esa-sceva/satcom-chunk-collection huggingface dataset:
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.5 Computational Complexity  
This section analyzes the computational complexity of the algorithm. In a two-stage embedding sub-algorithm, node mapping is a polynomial algorithm in terms of and \(|\text{node}^{v}|\), and [18; 22] are as well. Node mapping in [21] is a polynomial time algorithm in terms of \(|\text{node}^{e}|\), \(|\text{node}^{v}|\), and \(\max\{1,-\log\epsilon\}\) (\(\epsilon\) is a desired precision). The k-shortest path link mapping algorithm can be solved in \(O((|\text{node}^{e}|+|\text{link}^{e}|)\log(|\text{node}^{e}|+k))\) time [27]. The computational complexity of link mapping is the same as in [18; 21; 22]. PURE-ILP has exponential time complexity. The solution increases exponentially with an increase in network size. However, our constrained integer linear programming sub-algorithm reduces the number of binary variables in linear programming by constructing a candidate node set and candidate link set. Ref. [14] proved that the mapping can be completed in a limited time by this method. Thus, the computational complexity of our algorithm is between a heuristic algorithm and a linear programming algorithm.
# Overview of the Development of 3D-Printing Concrete: A Review
## 6 Print Quality Evaluation Index  
The forming quality of printed components is also a key issue of 3D-printing concrete technology. It mainly includes geometric quality problems such as the shape and size of the printed components and surface quality problems such as surface roughness. Basic requirements of the construction process are to print components with good forming quality. Therefore, it is very important to systematically evaluate the forming quality of 3D printed concrete components.  
The quality of concrete 3D-printing is affected by many factors such as printing materials, printer systems, pumps, control systems, print heads, and the shape of the discharge nozzle [88]. [PERSON] et al. [9] proposed that the surface quality of 3D-printing cement-based materials can be judged by the three criteria of no defects on the surface, clear square boundaries of the printed layer, and meeting the requirements of surface consistency and homogeneity.  
Printing parameters are the most critical factor affecting the surface quality of the parts; thus, scholars focus mainly on adaptive layering and optimization of various printing parameters. [PERSON] et al. [89] believe that, compared with equal layer thickness layering, adaptive layering can set a smaller layer thickness in the feature-intensive area of the model, so that the dense features are not easy to lose, thereby improving the surface quality of the part. The algorithm is complex; the quality of the algorithm determines the accuracy of the layering. If the algorithm is unreasonable, it will reduce the surface quality of the model. [PERSON] et al. [90] believe that the fused deposition printing parameters have strong coupling and influence each other during the printing process, which ultimately determines the surface quality of the parts. Optimizing the molding parameters can significantly improve the poor surface finish of the molded parts. [PERSON] et al. [91] effectively optimized the surface quality of the part by selecting the optimal forming angle, optimal layer thickness, and optimal extrusion magnification.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 2 Backgrounds and Motivation
### Motivation  
The current research on code generation based on GPT is still in the preliminary exploration stage and lacks cases from industrial domains, especially in the generation of code for safety-critical software in fields such as nuclear energy and the automotive industry. As software scales in the industrial domain continue to grow and the demand for software development efficiency increases, the support provided by large language models for code generation offers new possibilities for industrial software development. However, industrial software often contains domain-specific features, and conventional prompt engineering methods may not yield optimal results. The current benchmarks (HumanEval, MBPP, etc.) mainly focus on the ability of code LLMs to handle basic programming elements (such as integers and strings) with general-purpose algorithms. These benchmarks emphasize code generation capabilities for low-level requirements, but lack validation for high-level functional requirements. Furthermore, the current benchmarks primarily test code generation by LLMs from the perspective of multiple use cases and various algorithms, emphasizing their ability to generate code for different programming needs. However, there is a lack of research on how to refine prompts for a specific requirement to achieve the best code generation results. Therefore, this paper shifts the focus from the breadth of code generation to the depth of code generation, aiming to explore the application of LLMs in code generation for safety-critical domains by starting with specific domain requirements. This study proposes prompt engineering methods suitable for code generation in safety-critical domains.
# Building Ensemble of Resnet for Dolphin Whistle Detection
## 2 Materials and Methods
### Dataset
#### 2.1.1 Data Preprocessing and Tagging  
As described in [28], the collected data were subjected to a bandpass filter in the range of 5-20 kHz to align with the majority of dolphins' whistle vocalizations. The data were then passed through a whitering filter designed to rectify the hydrophone's open circuit voltage response ripples and the sensitivity of the sound card. The recorded audio files, which consisted of two channels, were averaged before the creation of spectrograms to decrease noise. In addition, the preprocessing pipeline eliminated signal outliers based on their length using the quartiles-based Tukey method [35], which led to the exclusion of signals that were longer than 0.78 s and shorter than 0.14 s.  
