esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Databases and Methods  
### Seismic Data  
Currently, extensive seismic exploration has been conducted in the Changling Graben of the Songliao Basin, providing valuable data for studying deep structures and strata (Figure 5). This study utilizes the latest 3D seismic data (40 m \(\times\) 40 m) provided by PetroChina, along with geological and well-log data. The 3D seismic data covers an area of approximately 13,000 km\({}^{2}\), achieving full 3D coverage with good seismic imaging quality and clear seismic reflection structural features. This ensures reliable support for systematically analyzing the structural and sedimentary filling characteristics of the Changling Graben's various sags. Based on the 3D seismic data, five representative crosslines and three inlines were selected. Through detailed stratigraphic and fault interpretation of these profiles, the regional geological framework characteristics of the Changling Graben were summarized. By utilizing the residual strata dip and the convergence trends of internal seismic reflectors, combined with the regional geological background of the Songliao Basin, a \"point-line-surface\" approach was applied to restore the denudation across the entire area. The denudation amounts for the aforementioned four denudation periods in the Changling Graben were restored at a density of 16 \(\times\) 16, involving a total of 400 seismic profiles. By analyzing the distribution characteristics of denudation amounts and the residual strata distribution patterns, the tectonic framework of the prototype basin was reconstructed. Combined with the tectonic background of the Songliao Basin, the evolutionary characteristics of the Changling Graben were analyzed, and the subsidence features and evolutionary patterns were summarized [50].  
Leveraging the orientation of the residual strata obtained from seismic data and the convergence patterns of co-phase axes within these strata, combined with the regional geological context of the Songliao Basin, this study systematically analyzes and estimates the denudation volumes for the four identified periods of denudation within the Changling Graben. By examining the distribution of denudation volumes and the spatial patterns of the remaining strata [51; 52; 53], the study reconstructs the original structural framework of the basin. Integrating this with the tectonic setting of the Songliao Basin, the study elucidates the evolutionary characteristics of the Changling Graben through various stages, highlighting the sedimentological features and underlying evolutionary trends.  
Figure 4: Typical geological cross-section of Changling Graben (section location in (M,M\({}^{\prime}\))).
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Mel-Frequency Cepstral Coefficients in Voiceprint Recognition  
Mel-frequency cepstral coefficients are widely used in speech and voice recognition for their ability to capture the essential characteristics of speech signals in a compact and computationally efficient form. By simulating the human auditory perception of sound frequencies, MFCCs provide robust features that enhance voiceprint recognition accuracy.  
In this study, MFCCs are extracted from audio data to serve as input features for the CNN model. The preprocessing involves transforming raw audio signals into MFCC features using techniques such as frame segmentation, spectral analysis, and cepstral transformation. This ensures that critical information such as phonetic patterns and vocal characteristics is preserved for subsequent classification.  
Delta and delta-delta cepstrum features, which capture the rate of change and temporal dynamics of the MFCCs, are also included to enhance the model's ability to generalize across diverse speech patterns. By combining static and dynamic features, the MFCC representation allows the CNN to learn both the fundamental and evolving attributes of voice samples, improving speaker recognition performance even with limited data.
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 3 Results and Discussion
### Effect of Fiber on Freeze-Thaw Durability of RPC
#### 3.2.2 Compressive Strength Loss  
The compressive strength of BFRPC with different basalt fiber contents was tested under the chloride-salt freeze-thaw cycle and the compressive strength loss rate was calculated by Formula (4), and compared with the PRPC and SFRPC to evaluate the impacts of basalt fiber contents and fiber type on the freeze-thaw durability of RPC.  
