esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Performance Comparison
### Computational Cost  
In Table 2, the proposed scheme is compared, in terms of computational cost, with the existing ones, that is, [PERSON] et al.'s scheme [4], [PERSON] et al.'s scheme [47], [PERSON] et al.'s scheme [48], and [PERSON] et al.'s scheme [49], hereinafter also referred to as the \"four chosen schemes\", on the basis of major operations. We considered hyperelliptic divisor multiplication as elliptic curve scalar multiplication, and bilinear pairings are the most expensive operations used in the relevant existing schemes. The variables _Am, em, bp_, and _mxp_ denote the hyperelliptic curve divisor multiplication, elliptic curve scalar multiplication, bilinear pairing, and modular exponential, respectively. It has been observed that a single scalar multiplication takes 0.97 ms for elliptic curve point multiplication (ECPM), 14.90 ms for bilinear pairing, and 1.25 ms for modular exponential [15]. The Multi-Precision Integer and Rational Arithmetic C Library (MIRACL) [68] was used to test the runtime of the basic cryptographic operations up to 1000 times to measure the performance of the proposed approach. The phenomenon was observed on a workstation having following specifications: Intel Core i7- 4510U CPU @ 2.0 GHz, 8 GB RAM and Windows 7 Home Basic 64-bit Operating System [19]. Similarly, the hyperelliptic curve divisor multiplication (HCDM) was assumed to be 0.48 ms due to the smaller key size\(-80\) bit key size [69].  
The computational costs provided in Table 3 and illustrated in Figure 6 clearly show that our proposed scheme, when compared with the \"four chosen schemes\" outperforms in terms of computational cost. The presented scheme is quicker than the existing ones by the following degrees:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **Signing** & **Verifying** & **Total** \\ \hline [PERSON] et al.’s scheme [4] & 16 \(em\) & 5 \(em\) & 21 \(em\) \\ [PERSON] et al.’s scheme [47] & 7 \(em\) & 1 \(bp+4\) \(em\) & 11 \(em+1\) \(bp\) \\ [PERSON] et al.’s scheme [48] & 5 \(em\) & 2 \(em\) & 7 \(em\) \\ [PERSON] et al.’s scheme [49] & 2 \(em+3\) \(mxp\) & 1 \(em+1\) \(bp+1\) \(mxp\) & 3 \(em+1\) \(bp+4\) \(mxp\) \\ Proposed & 1 \(\hat{\mu}_{m}\) & 0 \(\hat{\mu}_{m}\) & 1 \(\hat{\mu}_{m}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational cost.  
Figure 5: Architecture of the automated validation of internet security protocols and applications (AVISPA) tool v.1.1 [67].  
[PERSON] et al. [4] by 97.64% (\(20.37-0.48/20.37\times 100=97.64\%\)); [PERSON] et al.'s scheme [47] by 98.12% (\(25.57-0.48/25.57\times 100=98.12\%\)); [PERSON] et al.'s scheme [48] by 92.93% (\(6.79-0.48/6.79\times 100=92.93\%\)); and [PERSON] et al.'s scheme [49] by 97.89% (\(22.81-0.48/22.81\times 100=97.89\%\)).
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 8 Stopping Methods
### Naive Approach  
Our first implementation of canVisitMoreNodes (Algorithm 3) is based only on travel power cost. That is, the algorithm will calculate how much power is needed to visit the next node, \(n_{i}\), as \((d_{i}\cdot\Lambda_{m})\), which is the distance to reach node \(n_{i}\) times the power needed to move that distance, and check if the power left is enough to complete that trip (line 2). If there is enough power left, it returns \(True\), indicating to keep exploring further; otherwise, it returns \(False\), giving an indication to stop exploration. The time complexity of Naive (Algorithm 3) is \(O(1)\). It is clear that each operation is an arithmetic operation or comparison taking constant time, \(O(1)\), in the algorithm.  
