esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
## 2 Methods
### Visual Stimulation and Feature Extraction  
To elicit SSVEP, an LCD monitor displayed visual stimulation in the form of flickering boxes, and the BCI layout of the visual stimulation is shown in Figure 2. The subject was asked to sit at a distance of 55 cm from the monitor, measured from the subject's nasion to the BCI screen. For an LCD monitor, the refresh rate was typically 60 Hz; thus, the flickering boxes could flicker in a limited range of frequencies [19]. With such a refresh rate, it was possible to simulate different frequencies by displaying appropriate numbers of frames. Rendering an order of frames can generate a flickering box with a desired frequency. According to clinical experience, a visual stimulation ranging from 15-25 Hz can easily evoke an epileptic syndrome. Therefore, the selected frequencies were lower than 10 Hz in this study. The five orders of frames and corresponding frequencies that were used as stimuli are listed in Table 1. For an order of frames, the numbers 1 and 0 were used to represent a frame with an image and a black box, respectively. The duty cycle was therefore not always equal to 0.5, as the number of frames in one period is an odd number.  
Figure 1: Brain-computer interface (BCI) remote control system architecture.  
Figure 2: The BCI layout for an air swimmer drone vehicle.  
When the visual stimulation elicited signals in the visual cortex of the brain, EEG signals were simultaneously read from the occipital region of the subject's head by using a NuAmps EEG amplifier supplied by the Neuroscan Company. The EEG signals were then acquired from the Oz channel with reference and ground electrodes placed at A1 and A2. When an EEG signal \(x\) was acquired, a power spectrum sequence \(X\) with \(N\) frames was estimated by using the Hamming window and FFT. For the \(k\)th frame, \(X(k)\), a triangular window centered at\(f_{c}\) Hz was then applied to calculate the band energy for each stimulus with a flicker frequency of \(f_{c}\) Hz; it was treated as an input feature for the fuzzy tracking and control algorithm.
## 4 Experiments and Analysis
### Experimental Setup and Implementation Details
#### 4.2.2 Evaluation Metrics  
For quantitative evaluations, several objective evaluation metrics were employed to assess the performance of the proposed underwater image enhancement method. These metrics served as references for measuring image quality in a comprehensive manner.  
The first metric used was the Peak-Signal-to-Noise Ratio (PSNR) [52], which is a well-established full-reference image quality evaluation metric. It quantifies the errors between corresponding pixels in the enhanced image and the reference image. A higher PSNR score indicates better image quality in terms of minimizing pixelwise errors.  
To evaluate the visual quality of the enhanced images, the Structural Similarity Index (SSIM) [53] was employed. The SSIM measures the similarity between the enhanced image and the reference image in terms of three key features: brightness, contrast, and structure. A higher SSIM value implies a higher perceptual similarity between the enhanced and reference images.  
The Mean-Squared Error (MSE) [54] was another metric used, which calculates the average squared difference between the pixels of the enhanced image and the reference image. A lower MSE value indicates better image quality, as it quantifies the overall distortion in the enhanced image compared to the reference image.  
For a comprehensive evaluation of underwater image quality, two additional metrics were employed. The Underwater Color Image Quality Evaluation (UCIQE) [55] metric primarily measures the degree of detail and color recovery in distorted images. It is considered one of the most-comprehensive image-evaluation standards for underwater images. The Underwater Image Quality Metric (UIQM) [56] is specifically designed to assess color, sharpness, and contrast in underwater images. By considering both local and global image features, the UIQM offers a holistic evaluation of underwater image quality.  
By utilizing these objective evaluation metrics, the proposed method aims to provide a quantitative assessment of its performance in terms of fidelity, perceptual similarity, color reproduction, detail recovery, and overall image quality. These metrics serve as valuable references to measure and compare the effectiveness of the proposed underwater image enhancement technique.
## 2 Material and Methods
### WRF Model Setup  
The Weather Research and Forecasting (WRF) model [13] was selected as numerical weather model to accomplish all the experiments carried out within the framework of the STEAM project. It is a compressible non-hydrostatic model with mass-based terrain-following coordinates that was developed at the National Center for Atmospheric Research (NCAR) in collaboration with several institutes and universities for operational weather forecasting and atmospheric science research. In this work the adopted version for WRF is 3.8.1, while for the data assimilation package (WRFDA [22]) is 3.9.1. For this study the three domains setup shown in Figure 3 has been adopted. The domains have, respectively, a horizontal grid spacing of 13.5 (250 \(\times\) 250 grid points), 4.5 (451 \(\times\) 450) and 1.5 km (943 \(\times\) 883) with 50 vertical levels (Figure 3, all domains top reach 50 hPa).  
