esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimization
#### 3.1.4 Optimized Vapochromic Sensor Formulation  
The final optimized formulation for the sensor is seen in Table 4. A sulfonephthalen dye, bromocresol green, was chosen as the colorimetric indicator to detect the presence of a base environment inside a closed container. These dyes are known for their intense colors and widespread use in analytical chemistry [32]. It was hypothesized in the early 1900s that their color-changing mechanism was mainly due to the formation of a resonance stabilized quinone-phenolate ion complex [33]. As bromocresol green dye is deprotonated to form this complex, it can transition from an intense yellow color at low pH to green and then, finally, blue in conditions of higher pH. Figure 2 shows the initial sensor color in green (left) next to a blue sensor (right) exposed to the headspace of an ammonium hydroxide solution. After use, the sensor was found to be able to be restored to its initial green coloring after heating in the oven at 50 \({}^{\circ}\)C, indicating its potential for reuse.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reagents** & **Volume (\(\mu\)L)** \\ \hline
0.04\% \(w/v\) BCG solution & 480 \\ TEOS & 100 \\ TFA (0.1 M) & 34 \\ \hline NH4 OH (0.25 M) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Final sol–gel sensor formulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Initial** & **Analyte** & \
\begin{tabular}{c} **Reaction** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Reversible** \\ **Color** \\ **Change?** \\ \end{tabular} & **pH Range** \\ \hline Bromophenol Blue & BB & Vinegar & 1–3 min * & Yes & 3.0 (y)–4.6 (b) \\ Phenol Red & PR & Ammonium & \textless{}1 s & Yes & 6.8 (y)–8.4 (r) \\ Bromocresol Green & BCG & Ammonium & \textless{}1 s & Yes & 3.8 (g)–5.4 (b) \\ \hline Key: (r)–red; (y)–yellow; (b)–blue; (g)–green. * Started and finished changing color between the given timeframes. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three pH indicator dyes that changed color in the presence of either vinegar (5% acetic acid) or ammonium hydroxide (0.25 M).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Concentration of NH4 OH (ppm)** &
\begin{tabular}{c} **BCG** \\ **Time of Reaction \(\pm\) sd (s)** \\ \end{tabular} \\ \hline Stock (8760 ppm or 0.25 M) & 1.7 \(\pm\) 0.5 & 2.0 \(\pm\) 0.6 \\
500 & 4.7 \(\pm\) 0.8 & 31.8 \(\pm\) 4.3 \\
100 & 11.7 \(\pm\) 1.8 & N/A \\
50 & 29.7 \(\pm\) 7.9 & N/A \\
20 & 67.3 \(\pm\) 10.3 & N/A \\
10 & 132.0 \(\pm\) 15.0 & N/A \\
4 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between time of reaction of BCG and PR sensors to NH4 OH dilutions (n = 6). Concentration reported as concentration of NH4 OH in solution.
## 5 Evaluation
### Experiment 1  
We choose a circle topology in Experiment 1, as shown in Figure 9. First, we fix a central node \(O\) and the radius \(r\) of a circle. Then, we randomly generate an angle \(\theta_{1}\) between zero and 360. Node \(A_{1}\) is  
The comparison group is chosen to be SCAR [22], which uses round-robin scheduling. In this experiment, each simulation is repeated 10 times with different random seeds, and the measurements are averaged.  
The per-flow average throughput is shown in Figure 12, with the error bars showing the minimum and maximum per-flow throughput. Note that the points are slightly shifted along the \(x\)-axis for visual clearance. The exact \(x\) value should be the mid-point between each pair of data points. It is observed that DPSA can initially provide higher throughput than SCAR. This is because DPSA systematically schedules the packets to be coded together, minimizing the number of transmissions to clear packets in the queue. In addition, DPSA almost provides a straight line connecting the zero point to its highest achievable throughput. On the other hand, the curvature of the SCAR line is much higher.  
However, DPSA's maximum throughput level is lower than DPSA's. This happens when the network is fully saturated, _i.e._, the transmission capacity of the router node is smaller than the offered load. DPSA schedules packets in an order first to guarantee fairness. In terms of the coding graph, DPSA intentionally shifts transmission capacity from those well-connected vertices to the ones that have fewer neighbors. The result of this shifting is conspicuous. The throughput span is significantly narrowed, meaning smaller variance and better fairness. DPSA can fairly allocate bandwidth to all flows: the minimum throughput among all flows is roughly 70% of the average throughput. On the other hand, some flows receive only around 20% of the average throughput in SCAR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\mathbf{N}\) & \(\mathbf{e}\) & \(\mathbf{r}\) & \(\mathbf{R}\) \\ \hline Value & 10 & 10 & 100 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment 1 parameter settings.  
