esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction of the Novel Hydraulic Technology Mode  
### Background of the Novel Hydraulic Technology Mode  
A large amount of field data indicates that different coal seams (especially when they contain geological structures) need to have different permeability enhancing technologies applied. Because tectonic zones have abnormal changes in stress and gas distribution, the location of gas accumulation in these areas cannot be accurately predicted [22; 23; 24]. However, the gas content and pressure are balanced in nontectonic zones. Additionally, coal seams in tectonic zones are fractured and store a large amount of elastic energy [25; 26; 27]. The gas storage conditions are complicated. For example, a partition is formed by a fault in the fracture zone and a gas accumulation area. This partition will help store more gas and make the distribution of gas more complicated, whereas the type and physical properties of the coal and rock seams remain almost the same.  
Permeability enhancing technologies have been applied and researched throughout the world in recent years and have been summarized [4; 12]. These technologies can be divided into pressure relief technologies and fracturing technologies. Pressure relief technologies manufacture spaces in coal seams by methods such as drilling holes or flushing out coal. Stress in the coal seam will then be relieved, and the permeability of the coal seam will be increased. Examples of these technologies include dense borehole technology, hydraulic slotting technology, hydraulic flushing technology, and HOST. Fracturing technologies produce fractures in the coal seam by injecting high-pressure fracturing fluid or using other methods to produce fractures (or prop up natural fractures) in the coal seam and drastically increase the permeability. Examples of these technologies include traditional HFT, pulse HFT, SDHFT, tree-type HFT, and deep-hole blasting technology.  
In the present study, a novel mode is proposed by considering and summarizing the characteristics of coal seams, the difficulties in increasing coal seam permeability, and the advantages of different permeability enhancing technologies. The main idea behind this mode is to adopt different permeability enhancing technologies in different regions. Because a tectonic region stores a great amount of elastic energy and gas, the coal and rock seams are broken and cannot bear more energy input. Any injection of energy may easily cause CGO accidents. Therefore, pressure relief technologies are appropriate (HOST is one of the best technologies available at present). Coal and rock seams in nontectonic  
Figure 1: Schematic diagram of the novel hydraulic technology mode.  
regions are stable and can accommodate some energy; therefore, using fracturing technologies is better because they are highly efficient and effective. SDHFT is one of the best choices because of its balanced permeability enhancing effects.
# New Evolutionary-Based Techniques for Image Registration
## 7 Conclusive Remarks and Suggestions for Further Work  
The work reported in the paper addresses the problem of binary image registration for signature recognition purposes. We proposed a series of EC techniques in order to align a sensed image to a target one using the mutual information measure to compute the similarities between two images.  
Figure 1: (**a**) The target image; (**b**) The observed image.  
Figure 2: (**a**) The image obtained by ES-APSO2; (**b**) The image obtained by EO.  
We evaluated the quality of the results from both accuracy and computational complexity points of view. The accuracy was established in terms of the percentage of successful runs, the quality of the solutions being also calculated in terms of the signal-to-noise ratio. The computational complexity was defined based on execution time (run time).  
The first class of methods consists of APSO approaches, where the updating rules are defined based on the attributes range and the quality of current candidate solution. Based on the obtained results, we conclude that the algorithms are very fast and, in most of the cases, the accuracy is fairly good.  
The class of firefly-based techniques comprises very accurate algorithms, the success percentage being 100% for every tested pair of images. The main drawback of the proposed Firefly1 and Firefly2 methods is their execution time. However, the efficiency of firefly-based approach can be significantly improved by defining updating rules that consider, besides other criteria, the attractors quality.  
Finally, to find a trade-off between the accuracy and the efficiency of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive ES search to discover a global optimum solution with the strong quick convergence ability of APSO. The obtained algorithms proved very fast and are also highly accurate. Also, a series of experiments led to the conclusion that the proposed hybrid technique outperforms the well-known One Plus One Evolutionary Optimizer (EO) from the accuracy point of view.  
We conclude that the results are encouraging and entail future work toward extending the proposed approach to more complex perturbation models as well as more complex hybrid and memetic techniques.  
