esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Sensitivity of Parameters and Model Calibration
#### 4.2.1 Effect of Number of SPs  
SPs are released on boundaries and domains uniformly, according to the underlying mesh, as defined by a grid. Usually, a particle simulation suffers from too many particles in the total system [59]. Since the SP size can only be increased to a specific limit, the high number of SPs released in each channel can render the computation slow and costly. Varying the weights of the SP by annihilation and cloning reduces the non-uniformity in the computational particle number density [60,61]. A similar scheme of reducing the SP quantity balances the computation model cost and speed. The use of fewer SPs can be compensated for by a constant current scaling factor on the total current collected from the channels, thus mitigating the effect of SP quantities in the system [59,60].  
After the optimization, the flow channel contained 4031 superparticles, and for each carbon particle in an SP, 0.69154 positive ions were present for double-layer charging. Increasing the number of SPs would decrease the mass of the individual SP, facilitating efficient movement. Hence, more collisions could improve the charging of electrodes over time. Doubling the SPs also doubled the availability of positive ions for the charging process (Figure 8). This change in ion availability was much more prominent at higher voltages, supporting better carbon electrode utilization. The change in \(\beta_{Na}\) showed an almost linear relationship with increasing constant voltage, suggesting that only double-layer charging would be expected in a circular flow channel.
# Hemodynamic Analysis on the Anastomosis Angle in Arteriovenous Graft Using Multiphase Blood Model
## 1 Introduction
Therefore, the anastomosis angle is determined according to which vein is selected as the outflow vein and in which configuration the artificial graft is inserted. As mentioned above, wall shear stress is an important hemodynamic factor in AVG that induces stenosis. Therefore, it is important to precisely analyze the hemodynamics, including WSS, according to the anastomotic angle of AVG. Blood viscosity is a physical characteristic of blood that most directly affects WSS calculations by computational fluid dynamics. Among the various components of blood, plasma has Newtonian fluid characteristics; however, various blood substances, such as red blood cells, change the blood into non-Newtonian fluid with shear-thinning characteristics [21; 22]. Figure 1: Configuration of the forearm loop access. Either superficial veins located in the antecubital fossa (**A**) or the basilic vein right above the elbow joint (**B**) can be selected as an outflow vein. When comparing with the antecubital veins as the outflow, the basilic vein above the elbow joint provides an almost parallel course to the graft. (Arrow: venous anastomosis). Blood models, such as the Pow-law model, Casson model, and Carreau-Yasuda model, express the shear-thinning characteristics of blood well. However, because blood is assumed to be a bulk fluid, it has a limitation in that it does not consider local hematocrit changes according to the flow. Few blood models have been proposed to account for the effect of hematocrit on blood viscosity. [PERSON] et al. [23], based on the Quemada model, presented a blood model that considers the viscosity according to the concentration of red blood cells and the viscoelastic effect using the Oldroyd-B model. However, this model also has a limitation in that it is difficult to consider the local concentration change according to the red blood cell because blood is assumed as the bulk fluid. [PERSON] and [PERSON] presented a multiphase blood model using the multiphase computational fluid dynamics approach proposed by [PERSON], [PERSON], and [PERSON] [21]. This model assumed that the blood was plasma, white blood cells, and red blood cells. In addition, the multiphase blood model shows good accuracy within the range of 30-55% hematocrit. Since the multiphase model calculates the behavior of red blood cells, it has the advantage of considering the local concentration of red blood cells according to the flow. In particular, hematocrit is a factor that significantly affects blood viscosity, and the hematocrit increases dramatically during hemodialysis because the dialyzer removes water during hemodialysis. In addition, during hemodialysis, blood with a high hematocrit flows into a vein through the AVG. The flow into the vein through the AVG is affected by the angle of the anastomosis. Furthermore, hematocrit should be changed by the flow. Therefore, in this study, numerical analysis was performed using a multiphase blood model to consider the hemodynamic characteristics of AVG according to the anastomotic angle formed in the vein.
# New Insights into the Neuromyogenic Spectrum of a Gain of Function Mutation in SPTLC1
## Materials and Methods
### Coherent Anti-Stokes Raman Scattering (CARS) and Statistical Evaluation of Muscle Fiber Calibres  
5 \(\upmu\)m cryosections were generated for the coherent anti-Stokes Raman scattering (CARS) microscopy and stored at \(-80\)\({}^{\circ}\)C. For CARS and second harmonic generation (SHG) measurements, samples were dried under a constant nitrogen gas flow at room temperature and no further sample preparation was applied.  
