esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Different techniques might be used for the design of hybrid FiWi networks to deliver QoS-enabled services for mobile end-users, including traffic engineering and service differentiation, optimized buffering, and error-correction at the end-user. Moreover, QoE might be provided by using resource-aware mobile services, such as tuning the transmission rate according to the experience of an end-user. The variability of wireless channel conditions and mobility of wireless end-users might change the quality of received services which requires quick response times. In this paper, we focus on layer-2 QoS which largely depends on the performance of routing and resource management algorithms, including bandwidth allocation and channel assignment algorithms, with absolute or relative QoS assurances. The remainder of this paper is structured as follows. Sections 2 and 3 review enabling optical and wireless access-metro network technologies and their underlying QoS provisioning techniques, respectively. In Section 4, we elaborate on enabling FiWi technologies and FiWi network architectures. We also describe new QoS provisioning techniques for future FiWi networks. In Section 5, we study the performance of our proposed QoS provisioning techniques. Section 6 concludes the paper.
## 3 Case Study
### Data Pre-Processing
#### 3.2.3 Screening  
At the moment t, the data are screened by using whether the sum of rainfall at each rainfall station is equal to 0 as the basis for determining whether the moment is a clear day or not, in order to properly eliminate potential systematic errors. The screening methods are: rain only and no screening.
## 4 Discussion
### Recommendations for Future Work  
If one is to consider an expansion of the seep-spill discrimination developed throughout our investigations, we suggest, besides categorizing the oil-slick category, the investigation of the categories' classes or the type of oil, corresponding to [PERSON]'s Dissertation [28]. This means that, within the oil seep category, one can possibly use LDAs to separate different seepage clusters. Analogously, among the oil spills, the LDAs can be directed at differentiating the oil from different offshore oil and gas facilities.  
Another matter of interest is the application of our linear methodology (i.e., LDA-based algorithm) to a dataset containing oil (slicks) and non-oil targets (i.e., radar false targets; e.g., low wind or upwelling zones) in a similar fashion as accomplished by [28]. However, she used ANN, SVM, etc., to differentiate the on-water oil (spills) from look-alike features.  
An improvement to our seep-spill discrimination process could be the use of other variables to start the analysis. We suggest exploring the dynamic fractal [81], ratios accounting for the SAR signals inside and outside of the oil slicks to standardize for the wind influence [28], gapped pixel space from transect lines through the oil slicks [82], etc. These new attributes could bring further information to our capacity to differentiate the slick category. Their statistical correlation with the oil-slick size and SAR information explored in our analyses may not be significant, meaning they could improve our LDA-based algorithm's accuracy.  
Even though we meticulously and systematically analyzed all details of our exploratory data analysis, searching for improvements in the categorization of oil (slicks) into oil (seeps) and oil (spills) with a linear multivariate data analysis technique, it may be that the LDA approaches have reached their discrimination limits (i.e., \(\sim\)70% of overall accuracy) while using this multi-year satellite-field baseline training dataset. Because we reached similar effectiveness with fewer attributes in relation to our previous findings [29; 30; 31; 32], we suggest that other non-linear methods--for instance, cubist or random forest, or even other variants such as ANN or SVM [28; 63; 71; 72]--should be further investigated to attempt to improve on our oil-slick discrimination approach using satellite SAR measurements.  
We have demonstrated that non-linear transformations cause the largest impact on the success of the seep-spill categorization (Table 5). However, so far, we only have explored the simultaneous application of the same data transformations to all variables per LDA-based algorithm (i.e., \(\log_{10}\) to Area and to Per or cube root to Area and to Per, etc.). As such, another subject that we believe can further improve the discrimination power is to apply different non-linear transformations to different variables on the same LDA algorithm--e.g., \(\log_{10}\) to Area together with cube root to Per.
## 2 Correlation Algorithm
### Matrix Factorization  
Matrix factorization [21; 22; 23; 24; 25] is based on the concept of introducing implicit features onto the foundation of a \"co-occurrence matrix\". It translates the user-item rating matrix into implicit user ratings and implicit item ratings, ultimately yielding the final rating through a combination of these implicit ratings. This approach enhances the capability to handle data sparsity effectively.  
