esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Eye Metric Classification
#### 3.3.3 Synthetic Eye-Tracking Data Generation  
The use of Generative Adversarial Networks (GANs) for generating synthetic eye-tracking data represents a significant advancement in addressing challenges associated with data scarcity in this field. Two studies, led by the same research group, introduced SP-EyeGAN, a framework designed to produce synthetic eye-tracking data that closely emulates real-world human gaze patterns [9]. This approach aims to mitigate the high costs and privacy concerns of collecting real eye-tracking data while providing an alternative for pre-training machine learning models.  
SP-EyeGAN employs two distinct GANs: FixGAN, which focuses on generating micro-movements such as fixations, and SacGAN, which simulates macro-movements like saccades. While these networks share the same underlying architecture, they are trained separately to model distinct aspects of eye movements. The generated sequences incorporate key gaze features such as velocities and angles, allowing for the creation of data that statistically reflects human eye movement dynamics. The framework also incorporates a pipeline for pre-training neural networks using contrastive learning, where synthetic data serve as a foundation prior to fine-tuning models on smaller datasets of real labelled data for specific downstream tasks such as predicting reading comprehension or if it is the reader's native language.  
The results highlight the potential of SP-EyeGAN to enhance model performance in reading-related domains, such as comprehension and text difficulty assessment, where the synthetic data-driven approach has achieved state-of-the-art results. However, its impact is less significant in biometric contexts, including applications such as identity verification, ADHD detection, and gender classification, where synthetic data showed limited success [25]. Given the focus of this systematic review on reading applications, the success of SP-EyeGAN in these areas is particularly notable, demonstrating its ability to simulate the intricate gaze behaviours involved in text processing.
## 2 Methodology
### Inclusion and Exclusion Criteria  
During the initial search phase, we identified 156 scientific articles from the database query and last checked 5 July 2024. After eliminating 40 duplicates and disqualifying 41 records using automated tools, along with an additional 13 for various reasons, we narrowed down the selection to 62 publications for closer examination. Further scrutiny led to the exclusion of 12 more, and efforts were made to retrieve the remaining 50 reports. Unfortunately, four of these reports could not be retrieved, leaving us with 46 for a detailed eligibility assessment. Screening of titles, abstracts, and keywords resulted in the inclusion of 23 scientific articles. Subsequent in-depth full-text eligibility screening concluded with the selection of 22 articles for comprehensive analysis, including the following:* Articles that specifically explored e-fuel production utilising H2 and atmospheric CO2.
* Studies offering a TEA or an LCA of the e-fuel production process.
* In addition, studies that were published in English during the years 2013 to 2024. Conversely, exclusions were made based on the following:
* Articles that mainly focused on alternative renewable fuels without specifically addressing e-fuels.
* Publications that were deficient in substantial data or lacked clarity in their methodology.  
The process of the literature search and the presentation of findings were conducted in alignment with the directives laid out in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement summarised in Figure 4 [47; 48; 49; 50].
## 2 AE SHM Setup
### AE Module  
The Micro-SHM [22] is an AE system, which is cost-effective and reliable, targeting SHM applications. It is an ideal candidate for the purpose of our application since it can remotely monitor the joints of the carbody and the shell. Moreover, it can be used to monitor specific infrastructures such as railway bridges and tracks. This remote system is a tool, which offers data management and analysis procedures for AE data recording. The Micro-SHM has wireless capabilities to support remote monitoring and it also includes two channels for AE. In the configuration we are using for this paper, a 2 MSPS sampling was used with 2k samples depth. An analogue filter was used from 10 kHz to 1 MHz. Additionally, a digital butterworth bandpass filter was used, from 80 kHz to 200 kHz. This was to avoid any external source of noise that may take place when the monitoring procedure is active.  
There exist different options when it comes to wireless communication. Using the Wi-Fi or 4G capabilities that the system offers, we can transmit a packet with the values coming from the raw signal. Essentially, we may have to break the produced signal into a number of packets, and transmit them in order, so that the basestation is able to reconstruct the signal from the packets. The file that is produced by the Micro-SHM consists of the time relative to the first hit and the signal amplitude. The AE signal is continuously produced since it has to be able to detect cracks in real-time. Hence, we are considering a continuous approach for the transmission of the signal values.  
