esa-sceva/satcom-chunk-collection huggingface dataset:
# Metamorphic Testing of Relation Extraction Models
## 4 Experimental Set-Up
### Subject RE Models  
We conducted experiments on three RE models: BERT\({}_{\text{int}}\)+MTB, LUKE, and NCB. BERT\({}_{\text{int}}\)+MTB uses BERT [32] to represent textual relations and is a task-agnostic relation extraction model [33]. The core idea of the model is that two relational representations should be similar if they contain the same entity pair. The model uses a large amount of unsupervised data, and additional matching the blanks (MTB) tasks are added in the BERT pretraining process, which improves the performance of relation extraction in the pretraining stage.  
LUKE [8] is a transformer-based language model that is pretrained on large-scale text corpora and knowledge graphs while using entity information as an additional input embedding layer. It regards words and entities in the text as independent tokens and finally outputs the context-processed entity representation. Moreover, LUKE uses an entity-aware self-attention mechanism, which considers the category of the token (word or entity) when calculating the attention score.  
Noise-robust co-regularization BERT-large (NCB) [34] proposes a co-regularization framework for entity-centric information extraction, which consists of several neural models with the same structure but different parameter initializations to prevent overfitting to noisy labels. It can efficiently learn supervised models on noisy datasets without any additional learning resources.  
The details of three RE models are reported in Table 4. Among them, the latter two models achieved SOTA performance on the RE task ([[https://paperswithcode.com/](https://paperswithcode.com/)]([https://paperswithcode.com/](https://paperswithcode.com/)), accessed on 10 June 2022.)  
We implement BERT\({}_{\text{int}}\)+MTB using OpenNRE ([[https://github.com/thunlp/OpenNRE](https://github.com/thunlp/OpenNRE)]([https://github.com/thunlp/OpenNRE](https://github.com/thunlp/OpenNRE)), accessed on 20 June 2022), an open-source and extensible toolkit launched by the Natural Language Processing Group at the Department of Computer Science and Technology, Tsinghua University (THUNLP), which provides a unified framework for implementing relation extraction models. Based on huggingface's Transformers platform ([[https://github.com/huggingface/transformers](https://github.com/huggingface/transformers)]([https://github.com/huggingface/transformers](https://github.com/huggingface/transformers)), accessed on 20 June 2022), we implemented the LUKE model. For the NCB model, since only the source code was available, we locally trained it using the train split of the TACRED dataset ([[https://nlp.stanford.edu/projects/tacred](https://nlp.stanford.edu/projects/tacred)]([https://nlp.stanford.edu/projects/tacred](https://nlp.stanford.edu/projects/tacred)), accessed on 20 June 2022) and then conducted evaluations of the resulting models.
## 2 Materials and Methods  
### Sampling Site, Particulate Matter Sampling, and Gravimetric Mass Determination  
The city of Graz is located southeast of the Alps, within the valley of the river Mur, surrounded by mountains. The PM filter samples analyzed in this study were collected within the Styrian air quality network, maintained by the provincial government of Styria (Department 15). The prime monitoring sites of interest are Graz Don Bosco (DB, LON 15.41643\({}^{\circ}\), LAT 47.05702\({}^{\circ}\), 358 m asl) and Bockberg (BB, LON 15.49583\({}^{\circ}\), LAT 46.87139\({}^{\circ}\), 449 m asl). While DB represents an urban environment, BB is not directly influenced by anthropogenic emissions and thus represents a regional background site. At DB PM\({}_{10}\) and PM\({}_{2.5}\) were sampled with Digitel high volume samplers (air flow at operating condition: 30 m\({}^{3}\)/h) and PM mass was determined gravimetrically according to EN 1234:2014. PM\({}_{2.5}\) collected on quartz fiber filters (Pall Life Sciences) was provided for further chemical analyses. At BB PM\({}_{10}\) mass was determined with a beta attenuation mass monitor (MetOne BAM 1020; EN 16450:2017) with a flow rate of 1 m\({}^{3}\)/h.  