All the simulations are performed with the same set of physical parameterizations, described hereafter, that have already been successfully tested in the study of similar events [19; 23; 24]. Concerning the surface layer, the MM5 scheme has been adopted [25; 26; 27]. A convective velocity following [28] is used to enhance surface fluxes of heat and moisture. For the land surface processes, the Rapid Update Cycle (RUC) scheme has a multi-level soil model (6 levels) with higher resolution in the upper soil layer (0, 5, 20, 40, 160, 300 cm) [29; 30]. The planetary boundary layer (PBL) dynamics is parameterized with the diagnostic non-local Yonsei University PBL scheme [31]. The WSM6 microphysics six-class scheme is adopted to include graupel and its associated processes [32]. Finally, the radiative processes are parameterized by means of the longwave and shortwave RRTMG schemes [33].  
The initial and lateral boundary conditions are derived from NCEP-GFS (National Centers for Environmental Prediction Global Forecast System) analysis and forecast data available at a horizontal grid spacing of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a time resolution of 3 h, as well as the ECMWF-IFS (European Center for Medium-Range Weather Forecasts Integrated Forecasting System) at a horizontal resolution of 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) and a time resolution of 3 h.  
Figure 3: The three domains setup adopted for Weather Research and Forecasting (WRF) in the current study, showing the model orography in colours.
## 2 Material and Methods
### Quality Control of Flux Data  
The flux data were subjected to quality control checks to screen data for instrument malfunctions, rainfall, etc. A diagnostic output of LI-7200: automatic gain control (agc_li) was used to filter out half-hourly values when the instrument was performing poorly and for identification of outliers. Spike removal was carried out and then corrections for density fluctuations [25] were performed for variations in temperature and water vapor. The coordinate rotation correction was carried out following the method by [PERSON] and [PERSON] [26]. Sonic temperature correction for humidity was applied as per [PERSON] et al. [27]. The time lag compensations due to the sensor separations between CSAT-3 and LI-7200 were applied as per [PERSON] et al. [28]. Data quality was checked by applying the stationarity test and integral turbulence characteristics test [29] to the computed covariance. The rejection rate was 40% (10% for non-stationary, 40% for integral turbulence characteristics and 14% for both tests) in rice and 40% (15% for non-stationary, 40% for integral turbulence characteristics and 16% for both tests) in wheat. The rejection rate of data was larger when the canopy was small due to lower magnitude of CO\({}_{2}\) flux. The u* threshold of 0.1 m s\({}^{-1}\) was estimated by plotting the nighttime CO\({}_{2}\) flux (Fc) and frictional velocity (u*). Gap-filling of missing or discarded data was done by simple linear interpolation (1-3 consecutive missing data) and mean diurnal variation method (up to 4-6 consecutive missing data) and by look-up table approach for more than 6 h missing data using standard approach and available data points.
