esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis of Different Methods
### Computational Complexities of Different Methods  
We assume that \(M\) pulses are transmitted, with each pulse containing one frame of OFDM symbols. The subfragments encompass multiple pulses to demonstrate the computational efficiency of the three techniques. Each subfragment has a length of \(N_{sub}\) and contains \(N_{c}\) frequency points per symbol.  
For intra-frame and inter-frame coherent integration within one subfragment, the computational complexity in complex operations is  
\[\begin{split} C_{a,CI}&=\frac{N_{sub}N_{c}}{2}{\text {log}}_{2}(N_{sub}N_{c})\cdot{\text{I}}_{m,c}+\frac{N_{s}}{2}{\text{log}}_{2}(N _{s})\cdot{\text{I}}_{m,c}\\ &+2N_{sub}N_{c}{\text{log}}_{2}(N_{sub}N_{c})\cdot{\text{I}}_{a,c }+2N_{s}{\text{log}}_{2}(N_{s})\cdot{\text{I}}_{a,c}\end{split} \tag{31}\]  
For comparison, the computational complexities of the noncoherent method across different frames can be expressed as  
\[C_{a,NI} =\text{MI}_{a,c}N_{r}N_{c}N_{p} \tag{32}\]  
where \({\text{I}}_{m,c},{\text{I}}_{a,c}\) represent the complexities of complex addition and complex multiplication.  
Therefore, the complexities of MHI, MI and MTD in OFDM IRCS are as follows:  
\[\begin{split} C_{a,MHI}&=\frac{N_{sub}N_{c}}{2}{ \text{log}}_{2}(N_{sub}N_{c})\cdot{\text{I}}_{m,c}+\frac{N_{s}}{2}{\text{log}} _{2}(N_{s})\cdot{\text{I}}_{m,c}\\ &+2N_{sub}N_{c}{\text{log}}_{2}(N_{sub}N_{c})\cdot{\text{I}}_{a, c}+2N_{s}{\text{log}}_{2}(N_{s})\cdot{\text{I}}_{a,c}\\ &+N_{sub}{\text{I}}_{a,c}N_{r}N_{c}N_{p}\end{split} \tag{33}\]  
\[\begin{split} C_{a,HI}&=\frac{N_{c}}{2}{\text{ log}}_{2}(N_{c})\cdot{\text{I}}_{m,c}+\frac{N_{sy}}{2}{\text{log}}_{2}(N_{sy}) \cdot{\text{I}}_{m,c}\\ &+2N_{c}{\text{log}}_{2}(N_{c})\cdot{\text{I}}_{m,c}+2N_{sy}{ \text{log}}_{2}(N_{sy})\cdot{\text{I}}_{m,c}\\ &+\text{MI}_{a,c}N_{r}N_{c}N_{p}\end{split} \tag{34}\]  
\[\begin{split} C_{a,MTD}&=\frac{N_{c}}{2}{\text{ log}}_{2}(N_{c})\cdot{\text{I}}_{m,c}+\frac{N_{s}}{2}{\text{log}}_{2}(N_{s}) \cdot{\text{I}}_{m,c}\\ &+2N_{c}{\text{log}}_{2}(N_{c})\cdot{\text{I}}_{m,c}+2N_{s}{\text {log}}_{2}(N_{s})\cdot{\text{I}}_{m,c}\end{split} \tag{35}\]Given that both operations require only one machine cycle on advanced digital signal processors, the real multiplication operation \(I_{u,c}\) is considered as one real floating-point operation (Flop) in the analysis presented below. Four real additions, two real multiplications, and two real divisions constitute \(I_{m,c}\). Therefore, defining the computational complexity of the two methods as \(I_{m,c}=3\) Flops, \(I_{u,c}=1\) Flop, the complexity of the proposed method compared to other methods is as shown in Table 1.
## 3 Control Strategies
### Discrete Wavelet Packet Transform  
The DWPT is more efficient compare to the DWT in contrast to the decomposition approach. The decomposition approach in DWPT is determined on the scaling and wavelet coefficients at a specific decomposition level, maintaining uniformity in frequency bands [7; 31].  
