esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Surface Modification for DCR  
The DCR was treated using the ESO/TETA bio-oil-based modifier, where the light molecular fractions of the modifier not only adhered to the rubber surface via physical adsorption but also penetrated the voids within the rubber clusters. Upon microwave irradiation, the cross-linked network structure within the DCR was altered, converting it into free polymer chains [27; 28; 29]. This process also facilitated the decomposition of chemical groups in the ESO/TETA, leading to the formation of amide free radicals that covalently bonded with free radicals on the rubber. As shown in Figure 4, the FTIR spectra of the DCR, MDCR, and ESO/TETA show that the infrared spectra of the DCR and MDCR had strong absorption peaks at wavelengths of 2850 cm\({}^{-1}\)-3000 cm\({}^{-1}\), which indicates that the rubber powder contained \"-CH\({}_{3}\), =CH\({}_{2}\) and \(\equiv\)CH\". The spectrum for the DCR featured an absorption peak at 1537 cm\({}^{-1}\), attributed to the stretching vibration of the C=C skeleton, which indicates the presence of unsaturated bonds within the rubber powder. Notably, this peak was absent in the MDCR spectrum, suggesting the breakage of double bonds during the treatment. Conversely, new absorption peaks emerged at 1150 cm\({}^{-1}\) and 1095 cm\({}^{-1}\) in the MDCR spectrum, which were assigned to the formation of C-N bonds. This implies that the amide groups in the ESO/TETA reacted with the carbon free radicals of the DCR under the microwave irradiation to form C-N linkages, and at 1658 cm\({}^{-1}\), a C=O expansion and contraction vibration peak appeared, and the peak at 1738 cm\({}^{-1}\) was attributed to the ester characteristic peak in epoxy soybean oil, indicating a successful grafting of the ESO/TETA modifier onto the DCR surface via microwave irradiation. The  
Figure 3: \({}^{1}\)H-NMR spectrogram of (**a**) TETA, (**b**) ESO, and (**c**) ESO/TETA.  
introduction of these functional groups onto the rubber surface is presumed to increase its surface polarity. Such chemical modifications are expected to improve the compatibility and adhesive interactions between the modified crumb rubber and the asphalt, which is critical to the properties of modified asphalt.  
The EDS analysis of the DCR and MDCR is illustrated in Figure 5. The relative contents of C, O, N, S, and Si on the DCR and MDCR surface are shown in Figure 6. It is clear that the new element N appeared in the MDCR compared to the EDS of the DCR, and the relative content of O decreased by 3.4%. The results showed that during the microwave activation process, the oxidation reaction did not occur on the DCR surface, but the amide group reacted with the carbon radicals of the DCR to form the C-N group, which was consistent with the above FT-IR results.  
Figure 4: FT-IR spectra of DCR, MDCR, and ESO/TETA.  
Figure 5: EDS spectrum of DCR (**a**) and MDCR (**b**).
## 5 Test and Simulation
### Assessment of MWR Ranging Accuracy  
With extensive tests of MWR that were conducted in the 6-DOF platform, the results verified the micron-level high-accuracy specifications that could be applied to real space formation missions in the future. Table 3 provides the primary design parameters of the whole ranging system, with a relative distance of 100 km as an example. Figures 8 and 9 provide the real ranging test results from the 6-DOF moving platform under a stable longitudinal velocity of 5 \(\upmu\)m/s. The ranging error (the differential  
Figure 7: Six degree of freedom moving platform that achieved micron-level accuracy in position and an accuracy of milliarcseconds in attitude orientation.  
