esa-sceva/satcom-chunk-collection huggingface dataset:
# Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
###### Abstract
However, these methods for distinguishing negative samples are limited to annotated datasets and are not applicable in unsupervised learning scenarios. To address these aforementioned issues, we propose PrdSimCSE, an unsupervised contrastive learning framework based on prefix data augmentation. As illustrated in Figure 1, PrdSimCSE uses prefix data augmentation (Prd) to construct positive samples and differentiate negative samples. Next, Section 2 introduces the background knowledge on text augmentation, including the basic concepts of sentence embedding and contrastive learning. Section 3 details the prefix data augmentation method, including how to determine positive and negative prefixes and their impact on the model. Section 4 outlines the unsupervised PrdSimCSE, covering algorithm design, experimental environment setup, parameter settings, datasets, and baseline comparisons. Section 5 describes the progress of prefix data augmentation determined through ablation studies, where we further discuss how to determine positive and negative prefixes and the semantic bias caused by prefix data augmentation. Section 6 discusses the relationship between semantic similarity events and prefix data augmentation, including the advantages and roles of positive and negative prefixes. Section 7 concludes the paper by providing the contributions of prefix data augmentation to unsupervised sentence embedding learning and directions for future research. The contributions of this paper can be summarized as follows:  
* This paper introduces a novel text data augmentation method, prefix data augmentation. Positive samples are constructed using modal particle prefixes combined with dropout, thus preserving the original semantics as much as possible while alteringthe positional information of each token. This approach enhances and facilitates contrastive learning. * Prefix data augmentation can also be used to modify the original semantics. By constructing prompts that reverse the semantics and using them as prefixes, the modified sentences can serve as negative samples. This method allows for the creation of a richer set of negative samples from unsupervised corpora, thereby increasing the discriminability between different negative samples. * We developed a novel unsupervised sentence embedding learning approach, PrdSimCSE, which we used to construct both positive and negative samples through prefix data augmentation. Additionally, the approach treats other sentences within the same batch as negative samples. * We evaluated PrdSimCSE across various datasets, and the experimental results demonstrated that our proposed PrdSimCSE achieves superior performance in sentence representation compared with prior approaches. Furthermore, through ablation studies, we further examined the effectiveness of PrdSimCSE and discussed the advantages and limitations of our method in detail.
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
###### Abstract  
To cope with bacterial infections, broad-spectrum antibiotics such as sulfonamides have been largely used for intensive coastal aquaculture. Sulfonamides are stable and difficult to remove by conventional wastewater treatment. Environmental pollution will occur if sulfonamide-containing aquaculture wastewater is discharged into rivers and oceans. In this study, high salinity-tolerant bacterial strains A12 and L with sulfamethoxazole (SMX)-degrading ability from milkfish (_Chanos chanos_) culture pond sediments with SMX were isolated, identified, and characterized. The degradation of SMX and the changes in the bacterial community in milkfish culture pond sediments were assessed. Phylogenetic analysis using 16S rRNA gene sequences suggested that bacterial strain A12 was very close (99% sequence identity) to _Vibrio_ sp., and bacterial strain L was very close (99% sequence identity) to _Pseudomonas_ sp. Aerobic and anaerobic batch and continuous SMX addition experiments indicated that bacterial strains A12 and L could enhance SMX degradation in milkfish culture pond sediments. Different microbial community compositions under aerobic and anaerobic conditions exhibited different SMX-degrading abilities. The results of this study suggest that bacterial strains A12 and L provide a solution for treatment of wastewater and sediment from SMX-contaminated high salinity milkfish culture ponds.  
Keywords:sulfamethoxazole; milkfish pond sediment; bioremediation +
Footnote †: journal: Applied Sciences
# Co-Simulation of Multiple Vehicle Routing Problem Models
## 3 The Designed Model
### Structuring VRP Multi-Model
#### 3.2.1 Exchanging Data between Simulators: Input and Output Connections  
To predict the impact of co-simulation of VRP multi-model, we show the first model \(M_{1}\) which represents the solution of VRP using Algorithm 1, where the objective is to minimize the total traveled distance. The second model \(M_{2}\) implements Algorithm 2 that tries to minimize the total number of vehicles used to solve the same VRP. The aim in this case is to reproduce the VRP multi-model as a society of interacting and co-evolving models. We have to couple the two aforesaid models \(M_{1}\) and \(M_{2}\) using the concepts of Agent and Artifacts that are presented in Section 1. To couple \(M_{1}\) and \(M_{2}\) we need to define the input and output ports of each model and the connections between them.  
