esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Characterization
### Polygonal Model  
The polygonal model builds on the decomposition of the \(Q\)-\(\delta\) loops into skeleton and Bauschinger parts explained in Section 4.2. The shape of the skeleton can be determined either experimentally with the procedure explained in Section 4.2 (Figure 18) or numerically. The shape of each Bauschinger curve is approximated by two segments (dashed and dotted lines) as shown in Figure 22. The slope of the first segment of this bilinear approximation is taken equal to the initial elastic stiffness \(K_{e}\). The ordinate \(Q_{m}\) of the end point of the second segment (named point 11 in Figure 22) coincides with the maximum force attained by the damper in previous cycles of displacement in the same domain of loading. The abscissa of this end of point is determined from the deformation \({}_{B}\delta\). As discussed next, the value of \({}_{B}\delta\) of a Bauschinger curve is very well correlated with the plastic deformation accumulated on the skeleton part, \(\Sigma_{\mathrm{S}}\delta\), up to the point where the Bauschinger curve starts. Figure 23a shows the pair of values (\(\Sigma_{\mathrm{S}}\delta\), \({}_{B}\delta\)) calculated for each Bauschinger curve obtained from the tests conducted on the SS-TTDs in this  
Figure 21: Comparison of the \(\eta\)-\(\mu_{\mathit{CLM}}\) relationship between the selected references and this study.  
Figure 20: Normalized ultimate energy dissipation capacity against cumulative ductility factor.  
study. An approximately linear relationship is observed between \({}_{B}\delta\) and \(\Sigma_{S}\delta\) that can be expressed by the bold dashed line of slope \(\beta\) = 0.35, that is, \({}_{B}\delta\) = 0.35\(\Sigma_{S}\delta\). Finally, the ordinate of the intersection point of the two segments is taken as \(\alpha\Omega_{m}\), where the value of parameter \(\alpha\) is determined so that the areas enveloped by the actual Bauschinger curve and by the bilinear approximations is the same. Figure 23b represents, in the horizontal axis, the actual values of \(Q_{m}/Q_{y}\) for each Bauschinger segment obtained from the tests conducted on the SS-TTDs, and in the vertical axis, the value of \(\alpha Q_{m}/Q_{y}\) that satisfies the above condition. As seen in Figure 23b, \(\alpha\) can be taken as approximately equal to \(\alpha\) = 0.4.
## 4 Detection Performance and Discussion
### Actuator Experiment  
In this section, the data obtained using the actuator in an indoor environment is used to evaluate the proposed method. The actuator signal has an amplitude of 3 mm and a frequency of 0.333 Hz. In the experiment, the actuator is on a desk at a height of 1.3 m facing the antenna. The distance between the actuator and antenna was 400 cm and 700 cm. Figure 21a,b show the distribution of the kurtosis in slow time and the range estimation, respectively, at a distance of 400 cm. The corresponding results at a distance of 700 cm are given in Figure 21c,d. The error in the range estimates is 10.5 cm at 400 cm and 13.6 cm at 700 cm. The corresponding results with the AM and CFAR methods are given in Figure 22. The frequency estimation with the proposed and FFT methods is given in Figure 23. The estimated frequency with the proposed method is 0.321 Hz at both 400 cm and 700 cm, so the error is only 0.012 Hz. The results for the proposed method shown in Figure 23a,c show that the interference is significantly reduced compared to the results for the FFT method given in Figure 23b,d.  
Figure 20: The normalized respiration spectrum with (**a**) 30\({}^{\circ}\) and 45\({}^{\circ}\) azimuth angles using the proposed method, (**b**) the proposed and FFT methods at 45\({}^{\circ}\) azimuth angle, and (**c**) the proposed and FFT methods at 30\({}^{\circ}\) azimuth angle.  
Figure 21: Results for the actuator experiment, (**a**) kurtosis distribution at 400 cm, (**b**) range estimation at 400 cm, (**c**) kurtosis distribution at 700 cm, and (**d**) range estimation at 700 cm.  
