esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of PAH and Sample Treatments  
A dielectric barrier discharge (DBD) device as exhibited in Figure 1A was employed for cold plasma treatment. For PAH preparation, 800 \(\mu\)L hydrogen peroxide solution (3%, Sigma-Aldrich (Shanghai) Trading Co., Ltd., Shanghai, China) was added to 100 mL deionized water and then packed in a polypropylene container that was sealed with a polyethylene film (Ningjin Jinyi Packaging Materials Co., Ltd., Xingtai, China). The reason for using 0.8% hydrogen peroxide solution (800 \(\mu\)L H\({}_{2}\)O\({}_{2}\) in 100-mL deionized water) in the present study was to improve the antibacterial efficiency of PAS based on our preliminary experiments, which also showed that the 0.8% H\({}_{2}\)O\({}_{2}\) solution had a little antimicrobial effect on bacterial cells by itself. The container was placed between two electrodes and there was a dielectric barrier layer between the electrode and the sample. Processing parameters were as follows: the voltage = 60 kV; the frequency = 50 Hz; the working gas = air; and treatment time = 4 min. After treatment, the PAH was stored at room temperature for 6 h before further application.  
For an effect on _S. aureus_ biofilms, 10 mL PAH was transferred to the 6-well culture plate with formed biofilms and incubated at room temperature for 0, 10, 20, 30, and 40 min before biofilms were sampled for different analyses. At sampling times, the biofilm was washed with 0.85% sterile NaCl solution to stop the reaction between the biofilm and PAH [16]. To evaluate the effect of PAH on the viability of bacterial cells in biofilms, treated biofilms were transferred to 0.85% sterile NaCl solution, 0.5 mL of the mixed bacteria suspension was taken and diluted serially. Then, 100 uL of the dilution was plated on mannitol high salt agar. Bacterial colonies formed on the agar plate were counted after incubation at 37 \({}^{\circ}\)C for 24 h. Results were presented as the surviving bacterial population in log CFU/mL. Deionized water (DIW) and H\({}_{2}\)O\({}_{2}\) solution (prepared by adding 800 uL 3% hydrogen peroxide solution to 100 mL deionized water) treatment were chosen as negative controls.
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 3 Results and Discussion
### Water Consumption of the Slaughterhouse  
The slaughterhouse's fresh water consumption fluctuates with the daily production volume. This is usually between 4000 and 8000 chickens per day, which leads to a water consumption of 45 and 60 m\({}^{3}\), respectively. Table 2 shows how the water consumption for these two cases is divided between the individual production steps. The water consumption per animal decreases significantly with increasing production volumes. Figure 7 visualizes the water consumption of the individual processing steps in the slaughtering process. It is important to distinguish between water that has direct contact with the product and water that is only used to clean the vehicles and the cages, meaning without product contact. This indicates that, depending on the production volume, the potential for reusing wastewater in non-contact applications ranges from 24% to 30%. Lower quality requirements are sufficient for this portion compared to water with product contact. According to the EPA, U.S. Environmental Protection Agency [30], the pH value must be between 6 and 9, the turbidity NTU! 5, the BOD5! 10 mg\(\cdot\)L\({}^{-1}\), and the water must be germ-free (_E. coli_! 0 cfu in 100 mL).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{3}{c}{**4000 Chickens-d\({}^{-1}\)**} & \multicolumn{3}{c}{**8000 Chickens-d\({}^{-1}\)**} \\ \hline  & **Water [m\({}^{3}\)-d\({}^{-1}\)]** & \multicolumn{3}{c}{**Water [m\({}^{3}\)-d\({}^{-1}\)]**} \\ \hline Killing & 2 & 4.5\% & 2.2 & 4\% \\ Scadding & 4.5 & 10\% & 5 & 8.5\% \\ Defeathering & 14 & 31.5\% & 22 & 37\% \\ Evisceration & 6 & 13.5\% & 10 & 17\% \\ Bird wash & 4 & 9\% & 6 & 10\% \\ Cleaning & 14 & 31.5\% & 14 & 23.5\% \\ Total & 44.5 & 100\% & 59.2 & 100\% \\ \hline Demand per bird & 11.1 L & \multicolumn{3}{c}{7.4 L} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average daily water consumption in the abattoir based on slaughtering process and number of birds slaughterted.  
Figure 7: Water demand of the individual processing steps.
