esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The aBCI System
#### 2.2.3 ERP Trial Features  
In the study, the auditory BCI prototype first searched out sound-evoked potentials of the P3 peak and the N2 trough out of the ERPs gained from EEG after every trial. Thus, the system can obtain an N2P3 potential: the P300 potential minus the N200 potential. We used the values of N2P3, P300, and N200 to interpret the discriminative features and identify the best system accuracy in the post-analysis.  
Our aBCI system provides two options for the user in every trial: one audio story via the left earphone (L) and the other via the right earphone (R). If the participant focuses on the audio story in the right earphone, then the figure of the ERP components in this trial resembles Figure 4. In Figure 4, the green circles mark the locations of the P3 peaks in the ERP feature, while the red marks indicate the N2 troughs. The P300 potential minus the N200 potential in the same curve is the N2P3 potential. The results using any one component of ERPs, N2P3, P300, and N200, to distinguish which audio story the participant listened to all indicate that the participant focused on the option from the right channel in this trial. Thus, the red curve (R_2) in Figure 4 is what the participant chose in this trial.  
Figure 3: Two of the twelve audio stories (original text written in Chinese). Each story file includes a keyword (words rendered in boldface) that appears seven times in the story.
## 1 Introduction  
Transparent conducting electrodes (TCEs) are widely used in optoelectronic devices such as light-emitting diodes (LEDs), solar cells, liquid crystal displays, and touch screens. Owing to increasing demand for these products, significant research is being conducted to enhance their performance by manufacturing process optimization. Among many candidate materials, indium tin oxide (ITO) is generally used in many applications. However, ITO has drawbacks, such as having a high cost, unsatisfactory light transmission, chemical instability, and a weak ion barrier effect [1, 2]. In addition, ITO exhibits strong absorption in the ultraviolet (UV)/blue spectral range [3]. Therefore, it is necessary to find more suitable materials to applicate transparent electrode. Metal nanowires (NWs) such as silver NWs and carbon nanotubes have been studied as promising candidates for replacing ITO in optoelectronic devices [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. Among them, Ag NWs are especially promising because of their high conductivity (\(\sigma_{\text{Ag}}=6.3\times 10^{7}\) S m\({}^{-1}\)) compared to other metals commonly used as electrode materials for LEDs [15]. In addition, Ag is well known as a good material for obtaining surface plasmon (SP) resonance with a GaN LED, which increases the IQE and LEE [16, 17, 18, 19, 20]. Our group recently demonstrated the near-UV LEDs with high-quality solution-processed Ag NW TCEs with sheet resistances of 30-40 \(\Omega\)/sq and optical transmittances of over 85% to replace ITO-based TCEs [3]. This fabrication process is quite simple and relatively inexpensive, like that for ITO-based TCEs, suggesting that Ag NWs are promising candidate materials for use in next-generation TCEs in LEDs. However, many groups have only focused on replacing ITO-based TCEs with Ag-NW-based TCEs [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. By contrast, to enhance the properties of Ag-NW-based TCEs, our group recently reported TCEs based on Ag NWs and nanoparticles (NPs) in which localized surface plasmons (LSPs) are excited; consequently, the internal quantum efficiency (IQE) is improved by dipoles of quantum well (QW) and SP coupling of Ag metal, and the light extraction efficiency (LEE) is improved by NPs working as a leakage mode and act as scattering centers [16]. However, the optimal structure for adopting the Ag-NW/NP-based TCE in III-nitride LEDs is still unknown.  
In this work, we studied the improvement of the efficiency of LEDs with Ag-NW/NP-based TCEs. In addition, the use of Ag NWs and NPs also increases the IQE and LEE [14]. To improve the efficiency of LEDs with the Ag-NW/NP-based TCE, we attempted to control the spacing between NW and NP and the thickness of the p-GaN layer between the TCE and QWs. When a 45 nm thick p-GaN layer and 35 nm NPs at a high density of 8 \(\times\) 10\({}^{10}\) cm\({}^{-2}\) were used, the efficiency of the LED was significantly improved, owing to the phenomena described above.
