esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis
#### 2.5.1 Comparison with Camera-Based Measurements  
To evaluate the accuracy and reliability of the two GNSS-based methods (Method 1 and Method 2), we compared their _SL_, _SF_, and per-step _running velocity_ estimates with those obtained from the camera-based measurements. Due to the limited number of steps per subject and track section which could be fully captured within the calibrated areas, data were pooled across both subjects, both legs, and both track sections. For the _SF_ (\(n=20\); 2 subjects \(\times\) 2 sections \(\times\) 5 steps), five central steps per subject per section were analyzed to minimize edge effects, while the _SL_ and _velocity_ analyses (\(n=16\); 2 subjects \(\times\) 2 sections \(\times\) 4 steps) focused on steps entirely within the calibrated zones. This difference in sample size reflected the spatial constraints of the calibrated areas. We computed the root mean square error (_RMSE_), mean error (_bias_) with 95% confidence intervals (CIs), SD of differences, Pearson's correlation coefficient (_R_), and the intraclass correlation coefficient (ICC). Bland-Altman plots were generated to evaluate agreement and identify systematic biases, while scatter plots were used to visualize the relationships between the GNSS- and camera-derived measures. The strength of the correlations was interpreted according to the following guidelines [42]. Pearson correlation coefficients (_R_) of 0.90 and above were considered excellent, 0.70-0.89 were considered strong, 0.50-0.69 were considered moderate, 0.30-0.49 were considered weak, and below 0.30 were considered negligible. The reliability assessed by the ICC was interpreted based on the following criteria [43]. ICC values less than 0.50 indicate poor reliability, values between 0.50 and 0.75 indicate moderate reliability, values between 0.75 and 0.90 indicate good reliability, and values greater than 0.90 indicate excellent reliability.
## 2 Instrumentation Methods
### Engine Inlet Instrumentation  
The engine inlet (bellmouth) is instrumented to measure the engine air flow. The mass air flow (\(M_{af}\)) is defined as the multiplication of density (\(\rho_{s}\)), velocity (\(V\)) and cross-section (\(A\)) [9], where the density and velocity are functions which depend on multiple variables, such as: temperature, pressure and the Mach number.  
\[M_{af}=\rho_{s}(p,T)\cdot V(M,T)\cdot A;\ M_{af}=\frac{p_{s}}{R_{a}\cdot T_{s}} \cdot\frac{M}{\sqrt{k\cdot R_{a}\cdot T_{s}}}\cdot A \tag{1}\]  
According to the density (\(\rho_{s}\)) and velocity (\(V\)) mathematical definitions [9], the parameters that are possible to be measured are established, which are used in the density and velocity computation. One method, as presented in Figure 1a, is to instrument the bellmouth only for static pressure measurement (\(p_{s}\)), in order to measure the differential pressure (\(DP\)), to use the test cell air pressure (\(p_{0}\)), in order to determine the air velocity (\(V\)), and then to measure the test cell air temperature (\(T_{0}\)) which is used to determine the air density (\(\rho_{s}\)). The cross-section (\(A\)) is determined using the bellmouth diameter (\(D\)).  
Another method, which is more accurate, as presented in Figure 1b, is to instrument the bellmouth for static pressure measurement (\(p_{s}\)), for total pressure (\(p_{t}\)) and total temperature measurement (\(T_{t}\)), with approximately the same cross-section.  
The method presented in Figure 1a requires fewer parameters on the inlet and, based on the approximation of the pressure loss coefficient on the bellmouth, it results in a higher error. The method presented in Figure 1b uses the measured parameters directly and eliminates the error that is introduced by the approximation of the pressure loss coefficients, and, as a result, it has a lower error. It is to be mentioned that any method presents an error of measurement that is introduced by the instrumentation probes.
