esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Soil Moisture (SM) is one of the most critical factors for a crop's growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR's output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.  
soil moisture; RADAR; FMCW; non-invasive; capacitive sensing; precision agriculture; surface water content +
Footnote †: journal: Medical  
propagation, attenuation, and reflection depend on the medium's electrical conductivity and dielectric constant. The returned wave over time is used for measurement.  
Ground-penetrating RADARs (GPR) are commonly used to analyze the soil and the soil water content [8]. Usually, GPR devices have a center frequency between 50 MHz and 3.6 GHz [9].  
Automotive RADARs use the concept of Frequency-Modulated Continuous Wave (FMCW). Such devices have a more competitive price tag than the GPR, costing surely less than one-tenth of a GPR. Given the need to assess SWC and the interest in using an inexpensive RADAR system, the foremost goal of this research is to evaluate the viability of using automotive RADAR to characterize the SWC.
## 1 Introduction
Because of that, a preliminary feasibility study for a PR using the signal transmitted by PAZ is presented. This paper is structured as follows: In Section 1, the motivation and objectives of the paper are presented; the performance principle of PRs is summarized in Section 2, and the basic parameters related to resolution and coverage are analyzed taking into consideration the bistatic geometry and the use of satellite illuminators. Section 3 describes the main features of PAZ and provides the required information for the feasibility study. Finally, Sections 4 and 5 deal with the feasibility study and the analysis of a case study, respectively. Conclusions are presented in Section 6.
## 5 Discussion
### Comparative FT Analysis with Previous Study in HTB  
This investigation has identified the following eight rapid cooling events as described above: Early Permian (279 Ma), Late Triassic (222-213 Ma), Early-Middle Jurassic (193-169 Ma), Late Jurassic (151-147 Ma), Early Cretaceous (130-109 Ma), Late Cretaceous (92-77 Ma), Early Cenozoic (65-50 Ma), and Late Miocene (10-8 Ma). The comparative FT analysis of the HTB with previous works is necessary to better understand the evolution of the HTB. [PERSON] et al.'s (2010) [5] AFT analysis reveals that the HTB experienced an accelerated exhunation beginning at 12-10 Ma, which is in accordance with our result at 10-8 Ma. They concluded that HTB exhibited a southwestward titling pattern during the Late Miocene, based on the AFT data. Specifically, the date of the rapid uplift event is older in the south and younger in the north. However, our samples collected in the southern HTB exhibited a nearly identical young event of 10-8 Ma to their results (12-10 Ma) from the northern HTB, implicating that their FT evidence of hypothesis is insufficient. [PERSON] et al. (2007) [20] proposed the 4 fast cooling stages in HTB based on the FT analysis: 157-139 Ma, 121-97 Ma, 88-76 Ma, and 50-37 Ma. The chronology of the other rapid cooling events partially coincided with our research, suggesting a strong coherence as a whole, except for the youngest event in the Eocene (50-37 Ma). While their research indicates an extension or compression geological environment, a comprehensive geodynamic explanation for most episodes is lacking. [PERSON] et al. (2019) [4] discovered two distinct periods of exhunation in HTB using AFT data: the Late Cretaceous (90-70 Ma) and after 30 Ma. The first stage coincides with this study and both of them have a similar geodynamic, resulting from the horizontal shortening transmitted by the subduction between the Pacific and Eurasian Plates. These two studies in the second stage share the same geodynamics (northeastward expansion of the TP) but differ in timing. In summary, the FT ages in the Early Permian (279 Ma), the Late Triassic (222-213 Ma), the Early-Middle Jurassic (193-169 Ma), and the Early Cenozoic (65-50 Ma) have not been extensively convinced in the prior papers. Meanwhile, new ideas or additions to geodynamics are proposed, such as events that happened in the Late Jurassic (151-147 Ma), the Early Cretaceous (130-109 Ma), the Late Cretaceous (92-77 Ma) and the Late Miocene (10-8 Ma).
