esa-sceva/satcom-chunk-collection huggingface dataset:
# Erasure Codes for Cold Data in Distributed Storage Systems
## 5 Evaluation
### Read Performance Test
#### 5.2.2 The Performance in Random Reading  
The above tests were for the sequential reading performance. Next, we examined the performance of the system under different patterns and block sizes for random reads.  
From Figure 11, we can see that the trend for random reading was consistent with the trend for sequential reading. The larger the data block was, the lower the value of the IOPS. When we fixed the data block size to compare the different backup strategies, we can see that the replica strategy had certain advantages in terms of the IOPS relative to the erasure coding strategy. The test results were also consistent.  
Figure 9: The IOPS in the sequential reading.  
Figure 12 shows the throughput value for a random read. In the figure, we can see that the throughput value increased as the data block increased, but it was consistent with the sequential read. After the data block size reached 512 K, the change in the data block size had little influence on the value of the throughput. The throughput values were basically equal with data blocks 512 K and 1028 K, whether they were replica mode or erasure code mode.  
Figure 11: The IOPS in the random reading.  
Figure 10: The throughput in the sequential reading.  
Through the above four tests--the sequential read IOPS and throughput and the random read IOPS and throughput--we can see that the read speed of the replica mode was better than that of the erasure code mode, which was consistent with the theoretical value. This is because the replica mode can select the data block of the machine for reading, while the machine only stores part of the data and needs to call the data stored in other nodes through the network in the erasure code mode. At the same time, we can also see that the size of the data block had a great influence on the IOPS and the throughput value. Therefore, in conducting research to improve the performance of the storage system, it is important for us to set a reasonable data block size to improve the read performance of the system.
# Structure-Based Drug Design for Tuberculosis: Challenges Still Ahead
## 3 DNA Gyrase: A Validated Drug Target
These SAR observations helped to schematize synthesis and to rationalize how to balance antimycobacterial potency with oral exposure, safety, and synthetic complexity, leading to the identification of the 7-substituted-1,5-naphth-2-one core as a privileged LHS binder, the N-ethyl-4-aminopiperidines as a linker, and monocyclic aromatic rings with different substitution patterns as the best RHS binding option, in contrast to NBTIs bearing bicyclic rings as the RHS (Figure 2D, analogs 2 and 3) [101]. This work led to the identification of a subclass of naphthytidone/aminopiperidine-containing compounds that displayed activity against _M. tuberculosis_, both in vitro and in the mouse model, known as _'M. tuberculosis_ DNA gyrase inhibitors' (MGIs) due to structural and activity differences with respect to NBTIs [101]. More recently, the mechanism of action of two such compounds, i.e., GSK000 and GSK325 (Figure 2C; Table 3), was assessed on the _M. tuberculosis_ gyrase, showing that MGIs greatly enhanced DNA cleavage mediated by the bacterial enzyme, but they induced only single-stranded DNA breaks [102]. Their mechanism of action involves stabilizing covalent gyrase-cleaved DNA complexes and appears to suppress the ability of the enzyme to induce double-stranded breaks. Furthermore, these compounds maintained activity against mutant versions of DNA gyrase, bearing the three most commonly observed FQs resistance mutations, but displayed no activity against human topoisomerase II\(\alpha\)[102], suggesting good potential as candidate drugs, especially in the presence of FQ resistance. Modeling studies were carried out by the authors using the crystal structure of the _M. tuberculosis_ gyrase cleavage core and the NBTI crystal structure complex of _S. aureus_ gyrase [100] (Figure 2B). Most importantly, these studies allowed to confirm that FQs and MGIs do not share the same binding site and bind in a mutually exclusive manner (Figure 2), considerably reducing the risk of developing cross-resistance phenotypes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Family** & **Name** & **Structure** & **IC\({}_{\text{50}}\) (\(\mu\)M)** & **Reference** \\ \hline Fluoroquinolones & Moxifloxacin & 1 & [102] \\  & & 2.5 & [101] \\ SPR & SPR720 (VXc-100, VXc-486) & \({}^{\mu_{\text{NC}}}\) & ND & [92; 93] \\  & & & \\  & & & \\  & & & \\  &
## 1 Introduction
Compared with the traditional SCFDE, the prefiltered SCFDE (PF-SCFDE) uses a prefilter to transform the matched filter output sequence into a sequence that includes partially correlated noise and forward information. The output information sequence of the prefilter reduces the interference of the backward information from the matched filter. Subsequently, logarithmic likelihood estimation (LLR) is employed at the detection end to perform a weighted fusion of the forward information, which can improve the performance. To verify the PF-SCFDE performance, simulations were performed using MSK and GMSK signals under various channel conditions. We realized that, with similar complexity, PF-SCFDE can effectively improve the system performance over the channel with a low root mean square (RMS) delay extension. A sea trial comparison and analysis of PF-SCFDE and SCFDE using MSK UWA communication is presented in this paper, verifying that PF-SCFDE has superior performance in eliminating ISI over shallow UWA channels. The remainder of this article is organized as follows. Section 2 describes the binary CPM signal model and the transceiver structure of the UWA communication system. Section 3 presents the algorithm and symbol detection method of PF-SCFDE. In Section 4, MSK and GMSK are used to simulate and analyze the performance of PF-SCFDE under different channel structures. In Section 5, the performance of PF-SCFDE is analyzed using an MSK UWA communication sea trial. Finally, we conclude this study in Section 6.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 3 Results