Figure 12: Per-flow throughput in Experiment 1.  
As the offered load continues to rise, the average throughput of both protocols declines. This is mainly because of MAC-layer contention of source nodes. The router node has decreasing transmission capacity, because the media is more often occupied by the source nodes. In fact, we have designed a congestion control mechanism for our protocol. Such a congestion control mechanism can detect the congestion and reduce the amount of packets sent by the source nodes. Therefore, our protocol can maintain the highest throughput, even when an offered load further increases. However, this paper emphasizes the importance of a packet scheduling scheme, so this part is omitted intentionally.
## 3 Methodology
### Phase 4: Concept Clustering
After the completing the concept clustering procedure, further phases such as concept extraction and ontology construction are enacted. Because we use the soft clustering mechanism in this proposed model, a concept can be a member of more than one cluster. Regarding overlapping clustering, popularly known as the combination of soft clustering and hard clustering, it is worth considering that in a soft clustering mechanism such as the Fuzzy C-Mean algorithm, data values or concepts can be members of multiple clusters depending on membership degree values while in a hard clustering mechanism, the typical K-Mean method is not suitable for the semantic clustering of concepts. For example, when clustering a dataset containing information on movies, one movie may belong to many genres. As an example, an action movie may also fit the historical and romantic genres [63]. Similarly, a concept may be related to multiple key concepts or terms. Accordingly, it is more optimal to use a Fuzzy C-Mean clustering mechanism than a hard clustering mechanism for such problems.
## 4 Performance Metrics
### SU Packets' Performance Metrics  
The amount of SU packets that is blocked and leave per time slot is defined as SU packets' blocking rate \(B_{S}\).  
\[\begin{split} B_{S}=&\psi_{k,0,0,0}(f\tilde{\pi}_{11} \tilde{\pi}_{12}\alpha_{2}\tilde{\beta}_{2}+\tilde{\pi}_{11}\alpha_{12}\alpha _{2}\tilde{\beta}_{2}+\alpha_{11}\alpha_{2}\tilde{\beta}_{2})+\psi_{k,0,0,1}( \bar{\pi}\alpha_{11}\tilde{\alpha}_{12}\alpha_{2}+\bar{\alpha}_{11}\alpha_{12} \alpha_{2}+\alpha_{11}\alpha_{2})\\ &+\psi_{k+1,0,0,0}(f\tilde{\pi}_{11}\alpha_{2}+\bar{f}\tilde{\pi} _{11}\tilde{\pi}_{12}\alpha_{2}\tilde{\beta}_{2}+\bar{f}\tilde{\pi}_{11}\alpha _{12}\alpha_{2}+\alpha_{11}\alpha_{2})+\psi_{k+1,0,1,0}(f\tilde{\pi}_{11} \tilde{\pi}_{12}\alpha_{2}\beta_{12}+\bar{\pi}_{11}\alpha_{12}\alpha_{2}\beta _{12}+\bar{f}\alpha_{11}\alpha_{2}\beta_{12}+\bar{f}\alpha_{11}\alpha_{2} \beta_{12}\\ &+f\tilde{\pi}_{11}\alpha_{2}\tilde{\beta}_{12}+f\alpha_{11}\alpha _{2}+f\alpha_{2}\tilde{\beta}_{12})+\psi_{k+1,1,0,0}(\alpha_{11}\alpha_{2} \beta_{11}+\bar{\pi}_{11}\alpha_{2}\beta_{11}+f\tilde{\pi}_{11}\alpha_{2} \alpha_{2}\beta_{11})+\psi_{k+1,0,1,1}(\bar{\pi}_{2}\\ &+r\tilde{\pi}_{11}\alpha_{12}\epsilon_{12}+r\alpha_{2}\tilde{ \epsilon}_{12}+r\alpha_{11}\alpha_{2}\epsilon_{12})+\psi_{k+1,1,0,1}(\alpha_{ 2}\epsilon_{11}+\bar{\pi}_{11}\tilde{\pi}_{12}\alpha_{2}\epsilon_{11}+\bar{ \pi}_{11}\alpha_{12}\epsilon_{11})\end{split} \tag{25}\]  
The amount of SU packets that leaves because they are interrupted and cannot enter into the cache per time slot is defined as SU packets' loss rate \(L_{S}\).  