The short-time fast Fourier transform of the signal was computed using MATLAB's spectrogram function from the digital signal processing toolbox to create the dolphin whistle spectrograms. SFFT was performed with a Blackman function window with 2048 points, periodic sampling, and a hop size achieved by multiplying the window length by 0.8. The subsequent spectrograms were computed by shifting the signal window by 0.4 s. These spectrogram images were finalized by applying a gray-scale colormap, converting the frequency to kHz and the power spectrum density to dB, and restricting the \(y\)-axis between 3 and 20 kHz to emphasize the most significant (dominant) frequency range [36].  
The spectrograms were then manually labeled by a human expert in two steps: initial tagging and validation tagging. The first step involved precise annotation of 5 s spectrograms over ten days of data collection, which were used to train an initial version of a deep learning classifier. This classifier was then used to select new portions of recordings containing potential dolphin sounds, which made tagging the remaining data in the validation phase more efficient. The validation phase only required the verification of positive samples detected by the preliminary deep learning classifier.  
A human expert was tasked with identifying dolphin whistles as curving lines in the time-frequency domain and disregarding the contour lines generated by shipping radiated noise. When the discrimination process was complex, the expert directly listened to the recorded audio track to identify whistle-like sounds. The tagging resulted in a binary classification (whistle vs. noise) and a contour line marking the time-frequency characteristic of the identified whistle. This contour was used to assess the quality of the manual tagging by ensuring that the bandwidth of the identified whistle fell within the expected thresholds for a dolphin's whistle, specifically between 3 and 20 kHz. A second quality assessment was conducted by measuring the variance of the acoustic intensity of the identified whistle along the time-frequency contour, where the acoustic intensity of a valid whistle was expected to be stable.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 5 Simulation Results  
### Simulation Setting  
We have conducted extensive simulations to evaluate and compare the performance of ATML with other methods. The simulations are performed using MATLAB (R2016b). The ANs are equipped with square arrays with the dimension of \(M\times M\). The ideal radiation pattern (steering vectors) of the antenna arrays are adopted. The phase differences among the antenna elements are theoretical and the errors/deviations of the antenna gains are not considered. Suppose that the spacing between adjacent rows and columns is \(d\). We use the antenna at the position of \((1,1)\) as the reference point. Then, for the incident direction of \((\phi,\theta)\), the radiation pattern of the antenna at the position of \((m,n)\) in the array is \(s_{i,(m,n)}=md\cos\theta\cos\phi+nd\cos\theta\sin\phi\).  
The system parameters are listed in Table 1. Because the chip rate is 20 Mcps (chips per second), the chip duration equals 50 ns (the reciprocal of the chip rate) and the bandwidth is 20 MHz. The PN-sequences are transmitted circularly and received by the antenna arrays on the two ANs. The signals are superimposed with additive white Gaussian noise (AWGN) for various SNRs.
# Nanomaterials in Bone Regeneration
## 5 Conclusions  
Nanomaterials currently play an important role in bone tissue engineering research. Although bone itself has the ability to self-repair and remodel, in many cases, these processes are not sufficient, and external convention is required. Therefore biocompatible materials to substitute bone and promote regeneration are needed. Nanomaterials seem to be very promising for these purposes. Thanks to the nanoscale dimension, physico-chemical properties are significantly improved, and nanomaterials support and regulate cellular function, proliferation, and migration. A large number of nanomaterials, nanoparticles, and nanocomposites, which can be produced by a wide range of technologies, are currently being developed and researched. Together, they all attempt to mimic the natural structure of the bone as much as possible.  
Implants fabricated from nanostructured titanium have already been used in clinical medicine for quite a long time on a relatively wide scale. Various nanoparticles are being researched to functionalize the polymer matrix to improve the desired properties. These scaffolds are still at the stage of in vitro or in vivo testing; a large number of experiments still need to be performed before clinical use. The advantage of nanoparticles as a part of composites with polymers in the form of hydrogels is that they can be injectable into the defect site with a complex shape by a minimally invasive intervention. Further progress in the field of smart nanomaterials for bone tissue regeneration may bring new treatment options to medicine in the future.  
