esa-sceva/satcom-chunk-collection huggingface dataset:
Moment-Curvature Behavior of PP-ECC Bridge Piers under Reversed Cyclic Lateral Loading: An Experimental Study  
[PERSON]  
1 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (Y.B.)  
2 Earthquake Engineering Research Center of Yunnan, Kunming 650500, China 2  
[PERSON]  
1 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (Y.B.)  
2 Earthquake Engineering Research Center of Yunnan, Kunming 650500, China 2  
[PERSON]  
1 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (Y.B.)  
3 Department of Building Materials, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (Y.B.)  
3 Department of Building Materials, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (Y.B.)  
3 Department of Building Materials, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; [EMAIL_ADDRESS]  
4 Department of Civil and Environmental Engineering, Washington State University, Pullman, WA 99164, USA (RC) at the potential plastic hinge regions were performed experimentally. The bridge pier specimens were subjected to a combination of constant axial vertical loading and reversed cyclic lateral loading. The test variables include the reinforcement stirrup ratio, axial compression ratio, and height of the PP-ECC regions. Strain gauges were installed at the plastic hinge regions to determine the curvature. PP-ECC and RC bridge piers presented similar shapes of moment-curvature hysteretic curve. Regardless of the concrete type for the pier, the maximum moment and curvature were located near the bottom of the pier, which was consistent with the observed failure patterns. As greater peak moments and larger areas of hysteretic curves were observed for PP-ECC piers, this indicated that the use of PP-ECC at the potential plastic hinge regions significantly improved the deformation capacity and damage tolerance of bridge piers. Regarding the design variables, it was found that the axial loading ratio has a negative effect on enhancing the rotation capacity and plastic deformability, while the height of the PP-ECC portion and the amount of reinforcement stirrups displayed the opposite trend. Moreover, the contribution of stirrups in PP-ECC piers was more significant than that of RC ones.  
bridge pier; PP-ECC; hysteretic response; moment-curvature; plastic hinge
## 6 Reliability Prediction Model  
Bearing reliability prediction is realized by machine learning model training using dimensionality reduction features. Firstly, the key features are extracted from the original signal by feature engineering, and the redundant information of the features is reduced by the dimensionality reduction method, so as to improve the data processing efficiency. Then, the dimensionally reduced features are input into the model for training. Through the training and optimization of the model, an accurate prediction model can be obtained, which can effectively evaluate the reliability of bearings, make fault predictions, and provide important decision support for the maintenance and management of bearings.
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
###### Abstract  
Improving the quality of healthcare services is of the utmost importance in healthcare systems. Patient experience is a key aspect that should be gauged and monitored continuously. However, the measurement of such a vital indicator typically cannot be carried out directly, instead being derived from the opinions of patients who usually express their experience in free text. When it comes to patient comments written in the Arabic language, the currently used strategy to classify Arabic comments is totally reliant on human annotation, which is time-consuming and prone to subjectivity and error. Thus, fully using the value of patient feedback in a timely manner is difficult. This paper addresses the problem of classifying patient experience (PX) comments written in Arabic into 25 classes by using deep learning- and BERT-based models. A real-world data set of patient comments is obtained from the Saudi Ministry of Health for this purpose. Features are extracted from the data set, then used to train deep learning-based classifiers--including BiLSTM and BiGRU--for which pre-trained static word embedding and pre-training vector word embeddings are utilized. Furthermore, we utilize several Arabic pre-trained BERT models, in addition to building PX_BERT, a customized BERT model using the PX unlabeled database. From the experimental results for the 28 classifiers built in this study, the best-performing models (based on the F1 score) are found to be PX_BERT and AraBERTv02. To the best of our knowledge, this is the first study to tackle PX comment classification for the Arabic language.  
text classification; multi-label classification; deep learning; natural language processing; NLP; word embeddings; Arabic; patient experience; PX; LSTM; GRU; BERT
## 4 Materials and Methods
### Quantitative Real-Time PCR (qPCR)  
RNA was extracted from cells grown on MM containing 28 mM glucose and 100 \(\upmu\)M IPTG during the mid-exponential phase (OD600 nm 0.8-1.4) using the RNeasy Mini Kit (Qiagen GmbH, Hilden, Germany) with on-column DNA treatment. Total RNA was reverse-transcribed to cDNA with random primers using the RevertAid reverse transcriptase (Thermo Fisher Scientific Inc., Waltham, MA, USA). Total cDNA and specific primers (see Table S1) were added to Biozym Blue S'Green qPCR mix (Biozym Scientific GmbH, Hessisch Oldendorf, Germany). qPCR reactions were performed in a qTower 2.0 (Analytik Jena AG, Jena, Germany) and results were analyzed with the software qPCR 3.4 from the same manufacturer. _ftsZ_ gene was used as the reference gene.
