esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 3 AE-GRU Anomaly Detection Framework
### GRUs (Gated Recurent Units)  
A GRU is a type of recurrent neural network (RNN) designed to mitigate the vanishing gradient problem commonly encountered in traditional RNNs [33]. Introduced by [PERSON] et al. in 2014, GRUs employ gating mechanisms to selectively control information flow, reducing the risk of gradient vanishing or exploding during training [34]. Specifically, GRUs consist of two main components: the update gate and the reset gate (Figure 2). The update gate determines how much of the previous hidden state to retain and how much of the new input to incorporate, while the reset gate determines how much of the previous hidden state to forget and how much of the new input to focus on [35].  
For the input data \(x_{t}=\{x_{1},x_{2},\ldots,x_{n}\}\) and state \(h_{t}=\{h_{1},h_{2},\ldots,h_{m}\}\) vectors at time \(t\), the update gate \(z_{t}\) is computed as [36]:  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}), \tag{4}\]  
where \(W_{z}\) and \(U_{z}\) represent the weight matrix, and \(\sigma\) is the activation sigmoid function. The reset gate \(r_{t}\) in Equation (8) controls how much of the previous hidden state to forget [36].  
\[r_{t}=\sigma(W_{r}x_{t}+U_{z}h_{t-1}+b_{r}), \tag{5}\]  
These gates are combined using element-wise multiplication to determine the updated hidden state. The output of the GRU is then computed based on a combination of the current input and the updated hidden state [36]:  
\[h_{t}=z_{t}\odot h_{t-1}+(1-z_{t})\odot\hat{h}_{t}, \tag{6}\]  
\[\hat{h}_{t}=\tanh(W_{h}x_{t}+U_{h}(h_{t-1}\odot r_{t})), \tag{7}\]  
where \(h_{t}\) is the hidden state at time step \(t\) and \(\hat{h}\) is the candidate hidden state, representing the proposed update to the current hidden state. GRUs typically provide similar capabilities to LSTM units but with fewer parameters, ensuring computational efficiency while effectively capturing the long-term dependencies in sequential data.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### Elements of Molecular Imprinting
#### 5.3.5 Initiators  
The majority of MIPs are prepared by free radical polymerization, photopolymerization and electropolymerization processes. Usually compounds with azo- groups, shown in Figure 7, are widely used as initiators [115]. Among these azobisisobutyronitrile (AIBN) is the most commonly used initiator. Prior to polymerization, removal of the oxygen from the pre-polymerized solution is very important, which is performed by bubbling the solution using an inert gas such as argon or nitrogen.  
Figure 6: Chemical structures of common cross-linkers for MIP: (**a**) dicumyl peroxide (DCP); (**b**) ethylene glycol dimethacrylate (EGDMA); (**c**) divinylbenzene (DVB); (**d**) 1,3-diisopropyl benzene; (**e**) 2,6-biservloylamidopyridine; (**f**) trimethylpropane trimethacrylate (TRIM).  
Figure 7: Common initiator structures used in MIP synthesis: (**a**) azobisisobutyronitrile (AIBN); (**b**) 4-4’-azo(4-cyanovaleric acid) (ACID); (**c**) benzoylperoxide (BPO); (**d**) potassium persulfate (KPS).
## 2 System Model
### Modulation and Demodulation
#### 2.2.2 UHF MIMO RFID Reader Architecture
the \(L\)-th tag antenna, and \(\widetilde{\Gamma}_{L,i}\) indicates the \(i\)-th measured impedance reflection coefficient of the \(L\)-th tag antenna. To reduce errors, measurements are taken beforehand, and then compensated during the estimation. To measure the reflection coefficient offset, the tag was connected to the network analyzer, then, we obtain the value of \(\widetilde{\Gamma}_{L,i}\). The values of the measurements previously taken in our lab are shown in Table 2.  
