esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Application of Quaternary Ammonium Salts for Gas Hydrate Inhibition
## 1 Introduction  
In developing deep water gas fields (about 3000 m water depth), the development-gathering mode of \"drilling platform-underwater production submarine pipelines\" is often implemented [1]. The submarine pipelines are the crucial components that provide transportation means of natural gas to flow from the reservoir wellbore to different types of deep water floating platforms such as large multi-functional semi-submersible platform (Semi-FPS), tension leg platform (TLP) [2], compliant piled tower (CPT) and deep-draft single column platform (Spar). Due to low temperatures, high static water pressure, long tieback distance, and the composition of the gas, gas hydrate solids will possibly occur in riser, subsea trees or subsea pipeline. Gas hydrates are ice-like, crystalline compounds of gas and water [3] that exist at a suitable range of high pressures and low temperatures. It consists of variety of gas molecules such as methane, ethane, propane, isobutene, n-butane, nitrogen, carbon dioxide, hydrogen sulphide, etc. [4]. Gas molecules, known as 'guest' are entrapped in the hydrogen-bonded water molecules, called 'host' [5]. Among the unique features of hydrates are; they are not chemical compounds and are non-stoichiometric crystals [6; 7]. Due to no strong chemical bonds exist between the gas and water molecules [8], there is potential of the gas molecules to be released from the water molecules cage [9], which contributes to the flow assurance issues. The three most common gas hydrates structures are structure I (sl), structure II (sIf), and structure H (sH), which differs in cage size and physical shape. Typically, structure I consists of small hydrate lattices that can only hold small gas molecules such as CH\({}_{4}\). Structure II is more complex, quite larger in size, and able to entrap larger hydrocarbon molecules, whereas structure H is capable to contain much larger molecules such as isopentane [10]. Figure 1 shows the unit cells of sl, sII, and sH structures which are composed of different types of water cages. The conditions of gas hydrate solids occurrence is majorly governed by either its thermodynamic equilibrium or nucleation/growth rate. Chemicalinjection by adding the thermodynamic hydrate inhibitors (THIs) like methanol and mono-ethylene glycol (MEG) [5] or low-dosage hydrate inhibitors (LDHIs) such as kinetic hydrate inhibitors (KHIs) and anti-agglomerants (AAs) [11] is a common approach to either shift the equilibrium curve or delay the nucleation rate [12; 13; 14; 15; 16; 17].
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
###### Abstract  
With the rise of online education platforms, the vast number of accumulated data conceal patterns regarding the evolution of students' knowledge states. Tracking a student's knowledge state through their historical interaction records is known as knowledge tracing, a critical task in intelligent educational systems. Current research predominantly focuses on designing high-performance networks to enhance knowledge state tracking capabilities, often employing simplistic methods for question embedding, such as one-hot encoding or graph-based representations. This paper proposes a pre-training model for multi-concept question embedding (Pre-training Multi-concept Question Embedding, PMQE), aimed at providing a robust upstream tool for knowledge tracing and intelligent education fields. We first leverage the textual information in the dataset, representing concepts using their names or descriptive text embeddings. We then utilize the structural information from the question-concept graph, applying graph convolutional networks to derive question embeddings that integrate both textual semantics and structural information. Additionally, auxiliary information (such as question difficulty) is utilized for joint model optimization. The model outputs include question embeddings, question-concept weight matrices, and question difficulty, which can be used for knowledge tracing and other downstream tasks. Experiments conducted on two real-world datasets and multiple models validate the effectiveness of our approach.  
Keywords:knowledge tracing; pre-training; large language models; graph neural networks; intelligent educational systems +
Footnote †: journal: Applied sciences
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 3 Filter Performance Measurement
### Insertion Loss
The online insertion loss test setup version 2 with \(Z_{\text{in}}\) and \(Z_{\text{L}}\) close to power line impedance replicates realistic power grid conditions, thus offering an accurate way of evaluating the PLC filters [47; 48].
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 2 Disclose the Valuable Information
### Transformations
#### 2.1.2 Fourier Transform (FT)  
The Fourier transform (FT) is an extensively used mathematical tool for the analysis of frequency-domain signals. Discrete Fourier transform (DFT) is used where both frequency and time domain coefficients are discrete and, computed via fast Fourier transform (FFT). [PERSON] et.al. adopted full-cycle DFT (FCDFT) and half-cycle DFT to remove harmonics, dc components and to assess the phasor elements [27]. Half-cycle DFT is also used to calculate fundamental and harmonic phasors employed for fault-type classification [28; 29]. In [30], full-cycle FFT is used to find the fundamental components of voltage and current.
