esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition  
Two types of satellite images were evaluated, since the literature reveals that classification methods perform differently on different types of satellite imagery [33]. A total of seven satellite scenes were acquired from the USGS website from 7 July to 28 July 2019 (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellites** & \begin{tabular}{c} **Number** \\ **of Bands** \\ \end{tabular} & \begin{tabular}{c} **Resolution** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Acquisition Date** \\ **(Day/Month/Year)** \\ \end{tabular} & \
\begin{tabular}{c} **Path-Row/Tile** \\ **(Number)** \\ \end{tabular} &
\begin{tabular}{c} **Cloud** \\ **Cover (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Sentinel-2A} & \multirow{4}{*}{13} & \multirow{4}{*}{10–60} & 26 July 2019 & T20 TLS & \(\leq\)10 \\ \cline{3-5}  & & & 16 July 2019 & T20 TMT & \(\leq\)10 \\ \cline{3-5}  & & & 20 July 2019 & T20 TNS & \(\leq\)10 \\ \cline{3-5}  & & & 28 July 2019 & T20 TMS & \(\leq\)10 \\ \hline \multirow{3}{*}{Landsat-8} & \multirow{3}{*}{11} & \multirow{3}{*}{15–100} & 26 July 2019 & 008-028 & \(\leq\)10 \\ \cline{3-5}  & & & 26 July 2019 & 007-028 & \(\leq\)10 \\ \cline{3-5}  & & & 7 July 2019 & 007-027 & \(\leq\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of satellite images used in the study.  
Figure 1: Prince Edward Island, Canada.  
The Sentinels satellites are a constellation that consists of two twin satellites, Sentinel-2A and Sentinel-2B. When these satellites operate simultaneously from the same orbit, phased at 180\({}^{\circ}\) to each other, they can monitor the variability in land surface conditions every 5 days [34]. Sentinel-2 satellites acquire optical imagery at a resolution ranging from 10 to 60 m depending on the spectral bands. The satellite coverage limits are between 56\({}^{\circ}\) latitude South and 84\({}^{\rm o}\) longitude North with a swath width of 290 km.  
The Landsat-8 satellite is also an Earth observation satellite equipped with two payloads that collect 11 spectral bands with a spatial resolution ranging from 30 to 100 m. Landsat-8 was selected due to its enhanced thematic mapper in the range of visible bands compared to other Landsat satellites [27]. The Landsat-8 has improved capabilities from the previous generation due to the addition of new spectral bands in the blue spectrum, the use of two new thermal bands, and an enhanced duty cycle that has increased the daily image collection capacity of the satellite [35].  
The Landsat-8 satellite scenes were selected with the lowest cloud cover available to reduce the scattering and absorption of light in the atmosphere (Table 1). Additionally, the satellite scenes were taken from the collection-1 level-1, which was already geometrically and radiometrically corrected.
# Effects of Replacing Corn with Hydroponically
## 4 Discussion  
The cost involved in raising heifers is an important issue for dairy farmers. Additional animals typically account for 15-20% of milk production costs. The replacement of heifers ranks as the second or third largest component of production costs after feed and possibly labor on animal agriculture production. The costs can vary from farm to farm, depending on individual management strategies. The aim of raising heifers should be to minimize costs while still maximizing the returns on the costs incurred. Therefore, the objective of a dairy replacement heifer management program depends on the successful rearing of heifers to minimize costs without compromising future lactation performance. Corn is a primary energy source that is mainly used as an ingredient in preparing feed for ruminants in Korea. However, corns are generally imported from other countries, and the cost of feed is high. Therefore, farmers and researchers have placed more attention on searching for an alternative to corn meal. Dietary energy, DM intake, body weight, and weight gain are closely associated with growth performance in ruminants [17]. Therefore, in this study, we evaluated the effects of HSB on growth performance and biochemical metabolites in Holstein heifers in Korea. The results of the present study revealed that there were no significant changes in the live body weight of heifers fed with a 10% and 30% HSB diet when compared with the CON diet. Moreover, the average daily weight gain was determined from body weight measured monthly, and no significant difference was noted between the 10% and 30% HSB diet-supplemented heifers and the CON-supplemented heifers. Therefore, the study findings confirmed that HSB could be a substitute for corn meal in feed for ruminants in Korea. The regulation of feed intake and energy balance are influenced by many factors, including age, initial live weight, nutrition, breed features, and management program [18], and this feed intake and energy balance is predicted for metabolic, digestive tract, and external factors [19]. Limited research has been published to evaluate the feed value of sprouting barley. Early study results have shown that a 10-day sprouted barley diet in pigs induced lower weight gain as compared with ground maize, but in beef cattle, sprouted barley green fodder showed a 200 g higher daily gain as compared with a maize control diet [20].
