esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electrochemical Analysis
#### 3.3.3 Electrochemical Impedance Spectroscopy  
To further explore the corrosion behavior of N80 steel in different conditions, we conducted EIS tests at various stages. During the immersion period of 0 to 14 days, the EIS results showed the presence of high-frequency capacitive reactance arcs and low-frequency capacitive reactance arcs, as illustrated in Figure 10. Typically, larger diameters of the capacitive arcs indicated a more difficult charge transfer on the metal surface and a lower degree of corrosion [31]. As the immersion time extended, the complete capacitive arc shifted gradually towards the low-frequency region, and its diameter progressively increased, indicating a gradual reduction in the level of corrosion. As a result, the Bode figure showed a progressive increase in the impedance modulus at lower frequencies, accompanied by an increase in the maximum phase angle. This observation suggested an enhancement in the sample's ability to resist corrosion over time [32]. Concurrently, the buildup of products from corrosion on the metal exterior led to a gradual strengthening and densification of the film resulting from corrosion, which continued until the 14 th day of immersion. However, by the 28 th day, the intact corrosion product film either peeled off or suffered destruction, leading to a continuation of the corrosion intensification. This assessment was based on the comparison of the Bode plot data, where both the impedance modulus and the maximum phase angle at 28 days exhibited a decrease in comparison to the 14-day immersion period.  
The Nyquist diagram and the corresponding Bode diagram at different temperatures displayed distinctive trends. With the rise in temperature, there was an initial enlargement followed by a subsequent reduction in the radius of the capacitive arc. Notably, the capacitive arc at 80 \({}^{\circ}\)C exhibited a significantly larger radius compared to the other two temperatures. Moreover, the impedance modulus and the corrosion resistance also followed a similar pattern of first increasing and then decreasing with increasing temperature.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Conditions** & **E\({}_{\text{corr, SCE}}\)/V** & **I\({}_{\text{corr}}\)/A\(\cdot\)cm\({}^{2}\)** & **R\({}_{\text{p}}\)/\(\Omega\cdot\)cm\(
# Timing Analyses in FWE Evaluation
## 2 Problem Statement and Issues
### Implementation of Circuits with Two Coupled Lines
In conclusion, achieving a minimal jitter value is essential for maintaining the reliability, integrity, and synchronization of signals in various communication and electronic systems.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 3 Scaling up RINA Networks
### Providing QoS Guarantees
The scheduling capacity of the IPCP is constant, the QTA policy allows network designers and administrators to decide how this scheduling capacity can be allocated through different QoS classes within a RINA DIF. We performed a second experiment in a multi-layer scenario, to validate RINA's consistent QoS model across layers. The physical systems in the experiment setup are shown in Figure 10, with 41 nodes featuring a provider network that connects together 18 CPE routers belonging to two different metropolitan regions, via a core network. Each metropolitan region features a Metropolitan Area Network (MAN) that aggregates the traffic of all the CPEs in the region towards the core network. The DIF structure is the same as displayed by Figure 6. Only the residential customer DIF is equipped with QoS differentiation capabilities and the QTA scheduling policies, all the other DIFs just feature simple FIFO scheduling policies. The goal of this scenario was to increase the scale of the former QoS scenario experiments, in terms of the number of RINA-enabled nodes (from 16 to 41), DIFs (from 1 to 6) and flows (from 128 to 1296), as well as to validate the behavior in a multi-layer environment. Figure 9: End-to-end delay Cumulative Distribution Function (CDF) for flows between PEs, using QTAMux (**left**) and FIFO (**right**) schedulers in the DIF. QoS1 and QoS3 are the urgent classes. Each CPE router in the network is running an instance of the rina-echo-time server application and another instance from the rinaperf server application, accepting client flows from other CPE nodes. Each CPE node runs 4 rinaperf flows at 1 Mpbs and 4 rina-echo-time flows to all the other CPEs in the other MAN, each flow requesting a different level of quality. Hence each node is running 72 concurrent flows that result in 40 Mbps of traffic (16 \(\ast\) 1 Mbps or rinaperf traffic and 4 Mbps of aggregated rina-echo-time traffic). In total the scenario features 1296 concurrent flows. Figure 11 depicts result of this experimental setup, showing the delay and loss measured by the 18 rina-echo-time flows at nodes CPE63 and CPE41 respectively (the flows in other nodes exhibit a very similar pattern). The Figure show flows grouped in two very differentiated sets: flows belonging to high urgency classes experience a relatively short end to end delay (around 20-30 ms), while flows belonging to low urgency classes experience a very high delay (around 1300 ms). The QTA scheduling policies effectively guarantee low latency for high urgency classes, but the delay experienced by low urgency classes is too high--even taking into account that the bottleneck links are highly loaded (this is due to the lack of performance optimizations in the IRATI implementation).
