esa-sceva/satcom-chunk-collection huggingface dataset:
In this study, data from all available satellite altimeters were used to analyze the internal tides and describe their properties in the Arabian Sea. Specifically, we detected and reproduced surface mode-1 \(M_{2}\) internal tides. Using the obtained the data, we could provide a general description of the internal tides' characteristics, including their sources, propagation directions, and integrated energy flux and seasonal variations. We further analyzed the mechanisms that drive the seasonal variation in the internal tides with respect to the superposition of waves and horizontal changes in stratification. The remainder of this paper is organized as follows. Section 2 describes the data and methods. Section 3 presents the main results of the characteristics of the internal tides and their seasonal variability. A general discussion is provided in Section 4. Finally, Section 5 summarizes the main conclusions.
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 4 Holographic Metasurfaces for OAM Detection
This term rotates the \(m\)th beam and changes the interference status between the beam with other four beams. Therefore, by setting a proper value for \(\alpha_{m}\), we can weaken the constructive interference between adjacent beams, which is the main reason for the high side lobes. The optimal solution for \(\alpha_{m}\) is \(\alpha_{1}~{}=~{}1.0472\), \(\alpha_{2}~{}=~{}1.0472\), \(\alpha_{3}~{}=~{}2.0944\), \(\alpha_{4}~{}=~{}2.7925\), \(\alpha_{5}~{}=~{}4.5379\). The comparison results after optimization are shown in Figure 13f. When \(l_{0}=\pm 1,\pm 2\), the field intensity at a desired location is increased. A general trend of lowered side-lobe level can also be observed. It can be noted that the intensity becomes lower for \(l_{0}=0\). One may break the constructive interference between two adjacent beams but result in an enhanced interference between one of the adjacent beam with the other adjacent beam on the other side. Hence, it is not possible to achieve improvements for all the five incident cases, but there has to be a trade off. Overall, we can observe suppressed side lobes and increased field intensities at desired locations.
## 3 Analysis and Results of CGAN-IFCM  
The effectiveness of the proposed CGAN-IFCM is analyzed in four parts: (i) the performance of the proposed CGAN-IFCM; (ii) the necessity of using CGAN by comparing the performance of the detection models using CGAN-IFCM and IFCM; (iii) the necessity of using IFCM by comparing the performance of the detection models using CGAN-IFCM and CGAN-FCM; (iv) comparing CGAN-IFCM with two typical data generation methods (synthetic minority oversampling technique (SMOTE) and cost-sensitive learning (CSL)); (v) comparing the performance between CGAN-IFCM and existing methods. As previously noted, 10-fold cross-validation was adopted for performance evaluation, for which the value of 10 has been widely adopted in the literature [31; 32].
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 4 Machine-Learning-Based Models
### Decision Tree Regression Model  
Decision tree (DT) learning plays a critical role in solving classification and regression problems. The classification and regression accuracy for its performance, when compared to existing techniques, is sufficient. The classification model learned through these techniques is represented as a tree and is called a decision tree. ID3Q [84], C4.5Q [85], and CART [86] are decision tree learning algorithms. More details can be found in [87].  
The proposed decision tree creates a regression model that uses the tree structure form. It breaks down the measured dataset into small subsets while the corresponding decision tree is progressively developed, with the final output being a tree with decision nodes and leaf nodes. The decision node (an input) contains feature branches (e.g., Tx-Rx distance, AoA, Tx Height, frequency), each representing values for the attribute tested. The leaf node (e.g., the path loss value) represents a decision on the numerical target. The top-most decision node in a tree that associates with the best predictor is known as the root node. The DT regression model identifies ways to split the measured data via an algorithmic approach into smaller subsets. This approach is repeated several times until the best results are obtained. The optimum rules that lead to the best results are obtained by using variance reduction as a measure of impurity. These results are used to calculate the variance reduction for each output. A higher variance leads to a higher impurity, meaning that the corresponding conditions should be chosen as the optimum conditions for the model. Based on our model, the selected hyperparameters' values are the MAE as a variance calculation function, and the tree has 69 nodes.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Metrics  
The objective of performing simulations is to evaluate the performance of our proposed schemes in terms of average Packet Delivery Ratio (PDR), energy tax, end-to-end delay and Accumulative Propagation Distance (APD). These metrics are defined as:  
* Average PDR: It is defined as the total amount of data packets successfully received at the sink node to the total number of packets generated by the network nodes. It is calculated as: \(PDR=\frac{Packets_{Received}}{Packets_{Transmitted}}\).