## 5 Federated Learning for EEG Signal Classification: A Case Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Description** & **Application** & **Method/Framework** &
\begin{tabular}{c} **Privacy-** \\ **Preserving** \\ \end{tabular} \\ \hline \multirow{4}{*}{[71]} & Researchers developed a & \multirow{4}{*}{Emotion Recognition} & Dense Neural & \multirow{4}{*}{\(\blacktriangledown\)} \\  & federated learning system for & & & \\  & emotion recognition using EEG & & & Networks & \\  & signals and dense neural & & & \\  & networks. & & & \\ \hline \multirow{4}{*}{[72]} & Privacy-preserving federated & \multirow{4}{*}{E epileptic Seizure} & \multirow{4}{*}{Federated Learning} & \multirow{4}{*}{\(\blacktriangledown\)} \\  & learning framework for & & & \\ \cline{1-1}  & epileptic seizure detection from & & & \\ \cline{1-1}  & EEG signals within a & & & \\ \cline{1-1}  & fog-computing-based IoMT. & & & \\ \hline \multirow{4}{*}{[73]} & Group federated learning & \multirow{4}{*}{\(\blacktriangledown\)} & \multirow{4}{*}{\(\blacktriangledown\)} \\  & (Group-FL) for large-scale & & & \\ \cline{1-1}  & driver drowsiness detection. & & & \\ \hline \multirow{4}{*}{[74]} & Federated learning method for & \multirow{4}{*}{Emotion Recognition} & Federated Learning & \multirow{4}{*}{\(\blacktriangledown\)} \\  & emotion recognition based on & & & \\ \cline{1-1}  & EEG signals. & & & \\ \hline \multirow{4}{*}{[75]} & Federated learning approach & \multirow{4}{*}{\(\blacktriangledown\)} & \multirow{4}{*}{\(\blacktriangledown\)} \\  & for MI-EEG signal & & & \\ \cline{1-1}  & classification using CNN. & & & \\ \hline \multirow{4}{*}{[76]} & Federated transfer learning & \multirow{4}{*}{\(\blacktriangledown\)} & \multirow{4}{*}{\(\blacktriangledown\)} \\  & (FTL) for EEG classification. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison among various studies using federated learning to EEG signal. In conclusion, the evaluation of the models in these studies is as follows: In [71], FLER-Net was trained on the DEAP dataset of EEG signals, achieving accuracy scores of 89.30% for valence, 86.85% for arousal, and 88.16% for dominance, surpassing previous privacy-preserving emotion recognition models by utilizing a broader feature space and a federated learning-optimized architecture. Similarly, the proposed Fed-ESD model in [72] demonstrated strong detection performance in the standard setting, with accuracy at 79.2%, sensitivity at 76.8%, specificity at 81.74%, geometric mean at 79.25%, and an F1-measure of 79.28%, though results varied significantly between participants, likely due to random variations in data distribution and individual EEG patterns. Furthermore, extensive experimentation in [73] validated the effectiveness of each component within the framework, achieving mean scores of 81.0% for accuracy, 82.0% for F1-score, and 87.9% for the area under curve (AUC) on a publicly available dataset with 11 subjects, highlighting the robust performance of the framework in processing the data.
## 3 Results
### Studies Characteristics
#### 3.2.1 Subject Selection  
All studies were consistent in the sample recruitment process. Participants were first required to have a primary diagnosis of TS or CTD according to DSM-5 criteria. They were then asked to complete the Yale Global Tic Severity Scale (YGTS) to determine whether their motor and/or vocal tic severity was over the YGTSS cutoff for identifying TD according to the specific thresholds adopted by the authors (among the studies, a cutoff score of 14 was often used for TD). Another criterion concerned participants' intellectual level (IQ > 75 or 80) and proficiency in their native language. Moreover, most of the studies investigated participants' clinical history in order to identify potential comorbidities, such as Attention Deficit Hyperactivity Disorder (ADHD) [20, 21, 22, 29, 30, 31], anxiety disorders (all studies), and Obsessive-Compulsive Disorder (OCD) [15, 18, 20, 21, 22, 29, 30, 31]. Subjects with comorbidities were not excluded unless they required immediate treatment; however, the influence of these co-occurring conditions was not thoroughly analyzed. Subjects were excluded when they showed one of the following conditions: a current diagnosis of substance abuse/dependence, suicidality or self-injurious tics, psychotic disorders, a history of behavioral treatments for tics, recent changes (e.g., within the previous four weeks) in drug treatment status, or planned changes in drug treatment status over the course of the treatment.  
For studies involving Internet-based interventions, additional requirements included the cooperation of caregivers/parents [15, 18, 19, 20] and access to a computer with Internet access and a web camera.
