esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 IEC's Market Drivers
### Autonomous Vehicles  
Based on the 5G infrastructure, IEC is considered as the basic infrastructure of V2X (Vehicle to Everything) concept covering Vehicle to Vehicle (V2V), Vehicle to Device (V2D), Vehicle to Infrastructure, Vehicle to Home or Vehicle to Network [55]. Depending on the concept of (IoT) Automotive, V2X requires an important communication infrastructure where reliability and significant low latency are critical requirements [97]. To work efficiently under this category, many improvements have to be made including traffic mentoring [35; 98], sensing in vehicles continuously [99; 100], infotainment application support [10] and security [101]. These features are not fully adopted yet, but in the near future, 5G is expected to offer such flexibility, software and take advantage of emerging network-related technologies [102]. In addition, to enable IoT automatization, there is a need to improve RAN technologies where IEC has a major role here. IEC replacement within RAN gives a sufficient control for vehicles and radio network services [103]. Moreover, integration of IEC can overcome different technical challenges such as scalability, massive data handling, deployment strategies and issues of security and privacy. Drones or Unmanned Aerial Vehicles (UAVs) are another example of autonomous vehicles where they have the ability of sensing their environment without human involvement by using some cases such as smart agriculture, safety systems, and monitoring of the environment [104].
## 2 Materials and Methods
### Experiment Design  
ZnO-NPs (99.8% metal basis, 90 \(\pm\) 10 nm) purchased from Shanghai Macklin Biochemical Technology Co., Ltd. (Shanghai, China), were used in this study. ZnO-NPs were dissolved in a small volume of alcohol and then diluted with distilled water into different concentrations. Six treatments, including 0 mg\(\cdot\)L\({}^{-1}\) (CK), 10 mg\(\cdot\)L\({}^{-1}\), 50 mg\(\cdot\)L\({}^{-1}\), 100 mg\(\cdot\)L\({}^{-1}\), 150 mg\(\cdot\)L\({}^{-1}\) and 200 mg\(\cdot\)L\({}^{-1}\) of ZnO-NPs, were designed, and a total of six plots were created for each treatment containing 5 m\({}^{2}\) of tea trees each. The experiment was started on 24-26 March, 2021, with 1 L of ZnO-NPs sprayed on per treatment. The experiment was set up with three replications.  
Previous studies have suggested that high concentrations of ZnO-NPs may be toxic [19; 20]. However, in our unpublished data, the Zn contents in 0.1 g of dry tea leaves under all treatments were well below the normal Zn content in tea leaves. Hence, the concentrations of ZnO-NPs selected in this experiment would not bring about heavy metal contamination of tea trees, and the tea leaves could meet the drinking standard for making tea drinks.
# Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
###### Abstract  
The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable results. In this scope, equivalent polynomials represent an effective method to circumvent the problem while exploiting the standard Gauss quadrature rule to exactly integrate polynomials times step function. Certain scenarios, however, might require the integration of polynomials times two step functions (i.e., problems in which branching cracks, kinking cracks or crack junctions within a single finite element occur). In this context, the use of equivalent polynomials has been investigated by the authors, and an algorithm to exactly integrate arbitrary polynomials times two Heaviside step functions in quadrilateral domains has been developed and is presented in this paper. Moreover, the algorithm has also been implemented into a software library (_DD_EQP_) to prove its precision and effectiveness and also the proposed method's ease of implementation into any existing computational software or framework. The presented algorithm is the first step towards the numerical integration of an arbitrary number of discontinuities in quadrilateral domains. Both the algorithm and the library have a wide application range, in addition to fracture mechanics, from mathematical computing of complex geometric regions, to computer graphics and computational mechanics.  
numerical integration; XFEM; polynomial functions; Heaviside step function; subdomain quadrature
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 6 Model Test Result
### Influence of the Structure on Network Performance  
Since the test set does not participate in model training, the performance evaluation on the test set can reflect the generalization ability of the network. For the different network structures in Section 6, the classification performance, prediction performance, and inferring time are evaluated, as shown in Table 4.  
