esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 General Uses of Remote Sensing and GIS in Environmental Monitoring  
One of the significant advantages of remote sensing is the possibility of accessing an extensive open database that contains information from decades ago. This allows for the analysis of images obtained in different moments, which can be used to analyze the changes in the surface. In Figure 1(a),b, we show and focus on the most studied topics in the last decade (2013 to 2022) for both remote sensing and GIS. Then, in Figure 2(a),b, the most studied topics and subtopics are shown in depth for the last three years. Analyzing those figures, we find several similitudes and synergies between GIS and remote sensing research on environmental monitoring. We can see that in the last three years, topics such as climate change increased in published papers about GIS and remote sensing. Energy is another topic that is growing in published papers, but only in GIS publications. Regarding water quality, it is increasing in popularity in remote sensing publications but decreased in GIS publications.  
Figure 1: Published papers on the topics included over the last two decades. Note that the number of papers in 2022 was obtained at the end of July 2022.  
Figure 3: Most repeated topics and subtopics of papers published for (**a**) GIS + environmental monitoring and (**b**) remote sensing + environmental monitoring in the last three years according to [1].  
Figure 2: Most repeated topics of papers published for (**a**) GIS + environmental monitoring and (**b**) remote sensing + environmental monitoring in the last decade according to [1].  
Focusing on subtopics, the most important ones in the last three years were remote sensing images, water, and vegetation in remote sensing research. Meanwhile, for GIS publications, the most relevant subtopics were water resources, environment, land cover, land, visualization of results, and soil. According to [9], it is evident that remote sensing can be applied in almost every single aspect of our development. The authors showed how remote sensing could be used to evaluate the Sustainable Development Goals (SDG). They concluded that remote sensing can be applied to 13 of 17 SDGs. The ones in which remote sensing cannot be helpful are 4, 8, 10, and 16. Nonetheless, applying GIS to those SDGs would be possible to identify weak regions or study some differences in areas with similar characteristics.  
Focusing on the use of the GIS for environmental monitoring, in most cases, it is necessary to include information about remote sensing in the analyses. Although remote sensing data are not entirely needed in some cases, their use might help the experts interpret the results, enhancing the visual aspects of data presentation [10].
# An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
## 4 Case Studies
### DTIZ Test Functions
\begin{table}
\begin{tabular}{l l} \hline \hline
**Function Name** & **Mathematical Formula** \\ \hline \multirow{8}{*}{\(\text{DTLZ3}\)} & \(\min f_{1}(x)=(1+g(x_{M}))\cos(x_{1}\pi/2)\cdots\cos(x_{M-2}\pi/2)\cos(x_{M-1} \pi/2)\) \\  & \(\min f_{2}(x)=(1+g(x_{M}))\cos(x_{1}\pi/2)\cdots\cos(x_{M-2}\pi/2)\sin(x_{M-1} \pi/2)\) \\  & \(\vdots\) \\  & \(\min f_{M-1}(x)=(1+g(x_{M}))\cos(x_{1}\pi/2)\sin(x_{2}\pi/2)\) \\  & \(\min f_{M}(x)=(1+g(x_{M}))\sin(x_{1}\pi/2)\) \\  & \(g(x_{M})=100\Bigg{(}|x_{M}|+\sum\limits_{x_{i}\in x_{M}}(x_{i}-0.5)^{2}-\cos(2 0\pi(x_{i}-0.5))\Bigg{)}\) \\  & \(\min f_{1}(x)=(1+g(x_{M}))\cos(x_{1}^{4}\pi/2)\cdots\cos(x_{M-2}^{a}\pi/2)\cos(x _{M-1}^{a}\pi/2)\) \\  & \(\min f_{2}(x)=(1+g(x_{M}))\cos(x_{1}^{4}\pi/2)\cdots\cos(x_{M-2}^{a}\pi/2)\sin(x _{M-1}^{a}\pi/2)\) \\  & \(\vdots\) \\ \multirow{8}{*}{\(\text{DTLZ4}\)} & \(\min f_{M-1}(x)=(1+g(x_{M}))\cos(x_{1}^{4}\pi/2)\sin(x_{2}^{a}\pi/2)\) \\  & \(\min f_{M}(x)=(1+g(x_{M}))\sin(x_{1}^{4}\pi/2)\) \\  & \(g(x_{M})=\sum\limits_{x_{i}\in x_{M}}(x_{i}-0.5)^{2}\) \\  & \(\min f_{1}(x)=(1+g(x_{M}))\cos(\theta_{1}\pi/2)\cdots\cos(\theta_{M-2}\pi/2) \cos(\theta_{M-1}\pi/2)\) \\  & \(\min f_{2}(x)=(1+g(x_{M}))\cos(\theta_{1}\pi/2)\cdots\cos(\theta_{M-2}\pi/2) \sin(\theta_{M-1}\pi/2)\) \\  & \(\vdots\) \\ \multirow{8}{*}{\(\text{DTLZ5}\)} & \(\min f_{M-1}(x)=(1+g(x_{M}))\cos(\theta_{1}\pi/2)\sin(\theta_{2}\pi/2)\) \\  & \(\min f_{M}(x)=(1+g(x_{M}))\sin(\theta_{1}\pi/2)\) \\  & \(g(x_{M})=\sum\limits_{x_{1}\in x_{M}}x_{i}^{0.1}\) \\  & \(\theta_{i}=\frac{\pi}{4(1+g(x_{M}))}(1+2g(x_{M})x_{i})\) \\  & \(\min f_{1}(x_{1})=x_{1}\) \\  & \(\vdots\) \\  & \(\min f_{M-1}(x_{M-1})=x_{M-1}\) \\  & \(\min f_{M}(x)=(1+g(x_{M}))h(f_{1},f_{2},\ldots,f_{M-1},g)\) \\  & \(g(x_{M})=1+\frac{9}{|x_{M}|}\sum\limits_{x_{i}\in x_{M}}x_{i}\) \\  & \(h=M-\sum\limits_{i=1}^{M-1}\left[\frac{f_{i}}{1+g}(1+\sin(3\pi f_{i}))\right]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._It can be seen from Table 5, the performance index of the INSGA-II algorithm is better than those of the four compared algorithms in terms of test functions. From the above experiments, it can be seen that INSGA-II also has better convergence accuracy and distribution performance in high-dimensional multi-objective optimization problems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Function** & & **Comparison** & **Algorithm** & & \\ \hline  & **NSGA-II** & **MOEA/D** & **IBEA** & **SPEA2** & **INSGA-II** \\ \hline DTLZ1 & 0.131 & 0.236 & 0.097 & 0.089 & 0.085 \\ DTLZ2 & 0.377 & 0.576 & 0.284 & 0.262 & 0.205 \\ DTLZ3 & 1.741 & 2.464 & 1.074 & 1.191 & 0.807 \\ DTLZ4 & 0.561 & 0.896 & 0.320 & 0.334 & 0.231 \\ DTLZ5 & 0.142 & 0.264 & 0.092 & 0.089 & 0.053 \\ DTLZ6 & 0.278 & 0.529 & 0.108 & 0.095 & 0.052 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: IGD metric results for DTLZ function. Figure 3: Solutions with INSGA-II on DTLZ test function.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Heterodyne Beat Frequency Method  
The heterodyne method is known as the beat frequency method, which uses two lasers to determine the beat frequency. The wavelength difference between the two must be stable and continuously adjustable to ensure a frequency sweep within a certain frequency range. The linewidth of the reference light source is close to or negligible to that of the measured light source, as shown in Figure 1a. Two laser beams are mixed to generate interference light signals. The beat frequency power spectrum is collected using an electrical spectrum analyzer (ESA), and the measured laser linewidth is calculated based on the beat frequency bandwidth [25; 26]. The optical field function is:  
\[E_{1}(t)=E_{1}exp\{j[2\pi f_{1}t+\varphi_{1}]\} \tag{1}\]  
\[E_{2}(t)=E_{2}exp\{j[2\pi f_{2}t+\varphi_{2}]\} \tag{2}\]  
where \(E_{1}\) and \(E_{2}\) are the optical field intensities of the test laser and the reference laser, respectively, \(\varphi_{1}\) and \(\varphi_{2}\) are the initial phases of the laser, and \(f_{1}\) and \(f_{2}\) are the output light frequencies. After coupling two beams of light through a coupler, the beat frequency is detected, and a beat frequency signal is generated, which outputs the corresponding photocurrent. The optical beat current function is:  
\[I(t)=\beta E_{1}E_{2}cos(2\pi\bigtriangleup f+\bigtriangleup\varphi) \tag{3}\]  
