esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Overview
### Experimental Setup  
In the experimental setup, we use the _River_ Python module to implement and evaluate various tree-based models and algorithms on evolving data streams. Python has been widely adopted in scientific development in recent years due to its versatility, extensive support for libraries in machine learning and active community support. Therefore, it is used in this study. RiverML provides a robust framework for continuous learning and concept drift detection, which are essential for handling real-time data streams. Three CPS datasets are considered for the anomaly injection and adaptive online classification models: Fumehood data, Vacuum Pump data and Lithography data. The data for each CPS component consists of time-series data collected over a period of one month from different sensors attached to each CPS, as discussed in Section 4.1. Each dataset is split into 70% for training and 30% for testing. The raw data collected from the CPS sensors are considered normal, and to simulate drifts with anomalies, we inject different distributions of anomalies into the training and testing data as discussed in Section 4.2. The datasets are treated as binary datasets with two labels: \"normal\" for the unaltered data and \"abnormal\" for the samples where noise was injected in either the training or testing set.  
To evaluate the performance of the proposed TWIN-ADAPT's adaptive anomaly classification framework, we use hold-out validation. The model is trained on the training set and then evaluated on the test set. Given that the datasets are unbalanced, we utilize four different metrics to assess the model's performance: accuracy, precision, recall and F1 score. These metrics provide a comprehensive evaluation of the model's ability to distinguish between normal and abnormal states, reflecting its effectiveness in identifying CPS anomalies and attacks in real time.
# Research on Influence of Tank Sloshing on Ship Motion Response under Different Wavelengths
## 5 Simulation of Rolling Motion of Liquid Carrier in Transverse Waves Condition
### Influence of Liquid Loading Rate on Rolling Motion of Ship
It can be seen from Figure 21c that when the wavelength of the incident wave is 15 m, the order of the ship roll angle with different liquid loading rates is: \(\theta_{0.8}>\theta_{0.4}>\theta_{0}\). Under this condition, the sloshing of the tank also promotes the rolling motion of the ship. Tables 10-12 analyze the ratio of the natural frequency of the ship's roll to the frequency of the incident wave. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Liquid Loading Rate** & **Roll Natural Frequency** & **The Ratio of Natural Frequency to** \\  & **(rad/s)** & **Incident Wave Frequency** \\ \hline
0.8 & 2.417 & 0.533 \\
0.4 & 2.705 & 0.597 \\
0 & 3.634 & 0.802 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ratio of ship roll natural frequency to incident wave frequency when incident wave wavelength is 3 m.  
Figure 21: Rolling time history curves of ships with different liquid loading rates under different wavelengths. (**a**) Wavelength 6 m; (**b**) wavelength 11 m; (**c**) wavelength 15 m.  
The same conclusion can be drawn from the above three tables: the closer the incident wave frequency is to the natural frequency of the ship's rolling motion, the greater the amplitude of the ship's rolling motion will be. Therefore, it can be said that tank sloshing changes the amplitude of the ship's motion by changing the natural frequency of the ship's motion to make it different from the incident wave frequency.
###### Abstract  
The optical photoluminescent (PL) emission of Yb\({}^{3+}\) ions in the near infrared (NIR) spectral region at about 950-1100 nm has many potential applications, from photovoltaics to lasers and visual devices. However, due to their simple energy-level structure, Yb\({}^{3+}\) ions cannot directly absorb UV or visible light, putting serious limits on their use as light emitters. In this paper we describe a broadband and efficient strategy for sensitizing Yb\({}^{3+}\) ions by Ag codoping, resulting in a strong 980 nm PL emission under UV and violet-blue light excitation. Yb-doped silica-zirconia-soda glass-ceramic films were synthesized by sol-gel and dip-coating, followed by annealing at 1000 \({}^{\circ}\)C. Ag was then introduced by ion-exchange in a molten salt bath for 1 h at 350 \({}^{\circ}\)C. Different post-exchange annealing temperatures for 1 h in air at 380 \({}^{\circ}\)C and 430 \({}^{\circ}\)C were compared to investigate the possibility of migration/aggregation of the metal ions. Studies of composition showed about 1-2 wt% Ag in the exchanged samples, not modified by annealing. Structural analysis reported the stabilization of cubic zirconia by Yb-doping. Optical measurements showed that, in particular for the highest annealing temperature of 430 \({}^{\circ}\)C, the potential improvement of the material's quality, which would increase the PL emission, is less relevant than Ag-aggregation, which decreases the sensitizers number, resulting in a net reduction of the PL intensity. However, all the Ag-exchanged samples showed a broadband Yb\({}^{3+}\) sensitization by energy transfer from Ag aggregates, clearly attested by a broad photoluminescence excitation spectra after Ag-exchange, paving the way for applications in various fields, such as solar cells and NIR-emitting devices.  
