esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### MANCOVA Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Dependent** & **Type III** & & & \\  & **Sample** & **Sum of** & **df** & **Mean** & **F** & **Sig.** & **Partial Eta Squared** \\ \hline \multirow{4}{*}{Difficulty} & DCAT Posttest & 293.68 & 1 & 293.68 & 151.27 & 0.000 *** & 0.84 \\  & SAT Posttest & 7.56 & 1 & 7.56 & 34.48 & 0.000 *** & 0.55 \\  & TMTA Posttest & 251.27 & 1 & 251.27 & 64.53 & 0.000 *** & 0.70 \\  & TMTB Posttest & 56.95 & 1 & 56.95 & 46.96 & 0.000 *** & 0.63 \\ \hline \multirow{4}{*}{Pressure} & DCAT Posttest & 267.97 & 1 & 267.97 & 138.03 & 0.000 *** & 0.83 \\  & SAT Posttest & 13.31 & 1 & 13.31 & 60.66 & 0.000 *** & 0.68 \\  & TMTA Posttest & 27.62 & 1 & 27.62 & 7.09 & 0.013 * & 0.20 \\  & TMTB Posttest & 77.25 & 1 & 77.25 & 63.70 & 0.000 *** & 0.69 \\ \hline \multirow{4}{*}{Competition} & DCAT Posttest & 3.12 & 1 & 3.12 & 1.61 & 0.215 & 0.05 \\  & SAT Posttest & 0.68 & 1 & 0.68 & 3.12 & 0.088 & 0.10 \\  & TMTA Posttest & 0.02 & 1 & 0.02 & 0.00 & 0.945 & 0.00 \\  & TMTB Posttest & 2.02 & 1 & 2.02 & 1.67 & 0.207 & 0.06 \\ \hline \multirow{4}{*}{Difficulty *} & DCAT Posttest & 45.57 & 1 & 45.57 & 23.47 & 0.000 *** & 0.46 \\  & SAT Posttest & 2.66 & 1 & 2.66 & 12.14 & 0.002 ** & 0.30 \\ \cline{1-1}  & TMTA Posttest & 35.07 & 1 & 35.07 & 9.01 & 0.006 ** & 0.24 \\ \cline{1-1}  & TMTB Posttest & 22.81 & 1 & 22.81 & 18.81 & 0.000 *** & 0.40 \\ \hline \multirow{4}{*}{Difficulty *} & DCAT Posttest & 0.02 & 1 & 0.02 & 0.01 & 0.915 & 0.00 \\  & SAT Posttest & 0.36 & 1 & 0.36 & 1.62 & 0.214 & 0.05 \\ \cline{1-1}  & TMTA Posttest & 5.53 & 1 & 5.53 & 1.42 & 0.243 & 0.05 \\ \cline{1-1}  & TMTB Posttest & 1.95 & 1 & 1.95 & 1.61 & 0.215 & 0.05 \\ \hline \multirow{4}{*}{Pressure *} & DCAT Posttest & 2.30 & 1 & 2.30 & 1.19 & 0.285 & 0.04 \\ \cline{1-1}  & SAT Posttest & 0.14 & 1 & 0.14 & 0.65 & 0.427 & 0.02 \\ \cline{1-1}  & TMTA Posttest & 3.59 & 1 & 3.59 & 0.92 & 0.345 & 0.03 \\ \cline{1-1}  & TMTB Posttest & 2.31 & 1 & 2.31 & 1.91 & 0.178 & 0.06 \\ \hline \multirow{4}{*}{Difficulty *} & DCAT Posttest & 0.47 & 1 & 0.47 & 0.24 & 0.627 & 0.01 \\ \cline{1-1}  & SAT Posttest & 1.14 & 1 & 1.14 & 5.19 & 0.030 * & 0.16 \\ \cline{1-1}  & TMTA Posttest & 9.81 & 1 & 9.81 & 2.52 & 0.124 & 0.08 \\ \cline{1-1}  & TMTB Posttest & 0.60 & 1 & 0.60 & 0.50 & 0.487 & 0.02 \\ \hline \hline \end{tabular} Note: * \(p<0.05\); ** \(p<0.01\); and *** \(p<0.001\).  
