esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of GPEs on LPS-Induced NF-kB and MAPK Signaling Pathways in RAW 264.7 Cells  
We investigated the effects of GPE on LPS-induced NF-kB and MAPK signaling pathways in RAW 264.7 cells, because these are the primary signaling pathways regulating LPS-induced iNOS expression. GPEs suppressed the LPS-induced phosphorylation of p38 and JNK1/2 and slightly suppressed the phosphorylation of extracellular-signal-regulated kinase (ERK)1/2 in RAW 264.7 cells (Figure 3A). Interestingly, although NF-kB is a canonical signaling pathway involved in LPS-induced NO production and inflammation in immune cells including macrophages and neutrophils, GPEs did not affect the phosphorylation of p65 in RAW 264.7 cells (Figure 3B).  
Figure 1: Chromatogram and absorption spectra of GPI (**A**), GPS (**B**), BPI (**C**), and BPS (**D**).  
Figure 3: Effects of the GPEs on LPS-induced MAPK and NF-kB signaling pathways in RAW 264.7 cells. **(A)** GPEs suppressed the LPS-induced phosphorylation of JNK1/2, p38, and ERK1/2 in RAW 264.7 cells. **(B)** GPEs did not affect the LPS-induced phosphorylation of p65 in RAW 264.7 cells. Cells were treated with the indicated concentrations of GPEs and then stimulated with LPS (1 \(\upmu\)g/mL). The levels of phosphorylation and expression were detected by western blot.  
Figure 2: Effects of GPEs on LPS-induced nitrite production, cell viability, and phosphorylation of MAPKs and p65 in RAW 264.7 cells. **(A)** GPE suppressed the LPS-induced nitrite production in RAW 264.7 cells. Cells were pretreated with GPE in the presence or absence of LPS (1 \(\upmu\)g/mL) for 24 h. **(B)** GPEs did not affect the cell viability at the tested concentrations. The cells were treated with increasing concentration of GPE for 24 h. **(C)** GPEs suppressed the LPS-induced iNOS and COX-2 expression in RAW 264.7 cells. **(D)** Quantification of iNOS suppression by GPEs. Expression levels of iNOS, COX-2, and \(\upbeta\)-actin were determined by western blot. Data are presented as mean \(\pm\) SD of three independent experiments. \({}^{\#}p<\) 0.05 between control and LPS-exposed cells (no GPE); \({}^{*}p<\) 0.05; \({}^{**}p<\) 0.01.  
Effect of GPE on LPS-Induced Phosphorylation of c-Jun and c-Jun Translocation from the Cytosol to the Nucleus in RAW 264.7 Cells  
Because GPS and GPI only affected the LPS-induced phosphorylation of MAPKs, but not p65, we next evaluated whether GPS and GPI affected the LPS-induced phosphorylation of c-Jun and c-Jun translocation from the cytosol to the nucleus. Western blot analysis and immunofluorescence analysis results showed that although GPI suppressed LPS-induced c-Jun phosphorylation and c-Jun translocation from the cytosol to the nucleus, GPS only suppressed the LPS-induced phosphorylation of c-Jun, but not c-Jun translocation in RAW 264.7 cells (Figure 4).
# Satellite SAR Data-based Sea Ice Classification: An Overview
## 1 Introduction  
Sea ice cover is an essential component of the Arctic environment and plays an important role in the weather and global climate systems. In addition, sea ice has a significant impact on ice navigation and offshore activities in the polar region. Thus, sea ice classification and ice charting are of interest for making management decisions to ensure safety and efficiency of economic activities without damage for sensitive Arctic environment. Currently, remote sensing data are the main and very often only source of information about sea ice conditions in the Arctic.  