## 4 Results and Evaluation
### Statistical Analysis
#### 4.2.2 De-Correlation Analysis  
Surface reflectance of remotely sensed image in mountainous area is positively correlated with illumination condition (IC, cosine of the local incident angle) due to topographic effect [18,20]. Technically, the higher the correlation coefficient is, the stronger the terrain effect is. Thus, the residual correlation is commonly examined to quantitatively indicate whether or to what extent the topographic effects still exist after topographic correction [18,20]. (It is noteworthy that aspect-related correlation between reflectance and illumination condition may remain after fully topographic correction, and this will be discussed in the Discussion Section.) Near infrared (NIR) band is suggested to be used, because it is less influenced by atmospheric scattering than visible bands [4]. Figure 5 demonstrates scatter plots between reflectance and IC for the two TM scenes both before and after topographic correction by different models, respectively. Before plots being drawn, invalid reflectance pixels (negative reflectance resulting from atmospheric overcorrection), cloud and cloud shadow pixels were screened out.  
Before correction, as can be seen from Figure 5a,e, the reflectance shows a strong linear correlation with IC. The points are uniformly distributed in both sides of the fitted line, and correlation coefficients between NIR reflectance and IC reach 0.774 and 0.733 for spring and autumn scene, respectively. It implies that original images were subject to quite serious topographic effects. After topographic correction with different methods, correlation coefficients decreased to different levels. Among them, the proposed model in this paper always gained the lowest absolute value of correlation coefficients (\(-0.049\) and \(-0.0779\)), which are very close to zero. For results from Li model, the correlation coefficients are \(-0.117\) and \(-0.084\), a litter larger (in absolute value) than that from model proposed. Sandmeier model obviously performed the worst, with quite large negative coefficients (\(-0.416\) and \(-0.258\)), indicating serious overcorrection. Moreover, it can be seen that the overcorrections mainly concentrate in the low IC value area, and the corresponding points almost form a \"tail\" shape (Figure 5b,f), which is consistent with the phenomenon seen in Figure 2. This may suggest that Lambertian assumption need to be discarded when performing ter  
Figure 4: Reflectance intensity differences of band 4 between results from model proposed and Li model, (**a**) for the spring scene, and (**b**) for the autumn scene.  
mountainous region. Besides, according to some researches [19,30,53], slopes of the regression lines of the scatter plots are also important in indicating the topographic correction effects. From this point of view, the same conclusion for the model performances could also be drawn, since the slopes of regression lines before and after topographic correction show exactly the same variation trend with correlation coefficients (seen from equations in Figure 5).
## 4 Conclusions  
The varying heat dissipation is one of the main constraints of WAAM, degrading geometric accuracy and limiting productivity. Both reducing the heat input via adjusting the process parameters and enhancing the heat dissipation via thermoelectric cooling have the potential to overcome this obstacle. However, the latter exhibits great superiorities as demonstrated in this study. The following conclusions can be drawn from the present study:  
1. The upper and the lower layers could experience a similar heat dissipation condition with the aid of thermoelectric cooling, therefore resulting in similar thermal cycling, molten pool shape, and, ultimately, bead geometry with no need to adjust the process parameters. The case study shows a decrease of 56.8% in bead width error and an increase of 16.3% in material utilization.
2. The productivity of the WAAM process can be significantly improved with thermoelectric cooling, due to not only the increased ratio of bead height to bead width, but also the reduced interlayer dwell time. The case study shows a decrease of 60.9% in total fabrication time.
3. Much finer microstructures can be obtained in the middle parts of the deposited walls, and are attributed to the increased cooling rate. The average grain size is reduced from 7.96 \(\upmu\)m to 5.97 \(\upmu\)m.  
