esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Validation and Result Analysis
### Security Resource Scheduling Optimization Implementation
#### 4.3.2 Analysis of Implementation Results  
The implementation results of this study demonstrate that the dynamic scheduling model exhibits high adaptability and responsiveness. Based on forecasts of varying passenger flows across different time periods, the model can flexibly adjust the number of security screening lanes. During peak periods such as morning and evening rush hours, the model increases the number of lanes to reduce passenger waiting times and enhance overall operational efficiency. Conversely, during low-flow periods such as late night or early morning, the model decreases the number of lanes to avoid resource wastage.  
Quantitative comparisons performed using metrics such as average waiting time and total human-hours highlight the advantages of the proposed approach. Plan B, in particular, shows significant improvements, achieving a 10-20% reduction in average passenger waiting time during peak hours and a 5% reduction in total operational costs compared to fixed-shift and static scheduling methods. These findings underscore the model's ability to balance efficiency and cost-effectiveness under varying operational conditions.  
Sensitivity analysis reveals the impact of key parameters on the model's performance. Adjustments to factors such as passenger flow prediction accuracy and scheduling intervals demonstrate that extending adjustment intervals can effectively reduce the frequency of lane reconfigurations while maintaining optimal efficiency. This approach minimizes operational disruptions and ensures consistent service quality.  
The model also proves effective in handling specific scenarios such as holiday travel peaks, extreme weather conditions, or emergency evacuations. By setting upper and lower limits on security lane numbers, the system is capable of estimating maximum passenger wait times and allocating resources accordingly. This feature ensures both safety and efficiency in unpredictable conditions and supports contingency planning.  
Overall, the dynamic scheduling model demonstrates robust empirical results in optimizing security resource management. It provides high-speed rail stations with an effective, data-driven solution that enhances passenger experience, improves operational efficiency, and aligns with economic sustainability.
# Preparation and Application of ZIF-8 Thin Layers
## 3 Results
### Electrochemical Thin Layer Deposition and Effect of Synthesis Conditions
#### 3.1.4 Electrolyte Composition  
From the literature, it is known that the solvent plays an important role for electrochemical synthesis of MOFs in terms of solubility, ionic conductivity and deprotonation of the linker [30; 32; 38]. Thus, a lot of works studied the effect of different solvents onto the MOF formation, such as methanol and ethanol, especially for HKUST-1 [30; 38]. Only very few works were devoted to investigations of ZIF-8 [38]. We tested the effects of ethanol concentrations in aqueous solutions onto ZIF-8 deposition. According to the theoretical considerations, a reduced water content decreases the conductivity and increases the nucleation rate [30], which should lead to an increasing number of smaller crystals. This would directly affect the layer thickness and would be beneficial for a thin (mono-)layer of electrodeposited ZIF-8. However, all experiments showed a poor particle growth for the ethanol concentrations of 25% (K), 50% (L) and 75% (M) shown in Figure 4. Due to the poor film quality, a more detailed characterization via XRD was not possible, the crystal size differs from unregular shaped particles with an average diameter of 1 \(\upmu\)m (K and M) to more spherical shaped particles with a diameter of 200 nm. The poor deposition and yield are the result of the poor conductivity of the electrolyte and the poor solubility of the conductive salt. Therefore, there were no further investigations with other electrolyte additives.
## 2 Materials and Methods
### Characterization of MIL-88A Carriers
#### 2.2.3 Low-Temperature Nitrogen Sorption  
To evaluate the textural characteristics of the MIL-88A materials, nitrogen adsorption/desorption was conducted at a low temperature of \(-\)196 \({}^{\circ}\)C using the Quantachrome Autosorb IQ apparatus (Anton Paar GmbH, Graz, Austria). The Autosorb iQ apparatus allows for volumetric measurement of gas adsorption and desorption at relative pressure ranging from 0.001 to values slightly below 1.0. Before measurements, the MIL-88A materials were outgassed 12 h under vacuum at 150 \({}^{\circ}\)C. The Brunauer-Emmett-Teller (BET) method was used to calculate the specific surface areas of the samples, and the Barrett-Joyner-Halenda (BJH) method was applied for pore size and volume assessment. The micropore surface area was determined using the t-plot method.
