esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Packet Length Effect on Sparse Signal Transmission
#### 6.2.2 Performance under Varying Signal Sparsity (Various Applications)  
Since the measurement number threshold for signal reconstruction is correlated with sparsity, which is determined by the specific application, thus varying sparsity changes the signal/information transmission efficiency. Considering the sparsity of many signals, such as seismic wave, temperature, a density of \(\mathrm{CO}_{2}\) being about 5 to 10 [8], we use signals with \(K=5\) and \(K=10\) to show the signal's impact on signal  
Figure 18: Comparison between data and signal transmission efficiency.  
transmission efficiency. Figure 20 shows the relationship between signal transmission efficiency and packet length under various signal sparsities when \(BER=0.005\) and \(BER=0.01\). Apparently, the signal transmission efficiency is getting worse with the increasing signal sparsity, which makes a smaller optimal packet length. This is due to the reason that larger signal sparsity needs a greater measurement number to accurately reconstruct the sparse signal.
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 3 Indoor Reconstruction Methodology
### Indoor Component Reconstruction
#### 3.4.2 Smoothing Term  
A weight \(W_{i,j}\) is defined to determine the weights of adjacent cells, denoting the shared edges between the connected cells. By using the previous classification between occupied and empty cells the weights of edges between two occupied cells and between two empty are set to infinite and the weights of edges connecting an empty and an occupied cell are set to zero. In this way, cells forming the inner part of the room are prevented from being erroneously labelled as empty or vice-versa. The weights of the remaining edges between cells are fixed equal to the length of the edge between the cells. This means that the s - t cut problem is aimed at minimizing the length of guessed walls segments (Figure 5b).  
\[\begin{array}{l}W_{i,j}=S_{i,j}\big{(}l_{i}\,l_{j}\big{)}\\ =\left\{\begin{array}{ll}0&\text{\emph{if} }\left(l_{i}=occupied\ AND\ l_{j}=occupied\right)\ OR\ (l_{i}=empty\ AND\ l_{j}=empty)\\ \infty&\text{\emph{if} }\left(l_{i}=empty\ AND\ l_{j}=occupied\right)\ OR\ (l_{i}=occupied\ AND\ l_{j}=empty)\\ LE_{ij}&\text{\emph{other}}\end{array}\right.\end{array} \tag{10}\]  
Figure 5: Example of indoor component reconstruction using ’s–t cut’ for Room 1: (**a**) initial labelling considering ceiling occupancy, (**b**) graph construction and cost assignment; (**c**) final binary labeling with minimum cost; and (**d**) final complex labeling.  
To perform the \(s-t\) cut, the Kolmogorov's max-flow algorithms is used [40]. Once having computed the \(S\), \(T\) partitioning, the boundary of the occupied cells of the polygon partition gives the floor plan (Figure 5c,d).
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 5 Comparative Study for VOE  
### Data Collection  
The experiment was set up in the second-floor hallway of Building A, Building B, and Building C of Tan Trao University (TQU), Vietnam, as illustrated in Figure 5. We used the Intel RealSense D435 camera ([[https://www.intelrealsense.com/depth-camera-d435/](https://www.intelrealsense.com/depth-camera-d435/)]([https://www.intelrealsense.com/depth-camera-d435/](https://www.intelrealsense.com/depth-camera-d435/)), accessed on 6 May 2024) to collect the RGB-D image sequence, illustrated in Figure 6. The camera was mounted on a vehicle, shown in Figure 7. The angle between the camera's view and the ground was 45 degrees. The total distance traveled by the vehicle at one time is the forward direction (FO-D), (FO-D = 230.63 m), the opposite direction (OP-D), (OP-D= 228.13 m) and the width of 2 m. For every 0.5 m, a numbered marker with dimensions of 10 \(\times\) 10 cm was assigned for one marked corner. The total number of markers used was 332.  
The moving speed of the vehicle to collect data was 0.2 m/s; the data acquisition speed was 15 fps. The data were collected in RGB-D image pair with a resolution of \(640\times 480\) pixels. The direction of movement of the vehicle was always in the middle of the corridor. The data collection was performed four times over two days, each one hour apart. On the first day, the data of 1 ST and 2 ND were collected, and on the second day, the data of 3 RD and 4 TH were collected. We collected the data in the afternoon from 2:00 p.m. to 3:00 p.m. Each time, the direction of movement according to the blue arrow was in the FO-D, and the direction of movement according to the red arrow was in the OP-D. All data of the TQU-SLAM benchmark dataset are shown in the link ([[https://drive.google.com/drive/folders/16](https://drive.google.com/drive/folders/16) Dx_nORUvUHFg2 BU9 mm8 aBYMvtAzE9m7]([https://drive.google.com/drive/folders/16](https://drive.google.com/drive/folders/16) Dx_nORUvUHFg2 BU9 mm8 aBYMvtAzE9m7), accessed on 6 May 2024). The data we collected are shown in Table 14.  