DWPT constants for any level \(j\) are achieved from the convolution of the sampled original signal with low pass and high pass filters \(g(n)\) and \(h(n)\) respectively as follows:  
\[s_{j}^{2z}(k)=\sum_{n=-\infty}^{+\infty}g(n)s_{j-1}^{z}(2k-n) \tag{18}\]  
\[s_{j}^{2z+1}(k)=\sum_{n=-\infty}^{+\infty}h(n)s_{j-1}^{z}(2k-n) \tag{19}\]  
Here, \(s_{0}^{0}\) is the actual signal; \(z\) is the node number.  
Basically, the accurate measured values of harmonics are calculated on the selection of mother wavelet. Because of low spectral leakage, a large quantity of filter coefficients delivers more accurate harmonic measurement. However, at the same time, it needs a substantial amount of resources and more computation, which in turn decreases the efficiency of the computational method.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 5 Performance Evaluation
### The Impact of the Number of Mobile Users on Different Algorithms
Figure 7d shows that the throughput increases with the number of mobile users while the rate of increase gradually slows down. This is mainly because the limited resource capacities of edge servers can only deploy a limited number of VNFs and BVNFs. Meanwhile, our proposed algorithm is superior to the other two comparison algorithms. This can be explained by Figure 7b,c. Although there is no significant performance difference in terms of the number of instances in SFC, the average delay of DRPD is less than that of BMPD and SRPD.
# Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage
## 2 Hysteresis Modeling
### Dynamic Linear Sub-Model Identification Based on Least-Squares Algorithm  
To develop an RD hysteresis dynamic linear sub-model that can characterize the P-AS, a sinusoidal swept signal with the frequency of 1-100 Hz is adopted to drive the P-AS. A least-squares algorithm is employed to obtain the dynamic linear sub-model using the MATLAB system identification toolbox, as described in [18]. The dynamic linear sub-model is expressed as follows:  
\[G_{m}(s)=\frac{1.58*10^{4}s+10}{10^{-3}s^{3}+3.2s^{2}+1.58*10^{4}s+10} \tag{3}\]
## 3 Results and Discussion
### Swelling Behaviors and Weight Loss Properties of Scaffolds  
The evaluation of the swelling property of scaffolds produced for neural tissue engineering studies is important because it can offer information related to the normal behavior after implantation. Swelling of the scaffolds in aqueous media weakens some mechanical properties, while changing the pore size, allowing cells to adhere and migrate [42]. The lowest swelling rates were found to be about 40% in pure PLA scaffolds when the five scaffolds (17 PLA, 17 PLA/3 CS, 17 PLA/3 CS/0.01 LEV, 17 PLA/3 CS/0.03 LEV, and 17 PLA/3 CS/0.05 LEV) were compared with each other. In contrast, a swelling ratio noticeably increased after the addition of CS (nearly 200%) at the end of the 7 th day of immersion (Figure 4A). It was observed that CS added to the PLA solutions to a certain extent increases the water absorption potential up to approximately 400% in the literature [43]. As shown in Figure 4A, although it is observed a slight decrease in the swelling potential of levodopa-loaded scaffolds, their water absorption potential is still relatively high. A degradation test evaluates the structures exposed to PBS to dissolve over time, and their mass decreases [44]. The degradable polymers used in neural tissue engineering applications are preferred, thanks to their properties that reduce the need for surgical extraction. The rate and mechanism of degradation of a polymer affect the mechanical properties of the scaffold [45]. The weight-loss rates of the produced scaffolds were examined every 5 days for a month (Figure 4B). The lowest degradation was seen in pure 17 PLA scaffold (nearly %10) at the end of a month. The degradation rates of the 17 PLA/3 CS scaffolds and levodopa-loaded scaffolds, which show maximum swelling capacity, were found to be very high (approximately 30%). It was proven that materials that absorb more water and have better swelling properties show faster weight loss, according to the literature [46].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scaffolds** & **Tensile Strength (MPa)** & **Strain at Break (\%)** \\ \hline
17 PLA & 10.71\(\pm\) 6.22 & 2.20 \(\pm\) 0.86 \\
17 PLA/3 CS & 1.75\(\pm\) 0.14 & 0.93 \(\pm\) 0.70 \\
17 PLA/3 CS/0.01 LEV & 3.23 \(\pm\) 0.89 & 2.56 \(\pm\) 0.02 \\
17 PLA/3 CS/0.03 LEV & 2.64 \(\pm\) 0.44 & 2.23 \(\pm\) 0.06 \\
17 PLA/3 CS/0.05 LEV & 2.42 \(\pm\) 0.73 & 1.77 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tensile testing results of all scaffolds.
