esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Cross Dataset Evaluation  
We evaluated the generalization ability of our method by training our model on CID:IQ and testing it on VDID2014 without overlap between both. It is worth noting that cross dataset evaluation in our context is more difficult than those traditionally carried out in image quality assessment. Indeed, in addition to the difference in terms of content between both datasets, the two viewing distances considered by each of the databases are different. In other words, we evaluated here the efficiency of our method to predict the quality of unknown image for unknown viewing distances.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PCC** & **SROCC** \\ \hline
**PSNR (FR)** & 0.837 & 0.868 \\ \hline
**PSNR-HVS-M (FR)** & 0.887 & 0.916 \\ \hline
**PSNR-HA (FR)** & 0.893 & 0.915 \\ \hline
**C-PSNR-HA (FR)** & 0.882 & 0.916 \\ \hline
**C-PSNR-HVS-M (FR)** & 0.859 & 0.914 \\ \hline
**SSIM (FR)** & 0.737 & 0.909 \\ \hline
**CSSIM (FR)** & 0.918 & 0.915 \\ \hline
**CSSIM4 (FR)** & 0.921 & 0.908 \\ \hline
**WASH (FR)** & 0.441 & 0.445 \\ \hline
**VIF (FR)** & 0.515 & 0.684 \\ \hline
**MSSIM (FR)** & 0.745 & 0.911 \\ \hline
**SSIM2 (FR)** & 0.801 & **0.955** \\ \hline
**PSNR2 (FR)** & **0.950** & 0.946 \\ \hline
**CNN Quality (FR)** & 0.929 & 0.931 \\ \hline
**AQI (NR)** & 0.341 & 0.244 \\ \hline
**ARISMC (NR)** & 0.704 & 0.720 \\ \hline
**CPBD (NR)** & 0.472 & 0.481 \\ \hline
**CNN-VD (NR)** & 0.900 & 0.888 \\ \hline
**Our method (NR)** & 0.930 & 0.912 \\ \hline \hline \end{tabular}
\end{table}
Table 10: PCC and SROCC values whatever the viewing distance computed on VDID2014.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PCC** & **SROCC** \\ \hline
**CSSIM4 (FR)** & 0.700 & 0.679 \\ \hline
**WASH (FR)** & 0.468 & 0.454 \\ \hline
**VIF** & 0.665 & 0.659 \\ \hline
**MSSIM** & 0.716 & 0.790 \\ \hline
**SSIM2** & 0.495 & 0.602 \\ \hline
**PSNR2** & 0.507 & 0.482 \\ \hline
**CNN Quality** & 0.717 & 0.775 \\ \hline
**AQI (NR)** & 0.221 & 0.273 \\ \hline
**ARISMC (NR)** & 0.039 & 0.122 \\ \hline
**CPBD (NR)** & 0.325 & 0.261 \\ \hline
**CNN-VD** & 0.853 & 0.839 \\ \hline
**Our method** & **0.876** & **0.865** \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._Table 11 shows the correlations obtained for both viewing distances as well as the global performance. Compared to the individual evaluation, the performance decreased but still high. The same PCC value was obtained for the two distances. In addition to the viewing distances and the content (2.5*H and 5*H on CID:IQ against 4*H and 6*H on VDID2014), this decrease is certainly due to the fact that certain degradation types were not considered during the training step (White Noise on VDID2014 and Poisson Noise on CID:IQ).
