esa-sceva/satcom-chunk-collection huggingface dataset:
# Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life
## 2 Data Presentation
### Tensor Representation of the Data  
Since we will be using RNN-based models, representing data in the form of tensors is of particular interest. A tensor is defined as an N-dimensional array. It allows for an efficient way of representing multidimensional, complex data. For our particular purpose of vehicle predictive maintenance it will suffice to design 3-dimensional tensors which also can be visualized and understood more clearly [22]. The aim is to represent data in the sequential format, to be able to capture the history of each vehicle. Therefore, it is natural to think of one dimension as vehicles, the second dimension as time, and the third dimension as different features.  
Figure 1: Time to Event (TTE) and censored data.  
One challenge is that vehicle history, in principle, is of variable length. We address this by considering a fixed time dependency horizon. For example, if the time dimension is selected to be 10, each instance will contain 10 historical measurements. The output is always the TTE value of the most current measurement; therefore, the architecture of the RNNs is of many-to-one type. The only problem that can arise is for vehicles where the total length of history is smaller than the selected value: in that case zero padding will be applied for missing measurements. It is possible to apply either pre-padding (beginning of the series) or post-padding (end of the series). Each of them is suitable for different scenarios; in particular, in \"many-to-one\" architectures, the problem with post-padding is that the zeros at the end of the series could possibly cast a shadow over the effect of actual measurements (which comes before zeros). Since there is no such problem for pre-padding it is used in this study.  
Figure 2 depicts structures of the designed tensor. There are two advantages in representing the tensors in this format. One is that it is compatible with the way RNNs in Tensorflow [23] consume the input: \((n_{samples},n_{timesteps},n_{features})\). The other advantage is that since in this study the number of time steps and number of features are going to be varied (will be discussed in Section 5), this structure makes changing the size of each plane convenient.
# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 3 Materials and Methods
### Data Pre-Processing  
Before the classification step, each HS image was preprocessed. The HS image preprocessing chain was explained in [27; 29] and consisted of four main steps: image calibration, spectral bands removal and selection, noise reduction, and normalization of spectral signatures. The first step was the HS image calibration, which was performed to smooth out the raw spectral signatures and to compensate for the nonlinear response of the HS camera. This step was carried out by using two reference images, the first one obtained on a white surface (\(W_{ref}\)) at the place where the clinical procedure was performed, with the same lighting conditions and the second acquired by taking a capture with the shutter closed, generating a dark reference image (\(D_{ref}\)). Once these two images were obtained, the preprocessed HS image \(I(\cdot)\) was computed by a normalization step at the \(k\)-th pixel \(\mathbf{y}_{k}\):  
\[I(\mathbf{y}_{k})=100\times\frac{R(\mathbf{y}_{k})-D_{ref}(\mathbf{y}_{k})}{W_ {ref}(\mathbf{y}_{k})-D_{ref}(\mathbf{y}_{k})}\qquad k\in\{1,\dots,K\}, \tag{1}\]  
where \(R(\cdot)\) is the raw HS image acquired from the HS camera and \(K\) is the number of pixels in the HS image.  
In the second step, the spectral bands in the low and high frequency ranges were removed due to the high noise generated by the CCD sensor, producing low SNR in the first and last bands. For this reason, bands from 1 to 55 and from 700 to 826 were removed, resulting in an HS cube with 645 spectral bands that covered the spectral range from 440 to 902 nm. In a previous work [32], repeatability experiments were performed to demonstrate this statement. After this process, the spectral signatures were reduced by a 1:5 decimation procedure to avoid the redundant information between contiguous bands and also to reduce the execution time of the algorithms. Hence, each HS image was reduced to 128 spectral bands. The next stage involved a smoothing process by a Gaussian filter in the spectral domain. Finally, the last step in the preprocessing chain consisted of a normalization, also in the spectral domain, to avoid different radiation intensities produced by the non-uniform surface of the brain.  
Figure 1: Synthetic RGB images of six HS test datasets with the tumor area surrounded in yellow (first row) and ground-truth maps (second row). NT (normal tissue), TT (tumor tissue), HT (hypervascularized tissue), and BG (background) classes are represented in green, red, blue, and black color, respectively. White pixels correspond to non-labeled data in the images [29].
