esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Architectures and Training  
We evaluated three convolutional neural network (CNN) architectures--Xception, ResNet, and VGG16--each pre-trained on ImageNet and fine-tuned for deepfake detection. In each case, we froze the earlier convolutional blocks and appended a custom classification head consisting of global average pooling, a dense layer with 512 units (ReLU), and a final dense layer (softmax) for binary classification.  
* XCeption. Xception leverages depthwise separable convolutions to capture complex features with fewer parameters [8]. We initialized with ImageNet weights and fine-tuned on the DFDC dataset using a learning rate of 0.0001 (Adam optimizer, batch size of 32). The learning rate decayed by 0.5 every 10 epochs, and we employed early stopping (patience = 5) based on validation loss. We also adapted the classifier layers to the two-class (real/fake) scenario.
* ResNet. We experimented with ResNet-50 and ResNet-101 [9], focusing on an ensemble of their predictions to boost accuracy. Both variants were initialized with ImageNet weights with the final block(s) and new classifier layers unfrozen. Training used a learning rate of 0.00005, batch size of 16, and Adam with weight decay of \(1\times 10^{-5}\). We terminated training at 25 epochs if validation accuracy plateaued.  
VGG16. Characterized by stacked convolutional layers [10], VGG16 was fine-tuned with a learning rate of 0.0001, a batch size of 8, and a dropout rate of 0.5 to mitigate overfitting. Again, only the final convolutional block and newly attached dense layers were trainable. Training concluded at 20 epochs, which was subject to early stopping on validation loss.  
All models were implemented in TensorFlow 2.5 with GPU acceleration. Experiments were conducted on a machine equipped with an Apple M1 processor (16 GB RAM, 512 GB SSD). Approximate training times were 10 h per dataset for XCeption, 8 h for VGG16, and 12 h for the ResNet ensemble.
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### The Transmitter
Meyer wavelet can be implemented in fast wavelet transform (FWT) and DWT because of its symmetry and orthogonality, in addition to its fast ability of localization and decaying from the central peak than any inverse polynomial [14]. OWDM is a program which is very flexible and simple. It has lower complexity with a low filter than FFT processors. Furthermore, the filter type selected must be dependent on the channel situation or the information source. A Dmey is a discrete form of the Meyer wavelet. The Meyer wavelet and scaling function is defined in the frequency domain [14]. 1. A parallel-to-serial (P/S) operation is performed to obtain the time domain signal. 2. Pre-SDE is implemented by normalizing all data streams to ensure that the detected N-data have the same value of SNR. The Pre-SDE concept of an optical DCO-OFDM system is based on the VLC-MIMO N-channel imaging technique. The output optical power and the modulation index for every LED are P\({}_{\text{opt}}\) and \(\xi\), respectively. Pre-SDE is performed by using power allocation matrix A = diag (a\({}_{1}\); a\({}_{2}\); \(\cdots\cdots\); a\({}_{\text{N}}\)) which adjusts all electrical powers modulating data. Figure 2: Proposed transmitter model of Discrete Wavelet Transform (DWT)-Orthogonal Frequency Division Multiplexing (OFDM). Furthermore, the transmitted optical vector S could be calculated by Equation (2), where x is the modulated data vector [22]:  
\[\text{S}=\text{P}_{\text{opt}}\left(1+\xi,\text{A}\text{x}\right)\text{.} \tag{2}\]  
The modulation index (\(\xi\)) is supposed to be the same for all LEDs. Because of A, the electrical powers of different modulating data may be diverse. To preserve a fixed average electrical power as soon as exciting Pre-SDE, power restrictions are required to the diagonal elements of matrix [22]. The signal is normalized and a digital-to-analog (D/A) conversion has been performed. At every stream, a DC bias is applied to get a unipolar signal. Hence, the complex-valued OFDM signal is given by:  
\[\text{x}(\text{t})=\text{x}_{\text{Re}}+\text{j}\,\text{x}_{\text{Im}}\text{,} \tag{3}\]  
where x = (x\({}_{1}\) x\({}_{2}\), \(\cdots\)\(\cdots\) x\({}_{\text{N}}\))T is a vector of modulating signals and x\({}_{\text{i}}\) (i = 1, 2, \(\cdots\)\(\cdots\), N) is the normalized bipolar OFDM signal with variance \(\sigma^{2}\)xi = 1. The electrical powers of different modulating signals may be different. To maintain a constant average electrical power when performing Pre-SDE, a power constraint is imposed on the diagonal elements of matrix A. These elements, a\({}_{\text{i}}\), satisfy the following equation [22]:  
\[\frac{1}{\text{N}}\text{$\sum$}_{i=1}^{\text{N}}\text{a}_{\text{i}}^{2}=1 \text{.} \tag{4}\]
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
###### Abstract  
Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously. Secondly, a smart twin delayed deep deterministic (TD3) AmBC signal detection method is presented, based on deep reinforcement learning. Extensive quantitative and qualitative experiments are performed, which show that the proposed method is more compelling than the outstanding comparison methods.  
