esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Datasets and Evaluation Metrics  
#### 4.1.1 Dataset Analysis  
Our experiments against the SOTA methods were mainly conducted on the VisDrone2019 dataset, which includes 10,209 UAV aerial images (6471 for training, 548 for verification, and 3190 for testing). The top ten categories of interest, namely pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle were used for training and testing. The main difficulty for detection is that there exists a large number of small objects, and the object scales of the same class vary dramatically according to the drone's altitude. The distribution of target instance samples in the VisDrone dataset is shown in Figure 7. In addition, to further evaluate the generalization ability and robustness of the proposed model, we conducted the ablation experiments on the VisDrone2019 dataset as well as on the DOTA dataset. The DOTA dataset contains 2806 aerial images from different sensors and platforms with 15 categories and 188,282 objects. The distribution of target instance samples in the DOTA dataset is shown in Figure 8.  
Figure 8: The distribution of instance samples for each category in the DOTA dataset.  
Figure 7: The distribution of instance samples for each category in the VisDrone dataset.
## 2 Experimental Section
### Product Analyses  
The extracted bio-oil was analyzed both qualitatively and quantitatively using a GC-MS (Agilent 6890, Santa Clara, CA, USA) apparatus equipped with an HP-5 MS capillary column (30 m \(\times\) 0.25 \(\upmu\)m \(\times\) 0.25 mm). High-purity He was used as the carrier gas at a flow rate of 1 mL min\({}^{-1}\). The column temperature was initially maintained at 40 \({}^{\circ}\)C for 2 min and subsequently ramped up to 170 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\), where it was held for 5 min. Thereafter, the temperature was increased to 300 \({}^{\circ}\)C at the same heating rate (10 \({}^{\circ}\)C min\({}^{-1}\)) and held for an additional 2 min. The individual components of the bio-oil were identified by comparing their GC-MS spectra and retention times with those of reference compounds cataloged in the mass spectral library of the National Institute of Standards and Technology. Selective phenolic compounds (such as phenol, guaiacol, 4-methyl guaiacol, 4-ethyl guaiacol, 4-propyl guaiacol, eugenol, vanillin, acetovanillone, isoeguenol, and homovanillic acid) were quantified using an external calibration method, and their yields were determined using Equation (3):  
\[\text{Yield}_{\text{monomer}}\ (\text{wt.}\%)=(\text{Weight}_{\text{ specific monomer}}/\text{Weight}_{\text{ initial lignin}})\ \times\ 100. \tag{3}\]  
FT-IR analysis was performed using a PerkinElmer 400 FT-IR spectrometer (Waltham, MA, USA) operating at a resolution of 1 cm\({}^{-1}\) across a wavelength range of 500-4000 cm\({}^{-1}\). The molecular weight distribution of the bio-oil was determined using a GPC apparatus (Waters-2695, Milford, MA, USA) equipped with three styragel columns (styragel HR 3, HR 4, and HR 5E). Polystyrene was used as the calibration standard. The GPC analysis  
Figure 2: Procedures for DES-assisted lignin depolymerization and depolymerized product separation.  
was conducted at 35 \({}^{\circ}\)C, using tetrahydrofuran as the eluent at a flow rate of 1 mL/min. Each sample was subjected to GPC analysis twice, and the average values of the results were calculated. Elemental analysis of the KL feedstock and RL was conducted using a Vario MACRO cube analyzer (Hanau, Germany). The C, H, and N contents of the RL and lignin-derived bio-oil were directly measured using an elemental analyzer, while the O content was calculated based on the resulting difference. The high heating values (HHVs), serving as an indicator of the potential energy yield from a material, of both the KL and RL materials were calculated using their elemental compositions, including C, H, N, S, and O contents, according to the DIN 51900 standard [31, 32], as indicated in Equation (4):  
\[\text{HHV (MJ kg}^{-1})=[(34\ \times\ \text{C})+(124.3\ \times\ \text{H})+(6.3\times\ \text{N})+(19.3\ \times\ \text{S})-(9.8\ \times\ \text{O})]/100. \tag{4}\]
## 3 Proposed CC and CV Charging Modes and Parameter Modification  
### Proposed CC and CV Charging Modes  
According to the foregoing theoretical analysis, under the constraints of the ZPA conditions, the DS-LCC topology can realize a stable CC charging mode, and the LCC-S topology can implement a CV charging mode under dynamic loads.  
