esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results of the Tests  
Table 7 shows the results obtained for each flight by measuring the lengths of the stadias that were located in the study area (expressed in cm) and the signal-to-noise ratio for the error that was committed when measuring the distance, according to Equation (3). It is worth noting that the measurement of distance of the eight stadias was not possible for all the designed flights due to the luxvariance of the grass. However, its incidence is minimal and the signal-to-noise ratio is calculated with the average of the deviations that were obtained for each test, which range between seven and eight values.  
After considering the results of each flight, the statistic values related to each level of the different parameters (height of flight, forward and lateral overlap, and angle of inclination). For every parameter and level, an average signal-to-noise ratio and variability is obtained (Table 8). The latter expresses the maximum variation of the signal-to-noise ratio for each parameter, according to Equations (6) and (7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Parameters**} \\ \hline Focal length (pixels) & & 4255.70977 \\ \hline Principal point coordinates (pixels) & c\({}_{\mathrm{x}}\) & 1.77546 \\  & c\({}_{\mathrm{y}}\) & \(-13.6019\) \\ \hline  & k1 & 0.0807059 \\ Radial distortion coefficients & k2 & \(-0.258361\) \\  & k3 & 0.307656 \\  & k4 & 0 \\ \hline Tangential distortion coefficients & p1 & 0.000694868 \\  & p2 & 0.00221493 \\ \hline Affinity and Skew transformation & b1 & 0 \\ coefficients. & b2 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Self-calibration parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{6}{c}{**Measurement of the Stadia Length (cm)**} & \multirow{2}{*}{**S/N**} \\ \cline{2-3} \cline{5-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **  
According to the analysis of the variability, it can be stated that the influence of the inclination is much higher than those of the rest of the factors. Table 9 summarizes the results obtained from the calculation of the average signal-to-noise ratio, the sum of squares (SS) of the average variation, and the sum of squares for each parameter.  
From these data, the individual contribution of each parameter (Table 10) can be calculated according to Equation (7).  
On the light of the results the analysis according to Taguchi DOE Method, the suitable combination of factors to optimize the photogrammetric flight with respect to the geometrical goodness is the one that fixes the height of flight at its lowest level (15 m), the forward and lateral overlaps at their highest levels (80% for both), and the angle of inclination at its lowest level (nadiral).
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Task** & **Method** & **8 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **128 Bits** \\ \hline \multirow{6}{*}{I\(\rightarrow\)T} & CMFH & 0.3406 & 0.3437 & 0.3399 & 0.3409 & 0.3440 \\  & FSH & 0.3620 & 0.3732 & 0.3894 & 0.4014 & 0.4084 \\  & SCM-seq & 0.5013 & 0.5120 & 0.5422 & 0.5488 & 0.5483 \\  & SePH-km & 0.5256 & 0.5537 & 0.5627 & 0.5622 & 0.5698 \\  & DCH & 0.5840 & 0.5808 & 0.5907 & 0.5932 & 0.5843 \\  & LCMFH & 0.5955 & 0.6113 & 0.6286 & 0.6337 & 0.6412 \\  & SRLCH & 0.5789 & 0.5932 & 0.6378 & 0.6398 & 0.6529 \\  & DSAH & **0.6231** & **0.6432** & **0.6517** & **0.6667** & **0.6791** \\ \hline \multirow{6}{*}{T\(\rightarrow\)I} & CMFH & 0.3456 & 0.3498 & 0.3435 & 0.3486 & 0.3529 \\  & FSH & 0.3623 & 0.3717 & 0.3835 & 0.3973 & 0.4007 \\  & SCM-seq & 0.4709 & 0.4836 & 0.5067 & 0.5141 & 0.5161 \\  & SePH-km & 0.6102 & 0.6407 & 0.6515 & 0.6608 & 0.6651 \\  & DCH & 0.7106 & 0.7103 & 0.7098 & 0.7260 & 0.7223 \\  & LCMFH & 0.6765 & 0.7198 & 0.7389 & 0.7614 & 0.7667 \\  & SRLCH & 0.6874 & 0.6989 & 0.7567 & 0.7581 & 0.7875 \\  & DSAH & **0.7324** & **0.7596** & **0.7756** & **0.7834** & **0.8024** \\ \hline \end{tabular}
\end{table} 
Table 3: Performance comparison on NUS-WIDE dataset measured by mAP.