```
1:\(N_{d}\)\(\triangleright\) Set of distances between nodes \(=\{d_{1},d_{2},...,d_{s}\}\)
2:\(\Lambda_{m}\)\(\triangleright\) MCR moving power consumption per move unit
3:procedure canVisitMoreNodes(\(power\), \(i\))\(\triangleright\) power: amount of power remaining, i: index of next unvisited node
4:if\(power\geq 2\cdot(d_{i}\cdot\Lambda_{m})\)then\(\triangleright\) Multiply by 2 to accommodate for returning
5:return\(True\)
6:else
7:return\(False\)
8:endif
9:endprocedure
```  
**Algorithm 3** Naive approach for canVisitMoreNodes.  
The Naive approach is good at preventing the MCR from getting stranded. The Naive approach performs satisfactorily when the low-power sensor nodes in \(N\) are located close to \(B_{S}\) because there will be a high chance of including the lower power nodes in \(v\). However, the algorithm naively assumes it will be able to find the low power nodes before it runs out of power. The combination of Charge and Naive approach attempts to charge all the nodes it has visited so far, and only explore with the remaining power. This prevents the discovery of nodes with lower power that are further down the network. The network life extension is minimal or even inexistent if the charged nodes are part of the \(k\) nodes to drop. Naive fails to extend the life of the network if the low power nodes are not close to \(B_{S}\). The next approach attempts to address having low power nodes further down the network.
# A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells
## 3 Results
### Metabolic Difference between Groups RWPE-1 and PC-3 Cells  
As shown in Figure 2, 18 differentially expressed metabolites were identified, mainly two types of alcohols: arabinitol and mannitol; five amino acids: leucine, proline, isoleucine, glycine, and pyroglutamic acid; four carboxylic acids: propionic acid, 2-keto-3-methyl pentanoic acid, 2-keto-isocaproic acid, and 2-pyrrolidone-5-carboxylic acid; four organic acids: oxalic acid, succinic acid, malic acid, and 2-hydroxyglutaric acid; three sugars: tagtofuranose, fructopyranose, and glucose; and urea. Differences in the metabolites were compared between groups 1 (RWPE-1 cells) and 2 (PC-3 cells). The PC-3 cell group showed increased concentrations of all metabolites except 2-pyrrolidone-5-carboxylic acid compared to the normal RWPE-1 cell group. Leucine, proline, isoleucine, glycine, and pyroglutamic acid showed 6.3-, 78-, 92.8-, 113.3-, and 4.4-fold higher concentrations, respectively, in PC-3 cells than in RWPE-1 cells, whereas 2-pyrrolidone-5-carboxylic acid presented a 20-fold  
Figure 2: Bar graphs of the 18 most significant metabolites in the analysis of variance results comparing the two groups (i.e., RWPE-1 and PC-3 cells). Data represent the mean \(\pm\) SD. * \(p\) < 0.05, **_p_ < 0.01, and ***_p_ < 0.001 versus RWPE-1 cell group.  
lower concentration in PC-3 cells (Figure 2b). No glucose or urea was detected in the RWPE-1 group (Figure 2e).
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Segmentation Workflow: DeeplabV3+ Versus SegNet  
#### 2.3.1 Training Phase  
The input images used color images with the resolution of 533 pixels \(\times\) 800 pixels for both DeepLabv3+ and SegNet networks. Due to the different network architectures used, the setup parameters of each architecture were fine-tuned differently. For instance, the batch size of each model was adjusted individually considering the GPU limitations. However, the number of epochs in this study was set to be consistent to allow fair comparison. In order to avoid over-fitting, the training task would stop as soon as the loss computed on the validation set degraded over four consecutive epochs.  
DeepLabv3+ was trained by applying a stochastic gradient descent with momentum (SGDM) of 0.9 and a value of 0.005 for L2 regularization. The learning rate followed a piecewise schedule that reduced the learning rate from an initial value of 0.0003 by a factor of 0.3 every 10 epochs. A mini-batch size of eight training samples was used and the  
Figure 3: An illustration of SegNet architecture [60].  
model was trained for up to 30 epochs. For SegNet, the network was trained using the same value of SGDM but with a value of 0.0005 for L2 regularization. A mini-batch size of 4 training samples was used instead of 8 as in the DeepLabv3+ to reduce memory usage while training. The network updated the initial learning rate of 0.001 every 10 epochs by multiplying with 0.3 and training was accomplished over 30 epochs. All the models were trained and validated using MATLAB 9.8.0 (R2020a) on a system with the following configurations: Intel\({}^{\textregistered}\) CoreTM i7 CPU @ 2.60 GHz, 16 GB RAM with NVIDIA GeForce GTX 980M (5 GB RAM). Table 1 summarizes the hyperparameters used for each network architecture in this study.