reconfigurable intelligent surface; ambient backscatter communication; signal detection; multi-antenna; deep reinforcement learning +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning. _Sensors_**2022**, 22, 6137. [[https://doi.org/10.3390/s22166137](https://doi.org/10.3390/s22166137)]([https://doi.org/10.3390/s22166137](https://doi.org/10.3390/s22166137))
## 3 Results
### Comparison between RCV and LOO  
Table 5 and Figure 5 illustrate the difference in performance metrics resulting from the two validation methodologies. Overall, we can observe a striking drop in performance according to all metrics when adopting the more stringent LOO validation procedure. In particular, the accuracy decreased by almost 30% for both data sets, and the sensitivity decreased by 8.92% for CHB-MIT and 16.04% for our data set. The specificity decreased by 35% and 42% for CHB-MIT and our data set, respectively.  
The performance metrics' averages, standard deviations, and results of statistical significance testing (two-tailed student's t-test) are reported in Table 5. The estimated p-values indicate that the differences were always statistically significant; only the sensitivity metric measured on the CHB-MIT data set did not seem to become significantly worse when the LOO validation procedure was adopted.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 1 Introduction
The main contributions of this paper are as follows: (1) the displacement data and the influencing factor data are decomposed by VMD and respectively integrated into the trend displacement dataset, the periodic displacement dataset, and the random displacement dataset; (2) each dataset is substituted into the GA-Elman model to train the model and make predictions; (3) the effect of model prediction is compared with the evaluation index to obtain the optimal prediction model; (4) the validation dataset is substituted to verify the prediction effect of the optimal prediction model.
## 2 Materials and Methods
### Using a Machine Learning-Based Method  
Any machine learning method is based on a training/calibration data which the machine learns from to identify connections and relationships within the data. Here, a set of calibration (1500 pixels) and validation (300 pixels) was used. This data contained oil-contaminated pixels (hereafter termed oil) and pixels containing zero contamination (hereafter termed clean). These pixels were manually selected from an AISA-FENIX 1K hyperspectral image with 1 m GSD. The calibration and validation pixels were selected by crossing information from in-situ visits and measurements, examining the Fenix hyperspectral image and an Earth Resources Observation Satellite (EROS) high-resolution (70 cm GSD) panchromatic image taken by Image Sat International--ISI, several days after an oil spill. By doing that the calibration and validation pixels could be chosen with high certainty. Although the focus of this study is the implementation of our model, rather than details regarding our published method, it is worth to mention that we chose clean pixels which looked similar both spectrally and visually to oil pixels (e.g., dark soil and dry vegetation).  
Figure 1: Flow chart that demonstrates the methodology of this study.  
The Fenix image was acquired in April 2017 over the Evrona Nature Reserve in southern Israel (Figure 2), approximately two and a half years after the occurrence of an unfortunate oil spill accident. In this accident, millions of liters of crude oil flowed into the Evrona Nature Reserve, spreading through the narrow and dry streaming channels characterizing the Reserve.  
For more details regarding the oil spill, the acquisition and pre-processing of the Fenix image, and the pixels selection and location, the reader is referred to Section 2 in [7].
