esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Phosphate Solubilization Capability  
The strain growth on agar plates of Pikovaska medium allowed underlining that in the presence of glucose, phosphate solubilization is a capability shared by _G. diazotrophicus_, _H, seropedicae_, and _B. ambifaria_. For these strains, clearing zones around the colonies were observed, especially for _G. diazotrophicus_. In contrast, for the _A. brasilense_ strain clearing zones were observed only when the cultures were grown in modified Pikovaska medium (F- Pikovaska), with fructose instead of glucose.  
Similar results were obtained for NBRIP liquid medium: the colorimetric assay carried out on supernatants of broth cultures showed that the strain with the highest concentration of phosphate was _G. diazotrophicus_ (20.84 \(\pm\) 2.65 \(\mu\)\(\mathrm{PO_{4}}^{3-}\) mL\({}^{-1}\)), followed by _H. seropedicae_ (15.64 \(\pm\) 0.72 \(\mu\)\(\mathrm{PO_{4}}^{3-}\) mL\({}^{-1}\)) and _B. ambifaria_ (9.18 \(\pm\) 1.65 \(\mu\)\(\mathrm{PO_{4}}^{3-}\) mL\({}^{-1}\)). _A. brasilense_ in this estimation also showed positive results only in F-NBRIP broth cultures, with a phosphate solubilization capability of 12.40 \(\pm\) 1.34 \(\mu\)\(\mathrm{PO_{4}}^{3-}\) mL\({}^{-1}\).
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
## 3 Asymptotic Performance Analysis for the DPE
Thus, we obtain  
\[\mathbf{C}_{1}=\mathbf{I}^{-1}(\mathbf{\gamma})=\left[\frac{1}{\sigma^{2}}\frac{ \partial\mathbf{\mathrm{a}}\mathbf{D}(\mathbf{\gamma})}{\partial\mathbf{\gamma}^{\top}} \frac{\partial\mathbf{D}^{\top}(\mathbf{\gamma})\mathbf{\mathrm{a}}^{\top}}{\partial \mathbf{\gamma}}\right]^{-1}. \tag{14}\]By applying the chain rule, we have  
\[\frac{\partial\mathcal{L}_{\mathbf{y}}}{\partial\mathbf{\gamma}^{\top}}=\frac{\partial \mathbf{\omega}}{\partial\mathbf{\gamma}^{\top}}\frac{\partial\mathcal{L}_{\mathbf{y}}}{ \partial\mathbf{\omega}^{\top}}. \tag{15}\]  
Substituting Equation (15) into Equation (14), we can extend the derivative as follows:  
\[\mathbf{C}_{1}=\left[\frac{\partial\mathbf{\omega}}{\partial\mathbf{\gamma}^{\top}} \mathbf{I}(\mathbf{\omega})\frac{\partial\mathbf{\omega}^{\top}}{\partial\mathbf{\gamma}} \right]^{-1}, \tag{16}\]  
where \(\mathbf{I}(\mathbf{\omega})\) is the FIM of \(\mathbf{\omega}\) and is given by  
\[\mathbf{I}(\mathbf{\omega})=\frac{1}{\sigma^{2}}\frac{\partial\mathbf{a}\mathbf{D }(\mathbf{\omega})}{\partial\mathbf{\omega}^{\top}}\frac{\partial\mathbf{D}^{\top}( \mathbf{\omega})\mathbf{a}^{\top}}{\partial\mathbf{\omega}}.\]  
The expression on the right side of Equation (16) is recognized as the CRB for unbiased estimators of the parameter \(\mathbf{\gamma}\).
## 1 Introduction  
Rolling bearings constitute critical mechanical connectors between rotating and stationary components in transmission systems. The transient thermal characteristics of bearings are directly governed by multiple operational parameters, including rotational speed, applied load, lubricant performance, and external heat transfer/dissipation conditions. The evolving thermal states throughout the service life present significant challenges to bearing safety. Failure to accurately predict and control temperature surges caused by intrinsic degradation mechanisms (e.g., grease deterioration and mechanical wear) in conjunction with heat influx from adjacent overheating equipment during long-term operation or computational modeling scenarios may induce critical failures such as bearing instability and functional breakdown. These thermal-induced anomalies ultimately damage the operational stability and safety reliability of mechanical systems [1, 2].  