The compressive strength and compressive strength loss rate vs. freeze-thaw cycles are shown in Figure 7. It can be seen from Figure 7a that, for all RPC specimens, the compressive strength of RPC decreases as the freeze-thaw test continues, which indicates that the freeze-thaw durability of RPC gradually deteriorates under the chloride-salt freeze-thaw cycle. During the freeze-thaw test, the frost expansion of the pore solution inside the RPC leads to the initiation of micro cracks, thus reducing the compressive strength of RPC, which is consistent with the conclusion reported by [PERSON] et al. [31]. Comparing the compressive strength of SFRPC with that of BFRPC (12 kg/m\({}^{3}\) content), it can be known that the decline in the compressive strength of SFRPC is higher and the compressive strength of SFRPC and BFRPC is almost the same after 800 freeze-thaw cycles, which indicates that the corrosion of steel fiber not only increases the mass loss of RPC, but also has a negative effect on the compressive strength of RPC. It can be seen from Figure 7b that, for PRPC, BFRPC and SFRPC specimens, the compressive strength loss rate increases as the freeze-thaw test continues. At all freeze-thaw cycle levels, the compressive strength loss rate of BFRPC is lower than that of PRPC, and the higher the basalt fiber content, the lower the compressive strength loss rate of BFRPC, which indicates that adding basalt fiber could improve the freeze-thaw durability of RPC. This is because the addition of basalt fiber increases the number of harmless pores which could effectively reduce the freezing pressure of the pore solution, thus improving the freeze-thaw durability of RPC [37]. Moreover, comparing the compressive strength loss rate of SFRPC with that of BFRPC, the compressive strength loss rate of BFRPC (8 and 12 kg/m\({}^{3}\) content) is lower than that of SFRPC throughout the freeze-thaw cycle test. It indicates that steel fiber is not as effective at enhancing the freeze-thaw resistance of RPC as basalt fiber.
## 1 Introduction
For example, if the traveling range of the axis is up to 2000 mm, the expanded measurement uncertainty (\(k=2\)) of the mean positioning deviation is [3]:  
\[U\left(M\right)=\sqrt{U_{\text{instrument}}^{2}+U_{\text{misalignment}}^{2}+U_{ \text{m,machine}}^{2}+U_{\text{m,instrument}}^{2}+U_{\text{e,machine}}^{2}+U_{ \text{e,instrument}}^{2}+\frac{1}{10}U_{\text{EVE}}^{2}} \tag{4}\]  
where \(U_{\text{instrument}}\) is the expanded uncertainty due to measuring instrument, which can be determined by measurement instrument calibration; \(U_{\text{misalignment}}\) is the misalignment of measuring instrument to machine axis under test; \(U_{\text{m,machine}}\) and \(U_{\text{m,instrument}}\) are the expanded uncertainty due to measurement of temperature of the machine tool and the instrument, respectively; \(U_{\text{e,machine}}\) and \(U_{\text{e,instrument}}\) are the expanded uncertainty due to expansion coefficient for the machine tool and the measurement instrument, respectively; and \(U_{\text{EVE}}\) and \(U_{\text{m,instrument}}\) is the expanded uncertainty due to environmental variation error (e.g., drifting). Note that \(U_{\text{m,instrument}}\) can be zero if \(U_{\text{instrument}}\) includes the uncertainty due to the temperature measurement of the measurement instrument. Generally, misalignment of laser interferometer is smaller than 1 mm. Thus, if the machine axis under test is over than 300 mm, the measurement uncertainty of misalignment can almost be ignored. LaserTRACER can work on two modes calibration and ISO test. Calibration mode uses more than four LaserTRACERs or one LaserTRACER with time sharing (_i.e._, multilateration method) and error mapping to compute the 21 terms of error motion compensation for three-axis machine tools [20; 23]. Moreover, LaserTRACER can be applied to calibrate the rotary axes of machine tools [24] and can be applied to extra-small machine tool volumetric error compensation [25]. When LaserTRACER  
Figure 3: Multilateration requires more than four LaserTRACERs for target spatial position computing, where Point 1 to Point 4 represent the target points 1 to 4, LT1 to LT4 represent the position of the LaserTRACER No. 1 to No. 4.  
operated in ISO test mode, positioning accuracy of the machine tool causes measurement uncertainty because of misalignment. Since the behavior of misalignment of LaserTRACER is different than laser interferometers, for instance the angle between measurement axis and laser light is a function of measurement length for LaserTRACER, this study was performing three experiments to evaluating the influence of the test results due to misalignment which is caused by positioning accuracy of the machine tools.