Figure 22: Results for the actuator experiment at 700 cm using (**a**) AM method, and (**b**) CFAR method.  
The SNR obtained using (32) with the data from the actuator 1100 cm from the antenna is shown in Table 4. The best estimate of the actuator frequency is 0.334 Hz which was obtained using the proposed method. This estimate has a deviation of only 0.12% while the estimate is 0.116 Hz using the FFT method with a deviation of 65%. Further, the SNR improvement is significantly higher with the proposed method.
### Objectives  
This article aims to explore the role of real-time AI agents in enhancing the safety of autonomous maritime transportation systems. The specific objectives are as follows:  
1. To analyze the current state of autonomous maritime systems and the integration of AI agents within these frameworks;
2. To examine the AI techniques and methodologies employed for real-time data processing, obstacle detection, collision avoidance, and decision-making in autonomous vessels;
3. To evaluate the effectiveness of AI-driven safety mechanisms in mitigating risks and ensuring compliance with maritime safety and environmental regulations;
4. To identify the challenges and limitations associated with deploying real-time AI agents in autonomous maritime systems and propose potential solutions;
5. To present case studies and practical applications that demonstrate the implementation and impact of AI agents on maritime safety and sustainability.  
By systematically addressing these objectives, the article aims to deliver a comprehensive understanding of how real-time AI agents contribute to the safety, efficiency, and sustainability of autonomous maritime transportation systems. This exploration is intended to inform and guide researchers, engineers, policymakers, and industry practitioners involved in the development, regulation, and deployment of autonomous maritime technologies, ultimately fostering advancements that align with the evolving demands of global maritime trade.
\(\beta^{\prime}\) (IV), \(\beta\) (V), and \(\beta\) (VI) are frequently used to present the thermal stability and melting temperature order [7]. The relationship between those two different names has been described in [PERSON], 2011 [3]. It is reported that well-tempered chocolate is crystallized in form \(\beta\) (V), which can be obtained with a well-controlled crystallization processing via the tempering step in chocolate production [7]. However, \(\beta\) (V) form can transform into \(\beta\) (VI) form due to a long storage time and temperature fluctuations, leading to the undesirable fat bloom [9,10]. Hitherto, the amount and crystal types have been mainly measured by X-ray diffraction (XRD), differential scanning calorimetry (DSC), or pulsed nuclear magnetic resonance (pNMR) [3], while little research on the polymorphism of cocoa butter using terahertz spectroscopy has so far been carried out.  
Terahertz falls in the frequency range of 0.1-10 THz (1 THz = 10\({}^{12}\) Hz) and the photon energy (4 meV for 1 THz) is much lower than that of X-ray photons (100 eV-100 keV) [11, 12, 13], the radiation of which is much safer for an operator in comparison of X-ray based equipment. As an emerging and cutting-edge technology, terahertz spectroscopy (THz) has been intensively employed to detect biology threats and defects [14], residue detection [15, 16, 17], transgenic food [18, 19], foreign body detection [20, 21], dynamical changes in seeds [22, 23], and adulteration of different types of milk powder [24]. However, the application of terahertz spectroscopy in foodstuffs is still in the infant stage in comparison with the sophisticated and prevalent spectroscopic and imaging techniques like hyperspectral imaging [25, 26, 27, 28, 29, 30], Raman spectroscopy [31], and NIR images [32]. This is because water is a strong THz wave absorber, which is the major hurdle for the comprehensive application of THz in the food industry [33]. With its unique transmission capability, the current application of terahertz spectroscopy to chocolate mainly focuses on detecting foreign bodies and quality (e.g., reshape due to temperature fluctuance) in packed chocolates [34, 35, 36, 37, 38].  
Therefore, the objective of the current study is to study the feasibility of characterizing different types of crystallization from cocoa butter by using terahertz spectroscopy. This is the first study to investigate different types of crystallization from cocoa butter in the terahertz range. The feature frequency will be selected for identifying the different types of crystallization. This could potentially provide useful information for the evaluation of measurement systems, such as the measurable range and signal to noise, and the basic properties of crystallizations from different tempering procedures.
