esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wettability Measurement Methods
### Methods That Consider the Characteristics of Liquid Absorption  
#### 2.3.1 Infrared Spectroscopy (IR)  
The wettability of samples depends on numerous factors, including the chemical composition of their surface [80; 81]. This aspect can be investigated using infrared spectroscopy (IR spectroscopy), which is based on the absorption of infrared radiation by molecules of the substance. Absorption occurs due to the vibrational movements of molecules, leading to a reduction in the intensity of transmitted light. Fourier transform infrared spectroscopy (FT-IR) allows for the characterization of this attenuation through wavenumbers, as shown in Figure 7[24]. Different chemical bonds in the sample produce peak intensities at various wavenumbers.  
Figure 6: SEM images of the calcite surface before (**a**) and after (**b**) soaking in palmitic acid. Examples of wetting angle measurements on the calcite surface before (**c**) and after soaking (**d**) in palmitic acid [23].  
The advantage of this method is its ability to analyze chemical bonds on a sub-pore scale, as well as the relatively quick execution of the experiment. It should be noted, however, that IR spectroscopy is expensive to perform. Although primarily used for qualitative analysis and not for the direct quantitative measurement of chemical bond concentrations, IR spectroscopy provides valuable information on chemical changes in the sample after treatment with various substances [25; 26; 27; 28; 29]. For example, Figure 8 presents the IR spectra of calcite and dolomite before and after exposure to solutions with different concentrations of seawater. Despite the identical number of peaks, the differences in absorption intensity between fresh and treated samples indicate changes in their chemical and wettability properties [25].
## 3 Methodology
### Case Study Profile  
The new metric was tested and applied to evaluate a coal hauling company that operated in a coal mining site in Central Kalimantan, Indonesia. The company works 24 h a day, divided into two working shifts: day shift and night shift. Each shift has a 60 min break for rest and a meal. The working schedule consists of a 7-day week, and there is an over shift every Friday to take 12 h off. Depending on the circumstances and management decisions, several workdays are not available to work on. Every day the hauling operation team receives the production plan information from the mining owner, where the loading point and stockpile location are mentioned. The loading process starts at the loading point (mining pit). The coal is loaded into the trucks using an excavator. The truck is a rigid truck type with a vessel capacity of 24.5-25.5 metric tons.  
The process begins with the arrival of the truck at the loading point. An excavator loads the coal into the truck. Once the truck is fully loaded, the excavator operator gives a signal confirming that the truck is fully loaded and asks the truck to leave the loading point and travel to the port stockpile. The distance between the loading point and the port stockpile is around 30 km, with an allowable hauling speed maximum of 50 km/h. Before arriving at the port, the truck enters the weightridge, receiving documents from the weightridge operator that record truck weight information. Then, the driver enters the stockpile location where a port supervisor informs the driver and directs the truck to the dumping location for unloading. Once the truck is in the unloading position, the driver begins the process of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Round** & **Mining expert** & **Consultant** & **Lecture** & **Total** \\ \hline
1 & Round 1 & 11 & 2 & 1 & 14 \\
3 & Round 3 & 9 & 2 & 1 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Profiles of panelists in the three rounds of the Delphi Survey.  
unloading coal. The driver opens the tailgate of the truck and the hydraulic rams to lift the front vessel side, allowing the coal in the truck vessel to be dumped on the ground behind the truck. Once the unloading process is finished, the truck travels back to the loading point, and the coal is stored in the port stockpile.  
Once the driver returns to the loading point, they record the following data on the timesheet: time of entry and problems encountered during the hauling process cycle, including breakdown, refueling, stoppage, rain, slippery road, etc. The coal-hauling process is described in Figure 2. At the end of the working shift the driver completes and signs the timesheet, and submits it to the group leader, who confirms the document. If there is a difference, the group leader takes note of it and notifies the driver to verify the discrepancy. All data related to hauling operations are uploaded and stored in the mining control center (MCC). Operation data were collected for three months. For data reliability, we took the data from MCC that had already been verified by the operation control team.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Training Supervised Machine Learning Models
#### 3.6.4 Logistic Regression  
Logistic regression is a generalized linear model used for classification tasks and is valued for its simplicity and ability to provide interpretable results.  