Figure 5: Illustration of the data collection environment. In the environment, there are 15 important locations marked with a yellow background. The data collection process is conducted in two directions: the forward direction with a length of 230.63 m (follow the blue arrow) and the opposite direction with a length of 228.13 m (follow the red arrow).
## 3 Results and Discussion
### Surface Current Performance  
Surface current is an electric current induced by an electromagnetic field. The surface current distribution varies with frequency. Figure 12 depicts the surface current of several antennas at a frequency of 0.5 GHz while Figure 13 shows the surface current of 15 antennas at 2 GHz.  
In this case, we set the maximum surface current of all antennas at 0.5 A/m. Surface current varies for all antenna types, as seen in Figures 12 and 13. We designed the antenna by providing a distance between adjacent patches (copper) of 25 mm (as shown in Figure 1b) so that the surface current does not flow directly to adjacent patch elements (copper radiators) when the antennas are placed closely together. At 0.5 GHz, even though the copper radiators are separated by 25 mm, there is still a dispersion of surface currents with high intensity in nearby elements (indicated in red in the yellow circle with dotted lines) as demonstrated in Figure 12a,b. RPT and FCPT had larger surface currents in both neighboring patches than BCPT. This demonstrates that BCPT outperforms the rest in terms of S11 performance. There are various places with high surface current intensity in slots such as HFSPT, ESEPT, WWSPT, and HWSPT that are highlighted in red. Figure 12 shows that at a frequency of 0.5 GHz, the surface current in the elliptical structure and metamaterial is not excessive. Antennas with a lens structure that has been given an elliptical slot structure or metamaterial show increased surface current concentration at 4 GHz. A significant electric field is described by the existence of a high surface current.  
Figure 11: Radiation Pattern in the E-Plane of (**a**). RPT-CVA vs FCPT-CVA at 2 GHz, (**b**). RPT-CVA vs HFSPT-CVA at 2 GHz, (**c**) RPT-CVA vs ESE-CVA at 4 GHz, and (**d**). RPT-CVA vs MLPT-CVA at 4 GHz.  
Figure 12: Surface current performance of (**a**) Regular Palm Tree (RPT-CVA), (**b**) Front Cut Palm Tree (FCPT-CVA), (**c**) Back Cut Palm Tree (BCPT-CVA), (**d**) Hilbert Fractal Structure Palm Tree (HFSPT-CVA), (**e**) Vertical Wave Structure Palm Tree (VWSPT-CVA), (**f**) Horizontal Wave Structure Palm Tree (HWSPT-CVA), (**g**) Exponential Slot Edge Palm Tree (ESEPT-CVA), (**h**) Elips Lens Palm Tree (ELPT-CVA), and (**i**) Metamaterial Lens Palm Tree (MLPT-CVA).  
Figure 13: Surface current performance of (**a**) Regular Palm Tree (RPT-CVA), (**b**) Front Cut Palm Tree (FCPT-CVA), (**c**) Middle Cut Palm Tree (MCPT-CVA), (**d**) Back Cut Palm Tree (BCPT-CVA), (**e**) Complete Cut Palm Tree (CCPT-CVA), (**f**) Left Tilt Palm Tree (LTPT-CVA), (**g**) Right Tilt Palm Tree (RTPT-CVA), (**h**) Hilbert Fractal Structure Palm Tree (HFSPT-CVA), (**i**) Koch Fractal Structure Palm Tree (KFSPT-CVA), (**j**) Exponential Slot Edge Palm Tree (ESEPT-CVA), (**k**) Vertical Wave Structure Palm Tree (VWSPT-CVA), (**l**) Horizontal Wave Structure Palm Tree (HWSPT-CVA), (**m**) Regular Lens Palm Tree (RLPT-CVA), (**n**) Elips Lens Palm Tree (ELPT-CVA), and (**o**) Metamaterial Lens Palm Tree (MLPT-CVA).