###### Abstract  
In this paper, we present a system-level model of a digital multibeam antenna designed for Low Earth Orbit satellite communications operating in the Ka-band. We initially develop a suitable array topology, which is based on a thinned lattice, then adopt it as the foundation for evaluating its performance within a digital beamforming architecture. This architecture is implemented in a system-level simulator to evaluate the performance of the transmitter and receiver chains. This study advances the analysis of the digital antennas by incorporating both the RF front-end and digital sections non-idealities into a digital-twin framework. This approach enhances the designer's ability to optimize the system with a holistic approach and provides insights into how various impairments affect the transmitter and receiver performance, identifying the subsystems' parameter limits. To achieve this, we analyze several subsystems' parameters and impairments, assessing their effects on both the antenna radiation and quality of the transmitted and received signals in a real applicative context. The results of this study reveal the sensitivity of the system to the impairments and suggest strategies to trade them off, emphasizing the importance of selecting appropriate subsystem features to optimize overall system performance.  
L ow Earth Orbit (LEO) satellite communications; non-terrestrial networks; Internet of Things (IoT); Ka-band IEEEexample:BSTcontrol
## Materials and Methods
### Data Acquisition and Preliminary Processing  
Since the instantaneous phase calculation procedure by Hilbert transform employed in the signal mutual synchronization analysis is rather sensitive to random errors in measurements, an adaptive recursive filtering procedure has been applied to the original measurement sequences \(\{s_{i}\}\), where \(s\) denotes either systolic blood pressure (SBP) or pulse intervals (PI) aiming at the elimination of the anomalous measurements. This procedure is based on the analysis of the first differences of the pulse intervals and systolic blood pressure values. The threshold value for the exclusion of outliers is based on the analysis of their empirical distribution functions for the processed data series. The elimination procedure consists of two consecutive steps that are repeated iteratively: (i) marking of the potential outliers as candidates for future elimination and (ii) removing marked outliers. In the first step, one calculates the first differences \(s^{\prime}_{i}=s_{i+1}-s_{i}\) from the initial dataset \(\{s_{i}\}\) (that will be recalculated iteratively each time after a single outlier is eliminated). To mark the \(i\)th element of the dataset \(\{s_{i}\}\) as a candidate for being an outlier (\"OUT\") three conditions should be met simultaneously:  
1. The absolute values of the first differences \(\big{|}s^{\prime}_{i-1}\big{|}=\big{|}s_{i}-s_{i-1}\big{|}\) and \(\big{|}s^{\prime}_{i}\big{|}=\big{|}s_{i+1}-s_{i}\big{|}\) that are adjacent to the measured data \(\{s_{i}\}\) are both either greater or equal than the standard deviation \(\sigma\{s_{i}\}\) of the first differences.
2. The two consecutive first differences \(s^{\prime}_{i-1}\) and \(s^{\prime}_{i}\) have different signs.
3. The absolute values of the first differences normalized by its standard deviation \(\big{|}s^{\prime}_{i-1}\big{|}/\sigma\big{[}s^{\prime}_{i}\big{]}\) and \(\big{|}s^{\prime}_{i}\big{|}/\sigma\big{[}s^{\prime}_{i}\big{]}\) both exceed the critical value \(\sigma_{\text{crit.}}(N)\) determined as a quantile of the Student's \(t\)-distribution for the given dataset size \(N\).  
Those elements for which all three conditions are met are then marked as \"OUT\". Out of the marked data \(\{s_{i}\}\) the one with the largest normalized standard deviation is eliminated. After elimination of a single outlier the above procedure is repeated iteratively unless there are no values marked as outliers for the chosen elimination depth. For deeper details on the filtering algorithm, we refer to [35].  
Since the sequence of pulse intervals was non-equidistant due to its inherent variability, we next used the cubic interpolation and resampling with the desired sampling frequency, 5 Hz in our case. Therefore, both analysed datasets were now represented by sequences equidistant in time and taken at the same time points.