## Abstract  
To assess the efficacy of Pro-Argin with respect to the oral health-related quality of life (OHRQoL) and cervical dentine hypersensitivity (CDH) in subjects with CDH for 24 weeks compared to a control group. Three study centers (one each in France, Germany, and Switzerland) included 273 subjects for 24 weeks. Patients with CDH involving at least two non-molar teeth and those with Schiff scores (cold air sensitivity scale) of 2 or 3 were included. The primary study parameter was a change in the OHRQoL from baseline to 24 weeks, as assessed by the Oral Health Impact Profile (OHIP)-49 questionnaire within and between the randomized groups. Patients were randomly assigned to the test group (TG) or the control group (CG). Prophylaxis paste and toothpaste in the TG contained 8% arginine and calcium carbonate. The toothpaste in the CG contained sodium monofluorophosphate. The level of significance was set at 5% (\(p\) = 0.05). The OHIP-49 at 24 weeks was analyzed using the analysis of covariance (ANCOVA). The mean OHIP-49 score after 24 weeks (adjusted for baseline) was significantly lower in the TG than in the CG (\(p\) = 0.005, ANCOVA). The OHIP-49 pain-related items adjusted for the baseline values also showed significant differences between the groups (\(p\) = 0.025, ANCOVA). The treatment over a period of 24 weeks using products based on the Pro-Argin technology showed a significant improvement in the OHRQoL compared to the placebo application.  
Keywords:cervical dentine hypersensitivity; Pro-Argin; quality of life; patient health questionnaire +
Footnote †: journal: Applied Sciences
## 3 Span-Based Nested Named Entity Recognition Model
### Biaffine-Based Nested Named Entity Recognition Model  
The biaffine-based nested NER model classifies all span candidates in a sentence using the following formula:  
\[h=BERT(token_{seq}) \tag{1}\]  
\[h_{start}=FFNN_{start}(h) \tag{2}\]  
\[h_{end}=FFNN_{end}(h) \tag{3}\]  
\[s_{i,j}=biaffine\left(h_{i}^{start},h_{j}^{end}\right)=\left(h_{i}^{start} \right)^{T}U\left(h_{j}^{end}\right)+W\left(h_{i}^{start}\oplus h_{j}^{end} \right)+b \tag{4}\]  
\[y_{i,j}^{\prime}=argmax\left(s_{i,j}\right) \tag{5}\]  
Let \(tokens_{seq}\) be the words of the input sentence, which were encoded using Bidirectional Encoder Representations from Transformers (BERT) [16]. The encoded information was then processed using \(FFNN_{start}\) and \(FFNN_{end}\) to obtain \(h^{start}\) and \(h^{end}\), respectively. \(h^{start}\) and \(h^{end}\) represent all candidate spans in the sentence's start and end word information. For all candidate spans, a named entity classification was attempted using a biaffine classifier, and the detailed formula is presented in Equation (4). \(U,\ W,\ \text{and}\ b\) are all trainable weights, and finally classify the person's name tag for span \(i,j\ (i,j\ \text{is}\) the index of all words in the sentence).
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 7 Routing for Multi-hop WSNs with a Single Immobile Sink  
### System Model and Design Objectives  
A multi-hop wireless sensor network was considered in [2] that focuses on computing multi-hop routes from each node to a single immobile sink such that the network lifetime is maximized. The lifetime in [2] was defined as the time at which the first node runs out of energy. Each node can generate information due to its sensing capabilities and relay packets from other nodes to the sink node. The nodes' battery energies are limited and adjustments of transmission powers for each node is possible depending on the distance between nodes.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 2 Electrical Connection Faults
### Connectors  
Connectors transmit current through the contact between the pin and receptacle. On the microscale, the contact between the two metal surfaces occurs at discrete points produced by mechanical contact of asperities on the two surfaces, as shown in Figure 2. The electric current lines become increasingly distorted as approaching the contact interface and the current flow is separated to pass through the contact spots. These introduces  
Figure 1: Connection resistance is measured using a micro-ohmmeter.  
construction resistance, while the regions of the interface that are not in contact introduce capacitance effects. When degradation occurs on the electrical contact surface, the connection mode of metal-metal and metal-void-metal becomes metal-film-metal. This results in further film resistance and capacitance [11].  
