esa-sceva/satcom-chunk-collection huggingface dataset:
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Preprocessing
#### 2.3.2 Independent Component Analysis  
In biomedical engineering, independent component analysis (ICA) is considered a critical technique for blind source separation [15]. Under typical conditions, EEG data collected in research represent a blend of various signals. These signals encompass not only the brain's activity but also ocular artifacts, cardiac artifacts, noise due to poor electrode contacts, and external electrical interference. Given that artifacts, such as ocular and cardiac components in EEG signals, are produced by independent sources, ICA serves as a powerful tool to model EEG signals and distinguish different components originating from a single signal source. Using ICA to separate the original data enables the removal of artifacts present in the independent components, yielding clean EEG signals [16]. It is noteworthy that this method, when reconstructing EEG signals, not only preserves the temporal resolution but also enhances its spatial characteristics by identifying as many as several dozens of independent EEG signal sources active in different time segments, as well as their scalp projections [17].  
Figure 3: Zero-padding data formatting.  
MBPPE incorporates the fast independent component analysis (FastICA) module from the sklearn library for ICA. Compared to traditional ICA algorithms, FastICA employs parallel and distributed processing strategies. It analyzes and decomposes data based on maximizing non-Gaussianity, thereby reducing computational complexity and utilizing memory more efficiently [18]. In this implementation, FastICA adopts whitening, which streamlines computational demand and enhances the interpretability of the independent components. Employing the logcosh function as the default loss function confers high robustness, rendering it adept at processing complex data.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques
#### 4.2.5 Fingerprint  
RF-fingerprint-based positioning is a scene analysis method that compares the received information with a known information map to estimate the user position, which attracts much attention with the Wi-Fit technique. The principle is also applicable to a VLC-based system. The system in [73] is a two-dimensional IPS using fingerprinting of the power spectral density (PSD) of optical signals received from LEDs. During the offline initialization phase, prior knowledge of the PSD of each modulated visible light emitted by the LEDs is collected and stored as a database, namely a fingerprint map. For positioning, the real-time PSD received from a user can then be compared with that in the database for estimation in the online phase.  
There are three main popular fingerprint-based indoor localization techniques [74], namely the probabilistic method, \(k\)-nearest-neighbor, and neural networks. In [75], a robust asynchronous indoor localization using modulated LEDs is built according to the Bayesian probabilistic estimation method, taking into account possible obstructions of the LoS from the beacons. Each LED broadcasts an embedded unique chip sequence as a beacon to allow mobile users to conduct localization. Given the chip sequences and the locations of the LEDs, a mobile device can synchronize and determine its position based on the likelihood of receiving each signal strength with respect to collected information during the offline training phase. Another example using the probabilistic method is [76], which estimates user location through best matching with a pre-recorded radio map of observed light intensities and signal patterns of modulated LED beacons that contains unique transmitter IDs.  
In [77], the fingerprinting scheme estimates the position of a mobile device by comparing the received time samples of impulse responses to the collected power samples from the transmitters, known a priori, in the environment. In [78], the localization system exploits the signal extinction ratio distribution of LEDs. Each LED transmits its identification code in time division multiplexing in order to distinguish it.The receiver's position is then estimated by comparing the received extinction ratio to the reference collected during the training phase.
## 3 Results
### Validation of Results
#### 3.3.2 Simulation  
To confirm and validate our experimental results, we conducted a rigorous simulation using the ANSYS platform, specifically employing the computational fluid dynamics (CFD) module. The parameters essential for running this simulation are meticulously outlined and summarized in the table provided below, offering a comprehensive view of the key factors and settings utilized in our analysis.  
Figure 11 shows the mesh convergence for different arrangements. It can be concluded that values below 125 um were suitable for an accurate simulation result. Additionally, the image shows the element quality obtained in the model.  