between real measurement data from MWR and optical moving platform sensor) was less than 40 \(\upmu\)m during the test process, and 1.6 \(\upmu\)m/s range rate error was obtained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **Symbol** & **Unit** & **K Band** & **Ka Band** \\ \hline Frequency & \(f\) & GHz & 24.517002 & 32.692746 \\ Transmitting power & \(Ptx\) & W & 10 & 10 \\ Transmitting power & \(Ptx\) & dBW & 10 & 10 \\ Transmitting feed loss & \(Lftx\) & dB & 0.5 & 0.5 \\ Transmit antenna gain & \(Gtx\) & dBi & 16 & 16 \\ Effective isotropic radiated power & \(EIRP\) & dBW & 25.5 & 25.5 \\ Space distance & \(d\) & km & 100 & 100 \\ Flux density & \(Fd\) & dBW/m\({}^{2}\) & \(-\)85.49209857 & \(-\)85.49209857 \\ Space propagationloss & \(Ld\) & dB & \(-\)160.2311193 & \(-\)162.7308 \\ Power level of receiving antenna & \(Prxa\) & dBW & \(-\)134.7311193 & \(-\)137.2308 \\ Receiving antenna gain & \(Grx\) & dBi & 15 & 15 \\ Polarization mismatch loss & \(Lpol\) & dB & 1 & 1 \\ Antenna gain of receiving system port & \(Grxs\) & dBi & 14 & 14 \\ Receiving feed loss & \(Lfrx\) & dB & 0.5 & 0.5 \\ Power level of receiver input port & \(Prxi\) & dBm & \(-\)91.23111929 & \(-\)93.73080004 \\ Noise temperature of receiving antenna & \(Ta\) & K & 150 & 150 \\ Temperature of receiving feeder & \(Tf\) & K & 290 & 290 \\ Receiver noise factor & \(Rf\) & dB & 10 & 10 \\ Receiver noise temperature & \(Terx\) & K & 228 & 228 \\ Receiver noise temperature & \(Terx\) & dBK & 23.57934847 & 23.57934847 \\ ENT of Receiver port & \(Td\) & K & 393.2248687 & 393.2248687 \\ ENT of Receiver port & \(Td\) & dBK & 25.94640976 & 25.94640976 \\ G/T value of Receiver port & \(G/T\) & dB/K & \(-\)12.44640976 & \(-\)12.44640976 \\ ENT at the receiver system port & \(Trxs\) & K & 441.2055593 & 441.2055593 \\ ENT at the receiver system port & \(Trxs\) & dBK & 26.44640976 & 26.44640976 \\ G/T value of receiver system port & \(G/T\) & dB/K & \(-\)12.44640976 & \(-\)12.44640976 \\ Carrier-to-noise ratio & \(C/N_{0}\) & dB-Hz & 80.82247095 & 78.3227902 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of MWR ranging payloads.  
Figure 8: MWR ranging errors of 2400 samples, with data collected from a 6-DOF moving platform under a relative longitudinal velocity of 5 \(\upmu\)m/s. The ranging errors were less than 40 \(\upmu\)m during the test.
