esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 2 Background  
### Question Answering System  
As a critical field in artificial intelligence [12], intelligent question and answer is an essential branch of natural language processing, usually in a question-and-answer human-computer interaction to locate the user's desired knowledge and provide personalized information services. Unlike search engines, it allows computers to answer users' questions automatically in a precise natural language format. The history of intelligent question and answering dates back to 1950, when [PERSON], the father of computer science, came up with the Turing Test to determine if it possible for a computer might think accurately and correctly, thus opening the chapter on natural language human-computer interaction. Around the 1960s, the first question-and-answering systems were introduced, and [PERSON] et al. designed a Baseball program that could answer questions about baseball games in plain English. In 1966, [PERSON] designed and implemented ELIZA Chatbot [13], which can process simple problem statements. In 1971, another early chatbot [14] was developed by [PERSON], a psychiat at Stanford University, and named \"Parry\". These question-answering systems based on rule matching could not be widely applied due to lacking data resources at that time.  
As deep learning and natural language processing technology rapidly advances, the question-answering system gradually transitions from early rule matching to retrieval matching [15]. The core idea is to extract the core words in natural language questions, search for the relevant answers in documents or web pages according to the core words and return the corresponding answers using the correlation sorting algorithm. [PERSON] et al. [16] proposed the pseudo-correlation feedback algorithm based on the method of automatic document retrieval, which used the context information in the document to retrieve the most similar answers. The retrieval matching approach achieved good results when it was first proposed. However, as the number of data increased and the diversity of user questions emerged, the quality of answers extracted from documents or web pages by this approach varied, profoundly affecting system response time and the accuracy. Until the concept of knowledge graph was proposed, the KBQA is significantly improved in quality and has realized the form of extracting questions and answers from documents. At present, KBQA has received more and more attention from researchers, and it has become a topic of intense interest in the natural language field [17].
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
## 3 Metasurface Lens Design  
In order to demonstrate its applications to millimeter-wave bands, the metasurface lens and feeding-antenna array aimed at synthesizing plane waves at 26 GHz at a reduced distance from a feed position are discussed in this section. The feasibility of this lens-antenna scenario sets up a demonstration of the applications to millimeter-wave bands and the compactness of the proposed lens antenna as suitable for use in anechoic chambers as a radiated source.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## Materials
### Data Sets
Figure 2: Validation extent of the preprocessed S-1 image at the flood event (6 April 2017) for VV polarization (**a**). Digitized validation mask based on S-2 data (8 April 2017) (**b**). nearest drainage' (HAND) index [53] has been integrated as binary mask to exclude areas above the mentioned threshold of 20 m before classification [47]. The HAND index with about 90 m resolution is based on the height and flow direction information provided by the product \"Hydroheshes\" [54]. Figure 3b shows the HAND mask for the study area in Namibia. The separation between permanent open water surfaces and TOW areas was done by the use of SRTM Water Body Data (SWBD), which was applied as a permanent water mask. This product was created by the National Geospatial Intelligence Agency (NGA) [55] on the basis of data from the Shuttle Radar Topography Mission (SRTM30), which took place in February 2000, and it is freely available as a vector data set. SWBD was chosen for its resolution and global availability of SRTM data. Figure 3c shows the SWBD mask for the study area.
## 3 Methodology
### Key Frame Detection  
At this stage, the key frame detection is performed with the template matching technique on the eye region. The cover test is essentially a stimulus-response test. What we are interested in is whether the eye movements occur when a stimulus occurs. In the system, an entire process of tests is recorded in the video, which contains nearly 3000 frames at a length of about 50 s. We examined all frames between two stimuli. The stimuli we focused on are the unilateral cover test for the left and right eye, the alternating cover test for left and right eye, and the cover-uncover test for the left and right eye. In total, 18 stimuli are obtained with 6 types of stimuli for 3 rounds. The useful information accounts for about two-fifth of the video. Therefore, it is more efficient for the algorithm to discard these redundant information. The key frame detection is for the purpose of finding the frame where the stimulus occurs.  
The right iris region segmented in Section 3.2 is used as a template, and the template matching is applied to the eye regions. The thresholds \(\mathrm{TH_{1}}\), \(\mathrm{TH_{2}}\) are set for the right eye region and the left region, respectively, and \(\mathrm{TH_{2}}\) is smaller than \(\mathrm{TH_{1}}\), as the right iris region is used as a template. The iris region is detected if the matching result is bigger than the threshold. In the nearby region of the iris, there may be many matching points which present the same iris. The repeated point can be removed by using the distance constraint. Therefore, the number of the matching template is consistent with the actual number of irises. The frame number, the number of iris detected in the right eye region, the number of the iris detected in the left region are saved in memory. Then we search the memory to find the position of the stimulus. Taking the unilateral cover test for the left eye as an example, the number of iris detected is [1 1], separately, before the left eye is covered and then [1 0], after covering the left eye. Therefore, we can use the state change from [1 1] to [1 0] to determine the corresponding frame of the stimuli. The correspondence between state changes and stimulus is shown in Table 1. Thus, the frame number of the eighteen stimulus can be obtained.  