\[L_{S}=\psi_{k+1,0,0,0}(f\tilde{\pi}_{11}\tilde{\pi}_{12}+\tilde{\pi}_{11}\alpha _{12}\tilde{\beta}_{2}+\alpha_{11}\tilde{\beta}_{2}) \tag{26}\]The amount of SU packets with completed transmission per time slot is defined as SU packets' throughput \(T_{S}\).  
\[T_{S}=\alpha_{2}-B_{S}-L_{S} \tag{27}\]
## 3 Discussion
Interestingly, ROS were concentrated in the intercellular or extracellular regions, mirroring the initial onset site of petiole spot disease. This finding aligns with phenotypic observations (Figure 2). ROS production happens within the apoplast in stress conditions and is accelerated by the activity of NADPH oxidase (NOX) [33]. The results of a previous study show that under conditions of high nitrogen concentration, susceptibility traits exhibit heightened cell membrane permeability and plasma membrane peroxidation reactions, as evidenced by increased conductivity and elevated levels of MDA (lipid peroxidation products) [34]. Additionally, the over-application of nitrogen fertilizer can lead to an accumulation of ammonium nitrogen in cabbage petioles, potentially causing damage to the normal cell membrane structure [35], which causes the release of polyphenols and follows reaction catalyzed by polyphenol oxidase. This is consistent with previous research results, indicating that Chinese cabbage petiole spot can induce a large number of ROS dependent on excessive accumulation of NH4\({}^{+}\).
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 3 Materials and Methods
### Post-Processing  
Reconstruction-based methods typically generate anomaly maps by computing the Manhattan or Euclidean pixel-wise distance between an image and its reconstructed version. However, some studies suggest employing more robust dissimilarity metrics, such ascosine similarity, widely used to compute differences at the feature level [26; 29], or SSIM. Unlike pixel-wise intensity comparisons, SSIM evaluates luminance, contrast, and structural differences at the patch level, offering a more perceptually meaningful assessment. In this study, SSIM was explored as an alternative to residual distance in various methods, including VAE, r-VAE, f-anoGAN, H-TAE-S, OS-DDPM, DFR, and DAE. As in the hyperparameter tuning process, the approach (SSIM or the original residual function) that achieved the best validation performance was selected for final test evaluation (see Table 11).  
To refine the anomaly maps, the computed scores were multiplied by the foreground mask, effectively removing artifacts outside the body region. For image-level classification, global average pooling was applied. Note that this does not apply to the DRA and SupIC methods, which directly predict an anomaly score for the image.
# Strength Analysis of Real-Life Passwords Using Markov Models
## 2 Related Work
### Basic Password Strength Calculation  
The basic password strength calculation is done by a simple password policy or, as it is also called-a password rule system. Such a system has the ability to estimate the strength of a given password by analysing its structure i.e., the number of upper case, lower case, or whether it contains numbers or special characters. The estimation is a binary result which tells whether the passwords meets the requirements of the policy or not. One major weakness of a password rule system is that users and their textual passwords are still considered \"the weakest link\". Users tend to choose weak passwords and passwords that are easy-to-guess and can be found in a dictionary [17]. Because of this, such a password rule system fails when it comes to preventing weak passwords from entering the system. For example, a password \"Password11\" may be acceptable for the password rule system (it contains an upper case, a number and a special character), but it is still the most common and one of the easiest passwords to be cracked (with the use of a personal computer it can be cracked within seconds). We need more advanced password rules that can additionally check the possibility that a given password can be chosen by the attacker. This is where Markov models come to the rescue since they can be used as an aid to brute-force or dictionary password cracking.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 3 Traffic Flow and Its Characteristics
