esa-sceva/satcom-chunk-collection huggingface dataset:
# Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction
## 8 Conclusions  
In this paper, we put forward the limitations of applying existing Chinese Spelling Error Correction algorithms in practice. The model makes numerous unnecessary replacements that lead to a high False Positive Rate (FPR) value. In order to solve this problem, we proposed a method to perform a post-processing operation. This post-processing operation is based on many carefully choosed features, such as **sos**, **sop**, **topP**, **topK**, etc. These features can make full use of the pronunciation, shape, and semantics of Chinese characters. Unlike the traditional confusion set, we integrated these features directly into our model. More specifically, an SVM model is used to make the final correction decision. The post-processing operation can be broadly applied to various error correction models and significantly improve the model's performance in Chinese Spelling Error Correction applications, especially when the text contains only a few errors.  
In future research, we will design more valuable features to prevent the classification model from filtering out correct corrections, which might ease the harm of the recall after post processing. In addition, we will investigate how to combine the characteristics of Chinese characters to study in depth the problem that the model cannot accurately correct pronoun errors.  
Conceptualization, W.G. and Z.C.; methodology, W.G. and Z.C.; data curation, W.G.; original draft preparation, W.G.; review and editing, Z.C. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extra Data** & **Line** & **Avg. Length** & **Errors** \\ \hline NLPCC [38] & 3000 & 18.8 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics information of the extra data. We selected 3000 sentences from the 2018 NLPCC competition’s training set.  
Figure 7: The F1 score of the model after the post-processing operation under different error rates (the percentage of error sentences in the text). Among them, the three colors represent different test sets. The figure’s left side is the index results of error detection, and the right side is the index results of error correction. The straight line results from the original model and the dotted line results from adding the post-processing operation.
# Dissemination of Enterococcuscal Genetic Lineages: A One Health Perspective
## 3 Enterococcal Fitness Factors, Virulence Traits, and Antimicrobial Resistance  
### Fitness Factors  
Enterococci express various fitness and adaptive factors, referring to cumulative evolutionary mechanisms that allow them to survive and thrive in high-pressure selective environments (e.g., healthcare facilities and food industries). Their durability, and impressive multistress tolerance, optimizes enterococci as competitors of the host gastrointestinal tract and allow them to persist in healthcare environments, despite careful disinfection procedures, and they acquire antimicrobial resistance genes from other bacteria present in the same niche [25]. A key feature contributing to the enhancement of enterococcal fitness is the ability to produce biofilms (partly regulated by quorum-sensing mechanisms), leading to rapid adhesion onto abiotic surfaces, tissue colonization, and higher stress tolerance [26]. Some examples of molecular mechanisms involved in stress tolerance regulation include oxidative stress (in some _E. ficealis_ catalase-positive _katA_; peroxididases; and _sodA_ superoxide dismutase), thermal stress (heat stress regulated by heat-shock proteins; and cold stress by physiological changes, expression of cold shock proteins, and cold acclimation proteins), pH stress (alkaline mediated by Na\({}^{+}\) (V\({}_{1}\)V\({}_{0}\))-type ATPase and K\({}^{+}\)/H\({}^{+}\) antiporter in _E. hirae_; acid mediated by F\({}_{1}\)F\({}_{0}\)-ATPase), osmotic stress (ion transport systems, chaperones, and alterations in the physiochemical properties of the cell envelope), metal stress (copper, manganese, and iron), nutritional stress (nutrient-sensing global regulators CcpA, CodY, and (p)ppGpp messenger), antibiotic stress (associated mechanisms further explored in Section 3.3), host-related stresses (antimicrobial peptides, bile, and blood), disinfectants, among others (reviewed by [PERSON] and [PERSON] [25]). Overall, stress tolerance and pathogenesis are closely interlinked, considering that both contribute to enterococcal resilience and survival in harsh environments [25].
## 2 Methods: Modelling Approach
### Case Study Methods
#### 2.3.2 Benguela-Like Case Study: Addressing Pigment Variability  
The assemblages modelled in the first case study address optical changes due only to biomass (i.e., concentration of Chl \(a\) pigment) and size (assemblage \(D_{eff}\)), as the same set of generalised Chl _a_-carotenoid refractive indices is used for all phytoplankton particles represented. However, this approach addresses only a small subset of important changes in phytoplankton assemblage type, and in the presence of variability in dominant accessory pigments, the EAP model can be set to incorporate different refractive indices as appropriate for phytoplankton displaying accessory pigments other than carotenoids.  