All spectroscopic measurements were performed on a modified Leica TCS SP 8 CARS laser scanning microscope [13]. Generated CARS signals were detected in forward direction. A 40\(\times\) water immersion objective (IRAPO 40\(\times\)/1.10 WATER) was used for CARS imaging. CARS spectra were acquired for a field of 291 \(\times\) 291 \(\upmu\)m (2048 \(\times\) 2048 pixel) by tuning the pump laser from 804.0 nm to 826.4 nm with a step size of 0.7 nm, a pixel dwell time of approximately 10 \(\upmu\)s, and averaging of two images.  
Leica software LAS X (Version 2.0) was used for manual determination of the muscle fibers from the FCARS images. For cross-sectioned muscle samples, the length and width of fully imaged fibers was determined, and the caliber averaged from the two values. A total of 155 fibers were analyzed for the SPTLC1 patient. As reference, 1457 fibers were analyzed from five control samples.
## 2 Security and Privacy Challenges in IoE
### Next-Generation Attack Vectors in IoE
#### 2.2.2 Quantum Computing-Based Security Threats  
Traditional security mechanisms rely on asymmetric cryptography and hash functions. However, quantum computing technologies pose a threat to these cryptographic methods, with the potential to break encryption algorithms. In particular, traditional cryptography methods such as RSA and ECC are vulnerable to quantum attacks [66, 67]. For instance, [PERSON]'s and [PERSON]'s algorithms can facilitate the breaking of current IoE security protocols.  
[PERSON]'s Algorithm has the potential to render widely used cryptographic systems like RSA and ECC ineffective [66]. Authentication and data security mechanisms used in IoE systems could be compromised by such quantum attacks. On the other hand, [PERSON]'s Algorithm weakens hash-based security measures, posing a threat to authentication processes in IoE devices [68]. These threats represent a major risk, especially for blockchain-based IoE systems.  
As a solution, new-generation security approaches such as PQC and Lattice-Based Cryptography are recommended [69, 70].
## 3 Results and Discussion  
### Characterization with Shaped XUV Pulses  
The reflectivity characterization of the uncoated and CsI-coated steel surface is shown in Figure 6. Comparing the XUV reflectivity ratio for three different XUV spectra shows a good agreement between calculations using the tabulated values and evaluations of the Channeltron signals. The difference between calculation and measurement is approximately 3%. This confirms that the ratio of the two signals is a sensitive probe of the spectral shape of the incident XUV radiation.  
Moreover, the very good agreement between the theoretical calculations and the measurements is also a clear indication that the spectral response of our XUV spectrometer equipped with a CCD camera must be rather flat. Therefore, using a shaped spectrum and a set of two different mirrors allow for a quick check of the correct spectral response of the XUV measurement equipment.  
Figure 5: A typical HHG (high-harmonic generation) spectrum from a hollow-core fiber is shown in (**a**). By temporally shaping the driving laser pulse, a specified region of the XUV spectrum (blue area) can be enhanced (**b**) or suppression of harmonics (blue arrow) can be achieved (**c**).
## 2 Materials and Methods  
### Soil Gas Monitoring  
#### 2.1.1 Gas Sampling  
To monitor soil gas concentration, we used arrays of passive gas samplers which are similar to the sampler of [PERSON] et al. [78]. The gas samplers consist of a pipe made of polyvinyl chloride (PVC) which is closed at the lower end, a perforated stainless-steel pipe that leaves the bottom of the PVC pipe in an angle of 90\({}^{\circ}\) and a fine cannula at the bottom inside the PVC pipe (Figure 1, right). Gas vials can be introduced into the PVC pipe from the top and mounted onto the cannula which pierces the septum. The air inside the vial and the soil air around the perforated stainless-steel pipe equilibrate with time. A PVC cap is used to close the top of the sampler.  
Small shallow trenches (10-20 cm deep) were carefully due to install the soil gas samplers. The stainless-steel sampling pipe were then be gently inserted into the undisturbed wall of the trench at the requested depth. The trenches were then filled up again restoring the layering of the soil. The collection of soil air samples always started at least six months after installation, so that the roots could grow back and installation artefacts were minimized. This kind of installation and gas samplings allows a very accurate localization of the depth of the soil gas sampling. Several soil gas samplers per depth were installed at 10 cm, 5 cm, and 0 cm depth, to have a sufficient number of replications, with 0 cm being the interface between mineral soil horizon and the organic layer. Depending on the thickness of the organic layer further samplers were installed at 3 to 2 cm above 0 cm depth. Additionally, two modified samplers allowed taking gas samples of the local atmosphere at 5-10 cm above the soil surface. The gas samplers were attached to a solid rectangular aluminum frame (3 by 4 m) that was firmly anchored in the ground. Our design included five replicates per depth to allow the quantification of the uncertainty in the estimation of the soil gas concentration gradient.  
