esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 2 Related Work
[42] developed a method using social media data from Reddit to train a novel QuadArm model based on BERT to answer questions, aiming to identify COVID-19 cases and automatically extract reported symptoms. These NLP-based approaches typically identify patient severity by searching for similar results (e.g., words or documents) from text-based unstructured data. However, these approaches face significant challenges due to the peculiarities of medical terminology. Medical terms can be ambiguous, appearing in various forms, such as synonyms, homonyms, and abbreviations, which can vary in meaning depending on the context [43; 44]. For example, the term '_cold_' can refer both to a common viral respiratory infection and a general feeling of low temperature, leading to the potential misinterpretation of patient symptoms. Similarly, the abbreviation '_MI_' might be used to denote '_myocardial infarction_' (i.e., a heart attack) in one context, while in another, it could stand for '_mitral insufficiency_' (i.e., a valve disorder). Moreover, the presence of spelling errors, grammatical inaccuracies, and colloquial abbreviations can hinder the models' ability to accurately identify patient severity or lead to incorrect word extractions. To overcome these drawbacks and present a generalized model for predicting COVID-19 clinical severity, we performed severity prediction using a large patient dataset obtained from the KCDC and Korea University Ansan Hospital. To tackle the challenge of collecting clinical variables and implementing early triage, we utilized the epidemiological variables of patients based on categorical and numerical values, such as body mass index (BMI), body temperature, heart rate, blood pressure, and existing underlying diseases. These variables are readily available and can be quickly obtained at the time of patient admission. Moreover, considering the potential limitations of collecting patient data in real-world clinical settings, we grouped the clinical variables using data-driven strategies. We then evaluated the performance of various models using different combinations of these variable groups. We also analyzed important clinical variables using feature selection methods based on wrapper methods, including forward selection, backward elimination, and recursive feature elimination with cross-validation (RFECV). To improve performance, we addressed the issues of irregular sampling and data imbalance through missing value imputation and oversampling techniques. Finally, we presented a flexible machine learning model capable of accurately predicting the clinical severity of COVID-19 patients across various severity levels by employing an adaptive stacking ensemble technique with multi-layer structures, which helps reduce the risk of model bias.
## 2 Denoising Technique Comparison
In Figure 1, we generated two random signals with the same standard deviation, and they are, respectively, represented by blue and red lines. The black line is the average of the two signals, whose magnitude is significantly decreased compared to each of the original signals. In general, we can come up with a mathematical relationship between the noise level reduction and the sample size for averaging. Assume we have \(N\) numbers of Gaussian white noise samples with the standard deviation \(\sigma\). Each sample is denoted as \(z_{i}\), where \(i\) represents the \(i^{th}\) sample signal. Therefore, we can acquire:  
\[var(z_{i})=E({z_{i}}^{2})=\sigma^{2} \tag{2}\]  
where \(var(\cdot)\) is the variance, \(E(\cdot)\) is the expectation of the signal, and \(\sigma\) is the standard deviation. Figure 1: An example of noise level reduction by image averaging. By averaging \(N\) Gaussian white noise signals, we can write:  
\[var(z_{\text{avg}})\ \ var(\frac{1}{N}\sum_{i=1}^{N}z_{i})\ \ \frac{1}{N^{2}}N \sigma^{2}=\frac{1}{N}\ \sigma^{2}=\frac{1}{N}\ \sigma^{2}=\left(\frac{1}{\sqrt{N}} \sigma\right)^{2} \tag{3}\]  
where \(z_{\text{avg}}\) is the average of \(N\) noise signals. Equation (3) demonstrates that a total number of \(N\) samples is required to reduce the signal noise level by \(\sqrt{N}\). Since our goal is to reduce the image noise within a fixed threshold (represented by a constant standard deviation), a smaller deviation in the original image requires much fewer samples to make an equivalent noise-reduction estimation. Thus, it is worthwhile for the camera to move around, rather than being stationary, to find the best locations where the image noise level estimation is small. In general, we can reduce the noise level as much as needed by taking more samples. A comparison of the above denoising methods is presented in the following: In Figure 2, a default picture from MATLAB R2023b is polluted with Gaussian-distributed white noise with an SNR equating to 5 dB. A spatial filter (Gaussian filter), a wavelet filter (the wavelet threshold method), and the image-averaging technique (1000 average sample) were applied to denoise the noisy image. Figure 1 shows all techniques succeed in reducing the noise to some extent, while the wavelet filter and the image-averaging technique perform better than the spatial filter regarding denoising. The difference between the wavelet filter and the image-averaging technique is not obvious in Figure 2 but becomes more significant in the frequency domain. Figure 2: Original image, noisy image, and denoised images. Figure 4 shows the image generated from the Gaussian filter has a lower power intensity than that of the original image with high frequency, which indicates that a lot of high-frequency data in the original image has been filtered out as well. Although it performs better than a Gaussian filter, the wavelet filter still loses some details of the original image. Only the averaging method can reduce the noise level and retain all the information.