## 4 Trng
### Proposed Trng  
Figure 12 presents the algorithm to realize TRNG from the MRAM chip. First, the memory address is flushed, i.e., 0 s are written to all bits. Next, the write time is set to 75% switching probability and 1 s are written to the addresses. The 75% switching probability means the ratio of 1 s to 0 s will be 75%. The data written is considered as the first random number. Then, the same address is written again with all 1 s with the same write time. We notice that the ratio of 1 s to 0 s reduces to \(\approx\)50% (observation (b), Section 2) on the second write. This is the second random number. Similarly, when we write for the 3 rd time the 1 s to 0 s ratio drops to \(\approx\)35% which gives out the 3 rd random number. By repeating the same process for different write times and different addresses, we can generate more random numbers.  
Since it is impossible to bias individual MRAM bits to their 50% switching probability using a common write latency, we bias the MRAM using three different switching probabilities and extract all possible outcomes from n-bits. For example, biasing 4 bits at 50% switching probability means that on an average, 2 bits out of 4 will be set to '1'. This will produce 6 outcomes out of 16 possibilities (i.e., 1100, 0110, 1010, 1001 and 0101). Biasing at 75% switching probabilities mean 3 bits will be set to 1s producing 4 new outcomes (1110, 0111, 1011 and 1101). Biasing at 35% switching probabilities mean 1 bit will be set to 1s producing 4 new outcomes (0001, 0010, 0100 and 1000). Note that the remaining two outcomes (0000/1111) are also produced by TRNG but with lower frequency.  
Write operation of any spintronic memory suffers due to temperature and \(V_{DD}\) variation. Therefore, TRNG can be biased to some preferential values and behaves as Pseudo Random Number Generator (PRNG). Tracking of \(V_{DD}\) and temperature are needed for magnetic TRNG to change the biasing conditions accordingly and achieve desired switching probability.  
Figure 12: Algorithm of the proposed True Random Number Generator (TRNG).  
We assume that the processor can track \(V_{DD}\) and temperature to select appropriate write time. Figure 12(a) shows the proposed circuit and Figure 12(b) shows the timing waveform to implement variable write time. For this example, we have assumed that the processor runs at 1 GHz (time period 1 ns) and write time can vary from 9 ns to 13 ns depending on operating condition. Therefore, from this circuit we can select write time with a step size of 0.5 ns in that range (by asserting WR_ENABLEE and any of the other 8 pulses). If finer step size is required, more pulses can be generated with less duty cycle. However, in that case, the MUX will have more inputs (4:1 MUX (16 pulses in total) for 0.25 ns granularity).
## 2 Fundamental Background  
### Antenna Design Methodology  
The dimensions of a single element of the MIMO antenna can be estimated from the following equations [62]:  
\[W=\frac{c_{0}}{2f_{r}}\sqrt{\frac{2}{\varepsilon_{r+1}}} \tag{1}\]  
\[\varepsilon_{reff}=\frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}+1}{2} \bigg{[}1+12\frac{h}{W}\bigg{]}^{-1/2},\ W/h>1 \tag{2}\]  
where, \(W\) is the width of patch, \(h\) is the height of the dielectric substrate, \(\varepsilon_{reff}\) is the effective dielectric constant of the substrate, and \(\varepsilon_{r}\) is the dielectric constant of substrate.  
The dimension of the patch may be extended on each end by \(\Delta L\) due to fringing fields, which is the function of the effective dielectric constant \(\varepsilon_{reff}\) and width-to-height ratio \(W/h>1\).  
\[\frac{\Delta L}{h}=0.412\frac{((\varepsilon_{reff})+0.3)(\frac{W}{h})+0.264}{(( \varepsilon_{reff})+0.258)(\frac{W}{h})+0.8} \tag{3}\]  
The length of the patch antenna can be calculated while employing the subsequent calculations [62].  
\[L=\frac{c_{0}}{2f_{r}\sqrt{\varepsilon_{reff}}}-2\Delta L \tag{4}\]  
The length and width of the ground plane is given by the successive formulas.  