C.-L.C.: methodology, software, validation, analysis, writing--original draft preparation, writing--review and editing, supervision; A.S.: software, validation, analysis, writing--review and editing.  
This research received no funding.  
The authors declare no conflict of interest.
## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Assistance in Diagnosis
#### 4.1.2 Cancer Diagnosis and Surgical Interventions
Employing leave-one-site-out cross-validation, an SVM model was trained to categorize the captured images. The outcome yielded an 82.9% overall accuracy when compared against histopathological results, substantiating the potential of PS-OCT in offering reliable cancer diagnostic insights. Beyond the aforementioned primary SWIR imaging techniques, novel imaging systems have emerged in this spectral range. For example, an innovative technique grounded in orthogonal polarization imaging (OPI) within the SWIR range enabled size measurements of lymph nodes in both animal and human samples [78]. Given that cancer cells metastasize through the vascular and lymphatic system, this study harnessed deep U-Net models for high-contrast, label-free image analysis to automatically segment lymph nodes. These advancements highlight the profound impact of ML-assisted SWIR techniques in enhancing cancer diagnosis and surgical interventions.
## 3 Results  
We conducted a thorough comparison between the solar radiation estimates generated by the Heliosat-2 model and ground measurements across diverse geographic locations: the Middle East, East Asia, and Africa. This analysis encompassed various categories, including hourly comparison, diurnal comparison (includes both clear-sky and cloudy-sky conditions), monthly comparison, and seasonal comparison.
## 4 Results and Discussion
### Conceptual Framework and Validation
The HTMT can be used in two ways: (1) being a criterion; or (2) being a statistical test. If the value of the HTMT is higher than a predefined cutoff point (0.85 or 0.90), this suggests that the SEM model lacks discriminant validity. [PERSON] et al. [69] suggested that 0.90 is more suitable (referred to HTMT\({}_{0.90}\)) when the sample size is around 100 and the AVE values are not high. Alternatively, the HTMT can form the foundation of a statistical discriminant validity test. The bootstrapping procedure allows that confidence intervals for the HTMT can be constructed to test the null hypothesis (H0: HTMT \(\geq\) 1.0) against the alternative hypothesis (H1: HTMT \(<\) 1.0). If 1.0 (held in the H0) falls within a confidence interval, it can be concluded that the SEM model lacks sufficient discriminant validity; conversely, if 1.0 does not fall within the range of the interval, one may conclude that two constructs are empirically distinct. Tables 5 and 6 show the results of CFA which was conducted using the software SmartPLS 3. As indicated in Table 5, all the factor loadings were greater than 0.55, which were significant at the 0.05 level (critical \(t\)-value = 1.96). Besides, all the values of the Cronbach's alpha, AVE, and CR exceeded their respective cutoff values. Meanwhile, it could be seen from Table 6 that the \(p\)-values for the HTMT tests among the three hindrance groupings were below 0.05, and that the HTMT values were smaller than 0.90. Therefore, both the HTMT test and the HTMT\({}_{0.90}\) criterion indicated that the SEM model met the requirements of the discriminant validity. Thus, the measurement model was reliable and valid for the structural path modeling. In addition, the absolute model fit statistics that were available  
in the PLS-SEM outputs were acceptable. Standardized root mean residual was 0.066, below 0.10 which, as recommended by [PERSON] et al. [55], was the cutoff point, and the Chi-Square value (320.029) was large. Furthermore, the bootstrapping results in Figure 2 suggested that all the path coefficients (0.719, 0.449, and 0.414, respectively) for the three hypotheses were positive, with the \(p\)-values of 0.000. The coefficients of determination (\(R^{2}\)) for lack of collaboration and model integration and lack of continuous involvement and capabilities were 0.712 (with \(t\)-value at 11.662 and \(p\)-value at 0.000) and 0.512 (with \(t\)-value at 4.671 and \(p\)-value at 0.000), respectively. Hence, these statistics validated the conceptual framework (Figure 2). The subsequent sections will discuss this framework's rationale.
