esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.  
Keywords:cybersecurity; intrusion detection system (IDS); long short-term memory (LSTM); data imbalance +
Footnote †: journal: Applied Sciences
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 3 Proposed Method
### Optimization
Note that \(\mathbf{F}^{v}\) depends on \(\mathbf{W}^{v}\), and the iterative process is applied until convergence to obtain the global optimum. In general, the computational workflow of the proposed methodology is formally outlined in Algorithm 1. ```
0:
1: Initialize \(\mathbf{F}^{v}\), \(\mathbf{I}^{v}\),\(\mathbf{M}_{\text{sum}}\), \(\mathbf{M}\). 2:for each view \(v=1\) to \(V\)do
3: Generate basic partition \(\mathbf{P}^{v}\);
4: Compute the co-affinity matrix \(\mathbf{M}^{v}\) and accumulate: \(M_{\text{sum}}+=\mathbf{M}^{v}\);
5:endfor
6: Compute the global consensus matrix \(\mathbf{M}=\frac{M_{\text{sum}}}{\text{max}}\);
7: Calculate the consensus representation matrix \(\mathbf{Y}^{*}=E^{*}(\mathbf{\Lambda}^{*})^{\frac{1}{2}}\) through spectral decomposition;
8:for each view \(v=1\) to \(V\)do
9:repeat
10: Update \(W^{v}=\left(\left(X^{v}\right)^{T}X^{v}+\alpha F^{v}+\sigma I^{v}\right)^{-1} (X^{v})^{T}Y^{*}\);
11: Update \(\mathbf{F}^{v}=diag(\frac{1}{2||\mathbf{W}_{1}^{v}||_{2}+\mu},\frac{1}{2|| \mathbf{W}_{2}^{v}||_{2}+\mu},...,\frac{1}{2||\mathbf{W}_{2}^{v}||_{2}+\mu})\);
12:until\(\mathbf{W}^{v}\) converges
13:endfor
14:Output Rank the features based on \(\|\mathbf{W}_{1}^{v}\|_{2}\) and select the top \(r\) most discriminative features. ```  
**Algorithm 1** Consensus Guided Multi-view Unsupervised Feature Selection with Hybrid Regularization (CGMvFS)
# Microstructural Evaluation of Dental Implant Success Using Micro-CT: A Comprehensive Review
## 2 Principles and Applications of Micro-CT in Dental Implant Evaluation
A reduction in the source and detector distances (SD and DD) allows for the attainment of higher photon counts, thereby reducing the overall time required for the process (Figure 2). More sophisticated devices employ microscope objectives to achieve enhanced magnification and resolution. It is essential that the settings fulfill the conditions set out in Equation (1) [29]. \[PS\geq SS\cdot\frac{DD}{SD} \tag{1}\]  
While micro-CT offers the benefit of visualizing solid structures, such as bone, without the necessity for special preparation, it is not without limitations in terms of contrasting soft tissues. The use of contrast agents can facilitate the overcoming of these limitations; however, the specific choice of contrast agent is contingent upon the characteristics of the sample and the constraints associated with its utilization [30]. Micro-CT scanning can be a rapid alternative to other techniques, such as histology. However, the duration of the scan may vary depending on the specific scan parameters employed [31]. It is  
Figure 1: Schematic illustration of micro-CT system [28]. Figure 2: For optimal settings of the micro-CT scan, the spot size (SS) and the awaited pixel size (PS) are the parameters that set the source distance (SD) and detector distance (DD). possible that this may affect the radiation dose, which must be considered with particular attention in longitudinal studies. Nevertheless, it is not believed that X-ray radiation causes damage to the specimen's DNA. Nevertheless, the utilization of synchrotron tomography can circumvent this limitation, facilitating the generation of volumetric data sets with sub-micron resolution in a relatively short time frame [20]. Furthermore, micro-CT techniques are unable to accurately represent the original color of the sample. However, recent advancements have enabled the digitization of internal structures and natural surface colors [32; 33]. Additionally, the dimensions of the structure are a significant consideration, as structures below a specified threshold are not discernible by micro-CT [27]. The long-term storage of micro-CT data is also a significant challenge due to the considerable data volume involved. In conclusion, micro-CT is a powerful imaging technique that offers the advantages of non-destructive capabilities, high speed, and high resolution. The combination of micro-CT with other imaging techniques may prove an effective means of overcoming any potential shortcomings [20]. In the field of dental implant assessment, micro-CT provides a range of substantial benefits over traditional imaging techniques, including the following [27; 34; 35; 36; 37]:  
