esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 4 Discussion
### Long-Term Radiometric Stability  
In order to analyze the radiometric stability of the SAR instrument, the variation of radar backscatter for the different target types is investigated. The comparison of the derived standard deviation for the different target types and polarization channels is depicted by Figure 10 showing S-1A (blue) and S-1B (red) results. For this plot the same time range from Aug 2018 to Aug 2019 is selected  
Figure 9: Evaluation of the noise sensitivity for backscatter comparability after noise subtraction for the case shown in Table 4. The noise subtracted backscatters are balanced (0 dB) for noise values at the black line. The circle center (\(P_{noise}^{S1A}=-20.87\) dB, \(P_{noise}^{S1B}=-22.12\) dB) marks the annotated noise level for the given case which results in a 0.65 dB difference between S-1A and S-1B (near green line). A noise shift of only 0.1 dB (error bar) could reduce this difference to almost 0 dB or increase the discrepancy up to 1.2 dB.  
for all target types. Furthermore, the backscatter results are grouped by the given acquisition geometry for each target, i.e., each standard deviation shown corresponds to a certain antenna elevation angle.  
As visible in Figure 10, the derived standard deviation depends on the target type. As expected, the lowest standard deviations (below 0.15 dB) are found for the reference targets (corner reflectors and transponders) for co- and cross-polarized channel. The standard deviation derived for the ice areas are also very low; in particular for the cross-polarized channel the target stability is comparable to the artificial point targets. The rainforest regions and also the observed lake areas show a slightly higher backscatter variation but below 0.3 dB. Furthermore, the standard deviation in Figure 10, a measure for the observed stability, is nearly identical for both SAR instruments S-1A and S-1B. As the backscatter variation consists of contributions from both the observed targets and the SAR instrument, it is valid to use most stable targets for estimating the instrument stability. Thus, by focusing on stable targets (point targets and ice area) a radiometric stability of 0.1 dB can be verified for both SAR systems.
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 1 Introduction
In this paper, we propose a deep convolution classification network to achieve pixel-level classification of ice subsurface targets. This network is composed of filter processing, encoder and decoder. Our network has been validated on the radargram data set provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011, and the results show that our network can outperform an automatic classification system for the ice sheet subsurface targets based on support vector machine [10], Deeplab networks [17; 18; 19], object context network [20], and dual attention network for scene segmentation [21]. The main contributions of this paper are listed as follows: (1) for the first time in the literature, we introduce the deep convolution network to realize the classification of the ice subsurface targets, and the network realizes the end-to-end processing, which avoids the complex feature engineering for the ice subsurface targets; (2) in the encoder, the modified ASPP structure is used to obtain multi-scale features and improve classification accuracy by removing image level features and changing feature dimensions; (3) in the decoder, a reasonable method of feature fusion is used to solve the problem of long network training and testing times and further improve the accuracy; (4) we use a bilateral filtering algorithm to reduce the speckle noise of radar image to provide the high level of information from radar images. Figure 1: The typical radargram model includes free space, layers, echo-free zone (EFZ), bedrock, and noise.
## 5 Conclusions  
In this research, we proved the feasibility of using the spectral model, texture model, and spectral-texture model based on the Sentinel-2A imagery to identify cotton root rot and create prescription maps. The spectral model provided the best results for identifying root rot with an OA of 92.95%, while the OA based on the texture model and the spectral-texture model were 84.81% and 91.87%, respectively. The texture model could accurately detect root rot areas with a PA of 92.55%, while the PA based on the spectral and spectral-texture models were 89.73% and 90.80%, respectively. For creating the prescription maps, the spectral-texture model provided the best results with an OA of 91.40%, and the OA based on the spectral model and the texture model were 90.83% and 87.14%, respectively. Based on the texture model and the spectral-texture model, more areas will be assigned as the treatment zones in the prescription maps. In addition, the results of the research also indicated that the spectral model is appropriate for cotton fields with relatively severe infestation and more concentrated root rot distributions, while the spectral-texture model is more appropriate for cotton fields with small and scattered infestations. These results have confirmed that it is feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery and that the addition of texture features can improve the ability to identify root rot.  
