esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Study on the Waterstop of the Rodin Jet Pile Method in a Water-Rich Sandy Gravel Stratum
## 5 Test Results and Analysis
### Monitoring Data Analysis
#### 5.2.3 Pore Water Pressure Analysis  
The distance between JP1 and JP2 and the measuring hole P1 was 1.5 m. The distance between JP1 and JP2 and the measuring hole P2 was 2.0 m. The absolute values of the initial pore pressure at positions of -10 m and -24 m in the measuring hole P1 were 1.5 kPa and 9.8 kPa, respectively. The absolute values of the initial pore pressure at positions of -21 m and -32 m in the measuring hole P2 were 1.8 kPa and 82.6 kPa, respectively. The data were collected 12 times from each measuring point during the construction of each test pile. The absolute values of the pore pressure at positions  
Figure 10: Curve of the cumulative stratified settlement.  
Figure 9: Horizontal displacement monitoring curves of (**a**) point A, (**b**) point B, and (**c**) point C.  
of -10 m and -24 m in the measuring hole P1 were 0.5 to 2.5 kPa and 9.0 to 10.5 kPa, respectively. The absolute values of the pore pressure at positions of -21 m and -32 m in the measuring hole P2 were 1.6 to 2.2 kPa and 82 to 88.4 kPa, respectively.  
To analyze the influence of slurry pressure on the surrounding stratum, the relative change in pore water pressure was used to analyze the change in pore water pressure during the construction of ultra-high-pressure grouting. Figures 11 and 12 reflect the changes in pore water pressure relative to initial values during construction of JP1, JP2 and JP3. The following results were obtained:  
1. In the construction process of JP1, JP2 and JP3, the relative pore pressure at positions of -10 m and -24 m in the measuring hole P1 was within 1 kPa, and the variation was very small (see Figure 11). It showed that the ultra-high-pressure jet grouting did not form excess pore pressure, and the stratum outside the designed pile diameter range was nearly undisturbed during ultra-high-pressure grouting.
2. During the construction of JP1, JP2, and JP3, the relative pore pressure at a position of -21 m in the measuring hole P2 was small and was the same as that in the measuring hole P1. However, during construction of JP1, the pore water pressure at a position of -32 m changed by 2 to 6 kPa (see Figure 12). It showed that ultra-high-pressure jet grouting caused excess pore water pressure when construction was below the groundwater level.
3. According to the change in
## 1 Introduction  
Locating and tracking of mobile targets is mainly related to observation state variables such as angles, rate of angle changes and gradient of Doppler frequency, most of which are in line with nonlinear functions. In the traditional locating and tracking of DOA, estimation algorithms based on MUSIC, ESPRIT are widely used [1; 2]. However, the MUSIC algorithm needs Eigen decomposition and spectral peak search, rising higher computational complexity and can not satisfy the requirement, particularly when the system needs a high real-time performance [3]. ESPRIT algorithm decomposes the receipt signals into two sub-matrixes to estimate the DOA by using the phase difference between the different signals and sub-matrixes [4]. The disadvantages, including low accuracy and resolution and real-time performance, influence the communication system. To overcome this problem, [PERSON] proposed a means of estimation based on DILL whose delay of synchronizing signal is similar to that of the Least Mean Square (LMS) algorithm [5]. Using the error signal to update the estimating signal by iteration achieves good performance with easier computational complexity. However, the locating and tracking performance is not very good due to the nonlinearity and noise of the system. Therefore, this paper proposes a method to enhance accurate tracking by processing the signal in smoothing on the basis of estimating the signal DOA.  
There are Extended Kalman Filter (EKF) and Modified Gain EKF (MGEKF) algorithms for the processing of nonlinear system filtering [6, 7, 8]. The EKF algorithm carries linear processing of the nonlinear equation by Taylor expansion, and then it makes filter tracking by EKF. This algorithm may lead to linearization error, filtering divergence and degrading performance and is mainly used in the weak nonlinear approximated Gaussian environment. Unscented Kalman Filtering (UKF) chooses the Sigma sampling points set with mean value and same covariance based on the Unscented Transformation (UT) to estimate the posterior probability density distribution of the state vector by the statistical magnitude of sampling points after nonlinear transform. In place of the Page File (PF) random sampling by deterministic sampling, it eliminates the linear error in the EKF linearization [9,10]. Compared with the former two algorithms based on EKF, this algorithm enhances the convergence rate and filter tracking as well as the stability of the system. Therefore, the system can adopt the efficient UKF algorithm to do filter processing and reduce the noise interruption, decreasing the computational complexity and improving the locating accuracy and efficiency to maintain accurate tracking of the mobile targets.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 5 Neural Network (NN)-Based Phase-Field Brittle Fracture
### Data Collection
#### 5.1.2 Input and Output Relationship  
From the sensitivity analysis, it can be observed that the input and output relationships are highly non-linear. Hence, not much can be learned about the input-output relationship  
Figure 14: Scatter plots. Total stress vs. total strain throughout the finite element simulation of the phase-field modeling of brittle fracture. The stress unit is [N/mm\({}^{2}\)].  
from the scatter-plots of outputs and their respective inputs. Therefore, the model is trained using a different number of input features, and the architecture with better performance was chosen to be embedded in the finite element formulations.  
