esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 3 Preliminaries and Background
### Route Maintenance Phase  
In the dynamic changing network topology, the infrastructure continuously updates the routing table due to the addition and remove of vehicles. This activity involves an initialization and route-discovery process due to the addition of new vehicles. Furthermore, it monitors the current position of existing vehicles by sending messages; if the infrastructure does not receive the acknowledgement message from a particular vehicle, then that vehicle is discarded from the routing table, and a route-discovery process is initiated to ensure a single-route loop-free optimal path.
## 4 Remote Sensing Platforms Used in Peatland Research
### Multiple Platforms
#### 4.5.2 Biomass Estimation  
[PERSON] et al. (2020) demonstrated the potential of ultra-high-resolution remote sensing data, including hyperspectral drone data, in detecting peatland LAI and biomass patterns. They emphasised the importance of considering multiple remote sensing data sources and combining spectral, topographic, and structural information for accurate vegetation mapping [144]. Although hyperspectral imagery provided only slight benefits in analysing LAI and above-ground biomass patterns, combining spectral data with topographic and structural information is crucial for accurately mapping vegetation patterns in spatially heterogeneous landscapes like northern peatlands [144]. [PERSON] et al. (2017) highlighted the challenges and limitations of using vegetation indices as proxies for vegetation characteristics in temperate grassland ferns, particularly in the context of varying management intensities. LAI was observed to have limited predictive capability for photosynthesis potential (PP) and biomass, as its estimation from NDVI proved inaccurate [38].  
While NDVI was found to be a more reliable predictor of PP than LAI, applying NDVI as a proxy for LAI in photosynthesis models should be considered cautiously due to its high uncertainty. Further research and validation studies are needed to improve the understanding and modelling of carbon balance in peatlands [38].
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 3 Mechanical and Dielectric Properties of Nanofibers
### Dielectric Properties
#### 3.2.1 PAN Nanofibers and Carbon Fillers  
According to [PERSON] et al. (2010), the dielectric property depends on porosity and density [2]. The Figure 15 shows the dependence of density and porosity of PAN nanofiber membranes at the frequency of 1 MHz. The dielectric constant gradually increases with density ranging from 0.164 to 0.182 g/cm\({}^{3}\). Moreover, the dielectric decreases with increasing porosity from 84.4% to 86.1%. The apparent porosity can be found by Equation (6). Figure 16 shows the dielectric constant of PAN at 8 wt.% in the radio frequency range.  
\[\text{P}\ (\%)\ =\ (1-\ \frac{\rho_{M}}{\rho_{P}})*100 \tag{6}\]  
where P is the porosity, \(\rho_{M}\) is the membrane density, and \(\rho_{P}\) is the polymer density.  
[PERSON] et al. (2014) reported the dielectric constant of PAN and PMMA as a function of graphene nano flakes as in Figure 17. The physical properties, including the dielectric constant, were significantly increased with graphene concentrations [161].  
Figure 16: Dielectric constant of PAN at 8 wt.% in the radio frequency range [2].  
Figure 15: Dependence of density and porosity of PAN nanofiber membranes at frequency of 1 MHz [2].  
According to [PERSON] et al. (2011), the increase in dielectric property of permittivity increases the electromagnetic interference shielding effectiveness (EMI-SE), as shown in Figure 18a,b. The permittivity of carbon fibers with different proportions of ZrO\({}_{2}\) is shown in Figure 18a, and the EMI-SE of the same proportions is shown in Figure 18b. This relationship is more evident below 2000 MHz [162].  
Among many conventional carbon fillers, CNTs have been preferred for high dielectric constant. [PERSON] (2016) gave a detailed review of processing of CNTs as potential nanofillers to form nanocomposites [163]. CNFs have been widely used in electrochemical energy storage devices as reviewed by [PERSON] et al. (2016) [12]. Electrospun polymer/CNF or CNT fibers have been used in energy storage devices. The comparison between the CNF and CNT is shown in Table 3.  
Figure 17: Dielectric constant of PAN as a function of graphene nano flakes concentrations [161].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Allotropes of Carbon** & **Specific Gravity (gcm\({}^{-3}\))** & **Electric Conductivity (Scm\({}^{-1}\))** & **Thermal Conductivity (Wm\({}^{-1}\)K\({}^{-1}\))** \\ \hline CNF & 1.5–2.0 & 10\({}^{-7}\)–10\({}^{3}\) & 5–1600 \\ \hline CNT & 0.8–1.8 & 10\({}^{2}\)–10\({}^{6}\) & 2000–6000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of carbon nanotube (CNT) and CNF [12].  
Figure 18: (**a**) Permititivity (\(\varepsilon^{\prime}\)) and (**b**) electromagnetic interference shielding effectiveness (EMI-SE) of carbon fibers with different proportions of ZrO\({}_{2}\)[162].