Before the gas vials were installed in the gas sampler in the field, they were filled with helium (He, ambient pressure) in the laboratory. The gas vials remained in the sampler until the next sampling date. Diffusive exchange and equilibration via the cannula and perforated sampling pipe requires some hours [78]. Thus, the sampled gas in the vial represented an average concentration of the last day. Measuring all gas species with concentrations greater than 0.5% (N\({}_{2}\), argon (Ar), O\({}_{2}\), and possibly CO\({}_{2}\)) allowed for checking if the equilibration between vial and soil gas was accomplished. When soils were very wet, all soil pores were possibly saturated with water, and soil gas did not equilibrate with the gas volume in the sampling vial. In such a case a substantial quantity of He remained in the gas vial and the sum of N\({}_{2}\), Ar, O\({}_{2}\), and CO\({}_{2}\) was less than 90%. Such data were flagged, and data with less than 60% exchange were excluded.
## 3 Results and Discussion
### Zeta Potential and DLS Characterization  
Zeta potential and DLS measurement of nanoparticles are appropriate for characterizing nanoparticles and identifying the particle size and surface charge of the fabricated AgNPs.  
Figure 3a shows the AgNPs' hydrodynamic size distribution as determined by DLS. AgNPs' average size was determined to be 231.8 nm. Zeta potential measurements were used to test the colloidal stability of the AgNPs, as shown in Figure 3b, where the obtained value of \(-44.5\) mV indicates a high level of stability. Because of the formation of a hydrodynamic sphere around particles in aqueous media, which leads to a larger interpretation of the particle diameter when analyzing the scattered light in a DLS measurement, the mean size of the nanoparticles obtained by DLS tends to be larger than the values obtained from electron microscopy. It is believed that the zeta potential values reported above 30 mV in magnitude demonstrate that the nanoparticle sample is basically stable concerning colloidal stability [45].
### Results Overview  
Based on the above research, this paper proposes an intelligent recognition algorithm for earthquake emergency information based on a BERT pretrained language model to extract earthquake emergency information from text more efficiently and accurately. The algorithm is based on the BERT model for the feature representation of earthquake information in text and uses a bidirectional neural network combined with a self-attention mechanism to solve the problems of contextual semantic information in long texts, the varying length of input vectors, and the varying length of earthquake emergency texts.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
interfered by other wireless signals and undesirable environmental constraints and events. A packet with a mismatched checksum is regarded as a corrupted packet and is dropped by the network node. In other words, in traditional networking that adopts end-to-end retransmission, the destination node can receive a packet only when this packet is correctly transmitted through all network links. This probability diminishes exponentially with the number of hops.  
The widely adopted reliable communication protocol, TCP, is not designed for wireless communications. For example, packet loss in wireless communications may not be caused by congestion but can be attributed to interference and temporary shadowing. However, such a packet loss event will trigger the TCP congestion control mechanism and, as a result, reduce the transmission rate. Variations of TCP such as ATCP [12] were proposed to handle relevant packet loss scenarios. Advanced technologies such as Aspera [13] will only send feedback and retransmission requests when a packet is lost. However, these TCP-alike protocols are still end-to-end protocols. In other words, the default store-and-forward strategy is applied at the intermediate network nodes and an end-to-end retransmission is adopted. Any packet can reach the destination node only if it has not been lost at any network link. In a multi-hop wireless network (with packet loss), the end-to-end retransmission approach may further degrade the system performance due to the high chance of losing retransmitted packet at one of the lossy links.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using the JASP 0.18.1 software. The level of statistical significance was set at \(p<0.05\) for all tests. Initially, the normality of the data was checked using the Shapiro-Wilk test, which was appropriate for our sample size. The total number of burpee repetitions was performed using a repeated-measures ANOVA, with the tDCS condition (i.e., DLPFC, M1, and SHAM) as a within-subjects factor. If significant differences were detected, post-hoc comparisons with Bonferroni adjustment were performed to control for type I error due to multiple comparisons. For HR, RPE, and SmO\({}_{2}\), linear mixed models were used to examine variations across different time points (0%, 20%, 40%, 60%, 80%, and 100%) under each tDCS condition. This analysis would allow the assessment of both the main effects of time and condition as well as their possible interactions. Control variables, assessed by the POMS, BAI, and PSQI questionnaires, were analyzed by a repeated-measures ANOVA to explore differences in mood, anxiety, and sleep quality across sessions. In addition, the effect size was calculated using \(\eta_{p}^{2}\) and Cohen's \(d\), thus providing a measure of the effect size of the different tDCS conditions.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 7 Discussion and Evaluation
### Security Challenges of Using the Banking Card for Identity Transactions  
SSBI introduces the banking card to sign identity transactions. This raises additional security concerns related to the applet and APDU data exchange. Here, we present a set of these concerns alongside their corresponding mitigations:1. Malware attack by capturing the PIN or passphrase during customer entry--this is possibly mitigated using a secure keypad instead of a standard device keypad.