With increasing connector degradation, the thickness of the contamination film increases, which leads to increasing connection resistance \(R_{1}\) and the decrease of connection capacitance \(C_{1}\). For the contact surface between the connector pin and receptacle shown in Figure 2, the impact on signal transmission mainly occurs in the low frequency band; this is because the equivalent circuit of the contact surface contains capacitance, and the associated impedance decreases with the increase of frequency. Accordingly, high-frequency currents can be transmitted by coupling. As shown in Figure 3, for RF connectors, the maximum connection resistance of normal connectors obtained from our experimental testing is 10 m\(\Omega\). However, for higher precision circuits, the interconnection structure is often required to have a lower maximum connection resistance. Accordingly, for the purposes of this study, a lower value of 5 m\(\Omega\) is used in the current simulation model for the maximum connection resistance of a normal connector. This means that, when the connection resistance of a connector is greater than 5 m\(\Omega\), the connector is considered to be faulty. In addition, in the experiments, the faulty connectors were produced using an accelerated test, and the maximum measured fault connection resistance was 360 \(\Omega\). However, this is an extreme value. Thus, for the purposes of this study, the maximum connection resistance of a faulty connector is chosen to be 100 \(\Omega\) in the current simulation model [12,13].
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 3 Data  
### Data Set  
In this study, we used geophones to obtain seismic data for different vehicles at Kyushu University in July 2020. We placed the geophones in three stations 15 m apart, located 0.5 m from the road. The vertical motions (vibration) were recorded at a rate of 250 Hz. We tagged vehicles by size as large (e.g., buses and trucks), medium (e.g., private passenger cars), and small (e.g., motorcycles and scooters).  
Figure 4: The RNN architecture used in this study. The model contains two LSTM layers and two hidden layers. The model produces four values, indicating the probability of each vehicle class.  
During the experiment, a video camera was used to provide a visual guide for the manual preparation of the training data. Each event (the passage of a vehicle) lasted 2-3 s when the vehicle was close to the geophone. Based on signals at three stations, we estimated the speeds of the vehicles. The speeds of most vehicles used in this experiment were 25-35 km/h, and the maximum speed was 45 km/h. In the training process, we chose clear vehicle signals, eliminating all signals that contained surrounding noise or that overlapped with other vehicles to avoid overfitting the models. The selected events were extracted from the record in the form of windows 5 s long, containing 1251 data points (5 \(\times\) 250 Hz = 1250 samples). This duration was selected to guarantee the inclusion of the whole seismic waveform. We extracted, on average, 68 waveform windows per geophone station for each of the three-vehicle classes for a total of 612 windows. We also selected 318 waveform windows to represent the noise in our data as the fourth class. These include noise produced by strong winds, bicyclists, walkers, pedestrians pushing a trolley, road maintenance, and ambient noise. These 930 windows constituted the entire input to the three neural networks; examples of each class in the dataset are shown in Figure S1 in the Supplementary Material.
## 2 Position Domain Control for Contour Tracking
### Relative Derivatives and Position Domain Mapping  
To transform the dynamics of slave motions from time domain to position domain, a relationship has to be developed that relates time domain to position domain for master motion and slave motions. This relationship can be introduced by the relative derivative of the \(i\)th slave motion (\(q_{i}\)) with regard to the master motion (\(q_{m}\)):  
\[q^{\prime}_{si}=\frac{dq_{si}}{dq_{m}}=\frac{\dot{q}_{si}}{\dot{q}_{m}} \tag{3}\]  
Equation (3) indicates that \(q^{\prime}_{i}\), also called the relative position velocity, is the ratio of the slave motion speed over the master motion speed, and it describes the synchronized relationship of motions between the master and the slave motions.  
