esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Laser Scribing Machine and Parameters of Experiment  
### Laser Scribing Machine  
The laser scribing machine used in the test is a prototype of a six-axis five-linkage intelligent NC laser scribing machine tool, which is shown in Figure 10. Figure 10a show the structural principle and composition of the host machine. The main components include: machine bed and column, optical path installation platform, X/Y/Z linear feed axis, a/b optical rotation axis, c-axis mechanical rotation axis workbench, laser and optical path system, etc. In the process of laser scribing, due to the evaporation of the material inside the incision, the inert gas beam coaxial with the laser beam is often used to protect the optical system in the processing head, which can avoid contamination and oxidation of the workpiece.  
The laser and optical transmission systems are as follows: (1) laser: Firestar V30 CO2 laser from SYNRAD. After the laser beam emitted by the CO2 laser is expanded by a beam expander, the position and direction of the laser beam are adjusted by reflector M1 and M2, and the beam is shaped through an aperture. The reflector quarter-wave plate adjusts the laser beam to the circular polarization state. After adjusting the direction and position of the laser beam with the reflector M3, the laser beam enters the AB axis laser processing head and converges to the working surface through the focusing mirror for  
Figure 10: Structure and equipment of laser scribing machine. (**a**) Structure of laser scribing machine; (**b**) 6-axis 5-linkage laser scribing machine.  
scribing processing. Among them, the AB-axis laser processing head can achieve rotation of laser beam in three-dimensional space, the B-axis can make all optical elements behind it rotate 360\({}^{\circ}\) around the optical axis and the A-axis can make all optical elements behind it swing \(\pm\)120\({}^{\circ}\) around the optical axis, which realize the precise positioning in spatial position and five-axis high-precision linkage of the optical path flexible space transmission positioning device in the five-axis machining system. The laser uses the SIMATIC S7-1200 module from Siemens, Berlin and Munich, Germany to output the PWM impulse signal to control the output of the CO\({}_{2}\) laser, so the CO\({}_{2}\) laser is a quasi-continuous laser. It should be noted that after the beam passes through the focusing mirror, the parallel polarized light emitted by the laser becomes circularly polarized. The laser and optical path parameters are shown in Table 1.
# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Empirical Models of Lane Change  
The lane change empirical model is mainly based on actual observed data, taking the form of a large number of real vehicle lane change behavior samples for analysis, from which we can deduce the characteristics of lane change behavior. These types of lane-changing behavior models are mainly based on the driver's driving habits and experience. There are two common empirical models of lane change: the logit model [18] and reinforcement learning model.
## 5 Modelling the Communication Solutions  
Modelling the performance of each solution is for the purpose of performance prediction. Basing a model on features that are easily obtained from a network deployment is key to reducing the amount of testing required to characterise a deployment. The model can be used during runtime to compare solutions and advise on the best communication solution for a given set of requirements in a particular deployment.The goal of this is to reduce the need for extensive knowledge for application developers and to greatly reduce the amount of testing required on individual networks before deployment.
