esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 4 Model Setup and Validation
### Model Validation
#### 4.2.2 Tide Level Validation  
In this paper, four typical tide-level buoys were used to verify the simulation of Typhoon Mekkhala, namely the Sansha, Pingtan, Xiamen, and Jiangjunao buoys. Meanwhile, the simulation of the tide level was verified for Typhoon Maria using the Beishuang buoy and No. 4 buoy. The distribution map of each tide level station is shown in Figure 4b.  
Figure 6 shows that both simulated and measured values change periodically with time, which is in line with the semidiurnal tide near the Taiwan Strait. The relative error of the highest tide level at the Sansha buoy is 6%, and the relative error of the lowest tide level is 14%. On the Pingtan tidal buoy, both errors were 16%. At the Xiamen tide level buoy, the relative error of the highest tide level is 18%, and the relative error of the lowest tide level is 27%. This is due to the topographic accuracy, which leads to a large tidal error in the study area. At the Jiangjunao buoy, the errors are 2% and 9%, respectively. The measured tide level at the highest tide level at all verification points is consistent with the simulated time phase, so it can be determined that the model can better simulate the tidal conditions.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods  
### Study Area  
The Mekong Delta is a region in southwestern Vietnam where the Mekong River approaches and empties into the sea. It covers over 40,500 km\({}^{2}\) and is an important source of agriculture and aquaculture in the region. The area is also vulnerable to adverse impacts of climate change, including saltwater intrusion, coastal erosion, flooding, and drought, and efforts are being made to ensure greater productivity and climate resilience (Figure 1).  
Drought is a prevalent concern in the Mekong Delta because of climate change impacts [42]. The intensity, duration, and frequency of meteorological droughts in the delta have been studied using various indices [43]. The Vietnamese Mekong Delta (VMD) has experienced droughts that affect agriculture and aquaculture [44]. These findings contribute to a better understanding of drought patterns and their impact on agricultural output in the Mekong Delta, providing valuable insights for policymakers and practitioners in water resource management.  
From 1985 to 2022, the Mekong Delta in Vietnam had different levels of drought occurrences. Studies reveal that the area experienced several instances of drought, varying in intensity, throughout the years. The Mekong Delta in Vietnam had severe drought occurrences in 1988, 1998, 2010, 2016, and 2020, resulting in substantial economic losses as a consequence of crop devastation and detrimental impacts on the ecosystem and the livelihoods of farmers [12; 45].  
Figure 1: The geographical location and metrological stations in the Mekong Delta.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 2 Materials and Methods
### Reference Wind Speed Products
#### 2.2.3 Other Satellite Observations of Winds  
Wind speed measurements made by two other types of spaceborne microwave sensors are also used for intercomparison purposes. One is the ASCAT-B scatterometer flying on the Metop-B satellite. ASCAT-B is a backscatter radar that operates at C-Band with VV polarization and its operation is optimized for ocean wind observations [30]. The second spaceborne sensor is the GMI microwave radiometer flying on the Global Precipitation Measurement satellite. GMI is a multi-frequency/multi-polarization microwave radiometer designed to measure both atmospheric and surface geophysical parameters, including near-surface wind speed over ocean [31]. Notably, these two wind sensors rely on different remote sensing modalities and frequencies (radar backscatter at C-Band and passive microwave emission at X-, Ku-, K-, and Ka-Band) than CYGNSS (forward specular scatter at L-Band) to measure the wind speed, so their respective measurement errors and their sensitivity to ancillary geophysical parameters can be expected to be largely uncorrelated.
# Co-Firing of Fast Pyrolysis Bio-Oil and Heavy Fuel Oil in a 300-kW\({}_{\text{th}}\) Furnace
###### Abstract  
Combustion characteristics and pollutant emissions of burning fast pyrolysis bio-oil/heavy fuel oil (HFO) blends in a 300-kW\({}_{\text{th}}\) furnace are investigated. Lauan (shorea) wood is used as biomass feedstock for making bio-oil via a fast pyrolysis process. The bio-oil has high viscosity, high water and oxygen content, as well as a low heating value. A furnace test is conducted for pure HFO and bio-oil/HFO blends with various mixing ratios. The results show that instability occurs during the test for cases with more than 5% bio-oil in the emulsion. Notably, it is verified that burning a bio-oil/HFO blend with a 2.5% bio-oil and a 97.5% HFO exhibits similar furnace performance and has lower NO and SO\({}_{2}\) emission levels as compared to burning pure HFO. The reductions of NO and SO\({}_{2}\) emissions are 2.6% and 7.9% for this blend, respectively.  