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 3 Survey of Signal Processing Units based on FBGs
### Optical Pulse-Shapers
The synthesized grating profile obtained from this design approach, shown in Figure 11a, is much simpler than that obtained from previous approaches and readily feasible, even for a strong coupling grating. Figure 11c shows the measured spectrum of the input optical pulse. Figure 11d presents the measured transmissive power spectral response for the FBG-based pulse shaper, and Figure 11e the corresponding measured spectral phase response. For the FBG-based pulse shaper temporal characterization, the output signal is measured in amplitude and phase via Fourier transform spectral interferometry. The input (a 400 fs-FWHM Gaussian pulse) and the output time-domain signals are plotted in Figure 12. The experimentally obtained data and the ideally expected data are compared in that figure, showing high similarity and hence verifying the ability of the fabricated FBG to attain the target ultrafast pulse-shaping application. More recently, it has been presented the possibility of using FBGs with orthogonal impulses responses to perform coding operations aimed at implementing multidimensional quantum key distribution (QKD) protocols overcoming the low secret-key rate for quantum information processing [95]. Figure 11: Apodization profile (**a**) and period (**b**) of a rectangular pulse-shaper implemented on an FBG in transmission. The curves obtained from the synthesis algorithm are in blue, while smoothed profiles adapted to the restrictions of the fabrication method (sub-mm resolution) are in red; (**c**) Spectrum of the experimental input signal; (**d**) transmissivity and (**e**) ideal phase of the spectral response in transmission (blue line), analytical response restrained to fabrication limitations (red line) and experimentally measured response (dotted black line).
## 3 Characterization and Performance Testing  
### Freeze-thaw Aging Procedure  
In this paper, a freeze-thaw aging process was designed to simulate the repeated effect of temperature and moisture on asphalt pavement in spring-thawing season. The freeze-thaw aging procedure of asphalt binder sample is as follows.  
Figure 5: Nanosilica and nano hydrophobic silane silica (NHSS) particle size distribution statistics. (**a**) Nanosilica; (**b**) NHSS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technical Parameters** & **Unit** & **Value** \\ \hline \hline  & \(\mathrm{g/kg}\) & \(43\pm 5\) \\  & \(\mathrm{DPB}\) absorption & \(10^{-5}\) m\({}^{3}\)/kg & \(121\pm 7\) \\  & \(\mathrm{DPB}\) absorption of the compressed sample & \(10^{-5}\) m\({}^{3}\)/kg & 80–90 \\  & \(\mathrm{PH}\) value & - & 8 \(\pm\) 2.0 \\  & \(\mathrm{CTAB}\) surface area & \(10^{3}\) m\({}^{2}\)/kg & 36–48 \\  & \(\mathrm{Ash}\) content & \% & \(\leq\)0.7 \\  & \(\mathrm{45\text{-}\mu m}\) sieve residue & \(\mathrm{mg/kg}\) & \(\leq\)1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical parameters of carbon black.  
First, base asphalt and modified asphalt binders were heated to a fluid state and poured into a fixed-size plate to ensure the dimensions of asphalt binder samples is approximately \(6\times 250\times 250\) mm. The purpose of this was to ensure that the moisture could completely penetrate the asphalt and the preparation conditions of all samples were consistent.  
Then, the base asphalt and modified asphalt samples were submerged in a container containing water, and the container with specimens were placed in the precision temp-enclosure at \(-15\,\mathrm{\SIUnitSymbolCelsius}\) and frozen for 10 h.  
Finally, the base asphalt and modified asphalt samples were soaked in water at \(15\,\mathrm{\SIUnitSymbolCelsius}\) for 16 h through adjusting the temperature controller.  
As per the method described above, a complete freeze-thaw aging cycle was completed. Then, after 10, 20 and 30 freeze-thaw aging cycles, damaged samples were collected for physicochemical property test to explore the effect of NHSS modifier on the characteristics of asphalt under the freeze-thaw aging process. The photos of fresh and weathered specimen are shown in Figure 6.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 6 Performance Analysis
### Security Features  
We compared the security features of the proposed protocol with those of related protocols [8,17-22, 18-22]. We considered (SF1) \"preservation of anonymity\", (SF2) \"preservation of untraceability\", (SF3) \"preservation of perfect forward secrecy\", (SF4) \"resistance to stolen verifier attack\", (SF5) \"resistance to stolen smart card attack\", (SF6) \"resistance to session key disclosure attack\", (SF7) \"resistance to replay attack\", (SF8) \"resistance to MITM attack\", (SF9) \"resistance to ephemeral secret leakage attack\", and (SF10) \"resistance to privileged insider attack\". Table 7 summarizes the comparison of security features. The results show that the proposed protocol has superior security than other related protocols in fog-based VANET environments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Protocols** & **Communication Costs** \\ \hline
[17] & 5664 bits \\
[18] & 5568 bits \\
[19] & 4736 bits \\
[20] & 5632 bits \\
[8] & 5312 bits \\
[21] & 4352 bits \\
[22] & 6592 bits \\
**Proposed** & 4544 bits \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of communication costs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Security Features** & [17] & [18] & [19] & [20] & [8] & [21] & [22] & **Proposed** \\ \hline SF1 & O & O & O & O & O & O & O & O \\ SF2 & O & O & O & O & O & O & O & O \\ SF3 & O & O & O & O & X & - & O & O \\ SF4 & O & O & O & - & O & - & O & O \\ SF5 & O & O & - & - & X & O & - & O \\ SF6 & O & - & - & O & - & O & O & O \\ SF7 & O & O & O & O & O & O & O & O \\ SF8 & O & O & O & O & O & O & O & O \\ SF9 & X & O & O & O & X & O & O & O \\ SF10 & X & - & - & - & O & - & O & - & O \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of security features.  