1. Sub-micron resolution;
2. Non-destructive analysis;
3. Three-dimensional visualization;
4. Accurate quantification;
5. Bone-implant interface analysis;
6. Integration with advanced analyses, such as finite element analysis (FEA).
## 2 Results
### Method Validation  
When the best SPME conditions were selected, the method was validated with respect to limits of detection and quantification, linearity range, and recovery. Namely, 10 mL of 2.5% ethanolic solution was placed in 20 mL vial along with 2 g NaCl and 0.1 g EDTA, then 10 \(\upmu\)L of each standard working solution and internal standard (IS) (final concentration of IS was 0.09 \(\upmu\)g/L) was added to each vial. Samples were extracted using DVB/CAR/PDMS fiber at 35 \({}^{\circ}\)C for 30 min. Analyses were performed in three replicates, and the obtained results are presented in Table 3. Calibration curves were prepared in the range from approximately 0.08 to 80 \(\upmu\)g/L, depending on the analyte, by plotting the response from GC-MS against its concentration. Limit of quantification (LOQ) was established based on the standard deviation of the response and the slope of the calibration curve. Limit of detection (LOD) was calculated as one third of LOQ. Both LOQ and LOD were calculated with use of Agilent MassHunter Workstation Software Quantitative Analysis. For most of the studied compounds, the regression coefficient was above 0.99, with the exception of 1-propanethiol, 1-butanethiol, and 1-penthanethiol. For these compounds, the linearity was poorer (0.93-0.98). The calculated values of LOD and LOQ were in range of 0.001-0.171 \(\upmu\)g/L and 0.002-0.569 \(\upmu\)g/L, respectively. These concentrations are in most cases low enough to calculate the concentration of VSCs in real samples. The recovery experiments were carried out by spiking the analytes to real samples (pear, plum, and apple brandy, each in triplicate) and further extraction under optimized conditions. The value of recovery percentage was calculated from the difference between the concentration of the analyte in the spiked and nonspiked samples divided by the added concentration of each compound [35]. The results (Table 3) shows satisfactory accuracy (recovery in range of 90-110%) for most compounds. Higher error was obtained in the cases of 1-propanethiol (75-176%), 1-butanethiol (87-133%), and 1-pentanethiol (86-129%)--the same compounds that were characterized by poorer linearity. Additionally, the recovery percentage of dimethyl sulfide was unsatisfactory (78-107%), as this compound was found to undergo the chemical oxidation to dimethyl sulfoxide under adverse analysis conditions [1]. Moreover, for thiophenol and benzothiazole, the recovery rate was always higher than 100%.
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 5 Conclusions and Discussions  
In this article, we studied the trust-based service delegation problem in SIoT. Considering the bidirectionality of trust, we design a framework of the trust model and service delegation. On this basis, we propose a bidirectional trust evaluation method based on subjective logic. We have shown that by using this formulation, the SR and SP can quantitatively evaluate the trust of each other in a reasonable way. In addition, we consider the context of the task to ensure the feasibility of our model in the SIoT scenario. The task similarity and time window are presented for the calculation of evidence strength. The convergence operators including discounting and consensus operator are constructed for compositive trust quantification. The decision-making approach of the service delegation with comprehensive consideration of trust and utility is proposed to ensure the success of the task while improving the utility of the SR.  
However, the current work is in infancy. First, considering the computational complexity, the proposed model simplifies the condition setting to a certain extent. The evidence composition in evidence space only includes service attributes, bidirectional service evaluation information, service time, etc., without considering the relationships between device characteristics of IoT objects and service properties. Therefore, our proposed model is more suitable for the scenarios where the degree of heterogeneity and differentiation of IoT devices is low. The evidence-based descriptions of the characteristics of IoT devices and the relationship between these evidence-based descriptions and opinions will be our important future work. Second, with the development of the Internet of Things, some new architectures, such as multiple internets of things, are proposed. Therefore, we will further evaluate whether our model can be feasible and adaptive for various paradigms [39; 40; 41]. Moreover, we plan to extend this model and configure a real-world application scenario in order to make it more capable. The task simulations at different network scales will be carried out in the following research process to validate the effectiveness and practicability of our trust model and service delegation method. Furthermore, testing under different attack environments will be also further provided.  
Conceptualization, L.W., C.L. and Y.-B.L.; Methodology, L.W. and J.W.; Software, L.W. and Y.Y.; Validation, L.W., Y.Y., J.W., C.L. and Y.-B.L.; Formal Analysis, L.W. and C.L.; Investigation, L.W., Y.Y. and J.W.; Writing--original Draft Preparation, L.W., Y.Y. and Y.-B.L.; Writing--review and Editing, L.W., J.W. and Y.-B.L.; Supervision, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grants 62136006, 62073215 and 61873166.  
Not applicable.  
