esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
###### Abstract  
Halloysite nanotubes (HNTs) are natural occurring mineral clay nanotubes that have excellent application potential in different fields. However, HNTs are heterogeneous in size, surface charge, and formation of surfacial hydrogen bond, which lead to weak affinity and aggregation at a certain extent. It is very important to modify the HNTs' surface to expand its applications. In this review, the structural characteristics, performance, and the related applications of surface-modified HNTs are reviewed. We focus on the surface-modified variation of HNTs, the effects of surface modification on the materials and related applications in various regions. In addition, future prospects and the meaning of surface modification were also discussed in HNTs studies. This review provides a reference for the application of HNTs modifications in the field of new nanomaterials.  
halloysite nanotubes; surface modification; structural characteristics; controlled release; biocompatibility +
Footnote †: journal: Applied Sciences  
forces, such as van der Waals force and hydrogen bonding. To improve the performance of HNTs, surface modification is very desirable. For example, modified HNTs can be used as nanofillers in composite polymers to enhance mechanical strength [15] and as nanocarries to realize sustained drug delivery. In addition, it is also used as an adsorbent material to absorb or remove the dyes from aqueous solution [16], or as catalysts [17] to catalyze the reaction.
## 6 Discussion and Conclusions  
In order to track the GNSS signal in a degraded signal environment, an assisted tracking structure loop was used in a dual antenna receiver. The analytical formulations of the noise jitters of the proposed tracking loop were provided. The performance of the proposed tracking loop was analyzed in terms of dynamic stress, thermal noise jitter, and clock phase noise jitter. Simulation results show that the dynamic and clock noise in the slave loop could be eliminated with the assistance from the master loop. This ensures that a very narrow bandwidth could be used to suppress noise and improve the tracking performance of weak signals. Test results with a software receiver and signal collected from the hardware signal simulator show that the proposed tracking loop allow weak carrier signal tracking (18 dB-Hz) with a narrow bandwidth of 0.5 Hz for static applications. The integration of an IMU and the proposed dual antenna to provide Doppler aid for user motion compensation will be considered in future work.
## 4 Test Results
### Dynamic Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**YANP (50 km)**} & \multicolumn{2}{c}{**WNJU (90 km)**} & \multicolumn{2}{c}{**YOWL (140 km)**} & \multicolumn{2}{c}{**WULJ (225 km)**} \\ \hline \multirow{2}{*}{50\% Accuracy} & DGPS & 0.28 m & 0.40 m & 0.59 m & 0.82 m \\ \cline{2-6}  & FKP-DGPS & 0.20 m & 0.26 m & 0.25 m & 0.21 m \\ \hline \multirow{2}{*}{95\% Accuracy} & DGPS & 0.49 m & 0.79 m & 0.81 m & 1.16 m \\ \cline{2-6}  & FKP-DGPS & 0.40 m & 0.54 m & 0.44 m & 0.44 m \\ \hline \multirow{2}{*}{RMS (North)} & DGPS & 0.22 m & 0.22 m & 0.22 m & 0.17 m \\ \cline{2-6}  & FKP-DGPS & 0.18 m & 0.22 m & 0.22 m & 0.17 m \\ \hline \multirow{2}{*}{RMS (East)} & DGPS & 0.21 m & 0.41 m & 0.56 m & 0.83 m \\ \cline{2-6}  & FKP-DGPS & 0.15 m & 0.25 m & 0.17 m & 0.19 m \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Horizontal errors corresponding to baseline lengths. Figure 17: (a) DGPS horizontal error (WULJ); (b) FKP-DGPS horizontal error (WULJ); and (c) FKP-DGPS and DGPS vertical errors (WULJ). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Vertical**} & \multicolumn{2}{c}{**YANP (50 km)**} & \multicolumn{2}{c}{**WNJU (90 km)**} & \multicolumn{2}{c}{**YOWL (140 km)**} & \multicolumn{2}{c}{**WULJ (225 km)**} \\ \hline \multirow{2}{*}{50\% Accuracy} & DGPS & 0.33 m & 0.62 m & 0.94 m & 1.74 m \\ \cline{2-6}  & FKP-DGPS & 0.44 m & 0.71 m & 0.82 m & 0.45 m \\ \hline \multirow{2}{*}{95\% Accuracy} & DGPS & 1.19 m & 2.20 m & 1.93 m & 2.88 m \\ \cline{2-6}  & FKP-DGPS & 1.30 m & 2.35 m & 2.02 m & 2.23 m \\ \hline \multirow{2}{*}{RMS} & DGPS & 0.58 m & 1.06 m & 1.10 m & 1.89 m \\ \cline{2-6}  & FKP-DGPS & 0.67 m & 1.14 m & 1.08 m & 0.92 m \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Vertical errors corresponding to baseline lengths. that the FKP-DGPS can significantly contribute to the reduction in the spatial decorrelation effect in the position-domain and the improvement in the position accuracy.
