esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Ultimate Bearing Capacity  
Table 4 gives the ultimate bearing capacity test results for each test column. From Table 4, it can be seen that with the increasing stirrup corrosion rate, the ultimate bearing capacity of the axial compression columns decreased. When the measured stirrup corrosion  
Figure 11: The curves of \(\sigma\)-\(\varepsilon\).  
rate was 4.95%, the ultimate bearing capacity of the stirrup-corroded column decreased faster, and the decrease reaching 26.7%. Subsequently, the ultimate bearing capacity decreased slowly with increasing stirrup corrosion rate. When the stirrup corrosion rate was 12.57%, the ultimate bearing capacity decreased by 40% compared to that of the uncorroded column. It can be seen that the rate of reduction in ultimate bearing capacity was related to the corrosion rate of the stirrup, with more severe damage to the section of the test column occurring at a higher corrosion rate of the stirrups. Therefore, when predicting the ultimate bearing capacity of stirrup-corroded columns, the influence of the stirrup corrosion level and the damage level of the column section on the ultimate bearing capacity should be considered.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 4 Experiments and Results
### Ablation Studies
#### 4.4.1 RandAugment(++) Ablation Studies  
To confirm that the improvement of SmartAugment over RandAugment++ comes from the method differences, we study RandAugment with different optimization methods. For this purpose, we compare classical RandAugment with Grid Search, RandAugment++ with Random Search, and RandAugment with Bayesian Optimization as optimization algorithms. We chose a cheap-to-evaluate dataset (EM) for this ablation study. As the results in Table 6 confirm, SmartAugment outperforms RandAugment, independent of the selected hyperparameter optimization algorithm. An interesting observation from the study is that RandAugment++ improves over the classical RandAugment as shown in Table 6. It is worthy to note that these improvement gains were achieved with fewer iterations and less computational costs.  
Furthermore, Figure 3 shows that it can be sub-optimal to limit the number of applied augmentations to three, as is done in classical RandAugment. Therefore, increasing the search space as in RandAugment++ allows finding a better number of augmentation operations.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Method** & **HPO Algorithm** & **EM Dataset** & **\# Iterations** \\ \hline Rand & Grid Search (classic approach) & 78.54 & 93 \\ Rand++ & Random Search & 78.83 & 50 \\ Rand++ & Bayesian Optimization & 78.84 & 50 \\ Smart & Bayesian Optimization & **79.04** & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of RandAugment variants with SmartAugment on the EM dataset. For each of the results, we took the test mean IoU of the best three performing configurations evaluated on the validation set. The results show that SmartAugment outperforms RandAugment(++), independent of the hyperparameter optimization algorithm.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **p(aug)** & **col\_mag** & **geo\_mag** & **\#col\_ops** & **\#geo\_ops** \\ \hline KITTI & 0.13 & 0.12 & **0.24** & 0.14 & 0.03 \\ RaVeNNa & **0.46** & 0.04 & 0.06 & 0.06 & 0.05 \\ EM & **0.25** & 0.14 & 0.09 & 0.04 & 0.03 \\ ErFASst & 0.1 & 0.12 & 0.04 & **0.22** & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter importance study for different hyperparameters across different datasets on SmartAugment experiments. For instance, for the RaVeNNa dataset, the probability of applying a data augmentation strategy is responsible for 46% of mean IoU’s variability across the configuration space. The higher the importance value, the more potential it has to improve the performance for a given dataset.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 3 Results and Analysis
### Evaluation Metrics
#### 3.1.2 Information Transmission Rate Calculation Process  
The information transfer rate (\(ITR\)) is a frequently used metric for evaluating the performance of BCI, and its formula is as follows:  
\[ITR=\left(\begin{array}{c}\log_{2}N+A_{c}{\log_{2}}A_{c}{+}\\ (1-A_{c}){\log_{2}}(\frac{1-A_{c}}{N-1})\end{array}\right)\times\frac{60}{T} \tag{7}\]  
where \(A_{c}\) represents the classification accuracy, \(N\) represents the target number and \(T\) represents the entire detection time, including the target fixation time and the gaze shifting time. The optimal average data length and classification accuracy can be calculated by simulating the online trials, and then the theoretical \(ITR\) value can be obtained. As a result, if we want to improve \(ITR\), we might consider the three aspects above.