The authors declare no conflict of interest.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 5 User Study 1: Fitts' Law
### Discussion  
Similar to the performance of the traditional mouse in VR [6; 11], the finger mouse produced a higher throughput (Figure 5) and faster movement times (Figure 6) compared to the controller and the Digital Thimble. The throughput and movement times were relatively comparable between the Digital Thimble and the controller. However, as participants were less experienced with both the controllers and, particularly, the Digital Thimble, it is possible that their performance while using these devices could improve with practice. A longitudinal study is necessary to fully assess the effects of practice on throughput and movement times. The Digital Thimble demonstrated better precision (Figure 7) and accuracy (Figure 10) than both the controller and the finger mouse. This could be attributed to the Digital Thimble's form factor, which integrates seamlessly with the user's finger and does not require the use of an additional finger to pull a trigger or press buttons, unlike the controller or the finger mouse.  
The on-press selection method consistently outperformed the on-release method in terms of throughput (Figure 5) and movement times (Figure 6) across all devices. This is likely because the on-release method requires an additional action (e.g., pressing the left mouse button or the controller trigger) while simultaneously moving the cursor. The effect of the on-release method was more adverse with the Digital Thimble, likely due to the friction caused by the device while applying extra pressure during cursor movement. Interestingly, although the controller outperformed the Digital Thimble with both selection methods, the difference was not significant when using the on-press method. This suggests that with sufficient practice, the Digital Thimble using the on-press method could potentially match the performance of the controller.  
Additionally, the on-release selection method led to fewer target re-entries than the on-press method (Figure 7), enhancing users' precision. This is likely due to the speed-accuracy trade-off effect, where users made fewer errors and achieved greater precision due to the slower nature of the method.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 3 Materials and Methods
### Proposed Method
#### 3.2.3 Soft Voting  
Soft voting estimates the class probability with different algorithms having contrasting approaches to improve the accuracy of prediction. It assigns a larger weight to the important classifier, and the highest category is selected by summing the probabilities  
Figure 4: The proposed stacking model.  
Figure 3: Histograms for data distributions of 4 features.  
predicted by the model. The soft-voting classifier prediction is mathematically represented as follows:  
\[\hat{y}=\underset{i}{\text{argmax}}\underset{i}{\text{max}}\underset{j=1}{\text{ }\overset{m}{\text{ }}}w_{j}p_{ij} \tag{2}\]  
where \(\underset{i}{\text{argmax}}\) function is the function that outputs a maximum, and \(w_{j}\) represents the weight associated with the classifier prediction; \(p_{ij}\) is the probability of the classifier in predicting a certain class. In the process of assigning weights for soft voting, high-confidence models are given more weight based on the importance and accuracy of the classifier [26].  
The prediction ensemble system in this study uses soft voting to obtain the final prediction results. Soft voting usually requires different classifiers to compensate for their drawbacks. Based on the accuracy and algorithmic differences, this study chose LR, DT, NBC algorithms as level 2 classifiers, and the results of level 2 classifiers with different weights are used for soft voting.
## 2 Materials and Methods--Porosity Classification and Fracture Identification
### Pixel-Wise k-IN Approach to Determine Rock Fractures and Pores
#### 2.4.6 Shape Variance Using Length and Width of \(\mathcal{H}_{i}\)  
Apart from the above two measures, we also computed the ratio between the length and width of \(\mathcal{H}_{i}\). Here, the length is assumed to be the major axis of \(\mathcal{H}_{i}\). The major axis of \(\mathcal{H}_{i}\) is the longest straight line drawn through \(\mathcal{H}_{i}\) joining the endpoints (\(x_{i,1}\), \(y_{i,1}\)) and (\(x_{i,2}\), \(y_{i,2}\)) and its length can be computed as \(\operatorname{major}(\mathcal{H}_{i})=\sqrt{(x_{i,1}-x_{i,2})^{2}+(y_{i1}-y_{i 2})^{2}}\). The angle between the major axis of \(\mathcal{H}_{i}\) and the \(x\)-axis (also known as the orientation of \(\mathcal{H}_{i}\) has been computed as \(\operatorname{major}_{\text{angle}}(\mathcal{H}_{i})=tan^{-1}\big{(}\frac{y_{i,2}-y_{i,1}}{x_{i,2}-x_{i,1}}\big{)}\). Similarly, the width of \(\mathcal{H}_{i}\) has been assumed to be its minor axis, which is the longest line drawn through \(\mathcal{H}_{i}\) joining its two endpoints (\(x_{i,3}\), \(y_{i,3}\)) and (\(x_{i,4}\), \(y_{i,4}\)) and perpendicular to the major axis. Its length is computed as \(\operatorname{minor}(\mathcal{H}_{i})=\sqrt{(x_{i,3}-x_{i,4})^{2}+(y_{i,3}-y_{i,4})^{2}}\). The major and minor axes endpoints are found by computing the pixel distance between every combination of border pixels in the object boundary and finding the pair with the maximum length, where the two straight lines are perpendicular to each other. Since the shapes of the pores are assumed to be spherical or near-spherical, we restrict the range of \(\frac{\operatorname{major}\left(\mathcal{H}_{i}\right)}{\operatorname{minor }\left(\mathcal{H}_{i}\right)}\) between \([1,1.3]\) (\(\alpha=1,\ \beta=1.3\)), whereas for fractures, the ratio has been kept within (1.3, oo) in the current context. To reiterate, the authors encourage the readers to modify the regions based on their problem statements, the porosity of the rock sample, and its other necessary geophysical properties.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 1 Introduction