# Tube-Based Robust Model Predictive Control for Autonomous Vehicle with Complex Road Scenarios
## 2 Problem Statement and Preliminaries
### Model Integration
The error system is reformulated as  
\[e(k)=\overline{\xi}(k+1)-\overline{\xi}^{\prime}(k+1),\overline{u}_{e}(k)= \overline{u}(k)-\overline{u}^{\prime}(k), \tag{12}\]  
Equation (11) is rewritten as  
\[e(k+1)=A_{k,t}e(k)+B_{k,t}\overline{u}_{e}(k)+w(k) \tag{13}\]  
The Tube-RMPC controller is formulated to establish a specific control law for actual uncertainties while adhering to the input restrictions (9), ensuring that the trajectory of the perturbed system remains within the \"Tube\" centered on the nominal trajectory. Specifically, this relationship can be expressed as \(\overline{\xi}_{i}(k)=\overline{\xi}^{\prime}_{i}(k)\oplus Z\). where \(\overline{\xi}^{\prime}_{i}(k)\) denotes the nominal state, \(\overline{\xi}_{i}(k)\) represents the actual system state, and \(Z\) describes the interference invariant set. The Tube-RMPC control law, which constitutes the core of the proposed controller, is expressed as:  
\[u=K(\overline{\xi}-\overline{\xi}^{\prime})+u^{\prime} \tag{14}\]  
The control law consists of two components. The first component serves as an auxiliary feedback controller that ensures the actual system's state (9) closely follows the nominal state. The second component \(u^{\prime}\) is a control strategy tailored for the nominal system (10), which guides the nominal state toward the equilibrium point. Since \(u_{e}=u-u^{\prime}=K(\overline{\xi}-\overline{\xi}^{\prime})=Ke\), we can substitute it into (13) to obtain  
\[e(k+1)=(A_{k,t}+B_{k,t}K)e(k)+w(k) \tag{15}\]  
Secondly, it is necessary to determine the feedback control gain \(K\). This gain plays a crucial role in mitigating the effects of bounded disturbances on the system, ensuring that the dynamic system (15) with the error \(e(k)\) remains stable. Specifically, \(K\) is configured to ensure the stability of \(A_{k,t}+B_{k,t}K\). Additionally, as noted in [36], when \(A_{k,t}+B_{k,t}K\) it is stable, a robust positively invariant set is guaranteed for the error system. The feedback gain matrix \(K\) can be obtained by applying Theorem 1 presented below.
## 2 Concept of Vertical Seismic Isolation Device
### Vertical Design Displacement Limit  
Figure 2 presents the operation concept of the 3D seismic isolator. Figure 2b shows the schematics of static vertical displacement condition due to dead weight of the super structure, and Figure 2c shows the horizontal and vertical displacement condition during earthquake events. The housing of the vertical seismic isolator transmits the horizontal load of the superstructure to the LRB and guides the vertical seismic movement. The dimension  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Isolation Devices** & **Design Parameters** & **Design Value** \\ \hline \multirow{6}{*}{Horizontal Seismic Isolation Device (LRB)} & Outer diameter (mm) & 100 \\  & Lead plug diameter (mm) & 21.5 \\  & Total LRB height (mm) & 34 \\  & Shape factor (\(S_{1}\), \(S_{2}\)) & 9.9, 5.0 \\  & Design vertical load (kN) & 10 \\  & Design shear disp. (mm) & 35 (175\%) \\  & Beyond design shear disp. (mm) & 80 (400\%) \\ \hline \multirow{2}{*}{Vertical Seismic Isolation Device} & Housing diameter (mm) & 450 \\  & Total height (mm) & 140–360 \\  & Design vertical load (kN) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of 3D seismic isolator design parameters [16].  
Figure 1: Overall design concept of integrated 3-dimensional seismic isolator (half-symmetric view).  
design of such a housing is determined according to the shape and dimensions of the vertical spring and steel dampers, which are determined according to the determination of the design VIF. The most important aspect in this design concept is that the housing should be designed so that interference with the LRB does not occur during the beyond-design-basis earthquakes. To do this, the vertical seismic displacement of the spring should be properly limited by the determination of a design VIF and damper design.  
In this paper, the design target is established to limit the maximum vertical seismic displacement response within the static displacement of the spring by design vertical load of 10 kN. This will prevent detachment between the housing and the vertical spring.  
Table 2 presents the required vertical stiffness of the spring according to the design VIF for a design vertical load of 10 kN and their static vertical displacement values.