2. Presenting the banking card to sign the identity transaction is vulnerable to a change in the data package sent to the card by an attacker--this can be mitigated by establishing a secure messaging layer [55] based on a session key and securing all APDU data exchange.
3. The identity applet installed on the banking card is vulnerable to unauthorized updates or re-installation--this is mitigated by the default use of access to a chip key, which is a 3 DES key securely managed by the chip manufacturer and shared only with the issuer bank. Additionally, the issuing script performs key rotation to lock the Java Card state to avoid further installations.
4. Another layer of authentication could be a smart card with an embedded biometric fingerprint sensor to achieve a better user experience and security.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods
### Transmitter and Receiver Patterns  
The power of the received signal at the receiver is mainly determined by the GNSS transmitter antenna gain and the receiver antenna gain. A GPS Antenna Characterization Experiment (GPS ACE) reconstructed the transmit gain pattern for GPS blocks IIA, IIR, IIR-M, and IIF [4]. Since existing studies have not found a model for block III-A, we used the gain pattern of block IIA instead. The BDS transmitting antenna gain pattern was reconstructed using TJS-5 measurements by [PERSON] et al. [12]. However, there is no publicly available antenna pattern data for GLONASS and Galileo [5]; thus, the GPS Block IIF satellite antenna pattern was used instead. The values of the GNSS transmitting gain with off-boresight angle is shown in Figure 2.  
We assume that the spacecraft is equipped with a nadir antenna capable of acquiring multiple GNSS signals for the receiver antenna simulation. The received signal power is faint due to the high free space loss during GNSS signal transmission to the Moon. In this case, only high-gain antennas are able to receive weak signals, and following [5], the gain pattern is depicted in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Band** & **Frequency (MHz)** \\ \hline GPS & L1 & 1575.42 \\ BDS & B1I & 1575.42 \\ GLONASS & L1 & 1602 1 \\ Galileo & E1 & 1575.42 \\ \hline \hline \end{tabular}  
* In this simulation, the GLONASS frequency band is simplified to 1602 MHz due to each GLONASS satellite transmitting signals at a different frequency.  
\end{table}
Table 1: Band frequency for each GNSS system.  
Figure 3: Gain pattern of the receiver antenna.  
Figure 2: The transmitting gain patterns of the GNSS satellite antennas.
# Using Machine Learning Language Models to Generate Innovation Knowledge Graphs for Patent Mining
## 3 Methodology
### Graphical Patent Summarization
#### 3.3.1 Text Summarization  
After obtaining the vector of sentences, the quantified sentences can be compared for similarity to obtain the importance of the sentences, and the important sentences can be extracted from them. This study uses the LexRank algorithm [48] to calculate the importance of the sentences, which is a graph-based summarization method that uses the concept of a community network to obtain the importance of the sentences and the relationship between the sentences in an article, with the more important sentences having a higher centrality in the graph. The LexRank algorithm determines the importance of a single sentence, based on the number of other sentences linked to it. The greater the number of sentences linked to a single sentence, the greater the weight of the sentence. The LexRank algorithm is expressed in Equation (1).  
\[p(u)=\frac{d}{N}+(1-d)\sum_{v\in\mathit{adj}[u]}\frac{\mathit{Cosine similarity}(u,v)}{\sum_{z\in\mathit{adj}[z]}\mathit{Cosine similarity}(z,v)}p(v) \tag{1}\]  
where \(N\) represents the total number of sentences, \(d\) is the damping factor, \(\mathit{adj}[x]\) represents the set of nodes adjacent to node \(x\), and \(p(x)\) is the centrality of node \(x\).  
The LexRank algorithm uses the inverse document frequency (idf) modified cosine value to calculate the sentence-to-sentence similarity. However, this approach is still susceptible to the effect of sentence embedding due to the sentence length, which affects the final similarity calculation. Thus, Sentence-BERT is used to obtain the sentence vectors for the calculation of the residual similarity, and these vectors are used for the calculation of the residual similarity to increase the representativeness of the embedded vectors to the sentences, which are applied to the subsequent LexRank calculation, i.e., Equation (2).  
\[\mathit{Cosine similarity}=\frac{A\cdot B}{||A||||B||}=\frac{\sum_{i=1}^{n}A_{i} \times B_{i}}{\sqrt{\sum_{i=1}^{n}(A_{i})^{2}\times\sum_{i=1}^{n}(B_{i})^{2}}} \tag{2}\]  
where \(A_{i}\) denotes the component of vector \(A\), and \(B_{i}\) denotes the component of vector \(B\).