esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Result Discussion
### Analysis Strategy  
The first strategy of analysing the results of the study is to compare it with existing game models, which are Nash equilibrium, Bayesian Nash Equilibrium, and Stackelberg Game. The main justification behind the selection of these three existing game models mentioned above is as follows:  
* Adoption of Nash equilibrium assists in accurately detecting the best payoff for a player based on the decision of the player and other players. Hence, when it comes to estimating global trust in ad hoc environments, Nash equilibrium offers an unbiased estimation of all the individual trust scores that can be applied for the cause of both routing and security in large IoT scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \# & **Parameters** & **Values** \\ \hline
1 & Number of Nodes & 500-1000 \\
2 & Node deployment & Random \\
3 & Initialised Energy & 10 J \\
4 & Iteration & 1000 \\
5 & Sensing Range of Node & 10 m \\
6 & Sensing range of IoT access point & 100 m \\
7 & Dimension of one unit of IoT Cluster & 100 \(\times\) 100 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Simulation environment of the proposed scheme: (**a**) IoT cluster formation and routing initiation; (**b**) Inter-cluster data forwarding.  
* Adoption of Bayesian Nash Equilibrium depends on retaining a belief in target players aligned with the adopted strategies of remnant players. This is a better option than Nash equilibrium as the latter does not offer the players' belief score specifications. Eventually, it means that Bayesian Nash Equilibrium leads the nodes to converge into perfect strategically formulated beliefs with better consistency when exposed to vulnerable conditions. Hence, any deviation in trust score and belief will directly indicate the presence of malicious/misbehaved nodes.
* Stackelberg game is the most frequently implemented game model compared to other game models. According to this game mode, a sequential decision is prioritised over a simultaneous one. This model offers better decision making to the second prover, who can adopt a strategy based on action undertaken by the first prover. Apart from this, the best part of this model is the optimal acquisition of operational cost by the follower node. This eventually implies better decision making with higher clarity by the regular node, in order to perform secure routing upon detecting unknown/uncertainly behaved nodes in large-scale dynamic networks.  
The secondary strategy for analysis is to incorporate 5-10% of total nodes as misbehaving nodes whose identities are not predefined or shared with adjacent nodes in each IoT cluster. This setting allows deploying an unknown attacker node in the simulation area, which must be identified and isolated from active routing operations. The tertiary strategy of the proposed scheme is to carry out the analysis using standard performance metrics of identification accuracy, energy consumption, cost, and response time. The rationale behind adopting these metrics is stated in the following subsection.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Dynamic Data Filtering
#### 2.7.2 Histogram-Based Data-Density  
The first method to calculate the data-density is based on binning the normalised data in a 2D histogram. A suitable bin width for \(u_{r}\)' and \(\alpha^{\prime}\) is given by [PERSON] [22] as  
\[h_{n_{u_{r}}}=\frac{3.49\cdot\sigma_{u_{r}}{}^{T}}{\sqrt[3]{n^{T}}} \tag{9}\]  
and  
\[h_{n_{a}}=\frac{3.49\cdot\sigma_{\alpha^{\prime}}{}^{T}}{\sqrt[3]{n^{T}}} \tag{10}\]  
where \(\sigma_{u_{r}}{}^{T}\) is the standard deviation of \(u_{r}{}^{T}\), respectively \(\sigma_{\alpha^{\prime}}{}^{T}\) is the standard deviation of \(\alpha^{\prime}{}^{T}\), and \(n^{T}\) is the amount of data points for time interval \(\Delta T\).  
[PERSON] assumes that the corresponding variable has to be normally distributed to use this parametrisation. Although it has not been proven conclusively that the wind speed is normally distributed, [PERSON] et al. [23] have shown a great consistency of this theory for 10 min time intervals.  
Instead of normalising the amount of data within a bin with the total number of data points, we normalise with the maximum bin count. Thereby the data distribution dynamically refers to the measurement and requires no absolute values.  
