esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Object Detection in High-Resolution Images Using OPAI
### Re-Identification of the Targets  
To determine whether a moving object inside a flexible fluid bag is a foreign particle or an air bubble, continuous tracking allows for multiple observations of the target object, leading to a more stable and accurate assessment.  
To achieve this, each newly observed object from the input frame \(g_{t}\) in every frame is compared with the tracked objects and linked to the correct track by assigning a track ID. The observed object patch images are cropped from the original input frame \(f_{t}\) and registered under the track information. The registered object patch images are from the original-sized input frame because they need to be recognized by the deep learning inference even for small objects.  
Tracking is performed over a sequence of frames, typically around 2 s long. Tracks that are too short, having fewer than three frames of observation, are removed from the list as they are not reliable for judgment. Only tracks of a certain length are retained for further analysis using a deep learning model to determine whether they are foreign particles or air bubbles.  
After 2 s of tracking, all the collected object patch images are aggregated and converted into OPAIs. These OPAIs are then fed into the deep learning model to identify whether the objects are foreign particles or air bubbles. The deep learning model's inference results for each object patch can indicate either an air bubble, a foreign particle, or no detection.  
The judgment for each track is made by grouping the inference results of the object patches within the OPAI by track ID. If the number of patches identified as air bubbles or foreign particles exceeds a specific ratio of the total track length, the track is ultimately judged as containing air bubbles or foreign particles. The decision logic for each track is as follows:  
_If Bubble_Count < Enough_Track_Length:_  
_If there is at least one bubble:_  
_Declare the track is a bubble_  
_Else if there is no bubble:_  
_Declare the track is a particle_  
_Else:_  
_If Bubble_Count/Track_Length < Bubble_Ratio_  
_Declare the track is a particle_  
_Else_  
_Declare the track is a bubble_  
If all tracks within a particular flexible fluid bag are judged to contain only air bubbles, the fluid bag is considered normal. Conversely, if even one track is identified as containing foreign particles, the fluid bag is deemed to be contaminated. This approach enhances the robustness and accuracy of judgment by leveraging the power of deep learning and continuous observation across multiple frames. Using OPAIs and deep learning modelsensures precise differentiation between air bubbles and foreign particles, which is crucial for maintaining the quality and safety of medical products.
## 5 Performance Evaluation
### Recovery of the Public Key  
The first test aimed at identifying the time it takes to retrieve the public key of a user by connecting the gateway to the database, as shown in Figure 2. In the experiments, we have used PostgreSQL 9.4 database system, running on a host computer with Debian 9.8 OS, 4 GB of RAM, and Intel Core I5 1.6 GHz processor. The gateway was running on  
Figure 2: Architecture for monitoring an user for a specified time.  
another computer running Ubuntu 18.04 LTS with 8 GB of RAM and Intel Core I5 2.3 GHz processor. We have measured the time to fetch 1, 10, 100, and 1000 keys at a time, using three database sizes: 100,000, 1 million, and 10 million license plates. Table 2 summarizes the query execution time to retrieve a given number of keys for each database size. We can observe that the execution time of the queries varies according to the number of keys retrieved and also with the database size. It is worth noting that the higher number of license plates stored in the database is less the number of vehicle plates licensed in cities like New York, which do not reach the amount of 10 million private cars [45].
## 3 Calculation of Mixing Weight Importance Sampling Model  
As for the complicated mechanical assets, multiple failure modes are possible owing to the dynamic environment and load, coupled with the comprehensive mechanisms, which results in the multi-performance reliability analysis as well. Consequently, we investigate the mixing weight importance sampling aimed at addressing the mentioned practical issues. In detail, after constructing the surrogate model, the subsequent procedure utilizes the importance sampling approach to calculate the failure probability upon every failure mode. Crucially, weight coefficients are introduced to characterize the impact of each failure probability on system reliability, considering the different contributions of each failure mode to the system.  
Assuming one engineering system encompasses \(m\) failure modes, and the corresponding limit state function is represented as \(g^{(k)}(\mathbf{X})=0(k=1,2,\cdots,m)\), the rela  
Figure 2: Flow chart of clustering.  
tionship between the system failure and one single failure mode can be established as \(p_{f}=\Pr(\sum\limits_{i=1}^{m}g^{(k)}(\mathbf{X})\leq 0)\) if \(m\) failure modes are in a series association.  