* Average energy tax: It is defined as the average energy consumption per node when a data packet is sent successfully to the sink. It is measured in joules (J). It is computed according to Equation (6).
* Average end-to-end delay: It is defined as the average time to transmit data from the source to the destination successfully. It is measured in seconds (s). The expression to find out complete path delay is: \(\sum\limits_{h=1}^{h_{max}}\frac{D(h_{i},h_{j})}{V}\). \(h\) is the hop count of nodes \(i\) and \(j\). Vshows the speed of the acoustic signal, and \(D(h_{i},h_{j})\) is the distance between node \(i\) and \(j\).
* Average APD: It is the average accumulated propagation distance of all the data packets that are successfully sent to the sink nodes. It is measured in kilometers (km). The mathematical formulation is as follows: \(\frac{j}{i}D(i,j)\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Nodes & 100–500 \\ Sinks & 9 \\ Network Dimensions (km\({}^{3}\)) & 10 \(\times\) 10 \(\times\) 10 \\ Movement Speed of Nodes (m/s) & 2 \\ Acoustic Propagation Speed (m/s) & 1500 \\ Initial Energy (J) & 100 \\ Transmission Range (km) & 2 \\ Transmission Power (dB reuPa) & 90 \\ Total Bandwidth (kHz) & 4 \\ Sending Energy (W) & 50 \\ Receiving or Idle Energy (mW) & 158 \\ Header Size (bytes) & 11 \\ Payload (bytes) & 72 \\ Data Rate (kbps) & 16 \\ Size of ACK (bits) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
* Network lifetime: It is the time period for which the network remained operational. It is measured in seconds (s). The mathematical expression is: \(\sum\limits_{t=1}^{t_{max}}NL(t)\), where \(NL\) shows the network lifetime in unit time (t).
* Packet drop ratio: It is defined as the ratio of the number of packets transmitted, however not delivered successfully at the destination node. It is formulated as \(1-PDR\).
* Alive nodes: It is the total number of nodes still alive after the termination of network operations. The mathematical expression is \(AN=N-DN\), where \(AN\) is the number of alive nodes, \(DN\) denotes the quantity of dead nodes after complete battery depletion and \(N\) depicts the total number of nodes deployed in the network.
## 4 Conclusions  
The results presented in this paper illustrate that amide-coupling between a cathode and the CO\({}_{2}\)-fixing microorganism, _R. shareoides_, improves MES reaction. A graphite felt cathode was modified with a combination of chitosan and a carbodiimide compound. Negatively charged _R. shareoides_ cells (\(\zeta\)-potential, \(-\)31.88 \(\pm\) 3.31 mV) facilitated the formation of biofilm on positively charged modified cathodes. In particular, a robust biofilm of _R. shareoides_ was developed on the chitosan/carbodiimide compound-modified cathode. Therefore, a chitosan/carbodiimide compound-modified (GFE/Chit/CDI) cathode was adopted for the optimal condition of cathode modification in this study. This modification enhanced the biomass and succinic acid production from CO\({}_{2}\) conversion in a MES reactor (applied potential at \(-\)0.6 V vs. Ag/AgCl). The calculated faradaic efficiency of the MES reactor equipped with a chitosan/carbodiimide compound-modified cathode was 98.0% (coulomb to _R. shareoides_ biomass and succinic acid). This is a promising starting point for the development of future MES-driven CO\({}_{2}\) biorefinery process using _R. shareoides_ to produce various spectra of metabolites such as biofuels and platform chemicals.  
