esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result and Analysis
### Effect of Pavement Structure Parameters  
The maximum tensile stresses at PI (\(\sigma_{\text{x-d}}\)) are computed for different structure parameters listed in Table 6. The vehicle speed, the axle weight, and the roughness level are taken as 100 km/h, 30 tons, and D, respectively, as the tensile stress in JPCP is the largest under such loading conditions (Figure 10). The calculated \(\sigma_{\text{x-d}}\) at PI is shown in Figure 12.  
It is seen from Figure 12 that the maximum tensile stress \(\sigma_{\text{x-d}}\) at PI can be reduced by either increasing the slab thickness or reducing the elastic modulus of the slab. On the other hand, increasing the thickness or elastic modulus of the base layer improves the support conditions of the slab and thus reduces the tensile stress at PI as well; however, the tensile stresses are all greater than 2.25 MPa, beyond which fatigue failure is expected under repeated vehicle loads, while the lower fatigue limit is 45% of the flexural strength of 5 MPa [12]. It is therefore concluded that increasing the thickness or elastic modulus of the base layer is not an effective way to reduce the dynamic stress or to improve the fatigue life of JPCP. Increasing the thickness or the elastic modulus of the subbase layer and the foundation reduces the maximum dynamic tensile stress slightly. With the presence of the dowel bars, the tensile stress at P1 and the dynamic factor are reduced slightly by 7.4% and 2.6%, respectively. It is considered that the dowel bars are not effective in reducing the tensile stress under dynamic loads.  
The pavement structure parameters have minor influence on the dynamic factor, ranging from 1.48 to 1.67 under the \"100 km/h-30 tons-D\" loading conditions (Figure 12c), compared to which the dynamic load parameters have significant influence on the dynamic factor, ranging from 0.98 to 2.46 (Figure 11). In addition, the tensile stress of JPCP does not vary linearly with the change of the dynamic load parameters or the pavement structure parameters. This implies that the Duhamel integral, which is normally used for theoretically analyzing the dynamic response of a linear system, is not applicable for the dynamic analysis of JPCP.
## 2 Provisioning MB Optical Connections
### Considered MB Scenario and Channel Performance  
Figure 1a shows the considered MB scenario where a set of MB optical transponders (TPs) is available in sites A and B and can work in the different bands. The signals generated by the Txs are multiplexed and transmitted along the optical fiber, where MB Optical Amplifiers (OA) are employed, with the optical amplification structure consisting of using one OA per band, e.g., Erbium-Doped Fiber Amplifiers (EDFA) for the C and L bands and a Thulium-Doped Fiber Amplifier (TDFA) for the S-band, together with waveband (de)multiplexers.  
As already introduced, by transferring power from higher to lower frequencies, the ISRS effect increases the impact of fiber nonlinearities, which impact the QoT of optical connections using the different channels in a nonidentical manner. To illustrate such a differentiated QoT, Figure 1b shows the pre-FEC BER that an optical connection using a given route on the optical network will experience when different channels are used. Two routes (R1 and R2) are considered with different total lengths, with R2 being longer than R1.  
In general, we observe that the channels in the S-band experience a higher BER for the same route, and therefore they can support shorter optical connections, as compared with the channels in the C and L bands that can support longer connections. In view of the above, one approach for channel assignment could be to select channels in the C or L bands,as they are more versatile. However, this approach will result in faster exhaustion of the C and L bands, so new medium-to-long reach connection requests would get blocked with a higher probability. Another approach could be to find the channel that results in a BER that is high but under the threshold; e.g., channel _ch1_ in the S-band is selected over route R1 in Figure 1b. This has the potential to minimize the probability that new optical connections are blocked. The main drawbacks of this approach are: (1) the QoT has to be estimated for all the available channels along the route, which can require a long computation time depending on the method used for such an estimation; and (2) it might happen that any available channel can provide a BER under the threshold, so the connection request is blocked; e.g., in R2, channel ch2 in the C-band provides the best BER, but that is above the threshold.  
