esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby-Mori-Tanaka Theory
## 4 Study on Modified Soil Mix Proportions with Black Shale
### Analysis of Results
#### 4.2.2 Basic Mechanical Properties of Black Shale Modified Soil  
For specimens with the same black shale content, the slope of the stress-strain curve increases with the increase in cement content. Figure 9 shows the stress-strain curve for the modified soil with 40% black shale content (R40 series), and Table 3 presents the elastic  
Figure 8: (**a**) Relationship between dry density and cement-soil ratio of black shale. (**b**) Relationship between water absorption and cement-soil ratio of black shale. \(R_{40}\omega_{n}\) is the water absorption curve of the sample with 40% black shale content; \(R_{40}\rho_{d}\) refers to the dry density curve of the specimen with 40% black shale content. S15 and S25 refer to cement content of cement-soil of 15% and 25%, respectively.  
modulus values for the R40 series modified soil. It is observed that with a constant black shale content, the elastic modulus increases with the increase in cement content.  
Figure 10 shows the relationship between the unconfined compressive strength of black shale-modified soil and cement content. It is observed that the strength of the modified soil is generally positively correlated with the cement content, but the linear correlation is not significant. For samples with a 40% black shale content, the cement content increases from 6% to 18% in 3% increments, and the strength increases from 0.85 MPa to 2.70 MPa in order of 0.78 MPa, 0.20 MPa, 0.24 MPa, and 0.63 MPa increments. Referring to the standards for unconfined compressive strength of inorganic stabilized materials in the \"Highway Asphalt Pavement Design Specification\" [33] (JTG D50-2017), except for Y4S10, Y5S10, Y7S10, and Y7S15, the rest meet the requirements for subgrade layers of secondary and below-grade highways with medium to light traffic, as Table 4 shows.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Structure Layer**} & \multirow{2}{*}{\begin{tabular}{c} **Highway** \\ **Grade** \\ \end{tabular} } & \multicolumn{2}{c}{**Extremely**} & \multirow{2}{*}{\begin{tabular}{c} **Heavy** \\ **Heavy, Extra** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Medium,** \\ **Traffic** \\ \end{tabular} } \\  & & & & & **Light Traffic** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Cement \\ stabilized \\ class \\ \end{tabular} } & \multirow{2}{*}{Highway base} & High grade & 5.0–7.0 & 4.0–6.0 & 3.0–5.0 \\  & & Low grade & 4.0–6.0 & 3.0–5.0 & 2.0–4.0 \\  & & High grade & 3.0–5.0 & 2.5–4.5 & 2.0–4.0 \\  & & Low grade & 2.5-4.5 & 2.0–4.0 & 1.0-3.0 \\ \hline \hline \end{tabular} Note.: 1 High-grade: high-speed, first-class highways; 2 Low Grade: Grade II and below highways.  
\end{table}
Table 4: Seven day unconfined compressive strength standard of inorganic binder stabilized materials (MPa) [33].  
Figure 9: Stress–strain curve of R40.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 4 Solution of the MINLP Truss Synthesis Problem
### Pre-Screening of Discrete/Standard Dimensions  
In order to reduce the number of binary variables attributed to discrete dimensions and considering the fact that the optimal discrete dimensions are expected to be near the optimal continuous dimensions obtained in the previous phase, the pre-screening technique was applied after the first phase (after obtaining the optimal topology and continuous sizes).  