The determination of validity is based on a correlation of data in the normalised reference frame \(u_{r}\)-\(\alpha^{\prime}\). Calculating the contours for different densities, iso-lines form almost concentric circular shapes (Figure 4). Measurement points within the final contour will be marked as valid. To find the final contour that represents the separation line of valid and invalid data, we define an upper and lower threshold:  
Figure 5: Behaviour of parametrisation of fitted bi-variate Gaussian distribution of data density in relation to the different normalisation time intervals \(\Delta t\). The \(\alpha\)-axis fitted standard deviation is shown in turquoise, \(u_{r}\)-axis fitted standard deviation in dark blue, the maximum probability of occurrence in green and the residual of the original and the fitted data distribution.  
* The lower threshold value represents the lower percentage limit from which iso-lines will be calculated.
* The upper threshold can be seen as the reference shape that is based on the contour shape of the corresponding percentage density value.  
By empirical testing, we found a correlation to determine the separation line. The easiest reproducible condition with the least computationally effort is presented in the following:  
If the centre of a contour shape within the \(u_{r}\)-\(\alpha^{\prime}\) reference frame lies within the contour of the referenced shape corresponding to the upper threshold, all data points within this shape are marked as valid.
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 3 Leveraging Software Engineering for Sustainable Smart Cities with Electric Vehicles
### Integrating EV Charging Infrastructure with Smart Grids  
Integrating electric vehicle (EV) charging infrastructure with smart electrical grids is essential for building smart cities [19; 32]. Software engineers can develop systems that enable EV charging stations to communicate with smart grids, optimizing energy distribution and consumption while avoiding overloading the grid.  
Intelligent integration is achieved through a network of sensors, smart meters, and software platforms that analyze energy usage data in real-time [65]. Software systems can dynamically control EV charging rates or schedule charging sessions to minimize peak loads. These techniques optimize energy consumption, reduce waste, and save costs.  
Software engineers must overcome technical and design challenges by developing robust communication protocols and interoperability standards [66]. Incorporating advanced analytics, machine learning algorithms, and prediction models into software platforms enables intelligent and autonomous coordination between EVs and smart grids. Achieving optimal integration necessitates a holistic and systematic software engineering approach. Consequently, software engineering plays a pivotal role in developing sustainable and efficient transportation and energy solutions for smart cities.
## 3 Experimental Methodology
### Shock and Swept-Frequency Experiments of Metal Lattice-Polyurea Parallel Composite Structure
#### 3.2.2 Experimental Scheme  
Firstly, a shock test was conducted on the vibration isolation system comprising two metal lattice structures and the load mass m. The shock signal used was a semi-sinusoidal wave with a duration of 6 ms and a peak value of 1g, expressed as \(a=9.8\sin(\pi t/0.006)[\varepsilon(t)-\varepsilon(t-0.006)]\) m/s\({}^{2}\), where \(\varepsilon(t)\) is the unit step function. By analyzing the shock response curve, the stiffness and damping coefficients of the metal lattice structure under shock were determined. Through spectral analysis of the system load's response signal, the system's natural frequency was found to be 73.5 Hz. Consequently, the swept-frequency range for the subsequent swept-frequency experiment was set from 20 Hz to 100 Hz, and the linear sweep frequency method was adopted with a sweep rate of 10 Hz/min. To prevent structural damage due to excessive acceleration during resonance, a sinusoidal wave with a peak value of 0.05 g was used as the excitation signal in the swept-frequency test. Finally, the shock resistance and vibration isolation performance of the metal lattice-polyurea parallel composite vibration isolation system were tested through shock and vibration swept-frequency experiments, respectively.  
Figure 6: BCC–B metal lattice structure model and sample: (a) 3D model; (b) sample.
