esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 2 Methodology of the Literature Review  
This section will elaborate on the methodology of this literature review. In this study, a comprehensive and in-depth look was taken at 189 papers on recent examples of digital twins using different energy simulation software and emerging machine learning algorithms. There are two main reasons for choosing the method. On the one hand, by studying these papers in depth, we can have a more comprehensive understanding of the latest research trends and development directions; On the other hand, this is a general method that has been used in many review studies. The systematic approach is divided into several steps: identification, screening and classification, conformity assessment, and analysis.  
In the identification phase, the keywords, \"Digital Twin\", \"BIM\", \"BEM\", and \"Machine\" were searched extensively in Google Scholar and the University of Sydney Library's bibliography.  
For the screening and classification stage, after accumulating numerous papers, an initial screening was conducted based on the titles and abstracts of the articles to ensure that the papers were relevant to our research objectives. During this process, our focus was narrowed gradually to eliminate papers that were not relevant to our research objectives while we classified the literature. As the title and abstract are the most important parts to reflect the theme and content of an article, screening by title and abstract can effectively narrow the scope of research and improve research efficiency. Then, the literature found was put into three main categories:  
1. Those exploring background knowledge on the topic.
2. Those related to established software applied to building energy management.
3. Those related to emerging machine learning models being applied to building energy management.  
During the conformity assessment phase, an in-depth study was conducted on the selected articles to assess the quality and relevance to further narrow the study's scope. Factors considered in the assessment include the date of publication (with a significant emphasis on recent papers), the quality of the research methodology, the use of innovative software or machine learning algorithms, and the impact of the research in the field.  
Finally, a thorough analysis and summary of the final selected literature was conducted in the analysis phase. Papers are categorized according to the type of energy simulation software and machine learning algorithms used in the papers. An insight was provided into applying these techniques in the digital twins and BIM field. At the same time, we assess their strengths and weaknesses and possible directions for future development. When assessing strengths and weaknesses, we refer to previous studies and evaluations of these technologies to ensure the comprehensiveness and accuracy of our assessment.
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 2 Materials and Methods  
In this study, quasi-longitudinal waves were employed as a promising candidate to look into the behavior of pipe structures affected by impacts in water transmission mains. In addition, a wavelet-based approach was also exploited to enhance signal detectability and to reduce noise. Quasi-longitudinal waves are axis-symmetric waves, generated and developed in a fluid interacting with axis-symmetric cylinders, such as pipes, which allow the waves to propagate farther than elastic waves would by themselves. Therefore, the approach proposed here is identified as having high potential for long-range monitoring with non-invasive sensors, thanks to the interaction of the fluid and structure. On the other hand, the vibration sensors are vulnerable to noise from surrounding environments. This means that it is inevitable that the wavelet analysis will be deployed together with an additional signal processing technique. To make the wavelet analysis effective, it is necessary to determine the feature of noises, and an approach appropriate to making clear any distinctive features should be applied.
## 4 Proposed Network Model  
The used network to evaluate the proposed clustering protocol is composed of \(n\) nodes uniformly distributed in a field Q \(\times\) Q square meters. The existence of a link is specified only by the distance among nodes, without taking into account disruptions due to obstructions and interference of wireless signals. A cluster is formed by specifying the cluster head and its cluster members. The cluster member is directly connected to the cluster head. Figure 4 illustrates the proposed network architecture.