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 2 Materials and Methods
### Methodology  
This section outlines the methodology employed to assess the performance of the GPPs at different temporal scales. Data spanning from September 2000 to August 2020 were analyzed by directly comparing them to point rain gauge measurements, utilizing machine learning techniques such as classification and regression. For the spatial scale, we obviously chose the pixels covering the rain gauges. In this regard, it is worth noting that interpolation methods do not guarantee an accurate representation of precipitation distribution due to the limited spatial density of rain gauges [42]. Moreover, a bias correction framework was introduced for the GPM-F precipitation product, employing the artificial neural network (ANN) approach. The framework encompasses three modules: Construction and Training, Testing, and Precipitation Correction (Figure 2).
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 4 Quantitative Assessment Methods of Early Enamel Caries with OCT
### Quantitative Assessment Based on Lesion Depths
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **OCT System** & **Sample Preparation** & **Lesion Depth Result** \\ \hline \multirow{8}{*}{[PERSON] et al. [51]} & \multirow{8}{*}{1310-nm} & Method: occlusal areas were exposed to 14-day pH cycling, which consisted of 6 h demineralization and 17 h remineralization a day. \\ \cline{3-3}  & & Sample number: 6 human isolated teeth for 10 vitro detection & \(d=1.0804t+8.6128\) \\  & TD-OCT, axial resolution 10 \(\mu\)m. & Method: demineralization for 12, 24, 48, 72, 96 and 120 h. & \(d\) is lesion depth, \(t\) is demineralization time. \\ \hline \multirow{8}{*}{[PERSON] et al. [52]} & \multirow{8}{*}{1310-nm} & Sample number: 7 human 10 permanent teeth for 10 vitro detection & \(d=1.1266t+6.7342\) \\  & & & \(d\) is lesion depth, \(t\) is demineralization solution & demineralization time. \\ \cline{3-3}  & & Method: demineralization for 12, 24, 48, 72, 96, 120 h. & \\ \hline \multirow{8}{*}{[PERSON] et al. [53]} & \multirow{8}{*}{1310-nm SD-OCT, axial/lateral resolution \(<\) 7.5/15 \(\mu\)m, imaging speed 48–91 kHz, handheld scanning probe (Non-intraoral probe).} & \multirow{8}{*}{13 human molars, 25 human incisors, 11 premolars and 15 molars for ex vivo detection, 18 patients for in vivo detection.} & \\ \cline{3-3}  & & Sample number: 40 human enamel blocks & \\ \cline{3-3}  & & Solution: PH 4.8 & \\ \cline{3-3}  & & demineralization solution & Reduction in lesion depth after 15 remineralization for three \\ \cline{3-3}  & & and PH 7.0 & \\ \cline{3-3}  & & & \(\phantom{\rule{0.0 pt}{1.0 pt}}\) remineralization solution & \\ \hline \multirow{8}{*}{[PERSON] et al. [54]} & \multirow{8}{*}{840-nm SD-OCT, axial resolution 5 \(\mu\)m.} & \multirow{8}{*}{1310-nm PS-} & Method: 3 windows for each 311.80 (344.38) \(\mu\)m, 320.10 (244.36) \(\mu\)m, 312.70 (203.80) \(\mu\)m} \\  & & & \(\phantom{\rule{0.0 pt}{1.0 pt}}\) dimeneralized for 3 days and one window \\ \cline{3-3}  & & & \(\phantom{\rule{0.0 pt}{1.0 pt}}\) remineralized for 6 days by \\ \cline{3-3}  & & & \(\phantom{\rule{0.0 pt}{1.0 pt}}\) PH cycling. \\ \hline \multirow{8}{*}{[PERSON] et al. [55]} & \multirow{8}{*}{1310-nm PS-} & Sample number: 15 bovine 1000 enamel blocks for 10 vitro detection & \\ \cline{3-3}  & & Solution: pH 4.9 & \\ \cline{3-3}  & & & \(\phantom{\rule{0.0 pt}{1.0 pt}}\) dominantization solution & \\ \cline{3-3}  & & Method: 3 windows for each 3 block, demineralization for 9 days & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Lesion depths of caries can visually indicate the severity of caries to some extent. However, they can only reflect part of the characteristics of the initial stage of enamel lesions, as the amount of mineral loss may be different in a certain depth range [58]. Furthermore, there is a lack of solid criteria for determining the cut-off point to define lesion depth.
# Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses
## 2 Numerical Modeling  
### Constitutive Model Parameters and Validation  
#### 2.1.1 Numerical Model for Physical Model Test  
To verify the material model and apply it to the subsequence simulation of lateral blasting under static load, a physical single-hole crater blasting model test was conducted firstly to calibrate the material parameter, as shown in Figure 1a. The whole model is 400 \(\times\) 400 \(\times\) 200 mm. A borehole with a diameter of 8.0 mm and a length (\(L\)) of 40 mm is drilled in the center and the explosive with a diameter (\(d\)) of 8.0 mm and a length (\(l\)) of 12 mm is charged in the borehole. The cemented sand, which is composed of ordinary Portland cement (PC32.5), uniform-grained sand and water in the mass ratio of 3:3:1, is used as the model material to study the blast-induced damage of rock. The material mechanical parameters are determined by averaging the measured data from six mortar cubic blocks. The density \(\rho_{0}\) is 2456 kg/m\({}^{3}\); the compressive strength \(f_{c}\) is 48.3 MPa and the elastic modulus \(E\) is 32.36 GPa; Poisson's ratio \(\mu\) is 0.24; P-wave velocity \(v_{p}\) is 3828 m/s. According to the physical model, the single-hole crater blasting numerical model was developed for comparison with the test results, as shown in Figure 1b. The model consists of rock, explosive, and stemming. The size of the numerical model is same as the physical model and the total number of the meshed elements is 0.56 million, where the numerical convergence tests has been carried out and the calculation results of the model are convergent and accurate.
## 4 System Verification with Full-Vehicle Simulation
### Autonomous Emergency Braking System  
After detecting a darting out, AEBS predicts the trajectory of the ego vehicle and the darting-out object using the same method as PBS. Figure 9 is a diagram of the relationship between the ego vehicle and the darting-out object. The intersection of the two trajectories  
Figure 8: On-board sensor positions and the visibility when the ego vehicle is stopped.  
is the conflict area. The time until the ego vehicle enters the conflict area and the time until it exits it are as follows:  
\[T_{ego\_in}(t)=\frac{D_{ego\_in}(t)}{V_{ego}(t)} \tag{5}\]  
\[T_{ego\_out}(t)=\frac{D_{ego\_out}(t)}{V_{ego}(t)} \tag{6}\]  
where \(D_{ego\_in}\) indicates the distance until the ego vehicle enters the conflict area and \(D_{ego\_out}\) is the distance until it exits. Similarly, the time until the object enters the conflict area and the time until it exits are as follows:  
\[T_{obj\_in}(t)=\frac{D_{obj\_in}(t)}{V_{obj}(t)} \tag{7}\]  
\[T_{obj\_out}(t)=\frac{D_{obj\_out}(t)}{V_{obj}(t)} \tag{8}\]  
where \(D_{obj\_in}\) indicates the distance until the object enters the conflict area, \(D_{obj\_out}\) is the distance until it exits, and \(V_{obj}\) is the velocity of the darting-out object. AEBS decelerates the vehicle with 100% brake pedal stroke when the system determines that a collision is about to occur if the ego vehicle and the darting-out object maintain their current velocity. The AEBS activating condition is as follows:  
\[(T_{ego\_in}(t)-T_{obj\_out}(t)<0.5)\wedge(T_{obj\_in}(t)-T_{ego\_out}(t)<0.5) \wedge(T_{ego\_in}(t)\leq 1.4) \tag{9}\]  
Since the objective of this research is collision avoidance without emergency braking, the AEBS is a supplementary function for cases where PBS cannot decelerate sufficiently (e.g., when the darting-out velocity is much higher than predicted). Therefore, although the design of AEBS was described, its design is not the focus of this study.
## 4 Methodology
### Algorithm Description  
Algorithm 1 details the forward pass of our proposed method, which serves as the core computational engine within our broader experimental pipeline. This algorithm illustrates how a document \(D\) and set of financial queries \(\mathcal{Q}\) are processed to generate two key outputs: a final sentiment label and a set of interpretable query attention weights. These outputs are subsequently used in our evaluation pipeline (detailed in Section 5.2) to assess both the model's classification performance and the practical utility of its explanations.  