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 4 The Architecture and Complexity Analysis of the Proposed Decoder  
In this section, based on the proposed soft decision decoding algorithm, the decoder implementation for hardware evaluation is designed in a serial mode. Two key components in the decoder, the candidate sequence generator and the correlation discrepancy calculation unit, are both implemented based on the systolic arrays, which have lower complexity and reduce the length of the critical path. Furthermore, the implementation complexity of the proposed decoder is analyzed.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 5 Results Simulation and Analysis
### Experimental Environment  
The basic hardware environment based on the experiment is shown in Table 1. The experiment is based on the deep learning library of TensorFlow1.15. TensorFlow1.15 is an open source deep learning framework. TensorFlow can handle all kinds of neural networks conveniently. TensorFlow is used as the background program in the experiment, and matplotlib is used for visualization. We adopted TensorFlow1.15 version 1.15.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
## Abbreviations  
Al\({}_{2}\)O\({}_{3}\) Aluminum Oxide  
CuO  
Copper Oxide  
HTC  
Heat Transfer Coefficient  
SiO\({}_{2}\) Silicon Dioxide  
SPD  
Single Phase Dispersion Model  
SPH  
Single Phase Model  
TiO\({}_{2}\) Titanium Dioxide  
ZnO  
Zinc Oxide  
Symbols  
\(C_{p,nf}\) Specific Heat Capacity of Nanofluid (J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\))  
d\({}_{t}\) Tube Inner Diameter (mm)  
\(k_{eff}\) Effective Thermal Conductivity of Nanofluid (W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\))  
\(k_{disp}\) Dispersion Thermal Conductivity (W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\))  
L Length of the Tube (m)  
\(\mu_{eff}\) Effective Viscosity of Nanofluid (kg\(\cdot\)m\({}^{-1}\cdot\)s\({}^{-1}\))  
\(\mu_{disp}\) Dispersion Viscosity (kg\(\cdot\)m\({}^{-1}\cdot\)s\({}^{-1}\))  
\(\phi_{b}\) Volume Fraction of Nanoparticles (\(\%\))  
\(\rho_{nf}\) Density of Nanofluid (kg\(\cdot\)m\({}^{-3}\))  
Nu Nusselt Number  
T\({}_{m}\) Fluid Mean Temperature (K)  
U Mean Velocity of Nanofluid (m\(\cdot\)s\({}^{-1}\))
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 3 Results and Discussion
### Prediction and Analysis
#### 3.2.2 Experiment II: The Impact of Multiple Convolutions  
As observed from Table 4 and Figure 9, it is apparent that CNN-LSTM and MCNN-LSTM exhibit lesser prediction errors compared to the CNN and the MCNN, indicating that the combination models can utilize the advantages of multiple models to improve predictive accuracy. Specifically, the _MAE_, _MSE_, _RMSE_, and \(R^{2}\) values of the MCNN are 6.550, 68.181, 8.257, and 0.959, respectively, which are 17.96%, 33.69%, 18.57%, and 2.24% higher than those of the CNN. Similarly, compared to CNN-LSTM, the four error metrics of MCNN-LSTM have increased by 2.06%, 21.73%, 11.53%, and 0.62%. The above data indicate that using the MCNN with multiple parallel convolutional kernels can more comprehensively extract features of different scales and more accurately predict future wind power values.
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 5 Performance  
The main, albeit indirect, scientific objective of the MERLIN mission is to reduce uncertainties on CH\({}_{4}\) sources and sinks, from global to regional scales. This is done through atmospheric flux inversions, using the information (L2 products) on atmospheric CH\({}_{4}\) columns (XCH\({}_{4}\)) provided by MERLIN (see introduction and Section 2). To be confident in these uncertainty estimations, a multi-model assessment is necessary accounting for the full error budget for L2 XCH\({}_{4}\), including both random and systematic errors.