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Further Experiments  
#### 3.4.1 Effectiveness of Multi-Decoder Structure  
We also conducted experiments on the effectiveness of the number of decoders in multi-decoder structures. As listed in Table 4, we observed that the multi-decoder structures are more effective in obtaining detailed polyp regions than the single-decoder structure. In the multi-decoder structures, while all the number of decoders from two to five are effective, two decoders achieve slightly better performance with fewer learning parameters, justifying this selection as the basis of the number of decoders.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Batch.**} & \multirow{2}{*}{**\#Params**} & \multicolumn{3}{c}{**CVC-ClinicDB**} & \multicolumn{3}{c}{**Kvasir**} & \multicolumn{3}{c}{**ColonDB**} & \multicolumn{3}{c}{**ETTS**} & \multirow{2}{*}{**Mean FPS**} \\ \cline{3-3} \cline{5-12}  & & & **mDice** & & **MAE** & & & & & & & & & & \\ \hline PraNet & \(1\) & 32.55 M & 0.899 & 0.009 & 32.99 & 0.897 & 0.030 & 31.71 & 0.711 & 0.043 & 37.10 & 0.628 & 0.031 & 19.56 & 30.34 \\ COMMA & \(1\) & 31.10 M & 0.933 & 0.007 & **42.32** & 0.901 & 0.027 & **31.90** & 0.754 & 0.037 & **51.96** & 0.711 & 0.015 & **42.31** & **42.12** \\ COMMA & \(8\) & 31.10 M & 0.933 & 0.007 & **71.30** & 0.901 & 0.027 & **46.47** & 0.754 & 0.037 & **110.00** & 0.711 & 0.015 & **71.07** & **74.70** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inference analysis of four datasets in the same environment. FPS refers to the number of frames processed per second.  
Figure 2: Qualitative comparison with existing methods.
## 2 Observation Data and Data Processing Methods
### Acquisition of Lithospheric Magnetic Fields  
Based on the composition of the geomagnetic field, the lithospheric magnetic field F\({}_{\rm I}\) can be obtained by eliminating the components of the earth's main magnetic field F\({}_{\rm m}\), inductive magnetic field F\({}_{\rm i}\), ionospheric magnetic field F\({}_{\rm e}\), and magnetospheric magnetic field F\({}_{\rm s}\) from the geomagnetic data F\({}_{0}\) measured above:  
\[\rm F_{I}=F_{0}-F_{m}-F_{i}-F_{e}-F_{s} \tag{1}\]  
The main components of the inductive magnetic field F\({}_{\rm i}\) and magnetospheric magnetic field F\({}_{\rm s}\) are rapid changes within a period of hundreds of seconds or shorter. The geomagnetic measurement mode adopted in the SD profile is described as follows: the average value of the total geomagnetic intensity within hundreds of seconds is taken as the measured value of the total geomagnetic intensity. Therefore, the components of the induced magnetic field and magnetospheric magnetic field in the measured value can be disregarded.  
The continuous observation data of the Ulaanbaatar Geomagnetic Observatory, Mongolia, were selected as the reference data of ionospheric magnetic F\({}_{\rm e}\) variation in the SD profile; the diurnal variation in the measured values at all measuring points was reduced to eliminate the ionospheric magnetic field F\({}_{\rm e}\), and the reduction value F\({}_{0}\) of each measuring point was obtained [34]. The reduction time of the diurnal geomagnetic variation is 16:00-18:00 (universal time) on 31 August 2011.  
To eliminate the main magnetic field, IGRF-13 was selected as the main magnetic field reference model [35], and the numerical value F\({}_{\rm 13}\) of each measuring point of the model at the reduction time was calculated. The lithospheric magnetic field F\({}_{\rm I}\) can be obtained by subtracting the main magnetic field F\({}_{\rm 13}\) from the reduction value F\({}_{\rm 0}\) of each point.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 4 Results' Analysis
### Feasibility Analysis  
Though it has been claimed in the literature, standalone solar PV and hybrid solar PV/WT supply systems perform better [70; 71; 72]. It is less reliable since it depends on a single renewable energy source. The hybrid solar PV/WT system is not a reasonable solution because of the unfavorable wind profile in the chosen area. Due to the massive volumes of fossil fuel burned and the high fuel delivery costs in remote places, the standalone DG and hybrid solar PV/DG perform poorly, as we had expected. A hybrid solar PV/BG system is a desirable option for the isolated off-grid places, where the biomass energy potential is enough despite releasing a very small quantity of carbon dioxide. Additionally, by limiting the biomass burning for cooking purposes, the suggested approach indirectly lowers the greenhouse gas emissions.