## 4 Performance Validation
The training and validation losses inferred by the QWSA-HDLAR technique on the test UCF Sport dataset are reported in Figure 8. The figure identified that the QWSA-HDLAR algorithm has resulted in lower training and validation loss values. In order to report the enhanced performance of the QWSA-HDLAR technique, a comparison study with recent methodologies [28; 29] is performed on the UCF Sports dataset in Figure 9. The results implied that the AR-DT and LTP-HAR approaches showed poor performance with lower \(accu_{y}\) values of 78.21% and 78.84%, respectively. Figure 8: Training and validation accuracies of QWSA-HDLAR model on KTH dataset. Figure 9: Comparative \(accu_{y}\) analysis of QWSA-HDLAR model on UCF Sport dataset. This is followed by the average two-stream CNN and GMM- KFGRNN algorithms, which reached improved \(accu_{y}\) values of 88.30% and 88.51%, respectively. Though the DTR-DNN and GS-LOF techniques resulted in reasonable \(accu_{y}\) values of 95.83% and 95.54%, the QWSA-HDLAR method obtained a higher \(accu_{y}\) of 99.07%. The detailed results and discussion show that the proposed model has shown effectual performance on HAR over the other models.
# Equivalence between Fuzzy PID Controllers and Conventional PID Controllers
## 2 The Equivalent Fuzzy PID Controller Design
 \tag{13}\]  
Thus, the crisp output \(u(t)\) of the proposed linear FLC is given by  
\[u(t)=\frac{a_{u}}{3a_{e}}e(t)+\frac{a_{u}}{3a_{i}}\int e(t)dt+\frac{a_{u}}{3a_ {d}}\frac{de(t)}{dt}, \tag{14}\]  
which implies a linear PID controller with  
\[K_{P}=\frac{a_{u}}{3a_{e}},\quad K_{I}=\frac{a_{u}}{3a_{i}},\quad\text{and} \quad K_{D}=\frac{a_{u}}{3a_{d}}. \tag{15}\]  
Equation (15) shows that if a FLC design is based on the fuzzy knowledge from Figure 2 and the defuzzification process in Equation (7), then it will yield a linear PID controller and the resulting PID parameters have no relation with \(m\), the number of membership functions, but is strongly correlated to the operating ranges of the control input/output. With the derived important equivalence result, the designer can obtain a FLC design prototype based on a conventional PID controller design. In practical application, a FLC will be finished by digital implementation. When considering a digital PID controllers, the equation for the output \(u[n]\) at each sampling time will be  
\[u[n]=K_{P}e[n]+K_{I}T_{s}{\sum e[n]}+K_{D}\frac{e[n]-e[n-1]}{T_{s}}, \tag{16}\]  
where \(T_{s}\) is the sampling time. In the subsequent section, the performance of the FLC implemented in digital form is verified.
## 1 Introduction
One article [35] deals with the application of the acoustic emission method to control the process of corrosion and cracking in large diameter cast-in-place concrete piles. An acoustic emission signal filter, based on the amplitude and duration of the peak frequency, has been proposed. It is well-documented that joint analysis of acoustic emission signals and fractal dimensioning of coating cracks throughout the corrosion period allows global detection of local corrosion damage of piles, irrespective of sensor location. One study [36] uses probabilistic neural network architecture to diagnose the causes of damage to prestressed cast-in-place concrete piles; it is used to determine the general features of concrete pile damage and their causes. Most of the work related to neural network algorithms is used to solve regression problems in the broad sense--that is, predicting various values, such as predicting the pile load-settlement curve using the properties of the pile [37]; predicting the piles' lateral deflection [38]; predicting the bearing capacity of piles [39]; predicting the skin friction capacity of driven piles embedded in clay [40]; and predicting the pile settlement [41]. For these purposes, the ANN-model is used and requires complex computing power for its implementation. One paper [42] uses a classifier applied to numerical pile integrity tests considering concrete piles; however, to reduce the input data dimension and extract features, it is proposed to use the model obtained by the finite element method. Thus, the hypothesis of this paper can be formulated as follows. An algorithm based on an ANN classifier, its input vector being the characteristic points of the spectrum of the received signal and the output type of the pile defect, can be used as the main tool for the automatic interpretation of the results of pile integrity testing, conducted with the low strain integrity testing method.