# Computational Investigation of a Novel Box-Wing Aircraft Concept
## 3 Aero-Structural Characterization of CRM Aircraft
### Main Characteristics of CRM Aircraft  
The CRM aircraft [44; 45; 46; 47; 48] originates from a NASA initiative whose objective was to provide a benchmark case so as to help assess the computational capabilities employed for aircraft performances, worldwide. Since its introduction to the aeronautic research community via the AIAA Drag Prediction Workshop (DPW), the CRM model has been used by many research teams, whether as a means for validation or as a basis for comparison. Indeed, the CRM is a faithful representation of a long-range twin-aisle transport aircraft, very similar to the Boeing B777 (see Figure 3).  
It is designed for a cruise condition of Mach 0.85 and Reynolds number \(\text{Re}=40\) million (per reference chord), with an angle of attack \(\alpha=2.05^{\circ}\) leading to a nominal lift coefficient of \(\text{C}_{\text{L}}=0.5\). Table 1 lists some key characteristics of the CRM planform [44, 45]. The CRM model comes with several variants; besides a classical set of fuselage and wings, it can comprise (or not) a horizontal tail (of variable incidence angle, \(\text{i}_{\text{h}}\)), an empennage and/or a pair of engines (nacelle and pylon). As with most previous studies, the present work relies on the original CRM configuration, which solely comprises the horizontal tail (of zero incidence angle, \(\text{i}_{\text{h}}=0\)).
## 8 Implementation and Performance Evaluation
### Comparative Analysis with Other Protocols
#### 8.3.3 Suitability for Constrained Devices  
The enhanced GN-AK excels in scenarios where computation cost and energy consumption are critical. Its use of point halving and double-base chains aligns with hardware-accelerated binary fields found in  
* Smart cards (e.g., EMV chips)
* Sensor nodes (e.g., Zigbee, Bluetooth LE)
* RFID tags and embedded authentication modules  
Unlike HMQV and NAXOS, the enhanced GN-AK supports optional third-message key confirmation, improving resilience in mutual verification. Its modular scalar computation strategy can be adopted in other ECC frameworks with minimal disruption. These comparisons validate that the proposed GN-AK enhancement achieves better trade-offs between security and efficiency compared to related protocols.
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 2 Methodology
### Cost of Energy and Rate of Return  
In Figure 5, the LCOE and the IRR are presented. From the plot, it is evident that the PIP slip joint emerges as the most financially advantageous option among the three joint types evaluated. With an IRR of 43.42%, it surpasses the grout joint by 6.63% and the bolted flange joint by 6.16%, thereby providing the highest return on investment. Furthermore, it features the lowest LCOE at 0.0136 EUR/kWh, establishing it as the most cost-effective solution for energy production. This remarkable performance is corroborated by additional key metrics, including the highest NPV, the shortest payback period, the highest BCR, the highest ROI, and the lowest annualized cost, affirming its overall dominance across financial indicators.  
Figure 4: Comparing annual expenditure.  
While the grout joint and bolted flange joint do not demonstrate the same level of financial superiority as the PIP slip joint, their performance metrics are closely comparable. The IRR of the bolted flange joint is recorded at 40.9%, which is marginally higher than the IRR of the grout joint at 40.72%. Regarding LCOE, the bolted flange joint (0.013814 EUR/kWh) is only 0.12% lower than that of the grout joint. These minimal differences suggest that the decision between these two options may rely more on practical considerations, such as structural performance, installation complexity, or long-term maintenance, rather than exclusively on financial factors.  
All three joint types demonstrate strong financial viability, as evidenced by IRRs that notably exceed the typical discount rates used in offshore wind investment assessments (commonly ranging between 7% and 10%). Among them, the PIP slip joint stands out due to its exceptionally high IRR and lower Levelized Cost of Energy (LCOE), making it the most financially attractive option for stakeholders aiming to optimize investment returns and reduce energy production costs.  