Conceptualization, X.L.; experiment Q.Z., C.Y., X.L., W.H., J.T. and Y.T.; data collection, Q.Z., C.Y.; image processing X.L.; data analyzing, X.L., Q.Z., C.Y., W.H., J.T. and Y.T.; writing--original draft preparation, X.L., W.H., J.T. and Y.T.; writing--review and editing, Q.Z. and C.Y.; supervision, Q.Z. and C.Y.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Xinjiang Production and Construction Corps, grant number 2019 AB002.  
The authors wish to thank [PERSON] and [PERSON] at USDA-ARS in College Station, Texas for the collection of the airborne images and ground data. Thanks are also extended to the European Space Agency for the Sentinel-2A data. Mention of a commercial product is solely for the purpose of providing specific information and should not be construed as a product endorsement by the authors or the institutions with which the authors are affiliated.  
The authors declare no conflict of interest.
## 4 Bioavailability Assessment of Organic Pollutants Using DGT
### Determination of Hydrophilic Organics Using DGT  
Hydrophilic organic pollutants are widely found in water and soil, such as organophosphorus pesticides, antibiotics, and EDCs. These contaminants are often biotoxic and affect the survival and reproduction of organisms in the environment. They can cause long-term health effects, such as carcinogenesis, teratogenesis, and mutagenesis, when they enter the human body through the food chain and drinking water.  
The effective glyphosate of plants in soil was evaluated and compared by o-DGT. The results showed that the diffusion gradient in o-DGT can effectively measure the available glyphosate under typical soil environmental boundary conditions [108]. Sulfonamide antibiotics are mostly insoluble in water, but have strong hydrophilicity. Its molecular structure has both an aniline group (-NH\({}_{2}\)) that can be protonated and a sulfonamide group (-SO\({}_{2}\)-NH-) that is easy to lose protons, which is a typical amphoteric compound. [PERSON] et al. [109, 110] used o-DGT to evaluate the desorption kinetics and bioavailability of sulfonamide and trimethoprim antibiotics in soil, demonstrating its feasibility for polar organic compounds in soil. The antibiotic sulfamethoxazole (SMX) was used as a model compound, and XAD18 resin was used as a novel binding phase material. The uptake of SMX by o-DGT mimicked its bioavailability in soil [111]. The desorption kinetics of tetracyclines (TCs) in agricultural soils can also be assessed by o-DGT [112]. In recent years, DGT has been widely used in the monitoring of EDCs in aquatic environment. Compared with traditional methods, it is easy to perform multi-site deployment at the same time, pre-concentration of in situ analytes, and reduce matrix interference [95]. Compared with the Chemcatcher sampler, the DGT is less affected by the diffusion boundary layer and can provide higher measurements [113]. Moreover, the absorption of EDCs by DGT is independent of the water flow rate, so the field concentration can be deduced by the calculation method [95]. Through the combination of high-performance liquid chromatography/mass spectrometry (HPLC/MS) and yeast estrogen screening (YES) techniques [114], as well as the application of toxic equivalents and the ecological risk quotient (RQ) [115], DGT can show the pollution status of freshwater water bodies, indicating the threat of pollutants to aquatic organisms. In addition, DGT has also been well applied and studied in the measurement of EDCs in seawater [116].