sol-gel; silica-zirconia; glass-ceramics; Ag nanoaggregates; Yb\({}^{3+}\) ions; energy transfer; downshifting; photoluminescence +
Footnote †: journal: Applied Physics Letters  
**2020**, _10_, 2184; doi:10.3390/app10062184
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 4 Proposed CCB-Guided Adaptive Channel Denoising Mechanism
### Denoiser Parameter Selection Mechanism  
Algorithm 4 outlines the image processing process. First, the input image is processed to feature vector by the encoder. The transmitter will then check if it is the first transmission. If it is, the transmitter sends the point feature vector \(\mathbf{x}_{0}\), which is generated from a fixed image; the receiver receives \(\mathbf{x}_{0}^{\prime}\) after the wireless channel and compares it with the pre-stored fading vector \(\mathbf{x}_{n}^{\prime}\) (\(n=1,2,\ldots,N\)) to choose the closest vector \(\mathbf{x}_{\text{min}}^{\prime}\). When it obtains \(\mathbf{x}_{\text{min}}^{\prime}\), the denoiser will set the denoiser parameter \(\theta\), converted by \(\mathbf{x}_{\text{min}}^{\prime}\). After completing the above steps, the subsequent transmission image will pass the denoiser using this parameter model and enter the decoder for image reconstruction.
###### Abstract  
Facing engineering challenges of real-time and high-precision recognition of strongly heterogeneous formations during directional drilling, it is crucial to address the issues of sparse lithology geological labels and multi-source lithology identification from LWD data. This paper proposes a real-time intelligent recognition method for strongly heterogeneous formations based on multi-parameter logging while drilling and drilling engineering, which can effectively guide directional drilling operations. Traditional supervised learning methods rely heavily on extensive lithology labels and rich wireline logging data. However, in LWD applications, challenges such as limited sample sizes and stringent real-time requirements make it difficult to achieve the accuracy needed for effective geosteering in strongly heterogeneous reservoirs, thereby impacting the reservoir penetration rate. In this study, we comprehensively utilize LWD parameters (six types, including natural gamma and electrical resistivity, etc.) and drilling engineering parameters (four types, including drilling rate and weight on bit, etc.) from offset wells. The UMAP algorithm is employed for nonlinear dimensionality reduction, which not only integrates the dynamic response characteristics of drilling parameters but also preserves the sensitivity of logging data to lithological variations. The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. In the Hugin Formation of the Volve field in the Norwegian North Sea, the application of UMAP demonstrates superior data separability compared with PCA and t-SNE, effectively distinguishing thin reservoirs with strong heterogeneity. The CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3% in six lithology classifications. This approach delivers high-precision geo-engineering decision support for the real-time control of horizontal well trajectories, which holds significant implications for the precision drilling of strongly heterogeneous reservoirs.  
Keywords:heterogeneous formations; lithology recognition; Logging While Drilling; precise drilling; CatBoost +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Mechanical Properties  
The fumigation of alligator plants with gaseous ozone impacted selected mechanical properties of the leaves measured using an indentation test 10 days after the treatment (Table 1). The values of destructive force F\({}_{\text{max}}\) in the case of leaves from ozone-treated plants were lower than F\({}_{\text{max}}\) values identified in untreated leaves (0 min 0 ppm). The differences, however, were not statistically significant, and the values ranged from 3.06 N in leaves from the control sample (0 min 0 ppm) to 2.83 N in the case of leaves treated for 1 min with ozone at a rate of 10 ppm. The lower F\({}_{\text{max}}\) values identified in ozone-treated leaves of alligator plant most possibly can be linked to the fact that ozone penetrated through the cuticle and the upper epidermis into the deeper layers of leaves which resulted in a slight decrease in their resistance to the operation of external forces. Nevertheless, ozone penetrating into the deeper layers did not destroy the leaves since no necroses nor surface damage were observed in the ozone-treated leaves of _Kalanchoe dagremontiana_. The process, however, caused oxidative stress which induced an elevated production of secondary metabolites and small-molecule antioxidants (Figures 1 and 2). An additional effect produced may involve a change in the activity of proteolytic enzymes, which directly affects the condition of cell walls. As shown by [PERSON] [47] and [PERSON] [48], these enzymes directly affect the lysis of polysaccharides, which are the main building blocks of the cell wall; this may lead to changes in the mechanical properties of ozone-treated plants. A similar effect, observed in the case of flour treated with ozone [49], impacted the rheological properties of baked products made from this type of flour. The current findings also show a negative correlation between values of destructive force and total polyphenol contents, as well as antioxidant potential identified in leaves treated with the specific doses of ozone investigated in this study. A relationship between ozone treatment applied to plant material and F\({}_{\text{max}}\) value measured using an indentation test was also reported by other researchers. This effect, however, was varied depending on the plant organ which was subjected to ozone treatment. [PERSON] et al. [50] treated rhubarb petioles with ozone and found an increase in their resistance to external force measured during the indentation test and a process of compression of independent specimens. Similar findings were reported by [PERSON] et al. [23], who demonstrated that a higher dose of ozone applied corresponds to an increase in destructive force, as identified in the case of sea bucktchorn _Hippophae rhamoides_ L. On the other hand, when ozone treatment was applied to apples of the Ligol variety, the researchers found that the mean F\({}_{\text{max}}\) value measured using an indentation test was related to the colour of the fruit. The measurement showed that the F\({}_{\text{max}}\) value was higher on the blush side in
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 3 Results
### Sensitivity Analysis  
To investigate the relationship between the price of natural gas and the LCOH in Nigeria, more scenarios were generated using the sensitivity factor calculation. The results from the different scenarios are then analyzed to determine the changes that occur to LCOH when the gas price is varied.  