Figure 4: The flowchart of iris boundary detection. The result of the exact eye region detection is marked by the red window on the original grayscale image. The red circle represents the iris boundary.
# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 4 Experiments
### Experimental Results
#### 4.5.5 Loss Tuning Comparison Experiments  
In this paper, a weighted loss function consisting of CE loss, dice loss, focal loss, and Jaccard loss is used. Firstly, the default parameter values of CE loss and dice loss are chosen; A and B are 1 and 2, respectively. The default values are verified to be the most optimal choice through the experiments in Table 7. Then, according to the determined A and B values, the focal loss parameter C is used for the tuning experiments, and the values are taken from 5 to 100 from low to high. The best results were obtained when C was 20, as can be seen in Table 8. Finally, according to the determined A, B, and C, the tuning experiments of Jaccard loss parameter D are carried out, and D is set to 0.1-1.5 from low to high, respectively. Table 9 shows that the network is optimal when D is 0.9. According to the three loss tuning comparison experiments, it can be concluded that the IOU improvement is the largest when A, B, C, and D are 1, 2, 20, and 0.9, respectively, which is 0.18% higher than the IOU of the corresponding network when the original CE loss and Dice loss are combined. The experiments proved that the final parameter combination results in the optimal choice.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network** & **IOU (\%)** \\ \hline Vgg16 + RAFF + CBAM & **99.20** \\ Vgg16 + RAFF + RFB & 99.17 \\ Vgg16 + RAFF + CA & 98.14 \\ Vgg16 + RAFF + SE & 99.12 \\ Vgg16 + RAFF + ULSAM & 99.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Module comparison experiment. The bolded section indicates optimal parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**(A, B, C, D)** & **Precision (\%)** & **IOU (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline (1, 2, 0, 0) & 99.60 & **99.20** & 99.74 & **99.67** \\ (1, 1, 0, 0) & **99.62** & 99.16 & 99.68 & 99.65 \\ (1, 3, 0, 0) & 99.53 & 99.14 & 99.75 & 99.64 \\ (1, 4, 0, 0) & 99.53 & 99.15 & 99.74 & 99.63 \\ (1, 5, 0, 0) & 99.48 & 99.14 & **99.80** & 99.64 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameter adjustment experiment of CE loss and dice loss. The bolded section indicates optimal parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**(A, B, C, D)** & **Precision (\%)** & **IOU (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline (1, 2, 5, 0) & 99.73 & 99.31 & **99.72** & 99.72 \\ (1, 2, 10, 0) & 99.77 & 99.34 & 99.68 & 99.72 \\ (1, 2, 15, 0) & 99.78 & 99.35 & 99.68 & 99.73 \\ (1, 2, 20, 0) & 99.80 & 99.38 & 99.69 & **99.74** \\ (1, 2, 30, 0) & 99.81 & 99.36 & 99.67 & **99.74** \\ (1, 2, 40, 0) & 99.82 & 99.35 & 99.64 & 99.73 \\ (1, 2, 50, 0) & 99.81 & 99.33 & 99.64 & 99.72 \\ (1, 2, 100, 0) & **99.83** & 99.31 & 99.60 & 99.71 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameter adjustment experiment of focal loss. The bolded section indicates optimal parameters.
## 4 Analysis of Energy Storage System
### Study on Low-Temperature Characteristics of Battery  
The activity of the electrolyte of the lead-acid battery is easily affected by low temperatures, resulting in a decrease in battery capacity. The power supply system operating in Antarctica requires a long-term constant temperature treatment of energy storage system. Reasonable storage temperature needs to be determined, so as to reduce the energy consumption caused by maintaining the constant temperature as much as possible. Thus, a study on battery characteristics at low temperatures was designed and implemented.  
In order to study the low-temperature characteristics of lead-acid batteries, a battery capacity calibration experiment was designed. A low-temperature test chamber (MDF-86V340E, Zhongkeduling, Hefei, Anhui, China) was used to provide stable low-temperature environments from \(-\)50 \({}^{\circ}\)C to 0 \({}^{\circ}\)C. The key specifications of the low-temperature test chamber are presented in Table 6.  
