esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synthetic Data Test
The proposed method gives Q value results close to the true values. Then the Q value in the first layer are made to vary in the horizontal direction. The Q value estimation results in the second layer of the proposed method and the conventional attenuated travel time tomography method are compared (Figure 5). Due to the error in attenuated travel time, the Q value results of conventional attenuated travel time tomography method contains large errors, while the proposed method gives more accurate Q value results. When the offset distance is less than 300 m, the error between the conventional method and the true value is about 15%, and when the offset distance is more than 300 m, the error in the conventional method increases dramatically, and when the offset distance is 800 m, the error in the conventional method reaches 60%. In contrast, the improved method proposed in this paper is almost independent of the offset distance, and the Q value estimation results are always approximate to the true value. Random noise is then added to the model data for noise immunity test. The Gaussian random noises are 5%, 10%, and 15% of maximum amplitude, and the Q value results are shown in Figures 6-8. The proposed method gives more accurate Q results compared with that of the conventional method, especially for 15% Gaussian random noise. Both the conventional method and the proposed method in this paper have small errors in the first layer. This is because we selected the seismic reflection waveform horizontally to avoid the  
Figure 4: Comparison of Q value estimation results calculated using the proposed method and conventional attenuated travel time tomography method. Figure 3: Comparison of attenuated travel times by using the proposed method and conventional attenuated travel time tomography method. effects of the overburden. In the second layer, the influence of the overburden is received more, and the Q value calculation result of the conventional method far exceeds the true value, and the calculation error in the proposed method in this paper is still small. In the third and fourth layers, with the increase of Gaussian noise, the error in the conventional method increases exponentially, while the calculation error in the proposed method is still within the acceptable range with strong robustness and high accuracy. Figure 5: Comparison of Q value results estimated in the second layer calculated using the proposed method and conventional attenuated travel time tomography method. Figure 6: Comparison of average Q with 5% Gaussian random noise calculated using the two methods with true Q values. Standard deviation of Q are also plotted. Figure 7: Comparison of average Q with 10% Gaussian random noise calculated using the two methods with true Q values. Standard deviation of Q are also plotted.
## 3 Results  
### GW Climatology in the Indian Region  
Figure 1 presents the seasonal variation in the latitude-longitude distributions of the mean LS GW Ep over the Indian region during December 2019 and November 2021. The four seasons are defined as MAM (March-May), JJA (June-August), SON (September-November), and DJF (December-February). For the whole Indian region, the mean LS GW Ep is distinctly larger in JJA than in other seasons, and the peak mean LS GW Ep appears over the central part of the Indian Peninsula in JJA. On the one hand, as demonstrated by previous studies [30; 39; 52; 53], the peak mean LS GW Ep over the Indian region is closely related to the TEJ, which is prevalent in this region in JJA. The strong wind shears generated by TEJ are important sources of local GWs. On the other hand, in JJA, the Indian summer monsoon blowing from the ocean to the land brings large amounts of water vapor, enhancing the local convections and creating favorable conditions for the generation and propagation of GWs. In the following section, further analysis is carried out to investigate the relative importance of some main factors related to TEJ and monsoon activity, such as convective activity, average meridional wind speeds of 15-17 km, mean zonal wind speeds of 20-30 km and wind shear, on LS GW activity in the Indian region. In contrast, duringthe other seasons, the mean LS GW Ep over the Indian Ocean near the equator is generally larger than that over the Indian Peninsula.
# Structure-Property Relation of Trimethyl Ammonium Ionic Liquids for Battery Applications
## 2 Materials and Methods
### Conductivity  
Temperature-dependent specific conductivities of the ionic liquids and lithium salt solutions were determined by impedance spectroscopy with an SP-150 potentiostat (Biologic, Seysinnet-Pariset, France). The conductivity probe (WTW, Weilheim, Germany) consisted of two rectangular, platinized platinum electrodes fused into glass and had a nominal cell constant of 0.5 cm\({}^{-1}\). The actual cell constant was determined using commercial conductivity standards. The samples were sealed in the conductivity cell and placed in a ProlineRP 1845 thermostat (LAUDA, Lauda-Konigshofen, Germany) with \(T\)-stability of \(\pm\)0.01 \({}^{\circ}\)C. The temperature-dependent measurements were performed from 85 \({}^{\circ}\)C to 10 \({}^{\circ}\)C in steps of 5 \({}^{\circ}\)C. For each temperature, three impedance spectra with applied voltages of 5, 10 and 15 mV were recorded in the frequency range from 200 kHz to 1 Hz in 50 logarithmic steps. The resistance of the electrolytes, \(R\), determined this way were averaged with a deviation of about \(\pm\)1%. The specific conductivity \(\kappa\) was calculated from the cell constant \(k\) divided by the determined resistance \(R\). Molar conductivities were calculated using Equation (1):  
\[\Lambda_{\text{M}}=\frac{\kappa\ M}{\rho} \tag{1}\]  
with \(M\) being the molar mass of the mass of the ionic component, weighted with the stoichiometric coefficients in case of the salt mixtures.