To illustrate the effects of pigment variability, this case study simulates a transition from a high biomass _Myrioneta rubra_-dominated assemblage, to a high biomass peridinin (carotenoid)-containing dinoflagellate-dominated assemblage. _M. rubra_ is a fascinating but troublesome ciliate species, and enjoys an endosymphonic relationship with cryptophytes containing the diagnostic pigment phycoerythrin [77], and so \"borrows\" their characteristic red colour. _M. rubra_ blooms can reach extraordinary biomass, resulting in darkly pigmented'red tide' waters that have negative impacts both ecologically (depletion of nutrients, and the potential for anoxia as the bloom dies), as well as on the recreational use of coastal waters [77]. Again, assemblages are modelled using a Standard normal size distribution and the same values of \(\epsilon\) are used as for the carotenoids. This ensures that, in this example, all assemblage changes observed are due only to pigment-related differences.  
It should be made clear that the modelled transition is not intended to represent a likely ecological succession (except possibly a Lagrangian one, if a dinoflagellate bloom is advected into a previously _M. rubra_-dominated region), but rather to test what biomass and pigment differences are required for the detection of distinct optical conditions, particularly in the context of remote sensing.
# Deep Machine Learning for Path Length Characterization Using Acoustic Diffraction
## 2 Materials and Methods
### Phase-Amplitude Model  
The LSTM used in this experiment has five layers. This design includes an input layer, three LSTM layers, and an output layer. All input data (amplitude, phase, and acoustic path lengths) are normalized between zero and one. The layers of the model are detailed in this section.  
**Input**: The LSTM accepts input as seventeen pairs of phase and amplitude values in the form of a vector sized 17 \(\times\) 2. These have been normalized between 0 and 1 and represent the change in phase and amplitude over all lateral locations for each sample collected.  
**LSTM Layers 1 and 2**: The first two LSTM layers have a memory size of 17 units allowing the model to remember 17-time steps. Each of these layers also uses a hyperbolic tangent activation function.  
**LSTM Layer 3**: The second LSTM layer connects to a third layer with only 9 memory units so that only the most important half of the LSTM's memory is considered at the last layer. This layer again uses a hyperbolic tangent activation function.  
**Output Layer**. For the output layer, the final LSTM layer connects to an output layer with a single node. This layer is a dense layer that performs a regression to the ReLU activation function. The ReLU activation function led to the best performance when compared with the hyperbolic tangent function for the phase and amplitude pairs.  
The model is optimized using an Adam optimizer with a learning rate of 0.001. The learning rate of 0.001 with these models gave the best trade-off of training time and model performance. The model also uses mean square error as the loss function. The model is depicted in Figure 12.
## 2 Materials and Methods
### Methods  
#### 2.5.1 Anomaly Detection Algorithms  
For imbalanced data classification, the pattern of normal samples can be recognized by anomaly detection algorithms when the training set has only normal samples or it contains abnormal samples. The samples that do not fit the pattern are classified as anomalies. This is the basic principle of anomaly detection, which belongs to unsupervised learning. Three well-known anomaly detection algorithms are discussed for quality monitoring: Isolation Forest (iForest), One-Class SVM (OCSVM) and Local Outlier Factor (LOF).  
The iForest 'isolates' a sample from the rest of samples by adopting an ensemble of random trees as base estimators for the decision. The number of separations required to isolate a sample is the normality measure of iForest, as it produces noticeably smaller separation numbers for anomalies [25]. OCSVM has good performance for capturing the shape of the training set which contains normal samples only [26]. Samples outside the shape frontier are regarded as anomalies. LOF is a well-known density-based method which identifies the samples with much smaller local density as anomalies [27].
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 3 Parallelization on GPUs
### Face-Loop Parallelization
#### 3.1.3 Reduction  
The disadvantage of face colouring is that it loops over discontinuous surfaces, resulting in poor data locality. Therefore, another parallel strategy based on reduction thought was proposed. The principle of the reduction strategy is separating the gather and scatter parts of face-based loops [13]. The former does not have data conflicts so it can be parallelized. Face quantities were stored (without scattering) during the loop over faces and were then reduced for each cell independently in a second loop over cells. As illustrated in Algorithm 4, the reduction approach makes the residual update loop of Algorithm 1 able to run in parallel.  
