esa-sceva/satcom-chunk-collection huggingface dataset:
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 5 Conclusions  
We propose an inductive legal judgment prediction method based on graph neural networks. The method first constructs six kinds of text graphs for each document and then uses graph convolutional networks to transfer and update information within the text graphs to fully understand and extract feature information of documents of the judicial case. we also utilize an improved legal text differentiation extractor based on graph attention network to obtain the information about the differences of law articles between similar charges. In the graph fusion stage, we mixed the document features of the six kinds of text graphs and law articles information that carried the differentiated features. The experimental results show that our method performed well and achieved good results in all three subtasks of the legal judgment prediction task. Our future work is divided into two parts. First, we will investigate new methods for constructing graphs from texts, including information from multiple dimensions. Second, more efficient graph fusion methods with external knowledge will be adopted to improve the performance of multiple tasks in legal intelligence, including legal judgment prediction (LJP).  
Conceptualization, Q.Z.; data curation, Q.Z.; formal analysis, Q.Z.; funding acquisition, T.G.; investigation, S.Z.; methodology, Q.Z.; software, Q.Z.; supervision, T.G. and Y.W.; validation, S.Z. and D.L.; visualization, Q.Z.; writing--original draft, Q.Z.; writing--review and editing, T.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (grant no. 52130403) and the Fundamental Research Funds for the Central Universities (grant no. N2017002).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
###### Abstract
(3) Object-based image analysis methods (OBIA): Due to the limitations of pixel-based classification methods, such as the salt and pepper phenomenon in classification results, object-based classification techniques have been increasingly applied in remote sensing classification in recent years [37; 38]. Many successful cases of water body extraction using OBIA methods have been reported [39; 40; 41; 42; 43]. Given that urban functional zones (UFZs) are composed of diverse geographic objects, [PERSON] et al. [44] presented a novel object-based UFZ mapping method using very-high-resolution (VHR) remote sensing images. Based on object-oriented analysis technology and multi-source data, [PERSON] et al. [45] proposed a multi-level classification scheme based on goals and rules to study the changes of glacier environments. In addition, some studies also have used synthetic aperture radar (SAR) data to monitor the surface dynamics, because these data are insensitive to clouds [14; 46; 47]; the area of surface water can be extracted from SAR data based on textural analysis [48], change detection [49], automatic segmentation [50], and classification [51]. At present, machine learning algorithms to extract water bodies mainly include neural networks, support vector machines, and random forest algorithms. The studies carried out in the past have identified the best performing classification algorithm by comparing different classification algorithms. However, none of them provides a comprehensive comparative analysis of some popular classification algorithms [37; 52]. There are few studies on the evaluation of the transfer performance of each machine learning algorithm applied to remote sensing images in different periods. Based on Landsat-8 images, this study uses machine learning algorithms such as decision tree, logistic regression, random forest, and neural network to extract water bodies. First of all, the effect of each machine learning algorithm on the test set is discussed. After that, each machine learning algorithm is applied to three different local areas, and its effect on each local area is evaluated. At last, each machine learning algorithm is applied to remote sensing images in different periods to evaluate the model transfer performance of each machine learning algorithm, and three threshold methods are compared. The results could shed light on the future work of water body extraction based on remote sensing.
## 2 Methodology
### Mathematical Model
In general, the two-way approach provides a more accurate solution and is thus able to capture minor disturbances in the flow accurately. In considering the computational tools available, we note that two types of two-way coupling approaches, namely, monolithic and partitioned approaches, are used in the industry for FSI simulation [24]. The monolithic approach requires an engineer to write a code that is specific to the physical problem being addressed, whereas the partitioned approach preserves the software modularity by coupling the existing flow and structural solvers. Therefore, the partitioned approach requires only the use of existing software, which is an advantage. In summary, the ALE approach with two-way coupling is chosen based on the susceptibility of the screening mechanism to oscillatory flow conditions, requiring accurate fatigue crack analysis. This approach is preferred over one-way coupling due to its ability to capture minor flow disturbances accurately. Further, the analysis of the considered screening mechanism would require enhanced accuracy due to the oscillatory loading condition and subsequent susceptibility to fatigue cracks. Given that the two-way ALE approach can produce a more accurate result than its one-way counterpart whilst using already available software, adopting a two-way coupling approach to study the screening mechanism under the given loading conditions would be prudent.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 3 Results
### Small Ideal Reflector Array
#### 3.2.1 Retroreflected Energy Levels  
The small ideal reflector yielded results similar in pattern to those of the large reflector, but values were generally lower. Figure 11 shows results for the 2 m and 4 m distances for collocated source-receivers. The simple case has a wide range of values because some source-receivers are close to the reflector's edge.  