With regard to the low failure probability issues, enormous samples are required to ensure convergence in the practical calculation, which expenses large cost and time. The proposed importance sampling approach can intervene in the mentioned topic, whose core is replacing the original sampling density function with an importance sampling density function, increasing the probability of sample points falling into the failure domain, thereby improving sampling efficiency and accelerating model convergence.  
Additionally, a mixing importance sampling density function is constructed to address multi-performance reliability issues, where each failure mode possesses a different design point and failure area. As for the formulation of the function, firstly, determine the corresponding importance sampling density function for the single failure mode, then, consider the contributions of each mode to the final failure and construct the importance sampling density function via the proposed weight allocation method. The sampling density functions can be modelled in the following:  
\[h_{X}(\mathbf{X})=\alpha_{1}h_{X}^{(1)}(\mathbf{X})+\alpha_{2}h_{X}^{(2)}( \mathbf{X})+\cdots+\alpha_{m}h_{X}^{(m)}(\mathbf{X}), \tag{11}\]  
where \(h_{X}^{(i)}(\mathbf{X})\) is the importance sampling density function of the \(i\)-th pattern. \(\alpha_{i}\) is the weight of \(h_{X}^{(i)}(\mathbf{X})\) in the mixing importance sampling density function, which can be approximated as  
\[\alpha_{i}=\frac{p_{f}^{(i)}}{\sum\limits_{i=1}^{m}p_{f}^{(i)}}, \tag{12}\]  
where \(p_{f}^{(i)}\) denotes the failure probability of the \(i\)-th failure mode.
## 3 Experiments and Results
### Performance Comparison of Different Models
#### 3.7.2 Comparison of Performance and Computation Metrics for Alternative Models  
We measured the computational cost of the proposed model with the comparative models in Section 3.7.1 using floating-point operations (FLOPS) on an input image of size \(416\times 416\), using the number of parameters (Params) to represent the size of the model. The results are shown in Table 3.  
It can be seen that RIIAnet has a certain advantage in terms of the parameters and floating-point operations while achieving high performance. The MIOU, MPA, recall, and F1 score of RIIAnet all reached the maximum of 0.7705, 0.9868, 0.8047, and 0.8485, respectively. Compared with the best U-Net network model in the alternative models, the improvements were 0.0062, 0.06%, 0.0117, and 0.0029, respectively and its size of parameters is 0.04 times that of U-Net and 0.03 times that of DeepLabV3+, while the floating-point operations are 1/33 of the former and 1/10 of the latter, respectively. Since RIIAnet was not designed with a complex decoder module to make it suitable for the device with low GPU computing resources, satisfactory results are achieved in the tradeoff between parameters and accuracy.  
Figure 13 shows the PR curve, which is used to evaluate the generalization level and performance of the model from a qualitative perspective. It can be seen that the PR curve for RIIAnet on the test set completely exceeds those for the BiseNet, PSPnet, DeepLabV3+, and UNet++ models, indicating that its performance is better than these three models. This curve intersects with the Deepcrack, U-Net, and Segnet curves, and the performance of RIIAnet can be seen to be slightly higher than theirs based on the BEP performance metric (marked with an asterisk on the graph).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIOU** & **MPA** & **Recall** & **F1 Score** & **Params(M)** & **Flops(G)** \\ \hline BiseNet & 0.7056 & 0.9832 & 0.7339 & 0.7964 & 23.07 & 14.31 \\ Segnet & 0.7188 & 0.9830 & 0.7440 & 0.8121 & 29.44 & 105.99 \\ DeepLabV3+ & 0.7488 & 0.9839 & 0.7790 & 0.8309 & 54.71 & 54.92 \\ PSPnet & 0.6869 & 0.9829 & 0.7092 & 0.7870 & 39.05 & 46.71 \\ U-Net & 0.7643 & 0.9862 & 0.7930 & 0.8456 & 34.53 & 173.05 \\ UNet++ & 0.7138 & 0.9759 & 0.7481 & 0.8114 & 9.16 & 91.51 \\ Deepcrack & 0.7337 & 0.9849 & 0.7569 & 0.8243 & 14.71 & 53.14 \\ RIIAnet (proposed model) & 0.7705 & 0.9868 & 0.8047 & 0.8485 & 1.44 & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance and computation metrics for different models.