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Protocol** & **Year** & **Technique** & **Classification** & **Strengths** & **Weaknesses** \\ \hline DCHEP [29] & 2015 & \begin{tabular}{c} Remaining energy \\ measuring and \\ other sensors \\ connectivity \\ \end{tabular} & \begin{tabular}{c} Hybrid \\ clustering \\ scheme \\ \end{tabular} & \begin{tabular}{c} (1) Scalable and flexible solution \\ targeting intense WSNs with \\ random mobility; \\ (2) Achieves high growth in energy \\ performance and availability \\ compared to LEACH. \\ \end{tabular} & \begin{tabular}{c} (1) Residual energy calculated for \\ the nodes is unsuccessful in the \\ case of the nodes’ \\ rapid movement; \\ (2) Resulstin continuous \\ connectivity loss. \\ \end{tabular} \\ \hline \begin{tabular}{c} The Energy Efficient \\ Multi-Hop \\ Clustering Process \\ for Data \\ Transmission in \\ Mobile Sensor \\ Networks [30] \\ \end{tabular} & \begin{tabular}{c} Energy level \\ Measuring, \\ Stability, \\ Connectivity \\ \end{tabular} & \begin{tabular}{c} Hybrid \\ clustering \\ scheme \\ \end{tabular} & \begin{tabular}{c} (1) Introduces high bandwidth and \\ low delay in the network; \\ (2) Periodical cluster head election is \\ enhanced to form effective routing \\ and reduce energy consumption; \\ (3) Good connectivity and stability. \\ \end{tabular} & \begin{tabular}{c} (1) Periodically maintaining the \\ routing table makes the security \\ side weak; \\ (2) Residual energy calculating of \\ nodes is often unsuccessful in \\ rapid nodes movement, especially \\ in the critical cases; \\ (3) Delays due to the failure of \\ some nodes. \\ \end{tabular} \\ \hline LEACH [31] & 2002 & \begin{tabular}{c} A random CH \\ election \\ Adaptive, \\ cluster formation \\ \end{tabular} & \begin{tabular}{c} Deterministic \\ schemes \\ \end{tabular} & \
\begin{tabular}{c} (1) Distributes power waste evenly \\ across the sensors; \\ (2) High scalability; \\ (3) Robusts the security side; \\ (4) Simple, low overhead, and fast. \\ \end{tabular} &
\begin{tabular}{c} (1) The random method adds \\ more limitations; \\ (2) Some nodes cause a waste of \\ energy in transmitting data to an \\ area too far from cluster heads; \\ (3) High redundancy of data. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 4 Blockchain System Architecture for Safety Supervision of Special Equipment  
### System Users  
System users include equipment users, primary supervisors, inspection agencies, supervisory agencies at all levels, and third-party certification authority. All of them will use the system in their work. However, some system users will not participate in the establishment of the blockchain. Only system users whose work involves supervision information that needs to be recorded are blockchain participants. The organizations joining the Hyperledger Fabric blockchain network include enterprise using special equipment, primary supervision team, inspection agencies, and supervisory agencies at all levels. The most typical participants in a channel are 2-3 representatives of enterprise using special equipment, 2-3 primary supervisors, a representative of inspection agency, and a representative of a senior supervisory agency.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
###### Abstract  
This paper presents a non-intrusive solenoid valve fault diagnosis method, which adopts the eigenvalue extraction scheme and combines the time-frequency analysis and time-domain analysis of the current signal through the solenoid valve fault pattern recognition method. Specifically, the research group selected the solenoid valve driving end current value and its second-order change rate curve as the characteristic curve, and then it extracted the time-domain parameters and the corresponding frequency band energy of the second-order change rate as the characteristic value. Then, the research group conducted time-frequency analysis on the basis of the current signal, created a multi-feature fusion feature vector and then input it into a multi-class support vector machine (SVM) for solenoid valve fault diagnosis. To evaluate the method, the research group developed a working solenoid valve model that integrated a magnetic circuit, circuit and spool motion, and the research group examined four typical solenoid valve states (normal, spring broken, spool stuck and spool slightly stuck), for which the research group collected the current signals during solenoid valve energization and closing. The trials involved analyzing the current characteristics of the drive end, both in simulation and experimental setups, and the research group demonstrated that the proposed drive end current detection diagnosis method can improve the accuracy of recognition by 15.22% compared with traditional neural network recognition methods. The SVM strategy exploiting a multi-feature fusion strategy can improve recognition accuracy by 8.7% and verification accuracy by 42.11% compared with SVM on the basis of the energy feature values.  
s olenoid valve; fault diagnosis; current detection; multi-feature fusion; support vector machine key +
Footnote †: journal: Applied Sciences
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 4 Experiments
### Evaluation Settings  
The proposed method was implemented on PyTorch [27]. The model was trained using an Adam optimizer with \(\beta_{1}\) = 0.9 and \(\beta_{2}\) = 0.999. The input images were randomly selected and randomly cropped to 512 \(\times\) 256 size based on the random forest approach [28] during the training process. We set the maximum disparity (D) to 192.  