\end{table} 
Table 24: [PERSON]’s test of equality of error variances. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **F** & **df1** & **df2** & **Sig.** \\ \hline D-CAT post-test & 1.275 & 7 & 32 & 0.293 \\ SAT post-test & 0.683 & 7 & 32 & 0.685 \\ TMT-A post-test & 1.254 & 7 & 32 & 0.304 \\ TMT-B post-test & 0.453 & 7 & 32 & 0.860 \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Tests of between-subjects effects (MANCOVA).
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 4 Experimental Results and Analyses
### BDS-3+Heo Constellation
Figure 8: Estimated DOPs, positioning precision in longitude versus latitude, and the correlation between station height and receiver clock difference of the BDS-3+HEO constellation. (**a**) Number of visible satellites; (**b**) GDOP; (**c**) HDOP; (**d**) PDOP; (**e**) TDOP; (**f**) VDOP; (**g**) The ratio of positioning precision between the longitude component and the latitude component; (**h**) Correlation level between station height and receiver clock difference.
## 1 Introduction
### Recovery Mechanisms of Polymer Flooding
#### 1.2.2 Polymer Viscoelasticity  
Hydrolyzed polyacrylamide (HPAMs) are seemingly low-cost and commercially available polymers and hence are the most widely used in the oil industry [46]. Some HPAM polymers with high molecular weight may depict viscoelastic properties, depending on interstitial flow conditions [11]. Due to their non-Newtonian nature and viscoelastic behavior, HPAMs may depict a shear-thinning response at low shear rates and shear-thickening response at high shear rates [2; 7; 47]. Shear-thinning defines a decrease in shear viscosity, with an increase in shear rate and shear-thickening defined as an increase in shear viscosity with an increase in the shear rate, as discussed previously.  
The relaxation time of polymer molecules also defines the shear-thickening behavior while flowing through compression-elongation geometry (elongational viscosity) [7]. HPAMs increase displacement efficiency due to their elastic properties [28; 48; 49]. Thus, recovery efficiency (displacement efficiency \(*\) volumetric sweep efficiency) is increased by viscoelastic HPAM polymer flooding.  
Viscoelasticity of HPAMs used for EOR is restricted by various factors--as summarized in Table 1--such as the following:  
* Mechanical shear and chemical degradation [50; 51; 52]: Mechanical shear is caused during polymer solution preparation through mixing devices, injection pumps, flow lines, and valves from storage tanks to injection wells. Chemical degradation is caused by a chemical reaction. For example, the polymer may be sensitive to oxygen and thus oxygen scavengers are added to the solution.  
* Reservoir temperature [10; 53; 54]: Polymer solutions are sensitive to temperature. High temperature causes breakdown of the molecular backbone chain structure, which, in turn, decreases polymer viscoelasticity.
* Hardness of the reservoir brine [10; 55]: Hardness is the number of cations in the brine. A high number of cations will result in low polymer viscosity. Polymer's ball-like molecular structure is composed of negative charges, and the repulsive forces of negative charges result in a larger ball-like molecular structure. High concentrations of cations react with the negative charge of polymer, hence decreasing the ball-like molecular size. A reduction in molecular size results in the loss of polymer viscosity and hence reduces viscoelasticity.  
These degradation conditions negatively affect polymer performance in porous media and hence place a strain on project economics [56; 57].