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 1 Introduction
For example, the feature \"people\" appears frequently in the whole training set, so the importance of \"people\" is very high, calculated by the method of counting its frequency in the whole training set. However, such a feature may appear similar times in each category of the training set, and doesn't have category discrimination ability indeed. The category discrimination ability of features is an ability to distinguish one category from others. Therefore, the importance of features should be calculated from the perspective of the category discrimination ability. Features with strong category discrimination ability have high correlation with one category and low correlation with others. To the best of our knowledge, there are some researches on the calculation of feature importance based on the two aspects [22; 23]. They consider one aspect or two aspects abovementioned, design the feature importance calculation formula, and apply it to single-label text classification. In this paper, on the basis of these researches, the contributions of features to category discrimination are redefined from the above two aspects, and the importance of the features is obtained with the combination of them. A filter feature selection method for multi-label text is proposed in the paper. Firstly, multi-label texts are transformed into single-label texts using the label assignment method. Secondly, the importance of each feature is calculated using the proposed method of Category Contribution (CC). Finally, features with higher importance are selected to construct the feature space. Thus, the contributions of this paper can be summarized as follows. 1. The importance of features for classification is analyzed from two aspects of inter-category and intra-category, and then the formulas for calculating inter-category contribution and intra-category contribution are proposed. 2. A formula for calculating feature importance based on CC is proposed. 3. The proposed feature selection method is combined with Binary Relevance k-Nearest Neighbor (BRKNN) [24] and Multi-label k-Nearest Neighbor (MLKNN) [25] algorithms, achieving a good classification performance. The rest of this paper is organized as follows. Section 2 describes some related works. Section 3 introduces the details of the proposed method. Experimental results are shown in Section 4. Finally, Section 5 concludes the findings shown in this paper.
## 2 Related Research
### LEACH Protocol for WSN
#### 2.1.3 Limitation of LEACH Protocol  
The LEACH algorithm's probabilistic selection of CHs often leads to unbalanced clusters, as it does not consider factors like the spatial distribution and residual energy of nodes, resulting in some CHs handling excessive data loads and depleting their energy quickly. Since LEACH does not consider the distance between CHs and the BSs, nodes located farther from the BS may still be chosen as CHs, resulting in higher energy consumption for long-distance data transmission. These inefficiencies collectively shorten the network's lifetime, limiting LEACH's effectiveness in sustaining energy-efficient operation over extended periods. Consequently, while LEACH provides a foundation for clustering-based protocols, its limitations have driven research towards improved algorithms that consider node energy levels and spatial factors to achieve more balanced and efficient clustering.  