The ideal capacity of the battery to be tested is 2 V, 3 KAH under a normal temperature environment. The battery was discharged at a constant current of 15 A at different ambient temperatures from \(-\)50 \({}^{\circ}\)C to 0 \({}^{\circ}\)C. In addition, the battery capacity at different ambient temperatures can be obtained. The battery voltages were measured by an oscilloscope (MSO70404C, Tektronix, Beaverton, OR, USA). The discharge cut-off voltage was set as 1.6 V. The interval for the experimental temperature change was set to 10 \({}^{\circ}\)C. We obtained the correlation between battery capacity and voltage at \(-\)50 \({}^{\circ}\)C, \(-\)40 \({}^{\circ}\)C, \(-\)30 \({}^{\circ}\)C, \(-\)20 \({}^{\circ}\)C, \(-\)10 \({}^{\circ}\)C and 0 \({}^{\circ}\)C. At each ambient temperature, the battery continued to discharge at a constant current until the cutoff voltage was reached. During the experiment, the low-temperature test chamber could maintain the temperature. We took the average values of the voltage to minimize the statistical error and uncertainty. As the temperature decreased, the battery capacity also decreased. The standstill battery capacities were 98.42%, 98.11%, 97.62%, 96.77%, 95.83% and 94.12% at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Value** & **Unit** \\ \hline Rated power & 668 & W \\ Rated voltage & 220 & V \\ Temperature range & \(-\)60 to 86 & \({}^{\circ}\)C \\ Temperature adjustment accuracy & 0.1 & \({}^{\circ}\)C \\ Effective volume & 340 & L \\ Noise level & 52 & dB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Key specifications of the low-temperature test chamber.  
\(-20\)\({}^{\circ}\)C, \(-30\)\({}^{\circ}\)C, \(-40\)\({}^{\circ}\)C, \(-50\)\({}^{\circ}\)C, respectively. The discharge capacity of the battery was weakening due to low temperatures. Therefore, the storage temperature of the battery needs to be kept above 0 \({}^{\circ}\)C to avoid the low-temperature loss of the battery capacity.
## 4 Simulation Results
### Case 1: Both PMSMs Have the Same Speed and the Same Torque  
Since the maximum capacitor ripple current occurs around the middle range of the modulation index, both PMSMs are operated with 2000 rpm under a load torque of 9 Nm,  
Figure 6: Normalized dc-link capacitor current of dual VSIs dual PMSMs for various phase shifting angle; M = 0.6126 for both VSIs.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Rated speed & 4000 rpm \\ \hline Rated torque & 22.2 Nm \\ \hline Torque constant & 0.127 Nm/Arms \\ \hline Back Emf constant & 0.119 V/(rad/s) \\ \hline Rated current & 174 A \\ \hline Stator resistance & 14 mohm \\ \hline Inductance _d_-axis & 0.0245 mH \\ \hline Inductance _q_-axis & 0.0245 mH \\ \hline Pole pair & 6 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the PMSMs.  
which is the worst-case condition for the dc-link capacitor ripple current. The RMS value of the phase currents is 70.7 A. Thanks to the proposed method, the peak-to-peak ripple current of the dc-link capacitor is reduced, as shown in Figure 7a. The second carrier harmonic 20 kHz is the major harmonic component of the capacitor current, and it disappears when there is a \(\frac{\pi}{2}\) radian phase difference between the carrier waves, as shown in Figure 7b. In addition, the 6 th and 10 th harmonics are not present in the frequency spectrum of the capacitor current, and the magnitude of the remaining harmonics is either equal or less when there is a phase difference between the carrier waves. While the RMS value of the capacitor current is 90.8 A-RMS without a phase difference, it is reduced to 23.8 A-RMS with the proposed method.  
In addition, the PMSMs are simulated for varying speeds with a constant load torque of 9 Nm. The phase current of the PMSMs is 100 A (70.71 A-RMS), and this is constant for all varying speeds. The reduction in the capacitor current ranges from 40% to 75%, as shown in Figure 8a. In particular, the maximum reduction in capacitor current occurs at 2000 RPM, corresponding to almost half of the modulation indices where the second carrier harmonic is the major component. Figure 8b represents the RMS value of the dc-link capacitor current as the load torque varies. The speed of both motors is the same at 2000 rpm, and the load torque of both motors is identical under varying load conditions. The reduction in capacitor current ranges from 50% to 75% when there is a \(\pi/2\) phase difference between the carrier waves. Even when the load torque reaches its maximum value of 9 Nm, the dc-link capacitor current is 24 A-RMS under phase shifting conditions. This value is approximately equal to the dc-link capacitor current when the load torque is 2 Nm and there is no phase difference between the carrier waves.  
Figure 8: The RMS value of the dc-link capacitor current for varying speeds (**a**) and varying load torques (**b**): (blue) zero rad phase difference; (orange) \(\pi/2\) rad phase difference.  
Figure 7: The dc-link capacitor current (**a**) and frequency spectrum of it (**b**): (red) zero rad phase difference; (blue) \(\pi/2\) rad phase difference.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 4 Landing Site Detection  
During landing site detection, the UAV selects a portion of the flat area as its landing location based on the established color point cloud map. Qualifying the landing site as suitable involves assessing an acceptable slope range and confirming the absence of obstacles, a sufficient size, and proximity to the UAV. These factors were quantified by generating a cost energy map from the geometric and texture information of the area map to identify multiple potential landing sites. Areas with shallow water or heavy road traffic pose potential hazards for UAV landings; thus, landing site semantics determined the safest option for the UAV.