## Materials and Methods
### Evaluation Method  
The S1A soil moisture was evaluated in both the temporal and spatial dimensions. In-situ measurements are normally considered as the reference and are used in the evaluation of the original 1-km resolution S1A soil moisture (S1A_1K) in the temporal dimension. When considering that there is only one observation station in our study, the microwave products of SMAP, SMOS, ASCAT, and AMSR2 were used in the evaluation of the S1A soil moisture in the spatial dimension. Due to the differences in resolutions, an average of S1A_1K in the AMSR-2 0.25\({}^{\circ}\) grid (S1A_0.25) was produced to match the coarse resolutions of the products in the evaluation. The root-mean-square difference (RMSD), which signifies the closeness of two independent datasets representing the same phenomenon, is the most commonly used error characterization in the evaluations of retrieved soil moisture. As reported by [PERSON] et al. [52] and [PERSON] et al. [53], RMSD varies with the variability within the distribution of the error magnitudes and with the square root of the number of data, as well as with the mean average deviation (\(MAD\)). Due to the differences in spatial scale representativity and the number of observations of the microwave soil moisture, the RMSD is generally affected by bias. Therefore, in addition to \(MAD\) measuring the absolute deviation from the in situ reference andthe Pearson's correlation coefficient (\(R\)) for the correlation analysis, the unbiased root-mean-squared deviation (\(ubRMSD\)) was also used in the evaluation to achieve a more reliable and comprehensive estimation of the error. The metrics are formulated as follows, where \(\theta_{s}\) and \(\theta_{situ}\) are the soil moisture acquired from retrieval and in situ, respectively; \(N\) is the number of coincident observations:  
\[R=\frac{\sum{(\theta_{s}-\overline{\theta_{s}})(\theta_{situ}-\overline{ \theta}_{situ})}}{\sqrt{\sum{(\theta_{s}-\overline{\theta_{s}})^{2}(\theta_{situ }-\overline{\theta}_{situ})^{2}}}} \tag{2}\]  
\[MAD=\frac{1}{N}\sum_{n=1}^{N}\left|\theta_{s}-\theta_{situ}\right| \tag{3}\]  
\[ubRMSD=\sqrt{\frac{1}{N}\sum_{n=1}^{N}\left((\theta_{s}-\overline{\theta_{s}} )-(\theta_{s}-\overline{\theta}_{situ})\right)^{2}} \tag{4}\]
## 3 Results
### iWUE* and LUE Trend during 1982-2010
#### 3.2.2 LUE Trend during 1982-2010  
The LUE trend calculated by combining the MTE-GPP product and fAPAR3g is positive across the entire African continent but its significance is low, except for a few grid cells (Figure 4). The northwest of Sahel and Botswana show a positive MTE-LUE trend (15%-20% increase per decade) at 95% CI, whereas Cameroon (-2% per decade) and Tanzania (-7%) show negative MTE-LUE trends at 95% CI. In contrast to the MTE and fAPAR3g based product, the simulated LUE trend is positive only over the central African forest for both ORCHIDEE versions. The ORCHIDEE LUE trend varies between 2% and 5% per decade over central African forest. Note that the 2-layer version has a larger LUE trend at 95% CI of about 10% per decade in some grid cells over the southern of Angola (Figure 4). Overall, both versions of ORCHIDEE do not reproduce the observation-derived positive MTE-LUE trend in northwestern Sahel and the negative MTE-LUE trend over Cameroon and Kenyan zones (Figure 4).
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 3 QoE Maximization
The notation \((x)_{+}=\max\ (x,\ 0)\) ensures that the term is always positive. Constraint (7) guarantees that the clients do not experience any playback interruptions for the entire streaming duration. Constraint (8) ensures that the buffer level stays between 0 and \(B_{max}\). Finally, constraint (9) specifies that the discrete video rate downloaded by the client from the server belongs to the set of available video rates.
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
## 1 Introduction
In terms of the underground metro station, the existing several experimental studies only focus on the effect of the RVH on certain kinds of structural responses, such as structural relative displacement and dynamic internal force. Experimental studies of the effect of RVH on more comprehensive seismic responses also including the acceleration response and dynamic interaction of soil-station system, dynamic soil normal stress (DSNS) along the side wall of station, and structural dynamic tensile strain (DTS), are not available in the literature. Due to the limited number of the existing experiments, further experimental studies of the seismic behavior of the soil and metro station under different vertical earthquake actions are needed to draw more general conclusion. Therefore, in this study the effect of RVH on the seismic responses of an atrium-style metro station including the acceleration, dynamic soil normal stress DSNS, and DTS are the primary foci. Through series of 1 g shaking table tests, this paper presents the key experimental findings, and an attempt is made to evaluate the effect of the RVH both qualitatively and quantitatively. In addition, this paper also provides insights into the seismic behavior of the atrium-style underground structures, which will help researchers and practitioners to better understand the seismic aspects of these kind of structures to improve their seismic design and construction.