\[\text{Sensitivity factor}\;\frac{LCOH}{Gas\;price}=0.814 \tag{10}\]  
This translates to a rise of around USD 0.80/kg H\({}_{2}\) for every increase in gas price per MMBTU in LCOH.  
Given Nigeria's vast natural gas reserves, its current price of USD 2.42/MMBTU is significantly lower than what other countries, such as Germany and the Netherlands, pay, which ranges from USD 6 to USD 12/MMBTU due to their reliance on imports or market conditions. The sensitivity analysis in Figure 8 shows that the LCOH becomes highly competitive at lower gas prices. In other words, an increase in natural gas price usually leads to an increase in LCOH. This makes Nigeria's blue hydrogen highly competitive compared to other countries with higher gas prices. As a result, Nigeria could become a key hub for the commercial production of blue hydrogen.
## 3 Compression Algorithm Design
### Performance Analysis of the Proposed Algorithm
#### 3.3.1 PhysioNet Database  
PhysioNet [19] was established in 1999 as the outreach component of the Research Resource for Complex Physiologic Signals cooperative project. From this database signals were extracted and used as evaluation testbeds of the implemented algorithms from the following two specific subcategories:  
1. _Apnea-ECG Database_  
This database has been assembled for the PhysioNet/Computers in Cardiology Challenge 2000 [20]. From this database the ecgA04 apnea and ecgB05 apnea used in the evaluation process have been acquired.  
2. _CHB-MIT Scalp EEG Database_  
This database [21], collected at the Children's Hospital Bos-ton Massachusetts Institute of Technology (MIT), consists of EEG recordings from pediatric subjects with intractable seizures [22].
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 4 Enhanced Routing Solutions for Internet of Things Networks  
Currently, the routing solutions proposed for IoT/LLN are performed with the improvement of existent and well-known base protocols. Although several protocols have been used as inspiration, the principal and most recent approaches are based on the RPL and LOADng. Figure 4 presents a taxonomy of studied routing solutions, divided according to their proposals and motivation. Each considered solution is presented and described in the following subsections. Furthermore, at the end of each subsection, a summary table is shown to present the highlights of each approach, such as base protocol, objectives, descriptions, strengths, weaknesses, and application scenarios. The objective column presents the aims of the routing solution. The description column shows the method used by the proposal to reach its objectives. The strengths and weaknesses columns present, respectively, the main contributions and limitations of the solution. Finally, the application column shows the type of application to which the solution should, or intends to, be applied.  
Figure 3: LOADng control message transmissions. Arrows indicate the message flow. (**a**) Transmission of Route Request (RREQ) messages flooding all network nodes. (**b**) Transmission of Route Reply (RREP) and RREP_ACK messages, where bold lines indicate RREP messages that are forwarded through the path constructed by RREQ and dotted lines indicate RREP_ACK messages that are optionally sent after reception of each RREP, but not routed. (**c**) Transmission of data messages through the path selected by Snode after receiving RREP.
## 5 Conclusions  
Based on the in-situ observation of canopy SIF, GPP and meteorological factors of a subtropical evergreen coniferous forest at Qianyanzhou Station, the relationship between the SIF and GPP, and their responses to environmental changes were analyzed. The following conclusions were drawn:  
(1) Both SIF and GPP of the subtropical evergreen coniferous forest presented similar diurnal and seasonal variations, and both reach their highest values in summer, indicating that the potential of SIF in characterizing the variations of GPP.  