## 2 Numerical Modeling and Validation
### Validation of Numerical Modeling  
Figure 5 compares the results from numerical models with experimental results for the test walls described in Section 2.1 in terms of load-displacement hysteresis curves and secant stiffness degradation curves. It can be seen from the figure that the hysteresis curves obtained from the numerical simulation are in good agreement with the test results for both of the selected wall specimens. The magnitude of the peak load, the initial stiffness, and the hysteresis energy dissipation are all reasonably matched with the test results. Secant stiffness degradation can be calculated on the basis of the hysteresis curves, and it also shows good agreement between the simulation and the experimental results. Please note that Table 2 lists all of the values of force and displacement corresponding to the characteristic points for both the simulation and the experimental results in the skeleton curves (in both positive and negative directions). These characteristic points include yielding point, peak capacity point and ultimate capacity point.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wall ID** & \multicolumn{2}{c}{**Loading Direction**} & \(F_{\mathbf{y}}\) & \(D_{\mathbf{y}}\) & \(F_{\mathbf{p}}\) & \(D_{\mathbf{p}}\) & \(D_{\mathbf{u}}\) \\  & & **Direction** & **(kN)** & **(mm)** & **(kN)** & **(mm)** & **(mm)** \\ \hline \multirow{4}{*}{GSFCW-1} & \multirow{4}{*}{Test} & positive & 803.47 & 10.94 & 1079.54 & 52.90 & 92.60 \\  & & negative & 840.59 & 13.10 & 930.00 & 55.40 & 91.90 \\ \cline{1-1}  & & positive & 898.69 & 7.75 & 1078.70 & 20.00 & 75.57 \\ \cline{1-1}  & & negative & 890.34 & 7.52 & 1060.00 & 20.00 & 91.00 \\ \cline{1-1}  & & positive & 931.24 & 4.81 & 1223.11 & 26.64 & 66.80 \\ \cline{1-1}  & & negative & 885.27 & 7.55 & 1070.00 & 26.40 & 60.04 \\ \cline{1-1}  & & positive & 1019.34 & 7.92 & 1190.00 & 13.00 & 40.07 \\ \cline{1-1}  & & negative & 1002.53 & 7.90 & 1180.00 & 14.00 & 35.73 \\ \hline \hline \end{tabular}  
* Notes: \(F_{\mathbf{y}}\) and \(F_{\mathbf{p}}\) are the lateral force capacity of specimens at the yield and peak point, respectively; \(D_{\mathbf{y}}\), \(D_{\mathbf{p}}\) and \(D_{\mathbf{u}}\) are the lateral displacement at the loading point of the specimens at the yield point, peak point and ultimate point, respectively; and the ultimate point is the point at which the bearing capacity decreases to 85% that of the peak point.  
\end{table}
Table 2: Comparison of characteristic points of skeleton curves.  
Figure 4: The constitutive relationship of concrete: (**a**) tension; (**b**) compression; (**c**) loading–unloading protocol.
# The Efficient Processing of Moving \(k\)-Farthest Neighbor Queries in Road Networks +
## 5 MOFA Algorithm for M\(k\)FN Query Processing in Road Networks
Algorithm 3 computes the valid segments for \(\overline{q_{s}q_{e}}\) by using candidate facilities in \(\Sigma(q_{s})\cup\Sigma(q_{e})\). The computation of the valid segments for the example M&FN query is presented in detail in Section 6.2. First, \(\Phi(\overline{q_{s}q_{e}})\) is initially set to the empty set. While \(q\) moves along \(\overline{q_{s}q_{e}}\), the distance from \(q\) to each candidate facility \(f\) in \(\Sigma(q_{s})\cup\Sigma(q_{e})\) is calculated, i.e., \(\{(q,d(q,f))|q\in\overline{q_{s}q_{e}},\,f\in\Sigma(q_{s})\cup\Sigma(q_{e})\}\). Subsequently, \((q,d(q,f))\) is plotted in the two-dimensional space. For each query point \(q\) in \(\overline{q_{s}q_{e}}\), the \(k\)th FN \(f_{kth}\) and its distance to \(q\) are identified among the candidate facilities in \(\Sigma(q_{s})\cup\Sigma(q_{e})\) as