## 6 Results and Comparison
### Performance Metrics  
To create more efficient systems, it is essential to evaluate performance metrics, including slices, LUTs, throughput/area, and time. These metrics enable us to measure the effectiveness and efficiency of a design in terms of the utilized hardware resources and the speed of computation. Throughput/area, a valuable metric, is determined by Equation (4) and indicates the number of computations that can be executed per slice. This enables us to evaluate the area efficiency of a design and its capacity to perform computations per unit of hardware. The number of slices and LUTs utilized are crucial metrics in evaluating the hardware resources required for a design. By minimizing these metrics, we can lower the cost of the system and enhance its efficiency. Moreover, time is a critical metric in measuring the speed of computation, typically measured in microseconds. Enhancing the time required for a single PM computation can boost the system's overall efficiency and decrease the time needed to execute complex computations. \[\frac{throughput}{area}=\frac{throughput\left(Q=k\cdot p\,in\,\mu s\right)}{slices} \tag{4}\]  
The simplified form of Equation (4) is represented in Equation (5):  
\[\frac{throughput}{area}=\frac{\frac{10^{6}}{time\left(or\right)latency\left(Q=k \cdot p\,in\,s\right)}}{slices} \tag{5}\]  
The throughput formula, described in Equation (5), calculates the speed of computing one PM \(\left(Q=k\cdot p\,in\,s\right)\) by taking the reciprocal of the time taken. Slices refer to the area utilized on the FPGA device. The BEC curve has two points: \(P\) and \(Q\), representing its start and end points, respectively. The scalar multiplier is \(k\). The term \(10^{6}\) in Equation (5) converts the time (measured in microseconds) to seconds. To compute the time required \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Without-Pipeline** & **Two Stage Pipeline** \\ \hline Initial & 6 & 12 \\ Key Length & 233 & 233 \\ \(13\times(m-1)\) & 3016 & – \\ \(16\times(m-1)\) & – & 3712 \\ Inversion & 1276 & 2563 \\ Total Cycles & 4298 & 6287 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Timing information. for one PM, use Equation (6), and the values are presented in column 6 of Table 4. By optimizing these values, the system's efficiency can be improved, and the computation time can be reduced. \[time\left(or\right)latency=\frac{required\ (CCs)}{operational\ clock\ frequency} \tag{6}\]  
Equation (6) specifies the computation of the required clock cycles (CCs) necessary to perform one PM operation. The values for the required clock cycles are presented in Table 3, while the corresponding operational clock frequency (measured in MHz) is provided in column 3 of Table 4.  
Overall, by considering all of these metrics, we can design more efficient and effective systems that use the available hardware resources in the best possible way, and complete computations more quickly. This can result in significant improvements in performance and efficiency, making our designs more competitive and effective in their respective domains.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Different Methods for Anomaly Score Calculation
#### 2.4.3 Gaussian Mixture Models  
GMMs [35] represent a probabilistic model commonly employed for clustering tasks. The model assumes that the dataset is generated by a mixture of several distinct Gaussian distributions (i.e., multivariate normal distributions). Each cluster is represented by a specific Gaussian distribution, and the entire dataset is assumed to be a random sample drawn from \(k\) Gaussian distributions. Each distribution is characterized by its mean vector \(\mu_{i}\), covariance matrix \(\Sigma_{i}\), and mixing weight \(\pi_{i}\), where \(\pi_{i}\) denotes the proportion of that distribution in the overall dataset. These parameters enable GMMs to flexibly model complex cluster shapes.  
The probability density function (PDF) of a GMM is the weighted sum of its Gaussian components. The formula for the GMM PDF is given by the following:  
\[p(z|Y)=\sum_{i=1}^{k}\pi_{i}N(z|\mu_{i},\Sigma_{i}) \tag{13}\]  
where \(N(z|\mu_{i},\Sigma_{i})\) represents the multivariate normal distribution of the \(i\)-th Gaussian component, which gives the probability density of the data point \(z\) under that distribution. \(Y=\{\pi_{i},\mu_{i},\Sigma_{i}|i=1,\ldots,k\}\) denotes the parameters of all \(k\) distributions in the mixture. \(\pi_{i},\mu_{i},\Sigma_{i}\) are the mixture coefficient, mean vector, and covariance matrix of the \(i\)-th Gaussian distribution in the GMM, respectively.  