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
## 2 Materials and Methods
### Uniform Test  
According to the single-factor test, the fly ash-cement content was 2.84. The water -cement, bentonite-water, cement-sand, water reducer-cement, and latex powder-cement  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Title**} & **SiO2** & **Al2O3** & **CaO** & **MgO** & **SO3** & **Fe2O3** & **Na2O** & **K2O** & **Loss** \\ \cline{2-9}  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline Cement & 21.44 & 5.85 & 61.28 & 1.25 & 2.01 & 2.75 & — & — & 5.42 \\ Fly ash & 58.12 & 27.93 & 2.72 & 0.64 & 0.83 & 4.85 & 0.13 & 1.31 & 3.47 \\ Bentonite & 72.15 & 13.42 & 2.69 & 2.36 & — & 1.59 & 0.55 & 0.36 & 6.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cementitious material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. Test**} & **Cement** & **Fly Ash** & **Sand** & **Bentonite** & **Water Reducing Agent** \\ \cline{2-5}  & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** \\ \hline A1 & 95 & 270 & 570 & 65 & 0.00 \\ A2 & 95 & 270 & 570 & 65 & 0.37 \\ A3 & 95 & 270 & 570 & 65 & 0.73 \\ A4 & 95 & 270 & 570 & 65 & 1.10 \\ A5 & 95 & 270 & 570 & 65 & 1.46 \\ A6 & 95 & 270 & 570 & 65 & 1.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The test mix proportion for single admixtures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. Test**} & **Cement** & **Fly Ash** & **Sand** & **Bentonite** & **Water Reducing Agent** & **Latex Powder** \\ \cline{2-5}  & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** \\ \hline B1 & 95 & 270 & 570 & 65 & 1.46 & 0.00 \\ B2 & 95 & 270 & 570 & 65 & 1.46 & 0.18 \\ B3 & 95 & 270 & 570 & 65 & 1.46 & 0.27 \\ B4 & 95 & 270 & 570 & 65 & 1.46 & 0.37 \\ B5 & 95 & 270 & 570 & 65 & 1.46 & 0.46 \\ B6 & 95 & 270 & 570 & 65 & 1.46 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The test mix proportion for compound admixtures.  
ratios were 0.73-0.88, 0.20-0.35, 0.64-0.79, 0.1-0.4%, and 0.05-0.125%, respectively. Based on the above five factors, a uniform test was designed, and the number of tests was set to four times the number of test factors to improve the test accuracy. Sixteen groups of experiments were arranged using a uniform design table [28], as listed in Table 4.
## 7 Performance Evaluation  
### Case Study and Experimental Setup  
In this section, we evaluate our proposed model's performance on a TSN system based on a realistic vehicle use case, as shown in Figure 13a. A simplified representative network model for the vehicle is presented in Figure 13b with two switches and four end systems. The targeted end systems can be sensors, cameras, or actuators used in the connected vehicles to collect the relevant information from the surrounding environment.  
Figure 13: Graphical model for the applied use case: (**a**) networking elements for a realistic vehicle use case and (**b**) representative networking model for the vehicle.  
We assume that physical links connect these network elements with a 1 Gbps data rate in all experiments. As each networking node has to differentiate the traffic to multiple priority queues, the initial GCL for associated queues is listed in Table 2 with full isolation between TT open windows. Note that \(Q_{4}^{h}\) is considered a targeted queue to evaluate our proposed formulated model under a changeable overlapping ratio with other lower- and higher-priority windows. As a reference, we initiate the largest size of \(Q_{4}^{h}\) and \(Q_{4^{+}}^{h}\) frames (\(f_{4}^{h,max}\)\(\mathcal{N}_{4^{+}}^{h,max}\)) by 400 and 300 bytes, respectively. The period of all TT queues is 250 \(\upmu\)s, the duration of \(\mathcal{Q}_{4}^{h}\) open windows is 20 \(\upmu\)s, and the initial offset between every two adjacent nodes is 20 \(\upmu\)s for the fourth-priority queues, as depicted in Table 2.  
As our model is an NC-based model, we use the Java API of the Real-Time Calculus (RTC) toolbox [49] to construct the GCL schedules and calculate the worst-case end-to-end latencies for the targeted TT queue. The RTC toolbox is an effective tool in real-time systems implemented based on min-plus and max-plus algebra operators for network calculus theory. The algorithm is run on a computer with an Intel Core i7-3770 CPU at 3.40 GHz and 12 GB of RAM.
