esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical and Experimental Results
### Horizontal Fibonacci Sequence: Scaling Law
It is worth noticing that the above self-similarity between the transmission spectra requires the boundary condition E = 0 on the stub ends, otherwise one can check numerically that for the boundary condition H = 0 with the same geometrical parameters, this property is not fulfilled in all the frequency spectra. It has been found that the localization of modes in a Fibonacci structure exhibits critical behavior within the structure [80]. Indeed, contrary to disordered systems, these modes decrease according to apower law instead of an exponential law and the localization of these modes displays the property of self-similarity [81]. In order to understand the spatial localization of the different modes in Figures 3 and 4a,c illustrate the local density of states (LDOS) versus the space position \(x\) for the modes located at central gap frequency \(f_{c}=53\) MHz for generations \(S_{14}\), \(S_{11}\) and \(S_{8}\) respectively. The LDOS reflects the square modulus of the electric field inside the structure. The LDOS shows a self-similarity behavior [31; 82] around the main peak every three generations as it is shown in the insets of Figure 4b,c displayed for the 11 th and 8 th generations respectively. These results show that the electric field around the central frequency is neither extended nor localized. This particular field localization, which is due the quasi-periodicity arrangement of the blocks \(A\) and \(B\) in the FS, has been explained by a multifractal analysis to understand the critical behavior of such modes [31; 50; 62; 81; 83]. The behavior of the modes lying at the band edges is also analyzed as shown in Figure 5 for the mode \(f=68.08\) MHz in Figure 3f. The spectrum of LDOS is less regular compared to the band edge resonances occurring in the periodic structure (see the inset of Figure 5 for the mode \(f=61.40\) MHz). However, band edge resonances in periodic photonic crystals are non-localized states since they propagate throughout the system and are not evanescent [31]. In contrast, the Fibonacci band edge resonances may decay via a power law due to the non-periodic character of the blocks \(A\) and \(B\) in these systems [36; 84]. This kind of resonances has been shown to be useful in designing new complex cavity in 1 D photonic crystals that enables the feedback for laser action in the domain of random laser devices [85]. Figure 4: (**a–c**) Represent the spatial representation of the local density of states (LDOS) [in arbitrary units] of the mode lying at the central frequency \(f_{c}=53\) MHz for the 14 th generation. The insets of (**b,c**) correspond to the LDOS associated with the 11 th and 8 th generations at \(f_{c}\), respectively.
# Automatic 2D Floorplan CAD Generation from 3D Point Clouds
## 4 Experiments
### Experimental Environment  
We developed our method focused on the data of the seventh floor of the Robot Convergence Building of Korea University collected by a handheld laser scanner (ZEB-REVO). This data consists of multiple rooms with various types of clutter such as desks, chairs, and sofas as presented in Figure 4. Therefore, we experimented on another three datasets, as presented in Figure 4, to prove that the proposed method works robustly even for unseen data which were collected by different scanners. The data in Figure 4 are the second floor of the residential house collected by LIDAR (Velodyne HDL-32E), and this 3D point cloud data are very noisy and contain multiple rooms, clutters, and highly missed points in walls. The data in Figure 4,d are residential house and apartment which are from research that [PERSON] et al. created a benchmark for a new vector-graphics reconstruction problem [29]. They provide 155 scans collected by Google Tango phones (Lenovo Phab 2 Pro and Asus ZenFone AR). We selected two pieces of residential data from these datasets that have multiple rooms, little clutter, and the highly missed point in walls. In this paper, we referenced these two datasets as \"Tango Scan-1\" and \"Tango Scan-2\". The details of each data are presented in Table 1. The proposed algorithm used all 3D point clouds as an input, as presented in Figure 4. In the preprocessing step, we decreased by nearly 30% of number of input point clouds and conducted structuring in voxels with a leaf size of 0.01m in order to save time and retrieve more structural data.  