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 3 Background
### Dqn
The algorithm of DQN is described as follows (Algorithm 1):  
```
Initialize replay memory D to capacity N Initialize action value function Q with random weights \(\theta\) For episode = 1, M do  Initialize sequence \(s_{1}=\{x_{1}\}\) and preprocessed sequence \(\phi_{1}=\phi(s_{1})\) For t = 1, T do  With probability \(\epsilon\) select a random action \(a_{t}\)  Otherwise select \(a_{t}=\text{argmax}_{\text{Q}}(\phi(s_{t}),a_{t};\theta)\)  Execute action \(a_{t}\) in emulator and observe reward \(r_{t}\) next state \(x_{t+1}\)  Set \(s_{t+1}=s_{t},a_{t}x_{t+1}\) and preprocess \(\phi_{t+1}=\phi(s_{t+1})\)  Store transition \((\phi_{t},a_{t},r_{t},\phi_{t+1})\) in D  Sample random minibatch of transitions \(\left(\phi_{j},a_{j},r_{j},\phi_{j+1}\right)\) from D  Set \(y_{j}=\left\{ \begin{array}{ll}r_{j}&\text{if episode terminates at step }j+1\\ r_{j}+\gamma\text{max}_{\sigma}\text{Q}(\phi_{j+1},a^{\prime};\theta)&\text{ otherwise}\end{array} \right.\)  Perform a gradient descent step on \(\left(y_{j}-\text{Q}(\phi_{j},a_{j};\theta)\right)^{2}\) with respect to the network parameters \(\theta\) End for End for
```  
**Algorithm 1** DQN  
\(x_{t}\) represents the observation at the time-step \(t\). \(\theta\) is used as the weights of the neural network in DQN. \(\phi\) is the preprocessing function. The DQN algorithm is originally proposed to play the Atari games. Working directly with raw Atari frames, which are 210 \(\times\) 160-pixel images with a 128 color palette, can be computationally demanding, so DQN applies a basic preprocessing step aimed at reducing the input dimensionality. In our implementation, the input data are small. Therefore, we do not' use a preprocess function. T represents the terminate time-step. The agent will select a random action to explore the environment with probability \(\varepsilon\), which can be preset by the user.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 2 Background
### Vehicle Detection and Segmentation  
#### 2.1.1 Detection Using Background Subtraction  
Background subtraction (BS) is a widely used technique for applying a foreground mask for static cameras. Moving objects in the foreground are separated by calculating the foreground mask and subtracting it from the current frame and background model, containing the static part of the scene. Everything moving is considered characteristic of the foreground. BS is performed using two main functions: background initialization and background update. BS algorithms have already been implemented in open-source computer vision libraries such as OpenCV. In OpenCV, two background subtraction methods were implemented: k-nearest neighbor (KNN) algorithm [1] and Gaussian mixture-based background/foreground segmentation algorithm (MOG2) [2,3].  
Figure 1: Wrong-direction detection scenario (triangle represents camera’s visible region).  
BS showed instability on the false positive detections when it detected several closely located cars as one (Figure 2). The method is very sensitive to changing lighting conditions and, as an example, the results obtained during nighttime could be unacceptably inaccurate.
# Research on Response Postures of Subway Train in Straight Line Collision
## 6 Response Postures Analysis  
### Response Postures Summary  
As shown in Table 4, when the collision speed is less than 45 km/h, there is almost no derailment phenomenon in the collision between two trains. The trains maintain a frontal collision on the track, and there are multiple collisions between the carriages, mainly axial deformation. When the speed of hitting the obstacle reaches 45 km/h, the weak joints on the front frame generally fail. Under the condition of colliding with a rigid wall, the failure process of the train is accompanied by a variety of response postures (climbing, vertical arching, lateral buckling, carriage overturning, etc.). The lateral buckling is the most obvious in front-side collision with a rigid wall. In oblique collision with a rigid wall, large lateral movement of the train front is most noticeable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Response Attitude** & **Conditions** \\ \hline \multirow{2}{*}{Frontal impact} & Train-train collision (Speed \(\leq\) 45 km/h) \\  & Frontal collision with a rigid wall (Speed \(\leq\) 25 km/h) \\  & Accompanied by temporary derailment \\ \hline \multirow{2}{*}{Climbing} & Collision with a rigid wall (Speed \(>\) 25 km/h) \\  & Accompanied by severe derailment \\ \hline \multirow{2}{*}{Vertical arch} & Collision with a rigid wall (Speed \(>\) 25 km/h) \\  & Accompanied by severe derailment \\ \hline \multirow{2}{*}{large lateral movement of the train from} & Angled collision with a rigid wall (Angle \(>\) 5”) \\  & Accompanied by severe derailment \\ \hline \multirow{2}{*}{Lateral buckling} & Angled collision and front-side collision with a rigid wall \\ \hline \multirow{2}{*}{Carriage overturn} & Oblique and front-side collision with rigid wall \\  & Frontal collision with a rigid wall at 45 km/h \\  & Accompanied by severe derailment \\ \hline \hline \end{tabular}
\end{table}
Table 4: Response postures and corresponding conditions.  
Figure 20: Wheelset displacement under different front-side collisions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Front End** & **Middle Passenger Area** & **Rear End** \\ \hline C0 & 89.88 & 13.29 & 62.94 \\ C1 & 97.53 & 8.05 & 112.38 \\ C2 & 113.88 & 8.55 & 71.59 \\ C3 & 154.82 & 10.99 & 100.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy absorption per unit length of Carriage-2 in different parts at 36 km/h (kJ/m).
