esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Cost Function
\tag{16}\]  
The cost function \(f_{c}(c_{i},e_{i})\) can be written as:  
\[f_{c}(c_{i},e_{i})=\left\{ \begin{array}{ll}f_{cl}(c_{i}),&e_{i}=0,\\ f_{co}(c_{i}),&e_{i}=1.\end{array} \right. \tag{17}\]  
In (17), \(f_{cl}(c_{i})\) represents the local cost when \(c_{i}\) executes locally on UE and \(f_{co}(c_{i})\) is the remote cost when \(c_{i}\) executes remotely on MES. \(f_{cl}(c_{i})\) can be calculated as:  
\[f_{cl}(c_{i})=\delta_{1}(\frac{d_{li}+d_{di}}{d_{max}})+\delta_{2}(\frac{E_{li} +E_{dil}}{E_{max}}), \tag{18}\]  
where \(\delta_{1}\) and \(\delta_{2}\) are the weighting coefficients by which we can change the contribution and priority of delay and energy consumption in the cost function, respectively. \(d_{max}\) is the deadline time for a whole task to execute, and it can be calculated as the average value of delays for different tasks (the delay of a single task is the sum of delays for all \(n\) components per task). \(E_{max}\) is the maximum energy of UE's battery. Similarly, \(f_{co}(c_{i})\) can be written as:  
\[ \begin{split} f_{co}(c_{i})=\delta_{3}[d_{ii}(1-e_{i-1})+d_{ei} +d_{ri}+d_{pi}(2-e_{i-1})+d_{di}]\\ +\delta_{4}[E_{ti}(1-e_{i-1})+E_{ri}+E_{di}]+\delta_{5}f_{2}(r_{i})+ \delta_{6}f_{3}(k_{i}),\end{split}  \tag{19}\]  
where \(\delta_{3}\), \(\delta_{4}\)  
gives propagation delay only for reception if \(e_{i-1}=1\). \(f_{2}(r_{i})\) and \(f_{3}(k_{i})\) give the cost due to used CPU cores and subcarriers, respectively, and can be given as \(f_{2}(r_{i})=r_{i}/R\) and \(f_{3}(k_{i})=k_{i}/K\).
## 2 Subject and Methods
### Airway Measurements  
The sinus/airway module of Dolphin Imaging was used to segment the three-dimensional representation of the upper airway, as well as determine the most constricted cross-sectional area (MCCA) of the upper airway. The inferior boundary of the orophynx was set as a horizonal line from the anterior-superior corner of the third cervical vertebrae (C3), and the superior boundary was determined as a horizontal line from the posterior nasal spine (PNS). The nasopharynx extended from the superior limit of the orophynx (PNS-horizontal) to a line extending vertically from PNS (PNS-vertical). The airway sensitivity in the Dolphin Imaging software was determined based on the most accurate visual rendering of the proper airway volume in our study groups and was set to 30 (on a scale from 0 to 100) for all patients. The MCCA was found within either the orophynx or nasopharynx using the \"minimal axial area\" feature built into the Dolphin Imaging software. All measurements were done by a single investigator. A second investigator ([PERSON]) analyzed the airways of a random sample of 10 study patients. See Figure 2.  
Figure 1: Cephalometric images rendered from CBCT DICOM data and traced using Dolphin Imaging. The (**left**) is a patient from the control group and the (**right**) is a patient from the JIA group.
# Remote Sensing Analysis of Geologic Hazards
###### Abstract  
In recent decades, classical survey techniques (i.e., field measurements and aerial remote sensing) have evolved, and with the advent of new technologies--e.g., terrestrial radar interferometry [1, 2], digital time-lapse cameras [3], terrestrial and aerial laser scanners [4, 5] and platforms, e.g., UAV [6, 7]--remote sensing systems have become popular and widely used in geosciences. Contactes devices are not invasive and allow measuring without accessing the investigated area. This is an excellent advantage as earth surface processes often occur in remote areas and can be potentially dangerous or difficult to access [8]. Satellite and aerial remote sensing offer the possibility of surveying large areas, using hyperspectral optical [8, 9], synthetic aperture radar (SAR) [10, 11] and thermal infrared [12, 13] images and altimetric lasers [14]. The progressive rise in available public and private satellite constellations has permitted individuals to reach very high-resolution images at weekly to daily revisit time. On the other hand, ground-based surveys usually have higher acquisition frequency and spatial resolution compared to satellite systems, and they are able to observe the evolution of fast processes and their possible paroxysmal phase, e.g., volcanic eruptions [15, 16], glacier instabilities [17], landslides [18, 19], and floods [20, 21]. For their characteristics, proximal sensing applications are often used in monitoring activities at a short revisit time, as they can provide real-time or near-real-time information [22]. Therefore, they can be of great support in early warning procedures and risk assessment and management [23, 24]. Combined with aerospace sensors, contactless terrestrial devices are particularly suitable for data-fusion techniques, multi-scale approaches and supporting numerical model analysis [25, 26, 27, 28].  