## 2 A Theoretical Review of Feature Selection Methods
Commonly used correlation-based methods are the Pearson, Spearman, and Kendall methods, as well as the mutual information (MI) method. The Pearson correlation is the most widely used filter method for measuring the linear relationship between two variables. The Spearman and Kendall correlation methods, which employ non-parametric tests, are better suited for non-normally distributed data [18]. The degree of correlation between two variables is measured by the Spearman correlation, whereas the Kendall method measures and computes the interdependency between features [19]. Hence, the [PERSON] method is more accurate feature at identifying dependencies and correlations in relation to the target variable than Spearman method. [19]. Spearman correlation can be effective for non-linear and non-time series data but shows poor results in the domain of MTS problems. Another study used a feature selection method based on Pearson correlation and symmetrical uncertainty scores to compute non-linear and linear interactions between features and the target variable [20]. In this context, a correlated but important feature may be overthrown, leading to the wrong conclusion. Another well-known feature selection method is the mutual information (MI)-based method, which measures the uncertainty of random variables, termed Shannon's entropy [21]. A recent feature subset selection method based on merit score, also implemented by [PERSON] and [PERSON] [22], was used to identify relevant features in the MTS domain. In general, correlation-based feature selection (CFS) techniques are not effective in MTS problems [23]. As a CFS technique requires input streams in the form of vector representations, it may result in the loss of important information during vectorization. Recently, importance-based feature selection methods have been used in different domains, including the MTS domain. Commonly used methods are random forest (RF)- and extreme gradient boosting (XGB)-based feature importance ranking methods. For example, [PERSON] et al. [24] used the random forest method with Gini feature importance ranking to identify relevant features related to PUE prediction. However, these methods suffer from a high frequency and cardinality of features. This paper establishes SHAP value-assisted relevant feature selection for effectively modeling and characterizing DC operations.
## 3 System Model
### Mechanism Description
Based on the wakeup control mechanism, the state swtching process of the CH can be described in Figure 3.  
Combining Figures 2 and 3, we can summarize the access and forwarding behavior of two kinds of packets in each time slot as Figure 4.  
As shown in Figure 4, when there is an incoming emergency packet, if the CH is in the sleep state at this time, this emergency packet will directly access the CH for data forwarding and the CH will enter working state (if the CH stores a non-emergency packet at this time, this non-emergency will be preempted). If the CH is in the working state and the forwarded packet is a non\(-\)emergency packet at this time, this incoming emergency packet will preempt the forwarding priority of the non\(-\)emergency packet, and the interrupted non\(-\)emergency packet returns to the start of the cache to wait for further forwarding when there is a space in the cache; if the CH is in the working state at this time and the forwarded packet is an emergency packet, the incoming emergency packet will be blocked  
Figure 4: The access and forwarding behavior of two kinds of packets in each time slot. Figure 3: The state switching mechanism of the CH. and discarded. To sum up, as long as there is an emergency packet in the system, the CH will enter the working state. When there is an incoming non\(-\)emergency packet in the system, this non\(-\)emergency packet will access the end of the cache with access probability \(f\) to wait for data forwarding if there is a space in the cache. Otherwise, it will be discarded. Moreover, if other packets do not occupy the CH, the non\(-\)emergency packet at the start of the cache will access the CH (if the CH is still in sleep mode at this time, it only temporarily stores the non\(-\)emergency packet without forwarding; if the CH is in working state, the non\(-\)emergency packet in the CH will be forwarded directly). If the CH is in the sleep state and the amount \(N\) of packets in the cache reaches \(H\), the CH will switch to the working state and begin to forward this packet; otherwise, the non\(-\)emergency packets in the system will wait for next forwarding chance. To sum up, the access probability \(f\) only controls the possibility of non\(-\)emergency packets accessing the system. Whether the CH can switch from the sleep state to the working state depends on whether the number \(N\) of non\(-\)emergency packets in the system exceeds the wakeup threshold \(H\).
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 4 Analysis
### LEO-Enhanced PPP and PPP-AR
#### 4.2.3 PPP and PPP-AR Enhanced by Different LEO Satellite Numbers  
To further study the relationship between PPP and PPP-AR enhanced with the LEO constellation and the number of LEO satellites, we designed four experiments to control the number of LEO satellites. It should be noted that the LEO constellation used in this experiment was the L+M mixed constellation. Two LEO satellites have been taken as an example, to illustrate the method of controlling the number of LEO satellites in this study: when there were more than two LEO satellites, the first two satellites were retained according to the elevation angle. If one of the retained LEO satellites fell (i.e., the elevation angle became lower than the cutoff angle), the satellite with the higher elevation angle was selected for replacement.  