Figure 12 displays the simulation results, which serve as a visual confirmation of the validated mixing process achieved through our software-driven analysis. These results provide a tangible and illustrative representation of the intricacies of the mixing phenomenon, reinforcing the reliability and accuracy of our computational assessments. The 850 um width and 800 um deep tank size ensure the most efficient mixing process. According to the results, it achieves a mix index of 055 considering the fluid at the main channel.  
Figure 13 provides a visual representation of the results obtained from the mixing process within the tank of the device. This image encapsulates the culmination of the mixing operation, showcasing the intricate interplay of the fluids within the tank. It offers insights into the dynamic behavior of the substances being mixed, highlighting the effectiveness and efficiency of the mixing mechanism employed. By observing the distribution and interaction of the fluids within the mix tank, valuable information can be gleaned regarding the homogeneity, consistency, and thoroughness of the mixing process. Figures 13 and 14 clearly show the effectiveness of the simulation process compared with the experimental test.  
Figure 10: MATLAB analysis with color filter tool.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Pilot Studies Investigating Satellite Imagery in Support of Emergency Management
#### 4.3.1 Snow and Extreme Cold Incidents
Therefore, in both VV and VH data, the pixels classified as \"No Snow\" indicate areas not identified using the SAR analysis but are classified as snow pixels in the MODIS data (i.e., misclassified when using MODIS NDSI as a reference). From this, MODIS NDSI identified 7407 pixels as cloud-free snow pixels, with 32% of these also being identified by the differences in the VV channel and 56% by the VH channel. Whilst VH had better performance of the two, the results as to which polarisation  
Figure 4: High-level overview of snow and extreme cold processing pipeline. channel more effectively distinguishes the snow change are somewhat inconclusive, as the number of misclassified SAR-snow/MODIS-no-snow pixels cannot be adequately assessed due to the lack of any MODIS-no-snow pixels in the imagery. Validating change analysis is challenging due to the lack of cloud-free reference images. An alternative is using ground-based weather stations, but historical data are hard to acquire, and stations are sparse outside metropolitan areas. The snow extent decreased significantly between 14 February 2021 and 20 February 2021, as shown in VV and VH images presented in Figure 7. Although MODIS data could ultimately not validate these snow and ice cover maps, they demonstrate the potential of Sentinel-1 to indicate the areas of persistent snow cover and melt areas. Further work and validation of these results could be useful in determining the spatial patterns describing where snow cover builds and subsides over time. Figure 5: VV and VH change detection on 8 February 2021 with a threshold of \(-2\) dB. Figure 6: Snow pixel change detection: **(a)** from VV polarisations; **(b)** from VH polarisations. Figure 7: Snow extent VV and VH polarisations: **(a)** 14 February 2021 (note limits of image scene) and **(b)** 20 February 2021.
## 3 Results
### QPE Improvement
#### 3.2.1 Rainfall Mapping
In the first map (Figure 6a), the rainfall amounts were estimated on the basis of the Marshall-Palmer relationship (Equation (8)) The map indicates that the simple Marshall-Palmer-based approach resulted in overestimation of the true precipitation amounts, at approximately 85% of the stations. The highest rates of the overestimation (with the radar/gauge ratio \(r_{RG}\) exceeding 2.0) can be found in the southern parts of the country where lowland character dominates. Ratios with the most acceptable values (\(0.8<r_{RG}\leq 1.2\)) as well as underestimation can be found at a considerably smaller number of sites (Table 1). A completely different picture of the radar/gauge ratios can be found in the second map (Figure 6b), where the radar-based rainfall was estimated by means of a calibration using the true rainfall measurements from the rain gauge network, but with no quality information included. For the calibration, a completely different data set was used. Here, 40% of the sites are associated with an acceptable radar/gauge ratio. The most remarkable drawback of this figure is that, at more than half of the sites (predominantly in the NW, N, and NE, i.e., generally in the hilly and mountainous parts of the country), the true rainfall is underestimated. The number of cases with overestimation is practically negligible. Finally, the third map (Figure 6c) shows the results of a similar analysis where both the calibration and the quality information were used in the procedure of the radar-based rainfall estimation. The dominance of the green color indicating the most acceptable radar/gauge ratios (-72% of all cases) justifies the added value of the QIs in the QPE (Table 1). The percentage of sites with over- and under-estimated rainfall is the lowest among the three different approaches.