```
0: Pointers from face to cell \(f2 cl\), \(f2 cr\). Flow variables \(q\). Start cell index of a colour \(ic\).
0: Right hand side residuals \(RHS\)
1:for\(face\) = 0 to \(nFaces\)do\(\triangleright\) Traverse all the colors.
2:\(c_{0}\) = \(f2 cl[face]\)\(\triangleright\) This loop can be parallelized.
3:\(c_{1}\) = \(f2 cr[face]\)
4:\(c_{1}\) = \(f2 cr[face]\)
5:\(F_{ij}\) = InvicidFlux(\(q[c_{0}]\), \(q[c_{1}]\),...)
6:\(RHS[c_{0}]\) = \(F_{ij}\)
7:\(RHS[c_{1}]\) += \(F_{ij}\)
8:endfor
9:endfor
```  
**Algorithm 4** Separating the gather and scatter parts of convective flux calculating loop.  
Figure 2: A two-dimensional example of face colouring. Edges represent the faces in three-dimensional case. Faces of the same colour can be executed in parallel.
## 3 Laboratory and DEM Modelling Establishment
### Contact Model and Parameter Calibration  
In PFC software, compared with the parallel-bond contact model, using a flat-joint model can address the issues of particle interlocking and the mismatched tensile-to-compressive strength ratio [45; 46]. Hence, it is considered to be a constitutive model that can better simulate the failure and matching strength of rock samples and has been widely used in a number of studies [47; 48; 49]. In view of this, in the simulation test in this study, the flat-joint model was selected as the contact model for the rock and rock slab particles. The smooth-joint model is mainly used to simulate the friction action of rock discontinuities [25; 50]. However, in addition to friction action, force transmission also occurs at the interface position during the loading process, and the use of the flat-joint model can give consideration to these two actions. Therefore, the flat-joint model was also chosen as the contact model for the interface.  
The \"trial and error\" approach is the most commonly used method of parameter calibration [27; 29; 51; 52]. With a given set of microscopic parameters, the simulated samples are subjected to loading conditions that mirror those of the laboratory tests. If the simulation results fall within an acceptable error range and show good correspondence with the experimental macroscopic parameters and failure modes, the microscopic parameters are deemed suitable for other simulation tests involving the same rock type. Consequently, in this study, the results of the uniaxial and laboratory shear tests on limestone were reproduced through numerical simulation. To maintain uniformity and accuracy in the simulation parameters, the uniaxial simulation sample was cut directly from the shear sample, as depicted in Figure 5a. Through a process of constant trial and error, results that closely correspond with the experimental data were obtained, as evidenced in Figure 5. Table 2 gives the microscopic parameters used in this study. A quantitative comparison of the mechanical characteristics between the simulation and experimental results is shown in Table 3. The findings further validate the accuracy of the parameter calibration by showing that the errors between the two are within a reasonable range.
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology
### Heuristic Approach
```
Input: The network instance \(G(N,A)\) and origin \(s\) Output: The set of \(k\) simple shortest path labels \(P\)
1//Create a label \(d_{s}^{1}\) corresponding to the first path to the origin \(s\)
2\(d_{sx1}^{1}\gets s\)
3\(d_{sx2}^{1}\gets NULL\)
4\(d_{sx3}^{1}\gets 0\)
5//Create a temporary set \(T\) and move \(d_{s}^{1}\) into \(T\)
6\(T\leftarrow\emptyset\)
7\(T\gets T\cup\{d_{s}^{1}\}\)
8//Create an empty permanent set \(P\)
9\(P\leftarrow\emptyset\)
10//Initialize the label index and permanent label index for each node
11\(l_{s}\gets 1\)