For the 2 m distance, the optimum patch size for space-reversal (0.6 m) was within the optimum range previously observed for the large reflector. While the same could be said for the focusing space-reversal patches, the small reflector size was too small to identify an optimum (the largest patch returned the most sound). Only the focusing space-reversal patches provided cases where the retroreflected energy level was mostly above 0 dB.  
For the 4 m distance, retroreflected energy levels were reduced, with no configuration yielding a majority of values above 0 dB. The largest patch size reflected the most sound to the source for both focusing and non-focusing space-reversal patches. Furthermore, the full-surface focusing space-reversal patch yielded a reasonably small range of retroreflected energy levels in this case (unlike the 2 m distance small or large reflector). Although the levels were not high, the median is about 15 dB greater than the simple reflector's maximum at 4 m.  
All simulated results were lower than theoretical predictions. Simple patch results from the center of the source-receiver square were on average 0.58 dB less than in theory (Equation (2)), with a maximum deviation of 1 dB over the six reflector distances. For source-receivers at the focus of the 2.4 m focusing patch, levels were on average 0.34 dB less than in theory, with a maximum deviation of 0.4 dB.  
Figure 10: Examples of the time distribution of retroreflected impulse response for the four patch types for the large reflector: (**a**) source-receiver at a patch focus (**b**) source-receiver not at a patch focus. The envelope of the speech-A-weighted impulse responses (absolute value of the Hilbert transform) is shown in green, relative to the maximum peak. The cumulative sum of retroreflected energy is shown in magenta, relative to the greatest maximum value (i.e., focusing space-reversal).  
Mean retroreflected energy levels decreased quite consistently with surface distance. For many of the cases in Figure 12, the slope was about the same as the theoretical energy focus slope, suggesting that the retroreflected energy level was mostly related to the surface's solid angle. However, larger focusing space-reversal patches had a gentler slope, especially the single 2.4 m patch. Note that the Equation (2) theoretical flat-square reflector yields a stronger reflection than Equation (5) energy focusing beyond 2 m surface distance because of the surface's relatively small solid angle.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 6 Techniques for Fouling Control in Dead-End Submerged Membrane Systems  
As noted earlier, the submerged HF concept is widely used in the dead-end mode in the water-treatment industry. Strategies to mitigate fouling are required to avoid decline in the membrane permeability in dead-end submerged HF systems [282; 6; 283]. It is possible to minimize fouling both by choosing a suitable membrane material with a reduced tendency to adsorb substances in the feed and by optimizing the operating conditions in the system [5; 161; 284; 285]. The application of relaxation (intermittent cessation of permeation), backwashing (reversal of permeate flow through the pores), and air backwashing with or without air scouring are common physical approaches to remove fouling in submerged systems. It has been shown in many studies that relaxation and backwashing provide an effective removal of the fouling layer, thereby prolonging the filtration process in submerged membrane systems, especially at high imposed fluxes [88; 90; 131; 286; 287]. A significant challenge in the application of relaxation and backwashing is that only partial recovery of the permeability is achieved at the end of a filtration cycle, which implies a gradual loss of the effective filtration area due to fouling. Subsequently, in the next filtration cycle the less fouled areas will have to experience increased local fluxes to maintain the overall average flux, which in turn results in a higher fouling rate [94]. However, Figure 6, which shows a plot of the TMP versus time for both continuous and periodic backwashing and filtration, indicates that even with a partial recovery during each cycle, filtration with intermittent backwashing and relaxation outperforms continuous operation. A further discussion of backwashing and relaxation is provided in the following sections.  
Figure 6: TMP versus time for continuous filtration relative to periodic relaxation and backwashing of real seawater [26]. Reproduced with permission from [26], Copyright Journal of Membrane Science, 2010.