On the other hand, there is an indirect correlation between bearing failure and temperature. Extensive research identifies an abnormal increase in bearing vibration acceleration as a key indicator of both incipient failure and approaching end-of-life, a phenomenon typically observed as bearings near the imposed life time [3]. The root cause lies in the reduction of lubrication performance or excessive mechanical friction induced by prolonged exposure to high temperatures. Under high-intensity operation involving significant speed and load, combined with excessive heat transfer from adjacent components, the temperature of bearing components gradually rises. Prolonged exposure to these elevated temperatures causes degradation of the grease's performance. Consequently, the degree of mechanical friction between the bearing's inner/outer rings and the rolling elements increases. When the bearing's vibration level exceeds a critical threshold, warnings and eventual failure occur.
## 1 Introduction
This is to improve the safety of the users. A V2X framework was developed based on blockchain and MEC technologies. The developed V2X technology provides low latency and high reliability, while taking into account the specifics of the network, the high dynamics of network topology changes and the exchange of large numbers of data. Our novel contributions can be summarized as follows:  
1. Analyzing the indicators of reliability, sustainability, QoS and resource provisioning of the infrastructure facilities of the systems. 2. Designing and developing a model for the interaction of blockchain technology in the system \"roadside infrastructure objects (RSU)--objects of mobile edge computing\" to ensure stable and reliable delivery of information, as well as blockchain technology when organizing interaction between objects of mobile edge computing and the infrastructure of the operator's network core. 3. Designing and developing a complex mathematical model of the system, taking into account the interconnection of objects and channels for V2X information transmission. 4. Developing an energy-efficient offloading scheme for the developed MEC-based V2X systems. 5. Performance evaluation of the developed framework for heterogeneous scenarios. The rest of the article is structured as follows: Section 2 introduces recent related works that consider developing V2X systems based on MEC and blockchain technologies. Section 3 introduces the developed framework, including the distributed blockchain model, the MEC-based network structure and the offloading model. The performance evaluation, including the obtained results, is introduced in Section 4. Section 5 discusses the obtained results. Finally, the conclusions and future work are presented in Section 6.
## 8 Discussion
ELBSEP [45] improves energy efficiency by combining SEP with energy-levelestimation, but ELBSEP does not consider other factors like node distance to the base station or data transmission requirements, which could further optimise performance. Modified TEEN improves energy efficiency with multi-hop routing [43], but multi-hop and multi-path routing add complexity to the protocol. This requires additional path discovery and maintenance computations, which can increase the processing overhead on resource-constrained sensor nodes. The proposed TEEN-KNN protocol ensures compact clusters and eliminates dead CHs, while the modified TEEN in [43] does not explicitly optimise CH placement. Multitier DEC improves energy efficiency through hierarchical clustering [46], but does not explicitly eliminate overlapping CHs or reduce CH-o-BS distance. None of the works demonstrated a 20% reduction in energy consumption compared to the original protocols, proving the KNN method's effectiveness.
## 2 Material and Methods
### Statistical Analysis  
As this was a pilot study, the sample size of 31 patients per group was selected for convenience. Quantitative clinical variables were calculated per patient and per visit, and then by group. Means, standard deviations and 95% confidence intervals (CI) were calculated in clinical variables, such as per patient, per visit, and then by group. The Kolmogorov-Smirnov test was used to evaluate the normality and significant distribution of the samples, and ANOVA Test was used to identify significant differences between groups at the initial visit, and significant changes before, and after, treatment within each group. Wilcoxon signed-rank test was used to the non-normal distribution of the frequency of the indexes, with a significance level of \(p<0.05\). Statistical analyses were performed separately for the gingivitis and plaque evaluations. Comparisons, between the baseline scores for G.I. and P.I. of subjects in the three different groups, were performed separately using an independent t-test. Intra-group comparisons regarding baseline G.I. and P.I. Scores versus follow-up G.I. and P.I. Scores were performed using a paired t-test. Inter-group comparisons regarding baseline-adjusted G.I. and P.I. Scores at the follow-up examinations were performed using ANOVA. All statistical tests of hypotheses were two-sided and employed a level of significance of \(\alpha=0.05\). All analyses were performed using the IBM software package SPSS, v.20 (IBM Corp., Armonk, NY, USA).