Figure 9 shows the convergence times of one, two, three, four, and 'all' LEO satellites that enhanced the single-system PPP at different latitudes. One LEO satellite hardly accelerated the convergence, whereas two, three, four, and 'all' LEO satellites could accelerate the convergence time of the single-system PPP from approximately half an hour to 8.7, 4.4, 3.1, and 2.3 min, respectively. The convergence speed increased nonlinearly with the increase in the number of LEO satellites, and gradually reached saturation. Meanwhile, the latitude difference in the convergence time almost disappeared with the control of the number of LEO satellites. This also explained why the latitude difference in convergence time in Figure 7a was caused by the difference in the number of visible LEO satellites.  
Figure 10 shows the TTFF of one, two, three, four, and 'all' LEO satellites with enhanced single-system PPP-AR at different latitudes. As with the float solution, one LEO satellite hardly accelerated the first fixation of ambiguity, while two, three, four, and 'all' LEO satellites accelerated the TTFF of the single-system PPP-AR from approximately 15 min to 9.0, 7.5, 6.8, and 6.4 min, respectively. However, unlike the float solution, the TTFF for the fixed-solution PPP did not decrease significantly with an increase in the number of LEO satellites, and was close to saturation when there were four LEO satellites. This also indicated that the latitude difference of the TTFF in Figure 7b was not caused by the latitude difference in the number of visible LEO satellites, but by the latitude difference of the first fixed time itself.  
Figure 8: Comparison of convergence performance of PPP and PPP-AR enhanced with different LEO constellations in the same latitude region: (**a**) PPP; and (**b**) PPP-AR.  
Figure 10: Comparison of TTFF of one (1), two (2), three (3), four (4), and all (-) LEO satellites enhanced single-system PPP-AR in different latitudes.  
Figure 9: Comparison of convergence time of one (1), two (2), three (3), four (4), and all (-) LEO satellites enhanced single-system PPP in different latitudes.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 4 Discussion
### Accuracy Assessment for LULC Maps  
The SVM classifier performed well in the classification of Landsat imageries with overall accuracies ranging from 88% to 95%, which are above the minimum acceptable value of 85% recommended by [PERSON] [58]. These accuracies are due to the strengths of the SVM: a non-parametric classifier that is effective in handling any numerical data without assuming data distribution [32]. However, low producer accuracy (40%) and user accuracy (67%) were observed for the agricultural field class in 1998. This was maybe due to the limited number of samples that were used for training (38) and testing (15), as shown in Table 2. The limited number of samples for the agriculture class was due to the small size of agricultural land in the urban area. In the future, it is recommended to have more samples to improve the accuracy of LULC classification. This is in line with [PERSON] et al. [59], who stated that having a high number of sampling units is recommended to improve accuracies in remote sensing studies. It is also worth noting that the heterogeneity and topographic nature of Maseru also had an impact on the production of low accuracies for the classified maps. There was the \"mixed-pixel problem\" where some pixels were not entirely occupied by one class [60], considering that Landsat imageries with a 30 m resolution were used. This resulted in the misclassification of features with lengths and widths less than 30 m. The use of very high spatial resolution imageries such as WorldView-3 and Pleiades could be used to produce high accuracies in future studies.
## 3 Preliminaries  
### Encrypted Traffic Classification  
The aim of encrypted traffic classification is to classify the encrypted traffic into some specific categories which are applications or services at present, under the condition of determining the number and label of categories. Without considering the unknown applications or services, encrypted traffic classification is a typical supervised learning scenario.  