## 3 Results and Discussion
### Principal Component Analysis  
The mean concentrations of the various volatile compounds identified were used in a principal component analysis (PCA). From the PCA, the percent cumulative contribution of variance for the first two PCs was 63.06%, providing a good quality projection (Figure 7).  
Figure 6: Relative concentration per cultivar (ng:g\({}^{-1}\)) of terpenoids in 42 peach cultivars. Mean values of 3 fruit pureée samples per cultivar. Standard error (SE) bars are reported.  
From the score plot, the compounds hexyl acetate (E3) and 2-hexen-1-ol, acetate, (Z)-(E4) were highly associated with each other. The same was observed for the compounds 6-amyl-\(\alpha\)-pyrone (L5), \(\gamma\)-decalactone (L6), \(\delta\)-decalactone (L7), and \(\gamma\)-dodecalactone (L8) (Figure 7b). The extraction of the volatiles was performed on mature peach fruit confirmed by the presence of these compounds, mostly esters and lactones [10; 11]. This supports the findings from [PERSON] et al. [9]. The authors reported higher amounts of esters, lactones, and monoterepenes, such as linalool, in maturel peach fruits.  
From the PCA biplot (Figure 7a), it can be seen that some of the genotypes are unique with regard to their volatile compositions. \"Fiesta Gem\" had the highest amount of hexyl acetate (E3) and 2-hexen-1-ol, acetate, (Z)- (E4), followed by \"Blazeprince\", \"Fairtime\", and \"Harvester\". \"Majestic\" and \"Southern Pearl\" were also unique in that they had the highest amount of two of the characteristic peach aroma compounds \(\gamma\)-decalactone (L6) and \(\gamma\)-decalactone (L7). The latter are the most abundant in the literature, but the latter is the most abundant in the literature.  
Figure 7: Principal component analysis. (a) Biplot for the first and second principal components or factors obtained by the contents of the volatile compositions excluding A6 compounds. Cultivar IDs correspond to abbreviations used in Table 4. (b) Score plot of the variables. The letters and numbers represent the volatile compounds detected and correspond to the codes found in Table 2.  
and \(\delta\)-decalactone (L7) (Figure 7a). \(\gamma\)-decalactone was present in all the cultivars (Table 4) in an amount that was above their odor-threshold levels (11 ng\(\cdot\)g\({}^{-1}\) or 11 ppb [20]) and hence should contribute to peach aroma. In the cultivar \"Majestic\", which had the highest amount of \(\gamma\)-decalactones (502.8 ng\(\cdot\)g\({}^{-1}\)), the level was approximately 45-fold higher than the odor-threshold value. In these 42 cultivars, the benzaldehyde content (Table 4) was lower than its odor-threshold values (350 ng\(\cdot\)g\({}^{-1}\) or 350 ppb, [21]) and thus would contribute little to the peach aroma. Similar results were reported with \(\delta\)-decalactone since its levels were much lower than its odor-threshold values (400 ng\(\cdot\)g\({}^{-1}\) or 400 ppb) [20; 21].
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 5 Evaluation Scenario
### Simulation Design for Congestion Use Case  
We performed two sets of simulations for the congestion use case. The aim of the first set is to analyze the performance of three scenarios (each scenario with a different scaling factor) across different traffic strategies, while the second set is a batch of scenarios to design the decision-making algorithm.  
The results of the first set of simulations, discussed in Section 6.1, were obtained in two different scenarios: (i) without any traffic congestion or incidents; and (ii) with incident A located at the end of section S_6, as shown in Figure 4. The input data used for these simulations corresponds to the most congested hour and day in the 2022 dataset for the southbound direction, which was on 23 June 2022, at 17 h (cf. Figure 3). Scaling factors of 5, 4.75, and 4.5 were applied to this data. For each scenario, we tested different traffic strategies. The objective is to analyze and familiarize the behavior of the KPIs to decide the best strategy to mitigate congestion. The tested traffic strategies are as follows: (a) reducing the speed limit (from 120 km/h down to 50 km/h) in the section with the incident; (b) also reducing the limit in up to three previous sections; and (c) re-routing vehicles to reduce occupancy.  