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 1 Introduction
Because near-infrared algorithms perform in high-land-surface-reflectance conditions, they are only suitable for PWV retrieval in cloudless land and ocean glint areas during the daytime. Thermal infrared algorithms are developed based on thermal infrared split window channels. They are developed for and have been applied to sensors such as Advanced Very High Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) [18; 19]. Since these algorithms only use thermal infrared data to retrieve water vapor, they can be used for both day and night water vapor detection in cloud-free areas. Microwave algorithms are suitable for sensors with microwave observation capabilities and have been successfully applied to water vapor retrieval with sensors such as Calibration Microwave Radiometer (CMR) and Microwave Radiation Imager (MWRI) [20; 21]. Since microwaves have a certain penetration ability, these methods can be used for water vapor detection in both cloudless and cloudy areas, which is also a unique advantage over other water vapor retrieval algorithms. Since the DPC has a water vapor absorption channel in the 910 nm band, only a near-infrared water vapor retrieval algorithm can be developed for water vapor retrieval with the DPC. The water vapor absorption band at 910 nm and the nearby band at 865 nm located in the atmospheric window are used to quantify the water vapor absorption transmittance (WVAT) in the DPC 910 nm channel according to the difference in the top of the atmosphere (TOA) reflectance in these two bands. PWV is then retrieved based on the calculated WVAT. We evaluated the accuracy of DPC PWV using AERONET PWV data as ground-truth data and MODIS PWV data for intercomparison. The structure of this paper is as follows: We introduce the dataset used in Section 2.1 and the retrieval strategy for DPC PWV retrieval in Section 2.2. Then, the global distribution of DPC PWV is shown in Section 3.1. After that, we show the accuracy assessment of DPC PWV in Section 3.2, including validation against ground-based PWV and intercomparison with MODIS PWV data. In Section 4, we discuss the improvement of the existing bias of DPC PWV data. Finally, the paper is concluded in Section 5.
# Effects of Glyphosate and Its Metabolite AMPA on Aquatic Organisms
## 3 AMPA (Aminomethylphosphonic Acid)
### Environmental Fate  
AMPA has a lower water solubility and longer soil halflife than glyphosate. The presence of AMPA in freshwater, sediment, and suspended particulate is commonly measured in significant quantities [10; 89], and even more frequently (67.5%) than glyphosate (17.5%) [15; 90; 91]. The Water Framework Directive [92] provides a procedure to set Environmental Quality Standards for AMPA at level 450 mg/L. [PERSON] et al. [9] reported concentrations of AMPA in freshwater environments for Mississippi and Iowa were 2.6 ug/L and 0.02-5.7 ug/L. In France, AMPA was detected with the highest concentration at a level of 44 ug/L.  
A concentration of AMPA in soil was found from 299 to 2256 ug kg\({}^{-1}\) by [PERSON] et al. [93]. This study pointed out the difficulty of establishing specific conditions for the presence of AMPA in soils. It depends on complex and multifactorial processes, including agronomic conditions, local agrometeorological conditions, mineralogy, and soil conditions. Moreover, AMPA was detected in soil with no exposure time to glyphosate. It could be caused by surface runoff. This movement of soil particles can end up in surface water, and next, the substance can be desorbed, biodegraded, and accumulated in the bottom sediment.  
AMPA, like glyphosate, also degrades in water and soil but significantly slower. Because its adsorption to particulates is possibly stronger, its penetrability of cell membranes is lower. The concentration of AMPA in the sediment can fluctuate depending on its degradation rate relative to GLY (Figure 1) [93].
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
###### Abstract  
For high-fidelity quantum operations in ion traps, it is important to maintain the secular frequency of the trapped ions at a constant value. The radial secular frequency is proportional to the amplitude of the radio frequency (RF) signal applied to ion traps. Owing to the changes in the ambient temperature of a helical resonator and the minute vibration of the optical table, the amplitude can vary. Recently, a method for reducing the fluctuation in the RF signal amplitude, using a commercial universal proportional-plus-integral (PI) controller, has been introduced, which, in turn, reduces the secular frequency drift of the trapped ions. The method improves the capability to maintain the secular frequency at a constant value. However, the structure of the controller is fixed; thus, the control method cannot be changed to suit different experimental conditions, and the different feedback configuration cannot be implemented to increase the resolution. In this paper, we develop a field-programmable gate array (FPGA)-based feedback controller that allows the implementation of various automatic control methods and feedback configurations. In our experiments, the fluctuation in the amplitude of the RF signal was 1.806% using a commercial universal PI controller. The fluctuation was reduced to 0.099% using the developed FPGA-based PI controller, and to 0.102% using the developed FPGA-based lag compensator. By employing the developed FPGA control method, many other automating control methods can be applied to achieve a stable and high-performance control of the secular frequency.  