The solution is improved by successive iterations where every model, i.e., \(M_{1}\) or \(M_{2}\), shares the improved solution with the other. \(M_{1}\) and \(M_{2}\) read the input and try to improve their solution as illustrated in Figure 3.  
Figure 2: Model dependency overview.  
Specifically, when the model \(M_{1}\) finds new best solution in a given iteration, it sends the following inputs to the model \(M_{2}\)  
1. _Ed_: Set of edges belonging to the best solution (for each one of these edges the model \(M_{1}\) sends a pair of nodes delimiting this edge).
2. _P_: Set of pheromones trail of each edge belonging to the best solution and the model \(M_{2}\) retrieves these values of pheromone and modify its own values to have the new ones sent by \(M_{1}\).  
These sent pheromones trail to the model \(M_{2}\) will modify the pheromone trail of the same edges in \(M_{2}\) for improving the best solution finding. According to the ant colony system, in Algorithms 1 and 2 the ant chooses the next node to be added to the solution according to  
* The cost of the best solution represented by the total traveled distance;
* The edge with minimum distance to be traveled;
* The edge with high value of pheromone trail.  
The edge with high value of pheromone trail have a high probability to be added to the solution. Here, the model \(M_{2}\) have edges with elevated values of pheromone trail from:  
* Those of its own best solution;
* Those of \(M_{1}\)'s best solution.  
In the same way, when model \(M_{2}\) finds a new best solution, it sends its best solution to model \(M_{1}\) and replaces the current value of pheromone trail in this model by those of the new best solution in model \(M_{2}\). Figure 4 show more details on the behavior of models in interaction.
## 3 Methodology  
### Minimum Angle Particle Swarm Optimization  
In multi-objective evolutionary algorithms, the MAPSO algorithm was first proposed by [PERSON] [17] in 2005 as one of the pioneering angle-based evolutionary algorithms. In 2021, [PERSON] [18] improved upon this algorithm by using vector-based angles to select elite particles, addressing the rapid convergence issue of PSO algorithms and finding wide applications in various fields [19; 20].  
To address the high complexity and large scale of data transmission tasks, we employ and improve the MAPSO algorithm [18] to balance convergence and diversity in the external archive set. By utilizing the minimum angle strategy to select global guides, the algorithm effectively avoids local optima, thereby improving solution quality, reducing solution time, and enhancing the efficiency of satellite data transmission tasks.
## 5 Conclusions  
This study involved a comprehensive collection of geological data including regional tectonic background and lithology of the study area. Electrical resistivity tomography (ERT) was employed in the study area to obtain multiple ERT profiles through data inversion. By combining geological information from boreholes and other sources, the morphological characteristics and depth of the sliding surface could be determined. The following conclusions were drawn:  
1. In this study, the Lijiazu landslide was investigated using a grid ERT method for geophysical measurements. The measured 2-D resistivity profiles were compared and interpreted alongside drilling data to determine the depth and morphology of the landslide's sliding surface. Furthermore, three-dimensional visualization of the landslide information was constructed by combining the two-dimensional resistivity profiles, providing a more intuitive display of the internal morphology of the entire slope.
2. Through comprehensive analysis, it is evident that the landslide is currently in a creeping phase, with the possibility of unstable collapse. Considering the high cost of relocation, it is recommended to implement promptly measures to mitigate the landslide.
3. The application of ERT provides a new approach for landslide exploration. In subsequent landslide investigations, geological data can be combined with geophysical methods to further obtain three-dimensional structural information and geological characteristics of landslides, thereby providing valuable insights into the mechanisms and stability of landslides.  
