esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 2 The Impact of a Jammer on DLL Noise
From this figure, it is clear that the \(Q\) for the CBOC modulation is always higher than the \(Q\) for the BPSK signals. Note, however, that \(Q\) in this figure is independent of \(T_{c}\), so the fact that \(Q\) is always higher does not indicate that all CBOC GNSS signals are less affected by jamming than BPSK signals, as the \(T_{c}\) might be different. This is clearly visible in Figure 1b, where the \(T_{c}/Q\), as it appears in (3), is given for the GPS C/A, GPS P(Y), and Galileo E1-OS signals versus the bandwidth of an AWGN jammer. In this figure, a higher \(T_{c}/Q\) indicates a greater impact of the jammer. Clearly, the C/A code is most affected by the jammer, while the P(Y) code, although a BPSK signal, performs best because of the 10-times higher chip rate (smaller \(T_{c}\)). Upon observing Figure 1a, it also becomes clear that the CBOC modulation performs differently compared to the binary offset carrier (BOC) modulation, despite the fact that the spectra of both signals are quite similar (see Figure 2a). The difference in performance can be explained by the slight difference in the distribution of the power over the spectrum. The power of the CBOC modulation is spread out over a larger bandwidth, which is visible by the additional power at a frequency offset of around 6 MHz. As long as the power of the received signal within the bandwidth of the jammer is smaller for the CBOC signal, the signal will be less affected by the interference. The difference in bandwidth is also visible in Figure 2b, where the normalized received power of the signal is given as a function of the receiver's front end bandwidth. At a bandwidth of 10 MHz, the received power of a CBOC signal is slightly over 85%, while the received power of a BOC(1, 1) signal is around 93%. Even at a bandwidth of over 30 MHz, the difference is still around 1.5%. Finally, when looking at Figure 1a, it is notable that the impact of a jammer is maximized by minimizing the bandwidth of the jammer while transmitting at the frequency of the spectral maximum of the target signal. Further, for the (C)BOC signals, the impact of a jammer at the central frequency with a relatively small bandwidth (<0.5 MHz) is, in comparison to the other signals, small. This can be explained by the absence of signal power around the central frequency (see Figure 1a). If the same jammer is transmitting at the frequency of the spectral maximum, the impact is greater, though not as great as for the BPSK signals. Most jammers, however, do not transmit at a narrow bandwidth, as this may be filtered out by, for example, a notch filter. Once the impact of the jammer on the \(C/N_{0}\) is known, the impact on the accuracy of the DLL can be calculated using (1) and/or (2). Figure 1: (a) The jamming resistance quality factor \(Q\) for BPSK, BOC(1, 1), and CBOC as a function of the normalized bandwidth of the jammer. The larger the \(Q\), the smaller the impact of the jammer. (b) \(T_{c}/Q\) as function of the bandwidth of the jammer. The larger the \(T_{c}/Q\), the larger the impact of the jammer.