We evaluated the position and length of the detected walls by means of precision and recall, as used in pattern recognition, information retrieval, and classification. The main concept of these methods is the fraction of relevant instances among the retrieved instances. We computed these metrics based on the overlap between the areas of the ground truth and detected walls. We evaluated true-positive, false-positive, and false-negative cases. True-positive (\(TP\)) refers to the area of a detected  
Figure 4: Input 3D point clouds: (**a**) seventh floor of the Robot Convergence Building at Korea University, (**b**) second floor of residential house, (**c**) Tango Scan-1 and (**d**) Tango Scan-2.  
wall that is a wall in the ground truth, false-positive (\(FP\)) refers to the area of a detected wall that is not a wall in the ground truth, and false-negative (\(FN\)) is the area that is a wall in the ground truth but is not detected as a wall by the proposed algorithm. Based on \(TP\), \(FP\), and \(FN\), we calculated the precision and recall as follows:  
\[Precision=TP/(FP+TP) \tag{2}\] \[Recall=TP/(FN+TP) \tag{3}\]  
We defined thresholds by considering common buildings. During the wall detection process, \(thres=0.9\); in Algorithm 1, \(gridSize=0.01\) m; and in Algorithm 2, \(detArea=50\) pixels which is identical at 50 cm because \(gridSize\) is 0.01 m, \(minPoints=2500\), and \(minLen=100\) cm.
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 5 Discussion
### Advantages and Disadvantages of the Exhibition Environment Status Detection Device and System
#### 5.2.2 Disadvantages  
* High Initial Investment Costs: Artificial intelligence systems require high-performance hardware, such as high-efficiency servers, sensors, and cameras, which are typically expensive. Additionally, developing specialized AI software requires substantial resources, including professional development teams and long development cycles. These factors increase the initial investment costs, potentially becoming a barrier to widespread adoption.
* High Maintenance Requirements: The sophisticated nature of these systems means they require regular maintenance and updates to ensure optimal performance. This includes software updates, hardware repairs, and routine checks, which necessitate a team of skilled technicians. The need for specialized knowledge and expertise can further strain the resources of the institution.
* High initial installation costs: The high initial installation costs pose a significant barrier. Setting up the system involves substantial upfront expenses, including purchasing the necessary equipment, integrating it into existing infrastructure, and training staff to use the system effectively. These initial costs can be daunting and may deter institutions from adopting such advanced technologies.
# Influence of Fractal Disc Filter Flow Channel Parameters on Filtration Performance
## 3 Results and Discussion
### Influence of Various Flow Passage Parameters on Filtration Performance  
The optimal model of the disc filter was established by detecting the position and number of buffer slots, the tilt angle of the flow passage, the triangular shape and size of the internal section, and the taper, and then the optimal combination of laminated parameters was predicted. The input and output samples are defined, respectively, and then the model is established. The input data are trained, and when the error reaches the set value, it stops. The above neural network prediction model was constructed using MATLAB software (2022a). Among them, the maximum training time is 5000 times, and the learning rate of the network is 0.050. The error of the target to be reached is \(0.65\times 10^{-3}\).  
The model uses forty groups of numerical simulation results obtained by Fluent as data sets, including twenty-four groups of training sets, eight groups of verification sets, and eight groups of test sets. The learning results are shown in Figure 16.  
The predicted results have a high degree of fit with the numerical simulation results. The maximum relative error is 4.8%, and the average relative error is 0.96%. The predicted value of 62.5% of the data was absolutely consistent with the measured value. It indicates that the model has high reliability in predicting the head loss of the fractal disc filter and can be used to predict the optimal value of the head loss for a variety of flow passage parameter models. The prediction results are shown in Figure 17.  
Figure 16: BP Neural Network learning diagram.  