Specifically, lines 1-4 handle the initial setup and preprocessing, lines 5-10 describe the encoding of sentences and queries using the BERT encoder, and lines 11-13 utilize a GRU to capture sequential dependencies between sentences. The core of our contribution,the QGDA mechanism, is detailed in lines 14-18, where sentence-level and query-level attentions are computed and combined. Finally, lines 19-21 derive the final document representation and sentiment prediction.  
```
Input: Document \(D\) consisting of sentences \(S_{1},S_{2},\ldots,S_{m}\), and a set of domain-specific financial queries \(\mathcal{Q}=\{Q_{1},Q_{2},\ldots,Q_{k}\}\) Output: 1. Sentiment label \(\hat{y}\) for document \(D\).
2. Query attention weights \(v\) for interpretability.
3 Preprocess \(D\) into sentences with a specified padding size;
4 Add [CLS] and [SEP] tokens to each sentence and query;
5 Initialize pre-trained BERT model;
6 Initialize GRU sentence encoder;
7 for each sentence \(s_{i}\) in \(D\)do
8 Encode \(s_{i}\) using BERT word encoder to get representation \(S_{i}\);
9
10 end for
11 for each query \(q_{j}\) in \(\mathcal{Q}\)do
12 Encode \(q_{j}\) using BERT word encoder to get representation \(Q_{j}\);
13
14 end for
15 for each sentence representation \(S_{t}\) in documentdo
16 Update GRU hidden state \(h_{t}\) for sentence \(S_{t}\) using previous hidden state \(h_{t-1}\);
17
18 end for
19 for each query \(Q_{j}\)do
20 Compute sentence-level attention \(a_{j}\) using sentence representations \(h\) and query \(Q_{j}\);
21
22 end for
23 Compute query-level attention weight \(v\) using sentence representation \(h\) and sentence-level attention \(a_{c}\);
24 Compute combined sentence attention vector \(\widetilde{a}\) using \(a_{c}\) and \(v\);
25 Compute document representation \(D_{repr}\) as the weighted sum of sentence representations using \(\widetilde{a}\);
26 Compute final sentiment score using softmax layer over \(D_{repr}\);
27 Assign sentiment label \(\hat{y}\) based on the highest score;
28 return\(\hat{y}\), \(v\)
```  
**Algorithm 1**Hierarchical BERT-GRU Model with Query-Guided Dual Attention (QGDA) for Financial Sentiment Analysis
## 3 Results and Discussion
The local magnetic moments of Fe, Ga, C, and N atoms are 0.765 \(\upmu_{\text{B}}\), \(-\)0.007 \(\upmu_{\text{B}}\), \(-\)0.018 \(\upmu_{\text{B}}\), and 0.035 \(\upmu_{\text{B}}\), respectively. Obviously, Fe atoms are the most important contributors to the magnetic properties of GaFe(CN)\({}_{6}\). The local magnetic moments of Ga, C, and N atoms are very small. Because these three atoms have no magnetism, their magnetic moments are mainly induced by the influence of the Fe atom. In -Ga-N-C-Fe- chemical chains, the local magnetic moments between them show a sign change of \(-\)/+/\(-\)/+, which means that there is antiferromagnetic coupling between these atoms. Figure 9 shows the total and local magnetic moments of GaFe(CN)\({}_{6}\) under pressure. From 0 to 35 GPa, the total magnetic moment per formula unit of GaFe(CN)\({}_{6}\) is 1.0 \(\upmu_{\text{B}}\). In this pressure range, GaFe(CN)\({}_{6}\) has half-metal characteristics. When the pressure exceeds 35 GPa, GaFe(CN)\({}_{6}\) is no longer a half-metal and its molecular magnetic moment is no longer an integral value. The local magnetic moment of the Fe atom decreases with the increase of pressure, while the induced magnetic moment of the N atom increases slightly, although its value is very small. The local magnetic moments of Ga and C atoms are hardly affected by pressure. From the local magnetic moment signs of Ga, N, C, and Fe atoms, the change of pressure has no effect on the antiferromagnetic coupling of -Ga-N-C-Fe- chemical chains. Figure 2.1 Band structure of GaFe(CN)\({}_{6}\) under different pressures. (**a**) Majority-spin; (**b**) minority-spin. Figure 2.2 Total and local magnetic moments of GaFe(CN)\({}_{6}\) under pressure.