For the prediction of the fracture phase-field \(d\) at the current time-step, which would be the output of the NN model, _strains, stresses, driving force at the current time-step along with the phase-field \(d\) at the previous time step_ are considered the input to the NN model. Note that the stresses at the current time step have a direct dependency on the current fracture phase-field due to the degradation of stored elastic energy. In this contribution, the stresses are degraded with the previous value of \(d\). This results in a small increase in the global error, i.e., the performance of the neural network model; however, it is still in an acceptable range and yields good results. The formulation of the NN-based phase-field model defines a function in the following format:  
\[d_{NN}^{t}=FFNN(\mathbf{\varepsilon}^{t},\mathbf{\sigma}_{NN}^{t},d_{NN}^{t-1},\mathcal{ H}^{t},\mathbf{W},\mathbf{b}), \tag{23}\]  
where \(d_{NN}^{t}\) is the fracture phase-field at the current time-step, \(\mathcal{H}^{t}\) is the current driving force, \(\mathbf{\varepsilon}^{t}\) is the current strain with \(\mathbf{\varepsilon}=[\mathbf{\varepsilon}_{xx},\mathbf{\varepsilon}_{yy},\mathbf{ \varepsilon}_{xy}]\), and \(\mathbf{\sigma}_{NN}^{t}\) is the NN-model trained apriori to approximate the stress as follows:  
\[\mathbf{\sigma}_{NN}=FFNN(\mathbf{\varepsilon},\mathbf{W},\mathbf{b}), \tag{24}\]  
for given strains \(\mathbf{\varepsilon}\), weight matrix \(\mathbf{W}\), and the bias of neural network \(\mathbf{b}\).
# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
## 2 Preliminaries
### Artificial Potential Field (APF)
#### 2.1.1 Attractive Potential  
The attractive potential field is generated for driving the robot to the target location. The general idea is to increase the attractive \(U_{att}(q)\) field as the robot moves away from the goal. Among many mathematical functions, the quadratic potential, Equation (1), is used for this study:  
\[U_{att}(q)=\frac{1}{2}K_{att}\left\|q_{g}-q\right\|^{2} \tag{1}\]  
where \(K_{att}>0\) is used to control the scaling; \(q_{g}=\left[x_{g},y_{g}\right]^{T}\) is the location of the goal; \(q=\left[x,y\right]^{T}\) is the location of the robot; and \(\left\|q_{g}-q\right\|\) is the Euclidean distance between the robot and the goal. The force exerted by the goal is defined as the negative gradient of the attractive potential function [21].  
\[F_{att}(q)=-\
abla U_{att}(q)=-\frac{\partial U_{att}(q)}{\partial q}=-K_{att}(q-q_{g}) \tag{2}\]  
\(F_{att}(q)\) has two components: along the \(x\)-axis and the \(y\)-axis.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
###### Abstract  
Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users' video viewing satisfaction. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.  
U  
A Note: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))  
28 June 2019339431 July 20192 August 201928 June 2019339431 July 20192 August 2019
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 5 Analytical Solution for Linear Multipath Graphs
### LMG Model  
When designing automobile control applications, there are cases where a simpler data flow model is preferred instead of using the complex DAG model. The most common such case is when there are several independent parallel data flows from sensors to actuators. In Figure 6, \(r_{1}\) is the sensor runnable and \(r_{n}\) is the actuator runnable. Between them, there are \(m\) paths where each runnable in the middle part \(\{r_{2},r_{3},\cdots,r_{n-1}\}\) belongs to only one specific path among them. To distinguish such a particular application architecture from general DAGs, we specifically call them the _linear multipath graph_ (LMG) model. Although the LMG model can be applied to a limited range of applications, it is meaningful since there is an increasing need for integrating independent control algorithms to develop integrated control systems or multi-functional ECUs [36; 37; 38]. In such new systems, sensor data propagate through multiple independent paths of runnables to the actuators.
