esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Prediction Models
#### 2.3.3 Random Forest  
Random forest is a combined classification model composed of many decision tree classification models. It can ensemble a variety of decision rules to learn from data sets. Each decision tree classification model has voting rights to obtain the optimal classification results. This algorithm is widely used in multidimensional data and has good performance [25].  
The randomness of random forest is reflected in the random selection of data sets and the random selection of features to be selected. Random selection of data sets refers to taking samples with return from the original data set to construct a sub data set, and the data volume of the sub data set is the same as that of the original data set. Elements in different sub datasets can be repeated, and elements in the same sub dataset can also be repeated. Similar to the random selection of data sets, each splitting process of the subtree in the random forest is to randomly select some features from all features, and then select the optimal feature from these randomly selected ones.  
The construction process of random forest algorithm is as follows: (1) Use bootstrap method to select n samples from the original training set by sampling with replacement, take N times in total, and generate N training sets.; (2) Select features and train the decision  
Figure 3: Prediction flow chart based on K-nearest neighbor model.  
tree model for N training sets, respectively.; (3) For a single decision tree model, suppose the sample set contain M feature attributes, randomly select m attributes from M attributes as a subset (m < M), and select the optimal features from the subset with minimum Gini index to split; (4) Repeat the above steps, establish multiple decision trees to form a random forest, use N decision trees for classification prediction, and follow the voting mechanism to determine the prediction result of dropout rate. The key of decision tree learning is how to choose the best feature to split. This work uses \"Gini index\" as the index of decision tree to choose the best feature.  
Gini index is calculated as:  
\[G{I_{m}}=\sum_{k=1}^{k}{\hat{p}_{mk}(1-\hat{p}_{mk})} \tag{3}\]  
The importance of feature in node m, that is, the change of index before and after node m branching is,  
\[VIM_{ij}^{(Gini)}=G{I_{m}}-G{I_{l}}-G{I_{r}} \tag{4}\]  
where, \(VIM^{(Gini)}\) represents the fractional statistics of the variable \(X_{j}\), \(G{I_{l}},G{I_{r}}\) represent the indexes of the two new nodes after branching, respectively.  
If the node of the feature in the decision tree is in the set \(M\), the importance of the feature \(X_{j}\) in the tree \(i\) is  
\[VIM_{ij}^{(Gini)}=\sum_{m=1}^{M}VIM_{jm}^{(Gini)} \tag{5}\]  
Assuming that there are n trees in RF, the importance of features in RF is defined as  
\[VIM_{ij}^{(Gini)}=\frac{1}{n}\sum_{i=1}^{N}VIM_{ij}^{(Gini)} \tag{6}\]
# Regional Climate Change in Southeast Mexico-Yucatan Peninsula, Central America and the Caribbean
## 3 Results
### Climate Regional Drivers  
#### 3.2.1 Precipitation  
Recently, [7] showed an ENSO relation with precipitation for the southeast of Mexico. This relation follows an inverse correlation, in which the significant rain events are present due to the negative-phase of ENSO (La Niña phase), and severe dry conditions occur when the positive-phase is presented (El Niño) [7]. These results agree with previous studies for the area [14, 16, 64, 65, 66, 67, 68]. This effect is also present in other areas of this study (CA and Caribbean) (see Figure 3). The same behavior is present for the PDO. The duration of the PDO variability cycle is approximately ten years and, in agreement with the trend, Mexico and the Caribbean show a precipitation increase but CA shows a precipitation decrease. It is important to note that the climate in CA is driven by other climate regional factors, such as LLJ [69]. Although AMO shows a positive relation with the precipitation for all three study areas of the region, AMO variability is more than ten years, and it has a low frequency signal [70] that is reflected by the low spread in the data shown in Figure 3. The AMO phase is cold from the 1960s and hot since the 1990s. Although it is not clear from the data, the warm phase may have had an increased incidence of hurricanes, and there could be years that were better [71]. The frequency of ENSO and PDO is more widespread than AMO, and Figure 3 also shows this.