In conclusion, the technique offers an innovative way to regulate bead geometry during WAAM, significantly improving its capability in fabricating thin-walled structures in terms of geometric  
Figure 9: Microstructures in the middle parts of the deposited layers (**a**) with thermoelectric cooling; (**b**) without thermoelectric cooling. Microstructures in the bottom parts of the deposited layers (**c**) with thermoelectric cooling; (**d**) without thermoelectric cooling.  
accuracy, productivity, and microstructure. This method can also be extended to other layered manufacturing processes. Future work will focus on the thermal modeling of the WAAM process for accurate adjustment of the cooling power, and improvement of the active cooling system such that it could be fitted to parts with more complex shapes.  
This paper was supported by the National Natural Science Foundation of China (No. 51475009), China Postdoctoral Science Foundation (No. 2017M610726) and Postdoctoral Research Foundation of Chaoyang District (No. 2017 ZZ-01-09).  
[PERSON] developed the experimental setup and wrote the manuscript; [PERSON] designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data.  
The authors declare no conflict of interest.
# Computational Aspects of \(L_{0}\) Linking in the Rasch Model
## 2 \(L_{0}\) Linking in the Rasch Model
### Identified Item Parameters and Mean-Mean Linking  
To introduce the \(L_{0}\) linking method as a replacement for ordinary mean-mean linking in the Rasch model, we first outline the identification of item parameters in a group-specific estimation of the Rasch model when no DIF effects are present. In this case, the identified item parameters in the first group are \(\widehat{b}_{i1}=b_{i}\), where \(b_{i}\) represents the invariant item parameters across both groups. For identification, the mean of \(\theta\) in the first group is fixed at \(0\), while the standard deviation \(\sigma\) remains identifiable within this group.  
In the second group, the mean \(\mu\) and the SD \(\sigma\) can be identified when invariant item difficulties are assumed. Thus, \(\mu\) and \(\sigma\) represent group differences in \(\theta\). The identified item parameters in this group, estimated separately under the assumption of a \(\theta\) mean of \(0\) and an estimated SD \(\sigma\), are given by  
\[\widehat{b}_{i2}=b_{i}-\mu. \tag{3}\]Linking methods aim to recover the group parameter \(\mu\) using the group-specific item parameters obtained from separate Rasch model estimations.  
The MM linking method estimates the group mean \(\hat{\mu}\) for the second group as  
\[\hat{\mu}=-\frac{1}{I}\sum_{i=1}^{I}\left(\widehat{b}_{i2}-\widehat{b}_{i1}\right). \tag{4}\]  
The linking parameter \(\hat{\mu}\) in MM linking is obtained as the minimizer of the squared loss function (i.e., the \(L_{2}\) loss function; [19])  
\[\hat{\mu}=\operatorname*{arg\,min}_{\mu}H(\mu),\text{ with }H(\mu)=\frac{1}{I} \sum_{i=1}^{I}\rho\left(\widehat{b}_{i2}-\widehat{b}_{i1}+\mu\right),\text{ where }\rho(x)=\frac{1}{2}x^{2}. \tag{5}\]  
Thus, the estimate \(\hat{\mu}\) satisfies the estimating equation  
\[\frac{1}{I}\sum_{i=1}^{I}\rho^{\prime}\left(\widehat{b}_{i2}-\widehat{b}_{i1}+ \hat{\mu}\right)=0,\text{ where }\rho^{\prime}(x)=x \tag{6}\]  
and \(\rho^{\prime}\) denotes the derivative of \(\rho\) with respect to \(x\). Clearly, (6) is equivalent to (4).  
This paper investigates the computational aspects of MM linking in (5) when the squared \(L_{2}\) loss function is replaced with the \(L_{0}\) loss function, which aims to robustify the linking method in the presence of fixed DIF effects.