In the remainder of this section, we propose solutions to implement the second approach, i.e., to find the optimal channel assignment for a given route in an efficient way, as well as to devise mitigation methods to improve the QoT of those optical connections with channel assignments providing poor QoT that would otherwise be blocked.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Field Data
It may be useful to take fewer samples in categories that show little variability, such as water and forest, and increase the sampling in the categories that are more variable, such as agricultural areas [42]. Hence, though it accounted for the smallest percentage of the scene, we have collected more samples for the Rice class, as it is more variable over time, in addition to being the major focus of our study.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 2 Background
### Typical LLMs and Potential Applications  
Currently, LLMs have powerful multimodal data processing and reasoning capabilities that enable them to efficiently parse large amounts of sensor, image, audio, and text data from UAV flights. These data enable UAVs to detect abnormal behaviors and potential threats instantly. For example, in scenarios such as border patrols and emergency disaster relief, UAVs can use these data to achieve target identification, abnormal behavior judgment, and automatic response to safety and security events, thus significantly improving the overall protection and risk control effectiveness [78].  
LLMs can also be employed to accurately identify problems such as sensor spoofing and flight anomalies and quickly generate emergency response strategies in critical safety scenarios such as path planning and battery health monitoring, significantly improving the flexibility and safety of mission execution. In addition, owing to their long contextual processing and code generation capabilities, LLMs can integrate flight logs, laws, and regulations, as well as safety standard information, to conduct comprehensive safety audits, provide maintenance guidance, and generate safety training materials for UAVs. Moreover, when combined with cutting-edge technologies such as blockchain data verification, LLMs perform excellently in terms of antijamming, authentication, and cryptography. They can provide intelligent solutions for complex tasks such as UAV cluster collaborative security and cybersecurity monitoring, as shown in Figure 6.  
For example, [PERSON] et al. [79] highlight AI-driven forest surveillance for wild-fire management in Germany, aligning with LLM-enabled situational awareness in UAVs. [PERSON] et al. [80] explored UAV-AI integration for automating construction workflows, reflecting safety-critical UAV applications. [PERSON] et al. [81] introduced Chat-Drone, a system controlling UAVs via LLM prompts without traditional vision models. [PERSON] et al. [82] demonstrated LLM-generated flight paths from UAV footage using natural language prompts, validated through indoor tests with a Tello UAV, while addressing both autonomy potential and latency limitations.  
Figure 5: LLM main development process.  
As different LLMs have different characteristics, their applicability in the field of UAV safety and security varies. Therefore, we selected eight representative LLMs, outlined their core features, and discussed their applications in the UAV safety and security domain, as shown in Table 2.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 2 Related Works in the Literature
### Sentence Transformer Models for Semantic Sentence Embedding  
Third, we review studies on implementing _Sentence Transformers models_ for semantic sentence embedding. They used several variants of models, such as _MiniLM L6_, _MiniLM L12_, _MPNet_, and _DistilRoBERTa_ to obtain sentence representations that understand contextual meaning rather than focus on individual words.  
In [39], [PERSON] et al. proposed comparing _pre-trained sentence transformer_ models to accelerate the systematic review process in the medical field of peri-implantitis. In their study, they reported that _MiniLM L6_ and _L12_ are computationally efficient but compromise the accuracy of the generated semantic information. On the other hand, _MPNet_ and _DistilRoBERTa_ offer better semantic information accuracy but are significantly more time-consuming.  
In line with the study by [PERSON] et al. [40], which states that there is a trade-off between accuracy and computational efficiency based on the underlying _pre-trained_ model architecture, we adopt _MiniLM L6_ to assess semantic similarity between keyword extraction from PDF and query without compromising processing speed. Given that the _reference paper collection system_ requires fast response time while maintaining relevance accuracy.
## 4 Model Development
### Selection of Machine Learning Classifiers  
We chose five machine learning classifiers to evaluate the two datasets. These include three simple learners: Logistic Regression (LR), Support Vector Machines (SVM), and Decision Tree (DT) and two ensemble learners: Random Forest (RF) and Ensemble Majority Voting (EMV). RF uses a set of homogenous decision trees as its base classifiers while the EMV classifier was composed of the three simple learners LR, SVM, and DT, and used hard voting that considered the majority for predicting the class label for each instance in the test set. The rationale for choosing these is based on their previous performance reports in similar situations [9; 31; 32]. As our objective was to understand the factors contributing to the classification, we chose not to use any neural networks-based classifier in our analysis due to their \"black-box\" nature of interpretation of the model [34; 57].