**Author Contributions:** Conceptualization, V.B. and V.K.; writing--original draft preparation, P.B.K., P.C., L.W., J.D., R.V., A.N. and L.L.; writing--review and editing, V.B. and V.K.; supervision, V.B.; project administration, V.K.; funding acquisition, V.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by project No. CZ.02.1.01/0.0/0.0/16_019/0000787 \"Fighting Infectious Diseases\", awarded by the Ministry of Education, Youth and Sports of the Czech Republic, financed from The European Regional Development Fund and by the grant SVV-2020-260 540.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Anti-Breast Cancer Activity of Essential Oil:
## 3 Result
### Bioactivity of Essential Oils as Cancer Agents
#### 3.3.3 Colon Cancer  
Colon cancer is cancer that develops in the large intestine or at the bottom of the large intestine connected to the anus (rectum) [72]. Several studies reported the activity of essential oils, mainly the terpene group for alternative colon cancer treatment. Geraniol, a monoterpene found in essential oils of various fruits and herbs has been proposed to represent a new class of cancer agents for chemoprevention, because it has significant antiproliferative activity on colon cancer cells (Caco-2) [73]. Essential oils from _Afrostyrax lepidophyllus, Scotonycieris zenkeri_, and _Athanasia brownii Hochr_ showed a strong inhibitory effect on human colon carcinoma cell line HCT116 [74]. The essential oil of _Satureja khuzistanica_ significantly reduced the viability of SW480 colon cancer cells in a dose-dependent manner [75]. The essential oil of _Artemisia campestris_ exhibits significant antitumor activity against HT-29 colon cancer cells [76] while Thymoquinone inhibited the proliferation of a series of human colon cancer cells (Caco-2, HCT-116, LoVo, DLD-1, and HT-29) [77].
## 1 Introduction
To extract more convincing features, researchers developed the backbone for deep learning networks, such as GoogleNet [23] and Resnet [24], with skip architectures. [PERSON] et al. [25] proposed the framework of a 3D CNN with the residual block from Resnet and obtained deeper features for classification. One recently proposed HSI classification algorithm is the residual attention network (RAN), proposed by [PERSON] et al. [26]. RAN is a deep-learning-based algorithm that integrates residual connections and attention mechanisms to improve the classification performance. The residual connections help to alleviate the vanishing gradient problem and enable the network to learn more complex features, while the attention mechanism helps to focus on discriminative features and suppress noisy ones. Experimental results on several benchmark hyperspectral datasets demonstrated that RAN outperforms many state-of-the-art methods in terms of classification accuracy. The CNN-based HSI classification algorithms have been shown to be effective at achieving high levels of classification accuracy, particularly when large amounts of labelled training data are available. With the development of natural language processing (NLP) technology, the transformer architecture shows a strong feature extraction ability, especially when applied to the vision tasks. Vision transformer [27] has been applied to computer vision (CV) and shown exciting results. [PERSON] et al. [28] designed a BERT-like architecture, which flattens the HSI cube as a sequence of the transformer input. [PERSON] et al. [29] proposed the SpectralFormer network, which learns information from neighbouring bands. However, the current transformer-based methods for HSI classification introduce feature inconsistencies generated by a large number of differences between different bands when directly inputting samples into adjacent bands as a sequence, resulting in insufficient feature-extraction capabilities. Furthermore, the network fails to consider the correlation between the feature channels when modelling the input vector. We proposed a multidimensional spectral transformer with channel-wise correlation (MSTCC) to combine neighbouring band features and feed them to vectors used to classify their differences. The main contributions of this paper are as follows:  
* To overcome difficulties that arise when extracting global features with a CNN, we proposed a transformer-based network architecture to better extract long-range relationship features of cube bands from HSIs;
* To better combine the related features between bands of different dimensions, we proposed a channel-related feature extraction method;
* Combining all the above-mentioned points, we proposed a new method for HSI classification. We also validated the proposed model using several comparison methods, revealing that it achieved great results on the studied datasets.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Applications of the Car-Following Models  
As the core of traffic flow theory, the significance of car-following models is determined by their important cornerstone role in traffic flow theory. To be specific, understanding and then describing the car-following behavior in the right way are the basis for analyzing the operation and stability characteristics of traffic flow and evaluating the energy consumption and emission. Before these applications, the car-following model needs to be calibrated to make it suitable for describing the specific traffic system. In the V2X environment, with the development of relevant technologies and devices, it is easier to obtain field data sets with larger scale and higher precision, which creates new requirements for the model calibration methods. Based on these, the calibration of car-following models is first reviewed in this section. Then, applications of the car-following model are reviewed from two aspects: analyzing the traffic flow characteristics and evaluating the energy consumption and emission.
