esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datasets and Evaluation Metrics
### HDR Image Quality Assessment
#### 3.2.1 FovVideoVDP  
The Visible Difference Predictor for Wide Field-of-View Video is a video difference metric modeling spatial, temporal, and peripheral HVS aspects. Its computation involves complex stages derived from psychophysical studies: space-time contrast sensitivity functions (CSFs) adapted for foveal and peripheral vision, cortical magnification simulation, and contrast masking models. It processes video frames considering display parameters (luminance, size, resolution) and viewing distance. The output predicts the probability of visible differences over time and space. The full model details are in [95].  
VDP  
The original Visual Difference Predictor [108] estimates visual differences between two images using an HVS model. It typically involves amplitude nonlinearity (converting luminance to a perceptual domain), contrast sensitivity function (CSF) filtering (simulating optical blur and neural sensitivity to different frequencies), decomposition into frequency and orientation channels (cortical simulation), contrast masking calculations (predicting  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Applicability** & **Reference Type** & **Evaluation Type** & **Domain** & **Computational Cost** \\ \hline FovVideoVDP [95] & Both & FR & Global & Spatial and Temporal & High \\ \hline HDR-VQM [96] & HDR & FR & Global & Spatial & Medium \\ \hline HDR-VDP2 [35] & Both & FR & Global & Spatial & High \\ \hline HDR-VDP3 [36] & Both & FR & Global & Spatial & High \\ \hline BRISQUE [97] & LDR & NR & Local & Spatial & Low \\ \hline NIQE [98] & LDR & NR & Global & Spatial & Low \\ \hline PIQUE [99] & LDR & NR & Local & Spatial & Medium \\ \hline NIMA [100] & LDR & NR & Global & Spatial & Medium \\ \hline LPIPS [101] & LDR & FR & Global & Spatial & Medium \\ \hline SSIM [102] & LDR & FR & Global & Spatial & Low \\ \hline MS-SSIM [103] & LDR & FR & Global & Spatial & Low \\ \hline FSSIM [104] & LDR & FR & Global & Spatial & Low \\ \hline VSI [105] & LDR & FR & Global & Spatial & Medium \\ \hline PSNR [29] & LDR & FR & Global & Spatial & Low \\ \hline MSE [29] & LDR & FR & Global & Spatial & Low \\ \hline ColorVideoVDP [106] & Both & FR & Global & Spatial and Temporal & High \\ \hline MUSIQ [107] & LDR & NR & Global and Local & Spatial & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of characteristics metrics.  
how features mask each other), the calculation of detection probabilities for differences in each channel, and finally pooling probabilities into a final visibility map. The detailed steps and parameters are found in [109] and subsequent adaptations such as [110].  
ColorVideoVDP  
As an extension of FovVideoVDP, ColorVideoVDP evaluates distortions by jointly modeling luminance and color perception within the complex HVS framework used in FovVideoVDP. It incorporates models for color opponency and color contrast sensitivity. It predicts the visibility of spatio-temporal color and luminance distortions. Refer to [106] for the model architecture.
## 5 ACTOR Design
### Upper Confidence Bound (ACTOR)  
The optimal regret achieved through the UCB algorithm is the primary driving force of ACTOR. The UCB algorithm uses the upper bounds proposed in the previous section to handle the exploration-exploitation trade-off.  
The UCB algorithm measures the confidence in the knowledge of the rewards of each arm in which the term arm corresponds to the transmission power settings. The agent keeps track of the number of times each node has been selected, based on which the UCB index is calculated. The UCB index expresses the estimated upper bound for the rewards that an arm can achieve. At each iteration, the agent chooses the action with the maximum UCB index calculated as in Equation (7) [36].  
\[\text{UCB}_{a}(t) =\bar{X}_{t}(a)+\sqrt{\frac{2\sigma^{2}\log(t)}{N_{t}(a)}} \tag{6}\] \[Action(t) =\arg\max_{a\in A}[\text{UCB}_{a}(t-1)] \tag{7}\]  
The first term, \(\bar{X}_{t}(a)\), denotes the empirical mean of the rewards associated with action \(a\) by time \(t\). Equation (8) presents a recursive definition of the empirical mean of rewards. The reason we use a recursive method, rather than a simple \(\frac{1}{n}\sum_{t}X(t)\), is to reduce the memory footprint on the resource-constrained device. \(r\) represents the most recent reward. \(N_{t}(a)\) is the number of times action \(a\) has been chosen until time \(t\). For the calculation of \(\sigma\), we use Lemma 1.  
\[\bar{X}_{t}(a)=\frac{\bar{X}_{t-1}(a)\cdot N_{t}(a)+r}{N_{t}(a)+1} \tag{8}\]The second term is known as the exploration bonus or confidence width, and it denotes the degree of confidence in the expected reward, and it decreases with the number of times that arm is pulled. UCB shows more of a tendency to explore arms if they are either (a) promising, because \(\bar{X}_{t}(a)\) is large, or (b) not well explored due to a large confidence width.  
