esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 6 Simulation Results
### Impact of Clear Weather on PDM-SAC-OCDMA-Enabled FSO System  
The attenuation of CW is 0.14 dB/km [28]. This is small when compared to attenuation caused by severe weather conditions such as fog and snow. As such, it has little effect on the signal transmission. The impact of various propagation ranges on the performance of six users of the proposed PDM-SAC-OCDMA-enabled FSO system in terms of log(BER) is shown in Figure 3. It is seen that all six users, who are transmitted on two different polarization states (users 1, 2, and 3 are transmitted on \(X_{polar}\) and users 4, 5, and 6 are transmitted on \(Y_{polar}\)), can propagate up to 10 km with log(BER) values varying between \(-12.55\) and \(-9.24\).  
Figure 4 depicts the Q-factor versus FSO link for different users in our proposed model. It should be noted that users 1 and 4, who transmit their dataon \(X_{polar}\) and \(Y_{polar}\), respectively, reached a propagation range of 10 km with nearly the same Q-factor (6). A higher Q-factor of \(\sim\)7 is obtained at the same range for users 3 and 6. Users 2 and 3 have a Q-factor of 6.7 at 10 km.  
The eye patterns at 10 km propagation range for users 1, 2, 3, 4, 5, and 6 are displayed in Figure 5. From the results in Figures 3-5, we can see that user 3 and user 6, using the same code, performed better than other users (1, 2, 4 and 5). The main reason for the better performance of these channels is that they have the same code (101000). As such, they are occupied at the first point of the spectrum, leading to higher optical signal to noise ratio and so better log(BER). The other channels experience significant noise interference from neighboring channels due to the FBG bandwidth and reflectivity. Therefore, the size of eye diagrams for user 3 and user 6 are different than the other users. The large eye  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bit rate & 20 Gbps per user \\ Number of users & 3 on \(X_{polar}\) and 3 on \(Y_{polar}\) \\ Electrical-Bandwidth & 15 Gbps \\ CWL source transmit power & 15 dBm \\ CWL source linewidth & 10 MHz \\ Transmitter aperture diameter & 10 cm \\ Receiver aperture diameter & 20 cm \\  & CW: from 4 to 10 km \\  & LF: from 1 to 1.6 km \\  & MF: from 0.7 to 1 km \\ FSO range & HF: from 0.58 to 0.76 km \\  & WSF: from 1.05 to 1.2 km \\  & DSF: from 0.20 to 0.26 km \\ PIN photodetector responsivity & 1 A/W \\ Thermal noise power density & \(10^{-22}\) W/Hz \\ Absolute temperature & 300 K \\ Receiver load resistance & 1030 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters [5; 21; 24; 28].  
opening suggests a successful received signal at a 10 km FSO link with 120 Gbps overall transmission capacity.  
Figure 4: Q-factor for PDM-SAC-OCDMA-enabled FSO transmission under CW versus range.  
Figure 3: log(BER) for PDM-SAC-OCDMA-enabled FSO transmission under CW versus range.  
Table 5 lists the performance results of the six users of the PDM-SAC-OCDMA-enabled FSO transmission at 10 km under clear weather conditions.
## 2 Model Description
### HMW Dynamic Modeling
#### 2.2.2 Centroid Dynamic Model  
For the fuselage and wing, the velocity vectors can be expressed as  
\[\left\{\begin{array}{l}v_{0}=v_{f}=\frac{\mathrm{d}r_{0}}{\mathrm{d}t};\\ v_{S_{i}}=\frac{\mathrm{d}S_{i}}{\mathrm{d}t}=\frac{\mathrm{d}r_{S_{i}}}{ \mathrm{d}t}-\frac{\mathrm{d}r_{0}}{\mathrm{d}t}=v_{i}-v_{o}\end{array}\right. \tag{2}\]  
where \(v_{0}\) denotes the velocity of the fuselage, and \(v_{S_{i}}\) denotes the velocity of the \(i\)th wing.  