## 3 Model Details
### VCD Model Validation Using 3D Code
#### 3.2.2 Results of 1D Model  
In the 1D model, the mass fractions of carbon dioxide of the 1D model with different discretization lengths on the giving cross-section shown in Figure 11a are presented in Figure 12a. A discretization length of 32 mm (0.4 * Bore) is the recommended value for the intake system. However, the mass fraction of carbon dioxide with 32 mm is different from the result of the co-simulation model. For example, the mass fraction of the 1D model at 270 CA is about 6.4%. Correspondingly, the value of the co-simulation model is about 17.7%. The main reason for the large difference is that the discretization length of 32 mm causes the mixed gas homogeneous in a discretized sub-volume with the length of 32 mm. For the 1D model, as the discretization length decreases to 1 mm, the mass fraction of carbon dioxide reaches the maximum value of 18.3% at 230 CA, and keeps this value until the intake valves close at 415 CA. During 415-540 CA, the mass fractions of carbon dioxide change slowly due to the free diffusion of molecules.  
For the 3D code, the mass fractions of carbon dioxide on the giving cross-section shown in Figure 11a at different crank angles are presented in Figure 12b. As we can see, the mass fraction of carbon dioxide on the cross-section is less than 1% at 190 CA, which means that the exhaust gas does not reach the cross-section at the early stage. As the exhaust gas is continuously discharged into the intake runner, the mass fraction of carbon dioxide on most of the cross-section is about 17.7% at 230 CA, which indicates that the exhaust gas arrives at the cross-section. During 230-360 CA, the exhaust gas is continuously discharged into the intake manifold, and the mass fraction of carbon dioxide on the cross-section is kept at 17.7%. Since the intake valve closes at 415 CA, the mass fraction of carbon dioxide changes slowly because some exhaust gas still remains on the cross-section.  
The simulation results show that the mass fractions of carbon dioxide reach the maximum value at 230 CA for both the 1D model (discretization length of 1 mm) and the 3D code. The error of the maximum value is about 3%. After 230 CA, the mass fractions of carbon dioxide remain unchanged. In the whole process, the mass fraction changes with a discretization length of 1 mm are consistent  
Figure 12: Mass fractions of carbon dioxide: (**a**) 1D simulation; (**b**) 3D calculation.  
with the corresponding results of the 3D calculation. Finally, the discretization length in the 1D VCD model based on EMIV is tuned to 1 mm.
## 2 Optimization Model for the Adaptive Sine Multi-Taper Algorithm
### Focus of Optimization for the Adaptive Sine Multi-Taper Algorithm
In the 1 mHz to 10 mHz range, a smaller taper degree smoothing parameter is employed for smoothing, while in the 10 mHz to 100 mHz range, a larger smoothing parameter is used to reduce variance and improve estimation stability. On the other hand, for the sub-low-frequency range, optimization is achieved by leveraging parameter \(\beta\). Considering the stationarity and steepness of the actual process, this paper adopts a bisection method to determine the optimal parameter, with the best parameter for the taper degree in the improved adaptive sinusoidal multi-taper algorithm at the sub-low frequency set to 0.7. In terms of algorithm runtime, the computational complexity of the adaptive sinusoidal multi-taper algorithm is O(\(5N\cdot log_{2}(N)\)), which is significantly lower than other algorithms. Thus, through algorithmic improvements and adjustments, the adaptive sine multi-taper algorithm demonstrates superior performance in the systematic evaluation within the field of gravitational wave detection, which is one of the key focuses of this study.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 3 Results  
### SPEI Calculation  
Figure 3 shows the SPEI calculation results for 11 meteorological stations in the Mekong Delta region during various time periods (1, 3, 6, and 12 months). The Mekong Delta region has experienced droughts from 1985 to 1990 and from 2010 to 2016.  
* Stations with an SPEI \(<-2\) (extremely dry) include Can Tho. These stations received less rainfall than the other stations; hence, the SPEI was low.
* Stations with an SPEI \(\geq 2\) (extremely wet) included Ba Tri, Bac Lieu, Ca Mau, My Tho, and Soc Trang (1989). These coastal stations receive more rainfall than the other stations; hence, the SPEI is high.
## 4 Discussion
This research based on the combination of convolutional and dense neural network layers applied efficiently with the ground truth data for the mapping of 101 classes at a country scale should be a timely and important contribution. The recent studies in the classification of ecological communities using satellite images have emphasized the usage of multi-temporal satellite images for improving performance [107, 108, 109]. Consequently, [PERSON] et al. [110] achieved an F1-score in the range of 76-90% for the classification of 13 mountain forest and non-forest plant communities. Another study by [PERSON] et al. [111] obtained a Kappa coefficient of 75% using the Random Forests classifier for the classification of habitat communities. Similar results (80% Kappa coefficient) were obtained from the Random Forests classifier in the classification of coastal wetlands by [PERSON] et al. [112]. Since those accuracies were obtained in local-scale classification tasks, an achievement of 80.5% average accuracy in terms of Kappa coefficient across 48 prefectural regions in the current research for the discrimination of complex ecological communities using multi-temporal Sentinel-2 images is a significant contribution.
