esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case Background
### Engineering Disasters  
Since the project started in May 2019, due to the influence of groundwater, atmospheric precipitation, fissure water, etc., the moisture content of the surrounding rock has been as high as 29% or more, far exceeding the liquid limit value. During tunnel construction, the surrounding rock at the tunnel face often experiences a transition from plastic soil to water seepage to flowing water. Water immersion causes instability in the base, resulting in various engineering disasters such as deformation of the primary support and collapse  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Composite Lining**} & **Deep Buried Section** & **Deep Buried Section** \\ \cline{2-3}  & **SVI\({}_{\text{b}}\)** & **SVV\({}_{\text{b}}\)** \\ \hline Advanced support & \(\Phi\)108 \(\times\) 6 mm, L-40 m, 37 pieces/ring & \(\Phi\)60 \(\times\) 5 mm, L-30 m, 37 pieces/ring \\ \hline \multirow{3}{*}{Primary lining} & I22b steel arch frame, with a longitudinal & I22a steel arch frame, with a longitudinal \\  & spacing of 60 cm, made of 15 \(\times\) 15 cm steel & spacing of 75 cm, made of 20 \(\times\) 20 cm steel \\  & mesh and sprayed with 30 cm C25 concrete & mesh and sprayed with 30 cm \\ \hline Deformation & \multirow{2}{*}{35 cm} & \multirow{2}{*}{20 cm} \\ allowance & & C25 concrete \\ \hline Secondary lining & C30-reinforced concrete, thickness 60 cm & C30-reinforced concrete, thickness 50 cm \\ \hline Inverted arch & C30-reinforced concrete, thickness 75 cm & C30-reinforced concrete, thickness 70 cm \\ \hline \multirow{3}{*}{Substrate} & Using 6.0 m long \(\Phi\)60 cm rotary jet grouting & \multirow{3}{*}{/} \\  & piles (cement slurry 1:1) with a spacing of & \\ \cline{1-1}  & 1.0 \(\times\) 1.0 m; Three 6.0 m long \(\Phi\)140 \(\times\) 8 mm steel & \\ \cline{1-1}  & pipe piles are used at the corner of the wall & \\ \hline \end{tabular}
\end{table}
Table 1: Composite lining support design parameters for tunnel grade VI and V rock mass segments.  
Figure 2: Geological profile of the left entrance of the Tuanjie Tunnel.  
of the tunnel face. There have been 2 major deformations, 10 m of arch replacement, 13 m of slope cracking treatment, and over 20 collapses. In order to overcome the challenges of natural water abundance and excavation disturbance in tunnels, a scientific research project was initiated in August 2019 to make scientific decisions on the stability of surrounding rock, causes of disasters, and engineering disposal measures. Figure 3 shows a series of engineering disasters encountered during the construction of the Tuanjie Tunnel, including slope cracking, base water seepage, tunnel face collapse, and cracking and subsidence of the primary support.
## 3 Results and Discussion
### Correlation Analysis  
Table 6 shows how the main fatty acids were correlated in both teff and amaranth varieties. Palmitic acid was significantly negatively correlated with stearic acid (\(r=-0.443\), \(p=0.044\)) and oleic acid (\(r=-0.691\), \(p=0.001\)) for teff. However, the correlation of palmitic acid with both stearic and oleic acids was positive, although it was not statistically significant for amaranth. Stearic acid was significantly positively correlated with oleic acid (\(r=0.922\), \(p=0.000\)) and negatively correlated with linoleic acid (\(r=0.914\), \(p=0.001\)) in amaranth. Oleic acid was significantly negatively correlated with linoleic acid at \(p=0.001\) and \(p=0.000\) for teff and amaranth, respectively. Alpha-linolenic acid was significantly correlated with oleic acid (\(r=-0.753\), \(p=0.019\)) and linoleic acid (\(r=0.761\), \(p=0.017\)) in amaranth. [PERSON] et al. [37] reported a negative correlation between oleic and linoleic acid in amaranth. Similarly, in the present study, a higher negative correlation was obtained between oleic and linoleic acid in amaranth. On the other hand, the lowest positive correlation was obtained between stearic and \(\alpha\)-linolenic acid in teff. Pearson correlation analysis showed that teff and amaranth had different biosynthetic pathways for the synthesis of the major fatty acids, as the direction of the correlation was the opposite for the majority of fatty acid pairs where correlation was established.