On the other hand, the Micro-SHM and its accompanying software can obtain a number of AE features, which correspond to the measurements that have been taken in real-time from the sensors. The acoustic emission defect sources can be simulated using pencil lead breaks to composite materials and the sensors will output the generated signal as it appears in Figure 1, where the reader can see the response from an AE sensor with and without a defect, respectively.  
Here, we may assume that these measurements are transformed to values at the device level. The most important of these features can be found in the manual of the Micro-SHM and AEWin software that accompanies it. Note that these parameters are sent to the terminal computer in the form of minimum, maximum and average values of the AE hits. Hence, we can put them in a struct with arrays of three values per feature to form a packet.  
The nature of the AE monitoring requires that the features are promptly sent to the control centre in the case of a defect, especially if a serious one exists. Subsequently, the AE system that monitors the carbody needs to be equipped with the communication means to send the features without errors. This means that mechanisms, which ensure that the packets are sent without being erroneous, should be available and operate during the transmission operation. In the next parts of the paper, we provide the communication of the AE system, as well as the method to overcome errors in the communication.
# Techno-Economic Potential of Plasma-Based CO\({}_{2}\) Splitting in Power-to-Liquid Plants
## 3 Methodology  
### Goal and Benefit  
Both the advantages and optimization needs of plasma-based CO\({}_{2}\) splitting were mentioned in the last chapter. Since previous studies on PTL plants with Fischer-Tropsch synthesis (FTS) have only considered a path with the reverse water gas shift (RWGS) from a techno-economic point of view, the model in this work presented and its results offer insights into the integration of plasma-based CO\({}_{2}\) splitting as a possible CO source for synthesis gas. To obtain the necessary high temperatures in the RWGS, gas combustion is necessary, which is acheived by adding natural gas or destroying a part of the product produced. This is not necessary when using plasma technologies and is an advantage in the direction of defossilization. A first techno-economic classification of DBD plasma has already been performed by [PERSON] et al., but only for the experimental results obtained in the paper, which achieved high conversions but low efficiencies [34].  
Another important point is the interaction with other PTL components, especially the necessary oxygen separation. Three different scenarios, 2018/20, 2030, and 2050, which differed in the use of different boundary conditions and thus represented different degrees of technology development, were considered, and further increases in efficiency, etc., are discussed. One advantage of the developed MATLAB model is the possibility of extending individual components and, thus, increasing the level of detail of certain model parts. By integrating characteristic diagrams from previous experiments, finer control levers can be addressed, and the influences on a possible overall system can be discovered at an early stage. It must be kept in mind that, in summary the optimization of small levers can also result in considerable increases.  
Figure 5: Technical design of a gas diffusion electrode device for the removal of oxygen. (**a**) Full construction. (**b**) Sectional view to show the layers as well as inlets and outlets.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 5 Requirements for SFS Characterization
### Free CaO Content  
If the free CaO content is not high enough to be reasonably estimated by quantitative XRD, then another useful test is complexometric titration. Originally developed to determine the free CaO content in portland cement and clinker [276; 277; 278], this method uses hot ethylene glycol to complex with the free CaO, making it applicable to SFS [284; 29; 29; 29; 113; 279; 280; 281; 9]. However, the hot ethylene glycol complexes with Ca\({}^{2+}\) from both free CaO and Ca(OH)\({}_{2}\), so [PERSON] and [PERSON] [9] proposed using thermogravimetric analysis (TGA) to determine the Ca(OH)\({}_{2}\) content to refine the estimate of the actual free CaO content.  
