esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Analysis of Support Effectiveness
### Analysis of the Deformation of the Roadway Perimeter Rock  
The deformation characteristics of surrounding rock are an important index for evaluating the support effect, which directly reflects the stability of the roadway in the excavation and support process. In this study, three monitoring points were selected at the top plate and gang part of the roadway for displacement analysis; the distances of the monitoring points from the top plate were 0.2 m, 0.6 m and 1.0 m, respectively, and the results are shown in Figures 7 and 8. The position diagram of the measuring point is shown in Figure 9.  
Figure 7: Comparison of deformation of surrounding rock in unsupported condition and spray anchor net support condition.  
Under the unsupported condition, the deformation of the tunnel perimeter rock is more significant. The maximum displacement of the roof plate reaches 7.0 cm, which is mainly concentrated in the middle part of the roof plate, indicating that the peripheral rock in this area has insufficient bearing capacity and there is a greater risk of deformation. The maximum displacement of the gang section was 6.2 cm, which was mainly distributed in the middle and upper part of the gang section, indicating that the peripheral rock of the gang section also deformed significantly under the unsupported condition. In addition, the displacement of the top plate at 0.6 m was 6.5 cm, and the displacement of the top plate at 1.0 m was 5.9 cm, which further indicated that the deformation of the top plate gradually decreased from the surface to the depth of the roadway, but the overall deformation was still large.  
After adopting anchor net spray support, the deformation of surrounding rock was effectively controlled. The top slab displacement was reduced to 5.8 cm, which was about 17.1% less, indicating that the support system played a significant role in restraining the deformation of the top plate and surrounding rock. Gang displacement decreased to 5.1 cm, a reduction of about 17.7%, indicating that the support measures also have a good effect on the deformation of the surrounding rocks at the gang section. In addition, the displacement at 0.6 m of the roof slab decreased from 6.5 cm to 5.4 cm, a decrease of about 16.9%, and  
Figure 8: Distribution of monitoring points for tunneling and support of the perimeter rock roadway.  
Figure 9: Schematic diagram of measuring point location.  
the displacement at 1.0 m of the roof slab decreased from 5.9 cm to 4.9 cm, a decrease of about 16.9%. These data show that the support system can not only effectively control the deformation on the surface of the roadway but also inhibit the displacement of the deep surrounding rock, thus improving the overall stability of the roadway.  
By comparing the deformation characteristics under the two conditions, it can be found that the anchor net spraying support significantly reduces the deformation of the surrounding rock and improves the deformation distribution law of the roadway, which provides an important guarantee for the safe use of the roadway.
## 3 Biochar Adsorbents
### Properties of Biochar  
Biochar characteristics such as the chemical composition, surface chemistry, particle and pore size distribution, and physical and chemical stabilization mechanisms in soils determine its effects on soil functions and faecal contaminants control [21]. Studies into biochar have demonstrated potentials for its use in increasing nutrient [19,70,85,86,87,88] and water retention [89; 90; 91; 92; 93] in soils, filtering heavy metals [94,95], reducing transport of microbes [14,15,22], increasing C sequestration [96; 97; 98], infiltration, soil aeration, root development, soil density, cation exchange capacity (CEC), and pH value [99; 100, 101; 102]. The direct influence on soil structure, distribution of pore size, and density of the soil improves water holding capacity, aeration, and permeability [103,104, 91].  
Long-term properties including the stabilization of organic matter, slower release of nutrients from organic matter, and increased retention of cations have a huge impact to  
Figure 2: Scanning electron microscopy (SEM) pictures of pine sawdust biochar [72]. Reprinted from the reference with the permission, © 2021 John Wiley and Sons, Inc.  