\[L_{g}=6h+L \tag{5}\]  
\[W_{g}=6h+W \tag{6}\]
## 6 Discussion
### Proposed Hybrid Models
At present, the majority of automated sleep stage classification studies demonstrate a high performance with single-EEG channel, but only a small fraction (25%) evaluated the performance of their approaches on a mixture of signals (Figure 8). In summary, research on a mixture of PSG signals should be the main focus in the automated detection of sleep stages. The RNN/LSTM and hybrid models have yet to reach their peak performance in classifying sleep stages. Further research on these models to evaluate their performance across different databases could decrease the bias in these methods and help to identify architectures capable of processing mixed PSG signals. A model with optimal architecture could be employed in various applications and platforms, such as mobile, point-of-care monitoring devices. This review underscores the advantages (key points discussed) as follows:  
1. Numerous studies (15 from Figure 10) employed CNN models with EEG signals, and that CNN models are effective in recognizing characteristic features of sleep EEG. 2. One-dimensional (1D)-CNN models were used more often than 2D- and 3D-CNN models. From Figure 9b, 12, 8, and 2 1D, 2D, and 3D models were used, respectively. 3. Most studies (60% from Figure 8) used EEG signals and achieved high classification accuracy. Figure 14: Number of studies published between 2013 and 2020 describing implementation of various DL models for PSG recording analysis. * Summary statistics: year of the analyses for various deep learning models used in sleep classification. EEG signals were mainly used in studies that explored a mixture of PSG signals. In other words, EEG could be a reference signal when considering mixture of PSG signals to train and evaluate newly proposed models. The limitations of this review are as follows:  
1. It is difficult to compare various models and identify the best performing approach, because the majority of studies used data from only one sleep database to train and test the model. 2. There is a lack of studies that utilized other PSG recordings, such as EOG, EMG, or ECG signals. Studies that used these PSG recordings also did not perform equally well as those that used only EEG signals. Hence, this limits the implementation of these PSG recordings in real world applications for automated sleep stages classification.
# Study on Filling Material Strength and Dam Failure Characteristics of Loess Dam
###### Abstract  
In the Loess Plateau region, loess, as a widely distributed building material, is often used as a filling material for dams. When the water level reaches a certain height, the body of a dam is prone to shear failure due to the penetration of water. The change in the shear performance of local loess filler can affect the overall strength of loess dams. Therefore, the filler of a loess dam is selected to study the shear performance. The progressive failure process of a loess dam is simulated. The shear failure characteristics of loess filler under the influence of water content, confining pressure, and dry density were explored. The characteristics of the shear failure of a loess dam were analyzed. The remolded loess is prone to shear expansion failure under low confining pressure, low water content, and high dry density, and is prone to shear shrinkage failure under high confining pressure, high water content, and low dry density. When the water content is constant, the cohesion increases with the increase in dry density. When the dry density is constant, the internal friction angle generally increases with the increase in water content. However, when the dry density is high, the permeability of the remolded loess is weakened, resulting in uneven water distribution under a low water content, which affects the test results. The failure process of the loess dam is a progressive shear failure, which is affected by the water level and water pressure, and is destroyed under the action of pore water pressure and water body lateral pressure.  
unsaturated remolded loess; failure characteristics; confining pressure; water content; dry density; dam simulation +
Footnote †: journal: Applied sciences
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 2 Literature Review
### Feature Selection  
Feature selection helps to interpret data, reduce computational requirements, and reduce the impact of the curse of dimensionality, thereby improving the performance of the prediction model [22]. In 2010, [PERSON] [23] used eight feature selection methods in the Weka to select the most valuable attributes from 15 variables; the top five variables with the most votes were input into the prediction model. In 2016, [PERSON] [8] used five feature selection methods in Weka to select the 15 most important features from the original 60 variables using the majority vote process; these 15 features were input into the prediction model. In 2013, [PERSON] et al. [18] divided baseball data into three categories--batting, pitching, and team data--and used three methods (best subset selection, forward selection, and backward selection) to select features for each category; seven input variables were finally selected. In 2014, [PERSON] [7] used logistic regression for feature selection; sensitivity analysis was used to calculate the importance of each independent variable, and the independent variables with a relative importance of more than 80% were selected. In 2018, [PERSON] [9] used XGBoost for feature selection. XGBoost, decision trees, and random forest models all use tree-based models to rank features according to their importance and determine the best features.  