follows: \(\{(q,d(q,f_{kth}))|q\in\overline{q_{s}q_{e}},f_{kth}\in\Sigma(q_{s})\cup\Sigma (q_{e})\}\). The valid segments in \(\overline{q_{s}q_{e}}\) are computed while \(q\) moves along \(\overline{q_{s}q_{e}}\). Each valid segment has the same \(k\) facilities farthest from the valid segment. Specifically, for each \(q_{i}\) and \(q_{j}\) in the same valid segment \(\overline{q_{i}q_{m}}\), both \(q_{i}\) and \(q_{j}\) have the same \(k\) facilities farthest from them, that is, \(\forall q_{i},q_{j}\in\overline{q_{i}q_{m}}\), \(\Phi(q_{i})\)=\(\Phi(q_{j})\). Finally, the set of valid segments for \(\overline{q_{s}q_{e}}\), \(\Phi(\overline{q_{s}q_{e}})\) is returned. ```
0:\(k\): number of FNs requested for \(q_{i}\), \(\overline{q_{s}q_{e}}\): query segment, \(\Sigma(q_{s})\cup\Sigma(q_{e})\): set of candidate facilities for \(\overline{q_{s}q_{e}}\)
0:\(\Phi(\overline{q_{s}q_{e}})\): set of valid segments for \(\overline{q_{s}q_{e}}\) // \(\Phi(\overline{q_{s}q_{e}})\) is initially set to the empty set. 1:\(\Phi(\overline{q_{s}q_{e}})\leftarrow\emptyset\)
2:for each candidate facility \(f\in\Sigma(q_{s})\cup\Sigma(q_{e})\)do
3: evaluate \(d(q,f)\) while \(q\) moves along \(\overline{q_{s}q_{e}}\), i.e., \(\{(q,d(q,f))|q\in\overline{q_{s}q_{e}},f\in\Sigma(q_{s})\cup\Sigma(q_{e})\}\)
4: plot \((q,d(q,f))\) into the two-dimensional space
5:for each query point \(q\in\overline{q_{s}q_{e}}\)do
6: identify the \(k\)th FN \(f_{kth}\) while \(q\) moves along \(\overline{q_{s}q_{e}}\), i.e., \(\{(q,d(q,f_{kth}))|q\in\overline{q_{s}q_{e}},f_{kth}\in\Sigma(q_{s})\cup\Sigma (q_{e})\}\)
7: compute \(k\) facilities farthest from \(q\) while \(q\) moves along \(\overline{q_{s}q_{e}}\), i.e., \(\Phi(q)=\{f|d(q,f)\geq d(q,f_{kth}),f\in\Sigma(q_{s})\cup\Sigma(q_{e})\}\)
8: identify and add valid segment to the query result, i.e., \(\Phi(\overline{q_{s}q_{e}})\leftarrow\Phi(\overline{q_{s}q_{e}})\cup\{ \overline{(q_{s}q_{m}},\Phi(q_{i}))\}\)
9:return\(\Phi(\overline{q_{s}q_{e}})\)
```  
**Algorithm 3**\(compute\_valid\_segments(k,\overline{q_{s}q_{e}},\Sigma(q_{s})\cup\Sigma(q_{e}))\).
## 1 Introduction  
Bioactive glass (BG) is a biomaterial with the ability to form bone-like carbonated apatite in the living body by interacting with phosphate ions in body fluids. BG was developed by [PERSON] in 1969 and has been used as a bone substitute. When BG is implanted in a bone defect, it bonds directly to bone via an apatite layer, without the interposition of connective tissues [1].  
In recent years, BG-containing endodontic cements have been developed and marketed [2, 3]. Previous studies have reported that these cements exhibit favorable biocompatibility [4, 5]. In addition, these cements are thought to form an apatite layer at the interface with the dentin wall, dental pulp, or periodontal ligament [6]. The apatite layer formed between the material and dentin wall contributes to the prevention of bacterial leakage through the interface [7, 8]. In addition, the apatite layer formed between the material and dental connective tissues acts as a substrate for the attachment, proliferation, and differentiation of stem cells, thereby supporting tissue healing [9].  
Nishika Canal Sealer BG Multi (NBG) (Nippon Shika Yakuhin Co., Ltd., Yamaguchi, Japan) is a newly developed BG-containing cement indicated for both root canal fillings and vital pulp therapy. NBG comes in a semi-liquid, flowable consistency, although it can be prepared to a putty-like consistency by mixing the flowable NBG (F-NBG) with a designated BG-based powder (Nippon Shika Yakuhin Co., Ltd.). This putty-like consistency enables easier handling during vital pulp therapy.  
