esa-sceva/satcom-chunk-collection huggingface dataset:
# Aircraft Target Interpretation Based on SAR Images
## 5 Discussion  
We researched some methods in the literature, and transferred some methods to the SAR image interpretation field. These methods can improve the detection accuracy of aircraft targets in SAR images.  
We fused the Inception network and SENet attention mechanism to obtain the MR-FCA structure, which is beneficial for the detection of multi-scale targets. Figure 10[36] shows that the distribution of anchor box size is multi-scale in our datasets. The ablation experiment results in Table 4 showed that the designed MRFCA structure can enhance the detection performance for small aircraft targets. The \(mAP_{50-95}\) increased by 9.1% compared with the baseline algorithm for small aircraft targets. In order to further improve the  
Figure 9: Comparison results of small targets recognition between the original YOLOv5s and the improved YOLOv5s: (**a**) bounding boxes of the SAR images in the dataset; (**b**) results for the original YOLOv5s; (**c**) results for the improved YOLOv5s.  
detection ability for the small targets, we proposed adding one more detection head and decoupled processing to obtain the 4 DDH structure. The ablation experiment results in Table 5 showed that the improvement in the small target detection effect is significant. However, the MRFCA and 4 DDH modules increased the number of model parameters. We will attempt to use the lightweight network to reduce model parameters in further research. The number of SAR images in our datasets is limited. We proposed three data augmentation methods (flip, scaling, and mosaic) to expand the datasets and increase data diversity. The ablation experiment results in Table 5 showed that the \(mAP_{50-95}\) increased by 2.2%. The improvement in detection accuracy is not significant. We will attempt to obtain a richer dataset in further research. We adopted the K-means++ algorithm to cluster 12 anchor boxes for the 4 DDH module. The initial size of 12 anchor boxes helped to accelerate network convergence and improve detection accuracy. The ablation experiment results in Table 5 showed that the \(mAP_{50-95}\) increased slightly.  
Moreover, the fusion of SAR and optical images has become a prominent research direction in target recognition based on deep learning. SAR imaging has all-weather and penetrating clouds and mist. However, images are difficult to interpret. In contrast, optical imaging is susceptible to environmental impacts such as clouds and light. However, images are easy to interpret. Our further research topic is how to fuse SAR and optical images to achieve information complementarity and improve detection accuracy.
## 4 Sealing Evaluation of Coprocks in Three UGSs Planned or in Construction
### Geological Characteristics
#### 4.1.3 Zhujiadun Underground Gas Storage  
The Zhujiadun gas field is located in Yancheng city, Jiangsu Province [54]. The development of Zhujiadun gas field started in May 2003. Till December 2020, the cumulative gas production reached 4.57 \(\times\) 10\({}^{8}\) m\({}^{3}\). The caprock is mainly mudstone with a large thickness and stable lateral distribution. The main thickness of the caprock is approximately 400-600 m, the porosity is 0.06-0.60%, and the permeability is approximately 0.085 mD [40]. The construction of Zhujiadun gas storage started on 12 April 2022, with a designed storage capacity of 6.62 \(\times\) 10\({}^{10}\) m\({}^{3}\) and a working gas volume of 3.30 \(\times\) 10\({}^{10}\) m\({}^{3}\). At present, Zhu Chu 1-1, the first gas storage well in Zhujiadun gas storage has been completed, and the designed vertical well depth has reached 3835 m. Based on the geological conditions of three depleted gas fields, the main parameters for the evaluation of caprock's sealing performance can be summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Xu-2**[50] & **Xing-9**[52] & **Zhujiadun**[40] \\ \hline A & 8–50 (26.4) & 20–32.11 (24) & (47.75) \\ B & 0.0001–0.001 (0.0005) & 0.0116–0.551 (0.0223) & (0.085) \\ C & 60–160 (137) & (400) & 400–600 (500) \\ D & Medium–low brittleness 7.0–30.0 & Fragility 48.49–57.85 & Fragility 45.44–48 \\ E & Small amount & Small amount & Small amount \\ F & 0.01–0.074 (0.04) & 0.0484–0.067 (0.059) & (0.33) \\ \hline \end{tabular} Note.: The average values listed in brackets are used for evaluation and calculation in this paper.  
\end{table}
Table 7: Main parameters obtained from three selected depleted gas fields.