One of the primary tasks of sea ice charting is to accurately identify the location of the boundary between ice and open water (the ice edge) [1]. Estimation of ice concentration requires the ability to delineate ice and open water (OW) boundaries with finer spatial resolution. One of the main factors determining the ice navigation difficulties in the Arctic is the ice thickness. Other ice state parameters such as cracks, polynyas, fractures, slightly ridged ice, zones of decreased concentration, ridged features, and floes are very important for ice ship's routing. The procedure of recognition of all these navigation-significant sea ice parameters should be operational and close to the near-real-time regime. At present, sea ice charting by national ice services is based on expert analysis of different data received from all available information sources. This information can be derived from satellite data of various spectral ranges [2].  
In the last decades, many studies have been conducted to derive data on sea ice characteristics from high-resolution Synthetic Aperture Radar (SAR) images. SAR can provide measurements regardless of the weather conditions and natural illumination with a high spatial resolution. The national ice services of the United States of America, Canada, Russia, Norway, Denmark and Finland employ high-resolution SAR images as the main data source for monitoring sea ice cover for navigation ships, fishing and various marine operations [1; 3; 4; 5]. The national ice centers produce ice charts according to international and national standards defined by the World Meteorological Organization (WMO) [6]. More data are becoming available and more quickly accessible to the scientific community, generatingquickly expanding research results and new research questions. The ultimate objective of this paper is to consider existing techniques and to estimate the further development of the satellite data-based methods for the SAR sea ice classification.
# Metrology for Measuring Bumps in a Protection Layer Based on Phase Shifting Fringe Projection
## 3 Theoretical Model of Fringe Projection Profilometry
### Three-Step Phase-Shifting Technique  
The projected sinusoidal fringe pattern intensity \(I(x,y)\) on the camera plane for every pixel \((x,y)\) can be described as follows:  
\[I(x,y)=I_{0}(x,y)+I^{\prime}(x,y)\cos\phi(x,y) \tag{3}\]  
where \(I_{0}(x,y)\) is the background intensity proportional to the object reflectance and illumination intensity and \(I^{\prime}(x,y)\) is the fringe contrast. To determine the phase value \(\phi(x,y)\), we simply use the three-step phase-shifting algorithm. Three corresponding rows of pixels within the ROI for which the linear direction is parallel to the fringes takes the following forms of images:  
\[\begin{split} I_{1}(x,y)=I_{0}(x,y)+I^{\prime}(x,y)\cos\phi(x,y) \\ I_{2}(x,y)=I_{0}(x,y)+I^{\prime}(x,y)\cos(\phi(x,y)+2\pi/3)\\ I_{3}(x,y)=I_{0}(x,y)+I^{\prime}(x,y)\cos(\phi(x,y)+4\pi/3)\end{split} \tag{4}\]  
By solving the above equations, we can find the phase at each pixel point:  
\[\phi(x,y)=\tan^{-1}\left[\sqrt{3}\frac{I_{1}-I_{3}}{2I_{2}-I_{1}-I_{3}}\right] \tag{5}\]  
The average background intensity \(I_{0}(x,y)\) and fringe contrast \(I^{\prime}(x,y)\) respectively given by  
\[I_{0}(x,y)=\frac{I_{1}+I_{3}+I_{2}}{3} \tag{6}\]  
Figure 3: Fringe projection geometry: projection angle \(\theta_{i}\), viewing angle \(\theta_{c}\), sinusoidal grating pitch \(d\), and lens magnifications \(m\) and \(n\) of the projection and imaging modules, respectively.  
\[I^{\prime}(x,y)=\frac{\sqrt{3{(2I_{1}-I_{3})}^{2}+{(2I_{1}-I_{2}-I_{3})}^{2}}}{3} \tag{7}\]
# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 4 Experimental Results
### Quantitative Comparison
Figure 9 is plotted by the data in Table 3, which gives all the IoU in 21 categories. For all of the four methods, the highest and the lowest IoU scores are observed in category 0 and category 9, respectively. When compared with the FCN-32s, FCN-16s, and FCN-8s, the proposed algorithm has a higher IoU score in categories with the superscript \"*\" in Table 3.  
