esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Empirical Data and Preprocessing  
#### 3.2.1 Raw Data  
In this article, the data comes from the experiment of \"data set collection based on Wi-Fi connectivity between vehicles and base stations in an urban environment\" done by Microsoft Corporation in the United States. The experiment was completed in the Microsoft Experimental Park in Redmond, Washington. Using the experimental results of vehicle wireless locations in the vehicle road cooperative environment, the algorithm verification of this article is completed. There are 11 Wi-Fi fixed base stations set in the experimental park to transmit wireless signals; the red mark in Figure 2 below marks the base station setting.  
When the vehicle is driving along the road in the park covered by the wireless sensor network, it receives the wireless signal from each base station. The experiment is completed by collecting Wi-Fi signal data within the vehicle communication range, its own vehicle driving data, and GPS data during vehicle driving.  
The experiment uses the deployed fixed base station to obtain Wi-Fi signal data and analyzes the basic characteristics of the data between the base station and the vehicle. The experimental data package includes GPS data field information, Wi-Fi data field information, and vehicle data field information.  
1. GPS data fields  
The data names and data samples contained in the GPS data package can be found in Table 2.  
2. Wi-Fi data fields  
The data names and data samples contained in the Wi-Fi data package can be found in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Data Name** & **Data Sample** \\ \hline
1 & Machine time & T06:47:57.3593750-08:00 \\
2 & UTC time & T14:47:15.0000000 \\
3 & Latitude in degrees, followed by N & 47.644565 N \\
4 & Longitude in degrees, following by W & 122.13342 W \\
5 & Speed in knots (1 knot = 1.852 Kmph) & 0.13 \\
6 & Direction of motion & 157 \\
7 & Percent & 2.9 \\
8 & Horizontal & 1 \\
9 & Vertical dilution of precision & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GPS data fields.  
Figure 2: Experiment site settings.  
3. Vehicle data fields The data names and data samples contained in the Vehicle data package can be found in Table 4.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 3 Methodologies
### A New Method of Code Generation for Safety-Critical Software
#### 3.2.1 Software Requirement Analysis of Safety-Critical Software  
In order to conduct experiments on code generation, it is important to identify typical software requirements in safety-critical domains. First, we need to identify the domain from which the experimental examples come. Nuclear energy is a safety-critical area subject to national regulation. Its regulatory requirements for software are representative of safety-critical areas. The automotive industry is the most widely used field of software in the civil safety-critical field, and its software functions are representative. Secondly, safety-critical software mainly includes data flow control software that processes signals logically and state machine software that controls states or modes. The experimental examples must cover two types of software. Moreover, to ensure the representativeness of the research instance and facilitate the code generation experiments, the chosen instances should consist of relatively independent software modules with moderate complexity and conspicuous domain-specific features. Based on these principles, we have selected a reactor shutdown algorithm from the nuclear energy domain and cruise control software from the automotive domain. Once the software objects have been identified, requirement analysis needs to be conducted. The requirement analysis phase is based on the software development process in the nuclear energy and automotive domains. Domain engineers can conduct this requirement analysis, without the need for them to possess knowledge and expertise in code generation using LLMs. This approach minimizes the learning curves associated with applying new technologies.
# A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications
## 3 HT-Based Frame Synchronization Algorithm
### Algorithm
Extract the elements greater than \(ax_{max}\) from the vector \(X\), and find the one with the smallest subscript from them, and use the subscript value as the estimate of the frame length \(L\), which is recorded as \(L\). (\(\alpha\) will be determined in the simulation next);  
Step9. Let \(l\ =\ L\) and fill the image matrix \(C_{rcl}\) with the intercepted bit stream \(Y\);  
Step10. Record the count of each accumulator cell at \(\theta\ =\ 0^{\circ}\) in Hough space and recognize the synchronization word.
# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
### Related Studies
Mahalanobis distance control charts were used in determining anomalous behaviors in [20], which were compared with other methods, particularly the multivariate exponentially weighted moving average. It was found that using MD control charts were more effective in identifying outliers within the data set, and could moreover be used for detecting anomalies in data-extensive applications. Another study in [21] showed how the MD can be used in identifying diagnoses in non-linear systems. Since non-linear systems are more prevalent in real-world scenarios, it was advised that the MD be used to obtain more reliable diagnostics of systems. A different study conducted also assessed the structural health of offshore wind turbines, using autoencoders and the MD distance [22]. The combination of the MD and autoencoders resulted in a more robust unsupervised novelty detection pipeline for structural health monitoring. The proposed method in this study will focus on the use of operational data of the SCADA system paired with the MD approach in order to investigate abnormalities in the operation of wind turbines. This is an improved version of the related literature cited above, as this method will be able to relate the MD approach to the actual alarm event. This could be beneficial to the future of conditions monitoring for wind turbines.
## 1 Introduction
Therefore, in order to eliminate possible external influences such as different study setups, the influence of scanbodies, or measuring strategies [4; 16; 24; 25; 26] in the present study, two investigators assessed the transfer accuracy (trueness and precision) of three different IOS families using different hardware and software versions from 2012 to 2021 and compared them to a conventional impression (CI) on the same implant model over a period of ten years. According to ISO 5725-1, mean values for the deviations between the IOS results and the master model describing trueness and standard deviation describing precision for the different scanners and the CI [27]. The null hypothesis tested was as follows. There are no differences in the transfer accuracy for different IOSs with different hardware and software versions.
# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
## 2 Materials and Methods
### Materials
#### 2.1.2 Input Data  
In this study, a single aerial image captured by a UAV was employed for training purposes. Visual interpretation, a prevalent method in remote sensing image analysis, was used. An image analyst visually interpreted the image to discern diverse land surface features. The criteria for labeling typically encompass shape features, texture features, contextual information, scale, and direction. Leveraging these features, various elements were accurately identified and labeled through visual interpretation during the image annotation process. As a result, the corresponding labels for infected trees were obtained. The image boasts a resolution of 4608 \(\times\) 3456 pixels and is composed of three bands: red (central wavelength: 650 nm), green (552 nm), and blue (475 nm).  
To evaluate the model's effectiveness, the entire image depicting pine nematode-infested trees underwent manual labeling utilizing LabelMe. Subsequently, all sample data were randomly allocated into training, validation, and test sets at a 3:1:1 ratio. Validation was performed on training samples collected from three distinct locations, encompassing 100 UAV images in the validation set. Some example datasets are in Figure 4.  
Figure 3: Location of Yantai City and its study area. (a) Binhai East Road, Yantai City, Shandong Province, (b) Jingliu Road, and (c) Guanzhuang Road.
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
###### Abstract  
In mobile communication systems, device-to-device (D2D) communication and nonorthogonal multiple access (NOMA) are effective ways to improve spectrum efficiency and system throughput. In the NOMA-based D2D system, social relationship among D2D users is introduced to form D2D clusters, and NOMA is used for many-to-one communication in each D2D cluster. This paper proposes a joint channel allocation and power control algorithm which decomposes the resource allocation problem into two subproblems: channel allocation and power control. Matching theory is utilized to allocate channels for D2D clusters and sequential convex programming is applied to transform the optimization target to a convex problem before solving it via genetic algorithm. Simulation results indicate the superiority of our algorithm in improving the system throughput on the basis of meeting users' needs for files.  
D2D; NOMA; social relationship; matching theory; genetic algorithm Article
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 5 Zone Geographic Forwarding Rules  
A node in OLSR selects among its one-hop neighbors the minimum number of MPRs to cover its two-hop neighbors. However, it does not take into account the distribution of MPRs and its impact on network performance. Some MPRs are sometimes side-by-side, which intensifies the collision and decreases the reliability of the network. On the other hand, a node can receive a TC message more than once since it is the neighbor of multiple MPRs. These redundant transmissions consume resources on devices such as energy, another the network causing more loss of packets, and compromise the reliability of the network.  