Figure 6: Final biomass (attached and planktonic cells) and succinic acid production from microbial electrosynthesis reaction of _R. shareoides_ for 14 d under \(-\)0.6 V (vs. Ag/AgCl) and 5% CO\({}_{2}\). The weight of attached cells (g m\({}^{-2}\)) was normalized to the area of the graphite felt electrode. Bar chart, succinic acid (mgL\({}^{-1}\)). Square symbol (blue), planktonic cells (gL\({}^{-1}\)). Circle symbol (red), attached cells (g m\({}^{-2}\)).  
**Author Contributions:** Conceptualization, S.Y.L. and H.N.F.; methodology, J.L.; software, J.S.; validation, S.L., M.M. and Y.-K.O.; formal analysis, Y.R.L.; investigation, H.N.F. and M.P.; resources, J.L.; data curation, S.Y.L.; writing--original draft preparation, H.N.F.; writing--review and editing, S.Y.L.; visualization, J.L.; supervision, S.Y.L.; project administration, J.-S.L.; funding acquisition, J.-S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was conducted under the framework of the Research and Development Program of the Korea Institute of Energy Research (KIER-C1-2432). This research also supported by the R&D Innovation cluster of the Republic of Korea (2021-DD-RD-0033-01-202).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
## 3 Electrospinning of High-Performance Polymers
### Para-Aramid
Another innovation in the electrospinningning of p-aramid involved the use of sidechain substitutions to improve the solubility of the polymer [3]. Through N-substitutions, the solubility of p-aramid was improved. Side-chain substitutions were performed on conventional Kevlar(r) (p-aramid) fibres. The side-group substitutions were confirmed with NMR. The allyl-, propyl-, and pentenyl-functionalized fibres obtained were soluble in DMAc, DMF, and NMP. Benzyl-functionalized p-aramid showed solubility in DMAc and NMP. Finally, methyl-functionalized polymers were soluble only in NMP at high temperatures. The solution was then electrospun via needle-based electrospinningning ontoa stationary aluminum collector at a concentration of 20 to 50 wt%, a voltage of 20 kV, a flow rate of 0.1 mL/h, and a distance of 10 cm. In this instance, a coagulating water bath was not needed, as volatile solvents were used. Electrospinning at high voltage and low flow rate was generally successful, and fibres with an average diameter of 68 nm were produced using the propyl-functionalized p-aramid DMF at a concentration of 20 wt%. Fibre diameter was found to increase with polymer concentration, with a concentration of 50 wt% creating fibres with a diameter of 1.75 \(\upmu\)m. This study provided a first step in the scalable and continuous electrospinningning of p-aramid polymers. However, it did not fully explore the impact of the side-group substitutions on the mechanical strength of the resultant nanofibres. Since the addition of side groups can increase amorphous regions of the fibre, it is likely that this method would have a negative impact on the strength of the nanofibres. In summary, compared to the electrospinningning of other high-performance nanofibres, p-aramid nanofibres show considerable challenges due to the high crystallinity and low solubility of the polymer. The use of anisotropic solutions may improve the electrospinningning of the polymer but still requires the use of a highly corrosive solvent [15]. In contrast, side-chain substitutions provide a promising method to improve the electrospinningning of p-aramids, as the functionalized fibres can be dissolved in common solvents [3]. Further investigation into the impacts of side-chain substitutions on the properties of the resulting nanofibrous mats will be important in determining the best applications for these. The use of post-treatments to improve the mat performance will also be a vital step to further optimize the p-aramid nanofibres.
