell nuclear morphology was evaluated by confocal scanning microscopy, which revealed that treatment of NPs showed strong inhibitory action on HCT-116 cells (Figure 4B,C) as compared to control group cells (Figure 4A).  
There are several reports where magnetic nanoparticles have shown potential applications in drug delivery and other diagnostic assays [31; 32; 33; 34]. These results suggest that NPs selectively targeted both colon and breast cancerous cells and do not cause any harm to normal and healthy cells. There are reports of involvement of nanoparticles in cancer cell death where nuclear fragmentation and nuclear disintegration were prominent features [35; 36; 37; 38; 39; 40]. We recommend that Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2\times}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs possess a selective targeting capability to cancerous cells and could be a potential candidate for cancer treatments.
# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## Appendix A  
A convolutional neural network (CNN) is a type of deep learning technique, specially designed for image analysis. A CNN model learns from data directly without explicitly extracting the manual features. The common CNN model composes of convolution layer, activation layer, pooling layer, fully connected layer or dense layer, and other optional layers such as regularization and normalization.  
* The convolution layer is the core building block of a CNN, which is used to automatically extract the features from the input image and generate the feature map using the filters. Filters are applied to each training image at different resolutions, and the output of each convolved image is used as the input to the next layer. In CNN model, there are many convolutional layers that retrieve low to high-level features. Earlier layers extract the low-level features whereas the latter layers in the networks extract the high-level features. The output of the feature map relies on the input size, the filter size and its striding, and padding.
* Activation functions are non-linear functions that allow the network to learn non-linear mappings. It performs as the selection criteria to decide whether the selected neuron will activate. Only the activated features or neurons are carried forward into the next layer. Activation functions are usually embedded after the convolutional layer. The most common activation functions are sigmoid, Tanh, ReLU, and softmax.
* Pooling layer aims to simplify the output by applying nonlinear downsampling. Pooling can progressively reduce the spatial dimensions of the image, thereby reducing the number of parameters and computation in a network. It reduces the output feature map of the convolution layers by extracting important pixels and removing noise. The output of the pooling layer depends on the filter size and stride. The most common pooling layers used in CNN are max pooling and average pooling.
* The next-to-last layer of CNN is the fully connected layer, also known as the dense layer. It receives all extracted features from the previous layers as the input and used them to classify the image with the help of softmax or sigmoid function. The fully connected layer generates the vector that contains the probabilities for each class of any image being classified.  
There are various architectures of CNN models. In this study, we employed fine-tuning the pretrained CNN models with the transfer learning. In this case, 11 CNN architectures: (i) InceptionV3 [35], (ii) VGG-16, (iii) VGG-19 [36], (iv) XceptionNet [33], (v) Res-Net-50 [37], (vi) ResNet-152 [37], (vii) MobileNet [38], (viii) DenseNet201 [39], (ix) InceptionResNetV2 [40], (x) EfficientNetB7 [41], and (xi) NasNetMobile [42] which aretrained on ImageNet, are used as the pretrained models. Some of their properties are listed in Table 11 and each model is detailed in the following sections.
# Energy Analysis of Kalina and Kalina Flash Cycles Driven by Renewable Energy
## 3 Results and Discussion
### Effects of Flash Pressure in Kalina Flash Cycle (KFC)
Similar to the case of \(x_{b}=40\%\), the exergy efficiency (\(\eta_{ex}\)) curve has a maximum value; the exergy destruction ratio of the regenerator (\(Dr\)) increases, and the ratios of the condenser (\(Dc\)) and flash vessel (\(Df\)) decrease as the flash pressure increases. However, the exergy consumption ratios change quite gradually compared with the case of \(x_{b}=40\%\). The top three exergy destructions occur in the condenser, HRVG, and power components with the magnitude in descending order.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 5 Experimental Results
### Experimental Settings  
We considered the same hardware and software configurations for our method and all of the other methods. All of the simulations were performed using an Intel core i7-6700k CPU with 4.00 GHz and 32 GB RAM. We used keras for the simulations, which is a popular Python library. In the neural network, we deployed four layers with 100 neurons in each layer. To avoid overfitting, we used two drop out layers with values of 0.2 that randomly dropped 20 percent of the connections in each layer. Stochastic gradient decent (SGD) was used as the model optimizer with a learning rate of 0.001. Table 2 shows the simulation parameters that we considered for our experiments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline Number of layers & 4 & Model optimizer & SGD \\ Number of neurons & 100 & Learning rate & 0.001 \\ Weight initializer & Glorot uniform & Loss function & RMSE, MAE \\ Training data percentage & 75\% & Sequence length & 5–2000 \\ Batch size & 10 & Dropout rate & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for training the proposed mobility model.