## 4 Experiments and Analysis
### Main Results
Mesh-based methods, such as MeshNet, achieve moderate performance (91.90% class accuracy) but are constrained by the irregularity of mesh structures and computational complexity. Point-based methods, like Point2 Sequence and PointNet++, provide solid accuracy (90.40% and 91.90%, respectively) but require dense point clouds, which can lead to increased memory and computational overhead. Voxel-based methods, such as VoxNet (83.00%) and LightNet (86.90%), suffer from quantization artifacts and the loss of spatial resolution during voxelization, limiting their effectiveness. View-based approaches generally outperform mesh-, point-, and voxel-based representations due to their ability to capture rich spatial and structural features. Among these, MVCNN achieves a 90.10% class accuracy using 80 views, while MVMSAN improves to 95.68% with 20 views, marginally surpassing our method by 0.37% (95.68% vs. 95.31%). Despite the minor performance improvement, MVMSAN experiences a substantial increase in model size, expanding by 44.26% (from 6.1 million to 8.8 million parameters, as shown in Table 4), and incurs higher computational overhead. Similarly, MVDAN achieves 95.50% accuracy on 12 views, slightly exceeding our method by 0.19%, yet its parameter count is inflated dramatically by 20.31\(\times\) compared to our lightweight architecture. Furthermore, VCGR-Net, MVContrast, MVCVT, DILF, and SelectiveMV all exhibit inferior performance to our approach, failing to achieve competitive accuracy even with comparable or higher computational complexity. Sequential-view methods, such as SV2 SL (91.12%) and 3D2 SV (91.51%), leverage temporal dependencies but fall short of capturing intricate structural details. The confusion matrices of both our methods are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Representation** & **Points/View** & **Average Class** & **Average Instance** \\  & **Type** & **Number** & **Accuracy** & **Accuracy** \\ \hline MeshNet [13] & Mesh & - & 91.90 & - \\ Point2 Sequence [16] & Point & \(1024\times 3\) & 90.40 & 92.60 \\ FoldingNet [35] & Point & - & - & 88.40 \\ PointNet [8] & Point & - & 86.20 & 89.20 \\ PointNet++ [9] & Point & - & - & 91.90 \\ VoxNet [36] & Voxel & - & 83.00 & - \\ LightNet [37] & Voxel & - & 86.90 & - \\ MVCNN [9] & View & 80 & 90.10 & - \\ Geometry image [38] & View & 1 & 83.90 & - \\ MVDAN [44] & View & 12 & 95.50 & 96.60 \\ MVMSAN [39] & View & 6 & - & 96.84 \\ MVMSAN [39] & View & 12 & - & 96.80 \\ MVMSAN [39] & View & 20 & 95.68 & 96.96 \\ VCGR-Net [40] & View & 12 & 93.33 & 95.62 \\ MVContrast [45] & View & 12 & 90.24 & 92.54 \\ MVCVT [46] & View & 12 & - & 95.40 \\ DILF [47] & View & 6 & - & 94.80 \\ SelectiveMV [48] & View & 1 & - & 88.13 \\ PointOfView [49] & Point + View & -/6 & 92.17 & - \\ SV2 SL [22] & SeqView & 12 & 91.12 & 93.31 \\
3D2 SV [23] & SeqView & 12 & 91.51 & 93.40 \\ FusionNet [41] & Voxel + View & 20 & - & 90.80 \\ MSSAViT-TV (ours) & View & 12 & 94.91 & 96.07 \\ MSSAViT-SV (ours) & View & 12 & 95.31 & 96.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative results on ModelNet40.
## 4 Proposed Methodology: Dynamic Context-Aware Transformer (DCAT)
### Operational Workflow
#### 4.2.1 Training and Inference Process  
The DCAT model is meticulously trained and fine-tuned to ensure optimal performance in generating Tamil image captions. This process is underpinned by rigorous mathematical and algorithmic approaches.  
Training Process  
The training regimen of DCAT is bifurcated into pre-training and fine-tuning stages, each playing a vital role in the model's development.  
* _Pre-training:_ GPT-3, the language-generation component of DCAT, is pre-trained on a vast corpus of Tamil text. This stage is crucial for the model to capture the intricacies of Tamil syntax and semantics. \[J_{\text{pre}}=\sum_{i=1}^{N}\log P(w_{i}|w_{1:i-1};\Theta_{\text{pre}})\] (21)  
The objective function \(J_{\text{pre}}\) maximizes the likelihood of predicting the next word in a sentence, given its preceding words, thereby ingraining the language model with a robust understanding of the Tamil language.
* _Fine-tuning:_ Post pre-training, the DCAT model undergoes fine-tuning on translated image-caption pairs. This stage refines the model's ability to correlate visual features with corresponding textual descriptions. \[J_{\text{fine}}=\alpha\times J_{\text{NLL}}+\beta\times J_{\text{context}}\] (22) The fine-tuning loss function \(J_{\text{fine}}\) is a weighted sum of the Negative Log-Likelihood (NLL) loss and a contextual loss component, ensuring that the model not only generates linguistically correct captions but also captures the contextual essence of the images.  