It can be seen from the prediction in Figure 17 that the maximum head loss in the models with various flow passage parameters can reach 3.6 m, which is about 2.4 times the minimum head loss. The green-marked point in Figure 17 indicates that the minimum head loss of sample 1088 th is 1.49 m. The corresponding flow passage parameters are as follows: The buffer groove is located at sections 1-2\({}^{\prime}\), and the flow passage inclination is 25\({}^{\circ}\). The bottom side length of the inner section is 0.36 mm, and the height is 0.24 mm. The taper is 0.0080. According to the prediction results, the optimal values of multiple flow passage parameters are not completely consistent with those of a single flow passage parameter, but the taper with the greatest influence is consistent.
## 4 Analysis of the Effect of TBM Crossing Fault Fracture Zone Under Different Treatment Methods
### Analysis of Calculation Results
#### 4.3.2 Analysis of Tunnel Deformation and Ground Displacement
This shows that the surface pre-grouting scheme has the best control over surrounding rock deformation. Figure 14: Cloud map for tunnel deformation and geological deformation calculation. (**a**) Deformation cloud of the tunnel at location y = 30 m (30 m of tunnel model excavation). (**b**) Deformation cloud of the tunnel at location y = 75 m (fault fragmentation zone). (**c**) Deformation cloud of the tunnel at location y = 100 m (100 m of tunnel model excavation). Next, the deformation differences at the arch waist position under different working conditions are analyzed. As shown in Figure 15b, Condition II still results in the largest surrounding rock deformation (16.8 mm), which is a 1.78% increase compared to the arch position. Grouting schemes such as double-layer pipe shed grouting, chemical grouting + shield tail small pipe grouting + advanced pipe shed, and surface pre-grouting reduced the horizontal deformation at the arch waist by 11.97%, 10.33% and 56.21%, respectively, compared to Condition II. Notably, the double-layer pipe shed grouting method effectively controlled the horizontal convergence of the surrounding rock. Compared to the arch crown, the grouting scheme in Condition III reduced deformation by 1.64% more than in Condition I. This is because the double-layer pipe shed provides additional support, reducing the load on the lower structure and allowing it to be more evenly distributed. Finally, the deformation differences at the arch bottom position under different working conditions are analyzed, as shown in Figure 15c. In the double-layer pipe shed grouting scheme, the arch bottom uplift reached 16.78 mm, an increase of 22.6%, 23.2%, and 61.8% compared to Conditions I, II, and IV. This is primarily because the surface pre-grouting significantly improves the formation's integrity, while the pipe shed grouting reinforces the surrounding rock locally. However, this method does not provide comprehensive reinforcement, leading to stress concentration in certain areas. The double-layer pipe shed transmits the least load downward, resulting in a more pronounced uplift at the arch bottom. In summary, surface pre-grouting enhances soil stability, strength, and stiffness, reducing the risk of collapse and deformation. The injected slurry improves the soil's physical and mechanical properties, increasing its bearing capacity. This method accelerates construction, improves efficiency, and enhances safety. Three-dimensional numerical simulations show that surface pre-grouting maintains good surrounding rock integrity with minimal deformation, small relative displacement, and effective control over geological settlement. Therefore, surface pre-grouting is an effective technique for managing fault fracture zones.