helical resonator; feedback controller; FPGA; lag compensator; PI controller +
Footnote †: journal: Applied Sci. 2021, 11, 837. [[https://doi.org/10.3390/app11020837](https://doi.org/10.3390/app11020837)]([https://doi.org/10.3390/app11020837](https://doi.org/10.3390/app11020837))
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 2 Preliminaries
### Knowledge Graph Embedding  
A KG \(\mathcal{K}=\{\mathcal{E},\mathcal{R},\mathcal{T}\}\) contains a set of entities \(\mathcal{E}\), a set of relations \(\mathcal{R}\), and a set of triples or facts \(\mathcal{T}=\{(h,r,t)|h,t\in\mathcal{E};r\in\mathcal{R}\}\subseteq\mathcal{E }\times\mathcal{R}\times\mathcal{E}\). The symbols \(h\), \(r\), and \(t\) denote head entity, relation, and tail entity, respectively, in a triple \((h,r,t)\). An example of such a triple can be (Paris, isCapitalOf, France).  
Figure 1: An illustration of one time logical inference and forward chaining inference over given rules and a simple KG, which was adapted from [PERSON] [15].  
KG embedding aims to embed all entities and relations into a low-dimensional continuous vector space, usually as vectors or matrices called embeddings. A typical KG embedding technique consists of three steps as follows. First, the assumptions of the entities and relations vector space should be given. Entities are usually represented as vectors, i.e., deterministic points in the vector space, and relations are typically taken as operations in the vector space, which can be represented as vectors, matrices, or tensors. Then, a score function \(f:\mathcal{E}\times\mathcal{R}\times\mathcal{E}\rightarrow\mathbb{R}\) will be defined based on these assumptions to measure the plausibility of each fact in KG. Facts observed in the KG tend to have larger scores than those that have not been observed. Finally, embeddings can be obtained by maximizing the total plausibility of all facts measured by the score function.  
Based on the different design of score functions, the KG embedding techniques can be categorized into three types: translation based models, linear models, and neural network based models. We now briefly describe these types and introduce a commonly used model for each type. The readers can see [15; 16] for a thorough review.
## 2 Data Acquisition and Processing  
As part of the STARS project [18] (Spurring a Transformation for Agriculture through Remote Sensing) an intensive coordinated ground and remote sensing campaign ran during the 2014 and 2015 growing seasons in the Koutiala District (Mali) and the Bebeji Local Government Area (Nigeria). The aim was to assess the performance of remote sensing (RS) imagery for crop monitoring in smallholder farming systems. Remote sensing data acquisitions and analyses involved both VHR satellite and UAV imagery. A set of variables was concurrently collected _in-situ_, following a standardized protocol [19] for both sites, and synchronous with bi-weekly UAV and (cloud cover permitting) satellite data acquisitions. This paper reports on data collected in the Koutiala district (Mali) in 2014.
# A Hybrid Watermarking Approach for DICOM Images Security
## 2 Related Works
Since images are subject to attacks during their transfer, an attack of the RONI that does not affect the ROI may lead to the impossibility to recover the secret share that is required for the authentication process. The watermarking approach proposed in this paper aims at filling some of the gaps in the existing solutions. It takes into account the DICOM format and is based on the spatial domain. To enforce the security of medical image, it extracts patient information from the header of the DICOM image and combines it with specific image features to generate a strong authentication key that is only inserted in the RONI of the medical image, leaving the ROI unmodified to ensure the safety of the medical diagnosis. The proposed approach also integrates the insertion of multiple copies of the authentication key to cope with attacks that may modify parts of the RONI. The proposed approach is detailed in the next section.
