esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Petrography and Mineralogy  
Petrographic studies of the mudstone and shale shows that the rocks are predominantly made up of clay minerals (mostly kaolinite and calcite), quartz, and pyrite (Figure 5). The shales have lamination or fissility texture, allowing it to break along laminae planes. In cases where lamination on the mudrocks did not occur, thin to massive bedded mudstones were observed. Typically, detritus clay and silt size particles with some chemically precipitated cement and organic materials make up the argillaceous rocks. The sandstones are very fine to coarse grained and mostly poorly sorted (Figure 5). The grain morphology for the sandstones range from subangular to angular and are mostly made up of clay minerals and quartz. The XRD analysis shows that the dominant clay minerals in the mudrocks and sandstones are kaolinite, calcite and illite, ranging between 8.0-31.0%, 0.4-25.0% and 0.1-26.0%, respectively. The coal samples are dominated by kaolinite and graphite, ranging between 49.2-55.0% and 27.8-36.4%, respectively (Table 1).  
Figure 5: Thin section photomicrographs showing: (**a**) clay matrix and quartz vein in shale; (**b**) clay and silt size particles in shale; (**c**) alignment of mineral grains parallel to the lamination planes in shale; (**d**) clay minerals and silt size quartz grains in shale; (**e**,**f**) clay matrix in mudstone; (**g**) hematite staining in mudstone; (**h**) pyrite in mudstone; (**i**) nodular pyrite in shale; (**j**)silt and sand size particles (sandstone); (**k**) quartz overgrowth in clay matrix; (**l**) fractured feldspar grains; (**m**) quartz and metamorphic lithic (quartzite) in clay matrix; (**n**) heavy mineral (zircon) in clay matrix; (**o**) mineral inclusion (mica) in quartz.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**Borehole** & **Lithology** & **Depth** & \begin{tabular}{c} **Quartz** \\ **(wt.** \\ \%)** \\ \end{tabular} & \begin{tabular}{c} **Craphic** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Twitc** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Muscovite** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Kaolinite** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Flagiodase** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Dolomite** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Sliderie** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Calcite** \\ **(wt.** \%)** \\ \end{tabular} & \begin{tabular}{c} **Clotrie** \\ **(wt.** \%)** \\ \end{tabular} & \
\begin{tabular}{c} **Smectic** \\ **(wt.** \%)** \\ \end{tabular} &
\begin{tabular}{c} **Hernatic** \\ **(wt.** \%)** \\ \end{tabular} \\ \hline \multirow{11}{*}{OV125149} & Madotone & 21.0 & 36.0 & 0.0 & 7.0 & 0.0 & 17.0 & 6.0 & 2.0 & 1.0 & 0.0 & 11.0 & 3.0 & 1.0 & 15.0 & 1.0 \\  & Coal & 34.1 & 7.2 & 30.9 & 1.2
## 2 Methodology
### Objective Function
#### 2.3.4 Data Fitting Difference Formula  
The parameter reflecting the quality of inversion results, the root-mean-square (RMS) misfit, is defined as follows (Equation (11)), where \(\mathrm{N}=\mathrm{nt}\times\mathrm{nd}\) denotes the total number of sites. The parameter \(\alpha\) serves as a given data deviation used to assess the reliability of the measured data. In ideal circumstances, the disparity between the forward response and the measured data should be zero. A smaller value of \(\alpha\) indicates higher data reliability, while a larger initial RMS necessitates an increase in the number of iterations for the inversion to enhance the fitting degree.  
\[\mathrm{RMS}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\frac{d_{i}-f_{i}(m)}{\alpha }\right)^{2}} \tag{11}\]
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### Ferroelectric-Based Materials in Photoanodes for Water
#### 4.3.1 Ferroelectric Oxide Perovskites  
##### (1) BaTiO\({}_{3}\)  
BaTiO\({}_{3}\) (BTO) is an n-type semiconductor, and its ferroelectricity was discovered for the first time in 1945 by [PERSON] and [PERSON] [109]. Subsequently, the relation between photocurrent efficiency and the change in the electrode potential for the photooxidation of water was first investigated by [PERSON] [110] in 1976. This was a precursor to advanced studies on this promising ferroelectric material. Apart from possessing a favorable structure for water splitting [111], BTO is considered as a potential photoelectrode material because of its high stability in aqueous solution along with a suitable band edge position, and controllable electronic properties through lattice defect modification or oxygen stoichiometry [112; 113]. However, the only disadvantage of BTO is a relatively large bandgap of 3.2 eV, which limits its absorption spectrum to the ultraviolet region [114].  