bio-oil; fast pyrolysis; furnace; combustion characteristics; emissions +
Footnote †: journal: Applied Sciences
# Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems
## 2 Background and Related Work  
### Deep Learning Models in Recommender Systems  
Deep learning has transformed various fields within artificial intelligence and machine learning. These models resemble the structure and functioning of the human brain by using interconnected layers of artificial neurons to learn and recognize patterns in large amounts of data. The capabilities of deep learning have made it one of the most adopted tools, as they perform automated feature extraction and effectively handle time-series data as compared with traditional machine learning techniques. Deep learning models have proven to make remarkably accurate performance on large-scale data-driven tasks. The combination of pattern recognition, feature learning, and prediction performance makes deep learning have a transformative nature across a variety of industries and fields of research [11]. Deep learning models in recommender systems extract hidden patterns to alleviate common problems in traditional recommender systems: scalability, sparsity, and accuracy. Deep learning enhances recommender systems through the following:  
* Capturing non-linear relationships: deep learning models effectively allow for the extraction of non-linear relationships between users and items, which are often overlooked by traditional methods. This is crucial for understanding nuanced preferences and providing more accurate recommendations [12; 13].
* Handling high-dimensional data: deep learning models remain capable of handling extremely high-dimensional data, emerging as a challenge in recommender systems. Such models have the capacity to extract low-dimensional representations, thereby simplifying the complexity of problems while still preserving all informative contents, with a data sample size large enough to make conventional statistical methods ineffective [12; 13].
* Addressing data sparsity: deep learning models can effectively deal with the problem of sparsity of data, which can occur when information about user preferences or item attributes is limited. They can learn from incomplete data and hence make accurate predictions even when user interactions are limited [12; 13].
* Improving recommendation quality: deep learning models have generally surpassed traditional approaches in recommender systems, leading to more relevant and personalized recommendations [12; 13].  
Overall, deep learning models have become essential in modern recommender systems across numerous domains, including e-learning, e-commerce, e-health, e-tourism, and entertainment, increasingly enhancing their efficiency in delivering personalized user experiences [11; 13].
## 3 Results
### Comparison of Carbon Storage Estimation Results  
The appropriate combination of independent variables derived from GEDI L2B space-borne LiDAR data and Landsat 9 remote sensing images was selected by screening individual factors using random forest and Pearson correlation coefficient analysis. Model building for the selected variables and observed carbon storage was conducted using SVM, bagging, and RF, and the accuracy of each model was evaluated. Two indicators, R\({}^{2}\) and RMSE, were used to compare and analyze the prediction outcomes of various models, and the findings are presented in Figure 7a-c.  
Figure 6: Random forest feature importance ranking.  
Figure 7: _Cont._  
Comparing the R\({}^{2}\) and RMSE of the three models, it was found that RF had the highest estimation accuracy with an R\({}^{2}\) of 0.82 and RMSE of 11.92 t/hm\({}^{2}\). The bagging model had moderate evaluation precision, wherein the R\({}^{2}\) was 0.49 and RMSE was 19.69t/hm\({}^{2}\). In contrast, the SVM model had the worst results; the R\({}^{2}\) was 0.49 and RMSE was 23.97 t/hm\({}^{2}\). The results revealed that in the model established via SVM, the sample sites were discretely spread on each edge of the 1:1 line, and the fitting degree of the model was not high, while the random forest model had a higher precision in evaluating carbon storage. And the distribution of samples was more concentrated, and the estimation error was smaller when the carbon storage was higher or lower. In order to compare the accuracy difference of single remote sensing data source and joint multi-source remote sensing data to estimate carbon storage, we conducted feature optimization of variables extracted from GEDI and Landsat 9 images. Rv, sensitivity, modis_treecover, rh100, and landsat_treecover were selected in GEDI, while B7, VARI, B4, RGVI, and IPVI were selected in Landsat 9. The results are shown in Figure 7d,e, which were modeled via random forest. When only the variable of Landsat 9 was used, the R\({}^{2}\) was 0.69, RMSE was 15.38 t/hm\({}^{2}\) and the estimated average value was 37.18 t/hm\({}^{2}\). But the R\({}^{2}\) was 0.74, RMSE was 14.06 t/hm\({}^{2}\) and the average carbon storage was 38.09 t/hm\({}^{2}\) if only the variable of GEDI was used. These results demonstrated that the estimation results of carbon storage using GEDI or Landsat 9 alone were not as accurate as the estimation results of integrating GEDI and Landsat 9.