Inference Mechanism  
During inference, DCAT employs a feedback loop mechanism to iteratively refine the generated captions. This approach enables dynamic updates to the attention weights and context embedding, ensuring high-quality caption generation. Initial Caption = Generate-Initial-Caption(\(X,\Theta\)) while (Stop Criterion not met): \(A_{t},E_{t}=\text{Update-Attention-And-Context(}Y_{\text{init}},X;\Theta\)), \(y_{\text{next}}=\text{argmax}_{y}P(y|Y_{\text{init}},X,A_{t},E_{t};\Theta)\), \(Y_{\text{init}}=Y_{\text{init}}+y_{\text{next}}\).  
The inference process commences with generating an initial caption, which is then refined in a loop until a stop criterion (such as maximum length or end-of-sentence token) is met. At each step, the attention weights \(A_{t}\) and context embedding \(E_{t}\) are updated, influencing the generation of the next word in the sequence. This iterative refinement ensures that the final caption is not only contextually rich but also linguistically coherent.  
Through this comprehensive training and inference methodology, DCAT effectively bridges the gap between visual perception and natural language expression, particularly tailored for the Tamil language. This intricate process underscores the model's capability to produce captions that are both technically accurate and culturally resonant. The pseudocode of the proposed DCAT is shown in Algorithm 1.
## 3 Experimental Research and Analysis
### Dataset  
Model performance directly affects its effectiveness and reliability in practical applications. Testing the proposed model on datasets is an essential step to evaluate its validity and reliability. By testing the model on datasets, an objective and comprehensive assessment of its ability to detect complex scenarios can be made, and comparisons with existing methods can be conducted. This chapter introduces the datasets used, training configurations, and evaluation parameters for the model.  
In training deep learning models, selecting appropriate datasets is crucial. The quality, size, and diversity of datasets directly impact the training effectiveness, generalization ability, and practical performance of the model. To evaluate HSS-YOLO's object detection capability in real dim environments, this study used two datasets: a custom dataset and a public dataset. To enhance the recognition ability of the custom dataset in other scenarios, additional scenes from the public dataset were included. The public dataset is the DoorDetect Dataset [17].  
The custom dataset consists of 6.75 k handle images, containing three basic target categories: door, handle, and knob. The HandleData was split into training, validation, and test sets at a ratio of 8:1:1, with detailed label information shown in Figure 7: Display of local datasets. This study trained for 250 epochs to achieve better model convergence. The resolution of the datasets used in this experiment is uniformly 640 \(\times\) 640.  
Figure 6: Description of Inner-IoU.  
During training, selecting appropriate training parameters and hardware configurations is crucial for model convergence, generalization, and training efficiency. Incorrect parameter settings may result in poor model performance and negatively affect the model's effectiveness in real-world tasks.  