Figure 10 shows the difference value of IoU score between our algorithm and the FCN model. Obviously, our algorithm outperforms FCN model in large majority of categories. Especially, the IoU score of FCN model are 64.79%, 69.18%, and 69.73% in category 10. But, they are 6.25%, 1.86% and 1.31% lower than our algorithm. A detailed comparison is shown as follow:  
Figure 10: IoU difference value between our algorithm with FCN models. Figure 9: Segmentation accuracy of four methods. Table 4 shows the pair-wise comparison of IoU score between our method and the three other methods. When compared with FCN-32s, 20 categories have been improved by using our proposed method. The increased category ratio is 95.24%. When compared with FCN-16s, 15 categories have been improved. The increased category ratio is 71.43%. As compared with FCN-8s, 16 categories have been improved. The increased category ratio is 76.16%. The comparison proves our method can achieve a good performance. As shown in Table 5, our algorithm achieves mIoU score at 62.8% which outperforms others. Our algorithm achieves PA score at 77.14%, which is 3.86%, 1.41%, and 1.28% higher than that of FCN-32s, FCN-16s, and FCN-8s, individually. The scores of mIoU show that the proposed algorithm has an average improvement in the classification accuracy. The scores of PA indicate that our algorithm has a better fit for the object contour. The enhancement of these indicators has benefited from the application of BSLIC and the four criteria.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 2 Original Output Characteristics Test
### Sensitivity and Linearity Test  
The original sensitivity and linearity are tested first in the process of displacement-voltage (\(x-u\)) calibration. Moreover, the system flow is shown in Figure 3a and the calibration apparatus is shown in Figure 3b, where a high-precision linear translation stage (PI, M-521.DD1) is employed for producing absolute displacement with the repeatability of 0.1 \(\upmu\)m, and a voltage acquisition module (NI, PXI-6289) is employed to measure the sensor output voltage with the absolute accuracy of 2 mV.  
Figure 2: Measurement circuit and its simplified equivalent circuit.  
In the experiments, the displacement variation interval is 0.1 mm during the range of 0-6 mm, and the corresponding output voltages are collected at the same time. In this paper, four ECDSs (Kaman, KD2306-6U1, working temperature: 10-40 \({}^{\circ}\)C) are tested in order to avoid individual contingency. Consequently, the original calibration data is shown in Figure 4 and the original sensitivity and linearity of the tested ECDSs are calculated as shown in Table 1. In this paper, independent linearity [29] is adopted to evaluate the deviation of the ECDSs' actual performance relative to the ideal characteristic curve (straight line, whose position is not limited, to minimize the deviation between the straight line and the actual performance).  
Figure 3: Calibration system flow and apparatus: (**a**) system flow; (**b**) calibration apparatus.  
As shown in Figure 4 and Table 1, the original output range is nearly at \(-1\)\(\rightarrow\)1 V with the low sensitivity around 0.33 V/mm and the poor linearity from 95% to 98%, which limits the accuracy of subsequent measurements. Thus, high-accuracy calibration of ECDs is significantly essential to improve further the accuracy for high precision process control of parts manufacturing and assembling.