This study focuses on PM abundances during the time period of 2 January-31 March 2017. For chemical analyses 24 h samples were grouped to sample pools comprising 2 to 10 days with comparable DB PM\({}_{10}\) concentrations. The pools were formed on the basis of PM\({}_{10}\) mass concentrations determined at DB, and the ratio of PM\({}_{10}\) concentrations between DB and BB. PM\({}_{10}\) concentrations were only used to select sample pools or to calculate PM\({}_{10}\) mass ratios to compare the urban (DB) and background (BB) site. Chemical analyses were solely performed on PM\({}_{2.5}\) filters collected at DB.
## 4 Results and Discussion
### Analysis of the Subsoiling Resistance
#### 4.1.3 The Effect of Operation Speed on Subsoiling Resistance  
In this study, with the operating speed increasing, the subsoiling resistance increased slightly, but not significantly. The reason might be that under the soil conditions in this study, the diversion state of the soil on the subsoiler tip face was better, and the extrusion effect of the shovel tip on both sides of the soil was not significantly enhanced when the speed increased, resulting in an unremarkable increase in the subsoiling resistance. Compared with the resistance values of the shovel tip E and the shovel tip F at different speeds, it could be seen that with the speed increasing, the two shovel tips showed a slight decreasing trend of subsoiling resistance, which was due to the fact that with the speed increasing, the non-smooth surface structure could cause more effective soil disturbance and achieve better resistance reduction effect.
## 4 Experiments
### Ablation Experiments and Result Analysis  
To quantitatively analyze the contribution of each module of MDNet to the overall model performance, we conducted systematic ablation experiments on three benchmark datasets: LEVIR-CD, WHU-CD, and CLCD. By removing key modules in MDNet and comparing the analysis results under uniform experimental settings, the influence degree of each module was effectively quantified. The specific ablation experiment design and results are shown in Table 1 and Figure 6.
###### Abstract
[PERSON] and [PERSON] [23] used generative adversarial networks (GANs) to generate and predict flow fields for laminar and turbulent flows over a circular cylinder at various Reynolds numbers. These studies utilize neural networks with inputs of past and present velocity fields to predict or generate velocity fields for the present and future instances. The approaches mentioned above used the velocity field information in a given region as an input to predict the velocity field in the same region. Thus, they constructed neural networks having the same input and output physical quantities. On the other hand, [PERSON] et al. [24] adopted different physical quantities for the input and output variables of neural networks to predict flow over a circular cylinder. That is, in their study, pressures at multiple points on the cylinder surface were adopted as the input variables of neural networks to predict two-dimensional velocity field in laminar flow over a circular cylinder as the output variables of neural networks. With this approach, they demonstrated that the flow field over a cylinder could be accurately predicted even with different input and output variables of neural networks. However, the approach by [PERSON] et al. [24] assumed a two-dimensional flow even for the Reynolds numbers, where the flow is expected to be three-dimensional, having variations in the spanwise direction. Therefore, the present study aims to predict the velocity field over a circular cylinder having spanwise variations with the wall pressure input. Additionally, unlike the neural network by [PERSON] et al. [24], which utilized time series of wall pressure data as the input variables, we adopt instantaneous wall pressure data as the input for the neural networks in order to construct a more compact input dataset. To achieve these goals, we conduct numerical simulations of laminar flows over a circular cylinder at the Reynolds number of \(Re=60\) (two-dimensional) and \(Re=300\) (three-dimensional) and predict the transverse velocity field near a circular cylinder using neural networks constructed with instantaneous pressures on the cylinder wall as the input variables.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 3 Experiment Setup  
To validate the proposed numerical simulation model and explore the relationship between coil inductance response and flaw size and position, a flaw inspection experiment was carried out at room temperature. The experiment was primarily conducted using a signal sampling and processing unit, a system control unit, and a display module.  
