esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analytical Methods  
#### 2.4.1 Total Polyphenol Content (TPC)  
Total polyphenol content (TPC) was evaluated using the Folin-Ciocalteu method adapted from [PERSON] et al. [22]. Briefly, 0.1 mL of diluted extract was mixed with 7.9 mL of distilled water and 0.5 mL of Folin-Ciocalteu solution and kept for 10 min to allow interaction. Then, 1.5 mL of sodium bicarbonate (20% \(w/v\)) was added, and the samples were kept in the dark for 60 min at room temperature. The absorbance was measured using a UV-VIS spectrophotometer (Jasco V-750, Tokyo, Japan) connected with an immersion thermostat with a digital control Digiterm S150, Jasco PAC-743R and with a color LCD touch screen and Spectra Manager(tm) II software against the blank at 765 nm. A calibration curve with standard solutions of gallic acid was prepared and the results were expressed as mg Gallic Acid Equivalents/g dry weight raw material (mg GAE/g dw).
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 3 Applications Across Domains
### Healthcare Monitoring  
Time series analysis has become essential in healthcare for monitoring patient vital signs and predicting disease progression. Transformer models have been applied to analyze electrocardiogram (ECG) data with high accuracy [24]. Ensemble methods have also been used to detect anomalies in patient health records, enabling early intervention.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 3 Materials and Methods
### Energy Harvesting
In the presented prototype, we set \(C_{\text{ST}}=200\)\(\mu\)F, \(V_{\text{th+}}=5\) V, and \(V_{\text{th-}}=2\) V. Neglecting losses, the buffered energy supports an output power of 2.1 mW for one second, enough to take a sensor reading and transmit the result. A detailed evaluation of the harvesting performance is given in Section 4.1.
# Deep Learning in the Biomedical Applications: Recent and Future Status
## 5 Brain and Body Machine Interface
### Diseases Diagnosis  
The second part of the BBMIs concerns the diseases diagnosis. Many of these applications concern the automated detection of arrhythmia and cardiac abnormalities, which are irregularities in the rate or heartbeat rhythm automatically detected by the deep neural networks. DL has also been applied in other applications such as automated detection and diagnosis of seizure, screening of depression [238] or neonatal sleep state identification [239]. Table 5 gives an overview of papers using DL techniques for diseases diagnosis and abnormalities detection.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Purpose** & & **Signal Type** & **NN Type** & **Ref.** \\ \hline _Steady state evoked potentials_ & _(SSEP)_ & & & \\ _applications_ & Human’s affective and emotion states classification & EEG & DBN & [182; 183; 184; 185; 186; 187; 188] \\  & & & DMLP & [189; 190] \\  & & & CNN & [191; 192] \\ Auditory evoked potential & EEG & CNN & [193; 194; 195; 196] \\  & EMG & DMLP & [197] \\ Visual evoked potential & EEG & CNN & [198; 200; 201; 202] \\  & & & DMLP & [203] \\ Classification of mental load & EEG & DBN & [204; 205; 206] \\  & & CNN & [207] \\ \hline _P300 applications_ & & & & \\ The P300 speller & EEG & CNN & [208; 209; 210; 211] \\ Rapid serial visual presentation & EEG & CNN & [212] \\ Driver fatigue classification & EEG & CNN & [213] \\  & & CBN & [214; 215; 216] \\  & & DBN & [217] \\ \hline _Motor and sensorimotor rhythms (MSR)_ & & & & \\ _applications_ & Motor imagery tasks & ECoG & CNN & [218] \\  & EEG & CNN & [219; 220; 221; 222; 223; 224; 225; 226; 227] \\  & & DBN & [228; 229] \\  & & DMLP & [230; 231; 232] \\ \hline _Non-motor cognitive tasks_ & & & & \\ Mental arithmetics (MA), word generation (WG) and mental rotation (MR) & & & & \\ Mental task & EEG & CNN & [234] \\ Mental subtractions & fNIR & DMLP & [235] \\ \hline _Hybrid signals_ & & & & \\ Sleep stage classification & EEG, EOG, EMG, EOG, skin temperature, GSR, blood volume pressure, and respiration signals & DBN & [181] \\ Emotion analysis & EEG, EOG, EMG, skin temperature, GSR, blood volume pressure, and respiration signals & DMLP & [236] \\ Detecting driving fatigue & EEG, EOG & DMLP & [237] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of papers using DL techniques for brain decoding applications.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 2 Preliminary Understanding of VANETs
### Architecture of VANETs
Different types of cryptographic keys are also initiated by these TAs. Figure 3: Component-based architecture of VANET. Communication system:_ Vehicles can communicate with other vehicles using Dedicated Short Range Communication (DSRC) [15]. The information collected using this mechanism is not sufficient for managing vehicles and traffic. To get a wide range of information, the internet is used along with its infrastructure. Wireless Access in Vehicular Environment (WAVE) is also used for communication in VANETs [15]. On the other hand, 3G/4G/LTE cellular networks may be used for communication with other networks. Various types of communications take place in VANETs, which includes in-vehicle communication [16; 17], vehicle to vehicle communication (V2V) [18; 19], vehicle to infrastructure (V2I) [19], vehicle to pedestrians (V2P) [20], vehicle to grid (V2G) [21; 22], vehicle to broadband cloud (V2B) [23], and vehicle to everything (V2X) [24]. Based on the above communication schemes one classification may be done which categorizes the VANETs in three types: pure ad hoc networks, cellular, and hybrid [25]. In pure ad hoc networks, vehicles and RSUs use DSRC for communication, and networks are completely transient. On the other hand, cellular networks are fixed and persistent even if these are used in VANETs or not. RSUs may use these cellular networks for communication with other RSUs or trusted authority. Hybrid VANET architectures use a combination of both the architectures.
# Experimental Investigation of Anisotropic Diffusion Applied in Ghost Imaging Reconstruction
## 2 Experimental Setup and Principle
The iterative formula of IPGI is expressed as:  
\[\mathbf{T}_{IPGI}^{(k+1)}=\frac{1}{N}\mathbf{sT}+\frac{1}{N}\mathbf{nT}-\frac {1}{N}n^{\prime}\mathbf{T}_{IPGI}^{(k)}, \tag{9}\]  
\[n^{\prime}(\mathrm{x},\mathrm{y})=\left\{ \begin{array}{cc}n(\mathrm{x}, \mathrm{y})&n(\mathrm{x},\mathrm{y})<t\\ o&n(\mathrm{x},\mathrm{y})>t\end{array} \right. \tag{10}\]  
where the \(\mathbf{T}_{IPGI}^{(0)}=\mathbf{T}_{PGI}\) and the threshold \(\mathrm{t}\) varies from \(\min\left\{n\left(\mathrm{x},\mathrm{y}\right)\right\}\) to \(\max\left\{n\left(\mathrm{x},\mathrm{y}\right)\right\}\). A normalized threshold \(\mathrm{t^{\prime}=}\frac{t-\min\left[n(\mathrm{x},\mathrm{y})\right]}{\max \left[n(\mathrm{x},\mathrm{y})\right]-\min\left[n(\mathrm{x},\mathrm{y}) \right]}\) is utilized to simplify the experimental analysis [11; 13]. IPGI requires the initial image to be as clear as possible, otherwise the constructed noise interference terms may deviate from the actual noise and more noise will be generated after iterative operations. Although the reconstructed image of PGI, chosen as the initial image of IPGI, is clearer than that of other methods, it still has a lot of background noise. Anisotropic diffusion can solve the problem well; it adapts to both the image and the noise, and balances the tradeoff of noise removal versus preserving the image details. Therefore, our method has improved Equation (9) by combining anisotropic diffusion with IPGI; it is expressed by the Equation (11):  
