esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 6 Experimental Evaluation
### Sensitivity Analysis
#### 6.3.1 Considerations  
Prior to present the results, it is important to understand some issues of the WSN simulation:  
* First Order Radio Model [17; 24] was used to calculate the power consumption of the WSN due to its simplicity. It only considers the power consumption when the sensor node sends and receives a packet, i.e., it is not examined the power consumption when the sensor node processes a packet, only when it sends or receives one;
* The communication between the sensor nodes is perfect and has no obstacles to degrade the signal. For example, if sensor node \(A\) is inside of the range communication of the sensor node \(B\), sensor node \(A\) will receive the packets from sensor node \(B\). Allied to this, it was also considered that the link layer is perfect, e.g., it avoids collision of packets. This is not an actual scenario, but it facilitates to understand which factors most affect power consumption and reliability because we have no external interference in the WSN power consumption and reliability (e.g., noise or packet collision);
* We defined a region (called _Region I_) with only one sensor node located at an edge of the WSN, specifically, between coordinates (0,0) and (10,10). This region is used to evaluate the impact of the factors on the WSN reliability. In fact, we already know that the distance (combination of the factors Place Size and Place Algorithm) and the routing protocol influence the reliability of a region [16]. However, we do not know which factor most affects the WSN reliability. Putting the region away from the sink node, we will have a better idea of which factors (besides those mentioned above) have an impact on the WSN reliability; and  
Figure 18: Sensor nodes are deployed (**a**) closed and (**b**) far from sink node.  
* All sensor nodes send data to the sink node, which is located in the centre of the WSN. For example, the sink node will be located at the coordinate (50,50) and (250,250) when the _Place Size_ is 100 m \(\times\) 100 m and 500 m \(\times\) 500 m, respectively.
## 3 Discussion
### The UNIPA GNSS CORS Network Monitoring
Although fearing RFI by GSM repeaters was placed on surrounding buildings less than a kilomet away from the roof, results did not show electromagnetic fields that could interfere with GPS. In June 2007, during the first network trials, interferences were detected in the L2 signal. Using an _R&S FSP Spectrum Analyzer_ and a _Horn Ets Lindgreen 3115_ antenna, electromagnetic signals at L1 and L2 GPS frequencies were spatially explored, searching for the direction of the interfering signal (Figure 3). The approximate direction of the interfering signal was found, and it was probably of military use for investigative purposes. Data analysis in the graphical representation shows that the first interference occurred from June to August 2007, then became stronger from November 2007 to July 2008, when the signal-to-noise ratio (SNR) was low. Fortunately, the interference did not affect L1 values; multipath values were also high and returned to normal in August 2008. A posteriori data quality control by QC2 SKY confirmed the previous conclusions. To obtain a semi-quantitative estimate of the external signal interference on the choke ring antenna placed on the roof, the electromagnetic signal was irradiated with an apparatus consisting of an _R&S SMIQ03B Generator_, an _AR 15 SIG3 Amplifier_, and a _Horn Ets Lindgreen 3115_ antenna. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**CORS** & **ETRF2000 Epoch 2008.0** & **1 Gb08 Epoch 2008.0** & & \\ \hline X (m) & Y (m) & Z (m) & X (m) & Y (m) & Z (m) \\ AGRI & 4,936,331.298 & 1,194,330.469 & 3,845,900.834 & 4,936,330.975 & 1,194,330.805 & 3,845,901.113 \\ ALCA & 4,906,356.920 & 1,128,732.790 & 3,903,344.287 & 4,906,356.527 & 1,128,733.024 & 3,903,344.578 \\ CALT & 4,915,665.644 & 1,230,635.292 & 3,861,348.275 & 4,915,665.317 & 1,230,635.627 & 3,861,348.553 \\ CAMP & 4,933,161.054 & 1,115,797.238 & 3,873,024.268 & 4,933,160.640 & 1,115,797.477 & 3,873,052.139 \\ PALE & 4,889,534.473 & 1,160,203.648 & 3,914,738.514 & 4,889,534.146 & 1,160,203.926 & 3,914,738.891 \\ PART & 4,898,768.790 & 1,140,862.867 & 3,909,130.145 & 4,898,768.541 & 1,140,863.11 & 3,909,130.693 \\ PRIZ & 4,914,033.464 & 1,174,031.855 & 3,881,405.827 & 4,914,033.044 & 1,174,032.102 & 3,881,406.106 \\ TERM & 4,890,361.337 & 1,192,337.028 & 3,904,013.032 & 4,890,360.965 & 1,192,337.292 & 3,904,013.365 \\ TRAP & 4,911,563.389 & 1,092,566.455 & 3,906,585.782 & 4,911,562.977 & 1,092,566.697 & 3,906,586.062 \\ \hline \end{tabular}
\end{table} 
Table 1: UNIPA CORS geocentric coordinates in ETRF2000 and IGb08 systems. Figure 2: Location of the first group of the UNIPA GNSS CORS [103] in Sicily (Geocentric CRS RDN2008–EPSG: 6706). Geodetic positions in the ETRF2000 and IGb08 systems are shown in Table 1.
