esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning
## 4 Experimental Results
### Experimental Results on UTD-MHAD Dataset  
UTD-MHAD dataset contains a total of 27 action categories. We classify a subset with 7 action categories. If only the 3D space-time convolutional neural network is applied to the depth map sequence, the classification accuracy is 85.7%. While using the fusion framework proposed in this paper, the classification accuracy jumps to 95.34%, which is obviously improved compared with the result without fusion. This verifies the effectiveness of our fusion framework. Our method can extract more discriminative features from both global structure/motion information and local detail features from the actions.  
The confusion matrix of our proposed method is shown in Figure 6. From Figure 6 it can be concluded that our proposed method correctly identifies most of the actions. Some similar actions such as \"right arm swipe to the left\" and \"right arm swipe to the right\" can still be well separated. This again verifies the powerfulness of our proposed feature fusion scheme.
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 1 Introduction
### Research Queries  
The following research questions (RQ) will be investigated to attain the study purpose:  
**RQ.1:**_Which ensemble algorithms perform best for detecting Python-specific code smells, considering structural and syntactic differences from Java?_  
**Motivation:** This study investigates two method-level Python code smell datasets, LC and LM, originally introduced by [PERSON] et al. [3]. While their work applied six individual ML models, it did not explore the potential of ensemble learning techniques, which are known for improving generalization and robustness. Furthermore, Python's dynamic typing, lack of explicit access modifiers, and indentation-sensitive syntax present unique detection challenges not encountered in statically typed languages like Java. To address this gap, our study evaluates five ensemble classifiers, aiming to enhance predictive performance and provide a more robust benchmark for Python code smell detection.  
**RQ.2:**_Does chi-square-FST improve model performance in Python code smell detection, given Python's unique metric distribution influenced by dynamic typing and indentation-based structure?_  
**Motivation:** Prior research by [PERSON] et al. [3] demonstrated improved classification accuracy on Python code smells through Gain ratio-based feature selection. However, the effectiveness of other filter-based FSTs, such as the widely used chi-square technique, remains unexplored in this context. Python code metrics differ significantly in structure and distribution from those in Java due to the language's dynamic characteristics. This study investigates the impact of chi-square FST on ensemble models across the LC and LM datasets. It compares results with and without FST to determine its contribution to detection accuracy.  
**RQ.3:**_How does class imbalance in Python smell datasets influence model accuracy, and does SMOTE remain effective compared to its performance on Java datasets?_  
**Motivation:** Both the LC and LM datasets used in this study are highly imbalanced, containing 1000 instances each, with only 200 labeled as smelly and 800 as non-smelly. Class imbalance can significantly hinder classifier performance, especially in minority class detection. While the SMOTE method has shown effectiveness in prior Java-based code smell studies, its impact on Python datasets remains to be evaluated. This study applies SMOTE to the Python smell datasets and empirically assesses whether it improves model performance under these skewed distributions.
# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 2 Background
### Data Preparation
The data for the algorithm development were randomly split into three subsets: training, testing, and validation. During training, 70% of the data were used to train the model, while 10% served as the testing data to evaluate and optimise hyperparameters, helping to find the best combination and reduce overfitting. The remaining 20% was set aside as validation data for a final, unbiased evaluation of the algorithm's performance. To assess whether each class had sufficient data to train the diagnostic algorithm using these 11 features, the training dataset (comprising 10,938 instances) was categorised by class, with the distribution shown by the blue bars in Figure 5. As depicted, Class 1 contained a substantial amount of data. However, as the number of faults increased, the amount of available training data decreased correspondingly. For instance, when all components were in a failure state (Class 32), only 24 instances were available in the training data. In contrast to experimental methods, the simulation model offered an efficient way to generate additional data for classes with fewer data points. To ensure adequate training, a minimum of 200 datasets was deemed necessary per faulty class. The simulation model was rerun for data-deficient classes, introducing random measurement uncertainties (noise) to create 3880 new samples. This noise, primarily originating from atmospheric pressure and sensor readings, followed a Gaussian distribution with distinct standard deviations: atmospheric pressure noise was set to a standard deviation of 500 Pa, based on Met Office data, while each sensor's noise standard deviation varied with pump speed, as derived from  
Figure 4: Feature importance rankings: (**a**) minimum redundancy maximum relevance; (**b**) tree-based method. Figure 5: Distribution of the training data. experimental data. This expanded \"boosted dataset\" contained a total of 19,505 datasets. Consistent with previous splits, 70% of these data (13,650 sets) were designated as new training data, with the class distribution shown in Figure 5 (red bar). This approach highlights the effectiveness of simulation techniques in rapidly generating large, diverse datasets compared to traditional experimental methods.