The available Ca\({}^{2+}\) ions from the free CaO and Ca(OH)\({}_{2}\) form a complex with the ethylene glycol, thereby shifting the pH of the solution. Using phenolphthalein as an indicator, an acid is titrated into the solution until the pH is adjusted. Previous SFS studies have shown that ethylene glycol at \(95\pm 5\)\({}^{\circ}\)C mixed with a powdered SFS sample for 30 min is sufficient [279; 9]. The equation to determine the free CaO content is [277]:  
\[EGN=F\left[\frac{N(V-V_{b})}{10\ m}\right], \tag{1}\]  
where \(EGN\) is the ethylene glycol number that needs to be corrected based on the estimated Ca(OH)\({}_{2}\) content, \(F\) is a correction factor, \(m\) is the initial mass of the SFS sample (g), \(N\) is the normality of the acidic solution, \(V\) is the volume of acidic solution titrated (mL), and \(V_{b}\) is a correction for the volume of acidic solution titrated in a blank ethylene glycol sample (mL). For this configuration, the correction factor \(F\) is 28 [285; 9; 277; 279; 285]. Typically, \(V_{b}\) is 0 mL. Previous studies have shown that 0.05 N to 0.1 N hydrochloric acid is a reasonable solution for titration [283; 9; 279] since the volume titrated provides suitable resolution in the measurement (e.g., a very strong acid would not require much volume to titrate, which means that the volume titrated may be easily overestimated).  
However, one complication of the ethylene glycol method is that the free CaO is not fully complexed by ethylene glycol if it has formed as a solid solution with 20% or more by mass with other divalent metal oxides (e.g., MgO, FeO, MnO) [286].
# Advanced Financial Fraud Malware Detection Method
## 2 Related Work
### Hybrid Analysis-Based Malware Detection  
This section reviews several studies that have applied hybrid features, combining static and dynamic characteristics for Android malware detection. Table 3 presents a comparison of the studies discussed herein. In [47], StormDroid, a streamlined ML-based malware detection model that adopts a hybrid approach, was used. It leveraged various features, including permissions, sensitive API calls, sequences, and dynamic behaviors to classify Android malware. The model was evaluated using multiple ML algorithms, such as SVM, C4.5, multi-layer perceptron (MLP), naive bayes (NB), instance-based k (IBK), and Bagging predictors, on a dataset of approximately 8000 apps collected from Google Play [22] and Contagio [24]. StormDroid enables large-scale analysis by monitoring both static and dynamic behaviors and demonstrates an accuracy of 93.8%. Additionally, it achieved approximately three times the efficiency of the single-threaded models. MADAM, a cross-layer model that incorporates static and dynamic features such as system calls, short message service (SMS) messages, critical APIs, user activities, and app metadata, was introduced in [48]. The model was evaluated using various algorithms, including KNN, linear discriminant classification (LDC), quadratic discriminant classifier (QDC), MLP, parametric zero-cost proxies (PARZC), and radial basis function (RBF), on large datasets sourced from MalGenome [39], Contagio [24], and VirusShare [23], achieving an accuracy of 96.6%. Additionally, MADAM is designed to minimize battery consumption and imposes a low-performance overhead. In [49], Androtomist, an open-source tool, was designed for the static and dynamic analyses of Android applications. The tool offers two operational modes: a novice mode for beginners and an expert mode for advanced users. Androtomist was tested on three datasets using various ML classifiers, with an ensemble approach applied to average the results and determine the most significant features. It achieved a perfect accuracy of 100% on the Drebin [28] and VirusShare [23] datasets, whereas its accuracy was slightly lower at 91.8% on the AndroZoo [38] dataset. Another study [50] proposed a deep learning model for detecting Android malware using a hybrid analysis approach. The model utilized the MalGenome [39] and Drebin [28] datasets for static analysis and the CICMalDroid2020 [51] dataset for dynamic analysis. In total, 261 combined features were extracted for the hybrid analysis. The model was further evaluated using 311 application samples comprising 165 benign apps from the Play Store and 146 malicious apps from VirusShare [23]. The proposed model achieved an accuracy of
###### Abstract  
Wetland ecosystems are sensitive to climate variation, yet tracking vegetation type and structure changes through time remains a challenge. This study examines how Landsat-derived vegetation indices (NDVI and EVI) correspond with lidar-derived canopy height model (CHM) changes from 2000 to 2018 across the wetland landscape of the Peace-Athabasca Delta (PAD), Canada. By comparing CHM change and NDVI and EVI trends across woody and herbaceous land covers, this study fills a gap in understanding long-term vegetation responses in northern wetlands. Findings show that \(\sim\)35% of the study area experienced canopy growth, while 2% saw a reduction in height. CHM change revealed 11% ecotonal expansion, where shrub and treed swamps encroached on meadow and marsh areas. NDVI and EVI correlated significantly (\(p<0.001\)) with CHM, particularly in shrub swamps (\(\mathbf{r}^{2}=0.40\), 0.35) and upland forests (NDVI \(\mathbf{r}^{2}=0.37\)). However, EVI trends aligned more strongly with canopy expansion, while NDVI captured mature tree height growth and wetland drying, indicated by rising land surface temperatures (LST). These results highlight the contrasting responses of NDVI and EVI--NDVI being more sensitive to moisture-related changes such as wetland drying, and EVI aligning more closely with canopy structural changes--emphasizing the value of combining lidar and satellite indices to monitor wetland ecosystems in a warming climate.  