Satellite and terrestrial remote sensing are of paramount importance in specific tasks of geologic hazard analysis. This Special Issue has collected ten papers concerned with recent and upcoming advances in remote sensing applications in geologic hazard analysis. In particular, this Special Issue includes studies about satellite and terrestrial contactless devices for detecting, monitoring and analyzing geologic processes, as well as new data-processing and warning techniques (Figure 1).
## 6 Potential Solution for HCMSs with Larger-Amplitude and Wider-Range Negative Stiffness  
In real-world applications, a larger-amplitude and wider-range negative stiffness is desirable. To address this challenge, the number of layers of the Halbach cylinder is increased to look for potential solutions. Here, the 5-3-type and 3-5-type HCMSs are shown in Figure 17. As for these two HCMSs, two cases are considered. The first one is that each magnetic ring in the outer and inner Halbach cylinders has the same height (i.e., 10 mm), which is denoted as Type I. The other one is that the total heights of the outer and inner Halbach cylinders are the same (i.e., 30 mm), which is denoted as Type II. Other geometric parameter values can be found in Table 4. Then, stiffness curves of the 5-3-type and 3-5-type HCMSs are compared with that of the 3-3-type HCMS, as shown in Figure 18. The results show that (i) under the same volume, increasing the layer number of either the outer Halbach cylinder or the inner Halbach cylinder cannot enlarge the amplitude and the displacement range of negative stiffness; (ii) by keeping the same height of magnetic rings, increasing the layer number of either the outer Halbach cylinder or the inner Halbach cylinder can enlarge the amplitude and the displacement range of negative stiffness; and (iii) the 5-3 Type II HCMS is better than the 3-5 Type II HCMS in terms of compact design and the amplitude of negative stiffness.  
Despite the above promising characteristics, there are still the following disadvantages or limitations that deserve to be considered.  
(1) Compared with traditional single-layer magnetic springs, more magnetic rings are utilized in the structure of HCMSs. In this case, the total weight and volume of the magnetic spring will increase. At the same time, it needs to manufacture magnetic rings with different magnetization directions, leading to high costs.  
(2) The performance of QZS is sensitive to its structural integrity, which will impose strict demands on manufacturing and assembling magnets. As for the HCMS, there are multiple magnetic rings. Therefore, higher manufacturing and assembling precision is required. In return, the performance of the HCMS is susceptible to manufacturing or assembling errors in practice.  
(3) Both the design and the performance of HCMSs depend strongly on the material properties of the magnets. In engineering applications, however, the possibility of demagnetization or even complete loss of magnetism may happen in magnets due to long-term service or severe working conditions.  
(4) As for the HCMS, there is a conflict between the amplitude and the uniformity of negative stiffness. The reason may be because only an inner Halbach cylinder is used.  
Figure 16: The stiffness surface with respect to \(R_{2}\) and \(H\) (\(z=0\)).
## 6 Results
### Multiple Transmissions  
#### 6.4.1 Single-Path Transmission  
In this section, we selected three pairs of nodes, _Servera - Usera_, _Serverb - Userb_, and _Serverc - Userc_, for the experiment. They passed through a common transmission path: \(RouterA-RouterB\). We started the first set of transmissions at 12 o'clock with a bottleneck bandwidth of 36 Mbps and a link delay of 20 ms. Afterward, we added one set of transmissions every 5 s, totaling three sets of transmissions. We tested and recorded the throughput changes and the number of packet losses occurring in the link for the four schemes, as shown in Figure 9.  