where \(\beta\) is the ratio of the intensity of the two beams, \(\Delta f=f_{2}-f_{1}\) is the difference in the frequency signal between the two beams, \(\Delta\varphi(t)=\varphi_{2}-\varphi_{1}\) is the phase difference in the dual-frequency light waves, and the self-correlation function of the beat current can be obtained by Fourier transform to obtain the photoelectric current spectral density function:  
\[S_{b}=\bigtriangleup v/2\pi\Big{[}(v-v_{b})^{2}+\left(\bigtriangleup v/2 \right)^{2}\Big{]} \tag{4}\]  
where \(\Delta v\) is the beat frequency linewidth. When the reference linewidth \(v_{r}\) approaches the measured linewidth \(v_{t}\), the measured linewidth is half of the beat frequency linewidth \(\Delta v\), as shown in Figure 2a. When the reference linewidth \(v_{r}\) is much smaller than the measured linewidth \(v_{t}\), the beat frequency linewidth \(\Delta v\) will be approximately equal to the measured linewidth, as shown in Figure 2b.  
Figure 1: Basic schematic diagram of (**a**) the heterodyne beat frequency method, (**b**) the delayed self-homodyne method, (**c**) the MZI delayed self-heterodyne method, (**d**) the MI delayed self-heterodyne method, (**e**) the cyclic gain compensation delayed self-heterodyne method, and (**f**) the Brillouin second-order Stokes wave. ISO: isolator, PZT: piezoelectric ceramic, PD: photodetector, ESA: spectrum analyzer.
###### Abstract  
**Background:** With advancements in molecular diagnostics, including Highly Multiplexed Microbiological/Medical Countermeasure Diagnostic Devices (HMMDs) and the impending integration of Next-Generation Sequencing (NGS) into clinical microbiology, interpreting the flood of nucleic acid data in a clinically meaningful way has become a crucial challenge. This study focuses on the Luminex xTAG Gastrointestinal Pathogen Panel (GPP) for _Salmonella_ detection, evaluating the impact of MFI threshold adjustments on diagnostic accuracy and exploring the need for an \"indeterminate\" result category to enhance clinical utility in molecular diagnostics. **Methods:** A retrospective review of _Salmonella_-positive cases detected via the Luminex xTAG GPP was conducted from June 2016 to November 2023. Key metrics included patient symptoms, stool culture results, and potential infection sources. Results were analyzed using the assay's MFI cutoffs in Versions 1.11 and 1.12. Statistical comparisons between culture-confirmed and non-confirmed cases were performed using Kruskal-Wallis tests to assess MFI value distributions. **Results:** Among 2573 tests, 212 were _Salmonella_-positive under Version 1.11, while 185 were positive under Version 1.12. Adjusting the MFI threshold in Version 1.12 reduced false positives from 40.6% to 38.4% but led to one culture-confirmed positive case being missed. Statistically significant MFI differences were observed between culture-positive and culture-negative cases, suggesting that fixed binary cutoffs may not always yield clinically accurate interpretations. **Discussion:** The MFI threshold adjustment decreased false positives without fundamentally improving diagnostic accuracy, highlighting the limitations of binary interpretations in HMMDs. Introducing an \"indeterminate\" category, especially for cases with low MFI values, could aid clinicians in integrating molecular results with patient context. This approach offers a framework for future NGS integration, where nuanced interpretation will be essential to differentiate clinically significant findings from incidental data. **Conclusions:** Implementing an \"indeterminate\" interpretation category for HMMDs could enhance clinical decision-making and refine public health surveillance by focusing on clinically relevant findings. As NGS moves toward clinical application, establishing similar interpretive standards will be essential to manage the complexity and volume of molecular data effectively.  