# Design Study of Steel Fibre Reinforced Concrete Shaft Lining for Swelling Ground in Toronto, Canada
## 1 Introduction
In this study, an alternative shaft lining design, using SFRC, has been studied by combining several design codes to consider the possibility of SFRC replacing the original RC in terms of theoretical and geotechnical approaches. There was no design code that fully integrated the use of steel fibres with the design of a segment concrete section [41]. For the use of steel fibres, several performance criteria are presented, including German guideline DVB, RILEM TC 162-TDF recommendations, Italian guidelines CNR-DT 204, Spanish code EHE-08, and FIB model code [42]. The capacities of RC and SFRC lining designs were compared based on the Canadian concrete design codes CSA A23.3, together with RILEM TC 162-TDF in this study [43]. Four loading cases were applied to evaluate the load on the shaft lining. Numerical analysis using Fast Lagrangian Analysis of Continua (FLAC) 2D, which is a continuum finite difference analysis code, was performed to assess the maximum unrestrained convergence of the shaft wall due to the long-term time-dependent deformation (TDD) of the shaley rock prior to lining installation. For this numerical analysis, the \"swello\" TDD constitutive model developed by the author was used to simulate the swelling behaviour of the Georgian Bay shale [44]. In addition, the numerical analysis was carried out to evaluate whether it is appropriate to install the lining 90 days after excavation, as initially recommended by geological studies, and to determine the mobilised lining forces along the shaft wall to estimate long-term stability.
# Microbial Dynamics in Sour-Sweet Wine Vinegar: Impacts on Chemical and Sensory Composition
## 2 Traditional Balsamic Vinegar (TBV) Processing
### Ageing Process  
At least five casks (wooden barrels) are needed to perform the aging process. They are casks of different sizes and woods, as seen in Figure 1, arranged in decreasing scalar volumes. Every year, a small volume of the aged vinegar (the smallest barrel) is spilled. This barrel is then refilled with the contents of the previous barrel. This procedure is repeated up to the first and largest cask, which receives newly oxidized and fermented cooked must [20]. The operation is called \"rincalzo\" (refilling), and it must be repeated for at least 12 years for the vinegar to be defined as a traditional DOP.  
The sugar concentration increases along the barrel set due to the evaporation through the opening on the top of the barrel and the wood. In this procedure, every barrel will contain a blend of vinegar of different ages, and, naturally, the age increases from the bigger barrel to the smaller one. However, it is not easy to define the age of balsamic vinegar. In 2007, [PERSON] and [PERSON] [21] developed a theoretical model that allows predicting the age of traditional balsamic vinegar, considering the volume of vinegar transferred from barrel to barrel and the amount of newly cooked must added, aiming to provide a way  
Figure 2: (**A**) The mother of vinegar is removed from a wine vinegar pot. (**B**) Bacterial layer growing in red wine vinegar. (**C**) Mother of vinegar formation in home-made vinegar.  
to define the age of traditional balsamic vinegar and verify the requirements of the DOP legislation. The referred authors were able to prove that the age of traditional balsamic vinegar is correlated to the amount of vinegar withdrawn and/or to that of the newly cooked must. With their mathematical model, any producer could be able to validate the age of their vinegar, letting the consumer buy the vinegar at the stated age [21].  
At the end of the process, it is possible to obtain a dark-sugary/acid liquid with more or less 40% (\(w/v\)) of sugars (glucose and fructose in a 1:1 ratio) and with a pH of 2.3-3.2 and 2-5% (\(w/v\)) in acetic acid [22].
## 2 Related Work
In the Round-Robin routing, each source node has to elect two or more nodes to route each packet using a per-packet load balancing method. This technique considers that the application should allow the arrival of packets out of order in your destination; however, this technique cannot be feasible for many IoT applications. The weighted Round-Robin routing provides load balance mechanism that assigns a weight to each routing node proportionally to metric values. Although the protocol uses residual energy as routing metric, it does not present solutions for the problem of energy hole. Furthermore, the evaluation of the quality of link used in the experiments does not represent the end-to-end link quality during the process of route selection. Given the requirements, advances and challenges discussed above, it is possible to identify that existing routing schemes lack in providing an integrated QoS, reliability and energy-efficiency on flat-based solutions for WSNs/IoT applications. With this goal in mind, we propose a cross-layer routing mechanism with QoS, multipath support, resilience and energy-aware for WSNs with asymmetric link quality evaluation as expected and required for IoT scenarios.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
###### Abstract
This might affect the useful link and path durations, as well as the overall efficiency of the routing protocol. As a consequence, it is necessary for the routing process to find the best alternative route as soon as the route becomes unavailable due to node mobility. Therefore, building a mathematical model of the protocol can give a quick and easy impression of the performance of the protocol in specific circumstances, but when a deeper analysis of the protocol is required, the need for simulation programs still exists. Existing models cited in the literature studied this problem for the reactive routing category. In this study, we targeted proactive routing and analyzed one of its main protocols, called the DSDV. Thus, we have built a mathematical-based model to analyze the link and path durations of this protocol to find the best approximated values in real scenarios, which in turn can give a good indication of the factors that might affect its performance. Figure 1: Example of a routing mechanism of exchanging messages in the DSDV routing protocol. Besides the introduction section, the remaining parts of the paper are structured as follows: A review of the related work is discussed in Section 2. In Section 3, we thoroughly detail the proposed mathematical model and discuss the main assumptions. The validation analysis of the model along the simulation results are provided in Section 4. Finally, in Section 5, we derive the conclusion and provide some future works.