**Steps:**
* Initialized the logistic regression model and defined a parameter grid to optimize the C (regularization strength) and penalty settings;
* Used GridSearchCV to find the best hyperparameters;
* Trained the model using the optimal configuration and evaluated its performance on the test set.
Sensors_**2012**, _12_, 3964-3996; doi:10.3390/s120403964  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**An Overview of Data Routing Approaches for Wireless Sensor Networks**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department of Computer Systems and Communications, Universiti Teknologi Malaysia (UTM),  
Johor 81310, Malaysia; E-Mails: [EMAIL_ADDRESS] (A.H.A.); [EMAIL_ADDRESS] (S.A.R);  
[EMAIL_ADDRESS] (M.A.N.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 13 February 2012; in revised form: 12 March 2012 / Accepted: 16 March 2012 / Published: 27 March 2012_  
**Abstract:** Recent years have witnessed a growing interest in deploying large populations of microsensors that collaborate in a distributed manner to gather and process sensory data and deliver them to a sink node through wireless communications systems. Currently, there is a lot of interest in data routing for Wireless Sensor Networks (WSNs) due to their unique challenges compared to conventional routing in wired networks. In WSNs, each data routing approach follows a specific goal (goals) according to the application. Although the general goal of every data routing approach in WSNs is to extend the network lifetime and every approach should be aware of the energy level of the nodes, data routing approaches may focus on one (or some) specific goal(s) depending on the application. Thus, existing approaches can be categorized according to their routing goals. In this paper, the main goals of data routing approaches in sensor networks are described. Then, the best known and most recent data routing approaches in WSNs are classified and studied according to their specific goals.  
**Keywords:** Wireless Sensor Networks; data routing; energy; fault-tolerance; delay
## 3 Results and Discussion
### Plasma Characterization
Hence, the decrease in temperature that accompanies the electrical current drop could be attributed to the same process of plasma-induced monomer fragmentation, where the plasma-generated particles lose their energies as the inelastic collisions augment with the injection of monomer. DBD plasma polymerization involves a complex series of fundamental chemical and physical processes that occur between the plasma-generated species (monomer fragmentation, random recombination and diffusion of generated fragments) and at the surface/plasma interface [13]. The chemistry in the gas phase related to the fragmentation mechanisms of the monomer is a key issue addressed in the literature, on the understanding of which depend the preservation of the functionality of the monomer or its complete degradation [40; 41]. Previous research has revealed a mechanism for EG fragmentation by electronic impact, with a high probability of breaking the EG monomer skeleton at the C-C\(\sigma\) and C-O covalent bonds and the subsequent formation of various types of radicals such as CH\({}^{\star}\), CH\({}_{2}\)\({}^{\star}\), C\({}_{2}\)H\({}_{4}\)\({}^{\star}\), C\({}_{2}\)H\({}_{4}\)OH\({}^{\star}\), CH\({}_{2}\)OH\({}^{\star}\) and OH\({}^{\star}\)[42]. Later, by recombination with  
Figure 6: Variations with frequency of the calculated rotational (**a**) and vibrational (**b**) temperatures for DBD operated in He and He/ethylene glycol (EG) vapor mixture. neutral plasma species, these radicals can form larger molecular fragments and contribute to the growth of the pp-PEG film.