## 4 Results and Discussion
### Individual Tree Metrics  
At the point densities commonly used for the determination of forest metrics, area based approaches have been shown to produce comparable results to individual tree methods [48]. Furthermore, any increase in point density will likely improve the individual tree level results and allow for the derivation of new metrics such as stem quality [45]. The improvement in the repeatability of tree height and tree location derived through the comparison of the UAV point clouds and a set of decimated versions is demonstrated in Figure 8. The results shown in Table 5 indicate, through lower standard deviations, that increasing the sampling density of a tree crown results in an increased likelihood that the top of a tree will be sampled. Furthermore, if the tree top is not directly observed there will be a significant underestimation of the tree height.  
Figure 8: Example point clouds of Tree 1. Measurements of tree location and height from each of the four flights are shown with the (**a**) profile and (**b**) footprint of the original point cloud from flight 4 and the (**c**) profile and (**d**) footprint in the decimated point cloud from flight 4, where flight 1a is blue, 1b is green, 2b is yellow, 3b is black, 4a is red, and 4b is purple.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Tree**} & \multirow{2}{*}{**Measurement Count**} & \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Height (m)**} & \multicolumn{2}{c}{**Width (m)**} \\ \cline{5-7}  & & \(\boldsymbol{\sigma}\) (m) & **Mean (m)** & \(\boldsymbol{\sigma}\) (m) & **Mean (m)** & \(\boldsymbol{\sigma}\) (m) \\ \hline
1 & 6 & 0.44 & 10.94 & 0.05 & 8.26 & 0.25 \\
2 & 4 & 0.59 & 11.49 & 0.13 & 11.53 & 0.35 \\
3 & 3 & 0.55 & 12.71 & 0.11 & 12.93 & 1.02 \\
4 & 5 & 0.56 & 14.65 & 0.25 & 14.39 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean and Standard deviations (\(\sigma\)) of individual tree metrics derived from the (**a**) full density point clouds and (**b**) the point clouds decimated to 8 points per \(\mathrm{m}^{2}\).  
There is some slight improvement also seen in the repeatability of the measurement of canopy width. This measurement, however, is highly affected by the beam divergence as well as the low flying height and the angle of incidence of the laser beam. The combination of low flying height and large angles of incidence produces a shadowing effect which prevents the far edge of the canopy from being measured. The effect of shadowing could be reduced by choosing a survey design that further reduces the angle of incidence to within \(\pm\)15\({}^{\circ}\) over the targeted area. Furthermore, merging point clouds from multiple offset transects will increase the likelihood the whole crown will be sampled. This reduces shadowing and will significantly improve the crown width measurements.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 4 Proposed Approach  
The context is crucial and has a serious impact on the entire situation recommendation process. We propose a novel semantic-based probabilistic system that enriches and adapts dynamically user's situation rules in vast domains (city, work, shopping, travel, tourism, etc.). The objective of the situation enrichment system is to provide suitably adapted rules to users. These rules have to fit with users' preferences, users' current situations, and device's capabilities. Figure 2 exhibits the general architecture of the proposed recommendation system. This system is described in two main processes including respectively the learning process and the recommendation process.  
The learning process classifies situations rules according to their content after applying pre-processing and filtering techniques. It is based on four main phases, (1) rules profiles collection, (2) pre-processing and filtering rules, (3) rules analysis and classification and (4) situation rule storage in rules metadata repository. The recommendation process calculates the final score and recommends new rule items for the user. It is based on five main phases, (1) calculate the similarity of rules items, (2) calculate the probability-based context of rules items, (3) calculate the final score and sorting rules, (4) select compatible rules using SWRL (Semantic Web Rule Language) and (5) recommend new rule items. In the next section, we will highlight each part's different steps of the proposed recommendation system.  