reducing the contamination of water resources [104; 105]. Adsorption mechanisms studies showed that various types of interactions such as chemical bonding, chemical interaction, (complexation and/or precipitation), physical adsorption, ion exchange, and electrostatic attraction are largely responsible for binding faecal wastewater contaminants [22; 94; 95]. Physical sorption of metallic contaminants occurs on the surface area and pore volumes of biochar due to the high affinity of adsorption retained within the pores [74; 106]. Most positively charged contaminants are sorbed through electrostatic attractions, ligands specificity, and several functional groups (e.g., hydroxyl, Alternariol-AOH, carboxylate, ACOOH) on biochar because of their negatively charged surfaces [107]. The effect can also promote surface complexities and precipitation of these contaminates to their physical mineral phases, which immobilizes them [108]. Physical or surface sorption also happens by diffusional movement of organic and inorganic elements into sorbent pores [74]. Contaminant sorption also occurs through the exchange of ionizable cations or protons and chemical bonding on the biochar surface with those species in solution. Furthermore, biochar's high pH influences adsorption, because it affects charges on the surface, levels of ionization, and speciation of the adsorbent [74; 108]. These characteristics of biochar make it a viable soil and water quality amendment in studies associated with on-site sanitation systems and agricultural sludge usage. However, the effect of the ageing process on biochar properties has not been studied in detail; for example, adsorption capacities of biochar change with time [71].
## 4 Experimental Results
### Analysis of Wavelet Power Spectrum Images  
To demonstrate the advantage of the wavelet transform technique, we depicted the time-domain vibrational signals and their corresponding wavelet power spectrums for a normal and a failure stage in Figure 7. While periodic vibrations with low amplitude are observed in the normal stage, more severe vibrations appear in the failure stage. The wavelet power spectrum represents the variations of the energy distribution of the vibration signal for different frequencies over time. As shown in Figure 7, the wavelet transform can clearly distinguish between the normal stage and the failure stage signals. For normal operations, most of the energy is concentrated in high frequencies, but for a failure stage, due to the evolution of defects, the burst of energy is observed in a broader range.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing** & **Subset 1 Bearing 3** & **Subset 1 Bearing 4** & **Subset 2 Bearing 1** & **Subset 3 Bearing 3** \\ \hline Load (lbs) & 6000 & 6000 & 6000 & 6000 \\ \hline Speed (rpm) & 2000 & 2000 & 2000 & 2000 \\ \hline Defect type & Inner race & Roller element & Outer race & Outer race \\ \hline Endurance duration & 34 days 12 h & 34 days 12 h & 6 days 20 h & 45 days 9 h \\ \hline Number of healthy samples & 61,069 & 43,381 & 19,146 & 200,474 \\ \hline Failure threshold & 27 days 20 h & 19 days 20 h & 3 days 21 h & 41 days 2 h \\ \hline Number of faulty samples & 19,800 & 31,090 & 14,780 & 20,230 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of IMS bearing dataset [45]. The failure thresholds and the healthy and faulty samples were identified by the adaptive failure threshold.  
Figure 7: Comparison of time-domain vibration signals and power spectrum images for healthy and damaged signals: (**a**) raw signal (normal stage) [45], (**b**) wavelet power spectrum (normal stage), (**c**) raw signal (failure stage) [45], and (**d**) wavelet power spectrum (failure stage).
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 2 Preliminary Considerations  
### Markov Decision Process  
In the RL problem, the interaction process between the agent and the environment is usually modeled as a Markov decision process (MDP) [23]. The MDP can be denoted as \(M=(S,A,P,\rho_{0},r,\gamma)\), where \(S\) is the state space, \(A\) is the action space, \(P(S_{t+1}=s^{\prime}\mid S_{t}=s,A_{t}=a):S\times A\times S\rightarrow[0,1]\) is the probability of transitioning into state \(s^{\prime}\) upon taking action \(a\) in state \(s\), \(r(s,a):S\times A\rightarrow\mathbb{R}\) is the immediate reward associated with taking action \(a\) in state \(s\), \(\gamma\in[0,1)\) is the discount factor and defines the horizon of the RL problem, and \(\rho_{0}:S\rightarrow[0,1]\) is the initial state distribution.  
Given the MDP \(M\) and a parameterized policy \(\pi_{\theta}:S\to A\), the agent interacts with the environment following the trajectory \(\tau\):  
\[\tau=(s_{0},a_{0},r_{1},s_{1},\cdots,s_{T}) \tag{1}\]  
where \(s_{0}\sim\rho_{0},s_{t}\sim P(\cdot\mid s_{t-1},a_{t-1}),a_{t}\sim\pi_{\theta }(\cdot\mid s_{t}),r_{t}=r(s_{t},a_{t})\) and \(T\) is the horizon length.  