It can be seen from Table 4 that for the double-input network, when the VPP and constellation diagram or NDC are input, the classification and regression performance of the network are better, in which the classification accuracy is higher than 90%, the regression RMSE is lower than 0.8, and the MAPE is also lower than the other situations. The classification accuracy and regression performance of the three-input network are higher than those of the double-input network, but due to the complexity of the network increases, the training time is 1-2 ms longer than that without constellation.  
With the same network structure, the network input with as input performs better than that input with constellation diagram. Among them, the model classification accuracy of the three-input network with NDC is the highest (95.45%), and the lowest of RMSE and MAPE are 0.49% and 10.83%, respectively. However, compared to the constellation diagram, the input of NDC will enlarge the calculation amount of the model due to the enhanced identification of the color areas associated with the density of the points, thus expanding the inference time by approximately 4.7 ms compared to the constellation diagram.
## 3 Results  
### Seasonal Variations of Temperature, Salinity, and Sound Speed  
Figure 3 shows the regional distribution of depth- and time-averaged temperature, salinity, and sound velocity in the East Sea during August from 2000 to 2021. These figures highlight the regional differences in temperature and salinity during August. Temperature is the key factor affecting the speed of sound and sound absorption characteristics. When the overall variations across the East Sea were observed, August showed the most significant deviations among the months. Notably, the deviation in temperature (Figure 3a) showed a maximum variation of up to 3 \({}^{\circ}\)C in the southern coastal areas of the East Sea.  
Figure 3: Distributions of depth- and time-averaged (**a**) temperature, (**b**) salinity, and (**c**) sound velocity and corresponding standard deviations in the East Sea of Korea for August from 2000 to 2021.  
To further investigate the seasonal oceanographic changes in the East Sea, the mean and standard deviations of temperature (Figure S1), salinity (Figure S2), and sound velocity (Figure S3) are provided in the Supplementary Materials. During spring (April) and summer (August), surface water temperatures in some areas reached up to 25 \({}^{\circ}\)C, with salinity levels decreasing due to freshwater input and rainfall. In contrast, during fall (October) and winter (December), surface temperatures dropped as low as 2 \({}^{\circ}\)C, with salinity levels increasing due to reduced freshwater input and higher evaporation rates. The northern East Sea typically exhibited lower temperatures and higher salinity when compared with the southern part, leading to regional differences in sound velocity. These differences resulted in regional variations in sound velocity and acoustic properties.  
Therefore, this study focused on identifying the differences in the southern coastal region of the East Sea, where the greatest variations in water temperature were observed, and in situ MBES surveys were performed. To simplify the process of obtaining results, a MATLAB (R2023a) GUI was implemented, as shown in Figure S4.
## 1 Introduction
### Main Disparities between Communication and Radar Systems and Signals  
Despite the promising prospect of integration, distinct disparities arise between communication and radar systems, manifesting in their unique signal characteristics. Table 1 offers a succinct summary of these disparities. Radar waveforms typically encompass traditional pulsed and continuous waveforms, including chirp. Following this, we delve into two key features that exert a considerable influence on the cohesive structure of the combined system [6].
# Short Text Event Coreference Resolution Based on Context Prediction
## 4 Experimental Setup  
### Datasets  
In our experiments, we used the Chinese ECR dataset released during the 2021 National Conference on Knowledge Graph and Semantic Computing (CCKS 2021), which is specifically designed for the communication domain. In the CCKS 2021 dataset, there are various process-related knowledge in the communication domain, such as hardware installation, parameter configuration, integrated debugging, fault hiding, etc. The tasks of event extraction and event coreferential resolution are important means to achieve the sorting of fault context, troubleshooting steps, and recovery steps. The corpus in the CCKS 2021 dataset consists of publicly accessible fault handling cases from Huawei, incorporating a multitude of complex domain-specific terms, ambiguous events, shared elements, and diverse representations of event elements.  