# On the Design of a Decision Support System for Robotic Equipment Adoption in Construction Processes
## 3 Development of the Prototype
### Inference Engine
#### 3.3.1 MEU Computation  
By applying the principle of MEU, we choose the decision that yields the highest expected utility [32]. The preferences are captured by the utility function \(U(\mathrm{s})\) (1) that assigns a number to the different states (Table 5). The expected utility (\(EU\)) of a decision (\(d\)), given evidence (\(e\)) can be calculated by averaging the utility of the different outcomes with the probability (\(P\)) that the outcome can be achieved [32]:  
\[EU(d\,|\,\mathrm{e})=\sum_{s^{\prime}}P\big{(}\mathrm{Result}(d)=s^{\prime}|a, \mathrm{e}\big{)}\times U\big{(}s^{\prime}\big{)} \tag{2}\]  
\[decision=argmax_{d}\;EU(d|\,\mathrm{e}) \tag{3}\]  
In Table 6 we see the results of the MEU computation when we consider different weightings of preferences (p, q, or s). For equal weightings (p = q = s) we see that the best rational decision is the collaborative robot (R) with a MEU of 93.24.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 4 Implementation Results and Comparisons
### Comparisons
#### 4.3.2 Comparison to Throughput  
As shown in the last column of Table 3, the highest throughput of 18.867 Mbps is calculated for the 2-stage pipeline architecture of SIMECK32/64, described in [20] on Artix-7 FPGA. Comparatively, on a similar Artix-7 device, our proposed SIMECK32/64 architecture is 1.29 (ratio of 24.390 with 18.867) times more throughput-efficient. The highest throughput in [20] is achieved due to the utilization of a two-stage pipelining, while in our design, the parallel execution of smaller segments of the input message and key length result in higher throughput. Similarly, if we compare the throughput values with the design of [30] on Spartan-3 and Spartan-6 FPGA devices, our architecture results in 3.30 (ratio of 12.500 with 3.787) and 3.42 (ratio of 16.949 with 4.950) times higher throughput. Similarly, our architecture is 4.92 (ratio of 24.390 with 4.950) times more throughput-efficient as compared to the throughput values with SIMECK64/128 design of [31]. The reason for higher throughput in our design is the use of 32-bit message lengths and 64-bit key lengths, while in [30; 31], 64-bit message lengths along with 64-bit key lengths are considered. We utilize smaller messages and key lengths as the objective was to secure the low-resource embedded devices.  
If we compare the throughput values with a design of SIMON [15] (on Spartan-6) and LED [28] (on Spartan-3), our architecture is 14.91 and 5.38 times more throughput-efficient. As shown in Table 3, the throughput comparison to designs of [26; 27] is not attainable due to not having required information in the corresponding implementations.
## 4 Discussion
### Developing a Dynamic System  
A dynamic system capable of updating risk probabilities in real time based on project progress and external influences (e.g., weather conditions or changes in material costs) would significantly enhance the model's practicality and adaptability. Such a system would enable project managers to respond swiftly to emerging risks and optimize decision making throughout the project lifecycle. By integrating real-time monitoring tools, such as sensors, IoT devices, and predictive analytics, a dynamic system could provide continuous updates on key risk indicators. This would allow for proactive adjustments to project plans, resource allocation, and risk mitigation strategies, reducing the likelihood of major disruptions. The implementation of a dynamic system would bridge the gap between static risk models and the dynamic nature of real-world projects, ensuring more effective risk management and project resilience.
## 3 User Study
### Metrics  
#### 3.3.1 Objective Metrics  
To analyze the performance of the different devices for the semantic mapping task, the following metrics were utilized:  
* Three-Dimensional (3D) Intersection Over Union (IoU) (%): The user-generated semantic maps are evaluated using a known ground truth map by examining the volume of the intersection of the 3D bounding boxes of each object over the volume of the union of the bounding boxes. The ground truth map was created by measuring the dimensions of each of the six objects and their position and orientation relative to a fixed start point. Google's Objectron library is used for computing the 3D IoU given the user-generated 3D bounding box and the corresponding ground truth bound box [16].