## 6 Conclusions  
A thorough characterization of a LCoS SLM display (Holoeye-Pluto-BB_HR) working on reflection has been carried out to obtain the optimized LUT curves for achieving different phase modulations, ranging from \(\pi\) up to \(8\pi\). Wavelengths from the green region of the visible spectrum (514, 520, and 530 nm) have been used to measure the various phase variation ranges and to compensate for some nonlinearities of the SLM response. From these initial measurements, the corresponding LUT curves were determined so that \(\pi\), \(2\pi\), \(3\pi\), \(4\pi\), \(5\pi\), \(6\pi\), and \(8\pi\) phase ranges were linearly reproduced by the SLM device. By using these phase modulation ranges, a number of multiorder diffractive lenses were displayed on the SLM device, and their performances were compared experimentally with the results of a conventional first-order diffractive lens. Second- and third-order diffractive lenses have proved to be very useful to overcome the undersampling problems raised when encoding a lens of relatively low f-number or optical power exceeding the Nyquist condition.  
We have also shown the benefits of determining optimized LUT curves for each phase modulation range, since DOE performance exhibits better optical quality when the appropriate LUT with a full grey level range is used. According to the results obtained for high-order diffractive lenses, it is also preferable to have a sufficient range of available grey levels to smoothly display the phase function.  
The so-obtained optimized LUT curves were used to determine the LCoS-SLM response for extreme wavelengths of the visible spectrum in the blue and the red regions. This information permitted the design of an achromatic, multiorder lens with a residual chromatic aberration reduced to one-third of the chromatic aberration of a conventional first-order diffractive lens. A fourth-order lens design for a blue wavelength (\(\lambda_{0}=470\) nm) has a common focalization spot when it operates as a third-order diffractive lens for the red illumination wavelength \(\lambda_{\text{R}}=625\) nm, as demonstrated experimentally by our results.  
Conceptualization, methodology, validation, formal analysis, investigation, resources and writing--review and editing, M.S.M. and E.P.-C.; software, data curation and writing--original draft preparation, E.P.-C.; project administration and funding acquisition, M.S.M. This research was funded by the Spanish Ministerio de Economia y Competividad and FEDER Funds (ref. DPI2016-76019-R). Authors thank [PERSON] and [PERSON] for their contribution in the experimental LCoS calibration procedure. The authors declare no conflict of interest.
## 2 Materials and Methods
### Dual-Arm Air-Puff SS-OCT Setup  
We examined the corneal response to three types of air-puff stimuli under systematically controlled IOP. The setup (Figure 2A,B) comprised of: (i) a customized dual-sample-arm swept-source OCT (SS-OCT) system, (ii) an air-puff chamber, and (iii) an IOP-control system, consisting of a saline column connected to the eye via plastic tubing and monitored by a differential pressure sensor.  
The core component of the SS-OCT system was a short external cavity swept light source operating at a central wavelength of 1310 nm, with a bandwidth of 100 nm and a sweep rate of 50 kHz (Axsun Technologies Inc., Billerica, MA, USA). To enable simultaneous imaging of the anterior and posterior segments of the eye, a 50/50 fiber coupler was integrated into the sample arm, creating two sample arms with distinct optical path lengths. These sample beams illuminated the cornea and sclera, respectively (Figure 2A,B). The collected light from the sample interfered with the reference arm signal. The path lengths of the sample arms were adjusted to ensure that the corresponding OCT images did not overlap (Figure 2C). This approach utilized the depth multiplexing method, commonly employed in polarization-sensitive OCT and space-division multiplexed OCT systems [26; 27; 28]. The interference signal was detected using a balanced photodetector (PDB110C-AC, Thorlabs Inc., Newton, NJ, USA) and digitized with an acquisition board (Gage Compuscope 14200, Gage Applied Inc., Lockport, IL, USA; 200 MS/s, 14-bit resolution). The system's sensitivity was measured to be 99 dB for both sample arms. The measured depth range was 9.33 mm, and the axial resolution was measured to be 9.3 \(\upmu\)m.  