## 2 Materials and Methods
### Computational Fluid Dynamic Simulation  
Fluid dynamic analysis was performed using COMSOL Multiphysics (COMSOL Inc., version 5.5, 2020, Stockholm, Sweden). The designed microfluidic geometries were imported from SolidWorks software (SolidWorks, Version SP0, 2019, Dassault Systems) using the LiveLink function of COMSOL Multiphysics. In microfluidic devices with small microchannel geometric length scales, the movement of the fluid is governed by the continuity Equation (1) and the Navier-Stokes Equation (2) [40]. A physics-controlled mesh, which is a software default mesh, was used with an element size set to normal due to the limitations of both the computational capacity and processing time of the computer.  
\[\
abla\cdot\overline{\mathrm{U}}=0 \tag{1}\]  
\[\rho(\frac{\delta\overline{\mathrm{U}}}{\delta t}+\overline{\mathrm{U}}\cdot \
abla\overline{\mathrm{U}})=-\
abla\mathrm{p}+\rho\overline{\mathrm{g}}+ \
abla\cdot[\overline{\mathrm{u}}] \tag{2}\]  
where \(\rho\) is the density of the fluid under investigation (kg/m\({}^{3}\)), \(\overline{\mathrm{U}}\) = (u, v, w) is the flow velocity vector (m/s), \(\mathrm{p}\) is the pressure (Pa), \(\
abla\) is the divergence and \([\overline{\mathrm{u}}]\) is the shear stress tensor Pa. The computational fluid dynamic (CFD) simulation was implemented to solve the incompressible Navier-Stokes Equation (2) and the continuity Equation (1) to evaluate the velocity fields, the shear stresses, and the pressure inside the different microchannel geometries.  
The type of physics selected to solve the simulations was single-phase creeping flow, which is suitable for small-geometrical-length-scale microfluidic devices. The steady-state condition was selected. Subsequently, a no-slip boundary condition was applied to the walls. Different values of flow rate (2, 3, 5, 6, 7 and 10 \(\mu\)L/min) were used as inlet conditions for the four different microchannel geometries. The pressure at the outlets was set at P = 0 Pa, which refers to the inlets (no backpressure). Additionally, water was considered as a reference fluid to run the simulations as it is a Newtonian fluid, meaning that its shear stress (\(\tau\)) is proportional to the shear rate (\(\dot{\gamma}\)), and \(\mu\) is the fluid viscosity according to Equation (3) [41].  
\[\tau=\mu\dot{\gamma} \tag{3}\]  
The Reynolds number as the main controlling parameter in fluid flow within the microfluidic channels is very low (<1); therefore, the fluid within the microchannels is laminar. In addition, for the designed microchannel geometries, the fluid flow is laminar throughout the microchannel due to the low flow rate and the small overall microchannel dimensions. Thus, there will not be turbulences that may either disrupt or damage the cells [42]. Moreover, the rheological properties of water are similar to those of the culture medium used for cell cultures.  
Figure 4: The closed microchannel structure: (**a**) top view with high micropillar magnification; (**b**) front view; (**c**) side view.
## 3 Test Results and Analysis
### The Predict Model of Cumulative Generalized Shear Strain  
Figures 11 and 12 show the variation curves of the fitting parameters \(a\) and \(b\) with \(CSR\). For different \(\delta\) (i.e., different stress path), \(a\) increases with increasing \(CSR\). The values of the Monismth model parameters \(a\) and \(b\) in Figures 11 and 12 have been used in Figure 6. The growth rate gradually increases with increasing \(CSR\), and \(a\) has a good power function relationship with \(CSR\). \(b\) decreases with increasing \(CSR\), and the rate gradually decreases with increasing \(CSR\). \(b\) has a good negative power function relationship with \(CSR\). \(\gamma_{\mathcal{R}}\) calculated by the modified Monismth model, is defined as the cumulative generalized shear strain prediction \(\gamma_{\mathcal{R}}\). Then, the empirical relationship between \(a/b\) and \(CSR\) is as follows:  
\[a=A_{1}\times CSR^{A_{2}} \tag{3}\]  
\[b=B_{1}\times CSR^{B_{2}} \tag{4}\]  
Figure 11: Relationship between parameter \(a\) and \(CSR\) in Monismth model. (**a**) The relationship between parameter \(a\) and \(CSR\) when \(\delta=1\); (**b**) The relationship between parameter \(a\) and \(CSR\) when \(\delta=2\); (**c**) The relationship between parameter \(a\) and \(CSR\) when \(\delta=4\).  