For encrypted traffic classification, there are three elements to be quantified, which are sample, feature, and category. Assuming there are \(n\) samples to be classified, \(F\) is features used to input and \(C\) is different categories. The samples \(X\) can be described as \(X=\{x_{1},x_{2},\cdots,x_{n}\}\), where \(x_{i}\) refers to the \(i\)-th sample. The feature set can be described as \(F=\{f_{1},f_{2},\cdots,f_{m}\}\), where \(m\) represents the number of features in \(F\) and \(f_{j}\) refers to the \(j\)-th feature. An input sample \(x_{i}=[f_{1}^{(i)},f_{2}^{(i)},\cdots,f_{m}^{(i)}]\) where \(f_{j}^{(i)}\) is the \(j\)-th feature in \(i\)-th sample. Taking the application classification as an example, if the real category of \(x_{i}\) is \(a_{i}\), \(a_{i}=c_{k},c_{k}\in C\), the goal of the encrypted traffic application classification is to build a model \(\phi(x_{i})\) to obtain a predicted label \(\hat{a_{i}}\) which is expected to be the real label \(a_{i}\).
###### Abstract  
Frequency-locked detector (FLD) has been widely utilized in tracking loops of Global Positioning System (GPS) receivers to indicate their locking status. The relation between FLD and lock status has been seldom discussed. The traditional PLL experience is not suitable for FLL. In this paper, the threshold setting criteria for frequency-locked detector in the GPS receiver has been proposed by analyzing statistical characteristic of FLD output. The approximate probability distribution of frequency-locked detector is theoretically derived by using a statistical approach, which reveals the relationship between probabilities of frequency-locked detector and the carrier-to-noise ratio (\(C/N_{0}\)) of the received GPS signal. The relationship among mean-time-to-lose-lock (MTLL), detection threshold and lock probability related to \(C/N_{0}\) can be further discovered by utilizing this probability. Therefore, a theoretical basis for threshold setting criteria in frequency locked loops for GPS receivers is provided based on mean-time-to-lose-lock analysis.  
Keywords:GPS receiver; frequency locked loop; frequency-locked detector; mean-time-to-lose-lock +
Footnote †: journal: Sensors
## 3 Numerical Simulations
### Results with Different Noises
#### 3.2.2 Uniform on the Bridge with Random Noise Ranged (\(-1\), \(1\))  
Under the cases of moving-load noise shown in Table 3, this study also considers the situation where noise is uniformly distributed across the bridge deck. It is assumed that the noise follows a random distribution within the range of (\(-1\), \(1\)), i.e., the noise is distributed with a zero mean and amplitudes of 1.0% and 0.5% of the moving load magnitude. Given that the total length of the bridge is 360 m, an amplitude of 1.0% means that the overall random load amplitude can reach up to 3.6 times the magnitude of the moving load.  
Figures 8 and 9 represent the displacement data at points 1 (P1) and 3 (P3) under cases 5 and 6, respectively. It can be observed that the displacement under zero-mean random loads can be considered as fluctuating around the displacement caused by moving loads, with the fluctuation magnitude being proportional to the noise amplitude. In addition, it can be depicted that the mean value of the responses in three cases is the same, but the higher noise levels lead to stronger fluctuations in the vibration responses.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Load (kN)** & **Noise Amplitude** & **Remarks** \\ \hline
5 & 10 & 0.5\% & The mean of the \\
6 & 10 & 1.0\% & disturbance noise is zero. \\ \hline \hline \end{tabular}
\end{table}
Table 3: The simulation case with a uniform random distribution load.  
Figure 7: Comparison of predicted values from different calculation methods under a 5% noise intensity.
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 3 Treatment Approaches for \(\beta\)-Thalassemia
### Gene Therapy
#### 3.3.2 Genomic Editing Approaches for the Treatment of \(\beta\)-Thalassemia  
The primary strategies for employing gene editing in the treatment of \(\beta\)-thalassemia include correcting genetic mutations or enhancing the expression of HbF. The widely utilized gene editing technologies comprise zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPRRs/Cas9) [36]. CRISPRs/Cas9 can enable researchers to introduce specific gene sequences into sensors, thereby improving their functionality and broadening their range of applications [37]. The rapid advancement of these precise genomic editing tools has facilitated the targeted introduction of mutations. Currently, genomic editing tools have been successfully employed on the HBC1/HBC2 promoter to enhance HbF expression as a therapeutic strategy for \(\beta\)-thalassemia.  