## 3 Application and Validation of MuLiWEA
### Simulated Dual-Doppler Lidar Measurement Scenarios
The positions of the lidars should be chosen in order to have an appropriate relative azimuth angle \(\Delta\chi\), i.e., the angle between the line-of-sight direction of the scanner of the different lidars, as to reduce the numerical uncertainty of the estimated wind vector. [PERSON] et al. [32] provide a thorough analysis on the effect of \(\Delta\chi\) on the dual-Doppler lidar error. Based on that work, a general recommendation is to avoid having two lidars pointing in the same (\(\Delta\chi\approx 0^{\circ}\)) or opposite direction (\(\Delta\chi\approx 180^{\circ}\)), since linearly dependent measurements are performed that way. The inclusion of the continuity equation in MuLiWEA is intended to mitigate this issue. Four different lidar placements are considered in the simulations, which could be combined to form both favourable and unfavourable measurement setups. A top view sketch of these lidar positions together with their corresponding simulated PPI scan sectors can be seen in Figure 3. The wind turbine is located at (0, 0), and its wake is roughly indicated with a black trapezoid. The volume scan is illustrated by the side view in Figure 4. Specific altitude bounds have to be selected to determine which measurements are taken into account to give an estimate of the wind speed at hub height. Therefore, a dimensionless parameter \(C_{h}\) is defined, such that the altitude range is calculated by \(h_{h}\pm C_{h}\frac{D}{2}\). The volume scan and the single elevation scan have \(C_{h}\) values of 0.3 and 0.5, respectively. For both measurement scenarios, a radius of influence \(R\) of 6 m is used. These two parameters have been carefully defined through an error optimisation [33], the demonstration of which lies beyond the scope of this paper. Since the \(R\) value equals 1.5 times the grid resolution, the circles with radius of influence (see Figure 2) around different grid points overlap.
## 3 Proposed Methodology
### Cluster Head Selection
#### 3.2.2 TCH Selection Based on Trust Value  
To identify node behaviour, node quality, and node services, Trust Value (TV) is utilized. Additionally, it is utilized for sensor node routing, reconfiguration, and data aggregation. It offers a method for calculating the reliability of SNs. In this study, the trust value is employed to gather information and keep track of various node activities. Trust value is utilized to locate potential CH along with Energy based Timer (EBT). To maximize the effectiveness of optimal CH selection, tentative CH selection uses the EBT and TV techniques. The trust value of nodes is determined using Equation (18) below.  
\[Trust\ Value(TV)_{nodes}=\frac{N_{FD}}{N_{REC}} \tag{18}\]  
where NFD stands for the quantity of packets forwarded, and NREC for the quantity of packets received. The node with the greatest trust value is chosen as the temporary cluster leader after the trust values of each individual node are determined. The final CH process is then carried out. The outcome of the TCH selection is finally returned by the EBT and the TV.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 3 Preliminaries and Problem Formulation
### LBS Query  
An LBS query \(Lq\) is defined as \(Lq=(u_{id},\{(x,y),C,V\})\), where \(u_{\text{id}}\) denotes a user's identity; \((x,y)\) represents the user's location information, \(x\) and \(y\) represent latitude and longitude, respectively; \(C\) denotes the user's query content; \(V\) is the user's privacy preservation level.  