The fundamental principle of matrix factorization algorithms involves transforming the rating matrix into two sparsely interacting low-dimensional matrices, corresponding to latent rating features of users and items, represented as \(M=U^{T}V\). Where \(M\), \(U\), and \(V\), respectively, denote the rating matrix, implicit user rating matrix, and implicit item rating matrix. The matrix factorization model diagram is depicted in Figure 2.  
The prediction of a user's rating for a specific item is derived from the interaction between the user's latent rating features and the latent rating features of the corresponding item. For instance, consider that the latent rating vector for user \(u\) after factorization is \(u=[1,\,1,\,2]\), and the latent rating vector for item \(v\) is \(v=[1,\,0,\,1]^{\mathrm{T}}\). The three features of the user \(u\) signify preferences for action, romance, and comedy genres, respectively. The item vector \(v\) indicates that the movie has an action component of magnitude \(1\), no romance component (magnitude \(0\)), and a comedy component of magnitude \(1\). The user's predicted rating for the item is obtained by taking the inner product of these vectors, that is, \(u\cdot v=2\).  
Utilizing the feature vectors of users and items for rating prediction enables the attainment of enhanced prediction accuracy and concurrently mitigates the adverse impact of data sparsity.
## 3 Ensemble Margin for Imbalance Learning
### Ensemble Margin Definitions  
Different definitions of ensemble margin have been proposed [27; 35; 36; 68]. The decision by an ensemble for each instance is made by voting. The ensemble margin can be calculated as a difference between the votes according to two different well-known definitions [69] in both supervised [27] and unsupervised [70] ways.  
1. A popular ensemble margin, which has been introduced by [PERSON] et al. [27], is defined by Equation (1), where \(v_{y}\) is the number of votes for the true class \(y\) and \(v_{c}\) is the number of votes for any other class \(c\). This ensemble margin is in the range \([-1,\,+1]\) and the examples which are correctly classified have positive margin values. A large positive ensemble margin can be interpreted as a confident correct classification. \[margin(x) = \frac{v_{y}-\max_{c=1,\ldots,L\cap v\
eq y}(v_{c})}{\sum_{c=1}^{L} (v_{c})}\] (1) where \(L\) represents the number of classes.
2. The ensemble margin of a sample can also be obtained by the difference between the fraction of classifiers voting correctly and incorrectly, as in Equation (2) [69]. This second popular ensemble margin definition follows the same idea introduced by [PERSON] [27] but instead of using a max operation, it uses a sum operation [69]. \[margin(x) = \frac{v_{y}-\sum_{c=1,\ldots,L\cap c\
eq y}(v_{c})}{\sum_{c=1}^{L} (v_{c})}\] (2) This ensemble margin is also in the range \([-1,\,+1]\). However, correctly classified samples do not necessarily have positive margin values.
3. In [70], the authors proposed an unsupervised version of Schapire's margin (Equation (1)). This ensemble margin's range is from 0 to 1. It is defined by Equation (3), where \(v_{c_{1}}\) is the votes number of the most voted class \(c_{1}\) for sample \(x\), and \(v_{c_{2}}\) is the votes number of the second most popular class \(c_{2}\). \[margin(x) = \frac{v_{c_{1}}-v_{c_{2}}}{\sum_{c=1}^{L}(v_{c})}\] (3)
4. In this paper, we propose an unsupervised ensemble margin alternative defined as Equation (4), where \(v_{c_{1}}\) is the votes number of the most voted class for sample \(x\) and \(T\) represents the number of base classifiers in the ensemble. The proposed margin is an unsupervised version of the classic sum-margin referred to as Equation (2); it does not require the true class label of instance \(x\). Hence, it is potentially more robust to class noise. This new margin will be named as _unsupervised sum-margin_. \[margin(x) = \frac{v_{c_{1}}-\sum_{c=1,\ldots,L\cap c\
eq c_{1}}(v_{c})}{\sum_{c =1}^{L}(v_{c})}\] (4) \[= \frac{2v_{c_{1}}-T}{T}\]The proposed margin also has the advantage of being considered as a classifier evaluation function or adopted for classifier design in unsupervised or semi-supervised ensemble learning.  