Clearly, by combining the transfer characteristics of the two topologies and adopting reasonable switching control logic, a charger that can satisfy the CC/CV modes of the battery can be obtained. Therefore, this paper proposed a hybrid compensation topology by employing a shunt auxiliary capacitor \(C_{\text{F3}}\) and two additional ACSs (\(S_{1}\) and \(S_{2}\)) on the secondary side to achieve onboard charging, as shown in Figure 10.  
Figure 9: (**a**) Voltage gain of the LCC-S topology and (**b**) phase angle of the input impedance.  
The two additional ACSs (\(S_{1}\) and \(S_{2}\)) in the designed hybrid topology are shown in Figure 11. In general, ACSs can be designed via two MOSFETs connected back-to-back.  
As clearly shown in Figure 11(a), the control logic of ACSs \(S_{1}\) and \(S_{2}\) of the battery charger is converted from the CC to CV charging mode. Figure 11(b) represents the circuit diagram for performing the switching action. \(U_{\text{BAT}}\) represents the charging voltage of the battery, and \(k_{\text{v}}=U_{\text{REF}}/U_{\text{BAT\_MAX}}\) represents the ratio of the reference voltage \(U_{\text{REF}}\) and the boundary voltage from CC to CV mode. Initially, the WPT charger operates in CC mode (\(S_{1}=1\), \(S_{2}=0\)) while \(U_{\text{BAT}}<U_{\text{BAT\_MAX}}\). As the charging voltage increases, once it reaches the maximum \(U_{\text{BAT\_MAX}}\), the control logics of \(S_{1}\) and \(S_{2}\) are toggled and the WPT charger is operating in CV mode (\(S_{1}=0\), \(S_{2}=1\)), and \(I_{\text{BAT}}\) begins to drop until the charge end current is reached.  
Throughout the charging process, when the output voltage of the charger is less than \(U_{\text{BAT}}\), switch \(S_{1}\) is connected and switch \(S_{2}\) is disconnected, and the WPT charging systems operate in the CC stage, as clearly shown in Figure 11(b). In contrast, when the output voltage increases to \(U_{\text{BAT}}\), \(S_{1}\) is disconnected and \(S_{2}\) is connected, and the WPT charging systems operate in the CV charging stage, as shown in Figure 11(c).  
Figure 11: ACSs with two MOSFETs connected back-to-back.  
Figure 10: (a) Proposed hybrid compensation topology of the WPT systems; (b) DS-LCC topology for the CC output; (c) LCC-S topology for the CV output.
# Monitoring Forest Phenology in a Changing World
## 2 Monitoring Phenology
### Multi-Mode Collaboration Networks as a Solution for Ground-Based Monitoring  
Ground-based phenology monitoring techniques operate over relatively limited spatial extents and are not suited for addressing large-scale questions around the phenological impacts of global change. Collaboration networks--sometimes using citizen science--can begin to alleviate this issue by increasing the number of sites being monitored, leading to very high \"collective resolution\".  
Several long-term phenological networks have been set up to collect enormous amounts of phenology data across large spatial scales (USA-NPN [22], NECTAR [158], NEON [159]). For example, the NECTAR database encompasses phenological data for over 5000 plant species with time-series data for over 1500 species [158], and the German National Phenology Network has 474 sites with >50 years of data [160]. The data produced from these studies is valuable because most use status monitoring (e.g., presence or absence of leaves) rather than event-based monitoring (leaf flush), which provides explicit information on presence, absence, and duration of phenophases, as well as enabling integration of the phenology of sessile and mobile organisms [161]. Citizen scientists contribute to phenological data in networks such as the USA-National Phenology Network [22], with the benefit of producing datasets that are much larger--both spatially and temporally--than is feasible from a single research group and for a fraction of the costs [117]. As with all visual assessment monitoring, however, both citizen science and observer-based phenological networks are potentially limited by the subjective bias of observers [116], although collection apps that store photos for future consolidation and validation could help resolve this bias [155].  
Networks of phenological cameras provide a quantitative alternative for phenological monitoring networks. One such example is the Phenological Camera (PhenoCam) Network, predominately in North America but continuously expanding, with cameras placed above and within canopy at 440 sites in over 10 different biomes [24; 133; 151]. Similar large scale studies have also been set up in Europe (EuroPhen [162]), Japan (Phenological Eyes Network (PEN) [163]), and Brazil (e-Phenology Network [42]). Nevertheless, camera-based networks remain costly to implement, particularly if a tower needs to be constructed to mount the camera on and if this construction needs to be in a hard to reach location and/or surrounded by trees and undergrowth. Moreover, camera-based monitoring over multiple biomes with multiple cameras can suffer from the variation between environments in exposure, spectral indices used and sensor specifications so the assurance of standardised data requires multiple post hoc adjustments and editing, all of which can be time consuming [151]. Some networks, like PhenoCam, have begun to address these issues by categorising sites into standardised 'types', with Type 1 representing sites with cameras using standardised settings regardless of biome or region of interest [164].