## 3 Results and Discussion
### Bending Moment Distributions Along a Bearer
Furthermore, it provides damping and prevents positive bending moments of the selected point. In all of the graphs, the positive bending moments of the selected points are insignificant. This might be the advantage of using FFU sleeers since the response of the system is lower compared to case with concrete sleeers that are discussed in next section. In general, the graphs in Figure 29 show similar behavior in fully supported bearer case [12; 28; 29; 30; 31]. Symmetrical distribution in some bearers, such as bearers at normal track section or asymmetrical distributions in some bearers such as bearers at closure panel, could be observed. It is also noteworthy that the graphs are plotted for the specific point in a specific time, which means the graphs could exhibit a different profile under different selection criteria. Considering this fact, it could be concluded that comments on bending moment distributions are challenging in dynamic simulations. Nevertheless, the figures presented here give significant details on dynamic behaviors of turnout bearers. Figure 28: The flexural responses of unsupported bearers when the selected point have maximum and minimum bending moments: (**a**) Bearer at normal track section; (**b**) Bearer under switch blades; (**c**) Bearer at closure panel; (**d**) Bearer under crossing nose; (**e**) The longest bearer; (**f**) The shortest bearer.
# Demand Control Strategies of a PCM Enhanced Ventilation System for Residential Buildings
## 4 Demand-Controlled Ventilation  
### Method  
Demand-controlled ventilation is a personalized air supply based on the demand of each room. Instead of modelling the house as one thermal zone, the house is separated into several thermal zones. The house is separated into four thermal zones: living room, bedroom 1, bedroom 2, and bedroom 3. The HVAC and ventilation schedules are based on the demand of the zones and occupant behaviors.  
The internal heat load schedule of the rooms is the same as shown in Figure 9. The internal heat load level is set as 2 W/m\({}^{2}\) in the bedrooms and 5 W/m\({}^{2}\) in the living room.  
The family house with one master bedroom and two single bedrooms is occupied by four people. According to suggestions by [PERSON] et al. [32], the occupancy time for children's bedroom is 20:00-6:00, and for adults, it is 23:00-6:00 in New York time. The occupant schedules of the bedrooms and living room are set accordingly, as shown in Figures 23-25. Bedroom 2 and 3 are children's rooms, and are occupied 20:00-6:00. Day occupancy during the summer holidays is not taken into account.  
The HVAC schedule for each room is set according to the occupancy schedule. The HVAC is turned on when the room is occupied, as shown in Figure 26.  
The results of the optimized ventilation airflow rates in Section 3 are adopted: the maximum ventilation rate of the house during summer is 300 m\({}^{3}\)/h, and during winter 102 m\({}^{3}\)/h.  
The ventilation strategies of the rooms are the same as shown in Figures 3-6. The ventilation airflow rate of each ventilation inlet is set as the ratio of the room ventilation airflow rate and the number of the ventilation inlets of the room. For example, in the living room, there are three ventilation inlets (one PCMVW and 2 PCM facades). During the night in summer, the living room is ventilated with 0.042 m\({}^{3}\)/h (0.014 m\({}^{3}\)/h for each inlet). During weekday days (9:00-16:00), the occupant fraction is 0.25; thus, the airflow rate through each inlet is deduced to 0.0105 m\({}^{3}\)/h, as seen in Figure 27.  
During summers, the PCMVWs in bedrooms are not used due to no-cooling demand during the day. Instead, the night ventilation of the bedrooms is from outdoor directly to the room. For the living room,  
Figure 27: Ventilation schedule of each room during summer.  
Figure 26: The HVAC schedule for each room.  
Figure 25: Living room occupancy schedule.  
both PCMVW and PCM energy storage facade operate. The PCM energy storage provides pre-cooled air during the day, when ventilation pre-cooling is needed. During the night, night ventilation operates from 20:00-7:00, to discharge heat from the PCM energy storage as well as the thermal mass in the room.  