_applied sciences_  
_Article_  
**Control Design for Soft Transition for Landing Preparation of Light Compound-Wing Unmanned Aerial Vehicles Based on Incremental Nonlinear Dynamic Inversion**  
[PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1,2,2, [PERSON] 1,2 and [PERSON] 1,2  
1 College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (PC.); [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (J.W.)  
2 Key Laboratory of Unsteady Aerodynamics and Flow Control, Ministry of Industry and Information Technology, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China  
*Correspondence: [EMAIL_ADDRESS]  
**Abstract: This paper proposes a soft switching mode for electric vertical takeoff and landing (eVTOL) compound-wing unmanned aerial vehicles (UAVs) to achieve a smooth transition between modes. The proposed mode pre-compensates the lift loss with the rotary wing during the deceleration stage before UAV landing. The control law adopted in this paper consists of implicit nonlinear dynamic inversion (NDI) and incremental nonlinear dynamic inversion (INDI). The outer loop (attitude angle loop) control law is based on implicit NDI, while the inner loop (attitude angle rate loop) controller is based on INDI. An extended state observer (ESO) is employed to estimate the angular acceleration. This paper innovates by proposing a soft switching strategy that improves the robustness, safety, and smoothness of the transition for the compound-wing UAV, and applying advanced control law to mode transition design. For the future application of eVTOL aircraft in UAM scenarios, this paper evaluates the smoothness of transition and passenger comfort using normal overload as a physical quantity. The Monte Carlo (MC) simulation results demonstrate that the proposed mode can reduce the peak normal overload by about 89%.**  
**Keywords: eVTOL; compound-wing UAV; transition phase; soft transition; INDI; flight control design; normal overload**
# Simplified Plastic Hinge Model for Reinforced Concrete Beam-Column Joints with Eccentric Beams
## 4 Application of Simplified Plastic Hinge Model
### Comparison of Predictions to Test Results
The underestimation of the predictions with ACI 318-19 [12] (1.45 and 0.474) or ACI 352R-02 [13] (1.25 and 0.165) was due to the underestimated effective joint width (Table 3): the effective joint width of the eccentric beam-column joint specimens was symmetrically decreased at both sides due to the beam eccentricity (\(x\)) in ACI 318-19 [12] (Equation (1)), and the effect of beam eccentricity was indirectly considered in ACI 352R-02 [13] by using a factor \(m\) (Equation (2)). On the other hand, NZS 3101-2006 [14] directly considered the beam eccentricity (\(e\)) in the calculation (Equation (3)), and the calculated effective joint width of NZS 3101-2006 [14] was larger than that of ACI 318-19 [12] or ACI 352R-02 [13] but less than that of Eurocode 2 [15] without consideration of the beam eccentricity (Table 3). For these reasons, the simplified plastic hinge model using the effective joint width of NZS 3101-2006 [14] showed the best predictions for all test specimens. It is noted that in eccentric beam-column joints, the joint shear strength could decrease due to additional torsion and stress concentration developed by eccentric beams at the joint interface. Moreover, when a wide column is used, the joint hoop bar stress close to the eccentric beam would be much higher than that far from the eccentric beam [31]. For these reasons, the use of total amount of joint hoop bars could overestimate the performance in the simplified plastic hinge model, particularly for the beam-column joints with a wide column and large beam eccentricity. Of the test specimens, W0, W75, and W150 used wide columns (Tables 2 and 3). Among them, W150 had the widest column and largest beam eccentricity, and the overestimation in the onset of strength degradation in W150 may be caused by the use of total amount of joint hoop bars (Figures 6; 7; 8 and 9). However, to generalize the effect of joint hoop bars in the case of having a wide column and large beam eccentricity or to suggest an effective joint hoop bar area, existing test results relevant to the case are limited. Thus, further experimental studies are needed.
# Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations
## 4 Discussion  
Since the long-baseline positioning performance is mainly affected by the measurement noise of observation data, the atmospheric residuals, and the orbital residuals, it is necessary to evaluate the a priori success rate of the EWL combination in Section 3.2 under different error values. The a priori precision of code and carrier depend on the observation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} **Combination** \\ **Scheme (No.)** \\ \end{tabular} & \(R\) **\textgreater{} 1.5(\%)** & \(R\) **\textgreater{} 2(\%)** & \(R\) **\textgreater{} 2.5(\%)** & \(R\) **\textgreater{} 3(\%)** \\ \hline
1 & 100.0 & 99.2 & 96.1 & 91.9 \\
2 & 97.8 & 95.7 & 91.2 & 86.2 \\
3 & 99.9 & 99.0 & 95.8 & 91.7 \\
4 & 99.4 & 95.3 & 88.6 & 80.5 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The percentage of past ratio-test values with given thresholds in experiment 2.  
Figure 9: Experiment 2: long-baseline positioning performance of four combination schemes of BDS-3 + Galileo: (**a**) combination scheme No. 1, (**b**) combination scheme No. 2, (**c**) combination scheme No. 3, and (**d**) combination scheme No. 4.  
environments and the types of receivers. Assuming that the receivers used are professional geodetic receivers and that the observation condition is an open field, the DD pseudo-range and carrier precision are usually set as 0.5 m and 5 mm, respectively [32; 34].  
As shown in Figure 10, it is the relationship between the success rate of the EWL signals and the total errors in the theoretical situation. The total errors here include the second-order ionospheric residuals, the troposphere residuals, and the orbital residuals. It can be seen that, in theory, BDS-3 (1,\(-\)1,0,0) and Galileo (0,\(-\)1,1,0) can still maintain success rates of over 99% when the total errors reach 35 cm. The remaining EWL signals BDS-3 (0,0,1,\(-\)1), and Galileo (0,0,\(-\)1,1) and (0,\(-\)1,0,1) have success rates of more than 99% when the total errors reach 24 cm, which meet the important condition of long-baseline reliable positioning.  
It can be known from Reference [16] that, for a long baseline from 100 km to 500 km, the total errors range between 4.5 cm and 32 cm. Therefore, BDS-3 (1,\(-\)1,0,0) and Galileo (0,\(-\)1,1,0) can reliably fix the long-baseline instantaneously. Generally, the troposphere residuals of the long baseline do not reach the maximum value of 20 cm from Reference [16] but reach the more common 10 cm [35]. If more precise ephemeris data are used, the total errors are between 13 cm and 22 cm. The success rates of BDS-3 (0,0,1,\(-\)1), and Galileo (0,\(-\)1,1) and (0,\(-\)1,0,1) are greater than 99.4%, which can be fixed instantaneously in the first step. Therefore, the five EWL signals of BDS-3 and Galileo mentioned above can reliably fix the long baseline instantaneously.  
Therefore, the proposed optimal triple-frequency EWL/WL combinations with (B1I,B3I, B2a) and (E1,E5a,E6) signals are suitable for most long baselines, which can obtain the same precision as using four frequencies.
# Impact Features Extracting Method for a Reciprocating Compressor Based on the ABC-SGMD Model
## 1 Introduction
The diagnosis model based on the Artificial Bee Colony algorithm and Symplectic Geometric Modal Decomposition (ABC-SGMD) is proposed, and its effectiveness is validated through numerical simulations and experiments. The structure of the paper is outlined as follows:  
In Section 2, theoretical algorithms are introduced for optimizing Symplectic Geometric Modal Decomposition (SGMD) using the Artificial Bee Colony (ABC) algorithm, alongside the development of a multi-feature fusion model and modal identification method. Section 3 focuses on Numerical Simulation Verification, where fault mechanisms are analyzed and simulation signals of air compressor valve faults are generated. This section compares the advantages of the ABC-SGMD method in decomposing simulated signals with other approaches. Section 4, Fault Simulation Experiment, demonstrates the effectiveness of the proposed method on a laboratory reciprocating air compressor fault model. The results indicate that the method successfully addresses the challenges posed by traditional methods in handling nonlinear, non-stationary signals and in reducing the decomposition time. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Challenges and Limitations** \\ \hline STFT & Uses a fixed window function with single resolution, unable to balance time and frequency resolution. \\ WT & Encounters mode mixing issues with multi-component signals and varying results with different wavelet bases, lacking adaptive scale determination. \\ EMD & Experiences endpoint effects, mode mixing, over and under-envelope issues, and lacks noise robustness. \\  & Requires presetting penalty term parameters, initial center frequencies, and mode number, which significantly affect the results without a standard for evaluation. \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of the methods.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 4 Experimental Evaluation