In this section, we describe the two strategies Z-MPR and GFR, and the change we made to the message header to support both strategies and to implement the Z-GFR.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 5 Distributed Protocols
### Tdd & Tdma  
Time division duplexing (TDD) and TDMA are some of the oldest traditional protocols that are still in use. The TDD protocol is commonly assumed to be the preferred mode of operation for 5G mmWave systems because it allows for better usage of broader bandwidths and the use of channel reciprocity for channel estimation [59; 60]. The implementation of TDD/TDMA protocol in 60 and 73 GHz mmWave WPANs are presented in [61]. In a TDD system, time is divided between the users. The uplink and downlink transmissions are separated by a time interval in a synchronized manner. In this technique, uplink sounding signals can be used to collect the channel state information (CSI) at the transmitter in time division duplexing (TDD) systems.  
However, the network's performance may be limited by the limited UE power and the likely underdeveloped beamforming in the uplink communication reference signals. As a result, TDD in the mmWave frequencies must be limited to low-mobility circumstances. In the TDMA, several STAs share only one channel in entirely different time slots. The optimal spatial time division multiple access (STDMA) protocol utilizes some transceivers with minimal interference at a given resource block, providing the highest possible sum-rate for the whole network system [11; 62; 63; 64].  
Nonetheless, it requires a thorough understanding of network architecture [58], which is not supported by WPANs, particularly the devices in the move [30]. Time division multiple access (TDMA) reduces the overall overhead of STMDA. In TDMA protocol, every device is served one at a time. This sequencing ensures that no data is lost in the event of a collision.  
* Advantages: From the implementation, it is noticeable that the TDD and the TDMA scheme is an outstanding candidate for mmWave WPANs. The use of TDD, particularly dynamic TDD, makes the 5G system incredibly flexible and bandwidth and power efficient. Moreover, the tiny subframe time slot length makes it easier to achieve the URLLC target latency (1 ms) [65].
* Disadvantages: In mmWave WPANs, multipath interference in a common circumstance. The use of TDD/TDMA can be substantially affected by this occurrence. For mobile phones, especially handhelds, TDMA on the uplink channel necessitates a high peak power in transmit mode, which reduces battery life. The TDMA also necessarily requires significant signal processing for matched filtering and correlation detection in order to synchronize with a time slot [66].
## 3 An Automated Framework for DSM
### Outlier Detection and Warning  
The basic characteristic of an outlier is the presence of an isolated value significantly larger or smaller than \(t_{i-1}\) and \(t_{i+1}\) at \(t_{i}\). Persist anomaly detection (PAD) compares the value at \(t_{i}\) with the value in the previous time window and identifies the value as an outlier if it varies from the mean or median in the window to an unusually large degree. The key training parameters of the algorithm are the number of previous time series values contained in the time window (\(w\)) and the coefficient (\(c\)) used to determine the normal range boundaries based on the historical interquartile range. Three metrics, namely precision, recall, and \(F1\) score, are used to evaluate the outlier detection performance of the method. The greater the precision, recall, and \(F1\) score, the more accurate the PAD. The three metrics are calculated as follows:  
\[Precision=TP/(TP+FP);Recall=TP/(TP+FN) \tag{9}\]  
\[F1=2\times precision\times Recall/(Precision+Recall) \tag{10}\]  
Figure 5: Hybrid model for DSM.  
where \(TP\) is the number of outliers correctly detected, and \(FP\) is the number of data incorrectly identified as outliers. \(FN\) is the number of outliers incorrectly identified as valid data.  