The aim of the second set of simulations is to design the decision algorithm. It consists of a batch of 90 simulations where incidents are located at the end of different sections of the highway. These locations are indicated in Figure 4 as N_1 to N_8 in the northbound direction and S_1 to S_8 in the southbound direction. For the southbound direction, we used the same day and hour as in the previous simulations: 23 June 2022, at 17 h. However, for the northbound direction, the second most congested day--26 June 2022, at 13 h--was selected to test a slightly less congested pattern. Both cases use scaling factors of 5, 4.75, and 4.5.
## 3 Numerical Method and Loading Mode
### Loading Structure Method  
Figure 10 illustrates the schematic of the load distribution on the tunnel under the initial earth pressure conditions according to the load structure method. This method considers several key loads: the vertical earth pressure (\(P_{\rm{v1}}\)), the horizontal earth pressure (\(P_{\rm{x}}\)), the ground reaction force (\(P_{\rm{y1}}\)), and the tunnel structure's self-weight (\(G\)). The horizontal earth pressure is derived from the vertical earth pressure, multiplied by the lateral pressure coefficient (\(K_{0}\)), and it increases linearly with depth. To simulate the interaction between the ground and the tunnel segment, the study employs a circumferential spring model around the segment [33]. This model comprises three springs at each segment node: one normal and two tangential springs. The normal springs are primarily utilized to simulate the radial resistance of the strata against the tunnel during deformation processes, while the tangential springs are employed to represent the tangential frictional forces exerted by the strata on the tunnel. Notably, the normal springs are designed to compress without tension, meaning that they do not provide restraint when the contact pressure between the soil and the tunnel is zero. The ground spring's stiffness is set by the subgrade reaction coefficient, while the tangential springs' stiffness parameters are one third of the radial spring's, based on the research by [PERSON] [34] and [PERSON] [35]. The study, focusing on the loading structure method's impact on shield tunnel lining, divides the force on the lining structure into two stages. The first stage involves the initial earth pressure, and the second stage includes the additional load from ground surcharge. Based on the distributions of horizontal additional pressure and vertical additional pressure shown in 9, this study simplifies the linearly varying distributions, as depicted in Figure 10b, which shows the load pattern schematic for ground surcharge conditions.
## 1 Introduction  
Dementia is an overall definition of conditions and brain diseases causing a long-term and often steady decline in cognitive abilities and memory, which are severe enough to impair daily life functioning, often including emotional, social and behavioral deficits. The understanding of the biological mechanisms for developing dementia in the human nervous system is as yet widely incomplete.  
There are a variety of biomarkers used for attempting to diagnose different forms of dementia and more sensitive monitoring of progression, even at an early stage of onset. In particular, Alzheimer's disease (AD)--the most common cause of dementia (-70%)--and frontotemporal dementia (FTD) are well-examined conditions. Examples for biomarkers are cerebrospinal fluid biomarkers such as total tau, phosphorylated tau, and \(\beta\)-amyloid1-42 [1], or genetical biomarkers such as the APOE-\(\epsilon\)4 allele as one of the strongest non-Mendelian genetic risk factors [2] for AD. Similarly, various gene mutations for the FTD spectrum are known. Correlatively, structural magnetic resonance imaging (MRI) can reveal circumscribed atrophy patterns of brain lobes. Reduced cerebral blood flow (CBF) seems to be associated with neurodegenerative progression due to reduced brain metabolism; however, it is not yet clear which one precedes the other. Therefore, CBF could be a surrogate marker of brain function (neurovascular coupling) [3].  
Accordingly, arterial spin labeling (ASL), a relatively new method for analyzing brain perfusion and regional CBF, alongside dynamic susceptibility contrast perfusion or single photon emission computed tomography, has been suggested as an uprising biomarker to possibly detect early onset of dementia [4], which warrants regular consideration in the clinical setting. As opposed to other perfusion techniques, ASL is a completely non-invasive procedure without the demand for contrast agent or costly radioactive tracers. ASL scans are comparably short (approx. 4 min) and can be accomplished during the same examination of structural MRI, providing for an even more accurate diagnosis of the structure-function relationship [4]. Furthermore, MRI is easy to access and less expensive than other procedures, e.g., fluorodeoxyglucose (18F)- positron emission tomography (FDG-PET).  