Due to these advantages, QFG transistors have found widespread use in the design of amplifiers [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30] but also in high-performance current mirrors [31; 32; 33; 34; 35], mixers [36; 37], linearization of active resistors [38; 39], DC offset cancellation servo loop circuits [40], bootstrapped switches [5], current conveyors [41; 42; 30; 43], Voltage Feedback Operational Amplifiers (VFOAs) [44], Current Feedback Operational Amplifiers (CFOAs) [45], buffers [46; 47; 48; 49; 50; 51], rectifiers [35; 52], capacitance multipliers [35], transconductors [30; 53; 54; 55], digital-based analog circuits [56], DC-DC converters [57], continuous-time filters [58; 59; 60; 61], track and hold circuits [5], data converters [5; 62], capacitive sensing interfaces [63], particle detectors [23] and retinal prosthesis [64], to name some representative applications. The paper is organized as follows: Section 2 provides an overview of QFG transistors and their application to the design of amplifiers. Section 3 reviews various reported amplifier topologies based on QFG techniques. A new energy-efficient class AB amplifier designed and fabricated in a 130 nm CMOS process is described in Section 4. A discussion on the results is provided in Section 5, and some conclusions are drawn in Section 6.
## 4 Results and Discussion
### Residual Stress Distribution Around Actual Inclusions
In addition, \(\theta_{\text{sv},i}\), inclusion type also plays a significant role in the effect on local residual stress variation with \(\theta_{\text{sv},i}\). For the convex part of inclusion, the local residual stress of MnS inclusion increases the fastest with \(\theta_{\text{sv},i}\) among these four types of inclusions. When there is a convex angle of 25\({}^{\circ}\) on the inclusion surface, the local residual stress will increase about 1.64 times for MnS, 1 time for Al-Ca-O-S, 0.75 times for Mg-Al-O, and 0.53 times for TiN. For concave part of inclusion, the local residual stresses of Mg-Al-O and TiN inclusions increase the fastest, followed by MnS and Al-Ca-O-S. When there is the concave angle of 25\({}^{\circ}\) on the inclusion surface, the local residual stress will increase about 0.48 times for Al-Ca-O-S, 0.51 times for MnS, 1.32 times for Mg-Al-O, and 1.25 times for TiN. Although the local residual stress increases the fastest for convex part of MnS inclusion, the concentrated residual stress on the steel matrix side still does not exceed the other three types of inclusion under the same value of \(\theta_{\text{sv},i}\). However, for the concave part of inclusion, large \(\theta_{\text{sv},i}\) on the surface of TiN inclusion will lead to extremely large stress concentration on the inclusion side. This is also the reason that TiN inclusion itself often cracks in steel. These cracks inside inclusions can extend into steel matrix during the service of steel, causing failure of component [4]. Therefore, to obtain better performance it is important to avoid sharp edges of TiN inclusion. To clarify the effect of the shape on the maximum residual stress around different types of inclusions, the maximum residual stresses of perfect spherical inclusions and inclusions with actual shape are shown in Figure 9. The maximum residual stresses of perfect spherical inclusions of all types are obviously smaller than the cases considering inclusion shapes. When we assume the inclusion shape as a perfect sphere for simplicity, there will be a non-negligible error of around 28.0 to 48.0%.
## 1 Introduction
### Influence of Noise  
The assessment of the performance of CI users in noise has become of great interest as it is considered to be representative of daily listening conditions.  
In noise, the natural gaps in speech are filled and speech envelopes are distorted making speech recognition more difficult. The CP coding strategies may select noise-dominated channels, instead of the dominant speech channels, at low signal-to-noise ratios (SNRs) [13]. Unlike the CP strategies, FC strategies transmit the information of all available channels leaving the task of selecting the informational cues to the auditory system.  
The presence of noise reduces the effective dynamic range for CI users by compressing the region of audibility into the upper position of the dynamic range [14]. Good speech perception in noise is a target in the management of deafness [15, 16, 17, 18] and this aspect is also of great importance when CI coding strategies are concerned. Thus, tests in noise are more sensitive to changes in the fitting parameters and more ecological than tests in quiet conditions.