Conceptualization, M.S. and J.O.; methodology, M.S. and J.O.; software, L.Z. and R.L.; validation, J.L., L.Z. and M.S.; formal analysis, M.S. and R.L.; investigation, M.S.; resources, J.L.; data curation, J.O.; writing--original draft preparation, M.S.; writing--review and editing, J.O. and J.L.; visualization, L.Z.; supervision, R.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (42130810) and the Open Fund of Hunan Geological Disaster Monitoring Early Warning and Emergency Rescue Engineering Technology Research Center (42130810).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# N-Heterocyclic Carbene-Gold(I) Complexes Targeting Actin Polymerization
## 3 Results  
### AuL4 and AuL7 Induce Dramatic Cancer Cells Morphology Changes  
Our previous studies demonstrated the anticancer activities of **AuL4** and **AuL7** against different in vitro models, being particularly effective against HeLa and MDA-MB-231  
Figure 3: The three-dimensional structure of the nucleotide-binding cleft of actin (pink ribbon) is depicted. (**a**) **AuL4** pose; (**b**) **AuL7** pose; (**c**) binding mode of latrunculin B as determined by X-ray crystallography.  
cancer cells, respectively, and triggering the ROS-mediated intrinsic apoptotic pathway. Moreover, we proved that these complexes possessed a multi-target action, namely they inhibited human topoisomerases I and II activities and interfered with the microtubules dynamics. However, this latter feature resides in the ability of **AuL7** to inhibit the tubulin polymerization reaction, acting like the vinblastine, whereas **AuL4** behaves as the paclitaxel, thus stabilizing the microtubules, as demonstrated by immunofluorescence and direct enzymatic assays. In both cases, the exposure of HeLa and MDA-MB-231 cancer cells to **AuL4** (at its IC\({}_{50}\) = 1.63 \(\pm\) 0.5 \(\upmu\)M) and **AuL7** (at its IC\({}_{50}\) = 2.1 \(\pm\) 0.7 \(\upmu\)M), respectively, for 72 h, produced dramatic morphological changes, as visible by the observation of cancer cells (Figure 2), using an inverted microscope (20\(\times\) magnification). Indeed, **AuL4**-treated HeLa cells appeared round and shrunk, and **AuL7**-treated MDA-MB-231 cells exhibited both round and tread-like shapes, with respect to the vehicle-treated cells that have a normal morphology. These changes were attributed to the already proved interference with tubulin, but we wondered if our complexes could regulate the actin system, as well. This hypothesis sounded very challenging for us, because this would mean that the complexes possess another unexplored intracellular target, _viz._ the actin. In order to assess this supposition, we performed in silico and in vitro studies.
## Nomenclature  
X State of the vehicle in PF  
x Position of the vehicle in the map x-direction  
y Position of the vehicle in the map y-direction  
z Position of the vehicle in the map z-direction  
\(\theta\) & Yaw angle of the vehicle in map coordinate  
\(\sigma_{v_{z}}\) & The standard deviation of vehicle noise in the vertical direction  
\(\widetilde{X}_{k+1}\) & State at sample time k+1  
k Timestamp  
v\({}_{k}\) & Vehicle position in the x dimension at time k  
\(\theta_{k}(\Delta t),\ \dot{\theta}\) & Yaw rate of the vehicle at time k  
\(\theta_{k}\) & Yaw angle of the vehicle at time k  
\(\Delta t,\,\mathrm{dt}\) & Sample time  
\(z_{v,k+1}\) & Vertical noise of the vehicle  
d\({}_{i}\) & Distance between feature and vehicle  
\(\Delta\alpha_{[i]}\) & The relative bearing angle between feature i and vehicle  
x\({}_{v},Y_{y},z_{v}\) & x, y, z position of the vehicle in map coordinate  
x\({}_{i,k,k+1},Y_{i,k,k+1},Z_{i,k,k+1}\) & The relative distance at x, y, z direction between feature and vehicle  
\(\epsilon_{\Delta t}\) & Compound noise of distance measurement  
\(\epsilon_{\Delta\alpha}\) & Compound noise of angle measurement  
w\({}_{1,2,3,-i}\) & Weights of particle 1, particle 2,... particle i  
x\({}_{p,i}\), \(Y_{p,i}\), \(Z_{p,i}\) & x, y, z value of the ith particle  
P(x\({}_{p,i}\), \(Y_{p,i}\), \(Z_{p,i}\)) & Probability when the particle is x\({}_{p,i}\), \(Y_{p,i}\), \(Z_{p,i}\)  