To induce dynamic responses in the cornea, the corneal sample arm was coupled to an air-puff chamber dismounted from a commercial non-contact tonometer (XPert NCT; Reichert Inc., Buffalo, NY, USA) [7]. The beam illuminating the cornea was aligned collinearly with the air-puff stimulus direction. The pressure generated within the air-puff chamber during activation was measured using a pressure sensor (Ps1 in Figure 2A; SenSym, Milpitas, CA, USA) and recorded alongside the OCT signal. The optics of the sample arms and the air-puff chamber were mechanically separated to minimize interferometric signal instabilities caused by vibrations from the air-puff piston movement.  
Alignment of the imaging beam with the corneal apex was achieved via lateral scanning in preview mode. The apex was identified by observing the central reflex in both horizontal and vertical sectional images. The scleral sample arm was mounted on a positioning system (translational stages) to focus the optical beam on the posterior pole of the eye. After aligning the eye globe with respect to the illumination beams, a series of 4000 A-scans at both poles of the eye was acquired (M-scan) (Figure 2C).
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 4 Experiment and Results
### EEG Conformer Parameter Effectiveness  
In this ablation study on EEG Conformer parameter effectiveness, we evaluate the impact of different K values and attention dimensions on validation accuracy. The results show that for K values of 20, 30, and 40, the validation accuracies are 58.33%, 57.67%, and 58.67%, respectively, with the best performance at K = 40. However, for attention dimensions of 156, 16, and 56, the validation accuracies are 59%, 58.33%, and 59%, respectively, with no significant improvement as the attention dimensions increase, indicating  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Val Accuracy**} & \multirow{2}{*}{**No Pain**} & \multicolumn{2}{c}{**Val F1 Score**} & \multirow{2}{*}{**Mean**} \\  & & & **Low Pain** & & **High Pain** & \\ \hline EEG Conformer, B-6 & **58.67\%** & 0.00\% & 65.47\% & 59.66\% & 41.71\% \\ Bi-LSTM (48 units). B-6 [32] & 50.00\% & 10.53\% & 47.01\% & 59.55\% & 39.03\% \\ Bi-GRU (48 units). B-6 [33] & 49.67\% & 0.00\% & 54.04\% & 51.25\% & 35.10\% \\ Bi-LSTM (96 units). B-6 [32] & 45.33\% & 7.41\% & 46.87\% & 51.03\% & 35.10\% \\ Bi-GRU (96 units). B-6 [33] & 50.67\% & 5.71\% & 55.56\% & 51.26\% & 37.51\% \\ \hline EEG Conformer, B-1 & 58.67\% & 0.00\% & 63.61\% & 55.17\% & 39.59\% \\ Bi-LSTM (48 units). B-1 [32] & **59.00\%** & 0.00\% & 59.66\% & 61.38\% & 40.35\% \\ Bi-GRU (48 units). B-1 [33] & 55.00\% & 0.00\% & 57.98\% & 54.09\% & 37.36\% \\ Bi-LSTM (96 units). B-1 [32] & 57.00\% & 0.00\% & 59.80\% & 56.64\% & 38.81\% \\ Bi-GRU (96 units). B-1 [33] & 57.00\% & 0.00\% & 60.38\% & 55.56\% & 57.52\% \\ \hline \hline \end{tabular} Note.: All Bi-LSTM and Bi-GRU models consist of 2 layers with N units each of Bi-LSTM or Bi-GRU.  
\end{table}
Table 6: Validation accuracy and F1 scores of various neural network models using fNIRS signal.  
Figure 7: Vizualiation of K-means result.  
potential overfitting. Thus, we selected 16 attention dimensions to avoid overfitting and optimize efficiency. For detailed results, see Table 7.
# Adapting Mechanisms for In-Pipe Inspection Robots: A Review
## 3 Analysis of Adapting Mechanisms  
This section summarizes several types of adapting mechanisms for in-pipe inspection robots with several types of locomotion in terms of structure, how the pressure force on the pipe wall is obtained, the range of diameters that can be inspected, the possibility of ensuring movement through inclined or vertical pipes and overall dimension of the mechanism and robot where available.  
