esa-sceva/satcom-chunk-collection huggingface dataset:
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Diagnostics Techniques  
Different inspection techniques for _non destructive testing_ (NDT) of rails are available and currently in use. These can be divided in the following classes [5]: (a) _eddy current_ (_EC_)-_based methods_; (b) _ultrasonic techniques_; (c) _visual inspection techniques_; (d) _thermal techniques_; (e) _radiographic techniques_. These techniques require that some instrumentation is installed on a vehicle (i.e., a train or a special vehicle) to acquire the required measurements while moving on tracks.
## 5 Efficiency Analysis
### System Scalability and Management  
The modular design of the HMBAC architecture allows for scalable and efficient system management. Adding or removing users in Janus is handled independently by each organization (stakeholder). Each organization is able to issue attribute certificates and give access to the corresponding attribute keys to allow its users to: (i) post queries that will be accepted by the ACSC, based on the user's roles; and (ii) fully decrypt a response that has been partially decrypted by the KSSC. Adding/removing stakeholders within an organization, or changing the access policy of the domain, is handled at the domain level. Due to the use of independent DBCs per domain, managing functions within a domain will not cascade to affect the other domains. The use of the voting mechanism enables setting up elections at a domain level and in addition to define a majority threshold at the domain level for decisions affecting a particular DBC. Finally, adding new DBCs will require a majority vote by all stakeholders and will affect all domains, as this will require updating the smart contracts in the PBC.
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 3 Results
### Measurement Results for Surfaces with Different Textures  
The results obtained for the standard conditions provided a reference for further measurements. Standard conditions refer to imaging with DL illumination. A 7 \(\times\) 7 window size was used in the calculations for the variable kernel size operators. The contrast was ensured by adjusting the sensor exposure time to the maximum value without pixel saturation.  
Figure 9 shows the results obtained. Separate plots have been prepared for the two focus plane selection methods. The bar chart shows the AFE value. The colours of the bars correspond to the measurements made for the different surface types.  
The results for the maximum method show three operators with very high error (above 100): DRB, DRBT, and ICNT. These results significantly deviate from the other operators. The remaining operators have AFEs below a value of 3.  
The results for the CoG method differ from those presented for the maximum method. All values are in the range of up to 30. Only six operators have AFE results less than 3: DSL, DSLT, DPT, DPTT, LLPT, SSTD, and SVAR.  
In both cases, the values obtained for the operator are comparable for each surface type. Slightly larger errors were obtained for the wooden surface.  
The obtained RMES results are generally consistent with the AFE results (Figure 10). For the maximum method, some operators have values above 50. The best results--RMSE values below 6--are obtained for six operators: DSLT, DPTT, SETP, SSTD, SVAR, and SVRN.  
All results for the CoG method are in the range of up to 30. Results below 6 are obtained for the DSLT, DPTT, SSTD, and SVAR operators.  
A general relationship between the materials tested is visible. The lowest RMSE values are achieved for the metal surface and the highest for the wood surface.  
Applying the maximum method for focus plane selection returns acceptable values for six operators: DSLT, DPTT, SETP, SSTD, SVAR, and SVRN. However, using the CoG method, comparable results are obtained for four operators: DSLT, DPTT, SSTD, and SVAR. Due to the achieving of unacceptable results for most of the considered focus estimation operators, the remaining results presented in this chapter are narrowed down to six operators: DSLT, DPTT, SETP, SSTD, SVAR, and SVRN. This increases the readability of the results by excluding operators that did not already provide the required robustness for the standard conditions.  
Figure 9: AFE value as a function of the operator used for both focus plane selection methods. The results are grouped for the three considered surfaces.
## 3 Reinforcement Corrosion Monitoring Techniques
### Corrosion Assessment with Electrochemical Sensors
#### 3.1.3 Polarization Resistance (\(R_{p}\))
[PERSON] concluded that, the \(R_{p}\) measurements provided information that allows assessment of the corrosion process in RCS. However, the author also reported a wide dispersion in the \(R_{p}\) results, which makes its interpretation very challenging.