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work
### Basic Concepts and Problem Description
#### 2.1.5 Road Segment Grid Dictionary  
The road segment grid dictionary converts all GPS points within the road segments in the road segment dictionary into their corresponding grid points within the grid dictionary. The index of the dictionary represents the unique identifier for each road segment, while its corresponding value denotes the set of grid-based edges associated with that particular road segment. The structure of this dictionary is represented as [L1, L2, L3,... LN], where L = [CELL1, CELL2, CELL3,... CELLN].
## 4 Numerical Experiment  
We take two key subsystems--LRU1 and LRU2--of the fleet system in reference [30] as examples. LRU1 consists of four SRUs; LRU2 consists of three SRUs. Thus, we can verify the effectiveness and correctness of the joint optimization model through considering 7 different failure modes. The two-echelon service logistics system consists of one Depot location and three Base locations. The number of subsystems in the three Base locations are 13, 30, and 20. The annual demand of spare parts for the two key LRUs at the three Base locations are 3, 7, 5, and 2, 4, 3. The target availability of the fleet system is 95%.
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 3 Application of a Feature Vector Extraction Method for Simulating Chatter Signals
### The Chatter Vibration Signal Generator  
For the sake of demonstration, a BEMD is adopted to process a simulated chatter signal. According to the mechanism of the chatter and the characteristics of frequency and time domains in the grinding process, a chatter vibration signal generator was established through the Simulink module of MATLAB (2010a, MathWorks Company, Natick, MA, USA). The sampling frequency was set as 800 Hz, as shown in Figure 2.  
The principle of this chatter generation system can be described as follows. A vibration signal is generated by two sine wave generators and one white noise generator. The frequency of the first sine signal consists of 50 rad/s and 120 rad/s, and the other one consists of 40 rad/s and 80 rad/s. The phase of the second sine signal is shifted by 4 rad. Thus, the two sine signals and white noise can be expressed as:  
\[x(t)=4\sin(120t)+4\sin(50t)+0.3\text{ and }y(t)=2\sin(80t+4)+2\sin(40t+4)-0.2, \tag{17}\]  
\[c_{1}(t)=rand\ (2001,1)-0.5\text{ and }c_{2}(t)=0.4\ rand\ (2001,1)-0.2, \tag{18}\]  
Figure 1: The process of extracting feature vectors from grinding chatter signals.  
Figure 2: Grinding chatter signal generator.  
This system relies on the clock module to control current simulation time. When \(t\leq 1\) s, the output is a harmonic vibration signal, in order to simulate stable grinding. When \(1<t\leq 1.6\) s, the output is a harmonic vibration signal multiplied with a slant signal, so as to simulate grinding chatter. Furthermore, when \(1.6<t\leq 2.5\) s, the output is the original harmonic vibration signal multiplied by a gain coefficient, in order to simulate stable chattering. For the sake of simplicity, this chatter generation system can be expressed in a mathematical form:  
\[s(t)=\left\{\begin{array}{ll}x(t)+c_{1}(t)+i(y(t)+c_{2}(t))&0\leq t\leq 1\\ (x(t)+c_{1}(t))(1+4.5(t-1))+i((y(t)+c_{2}(t))(1+6(t-1))\\ 1<t\leq 1.6\\ 4(x(t)+c_{1}(t))+i4.5(y(t)+c_{2}(t))&1.6<t\leq 2.5\end{array}\right. \tag{19}\]  
The chatter signal generated by this system is shown in Figure 3. It is clearly seen that signal amplitude is small during stable grinding, but significantly increases after 1 s. Then after 1.6 s, the amplitude becomes steady when the grinder reaches stable chatter. Hence the trend and distribution are similar to experimental chatter images in [7; 32], meaning that this system simulates chatter signals well.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
###### Abstract  
Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth's surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19-21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution.  