## 2 Biosynthesis and Precursors of Terpenoids
### Sesquiterpenoid Chemical Compounds  
Sesquiterpenoids are C15 compounds with three isoprene (C\({}_{5}\)H\({}_{8}\)) units that are primarily present in fresh raw plant materials. They form the most diverse terpenoids group, and the biosynthesis of sesquiterpenoids is synthesized using mevalonic acid [46; 47; 48]. There are about 150 known sesquiterpenes compounds including artemisinin, \((-)\)-\(\beta\)-elemene, \(\beta\)-caryophyllene, aromaadendrene, trans-\(\beta\)-farmesene, \(\alpha\)-humulene, valencene, ledene, tranerollidol, cyanophyllene oxide, globulol, viridiflorol, \((-)\)-guaiol, \((+)\)-cedrol, \(\beta\)-eudesmol, \(\alpha\)-bisabolol, _cis_-muurola-4(15), 5-diene, germacrene D, costunolide, parthenolide, guatterin A, dihydromadolin, madolin-K, madolin-W, malayscaphiol, sarcanolides A and B, perovskanol, eudesmane-type I and II, chrysanolide A, isocyperotun-done, and 1,4-epoxy-4-hydroxy-4-5-seco-guain-11-en-5-1 [49]. Sesquiterpenoids are predominantly found in the taxonomic groups Poaceae, Solanaceae, Araceae, Rutaceae, Zingiberaceae, Cannabaceae, Myrtaceae, and Cupressaceae. However, they are most common in the Asteraceae family, where they are almost ubiquitous. Sesquiterpenoid compounds are associated with the following plant species: Farnesol (_Cymbopogon_ species), \(\beta\)-nerolidol (_Citrus aurantium_), \(\alpha\)-humlene (_Humulus lupulus_), Zingiberene (_Zingiber officinal_), \(\beta\)-Santol (_Santalum album_), artemisinin (_Artemisia annua_), nootkaton (_Citrus paradisi_), costunolide (_Saussurea costus_), and parthenolide (_Rosmarinus officinalis_) [50].
## 3 Results
### Antioxidant Activity  
Table 4 includes also the values of the antioxidant activity calculated using Equation (1). Significant antioxidant activity is exhibited by all the tested films. This indicates that all obtained films could be potentially used as active packaging films. More specifically, antioxidant activity increases as the content of OO@NaMt and OO@OrgMt nanostructure  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Code Name**} & \multirow{2}{*}{\begin{tabular}{c} **Avez. Film** \\ **Thick. (mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **W.V.T.R \(\times\)10\({}^{\text{d}}\)** \\ **(g/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **D\({}_{\text{W}}\)** \\ **(cm/m\({}^{2}\)/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Avez. Film** \\ **Thick. (mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **O.T.R.** \\ **(cc/m\({}^{2}\)/Day)** \\ **(Mean.Dev)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Pe\({}_{\text{O2}}\times\)10\({}^{\text{d}}\)** \\ **(cc/m\({}^{2}\)/Day)** \\ **(Mean.Dev)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Antibox. Activ.** \\ **after 24 h** \\ \end{tabular} } \\ \hline PS & 0.1 & 1.3704 (0.02) & 1.221 & 0.375 & 17150 (13.2) & 6431 (4.4) & n.d. \\ PS3 NaMt & 0.1 & 1.5000 (0.02) & 1.336 & 0.375 & 19340 (15.8) & 725.2 (5.3) & n.d. \\ PS3 OO@NaMt & 0.1 & 1.4593 (0.03) & 1.300 & 0.375 & 18850 (15.6) & 706.9 (5.2) & 28.1 (2.2) \\ PS5 OO@NaMt & 0.1 & 1.4482 (0.03) & 1.290 & 0.375 & 19320 (15.4) & 724.5 (5.1) & 31.2 (1.9) \\ PSCoMa & 0.1 & 1.3778 (0.02) & 1.227 & 0.375 & 17310 (13.2) & 649.1 (4.4) & n.d. \\ PSCoMAxOO@NaMt & 0.1 & 1.3482 (0.02) & 1.201 & 0.375 & 16840 (13.0) & 631.5 (4.4) & 27.3 (2.0) \\ PScoMAxOO@NaMt & 0.1 & 1.2407 (0.01) & 1.105 & 0.375 & 16020 (12.8) & 600.8 (4.3) & 32.6 (1.7) \\ PS3 OrgMt & 0.1 & 1.1630 (0.02) & 1.036 & 0.375 & 13250 (12.7) & 496.9 (4.2) & n.d. \\ PS3 OO@OrgMt & 0.1 & 1.1222 (0.02) & 1.000 & 0.375 & 12450 (12.7) & 466.9 (4.2) & 37.2 (2.1) \\ PS SOO@OrgMt & 0.1 & 1.0963 (0.01) & 0.977 & 0.375 & 11420 (12.6) & 428.3 (4.2) & 50.4 (1.8) \\ \hline \end{tabular}
\end{table}
Table 4: Average films thickness, Water Vapor Transmission Rate (WVTR), water sorption, obtained Oxygen Transmission Rate (O.T.R.), Oxygen Permeability (Pe\({}_{\text{O2}}\)), and Antioxidant Activity values of all tested films.  
