esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Response Analysis of the Wind Turbine Coupling Structure
### Stress Analysis on the Tower Body  
The 3D distributions of the radial displacement of the tower body and distributions of internal stress responses at the tower bottom under normal wind and typhoon loads are shown in Figures 20 and 21. It can be seen that: (1) the radial distribution of the tower body presented relatively similar distribution patterns under normal wind and typhoon loads. The radial displacement increased gradually going up of the tower body, forming two extreme regions on the windward side and leeside. The maximum positive and negative displacements were at 180\({}^{\circ}\) and 0\({}^{\circ}\); (2) Different wind field mainly affected the radial displacement at the middle and upper parts of the tower body. Under the typhoon loads, the maximum radial displacement of the lower tower body was 0.117 m, which was increased by 28.6% compared with that under normal wind; (3) Internal stresses at the tower bottom were symmetric along the windward side under normal wind and typhoon loads. Despite the slight difference in the meridian axial force at the tower bottom, other internal stresses presented basically consistent circumferential distribution laws under normal wind and typhoon loads. All of the internal stresses achieved the maximum value close to the circumferential 330\({}^{\circ}\) of the tower body; (4) Extreme internal stresses at the tower bottom under typhoon loads were stronger than those under normal wind loads. The maximum meridian axial force, maximum shearing force, maximum circumferential bending moment, and maximum meridian bending moment were increased by 36.1%, 34.8%, 34.2%, and 46.5%, respectively. All occurred close to the circumferential 330\({}^{\circ}\).  
Figure 19: Mode of vibration of the tower–blade coupling model at different orders. (**a**) First order; (**b**) Fifth order; (**c**) 10 th order; (**d**) 30 th order; (**e**) 50 th order; (**f**) 100 th order.  
Figure 20: 3D radial displacement of tower body under normal wind and typhoon loads. (**a**) Normal wind; (**b**) Typhoon.
## 1 Introduction  
In the era of Big Data, the intersection of large amounts of data becomes inevitable. Private Set Intersection (PSI) addresses the current need for a solution to compute the intersection of two or more datasets without disclosing information about the data of each party, except for the intersection itself. PSI has wide applications such as social discovery [1; 2; 3], document-like detection [4], joint learning in neural network models [5], suspect detection [6], privacy-preserving data mining [7], privacy-preserving retrieval systems [8], cloud-based applications [9], and so on. Many high-performance PSI protocols have been proposed recently [10; 11; 12; 13; 14; 15; 16]. Additionally, there are some PSI protocols [17; 18; 19; 20; 21] specifically designed for unbalanced scenarios, where the server has a large dataset and the client has a dataset much smaller than the server's size.  
Multiparty Private Set Intersection (MPSI) protocols are designed to solve the intersection problem among multiple participants. Traditional approaches for designing MPSI protocols mainly use the homomorphic encryption technology of public keys [22; 23; 24; 25; 26; 27; 28] and oblivious transfer methods [29; 30; 31; 32; 33; 34; 35]. For MPSI protocols, the unbalanced scenario is also a common real-life situation in which the server possesses a large dataset, while multiple clients have smaller datasets. For example, some WeChat (version 3.9.8) users want to find their mutual friends in order to create a friend circle or group chat. In this scenario, there are a server that has information about all registered users and their phone numbers and multiple clients with their own phone address books who want to perform MPSI with the server in order to find common phone numbers among them. We refer to this as a Multiparty Private Set Intersection for an unbalanced scenario, where the clients' address books consist of small datasets, while the server's database is very large.  
However, most of the existing MPSI protocols are not applicable to such unbalanced scenarios. For example, the protocol in [22] can only derive the approximate number of intersections; the protocols in [23; 24] require the participants' datasets to be of equal size; the protocol in [25] is only applicable to three participants and small datasets; the protocol in [28] can be applied to such scenarios, but the computation for the clients and the server is high.  
This paper presents a practical MPSI protocol for unbalanced scenarios in which the server with high computational power handles the majority of the computations, while the clients only need to perform a small amount of computation. Moreover, the protocol has a minimal impact on the execution time of clients as the number of participants increases.