## 2 Materials and Experimental Methodology
### Physical Properties Test  
The asphalt binder's workability was evaluated using the rotational viscometer test outlined in ASTM D4402 [32]. Using a 10.5 g sample and spindle number 27, this evaluation was performed at two critical temperatures of \(-\)135 \({}^{\circ}\)C and 180 \({}^{\circ}\)C (2 samples). Twenty readings taken at one-minute intervals allowed us to faithfully replicate field conditions. Understanding the handling qualities of the binder and its efficiency during the mixing and compaction stages of pavement building depends on knowing the viscosity at these temperatures. The viscosity of the binder shows its flow behavior at high temperatures, which is essential for correct compaction in pavement layers and effective mixing with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aging States** & **Test Properties** & **Test Result** \\ \hline \multirow{3}{*}{Asphalt binder (before aging)} & Viscosity @ 135 \({}^{\circ}\)C (cP) & 757 \\  & Viscosity @ 180 \({}^{\circ}\)C (cP) & 284 \\  & G\({}^{\circ}\)/sin \(\delta\) @ 64 \({}^{\circ}\)C (kPa) & 2.09 \\ \hline RTFO (short-term aged) & G\({}^{\circ}\)/sin \(\delta\) @ 64 \({}^{\circ}\)C (kPa) & 6.00 \\ \hline \multirow{3}{*}{RIFO + PAV aged residual} & G\({}^{\circ}\)sin \(\delta\) @ 25 \({}^{\circ}\)C (kPa) & 3405 \\  & Stiffness @ – 12 \({}^{\circ}\)C (MPa) & 189 \\ \cline{1-1}  & m-value @ –12 \({}^{\circ}\)C & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Original PG 64-22 binder properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Test Method** & **Test Result** \\ \hline Penetration (25 \({}^{\circ}\)C) & ASTM D5 [23] & 2–4 \\ \hline Density & ASTM D70 [24] & 1.42–1.44 g/m\({}^{3}\) \\ \hline Softening point & ASTM D36 [25] & 94–100 \({}^{\circ}\)C \\ \hline Flash point & ASTM D92 [26] & 257–268 \({}^{\circ}\)C \\ \hline Fire point & ASTM D92 (Cleveland open cup) [26] & 308–312 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trinidad Lake Asphalt (TLA) properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Method** & **Test Result** \\ \hline Density & ISO 2781 [27] & 0.94 g/cm\({}^{3}\) \\ \hline Hardness & ASTM D2240 [28] & 79 Shore A \\ \hline Toluene solution viscosity & ASTM D445 [29] & 13 cSt \\ \hline Volatile matter & ASTM D1416 [30] & \textless{}0.7\% \\ \hline Yellow index & ASTM D1925 [31] & \textless{}\textless{}\textgreater{} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of SBS polymer.  
aggregates. Since this temperature corresponds with normal mixing and application conditions, testing at 135 \({}^{\circ}\)C offers an understanding of the binder's performance during standard hot-mix asphalt processes. Evaluations at 180 \({}^{\circ}\)C, on the other hand, enable the analysis of the binder's behavior under highly demanding circumstances, such those experienced when particular additives change their thermal characteristics; ensuring the binder keeps its workability in practical paving uses depends on these tests.
## 3 Impact of external errors  
Only instrumental effects on the error budget have been reported yet. External effects need to be analysed and associated errors shall then be incorporated in the overall budget. The ones stemming from the errors on the POD and the radiometer measurement as well as the electromagnetic bias are already well known thanks to current missions. Their in-depth study is not the purpose of this section.  
