esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Heat Treatment on Water Absorption of Chinese fir Using TD-NMR
###### Abstract  
Knowledge of the dynamic changes in the water absorption process of heat-treated wood is important for providing a scientific basis for the reasonable application of heat-treated wood, especially for outdoor applications. Nuclear magnetic resonance (NMR) techniques provide detailed information about the moisture components and moisture transport processes in wood, which are not available with other methods. In this work, water absorption of untreated and heat treated Chinese fir (_Cumingham laucledata_ [Lamb.] Hook.) heartwood was investigated using various NMR methods. The heat treatment temperatures were varied between 160 \({}^{\circ}\)C and 220 \({}^{\circ}\)C. According to the spin-spin relaxation time (T\({}_{2}\)), there were two components of water in the samples heat-treated at 160 \({}^{\circ}\)C and 180 \({}^{\circ}\)C as well as the untreated sample, while three components of water were found in the samples heat-treated at 200 \({}^{\circ}\)C and 220 \({}^{\circ}\)C, and the mass of each component was calculated by the integral peak areas of the T\({}_{2}\) curve. The amount of bound water and free water in heat-treated samples were less compared to the untreated ones, and the water absorption decreased correspondingly, due to the increasing heat-treated temperature. The results obtained by one dimensional frequency coding indicated that the heat treatment made wood difficult to be accessed by moisture. Besides, NMR images revealed that the free water absorption in latewood was faster than in earlywood, but earlywood could absorb more water than latewood.  
moisture content; free water; bound water; CPMG sequence; relaxation time; NMR imaging +
Footnote †: journal: Applied Sciences
## 3 Results
### DPPH Radical Scavenging Activity  
Oleaster oil exhibited a concentration-dependent ability to scavenge DPPH radicals. As a reference for radical scavengers, ascorbic acid was used. At a concentration of 2 mg/mL, the scavenging activity of oleaster oil and ascorbic acid on DPPH radicals was 63.92% and 81.72%, respectively. The IC\({}_{50}\) (inhibitory concentration of 50%) value, which represents the amount of the antioxidant required to eliminate 50% of the DPPH radicals in the test solution, is a key indicator of DPPH scavenging activity. A lower EC\({}_{50}\) value is associated with stronger DPPH radical scavenging activity. In the case of oleaster oil, the EC\({}_{50}\) value for DPPH radical scavenging activity was determined to be 1.37 mg/mL (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Chemical Class** & **Volatile Compound** & **Mean** & **SD** \\ \hline Alcohols & ethanol & 15,050.6 & 589.2 \\  & 1-pentanol & 994.6 & 6.5 \\ Esters & isoamylacetate & 504.4 & 93.7 \\  & hexylacetate & 316.2 & 3.8 \\  & cis-3-hexenylacetate & 120.2 & 6.3 \\ Aldehydes & hexanal & 8248.1 & 493.1 \\  & trans-2-heptenal & 1561.3 & 23.0 \\  & nonanal & 4995.4 & 269.4 \\  & trans-2-nonenal & 532.0 & 46.7 \\ Aromatics & benzaldehyde & 160.7 & 5.1 \\  & guaicaol & 80.8 & 6.6 \\  & 2-phenylethanol & 481.7 & 75.6 \\  & m-cresol & 1286.3 & 36.4 \\  & 4-ethylphenol & 4742.2 & 605.0 \\  & D-limonene & 110.3 & 4.3 \\ Terpenes & \(\beta\)-cis-ocimene & 2224.6 & 42.3 \\  & \(\beta\)-linalool & 31.7 & 0.7 \\  & \(\alpha\)-farnesene & 775.6 & 63.6 \\  & acetic acid & 2015.4 & 61.0 \\ Acids & butanoic acid & 230.8 & 9.4 \\  & nonanoic acid & 1603.9 & 60.7 \\ Others & 2-pentylfuran & 480.1 & 35.4 \\  & 2-butyrolactone & 83.9 & 2.8 \\ \hline \end{tabular}
\end{table}
Table 1: Volatile compounds (ug kg\({}^{-1}\)) quantified in oleaster oil.
# Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review
## 4 Discussion
### Insights and Recommendations for Future Research
#### 4.3.2 A Standard for Algorithmic Graph Processing  
Standardization is another effort where collaboration between academia and industry benefits both communities [89]. In the last two years, the International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) have published two standards relating to property graphs. As of June 2023, the standard for SQL (ISO/IEC 9075-16:2023) includes SQL/PGQ [90], which standardizes ways to define property graph views over relational data. In April 2024, GQL (ISO/IEC 39075:2024) was published as a standard for graph query languages [91]. These standards have been developed with input from various national bodies, with participating organizations across academia, industry, and beyond. GQL itself builds on two industrial graph query DSLs: Neo4j's Cypher and Oracle's PGQL. However, a similar standardization effort has yet to be undertaken for graph algorithms. Standards increase confidence in a given solution, normalize implementation best practices, and encourage real-life adoption. Moreover, having a lingua franca for graph algorithms (similar to SQL for relational databases or the now emerging GQL for graph queries) enables the transportability of knowledge across different platforms and helps build a large user community for the language. The recent literature has already identified the standardization of graph algorithms as a worthwhile future direction in the roadmap of graph analytics [92; 93; 94].
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Study Areas  
To calibrate the processing chain, it was applied first on the plain in the immediate outskirts of Antananarivo, in the centre of the island. This calibration was conducted following a specific rainy season in 2018 characterised by continuous and heavy rainfall. This region was selected because it offers a wealth of information regarding previous flood events, experiences recurrent flooding, and possesses the most documented knowledge of flood-prone areas in the country [4]. These attributes greatly facilitated the calibration process and made it an ideal location for fine-tuning our flood detection methodology.  
Our chain was then tested during the passage of the Batsirai cyclone. Cyclone Batsirai swept across southern Madagascar on 5 and 6 February 2022 (Figure 1). Several zones along the island's eastern seaboard were declared disaster areas. Reference flood maps are available for a few zones [8; 10]. To showcase the chain's ability to detect floods over an extensive area, the detection period was extended to 12 days. Thus, some detected floods may not be related to the cyclonic event. More detailed analyses were conducted in areas where other flood map products were also available, notably in the districts of Mananjary and Manakara.
## 4 Review of Mask-Face Detection Methodologies
### Three-Class Mask-Face Detection Methodologies
#### 4.2.1 Attention-Based Approaches  
The attention mechanism is a component used in neural networks to model long-range interaction, which has led to great progress in both NLP and CV fields.  
In Computer Vision, motivated by human observation, the attention mechanism is a dynamic weight adjustment process based on features of the input images. In the study of three-class mask-face detection [57; 58; 59; 60], researchers added the attention mechanism after extracting the target features through the neural network, with the aim of reducing the effect of the class imbalanced problem on the detection performance.  
In particular, [PERSON] et al. [60] proposed a two-stage structure with the attention mechanism, Context-Attention R-CNN, which could reach mAP of 84.1%. The dataset contained 4672 images in three categories, including 3988 images without masks, 636 images wearing masks, and 48 images of incorrect mask-wearing. Among them, the incorrect mask-wearing category had the least number of images, presenting a large gap with the other two categories. In order to reduce the effect of this imbalance on the detection performance, the attention mechanism was added after extracting the target features through the neural network. It could weigh the extracted features to obtain more effective features of masked-faces from different dimensions and reduce noise features. The mAP of incorrect mask-wearing was improved by 5.5 percent using this method.
## 2 Materials and Methods
### Characterization Techniques  
X-ray powder diffraction (XRD) patterns were registered at room temperature with a PANanalytical X'PERT POWDER diffractometer using Cu (K\(\alpha\)) radiation with \(\lambda\) = 1.5406 A. Data were collected in the range \(5^{\circ}\leq 20\leq 120^{\circ}\) range with a step of 0.0167\({}^{\circ}\) and analysed by the Rietveld profile method [8] using the WinPLOTR/Fullprof suite program [9].  
High resolution transmission electron microscopy (HRTEM) was performed in a JEOL 300 FEG. The composition of the obtained materials was established by semi-quantitative chemical analysis using energy dispersive X-ray spectroscopy (EDXS). The samples were prepared by crushing the powders under n-butanol and dispersing it over copper grids covered with a porous carbon film. Scanning electron microscope (SEM) images and corresponding EDX spectra were obtained with a JEOL JSM 6335F microscope. Samples were mounted on SEM stubs using carbon adhesive and sputter-coated gold. Thermogravimetric analyses (TGA) were carried out under nitrogen by a Pyris thermogravimeter (PerkinElmer, Waltham, MA, USA). The analyses were carried out at a heating/cooling rate of 10 \({}^{\circ}\)C min\({}^{-1}\).  