keywords: highly multiplexed microbiological/medical countermeasure diagnostic devices (HMMDs); next-generation sequencing (NGS); clinical utility; reflex culture testing
# Strength Analysis of Real-Life Passwords Using Markov Models
## 2 Related Work
### Markov Models
In [13] Markov models were also proven to perform slightly better than the PCFG proposed by [PERSON] et al. in [19]. Through a systematic evaluation of many different password models using 6 real-world plaintext password datasets, with about 60 million passwords, they show that the model based on PCFG does not perform as well as the Markov model. A related study presents results regarding the performance of Markov models that counteract with the previous ones. Authors in [14] performed an experiment where they tested existing password scoring meters from the industry (e.g., Zxcvbn, KeePSM and NIST PSM) and academia (PCFG-based ones and Markov-based ones). Their results show PCFG-based meter performs best among existing password scoring meters. Their conclusion is that the PCFG-based model is better at measuring passwords, and the Markov-based model is better at cracking passwords. Furthermore, the authors present a novel password strength meter based on a fuzzy probabilistic context-free grammar. It can react dynamically to changes in how users choose passwords. A recent study [16] performs an extensive and empirical analysis of Chinese web passwords where the authors evaluate the security of these passwords by employing two state-of-the art cracking techniques: PCFG and Markov models. Their results show that Chinese passwords are more prone to online guessing than English passwords. Furthermore, this study explores how password scoring meters measure the password strength, which leads the authors to the claim that in order for a PSM to be accurate, its training set should be representative of the password base of the target site and that there is no single training set that can fit all PSMs. This claim is what we explore further in our study.
# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 5 Results
### General Measurement Results  
Here we report the descriptive statistics of the four measures (Table 1). The participants in this study had a mean VLAT score of 19.25 (\(SD=8.32\)) that ranged from \(-2.81\) to \(37.67\). Particularly, the VLAT scores were normally distributed as assessed by the Shapiro-Wilk test (\(W=0.994\), \(p=0.721\)) which facilitated score interpretation. The participants also had an average DRNT score of 12.13 (\(SD=1.91\)) that ranged from 3 to 15. The general distribution of the DRNT scores was skewed left. Furthermore, the participants had an average NCS score of 80.98 (\(SD=25.81\)) and an average VVQ score of 4.28 (\(SD=12.93\)).  
We also report correlations between all pairs of the four measurement scores (Table 2). We found a moderate positive correlation between the VLAT scores and the DRNT scores (\(r_{s}=0.565\), \(N=178\), \(p<0.001\)) (**H1 confirmed**). We also found a weak negative correlation between the NCS scores and the VVQ scores (\(r_{s}=-0.334\), \(N=178\), \(p<0.001\)). In particular, this result was in line with [PERSON] et al.'s [(61)] finding that individuals with high-need for cognition preferred verbal information. We found no significant correlations in any other pairs (**H2 and H3 not supported**).  
Because the primary interest of this study was to examine the correlation between user cognitive characteristics (i.e., numeracy, need for cognition, and visualizer-verbalizer style) and visualization literacy, further analyses focused on the correlation between visualization literacy scores and the high- and low-score groups in cognitive characteristics.