There are eight event types including \"Index Fault\", \"Soft Hardware fault\", \"Collect Data\", \"Check\", \"Setting Fault\", \"External Fault\", \"Set Machine\", and \"Operate Machine\". The event-specific trigger terms and event elements are diverse, such as \"Configuration\", \"Mismatch\", and \"Inconsistent\" in the \"Set Machine\" event. Meanwhile, the event descriptions are presented in the form of text pairs, accompanied by labels denoting \"True\" or \"False\" to indicate whether they are coreferent. Thus, the CCKS 2021 dataset can concurrently evaluate the tasks of event extraction and event coreferential resolution, and we can use the results of event extraction to supplement the auxiliary features in the task of event coreferential resolution. The CCKS 2021 dataset is open access and contains a total of 15,000 sample data which were randomly divided into training, validation, and test sets with a ratio of 6:2:2.
## Materials and Methods  
### Au NPs Preparation  
A Nd:YAG laser (1064 nm wavelength, 3 ns pulse width, 50 mJ pulse energy, 10 Hz repetition rate), was focused on a pure gold target (99.99%), placed into a glass tube filled with distilled water. The focused laser spot on the target was equal to 1 mm\({}^{2}\), irradiating with a fluence of 5 J/cm\({}^{2}\) and an intensity of about 3 \(\times\) 10\({}^{9}\) W/cm\({}^{2}\). The target surface was placed at 4 mm depth in the water and the volume of the liquid ranged between 1 cm\({}^{3}\) and 2 cm\({}^{3}\). The target moves during the laser irradiation due to the shock waves induced by the laser pulses; this changes the position of the laser ablation surface, producing a uniform erosion of the target surface in order to obtain a uniform production of the Au NPs in the liquid, as described in more detail in previous papers [19].  
The pulse laser generated plasma, not being able to expand due to the over imposed liquid, is produced at the solid-liquid interface and gives rise to gas bubbles, which expand in the liquid up to their implosion with the release of nanoparticles.  
Inside the bubble, before its implosion, high interaction between the energetic ions and the neutral species occurs with clusters and nanoparticles nucleation. The nanoparticles are released in the liquid having a volume delimited by the glass tube section (3.14 cm\({}^{2}\)) and by the water level in the glass of about 1.6 cm. Thus, the nanometric gold particles are dispersed in a liquid volume of about 5 cm\({}^{3}\). The difference in weight of the small Au target before and after the total laser irradiation allows to evaluate the ablation yield, which is generally of the order of 50 ng/pulse. With an irradiation of 33 min, a total ablation mass of 1 mg is produced, and the concentration of the Au NPs solution in water is of about 1 mg/cm\({}^{3}\). Thus, fixing the laser parameters, the solution concentration depends on the irradiation time, which generally ranges between 15 min and 45 min.  
Figure 1 shows the pulsed laser during the Au NPs production in water (a), the scheme of the experimental set-up (b), the absorbance measurements of the prepared solution of Au NPs with an average size of 20 nm in water (c), and the generated nanoparticles diameter  
distribution evaluated by using TEM microscopy (d). The absorbance peak reported in Figure 1c, centered at -520 nm wavelength, is due to the surface plasmon resonance (SPR) induced by the incident light on the metallic nanoparticles, which increases the light absorbance proportionally to the size of the conductive spherical nanoparticle [20].
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 4 Performance Evaluation
### Network Training and Comparison  
To evaluate the performance of the proposed 4 layer CNN-2 stacked LSTM, we compare it with other conventional networks. In addition, we present performance indicators of networks according to the length of the input timeseries. For fair comparisons, all networks were trained for 50 epochs with a batch size of 32. Relu was used as the activation function and Adam optimizer was applied. As the loss function, categorical cross entropy was used. Network training was performed on NVIDIA Titan V graphics card, 64 GB RAM, and Intel i7-7700k CPU environment.  