## 4 Discussion
In summary, we conclude that combining _Csd_ and microsatellite genotyping substantially enhances the resolution of genetic studies in honeybee colonies, offering valuable insights into genetic diversity, reproductive success, and social dynamics. The _Csd_ gene emerges as a promising tool for advancing genetic studies in honeybee populations. Genotyping using _Csd_ should allow for a reduction in the number of microsatellite markers used, contributing to the increased comparability of results between research teams and individual studies. Currently, this is significantly challenging, as the lack of consensus among scientists and the limitations imposed by the limited microsatellite marker diversity have led to a situation where different sets of microsatellite markers, amplified with differently labeled primers, are in use [37]. Conceptualization, M.C. ; Methodology, A.M., M.C. and R.M. ; Formal Analysis, M.C. and R.M. ; Investigation, M.C., R.M., J.N.-T., A.M., K.A.-W. and A.L. ; Resources, M.C. ; Data Curation, M.C. and R.M. ; Writing--Original Draft Preparation, M.C. ; Writing--Review and Editing, M.C., R.M., J.N.-T., A.M. and A.L. ; Supervision, M.C. ; Project Administration, M.C. ; Funding Acquisition, M.C. and A.M. All authors have read and agreed to the published version of the manuscript. The research was supported by the National Science Centre, Poland (grant No UMO-2018/31/B/N29/00969) awarded to [PERSON]. Ajda Moskric was funded through the ARIS research programme number P4-0133 =Sustainable agriculture. The funders had no role in the study design, data collection or analysis, the decision to publish, or the preparation of the manuscript. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. We would like to thank [PERSON] for providing us with brood from colonies ASP1 and ASP3. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Network Structure and Link Characterization  
### Disaster Area Wireless Networks  
In the aftermath of a natural or man-made disaster (such as a hurricane or a strong earthquake), regular communication services are disrupted due to infrastructure damage and power outages.  
Communication remains essential for conducting a well-managed recovery operation, finding survivors, or contacting friends and loved ones. In the absence of power, such communication must rely on battery-operated wireless devices such as cell phones and wireless routers powered by vehicular batteries. We term such communication networks as DAWNs. Figure 1 gives the network structure of a DAWN, which we proposed in this paper. In this scenario, rescuers (civil protection forces such as troops, fire brigades, rescue teams, etc.) carry portable devices that can communicate with each other as well as with ECVs by single or multiple hops. ECVs are located at the edge of disaster area, since roads are often completely destroyed by earthquakes, floods, etc. ECVs can gather information collected from disaster area by the mobile nodes carried by rescuers, and then transfer this information to the Command Center through a backbone network that may consist of satellites, base stations (BS), etc. Rescuers carry out their tasks all over the disaster area with communication devices to send or receive messages. They move frequently with the decreasing catastrophic intensity of any particular disaster area, thus causing the frequent link breakage and topology change. Restricted in power supply, mobile nodes have limited radio coverage. When there is no ECV nearby, mobile nodes have to turn to neighbor mobile nodes for data transmission. Unlike mobile nodes, ECVs are able to communicate with a backbone network without distance constraints because of their sufficient power supply and large radio coverage. Through one hop or multiple hops to ECVs, collected data can then be delivered to the Command Center by backbone networks, thus helping governors make suitable decisions for rescuing refugees trapped in the disaster area.  
In order to elaborate the network structure more clearly, we discuss a real multi-hop communication instance. \(A\), \(B\), and \(C\) are three mobile nodes involved in DAWN. Node A needs to transfer some rescue information to the Command Center while there are no ECVs in its communication coverage, so it has to transfer the information to next-hop, according to the routing table, as shown in Table 1. Node A selects neighbor Node \(B\) as the next-hop node for data transmission, then Node B selects Node C, and finally Node C transfers the data to the ECV directly.  
Figure 1: Disaster Area Wireless Network (DAWN) topology.
# An SSI-Based Solution to Support Lawful Interception
## 7 Related Work  
Self-Sovereign Identity is an emerging paradigm attracting the interest of governments and enterprises [14, 22, 24, 34, 35]. One of the main benefits of the SSI paradigm is its compliance with the General Data Protection Regulation (GDPR) [36, 37, 38, 39], making it an effective choice to develop privacy-by-design solutions. As a matter of fact, several concrete implementations of SSI frameworks are available to develop SSI-based solutions [31, 40, 41, 42]. Among them, we rely on Sovrin [18, 43, 44, 45], which is becoming a standard for deploying SSI solutions, as witnessed by several scientific papers [46, 47, 48, 49, 50, 51, 52] and industrial projects [53] proposing different solutions that utilize the Sovrin framework.  
In the literature, several solutions have been proposed that apply the SSI approach to solve different problems in different domains. For example, some of these domains are healthcare [54, 55, 56], industrial IoT [57, 58], and food supply chain [59].  