According to the momentum theorem, the dynamic equations of the centroid of the fuselage, the left and right wings can be obtained as  
\[\left\{\begin{array}{l}F_{af}+F_{1f}+F_{2f}+G_{f}=m_{f}\frac{\mathrm{d}v_{f} }{\mathrm{d}t}\\ F_{a1}+F_{f1}+G_{1}=m_{1}\frac{\mathrm{d}v_{1}}{\mathrm{d}t}\\ F_{a2}+F_{f2}+G_{2}=m_{2}\frac{\mathrm{d}v_{2}}{\mathrm{d}t}\end{array}\right. \tag{3}\]  
where the subscript \(f\), \(1\), and \(2\) represent the fuselage, the left wing, and the right wing, respectively. \(F_{ai}\) denotes the aerodynamic force of the \(i\)th rigid body; \(F_{ij}\), \(i\
eq j\) is the force of \(i\)th rigid body acting on \(j\)th rigid body; \(G_{i}\) denotes the gravity of the \(i\)th rigid body; \(v_{i}\) denotes the velocity of the \(i\)th rigid body; \(m_{i}\) denotes the mass of the \(i\)th rigid body.  
Organizing the above equations gives  
\[F_{a}+G=m\frac{\mathrm{d}v_{f}}{\mathrm{d}t}+\sum\limits_{i=1}^{2}m_{i}\frac{ \mathrm{d}v_{S_{i}}}{\mathrm{d}t} \tag{4}\]  
where \(F_{a}\) denotes the aerodynamic force of the HMW; \(G\) denotes the gravity of the HMW.  
Compared with fixed-configuration aircraft without morphing capability, the centroid dynamics of the HMW have the additional force \(F_{S}=\sum\limits_{i=1}^{2}m_{i}\frac{\mathrm{d}v_{S_{i}}}{\mathrm{d}t}\). For further derivation, we can obtain  
\[\frac{\mathrm{d}v_{S_{i}}}{\mathrm{d}t}=\frac{\mathrm{d}^{2}S_{i}}{\mathrm{d}t ^{2}}=\frac{\mathrm{d}}{\mathrm{d}t}\bigg{(}\frac{\delta S_{i}}{\delta t}+ \omega\times S_{i}\bigg{)}=\frac{\delta^{2}S_{i}}{\delta t^{2}}+2\omega\times \frac{\delta S_{i}}{\delta t}+\frac{\delta\omega}{\delta t}\times S_{i}+ \omega\times(\omega\times S_{i}) \tag{5}\]  
where \(\omega\) is the body rotational angular velocity.
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 3 Condition Analysis and Measuring Instruments
### Measuring Instrument  
For the acquisition of ship motion characteristic data, and to obtain the motion amplitude of each degree of freedom by wireless data transmission, a ship motion attitude acquisition device designed independently was selected. To monitor the height of the first wave of the landslide-induced surge, we used an ultrasonic wave acquisition analyzer of the National Waterway Regulation Engineering Research Center of Chongqing Jiao Tong University. It is equipped with seven sets of sensors to monitor the propagation law of swell waves in channel models of landslides. The data acquisition system mainly collects the data of wave height, wavelength, wave period, and relevant data of ship movement in different regions, with the collection time of 200 s and the acquisition frequency of 50 Hz.
## 2 Research Methodology  
This review is based on bibliometric analysis and content analysis of the literature body on the topic of using robots for inspection and monitoring. Previous literature review studies [3; 24] have used Web of Science (WoS) and Scopus databases to search for relevant papers on a topic. Both of these databases have a large range of high-quality academic publications in engineering and management domains [24]. To further ensure that all relevant papers are included in the review, the authors also used Google Scholar to find any relevant papers not indexed in WoS and Scopus. The keywords (robot* OR uav OR drone OR \"unmanned aerial vehicle\") AND (\"construction inspection\" OR \"infrastructure inspection\" or \"construction monitoring\" OR \"progress monitoring\" OR \"building inspection\" or \"inspection of building\" or \"monitoring of building\") were used to search in all the fields of the publications. The star (*) symbol was used as a wildcard to include words such as \"robots\", \"robotic\", and \"robotics\". After compiling the papers from all sources, 1538 results were generated in total (308 from WoS, 553 from Scopus, and 677 from Google Scholar). After removing the duplicates, 1264 sources were retrieved. Conditional formatting function in MS Excel was used to remove duplicate entries.  