Many feature selection methods exist; the main objective of feature selection is to reduce the number of features and select the most relevant features as input variables to improve the prediction accuracy of machine learning. The feature selection method used in this study is ReliefFAttributeEval, and this study investigated whether feature selection has an impact on model prediction performance.
## 2 Methodology & Case Study
### The Low-Cost & Portable Monitoring System
#### 2.1.2 Micro Electro Mechanical Systems (MEMS) Based Sensor Unit  
In each sensor unit, the MEMS tilt sensors can observe the rotation and abnormal displacement on the ground surface, which avoids the usage of a long extensometer wire. The installation and maintenance is relatively simple and cost-effective. The resolution of the MEMS tilt sensor is sufficient for landslide research, its measurement range is \(\pm\)30 degrees and the resolution of rotation angle is 0.0025 degree. High resolution, low noise and low temperature dependency, together with a robust sensing element design make this MEMS based inclinometer an appropriate choice to detect slope displacements and failures.  
Despite the displacements, the slope stability mechanism indicates that the soil matric suction and water content are significant for the state of stress and stability in shallow slopes, and the correlation of soil moisture and suction can manifest the soil hydraulic characteristics, so that both the soil moisture and the soil suction are essential for monitoring of rainfall-induced landslides. The soil moisture is detected at a shallow position of the slope, typically 30 cm deep. The soil moisture sensor measures volumetric moisture contents by detecting the corresponding dielectric constant of the surrounding soil. In addition, measurements of soil suction in unsaturated soils as well as precipitation are also achievable with the sensor unit by using tensiometers and rain gauges which do not need frequent maintenance. In this study, the measuring range of the soil moisture sensor (Decagon EC-5, Decagon Devices, Pullman, WA, USA) is technically 0-1 with an accuracy of \(\pm\)0.01, and the measuring range of the soil suction sensor (Tensiomark TS2, Stevens Water Monitoring Systems, Portland, OR, USA) is 0-300 kPa with an accuracy of approximately \(\pm\)0.15 kPa. The rain gauges (CAWS 100, Huayun Group, Beijing, China) can measure rainfall with a resolution of 0.2 mm and a 0-4 mm/min measurement range. These sensors can be integrated with the wireless sensor unit into the real-time monitoring system and are capable of detecting the magnitudes of the values of the parameters in this area.
## 3 Results
### Comparison of Optimized Model Results with Other Products  
#### 3.3.1 Comparison of GPP Estimation Results Based on Site Data  
Based on the GPP measurements from ChinaFLUX, this study compared the accuracy of forest GPP estimates from different products and the XGB model. From January 2009 to December 2010, a comparative analysis of monthly GPP observed at EC sites, alongside measurements from GOSIF, GLASS, and FLUXCOM, as well as estimates from an XGB  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Row** & **Variables** & **VIF** \\ \hline
1 & PAR & 119.89 \\
2 & SR & 119.12 \\
3 & NR & 8.46 \\
4 & EVI & 8.08 \\
5 & VPD & 6.97 \\
6 & Temp & 6.76 \\
7 & NDVI & 5.17 \\
8 & LAI & 2.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-collinearity assessment result.  
Figure 8: Performance evaluation of GPP estimation models across different variable combinations (1–7): (**a**) \(R^{2}\), (**b**) \(RMSE\) (g Cm\({}^{-2}\) d\({}^{-1}\)), (**c**) \(MAE\) (g Cm\({}^{-2}\) d\({}^{-1}\)). Box plots show median (central line), interquartile range (box, 25 th-75 th percentiles), and data distribution within 1.5\(\times\)IQR (whiskers). Mean values are represented by dots (\(\bullet\)) and squares (\(\square\)).  
model (Figure 9a), demonstrated that all three RS-based GPP products and the XGB-based estimates effectively captured the seasonal variations in GPP within China's forest ecosystems. Among them, the XGB model yields notably more accurate results in forest GPP estimation compared to other products. Specifically, the correlation coefficients (\(R\)) of the XGB model reached 0.92 (Figure 9b), which is notably higher than those of FLUXCOM, GLASS, and GOSIF. This indicated that XGB can capture the trend of GPP changes with higher accuracy (Figure 9a). In terms of estimate error, the \(RMSE\) of the XGB model was 1.55, the lowest among the four, further indicating its highest accuracy. For the other three data products, both GLASS and GOSIF had an \(R\) of 0.82, with an \(RMSE\) of 2.42 and 2.21, respectively, while FLUXCOM had an \(R\) of 0.72 and an \(RMSE\) of 3.28. Overall, the XGB model performed the best at the site-specific scale.