XPS studies were performed on a PHI Versa Probe II spectrometer (Physical Electronics, Minneapolis, MN, USA) with monochromatic X-ray Al K\({}_{\mathrm{\alpha}}\) radiation (100 \(\upmu\)m, 100 W, 20 kV, 1486.6 eV) and a dual-beam charge neutralizer. The instrument work function of the spectrometer was calibrated using Cu 2\(p_{3/2}\) (932.7 eV), Ag 3\(d_{5/2}\) (368.2 eV) and Au 4\(f_{7/2}\) (84.0 eV) photoelectron lines. The collected XPS spectra were analysed using PHI SmartSoft software (version 9.6.0, PHI, Minneapolis, MN, USA) and processed with MultiPak 9.1. Charge referencing was done against the adventitious carbon C 1\(s\) signal (284.8 eV). Recorded spectra were fitted using Gauss-Lorentz curves.  
The electrochemical tests of half cells were performed in Swagelok-type cells, using graphite paper as current collector (for both anode and cathode electrodes), Zn disk as the anode and Whatman GF/D borosilicate glass fibre sheets impregnated with solutions of 2 M ZnSO4.7H2O and 0.1 M MnSO4.H2O (Sigma-Aldrich, St. Louis, MO, USA). The synthesized samples were mixed with carbon SP (from Imerys Graphite & Carbon, Bironico, Switzerland) and sodium alginate in deionized water with mass proportions 80:10:10, respectively. The resultant slurry was coated on to the graphite paper and dried in the air overnight. The resulting electrodes had an active material loading of about 1-3 mg.  
Electrochemical testing was done using a Biologic 815 potentiostat. Cyclic voltammetry (CV) and galvanostatic charge and discharge (GCD) tests were conducted in a potential window between 0.8 and 1.8 V. Different scan rates were applied for CV testing, as well as several different applied specific currents were used for GCD testing. The applied current densities were normalized to the mass of manganese oxide on the electrode.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 2 Model Formulation  
### Signal Model and Crossband Filters  
Our study considers an arbitrary room with a single speech source. Let \(x(n)\in\mathbb{R}\) be the time-domain clean speech signal, and let \(x_{f,t}\in\mathbb{C}\) be the STFT representation of \(x(n)\), where \(f=0,\ldots,F-1\) and \(t=0,\ldots,T-1\) are the frequency and time bins, respectively. The speech signal is captured by an array of \(M\) microphones. In this work, we assume that the background noise is negligible. Hence, the observed signal in the \(m\)-th microphone, \(y^{(m)}(n)\), is given by  
\[y^{(m)}(n)=\sum_{i}h^{(m)}(i)x(n-i) \tag{1}\]  
where \(n\) is the discrete-time index, and \(h^{(m)}(n)\) is the RIR from the source \(x(n)\) to the \(m\)-th microphone. Based on the analysis in [28], the relation between the clean signal \(x_{f,t}\) and the observed signal \(y^{(m)}_{f,t}\) in the STFT domain is given by  
\[y^{(m)}_{f,t}=\sum_{f^{\prime}=0}^{F-1}\sum_{l}x_{f^{\prime},t-l}h^{(m)}_{f; f^{\prime},l}=\sum_{f^{\prime}=0}^{F-1}x_{f^{\prime},t}*h^{(m)}_{f;f^{\prime},t} \tag{2}\]  
where \(*\) denotes a linear convolution, and the coefficients \(h^{(m)}_{f;f^{\prime},t}\in\mathbb{C}\) are derived from the time-domain RIR \(h^{(m)}(n)\) and from the analysis and synthesis filters that are used to transform the signals from the time domain to the STFT domain and vice versa. Given a single frequency bin \(f\), we consider the time sequence \(h^{(m)}_{f;f,t}\) as the band-to-band filter, while the set of time sequences \(\left\{h^{(m)}_{f;f^{\prime},t}\right\}_{f^{\prime}\
eq f}\) are considered as the crossband filters associated with \(f\).