###### Abstract
except for fuel [32]. An aqueous methanol solution is supplied to the anode side, where it splits into electrons and protons. Protons in the form of H\({}^{+}\) pass through the membrane while electrons pass through the external circuit. Pure or moist oxygen is supplied to the cathode side, where it combines with protons and electrons. The byproducts are water and extract heat [33]. The type of membrane usually used in DMFC is Nafion. The overall chemical reactions are provided in Table S1. An anion exchange membrane fuel cell (AEMFC) operates in an alkaline environment, where hydroxide ions (OH\({}^{-}\)) move through the membrane, unlike proton exchange membrane fuel cells (PEMFCs) that rely on protons. AEMFCs consist of an anode, cathode, and membrane, and typically operate at 25-90 \({}^{\circ}\)C [34], though research is ongoing for high-temperature AEMFCs (>110 \({}^{\circ}\)C) [35; 36] [Figure 2]. Fuel, such as hydrogen or methanol, is oxidized at the anode, producing water, electrons, and heat, while oxygen at the cathode reacts with water and electrons to form hydroxide ions. Unlike PEMFCs, the ion flow in AEMFCs is reversed, moving from cathode to anode. Hydroxide ion transport occurs via two mechanisms: the Grotthuss mechanism, where OH\({}^{-}\) ions \"hop\" through hydrogen-bonded water molecules, and vehicular movement, where hydrated OH\({}^{-}\) ions diffuse through membrane channels. These mechanisms work together, with the Grotthuss mechanism dominating at low hydration and vehicular movement dominating at high hydration. However, challenges such as carbonation, dehydration, and chemical instability in alkaline conditions hinder performance. Optimizing membrane hydration, ion exchange capacity, and temperature is critical for improving AEMFC efficiency and conductivity.
## 4 Extension to the HS and LS Method  
In this section, we extend the idea above to the HS and LS method. The corresponding method is called the ZHS method and the ZLS method in which \(\beta_{k}\) is respectively defined by  
\[\beta_{k}^{ZHS}=\frac{g_{k}^{T}y_{k-1}}{max\{\mu\|d_{k-1}\|\ \|y_{k-1}\|,d_{k-1}^{T}y_{k-1} \}},\beta_{k}^{ZLS}=\frac{g_{k}^{T}y_{k-1}}{max\{\mu\|d_{k-1}\|\ \|y_{k-1}\|,-g_{k-1}^{T}d_{k-1}\}} \tag{29}\]  
where \(\mu>0\). It is obvious that \(\beta_{k}^{ZLS}=\beta_{k}^{ZPRP}\) since \(g_{k}^{T}d_{k}=-\|g_{k}\|^{2}\). Hence, we now only need to discuss the global convergence of the ZHS method.  
The following theorem shows that the ZHS method converges globally with the Wolfe line search (6).  