* Task Completion Time (s): The total time taken for a user to complete the mapping task.
## 3 Proposed Method
### Facial Emotion Description Point Location Based on AAM  
For cognitive emotion recognition, it is necessary to locate and track the emotion description points of a human face. An Active Appearance Model can accurately locate description points using facial shape features and texture features. The steps are divided into emotion description point marking, active appearance modeling, and description point matching.
# XLNet-Based Prediction Model for CVSS Metric Values
## 3 Methods
### Methodology Overview  
Figure 3 depicts this paper's two primary phases: XLNet transfer learning and vulnerability metric value prediction. By employing XLNet transfer learning, a fine-tuned model is developed to predict vulnerability metric values. Metric value prediction is a three-step process, i.e., text tokenization, transfer layer token embedding, and metric value prediction. These steps are shown in Figure 3. First, the vulnerability description content is divided into numerous tokens during the tokenization stage, and then tokens are embedded into the XLNet model. Finally, the softmax function to predict the likelihood \(\hat{y}\) of the vulnerability metric values is used. The remainder of this section will provide a detailed description of the framework.
## 1 Introduction
### Motivation  
Our motivation arises to seek measures to quickly detect and mitigate jamming, as it is one of the most aggressive attacks on energy consumption. Given that cybercrime against companies amounts to a loss of nearly USD 6 trillion per year, and it could cost the world USD 10.5 trillion annually by 2025 [9], the investment by companies to solve jamming has a prospect of growth of 7.9% at a Compound Annual Growth Rate (CAGR) from 2020 to 2025 [10]. We aspire to protect the WSN from jamming by monitoring the values of the WSN metrics. The performance metrics are altered in different ways according to the type of jamming (constant, deceptive, random, and reactive) and the scheme that the network uses to communicate, which can be cooperative (network-centric) or collaborative (node-centric).  
All of the above factors motivate us to propose an algorithm to detect jamming in real time, considering the above-mentioned two factors, especially in _industrial environments_. Since the behavior of the performance metrics varies depending on the different _node densities_, this article carries out two simulations to provide proper data history for the algorithm configuration. An experiment on a WSN with the same characteristics as an IWSN validates an algorithm that recognizes whether the network is under attack and reveals both the kind of jamming which is attacking the network. This learned information serves to improve the response to future attacks. With this algorithm, the foundations are laid for future works for the mitigation of jamming, its impact on energy consumption, and the economic benefits it entails to the factory.  
According to the research question, which arises from a need that the industry has, the objective of this work is to propose a jamming detection mechanism for IWSNs based on the performance metrics analysis under collaborative and cooperative schemes. It is desired that the proposed mechanism can be used as a starting point in future work to propose a jamming mitigation method that increases the availability and reliability of the network.  
The structure of this article is as follows: Section 2 describes the related works on jamming detection techniques, metrics, density of nodes and network schemes. Section 3 proposes a jamming detection mechanism and describes the simulation and experiment parameters. Section 4 analyzes the benefits that the collaborative WSN offers during a detected jamming attack on the network. Section 5 discusses the results. Finally, Section 6 concludes this article by making suggestions for future work.
###### Abstract  
Aiming at the problem of fault diagnosis in continuous time systems, a kind of fault diagnosis algorithm based on adaptive nonlinear proportional integral (PI) observer, which can realize the effective fault identification, is studied in this paper. Firstly, the stability and stability conditions of fault diagnosis method based on the PI observer are analyzed, and the upper bound of the fault estimation error is given. Secondly, the fault diagnosis algorithm based on adjustable nonlinear PI observer is designed and constructed, it is analyzed and we proved that the upper bound of fault estimation under this algorithm is better than that of the traditional method. Finally, the L-1011 unmanned aerial vehicle (UAV) is taken as the experimental object for numerical simulation, and the fault diagnosis method based on adaptive observer factor achieves faster response speed and more accurate fault identification results.  
fault diagnosis; nonlinear observer; adaptive algorithm; UAV; parameter estimation Article
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 2 Closed-Loop Stability Analysis  
Consider the following discrete-time system Equation (1) under the influence of parameter uncertainty:  
\[x(k+1)=(\Phi+\Delta\Phi)x(k)+\Gamma u(k)+d(k) \tag{1}\]  
where \(x(k)\in R^{n}\) and \(u(k)\in R^{1}\) stand for the system state and control input, respectively. \(R^{i}\) (\(i=1\) or \(n\), \(n\) is a positive integer) denotes the dimension of the vector. \(\Phi\) and \(\Gamma\) represent the system matrix and vector, respectively. \(\Delta\Phi\) and \(d(k)\) denote the bounded parameter uncertainty and external disturbance, respectively. \(k\) represents the \(k\)-th step in the discrete-time system. It is noted that \(\Delta\Phi\) is coupled with \(x(k)\).