Decoder  
Now, the maximum likelihood detection technique and Miller decoding are performed. After the CSI by the channel estimation, the reader receiving antenna uses its respective synchronization starting positions to obtain the starting positions of the received UID discrete signals, ready to be decoded by STBC. When the correlation is completed, the decoding result is the UID code (011010), as shown in the following Figure 7: \begin{table}
\begin{tabular}{c c} \hline \hline
**Reflection Coefficient** & **Tag Values** \\ \hline \(\widetilde{\Gamma}_{0,0}\) & \(0.4\angle-131.9^{\circ}\) \\ \hline \(\widetilde{\Gamma}_{0,1}\) & \(0.7\angle 37.69^{\circ}\) \\ \hline \(\widetilde{\Gamma}_{1,0}\) & \(0.35\angle-17.81^{\circ}\) \\ \hline \(\widetilde{\Gamma}_{1,1}\) & \(0.71\angle 130.34^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tag reflection coefficients at 925 MHz. Figure 6: Backscatter constellation.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 4 Results and Discussion
### Model Evaluation  
#### 4.1.1 Model F1-Scores  
Figure 4 demonstrates the performance of each of the models in terms of the F1-score based on the architecture and quantization types used. Note that the F32 model is the base.tflite model without any quantization applied.  
We can see that across the board, there seems to be a drop in the performance of the model as we quantize the model, with the QAT generally performing the worst on the set of testing data. To demonstrate this as a table, we can look at Table 6, which conveys the same information. Percentages highlighted on each row represent the best-performing model.  
Looking at the numbers more specifically from the table, we can more categorically identify the weak performance of QAT models. In many cases, however, the F16 quantized model results are comparable to those of the F32 models. This can be seen in Mobile-ViT_ViT_100 and MobileViT_ViT_175. As one final visual representation, the performance of each model in terms of the F1-score is included in Figure 5 with a bar chart.  
Figure 4: ViT model performances across quantization methods in terms of their F1-scores. Two dashed lines are included to show the minimum and the maximum F1-score values across the 7 different models for easier legibility.  
It is evident that the best-performing model based purely on the F1-score is the MobileViT_V2_175 model, but we will further analyze its performance by considering the size of the models as well. We have set a general bound of 0.9000 on the graph for easier visualization of models that perform well on testing data.
## 3 Experimental Results and Analysis  
### Failure Mode of Specim  
After the surface stripped concrete was treated, the damage characteristics of the five specimens were noted. The failure characteristic diagram of the specimens are shown in Figure 7. The following patterns could be found: (1) At the time of damage, due to the different ranges of the plastic hinge, the corners of the specimens all showed different degrees of crushing and spalling. (2) The W-1, W-2, and W-3 specimens were mainly damaged by bending. The W-4 specimen was mainly damaged by shearing. This difference indicated that for the same size and reinforcement, the choice of ceramic concrete as the load-bearing layer material for the sandwich wall panels is more in line with the seismic design requirements. (3) Compared with the damage to the W-4 specimens, the cracks formed during the damage to the W-1 specimens were denser. Additionally, the crack distribution during the damage to the W-2 specimens with additional fiber was more obvious. This indicated that the wall slab had a better ability to absorb and dissipate the seismic energy when ceramic concrete was used as the structural layer material. (4) The W-5 specimens had the phenomenon of concrete protective layer spalling at the lower abdomen position. The shear damage was more severe. In addition, a comparison of the cracking of the specimen W-5 with the remaining four sandwich wall panels revealed that the sandwich wall panels cracked earlier. Furthermore, additional fibers could improve the premature cracking of the ceramic concrete sandwich wall panels. By comparing the test results of specimen W-1 with W-2 and W-3, it could be seen that the additional fiber volume admixtures of 0.3% and 0.4% could increase the cracking displacement by 42.1% and 36.8%, respectively.  
Figure 7: Failure characteristic diagram of composite wallboard.