Blacklisting of the futile transmission power settings (based on RSSI) is performed during the initialization. In the example illustrated in Figure 1b, for the lowest three transmission power settings, the link is put in a disconnected region. Hence, ACTOR sets the PDR to 0 and virtually increases \(N_{a}(t)\). This will reduce the tendency of the exploration strategy to select those power levels.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Post-Processing: 3D Projection  
To generate a 3D projection from the 2D output, a post-processing step is required. This step involves aggregating the CT image slices and transforming them into a coherent 3D representation. In this process, the Mayavi [59] library in Python serves as a valuable tool for facilitating the conversion of the 2D model output into a 3D representation.  
The process begins by obtaining segmentation masks for each individual slice of the CT-scan. These masks provide information about the different regions within the lung. Next, the segmented slices are merged to create a unified representation. This merging process involves swapping the axes of the segmented data, ensuring that the resulting 3D projection can be visualized as a cohesive volume.  
The utilization of the Mayavi library streamlines the transition of the 2D model output to a 3D representation. This library offers robust functionality for transforming and displaying the segmented data in a three-dimensional space. By employing Mayavi, the proposed approach enhances diagnostic capabilities, providing clinicians with a comprehensive 3D visualization of lung structures and infection areas.
# Using Connected Vehicle Data to Evaluate National Trip Trends
### Current Survey Practices  
Although the comprehensive dataset collected by the NHTS and other such surveys is valuable for a variety of applications, the current data collection techniques are labor-intensive, difficult to scale, and take substantial time to process and obtain results. The 2017 survey alone recruited more than 252,000 households, and the final dataset containing information from more than 129,000 households was published in 2018.  
A major challenge with survey-based approaches is the cost of acquisition. For example, when the 2001 survey was conducted, the cost was expected to exceed $10 million [9]. Recruitment surveys with cash incentives for participation over mail, online, and telephone were conducted between March 2016 and May 2017. State and regional agencies often tend to offer monetary rewards for the public to respond to, as the data is invaluable to them, and the derived insights help determine follow-up survey requirements at a regional level [10; 11; 12].  
Additionally, there are opportunities to introduce biases due to the use of estimations and proxy interviews. For example, the trip distance attribute in the 2017 survey was estimated using the shortest distance recommended by Google Maps between the user's reported origin and destinations [13]. The relative infrequency of conducting these surveys (years 2001, 2008, and 2017) also makes it difficult to understand current trends and patterns [14] associated with fuel prices and new technologies such as micro-mobility. Finally, a rising concern with current practices is the standard use of mail-in diary methods for recording household surveys. These methods are receiving lower than desired response rates, leading to the data becoming less representative. The NHTS is supplementing and even replacing these mail-in methods with phone interviews, internet surveys, and mobile applications in an attempt to improve response rates and accuracy [15].
## 3 Experimental Test and Result Analysis
### Experimental Equipment
#### 3.2.1 SSH Remote Connection  
When we are debugging the car, we usually need to run the command line on the ROS host. However, if the display, keyboard, mouse, and other input devices are directly connected to the car to operate, when the car is in the process of movement, this method would be very inconvenient and may even affect the safety and efficiency of the operation. In order to avoid this situation and to ensure that flexible debugging and control are still possible when the car is moving, we adopted the method of remote control to realize the control of the car.  
Usually, we use secure shell (SSH) login for remote control. SSH is a widely used network protocol that provides security for remote login sessions and other network services. Through SSH, we can safely execute commands on the remote host on the local computer, just like operating directly next to the car.