# A Novel Optimization Layout Method for Clamps in a Pipeline System
## 2 Materials and Methods
### Process of the Optimization
#### 2.1.2 Prediction by Neural Network  
The flow chart of prediction by neural network is shown in Figure 3. Neural network usually consists of an input layer, a hidden layer, and an output layer. Each layer is distributed with a different number of nodes, and the nodes between the layers are connected. The transfer of data between neural networks is similar to the transmission of information in human neuronal structures. By training the neural network with data, the relationship between input and output can be obtained. The neural network topology is shown in Figure 4.  
The main steps of establishing a surrogate model for optimizing the pipeline system using a neural network are as follows:  
(1) Initialization of neural network  
(2) Randomly select the input and output samples to calculate the input and output of neurons in each layer.  
(3) Calculate the back-propagation error of each layer by comparing the network expectation and the actual output.  
(4) According to the calculation result, the weight is corrected on the basis of the correction formula.  
(5) Calculate the global error. If the error satisfies the preset accuracy requirement or the number of learning reaches the maximum convergence number, the correction is ended. Otherwise, the process will return to Step (3) and continue the loop until the convergence condition is met and a qualified neural network model is obtained.  
According to [PERSON]'s theory [26], a three-layer neural network with a hidden layer can simulate any nonlinear continuous function with precision. This makes the back-propagation neural network superior in nonlinear mapping ability, self-learning and self-adaptive to other algorithms. So, in this study, the neural network was used to construct the surrogate model.
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 2 Aluminum Plasmonics
### Treatment Mechanisms
#### 2.2.3 Hot Electron Therapy (HET)  
Hot carriers generated by coupling of the incident light to surface plasmons have been investigated for applications in plasmon-enhanced photocatalysis [67], photovoltaics [68; 69; 70], and photodetection [71; 72; 73]. Hot electron (HE) generation depends on the geometry of the plasmonic NPs [74].  
HE-based photodetectors [73] may find useful applications, especially in the UV spectral range. The limitations of UV-based photodetectors such as high dark currents and low quantum efficiency [75; 76] have previously been addressed using Au and Ag nanostructures. However, due to the poor plasmonic properties of Au and Ag in the UV range, other metals such as Al, Ga, and Cr have been used [15]. The broad tunable SPR of Al NPs may be useful in fabrication of HE-based photodetectors.
## Semantic Web Approach to Ease Regulation Compliance Checking in Construction Industry  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 2, [PERSON] 1 and [PERSON] 2**  
1 French Scientific and Technical Centre for Building (CSTB), 290 route des Lucioles, BP 209, 06904 Sophia Antipolis, France; E-Mails: [EMAIL_ADDRESS] (K.R.B.); [EMAIL_ADDRESS] (A.Z.)  
2 The I3S laboratory (Laboratoire d'Informatique, Signaux et Systemes de Sophia-Antipolis), University of Nice Sophia Antipolis and CNRS, BP 121, 06903 Sophia Antipolis, France; E-Mails: [EMAIL_ADDRESS] (C.F.Z.); [EMAIL_ADDRESS] (N.L.T.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-493-956-756; Fax: +33-493-956-733.  
_Received: 6 July 2012; in revised form: 23 August 2012 / Accepted: 27 August 2012 / Published: 11 September 2012_  
**Abstract:** Regulations in the Building Industry are becoming increasingly complex and involve more than one technical area, covering products, components and project implementations. They also play an important role in ensuring the quality of a building, and to minimize its environmental impact. Control or conformance checking are becoming more complex every day, not only for industrials, but also for organizations charged with assessing the conformity of new products or processes. This paper will detail the approach taken by the CSTB (Centre Scientifique et Technique du Batiment) in order to simplify this conformance control task. The approach and the proposed solutions are based on semantic web technologies. For this purpose, we first establish a domain-ontology, which defines the main concepts involved and the relationships, including one based on OWL (Web Ontology Language) [1]. We rely on SBVR (Semantics of Business Vocabulary and Business Rules) [2] and SPARQL (SPARQL Protocol and RDF Query Language) [3] to reformulate the regulatory requirements written in natural language, respectively, in a controlled and formal language. We then structure our control process based on expert practices. Each elementary control step is defined as a SPARQL query and assembled into complex control processes \"on demand\", according to the component tested and its semantic definition. Finally, we represent in RDF (Resource Description Framework) [4] the association between the SBVR rules and SPARQL queries representing the same regulatory constraints.  
keywords:ontology; semantic web; knowledge management; building industry; e-regulations; assisted checking; rule based system +
Footnote †: journal: Journal of the Royal Society Interface
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method  
Existing process monitoring methods of transition modes are mainly divided into qualitative and quantitative trend analysis methods, such as the artificial intelligence method, hard classification and soft classification, and the integrated learning method.