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 4 Herbs That Control Obesity
### Aframomu melegueta  
The common name of the herb is grains of paradise or Guinea grains. It is a perennial, herbaceous plant that belongs to the ginger family, Zingiberaceae. It is commonly found along the West African coast of Nigeria. The different parts of the herb possess specific bioactive compounds such as flavonoids, alkaloids, phenolic compounds, tannins, saponins, terpenoids, and cardiac glycosides that shows healing potential and therapeutic purposes [90,91]. These compounds possess various biological activities such as antimicrobial, anti-inflammatory, anti-allergic, antioxidant, anti-clotting, anti-cancer, anti-obesity, anti-diabetic, and hepatoprotective effects [91]. The anti-obesity effect of the herb is mainly due to the presence of vanillaoids such as 6-paradol and 6-gingerol. Specific to the liver and adipose tissue, 6-paradol and 6-gingerol controlled several gene expressions and AMPK phosphorylation. It appears that 6-paradol may affect adipose differentiation by preventing free fatty acid translocation, and 6-gingerol may, in particular, enhance hepatic metabolism by improving fatty acid \(\beta\)-oxidation. These components inhibit lipid accumulation and fatty acid consumption during adipogenic differentiation by regulating genes involved in biosynthesis. The enhancement of lipolysis, fatty acid \(\beta\)-oxidation, and the decrease in TG accumulation contribute to the decreasing adipocyte size, inhibiting the growth of WAT [92].
## 3 Results and Discussion
### Samples Characterization
#### 3.1.2 XRD Pattern  
Figure 3 reports the XRD pattern of the stabilized material 11 months after the synthesis procedure. The following crystalline phases are detected:--halite (NaCl), calcite (CaCO\({}_{3}\)), quartz (SiO\({}_{2}\)), hannebachite (Ca\({}_{2}\)H\({}_{2}\)O\({}_{7}\)S\({}_{2}\)), belite (Ca\({}_{2}\)SiO\({}_{5}\)), syyline (KCI) and bassanite (Ca\({}_{3}\)H\({}_{4}\)O\({}_{13.8}\)S\({}_{3}\)).  
The material is made of the same crystalline phases already detected in similar materials, stabilized by using amorphous silica sources [11; 18], which are analyzed weeks after the treatment. The XRD spectra collected on stabilized material 1 month after the material synthesis (data not shown) corresponds to the one already reported by [PERSON] et al. [21], due to the same stabilization procedure adopted. However, it is worth to notice the presence of belite in this sample. This phase was not found in previous works using the proposed stabilization methods [41] and is generally present as an additional main mineralogical component of calcium sulfoaluminate cement, which can be obtained by using FA [42]. Then its presence, although it was not expected, can be justified by previous works. It was already shown that FA and a source of lime (derived from FGD residues introduced in the sample stabilization procedure) respectively served as the principal raw silica and lime sources during the production of building materials such as clinker [43]. In the present case, the addition of BA allows us to increase the amount of CaO-Al\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) able to increase the reactive amorphous phase that contribute to the heavy metals stabilization [44]. The belite formation is probably due to the crystallization of C-S-H (calcium silicate hydrate) phase, due to the reaction between silicate species and calcium, which in cement is a hydration product.  
The stabilized material contains ceramic and silica-based material. This allows us to suppose that it may be reused to substitute some natural resources, with similar composition. One advantage of the obtained stabilized material is its physical form. Being a powder, it may be used as a filler in a composite, realized by mixing a matrix with a powder. In principle, this material may be used with all matrices requiring inorganic filler. In the present work, this filler was used to produce some composites by using an epoxy resin E-227, as described in the experimental section.