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 2 Related Work on Video Action Recognition Methods
### Methods Based on CNNs  
The existing CNN-based methods tailored for human action recognition mainly have two categories, 2D CNN and 3D CNN approaches. As a typical 2D CNN, TSN [15] leverages a sparse temporal sampling strategy to model long-term temporal dependencies. Meanwhile, Temporal Shift Module (TSM) [7] employs a zero-parameter mechanism that shifts specific channels along the temporal dimension, enhancing the flow of information between consecutive frames.  
In contrast, 3D CNNs inherently possess the capability to directly model temporal information with 3D convolutional kernels. Convolutional 3D (C3D) [8] adopts a straightforward yet effective spatio-temporal feature-learning method, effectively capturing dynamic video content for video action recognition tasks. However, resource-intensive 3D CNNs often entail a substantial number of model parameters, leading to computational burden and training inefficiency. To this, Inflated 3D Convolutional Networks (I3D) [9] extends 2D convolutional kernels into 3D by expanding along the temporal axis, thus enabling the efficient processing of spatio-temporal features within video data. Pseudo-3D Networks (P3D) [28] decompose 3D convolutions into a 2D spatial and 1D temporal convolutions. I3D [9] and P3D [28] successfully mitigate the computational burden while maintaining robust spatio-temporal feature extraction.  
Nevertheless, compared to 3D CNNs, 2D CNNs offer computational efficiency and simpler training pipelines, highlighting a trade-off between temporal modeling accuracy and resource demands.
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 3 Consideration for Design of Medical implants and Related Regulations
Most likely, inductive-based WPT systems mostly operate at a low operating frequency but range at a higher frequency compared to ultrasound-based and reach up to a few hundred MHz. Moreover, it can be concluded that operating frequency for most of the research based on inductive WPT lay below 50 MHz. Furthermore, 13.56 MHz is the interest frequency for the majority of the inductive WPT research field. On the contrary, most of the collected papers' research related to microwave-based WPT located at a higher operating frequency; compared to both ultrasound-based and inductive-based WPT, it ranged from hundreds of MHz to hundreds of GHz. On the other hand, it was noticed that the operating frequency of hybrid wireless power transmission (HWPT) varies from low to high frequency. This is due to the fact that HWPTcomprises a combination of more than one method to transfer power wirelessly, such as inductive-based and capacitive-based. Figure 10a presents the maximum dimensions of the receiver versus the operating frequency. In general, it can be noticed that most of the receivers' dimensions are less than 30 mm for all the WPT-based methods. This is due to the fact that miniaturization of the implanted device is highly recommended in the medical field to simplify the surgery procedures and to be more comfortable for  
Figure 10: A comparison of different approaches. (**a**) Maximum dimension of receiver versus frequency; (**b**) Efficiency versus frequency. the patients. Besides, it is shown in Figure 10a that the receiver dimensions of HWPT have a larger size compared to the other methods of WPT. Moreover, the receiver dimensions for most of the collected research on ultrasound-based and microwave-based WPT are below 20 mm. Additionally, a cluster of biomedical implant receivers using the inductive-based technique have a maximum dimension of less than 20 mm and operate at a considerably lower frequency compared to the microwave-based technique with almost the same size. Figure 10b presents the efficiency percentage versus the operating frequency. In general, it can be interpreted that the maximum power transmission efficiency is achieved via inductive-based WPT, whereas the minimum power transmission efficiency is achieved via HWPT; on the other hand, the maximum efficiencies achieved are 78% and 50% for microwave-based and ultrasound-based WPT, respectively. In the same way, we conclude from Figure 10b that the operating frequency is much higher for microwave-based WPT compared to inductive-based WPT, which has almost the same efficiency.