Preliminary shortlisting was performed by reading the titles and the abstracts of the papers. Only those publications were kept that utilized robots in some way for building and infrastructure inspection and monitoring. Papers on the use of robots for labor or construction jobs were not included. Research on general-purpose robots that were not specific to the built environment application was also removed. For the purpose of this review, a \"robot\" is any mobile system that can operate autonomously or manually and has sensors to navigate and collect data. Some examples of robots in the context of this study are unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), marine vehicles, microbots, wall-climbing robots, cable-suspended robots, and legged robots. Apart from the actual applications of robots, research on enabling technologies (such as computer vision) that make the use of robots more efficient and safer in the future while being independent of the type of robot has also been included in this review.  
After preliminary filtering, 575 sources were shortlisted for further review. This was followed by a secondary filtering, in which the shortlisted papers were analyzed by reading the major sections to ascertain the overall theme. The criteria discussed above were used to include or exclude the papers. Finally, 269 papers remained for in-depth analysis. The remaining papers were reviewed using the qualitative content analysis methodology used by past researchers [24; 25] to identify the application domains and research areas commonly studied in the literature. In this step, papers were tagged with custom keywords for classification. The resulting classes and categories are discussed in more detail in the following sections. The methodology for this review is illustrated graphically in Figure 1.
## 3 Dual-Stack Single-Radio Communication Architecture (DSSRCA)  
The proposed Dual-Stack Single-Radio Communication Architecture (DSSRCA) aims to provide a flexible communication infrastructure that supports UAV-to-WSN and UAV-to-Sink communications using a single radio. It is able to commute between best-effort and reliable communication strategies according to the different network QoS requirements present in UAV-to-WSN and UAV-to-Sink communications. An overview of DSSRCA organization in terms of partitions, layers, and sub-components is illustrated in Figure 1.  
Figure 1: Dual-Stack Single-Radio Communication Architecture (DSSRCA) overview: customized transport layer on top of 6 LoWPAN (**left**) and the small-footprint Wireless Sensor Network (WSN) protocol (**right**).  
The top layer in Figure 1 refers to the application level, which could be either at the Sink node or at the mobile data collector (MDC). On the left, it is the customized transport layer on top of 6 LoWPAN that constitutes the reliable protocol used in UAV-to-Sink communication. Finally, the small-footprint best-effort protocol used for UAV-to-WSN communication is shown on the right side.  
The following subsections detail the two main partitions that compose DSSRCA: Reliable and Best-Effort protocols. Additionally, details regarding the implementation and some experimental results are also presented.
# Simulation Study on Nanofluid Heat Transfer in Immersion Liquid-Cooled Server
## 3 Data Processing and Analysis
### Analysis of the Effects of Nanofluid Categories on Flow Heat Transfer
#### 3.1.2 Analysis of Average Nusselt Number \(Nu\) and Frictional Resistance Coefficient\(f\)  
The average Nusselt number \(Nu\) is a criterion number used to characterize the intensity of convective heat transfer, and \(Nu\) is introduced in this paper as an evaluation index for the heat transfer performance of nanofluid flow.  
\[Nu=\frac{hD_{h}}{\lambda_{f}} \tag{8}\]  
\[D_{h}=\frac{2 ab}{a+b} \tag{9}\]  
where \(h\) denotes the average convective heat transfer coefficient, W/(m\({}^{2}\cdot\)K); \(D_{h}\) denotes the characteristic size, m; \(\lambda_{f}\) denotes the fluid thermal conductivity, W/(m\(\cdot\)K); and \(a\) and \(b\), respectively, denote the server width and height, m.  
In addition, enhanced heat transfer should not come at the cost of a significant increase in frictional resistance, so an analysis of the frictional resistance coefficient needs to be carried out, with the frictional resistance coefficient \(f\) expressed as:  
\[f=\frac{2D_{h}\Delta P}{\rho Lu^{2}} \tag{10}\]  
where \(\Delta P\) denotes server import and export pressure drop, Pa; \(\rho\) denotes the fluid density, kg/m\({}^{3}\); \(L\) denotes the server length, m; and \(u\) denotes the fluid flow rate, m/s.  
As shown in Figure 8, under the same working conditions, the average \(Nu\) numbers of the five types of nanofluid under enhanced heat transfer are ranked as Al2O3-FC40 > Al-FC40 > TiO2-FC40 > CuO-FC40, and the frictional resistance coefficients \(f\) are ranked as Cu-FC40 > CuO-FC40 > TiO2-FC40 > Al2O3-FC40 > Al-FC40. Among them, the Cu-FC40 nanofluid has the smallest \(Nu\) and the largest \(f\); the Al-FC40 nanofluid has a slightly lower \(Nu\) than the Al2O3-FC40 nanofluid; and the Al-FC40 nanofluid has the smallest\(f\).  