Therefore, many previous reports [115; 116] were focused on doping BTO with Fe to extend its photoresponse for effective PEC activity. More importantly, it was reported that the ferroelectric polarization in BTO could enhance PEC performance in comparison with pristine TiO\({}_{2}\). This is a result of the facilitation of the separation of photogenerated electron-hole pairs along with the effectiveness of tuning of the electronic band structure (i.e., upward band bending) in heterojunction-based PEC devices of TiO\({}_{2}\)/BTO core/shell nanowire (NW) arrays as photoanodes [117]. However, the contribution of the ferroelectric polarization effect plays a predominant role, which is evidenced by numerous different measurements such as the PE hysteresis loop, dynamic contact electrostatic force microscopy (DC-EFM) characterization, and electric poling by different directions. As a result, the positive polarization switched by external electric field poling at the TiO\({}_{2}\)/BTO (5 nm of thickness) interface yielded an optimal photocurrent density of water oxidation (1.30 mA/cm\({}^{2}\)) that was 67% higher than that of a photoanode without BTO (0.78 mA/cm\({}^{2}\)). This result hints at the tremendous potential of ferroelectric photoelectrodes in enhancing PEC performance.
# Investigation of a Solar-Driven Organic Rankine Cycle with Reheating
## 2 Material and Methods
### The Developed Mathematical Formulation
#### 2.2.1 Modeling of the Solar System and Storage Tank  
Below, the basic formulas for the mathematical description of the used models are presented. These equations mainly regard the definition of efficiencies.  
The solar field thermal efficiency (\(\eta_{col}\)) is given by the following expression [25]:  
\[\eta_{col}=0.6869-1.4709\cdot\frac{T_{fluid}-T_{am}}{G_{T}} \tag{1}\]  
The useful heat product of the solar field (\(Q_{u}\)) can be found as follows:  
\[Q_{u}=\eta_{col}\cdot Q_{sol} \tag{2}\]  
The collector incident solar energy (\(Q_{sol}\)) is calculated according to the next expression:  
\[Q_{sol}=A_{col}\cdot G_{T} \tag{3}\]  
The solar field aperture (\(A_{col}\)) and the irradiation incident angle on the collector area (\(G_{T}\)) are used in the previous equation.  
The energy balance in the thermal tank can be expressed according to the following expression:  
\[Q_{st}=Q_{u}-Q_{loss}-Q_{trs,1}-Q_{trs,2} \tag{4}\]  
The heat output to the ORC is described by the terms (\(Q_{trs,1}\)) and (\(Q_{trs,2}\)), while the thermal losses with the term (\(Q_{loss}\)). It can be added that the thermal losses include the convective, radiation, and conductance parts. More specifically, the thermal losses of the tank (\(Q_{loss}\)) can be calculated by the next equation:  
\[Q_{loss}=\left(UA\right)_{tank}\cdot\left(T_{st}-T_{am}\right) \tag{5}\]  
The tank's thermal loss coefficient is chosen at \(U_{tank}\) = 0.5 W/m\({}^{2}\) K, while the outer area is calculated according to the tank geometry. It is useful to state that in this work, the tank is divided into five mixing zones and in every zone, the proper energy balance is conducted. The tank is selected to be a cylindrical one with the height to be the same as the diameter. A deeper description of the detailed modeling can be found in Reference [26].  
Furthermore, the stored thermal energy (\(Q_{str}\)) is written as:  
\[Q_{st}=\rho\cdot c_{p}\cdot V\cdot\frac{dT_{st}}{dt} \tag{6}\]  
The aforementioned equation is the energy balance in the storage fluid. The thermal oil density (\(\rho\)), specific heat capacity (\(c_{p}\)), tank volume (\(V\)), tank mean temperature (\(T_{st}\)), and time (\(t\)) are used in the previous expression. It is also important to say that this formula is the core of the dynamic simulation of the system.
## 4 Lab Experiment
### Validation of the Damage Localization Method
The significant divergence at \(-\)2.4 m and the good agreement at 2.4 m clearly indicate that potential damage is likely at \(-\)2.4 m, while no damage has occurred at 2.4 m.  