## 3 Results and Discussion
### Single Factor Pollution Index Evaluation  
#### 3.3.1 Single Factor Pollution Index Evaluation Results  
Nine water quality indicators were calculated for twenty-four sampling points in the Wanquan River basin, using Class III water as the target water quality to obtain the single factor pollution index for each sampling point in the Wanquan River basin (Table 4 and Figure 4).  
Of the 24 sampling points, 7 (29%) were in the no pollution class, 9 (38%) in the slight pollution class, 6 (25%) in the light pollution class, and 2 (8%) in the moderate pollution class (Table 4).  
Figure 3: Spatial distribution of various water quality indicators in the Wanquan River.  
For the nine water quality indicators, Hg has the highest average single factor index of 1.3 and is the most significant source of pollution. The average single factor index for the remaining eight indicators is less than 1. Among them, the average single factor index values for COD\({}_{\text{Mn}}\) and TN are 0.91 and 0.85, respectively, which are close to the lower limit of Class III water, and the relevant departments need to pay attention to them.
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 2 Materials and Methods
### Surface Morphology and Chemical Composition Analysis  
A field emission scanning electron microscope (FESEM, SupraTM 40, Zeiss, Germany), at an accelerating voltage of 2 kV was used to analyze the surface morphology of the generated LIG. The system was also equipped with an energy dispersive X-ray analyzer (EDS, Bruker, Germany). Gold coating was used while taking EDS measurements.  
A confocal Raman microscope InVia Renishaw (Renishaw, New Mills, UK) was used to analyze the laser-induced transformation to the graphene-based material. The measurement was obtained in high confocality mode, using a laser excitation wavelength of 532 nm at an average power of 300 uW.  
Fourier Transform Infrared Spectroscopy (FTIR) analysis was performed to study the LIG obtained on a flat polished aluminum substrate in reflection mode with a Shimadzu IR Tracer 100 FTIR Spectrophotometer along with a Shimadzu AIM-9000 Infrared microscope.
## 4 Enhanced Collaborative Localization Based on Belief Propagation
### Interactive Cooperative Localization Error Correction  
According to the position of the swarm member optimized by belief propagation, the swarm cooperative observation information is corrected. If the relative observation information is transmitted between the \(i\)th and \(j\)th members, the joint posterior probability distribution function of their positions is:  
\[p(\mathbf{X}_{i},\mathbf{X}_{j}\big{|}\mathbf{z})=N(\overline{\mathbf{X}}_{joint},\mathbf{P}_{ joint}) \tag{18}\]  
In the above formula, the mean value \(\overline{\mathbf{X}}_{joint}\) and covariance \(\mathbf{P}_{joint}\) of the joint posterior probability estimation of the \(i,j\)th member can be obtained by performing a Kalman filter update on the estimation of the \(i,j\)th member's positions \(N(\mathbf{X}_{i},\mathbf{P}_{i})\) and \(N(\mathbf{X}_{j},\mathbf{P}_{j})\).  
First, let \(\overline{\mathbf{X}}_{joint}=[\mathbf{X}_{i},\mathbf{X}_{j}]\) and \(\mathbf{P}_{joint}=[\mathbf{P}_{i},\mathbf{P}_{j}]\). Then, the linearization parameters \(\mathbf{A}\), \(\mathbf{b}\), \(\mathbf{\Omega}\), relative observation information \(\mathbf{z}_{i,j}\), and relative observation noise covariance \(\mathbf{R}_{i,j}\) are used to update the Kalman filter, and the observation and state estimation formulas are:  
\[\overline{\mathbf{z}}_{joint}=\mathbf{A}_{ij}\overline{\mathbf{X}}_{joint}+\mathbf{b} \tag{19}\]  
Filter gain:  
\[\mathbf{K}_{joint}=\frac{\mathbf{P}_{joint}\mathbf{A}_{i,j}^{T}}{\mathbf{A}_{i,j}\mathbf{P}_{ joint}\mathbf{A}_{i,j}^{T}+\mathbf{\Omega}_{i,j}}. \tag{20}\]State estimation equation:  
\[\overline{\mathbf{X}}_{joint}=\overline{\mathbf{X}}_{joint}+\mathbf{K}_{joint}(\mathbf{z}_{i,j}- \overline{\mathbf{z}}_{joint}) \tag{21}\]  
The optimized joint position estimation of the \(i,j\)th member is used to perform linearization decomposition of the observation information and update the linearization parameters of the relative observation information. By means of the belief-propagation algorithm, the marginal probabilities of the local sensors and the conditional probabilities are combined to generate a comprehensive posterior probability distribution. This posterior probability distribution provides an optimal estimate of the position and navigation state, considering the weights and contributions of all available information. Belief propagation is therefore able to integrate data from multiple sources and reduce the effect of single sensor errors, thus improving the overall system positioning and navigation accuracy.