## 1 Introduction
NLCs combine two features: they are colloidal carriers of submicron size and they are composed of a solid-lipid core consisting of a mixture of solid lipids blended with a liquid lipid to form a nanosized unstructured matrix and an aqueous phase containing a surfactant or a mixture of surfactants. The partially solid nanostructure enhances the stability of entrapped bioactive, enables high loading capacities, and offers sustained release profiles. The lipid matrix is made of biocompatible and biodegradable materials, which decrease the risk of acute or chronic toxicity. All the components are commercially available and/or approved by regulatory agencies [22; 23; 24; 25]. Silymarin (SLM) is one of the oldest traditional herbal medicines used to treat different disorders. SLM is mainly composed of four flavonoidignan isomers: silybin, isosilybin, silychristine andsilydianinin [26; 27]. Hepatoprotective effects of SLM have been demonstrated. It is mildly toxic and exerts significant anti-carcinogenic and anti-inflammatory effects. Recently, its beneficial effect in type 2 diabetes patients has been reported and a number of articles demonstrated its ability to decrease both fasting and mean daily glucose, triglycerides and total cholesterol levels [28; 29]. Antidiabetic effect of SLM is attributed to inhibition of gluconeogenesis in the liver and decrease of glucose-6 phosphatase activity [30]. However, it is a low water soluble and low permeable biopharmaceutic class IV type compound that faces stipulated requirements in delivery design. The aim of the study was to investigate the possibility to delivery lipophilic SLM into NLCs for the preparation of oral dosage form. NLCs were characterized in terms of average diameter, polydispersity, \(\zeta\)-potential, morphology, and physical and chemical stability. The free-drying process was also considered to increase long-term storage stability of the formulation. Storage stability and in simulated gastrointestinal conditions was also assessed. SLM in vitro release was studied in different pH media. The release results were applied to define the kinetic and mechanism of SLM release from the NLCs. The ability of NLCs to ameliorate the permeability of SLM was evaluated in vitro by using parallel artificial membrane permeability assay (PAMPA). Fluorescent nanoparticles were also prepared to verify the capacity of NLCs to enhance the permeability across Caco-2 cells layer. Preliminary cytotoxicity studies were conducted to confirm the optimized biopharmaceutical properties of the lipid carrier. The data obtained from the in vitro tests were compared to elucidate the permeation/absorption mechanism of SLM, which further could be correlated to in vivo behavior. Caco-2 cellular uptake, with or without endocytic inhibitors, was also evaluated, to identify the possible mechanisms of the internalization pathway of the developed NLCs.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 2 State of the Art on Digital Twins for Intelligent Transportation Systems
### Digital Twins in Autonomous Vehicles  
Another important application of digital twins is to speed up the development and testing of autonomous vehicles. The work presented in [22] identifies a gap between virtual environments and self-driving platforms and proposes a solution by introducing a Hardware-in-the-Loop (HiL) simulation system. To achieve this, the same Electronic Control Units (ECUs) found in self-driving vehicles are used in the closed loop of offline simulation. As a result, the offline simulation performs very similarly to the actual autonomous vehicle, and algorithms are implemented more quickly into self-driving vehicles. The study uses decision-making and path planning algorithms without machine learning.
## 3 Our Proposed FNNS  
### FNNRW Learning Algorithm  
Feed-forward neural networks have been widely used in many fields. FNNRWs are generally described as:  
\[f(x)=\sum_{i=1}^{N}\beta_{i}g(\omega_{i}\cdot x+b_{i}), \tag{1}\]  
where \(N\) is the number of hidden nodes; \(x=[x_{1},x_{2},\cdots,x_{n}]^{T}\in R^{n}\) is the input; \(\omega_{i}=[\omega_{i1},\omega_{i2},\cdots,\omega_{in}]\in R^{n}\) and \(\beta_{i}\in R\) are the input and output weights connecting the \(i\) hidden node and the output node, respectively; \(b_{i}\in R\) is the bias; \(g(\cdot)\) is the activation function; and the activation function generally adopts the common sigmoid function, as shown in Equation (2).  
\[\text{g}(x)=\frac{1}{1+e^{-x}}. \tag{2}\]  
Presented with a collection of practice data \(s=\{(x_{j},t_{j}):x_{j}\in R^{n},t_{j}\in R,j=1,2,\cdots,M\}\), satisfies expression \(H\beta=T\), where  
\[H=\left[\begin{array}{ccc}g(\omega_{1}\cdot x_{1}+b_{1})&\cdots&g(\omega_{N }\cdot x_{1}+b_{N})\\ \vdots&\cdots&\vdots\\ g(\omega_{1}\cdot x_{M}+b_{1})&\cdots&g(\omega_{N}\cdot x_{M}+b_{N})\\ \end{array}\right], \tag{3}\]  
is the hidden-layer output matrix, \(\beta=[\beta_{1},\beta_{2},\cdots,\beta_{N}]^{T}\) is the output weights, and \(T=[t_{1},t_{2},\cdots,t_{M}]^{T}\) are the target vectors.  