## 4 Discussion  
Based on the first principles calculation, the adsorption process of SF\({}_{6}\)-N\({}_{2}\) decomposition components (NO\({}_{2}\), SOF\({}_{4}\), SOF\({}_{2}\), SO\({}_{2}\)F\({}_{2}\)) on pristine and Ag\({}_{2}\)O-doped InN was simulated. By analyzing the adsorption energy, charge transfer, density of states, molecular orbital theory and recovery time, the adsorption mechanism of gas molecules on Ag\({}_{2}\)O-doped InN was further revealed. Pristine InN shows strong adsorption to SOF\({}_{4}\) molecules, but exhibits weak adsorption to NO\({}_{2}\), SOF\({}_{2}\), and SO\({}_{2}\)F\({}_{2}\) molecules. In particular, the charge transfer quantity for SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) adsorption on pristine InN are only 0.022 \(e\) and 0.007 \(e\). The gas sensing capacity of InN to SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) is poor. Compared with undoped InN, Ag\({}_{2}\)O-InN has a significantly higher adsorption energy for NO\({}_{2}\), and a significantly larger adsorption energy and charge transfer quantity for SOF\({}_{2}\). The large adsorption energy makes it difficult for NO\({}_{2}\) to desorb form Ag\({}_{2}\)O-InN, so Ag\({}_{2}\)O-InN is difficult to be a NO\({}_{2}\) sensor. At the same time, the moderate adsorption energy, large charge transfer, and obvious changes in conductivity make Ag\({}_{2}\)O-InN a good sensor for SOF\({}_{4}\), SOF\({}_{2}\), and SO\({}_{2}\)F\({}_{2}\). In conclusion, this paper not only explores the gas sensing mechanism of Ag\({}_{2}\)O-doped InN to SF\({}_{6}\)-N\({}_{2}\) decomposed components, but also provides a theoretical basis for fabricating Ag\({}_{2}\)O-InN sensors for online monitoring of partial discharge in GIS.
## 3 Parameter and Performance Comparison
### Execution Speed
#### 3.2.1 Execution Speed on ARM Platforms  
Arm Cortex M4 was chosen as the reference platform to test the performance of PQC algorithms on microcontroller systems. Almost all finalists were implemented and tested within a publicly available test platform [37] for various types of cryptographic scheme implementations (clean, reference implementation submitted to NIST, optimal in plain C, optimized for the Cortex-M4), as shown in Figure 6. The presented data include 3 versions of Dilithium (Dilithium2, Dilithium3, Dilithium4), 3 versions of Falcon (Falcon512, Falcon1024, Falcon512-tree), and 36 versions of Sphincs+ (see Section 2.3). A comparison is also shown according to [21] for Dilithium (Dilithium2, Dilithium3, Dilithium4) and Falcon [9], which roughly aligns with the comparison according to [37].
## 4 Discussion
In recent years, damage assessment in terms of changes in modal parameters has been widely studied. One example of the former is presented in [42]. The fundamental period of healthy and damaged Spanish buildings, according to the damage grade in the European Macroseismic Scale (EMS-98) [43], was estimated according to the following empirical equations:  
\[T_{\text{G0}}=(0.054\;\pm\;0.002)\;N \tag{6}\]  
\[T_{\text{G1}}=(0.065\;\pm\;0.002)\;N \tag{7}\]  
\[T_{\text{G2}}=(0.077\;\pm\;0.002)\;N \tag{8}\]  
\[T_{\text{G3 and G4}}=(0.089\;\pm\;0.008)\;N \tag{9}\]  
where \(N\) is the number of stories of the building and \(T\) is the period of the building at the corresponding damage grade, described in Table 4. These formulas are based on acceleration monitoring of 59 undamaged RC buildings and 34 damaged RC buildings, after the May 11 th 2011 Lorca earthquake (Spain). The monitored buildings shared similar structural characteristics to the _\"La Viha\"_ building studied in this work. According to Equations (6)-(9), it is estimated that the fundamental periods and frequencies of the _\"La Viha\"_ building, in terms of the damage grade in the EMS-98 scale, are as follows:  
According to the official damage report [44], the _\"La Viha\"_ building presented moderate to severe structural and no structural damage in agreement to the EMS-98 damage grade 2 and 3. Hence, the \begin{table}
\begin{tabular}{c c c c} \hline
**EMS Damage Grade** & **Damage Ratio (\%)** & **\(T\) (s)** & **\(f\) (Hz)** \\ \hline G0: No damage & 0 & 0.27 & 3.70 \\ G1: Negligible to slight damage; & 0–1 & 0.34 & 3.07 \\ G2: Moderate damage & 1–20 & 0.39 & 2.59 \\ G3: Substantial to heavy damage & 20–60 & 0.45 & 2.24 \\ G4: Very heavy damage & 60–100 & - & - \\ G5: Destruction & 100 & - & - \\ \hline \end{tabular}
\end{table} 
Table 4: Classification of damage grades according to the EMS-98 and estimation of the fundamental period/frequency of the _“La Viha”_ building as function of the damage grade. fundamental frequency of the building is estimated to be in the range of 2.24 to 2.59 Hz, consistent with the fundamental frequencies found with both the accelerometric and RAR methods. More research in this subject is necessary. However, estimating damage using equations such as those presented in [42] may be feasible.