This superior performance is largely driven by the PIP system's reduced initial capital expenditure, simplified installation process, and lower ongoing maintenance requirements, all of which contribute to lower annualized costs. These economic efficiencies not only enhance short-term profitability, but also support long-term operational sustainability.  
Figure 5: (**a**) Levelized Cost of Energy (LCOE) and (**b**) the internal rate of return (IRR).  
As a result, the PIP slip joint presents a compelling case for adoption in offshore wind developments where cost-effectiveness, streamlined deployment, and lifecycle performance are critical to project success.
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 4 Material and Methods
### WiMAX IEEE 802.16e Performance Metrics Used  
#### 4.1.1 Traffic Sent and Receive  
Traffic sent is the amount of data transmitted by the WiMAX MAC in packets/s or bits/s. WiMAX MAC sends average bits/s to the transport layer for network transmission,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **License Type** & **Language** & **Supported Operating** & \begin{tabular}{c} **GUI** \\ **Support** \\ \end{tabular} & \begin{tabular}{c} **Document** \\ **Available** \\ \end{tabular} & \begin{tabular}{c} **Ease of** \\ **Use** \\ \end{tabular} \\ \hline QualNet & \begin{tabular}{c} Commercial (separate license for \\ academicians and others) \\ \end{tabular} & C++ & \begin{tabular}{c} UNIX, Window-MAC, \\ Linux \\ \end{tabular} & Yes & Excellent & Moderate \\ \hline NetSim & Proprietary & C and Java & \begin{tabular}{c} Windows (7, Vista) and \\ Windows XP \\ \end{tabular} & Yes & Excellent & Easy \\ \hline SSFNet & Open-source & Java and C++ & \begin{tabular}{c} Linux, Solaris, and \\ Windows NT using \\ JDK1.2 and higher \\ \end{tabular} & Yes & Good & Hard \\ \hline OPNET & Commercial & C and C++ & \
\begin{tabular}{c} Windows XP, Vista, 7 \& \\ Windows NT 4.0 \\ \end{tabular} & Yes & Good & Easy \\ \hline Ns2 & Open-source & C++ and
\begin{tabular}{c} GNU/ Linux, Free BS D, \\ Mac OS X, Window XP, \\ Windows Vista and \\ Win. 7. \\ \end{tabular} & Limited & Excellent & Hard \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of some network simulation tools [22].  
file transfer protocol (FTP), and video conferencing for voice applications. Traffic received is the amount of data traffic transmitted successfully and received by the WiMAX MAC from the physical layer, either in packets/s or bits/s. For example, traffic received would be the average packet/data rates forwarded to the video conferencing application by the transport layer in video conferencing. The calculation to evaluate the traffic sent/received, and the study considered the WiMAX MAC packet delivery ratio (PDR) [26], which here presents the formula for packet delivery.  
\[\text{PDR}=\frac{\Sigma(\text{Total Packets Received})}{\Sigma(\text{ Packets Sent})} \tag{1}\]
## 3 Results
### Experimental Analysis of Stability  
The proposed algorithm demonstrates robustness in the presence of noise due to its effective noise suppression and fault feature extraction capabilities. To evaluate this, white Gaussian noise with a signal-to-noise ratio of \(-5\) dB was introduced to the acoustic array signal, generating new data samples. The processing procedure remained consistent with the previous experimental method, and the analysis results are depicted in Figure 16. In comparison to alternative methods, our proposed approach exhibits high accuracy in fault classification, achieving an average success rate of 98.04% across five cross-experiments. This further substantiates the advantages of the proposed method in effectively handling background noise.  
To assess the sensitivity of the proposed algorithm in scenarios with limited samples, we investigated the training sample size and feature processing capability of the algorithm. The training sample dataset was divided into three portions: 10%, 30%, and 50%. Table 6 presents the experimental comparison results for four methods. From Table 6, it can be observed that when the dataset size is set at 50%, there are no significant differences  
Figure 16: The results of different methods are compared when \(-5\) dB noise is added.  