## 2 Nanofluid Thermal Conductivity
### Effects of Surfactant on Nanofluid Thermal Conductivity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Nanoparticle** & **Base Liquid** & **Surfactant** & **Remarks** \\ \hline \hline \multirow{9}{*}{ \begin{tabular}{c} [PERSON] et al. \\ [39] (2015) \\ \end{tabular}  } & \multirow{9}{*}{FeC} & \multirow{9}{*}{Water} & Low-viscosity \\  & & & carboxymethyl cellulose \\  & & & sodium salt \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \multirow{9}{*}{ \begin{tabular}{c} [PERSON] et al. [22] \\ (2017) \\ \end{tabular}  } & \multirow{9}{*}{Al\({}_{2}\)O\({}_{3}\)} & \multirow{9}{*}{Water} & \begin{tabular}{c} Of the 3 surfactants tested, only SDBS \\ produced a stable nanofluid. \\ \end{tabular}  \\  & & & Adding SDBS surfactant resulted in a more \\  & & & & homogeneous distribution of nanoparticles \\  & & & & within the base fluid, and less clustering. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \begin{tabular}{c} [PERSON] et al. [33] \\ (2016) \\ \end{tabular}  & TiO\({}_{2}\) & Water & \begin{tabular}{c} CTAB, acetic acid (AA), \\ oleic acid (OA), SDS \\ \end{tabular}  & \begin{tabular}{c} Of the 4 surfactants tested, only CTAB and \\ AA produced stable nanofluids. \\ \end{tabular}  \\ \hline \hline \begin{tabular}{c} [PERSON] et al. \\ [16] (2005) \\ \end{tabular}  & TiO\({}_{2}\) & Water & \begin{tabular}{c} CTAB, OA \\ \end{tabular}  & \begin{tabular}{c} CTAB surfactant produced a more stable and \\ homogeneous nanofluid than OA. \\ The addition of surfactant prevented \\ nanoparticle clustering, creating a \\ homogeneous distribution of nanoparticle \\ within the base liquid. \\ \end{tabular}  \\ \hline \hline \begin{tabular}{c} [PERSON] et al. [23] \\ (2017) \\ \end{tabular}  & Ag & \begin{tabular}{c} Water \\ Ethylene Glycol \\ Hexane \\ \end{tabular}  & \begin{tabular}{c} Gum Arabic \\ OA \\ \end{tabular}  & \ \begin{tabular}{c} It was observed that the surfactants did not \\ affect the thermal conductivity of Ethylene \\ Glycol and Hexane. \\ Gum Arabic surfactant lowered the thermal conductivity of water, due to its lower \\ thermal conductivity in comparison to water. \\ \end{tabular}  \\ \hline \hline \begin{tabular}{c} [PERSON] et al. \\ [21] (2016) \\ \end{tabular}  & Al\({}_{2}\)O\({}_{3}\) & Therminol & OA \\ \cline{1-1}  &
# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 2 Materials and Methods  
This study utilized the graph-based machine learning algorithms GCN and SVM. These algorithms were implemented using StellarGraph, Pandas, Numpy, Sklearn, Tensorflow, IPython, matplotlib, and Pyvis libraries based on Python. The performance evaluations of the GCN and SVM algorithms were conducted using a confusion matrix, accuracy, precision, recall, and f1 scoring methods. The GCN algorithms were implemented using early-stopping methods to prevent overfitting.
## 3 IberSpeech-RTVE 2018 Evaluation Tasks
### Evaluation Data
#### 3.2.2 Diarization Scoring  
As in the NIST RT Diarization evaluations [[https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation](https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation)]([https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation](https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation)), to measure the performance of the proposed systems, DER was computed as the fraction of speaker time that was not correctly attributed to that specific speaker. This score was computed over the entire file to be processed, including regions where more than one speaker was present (overlap regions).  
Given the dataset to evaluate \(\Omega\), each document was divided into contiguous segments at all speaker change points found in both the reference and the hypothesis, and the diarization error time for each segment \(n\) was defined as:  
\[E(n)=T(n)\left[\max\left(N_{ref}(n),N_{sys}(n)\right)-N_{Correct}(n)\right] \tag{2}\]  
where \(T(n)\) is the duration of segment \(n\), \(N_{ref}(n)\) is the number of speakers that are present in segment \(n\), \(N_{sys}(n)\) is the number of system speakers that are present in segment \(n\), and \(N_{Correct}(n)\) is the number of reference speakers in segment \(n\) correctly assigned by the diarization system.  
\[DER=\frac{\sum\limits_{n\in\Omega}E(n)}{\sum\limits_{n\in\Omega} \left(T(n)N_{ref}(n)\right)} \tag{3}\]  
The diarization error time includes the time that is assigned to the wrong speaker, missed speech time, and false alarm speech time:* Speaker error time: The speaker error time is the amount of time that has been assigned to an incorrect speaker. This error can occur in segments where the number of system speakers is greater than the number of reference speakers, but also in segments where the number of system speakers is lower than the number of reference speakers whenever the number of system speakers and the number of reference speakers are greater than zero.