## 4 Experiments and Results
### Training Parameters and Time  
Table 6 presents quantitative measurements, in terms of parameters and time, for the _Hydra_, the state-of-the-art VDSR, and the traditional baseline approach for visual task analysis. We trained all models using a Windows system with the following configuration: (1) Intel Xeon CPU E3-1275 v6 3.80 GHz processor and (2) NVIDIA GeForce GTX 1050 Ti. Keras DL framework with Tensorflow was used for model training and evaluation.  
As shown in Table 6, the training parameters and time for the state-of-the-art VDSR are higher than the proposed _Hydra_ trunk. Similarly, the computation time, per image, of VDSR is higher than _Hydra_ trunk. In addition to the SISR models, Table 6 presents the training time and parameters as well as the computation time (per image) for _Hydra_ heads and the traditional baseline approach. As can be seen in Table 6, the training parameters and time for _Hydra_ classification head are notably lower than the baseline model. In case of segmentation, both _Hydra_ head and the baseline segmentation model have the same number of training parameters. However, the training time of _Hydra_ segmentation head, which was initialized using the trunk weights, is much lower than the baseline model. This suggests that using the weights of the _Hydra_ trunk leads to faster convergence. Finally, it is important to note that although the total training time for _Hydra_ (trunk and heads) is higher than the baseline approach, the majority of _Hydra_ time is coming from the trunk (SISR model). However, this trunk is trained only once and can be used to simultaneously solve N visual tasks in medical images.  
In addition to the traditional baseline models, we believe _Hydra_ has significantly lower computational time and parameters as compared to existing methods in the literature. Specifically, given that (1) the number of parameters for most SISR models in the literature ranges from 57K to 43M [27] and (2) the number of parameters for the standalone state-of-the-art classification models ranges from 7M to 146M [42], we believe the proposed _Hydra_ significantly decreases the training time and parameters for super-resolution image enhancement and visual task analysis in medical images. Besides being able to significantly reduce training and speed-up computations, similar to [43]_Hydra_ can tackle the issue of the high dependence on a relatively large corpus of labelled data as follows. The _Hydra_ trunk can be created using an unsupervised learning (no manual labeling) or residual learning (SISR), and then used as a shared backbone to simultaneously learn task-specific layers with relatively small numbers of training parameters; i.e., freezing the weights of the trunk and learning shallow task-specific layers can lead to enhanced performance in visual task analysis, especially for tasks with limited amounts of labeled data.  
Our experimental results are promising and prove the efficiency and superiority of the proposed approach for efficient medical image analysis.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 2 WSN Management
### Energy Management  
Energy management through software has been proposed [35] which minimises energy consumption of each sensor node by introducing a duty cycle to periodically turn on and then send the radio portion of a node to sleep. Implementing energy management schemes and energy efficient protocols maximizes the network lifetime [36]. Power management protocols providing the WSN with control over energy consumption resulting in energy efficiency have been presented severally including SenOS, Appsleep and energy level management [31]. Intelligent energy management through a prediction based dynamic method has also been proposed [37]. This technique uses a particle filter that estimates non-Gaussian processes and non-linear processes to obtain information about a target state required for optimized node sleep and sensing scheduling. This results in more energy efficiency for extended network lifetime, in addition further optimization can be performed using distributed node computing.  
Similar contributions to energy management based on software duty cycling have been made by others [38; 39], protocols such as Sparse Topology and Energy Management (STEM) [40] and SPAN [41; 42] enable sensor nodes to go to sleep when not forwarding data. To support such energy management techniques a WSN also requires a working network management scheme in place. [PERSON] et al. [35] mention the need for node synchronisation when using the duty cycle method to manage energy. Works on network synchronisation have facilitated the need for node synchronisation [35]. Other energy management schemes such as energy provisioning using batteries and energy harvesting have been been presented in the past years [43]. Algorithms are being developed to further enhance the energy harvest method resulting in network self-sustainability and one such algorithm is the Energy Aware Adaptive Sampling Algorithm (EASA) proposed by [PERSON] et al. [44]. This energy management technique is suitable for power hungry nodes with the capacity to harvest energy and given the required power to process the algorithm, it can be implemented on any WSN.