## 3 Proposed Cross-Working Condition RUL Prediction Method
### RAPP-Based Initial Degradation Detection
#### 3.1.2 Construction of RAPP via DACAEN  
After the training of DACAEN according to samples in the normal running stage, the reconstruction error of the normal samples has been reduced to a minimum value as far as possible, and the model lacks the reconstruction ability of the degraded samples. Therefore, the reconstruction error output by DACAEN can be used for early degradation monitoring of bearings. In the process of training the model, reconstruction loss is concerned with the Euclidean distance between the input sample and the generated sample. However, during the testing phase, differences between hidden layers were ignored. They cannot utilize the information available from the depth autoencoder projection path [26]. To address this problem, RAPP was introduced as a method for detecting novel samples using hidden space information, which provides an indirect method for calculating hidden reconstruction error by comparing the difference between the inputs and the DACAEN reconstruction, not only in the input space, but also in the hidden space [27]. Therefore, this paper combines DACAEN's powerful sample reconstruction capability and the comprehensive information representation of RAPP based on the projection pathway of deep autoencoders. RAPP based on DACAEN is proposed to realize early degradation monitoring of bearings, as shown in Figure 2.  
Given a training set \(X\), let \(x\) denotes the input vector from \(X\) and \(\hat{x}\) represent the reconstruction of \(x\). \(h(x)\) denotes the hidden representations from \(x\), and \(\hat{h}(x)\) is the hidden representations from \(\hat{x}\). Let \(d(x)=h(x)-\hat{h}(x)\) and \(D\) be a matrix whose \(i\)th row corresponds to \(d(x_{i})\), and let \(\overline{D}\) be the column-wise centered matrix of \(D\). \(\mu_{X}\) denotes the column-wise mean of \(D\). Then, singular value decomposition is conducted for normalization to obtain the singular values \(\lambda\) and right singular vectors \(V\). The traditional calculation method of the reconstruction error of Euclidean distance representation is replaced by \(RAPP\), and the formula of \(RAPP\) is as follows [26].  
\[\overline{D}=U\lambda V^{T} \tag{9}\]  
\[RAPP=\left\|(d(x)-\mu_{X})^{T}V\lambda^{-1}\right\|_{2}^{2} \tag{10}\]
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 2 Materials and Methods
### Preprocessing for Image Contrast Enhancement
#### 2.2.2 Contrast-Limited Adaptive Histogram Equalization (CLAHE)  
The basis of contrast-limited adaptive histogram equalization, which is frequently used for image enhancement, is based on histogram equalization. In histogram equalization, image quality can be improved by expanding the dynamic range of density with the entire image histogram. In histogram equalization, the intensity distribution of the image is normalized in order to obtain an output image with a uniform density distribution. Adaptive histogram equalization is also a modified histogram equalization process. The main idea of CLAHE is that the image is divided into rectangular regions in the form of a grid and the standard histogram equalization is applied to each region. After dividing the image into subregions and applying histogram equalization to each of them, the subregions are combined with the bilinear interpolation method to obtain an optimized whole image. However, adaptive histogram equalization shows poor performance when an image has various types of noise. In order to prevent the noise problem, it is necessary to limit contrast enhancement in homogeneous regions. Contrast-limited adaptive histogram equalization was developed to overcome the difficulty. In this method, each pixel is mapped using its four nearest neighbors. When the subregions are combined with bilinear interpolation, the regions are divided into groups according to their neighborhood.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Database** & **Normal Images** & **Diseased Images** & **Total** \\ \hline DRIVE & 33 & 7 & 40 \\ DIARETDB0 & 20 & 110 & 130 \\ DIARETDB1 & 5 & 84 & 89 \\ \hline \end{tabular}
\end{table}
Table 1: The specifications of the public datasets used in this work.  