CYGNSS; flood monitoring; GNSS-R; SMAP; flood mapping 2021
# FE-Study on the Effect of Gradient Concrete on Early Constraint and Crack Risk
## 1 Introduction
This paper presents a numerical study on the application of optimized concrete base materials in a prismatic mass concrete structural element. In this element, durable concrete is necessary near the surface and the requirement of low hydration energy is located in the center of the member. For this purpose, either a zoned distribution or a gradient material distribution in the cross-section could be used to have different materials in the cross-section. Finite element analyses using a simplified material model investigates the effect of a gradient concrete material distribution in a prismatic mass concrete structure showing that gradient concrete can contribute to lower the constraint stresses and therefore crack risk during concrete hardening. The paper investigates the effect of gradual distributed and zoned concrete with respect to homogeneous concrete properties, highlighting crack reduction. The practical application of the gradient concrete is not covered in this article. There are two main research questions:  
* How does stress and tensile strength develop in gradient mass concrete? * Is there a positive effect on the crack risk of concrete when using gradient concrete instead of homogeneous concrete? In this article, thermal stresses are calculated in young hardening mass concrete. Although there are already models in the literature (e.g., [28; 29; 30; 31; 32]) that are capable of detailed simulation of the concrete behavior, a simplified approach is used. Thermal and some mechanical (stiffness, Poisson-ratio, strength) aspects were considered in the present calculations, whereas the effect of moisture transport, creep and shrinkage is neglected in this study. These simplifications are made to keep the testing and modeling effort low in the phase of a preliminary study for this type of gradient concrete. Most of the neglected parameters (shrinkage humidity) are constant in a wide range of a mass concrete section. Especially if thin members of high-performance concrete are used, these effects are no longer negligible and should be tested and considered for all relevant mixing states. The concrete used in the calculation is gradient concrete in terms of hydration energy, modulus of elasticity, tensile strength and durability. The results are compared to a calculation with the same geometry but either homogeneous or zoned material properties.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
###### Abstract  
Following a large continental earthquake, information on the spatial distribution of triggered landslides is required as quickly as possible for use in emergency response coordination. Synthetic Aperture Radar (SAR) methods have the potential to overcome variability in weather conditions, which often causes delays of days or weeks when mapping landslides using optical satellite imagery. Here we test landslide classifiers based on SAR coherence, which is estimated from the similarity in phase change in time between small ensembles of pixels. We test two existing SAR-coherence-based landslide classifiers against an independent inventory of landslides triggered following the M\({}_{w}\) 7.8 Gorkha, Nepal earthquake, and present and test a new method, which uses a classifier based on coherence calculated from ensembles of neighbouring pixels and coherence calculated from a more dispersed ensemble of'sibling' pixels. Using Receiver Operating Characteristic analysis, we show that none of these three SAR-coherence-based landslide classification methods are suitable for mapping individual landslides on a pixel-by-pixel basis. However, they show potential in generating lower-resolution density maps, which are used by emergency responders following an earthquake to coordinate large-scale operations and identify priority areas. The new method we present outperforms existing methods when tested at these lower resolutions, suggesting that it may be able to provide useful and rapid information on landslide distributions following major continental earthquakes.  
landslides; emergency response; synthetic aperture radar 2019 11 237 2019 20 January 2019
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 3 LDPC-RS Concatenation Code
### Perturbation BP Decoder
However, a simple (6, 3) trapping set here contains 9 nodes and 9 edges, and it is impossible to use the whole Tanner graph of the LDPC code to analyze the gain of perturbation and yield a closed-form expression. We used Monte Carlo simulations to evaluate its performance.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike-Encoding Methods
#### 2.4.1 Rate Encoding  
Rate encoding can be implemented in three ways: count frequency encoding, density frequency encoding, and population frequency encoding [2; 17].  
Figure 4: Flow diagram of accurate mode.  
Figure 3: Flow diagram of accelerated mode.  
* Count Frequency Encoding Count rate (average over time) is the most common rate-encoding scheme. It is defined by the average emissivity \[v=\frac{N_{spike}}{T},\] (1) where \(N_{spike}\) is the spike count and \(T\) is the time window. [PERSON] and [PERSON] found that the bending of frog muscles with varying weights alters the bioelectric signal's firing rate [3]. Emissivity can be used to characterize any slowly shifting analog value in artificial contexts, such as pixel intensity or gas concentration.
* Density Frequency Encoding Neural activity is measured through different simulations, and the results of neural responses are displayed in stimulus time histograms to visualize spiking activity. The spike density is defined as \[p(t)=\frac{1}{\Delta t}\frac{N_{spike}(t;t+\Delta t)}{K},\] (2) where \(N_{spike}\) is the average number of spikes throughout all iterations, expressed as a fraction of \(t\) and \(K\) iterations. This method of coding is not reasonable for biological behavior. In nature, predators do not hunt by calculating multiple averages of the perfectly consistent motion trajectories of the same prey. In an artificial SNN, however, it may be useful to use the same input to calculate the average of multiple simulated runs due to the uncertainty of the information transmission path caused by the network structure.
* Population Frequency Encoding Based on similar properties of neurons in the population, its rate is defined by \[p(t)=\frac{1}{\Delta t}\frac{N_{spike}(t;t+\Delta t)}{N},\] (3)  
The number of spikes \(N_{spike}\) in the total population is determined in the time interval and divided by the duration \(t\) and the total number of neurons \(N\). This method of encoding is characterized by the fact that, for a given input value, the peak of the neuron can be uneven. If each neuron has a different activation curve for any input current value, then the overlapping spikes generated by a set of such neurons can be used to encode accurate information such as numbers, vectors, or matrices [18].