### Hysteresis
Research into hysteresis phenomena continues to evolve, with studies focusing on different aspects and applications. For example, [PERSON] et al. [69] analyzed large-scale vehicle trip data in South Korea, revealing distinct hysteresis patterns in macroscopic fundamental diagrams during weekdays and weekends. [PERSON] and [PERSON] [70] conducted a detailed analysis of hysteresis loops in Cape Town, showing that H2 hysteresis loops signify stable network recovery during peak periods. [PERSON] [71] observed a clockwise hysteresis loop in the study of phenomena under transient traffic conditions caused by incidents on a bidirectional motorway in Brussels, Belgium. [PERSON] et al. [72] examined the relationship between network flow and density, as well as trip completion rates, discovering that different patterns of hysteresis loops can emerge depending on the traffic conditions, with some even taking on counterclockwise forms. These findings underscore the complexity of hysteresis in traffic networks, where even the length of trips can influence the overall traffic dynamics. To better understand and simulate hysteresis phenomena, various models have been employed. [PERSON] and [PERSON] [51] used car-following models, while [PERSON] et al. [52] and [PERSON] and [PERSON] [53] applied cellular automata models. Additionally, [PERSON] et al. [73] and [PERSON] et al. [74] utilized microsimulations, and [PERSON] and [PERSON] [75] developed a modified full-velocity difference model. More recent approaches, such as the long-memory and short-memory deep learning car-following models by [PERSON] et al. [76], further demonstrate the dependency of hysteresis on small fluctuations in car-following behaviors. Ultimately, hysteresis in traffic flow is a multifaceted phenomenon, heavily influenced by the interplay of driver behaviors, traffic conditions, and roadway characteristics. Continued research and simulation efforts are essential to fully understand and mitigate its impact on traffic dynamics.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 6 Discussion  
Since its completion of the training, this network model has worked in parallel with the physically deployed TLCS for more than 12 continuous months, and this model works stably.  
When the color temperature and luminance of LED lamps are altered as intelligent lighting strategy requires, prediction values from this network model also meet the requirements to drive this TLCS work in the same way. If this network model is to run separately, it should be working as well as this physical TCLS. Indeed, in several daytime testing scenarios when this network model operated alone, the artificial light environment of each tunnel zone offered an adequate visual cognition distance for driving safety and comfort, the lighting indexes met expected lighting specifications, internal traffic flow ran smoothly and no obvious speed variability took place, feedback from actual drivers also indicated lighting quality satisfied visual performance for driving safety with little visual discomfort induced.  
Before this network model is introduced, this physical TLCS works with reliability index 0.95, that is \(Ra=0.95\), this new \(Ra\) is much improved in the parallel system:  
\[Ra^{\prime}=1-(1-Ra)\times(1-Ra)=0.9975.\]  
Figure 13: Simulation result for inside color temperature.
## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Statistical Methods  
Statistical methods rely on data to extract associations from historical data and predict future behaviors. Therefore, the accuracy of the forecast depends on the reliability of the historical data. Selecting a suitable training dataset is a crucial factor in achieving accuracy  
Figure 5: Forecast method classification by spatio-time horizon. This figure categorizes various forecast methods according to their spatial and temporal horizons. It provides a clear framework for understanding the different types of forecasting techniques, ranging from short-term to long-term forecasts, and their spatial scopes.  
in the developed model. Additionally, a large historical dataset is typically required because these methods benefit from the ability to correct errors associated with imprecise input data measurements [111].  
There are two distinct approaches based on historical solar irradiance data. The first approach involves regression methods that statistically analyze time series data, while the second approach involves non-linear artificial intelligence (AI) methods.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Sub-Space-Based Methods
The ICA coefficient is then weighted using the Piella fusion metric in accordance with the quality maximization criterion of the fusion image. 3. Non-negative matrix factorization NMF is a component-based object representation paradigm that seeks to decompose the original data matrix into the product of two non-negative matrices [45]. The approach uses a component-based object representation paradigm that is in line with how people perceive things. Therefore, NMF is frequently employed in the field of combining infrared and visible images. Traditional NMF requires a lot of time and is ineffective. [PERSON] et al. 's [46] enhanced NMF, which addresses the issue of random initialization based on singular value decomposition and converges more quickly than conventional random initialization NMF, was used to address these drawbacks.