Figure 3: Prototypes of wall pressed robots designed and manufactured by authors. (**a**) Prototype based on slider-crank linkage; (**b**) prototype based on a combined linkage; (**c**) prototype based on a combined linkage with three gearmotors; and (**d**) prototype based on a pantograph linkage.  
One of the most important issues in designing a wall pressed inspection robot is ensuring sufficient traction force to pull the robot itself and any auxiliary tools or devices through the pipe. The traction force is proportional to the friction coefficient and the pressure force between the drive wheel/track and the pipe surface. The friction coefficient is usually variable and depends on the material from which the wheel/track is made and the condition of the pipe surface. Excessive pressing forces can unnecessarily dissipate power and block the robot inside the pipe, and insufficient forces can cause the robot to fall. Therefore, the adapting mechanism must maintain enough pressure against the wall to ensure sufficient traction force and avoid slippage. Those mechanisms should also minimize the variation of the traction force if the diameter of the pipe changes. To achieve those goals, passive mechanisms rely on the force generated by a spring. The active ones instead use actuation systems with DC motors and in some cases, sensors that can detect changes in the pressing force. All this can be integrated into a control loop, which gives more flexibility to the adapting mechanism.  
All presented mechanisms are equipped with three sets of drive wheels or tracks evenly distributed at 120\({}^{\circ}\) around the central axis. Exceptions are the adapting mechanisms based on the pantograph linkage, which have the wheel sets located in the same plane.
# Practical Grammar Compression Based on Maximal Repeats +
## 4 MR-RePair  
The main strategy of the proposed method is to recursively replace the most frequent maximal repeats instead of the most frequent pairs.  
In this section, we first explain the naive version of our method called Naive-MR-RePair. Although it can have a bad performance in certain cases, it is simple and helpful in understanding our main result. Then, we describe the proposed MR-RePair.
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 5 Results and Analysis
### Fully Autonomous Recovery Verification Experiment  
#### 5.4.1 Test Results of Autonomous Recovery Success Rate for UAVs  
The weight of the small fixed-wing UAV used in the experiment was 1.618 kg. During the recovery process, the UAV was able to successfully hook the cable and fall into the recovery net. More than 10 arresting test experiments were performed, and the recovery success rate was calculated to be not less than 80%. The UAV recovery test process is shown in Figure 19.
###### Abstract
Moreover, the CBM exploration and development in the Southern Junggar Basin [11], Western Guizhou [12], Eastern Yunan [13], and Erlian basin [14] of China is also being carried out and thriving. The total number of surface CBM wells in China has exceeded 20,000 at present, and the total CBM production has achieved 117.7 \(\times\) 10\({}^{8}\) m\({}^{3}\) in 2023, which mainly comes from SQB and EOB. Unlike conventional natural gas, CBM is primarily stored in coal reservoirs through adsorption [15]. As a special organic rock mass, coal reservoirs are featured with developed pore and cleat system [16; 17], low mechanical strength [18; 19], strong heterogeneity [20; 21], etc. Meanwhile, coal reservoirs are very sensitive to pressure, temperature, and stress, leading to evident planar and vertical variations in CBM accumulation and development conditions [22; 23; 24]. The CBM accumulation in coal reservoirs is attributed to the synergistic effect of many geological factors, such as burial depth, coal rank, coal thickness, coal quality, coal adsorption, sealing condition, hydrogeology, structural condition, etc. [22; 24; 25; 26; 27; 28]. The CBM development is also influenced by many geological factors, such as gas content, gas saturation, coal permeability, porosity, critical desorption pressure, etc. [29; 30; 31]. Thus, the geological conditions evaluation of CBM is of great significance to CBM exploration and development. It is estimated that the total CBM resources, less than 2000 m in depth in the Sichuan Province of China, are 6715 \(\times\) 10\({}^{8}\) m\({}^{3}\), among which approximately 67% are in the Southern Sichuan Coalfield (SSC) [32]. However, the CBM exploration, development, and utilization works in the SSC started late and progressed slowly, and the basic geological research on CBM is seriously inadequate. The Guxu mining area is one of the few areas that have conducted CBM geological evaluation works in the SSC. Analyzing and evaluating the geological conditions of CBM can not only guide CBM exploration and development in the Guxu mining area; it can also play a positive demonstrative role for other CBM target areas of SSC. In this study, the Dacun Block of the Guxu mining area was selected as the objective area, and the CBM geological conditions, including gas content, gas saturation, porosity, permeability, reservoir pressure, adsorption capacity, and critical desorption pressure, were analyzed and discussed. The works in this study could provide a guide for the target area selection of CBM enrichment and high production in the Dacun Block and then promote the exploration and development of CBM in the Guxu mining area.