Additionally, LEACH uses a TDMA schedule to minimize interference between nodes and CDMA codes to prevent collisions among CHs. Nodes remain in sleep mode until their designated transmission time, reducing unnecessary transmissions and conserving energy. Despite these advantages, LEACH has significant limitations, including the random selection of CHs, which does not consider nodes' remaining energy, leading to early energy depletion in some CHs. Additionally, LEACH's single-hop transmission from CHs to the BS makes it unsuitable for large networks, as it can result in excessive energy consumption for distant CHs.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 5 Conclusions  
In this paper, an effective spectral-spatial feature extraction method called SPCM is proposed for HSI classification. It can not only characterize the non-linear relationship between different bands based on the kernel method, but also extract spatial structure features using the local neighbourhood construction method. The experimental results on three publicly available data sets have verified that the proposed SPCM-based classification method performs better than other base line methods, especially when the training set is small. The proposed method has achieved good results in respect to different indicators and visual effect. However, how to better combine spatial information and spectral information is still an open problem. In the future, we will further explore how to better learn spectral-spatial features to achieve better classification results.  
Conceptualization, W.C. and Y.W.; methodology, Y.W. and G.Z.; software, G.Z.; validation, G.Z., W.C., and Y.W.; formal analysis, G.Z., W.C. and Y.W.; investigation, G.Z., W.C., and Y.W.; resources, W.C. and Y.W.; data curation, G.Z., W.C., and Y.W.; writing--original draft preparation, G.Z.; writing--review and editing, W.C. and Y.W.; visualization, W.C.; supervision, W.C. and Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grants CCNU20 TD005 and CCNU22 QN013, and Knowledge Innovation Project of Wuhan under Grant 2022010801010274.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Results
### Relationship Between Ecological Factors and Biomass of Halophytes Under Salt Stress Conditions  
#### 3.4.1 Effect of Different Salt Concentration Treatments on Biomass of Halophytes  
The biomass of all three halophytes was affected to varying degrees with increasing salt concentration. As can be seen in Figure 10, the biomasses of Ha, Tc, and Pa all showed a decreasing trend with increasing salt concentration. The biomasses of Ha, Tc, and Pa were highest in the treatment with a salt concentration of 0 mmol\(\cdot\)L\({}^{-1}\) and decreased to a minimum at a salt concentration of 300 mmol\(\cdot\)L\({}^{-1}\). The biomasses of Ha, Tc, and Pa were 50.60, 31.70, and 18.00 g, respectively, at a salt concentration of 0 mmol\(\cdot\)L\({}^{-1}\) treatment. The biomasses of Ha, Tc, and Pa were reduced by 64.9%, 41.2%, and 83%, respectively, at a salt concentration of 300 mmol\(\cdot\)L\({}^{-1}\) treatment compared with the control. Biomass was analyzed by regression for salt concentration treatments and three halophytes. The fitted R\({}^{2}\) for biomasses of Ha, Tc, and Pa were found to be 0.663, 0.708, and 0.597, respectively, which were all greater than 0.5, providing a good fit (Table 3). Meanwhile, significant correlation (\(p<0.05\)) was found between salt concentration treatments and biomass of all three halophytes through ANOVA testing.  
Figure 9: Characteristics of daily changes in Wue of halophytes under different salt concentration treatments. (**a**) Ha. (**b**) Tc. (**c**) Pa. (**d**) Daily average of water use efficiency. Different lowercase letters in the graphs indicate significant differences under different salt concentration treatments (\(p<0.05\)).