## 3 Results
### MuSC-EVs Reverse Peroxide-Induced Mitochondrial Dysfunction  
Since MuSC-EVs effectively delivered protein to recipient muscle cells, we next explored the potential of these EVs to attenuate dysfunction caused by acute oxidative stress. Mitochondrial respiration was measured in C2C12 myotubes under control conditions and following a 24 h treatment with 50 uM hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). H\({}_{2}\)O\({}_{2}\) exposure resulted in a 42% decline in maximal oxygen consumption rate (OCR) (\(p\) = 0.0243; Figure 4A,D) as well as a 46% reduction in spare respiratory capacity relative to the untreated control group (\(p\) = 0.0185; Figure 4A,E). Subsequent treatment with MuSC-EVs (3.12 \(\times\) 10\({}^{8}\) SC-EV; 24 h) following H\({}_{2}\)O\({}_{2}\) exposure resulted in a 76% increase in maximal OCR (\(p\) = 0.0187; Figure 4A,D) and 84% increase in spare respiratory capacity in the damaged myotubes (\(p\) = 0.0198; Figure 4A,E), reversing H\({}_{2}\)O\({}_{2}\)-induced dysfunction. We also included myotubes treated with MuSC-EVs without oxidative insult. Maximal OCR and spare respiratory capacity were similar to control myotubes (Figure 4D,E). Further, the ratio of oxygen consumption rate to extracellular acidification rate indicates that treatment with MuSC-EVs restored the energetic phenotype of the damaged myotubes to that of the control group (Figure 4B). There were no significant differences between the basal respiration values of each group (Figure 4C).
# Optical Frequency Comb Expansion Using Mutually Injection-Locked Gain-Switched Lasers
## 3 Results and Discussion
### Expanded OFC Generation Using MIL-GS Lasers
#### 3.1.2 Comb Densification  
In comb densification mode, the PM is driven at a sub-harmonic frequency of the fundamental frequency \(\frac{f_{s}}{m}\), where \(m=2\), 4, 8, and 16. The resultant comb, with an FSR of 3.125 GHz, 1.5625 GHz, 781.25 MHz, and 390.625 MHz, is as shown in Figure 5a-d respectively. The insets within each of these figures show a magnified dotted region. Figure 5 shows clearly resolved comb tones with an OCNR ranging from \(\sim\)30-45 dB (within 20 MHz OSA resolution) and a comb bandwidth \(\sim\)180 GHz over the entire tuning range. The average comb output power remains constant at \(\sim\)9 dBm. In comb densification mode, as illustrated by the spectra in Figure 5c,d, the spectral flatness is degraded. This degradation is worse at the lower FSRs (\(\sim\)10 dB from spectral peak). Two important parameters of the frequency-divided OFC are presented in Table 3. This demonstration highlights the potential of a wide FSR tunability spanning from MHz to GHz with a simplified approach and fast tuning speed. It is worth noting that the fundamental FSR can also be tuned by varying the GS frequency to the slave lasers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**OFC Type** & \begin{tabular}{c} **Number of Lines** \\ **(5 dB from Peak)** \\ \end{tabular} & \
\begin{tabular}{c} **Comb** \\ **Bandwidth** \\ \end{tabular} &
\begin{tabular}{c} **Expansion** \\ **Factor** \\ \end{tabular} \\ \hline Individual GSL OFC & 15 & \(\sim\)93.7 GHz & – \\ Combined MIL-CSL OFC & 27 & \(\sim\)168.75 GHz & 1.8 \\ MIL OFC + PM (at 6.25 GHz) & 35 & \(\sim\)218.75 GHz & 2.3 \\ MIL OFC + PM (at 12.5 GHz) & 39 & \(\sim\)243.75 GHz & 2.6 \\ MIL OFC + PM (at 18.75 GHz) & 45 & \(\sim\)281.25 GHz & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the OFC expansion at FSR of 6.25 GHz.  
Figure 5: Optical spectra of comb densification MIL-GS OFC with FSR of (**a**) 3.125 GHz; (**b**) 1.5625 GHz; (**c**) 781.25 MHz; (**d**) 390.625 MHz. The insets depict a zoom in on the dotted region (5 dB/div scale).
## 2 Literature Review
### Big Data
#### 2.4.1 General Description  
Big Data can revolutionize how people drive. More data, if processed, can lead to more information [54]. As cars today collect the driving and other types of data from their surroundings and other cars and by analyzing and processing this data, car manufacturers and original equipment manufacturers create economic value [48]. Some of the key examples of big data applications in the car are: Inrix, an application that provides traffic information and navigation; GM's OnStar, where a digital crash signature is collected in case of an accident; Tesla's over the air software updates [55; 56].  