Figure 2: General architecture of the framework.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 3 Karman Vortex Street-Based Energy Harvesting
### Karman Vortex Street-Based PE-FBEHs
Another challenge is the power control circuit which can be worked upon as the output power of the reported harvester is comparatively low. The application of energy harvesting systems has extended to the sea and ocean as natural sources carrying huge quantities of energy [50; 51; 52]. Through energy harvesting technology, a tremendous amount of energy can be exploited for either large-scale power grid networks or small-scale dispersed off-grid electronic devices. Such types of large-scale energy harvesting are reported in [47]. The energy harvester in Figure 4b shows the prototype's guiding wings to turn with water flow. The guide-wing technology provides the harvesters with all-around multi-directional sensitivity, which will speed up the adoption of energy harvesters in oceans. Considering the orientation of the tip mass as an important aspect of VIVs energy harvesting, [PERSON] et al. [53] investigated the features of four different vortex-induced PE-FBEHs with emphasis on the effects of the bluff body's orientation on the natural frequency, damping, synchronization region and the output of the harvester. In continuation to achieve effective orientations of bluff bodies, [PERSON] et al. [54] designed a PE-FBEH to be used in rotational equipment. Their investigation shows that the diameter of the cylindrical obstacle has a less significant impact on the energy harvester's generated power output than the rotation speed and installation angle. [PERSON] et al. [48] studied how shedding-induced forces caused by an oscillating flap sheet interact with a piezoelectric beam as depicted in Figure 4c. According to their lumped parameter model, the distance between the piezoelectric beam and the bluff body was optimized for enhanced energy harvesting. Recently, [PERSON] et al. [55] reported a novel technique of topology optimization in VIV energy harvesters. A topological optimization analysis was performed on the distributions of the two piezoelectric materials. The long, trapezoid shape offered the maximum energy harvesting capacity, depending on the reinforcing material used in the design. When using piezoelectric material as a design variable, the short trapezium model provided maximum output from energy harvesting. It can be concluded that optimizing the distribution of reinforcement/substrate structures should be more important than focusing on the piezoelectric material itself.
# ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network
## 4 Experiments
### FPGA Implementation  
The ClipQ proposed in this paper has been applied to the CASANN-v2 convolutional neural network 8-bit accelerator. The detailed architecture of the accelerator is described in article [43], and the quantitative inference design borrows from [PERSON] [18]. We implement the accelerator on the XCUV19 PFSAv3824 FPGA, which has 2048 processing units and operates at 100 MHz. Since the hardware accelerator needs to consider quantization in the splicing layer, residual connection, upsampling, and other operators, the results of the accelerator are slightly different from the previous fake-quantization results (as shown in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Max** & **ACIQ** & **Percent** & **KL** & **ClipQ-** & **ClipQ** \\ \hline MobileNetV2 & 66.3\% \(\pm\) 0.2\% & 68.58\% \(\pm\) 0.3\% & 67.78\% \(\pm\) 0.3\% & 70.25\% \(\pm\) 0.2\% & 70.24\% \(\pm\) 0.2\% & 70.54\% \(\pm\) 0.1\% \\ MnasetV1 & 25.04\% \(\pm\) 0.3\% & 52.72\% \(\pm\) 0.4\% & 57.65\% \(\pm\) 0.3\% & 58.68\% \(\pm\) 0.3\% & 58.83\% \(\pm\) 0.2\% & 60.45\% \(\pm\) 0.3\% \\ PSPNet & 62.04\% \(\pm\) 0.2\% & 69.21\% \(\pm\) 0.3\% & 69.25\% \(\pm\) 0.3\% & 69.54\% \(\pm\) 0.2\% & 69.63\% \(\pm\) 0.1\% & 70.01\% \(\pm\) 0.2\% \\ DeepLabV3+ & 69.56\% \(\pm\) 0.2\% & 73.96\% \(\pm\) 0.3\% & 74.69\% \(\pm\) 0.2\% & 74.71\% \(\pm\) 0.1\% & 74.75\% \(\pm\) 0.1\% & 74.92\% \(\pm\) 0.1\% \\ YoloX & 28.6\% \(\pm\) 0.3\% & 30.4\% \(\pm\) 0.3\% & 29.7\% \(\pm\) 0.2\% & 30.1\% \(\pm\) 0.2\% & 30.2\% \(\pm\) 0.1\% & 30.7\% \(\pm\) 0.2\% \\ SSD & 20\% \(\pm\) 0.1\% & 20.3\% \(\pm\) 0.2\% & 20.7\% \(\pm\) 0.1\% & 19\% \(\pm\) 0.3\% & 20.4\% \(\pm\) 0.2\% & 20.7\% \(\pm\) 0.1\% \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of activation clipping methods.  