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 1 Introduction
The field of view (FOV) of each PIR sensor is modulated by a two degrees of freedom (DOF) segmentation, including bearing segmentation and circle segmentation, which provide the spatio-temporal information of the human target. The sensor nodes are attached to the ceiling; data fusion of adjacent sensor nodes will improve the localization accuracy. The speed of human locomotion could also be acquired. To achieve human activity classification, we propose a two-layer random forest (RF) classifier. Based on the location and moving speed of the human, the first layer of RF will label the activity type for each data frame. To boost the performance of our system, we incorporate prior knowledge of human activities. Because the duration of each kind of activity is a useful feature for activity classification, we employ a finite-state machine (FSM) to record the duration of the same activity for successive data frames. All of the features--including location, speed, and duration--will be input to the second-layer RF for final activity classification. The main contributions of this paper are two-fold:  
1. We propose a scalable framework that can decompose basic individual activities (\"walking\", \"lying\", \"sitting\", \"standing\", and \"transitional activities\") into simple PIR data streams. With relatively low communication burden, our system could be expanded to cover any size indoor environment and fulfill the real-time processing. Non-intrusive PIR sensors are embedded in the indoor environment to achieve ambient intelligence, which can reduce the feeling of obtrusiveness to the minimum. 2. We propose a two-layer RF algorithm that leverages three simple yet powerful features (\"location\", \"speed\", and \"duration of successive activity\") to achieve the recognition of human activities. Our approach is validated using data gathered from a mock apartment to make our results more confident. No human effort is needed to segment the monitored region for different activities.
# A Personalized Learning Path Recommendation Method Incorporating Multi-Algorithm
## 3 Methodology
### Design of Recommendation Algorithm
#### 3.3.2 Adaptability of Learners and Resources  
The fitness of learners and resources mainly refers to whether the recommended resources are suitable for the current learning level of learners. Different from traditional recommendation methods, today's online education environment can recommend suitable learning resources for different learners, that is, realize personalized recommendations. This paper quantifies fitness via learner characteristics to achieve personalized recommendations. According to the learner model constructed in this paper, the learner characteristics included in fitness quantification mainly include learning ability, learning style, and learning intensity. Learning ability mainly measures whether the difficulty of recommended resources is consistent with the current ability level of learners; learning style mainly selects appropriate resource types for learners; and learning intensity ensures that the learning time of the recommended resources is within the tolerance range of learners. Therefore, the specific calculation of the fitness of learners and resources is presented as follows:  
\[G_{f}=\lambda C_{i}\times\frac{1}{1+e^{-(|L_{i}-H_{j}|)(|S_{i}-D_{j}|)}} \tag{19}\]  
where \(G_{f}\) represents the matching degree between learner \(i\) and resource \(j\), \(C_{i}\) represents the learning style of learners, \(\lambda\) is the control parameter, specifically, the ratio of resource \(j\) type to learning style type, namely, \(\lambda=tr_{j}/t_{i}\), where \(t_{i}\) indicates the resource type to which learners with learning style \(C_{i}\) should correspond. The learning resources in this paper mainly include text, video, pictures, hypertext, animation, and tables. \(tr_{j}\) is the type of current resource \(j\). If \(\lambda=1\), resource \(j\) is suitable for learner \(i\); otherwise, it is unsuitable, that is, \(\lambda=0\). These conditions refer to learning intensity. \(H_{j}\) is the learning duration of resource \(j\), \(S_{i}\) is the learning ability, and \(D_{j}\) is the difficulty of the resource, directly obtained from the junior high school mathematics knowledge base.  
In summary, the objective function of personalized learning path recommendation is expressed as:  
\[F=\omega_{1}M_{r}+\omega_{2}G_{f} \tag{20}\]  
where \(F\) represents the objective function, \(\omega_{1}\) and \(\omega_{2}\) are the weights, and \(M_{r}\) and \(G_{f}\) represent the matching degree and fitness, respectively.
## 4 Findings and Discussion
### Discussion  
#### 4.4.1 Computational Complexity Challenge of XAI Implementation  
The computationally demanding and costly operation of SHAP is one of its disadvantages. Additionally, it has been discovered to be open to hostile attacks. In a subsequent study, the authors in [54] seek to assess the degree to which SHAP is susceptible to malicious attacks and look into potential new or improved defenses that might be utilized to enhance its robustness in IoT systems.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 5 Experimental Results
### Robustness Test for Simultaneous Attack  
To test the robustness of the template for signal distortion, we measured the BER when signal distortion and RST distortion occurred simultaneously. We compared the BER when recovering an image with the proposed template and ground-truth.  