\[\left\{ \begin{array}{cc}\mathbf{T}_{IPGI}^{(k,0)}&\overset{AD-\mathrm{ Denoising}}{\rightarrow}\mathbf{T}_{IPGI}^{(k,t+1)}\\ \mathbf{T}_{IPGI}^{(k+1,0)}&=\frac{1}{N}\mathbf{sT}+\frac{1}{N}\mathbf{nT}- \frac{1}{N}n^{\prime}\mathbf{T}_{IPGI}^{(k,t+1)}\end{array} \right., \tag{11}\]  
where \(\mathbf{T}_{IPGI}^{(k,0)}\) is the kth iterative result of IPGI, \"0\" means it is also the initial image before anisotropic diffusion denoising,\(\mathbf{T}_{IPGI}^{(k,t+1)}\) is the result of anisotropic diffusion smoothing of \(\mathbf{T}_{IPGI}^{(k,0)}\), and the number of diffusions equals \(t+1\). The imaging process of our method is shown in Figure 2. The reconstructed results of PGI, denoted as \(\mathbf{T}_{IPGI}^{(0,0)}\), are chosen as the initial image of IPGI, then \(\mathbf{T}_{IPGI}^{(0,t+1)}\), the result of anisotropic diffusion smoothing of \(\mathbf{T}_{IPGI}^{(0,0)}\), is used to construct the noise interference term of the first iteration. After the first iterative computation, the reconstruction result \(\mathbf{T}_{IPGI}^{(1,0)}\) is obtained. By repeating the computation method, the reconstructed image with less noise and better edge details can be obtained after several iterations. We verified the experiment results and found that the best quality reconstructed image produced by the IPGI process was achieved when the iterative times \(\mathrm{k}\) was equal to 3 [10]. Figure 2: The imaging process of our method.
## Appendix B RL Algorithm Candidates
### Bayesian Linear Regression Model  
The first candidate is to use the following reward generating model with action centering used in [1] for the posterior sampler:  
\[R_{i,t}=m(S_{i,t})^{T}\alpha_{i,0}+\pi_{i,t}f(S_{i,t})^{T}\alpha_{i,1}+(A_{i,t }-\pi_{i,t})f(S_{i,t})^{T}\beta_{i}+\epsilon_{i,t} \tag{10}\]  
where \(\alpha_{i,0}\in\mathbb{R}^{4}\) and \(\alpha_{i,1}\), \(\beta_{i}\in\mathbb{R}^{3}\). \(\pi_{i,t}\) is the probability that action \(A_{i,t}=1\) is selected by the RL algorithm for user \(i\) in state \(S_{i,t}\); we discuss how to compute this probability in Appendix C.2. The RL algorithm models \(\epsilon_{i,t}\) as being drawn from \(\mathcal{N}(0,\eta^{2})\) (the choice of \(\eta^{2}\) is informed by the ROBAS 2 dataset). Additionally, we put uninformative normal priors on the parameters: \(\alpha_{i,0}\sim\mathcal{N}(0,\sigma_{prior}I_{4})\), \(\alpha_{i,1}\sim\mathcal{N}(0,\sigma_{prior}I_{3})\), \(\beta_{i}\sim\mathcal{N}(0,\sigma_{prior}I_{3})\), where \(\sigma_{prior}=5\).  
Appendix B.2.2. Zero-Inflated Poisson Regression Model  
The second candidate is to use the zero-inflated reward generating model for the posterior sampler:  
\[Z_{i,t} \sim\text{Bernoulli}\left(1-\text{sigmoid}(m(S_{i,t})^{T}\alpha_{i, b}+A_{i,t}\cdot f(S_{i,t})^{T}\beta_{i,b})\right) \tag{10}\] \[Y_{i,t} \sim\text{Poisson}\left(\exp\left(m(S_{i,t})^{T}\alpha_{i,p}+A_{i, t}\cdot f(S_{i,t})^{T}\beta_{i,p}\right)\right)\] \[R_{i,t} =Z_{i,t}Y_{i,t}\]  
The above model closely resembles the zero-inflated Poisson model class used to develop the simulation environment in Appendix A.2; however, recall that the feature space used by the RL algorithm and the model to generate the environment is different. Additionally, here, we directly model the reward, rather than the raw brushing duration.  