# University Student Dropout Prediction Using Pretrained Language Models
## 5 Analysis  
### Ablation Study  
We conducted additional experiments to compare the performance improvements of each variable in terms of student information. To determine the effectiveness of the demographic (Dem. ) and academic (Aca.) information, we removed these variables separately in distinct experiments. Moreover, we conducted a separate experiment for course evaluation comments (Cou.), which are unstructured data, to compare the performance improvement when using structured and unstructured data from academic information in detail. The results of these experiments are shown in Table 5, along with the STUD model results for comparison. When demographic information was removed, STUD\({}_{BERT}\) exhibited a 54.95% performance drop in terms of the F1-score, whereas STUD\({}_{SimCSE-BERT}\) showed a 46.09% performance drop. When the structured academic information was removed, STUD\({}_{BERT}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Dropout Prediction**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic Regression & 0.766 & 0.255 & **0.675** & 0.370 \\ SVM & 0.847 & 0.339 & 0.533 & 0.414 \\ Decision tree & 0.821 & 0.296 & 0.546 & 0.384 \\ Random forest & 0.829 & 0.312 & 0.558 & 0.400 \\ MLP & 0.896 & 0.483 & 0.364 & 0.415 \\ MSNF & 0.877 & 0.405 & 0.442 & 0.422 \\ \hline STUD\({}_{BERT}\) & 0.894 & 0.478 & 0.416 & 0.444 \\ STUD\({}_{SimCSE-BERT}\) & **0.901** & **0.516** & 0.416 & **0.460** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between STUD and other baselines.  
demonstrated a 42.57% performance drop based on the F1-score, and STUD\({}_{SimCSE-BERT}\) exhibited a 33.26% performance drop. When the unstructured course evaluation comments were removed, STUD\({}_{BERT}\) showed an 8.11% performance drop based on the F1-score, and STUD\({}_{SimCSE-BERT}\) displayed a 9.78% performance drop.  
First, we observed the most significant drop in performance when demographic information was removed. These results indicated that demographic information was a much more influential variable than academic information for predicting student dropout. We assumed that this was because the students' academic information included only one semester, which was insufficient in terms of absolute information compared to demographic information. Second, we observed that both structured data (e.g., GPA and volunteer hours) and unstructured data (e.g., course evaluation comments) within academic information impacted a dropout prediction. This result demonstrated that course evaluation comments could not be utilized through machine learning and deep learning methods utilizing only structured data, which also influenced dropout prediction. This suggests that a combination of both structured and unstructured data is required for a more precise prediction of student dropout.
## 2 Contaminants of Emerging Concern (CECs)
### Personal Care Products (PCPs)  
This large category of emergent contaminants includes chemicals found in consumer products such as cosmetics, fragrances, disinfectants, antiseptics, UV filters, and insect repellents. Among the reviewed papers, fewer included PCPs monitoring.  
Triclosan and triclocarban, highly used disinfectants in personal care products, were measured in surface water in 16 sites along the Romanian side of the Danube and its three main tributaries, Jiu, Olt, and Arges rivers in 2014 in a range of 0.7-18.4 ng/L for triclorsan and 0.6-54 ng/L for triclocarban [50]. In a recent study on the Prut river, triclosan was reported in a range of 12.5-159 ng/L [26].  
The occurrence of the 10 organic UV-filters on the North-Western Black Sea coast was reported for the first time in 2020 by [PERSON] et al. [69]. High levels up to 5607 ng/L of BP3 (2 hydroxy-4-methoxy-benzophenone) were measured in the seawater. 234 HBP (2,3,4-trihydroxybenzophenone) reached a maximum level of 824 ng/L and BP1 (2,4-tdihydroxybenzophenone) of 600 ng/L in seawater. Salicylate derivatives (ethylhexyl salicate and homosalate) were also detected in high concentrations of 1286 ng/L and, respectively, 1262 ng/L in seawater, but reached a maximum value of 5823 ng/L of ethylhex salicate in sediments.  
Insect repellent DEET (N,N-Diethyl-meta-toluamide) was monitored along the Danube River and its tributaries within JDS3. In the Lower Danube waters, the measured levels were lower than 10 ng/L, while maximum levels in the Morava (81 ng/L) and Arges (37 ng/L) tributaries were reported [44]. In the Dniester River basin, a maximum concentration of 345 ng/L was reported [26].  