# A Comprehensive Survey on Real-Time Applications of WSN  
[PERSON]  
1 School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China; [EMAIL_ADDRESS]  
2 School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China; [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (S.I.)  
[EMAIL_ADDRESS]; Tel.: +86-156-2025-50992015 October 201715 October 201713 November 2  
one sensor to another sensor in a wide area via packets. WSN is ubiquitously used in various fields such as military surveillance [3], the health care industry [4], and other industries [5]. The emergence of3 rd millennium industries makes it interesting to research WSN. The actual implementation of WSN was started in 1950 and the US military used this technology. The first mature WSN was named the \"Sound Surveillance System\" (SOSUS). It was used to detect the threat of underwater submarines. Acoustic sensors (a type of sensor used to check the amplitude of the waves) are used for (SOSUS) and this technology is also being practiced nowadays [6]. Today, in every part of life,a sensor is used to detect sound, detect any kind of vibrations, check the water level, detect the temperature level [7], in smart houses [8], in smart phones, and most importantly, we use some kind of sensors in the human body [9]. The WSN consists of nodes, and through these nodes, sensors communicate with each other. The hierarchy of these nodes is like a star where every node connects to each other or like a mesh topology. Each node collects information from this sensor and communicates it to other nodes. A sensor node basically consists of a microcontroller, transceiver, external memory, power source,and one or more sensors. WSN consists of sensor nodes, a user, and an interconnected backbone. Figure 1 presents WSN Network architecture consisting of sensor nodes, which are used in environmental monitoring. In the context of wireless communication, the sensor nodes communicate with each other and send the processed data to the sink node. All the nodes send data to the sink node, which is further sent to users through the internet [10].  
With the advancement in the sensor field like an embedded system, Micro-Electro-Mechanical System (MEMS), wireless communication, and disseminated processing, lots of changes have recently been brought about in WSN. An Intelligent system is capable of getting data and processes huge volumes of data, i.e., traffic management, weather monitoring, air quality management [11; 12]. In this paper, we will discuss the real-time applications of WSN for monitoring the environment. These systems have replaced human efforts following an efficient method and could be able to gain functional requirements.
## 5 Discussion
### Comparison Models  
#### 5.1.1 The IRI Model  
For a given location, time, and maximum altitude, IRI obtains the TEC value from the lower boundary to the user-specified upper boundary by integrating the electron density along the signal propagation path [33]. IRI prediction data can be computed using the IRI model ([[https://kauai.ccmc.gsfc.nasa.gov/instantrun/iri/](https://kauai.ccmc.gsfc.nasa.gov/instantrun/iri/)]([https://kauai.ccmc.gsfc.nasa.gov/instantrun/iri/](https://kauai.ccmc.gsfc.nasa.gov/instantrun/iri/))) (assessed on 5 January 2024), with the following calculation formula:  
\[\text{TEC}=\int_{s_{1}}^{s_{2}}n_{e}(s)ds, \tag{7}\]  
where \(s_{1}\) represents the height of the user-specified lower boundary, \(s_{2}\) represents the height of the user-specified upper boundary, \(n_{e}\) represents the electron density, and \(s\) represents the propagation path. In this experiment, \(s_{1}\) is set to 50 and \(s_{2}\) is set to 2000, and the unit is meters.  
The optional parameters settings of the IRI model [14] are shown in Table 2:  
Figure 6: Comparison of observations (OBSs) and validation data (V-DATA) with RMSE in the Upper Left Corner: (**a**) April 2005; (**b**) May 2005; (**c**) June 2005; (**d**) July 2005; (**e**) August 2005; (**f**) September 2005; (**g**) October 2005; (**h**) November 2005; (**i**) December 2005; (**j**) January 2006; (**k**) February 2006; (**l**) March 2006.