### Oil Spill Classification Based on Fully Polarimetric SAR Features  
In the classification based on quad-pol SAR data, feature numbers from 2-10 are considered. The polarimetric features derived from quad-pol SAR data considered in the study are listed in Table 3. All of the features considered in this experiment are provided in Figure 3. In the display, all of the features are normalized to [0, 1]. In Figure 4, the tendency of overall accuracy achieved by three classifiers is plotted. The best classification result was achieved when considering eight features for SVM, nine features for ANN and four features for MLC. Generally, SVM achieved the best classification performance, followed by ANN. This result proved the superb capability of SVM in dealing with a large number of features. It can be observed that in all of the classifications, after the best four features have been introduced, the classification results began to fluctuate and did not change very much. These four features are: pedestal height, correlation coefficient, standard deviation of CPD and alpha angle. The eight features used for SVM classification are: \(S_{VV}^{2}\), pedestal height, entropy, DoP\({}_{HHVV}\), correlation coefficient, coherency coefficient, standard deviation of CPD and alpha angle. The nine features used for ANN are all of the features except ellipticity. As introduced in the previous session, all of these features have strong physical meaning, which enables them to largely contribute to the  
Figure 2: Pauli RGB image of RADARSAT-2 data.  
classification between mineral oil and clean sea surface/biogenic film. They are also not likely affected by the noise floor.  
Figure 3: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Feature** \\ \hline
1 & \(S_{VV}^{2}\) \\
2 & Pedestal Height \\
3 & Entropy \\
4 & DoP\({}_{HHVV}\) \\
5 & Correlation Coefficient \\
6 & Conformity Coefficient \\
7 & Coherency Coefficient \\
8 & Ellipticity \(X\) \\
9 & CPD Standard Deviation \\
10 & Alpha Angle \\ \hline \hline \end{tabular} Note: Features 4 and 8 were extracted from the Stokes vector considering the \(HH\) and \(VV\) channels.  
\end{table}
Table 3: Features that derived from quad-polarimetric SAR data.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 1 Introduction
While SSHs have demonstrated potential in spatial audio applications, a thorough investigation of them needs to be conducted. In this paper, we delve into the properties and performance of SSHs, uncovering their limitations and offering valuable insights for their future implementation. Specifically, we investigate and prove the following limitations of SSHs: (i) SSHs are not solutions of the Helmholtz equation due to the shifting and scaling of the input arguments for the associated Legendre polynomials and exponential function. (ii) The spherical Hankel and Bessel functions are not the corresponding radial functions for SSHs to perform radial extrapolation. (iii) There is severe distortion around the edge of the sector region when using SSHs for sound field reconstruction. Our proof shows that SSHs are better suited for decomposing and processing the sound field within the measured sector region without any radial extrapolation. Additionally, the measurement sector region should be larger than the target sector region to mitigate distortion. The main contribution of this paper is a comprehensive investigation of the performance and limitations of SSHs. This study will help future users identify suitable applications for SSHs and those that are not appropriate. The structure of this paper is as follows: Section 2 introduces the background theory of SSHs and discusses its three main limitations. Section 3 provides a mathematical proof showing that SSHs do not satisfy the Helmholtz equation. In Section 4, we further demonstrate that the spherical Hankel and Bessel functions are unsuitable for radius extrapolation in SSHs. Section 5 examines the distortion issues arising from these inappropriate functions. Section 6 highlights the limitations of SSHs and offers insights for implementation. Finally, Section 7 concludes the paper.
## 4 Verification of PSHRisk-Tool Results
### Existing Test Model  
The verification of a PSHA tool is essential to assure that the analysis results are reliable. The analytical verification process of PSHRisk-tool has been carried out in Section 4.1 considering three types of sources i.e., area, line and points. An additional test case from [PERSON] et al. (2018) is also considered for validation.  
PSHRisk-tool performs well under the framework of the selected case and is suitable for the development of probabilistic seismic hazard analyses as well as for further EPSHA and seismic risk assessment.  
The basic information for this case is shown in Figure 11 and Table 3. More detailed information related to this tested case can be found in the GEM technical report \"GEM1 Hazard: Overview of PSHA Software\" ([PERSON] et al., 2018).  