lidar; canopy height model; enhanced vegetation index; normalized difference vegetation index; trend analysis; boreal; Peace-Athabasca Delta
## 2 Materials and Methods
### Chemical Quantification of CHR Sorbed on MP  
The CHR concentration was measured in the aqueous extracts recovered after 0, 3, 6, 12 and 24 h of MP-CHR co-exposure and in the CHR control solution following the modified US EPA Method 8272 [38]. An aliquot (1.5 mL) of the aqueous extract solution was added to a vial with a small Teflon-coated stir bar and spiked with 0.1 mL of chrysene-\(d_{12}\) solution (5 mg/L in acetone) as an internal standard. Direct immersion solid phase microextraction (SPME) analyses were performed by directly exposing the commercially available polydimethylsiloxane (PDMS, polydimethylsiloxane-coated fused silica fiber, Supelco, Darmstadt, Germany) fiber into the sample solution under magnetic stirring. After 30 min of exposure, the fiber was inserted into the injector port maintained at 300 \({}^{\circ}\)C in the splitless mode of a Shimadzu GC-MS-QP2010 (Shimadzu, Kyoto, Japan). Separation was conducted with a Zebron ZB-35 column (95% dimethyl, 5% diphenylpolysiloxane 30 m, 0.25 mm, 0.25 \(\upmu\)m film; Phenomenex Inc., Torrance, CA, USA), using helium as the carrier gas. The following thermal program of the capillary column was used: from 50 \({}^{\circ}\)C to 310 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, then a hold for 2 min at 310 \({}^{\circ}\)C. Acquisition was performed under electron ionisation at 70 eV using the selected ion monitoring mode of the ion \(m/z\) 228 for the detection of chrysene-\(d_{12}\). Prior to sample analysis, blanks were performed first with a thermal desorption of the fiber alone and then after a 30 min exposure to a solution of deionized water. Further details on CHR calibration and recovery are reported in SM (Section S2, Figure S1). After absolute quantification, the concentration of CHR sorbed on MP was assessed by normalizing data per gram of co-incubating MP. A decrease in CHR concentration was generally detected between the CHR control solution and the aqueous solution extracted after MP/CHR incubation at all the intervals comprised between 3 and 24 h (Table 1). The CHR sorption on MP over time fitted with a non-linear (three-parameter) regression model (R\({}^{2}\) = 0.971; SM, Figure S2). An exponential trend of sorption was detected between 6 and 12 h of MP/CHR co-incubation, while it tended to stabilize at 10.8 \(\upmu\)g CHR per mg of MP at 24 h (SM, Figure S2). On these bases, 24 h of incubation was selected as the CHR/MP co-exposure timing to produce the CHR-loaded MP used in the following exposure treatments.