Figure 15: Accuracy of four methods during the training process.  
in test results among the methods. However, when reducing the training scale to 30% of the dataset, the proposed method remains highly stable with an accuracy of 98.32%, while DenseNet and LeNet-5 experience a notable decrease in accuracy. When further reducing it to only 10%, the DenseNet algorithm achieves an average test accuracy of merely 75.06%, whereas our proposed algorithm maintains a high accuracy rate of 95.30%. These experimental comparison results demonstrate that even under limited samples, the proposed method is capable of extracting effective features.
# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 5 Conclusions  
This paper proposes a lightweight UAV swarm detection model integrating an attention mechanism. First, the structure of the network is simplified and optimized by using the depthwise separable convolution method, which greatly reduces the total number of parameters of the network. Then, a SE module is introduced into the backbone network to improve the model\({}^{\prime}\)s ability to extract object features; the introduction of a CBAM in the feature fusion network makes the network pay more attention to important features and suppress unnecessary features. Finally, in the training process, a loss function based on DIoU can better describe the overlapping information and make the regression faster and more accurate. In addition, two data augmentation technologies are used to expand the UAVSwarm dataset to achieve better UAV detection. The proposed model is a lightweight model with high detection accuracy and only 3.85 MB in size, which is suitable for embedded devices and mobile terminals. In conclusion, the real-time performance and accuracy of the UAV swarm detection model proposed in this paper meet the requirements of rapid detection of UAVs in real environments, which has practical significance for the construction of anti-UAV systems. In our future work, we will continue to study and optimize the improvement strategy, so that it can achieve better recognition accuracy and real-time performance under the premise of minimizing the complexity of the model.  
Conceptualization, C.W.; methodology, C.W.; software, L.M.; validation, L.M., Q.G. and J.W.; formal analysis, Q.G., X.L. and F.D.; investigation, L.M.; resources, C.W.; data curation, L.M.; writing--original draft preparation, L.M.; writing--review and editing, C.W., Q.G. and T.W.; visualization, L.M., L.W. and E.W.; supervision, C.W.; project administration, C.W.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 61703287, 62173237, and 61972016), the Scientific Research Program of Liaoning Provincial Education Department of China (Grant Nos. LJKZ0218, LJKMZ2020556, and JYT2020045), the Young and Middle-aged Science and Technology Innovation Talents Project of Shenyang of China (Grant No. RC210401), the Doctoral Scientific Research Foundation of Shenyang Aerospace University (Grant No. 22 YB03).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Image Processing of Weld Seams
### Laser Stripe Center Extraction Algorithm
The RMSE calculation formula for the center points relative to the fitted line is as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(\text{d}_{i}-\overline{\text{d}} \right)}{n}} \tag{16}\]  
where \(n\) represents the number of laser center points, \(\text{d}_{i}\) denotes the distance from the current point to the fitted line, and \(\overline{\text{d}}\) is the average distance of all center points to the line. The distance \(\text{d}_{i}\) is calculated using the point-to-line distance formula, as follows:  
\[d_{i}=\left|\frac{A_{p}v_{i}+B_{p}u_{i}+C_{p}}{\sqrt{A_{p}^{2}+B_{p}^{2}}}\right| \tag{17}\]  
where \(v_{i}\) and \(u_{i}\) are the coordinates of the current extracted center point, and \(A_{p}\), \(B_{p}\), and \(C_{p}\) are the parameters of the fitted line equation \(A_{p}v+B_{p}u+C_{p}=0\). The root mean square error (RMSE) calculated by the three algorithms is shown in Table 1.  
The table shows that the proposed algorithm reduces error by 40% compared to the extremum method and by 37% compared to the grayscale centroid method. Experimental results confirm the algorithm's high accuracy, demonstrating its ability to effectively meet the relevant measurement requirements. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Extremum Method** & **Grayscale Centroid Method** & **Proposed Algorithm** \\ \hline RMSE (pixels) & 0.897 & 0.816 & 0.542 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of RMSE between the extracted center points and the fitted line for different algorithms. Figure 6: Comparison of laser stripe center extraction algorithms: (**a**) extremum method, (**b**) grayscale centroid method, and (**c**) proposed algorithm. Errors are highlighted with yellow ellipses, while noise is marked with blue ellipses.