esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### IEEE 802.11n (Wi-Fi 4)  
Wi-Fi 4 marked a substantial improvement over its predecessors, increasing the maximum network performance up to 600 Mbps and allowing the use of both the 2.4 GHz and 5 GHz frequency bands. This standard introduced several innovative technologies that transformed the user experience in terms of performance, reliability, and coverage range. Networks based on this technology support bandwidths of up to 40 MHz, doubling the available bandwidth compared to previous standards and enabling higher network performance, resulting in faster connection speeds and better performance for high-demand services. One of the key advances in Wi-Fi 4 is the use of Orthogonal Frequency Division Multiplexing (OFDM), which improves spectral efficiency and enhances data transmission by splitting the signal into multiple subcarriers. This method reduces interference and improves signal robustness, especially in environments with a high density of devices.  
This standard introduced modulation techniques, such as 64-QAM (Quadrature Amplitude Modulation), which improve spectral efficiency. It also incorporated frame aggregation, a technique that combines multiple data frames into a single transmission, reducing network overhead and latency while increasing network performance. Additionally, the Multiple-Input-Multiple-Output (MIMO) technology was added, which uses multiple antennas at both the transmitter and receiver to send and receive multiple data streams simultaneously. This improvement enhances network capacity and performance, providing a more robust connection. MIMO technology reduces interference, allowing the transmission of stronger and cleaner signals.
## 3 Science Data Product Performance
### The Ozone Mapping and Profiler Suite (OMPS) Instrument and Science Data Products
The NCEP uses OMPS EDR products towards (i) continuation of the long-term climate data record and climate monitoring that began with the SBUV/2, (ii) near real-time monitoring of the ozone hole, and (iii) UV index forecasts and for stratospheric intrusion/monitoring forecasts. Figure 15 shows an example of the UV index forecast product produced from the S-NPP OMPS observations [103]. Some research products include total column and tropospheric NO\({}_{2}\) (from NM), the Mg II index product, which is of interest with respect to solar activity (from NP), and stratospheric aerosol profiles derived from the LP, which help map out stratospheric aerosol loading due to natural eruptions. Table 14 shows the S-NPP OMPS SDR performance. Figure 14: OMPS total column ozone products (in Dobson Units) from MetOp-B GOME-2 (NOAA Version 8 algorithm), NASA EOS Aura OMI (NASA Version 8.6 algorithm) and S-NPP OMPS-NM (NOAA Version 8 algorithm) for 1 June 2015. (Figure courtesy: [PERSON], STAR OMPS Ozone team).
## 2 Vehicle Model
### Tire Model  
For the study of parameter estimation, considering the complexity, real-time performance, and model accuracy of the algorithm, the Duggoff tire model with fewer parameters is used. The mathematical expression for the longitudinal tire force \(F_{x}\) of a single wheel is:  
\[F_{x}=\mu F_{x}^{0}=\mu F_{z}C_{x}\frac{\lambda}{1-\lambda}f(L) \tag{7}\]  
\[f(L)=\left\{\begin{array}{c}L(2-L),L<1\\ 1,L\geq 1\end{array}\right. \tag{8}\]  
\[L=\frac{1-\lambda}{2\sqrt{C_{x}^{2}\lambda^{2}+C_{y}^{2}\tan\alpha^{2}}}\Big{(} 1-\varepsilon v_{x}\sqrt{\lambda^{2}+\tan\alpha^{2}}\Big{)} \tag{9}\]  
where \(F_{x}^{0}\) is the normalized longitudinal tire force, \(\mu\) is the road adhesion coefficient, and \(f(L)\) is the tire model correction factor; \(c_{x}\) and \(c_{y}\) are the longitudinal and lateral stiffness of the tire, whose values can be fitted based on the tire characteristics data provided by Carsim; \(\alpha\) is the slip angle of the tire, \(\epsilon\) is the speed influence coefficient used to correct the effect of tire slip speed on the tire force, and \(v_{x}\) is the longitudinal speed of the wheel.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 2 Related Work  
In practice, the time difference \(x(t)\), frequency difference \(y(t)\), and clock reading \(h(t)\) cannot be directly observed. The time calculation method according to the clock offset should be carefully researched. In this section, we survey two commonly used space-based time offset measurement methods, which are also compared with our proposed method in Section 5.