Table 6). However, the accuracy deviations are all within 0.2%, which can reflect the actual application performance of ClipQ.
## 7 Significant Computational Characteristics of the Proposed Model  
The proposed model aims to give a very good accuracy of its predicted numerical results in comparison with experimental data, provided either in the specimen level or in the form of scaled structures, while keeping the computational cost at a reasonable level.  
Extensive previous experience using classical fiber models has shown that their numerical accuracy depends on fine discretization of the fiber grid that leads to high computational cost, even with the use of computational parallelization, in regard to modern computers used in daily practice by engineers. When using a coarse discretization of the fiber grid for keeping a low computational cost, the numerical accuracy gradually falls due to the extremely approximative manner of integrating the concrete-compressive stresses \(\sigma_{c}\) over the compressive zone of the cross-section. As this computational integration procedure is embedded in the core of the algorithm of most of the inelastic models for RC beam elements, it has been proved that the low accuracy of computation of the concrete compressive resultant force \(F_{c}\) and its acting point leads to an increase in Newton-Raphson iterations and, therefore, in the computational cost (e.g., nonlinear analyses with SAP2000 or ETABS using alternatively coarse or fine fiber discretization). Thus, when focusing on relevant issues, such as:  
* the issue of computational accuracy with reference to the aforementioned stress integration and cross-section-stiffness computation,
* the cross-section geometric shapes,
* the computational cost of the model, and
* the convergence improvement of the algorithm,  
The basic computational characteristics of the proposed model, which give closer numerical results to the experimental data in comparison with the models of [PERSON] et al. [5] and [PERSON] et al. [6; 7; 8], are being summarized in the following subsections.
## 5 Results
### Experimental Setting  
In our experimental settings, all experiments were conducted on the NVIDIA 2060 RTX GPU and Google clouds. We trained our proposed model with a Mean Squared Error (MSE) as a loss function. We used Adam as an optimizer. We trained our epochs from 100 to 300 with a batch size of 16. The learning rate was \(10^{-3}\). We performed experiments on each dataset independently 6-7 times. The average result was compared with the previous state-of-the-art baselines models, as shown in Table 1. We used two real-world datasets belonging to Porto and Manhattan. We predicted the error distance rate to predict the next taxi destination with different POI at different times. The dataset is publicly available on Kaggle, and POI is extracted through the FourSquare social network.  
We performed our experiments on the Porto and Manhattan datasets. We used 200,000 and 350,000 taxi rides, respectively. In both datasets, we split our model randomly for training, test, and validation sets. The splitting ratio was 70%,15%, and 20%. According to trajectory Definitions 1 and 2, the Porto and Manhattan datasets are composed of full trajectory with pick-up and drop-off points. Our dataset nature is unbalanced, and the spatial nature of the problem of calculating the stand classification means accuracy and F1-score are not appropriate. Due to this nature of the datasets, they do not give the adequate quantification of the error. We used t the Error Distance Score (EDS) defined as Haversine distance. The Haversine distance predicts the distance between two points, the current destination and actual destination of the trip.  
\[EDS.kms=d_{haversine}(\hat{y},y) \tag{15}\]  
In above equation, \(\hat{y}\) is the predicted location and y is the exact destination.