However, since the LBS provider may be malicious, the user's location will be disclosed if the user directly sends \(Lq\) to the LBS provider. To preserve the location privacy, dummy locations method is used to preprocess \(Lq\). Hence, \(Lq\) is transformed to \(Lq^{\prime}\) as \(Lq^{\prime}=(u_{\text{id}},\{(x,y),(x_{1},y_{1}),\ldots,(x_{k-1},y_{k-1}),C,C_ {1},\ldots,C_{k-1},V\})\), where \((x_{1},y_{1}),\ldots,(x_{k-1},\)\(y_{k-1})\) are \(k-1\) dummy locations, \(C_{i}\) represents the query content sent at dummy location \((x_{i},y_{i})\), \(i=1,2,\ldots,k-1\).  
From \(Lq^{\prime}\), the adversary cannot determine the user's real location from \(k-1\) dummy locations. By this way, the vehicle location privacy can be protected.
## 3 Methods and Data Sources
### Regression Model Building and Testing  
#### 3.2.1 Construction of Machine Learning Regression Models  
The regression model indicators are derived from economic and demographic aspects. Five factors--sumNTL, registered population (in ten thousand), per capita disposable income, per capita consumption expenditure, and resident population--were selected to construct the influencing factor system of the regression model. Precise data from 2012 to 2020 across 16 administrative districts in Beijing were collected, totaling 144 entries. Subsequently, a ten-fold cross-validation method was employed; this is a commonly used machine learning model evaluation technique that divides the original dataset into ten subsets. Nine subsets are utilized for training the model while one subset is reserved for testing it. This process is repeated ten times with each subset serving as the test set once; the average result of these ten iterations serves as the final evaluation outcome.  
The advantages of this method include:  
1. Maximizing data utilization: By employing the entire dataset for model evaluation, maximum utilization of data can be achieved, leading to more reliable assessment results.
2. Reducing evaluation error: Via repeated testing and averaging results, ten-fold cross-validation minimizes evaluation errors and yields more precise assessment outcomes.
3. Assessing model generalization ability: Ten-fold cross-validation allows for an examination of a model's generalization ability--that is, its performance on unseen data. Testing different data subsets provides a comprehensive evaluation of model performance.
4. Avoiding overfitting: This approach helps mitigate overfitting issues where models perform well on training sets but poorly on test sets by evaluating them across various subsets.  
Figure 1: Changes in nighttime lighting brightness in Beijing districts (2012–2020).  
Furthermore, this study conducted tuning work on various characteristics of each model (such as hyperparameters like decision tree count and classification status in random forests). These parameters significantly influence the accuracy of model operation; thorough tuning efforts yielded favorable results in predicting waste generation volumes.
# DisA Limits RecG Activities at Stalled or Reversed Replication Forks
###### Abstract  
The DNA damage checkpoint protein DisA and the branch migration translocase RecG are implicated in the preservation of genome integrity in reviving haploid _ Bacillus subtilis_ spores. DisA synthesizes the essential cyclic 3\({}^{\prime}\), 5\({}^{\prime}\)-diadenosine monophosphate (c-di-AMP) second messenger and such synthesis is suppressed upon replication perturbation. _In vitro_, c-di-AMP synthesis is suppressed when DisA binds DNA structures that mimic stalled or reversed forks (gapped forks or Holliday junctions [HJ]). RecG, which does not form a stable complex with DisA, unwinds branched intermediates, and in the presence of a limiting ATP concentration and HJ DNA, it blocks DisA-mediated c-di-AMP synthesis. DisA pre-bound to a stalled or reversed fork limits RecG-mediated ATP hydrolysis and DNA unwinding, but not if RecG is pre-bound to stalled or reversed forks. We propose that RecG-mediated fork remodeling is a genuine in vivo activity, and that DisA, as a molecular switch, limits RecG-mediated fork reversal and fork restoration. DisA and RecG might provide more time to process perturbed forks, avoiding genome breakage.  