12\(l_{t}\gets 0,\forall\,i\in N\setminus\{s\}\)
13\(p_{i}\gets 0,\forall\,i\in N\)
14 Repeat
15//Pick the minimum cost label \(d_{u}^{pu+1}\) from \(T\) and move it to \(P\) making it permanent \(d_{u}^{pu+1}\leftarrow\min\limits_{d_{t}^{1}\in T}\left(d_{t}^{j}\right)\)
16//Since \(p_{u}\) labels are permanent for node \(u\), \(p_{u}+1\) is the next smallest temporary label index of \(u\)
17\(T\gets T\setminus\{d_{u}^{pu+1}\}\)
18\(P\gets P\cup\{d_{u}^{pu+1}\}\)
19//Increment the permanent label index of node \(u\)
20 For each node \(v\) adjacent to \(u\)do
21 If\(l_{v}<k\)then
22 IfCycleCheck(\(s\), \(u\), \(p_{u}\), \(v\)) is FALSE then // \(v\) is not on the path corresponding to \(p_{u}\)
23\(l\)//Create a new label \(d_{v}^{j}\)
24\(d_{v,3}^{j}\leftarrow\left(d_{u,3}^{pu}+C_{u,v}\right)\)
25\(d_{v,2}^{j}\gets p_{u}\)
26\(d_{v,1}^{j}\gets u\)
27\(T\gets T\cup\{d_{v}^{j}\}\)
28//Increment the label index of node \(v\)
29\(l_{v}\gets l_{v}+1\)
30
31 Else if\(\max\limits_{p_{v}<s\leq l_{v}}\left(d_{v,3}^{x}\right)>\left(d_{u,3}^{pu}+C_{u,v}\right)\)then
32 ifCycleCheck(\(s\), \(u\), \(p_{u}\), \(v\)) is FALSE then // \(v\) is not on the path corresponding to \(p_{u}\)
33\(d_{v,3}^{j}\leftarrow\left(d_{u,3}^{p_{u}}+C_{u,v}\right)\)
34\(d_{v,2}^{j}\leftarrow p_{u}\)
35\(d_{v,1}^{j}\gets u\)
36\(T\gets T\setminus\{d_{v}^{j}\}\)
37\(T\gets T\cup\{d_{v}^{j}\}\)
38 Until\(T\) is \(\emptyset\) or \(p_{i}\geq k\)\(\forall i\in N\setminus\{s\}\) // No more temporary labels to process or at least \(k\) permanent labels to every destination are found
```  
**Algorithm 3**KSSP_LL(\(G(N,A)\), \(s\)) \(\to P\)
## 2 Materials and Methods  
The overall workflow, as depicted in Figure 1, comprises five distinct parts. Firstly, training samples were selected through agricultural sites, field surveys, and visual interpretation, while test samples were masked from GLC_FCS30 data to extract the cropland distribution. The annual time series NDVI and EVI data of the sample sites were extracted from the processed MOD13Q1 data for the sample points. Secondly, two DL models named PB-Conv1D and PB-BiLSTM were developed and compared with the pixel-based Transformer (PB-Transformer) model, followed by the adoption of Snapshot and SWA to improve the classification accuracy of the models. Thirdly, the integrated classifier was obtained by combining DL models based on the ensemble learning method, comparing all the above models to obtain the optimal classification model. Finally, the proposed optimal model mapped the crop cultivation area for 2005, 2010, 2015, and 2020.
## 2 Background  
The functionality of MM is considered the backbone of seamless connectivity in wireless networks wherever users move within the network. It ensures that the connection of users is maintained and that their services are in excellent shape. The importance of MM could reach dropping calls and cutting off the data transfers, which would cause the user experience, overall, to suffer significantly. This becomes more critical in 6G networks, where applications like autonomous vehicles demand ultra-reliable and low-latency connections. Without efficient and smart MM mechanisms, such applications could face critical delays or even safety risks [10; 11]. Furthermore, MM plays a substantial role in managing the ever-increasing number of connected devices in future wireless networks, ensuring efficient resource allocation and optimal network performance for all users.  
The first subsection of this section provides a brief background on MM in 5G networks and its evolution in 6G, highlighting the impact of congested scenarios on network performance, while the second subsection provides the related works that focus on the cell load and its challenges in MM in 5G HetNets.