## 3 Results
### Analysis of Climatic Factors in Infection Rate of Tilapia Streptococcus  
The results of univariate logistic regression analysis indicate that four climatic factors (average atmospheric pressure, average temperature, UV index and rainfall) significantly related to streptococcal infection (Table 2). The multivariate logistic regression models with these four factors were then built as shown in Table 3. The factor average atmospheric pressure is negatively (Exp (B) = 0.958) correlated to streptococcal infection, while the other three, including average temperature, UV index, and rainfall, are positively (Exp (B) = 1.014, 1.071, and 1.384, respectively) correlated to streptococcal infection. However, the results were not statistically significant. For the factor of rainfall, the climatic data on the case were submitted on the day and a few days (3, 5 and 10 days) before the case submitted day was analysed. The results showed that if it rains on the case submitted day or three days before, the submitted case is more likely to be positive of streptococcus (Table 4).  
The linear regression models of the percentage of cumulated positive farms from all submitted tilapia cases related to the changing of average temperature, average atmospheric pressure and UV index are shown in Figure 5. When the average temperature is above 27.0 \({}^{\circ}\)C, the average atmospheric pressure is lower than 1005.1 hPa, or the UV index is above 7.2, the percentage of cumulated positive farms from all submitted tilapia cases was more than 50%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **No. of Total Bacteria** & _S. agalactica_ & _S. iniae_ & _S. dysgalactica_ & _L. garviae_ & _L. lactis_ \\ \hline
2013 & 25 & 24 & 1 & 0 & 0 & 0 \\
2014 & 82 & 37 & 42 & 1 & 2 & 0 \\
2015 & 158 & 142 & 14 & 0 & 1 & 1 \\ Total (\%) & 265 & 203 (76.7) & 57 (21.5) & 1 (0.4) & 3 (1.1) & 1 (0.4) \\ \hline \hline \end{tabular} Abbreviations: S. (_Streptococcus_); L. (_Lactococcus_).  
\end{table}
Table 1: Identification of Gram-positive cocci isolated from Tilapia from 2013 to 2015.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Climate Factor** & **B** & **S.E.** & **df** & **Sig.** & **Exp (B)** &
\begin{tabular}{c} **95.5\% C.I. EXP (B)** \\ **Lower** & **Upper** \\ \hline Average atmospheric pressure ( hPa) \({}^{*}\) & \(-\)0.072 & 0.018 & 1 & 0.000 & 0.931 & 0.899 & 0.964 \\ High and low pressure difference ( hPa) & \(-\)0.114 & 0.092 & 1 & 0.213 & 0.892 & 0.745 & 1.068 \\ Average temperature (°C) \({}^{*}\) & 0.086 & 0.025 & 1 & 0.001 & 1.090 & 1.038 & 1.144 \\ High and low temperature difference (°C) & 0.026 & 0.041 & 1 & 0.530 & 1.026 & 0.947 & 1.112 \\ UV Index \({}^{*}\) & 0.121 & 0.037 & 1 & 0.001 & 1.128 & 1.050 & 1.212 \\ Rainfall \({}^{*}\) & 0.488 & 0.203 & 1 & 0.016 & 1.630 & 1.095 & 2.426 \\ \hline \hline \end{tabular} \({}^{*}\) significant (\(p<0.05\)).  
\end{table}
Table 2: Univariate logistic regression analysis on tilapia streptococcus and climatic factors.