The alternative discrete dimension values in the vector \(\boldsymbol{q}_{ij,l}=\{q_{ij,l},\,q_{ij,2},\,\ldots\,\,q_{ij,l}\}\) from Equation (23) are positioned from the smallest discrete value in the first position to the largest value in the last position. A special parameter \(l_{i,j}^{\text{ps}}\) is then defined for each bar \(i\)\(\rightarrow\)\(j\) at the \(m\)-th MINLP iteration (MINLP\({}^{m}\), \(m\)\(\in\)\(M\)), as a \(q_{ij,l}\) vector's position counter \(l\), \(l\)\(\in\)\(L\), where the continuous dimension \(d_{ij,l}^{m-1}\), obtained in the previous NLP\({}^{m-1}\) sub-problem, is positioned next to its discrete standard value \(q_{ij,l}\); see Equation (25). The subsequent initialization of binary variables \(y_{i,j,l}^{\text{st}}\) for standard dimensions is for the current MILP\({}^{m}\) master problem, then performed by Equation (26):  
\[l_{i,j}^{\text{ps}}=l,\,\,q_{i,j,l}\leq d_{i,j}^{m-1}<q_{i,j,l+1},\,\,\forall \,\,(i,j)\Big{|}eI_{i,j}=1 \tag{25}\]  
\[\begin{array}{lcl}y_{i,j,l}^{\text{st}}=0&\text{if}&l-l_{i,j}^{\text{ps}}>n ^{\text{ps}+}\\ 0\leq y_{i,l,l}^{\text{st}}\leq 1&\text{if}&n^{\text{ps}-}\leq l-l_{i,j}^{\text{ps}} \leq n^{\text{ps}+}\\ y_{i,j,l}^{\text{st}}=0&\text{if}&l-l_{i,j}^{\text{ps}}<n^{\text{ps}-}\end{array} \tag{26}\]  
where \(n^{\text{ps}-}\) and \(n^{\text{ps}+}\), close to the previously obtained continuous dimension \(d_{i,j}^{m-1}\), define the number of neighboring lower and upper discrete dimensions, which will take an active part in the subsequent discrete sizing optimization. The binary variables lying inside the neighborhood limited by \(n^{\text{ps}-}\) and \(n^{\text{ps}+}\) are hence active (\(0\leq y_{i,j,l}^{\text{st}}\leq 1\)), while the rest of them are deactivated, i.e., set to zero (\(y_{i,j,l}^{\text{st}}=0\)). In this way, only the reduced set of active binary variables is involved in the optimization that significantly decreases the discrete combinatorial problem.
## 2 Materials and Methods  
### Test Prototype  
The test prototype was a 4-wheel independent steering, 4-wheel independent drive mechanical structure (4 WIS-4 WID), as shown in Figure 1. The body size of the test prototype was 120 cm \(\times\) 70 cm \(\times\) 70 cm (length, width, and height, respectively), with a left and right wheel tread of 54 cm and a wheelbase of 104 cm. With a lithium battery (24 V, 20 Ah) as the power source, STM32F103 ZET6 (32 bit, 72 MHz) was used as the main controller to receive the attitude information of the vehicle position (x, y) and orientation information \(\theta\) sent by UWB (ultra-wideband; positioning error: \(\pm\)5 cm) and electronic gyroscope (WT901C; error: \(\pm\)0.1\({}^{\circ}\)), respectively. The PWM signal controlling the vehicle steering and movement were generated by the path-tracking model were transmitted to the steering gear and drive motor. The steering of the wheels was independently controlled by four steering gears (DH-03X, 120\({}^{\circ}\), 38 N/M) at [\(-90^{\circ}\) 90\({}^{\circ}\)] (setting a positive angle for the wheel counterclockwise and a negative angle for clockwise deflection). The four hub motors were driven by AQMD6015 BLS drivers to realize prototype driving. The control process is shown in Figure 2.  
When tracking the prototype vehicle path, the main controller reads the angle data of the position coordinates (x, y) and the deviation data \(\varphi\) provided by the UWB wireless positioning system and the electronic gyroscope, respectively, according to the sampling period and then converts the position and attitude information into the lateral deviation \(d\) and heading deviation \(\theta\) relative to the desired path. A dual-input and single-output fuzzy controller was designed to construct a nonlinear transfer model between body deviation and the look-ahead distance \(LD\). The lateral deviation \(d\), heading deviation \(\theta\), and look-ahead distance \(LD\) are input into the pure pursuit model to calculate the steering radius \(R\) of the prototype under the current state, and then the rotation angle of each wheel \(\delta_{1}\), \(\delta_{2}\), \(\delta_{3}\), and \(\delta_{4}\) and the driving speed of the prototype wheel \(v_{1}\), \(v_{2}\), \(v_{3}\), and \(v_{4}\) are calculated by the four-wheel, Ackermann low-speed steering model. The main controller converts the rotation angle \(\delta\) and speed \(v\) of each wheel into PWM signals that act as input of the steering gears and the wheel motor to realize driving and steering so that the path-tracking control of the prototype vehicle can be accomplished. The fuzzy control process is shown in Figure 3.  