Figure 4: Variation of common pesticides in surface waters of Lower Danube basin compared to the PNEC values, according to the NORMAN Prioritization Framework.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 4 Pathfinding on Navigation Mesh  
### NavMesh Generation  
In the context of 3D navigation, navigation meshes (NavMeshes) play a pivotal role in representing traversable regions within a gaming environment. The process of creating a NavMesh from triangular meshes is commonly referred to as \"NavMesh generation\". This procedure involves the transformation of input polygons, specifically triangular meshes, into a heightfield. Each voxel within this heightfield stores information about the heights associated with occupied spaces. The generation of NavMeshes for our experiments leverages the Recast Navigation project accessible on GitHub [5]. The process unfolds as follows:  
Figure 8: Mesh reconstruction on the third floor in the Building of Engineering Building at NTU.  
Figure 6: Mesh reconstruction outside the Building of Engineering Building at NTU.  
Figure 7: The sparse map is constructed from the third floor in the Building of Engineering Building at NTU.  
* Voxelization: Triangular meshes are voxelized, converting them into a heightfield.
* Filtration: A filtration process considers surface inclinations and vertical clearance to eliminate voxels, defining areas conducive to traversal.
* Distance Field Computation: A distance field is computed within the voxel space, expressing the distance from each walkable voxel to the nearest non-walkable voxel within the heightfield.
* Watershed Algorithm: The volumetric cell-and-portal generation [27] algorithm is applied to the distance field, partitioning walkable voxels into non-overlapping regions.
* Polygonal Representation: These segregated voxel regions are transformed into polygonal representations using the Ramer-Douglas-Peucker algorithm [28].
* Triangulation: The resulting polygons are triangulated, yielding a navigational mesh suitable for path planning and execution.  
An illustrative example of this process can be observed in Figure 9, showcasing the conversion from meshes (depicted in white) to NavMeshes (in blue).  
It is worth noting that, by default, NavMesh structures render walls impassable, which aligns with the context of wheeled robots. However, considering parameters governing maximum climb and slope angles, as shown in Figure 10a, humanoid robots can bypass ramps and directly ascend walls, enabling access to higher levels.
# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Scheduler
#### 3.2.2 Scheduler with AI  
Many studies applied AI technology to a multipath TCP scheduler to refine the scheduling policy dynamically in accordance with its surrounding environments. In [80], [PERSON] et al. proposed a RL-based scheduler, namely, ReLes, to generate packet scheduling policy dynamically. Different from the traditional pre-defined fixed policies, ReLes is able to adjust the scheduling policy dynamically based on network environments. The scheduler takes the observations of the environments, such as RTT and throughput measurement, as an input, and generates a policy to allocate data on subflows as an output by using a neural network. The training part of the neural network is conducted in an offline way. In MPTCP networks, choosing the best path among all available paths is also challenging because it depends on the loss rates, RTTs, and data sizes. In most cases, the path state information is not available a priori, and the loss rates and RTTs will change over time. To overcome the limitation, [PERSON] et al. [81] proposed a method based on RL with deep Q network (DQN). The method is able to play a role in choosing the best path in MPTCP heterogeneous networks. The method can be enhanced by taking more network metrics into consideration to improve the selection accuracy. [PERSON] [82] applied deep reinforcement learning to improve packet scheduling. Instead of using fixed parameters such as SRTT and congestion window for characterizing the performance of an access path, a deep reinforcement learning agent is utilized to learn the strategy for distributing a packet optimally, thereby increasing the aggregated throughput of the multiple flows. The agent has two main phases, namely, the feedforward and backpropagation. In the feedforward phase, the agent interacts with the environment and acquires an optimal strategy to distribute the packets. The backpropagation phase is implemented in an offline way, and the deep neural network is updated by the previous experiences at this phase. However, the improvement is not big. [PERSON] et al. [83] mentioned that MPTCP can respond to network changes by performing congestion balancing across different paths without prior knowledge. Table 3 summarizes the studies on the MPTCP scheduler. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Metrics or Key Idea** & **Publication** & **Research Summary** & **Limitation/Advantages** \\ \hline Sorting every path & C. [PERSON] et al. [33] & A proposal that sorts each path and transmits the packets in descending order of ranks, so as to provide in-order delivery. & Load balancing/Consider bandwidth only \\ \hline Delay differences & [PERSON] et al. [65] & A proposal that considers the delay differences on different paths by re-injecting the segment causing HoL blocking into the other available subflows. & Considering the delay difference of paths/Reduction of congestion window size \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies on the MPTCP scheduler.