* Missed speech time: The missed speech time refers to the amount of time that speech is present, but not labeled by the diarization system in segments where the number of system speakers is lower than the number of reference speakers.
* False alarm time: The false alarm time is the amount of time that a speaker has been labeled by the diarization system, but is not present in segments where the number of system speakers is greater than the number of reference speakers.  
Consecutive speech segments of audio labeled with the same speaker identification tag and separated by a non-speech segment less than 2 s long were merged and considered a single segment. A region of 0.25 s around each segment boundary, usually known as the forgiveness collar, was considered. These regions were excluded from the computation of the diarization error in order to take into account both inconsistent human annotations and the uncertainty about when a speaker turn begins or ends.
## 4 Analysis of the Microseismic Event Monitoring Results
### Verification of the Monitoring Results and Site Conditions  
The microseismic anomaly area was surveyed to verify the accuracy of the microseismic monitoring and apparent volume energy prediction, and the survey results were consistent with the actual monitoring and predicted results based on the investigation of the field conditions in the microseismic event anomaly area at the 3736 m level. These findings suggest that the microseismic event monitoring of the over fault and the apparent volume and energy indices are reliable for the prediction of rockburst and large-scale ruptures that may be caused by fault activation. Figure 10 shows the channel fragmentation and deformation of the corresponding area at the 3736 m level on that day.  
Figure 9: Analysis module of the cumulative apparent volume and energy index time distribution characteristics.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 3 The Proposed Scheme  
In this research, we proposed a blockchain-based traceable and authenticated tobacco products logistics system with GPS and RFID technologies. The proposed system is constituted of the following twelve parties: ID issuer, tobacco ID tag, mobile reader, competent authorities, manufacturer, distributor, logistics, retailer, consumer, arbitrator, auditors, and blockchain center. The system framework is shown in Figure 2.  
Figure 2: System architecture diagram.
# Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index
## 2 Numerical Model
### Simulation Scheme  
The direct assignment of values to the shear strength indices (internal friction angle \(\varphi\) and cohesion \(c\)) of a specimen is not possible in PFC3D 6.0 software. Notably, the \(\mu\) value in PFC3D represents the contact friction coefficient in the particle contact model rather than the tangent value of the specimen's internal friction angle. Although no explicit relationship exists between the \(\mu\) value and the tangent of the sample's internal friction angle, they are generally positively correlated. In this study, the shear strength of a sample was considered to solely depend on the relative density during preparation. Stress-strain curves for samples with different relative densities under varying confining pressures were obtained using triaxial simulations. The peak internal friction angle \(\varphi\) and cohesion \(c\) were calculated from these curves. Subsequently, CPT simulations were performed under lunar conditions on the simulated foundations with varying relative densities to obtain cone resistance versus penetration depth curves. Finally, the relationship between normalized cone resistance and shear strength parameters was established. Four groups of triaxial simulations were conducted under lunar conditions with relative densities of 0.60, 0.68, 0.75, and 0.83. Simulations were performed under confining pressures of 5 kPa, 10 kPa, and 15 kPa for each relative density. Correspondingly, simulated foundations with four relative densities were created, and a CPT simulation was conducted for each. After preloading, gravitational acceleration was set to 1/6 g to stabilize the sample under lunar gravity. The van der Waals force switch was activated in the contact model, resulting in cone resistance-depth curves for different relative densities under lunar conditions.