## 3 Results and Discussion
### Microstructure Results  
As observed in Figure 12, the young coconut fiber (YCF) and mature coconut fiber (MCF) were long needle-like in shape at a magnification of 75 times (100 \(\upmu\)m scale bar). The MCF also had a roughness surface more than YCF. The rough surface of coconut fiber leads to improved bonding within the cement matrix. Figure 13 shows the SEM of fiber-cement flat sheets made with YCF and MCF. The flat sheets made with MCF had denser microstructure than the flat sheets made with YCF. This also supports the modulus-of-rupture results, which indicated that fiber-cement flat sheets were better in terms of using the MCF. The microstructure results also supported the modulus-of-rupture findings, indicating that the fiber-cement flat sheets with MCF exhibited better performance in terms of modulus-of-rupture properties than the fiber-cement flat sheets using YCF.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Verification of Data Integrity
#### 2.3.2 Dropped Packets  
Each board was set up to send 10,000 packets upon receiving the start packet and then stop sending. Because each packet was assigned a time-stamp, it was possible to track lost packets and identify any pattern of data loss. A high rate of dropped packets (e.g., >1%) would adversely affect the accuracy of the angle measurements, and a pattern of dropped packets would indicate a flaw in the system. The verification of the reliability of data transfer was crucial because the implemented transmission protocol, UDP, did not guarantee delivery of every packet. This experiment was conducted with two routers--a Google Nexus 6 Plus phone and then an O2 wireless box IV. A varying number of sensor boards were placed 1 m apart with their PCB antennas pointing towards the router, which was 1 m from the first board.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 2 Method Outline and Contributions  
We present the Unique Regions for Sketch map Interpretation and Matching (URSIM) method to find element-wise correspondences between a sketch map and an equivalent metric map representing the environment. URSIM takes as inputs two maps segmented in regions and finds the most distinguishable, or unique, regions in each map.  
We call such regions _anchors_. Both segmented maps are then converted to graphs where each region is a vertex and we have an edge between each region in contact. A vertex descriptor is created for each vertex based on the topology of the map, the position of anchors, and the size of regions.  
Figure 1: The metric map of the KTH dataset for SLAM in (**a**) and four corresponding sketch maps (**b–e**).  
Finally, using a planar graph matching algorithm, URSIM finds the most likely correspondences between both graphs' vertices.  
An illustration of the method can be seen in Figure 2.  
The contributions of this paper are:  
* A method to determine unique regions in segmented maps is proposed. By comparing the characteristics of each segmented region to other regions of the same map, we determine the most distinguishable, or unique, regions.
* A method to find two sets of equal length containing the most similar unique regions between two maps is presented. The unique regions in those sets are called anchors and are used later during the matching.
* A vertex descriptor using anchors in a graph with weighted vertices to compare the regions of two maps is introduced. Each vertex descriptor represents the topology of the environment around said vertex, relative to the position of anchors in the graph and the size of all other regions.
* An improvement of the graph matching algorithm used in our previous work [3] was confirmed. Instead of using a specific characteristic, such as a region being a crossing or a dead end, we changed the similarity measure used to compare vertices and compare the vertex descriptors presented in the previous point. A user-chosen similarity threshold determines when vertices are similar enough to be matched together.
## 2 Related Works
### Extreme Gradient Boosting  
XGBoost is an optimized distributed gradient boosting approach designed to be highly efficient and flexible. It has gained immense popularity in machine learning competitions and is widely regarded as the \"go-to\" algorithm for structured data. XGBoost has been optimized for both computational speed and model performance, making it highly desirable for real-world applications [23]. There are several advantages of decision-tree-based techniques [24].  
One of the most significant advantages of decision trees is their ease of interpretation. They can be visualized, and the decision-making process can be easily understood, even by non-experts. Decision trees are computationally inexpensive to build, evaluate, and interpret compared to algorithms like support vector machines (SVMs) [25] or ANNs. Unlike other algorithms that require extensive pre-processing, decision trees can handle missing values without imputation, making them more robust. Decision trees can also capture complex non-linear relationships in the data, which linear models may not capture effectively. Further, this approach can be used for both classification and regression tasks, making it very versatile.  
Gini impurity is a metric used to quantify the disorder or impurity of a set of items. It is crucial for the \"criterion\" parameter in the decision tree algorithm. Lower Gini impurity values indicate more \"pure\" nodes. The Gini impurity is used to decide the optimal feature to split on at each node in the tree.  
\[Gini(t)=1-\sum_{i=1}^{C}p_{i}^{2} \tag{1}\]  
Further advantages of using XGBoost are that of ensemble learning [26]. Ensemble methods, particularly boosting algorithms like XGBoost, are less susceptible to the overfitting problem compared to single estimators due to their ability to optimize on the error. By combining several models, ensemble methods can average out biases and reduce the variance, thus minimizing the risk of overfitting. Ensemble methods often achieve higher predictive accuracy than individual models. XGBoost, in particular, has been shown to outperform deep learning models in certain types of data sets, especially when the data are tabular.  