## 4 Design of the DRFTM
### Access to Evidence of Trust
#### 4.1.5 Recommendation Trust  
In UASNs, due to the sparse deployment and constant movement of nodes, the trust evidence obtained through direct interactions between nodes may not be sufficient to support the judgment of node trust, which requires the introduction of recommendation trust. In [33], [PERSON] et al. made a careful study of recommendation trust and distinguished between unreliable suggestions and dishonest nodes in the network. Here, we adopt the same computational method and do not elaborate on the specific process, and the recommendation trust is expressed as \(T_{i}^{rec}\).
## 4 Multi-Sensor Detection with Particle Swarm Optimization
### Combining the Detected Results of Two Phases at the Sink Node  
As illustrated in Figure 3, the estimated matrix \(\hat{\mathbf{b}}^{1}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) and \(\hat{\mathbf{b}}^{2}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) detected in phase 1 and phase 2 are combined at the sink node \(D\) to get the channel coded \(K\times M\) matrix \(\hat{\mathbf{b}}\) for the \(M\) bits channel coded frames of \(K\) source sensors, which is given by:  
\[\hat{\mathbf{b}}= \left[\hat{\mathbf{b}}_{1},\cdots,\hat{\mathbf{b}}_{k},\cdots,\hat{\mathbf{b} }_{k}\right]^{T}=\frac{\hat{\mathbf{b}}^{1}_{\mathbf{x}\mathbf{\triangleright}\mathbf{ \triangleright}\mathbf{\triangleright}}}{2} \tag{26}\]  
where \(\hat{\mathbf{b}}^{1}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) and \(\hat{\mathbf{b}}^{2}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) are the channel coded matrix derived from \(\hat{\mathbf{d}}^{1}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) and \(\hat{\mathbf{d}}^{2}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) after time-frequency decoding, respectively. For our proposed time-frequency coding scheme: \(\hat{\mathbf{b}}^{1}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}=\hat{\mathbf{d}}^{1}_ {\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}\) and:  
\[\hat{\mathbf{b}}^{2}_{\mathbf{x}\mathbf{\triangleright}\mathbf{\triangleright}}= \left[\begin{array}{cccc}\hat{d}^{2}_{1}(-(M\,/\,2+1))&\cdots&\hat{d}^{2}_{ 1}(-M\,)&\cdots&\hat{d}^{2}_{1}(M\,/\,2)\\ \vdots&&\vdots&&\vdots\\ \hat{d}^{2}_{k}(-(M\,/\,2+1))&\cdots&\hat{d}^{2}_{k}(-M\,)&\cdots&\hat{d}^{2}_ {k}(M\,/\,2)\\ \vdots&&\vdots&&\vdots\\ \hat{d}^{2}_{K}(-(M\,/\,2+1))&\cdots&\hat{d}^{2}_{K}(-M\,)&\cdots&\hat{d}^{2}_ {K}(M\,/\,2)\\ \end{array}\right].\]  
The monitoring information is transmitted to the ground monitoring center by sink node through the wired backbone bus to realize the comprehensive monitoring of the underground coal mine.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Circle Model-Based Feature Engineering
#### 2.2.3 Feature Preprocessing  
The normalization of features can improve the test accuracy of the classification algorithm. Here, a feature normalization method is developed based on the circle model. Take the circle model in Figure 3 for example. Since the arc trajectory for the fault voltage of each element must pass through the fault-free point P, the theoretical fault-free point P based on voltage features was selected as the new origin of the complex domain in the translation operation. The setting of the new origin is reasonable, because all types of faults of Linear Time-Invariant systems (LTIs) change around point P.  
Let \(\dot{U}_{t}=x_{t}+jy_{t}\) be an output characteristic voltage of the circuit. Then, the current voltage of the fault-free point P is \(\dot{U}_{0}=x_{0}+jy_{0}\), and the characteristic voltage after translation is \((x_{t}-x_{0},y_{t}-y_{0})\).  
As shown in Figure 6, the characteristic voltages of different states after translation were distributed evenly around the fault-free point, which facilitates the compression of the eigenvalues to [-1,1] and the application of the classifier.  
Figure 5: The 3D map of complex domain with frequency feature.  