The goal of the deep RL problem is to identify an optimal policy \(\pi_{\theta}^{*}\) that maximizes the expected discounted return:  
\[J=\mathbb{E}_{\tau}\left[\sum_{t=0}^{T-1}\gamma^{t}r_{t}\right] \tag{2}\]
## 3 Hysteresis Curve Characteristics of GRS
### Effect of Number of Loading Cycles on the Hysteresis Curve Parameters  
The morphology of the hysteresis curve of GRS changes dramatically with the increase of loading cycles, resulting in significant changes in its morphological parameters. As shown in Figure 4, it is observed that the higher the number of loads, the smaller the area and the degree of opening, while the larger the degree of tilt.  
Figure 5 plots the variation and trends of \(S\), \(k\), \(d\) and \(\varepsilon_{sp}\) for different fines contents with cyclic loads, respectively, and the following results can be obtained from the analysis: (1) From Figure 5a,c,d, it can be observed that with the increase of the number of loads, the decrease of \(S\), \(d\) and \(\varepsilon_{sp}\) is significant in the early loading period (\(N\leq 20\)), while the decrease in \(S\), \(d\) and \(\varepsilon_{sp}\) slows down and stabilizes in the late loading period (\(N>20\)). These observations are consistent with those obtained by [PERSON] et al. and [PERSON] et al. (2019); [PERSON] et al. (2019). The ratio of the decrease under the first 20 loads to the total decrease can be found in Table 2. The average reduction ratio for \(S\), \(d\) and \(\varepsilon_{sp}\) are 77.8%, 92.5% and 94.8%, respectively, with a maximum of 96.6% and a minimum of 72.9%. (2) Unlike the other three parameters, as shown in Figure 5b, \(k\) increases sharply in the early load stage and then increases slowly, and moreover, \(k\) approximately changes linearly with Log\(N\).  
The reason for these observations might be that in the early stage of loading (\(N\leq 20\)), the soil is rapidly compacted and the deformation of the soil is predominantly plastic ([PERSON] et al., 2019). At this stage, the internal structural damage and residual strain of the soil are most obvious, while in the late stage of loading, the soil compactness is enhanced with filled pores and increasing contact between soil particles, and the soil deformation gradually transitions from plastic to elastic, so the residual strain decreases and the soil stiffness gradually increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Hysteresis Curve Morphology Parameters** & **C0** & **C10** & **C20** & **C30** \\ \hline \(S\) & 72.9\% & 79.5\% & 79.2\% & 79.7\% \\ \(d\) & 90.5\% & 94.5\% & 92.6\% & 92.3\% \\ \(\varepsilon_{sp}\) & 94.4\% & 96.6\% & 95.0\% & 93.0\% \\ \hline \end{tabular}
\end{table}
Table 2: The ratio of the reduction of morphological parameters of the first 20 loads to the total reduction.  
Figure 4: Schematic diagram of hysteresis curve pattern with an increase of loading cycles (C0T0).
## 1 Introduction  
Milk-producing animals are typically kept overcrowded under unsanitary conditions suffering from various infections and for this reason they are administered antibiotics through their feed or water. Dairy cows are treated with antibiotics in therapeutic doses in order to treat diseases such as mastitis, bacteremia, diarrhea and pulmonary diseases or for prophylactic reasons. They are also administered at sub-therapeutic doses for growth promoting purposes. However, the excessive use of antibiotics in human and veterinary medicine has resulted in the development of antibiotic-resistant bacteria. Furthermore, antibiotic residues are found in milk and dairy products, posing a threat for human health, since they can cause allergic reactions and other toxic effects to sensitive groups. For this reason, the European Union has published the \"Commission Regulation (EU) No. 37/2010 of 22 December 2009 on pharmacologically active substances and their classification regarding maximum residue limits in foodstuffs of animal origin\" and every animal originated product should comply with [1; 2; 3].  
Analytical methods were developed for the determination of antibiotic residues in milk. Key priority in any analytical method is given to the analyte clean-up and preconcentration step, especially in complex matrices such as milk. Analytical performance is dependent to a well-developed sample preparation protocol. Sample preparation approaches have already been reviewed in the literature for the extraction and determination of antibiotics in milk samples [2; 4; 5; 6], along with other food samples [7; 8].  