In Figure 9, the upper half represents the amount of packet loss, while the lower half represents the transmission throughput. Figure 9 shows that all three comparison schemes would result in packet loss when new transmissions were added. The changes in the NewReno and Cubic schemes were similar, with the Cubic schemes experiencing more packet loss when new transmissions were added due to Cubic's more aggressive window-lifting algorithm. After experiencing packet loss caused by the addition of new transmissions, the original transmission rate of BBR rapidly decreased. This is due to the overestimation of the BDP by the later initiated connections, resulting in a queue backlog or congestion at network nodes. The RTT would also increase sharply for earlier connected transmissions, rapidly decreasing the transmission rate over several cycles. The Nodectl scheme did not generate congestion or packet loss. The graph shows that in the Nodectl scheme, the original transmission rate decreased before the new transmission rate increased. When a new transmission sends a negotiation request, the network node notifies the original transmission to adjust the rate. Currently, the newly transmitted data have not started transmission, avoiding congestion.  
Table 7 shows each transmission's average throughput after 10 s when three sets of transmissions were in progress simultaneously. It can be seen that the NewReno and Cubic schemes had higher transmission throughputs when starting transmission first. In the BBR scheme, the transmission throughput of the first initiated connection was greatly affected due to queue congestion at network nodes caused by connections created later. The Nodectl scheme allocates bandwidth among the network nodes, with almost the same transmission rates among the three groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **NewReno** & **Cubic** & **Bbr** & **Nodectl** \\ \hline (a) & 24.7\% & 37.9\% & 100.7\% & 99.8\% \\ (b) & 43.1\% & 46.9\% & 92.0\% & 100\% \\ (c) & 42.7\% & 53.1\% & 40.7\% & 86.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ratio of change in performance.  
Figure 9: Multiple single-flow transmission.
## 2 Experimental Method and Results
### Around Zero Dispersion Region  
Next, we discuss the dispersion region close to zero, where the net cavity dispersion \(D\) was from \(-0.0023\) to \(+0.0328\) ps\({}^{2}\). When film A was used and the spectral filter was not used, passive mode-locking was obtained within the \(+0.0065\) to \(+0.0328\) ps\({}^{2}\) range. In the range from \(+0.0065\) to \(0.024\) ps\({}^{2}\), smooth and mono-peak spectra without sidebands were observed. The spectral width was 8.4 to 11.6 nm. When \(D=+0.0328\) ps\({}^{2}\), a dissipative soliton pulse with steep edges was observed. The spectral width was as wide as 29.8 nm. When \(D=-0.0023\) ps\({}^{2}\), stable mode-locking was not obtained.  
Figure 7 shows the characteristics of output pulses when \(D=+0.0153\) ps\({}^{2}\). Mono-peak spectra without Kelly sidebands were observed stably, as shown in Figure 7a. The sharp spectral peaks were caused by the water vapor absorption. For the auto-correlation trace, the temporal width was 882 fs, as shown in Figure 7b, and the corresponding pulse width was 570 fs, under the assumption of a sech\({}^{2}\) shaped pulse. In the RF spectra, the SNR was more than 70 dB, as shown in Figure 7d, and good mode-locking was obtained.  
When the spectral filter and film A were used, as shown in Figure 4, stable mode-locking was not obtained in this dispersion condition. Here, we discuss the physical background for these results. Around the zero-dispersion region, stretched pulse mode-locking is the dominant mode-locking operation. In this operation, the spectral width of a mode-locked pulse becomes wider through the accompanying nonlinear effect. In order to sustain the broad pulse spectra, the wider bandwidth of a spectral filter is required for the generation of such pulses. In addition, previous studies have shown that a large  
Figure 6: Characteristics of output pulses when \(D=-0.144\) ps\({}^{2}\) and spectral filter was used, (**a**) optical spectra, (**b**) autocorrelation trace, (**c**) pulse train, (**d**,**e**) RF spectra of (**d**) fundamental frequency and (**e**) wide range.  
modulation depth is required to obtain a stretched pulse mode-locking operation [23]. Since the modulation depth of film A was smaller than that of film B, it was difficult to obtain passive mode-locking when film A and the spectral filter were used.  
Next, we focus on the condition in which film B was used and \(D\) ranged from \(-\)0.0023 to +0.0328 ps\({}^{2}\). Thanks to the large modulation depth in film B, stable mode-locking was achieved for these wider conditions, regardless of the usage of the spectral filter, as shown in Figure 4. The output power was lower than that obtained with film A.