(2) Along with the increase in time scales, the correlation between the SIF and GPP became more linear.  
(3) Ta and PAR significantly affect the changes in the SIF and GPP at different time scales, while SWC has no significant impact on them. With the increase in Ta, PAR or SWC, the linear correlation between SIF and GPP decreases gradually. When Ta or PAR is high, there is no significant linear correlation between SIF and GPP.  
Figure 8: Fluorescent equipment installation.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the study and completed the study. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is financially supported by the Special Project on National Science and Technology Basic Resources Investigation of China (2021 FY100701), the National Natural Science Foundation of China (No. 42141005) and Data reorganization and mining of ecosystem carbon, surface CH4 fluxes and wetland observation (KFJ-SW-YW043-6).  
**Data Availability Statement:** Due to the nature of this research, the participants of this study did not agree for their data to be shared publicly; therefore, supporting data are not available.  
**Acknowledgments:** We would like to thank Qianyanzhou Ecological Research Station for its technical support and data collection.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 2 Materials and Methods
### Improving Firefly Optimization Algorithm
#### 2.4.2 Dynamic Inertia Weight  
In the firefly algorithm, the concept of inertia weight is employed to balance the exploration and exploitation phases of the search process. Dynamic inertia weight (DIW) serves as an enhancement strategy that adaptively adjusts the inertia weight throughout the algorithm's iterations. Initially, a higher inertia weight encourages the fireflies to explore the search space more extensively, thereby reducing the likelihood of premature convergence to local optima. As the iterations proceed, the inertia weight is progressively decreased, enabling the fireflies to concentrate on exploiting the most promising regions, which enhances the precision of convergence. The dynamic inertia weight decreases linearly from 0.9 to 0.4. The inertia weight \(\omega\) can be dynamically adjusted according to the following formula:  
\[w=w_{\max}-(w_{\max}-w_{\min})\cdot t/T \tag{4}\]  
where \(w_{max}\) and \(w_{min}\) represent, respectively, the maximum and minimum values of the inertia weight, \(t\) is the current iteration number, and \(T\) is the total number of iterations.  
Through the dynamic adjustment of inertia weight, the algorithm initially emphasizes extensive exploration and subsequently accelerates convergence. This strategy enhances the firefly algorithm's adaptability in managing the trade-off between exploration and exploitation throughout the optimization process.
## 4 XAI Methods in Medicine
### Intrinsically Interpretable Models
#### 4.2.2 Interpretable Machine Learning Models  
As an alternative to knowledge-based systems, from the early days of medical decision making, statistical Bayesian, Hypothesis-Testing, and linear discriminant models were ML models that can be considered interpretable. They are based on the statistical relationships extracted from clinical databases which allow formal probabilistic inferential methods to be applied. [PERSON] and [PERSON] proposed the Bayesian approach in their pioneering article in the journal _Science_ in 1959 [57], with many of the alternatives first discussed in The Diagnostic Process conference [58]. Logistic regression is an effective statistical approach that can be used for classification and prediction. Generalised Linear Models (GLMs) [59] are also used in various problems among the literature [60; 61], while Generalised Additive Models (GAMs), an extension of these, allow the modelling of non-linear relationships and are used for prediction in medical problems as well [62; 63].  
Decision trees are considered transparent models because of their hierarchical structure which allows the easy visualisation of the logical processing of data in decision making processes. Moreover, a set of rules can be extracted to formalise that interpretation. They can be used for classification in the medical context [64; 65], however, they sometimes show poor generalisation capabilities, so it is most common to use tree ensembles (like the random forest algorithm [66]) that show better performance, in combination with post hoc explainability methods, as they lose some interpretability [67; 68; 69; 70].  
The generalisation of formal models through Bayesian networks [71], have become popular for modelling medical prediction problems [72; 73; 74; 75; 76], representing conditional dependencies between variables in the form of a graph, so that evidence can be propagated through the network to update the diagnostic or prognostic states of a patient [77]. This reasoning process can be easily visualised in a straightforward manner.  
Interpretable models can be used by themselves, but another interesting strategy is using them in ensemble models. Ensemble models consist of combining several different ML methods to achieve a better performance and better interpretability than with a black-box model alone [78]. These approaches can also include these interpretable models in conjunction with DL models such as neural networks [79; 80; 81; 82], as well as other post hoc explainability techniques [83; 84], such as the ones that will be presented later in this paper (Section 4.3). However, they pose increasingly, and as yet unresolved, complex interpretation issues, as recently emphasised by [PERSON] [85].