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
###### Abstract
**Adaptive stacking ensemble models for identifying various severity levels:** To overcome the limitations of single classifiers in predicting highly diverse severity levels, we propose an adaptive stacking ensemble technique that automatically combines multiple single classifiers. Our technique leverages the strengths of each classifier to capture diverse severity patterns and correlations within our dataset, thereby improving prediction performance. To construct the optimal stacking ensemble model, our technique adaptively selects base classifiers with low complexity and high diversity based on the correlation between their predicted outcomes and the performance of the candidate classifiers. We then employ a greedy search algorithm to automatically determine the optimal multi-layer combination of base and meta-classifiers. As a result, our technique represents a novel prediction approach that reduces the risk of misclassification and model bias by automatically combining selected single classifiers into a multilayer stacking ensemble learning model, after evaluating the impact of each classifier. To further improve the model's performance, we utilize missing-value imputation and oversampling techniques. These methods address the inherent challenges of clinical data that were not collected for research purposes, such as data loss due to irregular sampling and imbalanced datasets, which can degrade the performance of predictive models. The remainder of this paper is organized as follows: Section 2 provides a literature review of related work. Section 3 presents an overview and the detailed methods of the proposed technique. Section 4 provides a comprehensive experimental evaluation and discusses our findings. Finally, we conclude the paper in Section 5.
## 4 Aim and Objectives  
The main aim of this work is to review current research on the application of advanced monitoring procedures based on the integration between ground-based non-destructive testing (GB-NDT) methods and satellite-based MT-InSAR techniques, for transport infrastructure monitoring.  
To achieve the above aim, the main objectives are as follows:  
* to identify significant and recent applications in the field and analyse the suitability of medium- and high-resolution SAR data through the satellite-based PSI monitoring technique;
* to identify relevant studies based on the integration of data obtained by the PSI technique with data collected using GB-NDTs, aimed at improving upon the interpretation phase.
## 2 Materials and Methods
### Animal Model of Excisional Wound  
AgNP were tested in vivo in the form of a hydrogel. For this, an aqueous AgNP suspension was mixed with DermosoftTM (2%, \(w/w\)), and under vigorous stirring, Lecigel\({}^{\circledR}\) (2%, \(w/v\)) was added to obtain a hydrogel with 1.5 mg/mL of AgNP. The placebo was made with the described method, using water instead of AgNP. Characteristics of the hydrogel, such as color and odor, were tested, and the pH was measured at 20 \({}^{\circ}\)C using a 713 pH meter from Metrohm (Filderstadt, Germany). Mice were anesthetized with isoflurane by inhalation. The dorsal skin was shaved and cleansed with PBS at pH 7.4, and a 1 cm line was made using a scalpel. The animals were randomly assigned to three different groups: the first group (\(n=3\)), the negative control, was administered 100 \(\upmu\)L of the placebo gel; the second group (\(n=3\)) was administered 100 \(\upmu\)L (1.5 mg/mL AgNP) of the AgNP gel; and the third group (\(n=3\)), the positive group, was administered 100 \(\upmu\)L of Silvederma\({}^{\circledR}\), a commercial cream of silver sulfradiazine (10 mg/g). These treatments were administered daily from day 0 (after incision) until day 6. Mice were accommodated individually with access to food and water ad libitum. Body weight was recorded from the beginning until the end of the assay. During wounding and each following day, macroscopic wound photographs were taken (D4S model, Nikon, Tokyo, Japan), and the area was measured (ImageJ 1.29 xsoftware (National Institutes of Health, Bethesda, MD, USA)) to obtain a chronological healing profile. At the end of the assay on day 7, the mice were sacrificed. The wounded area was excised and handled for histological analysis. Hematoxylin and Eosin (H&E) staining was performed on a 4 \(\upmu\)m longitudinal tissue area. The assessment was carried out by an independent pathologist.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 5 The Proposed Secure Deduplication Protocol
### Subsequent Data Upload  
In the case of the initial data upload, the data are not yet stored in the cloud storage. This process involves generating the CS's \(ACL\) and the cloud storage's \(CTL\). In contrast, subsequent data uploads involve data already present in the cloud storage. In this scenario, client information needs to be added to both the CS's \(ACL\) and the cloud storage's \(CTL\). Additionally, the cloud storage conducts dynamic ownership updates, which entail refreshing the group key and re-encrypting data when ownership update occurs.  