## 2 Related Work
### The Science-Technology Association Detection Method
In comparison, the association detection method driven by topics can achieve deep semantic analysis of texts through algorithmic models, thereby effectively avoiding the interpretation bias caused by human intervention. The LDA2 vec symmetry model, which combines the advantages of the Latent Dirichlet Allocation Model [27] and the Word2 vec word embedding model, is an unsupervised learning approach capable of transforming unstructured textual information into semantically relevant word vector representations. This model combines the topic mining capability of the LDA model and the semantic embedding advantage of the Word2 vec model, overcoming the issue of semantic relationship loss between words that arises from the lack of deep semantic representation in traditional LDA models [28]. Consequently, it achieves fine-grained topic mining while significantly enhancing both the depth of semantic information extraction and the accuracy of topic recognition. This study uses the LDA2 vec model to extract the scientific and technological research topics in the field of network security from 2005 to 2024, forming science-technology topics and assessing their similarities. Subsequently, the collaborative topic pairs are classified into three categories: weakly correlated, moderately correlated, and strongly correlated. Finally, the strongly correlated topic pairs are selected to construct a topic collaborative evolution atlas in the form of a Sankey diagram to help researchers clearly understand the association between science and technology in this field. The key focus of this study is to address the following three core issues:  
Question 1: How to break through the one-way perspective of traditional topic evolution research and construct an identification method for science-technology topic association evolution in the field of cybersecurity? Question 2: What evolutionary types will be presented by science-technology topic association evolution in the field of cybersecurity? Question 3: What interactive effects will exist in the process of science-technology topic association evolution in the field of cybersecurity?
# Lower-Limb Flexibility Profile in 142 Competitive Female Football Players: A Cross-Sectional Study
## 4 Discussion
### Limitations  
When using the normative data presented in this study, several methodological considerations and limitations should be taken into account. First, all data were collected during the pre-season phase, as this was the only period authorized by the coaching staff of the participating women's football teams. Although this ensured that all players were evaluated under comparable performance conditions, it is possible that the recorded ROM values do not represent their maximal in-season capacity. Nevertheless, strength and conditioning coaches generally consider the pre-season to be the most appropriate moment to assess baseline physical capacities [59]. During the competitive phase, variations in training and match schedules across teams introduce heterogeneous training loads that would compromise the comparability required for a multicentric study of this nature [18].  
Furthermore, ROM values reported here correspond to the mean of the two most similar measurements obtained across three passive trials, rather than the single highest value. This approach was adopted to enhance the reliability of the data, as averaging reduces measurement error and increases consistency [60, 61]. All measurements were conducted after a standardized warm-up to minimize variability associated with viscoelastic changes in soft tissues due to temperature and stretching effects [62].  
A further limitation lies in the use of passive ROM testing. While passive techniques are recommended for monitoring flexibility due to their lower intra-individual variability and reduced susceptibility to measurement error, they may not fully reflect dynamic or functional ROM. Active or dynamic ROM tests are influenced by inter-subject variability in muscular strength, neuromuscular coordination (e.g., the balance between agonist activation and antagonist relaxation), and individual motivation levels [26]. Moreover, dynamic tests are prone to compensatory movements, which can produce misleadingly high ROM values (i.e., false negatives) [1]. For a more complete picture, passive assessments should ideally be complemented with kinematic and kinetic analyses of sport-specific movements involving large ranges of motion, in order to identify possible limitations or injury-related compensations.  
Finally, future research should explore the relationship between flexibility and other technical and physical parameters of the upper limbs and trunk, assessing their potential contribution to performance enhancement and injury prevention.