## 2 System Configuration  
The standard IEEE three-generator nine-bus system is considered as the test model in this study to inspect the performance of the proposed control schemes. The single line diagram of the test system is presented in Figure 1. This model consists of 3 generators, 3 loads, 6 transmission lines and 3 transformers whose simulation parameters are indicated in [43]. The model is updated by adding three wind farms. The total system installed capacity is 567.5 MW of conventional generation and 189 MW of average wind power generation (25 % penetration). Each wind farm is composed of 21 wind turbines and every turbine has a 3-MW rating. The fixed speed squirrel cage induction generator is used in this study to investigate its achievement under various operating conditions with/without the proposed control approaches.
## 3 Models Design
### Design of CNN+LSTM Hybrid Model  
The CNN+LSTM hybrid neural network structure in Figure 2 defines the shape of the input data through the Input layer; the Conv1D layer has 32 filters and a kernel size of five. The ReLU activation function is used to calculate the output. This is followed by down sampling using the MaxPooling1D layer and finally normalization via the Layer Normalization layer.  
The LSTM layer consists of 64 and 32 units, respectively, and it is used to model the representation and internal state of the sequence data. Here, the units of 64 in the LSTM model represent the number of neurons within the LSTM cell. The LSTM cell controls the input, forget, and output gates, and updates the state of the memory cell. To accomplish this, multiple neurons are utilized.  
Figure 1: CNN+Transformer hybrid neural network structure.  
The first units of 64 denotes the dimension of the hidden state, which is passed from the previous time step (t \(-\) 1) of the LSTM cell to the current time step (t) of the LSTM cell. The previous time step's hidden state is fed as an input into the current time step's LSTM cell and used in the current time step's computations. The second unit of 64 represents the number of neurons within the current time step's LSTM cell. These neurons are responsible for the calculations of the input, forget, and output gates, as well as the update of the memory cell, and units of 64 signifies that the LSTM cell contains 64 neurons internally.  
This indicates that the LSTM cell has a certain level of model complexity that is suitable for a specific task or set of data. Therefore, the appearance of units of 64 twice serves to denote the dimension of the hidden state and the number of neurons within the LSTM cell, respectively.  
Such configurations determine the model's complexity and expressive power, and choosing an appropriate number of neurons can significantly impact the model's performance and learning capabilities. Thus, in the given code, the CNN model and the LSTM model are used independently, where the output of the CNN model is passed as an input into the LSTM model for odor detection.
## 1 Introduction
The rationale for selecting a specific methodology is due to the underlying maintenance features' complex and nonlinear relationship. Specifically, we aim to identify the best-performing models and offer decision-makers better insight into the features' contribution. The MAI method helps understand the versatility across models by relying on the MDL (Mean Dropout Loss) algorithm. The MDL is not tied to a specific model but provides feature importance based on the loss function. In contrast to the traditional approach of simply relying on the model's performance and using features of the best-performing model, leading to biased estimates and ignoring essential features, we offer the quantitative impact of individual features and identify critical factors affecting maintenance performance. Such an approach compares features across models and aids in understanding feature contribution. In sum, the approach is considered more robust than solely relying on ML models' metrics that cannot be compared across models. Lastly, after extracting the most relevant features, we conduct a robust statistical analysis of individual features using a variety of hypothesis testing for categorical and continuous predictors. The rest of the manuscript is structured as follows. The second section describes the questionnaire-based survey, including the description of retrieved samples and features used in modelling. The section explains data (pre)processing, data wrangling, ML models with settings of (hyper)parameters, and the MDL algorithm. The third section offers in-detail results of descriptive statistics, ML performance metrics, and hypothesis testing findings derived from parametric and non-parametric analyses. The last section provides concluding remarks, as well as the limitations and implications of the study.
## 4 Control Analysis Based on Sensitivity
### Robustness  
The stability and robustness are analysed using the Nyquist trajectory defined as  
\[L(\text{I}\omega)=G_{\text{C}}(\text{I}\omega)G(\text{I}\omega) \tag{19}\]  
where \(\omega\) is the pulsation. Furthermore, adding 1 to (19) yields to \(S(\text{I}\omega)^{-1}\). Hence, it can be deduced that the stability is closely related to the sensitivity transfer function, \(S(s)\). In fact, the minimum distance from the point \((-1,0\text{I})\) to the Nyquist trajectory, \(\alpha_{s}\), matches the inverse of the maximum level of sensitivity defined as [35]  
\[\text{M}_{\text{s}}=\underset{\omega}{max}|S(\text{I}\omega)|=\frac{1}{\alpha _{s}}, \tag{20}\]  
Figure 8: Input sensitivity (\(S_{i}(s)\)) bode diagrams for the controller selection comparison.  