First of all, we will focus on the influence of the platform attitude. Two components of the three dimensional attitude angles are considered. On the one hand, yaw rotation is considered: indeed, as selected orbits can be sun-synchronous or not, the use of a yaw motion may be required to supply energy to the carrier satellite. It will be shown that a yaw rotation significantly degrades the spatial coverage; on the other hand, the impact of a roll motion is estimated, and will be shown to hugely amplify the height error.  
Figure 7: Antenna Pattern Orientation Optimization.  
Last, electromagnetic wave propagation perturbations through the ionosphere and the troposphere will be statistically reviewed. We will consider worst case scenarios as is usual when evaluating instrument performances and dimensioning systems.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 3 Mathematical Model
By adding this, one may see the effect of unmodelled dynamics in acquired model as mentioned in the following set of equations:  
\[\ddot{\theta} =\left(lu_{2}-\dot{l}_{yy}\dot{\theta}\right)/I_{yy}\]2 \[\ddot{\theta} =\left(lu_{2}-\dot{l}_{yy}\dot{\theta}\right)/I_{yy}\]3 (14) \[\ddot{\theta} =\left(lu_{2}-\dot{l}_{yy}\dot{\theta}\right)/I_{yy}\]4 (15) \[\ddot{\theta} =\left(lu_{2}-\dot{l}_{yy}\dot{\theta}\right)/I_{yy}\]5 (16) \[\ddot{\theta} =\left(lu_{2}-\dot{l}_{yy}\dot{\theta}\right)/I_{yy}\]6 (17) \[\ddot{\theta} =\left(lu_{2}-\dot{l}_{yy}\dot{\theta}\right)/I_{yy}\]7  
In the mentioned set of equations from (13) to (18), the variables are \(a_{1}=\left(I_{yy}-I_{zz}\right)/I_{xx}\), \(b_{1}=l/I_{xx}\), \(c_{1}=-J_{r}/I_{xx}\), \(a_{2}=\left(I_{zz}-I_{xx}\right)/I_{yy}\), \(b_{2}=l/I_{yy}\), \(c_{2}=J_{r}/I_{yy}\), \(a_{3}=\left(I_{xx}-I_{yy}\right)/I_{zz}\) \begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbols** & **Unit** & **Description** \\ \hline
**M** & \(\mathrm{Kg}\) & Mass of underactuated quadrotor \\
**G** & \(\mathrm{m/s^{2}}\) & Gravitational force \\
**L** & \(\mathrm{M}\) & Length of underactuated quadrotor’s arm \\
**R** & & Rotational matrix \\ \(K_{thr}\) & \(\mathrm{ms^{2}}\) & Thrust component \\
**BdragG** & \(\mathrm{Nms^{2}}\) & Dragging component \\ \(\Omega_{I}\) & Rev/min & Rotation rate of propeller fans \\ \(J_{R}\) & & Inertia of rotor \\ \(T_{I}\) & \(\mathrm{N}\) & Thrust created by each BLDC motor \\ \(F_{I}\) & \(\mathrm{N}\) & Forces in the respective directions \\ \(D_{I}\) & & Time depended on disturbances \\ \(I_{XX}\) & & Inertia at \(x\)-axis \\ \(I_{YY}\) & & Inertia at \(y\)-axis \\ \(I_{ZZ}\) & & Inertia at \(z\)-axis \\ \(\Delta\phi\), \(\Delta\theta\), and \(\Delta\psi\) & & Unmodelled system dynamics \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbols with their brief description and Units. \(b_{3}=1/I_{zz}\) and \(d_{i}=d_{si}+d_{ui}\), where the subscript \(i=1,2,3\). This term \(d_{si}\) shows the bounded disturbances, whereas the \(d_{ui}\) illustrates the unmodelled dynamic factors and external forces. In addition to this, one may note that the roll, pitch and yaw are the bounded input variables for our proposed underactuated quadrotor model. Thus, with bounded input and state variables, the derivative of input will also be bounded. This will lead towards the bounded change in the states as well and help in proving the stability of the proposed single-dimension fuzzy-based sliding mode control (SDF-SMC).