## 3 Experimental Scenario
### Performance Analysis of OST-SMC at Different Adhesion Coefficients  
In this subsection, the performance of the proposed method at different adhesion coefficients is analyzed at 20 m/s target speed. As shown in Figure 8, the effect of changing the friction coefficient seems to have a potential effect on the system at the same time it is found the proposed method takes care of the system to be in the desired state after the steady state is reached. The values \(\mu=1,\,\mu=0.5\), and \(\mu=0.2\) is selected to represent road surface parameters similar to dry road, wet asphalt, and snow, respectively.  
From the acceleration response Figure 7(b), we can see that the variation in acceleration is small with magnitude differences at different values of the friction coefficient \(\mu\). When \(\mu\) decreases, the vehicle seems to accelerate slowly. This behavior was found to be that lower friction leads to reduced control ability. Similarly, from the control input response, it is noticed that the lower the road friction the higher control is needed before the steady  
Figure 7: Proposed method performance analysis at different road slopes.  
state is reached this indicates that the control input takes care of the system by adjusting its ability to control the vehicle. When \(\mu=1\), the system appears well-damped and reaches the steady state faster; at \(\mu=0.2\), the system response tends to be a bit slower. This indicates that at a lower friction, the controller needs to work harder to maintain stability and tracking, which is why as seen in Figure 8c the proposed method copes with such a problem by applying appropriate control input for different road friction coefficients to make riding is comfortable and smooth. This result proves that the proposed method is robust for changing road conditions.
## 2 Related Works
The advantage of the table-filling method lies in its ability to introduce prior knowledge about relationships through preconstructed relationship tables. These relationship tables can contain rich domain knowledge and semantic rules, aiding the model in understanding the potential connections between entities and relationships. In recent years, many efficient models for triplet extraction have been developed. The TPLinker model was proposed in ref. [10], which considers the joint extraction problem as an end-to-end tokenizer linking task. This approach effectively avoids exposure bias. However, for each relationship, it requires two token-link matrices mapping entity boundaries, resulting in a large amount of redundant information. Consequently, the model convergence speed is slow and the decoding efficiency is low. In ref. [11], the authors proposed the OneRel model, which is an improvement on the TPLinker model, introducing the one-module one-step approach. They reduced the number of required label matrices to \(m\), where \(m\) is the number of predefined relationships, to improve decoding efficiency and reduce information redundancy. The authors of ref. [12] proposed the GRTE model, which increases the types of labels and incorporates two valuable global features. They used a transformer-based approach to capture more global information through iterative processes, achieving good results. Despite the state-of-the-art performance of these two methods, the scope of triplet extraction still requires \((n\times n\times m)\), where \(n\) is the sentence length, resulting in a large number of useless relationship tables. Additionally, both of these approaches require multi-label classification when extracting entity spans, increasing the difficulty of entity recognition. The authors of ref. [15] introduced the UniRel model, which encodes both relationships and entities as sequences to construct a unified representation. This method is similar to the prompt approach in relation classification and successfully unifies the representation of relationships and entities, reducing the relation matrix to one and further minimizing redundancy. However, this model has some issues. It can only extract single tokens, and its architecture is relatively simple, not fully utilizing the rich feature information provided by language models. Although the relation matrix is reduced to one, the cost is that the number of negative samples generated by this type of relation matrix far exceeds that in previous studies using individual tables. The differences in the table-filling methods are shown in Table 1:
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 4 Evaluation Results of Proposed Method
### Runtime  
The proposed model contains trainable parameters of 41,889, and the computational efficiency was evaluated on an NVIDIA Titan RTX (24 GB) GPU using FECG segments from the FECGSYN database. The average inference time per 4-s segment (1000 samples at 250 Hz) was \(40.39\pm 11.06\) ms. For continuous monitoring applications, processing one minute of ECG data required only 605.82 ms, which is equivalent to processing 1.7 min of FECG data processed per second. For future implementations, we will further explore its real-time deployment potential by applying Google's TensorFlow Lite (TF Lite Micro) [42].
## 3 Scalable On-line Anomaly Detection System (SOADS)
### Implementation
#### 3.2.4 Frequent Subsequence Miner  
The Frequent Subsequence Miner uses the PrefixSpan algorithm to derive subsequences. The PrefixSpan algorithm generates a pattern of the tree structure while omitting the process of generating candidate patterns in the database. Therefore, it is faster than the general sequential pattern algorithm. The PrefixSpan algorithm expands the tree of sequences that appear frequently in the projectedDB in which all sequences are stored by using depth-first search. In this study, to use the relation and frequency information of the _RELATIONAL DEPENDENCY TABLE_, the pyspark.mllib.fpm API source of the Spark MLlib was modified and used. Algorithm 1 shows the pseudocode of the modified PrefixSpan algorithm proposed in this study.  