Figure 7: Close\(-\)loop bode (\(T(s)\)) diagrams for the controller selection comparison.  
to consider the system as robust, a M\({}_{\text{s}}\) value below 2 p.u. is recommended by [36]. Furthermore, (20) provides more conservative but also more concise information about stability than GM and PM [37]. This yields to  
\[\begin{split}\text{GM}^{*}&=\frac{1}{1-\alpha_{s}}\\ \text{PM}^{*}&=2\arcsin\Bigl{(}\frac{\alpha_{s}}{2} \Bigr{)}\end{split} \tag{21}\]  
where the superscript \({}^{*}\) denotes an alternative to GM or PM.  
Figure 9 outlines the ending part of the Nyquist trajectory for the case under study.  
From Figure 9, it is possible to obtain Table 4. Note that \(f_{\text{M}_{\text{s}}}\) represents that frequency where M\({}_{\text{s}}\) occurs. Evaluating the results, both PR and FPR options present similar robustness, but the PRHC exhibits worse values.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### KPI Alarm Clustering Based on Correlation Analysis
#### 3.4.1 Correlation Analysis of Abnormal KPIs  
A sliding window is added to analyze the correlation of abnormal KPIs based on Coflux [2]. The time series of two KPIs with length \(l\) after feature amplification using the Coflux method are \(X=(x_{1},x_{2},\ldots,x_{l})\) and \(Y=(y_{1},y_{2},\ldots,y_{l})\). For fixed sequence \(Y\), we let the sequencing slide forward and backward, and calculate the inner products for each sliding step \(|s|\) of sequence \(X\). The process is shown in Equations (3) and (4):  
\[X_{s}=\left\{\begin{array}{l}\overbrace{[0,\cdots,0}^{|s|}_{0}x_{1},\cdots,x_{l-s}],for\ s\geq 0\\ [x_{l-s},\cdots,x_{l}\underbrace{0,\cdots,0}_{|s|}],for\ s<0\end{array}\right. \tag{3}\]  
With sliding s, the cross-correlation values of sequence X and sequence Y are calculated as follows:  
\[CC(X_{s},Y)=\frac{\sum\limits_{i=-l+1}^{l-1}X_{s}[i]\times Y[i]}{\sqrt{(\sum \limits_{i=-l+1}^{l-1}X[i]\times X[i])\times(\sum\limits_{i=-l+1}^{l-1}Y[i] \times Y[i])}} \tag{4}\]  
where CC refers to cross-correlation and FCC refers to flux-based cross-correlation. This enumerates all s that can calculate relevant row vectors of length \(2l-1\). The final correlation value, FCC, between two sequences for all values of s is selected through the following calculation process, and the shift values of two sequences are obtained. The calculation process is shown in Equations (5)-(7):  
\[\min CC=\min_{s}(CC(X_{s},Y)),s_{1}=\underset{s}{\arg\min}(CC(X_{s},Y)) \tag{5}\]  
\[\max CC=\max_{s}(CC(X_{s},Y)),s_{2}=\underset{s}{\arg\max}(CC(X_{s},Y)) \tag{6}\]  
\[FCC(X,Y)=\left\{\begin{array}{l}[\min CC,s_{1}],for|\max CC|<|\min CC|\\ [\max CC,s_{2}],for|\max CC|\geq|\min CC|\end{array}\right. \tag{7}\]  
Through the above correlation calculation steps, we can obtain correlation value FCC and shift value \(|s|\) between two KPIs, as well as the temporal direction of sequence \(X\) and sequence \(Y\), that is, the order of influence can be obtained via the positive and negative values of \(s\). When the FCC value is within \([-1,1]\), the closer the value to \(-1\) or \(1\), the stronger the correlation to sequence \(X\) or \(Y\). The shift value \(|s|\) indicates the possible time interval between sequences \(X\) and \(Y\). In the actual monitoring of KPIs, if two are affected by the same anomaly, the shift time interval is short, and so it needs to be analyzed according to different system data. If the time interval is exceeded, this indicates that there is no correlation between two KPIs, even if the correlation value is high. By analyzing the above problems, this paper adds a sliding window to the Coflux method to improve the calculation speed. The length of the sliding window is set according to the shift values of KPI and the abnormal duration. Finally, the correlation value, shift value, and temporal order after adding the sliding window are obtained.