Naturally, for two-class problems these definitions are quite similar. However, a major concern needs to be solved in relation to multi-class problems. For example, by Equation (2), the margins can represent a lower bound, since they can assume negative values even when the correct label gets most of the votes (when there is a plurality, but not a majority) [69].
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 4 Simulation Experiment and Result Analysis
### Simulation of Network Traffic and Attacks
#### 4.1.2 Generation of Normal Attack Scenarios  
Building upon the baseline of normal traffic, we introduce simulated attack scenarios to assess the resilience and responsiveness of the in-vehicle network. The attacks are categorized into several types, each with distinct characteristics and impacts on the network:  
1. Non-existing Attacks: This type of attack simulates the presence of messages that should not exist in the network under normal operation. The simulation incorporates such attacks by generating and transmitting packets with identifiers that do not correspond to any known ECU. Following this approach, we conducted a non-existing attack on the entire Ethernet network through a malicious module targeting control and video signals. The results, as depicted in Figure 14, show that the attack was successful, with the CANoe software trace confirming the malicious module's impact.
2. Wrong command Attacks: These attacks are characterized by the transmission of messages that are correct in format but incorrect in content. The simulation introduces wrong attacks by altering the payload of legitimate messages to contain incorrect data. As shown in Figure 15, the video device (TV_Receiver) and the GPS_Unit, acting as attack modules, conducted a wrong command attack on the Ethernet bus. This manipulation of data within the network demonstrates the potential for malicious actors to disrupt normal vehicle operations by sending misleading commands.
3. Tampering Attacks: Tampering attacks involve the modification of legitimate messages in transit. In the simulation, tampering attacks are recreated by intercepting and modifying legitimate traffic, changing data such as sensor readings or control commands. As illustrated in Figure 16, tampering attacks on control signals were conducted, demonstrating that effective control signal tampering can be achieved, particularly targeting the HeadUnit module. This manipulation of critical signals highlights the vulnerability of in-vehicle networks to unauthorized alterations that can lead to compromised vehicle control.  
Figure 14: Trace of non-existing attacks and malicious module attack results.  
Figure 15: Trace of wrong command attacks and malicious module attack results.  
Figure 13: Trace data panel.
## 6 Conclusions  
Based on the indoor test and the theoretical analysis of the tunnel limestone, the following conclusions can be drawn:  
1. With an increase in the limestone water content rate, the pores and micro-cracks inside the limestone are filled with water, which increases the stress required in the compaction stage of the limestone. At the same time, with an increase in the water content rate of the limestone, the elastic modulus and uniaxial compressive strength of the limestone gradually decrease.
2. With an increase in the water content rate of the limestone, the failure form is still mainly tensile failure. However, after an increase in the water content rate, the through-surface of the specimen failure gradually decreases. Then, based on the quantitative calculations, the brittleness index of the limestone will decrease with an increase in the water content rate.
3. With an increase in the limestone water content rate, the upper limit of the elastic strain energy storage will decrease because the pores and micro-cracks inside the limestone become filled with water. In addition, the dissipation energy of limestone will increase with an increase in the water content rate, thereby reducing the strength of the limestone. The proportion of pre-peak deformation among the total deformation increases with an increase in the water content rate of the limestone, i.e., the molding of the limestone increases.
4. This paper mainly studied the effects of the water content rate on the mechanical properties of limestone, which has certain limitations. The test results were also affected by the sample size. However, the final test results showed good regularity with a high reliability. Thus, this study could provide a reference for research on other kinds of rock hydrologic properties. In addition, since this research considered Tongzi Tunnel as its research background (based on the actual project), this work has very important practical significance. The results could provide theoretical support and guidance for the excavation of Tongzi tunnel, including the stability of the surrounding rock and maintenance during subsequent periods.  