## 3 Motivation for Using LSTM Network to Design the CMM
The operation is given by \[g_{t}=\tanh\bigg{(}\mathbf{W}_{g}\Big{[}\mathbf{y}^{\mathrm{T}}(t)\ \mathbf{h}^{\mathrm{T}}(t-1)\Big{]}^{\mathrm{T}}\bigg{)},\] (12) where \(\tanh(\cdot)\) denotes the hyperbolic tangent function and \(\mathbf{W}_{g}\) is a weight matrix corresponding to the cell gate. * Output gate (\(o_{t}\)): This gate combines the current input \(\mathbf{y}(t)\) and the previous hidden state \(\mathbf{h}(t-1)\) to decide how much to weight the cell state information to generate the output of the LSTM cell, which is also denoted as hidden state \(\mathbf{h}_{t}\). The operation is given by  
\[o_{t}=\sigma\bigg{(}\mathbf{W}_{o}\Big{[}\mathbf{y}^{\mathsf{T}}(t)\;\mathbf{h}^{\mathsf{T}}( t-1)\Big{]}^{\mathsf{T}}\bigg{)}, \tag{13}\]  
where \(\mathbf{W}_{o}\) is a weight matrix corresponding to the output gate. Finally, the cell state is updated according to  
\[\mathbf{c}_{t}=f_{t}\mathbf{c}_{t-1}+i_{t}g_{t}, \tag{14}\]  
which combines the amount of information from the previous cell state regulated by the forget gate and the amount of updated information. The output of the LSTM cell, i.e., the hidden state for time \(t\), will be the filtered version of the current cell state regulated by the output gate, i.e.,  
\[\mathbf{h}_{t}=o_{t}\text{tanh}(\mathbf{c}_{t}) \tag{15}\]  
The preservation of historical observations by the cell state \(\mathbf{c}_{t}\) over time is evident from Equation (14). Additionally, the cell state does not exhibit growth as the time index increases; rather, it adaptively updates its information content. We, therefore, use the cell state information as a mapping of historical observations. At each time sample, these historical observations are exploited to optimize CMM \(\mathbf{\Phi}\) using another DNN. At the end of all time iterations, the minimum variance distortionless response (MVDR) spatial spectrum estimation method is employed to estimate the signal DOAs.
## 3 Materials and Methods
### Methods
#### 3.2.3 Establishing Secure Data Channels with IOTA Streams  
To securely share data with authorized entities, IOTA Streams are used to create encrypted channels for data exchange. This approach ensures confidentiality, integrity, and control over who accesses the data. Key steps include  
1. Channel Creation and Key Management: The Raspberry Pi sets up an encrypted data channel on the Tangle, creating unique encryption keys for each authorized subscriber. These keys are securely stored and managed by IOTA Stranghold.  
Message Publishing and Access Control: The Raspberry Pi, acting as the _author_ of the stream, can grant access to trusted subscribers who wish to receive specific data from it. Access is managed through secure keyloads, which are encrypted messages containing decryption keys for authorized recipients.
* Data Encryption and Decryption: All messages are encrypted by the Raspberry Pi before publishing, ensuring that only subscribers with the correct decryption key can access the content. Unauthorized parties are unable to read the messages without the appropriate keyboard.
* Real-Time Updates and Subscription Management: The author can manage subscribers dynamically, adding or revoking access as needed. This allows for flexible and secure data-sharing arrangements that adapt to changing access requirements.
## 2 Background
### Shapley Additive Explanations  
To provide a clear representation of the model's performance, this research utilized the SHAP method. This approach is valued for its ability to offer understandable and direct explanations of a model's decisions, while avoiding the typical trade-off between accuracy and interpretability [62; 63; 64; 65]. To calculate the feature importance, a game-theory approach based on Shapley values [66] was employed, to enhance individual predictions.  
By determining the discrepancy between predictions and their averages [67], each participating party (feature) receives a collective reward based on their contribution.The distribution of payments in such way is referred to as Shapley values. When a feature is assigned a baseline value (mean), the SHAP assesses its impact concerning the model's prediction, attributing each feature an importance measure based on its individual contribution to a particular prediction. This approach yields valuable insights, minimizes the risk of underestimating a feature's importance, captures Shapley value generalization-based interaction effects, and enables the interpretation of a model's global behavior, while retaining local accuracy [68; 69; 70].
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
###### Abstract  
Under the action of gravity, buoyancy, and surface tension, bubbles generated by wave breaking will rupture and polymerize, causing the occurrence of high-speed jets and strong turbulence in nearby water bodies, which in turn affects sea-air exchange, sediment transport, and pollutant movement. These interactions are closely related to the shape and velocity changes in single bubbles. Therefore, understanding the motion characteristics of single bubbles is essential. In this research, a large number of experiments were carried out to serve this purpose. The experimental data were used to develop three machine learning models for the bubble final velocity, bubble drag coefficient, and bubble shape, respectively. The performance of the feed forward back propagation neural network (FBNN) models for the final velocity and drag coefficient were evaluated. The coefficient of determination (R\({}^{2}\)) and root mean squared error (RMSE) value of final velocity prediction model was recorded at 0.83 and 0.0518, respectively. Meanwhile, for the drag coefficient prediction model, the values are 0.92 for R\({}^{2}\) and 0.1534 for RMSE. The models can provide a more accurate output if compared to that from the empirical formulas. K-nearest neighbours (KNN), logistic regression, and random forest were applied as the algorithm while developing the bubble shape classification model. The best performance is achieved by the logistic regression.  