## 4 Experiments
### Results
#### 4.3.3 Detection-Based MOT Pipeline

Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**Tracking Method**} & \multicolumn{8}{c}{**Evaluation Metrics**} \\ \cline{3-13}  & & **\% MOTA\(\uparrow\)** & **\% MOTP\(\uparrow\)** & **TP\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** & **\% MT\(\uparrow\)** & **\% ML\(\downarrow\)** & **FM\(\downarrow\)** & **FPS\(\uparrow\)** \\ \hline \multirow{6}{*}{\(Y_{M1}\)} & SORT (\(IoU\)) & **24.86** & **78.88** & 12,485 & **8798** & 1288 & **1056** & 38.3 & **2.3** & 156 & **50** \\  & SORT (\(A\)) & 23.68 & 78.82 & **12,497** & 8986 & 1255 & 1077 & 40.0 & **2.3** & 163 & 49 \\  & SORT (\(W_{M}\)) & 24.30 & 78.82 & 12,495 & 8891 & 1253 & 1081 & 39.4 & **2.3** & 153 & 49 \\  & Deep-SORT (\(IoU\)) & \(-\)0.45 & 78.33 & 12,266 & 12,332 & 1178 & 1385 & 33.1 & 2.9 & 173 & 28 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & \(-\)8.75 & 78.31 & 12,334 & 13,631 & 1093 & 1402 & 38.9 & **2.3** & 125 & 28 \\  & Deep-SORT (\(A\)) & \(-\)8.15 & 78.38 & 12,364 & 13,573 & **1060** & 1405 & **41.1** & **2.3** & **122** & 28 \\ \hline \multirow{6}{*}{\(Y_{M2}\)} & SORT (\(IoU\)) & **43.49** & 81.20 & 13,242 & **6793** & 1001 & **586** & 53.1 & **2.9** & 108 & **50** \\  & SORT (\(A\)) & 43.16 & 81.19 & 13,250 & 6850 & 986 & 593 & 54.3 & **2.9** & 102 & **50** \\  & SORT (\(W_{M}\)) & 43.30 & 81.19 & **13,252** & 6831 & 980 & 597 & 54.9 & **2.9** & 104 & **50** \\  & Deep-SORT (\(IoU\)) & 23.61 & 81.36 & 13,041 & 9540 & 976 & 812 & 49.7 & **2.9** & 124 & 28 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & 16.07 & 81.32 & 13,147 & 10,764 & **870** & 812 & **56.6** & **2.9** & **93** & 29 \\  & Deep-SORT (\(A\)) & 15.49 & **81.40** & 13,099 & 10,802 & 896 & 834 & 56.0 & **2.9** & 95 & 29 \\ \hline \multirow{6}{*}{\(Y_{M3}\)} & SORT (\(IoU\)) & **65.25** & **81.34** & 13,637 & **3961** & 912 & **280** & 64.0 & 2.3 & 72 & **51** \\  & SORT (\(A\)) & 65.03 & 81.32 & **13,653** & 4009 & 887 & 289 & 63.4 & 2.3 & 76 & **51** \\ \cline{1-1}  & SORT (\(W_{M}\)) & 65.20 & 81.33 & 13,652 & 3983 & 896 & 281 & 64.0 & 2.3 & **71** & **51** \\ \cline{1-1}  & Deep-SORT (\(IoU\)) & 53.11 & 80.02 & 13,525 & 5649 & 932 & 372 & 58.9 & **1.1** & 124 & 30 \\ \cline{1-1}  & Deep-SORT (\(E_{D}^{IoU}\)) & 49.77 & 80.01 & 13,628 & 6248 & **815** & 386 & 64.0 & **1.1** & 99 & 30 \\ \cline{1-1}  & Deep-SORT (\(A\)) & 49.91 & 80.06 & 13,605 & 6204 & 825 & 399 & **66.3** & **1.1** & 109 & 30 \\ \cline{1-1} \cline{2-13}  & SORT (\(IoU\)) & 75.28 & **83.54** & 13,570 & **2406** & 106 & 191 & 56.6 & 3.4 & 70 & **52** \\ \cline{1-1}  & SORT (\(A\)) & 75.30 & **83.54** & 13,584 & 2418 & 1058 & **187** & 57.1 & 2.9 & 69 & 51 \\ \cline{1-1}  & SORT (\(W_{M}\)) & **75.34** & **83.54** & 13,585 & 2413 & 1057 & **187** & 57.1 & 3.4 & **68** & 51 \\ \cline{1-1}  & Deep-SORT (\(IoU\)) & 68.70 & 81.62 & 13,543 & 3355 & 1024 & 262 & 58.3 & 1.7 & 107 & 31 \\ \cline{1-1}  & Deep-SORT (\(E_{D}^{IoU}\)) & 66.95 & 81.56 & **13,629** & 3701 & 950 & 250 & 66.9 & 2.3 & 93 & 3