Figure 10: Relationship between terminal generalized shear strain and cyclic stress ratio.  
In summary, the cumulative deformation of seabed silt soil under various stress levels and loading models can be predicted by combining Equations (2)-(4).  
Figure 13 shows the relationship between the measured value of \(\gamma_{\text{g}}\) and the predicted value calculated by the modified [PERSON] model. It can be observed that the error between the predicted value and the measured value is roughly within 10%. The results show that the modified [PERSON] model can reasonably describe the \(\gamma_{\text{gp}}\) and \(CSR\) of saturated silt under different cyclic stress paths.  
Figure 12: Relationship between parameter \(b\) and \(CSR\) in Monismith model. (**a**) The relationship between parameter \(b\) and \(CSR\) when \(\delta=1\); (**b**) The relationship between parameter \(b\) and \(CSR\) when \(\delta=2\); (**c**) The relationship between parameter \(b\) and \(CSR\) when \(\delta=4\).
## 5 Conclusions  
This study integrated the CHTopoNER model and dynamic FSM to parse non-standard text addresses in Chinese and achieved good experimental results. The accuracy rate was 96.6% for standard placenames and 96.8% for non-standard placenames, which increased by 82.6% and 38.7%, respectively, compared to the integrated traditional and bidirectional FSMs. Integration with FSM supplemented the deficiencies of the CHTopoNER model, thereby improving the accuracy and robustness of address element parsing.  
In the future, more natural language processing techniques integrated with the FSM can be explored to improve the accuracy and efficiency of Chinese address parsing. For example, deep learning methods can be integrated with FSM to improve their ability to recognize and sort address elements. In addition, knowledge graphs [54] can be applied to address parsing to improve semantic understanding and address combinations. Moreover, integrating multimodal information into address parsing could be considered. For example, image recognition techniques [55] can be integrated to extract address information from pictures, which can be considered together with text information, thereby improving the accuracy and robustness of address parsing. The application and promotion of these new ideas and methods are expected in Chinese address parsing.  
Conceptualization, Z.Z.; software, M.Z.; validation, M.Z.; formal analysis, Y.Q.; resources, Z.J.; data curation, Z.J.; writing\(-\)original draft preparation, M.Z.; writing\(-\)review and editing, M.Z., J.M., Z.Z. and Y.Q.; project administration, X.L.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Joint Fund of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains, Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent processing, Ministry of Natural Resources, grant number 212102; and National Natural Science Foundation of China, grant number 42101454.  
Not applicable.  
Data Availability StatementData will be made available on request.  
The authors declare no conflict of interest.
## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### Bridge Monitoring Model
While some structures benefit from existing mobile or local networks, others, particularly civil structures like bridges, may be situated in areas with limited connectivity. Narrow-Band IoT (NBIoT) technology, offering a cost-effective and long-range connection service, proves advantageous for such scenarios. NBIoT's low energy requirements make it suitable for low-power devices, aligning perfectly with the presented application. Despite its low speed and high latency, the technology's capacity and security mechanisms (SSL/TCP/IP communication stack), which have been applied to this use case, prevent intrusion into the transmitted information and ensure that the information reaches its intended destination. This selection therefore makes it the right solution for the application described, facilitating immediate and cost-effective deployment. The server centralizes the processing and management of monitored structures. To accommodate scalability, a microservice-based development on Docker technology containers is adopted, which allows the migration of these microservices with minimum effort, thus avoiding dependencies on static architectures or specific service providers. These microservices communicate through REST API connections and handle tasks such as collecting information from deployed nodes, storing it in a non-relational database (MongoDB), and processing data according to different asset digitalization models. These models allow versatile management of structures as complete assets or compositions of different assets, enhancing asset management efficacy. Additionally, this microservice-based architecture allows the scaling of the system and the future updating of the algorithms applied by predictive applications based on artificial intelligence, thus preventing the obsolescence of the entire system. The server also implements a role management system to ensure secure and controlled access to system information (Figure 7). Figure 6: Node distribution along the platform.