# Variable Selection Using Deep Variational Information Bottleneck with Drop-Out-One Loss
## 4 Experimental Analysis
### Real Experiments  
We also conducted experiments on three real-world datasets for regression problems. These data were selected from the UCI machine-learning datasets, including Boston Housing Price (BHP), California Housing Price (CHP) and Diabetes. To be specific, the BHP dataset contains 506 observations with 13 input variables, including the per capita crime rate by town (CRIM), proportion of residential land zoned for lots (ZN), proportion of non-retail business acres per town (INDUS), Charles River dummy variable (CHAS), nitric oxides concentration (NOX), average number of rooms per dwelling (RM), AGE, weighted distances to five Boston employment centers (DIS), index of accessibility to radial highways (RAD), TAX, pupil-teacher ratio by town (PTRATIO), the proportion of black people by town (B) and lower status of the population (LSTAT). These variables are considered as relevant, and 13 irrelevant variables are generated from the distribution \(\mathcal{N}(0,1)\), which are denoted as \(irrel_{1},\ldots,irrel3\). The dataset CHP consists of 20,640 instances with eight variables, including the median household income (MedInc), median age of home (HouseAge), average number of rooms  
Figure 2: The \(gap\) vs. \(\beta\) in Example 2. (AveRooms), average number of bedrooms (AveBedrms), population, average occupancy (AveOccup), latitude and longitude. Similarly, we generated 22 additional irrelevant variables for the same operation. The irrelevant variables, denoted as \(\textit{irre1},\ldots,\textit{irre22}\), are added analogously. For the dataset Diabetes, 442 instances were collected with 10 attributes, including age, sex, BMI, BP, s1, s2, s3, s4, s5 and s6. An additional 20 irrelevant variables, denoted as \(\textit{irre1},\ldots,\textit{irre20}\), are generated analogously. Since the truly informative variables are unknown, Table 5 presents the two most-relevant variables and two most-irrelevant variables in BHP, CHP and Diabetes as selected by each algorithm. From Table 5, we can see that the proposed algorithm considered the pseudo-variables as irrelevant and the real variables as relevant in the three datasets. The selected relevant variables generally coincide with the results selected by other algorithms. In contrast, the Knockoff and SCAD algorithms incorrectly identified one real variable as irrelevant in the dataset BHP. The Lasso and Elastic Net algorithms identified one real variable, while the Knockoff algorithm identified two real variables as irrelevant in the dataset CHP. The SCAD, RF, AdaBoost and SpAM algorithms incorrectly identified one real variable as irrelevant in the Diabetes dataset. This also supports the superiority of DeepVIB for variable selection.
## 5 VSG-Enabled Grid Service for Power Systems
### Converter Station  
A high-voltage direct current (HVDC) is an economic solution for bulk power transmission. It can be classified into two categories: line-mounted converter (LCC)-HVDC and voltage-sourced converter (VSC)-HVDC. The LCC-HVDC has more economic efficiency and system stability for point-to-point long distance transmission, while the VSC-HVDC has more controllability and flexibility by independently decoupling active and reactive power [65; 66; 67]. The LCC-HVDC and VSC-HVDC can constitute a hybrid multi-terminal HVDC system that is able to utilize the advantages of the HVDC in both categories [68]. When VSC-HVDC stations are controlled by the VSG strategy, they provide virtual inertia and regulate the frequency, which enhances the system's controllability. Moreover, when feeding a weak AC system (in which the short circuit ratio (SCR) is below three) [69], the PLL in the conventional VSC-HVDC control block degrades the system's stability and restricts its transmission ability. On the contrary, a VSG-controlled VSC-HVDC can self-synchronize and feed an isolated system [70; 71].