For the single site of this test case, the magnitude distribution plot will same for all sites, but site-to-source distance distribution will change. Therefore, for site 1 up to the seismic hazard analysis, all steps have been shown in Figure 12. For the rest of the sites, Figures 13 and 14 show segmental views and probability density function of distance, respectively. To reduce the paper length the result has been shown up to the hazard curve for all sites and for comparing the seismic hazard curve between PSHRisk-tool and other reference software, the same graph has been used as shown in Figure 15.  
Figure 11: Area source-based test geometry and sites.  
Figure 12: (**a**) Segmental view of area source, (**b**) Probability density function of distance, (**c**) Illustration of probability density function for magnitude for a source with a truncated Gutenberg–Richter distribution, (**d**) Probabilistic seismic hazard curve, respectively for Site 1 by PSHRisk-tool.  
Figure 14: Probability density function of distance PSHRisk-tool for, (**a**) Site 2, (**b**) Site 3, (**c**) Site 4, (**d**) Site 5, (**e**) Site 6, (**f**) Site 7.  
Figure 13: Segmental view of area source by PSHRisk-tool for, (**a**) Site 2, (**b**) Site 3, (**c**) Site 4, (**d**) Site 5, (**e**) Site 6, (**f**) Site 7  
Figure 15: Hazard curves for area source-based test case, (**a**) Site 1, (**b**) Site 2, (**c**) Site 3, (**d**) Site 4, (**e**) Site 5, (**f**) Site 6, (**g**) Site 7.  
The selected test case is conducted considering the following software: OpenSHA, CRISIS 2007 and seismograph PSHA tool for the validation of the developed PSHRisk tool. The hazard curves obtained from different PSHA software for each site are shown in Figure 15, which shows that the analysis results are very similar to those of the CRISIS 2017 and Seismograph PSHA tool. Only for two sites (3 and 4), the result obtained from OpenSHA gives slightly higher values which show a similar statement as explained in [PERSON] et al. [54].  
From the above analysis of the PSHRisk-tool results with the analytical example and other software, it is optimized that the tool is suitable for the determination of PSHA and FR-Assessment.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 5 Results
### Decadal Regional ASL Trend
#### 5.3.1 Comparison between Long-Term and Decadal Trends  
By comparing the decadal (2000-2009 and 2010-2019) trends with the long-term trend, two major observations emerged: Firstly, both decades revealed a spatial variation in sea-level trend among the stations. The northern stations (stations #1, #2, #3, #11, and #13) exhibited higher rates compared to the southern (#4, #5, #6, and #7) and western stations (#8, #9, and #10) (Figure 9). A similar spatial trend pattern was also observed with the long-term trends (Figure 7). The increased rates in the northern stations were attributed to air pressure fluctuations and intensified westerly winds that push the water masses to the north-east [29; 63]. Additionally, for both examined decadal periods, the trend rates notably exceeded the long-term trend rates. For instance, the SA-derived values ranged from 2.7 to 9.6 mm/yr for the decadal trend compared to 2.5 to 5.4 mm/yr for the long-term trend (Figures 7 and 8). This implies a substantial increase (almost doubled) for the decadal period trends in comparison to the long-term trend (1995-2022) at several stations (e.g., Dirhami (#1), Kolobrzeg (#5), Swinoujscie (#6)), and even almost tripled (e.g., Sassnitz (#7)). Also, for each decade, there was a variety of differences amongst the stations.
## 3 Theoretical Background
### Isolation Forest  
The IF algorithm is considered a tree-based outlier-detection method. It is an unsupervised machine learning algorithm that detects outliers merely on the basis of isolation without using any distance or density measure. This algorithm selects a feature from a given feature set in a random manner, and then randomly picks a split value between the maximum value and minimum value of the selected feature to isolate observations. The partitioning process terminates when the algorithm has isolated all observations or when all remaining observations have the same value. The normality of an observation given a random tree is the number of splits required to isolate the observation, which is equivalent to the path length from the root node to the terminating node containing the observation. The IF algorithm takes the average path length calculated from all the random trees in an isolation forest, constructs a measure of normality, and establishes a decision function. Figure 4 illustrates the concept of an isolation tree.  
The contamination rate is a critical hyperparameter when applying the IF model. It is defined as the expected ratio of outliers in the dataset. This parameter varies between 0.0 and 0.5. In this study, the implementation of the IF algorithm was based on the ExtraTreeRegressor ensemble from the scikit-learn library [47].  
Figure 4: Concept of an isolation tree: (**a**) data points and isolation operations; (**b**) binary tree and the isolated point.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 6 Performance Evaluations  
The performance of our proposed method is discussed in this section. The evaluation aims to answer the following questions:  
* Is the proposed algorithm effective and accurate for detecting and predicting the traffic flow's singular points?
* How does the wavelet adaptive parameter \(a\) influence the traffic flow prediction performance?