```
A = {a1a2a3... an1a1...anE,a1...an is a sequence of read points } S = {sjsE is a set of sequences } R = {ai,a0}ai,a0A, a is a input activity, ao is a output read points} RF = {(r,f)rR, f is a frequency of r in T}
1: Function prefixSpanForSOADS(RF, S, minsup)
2: foreach RF do
3: if f \(\geq\) minsup then
4: P \(\leftarrow\) P \(\cup\) r/re RF
5: return prefixSpan(P, minsup, ProjectedDB(R, S))
6:
7: SubFunction prefixSpan(prefix, minsup, DB)
8: \(<\)b\(>\) \(\leftarrow\) extractPrefix(minsup, DB)
10: for each b do
11: prefix' \(\leftarrow\) prefix + b
12: P \(\leftarrow\) P \(\cup\) prefix'
13: P \(\leftarrow\) P \(\cup\) prefixSpan(prefix', minsup, ProjectedDB(prefix', DB))
14: return P
```  
**Algorithm 1**prefixSpan for SOADS  
The inputs of this algorithm are R\({}_{\text{F}}\), T, and minsup. R\({}_{\text{F}}\) is a set of relations and frequency pairs, and means the (relation-frequency) data of the _RELATIONAL DEPENDENCY TABLE_ in this study. S is a set of sequences, which is the read point list for each OID, that is, sequences extracted from the _EVENT LOG TABLE_. The extraction method is to use the RDD transformation, which is used in the Sequence Extractor. Minsup is the minimum support defined by the user.  
PrefixSpan finds the length-1 sequential patterns above the minsup, and generates a projectedDB that takes the patterns as prefix. Then the length-2 pattern above the minsup is found in the projectedDB, and a project DB that makes the corresponding patterns as prefix is generated. This process is repeated until length-N. The time required to find the length-2 pattern can be reduced by using the data of the _RELATIONAL DEPENDENCY TABLE_ proposed in this study, that is, R\({}_{\text{F}}\), because length-2 patterns above the minsup can be searched immediately.
## 1 Introduction  
Recently we described the results of a pump-probe experiment, in which the lifetimes of doubly excited states of neon dimers were measured [1]. The dimers were excited by absorption of two EUV photons from the Free Electron Laser (FEL) FERMI-1 [2] and probed via ionization by a UV laser pulse.  
The excited dimers decayed by Interatomic Coulombic Decay to stable dimer cations Ne\({}_{2}^{+}\), which were detected by a time-of-flight (TOF) mass spectrometer. Ionization by the UV pulse led to a repulsive state of the dimer, which dissociated, so that the yield of dimer ions was reduced, and the yield of Ne\({}^{+}\) increased. The dimer sample used in that work was very dilute with a large atomic background, as it was produced in a supersonic expansion of neon gas, with a yield of about 1%. The data were therefore noisy, and a substantial effort was needed to analyze and filter them. In this paper, we describe the methods used for that analysis.  
The data consisted of a number of temporal scans, each lasting approximately 30 min, in which the ion-TOF signal was measured as a function of the delay between pump and probe pulses, and these scans had poor signal-to-noise ratios. Simply averaging them did not give good results, probably because some of them suffered from poor FEL conditions or poor alignment of the pump and probe pulses. One should reject those scans that do not contribute significant signal, while avoiding human bias. A possible approach is to exploit the difference between signal and noise with respect to auto- and cross-correlation. This is an instance of the more general concept of \"matched filter\", i.e., the linear filter maximizing the signal-to-noise ratio (SNR) of a measured noisy sample [3]. Under the assumption of white noise, the matched filter is the complex-conjugate time-reversal of the signal sought, i.e., application of the matched filter returns the autocorrelation of the signal [4]. The concept is widely applied in signal processing, where one is primarily interested in extracting a burst of periodic signal from a noisy sample. As far as peak detection is concerned, a popular field of application is chromatography, but the general results obtained there apply equally well to our case; Ref. [5] explicitly discusses the use of a matched filter to determine amplitude and time shift of the peak being sought. We consider the use of auto- and cross-correlation for three purposes:  
1. validate the scans to be included or excluded from averaging
2. determine the weight with which each scan should enter the average
3. determine, if desired, by how much to temporally shift a scan prior to averaging
# Analysis of Measured Temperature Field of Unpaved Steel Box Girder
## 3 Experimental Results
### Changes in the Vertical Temperature Gradient  
In order to further study and analyze the vertical temperature distribution of steel box girders, based on a large number of measured data, two sunny days, 30 July and 26 December, were selected as the representative days of summer and winter. According to the period from 6:00 to 20:00 on the same day, for plate B (measurement points 1-2, 1-4-1-7, 1-9 of the east web) and plate D (measurement points 1-2, 1-11-1-14, 1-9 of the west web) monitoring data, the temperature field of the steel box girder was analyzed, and the time step was 2 h.  