As the analysis of the above data concluded that Al-FC40 nanofluid flow has the best heat transfer effect, so it was chosen for the subsequent study to discuss the effect of nanoparticle volume fraction \(\alpha\) and fluid flow rate \(u\) on the heat transfer performance.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 4 Experiment
### Experiment Result
#### 4.3.2 Quantitative Comparison  
We evaluate our method against current SOTA methods on the metal corrosion dataset, where each SOTA method is retrained and fine-tuned specifically for this dataset. The results of these comparisons are summarized in Table 4. Our MCIDN model achieves superior performance in both PSNR and SSIM metrics. Specifically, MCIDN attains a PSNR of 32.8645 dB on the blurry metal corrosion dataset, which is 1.2607 dB higher than NAFNet. Although the SSIM improvement over NAFNet is relatively smaller at 0.0019, it is still noteworthy.  
Our method also surpasses HINet by 1.3865 dB in PSNR and 0.002 in SSIM. When compared to DMPHN, our model exhibits a significant improvement of 5.2702 dB in PSNR and 0.0061 in SSIM. Additionally, our approach outperforms other competitive methods including Stripformer, M  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **PSNR** & **SSIM** & **Params(M)** & **FLOPs(G)** \\ \hline Blurry Image & 23.4609 & 0.9229 & - & - \\ Stripformer [20] & 26.2827 & 0.9543 & 19.7 & 170.46 \\ MPRNet [16] & 27.1775 & 0.9649 & 20.1 & 588 \\ Restormer [18] & 27.4252 & 0.9660 & 26.1 & 140 \\ DMPHN [15] & 27.5943 & 0.9607 & 21.7 & - \\ HINet [48] & 31.4780 & 0.9748 & 88.7 & 170.7 \\ NAFNet-64 [33] & 31.6038 & 0.9749 & 63.36 & 67.8 \\ MCIDN (Ours) & 32.8645 & 0.9768 & 28.59 & 35.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison of our proposed network with previous methods.  
Figure 6: The PSNR and SSIM curves comparing SOTA methods with our proposed method. The curves illustrate the performance improvements achieved by our method over the iterations.  
The outstanding performance of our MCIDN model can be attributed to the innovative integration of spatial and frequency attention mechanisms, which enhance the model's ability to capture and refine crucial features in blurry metal corrosion images. This integration allows for a more precise restoration of image details, leading to higher PSNR and SSIM values.  
Moreover, our model maintains a competitive range in terms of parameters and FLOPs, ensuring that the enhanced performance does not come at the cost of significantly increased computational resources. This balance between performance and efficiency makes our MCIDN model not only highly effective but also practical for real-world applications where computational resources may be limited.  
The significant improvements in both PSNR and SSIM metrics across various comparisons underscore the robustness and generalizability of our approach. These results demonstrate that our method sets a new benchmark for deblurring tasks in the context of metal corrosion images, making it a valuable tool for industrial applications where image clarity is critical for accurate analysis and decision-making.
## 2 Literature Review
### Feature Dimension Reduction Using WordNet Ontology  
Ontology-based research has recently become important in the search for linguistic patterns with the aim of increasing classification success. In a study on this subject aiming to classify clauses, entire lines, and sentences in legal text analytics work on contracts, smaller datasets were used, and fewer classes were focused on [13; 14]. In another study, a method for extracting specific entities related to market analysis was presented utilizing domain ontology [15].  
For the improvement of text classification performance, the information in knowledge bases such as Wikipedia and WordNet can be applied. Semantic correction was presented in [16] by including a priori information from WordNet in text classification. To incorporate semantic similarity between words, a smoothing matrix was employed, which was derived from WordNet. The smoothing was applied to TF-IDF feature vectors to improve semantic coherence. This causes the feature values of terms that are semantically related to increase. Another study that used WordNet to design a semantic smoothing kernel for text classification is [17]. The similarity between words was calculated based on their shared super concepts. [PERSON] et al. [18] used LSI to incorporate semantic relations between terms calculated into a kernel.  