Figure 13 represents the training accuracy when data with different timeseries lengths are used as input. The input timeseries length was evaluated as 5 types ranging from 1 s to 5 s. As a result of the comparison according to the length of the input timeseries, all networks showed the best performance when given a timeseries of 5 s. The training accuracy of LSTM was ~63%,which was the worst among the compared networks. 1D-CNN had an average of 24% higher performance than LSTM with a training accuracy of ~83%. 2D-CNN and CNN-LSTM had similar performance at a time series length of 1 s. However, as the timeseries length increased, the performance of 2D-CNN was superior to that of CNN-LSTM. In all cases, the 4-layer CNN-LSTM showed the second highest training accuracy among networks. Our proposed 4-layer CNN-2 stacked LSTM has superior results compared to other networks in all cases. At 3 s of timeseries input, the performance was similar to that of the 4-layer CNN-LSTM, but in other cases, it showed an average performance improvement of 1.75%. In addition, it showed the highest training accuracy with about 96.5% at a timeseries length of 5 s. Table 4 shows the training time of each network model. In terms of training time, 1D-CNN with a simple structure is the fastest, and 2D-CNN is the slowest because it trains images. CNN-LSTM is trained about 4.3 times faster than LSTM through the process of extracting and summarizing features through CNN. The 4-layer CNN-2 stacked LSTM with the highest training accuracy trained the third fastest, and was trained ~18.3% faster than the 4-layer CNN-LSTM with the second highest training accuracy. Therefore, it has been verified that the proposed 4-layer CNN-2 stacked LSTM has high training accuracy while training faster compared to other networks.
# Exopolysaccharides from Cyanobacteria: Strategies for Bioprocess Development
## 2 EPS from Cyanobacteria
### Chemodiversity  
The EPS in cyanobacteria is part of a complex network of extra polymeric substances, and it can also comprise proteins, nucleic acids, lipids, and secondary metabolites [10]. More than 13 different monosaccharides have been identified from C5 to C6 and underpinned on 40-50 glycosidic bonds tightly correlated to polysaccharide flexibility [9]. Glucose is the most common monosaccharide, however, some EPS strains were found to contain other dominant monosaccharides such as rhamn,xylose, arabinose, fucose, mannose, and uronic acids [27]. The latter have an exclusive presence in the cyanobacteria, being identified with a frequency of one or two units. Structurally monosaccharides can be grouped by their form: hexoses (glucose, galactose, mannose, and fructose); pentoses (ribose, xylose, and arabinose); deoxyhexoses (fucose, rhamnose, and methyl rhamnose); acidic hexoses (glucuron and galacturonic acid). The EPS of _Arthospira platensis_ strain MMG-9 exhibited rich EPS diversity, the CPS fraction the most diverse (fucose, galactose, glucose, mannose, rhamnose, ribose, and xylose) [28]. Methyl, pyruvyl, and succinyl groups can be present as well as sulfate groups, which are only found in archaea and eukaryotes. Additional types of monosaccharides such as N-acetylglucosamine, 2,3-O-methyl rhamnose, 3-O-methyl rhamnose, 4-O-methyl rhamnose, and 3-O-methyl glucose are also reported [29; 30]. At the macromolecular scale, these polymers are characterized by high molecular weight, which can range from kDa to MDa, whereas more than 75% of those characterized are heteropolymers [6]. The physico-chemical role of these building blocks is extremely rich. Although some exhibit hydrophobic character, which promotes adhesion to solid surfaces, others exhibit hydrophilic character sticking to minerals, nutrients, and water molecules. Notwithstanding, the combination of both moieties can promote an amphiphilic character allowing cyanobacteria to react in different ways to the surrounding environment [30]. Cyanoflan, an RPS isolated from the marine _Cyanothece_ sp. PCC 0010 has a high intrinsic viscosity and emulsifying activity in aqueous solutions [31]. Cyanobacteria constitute a prolific source of EPSs with physico-chemical properties. As a consequence of these natural complexities, a pitfall for structural elucidation burdens the number of available structures [9]. Non-exhaustive data are shown in Table 1.