## 5 Conclusions  
In this study, we researched the reason for the frequency drift of the oscillating longitudinal mode in an SLM laser. An LT etalon was designed to achieve original SLM oscillation and correct the frequency drift. The 400 V voltage applied to the LT etalon in 15 s restored the laser to SLM oscillation. Nevertheless, we supplied the voltage based on the longitudinal mode waveforms. In the next research, a frequency-feedback system will be developed to compensate for the mode's frequency drift. The system will monitor the frequency of the oscillating longitudinal mode in real time and apply a more accurate voltage to the LT etalon. We hope that the study could provide a reference for researchers on applying EO crystals in the frequency stabilization of SLM lasers.  
Figure 6: LT etalon’s (**a**) suppression on the second longitudinal mode and (**b**) compensation for optical path–length change with a 400 V voltage difference.  
**Author Contributions:** Validation, X.C.; data curation, Z.L.; writing--original draft preparation, X.C.; writing--review and editing, R.Y.; visualization, Z.L.; supervision, Y.J. and R.F.; project administration, X.L.; funding acquisition, Y.J., R.F. and X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (62375066, 62192774, 62305085), Natural Science Foundation of Tianjin, China (22 JCZDJC00080, 22 JCYBJC00520).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### t-SNE Clustering  
Figure 12 illustrates the results of clustering documents based on the t-SNE reduction in features derived from keyword frequency and first position of occurrence in each document.  
Each subfigure (a-e) corresponds to one of the five technology domains. The t-SNE method reduced the features to two dimensions to facilitate visualization of the clustering performed by k-means. Each point in the plot represents a single document in the normalized two-dimensional t-SNE space. As indicated in the legend, red and blue dots  
Figure 12: Visualization of the confusion index from the unsupervised ML for each technology domain.  
correspond to the two clusters (C0, C1) identified by the \(k\)-means algorithm. Gray circles mark documents labeled as relevant (R1) in the dataset, providing a visual representation of the confusion between relevant and irrelevant classifications. That is, significant overlap of gray circles across both clusters reflects higher confusion, whereas concentration in one cluster indicates better performance.  
Table 5 summarizes the quantitative results of the clustering for each technology domain.  
Key parameters include the following:  
* C0 and C1: Total number of documents assigned to Cluster 0 (C0) and Cluster 1 (C1);
* R1C0 and R1C1: Number of relevant documents in Cluster 0 and Cluster 1, respectively;
* Confusion Index (CI): Metric defined above to measure clustering confusion.  
Lower CI values indicate better separation of relevant documents into a single cluster, while higher values signify greater confusion. For the SSB case, relevant documents spread evenly across the clusters, resulting in a high CI of 0.41. This indicates significant confusion in distinguishing relevant documents from irrelevant ones based on the chosen features. For the EVC case, relevant documents are similarly distributed, resulting in a high CI of 0.51. For the CV case, documents concentrate in Cluster 1, leading to a low CI of 0.21. Hence, the separation of relevant documents improves significantly compared with SSB and EVC. For the eVTOL case, the distribution is uneven, leading to a low CI of 0.11 and showing good clustering performance. For the LiDAR case, relevant documents concentrate in Cluster 1, yielding the lowest CI of 0.09.  