## 2 Materials and Methods
### Anonymization and Information Loss Estimation
#### 2.2.2 L-Diversity, T-Closeness  
Another concept related to data privacy is sensitive attributes, the attributes that patients are not willing to be associated with, such as diagnosis codes [9]. The privacy provided by K-anonymity could be considered insufficient in some cases since potentially it can allow the disclosure of sensitive attributes that lack diversity through the use of background knowledge. Nevertheless, k-anonymity is the primary algorithm proposed for anonymization and is used as a baseline process [31; 32; 33; 34]. L-diversity and T-closeness are the most prevalent anonymization concepts that extend k-anonymity by considering sensitive attributes, the attributes that patients are not willing to be associated with. L-diversity focuses on the representation of sensitive attribute values in the anonymized dataset, requiring that each equivalence class have at least one value for each sensitive attribute [1; 9; 34]. T-closeness focuses on limiting the distance between the probability distribution of the sensitive attribute values in an anonymized group and that of sensitive attribute values in the entire dataset, requiring that the distance between the two distributions be no greater than a threshold \(t\)[9; 33].
# Parameterized Optimization in Uncertain Graphs--A Survey and Some Results
## 2 Distribution Models for Uncertain Graphs
### Random Failure Model  
Random Failure (RF) model is the most natural distribution model. In this sampling procedure, an edge \(e\in E\) is selected with probability \(p(e)\) independent of the outcome of every other edge in \(E\). Thus, each edge subgraph of \(G\) is a possible world and for an edge subgraph \(H\), the probability that \(H\) is the outcome of the sampling procedure is denoted by \(P(H)\) which is given by the equation \(P(H)=\prod_{e\in E_{H}}p(e)\prod_{e\in E\setminus E_{H}}(1-p(e))\).
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 2 The Methodology of the Work
### Theoretical Background
#### 2.1.3 Derivation of the Equations for Cutting Parameters Prediction  
The physical nature of the derivation is analogous to the beam curvature parameters (Figure 1a) at the place of the highest load, and the angle of rotation of the cross-section on the supports are among the important ones. At a certain depth of cut \(a_{p0}\), i.e., on the neutral plane (Figure 1b), similar to the beam, the tensile and compressive stresses are equalized. The significance of the used analogy to the beam shows that the so-called neutral depth of cut \(a_{p0}\) in terms of stress-strain state under the blade divides the deformation areas into elastic for \(a_{p}<a_{p0}\) and plastic \(a_{p}>a_{p0}\) with all mechanical consequences. This is confirmed particularly strongly for technologies with significantly greater depths of cut \(h>a_{p}\). These include drilling and machining with flexible tools such as abrasive waterjet, laser, plasma or ultrasound. With these machining technologies, the transition over the neutral plane \(h_{0}\) or \(a_{p0}\) is visible on the cuts to the naked eye. As a function of the roughness parameter, their waveform values \(Y(i)\), \(L(i)\) are linearly dependent. The design model methodology allows them to be tracked along the length of the beam and throughout the load process in connection with the spatio-temporal development of the distribution of the parameters of all the others, in the process of reshaping the involved functions, as shown in more detail below. Complex derivation of Equations (2)-(37) for machining is done in our patented works [24; 26] and publications [21; 22; 23].
## 5 Discussion
### Prospects for Engineering Applications  
As analyzed in the previous text, during the mining process of the working face, the plastic zone of the surrounding rock in the dynamic pressure roadway at the floor exhibits a \"butterfly-shaped\" development feature, and the development form has significant correlations with the vertical and horizontal distances. Undoubtedly, this brings numerous difficulties and unstable factors to the roadway support design. The proposition of this theory can offer the following guiding significance for practical engineering applications:  
1. Judgement of the maximum failure direction of roadway surrounding rock: through the theoretical deduction in this paper, the included angle between the maximum failure depth of the roadway surrounding rock and the horizontal axis under different mining progress has been obtained. This theory can offer a theoretical foundation for predicting the potential roof caving area of the roadway surrounding rock and is conducive to identifying potential dangerous areas in the roadway, taking preventive measures in advance, and avoiding the occurrence of disaster accidents;
2. Design and enhancement of roadway support schemes: through comprehending the butterfly-shaped failure mechanism, engineers can predict the failure mode and location of the roadway surrounding rock with greater precision, infer the failure areas of support elements (such as anchor bolts, anchor cables, etc.), and thereby devise more effective support schemes. Moreover, differentiated support approaches can be employed for distinct regions (such as the butterfly leaf zone and non-butterfly leaf zone) to enhance the targeting and efficacy of the support and reduce roadway deformation and failure resulting from improper support. The stage-by-stage instability of the anchor bolts in the \"butterfly\" plastic zone of the roadway is as shown in Figure 21;  
Figure 20: The determination of the reasonable spacing of the auxiliary retracement channel [35].  