## 5 Conclusions  
To handle satellite images and ground measurements, in this paper, we proposed a novel transformer-based model with dual encoders, named DualET, for short-term PV power prediction. To obtain cloud detailed information from satellite images, a two-dimensional wavelet transform block and a residual block were used in the remote-sensing information encoder. For the local seasonal information encoder, we conducted self-attention and series decomposition to learn the temporal patterns from local sequences. For the decoder, we employed three types of attention modules and series decomposition blocks to model the joint features of local and remote-sensing information and output the prediction. Specifically, a cross-domain attention module was proposed to learn the correlation between the temporal features and cloud information. Finally, the experiments on real-world datasets, including PV station data and satellite images, were presented to show the prediction performance of DualET. In addition, the ablation studies show the effectiveness of our design. In the future, we will attempt to improve the model architecture so that more data sources (e.g., NWP or other satellite remote sensing data) can be utilized to predict a longer horizon.  
Conceptualization, J.W. and H.H.; methodology, H.C. and J.Y.; software, H.C. and J.Y.; validation, X.Z., T.Y. and Y.W.; writing--original draft preparation, H.C. and X.Z.; writing--review and editing, T.Y., J.W. and Y.W.; visualization, J.Y. and X.Z.; supervision, J.W. and H.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2021 ZD0110403).  
Not applicable.  
The remote-sensing data used in this paper were processed from Himawari-8 satellite data supplied by the P-Tree System, Japan Aerospace Exploration Agency (JAXA). We also gratefully acknowledge the support of the MindSpore team.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## Abstract  
Recently we introduced the class of highly localized wavepackets (HLWs) as a generalization of optical Bessel-like needle beams. Here we report on the progress in this field. In contrast to pulsed Bessel beams and Airy beams, ultrashort-pulsed HLWs propagate with high stability in both spatial and temporal domain, are nearly paraxial (supercollimated), have fringe-less spatial profiles and thus represent the best possible approximation to linear \"light bullets\". Like Bessel beams and Airy beams, HLWs show self-reconstructing behavior. Adaptive HLWs can be shaped by ultraflat three-dimensional phase profiles (generalized axicons) which are programmed via calibrated grayscale maps of liquid-crystal-on-silicon spatial light modulators (LCoS-SLMs). Light bullets of even higher complexity can either be freely formed from quasi-continuous phase maps or discretely composed from addressable arrays of identical nondiffracting beams. The characterization of few-cycle light bullets requires spatially resolved measuring techniques. In our experiments, wavefront, pulse and phase were detected with a Shack-Hartmann wavefront sensor, 2D-autocorrelation and spectral phase interferometry for direct electric-field reconstruction (SPIDER). The combination of the unique propagation properties of light bullets with the flexibility of adaptive optics opens new prospects for applications of structured light like optical tweezers, microscopy, data transfer and storage, laser fusion, plasmon control or nonlinear spectroscopy.  
keywords:linear light bullets; ultrashort wavepackets; femtosecond pulses; localized wavepackets; generalized axicons; generalized needle beams; time-wavefront sensor; spatio-temporal autocorrelation; spatial light modulators +
Footnote †: journal: Journal of Scientific and Applied Physics
## 5 Conclusions  
In this study, we evaluated the effects of three modern PPBCs (ECPR, Bio-C Repair, and NeoPUTITY), along with two surface treatment modalities (Nd-YAG lasers and DBAs), on coronal tooth discoloration. Our study also highlights the importance of selecting CSCs with an optimized formulation to reduce the risk of complications in aesthetic areas. The findings revealed that none of the tested materials induced statistically significant or clinically perceptible discoloration over a 180-day period. In addition, the surface treatments did not significantly affect the color outcomes, likely due to the inherently low staining potential of the materials. Our study also highlights the importance of selecting CSCs with optimized formulations to reduce the risk of complications in aesthetically important areas. Within the limitations of this in vitro study, the premixed bio-ceramic cements tested exhibited favorable short-term color stability; nonetheless, when considering routine use in aesthetic areas, the findings of long-term in vivo research should be taken into account.  