During the winter, all the PCMVWs and PCM energy storage facades are in operation. The PCM energy storage provides pre-heated air, when a heating demand is present in the room. The ventilation airflow rate of each room is shown in Figure 28.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 2 Materials and Methods
### Filtering the Images
With other datasets, where multiple plants may be present together, a maximum plant size parameter can be employed to determine whether the largest green mask in the image corresponds to a single plant or a group of plants. If it represents a group, the image can be discarded, or it can be subdivided into smaller parts, such as tiles, for further analysis using texture and color features. However, this constitutes a distinct task. Figure 2: The image processing steps: (**a**) original image; (**b**) green mask; (**c**) artificial coloring of the regions; (**d**) the largest green object mask; (**e**) the mask was cut and resized to 256 \(\times\) 256 pixels; (**f**) the 256 \(\times\) 256-pixel mask was used on the image to have the largest plant image. Following the extraction of the \"one plant image mask\", the image was cropped, typically with an additional 5 pixels in every direction. This mask retained the same pixel/mm size as the original image and was utilized for distance transformation, which will be elaborated upon in subsequent sections. Concurrently, the mask was resized to 256 \(\times\) 256 pixels, and contours were computed, which is beneficial for area and Hu features. Next, the mask was utilized to obtain the original RGB plant image, where the plant retained its original color, while the background was blackened. This image was suitable for generating RGB histogram color features. Additionally, an RGB-to-HSV color space transformation was performed, yielding HSV histogram color features. Finally, a gray image comprising only one channel was generated, which is ideal for extracting texture features. Further details will be provided in subsequent sections.
## 3 Results and Discussion
### HMs in Plants and Invertebrates  
The characteristics of the accumulation of some HMs (Cu, Pb, and Zn) by plant (leaves) and invertebrate species occurring in soil potentially affected by the ex-operation of the copper smelter in Alaverdi Town were also investigated. The accumulation level was considered the difference between the total and reference concentrations of an element. The highest concentrations of anthropogenic Cu, Zn, and Pb were accumulated in the leaves of the species _Fraxinus excelsior_, _Robinia pseudoacacia_, and _Aesculus hippocampusanum_, respectively, with the lowest levels in the leaves of the species _Salix acutifolia_, _Populus canadensis_/_Ulmus minor_, and _Populus canadensis_, respectively (Figure 6). The plant species can be ranked according to decreasing HM accumulation level as follows: _Fraxinus excelsior-Acer platanoides-Robinia pseudoacacia-Aesculus hippocampusanum-Populus canadensis-Ulmus minor-Salix acutifolia_ in the case of Cu accumulation, _Robinia pseudoacaci-Fraxinus excelsior-Acer platanoides-Aesculus hippocampusanum-Salix acutifolia-Ulmus minor-Populus canadensis_ in the case of Zn accumulation, and _Aesculus hippocampusanum-Robinia pseudoacacia-Fraxinus excelsior-Acer platanoides-Salix acutifolia-Ulmus minor/Populus canadensis_ in the case of Pb accumulation (Figure 6).  
Figure 5: Overall hazard index (OHI) of HMs in the soil located at different distances from the Alaverdi copper smelter.  
Higher concentrations of anthropogenic Cu were accumulated in the tissues of the slug species _Deroceras caucasicum_ and _Limax flavus_, while anthropogenic Zn accumulated more in the earthworm species _Eisenia rosea_ (Figure 6). The investigated invertebrate species did not accumulate anthropogenic Pb (Figure 6).  
It can be stated that the investigated plant and invertebrate species showed different capacities for the accumulation of HMs from the soil, and the plant species _Frazinus excelsior_, _Acer platanoides_, _Robinia pseudoacacia_, and _Aesculus hippocastanum_, as well as the invertebrate species _Deroceras caucasicum_, _Limax flavus_, and _Eisenia rosea_ can be used for the selective removal of HMs from the soil over the copper smelting facility in Alaverdi Town.