In FNNRWs, as shown in Figure 1, input weights and biases are distributed uniformly at random, \(\omega\sim U(\omega_{min},\omega_{max})\), \(b_{i}\sim U(b_{min},b_{max})\). The well-known least-squares approach may be used to analytically compute the output weights:  
\[\min_{\beta\in R^{N}}\Big{\{}\|H\beta-T\|_{2}^{2}\Big{\}}, \tag{4}\]  
which gives \(\beta=(H^{T}H)^{-1}H^{T}T\). The least squares issue is typically poorly phrased, though. So we can use the \(l_{2}\) regularization method to solve this kind of problem, i.e.,  
\[\min_{\beta\in R^{N}}\Big{\{}\|H\beta-T\|_{2}^{2}+\mu\|\beta\|_{2}^{2}\Big{\}}, \tag{5}\]  
where \(\mu>0\) is a regularization factor. If \(\mu\) is given such that \(H^{T}H+\mu I\) is invertible, then the minimizer of Equation (5) is easily described as:  
\[\beta=(H^{T}H+\mu I)^{-1}H^{T}T, \tag{6}\]  
where \(I\) represents the identity matrix.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 1 Introduction
The inverse relationships between the \(GWT\) and \(R_{e}\), where a low \(GWT\) is related to a high \(R_{e}\), were demonstrated by the flux tower observation data in Palangkaraya of Kalimantan Province, Indonesia [26]. Previous studies have investigated the temporal and spatial distribution of CO\({}_{2}\) emissions from peatlands via bio-geophysical modeling and long-term CO\({}_{2}\) flux observations [27; 28; 29; 30]. [PERSON] et al. [13] discussed uncertainties and research needs in this field, e.g., accurate peatland area information, peat thickness distribution, drainage area classification, groundwater depths, and CO\({}_{2}\) emission rates. Another source of uncertainty in determining the CO\({}_{2}\) emissions from a terrestrial ecosystem on a peatland is in anthropogenic emissions, such as fire emissions (FE) [31]. Apart from the \(R_{e}\) that is related to the decomposition of soil and surface moisture conditions, FE contribute to drastic increases in CO\({}_{2}\) emissions [32]. The loss of biomass and the combustion of peat soils are huge sources of CO\({}_{2}\) emissions due to the high ratio of organic matter in peat soil [33]. Finally, the GPP, such as the moderate resolution imaging spectroradiometer (MODIS) GPP product [34], is required to balance the CO\({}_{2}\) budget. In this study, we established an empirical bio-geophysical model with inputs of satellite-based datasets to reveal the dynamics of the CO\({}_{2}\) budget of tropical peatlands. The ultimate goal of this study was to better understand the net carbon budget in the tropical peatland ecosystems via the more accurate estimated carbon balance components created by using satellite observations. The specific objectives of this study were (1) to estimate the spatial and temporal \(GWT\) of tropical peatlands in Indonesia including Borneo of Malaysia; (2) to compute fire emissions by using physically estimatedaboveground biomass, soil organic matter (SOM), and thermal anomaly data from MODIS; (3) to calibrate the satellite-based GPP of MODIS by fitting it to the flux tower-observed GPP; and (4) to integrate emission (\(R_{e}\), FE) components and an absorption (GPP) component into a CO\({}_{2}\) budget of target peatland areas.
## 4 State of Art about Data Availability in L2 Rollups
### Off-Chain (Validium Mode)  
Off-chain data availability, also known as validium mode, is an alternative option in the rollup architecture, designed to ensure that critical transaction data are available for state transition verification without being stored on-chain. Depending on cost and security tradeoffs, it may be suitable to store the information off-chain and employ other techniques to ensure data availability. Off-chain data storage can significantly increase the transaction throughput. Offloading the data storage to an off-chain solution leads to low data footprints on L1, which can significantly increase the scalability and performance of rollups. The number of transactions can reach up to 9000 per second or more. Moreover, shifting data availability to off-chain can significantly reduce the Gas cost associated with on-chain data storage, making transactions cheaper for end users.  
For instance, zk-SNARK-based rollup Polygon CDK [34] provides both options to store the transaction data off-chain or on-chain. Polygon CDK off-chain data availability ensures that state transitions can be verified without extensive on-chain storage by storing transaction data off-chain and submitting zk-SNARK proofs to the Ethereum mainnet. Similarly, Arbitrum Orbit [40] is an optimistic rollup that leverages an off-chain data availability mechanism for storing transaction data. Arbitrum batches transactions and stores the data off-chain to maintain the integrity and security of the rollup and prevent the Ethereum mainnet from overloading. This ensures that the required data for state verification are available during the challenge period.  