# Dynamic Load Test and Contact Force Analysis of the AERORail Structure
## 3 AERORail Dynamic Load Test and Analysis
### Dynamic Load Identification
#### 3.2.3 15 m Span Dynamic Load Identification  
The acceleration and displacement data measured over the 15 m span are processed and displayed using similar methods for the 5 m and 10 m spans. Using the speed and time information obtained by identification and the filtered mid-span acceleration signal, the vehicle-AERORail contact force-time curve shown in Figure 15 can be identified and obtained.  
The results of the 15 m span show the following rules: (1) the curve shape is bowl-shaped with a gentle trough; (2) the contact force fluctuation has obvious high-frequency fluctuations at higher speeds (greater than or equal to 15 km/h) and (3) the contact force is small at low speed. The single-valley curve reflects that the peak of the contact force occurs when the moving force passes through the mid-span, rather than after the start or end of the loading; the fluctuation of the contact force is mainly contributed by the coupled vibration of the vehicle-rail, and the vibration effect gradually increases with increasing vehicle speed. Characteristics such as the mean value and the maximum value of the 10 m span are further analyzed and the results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **5 km/h** & **10 km/h** & **15 km/h** & **20 km/h** & **25 km/h** & **Mean** & **Maximum Value** \\ \hline mean force (N) & 4520.0 & 6601.6 & 3809.1 & 3075.9 & 4586.8 & 4518.7 & 6601.6 \\ max force (N) & 9421.9 & 1,0573.0 & 8319.6 & 8043.7 & 1,1760.7 & 9623.8 & 1,1760.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical characteristic table of the 10 m span contact force curve.  
Figure 15: The contact force identification result of the 15 m span. (**a**) 5 km/h, (**b**) 10 km/h, (**c**) 15 km/h, (**d**) 20 km/h.  
The data with a span of 15 m do not have obvious linear reduction characteristics, and the data volatility is obvious. Specifically, it has the following three characteristics: the average contact force fluctuates between 1300 and 1900 N with the speed change; the maximum value of the contact force fluctuates to a certain extent with the change in vehicle speed, but the average value is relatively stable; there is no obvious correlation between the statistical properties of the contact force curve and the vehicle speed. The above characteristics are basically consistent with the analysis conclusion of the 10 m span.
## 4 Analysis of a Divisible Load Application in a Star Network Cloud  
### Modeling of Tasks Processing  
As discussed previously, each computing worker consists of three process components, including task receiving process denoted as Station 1, a computing process denoted as Station 2 and a transmission process denoted as Station 3. The Station 1 depicts the time delay needed for receiving a task from the master worker before starting the computing process. The Station 2 defines the time needed to execute the received task. The Station 3 depicts the time needed to transfer the result obtained at the Station 2 back to the master worker.  
These three stations, associated with a computing worker \(r\in\{1,2,...,R\}\), are connected in a tandem model without any queuing spaces between them. In the process of receiving tasks at the Station 1, the task flow arriving at the receiver is a Poisson process and the entire process of receiving tasks from master worker is exponential distribution with a mean value of \(\mu_{1,r}\) tasks per second since each task is accumulated by a bulk of data packets in sequence order with Poisson distribution. Likewise, the processing at the Station 2 is an exponential distribution with a mean value of of \(\mu_{2,r}\) tasks per second. The transmission of the results back to master worker at the Station 3 is performed in a manner that the results are packetized into data packets with exponential distribution, so that the transmission time of each packet is also exponentially distributed. Hence, the transmission of tasks at the Station 3 is exponentially distributed with a mean value of \(\mu_{3,r}\) tasks per second.  
A computing worker \(r\) is operating based on the following procedures:  
* The computing worker \(r\) is a tandem connected sequential processing chain.
* The master worker does not assign a new task to the computing worker \(r\) if an application task is in process at Station 1, even if Station 2 and/or Station 3 are empty.
* An application task is blocked when it completes the process at any Station and finds that the next Station is busy.