## 2 Background
### Challenges and Limitations of Existing Metrics  
The process of developing a successful performance prediction model is a very long and tedious task. It takes the analysis of a large number of factors from a broad stratum of projects. The data requirements are immense. Additionally, the project values, life-cycle, location, etc., are the variables that need to be accounted for. The time taken to develop the program, identify potential participants, introduce the concept, obtain feedback, revise parameters, and re-evaluate can be extremely long.  
Furthermore, it is a challenge to convince firms to provide data for on-going projects and data for any changes that may be observed after the suggested actions. Once a benchmark has been developed, it becomes a significant strategic asset. CII took almost eight years to derive a functional benchmarking model with a considerable number of projects to make meaningful assessments at the project level [40]. Despite the awareness and importance-performance measurement data, companies, or knowledge bodies have not been able to establish data banks [32]. Existing empirical studies only focus on a few factors and attempt to establish a relationship. These factors can be internal--that is, within the organization--and external, related to market conditions. Previous studies have focused on individual or combination of a few factors. Due to the limited scope, the whole system was not evaluated, thus making it necessary to understand and identify the underlying relationship between the factors amongst categories and across different categories that impact an organization's performance.  
The data from companies vary as the companies that execute small projects only form a fraction of the total industry turnover. Only large organizations can afford to execute projects with a minimal profit margin, as they have in house capabilities, established processes, established lines of credit, and qualify for large projects. However, it is essential to note that small organizations barely manage to stay afloat. It will thus be necessary to account for the organization size while developing a prediction model. Alternatively, different prediction models need to be developed depending on the organization size, specialty contractors, and types of contracts undertaken--for example, Engineering Procurement and Construction (EPC), Construction Management at Risk (CMR), Architectural and Design firms, General Contractor.
## 1 Introduction
The authors found that the drag encountered by the lorry model that had the linear AC-DBD plasma actuation being implemented at the trailer rear end was 5.1% higher than that encountered by the baseline trailer [29]. In contrast, 14.8% of drag reduction was achieved when the continuous serpentine AC-DBD plasma actuation was implemented at the rear end of the same trailer model. Since the authors in [29] did not investigate the flow physics downstream of the trailer rear end in both cases, the reasons causing the drag increase when implementing the linear AC-DBD plasma actuation at the rear end of a square-back trailer is unclear. The objective of the present experimental study is to investigate the flow physics along the wake region of a 1:20 scale square-back articulated lorry model, and in particular, to examine the alterations in wake flow field affected by the implementation of AC-DBD plasma actuator at the end of the trailer. Results obtained could provide an insight into the flow pattern along the wake region of such a lorry trailer with and without the linear AC-DBD plasma actuation being implemented. The data shown could also be used for validating Computational Fluid Dynamics (CFD) codes.
## 3 Results and Analysis
### Influence of TIL and Airflow Parameters on Temperature and Thermal Stress Evolution
#### 3.3.2 Influence of TIL Thermal Conductivity  
Figure 9 presents the variations in the temperature and thermal stress with the changed TIL thermal conductivity (\(\lambda_{i}\)). Increasing TIL thermal conductivity decreases the rock temperature and increases the associated thermal stress. The rock temperature \(T_{r}\) of rock surface presents a decrease from 166.99 \({}^{\circ}\)C to 114.41 \({}^{\circ}\)C as the TIL thermal conductivity \(\lambda_{i}\) increases from 0.05 W/(m-K) to 0.20 W/(m-K), whereas the associated thermal stress \(\sigma_{T}\) displays an increase from \(-\)14.78 MPa to \(-\)37.41 MPa. However, the insulation capacity enhancement via reducing per thermal conductivity gradually weakens, consistent with that of TIL thickness. The temperature growth, induced by decreasing the thermal conductivity \(\lambda_{i}\) from 0.20 W/(m-K) to 0.10 W/(m-K), is 29.52 \({}^{\circ}\)C, which reduces to 23.06 \({}^{\circ}\)C as the thermal conductivity \(\lambda_{i}\) decreases from 0.10 W/(m-K) to 0.05 W/(m-K). Similarly, the thermal stress \(\sigma_{T}\) exhibits a decrease of 12.73 MPa when the thermal conductivity \(\lambda_{i}\) reduced from 0.20 W/(m-K) to 0.10 W/(m-K), which decreases to 9.90 MPa when \(\lambda_{i}\) decreased from 0.10 W/(m-K) to 0.05 W/(m-K). Moreover, it is impossible to reduce the TIL thermal conductivity indefinitely limited to the technical. However, the insulation cost of high-temperature tunnels increases exponentially with thickening TIL and decreasing TIL thermal conductivity [43; 44]. Therefore, finding how to balance the TIL cost and insulation effect is the most crucial issue to address in high-temperature tunnel engineering [44].