## 3 Results
### Data Analysis
Because of this, and because our intention was only to study the influence of external factors on photogrammetric surveys, refraction was not considered in the comparison of the results obtained. In a study comparing the results of photogrammetric surveys for bathymetry in coastal waters with and without GCP in the submerged area, it was found that at a depth of 0.74 m, a difference of 35.2% was obtained in the survey [71]. In this study, the error obtained at 0.73 m varied between 9% and 18% when there was a constant turbidity of 4 NTU, which was lower than the parameters recorded [71], and showed an error of 13% when presented with a level of 21 NTU. Another photogrammetry study conducted in shallow waters obtained an RSME of 0.18 m [72]. This study recorded errors between 0.023 m and 0.324 m at different depths. Overall, the highest luminosity generated the lowest error up to a depth of 0.97 m. In addition, after assessing the variations in turbidity, two situations were observed: At shallower depths, and up to a depth of 0.49 m, the increases in turbidity positively contributed to error reduction. However, at depths exceeding 0.49 m, the increased turbidity level increased the error. This happens because in shallow waters greater turbidity decreases refraction [73]. Nevertheless, as depth increases, it becomes less relevant due to the angle, and light beams travel longer distances through the air and water [74]. These factors result in the two previously observed situations, wherein the error depends on the depth assessed, even when the same parameters are recorded in a survey [71]. Additionally, the average errors found in the underwater control points on the Z-axis of A1 and A2 were 0.04 m and 0.023 m, respectively, while X- and Y-axes presented errors under 2 cm. Unfortunately, values for the last flights couldn't be recorded due to turbidity. This is consistent with the limitations of photogrammetry in accurately surveying coordinates on the Z-axis. At depths exceeding 0.97 m, the effects from turbidity and luminosity cease to follow an established pattern. This is consistent with the findings of a study published by [PERSON] et al. [71], which concluded that UAV photogrammetry can be applied in bathymetric surveys conducted in shallow waters that do not exceed the depth of 1 m. In addition, the authors [71] conducted flight tests in sets consisting of two or three flight repetitions under the same conditions to compensate for possible flight complications, such as poor visibility. Figure 9: Assessment of the errors found at different depths, considering the variation in luminosity and turbidity during the surveys.
## 4 Results
### Mathematical Method to Maximum Drift Prediction  
A mathematical method based on a trigonometric function was proposed for the prediction of maximum drift in a range of water depths. The geometric configuration of the mooring system is shown in Figure 15 and its side-view layout in Figure 15A, where \(l\) is the length of the mooring line (835.5 m), \(d\) is the vertical distance from the platform fairlead to  
Figure 12: Maximum velocity of the floating platform and the time to new balance after the failure of mooring line #1.  
the seab, and \(X_{0}\) is the horizontal distance between the fairlead and the anchor. From the top view in Figure 15B, \(X_{d}\) is the maximum drift distance in the surge direction and \(\sqrt{l^{2}-d^{2}}\) is the orthographic distance between the anchor and fairlead, assuming that the mooring line was strained in the second position.  