Conceptualization, Q.Z.; methodology, Q.Z. and Y.L.; software, Q.Z. and Q.C.; validation: Q.Z., G.H. and Y.L.; investigation, Q.Z. and Y.L.; resources, Y.L.; writing-review and editing; Q.Z. and X.O.; visualization, Q.Z., J.T. and Q.C.; supervision, Y.L., G.H., X.O., Q.C. and J.T. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Yuanming Liu: Major Science and Technology Special Project of Guizhou Province (Qiankehe Major Special Project [2018] 3011); the Natural Science Foundation of Guizhou Province (Qiankehe Foundation-ZK [2022] General 082); Research on Key Technology of Guiyang Urban Rail Transit Tunnel Underpass Existing Railway (2020-13-ZB); and the Natural Science Foundation of Guizhou Province (Qiankehe Foundation [2019] No. 1057).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
# Vision Transformer-Based Tailing Detection in Videos
## 3 Proposed Methods
### Baseline Method 1: Handcrafted Rule-Based Tailing Detection
#### 3.3.1 Trajectory Similarity  
Moving objects change their locations over time and tracking them generates a sequence of points in time and space, called a trajectory [38]. There are various similarity measures to compare two different trajectories such as Euclidean and Hausdorff. To consider multiple points on trajectories, we used Frechet distance as the similarity measure. Frechet distance is a non-metric measure that takes into account both the location and ordering of the points along curves of the two trajectories [38]. When trajectories of tailing suspect and victim, \(T_{a}\) and \(T_{b}\) are given, the Frechet distance is defined as:  
\[F(T_{a},T_{b})=\inf_{a,\beta}\max_{t\in[0,1]}\{d(T_{a}(\alpha(t)),T_{b}(\beta(t )))\}, \tag{1}\]  
where \(\alpha\) and \(\beta\) are non-decreasing function from [0,1] onto [\(\alpha\), \(\beta\)] [39]. The shorter the Frechet distance, the more similar the two trajectories.
# A Survey of the Applications of Text Mining for the Food Domain
## 1 Introduction  
Food plays a crucial role in people's daily lives, significantly influencing their health and overall well-being [1]. The textual data related to food mainly come from ingredient lists, nutritional information, and other details found on food packaging. With the continuous advancement of information technology, the amount of accessible textual data in the food domain is rapidly increasing.  
Meanwhile, the rapid development of text mining methods [2; 3] makes it possible to deeply analyze the rich semantic information contained in the content of the text. It is used in various areas, especially in the food industry, where it plays an important role in analyzing food quality [4], providing gastronomic recommendations [5], warning about food safety issues [6; 7], and creating regulations for food security [8], among other things. Specifically, the work in [9] monitored food quality and safety issues based on user-generated content for timely corporate response. The work in [10] utilized big data to provide a comprehensive examination of the entire food supply chain, thereby increasing synergies and efficiencies throughout the supply chain and beyond. The work in [11] performed semantic analysis of food products based on reviews and menu descriptions, providing a way for users to discover their favorite dishes based on personal preferences and descriptions. Recommendation systems [12; 13; 14] use text mining techniques to provide guidance on healthy dietary choices, as well as to customize recipes based on individual and group preferences. These examples highlight the potential effectiveness of text mining in providing intelligent decision support in the food domain.  
Text mining techniques have the same potential in the food domain. However, food practitioners may not be familiar with the techniques and applications in this domain. This paper aims to fill this knowledge gap by providing a concise introduction to the major text mining techniques and their applications in the food domain. It will also review the latest technological advances in text mining in the food domain to help food practitioners better utilize text mining techniques to solve relevant problems and improve productivity and product quality.
# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 5 Experiments
### Experiment Results  
#### 5.4.1 The Prediction Performance  
Given the inherent difficulty in quantifying latent knowledge states, we operationalize model evaluation through response prediction accuracy--a well-established proxy metric for knowledge estimation fidelity in KT literature. To validate HGKT's efficacy, comprehensive benchmarking against state-of-the-art baselines was conducted, with comparative performance on learner outcome forecasting detailed in Table 2. All experiments employ standardized evaluation protocols using the receiver operating characteristic's area under the curve (AUC) and accuracy (ACC), both bounded in [0, 1]. Elevated metric values correspond to enhanced diagnostic capability, signifying superior model performance.  
From Table 2, experimental results across three benchmark datasets (ASSIST2012, ASSISTChall, and EdNet-KT1) demonstrate the consistent superiority of the proposed HGKT model over all baseline methods, achieving state-of-the-art performance in both AUC and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**ASSIST2012**} & \multicolumn{2}{c}{**ASSISTChall**} & \multicolumn{2}{c}{**EdNet-KT1**} \\  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline DKT & 0.72430 & 0.71265 & 0.72185 & 0.71963 & 0.68632 & 0.68903 \\ DKT+ & 0.72510 & 0.71312 & 0.72210 & 0.71352 & 0.64254 & 0.67352 \\ DKVMN & 0.71488 & 0.72415 & 0.70630 & 0.70198 & 0.67086 & 0.68231 \\ SAKT & 0.72213 & 0.72750 & 0.78743 & 0.74150 & 0.68041 & 0.68652 \\ AKT & 0.77814 & 0.75352 & 0.75621 & 0.71063 & 0.75827 & 0.71024 \\ LPKT & 0.77189 & 0.75936 & 0.79837 & 0.73774 & 0.77324 & 0.72026 \\ HGKT & 0.78812 & 0.76087 & 0.81305 & 0.74533 & 0.78372 & 0.72074 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of comparison of models on student performance prediction.  
ACC. Specifically, HGKT improves the AUC by 1.8% compared to the previous best model, LPKT. Its effectiveness is particularly notable on the ASSISTChall dataset, characterized by the longest learning sequences, indicating enhanced adaptability to extended learning contexts. Furthermore, from Figure 4, HGKT achieves superior AUC and ACC performance on the largest dataset, EdNet-KT1, highlighting its robustness in complex learning scenarios. The model significantly outperforms traditional LSTM-based approaches (e.g., yielding an average AUC gain of 11.879% over DKT). These results collectively affirm HGKT's effectiveness and adaptability for diverse knowledge tracing tasks.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 3 Overview of the SWARMs Architecture
### The SWARMs Architecture
#### 3.1.2 The Publish/Subscription Manager Component  
The Publish/Subscription Manager is a DDS-based component responsible to provide reliable non-blocking communications between the middleware and the underwater vehicles. It uses a publish/subscribe paradigm for data communications, generating and managing a collection of DDS topics. A detailed description of how DDS communications are managed in SWARMs is given at [21].
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 2 Materials and Methods
### Analysis and Characterization  
The fluoride content in the solution was measured using the fluoride ion-selective electrode method (GB 7484-87), employing the 232-01 reference electrode and the PF-2-01 fluoride ion electrode from Leici. The pore size distribution and specific surface area of Al-S957 were determined through nitrogen adsorption-desorption tests performed using a microporosity analyzer (BET, Micromerities 3 Flex; Micromerities Instrument Corporation, Norcross, GA, USA). The surface morphology of the material was analyzed through scanning electron microscopy (SEM, Hitachi Regulus 8100; Hitachi Limited, Tokyo, Japan). The aluminum content loaded onto the resin was quantified using inductively coupled plasma emission spectrometry (ICP-OES, Thermo Fisher iCAP PRO; Thermo Fisher Scientific, Waltham, MA, USA). X-ray photoelectron spectroscopy (XPS, Thermo Scientific K-Alpha; Thermo Fisher Scientific, Waltham, MA, USA) and Fourier transform infrared spectroscopy (FTIR, Thermo Fisher Scientific Nicolet iS20; Thermo Fisher Scientific, Waltham, MA, USA) were utilized to examine chemical bonding and functional group species before and after fluoride adsorption.