# Wettability of CNW/ITO Micro Structure for Modification of Surface Hydrophilicity
## 4 Conclusions  
Herein, we introduced results based on the wettability of the CNW/ITO micro-structure through this article. (i) The morphology of pristine-CNW and CNW/ITO micro-structure were characterized by SEM, and the wall thickness of the CNW/ITO micro-structure by ITO became thicker compared with pristine-CNW. (ii) The X-ray diffraction and Raman spectroscopy analysis were employed for the crystallization of ITO sputtered and defects with disordered carbon lattice in the CNW. The (222) plane was determined as the preferred orientation in the X-ray diffractions result. In Raman spectra, a reduced I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio and D\({}^{\prime}\) band were observed. (iii) The X-ray photoelectron spectra were performed for the investigation of the oxygen-functional group on the surface of the CNW/ITO micro-structure and showed an increase of oxygen-related chemical composition is proportional to the surface area of ITO. (iv) In the wettability results of CNW/ITO micro-structure by measuring CAs, pristine-CNW showed almost hydrophobicity (CAs \(>80^{\circ}\)). In contrast, CNW/ITO micro-structure exhibited further hydrophilicity (CAs \(<80^{\circ}\)). In addition, wettability of the CNW/ITO micro-structure maintained hydrophilicity for 4 h in an experiment for stability, and the wetting feature of CNW/ITO micro-structure by ITO sputtered for 45 min with organic solvents (acetone, methanol, isopropyl alcohol (IPA), and ethanol) appeared to have exceptional hydrophilicity (CAs \(<10^{\circ}\)). We expect that our experiment results adopted an eco-friendly surface structure modification which will have utilization and application in the field of surface coating and modification.  
Conceptualization, S.K. and W.C.; methodology, S.K., H.C.; formal analysis, S.K. and H.C.; software, S.K.; writing, S.K.; supervision, H.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20184030201900) and was also supported by Korea Electric Power Corporation [Grant number: R18 XA06-25]. The authors declare no conflict of interest. The authors declare no conflict of interest.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 2 Literature Review
### Pretraining on Programming Language  
Pretraining in the realm of programming languages is an emerging field, where recent efforts aim to adapt NLP pretraining techniques to source code. Two cutting-edge deep learning models, CuBERT [72] and CodeBERT [55], are commonly used in this area. CuBERT utilizes BERT's potent masked language modeling objective to derive generic code-specific representations, while CodeBERT goes a step further by incorporating a replaced token detection task [73] to learn cross-modal representations between natural language and programming languages. Apart from the BERT-style models, [PERSON] et al. (2020) [74] and [PERSON] et al. (2020) [75] employed a generative pretrained transform (GPT) and unified pretrained language model (UniLM) [76], respectively, for the code completion task. Additionally, transcoder [77] explores unsupervised programming language translation. In contrast, our approach involves encoder-decoder models based on T5 [19] for programming language pretraining, encompassing a more comprehensive set of tasks.  
The recent literature includes some emerging studies [78; 79; 80] that examine the T5 framework applied to code. However, these works have limited scope, concentrating solely on specific generation tasks without accommodating understanding tasks like our approach. Another encoder-decoder model called PLBART [81], built on BART, also supports both understanding and generation tasks. Nevertheless, prior to our proposal, all these previous works treat code in the same manner as natural language, largely overlooking the distinctive characteristics of code. In contrast, our approach focuses on leveraging identifier information in code for pretraining.  
In recent developments, GraphCodeBERT [54] integrated data flow extracted from code structure into CodeBERT, and [PERSON] et al. (2021) [82] introduced a deobfuscation objective to utilize the structural aspect of programming languages. These models are primarily concerned with enhancing the code-specific encoder during training. Conversely, [PERSON] et al. (2021) [83] proposed a method to capture relative distances between code tokens across the code structure. However, our specific focus lies in the identifiers that carry rich code semantics. We devised the C2B hybrid model that identifies tagging and prediction tasks to incorporate this valuable information into a Seq2 Seq model.
## 4 Application in the City of Dortmund
### System Dynamics Simulation Model
#### 4.1.4 Simulation and Verification  
We apply the SDSM based on public data of the city of Dortmund and using the e-commerce trends in the German context. Taking into account the volatility of the e-commerce sector, the SDSM evaluates the system components of APL for a planning horizon of three years (divided into 36 months). Table 1 shows the components used in the SDSM application and their values.