esa-sceva/satcom-chunk-collection huggingface dataset:
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
## 3 GNSS Single-Epoch Ambiguity Resolution
### GNSS Single-Epoch Ambiguity Resolution Based on Robust Estimation  
The algorithm mentioned above can yield the GNSS single-epoch AR over a short-range baseline. However, the AR success rate of BDS carrier phase observation is not 100%. The mis-fixing ambiguity will certainly bring an error to the tropospheric delay used to restrict the GPS/GLONASS observation equation. Moreover, it is difficult to avoid the effect of gross observational error on the data processing. In this study, to overcome the issues mentioned above, a robust estimation is used. The effect of these errors is rejected by using a reasonable weight.  
According to the theory of robust estimation, the M estimation is  
\[\widehat{X_{kM}}=\left(A_{k}^{T}\overline{P}A_{K}\right)^{-1}A_{k}^{T} \overline{P}L_{k} \tag{31}\]where \(\overline{P}\) represents the equal weight matrix. The common equal weight functions include the Huber weight function, Hampel weight function and Tukey function. According to the experimental analysis, we choose the IGG (Institute of Geodesy and Geophysics) weight function [28]:  
\[\overline{P}=\left\{\begin{array}{cl}\mathrm{P_{i}}&\mathrm{|V_{i}|<k_{0}}\\ \mathrm{P_{i^{\prime}}}\frac{\mathrm{k_{0}}}{\mathrm{|V_{i}|}}\frac{(\mathrm{ k_{1}}-\mathrm{|V_{i}|})^{2}}{(\mathrm{k_{1}}-\mathrm{k_{0}})^{2}}&\mathrm{k_{0} \leq|V_{i}|<k_{1}}\\ 0&\mathrm{k_{1}\leq|V_{i}|}\end{array}\right. \tag{32}\]  
In this stage, two key issues need to be solved [31]. The first is the definition of \(\mathrm{k_{0}}\) and \(\mathrm{k_{1}}\), the second is the choice of weight. Generally, \(\mathrm{k_{0}}\) and \(\mathrm{k_{1}}\)are set as constant, e.g., \(\mathrm{k_{0}\in[1.0\sim 1.5]}\), \(\mathrm{k_{1}\in[3.0\sim 8.0]}\). If we consider the robust equation of both the parameter matrix and the observation space, the mean value of the number of redundant observations is \(\sqrt{(n-m)/n}\); \(\mathrm{n}\) and \(\mathrm{m}\) are the numbers of observations and parameters, respectively. As a result, the value of \(\mathrm{k_{0}}\) and \(\mathrm{k_{1}}\) should be \(\mathrm{k_{0}=k_{0}}^{\prime}\cdot\mathrm{k}\) and \(\mathrm{k_{1}=k_{1}}^{\prime}\cdot\mathrm{k}\), where \(\mathrm{k_{0}}^{\prime}\in[1.0\sim 1.5]\), \(\mathrm{k_{1}}^{\prime}\in[3.0\sim 8.0]\), and \(\mathrm{k}=\sqrt{n/(n-m)}\). These initiatives make the selection of \(\mathrm{k_{0}}\) and \(\mathrm{k_{1}}\) vary with different \(\mathrm{n}\) and \(\mathrm{m}\), increasing the flexibility of the robust estimation. Generally, the estimation of Equation (32) usually involves the iterative method; the iterative solution of step t + 1 should be  
\[\widehat{X_{kM}}^{\mathrm{t+1}}={(A_{k}^{T}\overline{P^{t}}A_{K})}^{-1}A_{k}^{ T}\overline{P^{t}}L_{k} \tag{33}\]
## 2 Data Analysis and Preprocessing
### Correlation Analysis of NWP Data  
Due to the high fluctuation and the insufficiency of wind power data, it is inadequate to meet the demand of accurate power prediction only by mining its inherent data characteristics [28, 29, 30]. Utilizing NWP data, which are readily accessible and highly reliable, can aid in model extraction for comprehensive power feature analysis and extraction. In order to verify the validity of NWP data, the Pearson Correlation Coefficient (PCC) was used to calculate the correlation level between the data. Its calculation formula is as follows:  
\[\text{Corr}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{E[(X-\mu_{X})(Y -\mu_{Y})]}{\sigma_{X}\sigma_{Y}} \tag{1}\]  
where, \(X,Y\) show the power data and NWP data sequence; \(\text{cov}(X,Y)\) stands for the covariance between data; \(\sigma_{X},\sigma_{Y}\) mean the variance in data; \(\mu_{X},\mu_{Y}\) denote the average of data; and \(E\) denotes expectation.  