Tables 2-4 show the results of BER when Gaussian noise and RST distortion occur simultaneously. As can be seen from these tables, the larger the noise variance, the greater the error in the method with a template compared to the method with ground-truth. At 200 noise variance, the BER is very high in the method with a template. This is because the noise has corrupted the template as well as the watermark signal, and the image has not been correctly recovered. However, considering that the variance of the inserted template noise is less than 10, the noise variance of 200 is very strong, and in practice, such a large amount of noise barely occurs. In addition, there is little difference between the template-recovered method and the ground-truth-recovered method for scaling. This is because the watermark is inserted/decoded at a fixed image size of 512 \(\times\) 512 as mentioned above.  
Figure 9: Robustness results for geometric distortions. (**a**) rotation, (**b**) scaling, (**c**) translation. The method with the proposed template recovery and the method with ground-truth recovery show almost the same performance.
## 5 Conclusions  
In this paper, we propose a Monte-Carlo based reweighted factor selection method so that the SLMS-RL1 algorithm can exploit channel sparsity efficiently. Simulation results are provided to illustrate our findings. First of all, SLMS-RL1 can achieve the lowest MSE performance by selecting the reweighted factor as \(\varepsilon=0.005\) in different SNR regimes. Secondly, the convergence speed of SLMS-RL1 can be reduced by increasing the channel sparsity \(K\). At last, the steady-state MSE performance of SLMS-RL1 does not change considerably under different GMM impulsive-noise strength \(T\). In other words, SLMS-RL1 algorithm using the reweighted factor \(\varepsilon=0.005\) is stable under different GMM impulsive noises.
## 4 Experiments of Impact Source Localization in Stiffened Panel
### Experimental System  
In this paper, the fan-ring shaped high-stiffened panel of sealed bulkhead of manned spacecraft was taken as the research object. The stiffened panel had fan-shaped stiffeners and a rectangular mounting frame. The specific parameters of the panel and stiffener are shown in Table 4. The actual structure of the stiffened panel is shown as Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Angle (\({}^{\circ}\))** & 0 & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 \\ \hline
**Wave velocity (km/s)** & 5.43 & 5.46 & 5.47 & 5.42 & 5.40 & 5.45 & 5.41 & 5.43 & 5.46 & 5.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average wave velocity in each direction.  
Figure 9: The schematic diagram of wave velocity measurement experiment.  
The schematic diagram of the experimental system is shown in Figure 11, which consists of the acoustic emission instrument (Softland Times-DS2-16B), amplifiers (Softland Times-AE Amplifier), sensors (PAC-nano 30), a projectile launcher and the fan-ring shaped stiffened panel.  
The inner diameter was zero in the radial direction, the direction away from the center of the circle was the positive direction, the unit length was 50 mm, the right side of the flat plane side was the circumferential direction 0\({}^{\circ}\), counterclockwise was the positive direction, the unit angle was 1\({}^{\circ}\). The polar coordinate system was established. The three sensors were arranged on the flat side with coordinates S1 (450,29), S2 (100,19) and S3 (450,9) with a maximum distance of 819.6 mm between the sensors. Inside the isosceles triangle formed by the sensors, an impact point was set with the intervals of 50 mm and 2\({}^{\circ}\), for a total of 32 impact points. The distribution of impact points is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension Parameter** & **Value** & **Dimension Parameter** & **Value** \\ \hline Outer diameter (mm) & 2378.8 & Height of stiffener (mm) & 22.0 \\ Inner diameter (mm) & 1910.0 & Thickness of stiffener (mm) & 4.0 \\ Central angle (\({}^{\circ}\)) & 37.8 & Circumferential spacing (\({}^{\circ}\)) & 3.2 \\ Width (mm) & 468.8 & Radial spacing (mm) & 112.0 \\ Thickness (mm) & 3.0 & Material & Aluminum 5A06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of the fan-ring shaped high-stiffened panel.  
Figure 10: The fan-ring shaped high-stiffened panel actual structure diagram: (**a**) flat side; (**b**) stiffened side.  