# A Comparative Study for Control of Quadrotor UAVs
## 2 Materials and Methods
### System Control
#### 2.2.2 Inner Loop--LQR Control  
The linearized attitude dynamical model in (26) is exploited to design the LQR-based attitude stabilizer. LQR controllers are based on optimization (minimization) of the sum of the weighted energies (L\({}_{2}\) norms) of the state and the control command, with the constraints that the system dynamics are respected. According to the Linear-Quadratic Regulator method, since the linearized system is completely controllable, i.e., the controllability matrix has full rank, it is possible to define the following feedback control action,  
\[\mathrm{u}(t)=-\mathrm{K}\mathrm{x}(t) \tag{28}\]  
\[\mathrm{K}=\mathrm{R}^{-1}\mathrm{B}^{\mathrm{T}}\mathrm{P} \tag{29}\]  
\[\mathrm{A}^{\mathrm{T}}\mathrm{P}+\mathrm{Q}+\mathrm{PA}-\mathrm{PBR}^{-1} \mathrm{B}^{\mathrm{T}}\mathrm{P}=0 \tag{30}\]  
\[\mathrm{Q}=\begin{pmatrix}\mathrm{q}_{1}&0&0&0&0&0\\ 0&\mathrm{q}_{2}&0&0&0&0\\ 0&0&\mathrm{q}_{3}&0&0&0\\ 0&0&0&\mathrm{q}_{4}&0&0\\ 0&0&0&0&\mathrm{q}_{5}&0\\ 0&0&0&0&0&\mathrm{q}_{6}\end{pmatrix},\ \mathrm{R}=\begin{pmatrix}\mathrm{r}_{1}&0&0\\ 0&\mathrm{r}_{2}&0\\ 0&0&\mathrm{r}_{3}\end{pmatrix} \tag{31}\]  
where K denotes the feedback control gain, Q and R are diagonal fixed weight matrices for the weighted L\({}_{2}\) norms of \(\mathrm{x}\) and \(\mathrm{u}\), respectively. The dynamics of the closed-loop system depend on the relative values of the control parameters: \(\mathrm{q}_{i}\), \(\mathrm{r}_{i}\). P can be computed by solving the Algebraic Riccati Equation formulated in Equation (30). Refer to [26] for the theoretical foundations of LQR control and further details.
## 3 Numerical Simulation Analysis and Demonstration of Actual Extraction Sequence in Large Stopes
### Simulation Result Analysis of the One-Step High-Profile Working Face Extraction Process
The \"mining-filling-mining\" scheme can reduce the deformation of the roof and surrounding rock, but it involves significant closure work, a complex filling process, and high organizational difficulties in production, which reduces the production capacity of the working area and impacts the completion of mining tasks. Hence, it is only suitable for localized applications and not recommended for widespread promotion. The plan to reduce the width of the one-step mining area, by dividing a larger exposed surface into two surfaces to form triangular pillars, enhances the strength of the pillars. The simulation results show that the displacement of the rock on both sides of the void is significantly reduced, effectively protecting the pillars in the two-step mining area. Even if the rock on both sides has developed joints and fractures, the scheme retains a certain thickness of rock, providing adequate protection. When continuously applying this scheme in adjacent one-step mining areas, although the exposed area of the two-step void increases, there are no significant changes in the displacement of the fill material, roof displacement, or tensile stress variation compared to the original scheme. Although the compressive stress variation is not a significant change in the displacement of the fill material, the effect of the stress variation is not significant. Figure 10:7 Simulation calculation results of the two-step mining after applying this scheme. (**a**) Maximum principal stress cloud map; (**b**) minimum principal stress cloud map; (**c**) displacement cloud map of the roof; and (**d**) displacement cloud map of the pillars. stress increases, its range is small and has little impact. Therefore, it is recommended to actively adopt this scheme for the extraction of large one-step mining areas.
## 4 Evaluation Scope
### Target Domain
#### 4.1.1 Healthcare  
Various studies in our sample [44, 49, 57, 63, 74, 85] have been conducted in the healthcare domain. This domain is characterized by life-critical decisions that can immediately impact human lives, necessitating the consideration of ethical, legal, and regulatory aspects.  
Figure 2: Evaluation scope of the studies in our sample.  
Therefore, both doctors and patients must be able to trust AI systems and understand the decisions they make.  