Overall, the t-SNE and k-means approach performs better for the CV, eVTOL, and LiDAR technology domains, as indicated by lower CI values, suggesting effective separation of relevant documents. In contrast, SSB and EVC exhibit higher CI values, indicating significant confusion between clusters. The features (keyword frequency and first position of occurrence) seem more effective for separating relevant documents in domains with distinct terminologies, such as eVTOL and LiDAR. Domains with overlapping or broad terminologies, such as SSB and EVC, show greater clustering challenges. These results highlight the variability of clustering effectiveness based on the extracted keyword features across different technology domains. The outcome emphasizes the importance of feature selection and domain-specific considerations when using UML techniques for relevance filtering.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 3 Attached Fluid Mass Method Considering Liquid-Solid Coupling  
Structures in the liquid environment can be influenced by liquid-solid coupling, and the natural frequencies of the structure are changed accordingly. To accurately identify the damage, it is necessary to calculate the modal information of the liquid-solid coupling structures. The equation of structural motion based on liquid-solid coupling is  
\[M\ddot{x}+C\dot{x}+Kx=F_{s}+F_{Q}, \tag{2}\]  
where \(M\), \(C\), and \(K\) are the mass, damping, and stiffness matrices of the structure, respectively; \(F_{s}\) is the external load; \(x\) is the displacement of the structure; and \(F_{Q}\) is the additional hydrodynamic pressure exerted by the liquid on the structure.  
According to the fluid FE, the function of the flow field can be arranged into a typical finite element formulation as Equation (3). When the element's function takes the stationary value of node pressure, the following formula (Equation (4)) can be obtained.  
\[\Pi_{e}=\frac{1}{2}q^{T}H_{e}q-q^{T}F_{e} \tag{3}\]  
\[F_{e}=H_{e}q, \tag{4}\]  
where \(H_{e}\) is equivalent to the stiffness matrix of the structure, \(q\) is nodal displacement, \(F_{e}\) is a nodal load. According to the structure FE, the equation of additional hydrodynamic pressure can be acquired as  
\[F_{Q}=M_{p}\ddot{x}, \tag{5}\]  
where \(M_{p}\) is the attached fluid mass matrix, which represents the additional pressure of fluid caused by the vibration acceleration of the structure on the contact surface. Its specific calculation formula can be found in Equations (6) and (7). The detailed derivation of the attached fluid mass (see Equation (6)) is available in Ref. [38].  
\[M_{p}=\sum_{k=1}^{S_{E}}A_{k}^{T}L_{k}A_{k}H_{N}^{-1}B_{N} \tag{6}\]  
\[\left\{\begin{array}{c}L_{k}=\int N_{S}^{T}N_{S}dS\\ H_{N}=H_{nn}-H_{np}H_{np}^{-1}H_{nn}\\ H_{e}=\int\frac{\partial N^{T}}{\partial x}\frac{\partial N}{\partial x}+\frac {\partial N^{T}}{\partial y}\frac{\partial N}{\partial y}+\frac{\partial N^{T }}{\partial z}\frac{\partial N}{\partial z}dV\\ B_{N}=\int N^{T}\xi N_{S}dS\end{array}\right., \tag{7}\]  
where \(A_{k}\) is the position sequence matrix to extract the node hydrodynamic pressure matrix of an element from the hydrodynamic pressure matrix of all contact surface nodes; \(N_{S}\) is the interpolation function of the normal displacement on the contact surface; \(S_{E}\) is the number of fluid elements in contact with the structure; \(N\) is the interpolation function of the hydrodynamic pressure at fluid element nodes; \(\xi\) is the unitary surface mass of the fluid.  
Substituting Equation (5) into Equation (2), the new equation of structure motion is obtained:  
\[\Big{(}M+M_{p}\Big{)}\ddot{x}+C\dot{x}+Kx=F_{s}, \tag{8}\]The dynamic characteristics of the structure influenced by liquid-solid coupling can be analyzed by Equation (8), and the structural modal information can also be calculated accordingly.