# Role of Bimodal Water Retention Curve on the Unsaturated Shear Strength
## 5 Discussion
The proposed shear strength equation in this study can predict the measured data reasonably accurately, as observed in Figures 12 and 13. For both specimens, the linear relationship between shear strength and matric suction is observed when matric suction is less than their respective \(AEV_{2}\). Table 6 indicates that \(\phi^{\text{b}}\) of 70S30K is higher than that of 50S50K for the entire matric suction range. This trend may occur due to the higher percentage of the coarse-grained particles and the lower void ratio in 70S30K than those of 50S50K. This study was carried out on soil, which is associated with bimodal WRC without significant volume change. Therefore, the proposed bimodal shear strength equation in this study is not applicable to soil with bimodal WRC with significant volume change. In summary, the proposed bimodal shear strength equation can be used to minimize the cost and complexity of the unsaturated shear strength testing on soil with bimodal WRC. The outcome of this study is beneficial for geotechnical or geo-environmental engineers who needs to design slope cover. Future works should be carried out to incorporate the use of soil with bimodal WRC to minimize the rainwater infiltration into the slope as well as maintain some water contents to ensure the health of the plants. Based on the bimodal shear strength characteristics of the investigated soil in this study, the suction of soil for the slope cover should be maintained between \(\text{AEV}_{1}\) and \(\text{AEV}_{2}\). Previous studies by [PERSON] and [PERSON] [47], and also [PERSON] and [PERSON] [48] indicated the influence of fine particles especially those with significant volume change on shear strength characteristics. Other researchers [49; 50; 51; 52] also indicated that particle shape has an effect on the characteristics of shear strength. This study is limited to soils with negligible volume change. No investigation was carried out on the effect of particle shape on this study.
## 5 Evaluation and Discussion
### Memory Usage Evaluation and Comparision  
The memory usage evaluations of TKIFRPM and MTKPP methods is performed for the different datasets discussed earlier (i.e., mushroom, accident, and chess) using \(K\), \(\theta_{r}\) and \(T_{b}\) values (i.e., \(K\) = [100, 200, 300, 400, 500]), \(K\), \(\theta_{r}\) = 15 and \(K\), \(\theta_{r}\) and \(T_{b}\) = [3K for mushroom, 30K for accident, and 1.5K for chess]. The memory usage evaluations are conducted in same way as runtime evaluations (discussed in Section 5.2) by recording the memory usage of the methods in three iterations for each of the datasets. The average memory usage of the methods for the different values of \(K\) and each of the datasets are shown in Figure 9(a) (mushroom dataset), 9(b) (accident dataset), and 9(c) (ches dataset), respectively. The results depict that TKIFRPM is, memory-wise, more efficient than MTKPP in all scenarios. This is because MTKPP is an Apriori-based algorithm and follows no candidate pruning strategy and accepts the Apriori property. Therefore, it keeps large amount of data in the tree in the memory, which simultaneously grows with the increase in a dataset size. Thus, the memory usage of MTKPP is the highest not only compared to TKIFRPM but among all  
Figure 8: (**a**) Results of three iterations of TKIFRPM on the chess dataset. (**b**) Results of three iterations of MTKPP on the chess dataset.  
topmost-K frequent pattern mining techniques. On the other hand, TKIFRPM is based on the depth-first strategy and, apart from the threshold-based pruning, the TKIFRPM also follows the look-ahead pruning strategy. This strategy allows TLIFRPM to prune entire subtrees and eliminate them from the mining process. Therefore, it saves the memory which could be wasted in the candidate generations and subsequently used in the support calculation of the TKIFRPM. Conclusively, the TKIFRPM results in less memory usage than the MTKPP in all experiments.  
Figure 9: (**a**) Memory usage estimations of TKIFRPM and MTKPP on the mushroom dataset. (**b**) Memory usage estimations of TKIFRPM and MTKPP on the accident dataset. (**c**) Memory usage estimations of TKIFRPM and MTKPP on the chess dataset.
## 3 Methodology
### Delay Processing Model  
SOT methods are designed to distinguish the object from the background and allow a certain degree of deformation to handle appearance variation of the object. This mechanism will easily lead to insensitivity of the tracker when distractions are similar to the object within the search area under the MOT scenario. Hence, directly pushing a SOT method to MOT may easily lead to drifting problems. To tackle the drifting situation caused by occlusion or instance interaction, we propose a delay processing model. The consequences of occlusion can be seen as three main circumstances:  
1. Object deformation. This appears when \(s_{T_{k}}\) and \(s_{D_{j}}\) are at the same level but below the association threshold \(s_{DPthreshold}\). This kind of circumstance is usually determined as lost by the single object tracker, which leads to the ID switch problem.