In July, the sunrise in the Pingyao area appears at 6:00 a.m., midday occurs around 13:00, and sunset is around 19:30. As shown in Figure 6, during the period from 6:00 to 8:00, the vertical temperature distribution of the steel box girder was relatively uniform, and the temperature difference was negligible. From 10:00 to 12:00, plate B on the east side was first irradiated by sunlight. As the sun rose, the top plate's direct sunlight area increased, and the area of the bottom plate that was receiving the reflected radiation from the ground increased, so the temperature of both increased significantly. Since direct radiation efficiency is higher than reflection efficiency, the temperature of the top plate rises faster than that of the bottom plate. The maximum temperature difference occurs between 12:00 and 13:00. During the period from 14:00 to 18:00, with the change in the sun's azimuth, the area of plate D exposed to direct sunlight increases, and the solar radiation is strong during this period, so the temperature in the middle of the web is higher. After 19:00, the vertical temperature tends to be uniform. At 20:00, the vertical temperature distribution of the steel box girder is relatively uniform, and the temperature difference is not significant.  
Figure 5: Monthly variation curves of measured. (**a**) summer; (**b**) winter.  
Sunrise in winter is later than in summer, and sunset is earlier than in summer. From 10:00 to 18:00, the vertical temperature distribution of the steel box girder changed significantly, but the overall vertical temperature distribution and the rule of change with time were consistent with summer. However, due to the influence of the ambient temperature, the temperature rise in the top plate and the web plate of the steel box girder, and the temperature difference between the top plate and the bottom plate, were smaller than those in summer, as shown in Figure 7.
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 4 Experimental Study
### Preliminary Simulation Experiments  
Conditions of optimization experiments were proposed on the base of preliminary simulation experiments performed on the presented simulation model for three priority rules consequently in Witness simulator. The experiments served for understanding the simulation model's behavior under different loadings and its limits associated with production results.  
Different loadings were modelled by changing the time between arrivals (inter-arrival time) for the entry of each of four parts in the range 1 min to 20 min. Each of the parts entered in one piece lot size to the system.  
Simulation runs lasted 1440 min, which represented standard period of 1 day covering three shifts. The warm-up period of 90 min was found out as the period which is needed to produce one product of each four types using simulation.  
As system response, we observed values of five production indicators: Average flowtime (min.), average machine utilization (%), average work in progress (pcs), average number of products (pcs), and the average costs per part unit (\(\mathbf{\xi}\)) as outputs from simulation runs. These experiments showed production limits according to the design variables and rules setting.
## 4 Analysis of Experimental Results
### Linewidth and Discriminant Sensitivity Measurements  
The linewidth of the spectral line is generally defined as the full width at half maximum (FWHM), i.e., the width of the spectral peak. The multi-peak Lorentz fit to the SAS signal was performed by Origin, and the curve after subtracting the baseline is shown in Figure 12a. The laser frequency offset has a linear relationship with the scanning voltage, so the spacing of the saturated absorption peaks is proportional to the energy level spacing, and the corresponding linewidths can be measured using the scale method [29]. The following analysis takes \(T_{3}\) as an example. The energy level distance from \(C_{23}\) to \(T_{3}\) is 267 MHz/2 = 133.5 MHz. After Lorentz fitting, the FWHM of \(T_{3}\) can be obtained as 73. The spectrum linewidth of the \(T_{3}\) saturated absorption peak is calcu  
Figure 11: Wavelength changes after frequency locking.  
Figure 10: Automatic relock program.  
lated to be 73/616 \(\times\) 133.5 = 15.82 MHz. The slope of the zero-crossing point of the error signal reflects the frequency discrimination sensitivity, and the higher the value, the better the frequency stabilization effect. The slope of the over-zero point of the error signal is shown in Figure 12b. Firstly, the scale method is used to obtain the linewidth corresponding to the linear region of the zero crossing point of the error curve, which is 63/616 \(\times\) 133.5 = 13.65 MHz. Then, draw the frequency discrimination sensitivity \(k_{\text{f}}\) = 1.22 \(\times\) 10\({}^{-3}\) V/13.65 MHz = 8.94 \(\times\) 10\({}^{-5}\) V/MHz.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Dedicated Applications  
WSNs are in use in many areas, and most often with a varied architectural design. Moreover, sensor networks correspond to specific types of applications, as can be seen in Figure 7. A 2012 study by IDTechEx has reported that the worldwide market for sensor networks is growing, and is expected to grow from 0.45 billion to 2 billion between 2012 and 2022. We distinguish four types of WSN applications [32]: event-driven applications, periodic data-collection applications (also called time-driven applications), recommended execution applications or query-oriented applications, and hybrid applications.  
Figure 6: WSN protocol stack [33].