### Evaluation Process  
We choose proper measurements to quantitatively evaluate the 2D segmentation performance and 3D semantic model. We randomly labeled 16 images in PKU-M1 to test the segmentation performance. An example of PKU-M1 is shown in Figure 5. Table 1 gives class-wise statistics, where the _Building_ category is segmented very well, but _Vegetation_, _Road_, and _Vehicle_ are segmented relatively poorly. Since hand-crafted 3D semantic labeling is now still a challenging and tedious task, especially for large-scale scenarios, we have to evaluate the 3D semantic model indirectly. Notice that each 3D point is assigned a semantic label during the semantic fusion process; the label can be projected back to each camera coordinate by the geometric relation. We call this step re-projection. Then, we can indirectly evaluate the 3D semantic point cloud by re-projection images in a simpler manner. However, the re-projection map Figure 5d is quite sparse. Only foreground labels, which include _Vegetation_, _Building_, _Vehicle_, and _Road_, are countable for evaluation. So several common measurements for 2D segmentation are not suitable in our cases, such as MIoU (Mean Intersection over Union) and FWIoU (Frequent Weighted Intersection over Union). In our experiment, we choose Pixel Accuracy (Equation (13)) and class-wise F1-score (Equation (16)) for evaluation.
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 6 Nose-to-Brain Delivery of L-DOPA
### Lipid-Based Nanoparticles for Nose-to-Brain Delivery of L-DOPA  
[PERSON] et al. hypothesized a higher therapeutic efficacy of the intranasal treatment of PD if an effective oxidation protection of L-DOPA is provided and achieved it with a multi-lamellar liposomal formulation stabilized with ascorbic acid [186]. The authors concluded that the optimized cationic extruded liposomes, if formulated into a suitable sustained release system, could serve as a potential carrier for the nose-to-brain delivery of L-DOPA.  
Using a saturated phospholipid and various surfactants in differentiating ratios, [PERSON] et al. formulated L-DOPA-loaded mixed liposomes stabilized with natural antioxidants--L-ascorbic acid and quercetin [187]. The authors established their most promising formulation for intranasal delivery by combining 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and N,N-dimethyl-1-tetradcyclamine ammonium bromide (TDAB) in a 7:3 ratio, which was characterized by positive zeta potential (+21 mV), highest encapsulation efficiency, and strongest antioxidant activity.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Nano-Carrier System** & **Size/Zeta Potential** & **Active Targeting** & **Scientific Result** & **Reference** \\ \hline Hydroxypropyl methylcellulose microparticles & 27.04 \(\pm\) 1.00 \(\upmu\)m/n.a. & n.a. & Improved mucoadhesive properties & [180] \\ \hline Poly(lactic-co-glycolic acid) (PLGA) nanoparticles & 250 \(\pm\) 50 nm/n.a. & n.a. & Long-lasting effect of L-DOPA; noticeable progress in motor coordination & [172] \\ \hline Wheat germ agglutinin-grafted PLGA nanoparticles & 329.0 \(\pm\) 188.3 nm/ germ agglutin & Wheat germ agglutinin & Prolonged drug release; improved tissue deposition of L-DOPA; decreased cytotoxicity & [173] \\ \hline PLGA-nanosomal nasal drops & n.a. & n.a. & Increased bioavailability by 244\% in comparison to standard therapy & [181] \\ \hline Glutathione-modified Polyethylene oxide-poly-(\(\epsilon\)-caprolactone) nanomicelles & 145 \(\pm\) 3.2 nm/ +6.4 \(\pm\) 0.53 mV & Glutathione & Improved biocompatibility; lack of cytotoxicity & [182] \\ \hline Self-healing supramolecular hydrogel based on L-glutamine amide derivative and benzaldehyde & n.a. & n.a. & Improved biocompatibility; 4.1 times higher brain uptake of nasal L-DOPA & [183] \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._