In this paper, for the first time, we apply the SSI approach to the lawful interception domain. Lawful interception is an activity that involves legal [60, 61, 62], ethical [63], and technological issues [64, 65]. From a technological point of view, several proposals, also recent [5, 6, 66], are available in the literature, especially in the field of telecommunication [67, 68, 69, 70]. However, these works focus on the physical communication interfaces [70] and/or on the integrity and storage of the collected data [71].  
This paper addresses a slightly different problem, which is access control in the LI procedure. To the best of our knowledge, previous solutions propose access control models (such as RBAC [19] or VACM [20]) at a theoretical level, but their implementations fail to enforce these models effectively, leaving access control unimplemented in practice.
# Review of Image Forensic Techniques Based on Deep Learning
## 2 Image Forensic Techniques
### Active Forensics  
Digital watermarking is one of the most effective active forensics methods to realize piracy tracking and copyright protection. It can be classified into conventional methods based on manually designed features and deep learning-based methods. Early digital watermarking was mostly embedded in the spatial domain, such as least significant bits (LSB) [32], but it lacked robustness and was easily detected by sample pair analysis [33]. To improve robustness, a series of transform domain-based methods were proposed, such as DWT [34], DCT [35], contourlet transform [36], and Hadamard transform [37]. In recent years, with the continuous update and progress of deep learning technology, deep learning has been widely used in image watermarking and achieved remarkable achievements. In this survey, we will focus on the analysis of deep learning-based robust watermarking.
## 2 Design of Base Cells
### Description of the Cellular Configuration  
In the level set method, the solid and void regions are represented by different values of the level set function. The mathematical expression for the design domain can be described as [53; 54]:  
\[\left\{\begin{array}{ll}\Phi(x)>0,&\forall x\in\Omega\backslash\partial\Omega \qquad(Solid)\\ \Phi(x)=0,&\forall x\in\partial\Omega\cap D\quad(Boundary)\\ \Phi(x)<0,&\forall x\in D\backslash\Omega\qquad(Void)\end{array}\right. \tag{1}\]  
where \(D\subset R^{d}(d=2,3)\) denotes a reference design domain, \(\Omega\) denotes the set of possible shape in the domain, \(\partial\Omega\) is the structural boundary, and \(x\) denotes the point coordinates in \(D\). The readers can refer to [53; 54] for more details of the level set-based boundary description. By introducing the time variable \(t\), we can gain the following Hamilton-Jacobi partial differential equation (H-J PDE) [19; 46]:  
\[\frac{\partial\Phi(x,t)}{\partial t}-V_{n}\cdot\left|\
abla\Phi(x,t)\right|=0 \tag{2}\]  
Equation (2) reflects that the structural boundary evolution is described by the velocity field. In the level set method, the propagation of level set surface is triggered by the moving of velocity field [21]. Therefore, for a structural optimization design, the key step is to find an appropriate \(V_{n}\) to drive the change of structural topology and shape [21]. However, the H-J PDE is usually hard to solve, and the reinitialization scheme of the level set function consumes a lot of extra computing efforts. Thus, in this study, the more efficient PLSM is adopted. In the PLSM [51], the level set function is interpolated by a series of compactly supported radial basis functions (CSRBFs). More specifically, the discrete level set function is represented by a set of constant CSRBFs \(\varphi(x)\) and a number of varying expansion coefficients \(\alpha(t)\)[51]:  
\[\Phi(x,t)=\varphi(x)\cdot\alpha(t)=\sum_{i=1}^{N}\varphi_{i}(x)\cdot\alpha_{i} (t) \tag{3}\]  
where \(N\) is the total interpolation points in the design domain. Substituting Equation (3) into Equation (2), the complicated H-J PDE covers to an ordinary differential equation, and the velocity field used to propagate the level set front can be naturally extended from the boundary to the whole design region. As the CSRBFs \(\varphi(x)\) are constant during the optimization, the cellular structure configuration is mainly affected by the expansion coefficients \(\alpha(t)\). Therefore, the complex PDE-driven design problem for cellular structures is converted to a relatively simple parametric optimization problem with the design variables of \(\alpha(t)\). By using the PLSM, the cellular structure optimization can be conveniently solved by the gradient-based algorithms, including the OC and MMA. Moreover, we can use a level set function to denote a particular cellular structure configuration, which paves the way for the latter transitional cell method.