## 3 Presence of Micro- and Nanoplastics in Environmental Matrices and Food
### Soil  
Plastic particles pose a global concern in agricultural soil ecosystems, impacting soil biota biodiversity and food security. Agricultural plastic materials, primarily mulch films, along with irrigation water, street runoff, flooding, sewage sludge, agricultural compost, soil amendments, and atmospheric deposition, constitute significant sources of MPs and NPs in soil [40; 41; 42; 43; 44]. Modern agricultural techniques, often referred to as \"plasticulture\", rely on plastic to enhance crop productivity, quality, and sustainability, inadvertently leading to the production, dispersion, and retention of plastic fragments, particularly MPs and NPs, in rural ecosystems [45].  
However, the current database on soil pollution with plastic remains limited due to the absence of a validated method for plastic detection in soil, stemming from methodological, instrumental, and applicability constraints. It is estimated that from 63,000 to 430,000 tons and from 44,000 to 300,000 tons of MPs enter agroecosystems in Europe and in North America, respectively [46]. Plastic concentrations ranging from 2.38 to 1200 mg/kg have been reported in compost, while sewage sludge can contain between 1000 and 24,000 plastic items/kg. Additionally, irrigation with untreated and treated wastewaters, as well as surface freshwaters, serves as a pathway for MPs and NPs to enter the soil, with varying estimated concentrations [47].  
Other significant sources of MPs and NPs include roadside waste, illegal garbage disposal, road runoff, and atmospheric input. Most plastics, especially those larger than 1 \(\upmu\)m, are likely to become trapped in the soil, where they can persist for an unpredictable period, influencing groundwater quality; soil geochemistry; and biota comprising plants, earthworms, and microorganisms [48]. The fall of airborne MPs near roadsides from tire wear due to abrasion between the road and rubber further alters the biogeochemical properties of the soil [49].  
To address this emerging problem regarding soil quality, the European Commission has proposed a mission to ensure that 75% of soils are healthy by 2030, aiming to safeguard food quality, human health, ecosystems, and climate [50].
## Materials and Methods
### High-Throughput Sequencing  
Total DNA was extracted from fermented dough using a total DNA extraction kit (Omega Inc.). The hypervariable regions of fungi 185 rRNA gene were amplified with primers SSU0817F (5\({}^{\prime}\)-TTAGCATGAATRAATRRAATAGGA-3\({}^{\prime}\)) and 1196R (5\({}^{\prime}\)-TCTGAGC TGGTGAGTTICC-3\({}^{\prime}\)). For bacteria, the V3 and V4 hypervariable regions of 165 rRNA were amplified using primers 338F (5\({}^{\prime}\)-ACTCCTACGGGGAGGCAG-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACHVGGGWTCTAAT-3\({}^{\prime}\)). Purified amplicons were pooled in equimolar amounts, and paired-end sequencing was performed on Illumina MiSeq PE300 platform/NovaSeq PE250 platform according to standard protocols. Operational taxonomic units (OTUs) with 97% similarity cutoff were clustered using UPARSE version 7.1.
## 5 Applications
### Human-Robot Interaction  
Vision-based activity recognition has important applications in human-robot interaction (HRI). Giving robot the ability to recognize human activities is very important for HRI. This makes the robots useful for the industrial setup and well as in the domestic environment as a personal assistant. In the domestic environment, one of the applications of HRI can be seen as humanoid robots that could recognize human emotions from the sequence of images. A method based on the neural networks was proposed in [190] for the humanoid robot. In this method, six basic emotions (neutral, happiness, sad, fear, disgust and anger) were recognized from the facial expressions, and the topics embedded in the conversation were also identified and analysed. This method is effective for recognizing basic emotions but was not effective for compound emotions such as anger and surprise. In [191], an activity recognition method was proposed for HRI in industrial settings. It was based on the spatial and temporal features from skeletal data of the human workers performing the assembly task. The 3D coordinates of skeletal joints were acquired from Red Green Blue-Depth (RGB-D) data with the help of two Microsoft Kinect sensors. In order to select the best features, the random forests algorithm was applied. Finally, three groups of activities (movement, gestures, and object handling) were recognized using a hidden Markov model. Results indicated an average accuracy of 74.82%. However, this method was unable to recognize more complex activities such as entering and leaving the scene.  