Unlike models based solely on distances, GMM incorporates both distance and directional information through the full covariance matrix, allowing it to model more complex decision boundaries.  
To define the anomaly score, we calculate the negative log-likelihood probability based on the GMM model:  
\[A_{GMM}=-\ln(p(z|Y)) \tag{14}\]  
Figure 3: Visualization of K-Means clustering results for _valve_id_04_ (\(k=4\)).  
Here, \(A_{GMM}\) represents the anomaly score. A higher value indicates a greater likelihood that the data point is an anomaly.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 4 Simulation Results
### MSE Loss  
We first analyze the MSE loss of the proposed CSI-free DNN detector in the 4 \(\times\) 4 GOMIMO system. Figure 3a,b show the MSE losses versus the number of epochs for GOSM  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Room dimension & \(5\,\text{m}\times 5\,\text{m}\times 3\,\text{m}\) \\ Height of receiving plane & 0.85 m \\ Number of LEDs & 4 \\ Semi-angle at half power of LED & \(60^{\circ}\) \\ LED spacing & 2.5 m \\ Gain of optical filter & 0.9 \\ Refractive index of optical lens & 1.5 \\ Half-angle FOV of optical lens & \(72^{\circ}\) \\ Number of PDs & 4 \\ Responsivity of PD & 1 A/W \\ Active area of PD & 1 cm\({}^{2}\) \\ PD spacing & 10 cm \\ Number of activated LEDs, \(N_{a}\) & 2 \\ PAM levels, \(M\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **GOSM** & **GOSMP** \\ \hline Receiver locations & (2.5 m, 2.5 m, 0.85), (0 m, 0 m, 0.85) \\ \hline Number of input nodes & 4 \\ \hline Number of hidden layers & 4 \\ \hline Number of neurons & \(128\times 64\times 32\times 16\) & \(64\times 64\times 64\times 64\) \\ \hline Number of output nodes & 4 & 6 \\ \hline Hidden layer activation & ReLU \\ \hline Output layer activation & Sigmoid \\ \hline Loss function & MSE \\ \hline Optimizer & Adamax \\ \hline Learning rate & 0.01 \(|\) 0.001 & 0.01 \(|\) 0.005 \\ \hline Length of training set & 150,000 \\ \hline Length of validation set & 50,000 \\ \hline Scaling factor & \(1\times 10^{5}\)\(|\)\(2\times 10^{5}\) & \(1\times 10^{5}\)\(|\)\(1\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the DNN detector for GOSM and GOSMP.  
and GOSMP, respectively, where the receiver is located at the center of the receiving plane. As we can see, the MSE loss decreases rapidly with the increase of training epochs for both GOSM and GOSMP. Moreover, the MSE loss is much reduced when a higher training SNR is used, especially for GOSMP. It can be seen from Figure 3a that the MSE loss for GOSM converges quickly with only a few epochs. For GOSMP, as shown in Figure 3b, about 20 epochs are required for the MSE loss to converge. Hence, owing to the use of the minibatch technique, the CSI-free DNN detector only requires a very limited number of epochs for efficient training, indicating that it can be deployed rapidly in practical applications.
## 3 Simulation  
A numerical simulation is conducted in the first place in order to testify the effectiveness of basic the SSA filter on the ASOPS-based femtosecond laser ranging system. To this end, we constructed an arbitrary target trajectory consisting of a motionless state, an accelerated motion and a uniform motion by computer. The motion parameters have been listed in Table 1. Here, a positive velocity is defined as an increase of the distance, while a negative velocity indicates a decrease of distance. The initial distance value of the simulation is set to 200,000 \(\upmu\)m. Suppose the femtosecond laser ranging ideally reproduces the target trajectory; the retrieved target trajectory is shown as the red dashed line in Figure 3a. Here, the time span is 1.495 s, and the measurement sampling rate is 200 Hz (typical for ASOPS-based femtosecond laser ranging), containing 300 distance data points. The inset in Figure 3a shows the enlarged view for State ii, i.e., the transit from State i to State iii. In order to simulate the distance measurement uncertainty, a sequence of 300 Gaussian distributed random numbers with a standard deviation of 9.98 \(\upmu\)m have been added to the ideal distance array. The simulated target trajectory considering measurement uncertainty is shown as the black curve in Figure 3a.  