The signal sampling unit, depicted in Figure 5, was comprised of a cable, PE sheaths, a cable holder, and a positioning slide platform system. As shown in Figure 6a, the cable specimen consisted of parallel single-stranded galvanized steel wires arranged in a specific order, with a diameter of 7 mm. The whole cable was surrounded by PE material, which included a 1 mm PE sheath and six layers of 0.2 mm PE tape. The PE tape filled the space between the outer sheath and the cable. Flaws were systematically deployed in the middle of different layers. The diagrams of the flaw are presented in Figure 6c, where the axial length of the flaw is denoted by \(d\). To generate flaws of varying axial length \(d\), the distance between both sides of the wires was incrementally adjusted from 0 mm to 40 mm with a step size of 10 mm.  
Figure 4: Experimental data processing workflow.  
Figure 5: Signal Sampling Unit Diagram.  
The cable specimen was suspended by holders situated above the slide-way and traversed over a self-induction coil that was fixed to a mobile platform. The coil was composed of 300 winding of 0.3 mm enameled copper wire and possessed an external diameter of 80 mm, an inner diameter of 70 mm, and a thickness of 2.1 mm. The coil's inductance was accurately measured in real-time with an LCR bridge. A frequency range from 1000 Hz to 10,000 Hz was chosen to analyze the influence of the excitation frequency on the coil's self-inductance response and determine the most appropriate excitation frequency. Five anchor frequencies were selected to divide the frequency band, and the logarithm of the frequencies was employed to reduce the range span. The experiment involved moving the coil along the slide-way to detect flaws inside a cable. The scanning distance was 300 mm. The signals were then uploaded to a host computer for display and processing. Figure 7 presents the entire experimental procedure. Table 1 shows the detailed experimental parameters. The experiments are labeled as \(D_{i-j}\), where \(i\) represents the flaw location and \(j\) represents the flaw cross-sectional area on the \(i\)-th layer. For instance, \(D_{2-1}\) denotes the situation of \(P_{d}=2\) and \(PLA=\frac{1}{19}\).
## 1 Introduction  
Distributed systems provide convenience for dealing with online activities. With some enhanced techniques, such as cryptography, they provide secure and scalable architectures with a wide applicability. One of the most prominent examples is the crypto-currency system. In recent years, distributed crypto-currency systems have provided an architecture that offers transparency and enables a communication ecosystem that generates billions of transactions. For instance, Internet of Things (IoT) Tangle [1] is one architecture that uses a distributed Directed Acyclic Graph (DAG) structure. However, distributed systems might suffer from the lack of synchronization in databases. To overcome this disadvantage, a semi-synchronous architecture IOTA-Tango was introduced in [2] based on the basic architecture of IOTA-Tangle.  
Synchronicity is achieved when the transactions for validation are picked by the validators without ever being idle. The goal is to minimize the average waiting time for transactions without ignoring any transactions. It is important that the waiting time for an average transaction is minimized and no transaction is ignored. To ensure the system is synchronous with respect to the new transactions, an assignment mechanism was designed for scheduling those transactions to the verifiers with these following characteristics  
* transactions are seen across the underlying ledger system;
* first come first serve mode is used to release the transactions to the verifiers before they are sent to the controller;* the placement order of transactions in Tango is decided by the controllers. Adjacent steps (processes) must be mutually synchronous. In this paper, a brief review of our prior work on efficient injection protocols is provided. The Periodic Injection of Transaction via Evaluation Corridor (PITEC) was first introduced in [2]. The Probabilistic Injection of Transactions (PIT) was introduced in [3]. In this work, Adaptive Semi-synchronous Transaction Injection (ASTI) and Hybrid Adaptive Injection (HAI) protocols are further introduced. The rest of the paper is organized as follows. The reviews on PITEC and PIT are provided at Sections 3 and 4, respectively. A discussion on the ASTI protocol is at Section 5. For ASTI, the injection amount and the pulse period are used as the parameters for regulating the injection process. The hybrid protocol is introduced in Section 6 and finally, our conclusions are presented in Section 7.