The model validation experimental environment was established using an Intel(R) Core(TM) i7-13650 HX CPU(Manufactured by Intel Corporation) running at 4.90 GHz, with 32 GB of RAM, an NVIDIA GeForce RTX 4060 GPU (Manufactured by NVIDIA)with 8 GB of video memory, and Windows 11 Professional operating system. This setup used Python 3.10.15 programming language and the PyTorch2.0.0 deep learning framework. The specific hardware (Table 2) and software parameters (Table 3) are as follows:  
Relevant parameter settings (default values are used for those):
## 1 Introduction and Motivation
In summary, it can be seen that the identification of critical nodes and the evaluation of their impact in the WSN is important for the following reasons. Firstly, the provision of exhaustive protection to a given number of critical nodes can become viable. Secondly, the degradation that the WSN suffers when removing critical nodes is an upper bound; whatever node fails, its effect cannot exceed that degradation. Therefore, knowing this level of degradation is useful for the design and maintenance of the network and the adjustment of the service tolerance. In this paper, we focus on two metrics for network performance, namely latency and lifetime. The former refers to the amount of time needed to transmit data from one sensor to the base station, and we assume in this work that it is directly related to the number of hops or jumps needed for that end. The latter expresses the amount of time elapsed until the first node battery runs out of energy and thus becomes unable to gather and transmit data. Such elapsed time is divided into _time steps_, each of which represents the creation and transmission of a single data packet per node. Moreover, nodes are not endowed with any additional power source generator, such as photovoltaic cells that could alter the calculations of lifetime. In previous works, Mixed-Integer Programming (MIP) models have been proposed to identify the critical nodes and evaluate the degradation in terms of latency [11] and lifetime [7]. Although the MIP models provide exact results, they have two important limitations due to their complexity and long execution times required: (i) few critical nodes can be identified, and (ii) only WSNs with very limited number of sensors can be evaluated. In this paper, we propose a Greedy Randomised Adaptive Search Procedure (GRASP) meta-heuristic to overcome these limitations. The main contributions are:* A general framework for parallel GRASP execution;
* A specific GRASP meta-heuristic to identify the critical nodes when the performance metric is the latency and evaluate their impacts;
* A specific GRASP meta-heuristic to identify the critical nodes when the performance metric is the sensor lifetime and evaluate their impacts;
* A comparison between the MIP and GRASP meta-heuristic results in WSNs with 100 nodes and one and two critical nodes;
* An evaluation of the performance impact of one to five critical nodes in WSNs with up to 1000 nodes. The rest of the paper is organised as follows. Section 2 analyzes the state of the art. In Section 3, we explain the current scenario and the motivation behind the creation of a GRASP meta-heuristic for solving bigger networks. Section 4 defines the problem in detail. Then, in Section 5, the GRASP meta-heuristic for both latency and lifetime models are presented, as well as the features used for reducing the number of criticality tests to perform. Finally, obtained results are shown in Sections 6.1 and 6.2. Conclusions and future work are described in Section 7.
## 2 Design of Broadband CP Antenna
### Antenna Design Approach  
The reference antenna is designed by implementing the two-stage feeding technique to obtain the miniaturized size, as well as impedance matching of 50 \(\Omega\). The design has the two-stage feeding feedline and the E-shaped patch. The antenna is planted over Roger RT/Duroid 6002, which has 2.94 and 0.0012 relative permittivity and loss tangent. The simplified geometry of the reference antenna is given in Figure 1. The offered scattering is depicted in (b) of Figure 2. It can be noticed that the antenna offer a wideband of 25.5-29.5 GHz. The optimized parameters of reference work is as follows:  
Figure 1: Antenna schematic (**a**) top-view (**b**) side-view.  
A\({}_{x}\) = 12; A\({}_{y}\) = 8.5; A\({}_{1}\) = 3.7; A\({}_{2}\) = 2.3; A\({}_{3}\) = 3; A\({}_{4}\) = 3.5; A\({}_{5}\) = 3.5; g\({}_{1}\) = 1; g\({}_{2}\) = 0.5; g\({}_{3}\) = 0.6; g\({}_{4}\) = 0.5; g\({}_{4}\) = 0.5; H = 0.79. (All units are in mm).  
The reference antenna's final geometry is obtained after following three design stages. In the first stage of design, the rectangular patch antenna with two-stage feeding is designed for 28 GHz. The dimensions of the rectangular patch are obtained from the equation given in [25]. In the second stage, the square stub is etched from the rectangular patch antenna and the C-shaped patch is obtained. This design operates at a wideband of 26.5-29 GHz with a minimum return loss value of \(-\)14 dB. To improve the return loss and bandwidth, in the final stage, a rectangular stub is loaded in such a way to make the antenna patch of the E-shape. The final geometry operates at a wideband of 25.5-29.5 GHz with a minimum return loss of \(<\)\(-\)20 dB. The design stages of antenna and its impact on S-parameter is provided in Figure 2.