The detailed process of subsequent uploads is as follows:  
**Step 1. Upload pre-work.**  
* The client \(j\) calculates the hash value \(h(m)\) of the message \(m\).
* The client calculates \(m\oplus h(m)=M\) and a hash value \(h(M)\) to be sent to the CS.
* The client randomly selects \(r_{j}\) to be used to prove oneself.
* The client \(j\) stores \(h(m)\), \(h(M)\), and \(r_{j}\).
* The client \(j\) sends an upload request, \(M\), and \(r_{j}\) to the CS.  
**Step 2. Deduplicate data.**  
* The CS calculates the hash value \(h(M)\) of the received \(M\).
* The CS checks whether \(h(M)\) and \(r_{j}\) exist in the \(ACL\). For subsequent uploads, they are divided into two cases.  
1. The first case is that client \(j\) uploaded the same data before, but the client does not remember it and re-uploads the data. In this case, \(h(M)\) and \(r_{j}\) will exist in the \(ACL\) of the CS. If so, the CS notifies the client \(j\) that the data are already saved.
2. The second case is that information about \(h(M)\) exists in the CS's \(ACL\) but the client \(j\) is not registered as the owner. In this case, an update of the ownership group shall be made. The CS stores the client \(j\)'s random value \(r_{j}\) in the \(ACL\). And, the CS send a group key update request to the cloud storage.  
The process of adding ownership in subsequent uploads is described in Section 5.4.
## 3 Numerical Simulation of Oil Spill
### Simulation of Ice-Floes Drift  
The short-time prediction model of ice-floes movement was based on momentum conservation and mass continuity equations, therein, the critical step was to determine the constitutive relation of the ice internal force. The constitutive relation of the ice floes could be divided into two categories, based on the continuum and discrete medium. According to different ice densities, the viscoplastic constitution was adopted for continuous ice floes, and the collision-type constitutive relation was employed for the edge ice belts with discrete medium properties. Two types of equations were combined for the hydrodynamic model of ice floes, as shown in Equations (16)-(18). Equation (16) presented the momentum balance equation. Equations (17) and (18) presented mass conservation; and \(diffusion\) represented the term of energy diffusion. In the prediction model, the Coriolis force, the ocean surface slope and thermodynamic terms were assumed negligible.  
\[M\frac{\overset{\rightarrow}{U}}{Dt}\ =\ -M\overset{\rightarrow}{f}k\times \overset{\rightarrow}{U}+A\overset{\rightarrow}{\tau_{a}}+A\overset{ \rightarrow}{\tau_{w}}-Mg\cdot gradH+\overset{\rightarrow}{F} \tag{16}\]  
Figure 1: Velocity vector synthesis of oil-spill drift.  
\[\frac{\partial h_{a}}{\partial t}+\overset{\rightarrow}{U}\cdot\
abla h _{a} = \Phi_{h}+diffusion \tag{17}\] \[\frac{\partial A}{\partial t}+\overset{\rightarrow}{U}\cdot\
abla A = \Phi_{A}+diffusion \tag{18}\]
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Control Plane--Tiered Tunnel Automation  
To enable the automated creation of the encryption tunnels, a common control plane is required across the VNF Providers. This is similar to the current use of BGP as a global common control plane for internet traffic routing among Internet Service Providers. Such a control plane for NFV service chained traffic must support the exchange of service capabilities, flow-specific routes, and service chains. In principle, keys and VNF associations must be distributed between every Service Provider contributing to the SFC, in a per-hop > per-NFV service > per-user basis, while sharing such information globally imposes scalability limitations.  
Therefore, the control plane is defined architecturally as a three-tiered control channel with a key set-up mechanism. Figure 7 presents the tiered control channels within the control plane, between the providers A and B of the previous example. This tiered concept follows the IETF SFC [15] standardization and the BGP control plane RFC for NSH [10]. However, it extends the functionality by introducing three tiers of control plane routes that reflect the hierarchy of data plane headers. Next, Tier 1, 2 and 3 functionality is explained.