As one of the fundamental human needs, transportation also confronts the promises and challenges brought about by the big data era. Big data in the transportation arena is enabled by the rapid popularization of the intelligent transportation system (ITS) which has combined various technologies of the information era such as communication technologies, computers, sensors, the Internet, etc., in order to solve road traffic issues on a large scale [28; 57]. In the past few decades, the information is often collected continuously from different sources over vast geographic scales. While being huge in size and rich in information, the seemingly disorganized data could considerably enhance an experts' understanding of their system [7]. In addition, global positioning systems and other advanced communication technologies have made it possible to collect real-time traffic data from a wide range of heterogeneous sensor sources. This allows advanced traffic state estimations and predictions for important transportation management applications [49]. With the shrinking costs and increased accuracy of GPS, cellphone-based traffic monitoring makes extensive spatial and temporal coverage more accessible than the use of traffic sensors. Mobile phones are also considered the best means of data collection and transmission. The penetration rate for such smart phones is over 50% in the developing world and between 90 to 100% in developed countries [58].  
The methods of traffic data collection have been classified in following four categories as explained by [PERSON] et al. [49]:* Sensor-based traffic information collection technologies, such as inductive loop detectors, microwave radar, laser radar, magnetometers, and ultrasonic detectors.
* Video-based traffic information collection technologies, such as license plate recognition (LPR) systems.
* Radio-frequency-based traffic information collection technologies, such as radio frequency identification (RFID) and dedicated short-range communications (DSRC).
* Location-based traffic information collection technologies, such as GPS-equipped floating cars. In the automotive industry, the general purpose of these sensors, smartphones and embedded technologies is to facilitate traffic management and avoid certain obstacles.
## 1 Introduction  
The nature of the function optimization problem is to find the optimal solution of an objective function through iteration [1]. The function features are usually described as continuous, discrete, linear, non-linear, convex function, _etc._ In that the constraint function optimization problem can be converted into an unconstrained problem by using the designed special operators and penalty functions to make the solution always feasible, the unconstrained function optimization problem is the main research focus. The swarm intelligent optimization algorithm [2] is a kind of random search algorithm to simulate the biological population evolution and evolution, which solves the complex global optimization problems through individual cooperation and competition between species, and is applied in many fields, such as multi-objective optimization, data mining, network routing, signal processing, pattern recognition, _etc._ The typical swarm intelligence optimization algorithms include the ant colony optimization (ACO) algorithm [3], the genetic algorithm (GA) [4], the particle swarm optimization (PSO) algorithm [5], and the artificial bee colony (ABC) algorithm [6].  
The fireworks algorithm (FA) is a new swarm intelligence algorithm that was proposed by [PERSON] in 2010 [7], which has excellent optimization performance and aroused widespread concern in the world [8]. A hybrid fireworks-differential evolution (FWA-DE) algorithm was proposed by introducing the mutation operator, crossover operator and selection operator in the DE algorithm [9]. An enhanced fireworks algorithm (EFWA) was put forward by improving the minimum radius detection, mapping rules and spark selection strategy of the explosion sparks [10]. An adaptive firework algorithm (AFWA) was proposed to carry out the self-tuning for the blast radius [11]. That is to say, the distance between the optimal individual and the discussed individual is set as the next blast radius of the best individual. Through this kind of adaptive step size adjustment, the improved FWA shows good optimization performance. A dynamic search firework algorithm (DFWA) was proposed, which divides the fireworks population into core fireworks with optimal fitness value and non-core fireworks. By doing so, the former carries out the local search and the latter proceeds with the global search effectively [12]. In this paper, by balancing the global exploration and local search capability of FA through adjusting the explosion of fireworks, an improved fireworks algorithm based on grouping strategy of SFLA is proposed. The simulation experiments are carried out by comparing it with the SFLA and FA. The simulation results show that the hybrid algorithm can make the FA jump out of local optimum effectively and achieve the global convergence quickly. The paper is organized as follows. In Section 2, the fireworks algorithm is introduced. The FA-SFLA hybrid algorithm is introduced in Section 3. The simulation experiments and results analysis are introduced in details in Section 4. Finally, the conclusion illustrates the final part.