Additionally, the posterior sampling algorithm will put the following uninformative normal priors on the parameters: \(\alpha_{i,b},\alpha_{i,p}\sim\mathcal{N}(0,\sigma_{prior}I_{4})\) and \(\beta_{i,b},\beta_{i,p}\sim\mathcal{N}(0,\sigma_{prior}I_{3})\), where \(\sigma_{prior}=5\).
# Polarization Effect on the Performance of On-Chip Wireless Optical Point-to-Point Links
###### Abstract  
Optical on-chip wireless interconnection is an emerging technology that aims to overcome the communication bottleneck in computing architectures and in which multiple processing units are exploited for data-intensive applications. In this work, we propose an integrated dielectric Vivaldi antenna, which exhibits the same gain performances for both TE and TM input polarizations. Point-to-point on-chip communication links between two Vivaldi antennas are analyzed. Moreover, the effect of wave polarization on the link performances is numerically studied in on-chip multilayer structures in connection with the multilayer characteristic parameters, i.e., cladding layer thickness and refractive index. The numerical results show that, with the same antenna gain, TM polarization is affected by lower propagation losses when suitable cladding layer thickness and refractive index are considered.  
wireless optical interconnects; optical antennas; optical phased arrays; on-chip interconnection Article
## 2 Channel Model
### Channel Attenuation  
Underwater, when the acoustic signal is travelling from the source to the destination, its strength reduces as the channel has a great effect on the signal. The attenuation \(A\) depends upon the distance \(l\) of acoustic signal from source and its frequency \(f\) is modeled by the expression [39]:  
\[A(l,f)=A_{o}l^{c}\alpha(f)^{c} \tag{3}\]  
where \(\alpha\), \(A_{o}\) and \(c\) are s the absorption factor, normalization constant and spreading factor, respectively. The attenuation can be denoted as in decibel (dB).  
\[10 logA(l,f)/A_{o}=10 logd*k+10 log\alpha(f)*d. \tag{4}\]  
Figure 1: Transmission loss of acoustic waves.  
Thorp model for the absorption coefficient of higher and lower frequencies in kilohertz as  
\[10 log\alpha(f)=0.11+\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75*10^{-4 }f^{2}+0.003 \tag{5}\]  
\[10 log\alpha(f)=0.002+\frac{f^{2}}{1+f^{2}}+0.011f^{2}. \tag{6}\]  
Figure 2 shows the attenuation coefficient of the acoustic waves in seawater.
## 2 Materials and Methods  
### Subject Database  
This study was conducted with the approval of the research ethics committee of the Institute of Technology and Science at Tokushima University in Japan. A carbonated water tolerance test was performed using 20 male participants (age: 22.9 \(\pm\) 3.4, body mass index (BMI): 22.7 \(\pm\) 3.8) who had provided their consent to the research content and their participation. The test was conducted after 12 or more hours of fasting by the participants, over a 25-min period (comprised of a 10-min period of rest before consuming carbonated water and a 15-min period of rest after consuming carbonated water). During the test, sound data was recorded using a noncontact microphone (NT55 manufactured by RODE), an electronic stethoscope (E-Scope2 manufactured by Cardionics), and a multitrack recorder (R16 manufactured by ZOOM). The primary frequency components of BS have generally been reported to be present between 100 Hz and 500 Hz [15]. Based on these reports, sound data was stored at a sampling frequency of 4000 Hz and digital resolution of 16 bits. Furthermore, sound data was filtered by a third-order Butterworth bandpass filter with a cutoff frequency of 100-1500 Hz. The participants were in a supine position during testing, with an electronic stethoscope positioned 9 cm to the right of the navel and a microphone 20 cm above the navel [8].  
BS present in the sound data obtained using the noncontact microphone were also present in the sound data obtained using the electronic stethoscope. Based on this, as in our previous studies, we used audio playback software to listen carefully to both types of sound recordings, and classified as a BS episode any episode that was 20 ms or more in duration and could be distinguished by the ear at the same time position in both recordings [7].  