3. Reduction in engineering costs: accurate failure prediction can minimize unnecessary support reinforcement and prevent over-design, thereby lowering engineering costs. Meanwhile, through the optimization of the support design and construction processes, material waste and labor costs can be decreased, enhancing economic benefits.  
In the figure, when the development form of the plastic zone of the surrounding rock of the roadway is \"circular\", it is in the \"uninfluenced stage\"; when the development form of the plastic zone of the roadway is \"rectangular\", it is in the first stage; when the development form of the plastic zone of the roadway is \"butterfly-shaped\", it is in the second stage.
# Requirements and Hardware Limitations of High-Frame-Rate 3-D Ultrasound Imaging Systems
## 2 Materials and Methods
### Case Study: ULA-OP 256 towards 3-D High Frame Rate Imaging
#### 2.3.2 Identification of Bottlenecks  
The beamformer performance strongly depends on the efficiency with which the FE FPGA resources (362,000 logic elements, 1726 memory blocks on each FPGA) are exploited [40]. The FPGA firmware has been accurately optimized and the overall DAS beamformer output bandwidth (BBF) can currently sustain up to \(\approx\)630 MSPS with 32-bit resolution. Such a rate, although sufficiently high to beamform relatively small images at FR up to 1500 Hz [40] may represent a bottleneck in applications in which higher frame rates are needed.  
Each FE FPGA transfers, through direct memory access, the partially beamformed RF data to the dedicated memories of the DSPs. In turn, the FE DSPs transfer, with bandwidth BMC, the partially beamformed baseband samples to the MC board. As illustrated in Figure 2 (left panel), the communication between the FE boards and the MC board is based on a star configuration. In this data transfer topology, each FE board reconstructs a low-resolution, full-size image with the contribution of only 32 channels. The bandwidth  
Figure 1: Architecture of the research scanner ULA-OP 256. Blocks of 32 echo data are conditioned and partially beamformed by NFE FE boards before being sent to the MC board. B\({}_{\text{wr}}\) represents the write data transfer bandwidth required to the FE memory to save the 32 channels echo data.  
B\({}_{\rm{IQ}}\) needed to transfer the low-resolution image is thus the same needed to transfer a high-resolution (256-channel) image. Since, in real-time, all the N\({}_{\rm{FE}}\) FE boards simultaneously transfer their samples to the MC board, the overall transfer bandwidth, B\({}_{\rm{MC}}\), becomes equal to N\({}_{\rm{FE}}\)\(\times\) B\({}_{\rm{IQ}}\). B\({}_{\rm{MC}}\) must be sustained by the SRIO link, which, according to our experimental test, achieves a maximum transfer rate \(\approx\) 1.8 GB/s. When N\({}_{\rm{FE}}\) = 8, B\({}_{\rm{IQ}}\) is thus limited to about 200 MB/s. A second bottleneck due to communication congestion of the master control board may thus arise.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 2 Materials and Methods
### Active Point Extraction Method Based on Screening Model  
Edge points describe the contour of the obstacle; however, there are some edge points, such as isolated points, that do not contain line information. Moreover. there are other edge points, such as the point at the end of the contour, which only contain part of the line information. Taking these points as the starting point of the extraction line may yield unsatisfactory results; hence, this paper proposes a screening model based on the distribution of local pixels and uses the screening model to extract active points from the edge map, to provide a better starting point for the line extraction algorithm.  