## 1 Introduction
The decomposition of the HRV signals was performed using DWT (level 5, db08 mother wavelet). The parameters, namely, energy, sample entropy, approximation entropy, kurtosis, and skewness, were extracted from the wavelet coefficients, and the ranking of the parameters was carried out. The classification of diabetic and control groups could be performed with a maximum accuracy of 92.02% using the decision tree (DT) algorithm. Thus, the authors recommended that DWT can be considered as a potential candidate for the processing of the HRV signals. The WPD technique represents an extension of the DWT technique. It decomposes a signal like that of DWT. However, the difference lies in the fact that both the approximation and the detail coefficients of WPD participate in decomposition, unlike DWT, where only the approximate coefficient is decomposed at each level. [PERSON] (2017) proposed WPD (level 3, db02 mother wavelet)-based processing of HRV data for detecting congestive heart failure (CHF) [15]. The spectral exponent of the HRV data was computed and the author could correctly discriminate the CHF patients from the people who were having a normal sinus rhythm. Therefore, WPD has been suggested as a potential method for the processing of the HRV signals. Recently, [PERSON] et al. (2020) used WPD for the parameter extraction of HRV signals along with the Hilbert Huang Transform (HHT) and the Singular Value Decomposition (SVD) techniques for the development of an automatic sleep-staging decision support system [9]. The parameters extracted using WPD exhibited the highest classification accuracy for the random forest (RF) classifier suggesting the superiority of WPD compared to HHT and SVD methods of parameter extraction for HRV signals. Although the above-mentioned nonlinear signal decomposition methods have been used by many researchers in the processing of HRV signals to carry out the detection of various diseases, no studies could be found that have implemented them for the detection of any alteration in the CAR due to cannabis consumption. Taking motivation from the above-mentioned facts, the current article proposes the decomposition of the HRV signals using the EMD, DWT, and WPD methods to identify variations in the CAR activity in a habitual bhang (a cannabis product)-consuming population. Furthermore, an ML-based model for automatic detection of cannabis users has also been designed. In this manuscript, bhang has been represented as cannabis unless otherwise mentioned.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### Klobuchar Model  
The Klobuchar model was the first model developed to provide ionospheric correction through GPS signals. It is designed to be a computationally lightweight model while delivering a 50% decrease in the Root Mean Square (RMS) of the positioning error [23; 24; 25]. The algorithm, which runs on GNSS receivers, determines the ionospheric delay based on several parameters, such as the user's approximate geodetic latitude and longitude, elevation and azimuth angles of the visible satellites, and eight coefficients \((a_{i},\ \beta_{i};\ i=0,\ldots,3)\) that represent atmospheric parameters and are transmitted within the satellite's navigation message in GPS and BeiDou signals. It is to be remarked that the validity of these transmitted parameters is only a few hours, which is essentially different from our IAMM model, which has no expiration time. More information about the Klobuchar algorithm can be found in [18; 40]. The Klobuchar model is one of the benchmark algorithms we will use in our validation results.
## 2 Materials and Methods
### Data Analysis  
Principal Coordinate Analysis (PCoA) was performed based on Bray-Curtis dissimilarity to assess the distribution patterns of microbial and metabolite samples using the 'labdsv' package. One-way analysis of variance (ANOVA) and multivariate analysis of variance (PERMANOVA) were used to determine whether the sample classification exhibited statistically significant differences in diversity. Linear Discriminant Analysis Effect Size (LEfSe) was used to identify the taxa that distinguished the different sample groups. The OTU table was filtered to retain only those OTUs with a relative abundance greater than 0.01%, thereby simplifying the LEFSe analysis. Significant abundance differences among categories were identified using the Kruskal-Wallis rank-sum test (\(\alpha\) = 0.05), and the effect size for each OTU was assessed using the Linear Discriminant Analysis (LDA) score (threshold = 2.0). A supervised random forest classification model was used to identify the key determinants of wine regionality based on soil fungal and bacterial diversity (Shannon index), grape must fungal and bacterial diversity (Shannon index), soil properties, and weather conditions. The significance of each predictor was evaluated by examining the reduction in prediction accuracy, quantified by the increase in the mean squared error (MSE) between the observed values and out-of-bag predictions when the data were randomly shuffled. Partial least squares path modeling (PLS-PM) was used to analyze the direct and indirect relationships among soil fungal and bacterial diversity, grape must fungal and bacterial diversity, soil properties, weather conditions, and wine regionality. An a priori model was constructed based on established relationships among factors influencing the regional distribution of wine metabolites, with data preprocessed before model application. The path coefficients describe the strength and sign of the relationship between the two variables. The significance of the estimated path coefficients was evaluated using a 95% confidence interval. PLS-PM analysis was conducted using the 'plspm' package in R version 4.0.2.