In statistics, it is generally believed that, when \(\text{Corr}\geq 0.6\), there is a strong correlation that exists between the data; when \(0.4<\text{Corr}<0.6\), a moderate correlation is observed between the data; and, when \(\text{Corr}\leq 0.4\), a weak correlation is evident between the data. In this paper, the annual power data and NWP data are analyzed by PCC, and a correlation score of \(\text{Corr}=0.7044\) is derived, which shows that there is a strong correlation between them.  
In addition, we took T =96 as the sliding time window to select power data \(\{P_{i},P_{i+1},\cdots,P_{i+T-1}\}\in\mathbb{R}^{\text{T}\times 1}\) and weather data \(\{W_{i},W_{i+1},\cdots,W_{i+T-1}\}\in\mathbb{R}^{\text{T}\times\text{F}}\), and calculated the correlation coefficients represented by wind speed and wind power output separately. We obtained the correlation waveform within 2 weeks, as shown in in Figure 2(a).  
As can be seen, for most of the time, the data exhibit a significant degree of correlation, with only brief periods of low correlation observed. To observe the correlation level of data more intuitively, the wind power data within one of the time windows were selected for waveform comparison as shown in Figure 2(b). The correlation coefficient of this graph is 0.88. It can be seen that there is a high similarity between the trend in wind speed and the trend in power output over a 2-week time window. This also confirms that this set of data is highly correlated. Unlike the strong randomness in wind power, meteorological data such  
Figure 3: The chart of NWP data correlation analysis. All data are standardized by Z-Score. The sampling period is 15 min.  
as wind speed can be accurately predicted through physical modeling and other methods, and the prediction technology is also relatively mature. Therefore, this section proves the high correlation between NWP data and wind power data, and verifies the feasibility of using it as auxiliary information to improve the accuracy of model prediction after feature fusion with historical data.
## 5 Conclusions  
Three-dimensional computational fluid dynamic analysis was carried out to investigate an air-cooling Li-ion battery module with the aluminium foam heat sink (AFHS). The effects of porosity and pore density of homogeneous AFHS on the thermal management performance of the battery module were investigated from the aspects of pressure drop \(P_{d}\), battery average temperature \(T_{av}\) and temperature difference \(T_{d}\). Furthermore, four types of gradient porosity designs with the different porosity controlling parameter \(m\) were tested and compared. The results led to the following conclusions:  
Compared with not using AFHS, the use of AFHS significantly reduces the average temperature of the battery, but also brings the consequence of the increase of the pressure drop and temperature difference. The decrease of the porosity of AFHS reduces the battery average temperature, but increases the temperature difference. Increasing the pore density of the AFHS will lead to increases in pressure drop, but has little effect on thermal performance.  
The gradient porosity designed in the direction perpendicular to the flow can improve flow performance, and reduce the pressure drop. Although the thermal performance of the battery module with the AFHS of porosity-increasing gradient pattern (PIGP) is better than that with the AFHS of porosity-decreasing gradient pattern (PDGP), neither of the two types of gradient porosity designs reduces the average temperature of the battery compared to the homogeneous porosity AFHS. Considering the energy consumption and thermal performance, the porosity-increasing pattern with m = 0.1 is the best design among the six configuration designs.  
The gradient porosity designs in the flow direction have no obvious effect on the pressure drop. However, under the same porosity level, compared with homogeneous porosity AFHS, the AFHS of PDGF reduces the battery temperature difference, while the AFHS of PIGP increases the battery temperature difference. Using the AFHS of PDGF m = 1, the minimum temperature difference \(T_{d}\) = 3.10 \({}^{\circ}\)C is obtained at \(q_{m}\) = 7 gs\({}^{-1}\), which is 16.2% lower than that of AFHS of homogeneous porosity.  
Conceptualisation, P.W. and D.Q.; formal analysis, T.W.; methodology, P.W.; project administration, D.Q. and T.W.; supervision, D.Q.; validation, J.C.; writing--original draft, P.W.; writing--review and editing, P.W. and J.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program \"New energy vehicles\" of China (Project No.2018 YFB0106204).  