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model
### Integrated GPS/Galileo Code and Phase Observation Equations
#### 2.2.1 GPS/Galileo Loosely Combined Mode (LCM)  
In GPS/Galileo LCM, one pivot satellite is selected for each system, that is, just as the combination of (3) and (4). Afterwards, the common errors existing in each system can be eliminated by the double difference between the respective systems. In other words, two-times DD processing will be used in the GPS/Galileo combination and the number of observation equations is two less than that of satellites. After further linearization and modularization, (3) and (4) are transformed into:  
\[\left[\begin{array}{c}y^{G}\\ y^{E}\end{array}\right]=\left[\begin{array}{c}B^{G}x\\ B^{G}x+A^{G}N^{G}\\ B^{E}x\\ B^{E}x+A^{E}N^{E}\end{array}\right]+\left[\begin{array}{c}\xi^{G}\\ \xi^{E}\end{array}\right] \tag{5}\]  
where \(y\) is the observation vector consisting of both code and phase observations; \(x\) is the vector that contains the increments of the unknown baseline components and \(B\) is the corresponding transition matrix; \(N\) is the unknown DD integer ambiguity vector and \(A\) is the corresponding transition matrix; and, \(\xi\) denotes the unmodeled code and phase errors.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 3 Reinforcement Learning-Based Routing  
To achieve the above objective function, we define a routing methodology which involves three steps. In the first step, the devices form clusters to decrease the transmission energy. In the second step, route discovery takes place and end-to-end paths are found. Note that routing discovery is dependent on the type of underlying clustering schemes as different clustering schemes generate different network configurations or end-to-end paths. In the last step, based on routing discovery we apply RL to determine transmission paths to improve the EE of the network.  
As discussed earlier, clustering can impact route discovery, therefore, to optimize the route discovery we compare the performance of different clustering schemes.
## 5 Experimental Results and Discussion  
### M-ary Mode Identification  
Investigation is conducted to examine the identification accuracy, using CNN-based classifier, for each individual mode for 8-ary LG, 8-ary Mux-LG, and 16-ary HG formats.  
Figure 6 shows the classification accuracy in percent evaluated at SJR ranging form \(-5\) to \(3\) dB. For LG modulation set, all modes reach 100% accuracy at SJR equals \(0\) dB, except LG\({}_{01}\) which requires \(1\) dB more to reach 100% accuracy. However, for 8-ary Mux-LG and 16-ary HG modulation schemes, all modes can reach a recognition accuracy of 100% at SJR of \(-2\) dB. This shows an improvement of \(3\) dB over the 8-ary LG format. At low SJR (i.e., \(-5\) dB), the mode accuracy reduces to \(\sim\)85%, 96%, and 95% for LG\({}_{03}\), LG\({}_{02\pm 6}\), and HG\({}_{12}\). This can be interpreted from the confusion matrix shown in Figure 7 at \(-5\) dB SJR, where the diagonal values represent the correct classification accuracy ratios for each mode. However, the rest of the matrix values represent the misclassification with the other modes. For instance, the LG\({}_{02}\) provides 87.9% correct accuracy and confuses with LG\({}_{01}\), LG\({}_{03}\), LG\({}_{04}\), and LG\({}_{05}\) with 4.1%, 6.23%, 1.63%, and 0.13%, respectively. It can be observed that the misclassification ratio is large with neighboring modes and decreases as it moves away which is expected owing to the similarity of LG adjacent modes. It can be seen that mode LG\({}_{03}\) is confused with neighbouring modes LG\({}_{02}\) and LG\({}_{04}\). Moreover, in Figure 7b,c, the confusion for 8-ary Mux-LG and 16-ary HG formats is shown at \(-5\) dB SJR. For Mux-LG modulation, the mode LG\({}_{0\pm 6}\) is confused with G\({}_{0\pm 7}\) and G\({}_{0\pm 8}\). Moreover, for HG modulation, mode HG\({}_{12}\) is confused with HG\({}_{13}\) and HG\({}_{22}\).  
Figure 8 shows the average accuracy of 16-ary LG+Mux-LG and 32-ary LG+Mux-LG+HG modulation schemes. It can be noted that the average classification, for both modulation schemes, achieves \(\sim\)92% at \(-5\) dB SJR. Moreover, these modulation schemes need at least \(1\) dB SJR to reach 100% classification accuracy. This can be explained by the high effect of the jammer on the standard LG modulation schemes. Hence, combining the standard LG modes with Mux-LG and HG patterns, to increase data transmission rate, reduces the system performance under jamming threat.  
Figure 6: Individual mode accuracy for (**a**) 8-ary LG, (**b**) 8-ary Mux-LG, and (**c**) 16-ary HG modulation schemes.  
Figure 7: Confusion matrices for (**a**) 8-ary LG, (**b**) 8-ary Mux-LG, and (**c**) 16-ary at SJR of \(-5\) dB.