## 2 Materials and Methods
### Surface Characterization
#### 2.3.1 3D Surface Profiling  
Surface topography was evaluated by a 3D Surface Profiling System (Talysurf CLI 2000 from Taylor Hobson), equipped with an inductive gauge 2 \(\upmu\)m radius diamond stylus. Further, 0.5 \(\times\) 0.5 mm\({}^{2}\) surfaces were acquired, adopting the following setting: axis resolution (data spacing) 0.5 \(\upmu\)m, lateral resolution 0.5 \(\upmu\)m, and vertical resolution 2 nm. A surface analysis software (Taly Map Universal, release 3.1) was used to elaborate the acquired profiles and measure roughness by multiple parameters, with the possibility for applying different filters.  
The following 3D roughness parameters were analyzed: the absolute deviation of the surface (Sa, i.e., the arithmetic mean of the deviations from the mean, also known as average roughness), the density of summits of the surface (Sds, i.e., the number of peaks for unit area expressed in peaks/mm\({}^{2}\)), and the developed surface area ratio (Sdr, i.e., the ratio between the area of the real developed surface and the area of the projected surface); the latter is an expression of the surface complexity.  
Before the surface analysis, to separate the roughness profile from the primary profile, an 80 \(\upmu\)m Gauss' filters was applied on the acquired profiles. No less than four surfaces were analyzed for each surface type.
## 4 Materials and Methods
### Dataset  
#### 4.2.1 Meteorological Data  
The meteorological dataset employed in this study is the China Meteorological Forcing Dataset (CMFD), with a high spatial and temporal resolution, and which is published by the Cold and Arid Region Scientific Data Center. The spatial resolution of the dataset is 0.1\({}^{\circ}\), which was achieved by integrating the meteorological observation data of the China Meteorological Administration and various other meteorological reanalysis data, mainly including reanalysis data from Princeton University in the USA, data products from the global land data assimilation system, the NASA Global Energy and Water Cycle Experiment (GEWEX) surface radiation budget dataset, as well as data acquired by tropical precipitation measurement satellites [33]. The temporal resolution was 3 h, including meteorological factors, such as near-surface temperature, air pressure, air humidity, wind speed, downward shortwave radiation, and precipitation. The CMFD data are considered highly reliable by many scholars [34; 35], and the data used in this study were temperature, precipitation, air humidity, and solar radiation data acquired between 2003 and 2012. The daily maximum and minimum temperatures, daily precipitation, and solar radiation data were obtained after performing time scale and unit conversions. The time scale conversion was performed by taking the maximum and minimum values of the daily temperature as the highest and the lowest daily temperatures, respectively, while the sums of the daily precipitation and solar radiation were taken as the daily total precipitation and solar radiation data, respectively. The unit conversion was performed by converting the temperature unit to degrees Celsius (\({}^{\circ}\)C), the precipitation unit to millimeters (mm), and the solar radiation unit to megajoules per square meter (MJ/m\({}^{2}\)). The meteorological data were then resampled to ensure that the spatial resolution was the same as that of the leaf area index (_LAI_) data.
## 3 Methods
### Methods  
#### 3.3.1 LDA Topic Model  
The LDA topic model is widely used in text topic mining. The LDA model offers advantages for semi-structured documents like patent documents, including fast mining speed, comprehensive coverage, and high accuracy [32; 33]. Therefore, this paper applies the LDA topic model to mine and analyze the patent text to extract the technical topics and topic words in the patent text. This study uses the LDA topic model to analyze patent texts, which helps to reveal the implied subject terms within the text. It analyzes the evolution of technology and identifies trends in technology. The principle of the LDA topic model is shown in Figure 3. The solid circular nodes represent the observable variables and the directed edges between these nodes depict the conditional probabilistic dependencies between the variables. The rectangular boxes represent the process of repetition; the outer rectangular box reflects the process of repeatedly generating topic distributions for each document, and the inner rectangle reflects the process of repeatedly generating words from these topic distributions. LDA deconstructs the relationship between the input document and the original word vectors in two steps: selecting topics from the document and selecting words from the selected topics.  
'M' represents the total number of documents, 'N' represents the number of words in a single document, 'K' represents the number of topics, 'W' represents all the words in the corpus, and 'Z' represents all possible topics. The parameter \(\theta\) represents the distribution between documents and topics; \(\varphi\) represents the distribution of topics over words.  
Figure 3: LDA theme model.  