Based on the cosine law, the connection between \(X_{0}\), \(X_{d}\), and \(\sqrt{l^{2}-d^{2}}\) can be described as Equation (9), and following mathematical deduction we obtain the simple Equation (10):  
\[(\sqrt{l^{2}-d^{2}})^{2}={X_{0}}^{2}+{X_{d}}^{2}+2X_{0}X_{d}\cos(60^{\circ}), \tag{9}\]  
\[X_{d}=\sqrt{l^{2}-d^{2}-\frac{3}{4}{X_{0}}^{2}}+\frac{1}{2}X_{0}. \tag{10}\]  
In order to focus on the influence of various water depths in the prediction of maximum drift, the length of the mooring line is kept the same in the three water depths. Furthermore, the mooring restoring forces were set similarly in order to confirm the dynamic response of the platform was kept the same in the different water depths, as shown in Figure 16. The 50 m water depth restoring force (yellow dotted line in Figure 16) did not match well with the others restoring curves, as the shallow water effect allows a rapid increase in the restoring tension with respect to increased displacement. For example, the restoring force in 50 m of water depth is smallest before the platform moves to surge +20 m; however, the restoring force rises rapidly when the platform continues moving, and the curve matches the other two water depths.  
Figure 13: Mooring line #1 and line #2 tension change in time series; the tension unit is kN.  
Figure 14: Horizontal and vertical tension in mooring line #2 anchor point (the \(x\)-axis is time in sec, \(y\)-axis is tension in the unit kN).  
Figure 15: Geometric configuration of the mooring system and floating platform layout: (**A**) Side view and (**B**) top view.  
The largest sway drift occurred with the incident wind from 90\({}^{\circ}\), as seen in Figure 19. In addition, Figure 20 shows that an incident wave direction of 45\({}^{\circ}\), 90\({}^{\circ}\), and 180\({}^{\circ}\) induced negative motion because the incident waves forced the platform to rotate to face them and this rotation process led to a slightly negative value in the sway. For the same reason, in the 0\({}^{\circ}\)and 120\({}^{\circ}\) incident wave cases the platform already faced the waves and did not have any evidently rotational motion.
## 3 Secret Key Rate in Inter-Satellite Links
### Simulation and Discussion
Figure 6: The finite-size SKR of inter-satellite THz-CVQSS versus transmission distance (km). \(n=5,\xi_{0}=0.003,\eta=0.6,v_{el}=0.1,V_{M}=7\), and \(f=50\) THz. reconciliation efficiency exceeds 0.98. Thus, for the actual inter-satellite THz-CVQSS system, it is essential to employ an efficient reconciliation efficiency.
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.9 Contrastive Captioners Are Image-Text Foundation Models  
This work proposes a Contrastive Captioner (CoCa) [110] to pre-train a foundation model [108] jointly with a contrastive loss and a captioning loss. They employ a transformer-based encoder-decoder architecture. A distinguishing feature of their decoder is that the first half of the layers only attend to the unimodal textual representations, omitting the outputs of the image encoder. The second half of the decoder cross-attends to the encoder outputs as well to produce the multimodal image-text representations. The contrastive loss is applied to the image encoder and the unimodal section of the decoder whereas the captioning loss is applied to the multimodal section of the decoder, which enables it to generate textual tokens in an autoregressive manner. Their overall loss function is given by:  
\[\mathcal{L}_{CoCa}=\lambda_{Con}\cdot\mathcal{L}_{Con}+\lambda_{Cap}\cdot \mathcal{L}_{Cap}, \tag{7}\]  
where \(\mathcal{L}_{Con}\) and \(\mathcal{L}_{Cap}\) are loss weighting hyper-parameters. The pre-training is performed using alt-text data as well as annotated images; all labels are simply treated as text. CoCa unifies into a single model and single pre-training stage the three training paradigms of single-encoder (such as in image classification), dual-encoder (such as in contrastive learning for image-text alignment) and encoder-decoder (such as in image captioning and multimodal representation). CoCa gives state-of-the-art results on tasks pertaining to the categories of visual recognition, cross-modal alignment, image captioning and multimodal understanding. As an indication of the model performance in image captioning, they do not use CIDEr-optimization and yet their model performs competitively against other models that are CIDEr-optimized.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on LiDAR Data
#### 3.4.2 Status and Prospects  
Airborne LiDAR data are used in the 3D modelling of city road networks [77]. ALS provides direct 3D information for road extraction and is less affected by occlusions and shadows than do optical data. In addition, ALS provides 3D road information with elevation; this is especially useful in complex interchange areas. However, the area covered during the flight considerably limits the automatic road extraction process using ALS. Extensive investigations are still required to address the underlying issues, i.e., the development of fully automatic algorithms suitable for various landscape and road types, application of intensity data over large areas, reducing the number of false positives (car parks, squares, playgrounds, etc.) and identifying as well as connecting road segments shadowed by occlusions (e.g., trees and vehicles). Road markings must be taken into consideration in VHR. The national ALS datasets provide a good basis for mapping; however, these datasets are seldom updated. LiDAR sensors mounted on mobile mapping systems, UAVs or VHR satellite imaging instrument can be used for map updating. In addition, the new MS-ALS data [227; 228] and new dense point clouds created by collecting single photons enable the automatic detection of roads with higher accuracy.