# SNR-Based GNSS-R for Coastal Sea-Level Altimetry
## 3 Geodetic GNSS-R Model
Therefore, the total reflector height can be estimated as:  
\[H=H_{A}-H_{B}\;H_{B}=\frac{\phi_{B}^{(1)}\lambda}{4\pi} \tag{9}\]  
A single GNSS signal is processed to retrieve the height bias based on the above-mentioned inversion algorithm. Then, satellite tracks are partitioned based on track azimuth and satellite PRN, as well as ascending or descending status of the arc. The Quality Control (QC) of sea levels as a necessary step is done to detect outliers resulting from anomalous conditions using robust estimators and statistical intervals [24; 25]. It was found by [26] that tropospheric delay underestimates the reflector height retrievals. The interferometric atmospheric delay increases linearly with the reflector height and exponentially with the satellite elevation angle. Here, the Global Pressure and Temperature 3 model (GPT3) and Vienna Mapping Function 3 (VMF3) [27] are used to correct the sea level retrievals for the tropospheric delay corrections, as follows [28]:  
\[P_{d}=P_{d}^{R}G_{d}^{R}P_{r}=P_{d}^{R}X^{2}S^{2}X=R^{S}\sqrt{G_{r}^{R}}\exp \left(\imath\Phi_{r}^{R}\right)+R^{X}\sqrt{G_{r}^{L}}\exp\left(\imath\Phi_{r}^{ L}\right) \tag{3}\]0  
where \(\tau_{t}\) is the tropospheric delay and is defined as the difference between the radio length and the vacuum distance. Due to the tides, the sea level changes and so does the reflector height. Therefore, the interferometric phase rate, assuming a variable reflector height, can be written as follows [10]:  
\[P_{d}=P_{d}^{R}G_{d}^{R}P_{r}=P_{d}^{R}X^{2}S^{2}X=R^{S}\sqrt{G_{r}^{R}}\exp \left(\imath\Phi_{r}^{R}\right)+R^{X}\sqrt{G_{r}^{L}}\exp\left(\imath\Phi_{r}^{ L}\right) \tag{3}\]1  
where \(k_{z}\) is the vertical wavenumber and can be interpreted as the sensitivity of the geodetic GNSS-R station [29]:  
\[P_{d}=P_{d}^{R}G_{d}^{R}P_{r}=P_{d}^{R}X^{2}S^{2}X=R^{S}\sqrt{G_{r}^{R}}\exp \left(\imath\Phi_{r}^{R}\right)+R^{X}\sqrt{G_{r}^{L}}\exp\left(\imath\Phi_{r}^{ L}\right) \tag{3}\]2  
Therefore, the GNSS-R sea levels will be biased if they are not corrected for the vertical velocity and acceleration of the tidal waves, as follows [9]:  
\[P_{d}=P_{d}^{R}G_{d}^{R}P_{r}=P_{d}^{R}X^{2}S^{2}X=R^{S}\sqrt{G_{r}^{R}}\exp \left(\imath\Phi_{r}^{R}\right)+R^{X}\sqrt{G_{r}^{L}}\exp\left(\imath\Phi_{r}^{ L}\right) \tag{3}\]3  
where \(\dot{e}\), \(\dot{H}\), and \(\ddot{H}\) are elevation angle rate, sea vertical velocity, and sea vertical acceleration, respectively. Here, the elevation angle rate is derived from the GNSS precise ephemerides, while sea vertical velocity and acceleration are solved iteratively using the centered finite differences [9]. Finally, 15-min multi-GNSS corrected total reflector height is reported employing an 8 h moving window, as follows:  
\[P_{d}=P_{d}^{R}G_{d}^{R}P_{r}=P_{d}^{R}X^{2}S^{2}X=R^{S}\sqrt{G_{r}^{R}}\exp \left(\imath\Phi_{r}^{R}\right)+R^{X}\sqrt{G_{r}^{L}}\exp\left(\imath\Phi_{r}^{ L}\right) \tag{3}\]4
## 2 Related Works
### 3D Reconstruction Based on Binocular Stereo Vision with Random Speckle Projection  
Since random speckle structured light can effectively improve the accuracy of stereo matching, a lot of research on 3D reconstruction based on binocular stereo vision with random speckle projection has been reported. According to the number of projected patterns, they can be mainly classified into two types.  
The first type used the single speckle pattern. [PERSON] et al. [22] proposed an improved semi-global matching algorithm using the single-shot random speckle structured light. In this algorithm, a new penalty term was defined for alleviating the ladder phenomenon. Although it could achieve 3D dynamic reconstruction, the measurement accuracy was relatively low. [PERSON] et al. [23] designed an end-to-end stereo matching network for single-shot 3D shape measurement. It firstly used a multi-scale residual sub-network to extract feature tensors from speckle images, then a lightweight 3D U-net network was proposedto implement efficient cost aggregation. [PERSON] et al. [14] presented a high-precision 3D measurement scheme that used a single-shot color binary speckle pattern and an extended temporal-spatial correlation matching algorithm for the measurement of dynamic and static objects. However, when the object surface is colorful, the measurement effect may be affected.  