**Theorem 3**: _Let Assumption 1 hold. Consider the ZHS method and \(\alpha_{k}\) is obtained by the Wolfe line search (6), then_  
\[\lim_{k\rightarrow\infty}\|g_{k}\|=0. \tag{30}\]  
Proof of Theorem 3.: Suppose by contradiction that the conclusion is not true. Then there exists a constant \(\epsilon>0\) such that \(\|g_{k}\|>\epsilon,\forall k\geq 1\). From (29), it follows that  
\[|\beta_{k}^{ZHS}|\leq\frac{\|g_{k}\|\ \|y_{k-1}\|}{\mu\|d_{k-1}\|\ \|y_{k-1}\|}=\frac{\|g_{k}\|}{\mu\|d_{k-1}\|}. \tag{31}\]  
By (11) with \(\beta_{k}=\beta_{k}^{ZHS}\), we can get that  
\[\begin{array}{rcl}\|d_{k}\|&\leq&\|g_{k}\|+|\beta_{k}^{ZHS}|\ \|d_{k-1}\|+|\beta_{k}^{ZHS}|\ \frac{\|g_{k}\|\ \|d_{k-1}\|}{|g_{k}^{T}y_{k-1}|}\|y_{k-1}\|\\ &\leq&\|g_{k}\|+\frac{\|g_{k}\|}{\mu}+\frac{\|g_{k}\|}{\mu}\\ &=&(1+\frac{2}{\mu})\|g_{k}\|.\end{array} \tag{32}\]  
Hence, combing with (17),  
\[\sum_{k=0}^{\infty}\|g_{k}\|^{2}\leq(1+\frac{2}{\mu})^{2}\sum_{k=0}^{\infty} \frac{\|g_{k}\|^{4}}{\|d_{k}\|^{2}}<\infty. \tag{33}\]  
which leads to a contradiction. The proof is completed.  
The following result shows that the ZHS method with the Armijo line search (9) possesses global convergence.  
**Theorem 4**: _Let Assumption 1 hold. Consider the ZHS method and \(\alpha_{k}\) is obtained by the line search (9), then_  
\[\liminf_{k\rightarrow\infty}\|g_{k}\|=0. \tag{34}\]  
Proof of Theorem 4.: The proof is similar to the proof of the global convergent property of the ZPRP method given in Theorem 2 in this paper. We omit it here.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 3 Situation and Strategy
### Simulation Situation  
In order to investigate the performances of LEO constellation augmented GNSS RTK positioning with different length, we selected 19 Continuously Operating Reference Stations (CORS) run by U.S. National Geodetic Survey (NGS) at the mid-latitudes, which can provide the fixed station coordinates and support to track the real GPS constellation for ground-based observation simulation. The corresponding locations of the simulated stations are shown in Figure 2, mainly in 30-48\({}^{\circ}\)N region. As can be observed, these mid-latitude CORS stations distributed at different distances constitute short, medium and long baselines. The small scale map presents the area where the five stations make up short baselines, while the larger one covering a wider area and more stations provides the possibility of forming medium-to-long baselines. And the specific lengths of all baselines are also given in Table 3. Furthermore, considering that the low operating altitude of LEO satellites can cause the fast motion with respect to the ground stations in a relatively short period, the sampling interval of simulating observations is set 1 s in order to best analyze the effect of LEO satellites for GPS RTK positioning.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Orbit** & **LEO** & **LEO** & **LEO** & **LEO** \\ \hline Satellite Number & 60 & 96 & 192 & 288 \\ Altitude[km] & 1000 & 1000 & 1000 & 1000 \\ Constellation & 10 planes \(\times\) 6 & 12 planes \(\times\) 8 & 12 planes\(\times\) 16 & 12 planes\(\times\) 24 \\  & satellites & satellites & satellites & satellites \\ Inclination[deg] & 90 & 90 & 90 & 90 \\ \hline \end{tabular}
\end{table}
Table 1: Detailed orbital configurations of low earth orbit (LEO) constellations.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Slot** & **RAAN** & **Argument of Latitude** & **Slot** & **RAAN** & **Argument of Latitude** \\ \hline A1 & 272.847\({}^{\circ}\) & 268.126\({}^{\circ}\) & D1 & 92.847\({}^{\circ}\) & 135.226\({}^{\circ}\) \\ A2 & 272.847\({}^{\circ}\) & 161.786\({}^{\circ}\) & D2 & 92.847\({}^{\circ
## 4 Results
### Particle Characterization  
ZnO-NPs were shaped like spheres and rods with a mean diameter of 34 nm according to the manufacturer's declaration. The dispersion was analyzed by DLS. Using cell culture medium, the mean agglomeration size was 48 nm with a second population of large agglomerates measuring a diameter of more than 5000 nm. The size of ZnO NP agglomerates in human plasma was 59 nm. High variations in agglomerate size were seen in both groups. Single ZnO-NPs were visible by TEM within the cytoplasm sparing the nucleus in both groups, DMEM-EM and plasma. In the DMEM-EM group, larger counts of particles (4.2 single particle per slice) were seen compared to the plasma group (1.6 single particles per slice). The zeta-potential of ZnO-NPs was \(-\)11.2 mV (Figures 3 and 4).  