## 1 Introduction
The mode of action of ARF in auxin signaling is shown in Figure 1: When the cellular auxin concentration is low, Aux/IAA, encoded by the auxin early response gene family, binds to ARF and inhibits the activity of ARFs. When the auxin concentration is increased to a certain level, ubiquitinated Aux/IAA proteins are rapidly hydrolyzed. Aux/IAA protein's inhibition of ARF transcription factors is relieved, and ARFs then promote or repress the expression of downstream genes. ARF is commonly expressed in all periods of plant growth and development and in different parts of the plant, which also determines its comprehensive influence on plant life activities. In addition, [PERSON] et al. [5] found the gene family of strawberry (_Fragaria ananassa_) auxin response factors (FaARFs) were differentially expressed in the receptacle at four developmental stages by RNAseq. A large number of studies have shown that ARF is involved in the regulation of a variety of plant life activities, including leaf, flower, root development, and fruit ripening, senescence, and shedding. ARF is also involved in stress response and anthocyanin metabolism in plants. In addition, ARFs are  
Figure 1: Model of ARF involvement in auxin response. The process of auxin signaling is coordinated by the auxin receptor TIR1/AFB, as well as the signal response factors AUX/IAA and ARF, which together mediate the auxin signaling response. Auxin acts as a “molecular glue” and facilitates the interaction between SCF-TIR1/AFB and Aux/IAA proteins. At low concentrations of auxin, the AUX/IAA repressor binds to the ARF transcription factor and forms a dimer that recruits the co-repressor TPL (TOPLESS) to inhibit the ARF activity and the expression of auxin responsive genes. When the concentration of auxin is increased, Aux/IAA binds to the SCF TIR1/AFB complex and is ubiquitinated and then degraded by 26S protease, and then ARF transcription factors are released to activate the transcription of downstream genes. DBD, DNA-binding domain; MR, middle region; PB1, Phox and Bem 1.  
also involved in plant hormone crosstalk, and the relevant research results are valuable for us to fully understand the regulatory effects of plant hormones. In recent years, studies on the regulation of plant life through hormone signaling pathways have been carried out, and increasingly more genome-wide identification and expression analyses of ARF gene families in plants have been reported, as shown in Table 1, which are of great significance for a more comprehensive understanding of the functions of ARF. In this paper, we summarize recent research reports on the regulatory role of ARF in plant growth, discuss the key roles of ARF in the development of a variety of important organs in plants, propose challenges and opportunities for current ARF-related research, provide inspiration and references for future ARF research, and lay the foundation for the establishment of a functional network of auxin response factors.
## 4 Algorithm Design
### Resource Adjustment Among Categories
#### 4.3.1 Mutation in UAVs' positions  
The mutation probability of each UAV is set to \(PB_{um}\). For the \(n\)-th UAV, \(\forall n\in\{1,\dots,N^{U}\}\), the formulas for the mutation operation are described as follows:  
\[x^{u^{\prime}}_{n} =x^{u}_{n}+e^{x}_{n} \tag{13}\] \[y^{u^{\prime}}_{n} =y^{u}_{n}+e^{y}_{n}\] (14) \[e^{x}_{n} =random(-c_{1}x_{max},c_{1}x_{max})\] (15) \[e^{y}_{n} =random(-c_{1}y_{max},c_{1}y_{max}) \tag{16}\]  
where \(e^{x}_{n}\) and \(e^{y}_{n}\) are the horizontal and vertical coordinates of the \(n\)-th UAV. \(random(a,b)\) means that the value will be randomly determined between \(a\) and \(b\). \(x^{u}_{n}\) and \(y^{u}_{n}\) represent the coordinates before mutation, while \(x^{u^{\prime}}_{n}\) and \(y^{u^{\prime}}_{n}\) represent the coordinates after mutation. \(x_{max}\) and \(y_{max}\), respectively, denote the maximum horizontal and vertical distances of the IoT device deployment area. Parameter \(c_{1}\) ranges between 0 and 1 and is set to 0.1. Additionally, if the adjusted coordinates exceed the limits of \(x_{max}\) and \(y_{max}\), the coordinates will be adjusted to the edge values of the area.
###### Abstract  
This study outlines a methodology for determining the durability specifications of electronically controlled dampers by examining performance degradation observed on actual driving roads. It identifies areas of performance decline and the primary causes affecting major actuator dampers. Traditionally, the durability performance of automobile parts has been assessed by calculating damage based on load profiles. However, analyzing actual road conditions is essential because the control commands of electronically controlled suspension systems change in real time according to load conditions. Simulations based on Road Load Data Acquisition (RLDA) use statistically independent representative road surfaces to assess damped deterioration performance. Following this analysis, a rig test of the damper is performed to establish the durability specifications for damper actuator products. The primary form of performance degradation observed was a change in the tensile damping force, which was more substantial than the degradation observed on the compression side. Oil leakage and cavitation were identified as significant influencing factors from a Failure Mode and Effects Analysis (FMEA) perspective. The study concludes that additional design research is necessary, focusing on damper oil and leakage, while also considering the control algorithm's effects in designing electronically controlled dampers.  
continuous variable damper; semi-active suspension; RLDA; quarter car lab; FMEA; durability