## 6 Conclusions  
This study, conducted in Henan Province, China, utilized the RF-based downscaling method to enhance the spatial resolution of GOSIF data from 0.05 degrees to 1 km. GOSIF, NDVI, and LST with 0.05-degree spatial resolution were used as sample data to train the RF regression model. NDVI and LST were the input variables, with SIF as the predictor. Using the downscaled SIF result, the SIF anomaly index was calculated. Crop yield and areas affected by drought were selected to verify the drought index. Finally, the SIF anomaly index was used to monitor drought in Henan Province.  
The downscaled SIF result exhibited a higher correlation with MODIS GPP data than the 0.05-degree GOSIF, with correlation coefficients of 0.74 and 0.68, respectively. After resampling the downscaled SIF to a 0.05-degree resolution to match the GOSIF, a strong correlation was still observed. To show the superiority of the RF method, this study compared the downscaled SIF with direct resampling methods (nearest neighbour and bilinear). The results indicated that the downscaled SIF result showed enhanced spatial details, which was significant to study and monitor the regional GPP in Henan Province. Using the 1 km spatial resolution, the SIF anomaly index was established and calculated. Utilizing the 1 km resolution, the SIF anomaly index established strong positive correlations with crop yields (0.93 for wheat and 0.89 for maize), and a significant negative correlation with drought-affected areas (\(-\)0.58). The SIF anomaly index, based on the 1 km resolution downscaled SIF data using the RF method, proved to be a valuable tool for studying regional drought. Furthermore, the SIF anomaly index also presented researchers with a novel alternative for future drought monitoring. The findings presented in this research offer robust and scientifically sound data support for drought monitoring in Henan Province.  
Conceptualization, Z.Z.; methodology, Z.Z. and Z.G.; validation, Z.Z. and X.L.; formal analysis, Z.Z. and Y.Q.; investigation, X.L.; data curation, Z.S.; funding acquisition, Y.J.; writing--original draft preparation, Z.Z. and Z.S.; writing--review and editing, Z.Z. and Y.J. All authors have read and agreed to the published version of the manuscript. This research was funded by Key R&D Program of Shandong Province (2022 CXGC020416). Data are contained within the article. We thank the MODIS and land-used products. Meanwhile, we are very grateful for [PERSON] and [PERSON] who provided GOSIF data for this paper. The authors declare no conflicts of interest.
## 4 Adaptive OFDM-IM Method
### Scenario Model
#### 4.1.1 OFDM-IM Communication in Urban Traffic Scenarios  
In urban traffic scenarios, as illustrated in Figure 4, in addition to the LOS path, the NLOS components are reflected by scatterers such as trees, with the reflection coefficient of the trees being \(\mu=0.5\). At this point, the Rician factor of the channel model is 10 dB. Considering the good channel quality, convenience of charging electric vehicles, and users' requirements for communication reliability, high network throughput, and low energy consumption in urban scenarios, this study comprehensively examines the performance metrics of the BER, spectral efficiency, and energy consumption. It aims to identify the optimal parameter balance to enhance the system's performance and meet diverse demands.
## 3 Target Tracking Method
### The Spatial Registration Method  
#### 3.2.1 Indirect Observation Information of the Target Based on Mutual Observation  
Indirect observation information of the target based on mutual observation can be obtained by means of multiple UAVs. In this paper, we use two UAVs as an example.  
Based on the mutual observation information of UAV \(A\) to UAV \(B\) in Equation (16), we can obtain the observation information of UAV \(A\) to the target through UAV \(B\). The position of the target in the local geographic coordinate system centered on UAV \(A\) is  
\[\mathbf{X}_{ABT}=\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z }_{AB})+\mathbf{T}_{uts}(\mathbf{\mu}_{B})\mathbf{h}_{ctr}(\mathbf{Z}_{BT}) \tag{18}\]