\(\sigma_{x},\sigma_{y},\sigma_{z}\) & Compound standard deviation in x, y, z-direction  
\(\widetilde{x}_{i,k,k+1},\overline{y}_{i,k,k+1},\overline{z}_{i,k,k+1}\) & The transformed relative distance of feature i and vehicle at x, y, z direction in map coordinate  
H\({}_{\alpha x}\), \(\mu_{y,y}\), \(\mu_{z}\) & The feature position x, y, z in the pre-saved HD map  
N & Number of particles  
\(\lambda\) & Sigma point design parameter  
n\({}_{x}\) & Quantity of the state vector  
x\({}_{p}\)aukf, k & State of PAUKF  
\(\dot{\theta}\) & Yaw rate of vehicle
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Description of Levels, Areas, Segments and Items in the CTE Model  
The first and basic division in the model is the division into levels. This model has been defined by three levels:  
* Technical Level (TL)
* Business Level (BL)
* Environmental Level (EL)  
These three levels will consist of different segments, which are the second division of the model. Individual segments will be grouped into areas because all segments do not have the same importance in terms of the analysis of telecom potential [54; 55].  
The final number of different defined segments in this model is fourteen. These fourteen segments can describe any telecom operator potential from the top to the bottom. They are as follows:  
* Coverage and accessibility to users (TL),
* Technological development (TL)
* IT Development (TL)* Products Development (BL)
* Services Development (BL)
* Sales (BL)
* Customer Care (BL)
* Human Resources--HR (BL)
* Political Environment (EL)
* Regulatory Environment (EL)
* Law Environment (EL)
* Finance Environment (EL)
* Quality of Brand (EL)
* Presence in public (EL)  
The last divisions in this model are items. Each segment/area is defined by specially defined items that essentially describe and give value to the entire area. Each of the items is mathematically described, their sum revealing the total value of the area. The actual version of the CTE model has three levels, eight areas, fourteen segments and various items in each area. The format of the model is \"2-4-2\". All items in each area have a maximum worth of 0.1 (10%), and each area has a maximum value equal to 1 (100%). Table 1 shows and explains this distribution.  
The results will be presented as follows:  
* Total amount of CTE Model for individual telecom operator potential;
* Total Area Value (max. value of each area is 1);
* Each item's value in each area;
* Tabular and graphical forms for easier comparison of telecoms.  
Before a more detailed description of individual areas and items, a tabular representation of the relationship between areas, their mutual influence and connections to individual parts can be found in the article. The table will explain the following:  
1. Main targets: Assessment and calculation of telecom potential regarding the specific areas;
2. Forward links: Showing the influence of the observed area on other areas of the model;
3. Backward links: Showing which other areas have an influence on the observed area;
4. Chapter in the article: Link to chapter in article;
5. Appendix: Link to Appendix in article.  
Table 2 gives a brief overview and main goals of the analysis in individual fields, mutual links between fields and links to chapters and appendices in the text. In this way, it is easier to understand the principle and mode of operation of the model and to obtain additional information about individual areas and items within them.
# Efficient Filtering for Edge Extraction under Perspective Effect
## 2 Optimal Scale and Orientation Assignment
### Defining the Problem  
Detecting the local feature \(s(x,y)\) out of image patch \(p(x,y)\) has been the subject of many studies. This is especially true for local edges. The edges may be the real contour of the target or the other edges inside the target, with which we are not concerned. For a patch of natural images, if the noise is assumed to be additive, the noised patch can be expressed as  
\[p(x,y)=s(x,y)+n(x,y). \tag{1}\]  
Here, \(s(x,y)\) is the real context of the patch, and \(n(x,y)\) is the noise.  