Previous studies have reported that both F-NBG and putty-form NBG (P-NBG) induce apatite formation within 4 days of immersion in artificial body fluid (simulated body fluid [SBF]) with an ion concentration close to that of human blood serum [10; 11]; however, the apatite-forming ability (AFA) levels of F-NBG and P-NBG have not been compared. Since AFA depends on the presence of BG, the AFA of P-NBG, which is prepared by adding BG-based powder to F-NBG, may be superior to that of F-NBG.  
Most previous studies have used SBF to evaluate the AFA of biomaterials [12]; however, SBF is inorganic, whereas real body fluids contain non-negligible amounts of serum proteins [13]. Notably, albumin, the most abundant serum protein, is reported to suppress apatite formation on biomaterials [14; 15]; therefore, AFA testing using albumin-containing SBF will provide more clinically relevant findings than testing using inorganic SBF.  
The purpose of this study was to evaluate and compare the AFA levels of F-NBG and P-NBG in SBF and SBF with various concentrations of bovine serum albumin (BSA). We also compared the original elemental composition, calcium ion release, and alkalizing ability levels of F-NBG and P-NBG in order to explore the reason for their different AFA values.
# Model-Based Design and Simulation of Paraxial Ray Optics Systems
###### Abstract  
A model-based design allows representing complex, multi-domain systems as interconnected functional blocks, yielding graphical, intuitive information about the overall project, besides simplifying simulation. This work proposes using the modular approach as an optical engineering design and educational tool for developing paraxial ray optics setups, providing further integration with mechatronics subsystems and control loops. An expanded version of the ABCD transfer matrix modeling is implemented in MATLAB Simulink environment to simultaneously perform ray tracing and dynamic simulations. The methodology is validated for different problems, including paraxial cloaking, transmission through a multimode optical fiber, a Fabry-Perot interferometer, and an optical pickup with automatic focus, yielding reliable results with prospective applications in optical engineering design and for creating virtual labs devoted to multiphysics and mechatronics engineering courses.  
Keywords:optical design; optical engineering design; optical engineering education; mechatronics; ray tracing; systems modeling +
Footnote †: journal: Applied Sciences
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 2 Methodology and Resources
### Literature Review
The study of [22] introduces an approach based on Graph Spatio-temporal Neural Networks with Attention Mechanisms (GSTANN) to consider both the temporal and spatial correlation of features. Unlike the aforementioned investigations, satellite images are used together with terrestrial measurements for the power forecast in the subsequent 45 and 60 min. For this last forecast horizon, its results show that its GSTANN model is superior to the rest of the models tested in their research and, considering the present state of the art, this is why it will be our reference model: to overcome it. The comparison between the models from the literature review is presented in Table 4.
## 1 Introduction
Focusing the attention on the first case, when two semiconductors (a p-type semiconductor with a n-type semiconductor) are in contact, the diffusion of electrons and holes leads to the formation of a space-charge region at the interfaces. When irradiated, the photogenerated electrons are transferred from the CB of one photocatalyst (generally the p-type) to the CB of the other photocatalyst forming the heterojunction (generally the n-type), while the holes can migrate between the valence bands of the semiconductors, depending on the relative redox potentials; the phenomenon leads to more efficient charge separation and a lower electron-holes recombination rate. The heterojunction formation by combining TiO\({}_{2}\) (n-type semiconductor) and Cu\({}_{2}\)O (p-type semiconductor) is a viable route to improve solar light utilization [38; 39; 40; 41]. Among the different techniques reported in the literature to prepare Cu\({}_{2}\)O/TiO\({}_{2}\) composite material, a simple and industrially practicable ball milling procedure to dryness was selected as an alternate strategy for preparing photocatalysts in our recent study [42]. The results of the previous study allowed the selection of the best photocatalyst for hydrogen generation prepared by mixing Cu\({}_{2}\)O and TiO\({}_{2}\) (i.e., 1%wt. Cu\({}_{2}\)O in TiO\({}_{2}\) photocatalyst material prepared through ball milling method at a rotation rate of 200 rpm and milling time of 1 min). In the present work, we have chosen to use the best photocatalyst identified previously, to evaluate the possibility to use real solar conditions to produce hydrogen. Firstly, to optimize the photocatalytic hydrogen production in presence of the best-performing photocatalyst, the effect of the following variables on the hydrogen production is herein investigated:  
The concentration of the sacrificial species;  
Type of sacrificial species used;  
Temperature and pH of the system;  
Then, the photoefficiency under direct sunlight irradiation was evaluated. The quantum yield and the light-to-chemical energy are estimated under the optimal conditions seen based on the data.