## 6 Discussion
These measures ensure that robust defense mechanisms are in place and adaptable for peer-to-peer communication between multiple IoT agents. Lastly, the communication cost of our protocol is estimated at 70% of the usual three-party sum, significantly reducing overhead and contributing to the system's overall efficiency. By addressing these specific security concerns and efficiency challenges, the SMP-OTF4 IOT protocol advances the field of IoT security. It sets a new standard for implementing secure multiparty computation protocols in real-world applications. In terms of future work, we plan to propose a formal analysis of the protocol using tools such as Tamarin Prover or Proverif, as well as explore the scalability of the protocol to federated n-party key agreement and encryption to provide a more comprehensive understanding of the security properties of the protocol and highlight potential advantages or disadvantages, particularly in the IoT domain. Conceptualization, J.F. and P.G. ; software, R.N. ; formal analysis, J.F. and P.G. ; investigation, J.F., P.G., and F.E. ; writing--original draft preparation, J.F. ; writing--review and editing. J.F., F.E., and P.G. ; visualization, P.G. and R.N. ; supervision, J.F. and F.E. ; project administration, J.F. ; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript. This research was funded by project grant number DI21-0079, Universidad de La Frontera. Not applicable. ZeroC-Ice implementation code used for testing can be freely accessed [[https://gitlab.com/smpc-ufro/smpc](https://gitlab.com/smpc-ufro/smpc)]([https://gitlab.com/smpc-ufro/smpc](https://gitlab.com/smpc-ufro/smpc)). The authors extend their heartfelt thanks to all reviewers for their insightful comments and suggestions that contributed to the improvement of our research. The authors declare no conflicts of interest. Sponsors had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript:  
IoTInternet of Things  
OTPOne-time pad  
SMP-OTP4 IOSecure multiparty one-time pad protocol for IoT  
XORExclusive OR
## 3 Applications of the CDS for the Fiber Optic Link
### The Executive
#### 3.3.2 Advanced Actions for BER Improvement  
One of the important challenges in fiber optic communications is to evaluate the Shannon capacity in the presence of fiber nonlinear distortion [36; 37; 38]. Most current optical systems use the input signal distribution whose alphabets occur with equal probability. The performance of such systems can be improved by using more advanced input signal distributions. For example, improvement can be achieved by using geometric modulation shaping or probabilistic modulation shaping [36]. The BER being less than but close to the forward-error-correction (FEC) threshold is not desirable in practical applications because of the additional margin required to account for fluctuations in the fiber optic link [38]. Therefore, the system should do some actions to improve the BER in a fiber optic link. The advanced actions in actions space are the probabilistic modulation shaping and geometric modulation shaping that the executive uses to improve the performance of a nonlinear system. Probabilistic modulation shaping refers to uniform constellation points with the non-uniform probability distribution of symbols whereas geometric shaping refers to non-uniform constellation points with the uniform probability distribution of symbols [38]. Probabilistic shaping can be performed using modified FEC codes [39; 40]. However, these actions are not done blindly by CDS, and it can be done based on the desired goal to maximize \(P(X_{n}^{k}\,|Y_{n}^{k})\) based on taking actions to change \(P(\hat{Y}_{n}^{k})\) and \(P(\hat{Y}_{n}^{k}|\hat{X}_{n}^{k})\), by mapping to different constellation geometry or changing \(P(X_{n}^{k})\) (probabilistic shaping) using Equation (2).
# Line Clipping in 3D: Overview, Techniques and Algorithms
## 6 Evaluation and Experimental Results  
To determine the efficiency of the proposed algorithm, we evaluate it against the Cohen-Sutherland, Liang-Barsky, Cyrus-Beck and Kolingerova algorithms. Each algorithm had to clip a large number of arbitrary lines generated in a three-dimensional space. The boundaries of this space were defined by the points (\(-400\), \(-400\), \(400\)) and (\(400\), \(400\), \(-400\)). The clipping region has the following dimensions: \(200\) pixels width, \(150\) pixels height, and \(100\) pixels depth with its centre at the centre of the screen and the start of the axes X, Y, and Z (Figure 17). The lines were randomly generated anywhere in the three-dimensional space and each algorithm drew only the visible part of the lines inside the clipping region. The time that each algorithm needed to clip the lines was recorded in every execution. The whole process was repeated ten times and, in the end, the average time was calculated.  