# An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum
## 3 Method
### Knowledge Modeling and Query Formulation  
In this step, knowledge from domain experts was acquired through a series of intensive discussions and become the base of our ontology development. There are two groups of ontology development models that are widely used, namely waterfall and iterative-increment life-cycle models [40, 41]. Multiple prominent ontology engineering methodologies come under the latter group, including Methontology [42] and NeOn [40]. For example, Methontology has been successfully used to develop ontologies in the legal domain [43], while NeOn methodology in the human resources domain [44] and healthcare domain [41]. A combination of both methodologies was also possible, for example, integrating methods and models of assessing the quality of Internet services [45].  
To build our ontology, we adopted the ontology engineering methodology presented by [PERSON] and [PERSON] [46]. It belongs to the waterfall model group, where the stages of the ontology engineering process were performed sequentially [41]. The methodology was adopted because it provides flexibility to the ontology developers in our team to lead and drive discussions with domain experts. We performed the following stages:  
1. Identification of the purpose and scope of the ontology. As mentioned in Section 1, we share knowledge about the characteristics of Capsicum with non-expert users. Therefore, we expected that the ontology should cover characteristics of Capsicum identifiable by this group of users.  
Figure 1: Our research procedures and its evaluation.  
Building the ontology, which covers the ontology capture, coding, and integration with existing ontologies. We identified entities, properties, and data types, including how entities are related to each other. After that, we represented the identified objects using the Resource Description Framework (RDF) [47] and Ontology Web Language (OWL) [48]. For coding the ontology, we actively used the Protege ontology editor [49]. For integration with existing ontologies, we adopted the terms from the Plant Ontology [8].
3. Evaluation. We evaluated the ontology by using competence questions [46] to carry out reasoning with different characteristics of Capsicum. This evaluation ensures that a list of correct entities can be obtained when a common characteristic is provided. To order the obtained entities as facets, we use a ranking mechanism explained in Section 3.5.
4. Documentation. We generated the documentation of our ontology by using the WIDOCO tool [50]. It generated human-readable descriptions of terms and summaries with integration with other external information.  
In the query formulation step, a list of anticipated questions was formulated. We consider entities and properties from each question. To be precise, from each obtained question, we extracted entities, properties, and values from every property. As a result, a query list, represented as tuples (entity, property, and values), is obtained.
## 2 Methodology  
### Network Architecture  
The single-task network used in this paper is a FCRN from [PERSON] et al. [23], and the multi-task network is a hard parameter sharing network developed from the single-task FCRN. The multi-task network structure is shown in Figure 1. In order to better capture the low-frequency characteristics of seismic data, the first convolution layer of FCRN has 16 kernels of size \(299\times 1\). After the first convolution layer, three residual blocks are stacked, and each residual block is composed of two convolution layers. The first layer and the second layer, respectively, have 16 convolution kernels of size \(299\times 1\) and \(3\times 1\). To enable the network to complete multi-task inversion, two output channels are set after the residual block. Each output channel contains two one-dimensional convolution layers, and each one-dimensional convolution layer has a kernel of \(3\times 1\). The convolution step size is 1, and zero-padding is used to all convolution layers to ensure the same input and output sizes. Rectified linear unit (ReLU) and batch normalization (BN) are introduced into the network to accelerate network training and convergence.
## 7 Recommended Framework
### Optimising Communication Protocols  
Efficient communication protocols are fundamental for reducing energy consumption in IoT networks. Two critical protocols, Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT), play a central role in IoT data exchange. Challenges, such as high latency and inefficient bandwidth usage, arise when protocols like CoAP and MQTT are not optimised for energy efficiency. By modifying these protocols to better align with energy efficiency requirements, IoT networks can achieve significant improvements. For example, implementing a centralised resource model in CoAP can minimise the frequency of server updates, thereby conserving energy by reducing redundant transmissions. Similarly, fine-tuning MQTT configurations, such as adjusting Quality of Service (QoS) levels and employing message queuing, can reduce network load and lower power consumption.