# An Open Relation Extraction System for Web Text Information
## 2 Related Work  
### Supervised RE  
Relation extraction (RE) [15] aims at extracting relational fact triples of the form (head, relation, tail) from unstructured data. The main goal of RE is to classify relations between those entities mentioned from their context. PROPS [10], based on rule transformation, transforms the dependency parsing tree of sentences into a directed graph representing  
Figure 1: An illustration of how ORES utilizes 3 kinds of data for relation discovery based on distant supervision and bootstrapping.  
the association of relation tuples and then directly uses rules to extract triples from the directed graph. Traditional restricted-domain relation extraction mainly adopts supervised research methods. The pipeline method consists in entity recognition followed by relation extraction, and the two models are relatively independent. It is generally believed that the pipeline method suffers from error propagation, which affects the performance of relation extraction. The joint method unifies the two sub-models of entity recognition and relation extraction to use the mutual information of the sub-models to delay error propagation. Recently, [PERSON] [16] proposed a pipeline method research that broke this inertial understanding. It achieves several SOTA scores on standard benchmarks. Moreover, [PERSON] designed experiments to confirm that entity recognition and relation extraction learn different features, and joint modeling will interfere.  
Supervised relation extraction relies on labeled data, but the performance is degraded for open scenarios due to insufficient labeled data. Its essence is to train a relation classifier based on predefined relations. The limitation of traditional relation extraction methods is that they rely on manually annotated relational datasets. In addition, based on the predefined relation classification, the new relation extraction problem cannot be effectively solved. To solve the problem of traditional domain-limited relation extraction, semi-supervised relation extraction [17] has received widespread attention. Semi-supervised relation extraction uses a small amount of annotation information to learn for open scenarios. Its idea is to learn local features from a small number of labeled samples, learn the overall distribution from a large amount of unlabeled data, and then build a model. There are two main methods for semi-supervised relation extraction: bootstrap and distant supervision methods.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 4 Results
### Analysis of Baseline Models in Object Detection and Instance Segmentation  
In the context of this work, 'detection' refers specifically to the identification and localization of hemorrhages in retinal images, and should not be confused with the more general concept of object detection. For Faster R-CNN (R50-FPN), Table 3 reveals that the highest Average Precision (AP) is obtained at an Intersection over Union (IoU) threshold of 0.50:0.95 with all maxdets set to 100, with a value of 0.477. However, the Average Recall (AR) is low at 0.032 under the same setting. The model performs well for large objects, with an AP of 0.812 and an AR of 0.830 at IoU 0.50:0.95 with all maxdets set to 100. But it performs poorly on small objects, with an AP of 0.180 and an AR of 0.182 under the same setting. Table 4 indicates that Mask R-CNN (R50-FPN) has superior performance in both object detection and instance segmentation tasks, with a slightly better performance in object detection. The AP for object detection is 0.475 at IoU 0.50:0.95 with all maxdets set to 100, while the AR is 0.031 at the same setting. The AP for instance segmentation is 0.424 at the same setting. The AP is highest for medium-sized objects, with a value of 0.612 in object detection and 0.543 in instance segmentation. The objective evaluation of the accuracy and sensitivity of a Faster R-CNN (R50-FPN) model can be facilitated by visually comparing the model's output to the ground truth annotations, as demonstrated in Figure 7. This comparison enables the detection of discrepancies or errors in the model's predictions and provides insights into the degree of agreement with expert annotations. In the context of hemorrhage detection and segmentation, baseline models were evaluated, and the results are presented in Figure 8. The first image demonstrates early stages of DR without DME, with a marked area on the top right that indicates a failure in identification. The second image shows successful hemorrhage detection in a case with both DR and DME. These results showcase the potential of baseline models for detecting and segmenting hemorrhages, which can aid in the diagnosis and monitoring of DR in patients. Figure 6: Faster R-CNN (R50-FPN) in red and Mask R-CNN (R50-FPN) in blue over 30k iterations. (**a**) Illustrates accuracy, with Mask R-CNN (R50-FPN) having a higher overall accuracy of 99.34%. (**b**) Compares false negative rate, with Mask R-CNN (R50-FPN) performing better with a final false negative rate of 0.8%. (**c**) Demonstrates convergence of loss value, with Faster R-CNN (R50-FPN) demonstrating faster convergence and a lower minimum loss value. In conclusion, the summary of results shown in Table 5 indicate that both models have a relatively high average precision, with Mask R-CNN (R50-FPN) performing slightly better overall compared to Faster R-CNN (R50-FPN). However, the performance of both models can be improved by adjusting the IoU and maximum detection settings.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 4 Solder Joint Failure Modeling and Lifespan Prediction
### Simulation Validation  
The lifespan of all solder joints on the second flip chip of sample board number 1 was calculated and visually displayed in three dimensions. These results were then compared with those of finite element simulations, as shown in Figure 15.  