## 2 Literature Review
### Selection of Built Environment Factors  
Built environment factors affecting rail transit flow vary from city to city and from dataset to dataset. [PERSON] et al. (2004) [17] examined the number of people employed and residing within the buffer zones of rail transit stations in nine U.S. cities and found that there was a significant correlation with light rail patronage. [PERSON] and [PERSON] (2010) [18] conducted a model analysis and comparison using the passenger flow data of Seoul rail transit and [PERSON]'s research. It was found that the results of the two studies were similar, and it was found that employment, commercial building area, office building area, net population density, transfer times, and the number of feeder bus lines were significantly correlated with passenger flow. In China, city size is usually measured by population size [19]. In megacities, [PERSON] et al. (2018) [20] found that commuting activities generate weekday metro traffic and leisure activities, such as weekend metro traffic generated by shopping using influencing factors such as the population distribution and number of office locations in Taipei City. [PERSON] et al. (2023) [21] used POI and AFC data to find that tourist attractions, other types of POI, bus stations, and station accessibility are significantly related to the passenger flow of Nanjing Metro rail transit. Among megacities, [PERSON] et al. [2] found that population density and common residential land use were the main factors affecting morning and evening commuter flows in Guangzhou by comparing the different effects of day, time, and directional passenger flows. [PERSON] et al. (2019) [22] used OLS regression analysis and found that the effects of commercial land use, bus stops, and tourist attractions on rail transit patronage in Shanghai are independent of weekday time and all have significant positive effects. [PERSON] et al. (2022) [23] analyzed the \"7D\" built environment variables and found that the density of office facilities, the density of sports and leisure facilities, the density of medical service facilities, the density of buildings, and the plot ratio have a significant effect on the passenger flow out of each metro station in Beijing.  
Although these studies have analyzed in detail the mechanisms by which built environment factors affect rail transit patronage from the perspective of the characteristics of the respective cities, they have generally neglected the role of house prices as a key economic variable. House prices not only determine the spatial distribution of the built environment but also affect the geographic trend of patronage. Although [PERSON] et al. (2023) [24] and [PERSON] et al. (2023) [25] considered the factor of house price in their studies in Beijing and Chengdu, respectively, they did not bring house price into different models for comparative analysis to explore the significance of its impact on passenger flow. In addition, there is an even greater lack of studies on short-distance commuting-cum-cross-city access in megacities.
## 2 Analysis of Inherent Residual Current of Low-Voltage Distribution Networks
### Generation of Residual Current from Unbalanced Systems  
Residual currents emerge during regular operations due to line imbalances, asymmetric loads, and inadequate insulation at junction points. For instance, in the TT configuration, the asymmetry in ground conductivity results in the generation of residual currents: \(i_{tline}\) along the line and \(i_{tload}\) and \(i_{tRg}\) on the load side. This phenomenon is depicted in Figure 1, where A, B and C represent three-phase power lines, and N is a neutral line.  
In the low-voltage distribution station area, there are numerous lines where residual currents from rear equipment and lines accumulate in the front part. During normal system operation, the residual current behind household protection typically remains below 20 mA.  
In contrast, the capacitance current to the ground and equipment leakage current to the ground in medium and general protection can reach several hundred mA under standard conditions, posing challenges in setting up effective protection with poor sensitivity.  
The short length of low-voltage distribution lines results in minimal ground capacitance and consequently very low residual currents generated on the line. However, the complexity of load-side equipment and electrical environments leads to significantly higher amplitudes of leakage current and an increased likelihood of leakage compared to the line itself. Therefore, the primary source of inherent residual current in low-voltage distribution networks is the terminal load. While most equipment leakages do not trigger the action threshold of household protection, the cumulative leakage from multiple loads results in excessive residual current accumulation in the front section before the user-side measuring box.
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 1 Introduction
Sometimes, the exchange of signaling information is also performed via the UDP [2; 3; 4; 5]. As it is known in VoIP, the VoIP server is used only for managing the signaling information--creating, maintaining and dropping the calls. No voice or video streams pass through the server; i.e., it does not process them. They are exchanged and processed directly between the end devices (IP phones). But there is another group of VoIP servers where all the information (signaling, voice and video streams) pass through the server and are processed by it. Because of this feature, this type of VoIP server is interesting to subject to research to find out whether the UDP DoS attack affects the parameters of the voice or video streams that are processed by the VoIP server. Asterisk FreePBX is such a type of VoIP server where the voice and video streams from the VoIP calls pass through and are processed by the server [6; 7; 8; 9; 10]. This work is a continuation of a previous study that studied the impact of various TCP DoS attacks on the parameters of voice and video streams that are processed by the Asterisk FreePBX [11]. What is new and different in this study is that the Asterisk FreePBX is subjected to a UDP attack because the Asterisk FreePBX uses only the UDP protocol to carry signaling and utility information. Attacking the server with UDP packets will verify if this type of attack affects the performance of the server and the parameters of the voice and video streams.