The objective function optimized by XGBoost includes both a loss term and a regularization term, making it adaptable to different problems:  
\[\text{Obj}(\theta)=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum_{k=1}^{K}\Omega(f_ {k}) \tag{2}\]
## 3 Results and Discussion
### Parameter Study of the Adsorption of DR-81 onto the Cockleshell Powder
#### 3.2.2 Effects of Initial Dye Concentration and Contact Time  
The effects of the initial concentration and contact time are the two main factors to determine the maximum adsorption capacity for DR-81 dye removal by cockleshells in the interval 0-60 min, and to control the adsorption mechanisms at different concentrations from 10 to 100 mg/L. The effects of the initial dye concentration and contact time on DR-81 removal are presented in Figure 9.  
According to Figure 9, the first part of the curves (0-10 min) describes the rapid evolution of the adsorption capacity and is explained by the great availability of active sites on the solid surface. The second part of the curves (10-60 min) is a slow increase in and stabilization of the percentage of elimination, which indicates that the DR-81 molecules are in a phase of progressively occupying the available active sites and causing the saturation of the bio-adsorbent surface [41]. The increase in the concentration from 10 to 100 mg/L with an increase in the adsorption capacity from 1.47 to 6.79 mg/L is also explained by the decreasing attraction forces between the DR-81 dye and the solid surface [40].
###### Abstract  
The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer's side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer's energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.  
time series analysis; energy demand forecast; ARIMA; hybrid model; power theft +
Footnote †: journal: Applied Sciences
# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
###### Abstract  
As a key component of rail vehicle operation, the running condition of the wheelset significantly affects the operational safety of track vehicles. The wheel diameter, flange thickness, and flange height are key dimensional parameters of the wheelset, which directly influence the correct position of wheelsets on the track, and the train needs to be continuously monitored during the passenger operation. A prediction model for the key parameters of the wheelset is established based on LSTM (long short-term memory) neural network, and real measured data of wheelsets from the Shanghai Metro vehicles are selected. The predicted results of the model are compared and analyzed, and the results show that the LSTM-based prediction model for key parameters of wheelsets performs well, with the mean absolute percentage errors (MAPEs) for wheel diameter, flange thickness, and flange height being 0.08%, 0.42%, and 0.44%, respectively, for the left wheel and 0.07%, 0.35%, and 0.44%, respectively, for the right wheel. The prediction model for the train wheelset parameters established in this paper has a good prediction accuracy. By predicting the key parameters of the wheelset, the faults and causes of the wheelset can be found and determined, which is helpful for engineers to overhaul the wheelset faults, make maintenance plans, and perform preventive maintenance.  
Keywords:LSTM neural network; wheelset wear; parameter prediction; deep learning; reprofiling plan +
Footnote †: journal: Applied
## 2 Results and Discussion  
### Gene Expression of Monoterpenes Synthases Displays a Circadian Rhythm Pattern  
Monoterpenes and other volatile organic compounds (VOCs) synthesis differ throughout plant development as a result of the differential gene expression patterns in distinct cell types [24]. Most likely, genes involved in the synthesis of VOCs are transcriptionally activated, which coincides with the VOC emission [25]. We showed that linalool production is accumulated in the afternoon [26]. Therefore, we ask if lavender monoterpenes synthase gene expression is altered during the day.  
Figure 1: Monoterpenes biosynthesis in lavender. The reaction mechanisms of monoterpenes synthases initiate with the geranyl diphosphate substrate ionization and terminate by deprotonation or water capture. Limonene synthase catalyzes a-terpinyl to limonene using deprotonation, while linalool synthase, capturing a water molecule, catalyzes geranyl or linalyl cation to linalool.  
Precisely, the relative expression of _LaLINS_, _LaLINS_ and _LaTPS-1_ were monitored in the blooming heads of the inflorescest that contain flowers at the 3 rd to 5 th stage as determined by [26], every 3 h during two consecutive days using quantitative RT-PCR. The results showed that all three genes tested were expressed rhythmically. Remarkably, the highest gene expression was observed from mid-day until 15:00 (Figure 2). Specifically, the expression of _LaLINS_ increased 3-fold at mid-day and continued at a high level for the next three hours, followed by a rapid decline in the evening (Figure 2a). The next day, the gene expression profile was similar. Likewise, the limonene synthase gene expression was increased significantly, from late morning to a maximum at 15:00 (Figure 2b). On the second day, the accumulation of _LaLINS_ transcripts showed a similar profile, _albeit_ at a lower level (Figure 2b).  
Similarly, the expression of _LaTPS-1_ displayed a rhythmic pattern (Figure 2c). Although the biological role of _LaTPS-1_ is unknown [27], its transcript was expressed in stages 2 and 3 of the lavelander flowers [22]. In this study, the expression of _LaTPS-1_ is increased during mid-day, reaching a maximum at 15:00. On the second day, the expression pattern was similar, albeit at a higher level with a maximum on mid-day (Figure 2c). These data indicate that the transcription of monoterpenes synthases in lavelander flowers was affected by circadian rhythms.  