Data Availability Committees (DACs) and Data Availability Layers (DALs) are the most prominent techniques to ensure off-chain data availability in rollups.
## 4 Discussion
Therefore, mapping the Sao Jacinto sea outfall turbid plume catchment area corroborates the findings of [PERSON] et al. (2018), which states the plume's dominant dispersion direction is southward. The observations derived from the high-resolution true color images showed that, depending on the meteo-oceanographic conditions and on the lagoon's flow rate, the sea outfall's plume could exchange water with the coastal lagoon. These results corroborate those of [PERSON] et al. (2018), who suggested that the float concentration released by the outfall reached the adjacent critical zones during flood tides. [PERSON] et al. (2018) suggested that the dark color of the plume is due to the intense color of pulp mill industry wastewater, which accounts for more than 90% of the flow discharged. During this investigation, offshore transport of the wastewater plume was not observed at the surface as might be expected following the upwelling shelf circulation in that region. Commonly, the transport was to the south, in the direction of the inlet channel, or with an onshore component. The inshore dispersion of the waste-water plume observed here anticipates a possible release of pollutants into the inlet of the Aveiro coastal lagoon. In contrast, previous work by [PERSON] et al. (2018) suggested that the lagoon's eutrophication is unlikely to change due to the operation of the submarine outfall. Future improvements to this study could be achieved by running a retrospective numerical simulation of the sea outfall plume that allows for a comparison of the results obtained with available wastewater plume image data to predict their dispersion patterns. Exploring this numerical model in operational mode would permit simulation of the coastal environment when satellite data face restrictions that limit their applicability. Moreover, for validation proposes, in situ sampling should be planned to quantify the biogeochemical water parameters around the sea outfall, the Barra inlet channel, and beaches nearby. Additionally, it is essential to bring to light that the monthly mean area dataset may not be reliable, as satellite data were not available for some months. However, the objective of this study was to show the academic/scientific community that high-resolution satellite technology can be applied to support Sao Jacinto sea outfall plume detection. This methodology is helpful to evaluate whether the water exchange between the marine outfall and those ecosystems can threaten the adjacent environments or alter water quality, affecting the population's health and seafood quality; however, it is not a substitute for field measurements.
# Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
## 5 Results and Discussion
### Heartbeat Detection
At this point, recall that the training of HDM is conducted following an _intra-patient_ approach. In this way, the KNN classifier is trained for the morphology of each individual patient. Therefore, it is natural to expect that the results are improved. However, this implies that the KNN has to be trained for each specific patient and no global training data can be used. In future work, we plan to build classifiers that are able to be applied to new patients without using labeled data from them.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 3 Parameters to Be Considered for the Design and Analysis of a Flexible Antenna
### Radiation Pattern and Radiation Efficiency  
A flexible antenna for 5G needs a directional radiation pattern for applications such as cellular networks, base stations, global positioning systems (GPSs) etc., so that the radiation of the antenna becomes more concentrated in only some directions than in others. Applications, including wireless body area networks incorporate antennas with directional radiation patterns, such that the radiation should be focused on a particular direction. However, an omnidirectional radiation pattern is required in some specific flexible devices incorporating technologies, including Bluetooth, Wi-Fi, etc. Furthermore, another important factor that contributes to the performance of the flexible antenna is the radiation efficiency. It is the metrics that describe how efficiently the antenna transmits and receives the radio frequency signals. The radiation efficiency of the flexible antenna should be at acceptable level for the smooth working of a flexible antenna in 5G applications.
## 3 Numerical Simulation and Analysis
### Verification of the Collapsing Bubble Near the Solid Surface  
In this section, the pseudopotential MRT LB with the fluid-solid interaction is used to investigate the collapsing bubble near the solid surface. The simulations are verified compared with the experimental results.  