First, we trained the model with the Scene-Flow dataset [24] for 10 epochs with a learning rate of 0.001. For evaluation on the Scene-Flow dataset, the trained model was directly used. For validation on KITTI datasets, the model trained with the Scene-Flow dataset was used for fine tuning on for 300 epochs. For the first 200 epochs, we set the learning rate of fine tuning to 0.001, and for the final 100 epochs, to 0.0001. For KITTI 2012 and 2015 submission, we combined both KITTI 2012 and KITTI 2015 training sets to fine tune the pretrained model on KITTI for 500 epochs and used the KITTI 2012 and 2015 validation sets for the KITTI 2012 and 2015 submission, respectively. For the first 300 epochs, we set the learning rate of fine tuning to 0.001, and for the final 200 epochs, to 0.0001. We set the batch size to 12 for Scene-Flow and to 6 for KITTI. Training was implemented on an NVIDIA GTX 1660. We used smooth L1 loss. We set the loss weights of the three output stages to 0.3, 0.5, and 1.0.  
We evaluated the performance of our approach using two metrics. For the Scene-Flow dataset [24], we calculated endpoint error (EPE), which is the mean absolute error (MAE) between disparity prediction and ground-truth disparity. For the KITTI 2012 [25] and KITTI 2015 [26] datasets, we used three-pixel error (3 px), which is the percentage of pixels with a disparity of less than three pixels or less than 5% of the ground-truth disparity. To evaluate the computational costs, we relied on floating-point operations (FLOPs). The processing speed was clocked using Google Colab (Nvidia Tesla T4) in milliseconds (ms).  
Figure 4: Example scenes from datasets. From top to bottom: Monkaa, Driving, FlyingThings3D in SceneFlow, and KITTI 2012 and KITTI 2015. From left to right: left image, right image, and ground-truth disparity.  
The proposed method was evaluated against AAFS [12] and other state-of-the-art methods, namely PSMNet [10], DeepPruner-fast [20], and StereoNet [13]. The results are summarized and discussed in the next section.
## 3 Results  
### Cyclic Load Test  
Table 1 showed the mean cyclic number and broken number/total number of specimens tested until fracture. The result showed 11061.6 (11/15), 12278 (6/15), 15617 (5/15), and 8000 (1/15) for the implant abutments with occlusal surface areas of SA100, SA75, SA50, and SA25, respectively.  
\begin{table}
\begin{tabular}{|c c c|} \hline \multirow{2}{*}{**Abutment Occlal Surface Area (\%)**} & **Total Cycles** & **Broken Specimens/** \\  & **Mean \(\pm\) SD** & **Total Specimens** \\ \hline SA100 & 11,061.6 \(\pm\) 4602.6 \({}^{\text{a}}\) & 11/15 \\ \hline SA75 & 12,278 \(\pm\) 4139.3 \({}^{\text{abc}}\) & 6/15 \\ \hline SA50 & 15,617 \(\pm\) 3508.4 \({}^{\text{bc}}\) & 5/15 \\ \hline SA25 & 8000 \({}^{\text{bc}}\) & 1/15 \\ \hline P value \({}^{\text{*}}\) & 0.001 & \textless{}0.01 \\ \hline \end{tabular} Different superscript letters in the column represented statistical significance between groups (P < 0.05; post hoc Dunn test). Total cycles indicated total number of cycles ran before specimens broke under 300 N loading or 30,000 cycles if specimens remained intact. SD: Standard deviation. a, b, c are notations depicting different class variations.  
\({}^{\text{*}}\)Kruskal-Wallis test (K independent samples).  
[PERSON]’s correlation coefficients (\(r=0.475\)) in association with different abutment designs and occlusal surface areas to fracture resistance of zirconia crown.  
\end{table}
Table 1: Results of zirconia specimens on four occlusal surface areas of implant abutment after cycling test under vertical 300 N loading.  
The broken specimens of four occlusal surface areas showed specimens breaking into two to three fragments (Figure 3A-D). The cyclic number of broken specimens showed a significant difference among different groups of specimens (P = 0.001). [PERSON]'s honestly significant difference test indicated that the specimen breakage of SA100 was statistically significant when comparing to SA50 and SA25 (P = 0.001). Figure 4 showed that the percentage of intact specimens were 27% (SA100), 60% (SA75), 67% (SA50), and 93% (SA25), without visible fracture line found after 30,000 cycles. Specim breakage had a moderately strong association with the implant abutment occlusal surface area (r = 0.475).