Figure 2: Gene expression of (**a**) Lilanool synthase (_LaLINS_), (**b**) Limonene synthase (_LaLINS_) and (**c**) Terpene synthase-like (_LaTPS-1_) in _Lavandula angustifolia_ flowers during the day length. Error bars are presented as SEM of three replications. The values that are significantly different from the first day’s 6:00 sample are indicated with asterisks (* \(p\) < 0.05, *** \(p\) < 0.001, *** \(p\) < 0.0001).
# Application of the Parabola Method in Nonconvex Optimization
## 1 Introduction  
Zero-order methods are usually applied to problems in which the function being optimized has no explicit analytic expression. To calculate the values of such functions, it is necessary to solve non-trivial auxiliary problems of computational mathematics and optimization. In some cases, it is assumed that the calculation of a single objective function value may take from a few minutes to several hours of continuous computer operation. Developing an effective global optimization method requires identifying certain properties of the objective function (and constraints), for example, determining a good estimate of the Lipschitz constant [1; 2; 3; 4] or representing a function as a difference between two convex functions [5; 6; 7]. Such auxiliary problems do not always have a unique solution and are not often easily solvable. The effectiveness of global optimization methods often depends on the ability to quickly find a good local solution [8], which can significantly speed up some global optimization methods (for example, the branch and bounds method).  
The aim of the paper is to test several well-known zero-order methods on nonconvex global optimization problems a using special version of the multistart strategy. We check the ability of these methods not only to find a good local solution but also to find a global optimum of the problem. The multistart strategy was considered as a starting case for population-based optimization. It is necessary to note that the multistart approach can be considered as an overabundant population. Our aim is to check the efficiency of the suggested approach under this condition.  
Special attention is given to the Parabola Method in the present work. The reason for such an investigation is the following: if the objective function is differentiable, then in a small neighborhood of an interior local minimum point, the objective function behaves like a convex function. In [9], a more general statement of this property based on the concept of local programming is given. A combination of this method with different zero-order methods in multivariate optimization problems demonstrates quite high performance and global optimization method qualities. We tested several well-known zero-order multivariate optimization methods; nevertheless, application of this modification of the Parabola Method as the line-search procedure can be used in many other methods.  
The paper is organized as follows. In Section 2, we give a description of the Golden Section and Parabola Methods and perform numerical experiments on a set of univariate optimization problems. A numerical comparison of zero-order methods in combination with the Golden Section and Parabola Methods is presented in Section 3. We conclude the paper with a short discussion of the results in Sections 4 and 5.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Effects of Agro-Wastes on RC Elements
#### 4.3.4 The Impact of Agro-Waste on Energy Absorption and Torsional Strength  
Steel fiber-reinforced palm shell concrete beams have shown a significant increase in torsional strength and improved pre-crack torsional behavior due to enhanced concrete strength [83]. PO clinker concrete beams exhibit similar properties to those made with PO shells, although an increase in steel reinforcement ratio is associated with a reduction in ductility [75; 82].  
In terms of energy absorption, concrete beams prepared with PO fuel Ash demonstrate lower energy absorption compared to traditional concrete. However, combining such AW with steel fibers enables the optimization of energy absorption properties and enhances torsional strength [22].  
PO fuel Ash, categorized as AW, holds significant potential for improving the strength of reinforced concrete structures. Its use aligns with zero-waste initiatives, offering asustainable alternative material when employed as a partial adhesive substitute in concrete beams [22].  
AW Ashes exhibit notable potential as sustainable construction materials in RC beams. Incorporating these Ashes into concrete mixtures has led to significant improvements in load-bearing capacity, torsional strength, ductility, and deflection compared to traditional concrete beams. These advancements contribute to the development of more flexible and sustainable concrete structures. AW Ashes, such as WS Ash and RHA, have been highlighted as environmentally sustainable materials capable of enhancing concrete structure durability [71].  
Experimental investigations on concrete beams reinforced with WS Ash and RHA under static loading conditions reveal the impact of varying mixture ratios on structural performance. Results indicate that using these Ashes at an optimal 15% replacement ratio significantly improves flexural behavior, representing a balance between mechanical strength and structural sustainability [88].  
Recycled agricultural and industrial waste materials offer considerable advantages in geo-polymer RC beams. For example, geo-polymer concrete incorporating fibers derived from turning shops or workshops, combined with WS Ash, demonstrates both economic and mechanical superiority. These materials have enhanced load-bearing capacity compared to traditional RC beams and exhibit improved energy absorption and reduced crack propagation. The addition of steel and recycled turning fibers has resulted in higher ultimate load capacity and increased structural resilience, emphasizing the potential of recycled WS Ash as a sustainable alternative to steel fibers [21].