Furthermore, to mitigate the potential impact of vehicle stops and to enhance the visibility of divergences, we calculated the amplitudes at the \(-\)2.4 m and 2.4 m locations for both the intact and damaged bridge scenarios. These amplitudes are defined as the differences between the amplitudes of adjacent \"peak\" and \"valley\" points, as depicted in Figure 14a,b and Figure 15a,b. Figure 16 displays the ratio of amplitude differences between the reconstructed and measured bridge responses. Our analysis reveals two key findings: First, at the \(-\)2.4 m location, which is in close proximity to the damaged area, a significant divergence in the amplitude difference ratio is observed between the intact and damaged scenarios. This suggests that the transfer matrix undergoes a relatively substantial change after the introduction of artificial damage to the bridge. Second, the amplitude ratio exhibits minimal variation at 2.4 m locations between the intact and damaged scenarios. This observation further supports the hypothesis that the influence of bridge damage on the transfer matrix diminishes with increasing distance between the observation point and the damaged region. It is important to highlight that the distances between the damaged region and the two base cross-sections located at \(-\)4.4 m and 4.4 m are sufficiently large. This ensures that the modal coordinates at these sections are not influenced by the localized damage. Consequently, the transfer matrix, which relates the inclination between these three cross-sections--specifically, the base cross-sections at \(-\)4.4 m and 4.4 m, and the target cross-section at 2.4 m--remains substantially unchanged before and after the introduction of artificial damage to the middle span. In practical applications, it is also crucial to select base locations that are less likely to experience damage. This strategy is essential for maintaining the accuracy and reliability of the proposed method.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 2 The Equivalent Layers of Coal Seam Gas Emission
### The Influence Factor Group of Gas Emission  
The influence factor group pertaining to gas emission exerts a noteworthy influence on the volume of gas emissions during mining operations. This group encompasses all discernible, recognized, and quantifiable human and natural elements that are collectively designated as such. Each mining statistic comprises 17 distinct categories, with each category further subdivided into 46 individual items, as detailed in Table 1.  
The quantification of factors influencing gas emission relies on a rigorous statistical analysis of historical monitoring data. In mining production, it is common to encounter multiple, simultaneously interacting influencing factors. As such, their quantification process necessitates separate assessment, adjustment, and eventual fixation in the form of influence coefficients.  
The intensity of the impact of a given influencing factor on the type of equivalent effect layer and its associated mining gas emission varies, and its corresponding influence coefficient must be determined specifically for the equivalent effect layer and mining type involved.  
The quantified coefficients of the influencing factors are integrated with the basic quantities and subsequently entered into a database for utilization by gas prediction software (V1.0).
## 3 Application Description and Requirements
### Core Features
#### 3.2.3 Document Collaboration  
Multiple users will have the ability to collaborate in a real-time fashion on a shared resource in a text-editing interface where they are aware of each other's contributions. A peer will be able to see the other peer's actions applied in real time. Example of actions visible to a user in a collaboration session:  
1. Seeing added/deleted characters in a specific part of the document;
2. Seeing the cursor indicator of a user while navigating through the document;
3. Seeing actual logs of all the events received during a session.
## 3 Experiment and Analysis
### Experimental Results and Analysis
#### 3.2.4 Ablation Experiment  
In order to verify the effectiveness of the improved algorithm, ablation experiments were conducted with the same computer configuration and parameter Settings [27; 28]. We used \"y\" to indicate that an improvement point was added and \"n\" to indicate that an improvement point was not added. Table 3 lists the experimental results.  
As can be seen from Table 3, the introduction of the SK attention mechanism significantly improved the accuracy rate, which increased by about 2.7 percentage points. This was because the accuracy rate was greatly improved by increasing the amount of information obtained from the receptive field of the convolutional kernel. The addition of the RepVGG network structure also improved the speed (by about 2.8 percentage points). This was due to the extensive use of conv + BN layers, which reduced inference time and improved speed by merging layers. The improved algorithm recall rate increased by 3.9 percentage points and the mAP improved by 4.3 percentage points. In order to better explain and intuitively observe the performance improvement of the improved algorithm, the following is a diagram of various performance indicators used for the improved algorithm during training (Figure 7).