When the monitoring data are stored in the database, the proposed framework automatically calls the PAD algorithm to detect the value of each sensor, marks the sensor corresponding to the outlier in a red eye-catching font, and displays the information of the instrument such as the dam section and elevation, the measured value, and time. At the same time, the abnormal information is discarded and an alarm is generated to remind the managers of different departments and permissions to achieve the real-time sharing of warning information.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 2 Numerical Method and Computational Setup
### Turbulence Model  
In order to improve the predictive accuracy of turbulence models in highly separated areas, [PERSON] [42] proposed a method which combines RANS and LES, i.e., DES (detached eddy simulation). Its basic idea is to use RANS to solve the computation in the boundary layer near the wall and LES method in the boundary layer separation area. In this manner, not only the low cost of RANS method to solve the boundary layer area computation can be retained, but also the accurate prediction in the separation area is achieved. As a result, a more exquisite flow field information can be obtained. The simulation in this study is performed using ANSYS CFX 17.2 [30] commercial software. Hence, the turbulence model is DES model based on SST \(k\)-\(\omega\) and the governing equation is as follows:  
\[\frac{\partial\rho_{m}}{\partial t}+\frac{\partial(\rho_{m}u_{j})}{\partial x _{j}}=\ 0 \tag{3}\]  
\[\frac{\partial}{\partial t}(\rho_{m}u_{i})+\frac{\partial(\rho_{m}u_{i}u_{j})} {\partial x_{j}}=\ -\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j}} \bigg{[}(\mu_{m}+\mu_{t})\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac {\partial u_{j}}{\partial x_{i}}\bigg{)}\bigg{]} \tag{4}\]where, in the equation, the expression of turbulent viscosity coefficient \(\
u_{t}\) is expressed by:  
\[\
u_{t}=\frac{\alpha_{1}k}{max(a_{1}\omega,SF_{2})} \tag{5}\]  
in which, \(F_{1}\) and \(F_{2}\) are blending functions, \(S\) is the amplitude of curl, while \(a_{1}\), \(\alpha_{\omega 2}\), \(\beta^{*}\), \(\beta\), \(\alpha_{k}\), and \(\sigma_{\omega}\) are constants. The values of the above terms are: \(a_{1}\) = 0.31, \(\beta^{*}\) = 0.09, \(\alpha_{k1}\) = 2, \(\alpha_{k2}\) = 1, \(\alpha_{\omega 1}\) = 2, \(\alpha_{\omega 2}\) = 1.1682.  
Binding the function \(F_{1}\) and \(F_{2}\) is the key to the successful application of the turbulence model. The formula of the blending function can be obtained as:  
\[F_{1}=\ tanh\left\{min\left[max\left(2\frac{\sqrt{k}}{0.99\omega y^{\prime}},\frac{500\mu}{y^{2}\omega}\right)\frac{4\omega_{\omega 2}k}{CD_{\omega 3}y^{2}} \right]\right\}^{2} \tag{6}\]  
\[F_{2}=\ tanh\left[max\left(2\frac{\sqrt{k}}{0.99\omega y^{\prime}},\frac{500 \mu}{y^{2}\omega}\right)\right]^{2} \tag{7}\]  
where \(y\) is the nearest distance from the grid to the wall. When the y value is small, it tends to be the \(k\)-\(\omega\) model, while when the \(y\) value is large, it tends to be the \(k\)-\(\varepsilon\) model. In the SST \(k\)-\(\omega\) turbulence model, the diffusion term Y\({}_{k}\) caused by turbulence is generally represented by:  
\[\mathrm{Y}_{k}=\ \rho\beta^{*}f_{\beta}\text{-}k\omega \tag{8}\]  
while in the DES turbulence model, it is changed to be:  
\[\mathrm{Y}_{k}=\ \rho\beta^{*}k\omega F_{DES} \tag{9}\]  
\[F_{DES}=\ \max\left(\frac{L_{t}}{C_{des}\Lambda_{max}},1\right) \tag{10}\]  
where \(C_{des}\) is the calibration constant, which is 0.61, and \(\Lambda_{max}\) is the maximum edge length of the local computational cell.