## 3 Materials and Methods
### Characterization of Copozau Maturation Stages
#### 3.5.3 Carotenoids Analysis by HPLC-DAD  
Carotenoids were extracted from 250 mg of lyophilized copozau pulp using 4 mL of a methanol:formic acid solution (99:1; \(v/v\)). The mixture was ultrasonicated (Bransonic 1510R-MT, Danbury, CT, USA) for 20 min, and subsequently centrifuged (Thermo Electron, IEC Centra CL3, Needham, MA, USA) at 3500 rpm for 10 min, according to a previous report by [PERSON] et al. [71]. The resulting supernatant was evaporated to dryness. A stock solution at 10.000 ppm of the dried extract was prepared in methanol, filtered through a 0.45 \(\upmu\)m PTFE membrane, and subjected to HPLC analysis. HPLC analysis was performed using an (ultrahigh-performance liquid chromatographer Dionex UltiMate 3000 equipped with a diode array detector (DAD). The raw data were acquired and processed using Xcalibur 4.3 software (Thermo Scientific, San Jose, CA, USA). Chromatographic separation was carried out under previously published conditions [72] at 30 \({}^{\circ}\)C. A 10 \(\upmu\)L sample was injected into a YMC-C30 column (150 \(\times\) 4.6 mm i.d., 3 \(\upmu\)m particle size; YMC America, Inc., Devens, MA, USA) protected with a SecurityGuard Cartridge Phenomenex C18 (4 \(\times\) 2 mm, 3 \(\upmu\)m particle size) pre-column. The mobile phase consisted of 400 mg/L of ammonium acetate dissolved in a methanol:methyl tert-butyl ether:water mixture (80:18:2 \(v/v/v\), for solution A and 8:89:3 \(v/v/v\), for solution B). The total run time was 40 min. The DAD recorded absorbances across the entire UV-vis spectrum (240-600 nm), with carotenoids' characteristic absorbances specifically extracted at 450 nm. Experiments were realized with three biological replicates.
## 4 The Proposed DRLEA Approach
### Conventional Methods
#### 4.2.3 Pso  
In this paper, we modify the PSO antenna control method in [24] to reduce the number of low throughput users, as shown in Algorithm 3.  
```
1: Initialize the particles \(X\) and \(V\).
2:for\(i=0\) to \(E\)do
3:for\(k=0\) to \(P\)do
4: Update \(V\): \[\begin{split} V_{i}^{k}=& V_{i}^{k-1}+\omega_{1}*rand 1_{i}^{k}*(\textit{pbest}_{i}^{k}-X_{i}^{k-1})+\\ &+\omega_{2}*rand2_{i}^{k}*(\textit{gbest}_{i}^{k}-X_{i}^{k-1}), \end{split}\] (12)
5: Update \(X\): \[X_{i+1}^{k}=X_{i}^{k}+V_{i}^{k}\] (13)
6: Calculate \(\mathcal{R}\) by Equation (5) according to \(X_{i+1}^{k}\)
7:if\(\mathcal{R}_{i+1}^{k}\geq\mathcal{R}_{i}^{k}\)then
8:\(\textit{pbest}_{i}^{k}\gets X_{i+1}^{k}\)
9:endif
10:if\(\mathcal{R}_{i+1}^{k}\geq\mathcal{R}_{i+1}\) of other particles then
11:\(\textit{gbest}\gets X_{i+1}^{k}\)
12:endif
13:endfor
14:endfor
15:Output the best antenna parameters \(\textit{gbest}\)
```  
**Algorithm 3** PSO based algorithm for HAPS antenna control.  
Where \(X_{i}^{k}\) denotes the antenna parameters of the \(k\)-th particle in the \(i\)-th iteration, \(V_{i}^{k}\) denotes the \(k\)-th particle's velocity (the change of antenna parameters) in the \(i\)-th iteration, \(\textit{pbest}_{i}^{k}\) denotes the best \(X\) of \(k\)-th particle until the \(i\)-th iteration, \(\textit{gbest}\) denotes the best \(X\) of all particles in all the iterations, \(\omega_{1}\) and \(\omega_{2}\) denote two independent learning rates, \(rand_{1}\) and \(rand_{2}\) are two independent random numbers for increasing randomness. The PSO algorithm randomly generates \(P\) particles to search the optimal antenna parameters \(X\) in each iteration and record it into \(\textit{gbest}\). After each iteration, if the \(\textit{gbest}\) of the current iteration is larger than the previous \(\textit{gbest}\), record it into \(\textit{gbest}\) as the optimal solution.