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Atmosphere Fluxes
#### 3.2.3 Vegetation Water Fluxes
The advantage of such approach is that it gets rid of most of the large amplitude noise in SIF, while conserving to a large extent the photosynthetic activity dependence across biomes and climates. An alternative to SIF is the Photochemical Reflectance Index (PRI) [199] or combined products that can improve the signal-to-noise ratio. Figure 15: Relationship between Above ground Biomass (AGB) in Mg/hectare and Normalized Difference Vegetation Index (NDVI) data from X-band, C-band and L-band Vegetation Optical Depth (VOD, left panel). The right hand panel shows AGB data versus VOD from SMOS L-band observations (red dots) [183].
## 3 Materials and Methods
### Extracting the Representative CAR  
The representative CAR for each cluster must be extracted once we have identified all clusters to create a descriptive, compact, and useful associative classifier. In this procedure, the CAR closest to the cluster's center is selected as a representative; hence, the representative CAR must have the minimum average distance from all other rules. The method is described in Algorithm 1. ```
Input: a set of class association rules (CAR) in CARs array Output: A representative class association rule Initialization: assign length of CARs array to N, maximum value of Float to min_distance, zero to i and j while(i < N)do// Read all the CARs in CARs array assign zero to distance and j while(j < N)do// Find the average distance between CAR i and all other CARs distance \(\leftarrow\) distance + compute the similarity between rule i and rule j by using WCB | WCI | WCD distance metrics jump to next rule, increase the value of j by 1 End average_distance \(\leftarrow\) distance / N if(avg_distance < min_distance) then assign avg_distance to min_distance assign CAR i to representative_CAR
1 End return representative_CAR
```  
**Algorithm 1** A representative car based on cluster center (rc)  
Algorithm 1 gets a set of CARs within a cluster as an input. Lines 4-7 calculate the distance from the chosen CAR to all the other CARs using the distance variable (one of the distance measures described in Section 3.2.2 is used). In line 10, the starting value of min_distance is the maximum value of the float and is used to store the minimum average distance. In lines 9-12, the representative CAR that has the lowest average distance from all the other rules is identified. Algorithm 2 demonstrates our proposed approach step-by-step below:Algorithm 2 describes every step of the proposed model. The model is given a dataset with minimum support and confidence constraints, which are used to produce class association rules. CARs are organized into groups based on their class labels in line 3. For each group of CARs (lines 4-14), the hierarchical clustering algorithm complete linkage method (HCCL) builds the distance matrix using one of the distance measures described in Section 3.2.2 (line 5), computes the cluster heights (distances between clusters) using the distance matrix in line 6, and then uses these heights (distances) to calculate the ideal number of clusters (line 7). Then, to locate the clusters of CARs, the HCCL method is employed once again. Lines 10-14 extract the representative CAR for each cluster using the technique outlined in Section 3.4 and then incorporate it into our final classifier. The following classifiers were constructed: CCC1 was built based on WCB; CCC2 was formed based on WCI; CCC3 was formed based on WCD; and all algorithms used the RC method defined in Algorithm 1 for extracting a representative CAR (based on cluster center).
# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
## 2 Materials and Methods  
### Dataset  
#### 2.1.1 Data Collection  
In this study, images of three types of tree seedlings (dragon spruce (_Picea asperata_), black chokeberries (_Arontia melanocarpa_), and Scots pine (_Pinus sylvestris_)) were collected and analyzed. The images were captured with various angles that were selected at different shooting distances. In total, the original images were 660, including 60 for dragon spruce, 300 for black chokeberries, and 300 for Scots pine. Furthermore, the outdoor environment factors were also considered during the image capture. The image shooting was done on a sunny day with natural light to ensure the precision of shooting images. The RGB images of the three types of tree seedlings are shown in Figure 1. The data were augmented via 13 methods of the major data augmentation methods. The details of the tree seedling dataset are shown in Table 1.  
Figure 1: The original RGB images of the three types of seedlings, (**a**) dragon spruce, (**b**) black chokeberries, and (**c**) Scots pine.  
The dataset was acquired from different places, as follows. Dragon spruce images and black chokeberries images were captured in Tian Heng Mountain Harbin city, Heilongjiang Province-China, and Scots pine images were captured in the Acheng district, Harbin city, Heilongjiang Province-China.  