## 1 Introduction  
UCS is one of the most commonly applied rock mechanics parameters in any rock engineering project. Together with other parameters such as Young's modulus, Poisson's ratio, density, internal friction angle, and cohesion, UCS provides basic information to evaluate the intact rock strength and behaviour, which again are decisive for tunnel/cavern layout, support needs, etc. Traditionally, and still commonly used today, the UCS of a rock sample is obtained through a uniaxial compression test with apparatus and procedure, which were described in the ISRM-suggested methods and by the ASTM [1, 2, 3]. In addition to the traditional method, some authors also developed empirical correlations to obtain the UCS based on different parameters, such as pulse velocity (\(V_{p}\)), Schmidthammer rebound number (\(R_{n}\)), effective porosity (\(n_{e}\)), total porosity (\(n_{t}\)), dry density (\(\gamma_{d}\)), point load index (\(l_{s50}\)), shear wave velocity (\(V_{s}\)), Brazilian tensile strength (BTS), and slake durability index (SDI). Some of such empirical correlations for different types of rock are summarised and presented in [PERSON] [4], [PERSON] et al. [5], and [PERSON] et al. [6]. Examples of different empirical correlations for some rocks are presented in Tables 1 and 2. Recently, an artificial neural network (ANN)-based model was used to estimate the UCS based on results from three non-destructive tests [5; 6]. The selected three non-destructive tests were pulse velocity (\(V_{p}\)), Schmidt hammer rebound number (\(R_{n}\)), and effective porosity (\(n_{e}\)). In this way, a dataset and site-independent database comprising 274 datasets correlating the \(R_{n}\), \(V_{p}\), and \(n_{e}\) with the UCS of granite was used to train and develop the ANN models. Based on the results of the study, two closed-form equations were established to estimate the UCS [6]. Many other empirical relations and the ANN model can also be found in [PERSON] and [PERSON] [7] and [PERSON] et al. [8]. Moreover, as both the empirical and the ANN methods use indirect correlations or models to estimate the UCS, there could be a need for another option, which is a more direct method to estimate the UCS by utilising results from triaxial tests.
# The Efficiency of Large Hole Boring (MSP) Method in the Reduction of Blast-Induced Vibration
## 3 Comparison of Blasting Efficiency by Size of Empty Hole  
The blasting efficiencies of the MSP and cylinder-cut methods, which utilize empty holes, were compared according to the diameters of empty holes. Cylinder-cut generally includes 1-3 empty holes with a diameter of 102 mm, which is relatively less effective in reducing the vibration. Figure 5 shows the advance rate according to the drill length and diameter of the empty hole. The longer the drilling length, the lower the blasting efficiency, and the larger the empty hole, the better the blasting efficiency for the same drilling length [14]. Figure 5 does not contain detailed information for diameters over 203 mm. It can be expected that the MSP method has better blasting efficiency because it uses a 382 mm hole, almost twice as large as 203 mm.  
The volumes of free faces for the MSP and cylinder-cut methods were calculated to compare the effect of the free face. It was assumed that 1-3 holes of 102 mm diameter were drilled to 1.1 m and the cross-sectional area of the free face was calculated using Equation (1):  
\[A=\ \frac{\pi d^{2}}{4} \tag{1}\]  
where \(A\) is the cross-sectional area of the circle (m\({}^{2}\)) and \(d\) is the diameter of the drilled hole (m). The calculated areas of the free faces for 1 hole of 382 mm and 1-3 holes of 102 mm were compared, and the 382 mm diameter empty hole was found to increase the free face by 14, 7, and 4.67 times for 1-3 empty holes with the diameter of 102 mm, respectively. The MSP method, which normally bores an empty hole of 50 m depths at one time, is expected to have a greater blasting efficiency by securing an additional free face in the direction of excavation. In this case, the MSP method with a 50 m depth of the empty hole is expected  
Figure 4: Prediction of rock mass conditions ahead of tunnel face through borehole alignment tracking and ground exploration system.  
to have 213 times the effect of the free face compared with the cylinder-cut method with three empty holes.  
Equation (2) can be used to calculate the conversion diameter of empty holes with the same effect when using more than two empty holes as a free face:  
\[D=d\sqrt{n} \tag{2}\]  
where \(D\) is the conversion diameter of the empty hole, \(d\) is the diameter of the empty hole, and \(n\) is the number of empty holes. Assuming the same drilling length, a hole with the diameter of 382 mm has the same effect as 14 holes with a diameter of 102 mm, which is equivalent to 636 holes of 50 m depth [14].