## 4 Application
### Visualization of Traffic Density  
First, the received AIS data were organized into vessel-specific voyage data to identify the traffic density for area \"A\" of the target sea area. Subsequently, the cumulative data of the number of vessels passing through each cell were calculated for various grid sizes; the values are presented as a heatmap in Figure 7. Notably, the three different grid sizes (5, 6, and 7) tested revealed that the six-step grid provided the most suitable visualization of the maritime traffic density information.
# Biocompatible Dispersants for Carbon Nanomaterials
## 6 Biocompatible Dispersants
### Alkyl Chain Surfactants
Scanning electron microscopy data also show that these surfactants produce high-quality dispersions consisting of individual nanotubes [54] (Table 4 (7)). Gemini surfactants with cyclic linkers containing both ionic groups have also been reported [57]. Cyclic linkers could be more efficient than straight-chain linkers due to better packing of head groups resulting from their decreased mobility. However, this has yet to be investigated using CNMs. Little is known about the biocompatibility of these molecules. There is also a lack of literature on the use of Gemini surfactants to solubilise CNOs. However, the versatility and superior properties of these molecules may prove advantageous in producing stable, biocompatible CNO dispersions. Whilst anionic Gemini surfactants exist [58], there is no literature comparing them directly to their cationic counterparts in dispersing CNMs. Anionic surfactants may be more efficient than anionic Gemini dispersants because of the effects discussed earlier. However, more work will have to be done to investigate this. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Num.** & **Dispersant** & **CNM** & **Description** & **Application** & **Ref.** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{CTAB, SDBS} & \multirow{3}{*}{CNO} & \multicolumn{2}{c}{CNOs non-covalently functionalised} & \multirow{3}{*}{Dispersion efficiency and long-term stability studies} & \multirow{3}{*}{[48]} \\  & & & with two different surfactants to form & & \\  & & & two stable dispersions. & & \\  & & & A collection of ACS/CNO dispersions & & \\
2 & SDS, SDBS, CTAB & CNO & that includes a wide range of CNO- & & \\  & & & surfactant mass ratios & & \\
3 & SDBS, SDS, lauryl & MWCNT, & MWCNTs and graphene solubilised & & \\  & & & with a range of ACSs & & \\
4 & SDS, LDS, SDBS, & & & & \\  & TTAB & SWCNT & A range of SWCNT/surfactant dispersions & & \\  & & & & & \\
5 & SDS/Surfhorpe 1216 & MWCNT & MWCNTs dispersed via a surfactant & & \\  & & & substitution strategy, using SDS to & & \\  & & & de-bundle & & \\  & & & A series of alkyloxy & & \\
6 & IMIC6C12, and & MWCNT & methylimidazolium surfactants used & & \\  & & & to produce MWCNT dispersions & & \\
7 & 16-2-16 & & & A series of bis(quaternary & \\
7 & 12-12-12 & MWCNT & ammonium) Gemini surfactants used & & \\  & & & to solubilise MWCNTs & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Alkyl chain surfactant-based CNM dispersions. Whilst they may not be the most efficient surfactants, ACSs are ubiquitous in the literature and have found many uses in solubilising many different types of CNMs. They are often used as benchmarks for comparison against other dispersants. ACSs excel at de-bundling CNTs and can produce high-quality dispersions of single CNTs. Surfactant substitution methods enable the exploitation of these properties whilst combating ACSs poor efficiency. Gemini surfactants are an exciting new class of ACS that seek to improve their dispersing abilities, yet not much is known about them.
## 4 Biomedical Applications of ML-Assisted SWIR Techniques  
Table 1 summarizes the ML models and research highlights for a collection of recent SWIR work reviewed in this article. In general, ML can provide valuable support to SWIR techniques in three distinct ways: (1) Assistance in Diagnosis: ML can effectively analyze SWIR data and images to aid clinicians in making more accurate diagnoses; (2) Quantitative imaging and prognosis: ML can be leveraged to extract quantitative data from multifactorial SWIR datasets, enabling the extraction of precise information from biological systems (e.g., for disease staging); (3) Overcoming technological limitations: ML also has the potential to enhance the performance of SWIR technologies (e.g., increase imaging speed or image quality) without any hardware modification.