## 3 Experimental Results and Analysis
### Time Complexity Analysis  
Let the population size of SSA and ISSA be \(N\), the number of iterations be \(T_{\max}\), and the problem dimension be \(D\). From Section 2, two algorithms differ only in the way they update different species of sparrows, and they have the same algorithmic complexity in the steps of population initialization, boundary check, position update, and sort update, all of which be \(o(T_{\max}\cdot N\cdot D)\). Assuming that the proportions of producers, scroungers, and investigators in the sparrow population are \(r_{1}\), \(r_{2}\), and \(r_{3}\), respectively, then in SSA, the time complexity of the location update of producers is \(o(T_{\max}\cdot r_{1}\cdot N\cdot D)\), the time complexity of the location update of scroungers is \(o(T_{\max}\cdot r_{2}\cdot N\cdot D)\), and the location update of investigators is \(o(T_{\max}\cdot N\cdot D)\). The total time complexity of SSA is \(o(T_{\max}\cdot N\cdot D)\).  
In ISSA, the time complexity of the position update of the producer after introducing the golden sine strategy is \(o(T_{\max}\cdot r_{1}\cdot N^{2})\), and the position update of the investigators after introducing the particle swarm optimal idea is still \(o(T_{\max}\cdot r_{3}\cdot N)\). In addition, the time complexity added by introducing Gaussian perturbation to the optimal sparrow individuals is \(o(T_{\max}\cdot D)\). The total time complexity of ISSA is \(o(T_{\max}\cdot N\cdot D\cdot(1+r_{1}\cdot N))\).  
In summary, the time complexity of ISSA is slightly higher than that of SSA for the same number of iterations. However, the convergence speed of ISSA is fast, and the time consumption of ISSA may be reduced if no more changes in the search results are taken as the end condition. ISSA is more suitable for engineering applications that require high search accuracy and have lower requirements for computation time.
## 2 Train SINS/GNSS Integrated Navigation System Model  
In this study, the \"east-north-up\" (ENU) geographic coordinate system is selected as the navigation reference coordinate system, denoted as \(n\) The computed navigation reference coordinate system based on navigation solutions is denoted as \(n^{\prime}\). Additionally, the \"right-front-up\" (RFU) coordinate system is chosen as the carrier coordinate system with the inertial measurement unit (IMU) as the origin, denoted as the coordinate system. The e coordinate system is defined as a coordinate system in the equatorial plane, where x intersects the Greenwich meridian and z coincides with the polar axis North. To leverage the constraint that the train moves on the track and simplify the problem while capturing key elements, we assume that the train's motion is constrained to a two-dimensional plane. The motion of the train, either in straight lines or curves, is illustrated in Figure 1. During turning, there is a sideslip angle during turning, which is the angle between the direction of train velocity and the extension of the train carriage's central axis, as shown in Figure 2.  
Figure 1: Train’s straight-line motion and coordinate system definition. (black line: rail; red line: bogie axle; blue dot: blue dot: center point of train carriage).