## 4 Conclusions  
In the paper, the results of an extensive experimental work on sustainable improvement of a pyroclastic soil belonging to the Orvieto cliff (Central Italy) have been investigated in the perspective of its preservation from degradation. The suitability of Orvieto pyroclastic soil to lime treatment has been analysed by means of a multi-scale analysis. The link between chemo-physical evolution of the soil-ime-water system (microscale level) and mechanical behaviour of treated samples (macroscale level) has been thoroughly investigated. The main findings allow the following conclusions to be drawn, at different scales of investigation:  
* portlandite, chabazite and amorphous phases are the main phases involved in pozzolanic reactions induced by lime addition;
* by comparing diffractometer and thermogravimetric analyses, it is evident that secondary hydrated phases, mainly C-A-H and C-S-H, are formed since the very short time;
* the observed compressibility upon loading in oedometer conditions is relevantly affected by the formation of hydrated secondary phases; the reduction of compressibility and the increase of yield stress are directly linked to the amount of newly formed hydrated phases, in turn depending on the amount of added lime and duration of curing time.  
Methodology, M.C., C.C., D.D., G.R.; experimental tests, S.C., E.V.; SEM analyses and interpretation, D.D.; data interpretation, M.C., C.C. and G.R.; writing--review and editing, M.C., G.R. All authors have read and agreed to the published version of the manuscript.  
Part of this research was developed in the context of the Italian project \"Ricerca di Base, Sercizio 2018, University of Perugia, titled\" Rischio sismico di edifici stratiromontentali: rilivo e monitoraggio, aspetti stratturali e goctanci ed interreuti di ricostruzione post-sisma secondo una progettazione soleinibile\" and \"Ricerca di Base, Sercizio 2017, University of Perugia, titled \"Multi-scale analysis for the correlation of mechanical, micro-structural and chemo -mineralogical evolution of different lime treated soils\".  
The authors declare no conflict of interest.  
Figure 8: Compressibility curves from conventional oedometer tests on raw and lime-treated OR samples at increasing curing time.
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 1 Introduction  
For orthodontic treatment of malocclusions, the insertion of fixed multibracket appliances is a common and reliable treatment option. Since the introduction of the straight-wire technique, ideal positioning of bracket has become of eminent importance [1, 2, 3, 4, 5]. Incorrectly positioned brackets can lead to undesirable tooth movement and extended treatment time.  
Instead of chairside positioning (direct bonding), ideal bracket positions can be planned prior to treatment (indirect bonding). This approach was first described by [PERSON] and [PERSON] in 1972 [6] and has the advantage of unrestricted vision, reduced chair time, and increased patient comfort [7]. With regard to the rate of bracket loss in vivo and shear bond strength in vitro, direct and indirect approaches were reported to be comparable [8, 9, 10, 11]. In the classical indirect bonding technique, brackets are positioned on plaster models and transfer templates are fabricated in the laboratory [12, 13], which are frequently made of single- or double-layer silicones, vacuum-formed sheets of various thicknesses or a combination of both [14, 15, 16, 17, 18]. Nowadays, computer-assisted processes offer a timeefficient alternative. Software tools allow for semiautomated determination of ideal bracket positions and virtual design of transfer trays [19], which are commonly manufactured by means of 3D printing.  
Few in vitro studies compared transfer accuracy of CAD/CAM technology for indirect bracket placement to conventional ones and revealed comparable accuracies [20; 21; 22]. However, it is not yet clear whether severe crowding and the hardness of 3D-printed transfer trays impact on bracket transfer accuracy. Additionally, the workflow to assess potential bracket transfer inaccuracies remains to be validated, as artefacts from intraoral scanning may impair the measurements [23].  
Therefore, the present study aimed at investigating the impact of transfer tray hardness and crowding on bracket transfer accuracy, and to validate whether intraoral scanning is an eligible tool to assess potential errors.