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 5 Privacy Implications of Intrusion Detection Systems
### Type of Data  
Intrusion detection systems can be categorized on the basis of network data they use to detect malicious actors. Identification of correct data type not only affects the performance of the system, but also has an impact on the privacy of the users. For instance, if the detection system uses IP-address to analyze the behavior of a device, it can be easily traced back to the owner of this IP-address. The challenge in this regard is two-fold: (1) identifying the data type that provides optimum performance in terms of detection rate, and (2) ensure protection of the privacy of the network users. The existing intrusion detection systems use two types of data for detecting the malicious actors i.e., (1) application layer data logs, and (2) network traces data. The first type is data originated at the application level and is normally associated with specific type of data set. This type of data can provide information about the device architecture and can help in fingerprinting malicious and non-malicious devices. The second data-type is the IP traces of the network traffic and contains much more details about the behavior of the devices. Another data type that can be used is the content or payload of the information exchanged among devices, such as a temperature reading, a web-page, or meta data.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 4 Result and Discussion
### Diversity Properties of MIMO Antennas
#### 4.3.1 Envelope Correlation Coefficient  
To see the diversity properties of MIMO antennas, the envelope correlation coefficient ECC is calculated using the S-parameter. The ECC curves between ports 1 and 2 and the other ports in the MIMO antenna are shown in Figure 15a. Figure 15b shows the ECC curves between the remaining three ports as a function of frequency.  
\[ECC=\frac{\left|S_{ii}^{*}\times S_{ij}+S_{ji}^{*}\times S_{jj}\right|^{2}}{ \left(1-{S_{ii}}^{2}-{S_{ij}}^{2}\right)\left(1-{S_{ij}}^{2}-{S_{ji}}^{2} \right)} \tag{4}\]  
Figure 14: Gain and radiation efficiency of UWB MIMO antenna with/without introduced strips.  
Figure 15: ECC between antenna units: (**a**) between port 1 and other ports; (**b**) between ports 2, 3, and 4.  
From Figure 15a, it can be seen that the ECC of port 1 to port 2 (ECC12) and port 1 to port 3 (ECC13), and port 1 to port 4 (ECC14) is less than 0.002 within the operating frequency, especially the ECC between ports 1, 3, and 1,4 are less than 0.0013. In Figure 15b, it can be seen that the ECC of ports 2, 3, and 4 is less than 0.00009, and the ECC of port 2 to port 3 (ECC23) and port 2 to 4 (ECC24) is less than 0.00006, and the ECC of all ports is less than 0.002, which satisfies the requirements of MIMO system for MIMO antenna correlation parameters [23].
# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
###### Abstract  
Soil erosion is a global problem that negatively affects the quality of the environment, the availability of natural resources, as well as the safety of inhabitants. Soil erosion threatens the functioning of urban areas, which was the reason for choosing the territory of the Master Plan of Belgrade (Serbia) as the research area. The calculation of soil erosion loss was analyzed using the G2 erosion model. The model belongs to a group of empirical models and is based on the synthesis of the equation from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM). The estimation of soil degradation was analyzed in two time periods (2001 and 2019), which represent the time boundaries of the management of the Master Plan of Belgrade. The novel approach used in this research is based on using the land cover inventory as a dynamic indicator of the urbanization process. Land cover was identified using remote sensing, machine learning techniques, and the random forest algorithm applied to multispectral satellite images of the Landsat mission in combination with spectral indices. Climatic parameters were analyzed on the basis of data from meteorological stations (first scenario, i.e., 2001), as well as on simulations of changes based on climate scenario RCP8.5 (representative concentration pathways) concerning the current condition of the land cover (second scenario). A comparative analysis of the two time periods identified a slight reduction in total soil loss. For the first period, the average soil loss value is 4.11 t-ha\({}^{-1}\).y\({}^{-1}\). The analysis of the second period revealed an average value of 3.63 t-ha\({}^{-1}\).y\({}^{-1}\). However, the increase in non-porous surfaces has led to a change in the focus of soil degradation. Increased average soil loss as one of the catalysts of torrential flood frequencies registered on natural and semi-natural areas were 43.29% and 16.14%, respectively. These results are a significant contribution to the study of soil erosion in urban conditions under the impact of climate change.  