DNA repair; stalled fork; RecG; DisA; c-di-AMP; template switching +
Footnote †: journal: Cells  
10
## 3 Experimental Results and Discussion
### Single-Pass Sum-Frequency Generation (SFG) to 852.3 nm
The solid squares represent output power of 852.3 nm, while the circles represent the corresponding SFG conversion efficiency. When we fix the 1878.0 nm laser and scan the 1560.5 nm laser, we can obtain cesium absorption spectra by Cs atomic vapor cells. Then, we analyze the transmitted signal of a confocal Fabry-Perot cavity with a free spectral range (FSR) of 750 MHz while the 852.3 nm laser frequency is linearly scanned. Figure 9 shows a typical result, where the 852 nm laser can be tuned across more than 12 FSRs, which means that the continuously tunable range is at least 9.2 GHz. The scanning range is mainly limited by the DFB. If the scanning range of the fundamental frequency laser is sufficiently large, the sum-frequency 852.3 nm laser can also be scanned following. This is another advantage compared with cavity-enhanced scheme.
## 4 Results
### Regression Analysis Result
The meaning of this number refers to the degree to which the cause-and-effect variables are explained. Sig. of ANOVA tells whether the regression equation itself is significant [38]. That is, when Sig. is \(p<0.05\), it can be said that the regression equation is significant after analysis is performed. Table 6 satisfy the criteria for statistical significance. The results of the regression analysis in Table 6 are as follows. The antecedent variable Convenience (H1), which is expected to have a positive (+) effect on the Perceived value, was rejected. Usefulness (H2) and Enjoyment (H3) were adopted. However, Anthropomorphism (H4) was rejected. Among the negative antecedent variables, the hypothesis that Technical difficulties (H5) and Burden of expenses (H6) would negatively affect the Perceived value was rejected. However, the hypothesis that security threats (H7) will negatively affect Perceived Value is adopted, so the security problem of artificial intelligence speakers in the future is a factor to be resolved. The hypothesis that information reliability (H8) will also have a negative (-) effect on the Perceived Value was adopted, indicating that the reliability of information provision needs to be supplemented. Technical difficulty (H5) was rejected as an antecedent variable that could negatively affect perceived value, and cost burden (H6) and reliability of information (H8) were all rejected. In other words, it can be seen that the use of artificial intelligence speakers is not an improvement. However, it was adopted that the threat of security (H7) would have a negative impact. In other words, it can be said that improvement is necessary. First of all, the opposite results were derived from the facts that could be known through user interviews prior to this study. In the pre-interview, several people said that they recognized that there were many things that could not be answered or given a fixed answer as to the reliability of the information. It was found that there is no burden in paying the cost by dividing it into a monthly fee as an additional service of a telecommunication company rather than as a direct purchase. In terms of technical difficulties, it is somewhat inconvenient, but it can be said that it is satisfactory to search for desired content, such as voice recognition. However, there are still no technical difficulties, but it is not at the level to deliver convenience. In particular, with regard to security, it can be seen that personal information transmitted through the network and the connection between peripheral devices can be hacked, and improvements are needed for things that can cause malfunctions. Finally, in Table 7, the hypothesis (H9) was adopted that perceived value would have a positive effect on recommendation intention. In other words, it is concluded that the currently used artificial intelligence speaker provides usefulness and enjoyment, has no technical difficulties and costs, and has reliable information. However, it can be seen that security threats need improvement.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 2 Operation Mechanism and Design Principles of Edge Couplers