## 2 Results
### Identification of Metabolites Responsible for Differences  
The difference between BmsR and the other four species of _Bupleurum_ mainly lies in some individual components and their contents. To find the different compounds between the five _Bupleurum_ species/varieties, the supervised method, OPLS-DA, and Student's _t_-test were used. To clarify the discrepancy in the volatile metabolites among the five _Bupleurum_ species/varieties, the key differences between BmsR and the other four species/varieties were explored, respectively (FC \(\geq\) 2 or FC \(\leq\) 0.5, value of \(p\) < 0.05 and VIP \(\geq\) 1) (Figure 4A-F and Table S4). Figure 3: Overview of the detected metabolites in the five species/varieties: (**A**) heat map of HCA of dried samples. In the heatmap, color-coding consists of shades of red and green, in which a higher intensity of red stands for very high concentration, and a higher intensity of green stands for very low concentration; (**B**) the 3D PCA plot of dried samples; (**C**) distribution of different types of metabolites in dried samples; (**D**) IS standardized total peak areas of volatile metabolites in different samples. A comparative analysis was conducted between BmsR and the other species. The contents of some metabolites amount to the main differences among the five species/varieties. In BcR, the relative contents of hexanal, _o_-cymene, (\(2E\),\(AE\))-nona-2,4-dienal, and 2-isopropyl-1-methoxy-4-methylbenzene were higher and were significantly up-regulated compared with BmsR. The relative contents of 2,4-hexadienal, (\(2E\),\(AE\))-nona-2,4-dienal, dodecanal, 4-pentenyl acetate, _n_-decyl acetate, and other compounds were higher in the BcR_W and were significantly up-regulated compared with BmsR. Compared to BmsR, the relative contents of sabinen, \(\beta\)-pinene, _n_-decyl acetate, \(\alpha\)-pinene, dodecanal, _o_-cymene, \(\beta\)-phellandrene, citral, and \(\gamma\)-terpinene were higher in BsR and its wild samples and significantly up-regulated. In BmR, the relative contents of 2-methyletrahydrothiophen-3-one, heptanal, \(\beta\)-pinene, sabinene, and benzyl angelate were higher, and \(\alpha\)-maaliene, \(\beta\)-elem, and dodecanal were significantly up-regulated compared to those of BmsR. Compounds with higher relative content in BsmR, such as _a_-pinene, sabinen, \(\beta\)-pinene, \(\beta\)-myrcene, \(\beta\)-guaiene, 2-amylfuran, and so on, were almost all significantly up-regulated when compared to BmsR. Moreover, we also identified 66 metabolites that were significantly up-regulated in BmsR compared with other species, mainly including esters, hydrocarbons, and ketones; the top 20 characteristic differential metabolites (arranged by the standardized peak area) are shown in Table 1.
## 2 Materials and Methods
### Experimental Measurements and Battery Simulation
#### 2.3.3 Battery End-of-life Assessment  
The SOC estimator was used to provide an estimate of the daily depth of discharge (DOD) of the battery pack. This information can be used to predict the overall lifespan of the battery pack as a function of the number of charging/discharging cycles. As is known from the literature, a battery is often regarded to have reached its end-of-life for vehicle application when one of the battery's performance values has fallen below 80% of the SOH; that is, the end-of-life is reached when the battery capacity has dropped below 80% of its initial value [32].  
In [33], the authors conducted a deep study on the ageing of lithium cell batteries undergoing dynamic stress tests (DSTs). The test cycle was defined as: the cell first starts at a specified SOC level and the DST profile is applied repetitively until the specified stop level is reached. The cell was then recharged back to its starting level at the 1 C-rate to finish one test cycle. In [33], experimental data are reported as a function of the number of cycles, the SOC, the DOD, and the cell temperature. In [30], the KiBaM is deeply investigated for the same battery cell as that used in the current study, and the values of the main parameters of KiBaM are evaluated under steady state conditions and after several discharge/charge cycles to estimate the ageing of the battery cell.  
In the current study, the starting SOC level was 100% and the ending SOC level was determined by the Simulink estimator, that is the DOD. A further simulation, based on the battery model shown in Section 2.3.2, was considered to estimate the number of charge/discharge cycles to achieve 80% of the battery SOH, that is, its end-of-life. To perform this estimate, the model of the SOC estimator was made to cycle the collection scenario 3000 times and, to simulate the progressive aging of the battery pack, the parameters k and c were intentionally varied during each cycle; in this model, k and c are the parameters that account for the ageing of the battery performance. Each cycle corresponds to a collection scenario for the discharging phase and the 1/3 C-rate charge for the charging phase.  
The results obtained by the SOH simulation were compared with the results in [33] to validate the reliability of the simulation. The SOH simulation was finally used to estimate the number of charge/discharge cycles to reach the end-of-life of the battery pack.