The dataset was acquired in two ways. First, dragon spruce images were taken by DJI Inspire1 professional drone with a wheelbase of 559-581 mm and a maximum flight time of 18 min. Its weight is slightly less than 7 lbs (2935 g), and it flies at a speed of 72 feet (22 m) per second. The UAV has a 12.4 million FC350 camera and SONY EXMOR 1/2.3 sensor, which can take a single shot, multiple continuous shooting, and a maximum 4096 \(\times\) 2160 p resolution HD video recording. The tilt photography scheme is performed in one ortho and four oblique directions. Second, black chokeberries and Scots pine images were taken by a Huawei Enjoy 20 Pro mobile phone equipped with an Octa-core CPU (4 \(\times\) 2.0 GHz Cortex-A76 & 4 \(\times\) 2.0 GHz Cortex-A55) and a triple main camera (48 MP, f/1.8, 26 mm (wide), 1/2.0\", 0.8\(\upmu\)m, PDAF), (8 MP, f/2.4, 120\({}^{\circ}\) (ultra-wide)), (2 MP, f/2.4, (macro)) and 4000 \(\times\) 1080 p resolution video recording.
## 5 Discussion and Comparison to SCPT Results
### Comparison of the Maximum Load  
The experimental shear failure load of B1-1 (\(V_{\mathrm{u}}\)) is compared to the ultimate shear capacity according to the SCPT (\(V_{\mathrm{u,SCPT}}\)) and to the current EC2 (\(V_{\mathrm{u,EC2}}\)) [41] (Table 3). For calculation of the shear capacity according to EC2, a factor \(C_{\mathrm{Rm,c}}=0.192\) was used to account for a mean instead of a characteristic value [42] to determine the ultimate capacity. The maximum shear force at failure according to the SCPT is taken as the load when the maximum stress \(\sigma_{\mathrm{zo}}\) is reached and no further equilibrium can be found. At this point, a significant reduction of the shear capacity in the uncracked zone also took place, and the decreasing crack propagation angle \(\beta\) was not able to compensate the reduction of the shear stress capacity due to vertical stresses \(\sigma_{\mathrm{zo}}\) at the crack tip. Detailed information on the shear stress distribution and the influence of propagating shear crack angles may be taken from reference [25]. With regards to the values shown in Table 3, it is stated that both the calculated capacities (SCPT and EC2) give a good prediction of the ultimate shear capacity resulting from the experimental test.  
Figure 7: Experimental and calculated (SCPT) results for: (**a**) the crack opening \(\mathrm{w_{reinf}}\) at the height of the reinforcement layer and (**b**) the maximum concrete compressive strain \(\epsilon_{\mathrm{top}}\) in the top fiber of the uncracked zone.
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 4 Proposed Technique
### Task Offloading Results  
We implement the proposed offloading technique and compare it to three other techniques: the matching-based task offloading technique [30], the knapsack-based task offloading technique [42] and the exhaustive search algorithm. The comparison is conducted using different ratios of vehicles generating the task. In Figure 10, the results show that the proposed task offloading technique computes all tasks within 5 s at a high task workload scenario, i.e., when 90% of vehicles have a computation task. On the other hand, the matching-based and knapsack-based task offloading techniques show inferior results, with a total delay of around 7.8-8 s in the high task workload scenario. Lastly, the exhaustive search technique shows the best results because it tries all combinations and selects the best combination with the lowest task delay. It can be seen that the proposed technique achieves a similar performance to the exhaustive search technique, with a slightly higher task delay.  
Figure 11 shows the task delay of the proposed technique in comparison to when CQI-based task offloading is not used and instead, a fixed 50% of the task is offloaded to  
Figure 10: Task offloading delay with different numbers of task-generating vehicles.  
Figure 9: Three-seconds-ahead prediction of computation availability.  
the fog node. The results show that without using CQI-based offloading, the task delay goes up to 10 s. This is because the channel conditions between the source vehicle and fog node are not considered, thus resulting in lower transmission delay.  
Figure 12 shows the task delay when cooperative computing vehicles are either selected without ANN-based prediction or are selected randomly. It can be seen that when cooperative computing vehicles are randomly selected, task delay goes up to 12 s. Without using ANN-based prediction and only making decisions based on the current values of link connectivity time and computation availability time, task delay increases by up to 20% compared to using the proposed technique.  
Figure 11: Task offloading delay with and without CQI-based offloading.  
Figure 12: Task offloading delay with and without ANN prediction.