## 4 Summary and Conclusions
As a result of the atmospheric warming and surface cooling, a considerable planetary cooling is found over the areas of dust transport, as strong as \(-\)40 W/m\({}^{2}\) at 12:00 UTC, which turns to an appreciable planetary warming (up to 70 W/m\({}^{2}\)) over highly reflecting African desert areas overloaded by dust. * The above-mentioned SW DDREs significantly perturb the regional solar radiation budget, in particular over the Mediterranean areas undergoing the dust export, by up to 45% in the atmosphere and 35% at the surface, thus constituting an important factor for processes such as the modification of temperature or humidity, and for dynamicaleffects, such as the creation of horizontal and vertical temperature gradients, leading to changes in air motions and local circulations. * The vertical resolution of the obtained RTM results gives insight into the existence of strong differences in SW atmospheric DDREs with height. Thus, along the pathways of the dust export, and within the layers having the highest dust loadings found up to 5-6 km in height, the largest DDREs, up to about 13 W/m\({}^{2}\), are observed in the second day of the DAEC, namely on 17 June 2016, near 5 km. Apart from the significant vertical differences in magnitude, the model results reveal even a change in the sign of atmospheric DDREs, resulting in limited atmospheric cooling. * The atmospheric solar radiative dust DREs cause a significant heating effect, producing heating rates up to 0.14 K/3 h on 17 June 2016, 12:00 UTC, being maximum within layers with the highest dust loadings, namely at heights between 3 and 5 km, but also with significant values down to the surface. Such important heating rates within air masses that carry dust loadings create significant horizontal and vertical temperature gradients, which can alter the distribution of isobars and induce horizontal pressure gradients with consequent effects on the dynamics of the regional atmosphere. * The atmospheric heating induced by the absorption of solar radiation by DAs produces a buoyancy, with a maximum in the second day of the DAEC, which either favors or suppresses the vertical air motions, depending on the prevailing conditions. The vertical acceleration caused by dust aerosols was as large as 0.014 m/s\({}^{2}\) on 17 June 2016 at 12:00 UTC within the elevated dust layer, near 5-6 km. This dust-induced buoyancy produces significant modifications to the vertical air velocity. For example, according to the model results, should an acceleration equal to 0.014 m/s\({}^{2}\) persist for 10 min, the change in air velocity would be as large as 8.6 m/s, being of the same order of magnitude and comparable to the prevailing vertical velocities. Such modifications of vertical air motions due to dust can be related to the observed (e.g., [50]) effects on cloud formation, especially the high-level ones.
###### Abstract
Then, based on the determined network scenario, the protocol applies the appropriate input parameters to a fuzzy logic system, which adapts the beaconing rate. Afterwards, HACEAB further optimizes the performance by adapting the transmission power to variations in node density and distribution. The remainder of this paper is structured in the following manner: In Section 2, we present a review of related work on beaconing in VANETs. Section 3 offers an overview of the fuzzy logic system and its application in VANETs. Section 4 explores the effects of various parameters on beaconing. Our proposed beaconing protocol is detailed in Section 5. Section 6 delves into the performance evaluation and simulation results. Finally, Section 7 provides the conclusion of the paper.