# The Role of Macrophage Dynamics in Atherosclerosis Analyzed Using a Petri Net-Based Model
## 2 Materials and Methods
### Petri Nets
#### 2.2.4 Macrophage Polarization in Atherosclerosis  
Macrophages are key immune cells involved in regulating inflammation, clearing infections, and repairing tissues through processes like phagocytosis and polarization [9; 48]. Macrophage polarization refers to the ability of these cells to change their phenotype and exhibit different functions depending on the surrounding environment. In atherosclerosis, macrophages polarize into M1 and M2 subtypes [49; 50] due to various signals, each influencing the disease's development differently [1].  
M1 macrophages, derived from Ly6C\({}^{high}\) monocytes, promote inflammation through pro-inflammatory cytokines, accelerating atherosclerosis. They are activated by factors like LPS and INF-\(\gamma\)[33; 48; 49; 51]. On the contrary, M2 macrophages from Ly6C\({}^{low}\) monocytes reduce inflammation and aid tissue repair, influenced by cytokines such as IL-4, IL-13, IL-1, or vitamin D3. They release anti-inflammatory substances, such as IL-10, that aid in the healing of lesions [33; 48; 49; 51].
## 2 Materials and Methods
### General Process  
Figure 1 depicts the general process of irradiance forecasting 1 h, 6 h, and 12 h in advance. Each step of the process is described below:  
1. Meteorological data, including variables such as irradiance, precipitation, temperature, and wind speed, are collected from a weather station located in the south of Ecuador.
2. The collected data are preprocessed to prepare the information for the model. This process includes normalization, treatment of missing values, and elimination of outliers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline N\({}^{\circ}\) & **Variable Name** & **Mean** & **Min** & **Max** & **Std. Dev.** \\ \hline
0 & Air\_Temp\_C (\({}^{\circ}\)C) & 17.50 & 7.80 & 28.00 & 3.04 \\
1 & Air\_Humidity\_Pct (\%) & 75.42 & 26.00 & 100.00 & 15.48 \\
2 & Rain\_mm (mm) & 0.004 & 0.00 & 4.00 & 0.07 \\
3 & Solar\_Irradiance\_Wm2 (W/m\({}^{2}\)) & 137.20 & 0.00 & 999.00 & 221.35 \\
4 & Wind\_Direction\_Deg (\({}^{\circ}\)) & 220.73 & 0.00 & 360.00 & 107.69 \\
5 & Wind\_Speed\_ms (m/s) & 1.32 & 0.00 & 8.50 & 1.00 \\
6 & Battery\_Voltage\_V (V) & 12.82 & 12.00 & 14.00 & 0.78 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical information of variables collected from Weather Station 5001.  
Figure 1: General flow chart of the methodological process used in this study.  
3. The most strongly correlated features are selected as input to the model, improving its accuracy and efficiency.
4. The dataset is divided into three subsets for each year's seasons, winter and summer.
5. The irradiance is decomposed using CEEMDAN into several intrinsic mode functions to extract patterns from the data.
6. The decomposed irradiance variables and other meteorological variables are concatenated to form the model input feature set.
7. The CNN-ATT-LSTM model parameters are initialized, preparing the network for training.
8. The CNN-ATT-LSTM model is trained using the training set. This process involves adjusting the model weights to minimize error.
9. The model is evaluated using the validation set to ensure generalization of data not seen during training.
10. The model performance is checked to see whether it is optimal. If not, the hyperparameters are updated, and the training and validation process is repeated.
11. Once the model has been trained and validated with optimal results, the irradiance in the test set is predicted.
## 2 Survey for Outdoor Agricultural UAV Applications
### Crop Monitoring  
Crop monitoring involves taking measurements for the efficient cultivation of a crop based on images of the crop captured with remote sensing methods. In this context, yield, growth and diseases of the product are generally estimated. These controls are difficult to perform manually, especially on very large farms. Very large farms are often monitored via satellites and aircraft. However, satellites and airplanes involve difficulties such as high altitude, higher costs and the effect of clouds on the ability to take clear pictures [33]. These conditions also do not meet the requirement for precision crop monitoring. However, UAVs can obtain high-resolution images from close range at a low cost [24]. For this reason, it is reasonable to perform these tasks with a UAV at minimum cost. In addition, thanks to the different cameras in the UAV, different index information about the crops is obtained; these indexes are also important for crop yield control [34]. The sensors used for this purpose are generally thermal, RGB, multispectral and hyperspectral sensors (see Figure 2) [24].