## 4 Experiment and Results Analysis
### Experimental Results  
The comparison results between the model and each model in this paper are shown in Table 2. Glove indicates that the model is based on the Glove [28] word vector model, and BERT indicates that the model is based on the \"BERT-base-uncased\" model of the pre-trained model BERT. P stands for Precision, and R stands for Recall.  
Through the comparative analysis in Table 2, it can be seen that the effect of the Glove-based model is significantly lower than that of the BERT model, indicating that the BERT model is better than the Glove in this task.  
On the Laptop14 dataset, our model is the best on all three metrics and 0.4% higher than the best DCRAN on F1. On the Restaurant14 dataset, our model has the best results  
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Laptop14**} & \multicolumn{3}{c}{**Restaurant14**} & \multicolumn{3}{c}{**Twitter**} \\ \cline{3-10}  & & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{4}{*}{GLove} & LM-LSTM-CRF & 53.31 & 59.40 & 56.19 & 68.46 & 64.43 & 66.38 & 43.52 & 52.01 & 47.35 \\  & E2E-TBSA & 61.27 & 54.89 & 57.90 & 68.64 & 71.01 & 66.60 & 53.08 & 43.56 & 48.01 \\  & IMN & - & - & 57.66 & - & - & 68.32 & - & - & 51.31 \\  & DOER & 61.43 & 59.31 & 60.35 & 80.32 & 66.54 & 72.78 & 55.54 & 47.79 & 51.37 \\ \hline \multirow{4}{*}{BERT} & BERT-E2E & 61.88 & 60.47 & 61.12 & 72.92 & 76.72 & 74.72 & 57.63 & 54.47 & 55.94 \\  & SPAN & 66.19 & 58.68 & 62.21 & 71.22 & 71.91 & 71.57 & 60.92 & 52.24 & 56.21 \\  & DREGCN & - & - & 63.04 & - & - & 72.60 & - & - & - \\  & DCRAN & - & - & 65.18 & - & - & 75.77 & - & - & - \\ \hline \multirow{4}{*}{BERT} & Our method & 67.73 & 63.56 & 65.58 & 76.92 & 77.05 & 76.98 & 62.22 & 60.66 & 61.43 \\ \cline{1-1} \cline{2-10}  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compare experimental results (%).  
on the Recall and F1 metrics, with F1 values 1.21% higher than the best DCRAN and DOER the best on Precision, but its Recall is low and lower than most models, indicating that their model can predict a small number of correct ones. On the Twitter dataset, our model is the best on three indicators, especially on Recall, which is 6.19% higher than the best BERT-E2E and 5.22% higher than the best SPAN on F1 values, which indicates that our model has good predictive performance, not only predicting more but also correctly.  
Compared with DREGCN, using dependent syntactic knowledge enhancement, the results of this model are greatly improved, which shows that we can make good use of the context information and dependencies of text using the multi-head attention mechanism.  
In addition, we have also completed experiments on the Restaurant15 dataset; the results are in line with our expectations, and all indicators are higher than these models.
# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
## 1 Introduction  
### Background  
Unmanned Surface Vehicles (USVs) have been widely implemented in marine and inland waterway research and operations. Their expansive utility encompasses the realms of environmental surveillance, search and rescue, and penetrating the intricate domain of scientific exploration [1, 2, 3]. As USVs shoulder an escalating gamut of sophisticated missions, the call for innovative, versatile navigation systems has intensified manifold. Especially when considering intricate terrains such as river systems and inland water bodies, the prerequisites for USVs are acute sensory perception and advanced navigational autonomy. The imperative for such capabilities is underscored when USVs are deployed in missions such as monitoring aquatic ecosystems or detecting submerged infrastructural elements, necessitating precision navigation in proximity to riverbanks [4, 5, 6].  
Navigating along the shapes of riverbanks presents a significant challenge for autonomous systems. These river boundaries are continuously transformed, influenced by tidal patterns and atmospheric forces. This dynamic nature complicates the process of manually designating waypoints for USVs, often leading to deviations and inaccuracies [7, 8, 9, 10], hindering their precision in autonomously following the riverbanks.  
Recent advancements are tending toward harnessing the combined capabilities of multi-modal sensors. Comprehensive environmental awareness is achieved by integrating technologies such as LiDAR, optical imaging, and state-of-the-art GPS modules into the framework of USVs. This amalgamation facilitates USVs in pinpointing their geospatial positions with heightened accuracy, significantly enhancing their capabilities in autonomous riverbank navigation [11]. Yet, achieving this sensory augmentation has its challenges. Beyond the tangible task of harmonizing diverse hardware components is the computational intricacy of crafting algorithms adept at processing vast amounts of heterogeneous sensor data to derive dependable navigation directives.  