### Performance Metric Parameters and Design Principles of Edge Couplers
In summary, it is intuitive to exploit some basic design principles toward high coupling performance according to the operation mechanism and structure of edge couplers. Silicon photonic integrated circuits are compatible with CMOS fabrication process. As depicted in Figure 2b, typical silicon photonic waveguides are fabricated on the standard silicon-on-insulator (SOI) wafers where silicon waveguides lie on the buried silicon dioxide (BOX) layer with a thickness of two to three microns and are coated with a silicon dioxide (SiO\({}_{2}\)) top cladding. Underneath the BOX layer is a silicon substrate with a thickness over 700 microns [40]. As for the commonly used commercial optical fiber that has been revealed in Figure 1, it is composed of two parts, namely a fiber core and the cladding. Light transmits through the optical fiber following the total internal reflection (TIR) principle, and the fiber core has a slightly higher refractive index than that of the outer cladding. The key factor to achieve high coupling efficiency is to reduce the modal mismatch between the fiber core and the Si photonic edge coupler facet regarding aspects of both the modal size and the mode effective index. The basic structure of an edge coupler based on a standard single inverse taper can convert the mode within a rather large length of several hundred microns, which is not compact and efficient enough for most applications. Hence, it is necessary to transform the structure of an elementary edge coupler in horizontal or vertical dimensions to achieve better coupling performance. In following two sections, different types of horizontally and vertically transformed edge couplers are presented from aspects of their configurations, operation mechanisms, fabrication feasibility, as well as characteristics and limitations. To have a consistent coordinate system definition in this review paper, structural transformations in the horizontal direction and vertical direction refer to structure modifications in the XY plane and YZ plane, respectively. Figure 2: (**a**) Propagation of transverse electric (TE) and transverse magnetic (TM) mode; (**b**) layer composition of Si photonic circuits in a silicon-on-insulator (SOI) platform.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 5 Assessment of Combined GPS/LEO RTK for Different Length Baselines  
With the aforementioned characteristics of the LEO constellation augmented GPS RTK positioning, the RTK computations for 15 different length baselines, see Table 3, are conducted in this section, just like 'P339-P341' baseline scheme. Afterward, we made simple statistics on the average convergence time and TTFF for each baseline type, and analyze the influences of LEO constellations on short-, medium-, and long-baseline solutions, respectively. Furthermore, it is noteworthy that  
Figure 5: Average convergence time, time to first fix (TTFF), and fixing ratio of GPS RTK solutions in combination with different LEO constellations, for ‘P339-P341’ baseline scheme.  
Figure 6: The 1-h sky plots (azimuth versus elevation) of GPS with the augmentations of different LEO constellations: (**a**) GPS+60 LEO; (**b**) GPS+96 LEO; (**c**) GPS+192 LEO; (**d**) GPS+288 LEO. Circular markers indicate the satellite (GPS in blue, LEO in red) positions at the initial epoch.  
the distinguishing atmospheric processing strategies must be taken into account for achieving better positioning performance under different circumstance.
## 3 Background and Previous Related Work
### Dlt
#### 3.3.7 Privacy-Enhancing Techniques  
Cryptographic techniques are also used to preserve privacy in DLT. Some examples include:  
1. Zero-knowledge proofs (ZKPs): ZKPs enable users to establish the authenticity of a statement without disclosing additional details. Examples of ZKP systems used in DLT are zk-SNARKs (used in Zcash) and zk-STARKs.  
2. Confidential transactions: These techniques hide transaction amounts or other sensitive data. Examples include Pedersen commitments and Bulletproofs (used in Monero and Mimblewimble-based protocols).
3. Ring signatures: Ring signatures obscure the sender's identity in a transaction by making it indistinguishable from other users in the same group. An example of a ring signature implementation in DLT is CryptoNote (used in Monero).  
These cryptographic techniques and mechanisms form the foundation of security, privacy, and data integrity in DLT systems. As the technology evolves, new cryptographic techniques may be developed and adopted to address emerging challenges and enhance the capabilities of DLT.