Molecularly Imprinted Polymers (MIPs) are synthetic polymeric materials with imprinted sites complementary to a specific molecule and high affinity over analytes with analogous molecular structure. Sample preparation protocols can benefit from the selectivity of the MIPs over specific molecule or group of molecules for improved clean-up efficiency. MIPs have already been reviewed as sorbents in sample preparation techniques [9; 10; 11] such as solid-phase extraction (SPE) [12; 13; 14], solid-phase microextraction (SPME) [15; 16] and on-line extraction techniques [17] for bioanalysis [18], pharmaceutical [19], environmental [20], food [21], and forensic analysis [22]. MIPs have also been reviewed for the extraction of non-steroidal anti-inflammatory drugs and analgesics [23], drugs of abuse [24], personal care products [25] and proteins [26]. Apart from sample preparation purposes, MIPs have already been reviewed as monolithic columns in high-performance liquid chromatography (HPLC) and capillary electrophoresis (CE) [27; 28], in biosensors [29] and gas sensors [30], in drug delivery [31], for diagnostic purposes [32; 33], in wastewater treatment [34] and for biomolecule identification and separation [35].  
This review focuses on the basics of molecular imprinting technology and polymerization techniques, as well as the application of MIPs for the chromatographic determination of various antibiotic categories in milk found in the recent literature.
## 2 Material and Methods  
### Data and Model Development  
Data for this study was collected from previous in vitro shoot regeneration studies [3; 28; 29; 30; 31; 32; 33; 34]. Different types and concentrations of phytohormones, explant types, and genotypes on percent shoot regeneration frequency are summarized in Table S1. To construct the GRNN model (Figure 1), different genotypes, various explants, and different concentrations of 6-benzylaminopurine (BAP), kinetin (Kin), 2,4-dichlorophenoxyayacetic acid (2,4-D), indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), Zeatin, and CuSO\({}_{4}\) were considered as inputs, and shoot regeneration frequency was considered as output for modeling wheat in vitro shoot regeneration. For model development and validation, the dataset was randomly divided into two subsets of 70% for training and 30% for validation.  
GRNN with a very fast training process was established by [PERSON] [13]. The input layer, pattern layer, summation layer, and output layer are four layers of GRNN. The input layer is completely joined to the pattern layer. Each neuron of the pattern layer is linked to S-summation and D-summation neurons from the summation layer. S-summation and D-summation neurons, respectively, measure the sum of the weighted and unweighted outputs of the pattern neurons. The connection weight between S-summation neuron and a neuron of the pattern layer is equal to the target output, while the connection weight for D-summation is unity. The output layer obtains the unknown output value corresponding to the input vector, only via dividing the output of each S-summation neuron through the output of each D-summation neuron. GRNN uses the following equations to calculate an output:  
\[\hat{y}=\frac{\sum_{i=1}^{n}y_{ie}\ exp\bigg{(}-\frac{D_{i}^{2}}{2\sigma^{2}} \bigg{)}}{\sum_{i=1}^{n}exp\bigg{(}-\frac{D_{i}^{2}}{2\sigma^{2}}\bigg{)}} \tag{1}\]  
\[D_{i}^{2}=(x-x_{i})^{T}(x-x_{i}) \tag{2}\]  
where \(n\), \(\hat{y}\), \(y_{i}\), \(\sigma\), and \({D_{i}^{2}}\), and \(T\) are the number of input-output datasets, the output data (average of all the observed values), \(i\)th output data (connecting weight), width parameter, a scalar function, and the target related to the \(i\)th observation, respectively. In each iteration, a model was developed by using the training data and used to predict the outcome of the validation set. To assess the predictive ability of the GRNN model, three performance measures including R\({}^{2}\) (coefficient of determination), root mean square error (RMSE), and mean bias error (MBE) were used. Greater values of R\({}^{2}\) and smaller values of RMSE and MBE indicate the higher predictive ability and performance of the constructed model.  
Figure 1: Schematic diagram of the proposed generalized regression neural network (GRNN) model.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 9 Conclusions  
Wind power forecasting plays a vital role in dealing with the intermittency and uncertainty characteristics of wind. Power system operations require a high accuracy forecasting model, since probabilistic wind power forecasts and NWP calibrations are still very challengeable. In this paper, a hybrid model has been proposed for three-hour-ahead wind power forecasting, which includes a dimensional reduction using autoencoders in the preprocessing step, the XGBoost training model for deterministic forecasting, and the consideration of different NWP models such as WRFD, RWRF, and WEPS. For evaluating the developed method, actual measured data from the Hu-Si wind farm located on Penghu island, Taiwan have been used for testing, and RMSE, PICP, PINAW, and CWC have been applied to identify the model performance.  