## 4 Experiments and Results
### Experiment Results and Analysis
#### 4.4.1 Relevant Comparison Experiments  
We conducted comparative experiments with five methods published in recent years, as follows:  
1. In 2015, [PERSON] et al. [41] proposed a solution based on word embedding and bidirectional LSTM recursive neural networks for unified tagging.
2. In 2015, Baidu-Research [5] proposed a solution for text annotation using bidirectional Long Short-Term Memory (LSTM) networks and Conditional Random Fields (CRF).
3. In 2018, [PERSON] et al. [18] proposed BERT, a deep bidirectional pre-training language model based on Transformer.
4. In 2020, [PERSON] et al. [42] proposed a research method based on the BERT-BiLSTM-CRF model.
5. In 2021, [PERSON] et al. [17] proposed LEBERT, a model for solving Chinese sequence labeling through integrating lexical information into the underlying encoding process of BERT.  
To verify the practicality of the method proposed in this study, we selected BiLSTM, BiLSTM-CRF, BERT, BERT-BiLSTM-CRF, and LEBERT as baseline models for comparative experiments. The results of the comparative experiments are shown in Table 5.  
As shown in Table 5, the entity recognition performance of the method proposed in this study on the mine hoist dataset is significantly superior to that of models such as BiLSTM, BiLSTM-CRF, BERT, and BERT-BiLSTM-CRF. This is primarily due to the specificity of the text describing mine hoist faults, which can easily lead to entities belonging to different types due to varying contexts. Consequently, the BiLSTM method's lack of perception of local features and information loss becomes more apparent. However, the introduction of BERT has led to a particular improvement in the accuracy, recall, and F1 score of entity recognition compared to the BiLSTM and BiLSTM-CRF models, effectively addressing the issue of entity polysemy. After the LEBERT model incorporated word features into text embedding, compared to BERT on the mine hoist dataset, the precision of entity recognition increased by 13.74%, and the F1 score increased by 14.33%. Through constructing a mine hoist domain dictionary, the method proposed in this study is more sensitive to semantic changes at the word-level brought about via domain-specific terminology. Compared to BERT, the accuracy of entity recognition increased by 14.06%, and the F1 score increased by 14.59%. In comparison with LEBERT, the accuracy of entity recognition only increased by 0.32%, and the F1 score increased by 0.26%. The domain dictionary can effectively enhance the entity recognition effect in domains with scarce datasets.
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 2 Materials and Methods
### Materials
#### 2.1.2 HL7 FHIR  
For the sharing of medical imaging and medical data, an HL7 Fast Healthcare Interoperability Resources (FHIR) standard server was used. HL7 is a standards committee established to promote the exchange of information between different medical information systems and recognized worldwide with countries such as the United States, United Kingdom, Australia, Japan, Taiwan, New Zealand, and others designating and using HL7 as national standards [10]. The HL7 Application Programming Interface (HAPI) FHIR server is the official FHIR test server of HL7 [11]. There are multiple test servers such as \"Graham's Test Server\", \"Healthcare Services Platform consortium (HSPC) Sandbox\", and \"Vonk\", among others, but the HAPI FHIR server is open-source and can be operated on local systems. Operation guidelines are also available and were used in this study.  
The HAPI FHIR server environment was executed on Windows 10 with \"jdk1.8.0_211\" and \"jre1.8.0_211\" used for Java based application development software. Library builds and management needed for server operations used Apache Maven with Apache Tomcat 8 used to execute the server.  
HL7 FHIR designates XML and JavaScript Object Notation (JSON) as the data format standard and uses the term \"resource\" as the smallest unit of exchanged information [12]. HL7 officially lists resources and the respective resource used for different layouts is listed in Table 1.  
Resources are constantly being added and edited following further research by HL7, and there are currently 146 resources listed as of 2021 [13].
## 4 PPP Accuracy Evaluation
### Kinematic PPP Performance
The consistency of performance improvement among systems based on multi-GNSS data quality, broadcast ephemeris accuracy, and precision positioning performance is analyzed. It can be found that the observation data quality of GPS is improved by 28.11%, and the improvement for SISRE, static PPP, and kinematic PPP is 75%, 44.31%, and 69.24% compared to GLONASS, respectively. Compared to GLONASS, the improvement of data quality, SISRE, static PPP, and kinematic PPP of Galileo is improved by 32.71%, 75%, 5.52%, and 28.37%, respectively. The improvement of BDS-3 is 33% and 32.78% compared to BDS-2 in terms of observation data quality and SISRE, respectively. These results show that there is a consistency between data quality, SISRE, and PPP. Figure 11: Mean convergence time for kinematic PPP in each station.