## 5 Experimental Results
Energy consumption increases as the IP-WSN node density increases. Our proposed scheme increases the performance linearly compared to MIPv6 and PMIPv6. Therefore, the energy consumption increases more rapidly as the density of IP-WSN node increases. Figure 21(a,b) represents packet delivery ratio versus number of hops. Packet delivery ratio is measured for 1 packet per 30 seconds, 1 packet per 10 seconds and 1 packet per one  
Figure 18: Number of Hops _vs._ Mobility Cost. Figure 19: Data payload _vs._ Energy consumption. second with respect to the number of hops. Increasing number of hops between sources and destinations shows a small negative effect on packet delivery ratio for the IP-WSN scenario. The higher the traffic loads, however, the lower the packet delivery ratio. Figure 21(a) shows multiple nodes to SMAG node packet delivery ratio in IP-WSN. The delivery ratio is defined as the number of successfully delivered packets divided by the number of packets generated at sources. In this case the proposed routing and addressing scheme shows excellent delivery ratio except for 1 packet per second. In case of 1 packet per second traffic in which the end-to-end packet delivery suffers more contention and thus more MAC layer retransmission. Figure 21(b) considers a typical IP-WSN scenario where a single source node sends data periodically to a SMAG node over multiple hops. In this case also the proposed routing and addressing scheme shows excellent delivery ratio. Figure 21: Delivery ratio _vs._ number of hops. (**a**) Multiple nodes to SMAG delivery; (**b**) End-to-end delivery. Figure 20: Node Density _vs._ Energy Consumptions. Figure 22(a,b) depict the end to end delay versus number of hops. Both figures show increasing amount of delay along with the increasing number of hops. The end-to-end delay is almost the same for both cases, except for the case of 1 packet per second traffic in which the end-to-end packet delivery delay suffers more contention and thus more MAC layer retransmission. Figure 22(a) considers a typical IP-WSN scenario where multiple nodes send data periodically to a SMAG node. Figure 22(b) is performed to show the end-to-end delay for end-to-end packet delivery scenario in IP-WSN. In both cases the proposed routing and addressing scheme minimize the end-to-end delay significantly. Figure 21: _Cont._  
Figure 22: End to end delay _vs._ number of hops. (**a**) Multiple nodes to SMAG delivery delta; (**b**) End-to-end delay.
## 3 Shield Tunneling Anomaly Detection and Scenario Identification
### Anomaly Detection Model Based on Dynamic Threshold  
The setting of the threshold in an anomaly detection model has an impact on the accuracy of anomaly detection. When the threshold is set too high, the anomaly detection model becomes conservative, leading to cases of missed anomaly detection. On the other hand, if the threshold is set too low, the model becomes overly sensitive, resulting in unnecessary false alarms. A reasonable and accurate threshold setting can reduce false alarms while capturing more abnormal data effectively. Furthermore, noise data and differences in data characteristics under different shield tunneling scenarios can introduce more interference in determining the threshold for the anomaly detection model. Therefore, to improve the performance of the anomaly detection model, we make improvements to the VAE-LSTM model and design an anomaly detection model with a dynamic threshold to monitor energy consumption in the shield tunneling process. The process of shield tunneling detection model using the dynamic threshold, DT_VAE-LSTM (VAE-LSTM with Dynamic Threshold), is illustrated in Figure 2.  
The DT-VAE-LSTM model can be divided into three modules: offline training, dynamic threshold setting, and online detection. The offline training module combines the VAE and LSTM algorithms to implement a reconstruction-based anomaly detection model, using anomaly-free data to train the model and learn data features in normal scenarios. Specifically, the VAE algorithm is used to learn the latent distribution of the data, while the LSTM algorithm can capture the temporal features. The dynamic threshold setting module will dynamically update the threshold with each iteration of model training, which is set based on the reconstruction error of the training data. The online detection module uses the trained anomaly detection model to reconstruct the test data, obtaining the reconstruction error. Finally, the final anomaly detection result is obtained by comparing the reconstruction error of the test data with the threshold.  
DT_VAE-LSTM first preprocesses the collected data. DT_VAE-LSTM primarily monitors the operational segment where the shield machine is working. Therefore, the data preprocessing stage initially removes data at the time of cessation. In addition, data are smoothed and normalized. After data preprocessing, the training data and the data to be detected enter the offline training phase and the online detection phase, respectively.
## 1 Introduction  
### Background  
The Internet of Things (IoT) is transforming numerous sectors by enabling interconnected devices (also known as nodes) to communicate and perform tasks seamlessly over the Internet. This advanced connectivity is driving unprecedented convenience and efficiency across various domains, from healthcare to smart cities and industrial automation. The rapid expansion of IoT is underscored by its projected requirement of three to six devices per person in the near future, highlighting its integration into daily life [1]. However, the increased complexity of these systems, driven by efforts to boost security, accuracy, and functionality through sophisticated algorithms, has led to challenges such as heightened power consumption and energy inefficiency [2].  