# Advances in Hyperspectral and Multispectral Optical Spectroscopy and Imaging of Tissue
## 1 Introduction  
Optical imaging and characterization of tissue has become a huge applied field due to the advantages of the optical analysis methods, which include non-invasiveness, portability, high sensitivity, and high spectral specificity. This research field continues to grow and spread in many different directions due to the development of new light sources and detectors, such as, for example, the supercontinuum and tunable lasers, portable highly sensitive spectrometers, multiwavelength photoacoustic images, due to novel methods of data analysis, such as the machine-learning methods of spectral analysis, and due to novel applications, such as the imaging of embryogenesis or monitoring of the cerebral oxygen metabolism. Polarization analysis of the anisotropy of optical, mechanical, and electrical properties of materials is another active research direction in hyperspectral imaging from THz to IR spectral ranges.  
The purpose of this Special Issue is to provide an overview of recent advances in the methods of tissue imaging and characterization, which benefit from using large numbers of optical wavelengths.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 8 Conclusions  
Blockchain technology has received substantial attention for its ability to address security, privacy, and reliability challenges inherent to decentralized energy trading solutions. However, the diversity of the energy markets and the absence of an extensible framework impede the widespread adoption of blockchain-based P2P-ET solutions globally. In this study, we introduce a novel unified and extensible blockchain-based P2P-ET framework, BPET, to bridge this gap. This framework adopts the performant private Hyperledger Besu blockchain and microservices as its infrastructures and enables developers to swiftly build a localized P2P-ET system on top of it. We designed and implemented a prototype of BPET by developing modular smart contracts, deploying them onto the blockchain network, and constructing a decentralized application. To validate the proposed solution, we conducted a case study using real AESO energy data and evaluated the performance of smart contracts using the Hyperledger Caliper benchmark tool. The experimental results demonstrate the system's capability of handling energy trading transactions effectively and efficiently within the Alberta electricity wholesale market. For future research topics, it is of interest to explore the integration of automated market maker mechanisms to enhance market liquidity and efficiency. It is also interesting to develop and incorporate more energy markets as practical examples to enhance the usability of the BPET framework. Additionally, our interest is to investigate the feasibility of incorporating cutting-edge zero-knowledge machine learning technologies into P2P-ET to enhance the intelligence and privacy of blockchain-based energy systems.  
Conceptualization, C.F.; methodology, C.F., H.K. and P.M.; software, C.F.; validation, C.F.; formal analysis, C.F. and H.K.; investigation, C.F.; resources, P.M.; data curation, P.M.; writing original draft preparation, C.F.; writing, review, and editing, C.F., H.K. and P.M.; visualization, C.F.; supervision, H.K. and P.M.; project administration, P.M.; funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Huawei-ECE Research Initiative at the University of Alberta.  
All original data used in this research are from the Alberta Electric System Operator. The real-time and historical provincial electric wholesale market data are publicly accessible at [[http://ets.aeso.ca/](http://ets.aeso.ca/)]([http://ets.aeso.ca/](http://ets.aeso.ca/)) (accessed on 5 May 2024).  
Digital Research Alliance of Canada (alliancecan.ca) and Cybera (cybera.ca) partially supported this research work through their cloud computing resources.  
The authors declare no conflicts of interest.
## 4 Voltage Vector Distribution Method
### Linear Partition Method
Equation (26) is also suitable for this occasion. There are four examples shown in Figure 8. In the first situation, line \(l_{1}\) intersects \(\overset{\rightarrow}{u_{s}^{*}}\) right inside the feasible range of voltage vector distribution, thus the first calculated \(\overset{\rightarrow}{u_{s1}^{*}}\) is valid. Under this circumstance the output power of inverter1 follows \(P_{inv1}^{*}\) accurately. In the second situation, \(P_{inv1}^{*}\) is too big so line \(l_{1}\) is completely out of inverter1's modulation range. Thus, \(\overset{\rightarrow}{u_{s1}^{*}}\) has to be shortened to theboundary of the hexagon centered on \(O_{1}\). In the third situation, the first calculated \(\vec{u^{*}_{s1}}\), which makes the output power of inverter1 equals to \(P^{*}_{imv1}\), is out of feasible range of voltage vector distribution because the corresponding \(\vec{u^{*}_{s2}}\) is beyond its modulation range. Thus, after \(\vec{u^{*}_{s2}}\) is shortened to the hexagon centered on \(O_{2}\), \(\vec{u^{*}_{s1}}\) is recalculated to compensate the shortage. In the fourth situation, the amplitude of \(\vec{u^{*}_{s}}\) is too big so that the two hexagons centered on \(O_{1}\) and \(O_{2}\) have no overlapping region. Thus, \(\vec{u^{*}_{s1}}\) and \(\vec{u^{*}_{s2}}\) both reached their maximum amplitude collinearly with \(\vec{u^{*}_{s}}\) in the opposite direction to synthesize \(\vec{u_{s}}\), which is the best available result.