Figure 1: Test prototype and test environment. (1) Greenhouse; (2) UWB anchor; (3) test prototype; (4) UWB signal receiver; (5) steering gear; (6) drive motor; (7) test path.  
Figure 2: System control process.
# Review of Satellite Interferometry for Landslide Detection in Italy
## 8 Future Perspectives
Over the next few years, the EGMS will deliver consistent, regular, standardized, harmonized, and reliable information regarding natural and anthropogenic ground motion phenomena (including landslides) over Europe. Considering the successful experiences and the frequency of impactful landslide events, such service will constitute a reliable baseline for landslide studies, hopefully guaranteeing a wider uptake of satellite interferometric data. * Despite the maturity of the interferometric techniques, the repeat pass time is still a major hurdle for the use of satellite InSAR as tool for systematic monitoring, especially when dealing with very short period events, such as landslide accelerations. Geological processes with strong deformation magnitudes with respect to the temporal sampling may yield aliased results or be missed altogether. In this scenario, another promising opportunity is represented by geosynchronous satellite, whose observing capability can provide a novel set of measurements, specifically InSAR at high temporal resolution (e.g., 1-12 h). This can be a game changer for solid earth applications. Hydroterra is an example of a project exploring such new InSAR horizons [199]. Hydroterra is currently a phase 0 candidate mission for the 10 th Earth Explorer Programme of the ESA. The aim of the project is to observe key processes of the daily water cycle by placing a single SAR satellite in geosynchronous orbit. The Mediterranean, the Alpine Arc, Sahel, and other African regions would be covered from a geosynchronous orbit slot positioned over Africa. The main science goals are to improve the prediction capability of intense rainfall and related flooding and landslides, improving the understanding of the diurnal water cycle. These goals will be supported by providing estimates of soil moisture, integrated atmospheric water vapor changes, flood extent, and information on snow melt. Secondarily, rapid response imaging of ground motion will enable new science applications and near-real-time services. The following are available online at [[http://www.mdpi.com/2072-4292/12/8/1351/s1](http://www.mdpi.com/2072-4292/12/8/1351/s1)]([http://www.mdpi.com/2072-4292/12/8/1351/s1](http://www.mdpi.com/2072-4292/12/8/1351/s1)), Video S1: GIF of the temporal evolution of interferometric applications for landslide studies in Italy. GIF created using the bar chart race of Flourish Studio and Screencast-O-Matic. Conceptualization and writing, L.S. ; bibliographic review and creation of the database, L.S., M.D.S., A.B., and P.C. ; methodology and figures preparation, S.B. and F.R. ; review and homogenization, N.C. and M.C. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the DEMOS project \"Deformation monitoring using Sentinel-1 data\" (Ref: CGL2017-83704-P). The authors declare that there is no conflict of interest.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 5 Discussion  
While comprehensive experiments validated the effectiveness of the loss function with directional guidance and the TRSM module, further analysis of their characteristics is still necessary. Our method optimizes the object-tracking algorithm separately in the training and tracking phases and is not dependent on any specific algorithm, making  
Figure 5: Comparison of visual tracking results on the LaSOT dataset.  
it applicable to various deep learning-based object-tracking algorithms to improve their tracking accuracy. When applied to the STARK algorithm, our method can effectively improve its performance with only a small increase in parameters. Although the algorithm in this paper is not compared with the most advanced algorithms, this demonstrates the importance of our method in improving tracking accuracy. Specifically, the TRSM module is applicable to long-term object-tracking algorithms, which determine target loss based on the reliability of tracking results before conducting target re-detection.  
However, additional research is still required for the proposed method. Firstly, the TRSM module must rely on the object-tracking algorithm for training, which may prove inconvenient. Therefore, our future research work will focus on optimizing the TRSM module and conducting independent training to make it a completely independent module for easier application to various object-tracking algorithms. Secondly, the hyperparameters set in the loss function were based on research in the object detection task, which could have an adverse impact on the object-tracking task. In the future, we will investigate the sensitivity of these hyperparameters in the object-tracking task.