For the analysis, we divided the sound data into sub-segments with a window range of 256 samples and a shift range of 64 samples. The STE method was used to calculate the power of each window range, making it possible to detect sub-segments above a certain signal-to-noise ratio (SNR). SNR, as used in this study, is defined as follows:  
\[SNR=10 log\delta_{10}\frac{P_{S}}{P_{N}} \tag{1}\]Here, \(P_{S}\) represents the signal power and \(P_{N}\) represents the noise power. \(P_{N}\) can be calculated based on a one-second interval of silence determined by conducting the abovementioned listening process, and it is a time-averaged value. Sub-segments detected successively using the STE method are treated as a single segment (also called sound episode (SE)). If a detected segment corresponds to a BS episode, then it is defined as a BS segment; otherwise, it is defined as a non-BS segment.
## 5 Results and Discussion  
### Validation and Characteristics of GRACE TWSA in the PRB  
The GRACE RL06 TWSA products provided by CSR, GSFC, and JPL are distributed in the form of gridded datasets on a global scale. In this study, further post-processing and spatial extraction were conducted to derive the gridded cumulative TWSA time series specific to the PRB area. The GRACE data used in this section span from April 2002 to December 2023. A comparative analysis of TWSA time series data derived from the GRACE satellite is conducted in Figure 4. The focus of the analysis is on evaluating the degree of agreement and divergence between the datasets by calculating the Pearson correlation coefficient (r) and the root mean square Error (RMSE).  
The Pearson correlation coefficients, which measure the linear relationship between the datasets, indicate a very strong positive correlation among CSR, GSFC, and JPL. Specifically, CSR and GSFC exhibit a correlation of \(r=0.964\), while CSR and JPL show an almost identical correlation of \(r=0.965\). The correlation between GSFC and JPL is slightly lower but still robust, at \(r=0.950\). These high correlation values suggest that all three datasets capture similar temporal dynamics in TWSA, with minimal divergence in their overall patterns.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Term** & **Types** & **Explanation** \\ \hline TP & True Positive & the FPI correctly predicts a flood event \\ TN & True Negative & the FPI fails to predict a flood event \\ FP & False Positive & the FPI incorrectly predicts a flood event \\ FN & False Negative & the FPI correctly predicts the absence of a flood event \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix for evaluating the accuracy of flood events identification.  
To quantify the differences in magnitude between the datasets, the RMSE for each pair is calculated. The CSR-JPL comparison results in the lowest RMSE of 1.80 cm, indicating the closest agreement in absolute TWSA values between these two institutions. The CSR-GSFC comparison produces a slightly higher RMSE of 2.27 cm, while GSFC and JPL have an RMSE of 2.14 cm. These deviations, though small, reflect some degree of variation in the retrieval algorithms or post-processing techniques used by the respective institutions. Nonetheless, the overall agreement between the datasets remains high, as evidenced by the combination of strong correlation and low RMSD values.  
Correlation coefficients compared across the 2002-2023 period demonstrate a strong agreement. The TWSA estimates derived from CSR, JPL, and GSFC mascons show a consistent and acceptable level of agreement.
## 4 Hierarchical Incremental Maps
### Node Level Loop Closure  
The similarities between each reference node and a new image \(I_{q}\) are evaluated using this method. Nodes are clusters that represent compact zones of the environment, and they contain images with similar features. Each image is described using one of the global appearance descriptors presented in Section 3. Each node \(N_{l}\) is represented through the mean descriptor \(\vec{\mu}^{N_{l}}\) and the covariance matrix \(\sum_{d}^{N_{l}}\) computed from the descriptors of the images contained in the node.  
Similarities between a node and an image are evaluated using the Mahalanobis distance [62]. If \(\vec{d}_{q}\) is the descriptor of the image \(I_{q}\), the distance \(\Delta_{\vec{d}_{q}}^{N_{l}}\) between the descriptor \(\vec{d}_{q}\) and the node \(N_{l}\) can be calculated as:  
\[\Delta_{\vec{d}_{q}}^{N_{l}}=(\vec{d}_{q}-\vec{\mu}^{N_{l}})^{T}(\sum_{d}^{N_ {l}})^{-1}(\vec{d}_{q}-\vec{\mu}^{N_{l}}) \tag{3}\]  
where i = 1,2,...,\(C\) and \(C\) is the current number of clusters.  