This method is formulated based on the fact that the image is divided into several regions of nearly equal size that do not overlap with each other. In the literature, contrast-limited adaptive histogram equalization has given successful results on medical images [33; 34; 35].  
In Figure 4, we can see the original color retinal image and the enhanced fundus image, which was obtained by using CLAHE with a healthy image and a diseased image.  
Figure 3: Retinal fundus images and their green channel images.
## 3 Results and Discussion  
### Electronic Nose Detection  
#### 3.1.1 Analysis of the Primitive Response Signal  
The Trapezoidal Rule is employed to integrate the process of collecting gas samples from oysters. As illustrated in Figure 2, during the 600 s of the gas collection cycle, the first 300 s are dedicated to gathering gas sample data from the oysters, while the subsequent 300 s are allocated for cleaning the gas chamber with air. Consequently, only the integral area within the initial 300 s is computed in this context. The Trapezoidal Rule is a numerical integration technique utilized to estimate the value of a definite integral. The integration interval \([a,b]\) is divided into several small trapezoids, which approximates the area under the curve and provides an estimated value of the definite integral.  
Specifically, the interval \([a,b]\) is divided into \(n\) equal parts, each having a length of \((b-a)/n\). For instance, in Figure 3, the integration interval for this region spans the first 300 s, which are divided into 300 equal parts. The function values corresponding to the endpoints of each sub-interval are then obtained. The small intervals depicted in the figure are for illustrative purposes; in practice, the intervals are divided based on the irregularity of the area of the sub-divided intervals. The more irregular the division, the greater the number of intervals used. The heights at the two endpoints of each interval are multiplied by \(h\), where \(h\) represents the response voltage at those endpoints. Subsequently, the area of the trapezoid in each sub-interval can be calculated. By summing the areas of the trapezoids across all sub-intervals, an approximate area under the entire interval \([a,b]\) is obtained. This method is applied to the gas collection data from the oyster sample during the first 300 s, allowing for the calculation of the integral area of this adsorption process, as shown in the Equation (2).  
\[\int_{a}^{b}f(x)dx\approx\frac{h}{2[f(a)+2f(x_{1})+2f(x_{2})+\dots+2f(x_{n-1}) +f(b)]} \tag{2}\]  
Among them, \(x_{1},x_{2},\dots,x_{n-1}\) are the right endpoints of the \(n-1\) subintervals obtained by subdividing the interval \([a,b]\).  
In summary, the entire adsorption process was analyzed by calculating the integral area for oyster samples grouped in 5 min intervals. The integral areas of S1 at four different temperatures were aggregated and plotted as a line graph in Figure 3. It is evident that the change in integral area in response to the S1 sensor for oysters at 4 \({}^{\circ}\)C is not significant. However, as the temperature increases, the changes in integral area become  
Figure 3: S1 sensor integral area line chart.  
Figure 2: Schematic diagram of integral area in adsorption process.  
more pronounced, although this does not facilitate direct analysis. A review of the literature suggests that the rate of volatile gas evaporation in oysters does not necessarily increase consistently with temperature variations. Consequently, the initial processing of the raw response signals does not provide a direct means for assessing oyster freshness.
## 7 Simulation and Evaluation
### Sensitivity Analysis of Key Simulation Parameters  
To further evaluate the robustness and adaptability of the MMS-DSR protocol, a sensitivity analysis was performed by varying key network parameters such as latency, bandwidth, and reliability. This analysis aims to explore the protocol's performance under different conditions, ensuring its effectiveness in dynamic urban vehicular environments, as previously discussed in the Simulation and Evaluation section.  