## 4 Experimental Results
### Evaluation of ROI Extraction Module
#### 4.2.1 Training Phase  
In order to evaluate this module, we used the cost function described in Section 2.1. In our experiment, the training procedure was considered to be completed after 100 epochs. Figure 5 illustrates the predicted boxes during different epochs in the training procedure.  
As can be seen, the bounding box fit the intended ROI during epochs. The learning parameters of each epoch were supposed as the inputs of the cost function depicted in Figure 6.  
Based on this plot, the cost of palmprint localization dramatically decreased during the early steps while it started to become steady after approximately 100 epochs.
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
## 4 Scattering Noise Model
Finally, the scattering noise variance is obtained by integrating (10) over the system bandwidth \(BW\), which is given as:  
\[R(t^{\prime})=\lim_{T\to\infty}\frac{1}{T}\int_{-\frac{T}{2}}^{\frac{T}{2}}I(t )I(t+t^{\prime})\,dt=\lim_{T\to\infty}\frac{q^{2}}{T}\int_{-\frac{T}{2}}^{ \frac{T}{2}}\sum_{j=1}^{N-N^{\prime}}\delta(t-t_{j})\sum_{j^{\prime}=1}^{N-N^ {\prime}}\delta(t-t_{j^{\prime}}+t^{\prime})\,dt. \tag{2}\]0  
In the clear water, \(\kappa\) is near zero for short link transmission distances, and therefore the scattering noise can be ignored, whereas in the turbid and harbor waters, almost all received photons will experience scattering, thus \(\kappa\) approaches the unity.
## 5 Handling Stability Simulation and Comparison
### Simulation and Comparison of the Braking-in-Turn Tests  
The braking-in-turn analysis is one of the most critical analyses encountered in everyday driving. This analysis examines path and directional deviations caused by sudden braking during cornering. Typical results collected from the braking-in-turn analysis include the steering wheel torque, the yaw rate, and the truck sideslip angle [40]. The simulations of the braking-in-turn tests were conducted in Adams based on the two truck models, respectively. According to the international standard ISO/975-85, the simulation settings are as follows: the brake deceleration 6.3 m/s\({}^{2}\), the lateral acceleration 5.1 m/s\({}^{2}\), the turn radius 30.0 m, the maximum brake duration 5.0 s, the initial straight-line distance 15.0 m. Figure 15 provides the simulation diagram of the braking-in-turn test based on Adams. Figure 16 provides a comparison of the simulated dynamic responses. From Figure 16a, it can be seen that the steering wheel torques from the two truck models are basically the same. Figure 16b depicts that there are almost no differences for the yaw rate. Figure 16c shows that the sideslip angle of the truck with the taper-leaf spring suspension is almost the same as that of the truck with the multi-leaf spring suspension. From Figure 16d, it can be seen that the two truck models show almost the same bushing longitudinal force at the spring eye during the braking-in-turn test. This proves that the replacement of the leaf spring has little impact on the in-train longitudinal forces during braking.  
Figure 16: _Cont._  
Figure 15: The simulation diagram of the braking-in-turn test based on Adams.