## 4 Results and Discussion  
### Steady-State Rolling Analysis  
#### 4.1.1 Contact Stress and Contact Area  
To analyse the contact stress and contact area of the simplified pavement according to aggregate grain size, the 3D type-pavement contact model was used to simulate the steady-state rolling of a vehicle at a driving speed of 60 km/h, as shown in Figures 11 and 12.  
As shown in Figure 11a-c, the general extent of contact stresses between the tire and each simplified pavement was similar; however, the different aggregate sizes resulted in different magnitudes of contact stress on each pavement. The peak stresses on the simplified pavements with aggregate grain sizes of 7, 9, and 13 mm were 6.77, 8.28, and 9.67 MPa, respectively. As shown in Table 3, the _MTD_ values of the simplified pavements with aggregate grain sizes of 7, 9, and 13 mm were 1.61, 2.07, and 2.86 mm, respectively. These results show that both the _MTD_ values and peak contact stresses on the simplified pavements were positively correlated with the aggregate grain size.  
As shown in Figure 12a-c, the overall shape of the contact area between the tyre and each simplified pavement was similar; however, the size of the contact area decreased with the change in the macrotexture of the simplified pavement corresponding to the increase in aggregate grain size. To quantitatively simplify the contact area characterisation of the pavement according to aggregate grain size, we outputted the contact area of each simplified pavement in the software post-processing, as shown in Figure 13.  
Figure 11: Contact stresses of simplified pavement during steady–state rolling. (**a**) Aggregate grain size 7 mm. (**b**) Aggregate grain size 9 mm. (**c**) Aggregate grain size 13 mm.  
Figure 12: Contact areas of simplified pavements during steady–state rolling. (**a**) Aggregate grain size 7 mm. (**b**) Aggregate grain size 9 mm. (**c**) Aggregate grain size 13 mm.  
As shown in Figure 13, the maximum contact areas of simplified pavements with aggregate grain sizes of 7, 9, and 13 mm were 1292.5, 1137.2, and 744.3 mm\({}^{2}\), respectively. These results demonstrate that the contact area of the simplified pavement decreased as its aggregate grain size increased, indicating that pavements with large _MTD_ values will have small contact areas.
## Nomenclature  
\[\begin{array}{ll}\overrightarrow{v}_{RST}(t)&\text{vector of grid voltages}\\ V_{M}&\text{grid voltages magnitude}\\ \varphi&\text{currents vector angle With Respect To. voltages vector angle}\\ \overrightarrow{i}_{RST}(t,\varphi)&\text{vector of grid currents}\\ I_{M}&\text{grid currents magnitude}\\ PF=\cos\varphi&\text{power factor}\\ S&\text{total apparent power}\\ \overrightarrow{m}_{RST}(t)&\text{vector of modulation indices}\\ M(t)&\text{modulation indices magnitude}\\ m_{0}(t)&\text{zero-sequence modulation component}\\ \overrightarrow{v}_{DC}(t)&\text{vector of partial DC link voltages}\\ v_{DC1}(t),v_{DC2}(t)&\text{individual partial DC link voltages}\\ \overrightarrow{p}_{RST}(t,\varphi)&\text{instantaneous AC-side phase power vector}\\ p_{RST}(t,\varphi)&\text{total instantaneous AC-side power}\\ \overrightarrow{v}_{ABC}(t)&\text{vector of converter imposed AC-side voltages}\\ V_{DC}^{*}&\text{DC link voltage reference value}\\ V_{DC1}^{*},V_{DC2}^{*}&\text{split DC link set point voltages}\\ C_{DC1},C_{DC2}&\text{split DC link capacitances}\end{array}\]
# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 2 Literature Review
### KeyBERT  
KeyBERT is a keyword extraction method in the Python library developed through research and development led by [PERSON]. The model enables users to extract keywords or key phrases from the given text and embed sentences or documents into high-dimensional vector representations using BERT. KeyBERT builds on top of the hugging face library, which provides a user-friendly environment for working with pre-trainedBERT models [6]. KeyBERT also uses the TF-IDF and Maximal Marginal Relevance (MMR), which can be used to extract the most relevant and diverse keywords from given texts. In addition, KeyBERT also provides sentence embedding capabilities, which allow users to transform sentences or documents into high-dimensional vector representations that can be used for text classification or clustering. [PERSON] et al. [7] proposed KeyBERT for domain adaptation. They extract the abstractive summaries through neural language models and use these summaries for keyword extraction in semantic analysis. [PERSON] et al. [8] used the KeyBERT model to extract the keywords from documents and match them with author-assigned keywords. They compare their model with other keyword extraction methods and find that KeyBERT extracts better keywords than others. [PERSON] et al. [9] used the KeyBERT model to extract true and fake news related to COVID-19 and identified the keywords as false and true news. [PERSON] et al. [10] proposed a KeyBERT model for extracting the keywords from medical and non-medical service guidance. [PERSON] et al. [11] used the KeyBERT model with other methods, such as YAKE and RAKE, to extract the lightweight keywords from new articles in a multilingual setup. Figure 1 illustrates the concept of how KeyBERT works.  