Figure 19: FOM with AFHS with gradient porosity designed in the \(X\)-axis direction.
## 4 Design of the Wireless MEC Sensor
#### Accelerated Corrosion Test Setup and Results  
An accelerated corrosion test was set up to test the sensor for real-time application. Diluted sulfuric acid is reported to be highly corrosive to mild steel [37]. A corrosion rate as high as 110 gm/dm\({}^{2}\)/day was observed when the carbon steel samples were soaked in 10% \(v/v\) sulfuric acid for 24 h at room temperature [38]. The addition of water in the concentrated sulfuric acid results in an increase in the corrosion rate of carbon steel due to the solubility of ferrous sulfate protective layer formed by the acid on the steel surface [39].  
Therefore, a 20% \(v/v\) sulfuric acid solution was used to carry out the test. An 8 mm thick S275 mild steel plate sample was kept in a cell containing the acid for 72 h. A temperature and humidity sensor (AM 2302) was also installed at the test site to record the environmental data. The sensor was programmed to record the inductive reactance after every minute and transmit it to the data cloud. The experimental setup and the corroded steel plate are shown in Figure 14.  
At stages through the procedure, the corrosion products were cleaned for accelerating the corrosion process. There was a measurement noise in the signals due to the electronic components. A 10-point moving average filter was used to de-noise the signals. The results from the corrosion and AM 2302 sensors are shown in Figure 15 and Figure 16, respectively.  
The graph of rate of change of reactance with respect to time in hours is shown in Figure 17. It is evident from the Figure that the corrosion rate is high at the start of the test due to the absence of corrosion products that form a protective layer around the sample.  
Comparison of the data from the sensors shows that the sensor is sensitive to corrosion defects. There are no subtle differences in the signals due to the variation in temperature or humidity. There are some points where the sensor readings have a huge variation from the trend due to the movement of the sample for cleaning of the corrosion products.  
Figure 13: The electronic design of the sensor.  
Figure 14: Experimental set up and corrosion in the steel plate at different stages: (**a**) experimental set up, (**b**) corrosion after 12 h, (**c**) corrosion after 24 h, and (**d**) corrosion after 72 h.  
Figure 15: The signals of the MEC sensor recorded for 72 h.
## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System
### Final Classification After Treatment Module
#### 4.3.3 Achieved Results
* **Moderate performers:** Models such as **gradient boosting**, **Extra Trees** and **CatBoost** showed competitive performance with a slight decrease in accuracy compared to the top models (from around 0.87 to 0.88). These results suggest that these models perform well but require further fine-tuning or adjustments. * **Traditional and linear models: Logistic regression** and **SVC** achieved mean accuracy scores of 0.92 to 0.89. These models performed reliably but with slightly less accuracy than the ensemble methods. * **K-nearest neighbors, AdaBoost**, and the **MLP classifier** also showed decent performance (from 0.85 to 0.87), but their results indicated limitations in handling the dataset's complexity. The results suggest that ensemble methods, particularly **LightGBM**, **random forest**, and **Bagging**, offer the best performance for this classification task. Their high accuracy and low variability make them suitable candidates for clinical decision support systems where reliability and precision are crucial. On the other hand, **AdaBoost** underperformed significantly, highlighting the importance of selecting and tuning models appropriately for the dataset. The findings also emphasize that traditional methods like **logistic regression** and **SVC** can perform well in many scenarios, although they may not be as robust as ensemble methods. These insights guided the selection and further optimization of models in the clinical decision-making framework for FSGS treatment outcomes. \begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Mean Accuracy** \\ \hline Random forest & 0.93 \(\pm\) 0.01 \\ \hline Support Vector Classifier (SVC) & 0.89 \(\pm\) 0.01 \\ \hline Logistic regression & 0.92 \(\pm\) 0.02 \\ \hline XGBoost & 0.86 \(\pm\) 0.01 \\ \hline K-nearest neighbors (KNN) & 0.85 \(\pm\) 0.02 \\ \hline Decision tree (DT) & 0.89 \(\pm\) 0.01 \\ \hline Gradient boosting (GB) & 0.87 \(\pm\) 0.01 \\ \hline Adaptive boosting (AdaBoost) & 0.86 \(\pm\) 0.04 \\ \hline Extra Trees (ET) & 0.87 \(\pm\) 0.02 \\ \hline Bagging & 0.92 \(\pm\) 0.01 \\ \hline LightGBM & 0.93 \(\pm\) 0.01 \\ \hline CatBoost & 0.88 \(\pm\) 0.02 \\ \hline Linear SVC & 0.89 \(\pm\) 0.02 \\ \hline Naive Bayes & 0.86 \(\pm\) 0.01 \\ \hline MLP classifier & 0.87 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cross-validation accuracy scores for the evaluated models.