# Water Temperature and Salinity Measurement Using Frequency Comb
## 2 Measurement Set-Up  
### Temperature Measurement Setup  
As shown in Figure 1, the experiment setup is presented. Above all, the whole experiment configuration can be considered as two sections. One section is used for sensing the optical distance variations caused by temperature; the laser source we employ in this section is frequency comb FC (Menlo system orange, FC1000, 518 nm, 100 MHz, 500 mW), links to the frequency reference, Rb clock (Microsemi 8040). In the picture above, the frequency comb sends out a laser beam, which is split from the Beam splitter 1 BS1 (Thorlabs, BSW10R) into two parts. One is reflected by mirror 1 M1 (Thorlabs, PF10-03-P01) fixed in a precision displacement PDP (PI, M521.DD1), which serves as Reference arm RA. The other is divided by BS2. The transmission beam is traversing part of a quartz glass water tank full of water, and reflected by M2, leading into a measurement arm 1 (MA1). The reflection beam is going through the empty part of water tank, and reflected by M4, which introduces the measurement arm 2 (MA2). Three arms meet at the output of BS1, and detected by PD1 (Thorlabs, PDA36A-EC), and waveforms can be observed from the Oscilloscope Scope (LeCroy, WaveRunner9000-MS). The second section is led by a Cw laser (Spectra-Physics, Excelsior-532-300-CDRH, 532 nm), which is used to get a precise position of the scanning mirror M6 by counting the quantity of fringes. This whole section works as a Cw laser interferometer. Please note that M1 and M6 are connected and scanned together. In addition, in this configuration, temperature is measured as a reference by temperature sensor TS (Seabird, SBE56 Temperature Logger) in real time. Besides, the water tank is divided into two partitions: one is full of water, the other is empty. Lastly, the laser beam should be perpendicular to the glass wall to minimize measurement uncertainty, and the distance \(L_{1}\) in Figure 1 are set very close to make sure the glass temperature of the empty part is almost the same with the water part.
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 4 Experiments
### Ablation Studies
#### 4.3.3 Category-Wise Performance Analysis  
Table 5 presents a detailed analysis of our model's performance across different layout categories. The model achieves the highest AP\({}_{50}\) for the Figure (0.903) and Text (0.895) categories, which are well represented in the dataset, as shown in Table 1. The Figure Caption and Table Caption categories, which have fewer samples, show relatively lower performance (0.836 and 0.824 AP\({}_{50}\), respectively), highlighting the impact of class imbalance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Direction Representation** & **mAP \(\uparrow\)** & **DA \(\uparrow\)** & **DMAE \(\downarrow\) (\({}^{\circ}\))** \\ \hline Direct Angle Regression & 0.693 & 0.857 & 4.8 \\ Classification (180 classes) & 0.702 & 0.895 & 3.2 \\ \((\sin\theta,\cos\theta)\) Vector & **0.715** & **0.923** & **2.5** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different direction representation methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Asymmetric** & **AIR** & \multirow{2}{*}{**DFEM**} & \multirow{2}{*}{**DFA**} & \multirow{2}{*}{**Direction-Aware**} & \multirow{2}{*}{**AP\({}_{50}\uparrow\)**} & \multirow{2}{*}{**mAP \(\uparrow\)**} & \multirow{2}{*}{**DA \(\uparrow\)**} \\
**Conv** & & & & & & 0.793 & 0.635 & 0.768 \\ ✓ & & & & 0.815 & 0.652 & 0.784 \\ ✓ & ✓ & & & 0.832 & 0.671 & 0.809 \\ ✓ & ✓ & ✓ & & 0.849 & 0.687 & 0.835 \\ ✓ & ✓ & ✓ & ✓ & **0.867** & **0.715** & **0.923** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study on key components of our model.  
The F1 scores, which provide a balanced measure of precision and recall, follow a similar pattern, with the Figure (0.92) and Text (0.91) categories achieving the highest values. This indicates that our model maintains a good balance between precision and recall for these common layout elements. The slightly lower F1 scores for the two Caption categories (0.85 and 0.84) suggest that these categories remain challenging due to their smaller size, variable appearance, and relative scarcity in the training data.  
The direction prediction performance, measured by DMAE, is consistently strong across all categories, with the best performance for the Figure (2.0\({}^{\circ}\)) and Text (2.1\({}^{\circ}\)) categories. The slightly higher DMAE for the two Caption categories (2.8\({}^{\circ}\) and 3.0\({}^{\circ}\)) can be attributed to their smaller size and more variable orientation. The micro- and macro-averages are similar, indicating that our model performs well across all categories despite the class imbalance in the dataset.  
Interestingly, we observe a correlation between F1 scores and direction prediction accuracy, with categories with higher F1 scores generally showing lower DMAE values. This suggests that accurate classification and localization contribute to better direction prediction, and vice versa, demonstrating the effectiveness of our joint optimization approach in the direction-aware detection head.