bubble shape; bubble velocity; drag coefficient; machine learning; ocean +
Footnote †: journal: Applied Physics Letters
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 6 Discussion  
In this paper, we have proposed an architecture of a hybrid LPWAN-satellite communication network that takes into account the present state-of-the-art of the Internet of things with its standards and protocols, and makes it possible to deploy IoT systems in remote areas. Focusing on data transfer optimization, we proposed a data format change on the presentation layer that can be implemented in the gateway supplemented with a packaging algorithm.  
Our experimental results related to the Iridium network align well with the previous findings of other authors [3; 13; 15; 17]. The average latency in SBD mobile-originated (MO) message transmission is about 10 s. Such a value is certainly significant enough to limit the application scenarios to the ones which deal with delay-tolerant data only. In this sense, the case of the Internet of things with sensors measuring environmental parameters that change slowly, this seems to be a perfect application area.  
With sensors deployed in an area without any existing network infrastructure, the LoRa technology seems to be one of the best choices currently available. The task of aggregating data from IoT sensors and transmitting them to the global network over satellite involves many levels of optimizations. In the present paper, we only discuss those which happen on the edge of the two networks: LPWAN and satellite.  
Optimizing data transfer enables the ability to either increase the number of sensors using the same gateway or to lower their power consumption, which is extremely important for scenarios of autonomous battery-powered devices or devices that use alternative energy sources.  
It has to be mentioned that using satellite communication has very significant limitations--high cost of service, low throughput and long delays. However, we still believe it to be the most feasible option when building a data acquisition system in remote regions without any terrestrial communication infrastructure.
## 4 Experiment
### Ablation Study  
To demonstrate the rationale and effectiveness of our proposed method, we designed an ablation study. For the four input modalities of the model--image, text, audio, and olfactory information--we conducted the following experiments: removing the image modality while retaining text, audio, and olfactory modalities; removing the text modality while retaining image, audio, and olfactory modalities; removing the olfactory modality while retaining image, text, and audio modalities; and removing the audio modality while retaining image, text, and olfactory modalities. In each experiment, we kept the rest of the model unchanged and used the same training and testing sets to ensure the comparability of the experimental results. The experimental results are shown in Figure 7.  
To quantify the contribution of each modality, we used ACC and F1 as evaluation metrics. In each ablation experiment, we compared the model's performance changes in these metrics to assess the impact of removing a particular modality on the model's performance. The experimental results are shown in Table 7.  
Based on the results of the ablation experiments, we can observe that the model's performance declines when certain modalities are removed. Analyzing the experimental results, we can see that when the text modality is removed, both the F1 score and accuracy of the model drop significantly, indicating that fault text information plays a crucial role in fault description and diagnosis. Similarly, the removal of the image modality leads to a noticeable performance decrease, suggesting that image fault information also has a significant impact on diagnostic performance. In contrast, the audio and olfactory modalities have a relatively smaller impact on the model's final decision. While these modalities can provide useful information in specific scenarios, their effect on overall model performance is less pronounced than that of the image and text modalities. The results of the ablation experiments validate the rationale behind the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ablation Experimental Conditions** & **F1 (\%)** & **Accuracy (\%)** \\ \hline Ours & 80.8 & 92.1 \\ Image Modality Removed & 63.2 & 75.5 \\ Text Modality Removed & 53.7 & 60.0 \\ Smell Modality Removed & _77.7_ & 85.7 \\ Audio Modality Removed & 76.5 & 84.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The impact of different modalities on model performance.  
Figure 7: _Cont._