## 5 Numerical Results
The residential and business sub-area probabilities \(p_{r}\) and \(p_{b}\) are chosen equal to 0.5 and their peak offered traffic in the PHI are \(A_{o,p}=26\) Gbps and \(A_{o,r}=16\) Gbps. The reference area is composed of \(T=200\) sub-areas and each optical ring interconnects \(S=10\) Access Switches, each one handling the traffic of \(D=\frac{T}{SR}\) sub-areas. Each RRU can chose one of \(Q=5\) MIMO configurations according to the offered traffic. The configurations provide the user capacities \(C_{0}^{RRU}=0\), \(C_{1}^{RRU}=75\) Mbps, \(C_{1}^{RRU}=150\) Mbps, \(C_{1}^{RRU}=300\) Mbps and \(C_{1}^{RRU}=600\) Mbps and generate CPRI traffic flows at bit rate \(f_{0}^{CPRI}=0\) Gbps, \(f_{1}^{CPRI}=1.2288\) Gbps, \(f_{2}^{CPRI}=2.4576\) Gbps, \(f_{3}^{CPRI}=4.8152\) Gbps and \(f_{4}^{CPRI}=9.8304\) Gbps, respectively. An OTN with two layers (ODU2 and ODU4) is considered with wavelengths carrying 100 Gbps flows. Figure 6: Comparison of the number of wavelengths per optical ring for the static and dynamic scenario cases. The number of wavelengths per optical ring is reported as a function of the used RRU percentage \(\gamma\) in the case of number \(R\) of optical rings equal to 1, 2 and 4. The values of the other parameters are chosen as the ones in Figure 5.
## 4 Discussion
's studies were identified for functional near-infrared spectroscopy devices. We showed that a spectral correction (see Section 2.2) is required when a hyperspectral mosaic sensor is used [10]. The spectral correction aims to reduce the quantification errors in the measurements and allows a better follows-up of the temporal hemodynamic and metabolic variations; see Table 5.  
We also compared hyperspectral imaging to RGB imaging for the computation of hemodynamic and metabolic brain maps. Hyperspectral imaging is the suitable solution to compute hemodynamic maps and metabolic maps thanks to its ability to acquire the intrinsic signal in the near-infrared range [23]. A very important crosstalk between HbO\({}_{2}\), Hb, and oxCCO can be observed when the RGB camera is used for the computation of hemodynamic and metabolic brain maps; see Figure 13. Therefore, RGB imaging is not a suitable solution to compute metabolic brain maps. An RGB camera could be however a low-cost solution to compute hemodynamic maps; see Figure 7.0 This solution is not accurate to quantify \(\Delta C_{HbO_{2}}\), but is very accurate for the quantification of \(\Delta C_{Hb}\). This original result is interesting because most surgical microscopes used in the operating room are equipped with standard RGB cameras. It is known that the BOLD signal used in fMRI studies is predominately due to the paramagnetic properties of deoxygenated hemoglobin [47]. This result indicates that the \(\Delta C_{Hb}\) quantified with RGB imaging can be used in a robust way for intraoperative functional mapping based on SPM analyses. We incorporated the camera noises in our simulation to find the most robust and reliable spectral configuration. In Figures 8 and 9, we show that the SNR of the imaging system has a drastic impact on quantification performances. An accurate quantification could be obtained with a high SNR value. Therefore, the light source and the camera specifications and settings have to be carefully chosen in order to guarantee an optimal SNR. This simulation framework could be a great tool for industry or researchers working on intraoperative functional brain mapping solutions to help them in the choice of commercial camera. However, our simulation framework needs to be improved. For the moment, a homogeneous volume of grey matter was considered. A realistic mapping of the exposed cortex could be simulated as suggested by [PERSON] et al. [28]. This will be considered in future studies. Moreover, the hemodynamic and metabolic changes following neuronal activation were homogeneously simulated in the volume of grey matter. These events are obviously not consistent with those appearing in a real cortical tissue. This modeling also has to be taken into account to improve our method of identification of the optimal spectral bands of a hyperspectral camera for brain hemodynamic and metabolic monitoring.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 2 Datasets  
In this study, three publicly available remote sensing image data sets--the UC Merced land-use dataset [19], RSSCN7 [20], and WHU-RS19 [18]--were used for testing the performance of the proposed method.