## 2 Methods
### Time-Dependent Three-Dimensional Dataset  
Since the main objective of this work is to reveal the temporal transformation of a three-dimensional and time-dependent dataset, let us first introduce some notation in order to clarify the mathematical ideas to be used. Let us define the discrete time-dependent data to be the set of points \(\mathcal{P}=\left\{p_{i}\right\}\), with \(i=1,2,\ldots,m\), being \(m\) the total data. The values in each one of the three dimensions can be seen as scalar coefficients for a set of basis vectors. This tuple of components compose the vector that we call the _position_ or _coordinate_\(\mathbf{x}_{i}=\left[x_{i,1}\quad x_{i,2}\quad x_{i,3}\right]^{\top}\), with the superscript \(\top\) denoting the transpose operation, the first subscript referring to the point \(i\) and the second to the dimension. Hence, we call \(\mathcal{X}_{n}\left(t\right)=\left\{\mathbf{x}_{1}\left(t\right),\mathbf{x}_{2}\left( t\right),\ldots,\mathbf{x}_{i}\left(t\right),\ldots,\mathbf{x}_{n}\left(t\right) \right\}\in\mathbb{R}^{3}\) the _positions_ of the points in a certain time \(t\). Let us consider a uniform partition of the time span \(t\in[t^{d},t^{g}]\) in a sequence of discrete time-steps \(t^{d}=t^{0}<t^{1}<\ldots<t^{n}<\ldots<t^{N}=t^{g}\), with \(\delta t>0\) the time-step-size defining \(t^{n+1}=t^{n}+\delta t\) for \(n=0,1,2,...,N\). Thereby, we use the superscripts to denote the discrete time-steps, with the only exception of denoting the transpose operation with the superscript \(\top\).  
Since the time-dependent dataset of the case study comes from a PCA reduction of a higher-dimensional multivariate dataset \(\mathcal{Y}_{n}\left(t\right)=\left\{\mathbf{y}_{1}\left(t\right),\mathbf{y}_{2} \left(t\right),\ldots,\mathbf{y}_{i}\left(t\right),\ldots,\mathbf{y}_{n}\left(t\right) \right\}\in\mathbb{R}^{k}\), with \(k>>3\) and \(t\in[t^{d},t^{g}]\), into a lower-dimensional one \(\mathcal{X}_{n}\left(t\right)\), \(t\in[t^{d},t^{g}]\) that possesses only three independent dimensions, namely Principal Component 1 (PC1), Principal Component 2 (PC2), and Principal Component 3 (PC3), we use the Cartesian coordinate system straightforwardly with each principal component being a dimension. That is, \(\mathbf{x}_{i}\left(t^{n}\right)=[x_{i,\text{PC1}}\left(t^{n}\right)\quad x_{i, \text{PC2}}\left(t^{n}\right)\quad x_{i,\text{PC3}}\left(t^{n}\right)]^{\top}\). Hence, the discrete time-dependent dataset can be thought of as a cloud of points in the three-dimensional space that deforms gradually over time.
## 3 Results
### Prediction of \(ESV\) for the Southern Jiangsu Urban Agglomeration in 2035 Using the PLUS Model
#### 3.5.2 Projected ESV Under Various Scenarios for 2035  
Figure 10 illustrates the changes in ESV for land use types under various scenarios, with the unit being in billions CNY. By land use type, for farmland, the ESV is highest under the FPS at CNY 63.468 billion, followed by the EPS and the CDS, with the NDS having the lowest value. This suggests that the scenario setup is sound and has a high degree of alignment with farmland protection policies. For grassland and forest land types, the ESV under the EPS is the second highest, exceeding the ESV of the NDS and falling below the ESV of the CDS. This indicates that, compared to the NDS, the EPS provides better protection and development for forest land and grassland. Looking at the total ESV, the ranking is as follows: NDS > EPS > CDS > FPS, with specific values of CNY 358.312 billion, CNY 348.616 billion, CNY 339.402 billion, and CNY 333.999 billion, respectively.  
The analysis in Figure 11 indicates that the spatial distribution of ESV under various scenario predictions is generally similar to the actual ESV spatial distribution from 2000 to 2020, displaying a distribution where values peak in the eastern areas and diminish in the western areas. Areas of elevated value are still primarily located in the eastern part of the study area, particularly in water body areas such as Lake Tai; low-value areas continue to be predominantly found in the western part of the agglomeration, mainly in areas of farmland and built-up land. Areas with comparatively lower values are located in the easternmost section of the study region. From Figure 12, it is evident that the differences in ESV distribution under the four scenario predictions are mainly concentrated in the medium and relatively high values, manifested in the study area's southwestern sector. Specifically, under the NDS and EPS, this region exhibits medium and relatively high ESV distributions, while under the FPS and CDS, the area shows medium and relatively low ESV distributions.  
Figure 10: ESV changes for land use types under simulated scenarios.