The hardware, as well as the software, specifications for our evaluation were as follows: (a) AMD FX Quad-Core [EMAIL_ADDRESS] GHz CPU, (b) RAM 16 GB, (c) NVIDIA GeForce GTX-1050 GPU, (d) Windows 10 Professional operating system, (e) C++ with OpenGL/FreeGLUT under the Code::Blocks environment. Each algorithm should have clipped and drawn one million lines in every execution. The visual result was a rectangular parallelepiped volume full of clipped lines (Figure 18), whereas the evaluation results are shown in Table 1.  
Using the above results, a graph for the clipping times of each algorithm was created (Figure 19).  
By using the formula  
\[\frac{proposed-other}{proposed}\cdot 100\]  
we can evaluate the speed of the proposed algorithm in percent compared to the other algorithms. Compared to the Cohen-Sutherland algorithm, the proposed algorithm was faster by 57.16%; compared to the Liang-Barsky algorithm, our algorithm was faster by 52.73%; compared to the Cyrus-Beck algorithm, our algorithm was faster by 109.75%; and compared to the Kolingerova algorithm, our algorithm was faster by 102.66%.  
The experiment was also performed for similar types of line segments, e.g., segments that were strictly inside the clipping region and did not intersect it. The results were in accordance with the above results; there were no different running times for all algorithms.
# Thermal Performance Analysis of a Double-Pass Solar Air Collector: A CFD Approach
## 3 Results and Discussion
### Hydraulic Behavior  
Figures 4 and 5 show the velocity streamlines with the two analyzed air mass flow rates (flow 1: 0.01 kg/s and flow 2: 0.02 kg/s, respectively). In Figure 4 (flow 1: 0.01 kg/s), the configurations with an inlet from below (a and b), a sudden expansion occurs near the inlet of the cavity, forming an eddy in the upper part of the collector. While in the configurations with the entrance at the top (c and d), the eddy forms at the bottom. Further, in all configurations except (b), it is observed that after the air enters the cavity, several families of eddies form until the end of the collector (z = 2.0 m). On the other hand, in configuration (b), a large eddy is observed in the first half of the cavity, and then the formation of some smaller eddies. Finally, it is essential to note that the highest magnitude velocities are generated at the elbow and at the beginning of the duct (second pass).  
Figure 4: Streamlines at the collector cross-section with air mass flow 1: 0.01 kg/s. (**a**) Down–Down; (**b**) Down–Up; (**c**) Up–Up; (**d**) Up–Down.  
In Figure 5, a similar behavior to the one described for flow 1 is shown for flow 2 (0.02 kg/s), where a sudden expansion of air near the inlets occurs. For configurations (a) and (b) (inlet from the bottom), a visible jet can be observed at the bottom of the cavity; here, an eddy at the upper region can also be observed. Contrarily, in the configurations with an inlet from above, the jet is formed at the top, whereas the eddy occurs at the bottom region. Additionally, in configurations (a) and (c) (either both inlet and outlet from above or below), there is a primary air current with high speed. In configuration (a), the current goes up and down the cavity, generating several eddies at the upper and bottom sides opposite to the main flow. Contrarily, in configurations (b) and (d), which have inlet and outlet in opposite positions on the \(y\)-axis, a large eddy is formed near the cavity inlet, observing a larger eddy in configuration (b) that moves towards the exit in a disorderly manner. Moreover, the largest speed occurs at the elbow of the collector, having higher speeds in the configurations with a bottom outlet.  
One reason that explains a disordered and asymmetric flow is because of the collector tilt (see Figure 1). In addition, the pressure increase justifies the phenomenon of the sudden air contraction caused by the elbow area reduction. Furthermore, a higher mass flow influences the amplitude and turbulence of the eddies found in the first section of the cavity.