## 3 Self-Triggered Control for Islanded Microgrids under DoS Attacks
### Improved Ternary Self-Triggered Control Law Under DoS Attacks
\tag{15}\]  
where \(t_{k}^{ij}\) and \(t_{k+1}^{ij}\) are the \(k\)th and (\(k\)+1)th successful communication time instants, respectively. Additionally, \(\delta^{ij}_{l}(t):0\to 1\) denotes the latest detection that the DoS attacks shifts to a sleep period. For the sake of clarity, our improved ternary self-triggered control law is summarized in Algorithm 1 and illustrated as below. ```
Initialization: For agent \(DG_{i}\)\((i=1,\ \ldots,N)\) and \(j\in N_{i}\), set clock variable \(\theta^{ij}(0)=0\), detection function \(\delta^{ij}(t)=1\), control input \(u^{ij}(0)=0\), and state variable \(\dot{x}^{AVE}_{i}(0)=\sum\limits_{j\in N_{i}}u^{ij}(0)\). for\(i=1\) to \(N\)do for\(j=1\) to \(N_{i}\)do while\(\theta^{ij}(t)>0\)do \(\dot{x}^{AVE}_{i}(t)=\sum\limits_{j\in N_{i}}u^{ij}(t)\); endwhile if\(\left\{(i,j)\in S_{1}(t)\ \wedge\ \delta^{ij}(t)=1\right\}\text{or }(i,j)\in S_{2}(t)\)then update \(u^{ij}(t)=\delta^{ij}(t)\text{sign}_{\varepsilon}\Big{(}\text{dev}^{ij} \Big{)}\);  update \(\theta^{ij}(t)=f^{ij}(x(t))\); endif if\((i,\ j)\in S_{1}(t)\ \wedge\ \delta^{ij}(t):1\to 0\)then update \(u^{ij}(t)=0_{\widetilde{i}}\); endif if\(\delta^{ij}(t)=0\)then \(DG_{i}\) constantly sends test data packets to \(DG_{j}\);  When \(DG_{i}\) receives the ACK message from \(DG_{i}\), update \(\delta^{ij}(t):0\to 1\); endif endfor endfor
```  
**Algorithm 1** Improved ternary self-triggered control law  
**Remark 4:**_It is worth noting that when \(DG_{i}\) is plugged out, the neighbors of \(DG_{i}\) are unable to receive its state information either. In order to accommodate for the topology changes and plug-and-play operation, each \(DG\) is given a unique ID and equipped with a local information table to store the connectivity information of the communication network. This table can be represented by a \"0-1\" matrix, where \"0\" indicates the corresponding communication link is absent and \"1\" indicates the corresponding communication link is present. When \(DG_{i}\) is plugged out, it will send a message to its neighboring \(DG_{i}\) (\(j\in N_{i}\)), and \(DG_{j}\) will delete the corresponding ID and update the local information table as well as its node degree \(d_{j}\). When a new \(DG\) (named \(DG_{N+1}\)) is plugged into the microgrid, it will be given a new ID and try to find the nearest neighbors with the flooding-based graph discovery algorithm [36]. The neighbors of \(DG_{N+1}\) will update their local information table and node degrees, and then interact with \(DG_{N+1}\) to implement the secondary control scheme proposed in this paper._
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 5 Discussion  
The experimental results presented in Section 4 demonstrate the efficacy of the proposed VAE-CNN framework for real-time anomaly detection in V2X BSM data. This section discusses the interpretation of these results, the implications for V2X security, the limitations of the current study, and directions for future research.