Figure 4: ZnO-NP disposition. TEM analysis of hMSCs cultivated with DMEM-EM (**A**) and plasma (**B**) after ZnO-NP exposure. In both pictures, ZnO-NP can be detected within the cytoplasm.  
Figure 3: ZnO-nanoparticle (NP) characterization. The dispersion was analyzed by DLS. The sizes of ZnO-NPs in plasma and DMEM-EM were evaluated. In both groups, high variations in agglomerate size could be detected.
# Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand
## 4 Algorithm Design
### Simulated Annealing--Ant Colony Hybrid Algorithm  
Figure 2 shows the principle of ant colony algorithm coding. As shown in Figure 1(a), in a general transportation network, there is only one mode of transportation between the nodes. Whereas, in multimodal transportation networks, the node coding used by the traditional ACO algorithm is no longer applicable due to the possibility of multiple modes of transportation between neighboring nodes. Therefore, it is necessary to convert the multimodal transportation node codes to general network node codes. For the case in Figure 1(b), each transportation mode of node 2 is encoded as a new node. In this way, the ACO algorithm can be used in the converted network. The converted nodes are shown in Figure 1(c), where H denotes highway, R denotes railroad and A denotes aviation.  
In the ant colony algorithm, due to the positive and negative feedback, the ants cannot find the globally optimal path, resulting in the algorithm easily erring towards the locally optimal solution and then the problem of \"premature convergence\". However, by adding the simulated annealing mechanism into the ant colony algorithm, the algorithm can effectively prevent the problem of \"premature convergence\" and obtain a better convergence speed. The specific improvements are as follows: when the ants complete a cycle and generate a new solution based on the optimal route, the generation strategy is to randomly select two neighboring nodes in the generated optimal route, change the transportation mode between them and leave the rest unchanged.
# Machine Learning Applications for Short Reach Optical Communication
## 1 Introduction
Compared with [1], our paper reviews the recent developments of ML-based techniques in short-reach communications instead of introducing various impairments and related theories that affect the transmission of short-reach systems. Compared to [82], our paper not only covers ML-based equalization techniques in short-reach communications, but also introduces the application of ML in OPM, MFI, and in-building and indoor positioning. The following is a list of our contributions:  
* Review the state-of-the-art of short-reach optical communication systems and networks including inter-datacenter networks, intra-datacenter networks, optical access networks, mobile front-haul communications, and in-building and indoor optical wireless communications. * Comprehensively review ML-based OPM and MFI related research in short-reach optical communications, including the comparison of the different ML techniques, how the OPM task is formulated and the ML technique is utilized, and our analysis of, advantages and limitations of these studies. * Comprehensively review the recent progress on ML-based signal processing techniques in short-reach optical communications, including ML-based equalization techniques as well as other ML-based signal processors, such as auto encoder/decoder, digital pre-distortion and soft-demapping applications. * Review the ML-based applications in in-building/indoor OWC systems, including the ML-based signal processing techniques, and the application of ML algorithms in indoor user positioning. The remainder of this survey is organized as follows. In Section 2, we first describe and compare different ML-based OPM and MFI techniques. Then, we discuss the applications in direct detect and coherent short-reach optical communications in two subsections. In Section 3, we provide a review of recent progress on ML-based signal processing techniques for short-reach optical communications, including different ML-based linear and nonlinear equalizers, auto-encoder, digital predistortion and soft demapping. In Section 4, we review and discuss recent ML-based applications in in-building/indoor OWC systems, including both wireless signal processing and user positioning. Finally, in Section 5, we summarize this paper and provide our views on the potential future research directions.