## 4 Discussion
### Power Shift
#### 4.2.3 Effects of a Ramp on the Injected Signal  
We compare the power-shift method with and without the linear ramp on the control parameter. In Figure 13 we show the resulting attractor vs. TAP. The blue dots represent the stepwise injected signal at \(Y_{0}=1.63\) for base AIII with the \(Y_{PS}=1.84\), i.e., with no \(T_{lag}\). The orange \(xs\) represent the linear ramp with \(T_{lag}=15\tau\). The orange \(xs\) show that the effect of the ramp is minimal. In fact, access to AV is improved by \(2.65\%\). There is a shift to the left by the orange \(xs\) suggesting that a power shift should be conducted slightly earlier to compensate for the lag effects that may appear. Looking at the main tranche we see the orange \(xs\) fall off \(7\tau\) earlier than the blue dots and also returns \(11\tau\) earlier, giving us a net time increase of \(4\tau\). This shows that a slight lag in the power shift might help instead of hinder access.
## 2 Materials and Methods
### Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC)  
The microorganisms evaluated were _Escherichia coli_ ATCC 35401, _Enterococcus faecalis_, _Salmonella enteritidis_ ATCC 13076, and _Shigella flexneri_ ATCC 12022. Both the MIC and MBC were determined using a 96-well plate. The four evaluated bacterial strains were cultured in Petri dishes on Mueller-Hinton medium at 37 \({}^{\circ}\)C for 16 h. One to two colonies were added to a test tube containing saline solution (0.85% NaCl) until turbidity equivalent to that of a 0.5 McFarland standard was reached (1.5 \(\times\) 10\({}^{8}\) CFU/mL). This solution was diluted to 1.5 \(\times\) 10\({}^{6}\) CFU/mL, and 50 \(\upmu\)L was added to each well, followed by the addition of 50 \(\upmu\)L of the solution (ME, EAF, or MF) to be tested at one of eight concentrations (0.625, 1.25, 2.5, 5, 10, 20, 40, or 80 mg/mL). In the case of synthetic metabolites (titaconic anhydride (ITAN), maleic anhydride (MA), and 5-hydroxymethylfurural (5-FIMF)), the concentrations were: 0.39, 0.78, 1.56, 3.125, 6.25, 12.5, and 25 mg/mL. The positive control was ampicillin, the negative control was bacteria alone, and the toxicity of 0.5% DMSO was evaluated. The 96-well plate was incubated at 37 \({}^{\circ}\)C for 18 h. The lowest concentration that did not present bacterial growth was taken as the MIC value. The MBC was measured by taking a sample from each well without turbidity, inoculating it in a Petri dish containing Mueller-Hinton medium, and incubating it at 37 \({}^{\circ}\)C for 18 h. The dish without observable bacterial growth was taken as the MBC.
# Field Pull-Out Tests of Percussion Driven Earth Anchors (PDEAs)
## 7 Summary and Conclusions  
In this study, the pull-out behavior of percussion driven earth anchors (PEDAs) in clayey soils was investigated by conducting both field and laboratory tests. The anchors were field-tested after being installed in lean silty clay (CL), the type of soil dominant on the project site. The anchors were installed around 3 m into the slope and were subject to field pull-out tests. The maximum experimental field pull-out resistance was found to be in the range of 13.2 kN to 17 kN. The variation was due to the strong spatially variabilities of soils, for example, moisture content, within the slope embankment. Additionally, an empirical equation was used to estimate the ultimate pull-out capacity based on the undrained shear strength obtained from laboratory and in situ TCP test data. Compared to the field maximum pull-out load, the pull-out resistance estimated by empirical equation 1 using laboratory-acquired C\({}_{\text{u}}\) values is an average of 37% lower, while based on the C\({}_{\text{u}}\) values found from the in situ TCP values, the estimated ultimate pull-out load is much closer to the pull-out test results, with only an 8% average variation. Therefore, the prediction equation is promising for estimating the pull-out capacity, but is highly affected by the accuracy of the shear strength value of the soil. Even though a considerable discrepancy exists between the calculated and experimental pull-out resistance, the equation can help estimate field pull-out strength during the planning stage. Moreover, it is recommended that the field pull-out test methodology be further refined and more field pull-out tests be performed in different conditions to develop an accurate empirical model to estimate the ultimate pull-out resistance of these types of anchors. In addition, future studies on the size effects of anchors on the pull-out behavior need to be performed. The failure modes for these anchors in clayey soils also need to be studied further.  