In recent years, various innovative gene editing therapies for \(\beta\)-thalassemia have emerged. Studies have demonstrated that combining CRISPR/Cas9 with transposons can significantly enhance HbF expression [38]. This research illustrates the effectiveness of increasing HbF expression through the concurrent utilization of two mechanisms within a single cell. Furthermore, the study conducted by [PERSON] et al. [39] employed a combined Cas9/AV6 genome editing approach, which successfully replaced the endogenous HBA1 gene and significantly improved the balance between \(\beta\)-globin and \(\alpha\)-globin. However, upon transplantation into a mouse model, this method experienced a decline in gene targeting frequency, highlighting the need for further improvements. Additionally, [PERSON] et al. [40] proposed correcting the imbalance between \(\alpha\)-globin and \(\beta\)-globin by introducing \(\alpha\)-thalasemia, a method that shows potential efficacy in promoting erythroid differentiation.  
In summary, the aforementioned studies present novel approaches for the treatment of \(\beta\)-thalasemia. However, the effectiveness of these methods necessitates additional research and clinical trials for validation. In the future, it may be essential to utilize additional animal models to explore and validate these techniques of genome editing, thereby identifying improved gene therapies aimed at addressing the molecular mechanisms of \(\beta\)-thalasemia.
## 2 Methods  
The program of Dynamic Maritime Traffic Pattern Recognition includes Trajectory Preprocessing and Trajectory Clustering. Trajectory Cleaning, Compression and Partition belong to Trajectory Preprocessing. All methods must be performed in an online mode.  
AIS data need to be preprocessed before clustering. Preprocessed tasks include trajectory cleaning, simplification, and partition. First, some problems, such as wrong setting of the AIS transmitter, equipment failure, or weak information lead to a chaotic trajectory, position error, and signal interruption. If the trajectory is not cleaned, the experimental results would be severely affected. Second, because the AIS data size is large and contains many redundant points, if redundant data are not deleted, the system bears expensive data storage costs and time-consuming calculations. One of the best solutions is compressing AIS data using the trajectory simplification algorithm. Last, a ship may anchor, berth, enter, or leave the monitoring area according to the navigation plan. If the whole trajectory is used to cluster, many conventional local patterns would not be recognized. Particularly as some ships may sail in the harbor for a long time, in which case the AIS data would be received continuously, leading to memory overflow and clustering inability. Thus, a trajectory needs to be partitioned into several meaningful sub-trajectories for clustering.  
The aim of trajectory clustering is to identify the common traffic pattern in a dynamic environment. The new trajectories can preserve or generate the existing traffic patterns and the old patterns can be deleted due to environmental changes. Therefore, the trajectory clustering algorithm should be able to quickly merge the new trajectories into existing traffic patterns or create new ones. Meanwhile, to improve the cluster speed and adapt to environmental changes, expired trajectories should be deleted and the traffic patterns adjusted accordingly. To achieve this goal, an effective incremental cluster algorithm is needed to perform the ship trajectory cluster, and the algorithm must include the functions of trajectory addition and deletion.
## 4 Simulation Test and Result Analysis
### Experiments on FDIA Detection of Grid Loads
#### 4.4.2 Different Attack Intensity  
In this experiment, a baseline 2-norm value of 1 was employed, and attack vectors characterized by five distinct 2-norm intensities were developed, ranging from 0.5 to 2.5. The primary objective was the exploration of how these varied 2-norm magnitudes impact the accuracy of the four distinct detection methods. Figure 10 illustrates the fluctuation in accuracy across the four detection methods, as the 2-norm values undergo alteration.  
The experimental results reveal a clear trend: As the 2-norm value of the attack vectors increases, the accuracy of the detection method gradually increases. This can be explained by the fact that the increase in attack intensity leads to an increase in the differentiation between normal and false data. When the 2-norm intensity is low, the two types of data may be more similar, thus increasing the likelihood of misclassification. However, as the attack intensity increases, the distortion characteristics of the attack data are more pronounced, making it easier for the detection method to distinguish them from normal data, and the accuracy of the detection increases. Therefore, this method overcomes the shortcomings of traditional state estimation methods and is able to detect FDIAs efficiently, and the experiments verify the advantages of the method in terms of detection efficiency and classification accuracy.