After the translation, the translated eigenvalues were compressed by a ratio equal to the difference between the maximum and minimum of the voltage features in the new coordinate system. Considering the effect of tolerances, the feature normalization formula can be expressed as:  
\[\left\{\begin{array}{l}x=\frac{x_{t}-x_{0}}{(x_{\max}-x_{\min})(1+tor^{\%}_{ \infty})}\\ y=\frac{y_{t}-y_{0}}{(y_{\max}-y_{\min})(1+tor^{\%}_{\infty})}\end{array}\right. \tag{18}\]  
where, tor% is the tolerance parameter; \(x_{t}\) and \(y_{t}\) are the real and imaginary values of the translated circuit. The maximum and minimum of the voltage features were identified quickly by drawing a circle from three points. Then, the compression ratio was determined as the difference between the maximum and the minimum. On this basis, the voltage features were normalized by Formula (18), completing the feature preprocessing.
## 3 Results and Discussion:
### Statistical Analyses  
The compressive strength results presented variability (Figure 8), so data analyses were performed to identify possible outliers. Statistical methods such as the standard deviation or boxplots are commonly used to estimate outliers in the data. The experimental data obtained in this work were statistically analyzed using the Z-score method based on the standard deviation (sd) and [PERSON]'s methods based on boxplots [30].
## 2 Results and Discussion
### Phenolic Profile in Egyptian Dukkah and Syrian Za'atar (ug/g)  
To complement the profile of volatiles using GC-MS, HPLC was further employed for phenolics profiling in these spice blends to account more for their health benefits and food preservative actions. HPLC revealed that Egyptian dukkah as well as Syrian za'atar were rich in phenolics (Table 2). The thyme-based Egyptian dukkah (EDT) showed the highest levels of identified phenolics such as rosmarinic acid (338.30 ug/g), fertile acid (325.79 ug/g), and rutin (302.06 ug/g), respectively. It was obvious that the quantities of these compounds in the various herbal combinations under study varied and accounted for differences in antioxidant activities. Catechin was found at significant levels in EDT and SZT (14.7 and 14.8 ug/g, respectively), but not in EDN. Catechin has very potent antioxidant effects through different mechanisms [34], which could be related to the high antioxidant activity of EDT and SZT using diverse assay techniques. Caffeic and sinapic acids are two hydroxycinnamates found at substantially higher levels in EDT than in other samples (194.4 and 28.8 ug/g, respectively). They are among the most important hydroxycinnamic acids with strong antioxidant properties [35]. Chlorogenic acid is a polyphenol and an ester of caffe acid and quinic acid found in the four samples under investigation. Chlorogenic acid and its isomers were shown to have substantial antioxidant and DNA-protective properties [36]. Ferulic acid was found in the four samples with much higher concentrations in EDT, followed by SZT (325.8 and 91.3 ug/g, respectively). Ferulic acid has low toxicity and possesses numerous biological activities (anti-inflammatory, antioxidant, antimicrobial activity, anticancer, and antidiabetic effects) [37]. Rutin, a flavonoid glycoside of quercetin, was detected at 302.1, 12.5, 68.6, and 29 ug/g in EDT, EDN, SZT, and SZS, respectively. Rutin exerts potential antioxidant, anti-inflammatory, and neuroprotective properties, implying a substantial role in the treatment of neurodegenerative and other disease conditions [38]. Apigenin-7-O-glucoside is a flavone O-glycoside that was detected only in EDT (7.76 ug/g). Rosmarinic acid, a polyphenol (an ester of caffeic acid and 3-(3,4-dihydroxyphenyl) lactic acid), was found at high levels in both EDT and SZT (338.3 and 149.6 ug/respectively). It has potent antioxidant effects and showed significant cardioprotective activity in diabetic rats [39]. Daidzein and genistin are examples of isoflavonoids, with daidzein found in all samples except SZT, while genistin was detected in all blends except SZS.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Axial Ratio Parameter Analysis  
An antenna's axial ratio represents how effectively the radiation element preserves the transparency of the polarization. Better polarization performance is indicated by a lower axial ratio, and this is important for reducing signal distortion in communication systems. Figure 11 shows the comparison results of the single-port antenna, the MIMO antenna, and the MIMO antenna with an FSS.  
Figure 8: (**a**) Back view of the MIMO antenna, (**b**) side view of the MIMO antenna.  
Figure 9: MIMO antenna with an FSS: various S-parameters.  
In this work, the various stages of the antenna maintained the AR value of \(\leq\)3 dB at the resonance frequency of the 5G and GNSS bands. At the frequencies of 1.2-1.3 GHz, it achieved the axial ratio of 1.105 dB and circular polarization. At the higher frequency of the 5G band, the axial ratio was greater than 3 dB, with linear polarization.