The parameter of latency is critical in time-sensitive applications like vehicular ad-hoc networks (VANETs), where quick decision-making is crucial. The analysis examined latency variations ranging from 10 ms to 100 ms, assessing their impact on end-to-end delay, route discovery time and PDR. As expected, increasing the latency led to a proportional rise in end-to-end delay. However, the MMS-DSR protocol demonstrated resilience, with only a moderate increase in delay ( 15%) even as latency reached 100 ms. The route discovery  
Figure 15: Scalability comparison performance across increasing vehicle density.  
time and PDR remained largely unaffected by this latency increase, maintaining a high level of performance. This observation is consistent with the findings presented in the Average End-to-End Latency subsection, as shown in Figures 9 and 10, where MMS-DSR consistently outperformed other protocols (SOL-DSR and Enhanced OLSR), particularly under higher latencies.  
The sensitivity analysis explored the impact of varying bandwidth from 100 Mbps to 1000 Mbps, observing the corresponding effects on network throughput and routing overhead. The results showed a significant improvement in throughput as bandwidth increased, with an approximate 30% gain in throughput observed when bandwidth was increased to 1000 Mbps. Despite this improvement in throughput, the protocol maintained minimal routing overhead, as evidenced in Figures 7 and 8, where MMS-DSR consistently displayed superior scalability and efficiency across varying vehicle densities and speeds. These findings further align with the Routing Overhead results in Figures 13 and 14, where MMS-DSR exhibited significantly lower overhead compared to SOL-DSR and Enhanced OLSR, even in high-bandwidth scenarios.  
Reliability was tested by introducing varying levels of packet loss, ranging from 0% to 15%, to evaluate the protocol's robustness under adverse conditions. The analysis demonstrated that even under high packet loss rates, MMS-DSR maintained a PDR of around 90%, underscoring its resilience. The machine learning-based prediction model integrated into MMS-DSR (via CNN-LSTM) allowed the protocol to anticipate network disruptions and adjust routing decisions dynamically, thus minimizing the impact of packet loss on overall performance. This adaptive capability aligns with the Packet Delivery Ratio results discussed in the simulation section, where the MMS-DSR protocol showed superior reliability in comparison to traditional DSR and other advanced routing protocols, even under fluctuating network conditions.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 5 Performance Analysis
### Computational Complexity Comparison  
We present a computational complexity comparison of our scheme with [PERSON] et al. 's [30], [PERSON] et al. 's [71], [PERSON] and [PERSON]'s [4], and [PERSON] et al. 's [5] schemes in Table 5. We can ignore the time taken for computing XOR operation because the value is too low to influence the result. [PERSON] et al. 's [71], [PERSON] and [PERSON]'s [4], and [PERSON] et al. 's [5] schemes take morecomputational cost than [PERSON] et al. 's [30] and ours. [PERSON] et al. 's scheme [71] takes the most computational cost, and the reason may be that [PERSON] et al. 's scheme [71] is the only scheme which performs exponentiation operations among them. [PERSON] and [PERSON]'s [4] and [PERSON] et al. 's [5] schemes take more computational cost than [PERSON] et al. 's [30] and ours because [PERSON] and [PERSON]'s [4] and [PERSON] et al. 's [5] schemes perform more not only one-way hash function operations but elliptic curve point multiplications. The results have proven that performing an elliptic curve point multiplication takes more time than a Chebyshev chaotic maps operation, and, compared to RSA and ECC, Chebyshev polynomials can offer smaller key size and faster computation [42; 43; 72; 73; 74]. However, [PERSON] et al. 's scheme [30] performs only two elliptic curve point multiplications in total while our scheme performs six Chebyshev chaotic maps operations. For the above reason, [PERSON] et al. 's scheme [30] takes less time than our scheme. Although [PERSON] et al. 's scheme [30] is more efficient than our scheme by a narrow margin, [PERSON] et al. 's scheme [30] cannot provide key confirmation because of lacking session key agreement, and neither can [PERSON] and [PERSON]'s scheme [4]. Moreover, [PERSON] et al. 's scheme [30] cannot provide mutual authentication, anonymity, traceability of anonymity, unforgeability, and non-repudiation. Figure 8. illustrates computational complexity of receiver/gateway node with varying number of devices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Security Requirements** & **[PERSON] et al. [30]** & **[PERSON] et al. [71]** & **[PERSON] and [PERSON] [4]** & **[PERSON] et al. [5]** & **[PERSON]** \\ \hline Mutual authentication & X & O & X & O & O \\ Session key confirmation & X & X & X & O & O \\ Anonymity & X & O & O & O & O \\ Traceability of anonymity & X & X & X & X & O \\ Unforgeability & X & X & X & X & O \\ Non-repudiation & X & X & X & X & O \\ Without RC & O & O & X & X & O \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Security requirements comparison.