The screening model is shown in Figure 2. Figure 3 shows the active point extraction process based on the screening model.  
(1) Figure 3a shows the edge map used as input.  
(2) For the edge point \(p_{t}\), if one of its neighbor points \(p_{i}\) is an OBSTACLE grid, and at least one of the three tail points of \(p_{i}\) is an OBSTACLE grid, then \(p_{t}\) will be marked as an  
Figure 3: (**a**) Edge map; (**b**) a grid in the top of the edge map; (**c**) a grid in the center of the edge map; (**d**) active grids.  
Figure 2: Screening model. (**a**) The target grid \(p_{t}\) has eight neighbor points, denoted as \(\{p_{0},p_{1},\ldots,p_{7}\}\). (**b**) Each neighbor point has three tail points, denoted as \(\{p_{i0},p_{i1},p_{i2}\}\).  
active point. Taking Figure 3b as an example, the neighbor point \(p_{2}\) of the target point \(p_{t}\) is an OBSTACLE grid, and the tail point \(p_{21}\) is also an OBSTACLE grid; thus, \(p_{t}\) is determined as an active point.  
(3) Repeat (2) until all edge points are processed.  
(4) The final result is shown in Figure 3d.  
It can be seen from Figure 3 that points at the end of the contour (indicated by black circles in Figure 3d) and isolated points (indicated by yellow circles in Figure 3d) are regarded as inactive points, which do not contain complete line information. On the contrary, the extracted active points contain complete line information, which is beneficial to the line extraction algorithm to obtain better results.
## 3 Materials and Method
### Data Augmentation
#### 3.2.1 Financial Dataset Imputation and Cleaning  
Financial data typically appear in the form of time series, such as stock prices, trading volumes, and market indices. These data are strongly time-dependent, but missing values often arise due to reasons like market closures or data collection failures. To ensure data completeness and consistency, missing values must be imputed in a reasonable manner. The basic idea behind missing value imputation is to predict the location of the missing values based on known data points. Common methods include interpolation and time-series forecasting. Interpolation estimates missing values by analyzing the trend between known data points. Let \(y_{t}\) denote the value at time \(t\), and assuming that a missing value exists at \(t\), the value can be estimated using linear interpolation as follows:  
\[y_{t}=y_{t-1}+\frac{y_{t+1}-y_{t-1}}{t_{t+1}-t_{t-1}}(t-t_{t-1}), \tag{6}\]  
where \(t_{t-1}\) and \(t_{t+1}\) are the nearest two non-missing time points. This method assumes linearity between data points and is suitable for small gaps. For time series with more complex nonlinear trends, time-series forecasting models, such as ARIMA or LSTM, can be used to fill in missing values. For example, in an LSTM model, the input time-series data \(X=[x_{1},x_{2},\ldots,x_{t}]\) pass through the neural network to obtain a predicted value \(\hat{x}_{t+1}\) as follows:  
\[\hat{x}_{t+1}=f(X;\theta), \tag{7}\]  
where \(\theta\) represents the model parameters and \(f(\cdot)\) is the mapping function of LSTM. The predicted value \(\hat{x}_{t+1}\) is used to fill in the missing value, which helps better capture the time dependence in the data. After completing the missing value imputation, financial data also need to undergo normalization to meet the requirements for model training. Normalization maps data to a specific range, such as \([0,1]\), reducing the impact of different feature scales. Let \(x\) denote the data value, with \(x_{\text{max}}\) and \(x_{\text{min}}\) being the maximum and minimum values of \(x\). The normalized value is calculated as  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}. \tag{8}\]  
Normalization helps improve the convergence speed of the optimization process and is particularly useful for financial data training tasks.
## 5 Environmental Applications  
SPME has been widely applied to the sampling and analysis of environmental matrices including air, water, soil, and sediment samples, in on-site or off-site analysis [20; 16; 18]. Hundreds of papers reporting SPME applications in the field of environmental analysis have been published in recent years (see Table 4), the vast majority of which referred to aqueous samples (see Figure 10).