To decide which are the candidate nodes for the closure, the _Mahalanobis distance_ has to satisfy the condition of similarity presented in Equation (4), where \(\mu_{ns}^{N_{l}}\) and \(\sigma_{ns}^{N_{l}}\) are the mean and the standard deviation of a Gaussian distribution. When every node is built, 80% of the images are used to model the node, creating with them the mean descriptor \(\vec{\mu}^{N_{l}}\) and the covariance matrix \(\sum_{d}^{N_{l}}\). The other 20% of images are used to build a Gaussian distribution, where \(\mu_{ns}^{N_{l}}\) and \(\sigma_{ns}^{N_{l}}\) represent mean and standard deviation of the distances between each of these 20% of images to the mean descriptor of the node. Also in Equation (4), \(x\) is a parameter that must be tuned, and whose value must depend on the number of clusters. The lower \(x\), the more restrictive the condition to create a new node. Values of \(x\) used in this work vs. number of clusters can be seen in Figure 5. As shown in this figure, \(x\) is less restrictive for a low number of clusters, but it is necessary to limit it from a specific number of clusters. This limit is established depending a parameter (\(\Omega\)) which has to be tuned (Table S4). \(x\) remains constant at \(\{1.7,1.85,2,2.15,2.3\}\) when \(C\geq 9,C\geq 8,C\geq 7,C\geq 6\) or \(C\geq 5\) respectively.  
\[\left|\Delta_{\vec{d}_{q}}^{N_{l}}-\mu_{ns}^{N_{l}}\right|\leq\left|x\sigma_ {ns}^{N_{l}}\right| \tag{4}\]  
Figure 4: Graphical summary of the proposed method to create hierarchical maps incrementally.  
At the end of the Node Level Loop Closure, all the nodes that satisfy Equation (4) are considered candidate nodes and introduced in the set \(N^{*}\). After that, the Image Level Loop Closure is activated to try to retrieve the specific image from these nodes that closes the loop. In some occasions, the Node Level Loop Closure may not find any candidate node that closes the loop. In that case, it outputs an empty set of nodes, and it is considered that the new image does not close the loop with any of the existing nodes so far.
## 3 SLedge Model
### Application Scenario  
The application for SLedge is real-time IoT analytics performed over a fog infrastructure. A fog-dedicated infrastructure gives complete responses based on local real-time analysis over data generated nearby. To exemplify, we consider an image processing application that can be in charge of performing face recognition in images of a security camera. The application receives 60 pictures (frames) per second, converts the image to black and white, detects faces in each image, crops them, and recognizes the face in the cropped images. This type of analysis can be helpful when securing airports, large shopping areas,or inside public transportation. Besides security, this application can count people or other objects for dynamic planning in a smart city context or intelligent traffic systems.  
We are mainly motivated by disaster scenarios, where there is the need to perform situation awareness, and processing nearby images to detect people that need help. Latency is crucial under these circumstances, and Internet connectivity to a cloud service should not be taken for granted. We assume there is no global infrastructure and expect to have only local resources available, such as mobile nodes, for example. In this context, volunteer teams can go with a camera to the affected field, take pictures of the surroundings, and locally process the streams of data generated in the neighborhood to obtain situational awareness for the fast response of first rescue teams. The system can detect people and obstacles that have changed the city map after the disaster.  
Moreover, this approach can build a cloud-fog collaboration so that edge analytics can complement the centralized architecture. A cloud-fog scenario generally offers real-time partial responses to clients close to where the data is being generated, avoiding raw data traveling through the Internet backbone towards a data center. Besides improving latency, this approach decongests the network, considering a growing amount of data generated at the edge of the network, for example, in the IoT context expected to dominate Internet traffic in the future. In our particular scenario, only the cropped faces resulting from the stream processing system may travel towards the Cloud for a centralized analysis, but also a fast response is provided to the local stakeholders.