However, this study has certain limitations, including being conducted under in vitro conditions and a limited observation period. Variables such as pulp vitality and oral fluid exposure were not represented under clinical conditions. Therefore, longer-term in vivo studies with increased sample size are needed to evaluate the material-dentin bonding and verify the long-term color stability and clinical performance of these materials in intraoral environments. Our study contributes to the literature by evaluating surface modification and color change together. These two issues have generally been examined separately in the literature; however, evaluating aesthetics and biocompatibility together is very important, especially in young permanent teeth. This approach sheds light on current clinical practices in terms of meeting both functional and aesthetic requirements.  
N.K.S.: Designed the research, collected the data, reviewed the literature, analyzed the data, performed the experiments, analyzed and interpreted the data, prepared the figures, and wrote the paper. L.B.A.: Performed the experiments, analyzed and interpreted the data, and edited and reviewed the paper. H.S.: Designed the research, performed the experiments, analyzed and interpreted the data, and edited and reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the scientific research projects coordination of the Ordu University Presidency (Project No. A-2411).  
This investigation was approved by the Ethics Committee of the Faculty of Dentistry at Ordu University (2024/49) and was conducted in accordance with the Declaration of Helsinki ethical principles. Informed consent to participate was obtained from all participants in this study.  
Not applicable.  
All datasets of the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
## 4 Simulations and Results  
We investigate the co-channel coexistence performance of IEEE 802.11p and NR-V2X, possibly amended with the mitigation methods eM\({}_{\text{A}}\)-time-split, M\({}_{\text{B}}\)-E-signals, dM\({}_{\text{C}}\)-preamble, M\({}_{\text{C}}\)-preamble-no-SF, and M\({}_{\text{F}}\)-CTS-to-Self. Results are provided using the open-source V2X simulator WiLabV2 Xsim [15].
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 3 Research Areas and Data  
The Chengbi River Karst Basin is located in the northeast of Baise City, Guangxi and belongs to Xijiang River system. Chengbi River Karst Basin covers an area of 2087 km\({}^{2}\), of which the karst landform area is about 1123 km\({}^{2}\), which is a typical karst basin. Above the middle of the basin are mostly soil mountains and karst landforms, while below the middle of the basin are alluvial along the river, and the terrain is relatively flat, belonging to hilly landforms [34]. Due to the complexity of the karst basin, the underground hydrological characteristics of the basin are difficult to obtain. Meanwhile, the sparse meteorological stations in the Chengbi River basin before 2001 could not provide accurate meteorological data input to the process-driven model. This difficulty in obtaining underground hydrolog  
Figure 2: Data structure.  
ical characteristics and lack of long-term meteorological data is common in karst basins. Therefore, it is necessary to explore data-driven models based on runoff series. The catchment area above the Chengbi River Reservoir dam site covers 2000 km\({}^{2}\), which is 95.8% of the total catchment area of the whole basin. The annual average precipitation is 1560 mm, with about 87% of it occurring during the flood season. The average annual flow is about 37.8 m\({}^{3}\)/s, and the basin belongs to the subtropical monsoon climate with long summers and short winters. The basin is hot and rainy in spring and summer due to the influence of the ocean monsoon. At the same time, due to the existence of more tributaries in the basin, floods with longer duration are often formed in the basin, and the flood season in the basin lasts longer, starting from April to October. The Chengbi River Reservoir can provide residential water, flood control, power generation, agricultural irrigation, and other social benefits, which play very important roles in the development of Baise City. Therefore, it is essential to establish an appropriate and high-precision runoff forecasting model, which can effectively adjust the optimal scheduling model of Chengbi River Reservoir and make full use of water resources in flood season. A total of 12 telemetric rainfall stations are set up in the Chengbi River Karst Basin. This paper focuses on Bashou station and uses a dataset of 492 monthly runoff data from 1979 to 2019 for runoff forecasting. The runoff data are provided by the Chengbi River Reservoir Authority, and runoff depth data are obtained by dividing the runoff data of the designated section by the catchment area of the reservoir. The Chengbi River Karst Basin's approximate location is depicted in Figure 3.
# Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks
## 4 Results and Evaluations
### Performance Results and Analysis
As shown in Table 7, the network load for a single client in one round is twice the weights size, as each client sends its weights to the edge and receives the newly aggregated weights. In H-FL, the network load slightly increases because the main server sends the global model weights to the edges and receives the weights from each edge for aggregation. Although H-FL introduces an additional layer of communication between the server and \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Clients**} & **Number of** & **Average ANN** & **Average LSTM** & **Total Training** \\  & **Training Data** & **Training Time (S)** & **Training Time (S)** & **Time (S)** \\ \hline Client 0 & 75,341 & 1.45 & 4.49 & 5.94 \\ Client 1 & 956,504 & 14.47 & 56.02 & 70.49 \\ Client 2 & 140,115 & 2.53 & 10.61 & 13.14 \\ Client 3 & 440,494 & 7.25 & 27.03 & 34.28 \\ Client 4 & 410,876 & 6.88 & 27.09 & 33.97 \\ Client 5 & 140,044 & 2.58 & 0.41 & 2.99 \\ Client 6 & 1,118,686 & 16.71 & 58.64 & 75.35 \\ Client 7 & 405,376 & 6.34 & 17.77 & 24.11 \\ Client 8 & 131,100 & 2.39 & 7.55 & 9.94 \\ Client 9 & 162,991 & 2.93 & 2.28 & 5.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average training time per client for one round. Figure 11: Distributed loss across communication rounds. Figure 12: Average F1-score results non-IID levels across communication rounds for car hacking: Attack & Defense Challenge 2020 dataset [26]. edges, which is absent in standard FL, it reduces bottlenecks by distributing clients across edges, effectively dividing the network load and improving efficiency. One of the main challenges in implementing FL is the memory consumption of learning models trained locally on individual vehicles. High-end automobiles, such as those produced by Tesla, utilize advanced deep learning algorithms for tasks like object detection and various driver assistance features. To handle the substantial computational demands of data from cameras and sensors, these vehicles are equipped with dedicated GPUs. For example, Tesla's latest Model S and Model X are powered by AMD RDNA 2 GPUs, which provide 16-32 GB of memory for processing tasks [41]. Given that our IDS also relies on GPUs, we evaluate a vehicle's memory requirements by analyzing both the size of the learning models and the memory needed to process the dataset. The total model size is 2.98 MB. To optimize memory usage during training, we process the data in batches of 256 rather than loading the entire dataset at once. This approach significantly reduces the memory required for training data from 104.25 MB (for the full dataset) to just 0.02 MB per batch. Consequently, the model requires approximately 1 GB of memory for operation, which is well within the capabilities of current vehicle-level machines.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction
### Previous Survey Works
AI/ML techniques for aerospace robotics include (i) ANN for delay, connectivity and security improvement, as well as channel prediction; (ii) particle swarm optimization for determining UAV trajectory; (iii) DL for improved UAV connectivity; and iv) ML for user content request prediction from the UAVs. Then, the article in Reference [33] focuses on a multi-layered communication system comprising space (satellite), air (UAV), and ground segments, namely space-air-ground integrated network (SAGIN). It is found that DL, mainly convolutional neural networks (CNNs), with different DL architectures and training methods can improve the network performance. Next, the tutorial in Reference [34] provides a classification of ANNs for wireless communications. Among several applications (e.g., caching, multiple radio access technologies (RATs), and IoT), the authors refer to UAVs and discuss RL for coverage, connectivity, trajectory, resource, and path planning optimization. A cellular use case for applying AI is considered, where UAVs are part of cache-enabled BSs. Finally, recurrent neural network (RNN) and especially echo state network (ESN) algorithms at the conceptor are studied for mobility prediction. Departing from these works, in this survey, we focus on the application of a broad set of AI/ML techniques for UAV-based communications without focusing on particular AI/ML categories or UAV applications. More specifically, the following contributions are given:  
* An exhaustive overview of AI/ML solutions from all possible categories and their application in UAV-based networks is presented. * A wide range of UAV-enhanced wireless communication issues, ranging from physical layer and resource management aspects up to trajectory design and caching is studied, while wireless security and public safety applications are comprehensively discussed. * Open issues are identified for both networking and security areas, stimulating further research for the application of AI/ML techniques in UAVs-based networks. Table 1 provides brief descriptions of the relevant surveys and the research areas and scope in the context of AI/ML that they discuss.