To address these issues, the riverbank following planner (RBFP) leveraging the granular data acquisition capabilities of 2D LiDAR, the RBFP undertakes meticulous topographical analyses of riverbanks. The subsequent transmission of this distance point cloud data to the USV's central computational unit ensures precise riverbank following. Moreover, the integrated Proportional-Integral-Derivative (PID) control mechanism acts as a responsive sentinel. In instances of unintentional off-course drifts or close proximity to riverbanks, this controller orchestrates real-time modulations in the USV's propulsion and orientation, assuring unwavering adherence to the delineated path. This capability paves the way for a novel USV navigation paradigm that champions autonomous riverbank contouring without the tether of manual waypoints or reliance on pre-configured path blueprints.
# A Conceptual Model for Joint Graphic Representation of
## 6 Discussion  
Figure 21 represents the example of the linear servo axis of Section 4.3 in a 3D computer-aided design application. The full 3D design view (Figure 21a) and the MMCS view are depicted (Figure 21b). Although the difference between the two representations may not seem relevant, it should be noted that the MMCS view provides two important pieces of information for the automation perspective, in a much more explicit way than the classic 3D view.  
The first is that the MMCS view clearly identifies the moving part and its dimensions, and the second is that it provides an explicit representation of points that are ends of the movement.  
For example, in Figure 21a (3D mechanical representation of the axis), it is not clear what the end points are of the movement of the axis. These depend on how the axis is internally built. It would be necessary to analyze in detail the \"hidden building elements\" of the 3D mechanical view to deduce it. However, in its MMCs view (Figure 21b), both actual moving part dimensions and movement end points are explicitly represented. It should be remarked that the movement end points specified in Figure 21b do not coincide with those that the moving part of the axis in Figure 21a are apparently to reach.  
On the other hand, for the same axis, the part that behaves as a guide and the moving part can vary depending on the internal mechanics and the assembly of the structural supporting parts, and it is not always obvious to identify them in a purely mechanical representation.  
As previously indicated in Section 4, the MMCS representation clearly distinguishes between these two situations. For the case of a single axis or a simple configuration of axes, the individual movements and the trajectory of the end tool (TCP) can easily be deduced. However, as the mechanical structure is more complex, it can be seen that the representations in MMCS are much more meaningful, as Figure 22 illustrates. In the MMCS view of the figure, the machine kinematic chains are clearly shown.  
Moreover, virtual motion control resources, but with a direct translation to the actual movements, such as virtual axes, virtual gearing, etc., may be explicitly represented in MMCS. The behavior of many modern mechatronic machines would be difficult to understand if these virtual elements were not considered.  
Figure 21: Linear servo axis computer-aided design (CAD) layers. (**a**) 3D CAD layer; (**b**) MMCS CAD layer.
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 4 The Proposed Model
### Multi-View Information Fusion  
Since the subviews are all divided by the same HIN, the information of each subview is intersecting and complementary. Through the multi-view fusion module to fuse the node information in each subview, we can obtain rich and complete HIN embedding.  
For multi-view information fusion, we use spatial matrix fusion. By introducing the weight space matrix \(W\), we can learn the pairwise correlation of the same node among subviews. For a HIN \(\mathcal{G}\) with \(N\) subviews, firstly concatenate the embeddings of the node \(i\) in subviews \(\mathcal{G}^{m}\) and \(\mathcal{G}^{n}\). Then, multiply the concatenated representations with the spatial matrix and sum all subviews. After the multi-view information fusion, the embedding of the node \(i\) is represented as \(h_{i}^{\mathcal{G}}\):  
\[h_{i}^{\mathcal{G}}=\sum_{\begin{subarray}{c}m,n=1\\ m\
eq n\end{subarray}}^{N}W_{mn}\cdot[h_{i}^{\mathcal{G}^{m}}||h_{i}^{ \mathcal{G}^{n}}] \tag{4}\]  
where \(W_{mn}\) is the corresponding value of the subview \(\mathcal{G}^{m}\) and \(\mathcal{G}^{n}\) in the spatial matrix \(W\), indicating the correlation between subviews \(\mathcal{G}^{m}\) and \(\mathcal{G}^{n}\). The weight space matrix \(W\) is composed of \(W_{mn}\) and is constructed by two-dimensional correlation coefficients:  
Figure 4: Calculation of normalized attention value \(\alpha_{ij}\).  