The experimental system is shown in Figure 13. During the experiment, the steel projectile was launched at the impact point using the launching device to generate the impact signal in turn, and the experiment was repeated three times per point.
## 4 Simulation Experiment
### Simulation Results
The average relative errors and the running time of the two methods are compared in Table 1. As can be seen in Table 1, with different exposure times, the average relative error between the MTFs calculated by the method proposed in this paper and the simulation method are 4.57%, 1.37%, 1.91% and 4.59%, separately. The average relative errors between the MTFs calculated by the former method and the simulation method are 39.71%, 65.29%, 106.6%, and 891.86%, separately. Table 1 also shows the running time of the two methods. Figure 8: The grayscale image of complex motion. \[\mathrm{error_{relative}}=\frac{1}{\mathrm{k}}\sum_{n=1}^{\mathrm{k}}\left| \frac{\mathrm{MTF}_{n}-\mathrm{MTF}_{n}^{\prime}}{\mathrm{MTF}_{n}^{\prime}} \right|\times 100\%, \tag{27}\]0 The simulated MTF and theoretical MTF for sinusoidal motion. As can be seen in Figure 9, the trend of MTF calculated by the proposed method varies with the exposure time, caused by the change of the blur radius of the linear motion and the frequency of the sinusoidal vibration. We can see from Table 1 that the errors between the MTF calculated by Equation (26) and the simulation results increase with the increase of exposure time. We can conclude that, despite the increase in running time, the MTFs calculated by our method, resulting in a less than 5% average relative error, are more similar to the simulation results than the former method.
## 4 Estimation Techniques
### Particle Swarm Optimization (PSO)  
PSO is a swarm intelligence and mobility-based stochastic optimization approach. In PSO, the concept of social interaction is used to address issues. It makes use of a swarm of particles (agents) that move around in the search space looking for the best solution. Each particle in the swarm looks for its positional coordinates in the solution space, which are connected to the best solution that the particle has identified so far. \(P_{best}\), or personal best, is the term for it. Another best value that the PSO keeps track of is \(g_{best}\), or global best. This is the best possible value produced so far by any particle around that particle. In PSO, each particle has a velocity and a position. We need to update the velocities of the particles and move the particles to their new positions. The particles move to their new position according to the following relation:  
\[P_{i}^{t+1}=P_{i}^{t}+v_{i}^{t+1} \tag{55}\]  
The velocities are calculated as:  
\[V_{i}^{t+1}=W\cdot V_{i}^{t}+c_{1}U_{1}^{t}\left(P_{b_{1}}^{t}-P_{i}^{t}\, \right)+c_{2}U_{2}^{t}\big{(}g_{b}^{t}-P_{i}^{t}\big{)} \tag{56}\]  
In (56), \(W\) is the inertia weight and is a positive constant; this parameter is important for balancing the global search. \(V_{i}\) is the velocity of the particle, \(U_{1}\) and \(U_{2}\) are uniformly distributed random numbers; \(c_{1}\) is the cognitive constant and \(c_{2}\) is a social constant, \(P_{b}\) is the personal best, and \(g_{b}\) is the global best. [PERSON] and [PERSON] [43] defined a general rule for the calculation of inertia weight, cognitive, and social constants in terms of constriction coefficients. The constriction coefficient is given as:\[\chi=\frac{2k}{\left|2-\varphi-\sqrt{\varphi^{2}-4\varphi}\right|} \tag{57}\]  
Here, kappa (\(k\)) is constant and \(\varphi\) is also a constant whose value is greater than or equal to 4; \(\varphi\) can be written as:  
\[\varphi=\varphi_{1}+\varphi_{2}\geq 4 \tag{58}\]  
Then, \(W\), \(c_{1}\), and \(c_{2}\) can be calculated as:  
\[W=\chi\]  
\[c_{1}=\chi\varphi_{1}\]  
\[c_{2}=\chi\varphi_{2}\]  
Similar to GA, PSO is also very popular and used in several optimization problems such as antenna array synthesis, modulation, and demodulation in digital communication, resource allocation in networking, etc.