## 3 System Model
The propulsion power consumption with speed \(v(t)\) can be modeled, as follows:  
\[P_{p}(v(t))\triangleq\underbrace{P_{o}\bigg{(}1+\frac{3v(t)^{2}}{U_{tip}^{2}} \bigg{)}}_{\textit{blade profile}}+\underbrace{P_{i}\bigg{(}\sqrt{1+\frac{v(t)^{4}}{4 v_{o}{}^{4}}}-\frac{v(t)^{2}}{2{v_{0}}^{2}}\bigg{)}^{1/2}}_{\textit{ induced}}+\underbrace{\frac{1}{2}d_{0}\varrho sAv(t)^{3}}_{\textit{parasite}}, \tag{5}\]  
where \(P_{o}\) and \(P_{i}\) are the constants defined in (3). The propulsion power consumption of the UAV consists of three components, i.e., the blade profile, induced, and parasitic powers. The blade profile and parasitic powers are quadratically and cubically increase with \(v(t)\), respectively. In contrast, the induced power decreases as \(v(t)\) increases.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 5 Experimental Results and Analysis
### Applicability and Limitations Analysis  
The proposed scheme works well for most types of images, but it inevitably has limitations. Feature point-based algorithms are limited by the feature point operator itself. For images with simple texture, the feature points are often unstable when under a severe image quality degradation. Therefore, for images with simple texture, the proposed method may not achieve accurate watermark synchronization, which will probably cause watermark detection to fail.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hypothetical Scenario** & **Scenario (a)** & **Scenario (b)** & **Scenario (c)** & **Scenario (d)** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Examples of Four Hypothetical Scenarios.  
Figure 17: Watermark detection results against screen-cam with common attacks. (**a–d**) represent scenarios (**a–d**), respectively.  
Another limitation is that the proposed scheme is not applicable to this situation, where the image displayed on the screen is greatly zoomed out before we capture it with a camera. Because in this case, the image displayed on the screen is resampled, which will cause a massive loss of image details. Unfortunately, the screen-cam process will amplify this distortion. Especially for high-resolution images, the users are most likely to zoom out to view the entire image. Therefore, the proposed scheme could be used with access control systems or other specific applications to avoid this situation.  
Furthermore, because the motivation of this method is to hold accountability for leakage behavior, the time complexity of algorithm is not a very important consideration. However, in other words, time complexity is also one of our limitations. The computation time of watermark embedding includes two parts: LSFRs construction and message embedding. Based on a personal computer, which CPU is Intel Core i7-9700 CPU and RAM is 32 GB, the average computation time of LSFRs construction and message embedding for the host images are 7.041 s and 0.106 s, respectively. The Harris-Laplace operation involves multiscale and iterative calculations, which cost most of the computation time. Based on the algorithm, the time complexity of embedding algorithm is \(O(Length\cdot Width)\), where _Length_ and _Width_ define the length and width of the image, respectively. Hence, for high-resolution images, the computation time will vary according to their size. With respect to watermark detection, the process of finding candidate LSFRs is similar to the process of constructing LSFRs. Although the message extraction process iterates the message extraction algorithm within our defined detection range, the computation time is still insignificant compared with the process of finding candidate LSFRs. Hence, after the manual perspective correction process, the time complexity of watermark detection is similar to watermark embedding. Therefore, considering the user experience, the algorithm is not recommended for real-time applications for now.