# Translating Workflow Nets to Process Trees: An Algorithmic Approach
## 1 Introduction
Furthermore, any algorithm optimized for process trees, e.g., by exploiting the hierarchical structure, can also be applied to WF-nets of such a type. For example, in [5], it is shown that the computation time of _alignments_[6], i.e., explanations of observed behavior in terms of a reference model, can be significantly reduced by applying Petri net decomposition on the basis of model hierarchies. Hence, computing a process tree representation of the WF-net can be exploited to reduce the computational complexity of the calculations mentioned. Furthermore, recently, an alignment approximation technique was proposed that explicitly exploits the tree structure of a process tree [7]. Additionally, computing the underlying canonical process tree structure of two different process models allows us to decide whether or not the two models are _behaviorally equivalent_, e.g., as studied  
Figure 1: A simple example process tree [1], describing all basic control-flow constructs. in [8; 9]. In a similar fashion, the process tree representation of a workflow net can be exploited to translate it to a different process modeling formalism, i.e., known as process model transformation [10]. In this paper, we present an algorithm that determines whether a given WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the original WF-net is sound, and the obtained process tree's language is equal to the language of the original WF-net. A corresponding implementation, extending the process mining framework PM4 Py [11], is publicly available. Using the implementation, we conducted several experiments that show a quadratic time complexity in terms of the WF-net size. Furthermore, our experiments indicate that the algorithm can rediscover process trees, i.e., the process models used to generate the input for the experiments are rediscovered by the algorithm. The remainder of this paper is structured as follows. In Section 2, we present preliminary concepts and notation. In Section 3, we present the proposed algorithm, including the proofs with respect to soundness preservation and language preservation. In Section 4, we evaluate our approach. In Section 5, we discuss related work. In Section 6, we discuss various aspects of our approach, e.g., extensibility, in more detail. Section 7 concludes the paper.
# Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
## 5 Discussion  
### Warehouse Representation Limitations  
Cellular decomposition is a technique used in robotics and computational geometry to represent large continuous spaces as an aggregation of smaller, discrete units called cells. The dimensions of these cells in a grid representation, especially in the context of a warehouse with variable-sized shelves, are critical for effective and accurate modeling.  
1. Cell size and shelf representation: The dimensions of a cell should ideally be chosen such that the most common or average shelf sizes in the warehouse can be represented using an integer number of cells. This ensures that the majority of the warehouse can be accurately represented without significant distortions. However, it is essential to recognize that in real-world scenarios, shelves might not always conform to these standardized sizes. Some might span across the border of a cell or occupy a fraction of it.
2. Impact on the AP algorithm: The effectiveness of the AP algorithm hinges upon the granularity of the grid representation. If the cells are too large, the nuances in robot traffic patterns might be lost, leading to sub-optimal clustering. Conversely, if the cells are too small, the algorithm might become computationally expensive, and the resulting clusters could be too granular to be practically useful. Moreover, misalignment between cell boundaries and actual physical structures like shelves could lead to clustering that does not accurately reflect the true traffic patterns.
3. Inherent challenges of ACD: This dilemma is not unique to the scenario described but is a characteristic challenge of all approximate cellular decomposition methods. When decomposing a continuous space into discrete cells, there is always a trade-off between representation accuracy and computational efficiency. Too fine a decomposition can capture the environment's nuances but may be computationally prohibitive, while a coarser decomposition might miss critical details. Additionally, real-world environments are seldom perfectly regular. Objects, such as shelves, might not neatly align with the imposed grid, leading to representation errors. These discrepancies can influence pathfinding, task allocation, and other algorithmic processes reliant on the grid.
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 4 Soft Sensor for Pollution Monitoring
### CO\({}_{2}\) Emission Calculation Methods  
In order to achieve better fuel efficiency and to reduce vehicle emissions, the amount of air entering the engine must be calculated correctly. This subsection aims to show some methods that the vehicle's on-board computers use to determine such an amount of air. This is one of the main procedures used by the urban vehicular pollution monitoring tool developed in this work.  
The ECU can control several components that perform the task of defining the amount of air necessary to make the combustion have the highest yield. In addition, the ECU can determine the ideal air temperature and pressure, the current \(\lambda\) value of the vehicle, and the actual air flow that the engine requires at any given time.  
The main methods used to obtain such values are: alpha-N, speed density, and mass air flow, described as follows:  
* Alpha-N is a method rarely used today. The _Alpha_ consists of the angle of the throttle position sensor, and \(N\) refers to the engine speed in measured Revolutions Per Minute (RPM). The method does not estimate the airflow directly, but instead, it uses a mapping between the _Alpha_ and \(N\) to obtain such a value. It can be considered a simple method and was originally developed for engines used in racing [50].