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 2 Methodologies  
### Fmd  
FMD decomposes a signal into multiple sub-signals by utilizing an FIR filter bank and employs correlation kurtosis (CK) in conjunction with signal impulse and periodicity for deconvolution. The mode is selected based on the correlation coefficient (CC) of the sub-signals, discarding those with lower CK values until the number of retained sub-signals aligns with the expected number of modes. The specific implementation process is detailed as follows [19]:  
Step 1: Input the original signal \(x(t)\) and the parameters of FMD initialization: mode number \(n\), filter length \(L\), frequency band cutting number \(K\), and cycle period \(m\).  
Step 2: Initialize the FIR filter bank through \(K\) Hanning windows, usually set in the range of 5 to 10, and initialize iteration \(i=\)1.  
Step 3: Obtain the filtered signal (i.e., the decomposed modal component) through \(u_{k}^{i}=x^{*}f_{k}^{i}\); \(u_{k}^{i}\) represents the decomposed mode obtained from the \(k\)-th filter during the \(i\)-th iteration of the process, where \(i=\) 1, 2, 3, \(\ldots\) represents the iteration index, indicating the current step in the iterative process, \(k=\) 1, 2, 3, \(\ldots\) indicates the filter index, denoting the \(k\)-th filter in the filter bank, and * is the convolution operation.  
Step 4: Update the filter coefficients using the original signal \(x(t)\), the decomposed modal component \(u_{k}^{i}\), and the estimated fault period \(T_{k}^{i}\), where the estimated fault period \(T_{k}^{i}\) is determined by the autocorrelation spectrum of \(u_{k}^{i}\). After the zero point, the moment corresponding to the local maximum value \(R_{k}^{i}\) is reached and \(i=i+1\) is set.  
Step 5: Determine whether the current iteration reaches the maximum number of iterations. If it is not reached, return to step 3; otherwise, continue to step 6.  
Step 6: Calculate the correlation coefficient between every two modal components, construct a \(K\times K\) correlation matrix CC (\(K\times K\)), select the two modal components with the largest correlation coefficient, pass the estimated failure period \(T_{k}^{i}\), calculate their correlation kurtosis (CK), and then select the modal component with the larger correlation kurtosis (CK) as the decomposed modal component and set \(K=K-1\).  
Step 7: Determine whether the current number of modes meets the specified threshold, \(n\). If it does, the decomposition process will conclude, and the results will be presented. If it does not, return to step 2 and continue the previous operations. The specific FMD process is illustrated in Figure 1.
## 4 Experiment and Results
### Contrast Experiments  
To compare the performance of the algorithm, some other algorithms were selected for comparison. See Table 2 for the detection performance of the algorithms in this chapter and other algorithms.  
It can be seen that the algorithm in this chapter still has a similar performance compared with other advanced methods. This paper algorithm has achieved the highest precision (95.63%) and F1 Score (95.61%). Compared with the other two methods, this algorithm achieved the highest F1 Score while simultaneously ensuring precision and recall rate.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Estimated Performance Evaluation of Critical Latency
#### 4.3.2 Summary of Performance Evaluation Results  
Performance evaluations for the four scenarios described in Section 4.3.1. demonstrated that the location of the two points of potential OS-TS collision--M and C--and the collision time can be estimated. A summary of the results is as follows:  
* The time at both points decreases as the encounter bearing decreases;
* The time difference between the two points also decreases as the encounter bearing decreases;
* The smaller the time for two potential collision points and the smaller the time difference between the two points, the closer the collision is. Therefore, the proposed technique is expected to be applicable for collision avoidance under COLREGs, which will be discussed thirdly in Section 5 (Discussion).