In the following, the basic SSA filter has been applied to the distance sequence with noise so as to retrieve the real target trajectory. As described in the Methods Section, only the lagged window length matters for effective implementation of an SSA filter. In order to choose an appropriate window length, we process the simulated distance signal under nine different window lengths, which ranges from 10 to 100 pts, corresponding to a time span from 0.05 to 0.5 s. The filtered distance sequence in each simulation is compared with the ideal target trajectory, and the effect of the SSA filter is evaluated by their correlation coefficient and the RMS residual that is simply the standard deviation of the errors obtained by subtracting the filtered distance sequence from the ideal one. The obtained correlation coefficient, as well as the RMS residual at different SSA filter window lengths are shown in Figure 3b. Obviously, the window length of 50 pts (0.25 s) provides the highest correlation coefficient, as well as the minimum RMS residual of 1.1 \(\upmu\)m. The retrieved target distance sequence obtained by SSA filtering at a 50-pts window length is plotted as the blue dotted line in Figure 3a. The result explicitly demonstrates the effectiveness of the basic SSA filter for retrieving the target trajectory from Gaussian white noise. Note that a priori knowledge on the target kinetic behavior is not required.  
Figure 2: Principle of femtosecond laser ranging based on asynchronous optical sampling.
# Adaptive Authentication Protocol Based on Zero-Knowledge Proof
## 3 Zero-Knowledge Authentication Protocols: Advantages and Disadvantages  
The presence of the verifier (V) and the prover (P) is required for authentication using the zero-knowledge protocol. The prover must prove to the verifier that he owns a certain secret without disclosing it during the execution of the protocol. The protocol is built in such a way that the verifier can verify the truth of the prover's answers without knowing its secret [34].  
One of the first ZKAPs was the Fiat-Shamir protocol [35]. The protocol must be executed within 40 rounds to ensure high imitability. The Feige-Fiat-Shamir ZKAP can reduce the time spent on authentication. Increasing the number of questions to 5 bits reduces the number of rounds to 8 [34; 36]. Feige-Fiat-Shamir protocols are used in smart cards [37; 38], the Internet of Things [39], and RFID automatic object identification systems [40; 41] due to their high imitability. In reference [42], it was shown that the use of Feige-Fiat-Shamir ZKAP in IoT devices provides high imitability with the use of a 20-bit key and 20 rounds of authentication.  
The Schnorr ZKAP has a higher authentication speed [43]. This result was achieved by reducing the number of rounds in the protocol. However, this protocol does not provide a minimal authentication time. This is due to the fact that the considered protocols use high prime numbers, of 512 digits and more, to ensure a high level of confidentiality. This leads to an increase in the time spent performing the authentication procedure. This problem can be solved by developing the ZKAP, which uses session keys. This will reduce the number of authentication steps, providing a high level of confidentiality with a lower bit depth for the processed data.
## 2 Background and the Related Work
### Related Works
#### 2.2.2 Security Policy Deployment in SDN  
[PERSON] et al. (2018) introduced Brew, a security policy analysis framework for distributed SDN-based cloud environments. It detects and resolves security policy conflicts across several layers so that implementation and effectiveness is consistent.  
[PERSON] et al. (2018) put forth a new design for an SDN switch that works by discovering users automatically based on their behavior and deployed a personalized network policy. This demonstrates efficient network security management through traffic pattern analysis using k-means clustering for user classification conducted by the SDN controller.  
[PERSON] et al. (2018) introduced \"DEPO\", a framework for evaluating and managing policy deployment in software-defined infrastructure (SDI), utilizing knowledge modeling, data analysis, machine learning, and emulation techniques to assess the impact of service-level policies on SDI orchestration and configuration within test-bed environments.  
[PERSON] et al. (2018) conducted an empirical study that outlines techniques to identify and analyze SDN policy parameters and network topology for enhancing security by preventing adversaries from exploiting data plane vulnerabilities through empirical latency-based approaches and configuration analysis. [PERSON] et al. (2018) introduced \"SIMPLE\", an SDN-based solution facilitating efficient middle-box-specific traffic steering validated through implementation using a POX controller.  
Although the proposed frameworks offered significant contributions to policy analysis, personalized policy deployment, and traffic management in SDN, they lack a comprehensive approach to integrating encryption as policies and addressing challenges with the interception of packets that occurs during transmission in data planes.