## 3 Design of Neural Network and Production of Training Data
### Design of Classification and Regression Neural Network
#### 3.1.2 Structure of CPR-Net  
Although the aforementioned CPD-Net can provide high-accuracy clock phase recognition, its phase is always a fixed category, and the precision of phase extraction is not high enough. In response to this problem, we have proposed a regression-based CPR-Net; the network structure is shown in Figure 7. The network is mainly composed of four convolutional layers and eight fully connected layers, and the ReLU activation function is used behind each layer. Four convolutional layers constitute an encoder and decoder. The first two convolutional layers are used as encoder to extract features from the discrete single photon pulse, the latter two convolutional layers are used as decoder to reconstruct the time slot synchronous clock phase value from the extracted features. Eight fully connected layers gradually reduce the dimension of the data output by the decoder. Finally, the phase prediction value is output in the output layer. In summary, the model of CPR-Net can be expressed as:  
\[\hat{S_{l}}=f_{n}(f_{n-1}(\dots f_{1}(U_{n}))) \tag{3}\]  
where \(U_{n}\) represents the input data and \(f_{n}\)(\(n\) = 1,2...,12) represents the mathematical operation of each layer of the network. Since CPR-Net is a regression network, the cross entropy is no longer used as the loss function. The mean square error is used as the loss function, so its loss function can be expressed as:  
\[l_{cpr}=\frac{1}{n}\sum_{i=1}^{n}\left(s_{i}-g_{i}\right)^{2} \tag{4}\]  
where \(g_{i}\) is the expected output value and \(s_{i}\) is the actual output value of the network.  
Figure 6: The structure of CPD-Net.
## 2 Outline  
Section 3 provides a concise review of the basic principles of pulsed operation and mode-locking techniques. In this section, the mechanism of pulse formation is described in a qualitative and intuitive manner. In addition, a general description is given to other mode-locking techniques, which preceded Kerr lens mode-locking. Section 4 describes the fundamental concepts of Kerr lens mode-locking. Nonlinear effects in the spatial and temporal domains are described quantitatively. Analysis and calculation of the cavity mode in continuous-wave (CW) and mode-locked (ML) operations are reviewed in Sections 5 and 6, respectively, where we describe the standard configuration of a KLM Ti:sapphire cavity and its operating regimes. Specifically, we review the calculation of linear astigmatism and methods for compensating for it and provide an analysis of the intra-cavity fundamental Gaussian mode for both CW and ML operation. Section 7 discusses the different types of mode-locking techniques in a Ti:sapphire oscillator: hard and soft aperture techniques are described in detail, including both quantitative and visually intuitive descriptions. Specific attention is given to the \"virtual hard aperture\" method, which is less discussed in the literature, where diffraction losses in an unstable resonator produce an effective hard aperture for the laser. Section 8 addresses the problem of nonlinear astigmatism in the cavity and how it affects the spatial mode and nonlinear response in ML operation, and the standard compensation for nonlinear astigmatism (and its disadvantages) are discussed. Dispersion management is addressed in Section 9: analytic expressions for calculating the group delay dispersion are provided and common dispersion compensation devices are briefly reviewed, such as prism-pairs and chirped mirrors. Section 10 provides a qualitative review of the common experimental behavior of a standard designed KLM Ti:sapphire cavity: the typical behavior of the mode-locking power, threshold and efficiency are described for typical and commonly used mode-locking techniques. Section 11 reviews advanced cavity designs, specifically developed to improve laser performance and to overcome several inherent limitations of standard designed cavities, such as nonlinear astigmatism, Kerr lensing efficiency and intra-cavity control of the oscillation spectrum. Appendix provides further discussion regarding the compensation for linear astigmatism in its most general form.