Similarly, the relative position acceleration of the slave motions can be expressed as the second relative derivative of Equation (3):  
\[q^{\prime\prime}_{st}=\frac{dq^{\prime}_{si}}{dq_{m}} \tag{4}\]  
According to Equations (3) and (4), a one-to-one transformation from time domain to position domain can be defined as:  
\[\begin{cases}\dot{q}_{st}=\dot{q}_{m}q^{\prime}_{sl}\\ \ddot{q}_{si}=\ q^{\prime\prime}_{st}(\dot{q}_{m})^{2}+\ddot{q}_{m}q^{\prime}_ {si}\end{cases} \tag{5}\]  
Equation (5) shows the relationship between the absolute motions and the relative motions, and relate the absolute velocities and accelerations in time domain to the relative velocities and accelerations inposition domain. These two equations are used to transform a dynamic system from time domain to position domain [9; 10; 11].
# Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism
## 3 Model Structure  
URLs themselves have strong requirements for sequence order, and GRU is a model that can process sequence data. At the same time, BiGRU can deeply mine and make full use of the relevant information in the obtained URL data. Therefore, this paper uses BiGRU as the basic model structure. In order to prevent the neural network model from overfitting in the training process, that is, a situation where it performs well and has high accuracy on the training set, but it performs poorly and has low accuracy on the test set. In this paper, a layer of dropout mechanism is added before URL data enter the model, which avoids the excessive influence of some weights on the network model to a certain extent and reduces the model's deviation.  
URLs are not complicated, but they have certain requirements for sequence relations. Therefore, this paper first uses a layer of BiGRU, and the number of neural nodes is set to 128 to learn the characteristics of URLs. Then, the learned information is outputted to the attention layer to improve the full use of key information. Finally, the full connection layer using the tanh function as the activation function is added, and the softmax function is used for the final classification. Thus, a dropout-attention bidirectional gated recurrent unit (DA-BiGRU) is formed. The DA-BiGRU model structure is shown in Figure 7.  
Figure 6: Dropout Mechanism.  
This method firstly preprocesses the URL data set using Word2 Vec to construct a word vector and segmenting the data set into a digital vector. Secondly, the preprocessed data set is constructed into the data format required by the model through embedding layer. Then, we train the proposed model DA-BiGRU. Finally, utilizing the best-performing model, we can perform the malicious URL detection task and obtain the classification result. The calculation equations are shown below.  
The input at \(t\) time \(x_{t}\) becomes \({x_{t}}^{\prime}\) by dropout mechanism:  
\[{x_{t}}^{\prime}=dropout(x_{t}). \tag{8}\]  
The hidden state output of forward propagation GRU and back propagation GRU at \(t\) time can be defined as:  
\[\overrightarrow{h_{t}}=GRU({x_{t}}^{\prime},\overrightarrow{h_{t-1}}). \tag{9}\]  
\[\overleftarrow{h_{t}}=GRU({x_{t}}^{\prime},\overleftarrow{h_{t-1}}). \tag{10}\]  
where \(\overrightarrow{h_{t}}\) and \(\overleftarrow{h_{t}}\) denote the hidden state output of forward propagation GRU and back propagation GRU at \(t-1\) time, respectively.  
The hidden state output of BiGRU at \(t\) time is the weighted sum of forward and backward:  
\[h_{t}=\overrightarrow{W_{t}}\cdot\overrightarrow{h_{t}}+\overleftarrow{W_{t} }\cdot\overleftarrow{h_{t}}+b_{t}. \tag{11}\]  
where \(\overrightarrow{W_{t}}\) is the weight of \(\overrightarrow{h_{t}}\), \(\overleftarrow{W_{t}}\) is the weight of \(\overleftarrow