## 4 Discussion
### Limitations  
Some limitations of this study should be acknowledged. Firstly, data acquisition and processing faced challenges, such as difficulty in obtaining historical data and insufficient accuracy of single data sources. The limited number of observation points covered by panoramic static images means they cannot capture the greening of all urban areas, potentially affecting our results. Additionally, the growing season of plants directly impacts the proportion of visible green vegetation. Images taken early in the planting season and not updated will result in lower GVI values. Future studies could apply larger street-view datasets, such as Global Streetscapes [71], and integrate 2D remote sensing data to enrich these datasets. Additionally, other potential geospatial datasets, such as LiDAR and multi-spectral imagery [72], could provide valuable information on vegetation height, density, and health, thereby enhancing GVI assessments. Moreover, the population estimates for aggregated residential points, derived from total household counts based on POI data, may lack precision. Secondly, the impact of grid resolution on the study results was not discussed. Thirdly, this study primarily employed qualitative analysis to assess changes in equity, using specific examples for support. However, the lack of quantitative examination of influencing factors limits our understanding of the driving forces and their specific impacts. Future research should apply correlation analysis to gain deeper insights into the factors driving equity evolution and their interactions. Finally, this study only considered the spatial attributes of urban residents. Future research could extend equity studies to encompass cultural, economic, and other social dimensions. For instance, social surveys may help capture the diverse needs of different social groups, particularly vulnerable populations. Such insights would support scientifically grounded, targeted strategies for integrating equity considerations into urban spatial planning.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 4 Experiment
### Baselines  
We compare our model with the following baseline models. To highlight the advantages of multimodal feature fusion, we separately evaluate MemCapsNet and AgCapsNet for single-modal aspect-based sentiment analysis. ATAE-LSTM, MemNet, and IACapsNet are used as single-text baseline models on the SemEval datasets for aspect-based sentiment analysis. Since there were no publicly available methods and datasets for aspect-based image sentiment analysis on review data, we compare the results of AgCapsNet with the reproduced results on VGG-16 [54] and Capsule Network (CapsNet) [55] only using the image data in Multi-ZOL and MTCom. Finally, the comparison experiments are conducted for our VECapsNet and the strong baseline method MIMN on the Multi-ZOL dataset and our MTCom dataset.  
ATAE-LSTM [6]: An aspect-based text sentiment analysis model, which uses the attention mechanism between hidden states and aspect words to obtain aspect-related keyword representations for text sentiment analysis.  
MemNet [26]: A memory model using multiple attention mechanisms on a memory matrix stacked by inputted word vectors.  
IACapsNet [33]: A capsule network model using a cross-attention mechanism for textual aspect-based sentiment analysis.  
MIMN [10]: A multimodal aspect-based sentiment analysis model proposed by [PERSON] et al., which uses two interactive memory networks with four types of attention to supervise the textual and visual information with the given aspect. They presented the task of aspect-based multimodal sentiment analysis for the first time, and provided a multimodal aspect-level sentiment dataset, Multi-ZOL.
## 6 Localization Systems
### Outdoor Localization Systems
By synchronously integrating the values of all sensors, the microprocessor elaborates these and sends, in real time, the reconstructed parameters to a portable device via Bluetooth [24; 103]. Another application of this technology was to analyze the football player performance model to evaluate valuable aspects such as physiological parameters and fatigue's role during matches (using GNSS Live 50 Hz) [202; 203]. Furthermore, the recent developments in an emerging technology (Real-time Kinematics--RTK GNSS) made available a family of low-cost GNSS navigation receivers connected to an RTK base station, promising centimeters of performance in the location measurements [204; 205]. This technology allows the GNSS receivers' incorporation of correction to increase the position accuracy of the receiver. Primarily, the receiver is under control remotely. The RTK base station is a stationary GNSS receiver of a known location that transmits correction data through radio or the internet. The RTK rover can achieve high-precision positioning with the help of an RTK base station within a 40 km radius. The RTK base station transmits correction data at a frequency of 1 Hz. Another possibility is to determine the position of the RTK base station using a method called precise point positioning (PPP), which is based on an additional satellite phase of the bias information generated from a network of global reference stations [Networked Transport of RTCM (Radio Technical Commission for Maritime Services) via Internet Protocol--NTRIP] [206] (Figure 9).
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 5 Question 1: Which mPLM Is Best for QE Tasks?