In this study, we compare morphological and functional MR neuroimaging of patients suffering from clinically diagnosed FTD, AD and MCI to determine regions of significant brain atrophy and correlating CBF and/or relative CBF (rCBF) via pulsed ASL (pASL). Unlike prior studies, data were divided into much smaller sections of brain regions by a comprehensive set of pre-defined brain structures including gray and white matter (286 regions) via an automated online application tool to give more detailed insight.
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
## 3 Results and Discussion
### In Vitro Evaluation of Fungal Adhesion to the Nail Plate
#### 3.2.2 Microbiological Assessment
As was seen in the multiple-treatment procedure, the antiadhesive activity of the AP after only one treatment was higher at each time against _Trichophyton rubrum_ than against _Candida albicans_; the higher activity seemed to be after 5 h of contact, being 3.12 \(\pm\) 1.27 and 2.08 \(\pm\) 0.78, respectively. Moreover, considering the obtained values of the BC, different behavior was noted between the two fungal species, with negative AE values in the first 5 h for _C. albicans_ and weakly positive values for _T. rubrum_. This was due to the different metabolisms of the two fungi. In fact, the great adaptability of _C. albicans_ translates into an ability to rapidly respond to stress factors, take up nutrients or multiply under different conditions. As such, this yeast showed a greater rapidity of growth and adhesion compared with _T. rubrum_, which has a slower cell duplication time [36]. The AE values obtained from a single treatment with the BC confirmed that this product did not show antiadhesive effects. Furthermore, taking into account the results obtained at all testing times, 5 h appeared to be the best time to test antiadhesive activity. Therefore, in order to verify the potency of an AP, it could be interesting to compare the activity detected after multiple treatments with the activity after a single application. Table 1 reports the summary of the statistical analysis carried out on all antiadhesive effects (AEs) obtained from all groups after multiple treatments of an AP compared with the one obtained after just a single application of an antifungal product. Figure 7: Antiadhesive effect in the single-treatment procedure of group 4 with an antifungal (AP) product and group 5 with a base coat (BC) product against (**A**) _C. albicans_ and (**B**) _T. rubrum_. Statistical significance: *, \(p\) < 0.05 is significant; **, \(p\) < 0.01 is strongly significant; *** \(p\) < 0.001 is very strongly significant. The results clearly show that the prevention activity changed dramatically in relation to the fungal species. In fact, amorolfine could exert statistically significant antiadhesion activity only against _T. rubrum_. Furthermore, the lack of a statistically significant difference between multiple treatments and one single application of an AP highlights that the drug could penetrate into the membrane to maintain its activity against _T. rubrum_ even when washed.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 4 Experiment
### Data set Configuration and Experimental Conditions  
#### 4.1.1 Training Dataset Parameters  
Based on the signal modeling, data generation, and automatic labeling methods mentioned in Section 2, the communication model is built by Matlab 2020b, and the parameters of the two-dimensional time-frequency image data set for training are shown in Table 1.  
In the final training data set composition, we generate 20,000 time-frequency images, of which 80% are divided into the training set and 20% into the validation set.
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 8 Conclusions  
This paper proposes and evaluates LQE, a Linear Quadratic Optimization-based ABR scheme, which chooses a proper video bit rate to reduce playback stalls due to rebufferings. We introduce several tweaks to utilize the complex control theory appropriately for mobile video streaming. Our simulation results show that LQE successfully mitigates rebufferings by decreasing their duration when they occur compared to the existing buffer-level based ABR schemes, while still preserving similar video streaming quality.  
Conceptualization, Y.-m.K. and Y.-s.L.; methodology, Y.-m.K. and Y.-s.L.; software, Y.-s.L.; validation, Y.-m.K.; formal analysis, Y.-m.K.; writing--original draft preparation, Y.-m.K.; writing--review and editing, Y.-s.L.; visualization, Y.-m.K.; supervision, Y.-s.L.; project administration, Y.-s.L.; funding acquisition, Y.-s.L. Both authors have read and agreed to the published version of the manuscript.  
This work was supported by the Sungshin Women's University Research Grant of H20200096.  
Not applicable.  
The authors declare no conflict of interest.