Since robots are becoming part of our lives, it is very important that robots should understand human's emotions, intentions, and behaviour. This problem is termed as robot-centric activity recognition. A method for robot-centric activity recognition was proposed in [192] from the videos recorded during HRI. The purpose of this method was recognition of the human activities from the actor's own viewpoint. Unlike conventional third-person activity recognition, the actor (robot) wearing the camera, was involved in ongoing activity. This is not only recognition of activity in real time but also recognizing the activity before its completion, which is really a challenging task. A method for this type of activity recognition was proposed in [193] from RGB-D videos captured by the robot while physically interacting with the humans. The authors used four different descriptors (spatio-temporal points in RGB and depth data, 3D optical flow, and body posture descriptors) as features. With the combination of these descriptors and SVM, authors achieved recognition accuracy of 85.60%.
## 4 Results and Discussion  
### Results of Simulation and Measurement  
The same amplifier groups are used to collect the anode signal, and pulse height distribution of the top and bottom anode are measured, as shown in Figure 9. The charge ratio of top and bottom anode is about 1.9, which is consistent with the simulation results. Table 2 shows the simulated and measured capacitance for the XS anode. The relative norm difference of the results is used and defined as:  
\[difference=\left|\frac{C_{S}-C_{M}}{C_{M}}\right|\times 100\% \tag{7}\]  
where \(C_{S}\) is the simulated capacitance and \(C_{M}\) is the measured capacitance. It can be seen that the simulated capacitance is about 9% larger than those of measured values. Due to machining error and geometry defect of the anode being neglected, the errors of inter-electrode capacitance are introduced. However, the minor difference between the simulation and measurement proves the reliability of the model.
# Model for Cryogenic Flashing LNG Leak
## 4 Discussion and Conclusions  
The existing modelling work performed for flashing leak is for non-cryogenic pressurized sources, such as propane and butane. These models are not compatible with cryogenic liquids, such as LNG. This paper is an attempt to produce a source term model for cryogenic flashing leak that will be useful for risk assessment, such as LNG gas dispersion. One potential use of the leak model is providing an equivalent leak boundary condition for CFD simulation and helping to reduce computational time. The leak model formulation is based on the recent LNG experiment SPARCLING findings and the Isenthalpic Homogeneous Equilibrium Model (HEM). A comparison of the LNG bunkering technical reference, TR56, with the FLACS simulation using the leak model did not show contradicting results.  
To further verify the leak model fidelity, a comparison can be made with commercial software, such as Process Hazard Analysis Software (PHAST) [31]. However, commercial software uses an integral model, which is unable to analyse impinged leaks. Considering the rise in LNG usage in the marine industry, reserving 60 m (refer to Figure 7) for LNG bunkering will be a challenge for port facilities. The quickest way to reduce the LFL distance would be to use a barrier to redirect LNG dispersion. CFD software coupled with the leak model could potentially be used to evaluate the barrier effectiveness to reduce gas dispersion. Hence, it would be worthwhile to conduct an LNG leak experiment to validate the leak model and CFD software. Additionally, the experiment can confirm the model assumption of when vaporization is completed. Finally, the experiment should explore leak profiles of between 10 mm and 25 mm, which is currently lacking in the modern literature.  
Conceptualization, B.H.L. and E.Y.K.N.; methodology, B.H.L.; investigation, B.H.L.; writing--original draft preparation, B.H.L.; visualization, B.H.L.; supervision, E.Y.K.N.; writing--review and editing, E.Y.K.N.; funding acquisition, B.H.L. and E.Y.K.N. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Distance to LFL as a function of stagnation pressure for 25 mm-diameter hole leak.  
**Funding:** This work was supported by the Industrial Postgraduate Programme (IPP) initiated by the Singapore Economic Development Board (EDB).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to academic license agreement.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance of Satellite-Based Evapotranspiration
## 3 Methods
### Surface Energy Balance Models and Eapotranspiration Modelling
The \(\rm ET_{0}\) was estimated using the standardized Penman-Monteith equation [59]:  
\[\rm ET_{0}=\frac{0.408\Delta(R_{N}-G_{0})+\gamma\frac{900}{T_{a}}+u_{2}(e_{s} -e_{a})}{\Delta+\gamma(1+0.34u)} \tag{15}\]  
where \(\rm u\) is wind speed [m s\({}^{-1}\)], \(\rm e_{s}\) is the saturation vapor pressure (kPa), \(\rm e_{a}\) to the actual vapor pressure (kPa), and \(\rm\Delta\) is the slope of the vapor pressure [kPa \({}^{\circ}\)C\({}^{-1}\)]. Modeled \(\rm ET_{a}\) was compared to measured \(\rm ET_{c}\) at the Oromo calibration site using the coefficient of determination (\(\rm R^{2}\)), root mean square error (RMSE or RMSD), the standard deviation of the residual values (\(\rm\sigma\)), the bias of residual values and the mean absolute error (MAE) statistics.