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 4 Results and Discussion
### Case Study 1: Singularity Analysis of VS Methods Using Setup 1  
The performance of the robot in encountering singularities was analysed using four different VS techniques (Figure 5). As shown in Figure 5a, the position-based controller failed to track the desired features. This failure is caused by an error in 3D estimation of the target, created by the uncalibrated camera. Since DHVS compensates for two of six velocities directly from the image information, therefore the errors converge to zero (Figure 5d). This robustness is due to the fact that the image-based approaches do not require object pose estimation, therefore, it provides robust control in terms of calibration and regulating the errors. Not to mention that the overshoots in Figure 5d are caused by the uncalibrated camera parameters, but the key point is that the controller still managed to completethe task successfully.  
As shown in Figure 5b, in IBVS the errors have not converged to zero because the controller generates a high velocity in the \(Z\)-axis, causing the robot to reach its joint limits. The main explanation for this restriction is that the controller is just considering the 2D information of the image. Consequently, the camera moves away from the target to compensate the rotation error about the \(Z\)-axis (camera retreat phenomenon). Nevertheless, in the DHVS method, the \(Z\)-axis errors and task space rotations are compensated by 3D calculation of the target. In Figure 5d, it is illustrated that the errors are successfully converged to zero by DHVS approach Figure 5d.  
Figure 5: Performance analysis of four different VS methods in completing a similar task in presence of singularity. (**a**) Failure in completing the VS task with PBVS approach. (**b**) Failure in completing the VS task with IBVS approach. (**c**) Failure in completing the VS task with HVS approach. (**d**) Success in completing the VS task with DHVS approach.  
In the traditional HVS approach, the controller compensates for translation in \(Z\)-axis from the 2D information given by the camera. As a result, the controller continues to generate a non-optimized trajectory for the camera, which result in generating robot singularity. Figure 5c clearly shows that the controller using the HVS method failed to converge the errors to zero.  
In summary, Figure 5 suggests that the proposed DHVS approach could avoid the singularity, mitigate the discontinuities, and complete the VS tasks without the use of complex and time-consuming methods (Figure 5d), while the other VS methods failed to complete the task successfully.
## 3 Signal Processing
### Geometric Models  
The collection geometry of a ViSAR system operating on a circular flight path is shown in Figure 6. The radar moves along a circular flight path at a constant speed, \(v\). \(R_{b}\) is the radius of the flight path, \(R_{a}\) is the slant range between the radar and the scene center, \(R_{z}\) denotes the altitude of the aircraft, \(\theta_{a}\) is the azimuth beamwidth of the antenna, \(\theta_{S}\) is the grazing angle between the antenna beam axis and the ground plane, and \(R_{t}=\
icefrac{{5}}{{2}}\) is the radius of the scene area.  
Figure 7 illustrates the antenna geometry of the proposed MIMO ViSAR system. We suppose that there are \(M\) Tx antennas and \(N\) Rx antennas. We assume in this paper that, in general, the virtual array  
Figure 5: Scene size limitation of polar format algorithm (PFA).  
Figure 6: Collection geometry of video SAR on a circular path.  
formed by the MIMO antenna is a uniform linear array in the azimuth. Therefore, a large co-array can be realized using the MIMO concept. Also, it is advantageous to increase the number of phase centers if \(M\) and \(N\) are greater than two, since \(M\) Tx antennas and \(N\) Rx antennas give \(MN\) antenna elements in the virtual array.