The second type used multiple different speckle patterns. To acquire high-quality results, many current studies employ stereo matching methods based on spatiotemporal correlation. [PERSON] et al. [24] proposed an improved 3D reconstruction method through combining advantages of both spatial correlation and temporal correlation methods. Meanwhile, by analyzing the relationship between the correlation window size and the number of speckle patterns, the tradeoff between the accuracy and efficiency was found. [PERSON] et al. [25] proposed a 3D face reconstruction scheme based on space-time speckle projection. In order to enhance the computing efficiency, several optimization strategies were used and a popular similarity metric namely zero-mean normalized cross correlation (ZNCC) was used to compute the matching cost. For measuring dynamic scenes that include static, slow and fast moving objects, [PERSON] et al. [26] proposed an adaptive spatiotemporal correlation method, where the weights were introduced for automatically adjusting the matched image regions.
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
## 1 Introduction
We adopted a channel attention mechanism based on contrast-aware. Then the retained feature maps are fused through concatenation at the end. The feature fusion is carried out according to the importance of the feature maps. In the upsampling steps, we adopted a multi-factor network, which includes position projection, weight prediction, and feature mapping. As shown in Figure 1, our IDMF-SR achieves better visual results compared with state-of-the-art methods. The contribution of this paper can be summarized as the following four points:  
* We propose the multi-scale factor image super-resolution network (IDMF-SR) based on information distillation for significantly reducing the number of parameters. Our IDMF-SR is an end-to-end network model, which can utilize hierarchical features more than previous CNN-based methods and balance performance against applicability;
* We put forward a new information distillation network to gradually extract and cascade features. IDN divides the feature map extracted from each layer into two  
Figure 1: Visual results under \(\times\)4 upscale factor. (**a**) VDSR; (**b**) Lap-SRN; (**c**) Meta-SR; (**d**) RCAN; (**e**) IDMF-SR; (**f**) HR; (**g**) Urban100 img\(\_\)76 (3\(\times\)). parts. One of the parts flows into the next convolutional layer, and the retrained part is cascaded in the end;
* We propose a contrast-aware channel attention mechanism (CCAM) in the information distillation network. The traditional channel attention mechanism obtains the importance of the channel through the squeeze-and-excitation module, which is conducive to improving the PSNR value. Our CCAM can further enhance image details, such as edges, textures, and structures;
* IDMF-SR is inspired by meta-learning, and the network achieves image magnification by predicting filter weights by scale factors. Only training one network model can realize the image magnification at any multiple, which is conducive to application in the real scene.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 3 Problem Formulation  
As mentioned in Section 2.2, the NCC allocates the time slots in a BH time window to the satellite terminals and the terrestrial BSs. The objective of resource allocation depends on the practical needs of the operators, such as optimizing system spectrum efficiency or guaranteeing fairness between the satellite and terrestrial systems. In this section, resource allocation problems are formulated from the following two perspectives, respectively:  
* Maximizing the weighted sum of capacity (MWSC) to improve the spectrum efficiency of the ISTN.
* Maximizing the minimum capacity-to-demand ratio (MMCDR) to guarantee fairness in the ISTN.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 6 Conclusions  
In this paper, we proposed an efficient continuous event-monitoring and data-gathering scheme called TPEG based on fog nodes in VANETs. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode for data generation. These data are then sent to the RSUs, and the confidence of events is calculated. If the confidence exceeds some threshold, nodes would transfer to event-checking phase, and some nodes would be selected to transfer to deep sensing mode to sense more accurate data of the environment. TPEG adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Experimental studies demonstrate that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while at the same time, it detects events and gathers the event data with some delays.  
For future work, we are going to use real-world vehicular trajectories and log data of vehicles to demonstrate the effectiveness of the proposed scheme. Furthermore, we are going to broaden the definition of events and use the scheme as a basic event-monitoring and data-gathering block to construct specific VANET applications.  
This research is supported by the Natural Science Foundation of China (61672441), the Scientific Research Project of National Language Committee of China (YB135-49), the National Key Technology Support Program (2015 BAH16 FF01) and the State Scholarship Fund of China Scholarship Council (201706315020).  
[PERSON], [PERSON] and [PERSON] developed the algorithms and wrote the manuscript. [PERSON], [PERSON] and [PERSON] processed the trajectory dataset. [PERSON], [PERSON] and [PERSON] conducted the experimental simulations. [PERSON] and [PERSON] performed the analysis. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.