# Evaluation of Yogurt Quality during Storage by Fluorescence Spectroscopy
## 3 Results and Discussion  
### Spectral Investigation  
Fluorescence spectra were obtained in the wavelength range of 339.98 nm to 1028.70 nm with a resolution of 0.38 nm. Each spectrum includes 2048 data points which are decided by the linear CCD array of USB 2000+ spectrometer. To reduce the number of data points available for multivariate analysis, the spectra in the wavelength range of 410 to 910 nm, where all available fluorescence signals are included, was selected. The average fluorescence spectra measured from five different kinds of yogurt are shown in Figure 1. As can be observed clearly, there are several spectral wavelength bands that might be particularly interesting. The prominent emission band with a peak wavelength of 525 nm located in the wavelength range of 500 to 600 nm has almost the same spectral shape for five kinds of yogurt, which means that the emission spectra in this range should be attributed by the fluorophores presenting in raw milk, such as tryptophan, nicotinamide adenine dinucleotide (NADH), vitamin A, riboflavin, and Maillard products [20; 21]. Another interesting region is from 600 to 750 nm, where five different narrow emission bands occur. The first emission band appears approximately at 633 nm, which has a similar spectral shape for different kinds of yogurt samples. An apparent double peak appearing at about 661 nm and 672 nm have a slight difference in the spectral intensity, for example, the peak intensity at 661 nm of class D is far less than that at 672 nm. These narrow emission peaks in the red and near-infrared (NIR) region might be produced by the presence of porphyrin and chlorin compounds in yogurt, most likely protoporphyrin, hematoporphyrin, chlorophyll a and chlorophyll b [22; 23]. In addition, a small peak appearing at about 420 nm is the same for both the spectral shape and intensity for the five kinds of yogurt, which should be attributed to the fluorescence signal from the high-pass filter when excited with 405 nm laser so this peak can be ignored in the present study.  
Figure 1: Average fluorescence spectra of five brands of yogurt (HuaHuaNiu, JunLeBao, MengNiu, YiLi, and GuangMing).
# Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study
## 2 Materials and Methods
### Analysis Pipelines in FieldTrip and Brainstorm
#### 2.4.3 Source Reconstruction  
The first step in localizing sources is the construction of a forward model and lead field matrix. The forward model allows one to calculate an estimate of the field measured by the MEG sensors for a given current distribution in the brain and is typically constructed for each subject. The lead fields or the solution to the forward problem are evaluated using various algorithms, such as a single sphere [39], overlapping spheres [40], a spherical harmonics approximation of realistic geometries [41], and boundary element methods [42].  
The forward solution was computed in Brainstorm analysis using the overlapping spheres method, which is the default. The number of cortical sources was kept at 15,000 as recommended [37]. On the other hand, in FieldTrip, we applied a semi-realistic head model developed by [PERSON] [41] called a single-shell model, which is based on the correction of the lead field for a spherical volume conductor by a superposition of basic functions, gradients of harmonic functions constructed from spherical harmonics. We thus discretized the head volume with a grid with a 0.7-cm resolution and obtained a source space consisting of9025 voxels. The lead field matrix was calculated using each grid point [41]. Thus, in Brainstorm, a cortical surface model was used, and in FieldTrip, a volumetric one.  
The next step is calculating the inverse solution to estimate the location and strength of neuronal activity, which can be computed via multiple options, including dipole fitting based on nonlinear optimization [43], minimum variance beamformers in time and frequency domains [44; 45; 46], and linear estimation of distributed source models [47; 48]. In both software analyses, we used standardized low-resolution brain electromagnetic tomography (sLORETA) [49].  
The sLORETA family of solutions was validated against numerous imaging modalities [50; 51; 52] and simulations [53; 54]. sLORETA uses standardized current density images to calculate intra-cereb generators. Although the image was blurred, sLORETA was found [55] to have the exact zero-error localization when reconstructing single sources in all noise-free simulations, i.e., the maximum of the current density power estimate coincided with the exact dipole location [48]. Meanwhile, in all simulations with noise, sLORETA had the lowest localization errors when compared with the minimum norm solution.  
Note that when working with multiple sensor types to form a joint source model, the empirical noise covariance is used to compute the weights of each sensor in the overall model. For this purpose, noise covariance matrices are typically computed from empty-room recordings that capture instrumental and environmental noise in the absence of subjects.