## 4 Design and Implementation
### Product Design Workflow
In addition to receiving sensor information, each device itself can determine the local environment model according to its location, incorporating year-round sun trajectory and temperature and humidity information, to adjust the device. These devices also do not operate independently; they can form an information collection and communication network. Several nearby devices can collect environmental information in the region, ensuring the accuracy of the mutually authenticated data. After a long period of environmental information collection, they can use these data to further optimize the environment.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Evading Immune Response  
The sRNAs discussed in the previous sections also contribute to the growth of bacterial pathogens inside the host and at the same time equip the bacterial pathogen with defense to evade the host immune response. For example, RyhB and OxyS are known to regulate iron homeostasis and oxidative stress response to mitigate host immune defense and thus have emerged as major players of virulence in pathogenic strains of _E. coli_[105; 106; 107], _Vibrio_[108], _Salmonella_[65], and other Gram-negative bacteria. Similarly, bacteria have evolved diverse strategies to evade the host immune response, enabling their survival and persistence within the host [12; 14]. One common approach is the modulation of surface structures to avoid recognition by the host's immune system. For instance, altering or masking pathogen-associated molecular patterns (PAMPs), such as lipopolysaccharideradides or flagellin, prevents the activation of Toll-like receptors (TLRs) on immune cells [109; 110]. Bacteria also secrete effector proteins through specialized secretion systems (e.g., Type III and Type IV secretion systems) to manipulate host cell signaling pathways, suppress pro-inflammatory responses, and inhibit apoptosis [61; 111] (Table S3). Additionally, some pathogens form biofilms, a protective extracellular matrix that shields them from phagocytosis and antimicrobial agents [112]. Intracellular pathogens like _Salmonella_ and _Listeria_ exploit host cells by residing in modified vacuoles or escaping into the cytosol, thus avoiding lysosomal degradation [113; 114]. There is growing evidence about the role of sRNAs in immune evasion. For example, sRNAs modulate the expression of virulence factors that interfere with cytokine production, or inhibit oxidative stress pathways to enhance bacterial survival [115]. These multifaceted evasion tactics underscore the sophisticated interplay between bacteria and host immune defenses, as discussed in the following examples.
## 3 Results and Discussion
### In Silico Calculations  
The compound's drug-likeness and ADMET characteristics have also been theoretically predicted using an in silico approach. When this non-covalent contact is included in the  
Figure 5: The distributions and energy surfaces of the molecular orbitals of SQ.  
drug design, it causes the conformational restriction of tiny drug molecules, which results in increased pharmacological activity, lipophilicity, and membrane permeability [46; 47].  
To be deemed a potential drug candidate, a molecule must fulfill three requirements: it must enter the body, reach its pharmacological target, accumulate sufficient concentration at the target site, and remain in active form for an extended period of time to demonstrate the expected biological activity. Several substances failed as medicines due to their limited bioavailability and poor pharmacokinetics.  
PASS Online predicted the anti-inflammatory and antioxidant activity of 5.  
The ADME profile of the SQ was clarified using the SwissADME web tools (Table 1). Eight physicochemical characteristics, including the four factors outlined in [PERSON]'s \"rule of five\", are used to measure a compound's drug-likeness [48; 49; 50]. A molecular weight less than 500 g/mol, XLOGP3 (\(-\)0.7 to +5.0), topological polar surface area between 130 A2, predicted solubility not exceeding 6, and flexibility or rotatable bonds not exceeding 9 are some of the requirements.  
The synthesized SQ salt satisfied the standard values of the drug-likeness notion by having three rotatable bonds, a molecular weight of 483.58 g/mol, and a TPSA of 107.41 A2. Good oral absorption was associated with the compound's bioavailability score of 0.55 [51]. It also has an excellent synthetic accessibility score of 3.22, which is a crucial factor in the process of finding new drugs. Cytochrome P450 (CYP) inhibition results in pharmacological toxicity [52; 53]. The named molecule is not anticipated to inhibit the CYP1A2, CYP2C9, CYP2C19, CYP2D6, or CYP3A4 isoforms, according to the calculations. With a log Kp of \(-\)4.62, the compound demonstrated good skin permeability. The SQ's toxicity falls into the safer zone (toxicity class: 5), according to acute toxicity data (ProTox-II). Since the compound's calculated LD\({}_{50}\) was 5000 mg/kg, it can be regarded as nearly non-toxic. While there was no evidence of other organ toxicities, immunotoxicity was expected to be present (0.71% chance).  
To confirm the predicted biological activities, the compound was assessed in vitro for its antioxidant capacity and in vitro/ex vivo for its anti-inflammatory potential.