In our work, the signal is defined as a step edge at the original with limited extension, which works as the localized target size. For the 1D case, \(s(x)\) is a pulse of width \(d\) extended from \(x=0\) to \(x=d\):  
\[s(x)=\begin{cases}1&\text{for }x\in[0,d],\\ 0&\text{otherwise.}\end{cases} \tag{2}\]  
For the 2D case, \(s(x,y)\) is a strip of width \(d\) along the \(y\) axis:  
\[s(x,y)=\begin{cases}1&\text{for }x\in[0,d]\times[-\infty,\infty],\\ 0&\text{otherwise.}\end{cases} \tag{3}\]  
In these two cases, the parameter \(d\) corresponds to the localized target size. In many works, such as that of [PERSON], the signal is defined as an unbound step edge, which can enlarge the scale of the filter to suppress the noise. Usually, the problem of local feature detection is to design a certain filter or a certain set of filters to detect the local features using specific criteria. Here, \(h(x,y)\) is the impulse response of the filter (or one of the filters in the set). Here, we restrict ourselves to linear filtering, so the output of the filtered version of the local patch is as follows:  
\[\widehat{p}(x,y)=s(x,y)\otimes h(x,y)+n(x,y)\otimes h(x,y). \tag{4}\]  
Here, \(h(x,y)\) is supposed to be designed to detect \(s(x,y)\) from \(p(x,y)\). This problem has been addressed many times. Now, we are going to re-examine this problem when \(s(x,y)\) is the real contour of the target that we want to detect, and from certain method, we happen to know the localized target size and the localized target orientation of the contour.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 4 Simulation Analysis
### Building the Retrieval Function for Lossy Samples  
In contrast, the permittivity of lossy samples involves real part and imaginary part (or loss tangent). To retrieve their permittivity, one needs to take into account both the quality factor and the corresponding resonance frequency. It can be seen from Figures 7 and 8 that the resonant frequency is almost determined by the real part \(\varepsilon_{\text{r}}^{\prime}\). In light of this, only \(Q_{\text{MUT}}^{-1}\) versus \(\tan\delta\) is shown in Figure 10. Clearly, higher loss tangent causes smaller \(Q_{\text{MUT}}\). The linearity for all cases is pretty good. Such an observation will make the retrieval results sufficiently accurate.  
To reach a two-variable function, a three-dimensional plot is shown in Figure 11. The fitted quartic equation is:  
\[\varepsilon_{\text{r}}^{\prime\prime}=\varepsilon_{\text{r}}^{\prime}\frac{F( Q_{\text{MUT}}^{-1},\varepsilon_{\text{r}}^{\prime})}{G(Q_{\text{MUT}}^{-1}, \varepsilon_{\text{r}}^{\prime})} \tag{6}\]  
where  
\[\left\{\begin{array}{c}F(Q_{\text{MUT}}^{-1},\varepsilon_{\text{r}}^{\prime })=-2.75\times 10^{4}-3.79\times 10^{2}\varepsilon_{\text{r}}^{\prime}+7.50 \times 10^{6}Q_{\text{MUT}}^{-1}\\ \hskip 14.226378 pt-5.09Q_{\text{MUT}}^{-2}+5.50\times 10^{4}\varepsilon_{\text{r}}^{ \prime}Q_{\text{MUT}}^{-1}\\ G(Q_{\text{MUT}}^{-1},\varepsilon_{\text{r}}^{\prime})=1+1.84\times 10^{2} \varepsilon_{\text{r}}^{\prime}+2.189\times 10^{5}Q_{\text{MUT}}^{-1}+0.26( \varepsilon_{\text{r}}^{\prime})^{2}\\ \hskip 14.226378 pt-3.00Q_{\text{MUT}}^{-2}-1.98\times 10^{4}\varepsilon_{\text{r}}^{ \prime}Q_{\text{MUT}}^{-1}\end{array}\right. \tag{7}\]  
Figure 9: The relationship between \(\varepsilon_{\text{r}}^{\prime}\) and \(\Delta f_{\text{r}}\).  
The RationalTaylor model [37] was selected to fit all points, so that the nonlinear surface covers all points in the coordinates as much as possible. In this case, the coefficient of determination \(R^{2}\) is 0.99943. This is also good enough for data retrieval. By using Equations (6) and (7), the complex permittivity of the sample can be readily restored.