Figure 7: Visualization of communication costs comparison [8,17-22].
## 3 Piezoelectric Nanogenerator Device Fabrication and Piezoelectricity Measurements  
The PVDF NF-based ferroelectric nanogenerator (FENG) devices were fabricated according to the already established method. The NG was fabricated by taking the required dimension (2 \(\times\) 2 cm) of each NF sample and the copper electrode was sputtered on the top and bottom surface of the NF film. Two copper wires (0.1 mm) were attached with copper electrodes with the help of conducting silver paste and finally polydimethylsiloxane (PDMS) was used as a packaging layer for the NG to protect the device from external conditions.  
The piezoelectric property of the samples was analysed by means of an experimental setup consisting of a frequency generator and an output measurement system. The sample was placed on the sample holder and the desired frequencies of vibration according to different mechanical loads were selected. As a result, the vibrating nob applies the desired frequency of vibration on top of the sample to induce electrical response. The output voltage generated by the sample was measured by a digital oscilloscope (Yokogawa, DL9040) to evaluate the piezoelectricity. Furthermore, the piezoelectric coefficient d33 for PVDF R, PVDF GO R, and PVDF GO A was calculated by using the equation [38].  
\[\mathrm{d}_{33}=(\mathrm{C}^{\star}\mathrm{V})/\mathrm{F} \tag{4}\]  
where F is the applied force and V is the generated piezoelectric potential over a capacitor (C = 70 pF) in parallel with the FENG device.  
In order to check the efficacy of the PEGAN FENG as piezoelectric sensors, the as prepared FENG device was attached to a finger, on the sandal and on the handle of a chair. The stress induced piezoelectricity was analysed as described above using the digital oscilloscope.
## 4 Discussion
### The Need for More Research on Vibration and Acoustic Technologies  
Accelerometers and microphones were adopted as sensors for vibration and acoustic studies, respectively. While most of the studies attempted in vibration and acoustic technologies separately were related to pattern recognition and differentiation, no articles discussed limitations in hardware, which could explain the limited extent of attempts to apply both technologies simultaneously. Understanding the hardware's limitations and exploring the potential of converging the two technologies represent another opportunity for further research.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
### Lithium Titanate Oxide (LTO)  
LTO, when used as an anode active material in Li-ion cells, significantly enhances both the power output and cycle life of the battery. These attributes are not just beneficial but crucial in maritime applications. However, it is important to note that LTO is characterized by a relatively low specific energy, typically ranging from 50-80 Wh.g\({}^{-1}\), attributed to its low cell voltage. This lower energy density necessitates using more batteries to fulfill maritime vessels' energy requirements, consequently leading to increased battery sizing and elevated costs [27]. Li-ion cells typically have an open circuit voltage ranging from 3.2 to 3.9 V. To meet specific voltage and current requirements, these cells are combined to form modules, which are assembled in series and/or parallel configurations to create complete battery packs. The battery management system (BMS) and the thermal management system (TMS) are crucial components of maritime battery systems. The BMS plays a key role in linking the battery cells with the power management system (PMS) to ensure optimal performance. It carefully monitors and controls each cell's electrical, mechanical, and thermal parameters, managing charge and discharge activities to maximize the efficiency and longevity of the battery pack [110; 117; 118].