## 2 Negative-Stiffness Device and Its Mechanical Model
### Parameter Impact Analysis  
From the expression of \(F_{NSDspring}\), the parameters that affect the mechanical properties of the device can be found. Using the control variable method, the frequency of orbital surface function frequency, \(\beta\); the peak value of track surface function \(b\); the stiffness of Belleville spring, \(k\); the preload, \(\Delta l\); and the friction coefficient of track surface \(\mu\), are analyzed, respectively.  
3.1 Orbital Surface Function Frequency, \(\beta\), and Peak Value of Orbital Surface Function, \(b\), Influence on \(F_{NSD}-x\) Curve  
If we take \(\Delta l=0.5\), \(k=50\), and \(\mu=0.2\) from Equation (6), the force displacement curve under different \(\beta\) values and the force displacement curve under different \(b\) values can be obtained, as shown in Figures 8 and 9.  
Figure 8: Force–displacement curve under different \(\beta\) values.  
Figure 7: \(C=15\) and \(\beta=10\) damping mechanical model.  
It can be seen from the above two figures that changing the value of \(\beta\) will change the change cycle of force, and the stronger the value of \(\beta\), the greater the change frequency; changing the \(b\) value of the track surface will change the magnitude of the force peak, but it will have no effect on the change frequency of the force.
# Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training
## 2 Materials and Methods
### Software Architecture
#### 2.2.2 Camera Calibration
The position of the cube center is estimated in each camera reference system from the position of the two diamond markers \((\overset{\rightarrow}{O}_{\text{Top}}\) and \(\overset{\rightarrow}{O}_{\text{Lat}})\), the orientation of their \(Z\)-axis \((\hat{z}_{\text{Top}},\ \hat{z}_{\text{Lat}})\) expressed in the tracking camera reference system, and the size (\(l\)) of the ArUco Diamond marker according to Equations (1) and (2). \[\overset{\rightarrow}{P}_{\text{Top}}=\overset{\rightarrow}{O}_{\text{Top}} +\frac{l}{2}\hat{z}_{\text{Top}}\] (1) \[\overset{\rightarrow}{P}_{\text{Lat}}=\overset{\rightarrow}{O}_{\text{Lat}} +\frac{l}{2}\hat{z}_{\text{Lat}}\] (2) where \(\overset{\rightarrow}{P}_{\text{Top}}\) is the position vector from the origin of the \(\text{Cam}_{\text{Top}}\) reference system to the calibration cube center, and \(\overset{\rightarrow}{P}_{\text{Lat}}\) is the position vector from the origin of the \(\text{Cam}_{\text{Lat}}\) reference system to the calibration cube center. 4. Steps 1-2-3 are repeated at least three times moving the cube in the camera FOV to collect two clouds of \(n\)-positions (\(n\geq 3\)). 5. A rigid point cloud registration algorithm based on singular value decomposition (SVD) is used to calculate the transformation matrix \((\overset{\rightarrow}{\text{Top}}\text{T}_{\text{Lat}}\) in Figure 2b) between the reference systems of the two cameras from the collected clouds of positions (\(n\)positions of the center of the calibration cube expressed in the reference systems of the two cameras).