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 5 Evaluation Result  
### Analysis of the Retrieval Effectiveness  
Tables 4 and 5 showed the retrieval effectiveness of IR models on the datasets. The results showed that PLM-based IR models demonstrate significant improvements in retrieval performance compared to traditional IR models and neural network-based IR models. In comparison to the original Dense Retriever (DR) and Reranker methods, our proposed method also exhibited superior performance. We believed that, under the condition of using the same ranking training method, the contrastive training method using language model-augmented query variations could fine-tune the language model to enhance the representation performance of query intent, reduce the risk of overfitting, and improve the generalizability of the model. Additionally, we also found that the retrieval performance of models based on Dense Retriever was generally inferior to that of the Reranker.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAP** & **MRR@10** & **MRR** & **nDCG@10** & **P@10** \\ \hline BM25 & 0.569 & 0.575 & 0.601 & 0.659 & 0.110 \\ RM3 & 0.575 & 0.586 & 0.587 & 0.672 & 0.113 \\ DRMM & 0.612 & 0.617 & 0.620 & 0.698 & 0.113 \\ KNRM & 0.645 & 0.657 & 0.659 & 0.723 & 0.112 \\ DR & 0.781 & 0.792 & 0.793 & 0.833 & 0.115 \\ Reranker & 0.830 & 0.843 & 0.848 & 0.873 & 0.117 \\ Our (DR) & 0.785 & 0.801 & 0.801 & 0.841 & 0.117 \\ Our (Reranker) & **0.845** & **0.862** & **0.862** & **0.888** & **0.119** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Retrieval performance of each model on WikiQA. The best result is **bold-faced**.
## 2 Materials and Methods
### BoW Model and [PERSON] et al. [32] first introduced the image method based on the BoW model. They believed that an image can be analogized to a document and the \"words\" of an image can be defined as feature vectors. The basic BoW model regards an image as a set of feature vectors and statistics of occurrence frequency of feature vectors, which are used for terrain classification. The BoW model can be set up by a clustering algorithm that is used to obtain the visual dictionary and the steps are as follows. Feature extraction: \(m\) images (\(m\geq 50\)) are collected for each terrain type and each image is extracted by SURF-BRISK to obtain \(n(i)\) feature vectors. All terrain images form a total sum (\(n(i)\)) of feature vectors (words). Generation of dictionary/codebook: The feature vectors obtained from the previous step are clustered (here, the \(k\)-means clustering method is used [33]) to get \(k\) clustering centers in order to build  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Descriptor Type** & **Detection Time (ms)** & **Matching Time (ms)** \\ \hline SURF & 852 & 1251 \\ BRISK & 127 & 98 \\ SURF-BRISK & 765 & 142 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection times of different descriptors. Brisk: binary robust invariant scalable keypoints.  
Figure 5: Matching results of different algorithms: (**a**) SURF; (**b**) BRISK; (**c**) SURF-BRISK.  
the codebook. A histogram is generated according to the codebook. The nearest neighbor calculation of each word of the picture is used to find the corresponding words in the codebook in order to form the BoW model.  
SVM is an excellent learning algorithm developed on the basis of statistics theory and is widely used in many fields, such as image classification, handwriting recognition, and bioinformatics. The input vector is mapped to a high-dimensional feature space by nonlinear mapping (a kernel function) and an optimal hyperplane is constructed in this space. Compared with the artificial neural network, which suffers from an overfitting problem, the support vector machine has better generalization ability for unknown samples [34]. SVM can be divided into three groups: linear separable, nonlinear separable, and kernel function mapping. Linear classifier performance is limited to linear problems, because in nonlinear problems constraints of excessive relaxation can lead to a large number of error samples. At this point, it can be transformed into a linear problem in a high-dimensional space using nonlinear transformation in order to obtain an optimal classification hyperplane.