## 5 Model Setup
### Sensitivity Analysis  
The sensitivity analysis was performed on various factors in the LSTM prior to evaluating the performance of the forecasting model and the performance of the newly proposed hybrid activation function in this study. The simulation cases for the sensitivity analysis are summarized in Table 4. The four parameters can be set in LSTM. The hydrological data were used for 1 year as input data. The forecasting data concern water surface elevation after 3 h because the leading time of the flood forecasting in river is within 3 h. The uncertainty of each parameter was expressed as the standard deviation and the average CPU time was verified with 30 iterative simulations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Data** & **\(t\)-score** & **\(p\)-value** & **Significant Factor for Forecasting Flood** \\ \hline Precipitation & 8.9 & \(\sim\)0.00 & O \\ Outlet Discharge & 41.2 & \(\sim\)0.00 & O \\ Water Elevation & 37.8 & \(\sim\)0.00 & O \\ Tidal Level & 2.2 & 0.03 & O \\ Wind Speed & 1.9 & 0.06 & X \\ Concentration of Dissolved Oxygen & 1.0 & 0.29 & X \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(t\)-test analysis for input data.  
Figure 3: \(p\)-value derivation process.  
The standard deviation and the CPU time were considered to derive the optimal setting value for each parameter. The standard deviation and CPU time for each parameter are shown in Figure 4 and the results of sensitivity analysis are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Evaluation** \\ \hline Hidden Layer (A) & 1 *, 3, 5, 10, 20 & \\ Learning Rate (B) & 0.005, 0.01 *, 0.02, 0.05, 0.1 & Standard deviation \\ Sequence Length (C) & 1 *, 3, 6, 9, 12, 24 & (Uncertainty) and CPU Time \\ Iterations (D) & 1, 10, 100 *, 1000, 10,000 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity analysis for LSTM for forecasting flood (*: reference value).  
Figure 4: Results of the sensitivity analysis ((**A**): hidden layer; (**B**): learning rate; (**C**): sequence length; (**D**): iterations; dashed lines: average value of observed data; \(\equiv\) CPU Time, \(\equiv\) Average forecasting value).  
As a result of the sensitivity analysis, the optimal values for each parameter were those with a hidden number of layers of 10, the learning rate was 0.005, there were 1000 iterations, and the sequence length was 1 h in this study. However, in the case of sequence length, the setting value can be changed depending on how the engineer sets the leading time. The recommended time of the sequence length is more than 5 h, according to previous studies [25].
## 4 Discussion
### Challenges  
Challenges are found throughout this study. Foremost, the RF model is designed professionally for structural and tabular applications such as stock market price predictions that use a specific number of vectors; simple image classification; or recognition tasks, such as satellite imagery object detection. However, the RF model capability can be limited when faced with large-scale training involving a significant number of classes, such as when using the ImageNet dataset [42] with its 1000 classes and 1.2 million images. To tackle these challenges, a DNN model was developed. It excels in extracting features from high-dimensional vector spaces and large datasets while requiring less time for training. We use this RF model as a prototype for our analysis, and we highlight that a potential robust defense mechanism exists if we can adopt a different architecture.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 2 Data Sources and Preprocessing  
In this study, radar backscatter measurements from the current operating Ku-band HY-2B/HSCAT and CFOSAT/CSCAT, and C-band ASCAT on board the MetOp-A, MetOp-B, and C satellites are used. More relevant information about the scatterometer data is presented in Table 1.
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 4 Experimentations and Results
### Machine Translation Experiments
#### 4.2.1 NMT  
In our Vietnamese-Korean NMT systems, the baseline KrUTagger tool and the VnPOS MT system are implemented, based on the NMT model in OpenNMT [13]. NMT is a sequence-to-sequence encoder-decoder model with an attention mechanism. The encoder is a bi-directional RNN, which uses a context-free model (word2 vec) to convert input sentences into input vectors. Using a bi-directional RNN, the encoder generates the corresponding representation of the input sentence. The decoder is also an RNN; it takes the representation of the input sentence, the previous hidden state, and the initial output word prediction, and provides a new output word prediction. The attention mechanism creates a shortcut that directly links the words in the target language with the corresponding words of attention in the source language [41].