## 2 Optimization Strategies
### Optimization Algorithms
#### 2.2.3 Bio-Mimic Algorithms  
Modern optimization algorithms are often nature-inspired/bio-mimetic, and they are suitable for global optimization. There exist a diverse range of bio-mimic or metaheuristic algorithms for optimization, including Particle Swarm Optimization (PSO) [25], Genetic Algorithm (GA) [26], Ant Colony Optimization (ACO) [27], Cuckoo Search (CS) [28], Bat Algorithm (BA) [29], _etc._ The right choice of an optimization algorithm can be crucially important in finding the right solutions for a given optimization scenario.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 2 Operation Principles
### Optimize Time-Domain Equalization Implementation  
With the continuous development of commercial coherent transceivers, higher efficiency requirements are put forward for the CDE algorithm to meet the requirements for power consumption. The previous TD-CDE method needs to be revisited to (I) improve the hardware implement ability of time CDE algorithms; (II) overcome the drawbacks mentioned above; (III) reduce dependence on subsequent adaptive equalization modules.
# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
## 3 Problem Definition and Features Designation
### Features for Bitcoin Price Fluctuation Prediction
#### 3.2.3 Denoising Autoencoders Features  
The features summarized above are limited, and many features are hidden in the time series financial data. To address the problems mentioned above, we adopt denoising autoencoders (DAEs) [33], which have the ability to extract deep features and eliminate the noise from the input time series with a deep neural network architecture. The DAEs as a type of unsupervised learning structures include three components: encoder, decoder, and training module.  
The main process of DAEs is described in Algorithm 1. In particular, first, we add low Gaussian noise \(q=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(q-z)^{2}}{2\sigma^{2}}}\) to the initial time series \(o_{t}\) and obtain \(\tilde{o}\). Then, the encoder network \(f_{\theta}\) maps \(\tilde{o}\) to \(s\), and the decoder network \(g_{\theta^{\prime}}\) maps \(s\) to \(z\). Both encoder and decoder networks adopt LSTM networks since LSTM can better capture time dependency of the input time series data. The reconstruction error is measured by the loss \(L_{2}(o,z)=\left\|o-z\right\|^{2}\), and the parameters \(\theta\) and \(\theta^{\prime}\) are updated through minimizing the error \(L_{2}(o,z)\). Finally, the high-level hidden representation \(s\) is regarded as the hidden features.  
```
0: time series \(o_{t}\). encoder parameters \(\theta\). decoder parameters \(\theta^{\prime}\).
0: hidden features \(s_{t}\). for each \(t\) in \(\{1,...,n\}\)do  adds tiny noise to \(o_{t}\), obtain \(\delta\) ;  Encoder: \(s_{t}\sim f_{\theta}(\delta)\) ;  Decoder: \(z_{t}\sim g_{\theta^{\prime}}(s_{t})\) ;  Training: \(\underset{\theta,\theta^{\prime}}{\operatorname*{argmin}}||s_{t}-z_{t}||^{2}\) ; endfor  return \(s_{t}\) ;
```  
**Algorithm 1** DAEs