* Is the proposed algorithm better than other traffic flow prediction methods?
## 1 Introduction
### Contributions of This Paper  
The major contributions of this paper are listed as follows:  
1. We demonstrate the feasibility of implementing rule- and a fuzzy inference system-based longitudinal navigation control systems on a pure electric platform. In both cases, power can be regenerated in the battery with appropriate design considerations. Previous efforts are mostly based on an internal combustion engine and hybrid vehicles, with little or no focus on electric power consumption and regeneration. These techniques are implemented for a B-class electric vehicle. The fuzzy inference system outperforms the rule-based strategy by a magnitude of two in terms of power regeneration. A benchmark experiment is performed in a crossroad intersection environment with a traffic light. Both strategies implement an eco-approach and departure strategy based on a coupled green light optimal speed advisory/regenerative braking system.
2. Lateral, longitudinal, and rolling resistance forces are considered in the dynamic model. They directly impact on the navigation performance and power consumption and regeneration. Works on vehicle navigation usually rely on simplified kinematic models where no forces are considered. Longitudinal and lateral vehicle dynamics provide realistic behavior in terms of powertrain effort and vehicle motion. This differs from most state-of-the-art efforts, where dynamics are partially or totally neglected.
3. A fully characterized electric machine that matches the vehicle powertrain is often neglected in the literature. Ideal actuators are a common choice. Efficiencies are assumed to be ideal or constant. From an energy standpoint, the vehicle propulsion electric motor is represented by its loss map. This allows computing a realistic bidirectional power demand and conversion efficiency.  
In summary, the SPaT data usage, sensor fusion, longitudinal and lateral controllers, electric motor, and dynamic vehicle models add realism to the connected automated vehicle control system.  
The paper is organized as follows: Section 2 presents the theoretical framework. The rule set and fuzzy inference system used for longitudinal control can be found in Section 3. Section 4 explains the rationale for the experiment design. It also contains the results of the experiment. Finally, Section 5 concludes this work and proposes future research directions. For notation clarity, a nomenclature list is provided at the end of this paper.
## 3 Methods
### Building Our Model  
Figure 6 presents the architecture of our proposed model, which is composed of four components. The left three components capture spatio-temporal correlation of bus traffic flow, which shared the same network structure. The single right component captures bus scenario patterns.  
We first obtained the bus boarding flow and alighting flow of each time interval using Definition 1, which can stack together into a two-channel image-like matrix. Then, in order to capture the temporal correlation of bus traffic flow, we divided these matrices of all time intervals into three parts denoting adjacent time, near time, and far time, i.e., temporal closeness, period, and trend, respectively [10]. More specifically, to predict the \(Y_{t}\) of future time interval t, the closeness can be denoted as \([Y_{t-l_{c}},Y_{t-(l_{c}-1)},\ldots,Y_{t-1}]\), which is a sequence that contains the past \(l_{c}\)-length consecutive time interval observations. Then, we concatenated the sequence to a tensor \(Y_{c}\), which is the input of Input1. Likewise, the period can be denoted as \([Y_{t-p,l_{p}},Y_{t-p,(l_{p}-1)},\ldots,Y_{t-p}]\), which is a sequence that contains past \(l_{p}\)-length observations with time interval of p, when p is set to one-day. The trend can be denoted as \([Y_{t-q,l_{q}},Y_{t-q,(l_{q}-1)},\ldots,Y_{t-q}]\), which is a sequence that contains past \(l_{q}\)-length observations with time interval of q, q is set to one-week. Then, the \(l\)-length sequence of closeness, period, and trend were concatenated into a tensor \(Y\in R^{2l\times m\times n}\), respectively, which are inputs of the left three components, namely Input1, Input2, Input3.  
Figure 5: Bus total traffic flow. Each subgraph represents the total traffic flow of all regions at each time interval in one day.  
The left three components share the same structure, that is each component contains a convolutional neural network layer named ConvIn, then connect to two improved residual blocks, and finally through a ReLU [19] activation function followed by another convolutional neural network layer named ConvOut; these convolutional networks can capture the spatial dependency of each region. The output of the left three components is weight-fused by the parametric-matrix-based method [10], which denotes the different influence of spatio-temporal correlations of each component to obtain the final spatio-temporal output \(Y_{st}\).  
The single right component of our model captures the bus scenario patterns. We first obtained the sum of bus boarding flow and alighting flow of each time interval, and then we normalized the flow sum and encode the normalized value into a one-dimension matrix, next we fed it into a two-layer fully connected neural network to get the output of bus scenario patterns \(Y_{bs}\). Then, we merge the spatio-temporal output \(Y_{st}\) and bus scenario patterns output \(Y_{bs}\), followed by a Tanh activation function. Finally, we obtained the prediction of bus traffic flow \(Y_{pred}\).