soil erosion; G2 model; urban landscape; climate change; land cover change +
Footnote †: journal: Applied Sciences
# Grain Sieve Loss Fuzzy Control System in Rice Combine Harvesters
###### Abstract  
The main working parts of the cleaning device of a rice combine harvester can be controlled by an established control strategy in real time based on the monitored grain sieve loss. This is an efficient way to improve their cleaning adaptability, since as a consequence, the main working parameters of combine harvesters can automatically adapt to crop and environment changes, and the corresponding cleaning performance can be improved. To achieve the target of cleaning control based on the monitored grain sieve loss, a fuzzy control system was developed, which selected S7-1200 PLC as the main control unit to build the lower computer hardware system, utilized ladder language to complete the system compilation, and used LabVIEW 14.0 software to design the host-computer interface. The effects of fan speed, guide plate angle, and sieve opening on the grain sieve loss and grain impurity ratio have been investigated through a large number of bench tests. The relevance level of the operating parameters on the performance parameters has been determined also, and finally, a fuzzy control model was developed for the cleaning system. The experiment results indicated that the designed fuzzy control model can control the cleaning section settings, such as fan speed and guide plate angle automatically, and reduce the grain sieve loss to some extent.  
Keywords:rice; combine harvester; cleaning system; sieve loss; control; experiment +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Basic Watermarking Procedure
#### 2.1.1 Watermark Synchronization  
The watermark synchronization is the vital factor related to robustness when a watermarking algorithm suffers the geometric type of attack such as image cropping, image rotation and so on. In fact, sometimes the watermarked values can remain correct but the error of synchronization will lead to the failure of watermark extraction. Specifically, consider the watermark sequence _WM_ being represented as:  
\[WM=wm[i](wm[i]\in\{0,1\}\ \ i=1,2,\ldots,L), \tag{1}\]  
where \(L\) is the length of _WM_. Under geometric attack, embedding \(wm[i]\) and directly extracting it could lead to the loss of \(i\). So \(i\) is an important information part in both processes of watermark embedding and extraction. Therefore, the connections between \(i\) and \(wm[i]\) represent the watermark synchronization. Commonly, a stable watermark synchronization definitely ensures the watermarking algorithm robustness against image cropping attack. So far this mechanism has been implemented in different ways, such as geometric reference points [6], auxiliary location information [10; 34], mapping method [35], etc.
## 3 Formulation of the Mathematical Model
### Modified Effective Pore Diameter with Effects of Adsorption/Desorption and Stress-Sensitivity
#### 3.2.1 Stress-Sensitivity  
The effects of stress-sensitivity on gas flow within shale reservoirs incorporate two aspects: (1) matrix and fracture permeability, as well as porosity, which decreases with decreasing reservoir pressure (increasing effective stress); and (2) matrix shrinkage with increasing effective stress decreases the effective pore diameter.  
[PERSON] and [PERSON] [34] studied the variation of fracture conductivity with respect to changes in reservoir pressure. The fracture conductivity is defined as the product of fracture width and fracture permeability, as given by:  
\[F_{c}=w_{f}\cdot k_{f}, \tag{11}\]  
where \(F_{c}\) is the fracture conductivity, \(w_{f}\) is the fracture width, and \(k_{f}\) is the fracture permeability.  