###### Abstract  
In resource-intensive Internet of Things applications, Lightweight Stream Ciphers (LWSCs) play a vital role in influencing both the security and performance of the system. Numerous LWSCs have been proposed, each offering certain properties and trade-offs that carefully balance security and performance requirements. This paper presents a comprehensive evaluation of prominent LWSCs, with a focus on their performance and resource consumption, providing insights into efficiency, efficacy, and suitability in the real-world application of resource-intensive live video feed encryption on an ARM processor. The study involves the development of a benchmarking tool designed to evaluate key metrics, including encryption frame rate, throughput, processing cycles, memory footprint, ROM utilization, and energy consumption. In addition, we apply the E-Rank metric, which combines key performance and resource metrics to derive a unified comparative measure for overall software performance.  
lightweight cryptography; stream cipher; performance evaluation; Internet of Things; resource-intensive application [table]capposition=top  
+++ ==WARNING: Truncated because of repetitions==
position=top  
++  
suitability of these LWSCs for the real-time, resource-intensive encryption of live video feeds. The experiment was carried out using a Raspberry Pi Zero W, featuring a single-core ARM11 processor and enabling the seamless integration of the camera module. To precisely assess the performance and resource consumption of each selected LWSC, we developed a benchmarking framework that considers the following measurement metrics: encryption frame rate, throughput, Cycles per Byte (CpB), memory footprint, ROM utilization, and energy consumption. Energy consumption during LWSC operations is measured using an Arduino coupled with a power measurement sensor. Furthermore, we utilized the E-Rank metric established in our recently conducted study, derived from the Rank [4] metric and incorporating insights from the FoM [5] metric. This metric serves as a comprehensive and optimal means of evaluating overall software performance by balancing performance, energy consumption, and resource utilization into a unified comparative measure.  
This paper is organized as follows: Section 2 offers a brief explanation of the structure of the selected LWSCs and reviews a few existing benchmarking tools, pointing out their limitations and explaining why they are not suitable for our research. Section 3 details our research methodology, including the development of the tool, selection criteria for LWSCs, hardware requirement factors, and benchmarking metrics. Sections 4 and 5 visually present our results through graphs and offer a detailed analysis and discussion on the implications and explanations behind the performance, resource usage, and overall software performance of the selected LWSCs. Finally, Section 6 presents a comprehensive summary of the main findings and insights from the study while also highlighting potential avenues for future research.
## 4 Comparison between Numerical Simulation and Analytical Methods
### The Morison Equation Is Used by Calculating the Water Pressure  
The Morison equation is one of the most commonly used methods for analyzing the forces on small-diameter rigid columns under the action of water flow. It assumes that the structure has no elastic deformation under the action of water flow, so the water flow force on rigid structures is only related to the incoming water flow. The Morison equation was applied to theoretically calculate the water pressure acting on temporary piers. The Morison equation assumes that the water pressure at any height of the structure consists of two parts: drag force \(f_{D}\) and inertia force \(f_{t}\):  
\[F=f_{D}+f_{t}=\frac{1}{2}C_{D}\rho A(u_{x}-u_{t})|u_{x}-u_{t}|+C_{M}\rho V_{0} (\frac{du_{x}}{dt}-\frac{du_{t}}{dt}) \tag{12}\]  
where \(u_{x}\) is the water flow velocity, \(\frac{du_{x}}{dt}\) is the water flow acceleration, \(u_{t}\) is the structural velocity and \(\frac{du_{t}}{dt}\) is the structural acceleration.  
In this study, the water flow is assumed to be uniform, and the vibration velocity of the temporary pier under the impact of water flow is negligible in comparison with the water flow velocity. Therefore, the first item is mainly considered, where the water flow force is \(\frac{1}{2}C_{D}\rho A(u_{x})|u_{x}|\). \(C_{D}\) is the drag force coefficient, \(C_{M}\) is the inertia force coefficient and the values of \(C_{D}\) and \(C_{M}\) are clearly specified and explained in national regulations.  
According to the Morison equation, the formula for calculating the flow pressure is \(f=\frac{1}{2}C_{D}\rho(u_{x})|u_{x}|\), where \(C_{D}\) is taken as 0.65 according to the American API specification, and the shielding factor is taken as 0.75.