## 2 Direct and Inverse Problems
### Direct Problem  
For simplicity, in demonstrating the estimation methodology, a two-dimensional equation is assumed:  
\[\frac{\partial C}{\partial t}=a\frac{\partial^{2}C}{\partial x^{2}}+b\frac{ \partial C}{\partial x}+c\frac{\partial^{2}C}{\partial y^{2}}+d\frac{ \partial C}{\partial y}-eC+f(t)\delta(x-x_{0},y-y_{0}),\ \ (x,y)\in\mathbb{R}^{2},\ \ t>0 \tag{1}\]  
with initial conditions  
\[C(x,y,0)=C_{0}(x,y),\ \ (x,y)\in\mathbb{R}^{2}. \tag{2}\]  
Here, the unknown \(C(x,y,t)\) is the concentration. The time-varying concentration source \(f(t)\) is located at \((x_{0},y_{0})\in\mathbb{R}^{2}\). For ease of presentation, we will assume constant coefficients in (1) such that \(a=\text{const}>0\), \(c=\text{const}>0\).  
By using  
\[C(x,y,t)=\exp(\beta t+\mu x+\eta y)u(x,y,t), \tag{3}\]  
where  
\[\mu=-\frac{b}{2a},\ \ \eta=-\frac{d}{2c},\ \ \beta=-e-\frac{1}{4a}(b^{2}+d^{2}),\]  
then the problem (1), (2) is transformed into  
\[\begin{split}\mathcal{L}u&=F(x,y,t)=\exp(-\beta t -\mu x-\eta y)f(t)\delta(x-x_{0},y-y_{0}),\\ \mathcal{L}u&\equiv\frac{\partial u}{\partial t}-a \frac{\partial^{2}u}{\partial x^{2}}-c\frac{\partial^{2}u}{\partial y^{2}}, \ \ (x,y)\in\mathbb{R}^{2},\ \ t>0,\end{split} \tag{4}\]  
where  
\[u(x,y,0)=u_{0}(x,y)=\exp(-\mu x-\eta y)C_{0}(x,y). \tag{5}\]
## 5 Field Application and Monitoring  
### Support Scheme  
In collaboration with the high-strength double anchor cable truss support system on the top wall and the high preload, as depicted in Figure 18, I will assess the new support scheme and its specific requirements. Furthermore, I will analyze the deformation characteristics of the surrounding rock in the 1409 airway.  
(1)be 8 m deep. The anchoring length will be 2250 mm. The bottom span of the anchor cable system will be 2.1 m, and the angle between the anchor cable drilling and the vertical line will be 20 degrees. The preload of the anchor cable should not be less than 200 kN. The anchor cable orifice will be located 1050 mm from the coal wall.  
(2) Roadway support  
The road is supported by a combination of bolts, metal mesh, and steel belts.  
1 Bolt support  
The anchor bolts used are medium-speed resin cartridge anchorages with a diameter of 23 mm and a length of 550 mm. The borehole diameter is 28 mm, and the anchoring length is 1650 mm. The side anchor bolt is tightened with a pre-tightening moment of at least 300 N-m. The anchor bolts used are high-strength anchor bolts with a diameter of 20 mm, made of silicon manganese steel, with left-handed threads.  
There are four anchor bolts in each row, with an 800 mm gap between each row. The distance between the anchor bolts is also 800 mm. The top-side anchor bolt has an included angle of 20\({}^{\circ}\) with the horizontal plane. The two center anchor bolts are positioned horizontally, while the bottom anchor bolt has an included angle of \(-10^{\circ}\) with the horizontal plane.  
2 Channel steel connection anchor cable support  
For the anchor cable, a diameter of 28 mm and a length of 5.3 m are used. The anchor cable is inserted to a depth of 5.0 m with an anchorage length of 2250 mm. Alternatively, a diameter of 21.8 mm can be used with 19 strands of high-strength and high-elongation prestressed steel.  
The channel steel used to link each row of two single anchor cables in the side anchor cables has a row spacing of 1600 mm. The upper anchor cable hole has an included angle of 20\({}^{\circ}\) with the horizontal line and is located 1.0 m from the top plate. The two anchor cable holes are separated by 1.2 m. The lower anchor cable's drilling forms a \(-10^{\circ}\) angle with the horizontal line, and the hole opening for the lower anchor cable is located 0.6 m from the bottom plate.