### Experimental Results for Question 1
#### 5.3.2 Sub-Task 2  
The finetuning results for sub-task 2 (sentence-level post editing effort) are shown in Table 3. High performances were achieved in the descending order of XLM-TLM, XLM-R-large, mBART, XLM-R-base, mBERT, XLM-MLM-17, XLM-MLM-100, XLM-MLM, and XLM-CLM based on the average Pearson correlation coefficient. As a result of this experiment, XLM-TLM showed the highest performance based on the average, minimum, and maximum Pearson correlation coefficients, similar to the previous experimental results for sub-task 1. As analyzed in sub-task 1, because XLM-TLM was induced to learn alignment information for language pairs using parallel corpus, it can be predicted that this process contributes significantly to its performance improvement for QE, which requires knowledge of relationships between languages. In sub-task 2, the XLM-R-large model showed the best performance after XLM-TLM. A fairly comparable performance was demonstrated with an average Pearson correlation coefficient of 0.498. XLM-R-large is the latest model among the mPLM models considered in this study. As mentioned in Section 3.3, a state-of-the-art performance among cross-lingual models was achieved by expanding the number of parameters considering the large amount of data and the curse of multilinguality. Nevertheless, XLM-R did not learn the relationship between the source and target sentences because it learned the mono corpus in an unsupervised manner. In QE, the source sentence and MT output are referenced together to determine which part has been incorrectly translated, and, therefore, this characteristic did not produce an optimal effect compared to XLM-TLM. Although mBART is a sequence-to-sequence model, it ranks third in the regression task with a higher performance than all XLM models. As an extension of MLM, mBART uses a pretraining scheme referred to as text infilling and sentence permutation, and an average Pearson correlation coefficient of 0.463 was obtained. This result was significantly higher than those of XLM-MLM (0.334), XLM-MLM-17 (0.415), and XLM-MLM-100 (0.409), which used only MLM. Therefore, it can be confirmed that the additional strategy of mBART had a positive effect on the improvement of QE performance during finetuning. mBERT showed an average Pearson correlation coefficient of 0.417 in sub-task 2 and did not demonstrate a very high performance when compared with the sub-task 1 results. Considering the comparison of the various XLMs, XLM-MLM-17 performed slightly better than XLM-MLM-100 (as in sub-task 1), while XLM-CLM ranked lower than XLM-MLM, which exhibited the lowest performance in sub-task 1.  
To answer subtask 2, we refer back to the question we posed. Which mPLM is best for QE tasks? For the question, we can explain that the XLM-TLM model also performed best in sub-task 2.  
Question 2: Does the Input Order of the Source Sentence and the MT Output Sentence Affect the Performance of the Model?
## 3 Proposed Method for Attribute Weighting
### The Description of Principal Component Analysis and the Entropy Method
#### 3.2.1 Principal Component Analysis
Therefore, the eigenvalues are arranged in descending order, facilitating the identification of the most significant eigenvectors in terms of the total explained variance [30]. Step 4: Data Projection into the New Principal Component Space  
The transformation of data into the new principal component space is accomplished using Equation (5). \[Y=X\times V, \tag{5}\]  
where \(Y\) is the matrix of data projected into the new space, \(X\) is the original data matrix, and \(V\) is the eigenvector matrix. Step 5: Explained Variance  
The proportion of variance explained using the principal components is calculated by considering the sum of the first k eigenvalues over the total sum of all eigenvalues, using Equation (6). \[R^{2}=\frac{\sum_{i=1}^{k}\lambda_{i}}{\sum_{i=1}^{n}\lambda_{i}}, \tag{6}\]  
Step 6: Selection of Principal Components  
Determining the optimal number, k, of principal components to retain in a Principal Component Analysis (PCA) involves, firstly, evaluating the cumulative proportion of explained variance. However, this criterion is not isolated, as there are other approaches that can be considered for the selection of the number of principal components. The ultimate outcome of a Principal Component Analysis reveals the projection of the original data into a space of equal dimensions, where the new set of variables represents a linear combination of the original variables. The significance and essence of the original dataset are predominantly manifested in the initial variables or components of this newfound set. In the context of our research, the linear combination provided by PCA plays a pivotal role in facilitating the assignment of weights to each variable concerning the original set for every record. This capability for weight assignment enables a detailed understanding of the individual contribution of each variable in the analysis, thereby enhancing our overall comprehension of the dataset.