The complexity of IoT systems demands considerable processing power, which can strain devices and networks, especially given the need to process vast data volumes. These challenges are compounded by the reliance of IoT networks on efficient communication protocols, task scheduling, and resource allocation strategies. Addressing these issues has become a priority to ensure sustainable IoT deployment, making energy optimisation critical for balancing functionality with efficiency [3].
## 4 Findings and Discussion
### Explainable Frameworks in XAI
#### 4.3.1 What Is SHAP and What Is Its Applicability to IDSs for ICVs?  
SHAP is a method for explaining the output of any machine learning model [54; 72]. The concept of Shapley values from cooperative game theory [72; 85] allows a fair distribution of values among a group of individuals. The method assigns each feature an importance value for a particular prediction. The values can be for both global and local interpretation of a model's predictions, and it is considered a state-of-the-art method for feature importance in XAI. It is also model-agnostic, meaning that it can explain the output of any machine learning model, regardless of its architecture. SHAP aids the perception of the contribution of each data feature to the particular prediction made by a machine learning model. Intrusion detection in ICV is a critical task, as it helps ensure the vehicle's security and safety and its passengers. SHAP explains the decisions made by an IDS in ICVs, which can help to improve transparency and trust in the system. Applying SHAP aids the understanding of the feature importance and contribution of each feature to the predictions made by the model for detecting intrusion attempts.  
Based on the SHAP values approach, the following criteria are used for providing both global and local explanations for XAI models [54]: SHAP Force Plots, SHAP Importance Plots, SHAP Summary Plots, and SHAP Dependence Plots. SHAP Summary Plots give a global summary of the features of the SHAP values distribution in the dataset. They demonstrate how each feature's relevance is distributed. This makes it easier to see the dataset's features.  
Dependence plots, according to [54], are deemed to be the most straightforward global interpretation plots. The link between the value of a feature and the related Shapley values for each instance in the dataset is displayed in the dependence charts. The SHAP dependence plots display the precise structure of the relationship, whereas summary plots only display the relationship between a feature value and its influence on the prediction.  
The SHAP Force Plots aid in the visualization of feature attributions of specific occurrences, where each feature value is a force that either confirms or refutes a prediction. The Force Plots were utilized by the authors of [54] to depict the packet flow characteristics that support or refute the hypothesis made by our IDS model. The size or effect that each feature has on the forecast is also displayed through Force Plots.  
Additionally, SHAP Importance Plots for Shapley values are employed to determine the level of importance or significance of instances in the dataset. Large absolute Shapley values in this plot are classified as key features since they have a greater average impact on the model output [54].
# A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
## 4 Discussion  
The presented work proposed a semi-supervised methodology to detect alarm situation in a WWTP. The importance of the proposal lies on the fact of implementing classifiers that take into consideration all system variables registered from normal operation with no need of human expertise to determine unexpected and anomalous events. In the current context of digitalization and quality standard increment, this tool takes advantage of data availability to enhance the supervision and diagnostics in critical infrastructure. The proposal can complement traditional diagnosis and supervision systems, offering an alarm in case of early anomaly detection. This contributes to maintenance decision making, easing effectiveness, and economic and energy efficiency. Furthermore, the results show how classifier performance improves significantly when the deviation is greater, resulting in a more dangerous situation. Despite the clear advantages of the proposal, there are several factors to which a one-class approach cannot contribute. As this is a semi-supervised methodology, it is necessary to have an initial set of normal operation instances, so human expertise is needed at least during the first data registration stage to ensure data quality. Once this limitation is overcome, the combination of the proposal with other supervised techniques will be suitable for detecting sensor misreadings to complement the one-class classifier.