In present work, the computational domain for the collapsing bubble near the solid surface is established in 700 \(\times\) 700 lattice system which is shown in Figure 3. The open condition [31] is applied in the top boundary and the periodic boundary conditions are employed in a vertical direction. The bottom boundary uses the nonslip boundary scheme. The density field is the same as that in the Equation (16). In Figure 3, \(R_{0}\) represents the radius of the bubble and \(b_{0}\) denotes the distance between the center of the bubble and the solid surface. The dimensionless position offset parameter \(\lambda_{0}=R_{0}/b_{0}\) which represents the ratio of the radius to the distance between the bubble and the solid surface, was adopted to compare the dynamic process of cavitation bubbles collapsing near the solid surface in different cases [32]. The parameters \(p_{\infty}\) and \(p_{\circ}\) represent the ambient pressure and the vapor pressure in the bubble, respectively.  
In the simulation, the initial \(R_{0}\) and \(\lambda_{0}\) were 80 and 1.3, respectively. The constant pressure difference \(\Delta p=p_{\infty}-p_{\circ}\) was 18.334 to ensure the bubble collapse. The density ratio was 500 with \(\rho_{l}=500\) and \(\rho_{\circ}=1\). The parameter \(G_{w}\) in fluid-solid interaction was 0 for this case [33].  
Figure 3: The computational domain for the collapsing bubble near the solid surface.  
Figure 2: The contact angle with different \(G_{w}\) (hydrophobicity: contact angle \(\theta>90^{\circ}\), hydrophilicity: contact angle \(\theta<90^{\circ}\) ).  
Figure 4 shows a comparison between the numerical and experimental results. It is clearly shown that the numerical results were in good agreement with the experiment. The spherical bubble shrank from the top pressure in the initial state. As time passes, the sunken region of the bubble grows, and, in the final stage, the jet of the bubble will form when the bubble is collapsing.
## 2 Materials and Methods  
### Participants  
This study used CBCT records found in the resources of the Department of Maxillofacial Orthopaedics and Orthodontics of the Wroclaw Medical University. CBCT images of 67 patients (35 women and 32 men) aged 13 to 49 years were analysed. CBCT scans taken between 2017 and 2021 prior to orthodontic treatment were used as part of the orthodontic diagnostic process.  
The criteria for inclusion of the patient's CBCT records in the study group were as follows:  
1. Complete development of the upper incisor roots.
2. Clearly visible maxilla.
3. Patients with permanent dentition.
4. Without a history of orthodontic treatment.
5. Generally healthy patients with no history of bone diseases. Exclusion criteria:
1. Missing maxillary incisor(s).
2. Maximillary incisor pathology.
3. Developmental anomalies of the incisive canal (additional canals in the area of the incisive canal, as well as clear asymmetries).
4. Poor quality of CBCT, preventing accurate measurements.
5. The presence of imaging artifacts hindering measurements in the anterior maxillary region.
6. Incomplete root development.
7. Root resorption due to trauma or inflammation.
8. History of previous orthodontic treatment.  
The participants were classified into groups according to sex and age. Four age groups (16-18 participants) have been created:  
1. 13-20 years.
2. 21-29 years.
3. 30-39 years.
4. 40-49 years.  
Ethical approval from the Bioethical Committee of the Wroclaw Medical University was granted for our study (nr KB-231/2021, accessed on 19 March 2021), providing that all the data were anonymised (as was the cases in our study). In accordance with the decision of the bioethics committee, patients were comprehensively informed about the assumptions, risks, consequences for the body, and benefits of the study. Patients received a full written justification and information that they could withdraw from the study at any time without any consequences for their participation in the comprehensive orthodontic treatment process. Patients under 18 years of age as well as their parents signed with informed consent to participate in the study.  
Patients over the age of 13 were included in the study because the treatment of patients of this age requires the use of fixed appliances, which are used to carry out precise, large-scale tooth movements. Significant tooth movements during treatment with fixed appliances significantly affect the possibility of roots of the central incisors contacting with the incisive canal. In treatment with removable appliances at an earlier age, such significant shifts are not made, which is associated with a lower risk of contact of the roots of the central incisors with the incisive canal. Therefore, the potential benefits of such diagnostics outweigh the health costs associated with exposing younger children, even before their growth spurt, to the significant risks from absorbed radiation associated with CBCT projection.