2. Object misalignment. Here the object track is assigned to a false detection with similar appearance and motion features, usually expressed as an abrupt change of location, receiving a reasonable estimation score \(s_{T_{k}}\) and similarity score \(s_{D_{j}}\).
3. Doppelganger initialization. This occurs if the object experiences partial occlusion or slight variation of appearance, where the tracker finds a new location at the current frame without verification of any detection and keeps tracking for upcoming frames. Hence, a doppelganger track will be initialized using the detection supposed to be assigned to the object, causing an overlap problem. The three circumstances set \(\mathbb{C}\) is thus formed as follows: \[\mathbb{C}=\begin{cases}{}^{\prime}deformed^{\prime}&if\ s_{T_{k}}\in s_{D_{j}} \cdot(1\pm\sigma)<s_{DPthreshold}\\ {}^{\prime}misaligned^{\prime}&if\ s_{T_{k}}\in s_{D_{j}} \cdot(1\pm\sigma)>s_{DPthreshold}\\ {}^{\prime}doppelganger^{\prime}&if\ s_{T_{k}}>s_{DPthresh },s_{D_{j}}>s_{DPthresh}\\ \end{cases}.\] (5)  
Here, \(\sigma\) is set to 0.2. To solve the aforementioned problems, we propose a delay processing model consisting of three parts which specifically target those circumstances. Such model is not only able to to avoid the noisy appearance features that might contaminate the training space of the single object tracker, but to change the update strategy of the tracker to ensure the tracking performance.
## 3 Experimental Setup  
### Experimental Apparatus  
Figure 10 shows a schematic diagram of the experimental apparatus. The UVP measurement with a phase-separation technique called Developed-UVP consists of a 4 MHz ultrasonic transducer, an ultrasonic pulser/receiver, a function generator, a digitizer, and a computer with LabVIEW software  
Figure 8: Effect of the window length in generating a spectrogram on the measured peak frequency.  
Figure 9: Example of the Doppler frequency classification for proposed technique: (a) single-phase flow (only particle), (b) two-phase bubbly flow (with particle and bubble).  
as shown in Table 3. The pulser/receiver emitted (the pulse shape was programmed by function generator) and received ultrasonic pulses through an ultrasonic transducer. The echo signals received by the pulser/receiver were converted to a digital signal by the digitizer, with a sampling rate of 100 MS/s. The digitizer and pulser/receiver were connected and synchronized with each other. Data from the digitizer were recorded on the computer via USB port. Calculation and analysis were performed using LabVIEW software. The measurement test section was located on a vertical pipe made up of acrylic with an inner diameter (\(D\)) of 20 mm. The measurements were conducted downstream at a distance of 50 \(D\) from a bubble injector. The transducer was positioned on a clamper at the outer surface of the pipe with an incident angle of (\(\theta\)) 45\({}^{\circ}\). This incident angle, the ultrasound transmission ratio is maximum. Also, two wave mode (Longitudinal and Shear wave) does not occur. Therefore, incident angle 45 degree was chosen in this study. [27]. The working fluid was water. Its temperature was measured by a thermocouple and controlled to produce a small deviation at 25 \(\pm\) 2 \({}^{\circ}\)C by a cooling system. The most important liquid phase properties on two-phase flow regime is liquid viscosity. [PERSON] et al. [28] investigated the influence of liquid viscosity and pipe diameter on the flow regime. They concluded that liquid viscosity and pipe diameter did not significantly affect the transitions between flow regimes. However, in the UVP measurement, liquid temperature is very important properties. The speed of sound is a function of temperature; therefore, the liquid temperature should be monitored during the experiment. Then, nylon particles of 80 \(\upmu\)m were dispersed into the water as tracer of the liquid. The air was injected from a bubble injector to generate the bubble using an air compressor. In this work, the basic frequency of ultrasonic wave was 4 MHz which is in high frequency range. High frequency will reduce cavitation effect because the negative pressure produced by rarefaction cycle is insufficient in duration and intensity to initiate cavitation. The sound pressure in the liquid at this frequency is very small. Hence, the bubbles (cavitation) are not produced [29]. As a consequence, condensation and evaporation process could not occur in this experiment.  