## 2 Materials and Methods
### Isothermal Fatigue Loading of P91 and HiperFer-17 Cr2  
The fatigue specimens were loaded in isothermal fatigue tests using a servohydraulic test system MTS 100 kN equipped with a radiation furnace to achieve test temperatures of 600 \({}^{\circ}\)C, 635 \({}^{\circ}\)C and 650 \({}^{\circ}\)C, according to a previous study [12]. To measure and control the temperature of the specimens, a thermocouple of type K was applied in the middle of the gauge lengths of the specimens. The specimens were loaded with two different types of load scenarios, i.e., upper High-Cycle Fatigue (HCF) loads with \(f=5\) Hz and lower stress amplitudes, leading to failure at around 40,000 cycles, as well as Low-Cycle Fatigue (LCF) loads with \(f=0.05\) Hz and higher stress amplitudes, leading to failure at around 4000 cycles (Table 2). Furthermore, the fatigue specimens were loaded with stress amplitudes \(\sigma_{a}\) leading to similar numbers of cycles to failure across all three temperatures and for both materials, which were approximately 2000 cycles for the LCF tests and 20,000 cycles for the HCF tests (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{
\begin{tabular}{c} Loading \\ parameters/materials \\ \end{tabular} } & \(f\) [Hz] & \multicolumn{3}{c}{0.05 (LCF)} & \multicolumn{3}{c}{5 (HCF)} \\ \cline{2-9}  & \(T\) [\({}^{\circ}\)C] & 600 & 635 & 650 & 600 & 635 & 650 \\ \hline \multirow{2}{*}{HiperFer-17 Cr2} & \(\sigma_{a}\) [MPa] & 280 & 260 & 245 & 220 & 210 & 190 \\ \cline{2-9}  & Interruption after & \multicolumn{3}{c}{10; 40; 300; 2000 cycles} & \multicolumn{3}{c}{20; 2000; 20,000 cycles} \\ \hline \multirow{2}{*}{P91} & \(\sigma_{a}\) [MPa] & 320 & 280 & 250 & 310 & 270 & 260 \\ \cline{2-9}  & Interruption after & \multicolumn{3}{c}{10; 300; 2000 cycles} & \multicolumn{3}{c}{20; 2000; 20,000 cycles} \\ \hline \end{tabular}
\end{table}
Table 2: Loading conditions applied on the specimens of P91 and HiperFer-17 Cr2.  
At the given loading conditions, fatigue specimens were loaded in intervals with a defined number of cycles and then the tests were interrupted to detect the corresponding microstructural changes and the respective mechanical properties. In addition to hardness, the plastic strain amplitude at interruption \(\varepsilon_{a\text{-}p\text{-}inter}\). was determined for every investigated specimen as a mechanical parameter, providing information about changes in the cyclic deformation behavior between the intervals. This enabled the detection of the microstructural evolution and the mechanical and the micromagnetic properties during the isothermal fatigue loads. The number of cycles for the test interruptions was determined based on the changes in the cyclic deformation curves obtained, which were explained in detail in [12].
## 4 Discussion
### Link between Parameter Sets and Model Structures
This modification improved the performance of the MODIS GPP algorithm for a tropical forest. Separating the canopy into sunlit and shaded leaves, [PERSON] _et al._[13, 15] developed a two-leaf light use efficiency model based on the MODIS algorithm, and the model properly described differences in the light use efficiencies of sunlit and shaded leaves. Further improvement should focus on the photosynthesis models' capacities in capturing seasonal change. In this study, all models exhibited large uncertainties in winter and spring, even if the key parameters in the two-leaf Farquhar model have been well optimized (Table 3, Figures 3 and 4). This result is the same as the simulations from 26 models at 39 EC sites [36]. Fortunately, many approaches could be referred to quantify seasonal variation of key variables in GPP estimates. A comparison between using the dynamic maximum velocity of carboxylation (\(Vc_{max}\) in Equation (A3)) and the constant \(Vc_{max}\) in the Farquhar models by [PERSON] _et al._[99] indicated that \(Vc_{max}\) variation had remarkable effects on GPP, and an overestimate of 15% was caused by assuming \(Vc_{max}\) to be constant in a cool-temperate deciduous broadleaf forest. [PERSON] _et al._[100] upscaled the ecosystem parameters \(Vc_{max}\) with _LAI_ for 81 EC sites, but the seasonal variation of \(Vc_{max}\) could not be sufficiently explained at the ecosystem scale. By seasonally changing the photosynthetic parameters in a Farquhar-type biochemical model, [PERSON] _et al._[101] successfully reproduced the observed response in net assimilation rates at leaf scale. According to satellite data and the Biome-BGC terrestrial ecosystem model, [PERSON] _et al._[102]  
Figure 5: The annually averaged soil water factor (\(\beta_{t}\)) _versus_ the optimized maximum light use efficiency (\(\varepsilon_{max}\)) used in the LUE and LUE-SW, excluding four sites with \(\varepsilon_{max}\) greater than 1.3 gC\(\cdot\)MJ\({}^{-1}\) (gray symbols). The bar represents \(\pm\)0.5 standard deviation of \(\beta_{t}\). suggested that proper setting of the root depths was important to simulate GPP seasonality in tropical forests. On the basis of these studies, a widely accepted concept is needed to improve seasonal change in GPP estimate.