LDA extracts potential themes from unstructured text, which is useful for understanding and analyzing patent content. Patent texts are often highly diverse and complex, containing a great deal of specialized terminology and technical details. LDA models are able to adapt to different types of text data and deal with noise and redundant information in the text.  
At the same time, the text vectorization method of term frequency-inverse document frequency (TF-IDF) is used to allocate weights to the words in the patent text. TF-IDF tends to filter out common words and retain important words, which can reduce the impact of invalid high-frequency words on technical topics. It can be calculated as follows.  
\[TF-IDF(w_{i},D_{text})=\frac{F_{w_{i}}}{F}\times ln\frac{D_{text}}{(d_{i}:w_{i} \in d_{i})+1} \tag{1}\]  
where \(D_{text}\) represents the entire text set. \(d_{i}\) is the text in set \(D_{text}\). \(w_{i}\) represents the words. \(F\) represents the total number of times a word appears. \(F_{w_{i}}\) represents the number of times the word appears in \(D_{text}\) text. The higher the TF-IDF value of a word, the greater the importance of the word in the text, and the more likely it is a keyword of the text.
## 5 Test Results  
In the experiments in this work, the performance of the DDZMR network in computationally reconstructing the degraded images of HDOE is first tested based on the prototype SHDCIS. Compared with the cross-channel convolutional neural network proposed in [10], DDZMR performs better on degraded diffraction images, and it can also perform 2\(\times\) image super-resolution simultaneously. Then, the enhancement effect of DDZMR network on SHDCIS imaging resolution was tested in the dark room, which showed that DDZMR could improve the resolution by 1.412 times. Next, an ablation study is performed on the DDZMR component, which verifies the effectiveness of the image regional division and channel selection module and multimodal loss function. Finally, we test the robustness of the ISR capability of the DDZMR network using publicly available datasets and compare it  
Figure 10: (**a**) Comparison of the performance of DDZMR networks and the state-of-the-art networks on dataset B100 [34]; (**b**) Comparison of the performance of DDZMR networks and the state-of-the-art networks on dataset Urban100 [35].  
with the SOTA method; DDZMR shows excellent performance, and it has image restoration performance that other ISR networks do not have.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 4 Applications of Antimicrobial Nanoparticles
### Fabrics and Fibres  
Textile fibres have been incorporated with nanoparticles to produce clothing and shoe pads with special properties [123]. The addition of antimicrobial nanoparticles is primarily used to exploit their antimicrobial properties to decrease odour-producing bacteria and fungi, but recent studies have investigated applications to reduce bacterial contamination that may cause disease [124; 125; 126; 127]. Studies have shown a variety of ways to produce nanoparticle incorporated fibres, with Ag as the common antimicrobial nanoparticle. A study by [PERSON] et al. suggested two types of spun fibres containing Ag NPs in sheath-part with, respectively, 0.3 and 1.5 wt% inhibited 99.9% of _E. coli_ and _K. pneumoniae_[128]. In another study, [PERSON] et al. prepared Ag-tricalcium phosphate nanoparticles using flame spray synthesis and generated polyamide fibres incorporated with the nanoparticles through the process of extrusion and melt-spun fibres [129]. The polymer fibres were able to effectively reduce 99.999% of _E. coli_ and 99.6% of _S. sanguini_ within 24 h. In another case, [PERSON] et al. immersed cotton fabrics with chitosan and AgNO\({}_{3}\) nanoparticle solutions to produced cotton fabrics with Ag NPs. The fabrics were able to inhibit _E. coli_ and had less fabric colour change after 81 washes [130]. Lastly, pressurised gyration was used by [PERSON] et al. to produce nanoparticle (including Cu, Ag and W) embedded polymer fibres with the potential antimicrobial applications for water and air filter systems. These filters had shown successful inhibition of _P. aeruginosa_ (over 70% reduction). The SEM images of these antimicrobial filter mats are shown in Figure 5[11].  
Although all the produced nanoparticle incorporated fabrics and fibres mentioned have shown effective antimicrobial activity, the examples are based on research studies and are not marketed currently.