# Modulation Decoding Based on K-Means Algorithm for Bit-Patterned Media Recording
## 3 Proposed Modulation Decoding Scheme
### Conventional Modulation Encoding and Decoding Scheme  
Imposing constraints in modulation coding schemes helps improve performance and eliminate patterns that cause interference. In the BPMR system, the effect of ITI has greater impact on the error performance than that of ISI, as the patterns of [\(-\)1, \(+\)1, \(-\)1]T and [\(+\)1, \(-\)1, \(+\)1]T have greater impact. To prevent patterns that degrade the performance, we used 4/6 modulation codes, and Figure 2 illustrates the codeword list of the modulation codes [15]. The codewords can prevent the occurrence of [\(-\)1, \(+\)1, \(-\)1]T and [\(+\)1, \(-\)1, \(+\)1]T in each codeword. To encode four bits \(\mathbf{a}=[a_{i},\,a_{i+1},\,a_{i+2},\,a_{i+3}]\) of user data to a 3 \(\times\) 2 (= 6 bits) array of codewords (coded data sequences) \(\mathbf{c}^{l}=[c_{j-1,k^{\prime}}^{l},\,c_{j-1,k+1}^{l},\,c_{j,k^{\prime}}^{l},\, c_{j+1,k^{\prime}}^{l},\,c_{j+1,k+1}^{l}]\) for \(l\) = 0, 1,..., 15, one-to-one mapping and a look-up table of the codeword list are used. In the decoding process, a soft decision overcomes the decoding loss of a hard decision and is mainly used to enhance the decoding process; Euclidean distance, which enables the soft decision for modulation decoding, is used. The Euclidean distance \(d^{l}\) between the received sequence \(\mathbf{\hat{c}}=[\hat{c}_{j-1,k},\,\hat{c}_{j-1,k+1},\,\hat{c}_{j,k},\,\hat{c}_{j,k+1},\,\hat{c}_{j+1,k},\,\hat{c}_{j+1,k+1}]\) after the VA or SOVA detector and codeword \(\mathbf{c}^{l}\) is calculated by  
\[d^{l}\left(\mathbf{\hat{c}},\mathbf{c}^{l}\right)=\sqrt{\sum_{j=-1}^{1}\sum_{k=0}^{1} \left(\hat{c}_{j,k}-c_{j,k}^{l}\right)^{2}}. \tag{12}\]  
The soft-decision decoder determines the decoded data sequence corresponding to the minimum Euclidean distance and implements one-to-one demapping.
## 2 Materials and Methods
### Data Description  
To evaluate the performance of the proposed method, we apply it to real and synthetic networks. The real networks include: USAir (American aviation network) [43; 44], Netscience (Scientist cooperation network) [45; 46], Infectious (People infection network) [47; 48], USAirport (American airport network) [49; 50], Yeast (Protein interaction network) [51; 52] and Power (Power grid of the western United States) [2; 39]. The statistical properties of the six networks are presented in Table 1. It can be seen from the table that the six real data sets used in this paper have their own characteristics and are representative, which can well verify the effectiveness of the algorithm. In addition, one synthetic small-world network is used, which was generated by a Watts-Strogatz model [2] with the parameters \(N=6000\), \(K=6\) and \(p=0.1\). The synthetic network is denoted as WS in this paper.