In another study, sentiment classification was performed using 1,578,627 tweets. Classification success was measured using BoW and Semantic BoW. Here, the similarity calculation between the words was performed using WordNet, and the attribute size was reduced by reducing similar words to one. By using the AdaBoost Classifier and theKNeighbors Classifier together with Semantic BoW, it was observed that the classification success increased by 1-4% compared to the classical BoW method. However, since the accuracy when using the Semantic BoW method only reached 69%, an improved success rate is needed [19].  
Ontology-based dimension reduction methods have become important in classification studies. In a study in which feature dimension reduction was carried out by avoiding word repetition with WordNet, a structure was proposed in which classification was performed with CNN [20]. In another study on this subject, the authors compared Naive Bayes, Jrip, J48 and SVM classification methods with PCA and ontology-based feature reduction methods, and it was seen that ontology-based reduction gave better results than PCA. The highest success rate was obtained by SVM, at 85%. In the study, 15 imbalanced categories belonging to the Reuters-21578 dataset were used [21].
## 4 Core Algorithm
### Explorable Node Selection  
A stage in the algorithm, Explorable Node Selection, generates a set of nodes \(S_{explored}\), which contains all the camera's next explorable locations from the current location \(P^{C}\) with available current energy for moving \(E_{bound}\). The explorable set is found using the following steps (Figure 7):  
1. With current energy bound \(E_{bound}\), find all nodes in the space that can be reached from the current node \(P^{C}\). In other words, all feasible nodes are in the set: \[S_{feasible}=\{Node_{index}|E(P^{C},P_{index})\leq E_{bound}\}\] (8) where \(P\) is the position of a node, and \(E(P_{a},P_{b})\) is the estimated energy cost from \(P_{a}\) to \(P_{b}\). When the current energy bound \(E_{bound}\) is greater than a predefined energy threshold \(E_{T}\) (\(E_{bound}>E_{T}\)), then the algorithm is safe to explore all nodes in the feasible range \(S_{feasible}\) in the next iteration loop. In other words: \[S_{explorable}=S_{feasible},\;\;\;\;if\;E_{bound}>E_{T}\] (9)Note: \(E(P_{a},P_{b})\) is generated from a dynamic model of a robot-arm control system. The details of this function are developed in the following section of this paper;
2. When \(E_{bound}\leq E_{T}\), step 2 and step 3 are applied to find the explorable set. In the feasible set, look at all explored nodes and find the one that has the smallest number of pictures. In other words: \[S_{feaskeep}=\{Node_{index}\,|\,Node_{index}\in S_{feasible}\cap S_{explored}\,\}\] (10) \[\exp\_min=\{index\,|N_{\text{exp\_min}}=\text{min}\Big{(}N_{index}\Big{|} Node_{index}\in S_{feaskeep}\Big{)}\,\}\] (11) where \(S_{explored}\) is the set of all explored nodes. \(S_{feaskeep}\) is the intersection set between \(S_{feasible}\) and \(S_{explored}\);
3. The estimation function may not give accurate results for some unexplored nodes. Therefore, it is possible that the algorithm may make the camera end up in a node that has a large number of pictures in some iteration loops. Therefore, our algorithm needs to make sure, at worst, it has enough energy to go back to the best (minimum number of pictures) node that was explored when the available energy is low (\(E_{bound}\leq E_{T}\)). This further reduces the feasible set \(S_{feasible}\). The explorable set \(S_{explorable}\) can be written as: \[S_{explorable}=\{Node_{index}|E_{1}+E_{2}\leq E_{bound}\},\quad\text{ if }E_{bound}\leq E_{T}\] (12) \[E_{1}=E(P^{c},P_{index}),\ E_{2}=E(P_{index},P_{\text{exp\_min}})\] (13) Figure 7 illustrates the selection of explorable node sets.  
Figure 7: Find the explorable set \(S_{explorable}\) when the global minimum (red star) is inaccessible. (**a**) Step 1: Obtain the feasible range \(S_{feasible}\) (green dashed line). (**b**) Step 2: Find the explored node with a minimum picture number in the feasible range. Initial nodes (blue stars) are explored (blue circles). \(Node_{1}\) is \(Node_{exp\_min}\). (**c**) Step 3: Obtain explorable range when \(E_{bound}\leq E_{T}\). The explorable range \(S_{explorable}\) (blue dashed line) is a subset of \(S_{feasible}\).
# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 3 Event Detection and Sensory Data Aggregation
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