Figure 15a presents the lifespan predictions for each solder joint on the second flip chip of sample board number 1 using finite element simulation techniques. The failure cycles of solder joints on the inner ring are marked in cyan, while those on the outer ring are marked in purple, the solder joint No.1 on the chip is marked in red, and three special solder joints in the central region are marked in blue. Clearly demonstrating a trend of decreasing  
Figure 14: Determination of failure time for solder joint 107 on flip chip 2 of sample 1. (**a**) Solder joint failure curve; (**b**) initial failure point A and termination point B.  
quality with an increasing number of thermal cycling tests. The simulation data reveal that solder joints located at the center of the inner ring exhibit longer lifespans, capable of withstanding approximately 400 thermal cycling tests, whereas those at the corners of the outer ring show shorter lifespans, enduring only about 100 cycles. The lifespan of the solder joints is represented in a normal distribution.  
Figure 15b displays the solder joint lifespan results obtained using the method proposed in this paper. The failure cycles of solder joints on the inner ring are represented in purple, while those on the outer ring are shown in green. Due to the multiaxial stress endured by the solder joints during the thermal cycling tests, the actual lifespans are slightly less than those predicted by the simulations. The method reveals that solder joints in the inner ring tend to have longer lifespans compared to those in the outer ring, and this finding aligns closely with the lifespan distribution trends observed in the simulation results. The conclusions derived from this method provide significant guidance for understanding the characteristics of solder joint lifespan and influencing factors, offering substantial data support and theoretical basis for further in-depth research.
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results  
The analysis reveals that the landslide studies in the region are quite biased towards Uttarakhand region, while there are few to no studies in the northeast Indian region (Figure 5).  
This bias can be attributed to the high population density in the western sector of the Indian Himalaya, leading to a larger number of casualties and a generally higher level of landslide risk. [PERSON] et al. [13] calculated the number of casualties due to landslide incidences during 2007-2015, which came out to be 5228, a staggering 82.9% of all disaster casualties in the region. To understand the type of studies being conducted, we divided them into seven broad categories: description of main events; identification; forecasting; monitoring and investigation; river damming; extreme events and climate change; and susceptibility mapping.
## 5 Development of an Auto-Response Early-Warning System
### Input and Output of the Warning System  
The system was designed to simplify the operation through the pre-installed INMARSAT email communication instrument (C/F/FBB station) on the ship. For example, if any crew sends out a request including the following phrase \"/LAT/30.4/LON/123.1/\", they will receive text message within 10 min via F station (or 25 min via C station).  
In Figure 11, the first line notes the nearest grid's latitude and longitude. In the following lines, the first value on the left marks the starting date and hour in UTC (e.g., 052912Z means 1200 Z at 29 May), then the accumulated CP and LSP forecast value within each six-hour interval (e.g., 052918Z/CP/13/means an accumulated 13 mm would be expected during the previous six hours until 1800 Z on 29 May).  
Nowadays most inland and offshore river cruise boats are running in liner ship mode, that is to say, the manager and navigator know the ship's position at the exact time before the departure. Thus, a future improvement of the above warning system could be to generate the predicted CP or wind profile, like Figure 6 or Figure 7c, before the trip and even after the trip begins. By reading the figures, the ship officer could know which part of the trip is most dangerous and then take necessary action to avoid it. Under this  
Figure 11: Return message to the sender with location request.  
Figure 10: Structure of the automatic weather navigation system scheme.  
anticipation, the ship's resilience toward convective wind gusts can be improved with enhanced object-oriented service. However, this work is still under development.  
The authors of [31] stated that \"With such an effective means of navigation, backed up by the continuing improvements in the provision of weather information and navigational warnings, the small craft of the future, whether at sea or in the air, should be capable of operation with minimal interference with larger commercial craft.\" Our research bears a wishful small step toward this direction.