Conceptualization, X.Y. and L.H.; methodology, X.Y., L.H. and G.L.; validation, X.Y., N.T.A. and G.L.; formal analysis, X.Y., N.T.A. and G.L.; investigation, N.T.A., M.A., A.P. and G.L.; data curation, N.T.A., M.A. and A.P.; writing--original draft preparation, N.T.A.; writing--review and editing, X.Y. and G.L.; visualization, N.T.A. and G.L.; supervision, X.Y.; project administration, X.Y., L.H. and G.L.; funding acquisition, X.Y. and L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Texas Department of Transportation: 02-0 XXIA001.  
Not applicable.  
This study was supported by TxDOT as part of the performance measurement of these types of anchors. The authors are grateful for the support and assistance received from the agency.  
The authors declare no conflict of interest.
# Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm
## 6 Experiments, Results and Discussions
### Feature Comparison  
We compared three types of features: (1) only CH; (2) only FRFE; and (3) CH with FRFE, then reduced by KPCA. Classifiers were all set to Jaya-FNN. The measure was chosen as average sensitivity rate (ASR) based on a 10 \(\times\) 10-fold SCV. The ASR is defined as the average value of sensitivity rate of 10 runs over the 10 validation folds. Table 6 lists the feature comparison results.  
Table 6 shows that 64 CH yields ASRs of 94.7% \(\pm\) 0.4%, 96.3% \(\pm\) 0.5%, and 95.4% \(\pm\) 0.5% over green, Oolong, and black tea, respectively. The 25 FRFE yields ASRs of 95.3% \(\pm\) 0.3%, 98.2% \(\pm\) 0.4%, and 96.6% \(\pm\) 0.4% over green, Oolong, and black tea, respectively. Finally, their combination reduced to four PCs yields the largest ASRs of 97.3% \(\pm\) 0.4%, 98.9% \(\pm\) 0.3%, and 97.6% \(\pm\) 0.3% over green, Oolong, and black tea, respectively. The results demonstrate that the combined feature performs better than single feature set, hence, the combination strategy is effective. Besides, the FRFE gives better results than CH, which suggests that the fractional features are more suitable to identify tea categories than color features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Green** & **Oolong** & **Black** \\ \hline
64 CH & 94.7\% \(\pm\) 0.4\% & 96.3\% \(\pm\) 0.5\% & 95.4\% \(\pm\) 0.5\% \\
25 FRFE & 95.3\% \(\pm\) 0.3\% & 98.2\% \(\pm\) 0.4\% & 96.6\% \(\pm\) 0.4\% \\ (64 CH and 25 FRFE) reduced to 4 PCs & 97.3\% \(\pm\) 0.4\% & 98.9\% \(\pm\) 0.3\% & 97.6\% \(\pm\) 0.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature Comparison.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **CCP** \\ \hline All & MIE = 1000, Population = 20, RT = 50 \\ \hline
**Algorithm** & **ASP** \\ BP & LR = 0.01 \\ MBP & LR = 0.01, MC = 0.9 \\ PSO & MV = 1, IW = 0.5, AC = 1 \\ SA & TDF = “Exp”, IT = 100, FT = 0 \\ GA & CP = 0.8, MP = 0.1 \\ Jaya & No ASPs \\ \hline \hline \end{tabular} (MIE = Maximum Iterative Epoch, RT = Run Times, LR = Learning Rate, MC = Momentum Constant, CP = Crossover Probability, MP = Mutation Probability, TDF = Temperature Decrease Function, IT = 100, FT = Final Temperature, MV = Maximal Velocity, IW = Initial Weight, AC = Acceleration Coefficient).  
\end{table}
Table 4: Parameters obtained by trial-and-error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposed Method** & **Mean** & **SD** \\ \hline BP & 0.1242 & 0.0269 \\ MBP & 0.0939 & 0.0228 \\ SA & 0.1043 & 0.0317 \\ GA & 0.0266 & 0.0105 \\ PSO & 0.0197 & 0.0040 \\ Jaya & 0.0072 & 0.0015 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result details of training methods.