## 4 Security Analysis and Simulation Verification
### Performance Analysis and Security Comparison
#### 4.4.3 Interpretation and Result Evaluation  
Performance analysis demonstrates that the SE-UAVAKA protocol achieves a key negotiation time of 8.9328 ms through optimized scalar multiplication operations (limited to four iterations) and lightweight implementations of hash and XOR functions. This represents a significant reduction compared to benchmark protocols such as Chaudhry, Ren, and Tsobdiou, confirming adaptability to resource-constrained UAV environments. Security evaluations confirm the protocol's comprehensive fulfillment of critical requirements, including impersonation-attack resistance, replay-attack prevention, user anonymity, and perfect forward security. Notably, it addresses a critical limitation in existing solutions by effectively resisting known session-specific temporary information attacks. The protocol thus establishes an optimal balance between cryptographic robustness and operational efficiency, meeting the dual demands of low-latency communication and high-grade security in UAV networks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Number of Tests** & **Agreement Time (PC)** & **Agreement Time (ARM Cortex-M3)** \\ \hline Chaudhry & \(8 Mul+2 Add+8 Hash\) & 17.621 ms & 697.24 ms \\ Tsobdiou & \(6 Mul+2 Xor+8 Hash\) & 13.235 ms & 522.72 ms \\ Ren & \(6 Mul+8 Hash\) & 13.2168 ms & 522.52 ms \\ Proposed & \(4 Mul+6 Hash+12 Xor\) & 8.9328 ms & 349.59 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computation cost for the authentication and key-agreement phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **Ren** & **Chaudhry** & **Tsobdiou** & **Proposed** \\ \hline IAR & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ RAR & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ UA & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ MA & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ PFS & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ KKS & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ KSTR & \(\mathbf{\mathsf{X}}\) & \(\mathbf{\mathsf{X}}\) & \(\mathbf{\mathsf{X}}\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Protocol security comparison.
## 2 Materials and Methods
### Crop Yield, Protein Concentrations, N Uptake and N Balancing  
Grain yield for each experimental plot was precisely measured using a plot combine harvester. Laboratory analyses were performed to determine the grain's dry matter (DM) content after drying at 60 \({}^{\circ}\)C. Fresh matter (FM) yields, expressed in t ha\({}^{-1}\), were standardized to a dry matter content of 86%. Grain N content was quantified using a C/N Analyzer [33] and N uptake was calculated by multiplying the grain DM yield by the N content determined through laboratory analysis. By-products, including straw, were excluded from harvesting and were not factored into the calculation of N output.  
The N surplus (kg ha\({}^{-1}\)) was calculated:  
\[\mathrm{N_{surplus}=N_{input}-N_{output}}\]  
The NUE (%) was determined using the difference method:  
\[\mathrm{NUE}=(\mathrm{N_{output\_fertil}-N_{output\_unfertilized}})/ \mathrm{N_{input}}\]  
\(\mathrm{N_{input}}\) is defined as the amount of mineral N fertilizer applied to each plot.  
\(\mathrm{N_{output}}\) is represented as the grain N uptake in the fertilized treatment and unfertilized treatment, respectively.
## 3 Therapeutic Management of EGPA
### Anti-IL-5-Targeted Therapies
#### 3.2.2 Reslizumab  
Fewer evidence is available for reslizumab as a controller drug in EGPA. A study involving nine patients with refractory, steroid-dependent EGPA patients with severe eosinophil asthma were treated with reslizumab infusions at a dose of 3 mg/kg every 28 days [37], according to the regimen approved as add-on therapy for treatment of severe asthma with an eosinophilic phenotype [38]. Patients also had other EGPA features, namely, paranasal sinus disease (100% of patients), neurological involvement (44% of patients), and cardiac involvement (33% of patients). At the end of the 48 weeks of treatment, 7 out of 9 patients (~78%) had their steroid doses reduced to or below 7.5 mg/day (consistent with remission [39]), while two patients were able to completely stop the drug. No increases in exacerbation frequency or hospitalization were seen and no treatment-limiting adverse effects were recorded. Since no significant improvement in overall BVAS scores was seen, the authors concluded that reslizumab may be less effective in controlling extrapulmonary manifestations of EGPA, namely, neuropathy, than in quenching activity of airway disease [37]. Thus, reslizumab may be a therapeutic option in EGPA patients mainly troubled by severe eosinophilic asthma. Another open-label, pilot study involving 10 patients with EGPA [40] demonstrated a significant reduction in the daily oral glucocorticoid dose after 7 monthly reslizumab treatments (equivalent to a 24-week treatment phase). Although only 6 of the 10 subjects completed all the study visits, 8 patients could be nonetheless analyzed; 5 out of these 8 patients were considered true responders to reslizumab because of successful tapering of oral steroids, with no exacerbations during the treatment phase. Overall, three exacerbations were recorded during the treatment, and the most common was the worsening of asthma symptoms. One patient suffered from a severe adverse event, deemed to be causally linked with the study drug, requiring removal from the study [40]. Because of the small numbers of patients and the design of these studies, randomized, controlled clinical trials are needed to validate the efficacy and safety of reslizumab for EGPA.