Figure 3: Subview information extraction.  
\[W_{mn}=\frac{\sum_{i}\sum_{j}\left(W_{m}^{ij}-\tilde{W}_{m}\right)(W_{n}^{ij}- \tilde{W}_{n})}{\sqrt{\left(\sum_{i}\sum_{j}\left(W_{m}^{ij}-\tilde{W}_{m} \right)^{2}\right)\left(\left(\sum_{i}\sum_{j}\left(W_{n}^{ij}-\tilde{W}_{n} \right)^{2}\right)\right)}} \tag{5}\]  
where, \(W_{m}\) and \(W_{n}\) are the weight matrices of subviews \(\mathcal{G}^{m}\) and \(\mathcal{G}^{n}\), respectively, \(W_{m}^{ij}\) and \(W_{n}^{ij}\) are the values of row \(i\) and column \(j\), \(\tilde{W}_{m}\) and \(\tilde{W}_{n}\) are the mean values of the entire matrices of \(W_{m}\) and \(W_{n}\), respectively, and calculated as:  
\[\tilde{W}_{m}=\frac{\sum_{i,j}W_{m}^{ij}}{i\times j} \tag{6}\]  
\[\tilde{W}_{n}=\frac{\sum_{i,j}W_{n}^{ij}}{i\times j} \tag{7}\]  
\(h^{\mathcal{G}}\)is the final embedding of HIN \(\mathcal{G}\). Then, we can combine different downstream tasks, design different loss functions to optimize the model, and obtain the most suitable heterogeneous graph representation.
## 2 Materials and Methods
### Determination of Carbohydrates  
The extraction of saccharides involved heating of the grain sample at 60 \(\pm\) 1 \({}^{\circ}\)C for 30 min, followed by ultrasound-assisted extraction at a 50 kHz and 360 W for another 30 min at 25 \(\pm\) 1 \({}^{\circ}\)C using an ultrasonic bath (Ultrasons, J.P. Selecta(r), Barcelona, Spain). A 1.00 \(\pm\) 0.01 g sample was combined with 10.0 mL of 50% aqueous acetonitrile solution (H\({}_{2}\)O:CH\({}_{3}\)CN \(v/v\)) in conical plastic tubes (Sarsted AG & Co. KG, Numbrecht, Germany). After shaking with a ZX3 vortex mixer (Velp(r) Scientific, Usmate Velate, Italy), the samples were centrifuged at 3169\(\times\) g for 10 min at 19 \(\pm\) 1 \({}^{\circ}\)C using a 2-16 KC centrifuge (Sigma Laborzentrifugen GmbH, Osterode am Harz, Germany). Before HPLC-RID analysis, the supernatant was filtered through a hydrophilic PTFE membrane filter (Cromafil(r) Xtra H-PTFE, Macherey-Nagel GmbH & Co. KG, Duren, Germany). Quantitative analysis of carbohydrates was conducted using Water Alliance HPLC system (model e2695) equipped with a 2414 RI detector and a 2998 column heater (Waters Corporation, Milford, MA, USA). Carbohydrates were separated using an Altima Amino column (4.6 \(\times\) 250 mm; 5 \(\upmu\)m; Grace(tm), Columbia, MD, USA), with temperatures in the column and flow cell maintained at 30.0 \({}^{\circ}\)C, an injection volume of 15.0 \(\upmu\)L was used, and the sample loop was washed with ultrapure water (H\({}_{2}\)O) and acetonitrile (CH\({}_{3}\)CN) in a 50:50 \(v/v\) ratio. The mobile phase flow rate was set at 1.0 mL/min, and data analysis was performed using Empower3 chromatography data software (build 3471) (Waters Corporation, Milford, MA, USA) [32].  
Starch content was determined by the Ewers polarimetric method according to ISO 10520:2001 [33].  
The content of monosaccharides, disaccharides, and starch was reported in g per 100 g DW.
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 2 Background  
In this section, we give some preliminaries. First, a summary of Petri Nets is given, and then a description of Stochastic Petri Nets is provided. Finally, several active defense technologies are presented.