Figure 10: Schematic of the experimental apparatus.
## 3 Test Setup and Test Matrix  
Three-point bending tests were performed on the ends of the girders to obtain the shear capacity of the girder. Due to the localized nature of end damage, a short shear span of 1.25 \(d_{p}\) (\(a/d=1.25\)) was selected to investigate the shear behavior of the girder. Given the depth to the centroid of the prestressing strands (\(d_{p}\)) is 863 mm, the distance from the center of the support to the center of loading plate was 1079 mm. The center-to-center distance of the girder supports was chosen as 6400 mm such that the other end of the girder was unaffected from the loading. The test setup is illustrated in Figure 3. An actuator with capacity of 1200 kN was used in the test. The vertical deflection of the girder was measured by placing a linear variable displacement transformer (LVDT) under the girder at the loading point. Three LVDTs installed on the web at 0\({}^{\circ}\), 45\({}^{\circ}\) and 90\({}^{\circ}\) formed a rosette configuration to measure the averaged strain within web during loading and calculate the principal strain afterwards. Two strain gages were placed at top and bottom flanges to measure the compressive and tensile strain of concrete during loading. Figure 4 shows the instrumentation used in the test.  
Figure 3: Three-point bending testing.  
Figure 2: AASHTO Type II PC girders: (**a**) full-scale PC girder and (**b**) section details.  
The test matrix is presented in Table 1. The testing included the control, damaged, mortar repaired and carbon fiber reinforced polymer (CFRP) repaired cases. The compressive strength of the mortar that was used in the repair process on the day of testing is also listed in the table.
## 4 Experimental Details
### Evaluation Metrics  
In this study, we used a set of four key metrics to evaluate the performance of the detection model, specifically, Precision (P), Recall (R), mAP50, and mAP50:95. Moreover, in order to measure the performance of the model in terms of lightweight, we use the number of model Parameters (Params), FLOPs, and Model Size (Size) for comparison.  
\[\left\{\begin{aligned} \text{Precision}&=\frac{TP}{TP+FP} \\ \text{Recall}&=\frac{TP}{TP+FN}\\ AP&=\int_{0}^{1}\text{Precision}(R)\:dR\\ mAP_{50}&=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\\ mAP_{50:95}&=\frac{1}{10}\sum_{t=50\%_{i}}^{95\%_{ i}}\frac{1}{N}\sum_{i=1}^{N}AP_{i}(t)\\ \text{Parameters}&=\sum_{l=1}^{L}(\text{ Weights}_{l}+\text{Biases}_{i})\\ \text{FLOPs}&=\sum_{l=1}^{L}\text{Number of Operations in Layer}_{l}\\ \text{Model Size}&=\text{Total size of all parameters in bytes}\end{aligned}\right. \tag{3}\]  
where \(TP\) refers to true positives, i.e., the number of samples that the model correctly predicts as positive. \(FP\) is false positive, which is the number of samples that the model incorrectly predicts as positive. \(FN\) is false negative, which is the number of samples that the model incorrectly predicts as negative. \(N\) is the total number of categories for the detection task. t represents the average precision (\(AP\)) at a specific recall (\(R\)) or intersection over union (IoU) threshold t. \(R\) is the recall. L denotes the number of layers of the model. The weights refer to the weight parameters associated with each layer. Bias refers to the bias parameter in each layer. The Model Size is the total size of all parameters in bytes, where each parameter is represented as a floating-point number.