# A Survey of Information Extraction Based on Deep Learning
## 3 Event Extraction Based on Deep Learning
### Summary  
As mentioned in this section, in terms of the accuracy and prediction effect of trigger word recognition and argument recognition, researchers currently prefer joint multi-task extraction methods. The experimental results have also proven many times that, compared with the pipeline or single-task extraction method, the multi-task joint extraction method causes trigger words and argument information exhibits a mutually reinforcing extraction effect. Since the information obtained from the extraction of isolated sentences is likely to be incomplete, researchers have shifted their focus to chapter-level extraction. However,  
Figure 9: The Architecture of DCFEE, which was designed to divide the event-extraction process into sentence-level extraction and document-level extraction into two parts [87].  
the accuracy of many experimental results does not meet the requirements of practical applications. However, there are still many other issues that need to be addressed. For example, in the current study of event extraction, most were extracted based on the existing annotated corpus. There are few studies and poor results, and current methods are only useful for specific event types. Therefore, establishing more standard and mature corpora and improving the extraction effect of unlabeled corpora are worthy of further research. For chapter-level event extraction, how to effectively integrate the information extracted from different sentences is also a major challenge to be solved, which requires researchers to overcome the lack of semantic understanding techniques within and across chapters. In addition, system performance and portability are the two most important factors restricting the widespread application of event extraction technology. Future research can focus on how to overcome and solve these two problems, continuously improve the performance of the event extraction system, and enhance its portability.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 2 Element Test of Viscoelastic and Rotational Friction Damper
### Damper Design and Material  
Figure 2a presents a model representing the behavior of the Kelvin solid model, while Figure 2b depicts the shear deformation of viscoelastic material. Parameters of the VE damper are obtained as below in Equations (1)-(4) [5].  
\[k=\frac{G_{E}A}{h} \tag{1}\]  
\[G_{C}=\frac{G_{E}\eta}{\omega} \tag{2}\]  
\[c=\frac{G_{C}A}{h} \tag{3}\]  
\[F(t)=kX_{0}(t)+c\dot{X_{0}}(t) \tag{4}\]  
Figure 1: VE and RF dampers in element test: (**a**) front view, (**b**) top view.  
Figure 2: VE damper schematic diagram: (**a**) Kelvin solid model, (**b**) VE material deformation.  
where  
\(k\) is elastic stiffness;  
\(c\) is damping coefficient;  
\(G_{E}\) is elastic shear modulus;  
\(G_{c}\) is shear viscous damping constant;  
\(A\) is VE surface area;  
\(\eta\) is loss factor;  
\(h\) is VE thickness;  
\(\tau_{s}\) is shear stress;  
\(\gamma_{s}\) is shear strain;  
\(\omega\) is angular frequency;  
\(X_{0}\) is displacement.  
Figure 3 shows the schematic diagram of RF damper which represents the activation moment. Equation (5) defines the moment capacity of the rotational friction damper, with the following parameters: number of friction surfaces (\(N=2\)), internal radius (\(r_{i}=5\) mm), and outer radius (\(r_{o}=75\) mm).  
\[M_{y}=\frac{2 NQ\mu({r_{o}}^{3}-{r_{i}}^{3})}{3({r_{o}}^{2}-{r_{i}}^{2})} \tag{5}\]  
where  
\(M_{y}\) is moment capacity;  
\(N\) is number of layers;  
\(r_{i}\) is internal radius;  
\(r_{o}\) is outer radius;  
\(\mu\) is friction coefficient;  
\(Q\) is clamping force.  
Figure 4a illustrates the front view of the disassembled section, while Figure 4b presents the cross-section view of the damper assembly. The viscoelastic damper was designed with a capacity of 3 kN, which is sufficient for a six-story frame. The selected VE polymer material exhibits a storage shear modulus (\(G_{E}\)) of 0.174 N/mm\({}^{2}\) and a loss factor (\(\eta\)) of 0.19, ensuring optimal energy dissipation performance. The VE damper comprises two VE layers, each measuring 40 mm in width, 70 mm in length, and 5 mm in thickness. The RF damper consists of four friction disks, each with a 75 mm radius and 12 mm thickness, paired with four SUS plates (156 mm \(\times\) 156 mm \(\times\) 2 mm) serving as sliding surfaces. The sliding interface between the friction disk and SUS plate provides a friction coefficient of \(\mu=0.2\) for the damper. Table 1 summarizes the material specifications of the dampers.  