# Nitrogen-Related Defects in Crystalline Silicon
## 2 Background Information of N-Related Defects in Si
### The V\({}_{m}\)N\({}_{2}\)O\({}_{n}\) (\(m\), \(n\) = 1, 2) Defect  
It has been found that it is energetically unfavorable for the N\({}_{2}\)V\({}_{2}\) defect to trap further vacancies [77; 98]. However, oxygen atoms can be readily captured by the N\({}_{2}\)V\({}_{2}\) defect to form larger structures [14; 52; 53; 76; 99] generally labeled as N\({}_{2}\)V\({}_{2}\)O\({}_{n}\) (n = 1, 2) complexes, which can act as nuclei for oxygen precipitates, but also enhance the formation of oxygen precipitates [14; 28; 53; 100; 101]. The structures of the VN\({}_{2}\)O, V\({}_{2}\)N\({}_{2}\)O (refer to Figure 8), VN\({}_{2}\)O\({}_{2}\), and V\({}_{2}\)N\({}_{2}\)O\({}_{2}\) complexeshave been studied in the literature, and a number of LVMs have been correlated [14; 52; 56] with these structures. Certain correlations between  
Figure 7: A schematic representation of the NO\({}_{2}\) defect [31].  
theoretically calculated LVMs and experimentally detected infrared lines have been made. For instance, the LVMs at 729 cm\({}^{-1}\) of V\({}_{2}\)N\({}_{2}\)O\({}_{2}\) and the 731 cm\({}^{-1}\) of V\({}_{2}\)N\({}_{2}\)O might be assigned [14; 52] to the FTIR line at 739 cm\({}^{-1}\), which was observed experimentally [43] in nitrogen and oxygen implanted silicon. Additionally, the LVMs at 810 cm\({}^{-1}\) of V\({}_{2}\)N\({}_{2}\)O\({}_{2}\) and 819 cm\({}^{-1}\) of the V\({}_{2}\)N\({}_{2}\)O matches with the FTIR measured lines at 806 cm\({}^{-1}\) and 815 cm\({}^{-1}\) absorption lines for N-O defects [52]. Apparently, more experimental work is necessary in order for definite assignments of certain IR bands with corresponding N\({}_{2}\)V\({}_{2}\)O\({}_{n}\) (n = 1, 2) complexes to be established.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 3 Methodology
### TCN-LSTM Model  
In short-term passenger flow prediction, the change of passenger flow has multiple complex characteristics, such as strong nonlinearity, weak periodicity, and correlation. A single passenger flow prediction model cannot fully capture each feature of the data. However, the combined model can alleviate this problem to a certain extent [37]. In this paper, combining the temporal convolutional network (TCN) model with the long short-term memory network (LSTM) model, a deep learning-based TCN-LSTM model is established to predict the short-term passenger flow of URT. The model structure is shown in Figure 5. The specific workflow is: first, feature extraction is performed; the parameters in each layer are normalized, and the feature data are transmitted to the TCN layer for convolution calculation. Then, the TCN layer obtains more complete sequence features through dilated convolution and causal convolution calculations, so as to extract more dilated information dependencies. Finally, the output of the TCN layer is used as the input of the LSTM network layer to further extract features while retaining the features extracted in the TCN and then merged with the features captured by the LSTM network layer. In this way, the short-term trend of passenger flow data can be captured, and the prediction results of the combined model can be obtained.  
Figure 4: Structure of LSTM memory unit.
## 2 Materials and Methods
### Satellite Imagery Selection and Processing
#### 2.5.5 Digital Elevation Model and Final Study Area Extent  
A DEM was derived from the time series of intercalibrated Sentinel-2 images following the method first described in [PERSON] et al., (2021), which includes the use of a modified logistic function and corrections of water height due to spatial differences in the tidal phase (hence water height) across the entire extent of the scene. This approach builds on the fact that emerged intertidal areas will have higher near-infrared (NIR) reflectance values, which will then abruptly drop at the moment they are submerged [71; 72]. However, there are expectable differences in the tidal stages across the entire Bijagos Archipelago as it extends over ca. 100 km in latitude and longitude, which will affect the estimation of the elevations. To overcome this, we followed the method developed by [PERSON] et al., (2021) and estimated the tide lags for the whole study area. Analyses were carried out using the _mgcv_ and _nplr_ packages [74; 75] in R and are detailed in [PERSON] et al., (2021). We thus obtained a DEM of the intertidal area corrected for tidal lags, to be used as a predictor in the supervised classifications.  
The lowest tide height used to extract the intertidal area was 1.04 m (referenced to hydrographic zero; see Section 2.5.4), but the tide heights in the images selected for the classification were higher (Sentinel-1 image was acquired at a tide height of 1.11 m; Sentinel-2 at 1.55 m). To extract the final study area, we used the DEM to identify and exclude pixels that would be under water at the time of the acquisition of those images. Thus, by taking a conservative approach to reduce the chance of the inclusion of areas under water, the pixels below 1.65 m were eliminated.