## 3 Mathematical Model
We can describe \(\mathbf{p}^{T}\) using a system of equilibrium equations:  
\[p(\mathbf{n})\Bigg{(}\sum_{k=1}^{K}n_{k}h_{k}(\mathrm{I}(k\in \mathcal{K}_{\mathrm{s}})+x_{k}(\mathbf{n})\mathrm{I}(k\in\mathcal{K}_{ \mathrm{e}}))+\sum_{k=1}^{K}\lambda_{k}\mathrm{I}\Big{(}\mathbf{n}\in\mathbb{ S}_{k}^{(1)}\Big{)}+\sum_{k=1}^{K}\lambda_{k}\mathrm{I}\Big{(}\mathbf{n}\in \mathbb{S}_{k}^{(2)}\Big{)}\Bigg{)}=\\ =\sum_{k=1}^{K}p(\mathbf{n}-\mathbf{e}_{k})\lambda_{k}\mathrm{I} \Big{(}\mathbf{n}-\mathbf{e}_{k}\in\mathbb{S}_{k}^{(1)}\Big{)}+\sum_{k=1}^{K} p(\mathbf{n}-\mathbf{e}_{k}+\mathbf{m}_{k}(\mathbf{n}))\lambda_{k}\mathrm{I} \Big{(}\mathbf{n}-\mathbf{e}_{k}+\mathbf{m}_{k}(\mathbf{n})\in\mathbb{S}_{k}^ {(2)}\Big{)}+\\ +\sum_{k=1}^{K}p(\mathbf{n}+\mathbf{e}_{k})(n_{k}+1)\mu_{k}( \mathrm{I}(k\in\mathcal{K}_{\mathrm{s}})+x_{k}(\mathbf{n}+\mathbf{e}_{k}) \mathrm{I}(k\in\mathcal{K}_{\mathrm{e}}))\mathrm{I}\Big{(}\mathbf{n}\in \mathbb{S}_{k}^{(1)}\Big{)}, \tag{18}\]  
with the intrinsic normalization condition \(\sum_{\mathbf{n}\in\mathbb{S}}p(\mathbf{n})=1\). Due to the implementation of the Dynamic Resource Reallocation (DRR) method with service preemption, the Markov process \(\mathbf{X}(t)\) is not reversible. Therefore, we can numerically compute the stationary probability distribution \(\mathbf{p}^{T}\) using an iterative method ([21], pp. 103-204) ([22], pp. 106-179) [23; 24; 25] to solve Equation (18) rewritten as \(\mathbf{p}^{T}\mathbf{A}=\mathbf{0}^{T}\), \(\mathbf{p}^{T}\mathbf{1}=1\), where \(\mathbf{A}=\left(a(\mathbf{n},\mathbf{s})\right)_{\mathbf{n},\mathbf{s}\in \mathbb{S}}\) is the infinitesimal matrix (the matrix of transition intensities):  
\[a(\mathbf{n},\mathbf{s})=\left\{ \begin{array}{l}\lambda_{k},\;\mathbf{s}= \mathbf{n}+\mathbf{e}_{k},\;\mathbf{n}\in\mathbb{S}_{k}^{(1)},\;k=\overline{1,K},\\ \lambda_{k},\;\mathbf{s}=\mathbf{n}+\mathbf{e}_{k}-\mathbf{m}_{k}(\mathbf{n}), \;\mathbf{n}\in\mathbb{S}_{k}^{(2)},\;k=\overline{1,K},\\ n_{k}\mu_{k}(\mathrm{I}(k\in\mathcal{K}_{\mathrm{s}})+x_{k}(\mathbf{n}) \mathrm{I}(k\in\mathcal{K}_{\mathrm{e}})),\;\mathbf{s}=\mathbf{n}-\mathbf{e}_ {k},k=\overline{1,K},\\ -\sum_{k=1}^{K}\lambda_{k}\mathrm{I}\Big{(}\mathbf{n}\in\mathbb{S}_{k}^{(1)} \Big{)}-\sum_{k=1}^{K}\lambda_{k}\mathrm{I}\Big{(}\mathbf{n}\in\mathbb{S}_{k}^ {(2)}\Big{)}-\sum_{k=1}^{K}n_{k}\mu_{k}(\mathrm{I}(k\in\mathcal{K}_{\mathrm{ s}})+x_{k}(\mathbf{n})\mathrm{I}(k\in\mathcal{K}_{\mathrm{e}})),\;\mathbf{s}= \mathbf{n},\\ 0,\;\text{otherwise}.\end{array} \right. \tag{19}\]  
Once the stationary probability distribution \(\mathbf{p}^{T}\) is obtained, one can compute the system Key Performance Indicators (KPIs) using expressions in analytic form. We analyze the system functioning in terms of the utilization and accessibility KPIs [3].