Figure 3: Schematic diagram for calculating the activation moment of RF damper.
# Development of Dialogue Management System for Banking Services
## 4 Methods
### Tokenization and Feature Extraction
In our work, we implemented the CRF entity extraction using different tagging methods to handle the multi-word entity problem, for example \"credit card\". One of the methods is known as Beginning Inside Last Out Unit (BILOU) tagging [23; 52]. BILOU tagging is mostly used to label multi-word entities. For example, we have a \"albali credit card\" which is the \"card_type\" entity. To label this entity in our training data we use [albali](b-card_type) [credit](i-card_type) [card](l-card_type). Here b-, i-, l- are prefixes meaning beginning, inside, and last, accordingly. Additionally, u- (unit, for example [ubank](u-app)) tag is used to label one-word entities, and o-(out) is used to label words that are not the entities. The second method is using the part(particle) tag to note the word being the part of a multi-word entity. For instance, the entity example above will be tagged as [albali](part) [credit](part) [card](card) type. Another technique applied to handle multi-word entities is training sentence samples, which include multi-word entities separately from the sentences that include only one-word entities. We also applied NN for NER classification. However, performance of the NN were similar or lower than CRF for our dataset. For this reason, we proceeded further with only the CRF entity extractor.
## 4 Performance Analysis of Multi-Constellation Kinematic PPP
### Results and Analysis
#### 4.2.2 Positioning Accuracy and the RMS of the Kalman Filter  
To assess the kinematic positioning accuracy of the multi-constellation PPP, the double difference RTK results were regarded as the true coordinates. These results were computed using the GNSS/INS tightly coupled resolution of Inertial Explorer 8.60 software (IE 8.60), using the multi-constellation signals. The positioning accuracy of this software can reach 1-2 cm. Figure 4a shows the positioning results of multi-constellation kinematic PPP and RTK. When we zoomed in it in Figure 4b, we found that the position of GPS only was very different from that of RTK. There was also little error in the  
Figure 3: Coordinate increments in the following directions: north, east and up.  
Figure 2: Numbers of satellites and positional dilution of precision (PDOP) values of multi-constellation PPP. BDS: BeiDou Navigation Satellite System; GLONASS: Globalnaya Navigatsionnaya Sputnikovaya Sistema; GPS: global positioning system; G: GPS only; G + R: GPS/GLONASS combination; G + C: GPS/BDS combination; G + R + C: GPS/GLONASS/BDS combination.  
results of the GPS/GLOAASS PPP. The positions of the GPS/BDS and GPS/GLOAASS/BDS were very similar to those of the RTK. The differences between these RTK coordinates and the PPP results of the GPS only, GPS/GLOAASS, GPS/BDS and GPS/GLOAASS/BDS were compared to assess the performance of the multi-constellation kinematic PPP in detail. The mean square errors of these differences are shown in Table 3. From Table 3, we can see that the mean square errors of GPS were 0.045 m and 0.085 m in the east and north directions, respectively, which were much larger than those of GPS/BDS. With the GPS/BDS combination, accuracy improved by 71.11% and 41.18% over GPS only in the north and east directions, respectively. However, the positioning accuracy of GPS/GLOAASS was slightly poorer than that of the GPS/BDS. The reason would be that there were more visible GPS/BDS satellites than which of GPS/GLAONSS, and the PDOP of GPS/BDS was better than that of GPS/GLAONSS) (Figure 2). The accuracies of GPS/GLOAASS/BDS PPP were better than those of GPS/BDS by 30.77% and 56.00%.  
The standard deviations (STDs) of these differences are shown in Table 4, and the reference coordinates are the positioning results of the RTK. Smaller STDs indicate better results. As shown in Table 4, the STD of the GPS-only system was relatively large because the number of visible satellites was low (Figure 2). In contrast, the STD of the GPS/GLOAASS/BDS PPP was clearly the best.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **N** & **E** \\ \hline GPS & 0.067 & 0.091 \\ GPS/GLOAASS & 0.062 & 0.081 \\ GPS/BDS & 0.056 & 0.073 \\ GPS/GLOAASS/BDS & 0.042 & 0.060 \\ \hline \hline \end{tabular}
\end{table}
Table 4: STDs of the positioning errors for the four processing cases (unit: m).  
Figure 4: Multi-constellation kinematic PPP and real-time kinematic (RTK) positions.