Based on the experimental data, the relationship between the fracture conductivity multiplier (a ratio of fracture conductivity at different pressures divided by fracture conductivity at the initial pressure) and pressure was obtained through software modeling using CMG [35]. The results indicatedthat the fracture conductivity multiplier exponentially decreased with decreasing pressure (Figure 3). In addition, other scholars have described the relationship between fracture permeability and reservoir pressure [31], as follows:  
\[k_{fi}=k_{f0}e^{-c_{f}(p_{0}-p_{i})}, \tag{12}\]  
where \(k_{fi}\) is the fracture permeability for a given reservoir pressure, \(k_{f0}\) is the initial fracture permeability, \(c_{f}\) is the compressibility for the fractures, and \(p_{0}\) and \(p_{i}\) are initial reservoir pressure and a given reservoir pressure, respectively.  
The results from numerical simulation and experimental data showed that both matrix permeability and matrix porosity decreased exponentially with an increase in effective stress. Furthermore, the relationships between matrix porosity, matrix permeability, and reservoir pressure were obtained by [21; 36]:  
\[\varphi_{i}=\varphi_{0}\cdot e^{c_{f}\cdot(p_{0}-p_{i})} \tag{13}\]  
\[k_{i}=k_{0}\cdot e^{c_{k}\cdot(p_{0}-p_{i})}, \tag{14}\]  
where \(\varphi_{0}\) is the initial matrix porosity, \(k_{0}\) is the initial matrix permeability, \(\varphi_{i}\) and \(k_{i}\) are the matrix porosity and matrix permeability with respect to a given reservoir pressure \(p_{i}\), and \(c_{p}\) and \(c_{k}\) refer to compressibility for porosity and compressibility for permeability, respectively.  
Furthermore, the pore diameter is calculated by [6]:  
\[d_{i}=4\sqrt{2\tau}\sqrt{\frac{k_{i}}{\varphi_{i}}}. \tag{15}\]  
Based on Equations (13) and (15), the change in effective pore diameter is defined as:  
\[\Delta d_{s}=d_{i}-d_{0}=d_{0}\Big{(}e^{\omega(p_{0}-p_{i})}-1\Big{)}. \tag{16}\]
# Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile
## 3 Results
### Experiments and Theory
First, the global twist was resolved hypothetically to form a perfectly planar structure by forcing the helicity from 10.5 to 10.67 bp/turn. The twist angle of 8.23\({}^{\circ}\) per 16 bp was calculated considering the difference in the designed and natural DNA helicity (10.67 and 10.5 bp/turn). Given spring constants of dsDNA bundles, the energy needed for flattening the tile was calculated as \(\sim\)25.6 kcal/mol. Next, the planar structure was bent into an ideally circular shape (as shown in the Figure 3d). This was realized by twisting of dsDNA bundles and bending of crossovers. These two components were modeled as elastic springs connected into a network whose effective spring constant was calculated by considering the springs in parallel and series. With knowledge of the effective spring constant of the entire origami, the energy necessary for bending at 360\({}^{\circ}\) was approximately 6.7 kcal/mol. Combining the two steps, the origami tile transformed from a twist conformation to a planar tile and then to a cyclized tube. Thus, the driving energy for the cyclization process was acquired by summation of the energies for flattening and flexing, yielding \(\sim\)32.3 kcal/mol [27]. This theoretical value agrees almost perfectly with the experimentally determined activation energy.
## 4 BER Computational Strategies for Decision Unit--Background Tutorial
### BER Estimation Schemes--A Brief Review  
Many techniques may be used to conclude bit error rate estimation. This subsection provides a synopsis of the conventional MC simulation to reveal that the execution time for low BER is very long. This subsection presents three techniques: quasi-analytical estimation, importance sampling theory, and tail extrapolation probabilities.  
Such solutions demand assumptions regarding the actual system behaviour, and the effectiveness is greatly dependent on the presumed parameters, which likely have to be altered for various systems of communication. Predominantly, finding the ideal model or suitable parameters is not easy. Next, a number of new BER estimators based on the LLR distribution were introduced; nevertheless, they have a few shortcomings, such as being dependent on the SNR uncertainty estimation and the specific channel features. However, all the aforementioned approaches demand awareness of the transmitted bit stream, while the estimator certainly does not know transmitted data in practical situations. In contrast, our new CNN imaging computational processor requires no prior information.