## 6 Challenges and Future Research Scopes
### Distributed Architecture Design for Converged NFV, SDNs, and Edge Cloud  
Edge cloud is a distributed system with considerable heterogeneity in computing, storage, and network resources that is required to exchange network status such as computational resources, load, bandwidth, etc. It needs to be integrated into a common infrastructure and managed in a holistic manner. SDNs separate the control plane and data plane of networks as we discussed before. The control plane could be designed to exchange computational capability, load, bandwidth, etc., in the edge cloud system. Virtual network functions (VNFs) created by NFV can be dynamically launched or terminated based on demands [8; 23]. Integrating AI with SDNs and NFV within edge cloud architecture could provide a promising solution to the challenges mentioned above [51]. Therefore, integration of an AI-enabled SDN controller, NFV, and edge cloud could increase performance and flexibility of the system using programmability, and configurability of the infrastructure layer and service layer. Moreover, convergence of SDNs, NFV, and edge cloud could potentially open a new avenue for innovation of cost-effective services and application delivery. Therefore, this is a huge potential area of future research.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
### RO Missions
FY-3C and D were launched in 2013 and 2017, respectively. The GNSS occultation sounder (GNOS) receiver onboard the FY-3C/D satellites is the first GPS and BeiDou-compatible sounder [17; 18; 19; 20]. FY-3C can provide approximately 450-500 RO soundings per day for NWP. The FY-3D launched in 2020 can provide even more RO soundings, approximately 1000 per day. The GNOS-RO data have been used to study climate and TCs [21; 22]. GNOS data have been assimilated into the operational NWP system of the ECMWF, Deutscher Wetterdienst (DWD), and Met Office [23]. Moreover, the FY-3-series satellites contain FY-3E/F/G/H and FY-3R, which are intended for rainfall observation. These missions are expected to be launched by 2025 and will provide ten years of continuous RO data. An advanced GNOS receiver (GNOS II) has been designed for the FY-3E satellite, making FY-3E applicable for monitoring sea waves as well [24]. With the rapid development of GNSS, such as the Chinese BeiDou Satellite (BDS), Russian GLONASS [25; 26], European Galileo [27], GPS, and more LEO satellites deployed with GNSS signal receivers, the frequency and number of RO soundings are expected to improve remarkably [17; 18; 28; 29; 30].
# An Improved Particle-Swarm-Optimization Algorithm for a Prediction Model of Steel Slab Temperature
## 3 Improved Particle-Swarm-Optimization Algorithm  
### The Basic PSO Algorithm  
The PSO algorithm originates from a researcher observing the social behavior of a flock of birds or fish [28]. Each particle in the population is a potential solution to the objective function. The particle has two attributes: velocity \(V_{i}=\left[v_{11},v_{12},\cdots,v_{ij},\cdots,v_{ND}\right]\) and position \(X_{i}=\left[x_{i1},x_{22},\cdots,x_{ij},\cdots,x_{ND}\right]\), where \(i=1,2,\cdots,N\)--\(N\) is the size of particle swarm; and \(j=1,2,\cdots,D\)--\(D\) is the dimension of the solution space.  
In each iteration, the information of individual particle is filtered to find the information about the historically optimal position \(p\) of the individual and the historically optimal position \(g\) of the population. The known information is brought into the velocity updating equation, Equation (3), and the position updating equation, Equation (4), to adjust the search direction of the population, so that the particle swarm approaches the global optimal solution, which is the optimal position of the population.  
\[v_{ij}(k+1)=wv_{ij}(k)+r_{1}c_{1}\left(p_{i}-x_{ij}(k)\right)+r_{2}c_{2}\left( g-x_{ij}(k)\right) \tag{3}\]  
\[x_{ij}(k+1)=x_{ij}(k)+v_{ij}(k+1) \tag{4}\]  
where \(w\) represents the inertia weight; \(r_{1},r_{2}\in[0,1]\) are two uniformly distributed random values; \(c_{1},c_{2}\) are the acceleration parameters, which are non-negative constants; k represents the current iteration; and \(k=1,2,3,...,G\), where \(G\) is the maximum number of iterations. The velocity updating of the particle is influenced by three factors: the current moment particle velocity \(V_{ij}(k)\), the particle's self-experience \(\Delta V_{P}\) and the experience of particle swarm \(\Delta V_{g}\). \(\Delta V_{P}\) is the part of the particle that learns from its historical information, and \(\Delta V_{g}\) represents the part of the particle that learns from the historical information of other particles within the population.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 3 Materials and Methods
### Work Methodology and Planning  
This research, development and validation work was planned in six main phases, namely, (PH1) initial research, for analysing captive-portal implementations, WebAuth technology, and configuring the environment; (PH2) protocol design, for grouping the main requirements; (PH3) authentication server development, to develop the web application with WebAuth authentication; (PH4) system integration, where the developed web application is integrated with the captive-portal authorisation; (PH5) usability improvement, where the error handling is designed and user interface is improved; and (PH6) validation, where the result is validated through compatibility tests and a usability experiment with real users.