* Speed density: This method performs an estimation of the air mass based on the ideal gas law. To do this, it uses readings of temperature and air pressure sensors in the vehicle, in addition to using the volumetric efficiency of the engine. This method can be used with the MAP sensor, as it measures the absolute pressure.
* Mass air flow: This method directly uses the Mass Air Flow (MAF) sensor present in the vehicles to obtain the reading of air flow. The value obtained is in the unit mass/time.  
The methods that were used in this work to obtain the amount of air entering the combustion chamber were the speed density and mass air flow methods. By making a brief comparison between both of them, one can see that the speed density method has a greater mathematical complexity than the mass air flow method. That is because it needs a combination of sensors to calculate the desired estimations indirectly, whereas the mass air flow method directly uses the MAF sensor to do the same. On the other hand, the MAF sensor may not be found in all vehicles.  
In order to develop the algorithm needed to calculate the CO\({}_{2}\) estimate, the Python programming language was used with the Jupyter Notebook development environment. This tool combines code compilation, Rich Text Format (RTF), mathematical functions, plotting charts, and maps, among other features that facilitate the development of code and data analysis in the same environment.
###### Abstract  
This study proposes a risk assessment method for ethane tank leakage based on Fault Tree Analysis (FTA) and the Fuzzy Bayesian Network (FBN). It aims to diagnose and probabilistically evaluate system risks in scenarios where leakage data are imprecise and insufficient. Initially, a fault tree for ethane tank leakage risk is constructed using the connectivity of logical gates. Then, through the analysis of minimal cut sets, the fundamental causes of ethane tank leakage risk are identified, including cracking, instability, and corrosion perforation. Subsequently, the fault tree is mapped into a Bayesian network, which is then integrated to transform it into an FTA-FBN risk diagnostic probability model. Prior probabilities of parent nodes and conditional probability tables are obtained through fuzzy mathematics principles and expert guidance. These are combined with Bayesian inference to derive posterior probabilities, thereby determining the contribution of each basic event to the ethane tank leakage risk. By leveraging the advantages of the fuzzy Bayesian network in handling uncertain problems, the model and analysis effectively address the ambiguities encountered in real-world scenarios. In order to better cope with the uncertainty of leakage, the weakest t-norm algorithm and the similarity aggregation method are introduced for the parameter learning of the fuzzy Bayesian network to achieve an accurate solution of the model. Finally, this integrated model is used in a real case to study the causes of ethane storage tank leakage. The research results are of great scientific significance for revealing the evolution mechanism of ethane storage tank leakage accidents and ensuring system safety throughout the life cycle.  
fault tree; fuzzy Bayesian network; ethane tanks; leakage risk diagnosis
## 2 Materials and Methods
### RNA Isolation and RT-PCR Analysis  
Bacteria were grown to OD\({}_{600}\) = 0.1 (t = 0) in LB and treated with SFN doses equal to 1 \(\times\) MIC, \(\frac{1}{4}\times\) MIC and \(1/16\times\) MIC, or SHX (1 mM), mitomycin C (0.5 \(\upmu\)g/mL); for negative control untreated culture was used. Samples for RNA purification were collected 180 min after treatment. Total RNA was extracted in duplicate from two biological replicate cultures of each strain using the RNeasy MINI kit (Qiagen GmbH, Hiliden, Germany) according to the manufacturer's protocol. EvoScript Reverse transcriptase (Roche, Basel, Switzerland) and random primers were used to create cDNA from RNA samples. Quantitative real-time PCR (qRT-PCR) was performed using LightCyclter 480 (Roche Diagnostics AG, Rotkreuz, Switzerland) and the following primers: stk1--CGATGTTACGTTTGTTACTGTGACAGC; stx1R--AATGCACGCTTCCCAGAATTG; recAF--CTGGAAAATCTGACGCCCT; and recAR--ATGCCCTGGCTCCATCATACG. All data were normalized to levels of _recA_, and analyzed using the comparative cycle threshold (CT) method. Target gene expression levels were compared by the relative quantification method 2\({}^{-\Delta\Delta\mathrm{CT}}\)[25].