Regarding this, [PERSON] et al. [44] evaluated medical explanations to enhance clarity and trust for healthcare professionals. [PERSON] et al. [63] evaluated the structuring and presentation of explanations within clinical decision support systems. [PERSON] et al. [57] evaluated to what extent explanations make the AI diagnostic process more transparent for clinicians and patients. [PERSON] et al. [74] evaluated what kinds of explanations are preferred by healthcare providers and patients in clinical decision support systems. [PERSON] et al. [49] evaluated how explanations help patients with diabetes better understand and manage their condition. [PERSON] et al. [85] evaluated explainable AI in the health domain by examining the usefulness and effectiveness of activation maps in aiding radiologists in detecting vertebral fractures.
## 4 Discussion
Our results agree with previous findings that Class III craniofacial growth pattern tends to be re-established after active orthopedic treatment with RME/FM [4]. As for the vertical skeletal relationships, both groups showed a counterclockwise rotation of the mandible that was associated with a reduction in intermaximillary divergence (Table 4). This finding is also in agreement with previous reports on RME/FM [4]. The analysis of the outcomes during the T1-T3 interval can be considered the medium-term results for both treatment protocols (Table 2). The comparison of the results showed that the greater maxillary advancement was performed by the RME/FM protocol in comparison with the RMR protocol (SNA +1.5 degrees). This result agrees with previous studies [22; 23] that showed respectively a maxillary advancement of 1.2 mm and 1.4 mm of Point A to Nasion perpendicular in Class III patients treated by RME/FM with respect to subjects with untreated Class III malocclusion when evaluated at a postpubertal observation. No significant differences were found between the two protocols in the correction of mandibular protrusion. The RME/FM protocol showed greater efficacy than RMR protocol in the correction of skeletal sagittal Class III relationship (ANB and Wits appraisal +1.9 degrees and +2.2 mm, respectively). Both protocols exhibited a counterclockwise rotation of the mandible that was associated with a decrease in intermaximillary divergence (Table 2). This result for the RME/FM protocol is in agreement with previous investigations [22; 23]. As for the dental changes the RME/FM protocol produced a significantly greater correction of the molar relationship (\(-\)1.5 mm) with respect to RMR that actually induced a worsening of the Class III molar relationship (+1.2 mm). In the RME/FM group 52% received phase 2 treatment before facial growth end be-cause there was lack of space in the upper arch for the eruption of the maxillary permanent canines or because it was decided, in agreement with patients' parents, to re-fine the occlusion. A limitation of the present study was its retrospective nature that could increase the risk for selection bias. We tried to control this bias by including in both groups all consecutively treated patients who presented with the inclusion criteria. Another limitation was the absence of a control sample of subjects with untreated Class III malocclusion. However, it is very difficult to find a contemporary sample with untreated Class III malocclusion mainly for ethical reasons. Finally, the last observation of the present study was taken at a postpubertal phase of skeletal maturation while, ideally, the final follow-up should have been collected at the end of active growth. The results of the present study clearly demonstrated that if the aim is to produce a favorable correction of Class III skeletal imbalance, the RME/FM protocol should be preferred with respect to RMR. RMR can be regarded as a useful protocol in the treatment of pseudo-Class III malocclusion with mandibular functional anterior shift or as a retention appliance after RME/FM.
# A General Euler Angle Error Model of Strapdown Inertial Navigation Systems
## 4 Discussion  
There are some limitations of the proposed method. These above-mentioned experiments raised the idea that the proposed method can achieve sufficient accuracy for SINS. However, it does not perform well when there are three singularities. Therefore, the singularity problem of the Euler angle error method cannot be avoided by different Euler angle rotation orders. In practice, it is rare that the  
Figure 10: The Euler angle error propagation with the coupling of three initial Euler angle errors and rotation in the \(Z\) axis.  
Figure 9: The Euler angle error propagation with coupling of three initial Euler angle errors and rotation in the \(Y\) axis.  
Euler angles \(\varphi\), \(\theta\), and \(\gamma\), in turn along with head, pitch, and roll axes, have singularities at 90 degrees at the same time. Meanwhile, Euler angles are the direct output of the attitude change, and Euler angle errors can more accurately and rapidly characterize the attitude error propagation. The computational burden of the proposed method is reduced by avoiding attitude transformation compared with the conventional misalignment method. Consequently, the proposed method is applicable in most cases. Future work will address its application in a nonlinear environment.