The procedure for extracting keywords from a document using KeyBERT involves installing the library, loading the target document, initializing the KeyBERT model with a pre-trained language model, calling the extract keywords function to obtain a list of top keywords along with their similarity score, and utilizing these keywords for various purposes such as improving search engine optimization, categorization, or identifying critical topics [11]. Overall, KeyBERT's keyword extraction process entails utilizing a pre-trained language model to identify the most relevant and informative words in a given document. To obtain representative keywords, the embeddings of each word and document are computed using Equation (1). Subsequently, the resulting embeddings are sorted in descending order, and the top n items are selected based on the highest similarity between the word and document embeddings. This similarity indicates the degree of representativeness of the document, with higher values indicating greater representativeness.  
\[Similarity\ (A,B)=\frac{A\cdot B}{||A||*||B||}=\frac{\sum_{i=1}^{n}Ai*Bi}{ \sqrt{\sum_{i=1}^{n}A_{i}^{2}*\sqrt{\sum_{i=1}^{n}B_{i}^{2}}}} \tag{1}\]
# In Vitro Gene Expression Responses of Bovine Rumen Epithelial Cells to Different pH Stresses
###### Abstract  
Ruminal acidosis often occurs in production, which greatly affects animal health and production efficiency. Subacute rumen acidosis (SARA) occurs when rumen pH drops rapidly to 5.5-5.8, and acute rumen acidosis (ARA) occurs when rumen pH drops below 5.0, but the molecular regulation mechanism of the rumen epithelium after the rapid decrease in pH is still unclear. Bovine rumen epithelial cells (BRECs) were cultured at pH = 7.4 (control), 5.5 (SARA), and 4.5 (ARA). Transcriptome and metabolomic methods were used to obtain the molecular-based response of BRECs to different pH treatments; pH = 4.5 can significantly induce apoptosis of BRECs. The RNA-seq experiments revealed 1381 differently expressed genes (DEGs) in the control vs. SARA groups (\(p<0.05\)). Fibroblast growth factor (FGF) and tumor necrosis factor (TNF) were upregulated 4.25 and 6.86 fold, respectively, and TLR4 was downregulated 0.58 fold. In addition, 283 DEGs were identified in the control vs. ARA comparison (\(p<0.05\)), and prostaglandin-endoperoxide synthase 2 (PSTG2) was downregulated 0.54 fold. Our research reveals that the MAPK/TNF signaling pathway regulates the inflammatory response of BRECs. Metabolomics identified 35 biochemical compounds that were significantly affected (\(p<0.05\)) in control vs. SARA and 51 in control vs. ARA. Bioinformatics analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database revealed that drug metabolism-cytochrome P450 metabolic and alpha-linolenic acid metabolism changes occurred. These transcriptional and metabolic changes are related to the adaptation of BRECs to low-pH stresses. In conclusion, the combined data analyses presented a worthy strategy to characterize the cellular, transcriptomic, and metabonomic adaptation of BRECs to pH in vitro. We demonstrated transcriptional expression changes in BRECs under pH stress and activation of the molecular mechanisms controlling inflammation.  
apoptosis; dairy cows; inflammation; metabolomics; pH; RNA-Seq 2022 20