This study demonstrates that the use of the machine learning model for training as well as the deep learning model for preprocessing input data is very important to improve forecasting performance. In addition, different NWP models should be considered in the forecasting module. Finally, the forecasting results herein in both deterministic and probabilistic forecasts show that the proposed model has better accuracy, stability, and reliability than other methods for short-term wind power forecasting.  
Conceptualization, Q.T.P. and Y.K.W., methodology, software, Q.T.P., validation, Y.K.W. and Q.D.P., formal analysis and investigation, Q.T.P. and Y.K.W., writing--original draft preparation, Q.T.P., writing--review and editing, Y.K.W. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 108-3116-F-194-001-. Project title: Development of Renewable Power Forecasting Technique Combing Numerical Weather Prediction and Artificial Intelligence.  
The authors declare no conflict of interest.
## 7 Future Scope  
This paper discusses most of the current clustering algorithms based on soft computing techniques, with their merits and limitations, to meet the need for applications. Nevertheless, we find several study gaps where the science is either not studied or not completely utilized to its maximum potential:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**CI Techniques** & **Advantages** & **Disadvantages** \\ \hline PSO & Because of its simplicity in executing on hardware or software, and highly optimum result, PSO is the most prevalent soft computing technique used in VANETs. & The PSO’s iterative design does not make it ideal for real-time services. \\ \hline K-means & Comparatively easy to implement. Guarantees convergence. & Difficult to approximate K-value. It did not fit properly with a global cluster. \\ \hline NN & NNs will deal with incomplete sets of data. NNs are useful for prediction. & In complex ANN systems, excessive training can be needed. \\ \hline FA & For determining the fitness function, the FA has relatively fewer parameters. & It is not successful in defining the high-performing regions in the search space for complicated issues. \\ \hline FL & The least system improvement cost, design time, and computing memory are needed to execute FL. & Fuzzy laws do not conform to the complexities of the network and need to be re-learned under difficult network circumstances. \\ \hline ABC & The ABC algorithm attains global optimization through discovery by artificial scouts, while local optimization is achieved by exploitation of onlookers and employed bees. & Because of the random solution, ABC has a sluggish convergence issue. \\ \hline GA & The inherent parallel environment makes GAs appropriate for the process of data collection. & GA has a slow speed of convergence that limits its execution in real-time applications. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of bio-inspired techniques.  
Figure 8: Network performance metrics.  
* In VANET, due to high-speed network situations, the location of vehicles often changes. In an uneven VANET vehicle density structure, clustering protocols should have a greater ability to predict the exact vehicle location. Clustering techniques based on traffic density may be implemented. The role of the cluster protocol in an uneven vehicle density scenario for precise location prediction may be comprehensively analyzed and can be worked upon.
* In the trustworthiness verification process, vehicles require to discover the requested data. It is hard to handle both trust and user privacy for such procedures. Therefore, to guarantee the trade-off between privacy and trust, a need for a robust cluster-based architecture motivates the researchers to work in this emerging area.
* The proposal on clustering protocols has taken into account urban or highway situations in the literature work. Although, for a hybrid road scenario, a special investigation is needed to improve various network performance parameters. Further, a hybrid approach can also include the real highway speed vehicles in a bidirectional road scenario.
# Neural Graph Similarity Computation with Contrastive Learning
## 3 Methodology
### Contrastive Learning
#### 3.3.1 Graph Augmentations  
Data augmentation is the prerequisite for contrastive learning, which creates new data without hurting the semantics. In the context of graphs, typical augmentation strategies include the modifications of nodes, edges, and node/edge labels [48]. In this work, inspired by previous works [47], given a graph \(G\), we make augmentations by randomly removing nodes with probability \(p_{r}\), randomly adding or removing edges with probability \(p_{e}\) and masking node and edge labels with probability \(p_{m}\), resulting in two augmented graphs, i.e., \(\hat{G}_{n}\) and \(\hat{G}_{b}\).