## 1 Introduction  
Concrete structures deteriorate as they age [1]. Therefore, maintenance techniques to extend the lifespan of concrete structures have been constantly developed [2]. Nevertheless, problems with concrete structures continue to occur. Typical problems include corrosion of the internal steel rebar [3], alkali-aggregate reactions, and carbonation of the concrete [4; 5; 6]. Many strengthening methods have been adopted to improve the performance of deteriorated concrete structures, including the use of sole plate reconstruction, externally bonded (EB) steel plates, externally bonded fiber-reinforced polymers (EB FRPs), and external steel tendons [1]. Among these, the use of external steel tendons is the most common [7], because they offer good applicability and high strengthening efficiency. In addition, external steel tendons are easy to analyze structurally, economically viable [8], and do not impose an additional load on the beams. Furthermore, they are easy to install, require a short construction time [9], and do not interfere with the maintenance of the structure after installation. Furthermore, the degree of strengthening can be adjusted by controlling the prestress levels in the external tendon [10].  
However, the use of external steel tendons may result in problems if the anchorage system fixing the prestressing tendons is not providing sufficient load resistance [11; 12; 13]. A further problem is the wide range of anchorage systems. The majority of end anchorages used with external tendons are installed via premade lifting holes in the concrete beams or prestressed concrete girders. [PERSON] _et al._[14] conducted an experiment to examine the performance of three types of existing anchorages that are frequently used in structures. The results of their experiments identified several problems, such as cracking around the lifting hole, anchor bolts pull-out, and concrete delamination around the anchorage [15].  
This study addresses a novel type of end anchorage as a means of overcoming the drawbacks associated with the conventional end anchorages. This newly proposed anchorage minimizes the use of stud anchors, which can damage reinforced and prestressed concrete beams. It is shaped in a way that allows it to efficiently transfer the prestress forces in an external steel tendon to a beam. To evaluate the performance of the new anchorage, it was compared with three types of conventional anchorages by subjecting them to a static loading test.
## 1 Introduction
This paper considers the path planning problem in a space with multiple obstacles and multiple robots, and those obstacles and robots are not points but have body size. The generalized Voronoi diagram (GVD) [25] is used to deal with the problem. [PERSON] [26] presents a nonuniform sampling technique to find an optimal path on the map, the map uses the generalized Voronoi graph to initialize, and the path is calculated by a heuristic path method. To improve the motion planning efficiency of the rapidly exploring random tree and its variants (RRTs), [PERSON] [27] presents a GVD-based heuristic path planning algorithm to generate a heuristic path and guide the sampling process of RRTs. In this paper, a navigation strategy with path priority (NSPP) is proposed to solve the problem of multiple robots moving in a large space with some static or/and dynamic obstacles. The main contributions of the paper are as follows. 1. The Voronoi diagram for a single-robot path planning is extended to multiple robots. 2. Each robot has its path-priority order to make the path of each robot more efficient. Suppose a control center manages an unmanned factory where each robot is set a unique path-priority order depending on the importance of its task. Each robot moves from its initial position to the target position without colliding with other robots or obstacles. Section 2 defines the problem to be solved and explains what the path-priority order of the robot is. Section 3 is the main session to build the navigation strategy with path priority (NSPP). Simulations and comparisons are carried out in Section 4. Finally, the conclusion is given in Section 5. The main idea of the algorithm is that the Voronoi diagram for single-robot path planning is extended to multiple robots. Suppose a control center manages an unmanned factory where each robot is set a unique path-priority order. The path-priority order for each robot depends on the importance of its task so that all robots with priority orders moving in the factory will be more efficient.