increases. Higher antioxidant activity values were obtained for OO@OrgMt based films than for the OO@NaMt based films. As it was mentioned in the preparation section the highest OO content was calculated gravimetrically for OO@OrgMt nanostructure (i.e., 36.0%) than for OO@NaMt nanostructure (i.e., 20.1%).
# Investigation of the Mechanical Behavior of Polypropylene Fiber-Reinforced Red Clay
## 3 Results and Discussion
### Shear Strength of Fiber-Reinforced Red Clay
#### 3.1.3 Effect of Cell Pressure on Strength  
Figure 4 exhibits the test results for the reinforced specimens with _FC_ of 0.25% and 0.50%. As shown in the figure, the specimens with different _FL_ had similar stress-strain curves. The strength of the fiber-reinforced clay increased with cell pressure. [PERSON] et al. [30] and [PERSON] [31] studied the strength of basalt fiber-reinforced clay and silty clay, respectively, and reached consistent conclusions with this study. [PERSON] et al. [22] investigated marine clay soil with nanomodified coil fiber and found that the treated soil had peak strength, and it increased with an increasing level of cell pressure. The red clay specimen reinforced with 0.50% 19 mm fiber filaments subjected to cell pressures of 100, 200, and 300 kPa had strengths of 914, 1030, and 1143 kPa, respectively, at an axial strain of 15%. For a given cell pressure, strength increased with the increase in _FC_. This positive relationship was more notable at large axial strains. The specimens mixed with 0.25% and 0.50% 19 mm fiber filaments had strengths of 949 and 1143 kPa, respectively, at a cell pressure of 300 kPa.
# Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
## 3 Accented Speech Recognition Based on End-to-End Domain Adversarial Training  
### Domain Adaptation for Accented Speech Recognition  
Deep neural networks require a large number of training data to construct the ASR model. In standard speech recognition tasks, a lot of normal speech data are available, while accepted speech data are difficult to collect. Thus, it is difficult to construct the ASR model for accented speech recognition using only accented speech data. Furthermore, fully depending on standard speech data (i.e., non-accented data) for model training may degrade the ASR accuracy when accented speech is inputted, as the acoustic characteristics of standard speech data are quite different from those of accented speech data. Domain adaptation can be a solution for this problem.  
Domain adaptation is a technique used to train models when the training and test data domains are mismatched [7]. This technique utilizes the well-organized information provided by the source domain data to handle the target domain data. Thus, it aims to obtain models that retain the characteristics of the target domain data from source domain models that are well trained with a large number of source domain data. With the help of domain adaptation, the distribution differences between the source and target domains are reduced. Figure 2 shows the effect of domain adaptation. Through the domain adaptation, the distance between the source and target domains becomes closer. In other words, the districts of the two domains are almost integrated.  
In accented speech recognition, standard speech data can be regarded as the source domain data, whereas accented speech data are considered as the target domain data. The main idea of this study was to construct a reliable model for accented speech recognition with a small number of accented speech data from a standard ASR model by using the domain adaptation technique.
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
## 4 Results
### Discrimination  
Discrimination results for all test cases are presented in Figure 5, each case presenting the chance-corrected discrimination rate (a cross) and its one-tailed 95 percent Confidence Interval (CI). In addition, the selected detection threshold \(\mathrm{P_{d}}=33.33\%\) and chance rate \(\mathrm{P_{0}}=6.25\%\) have been visualized. The numerical values of the results are tabulated in Table 1.  
In the small room case, five out of six comparisons were significantly recognizable. Especially U14 has been clearly separated from the others. Both cases involving U22 were also recognized by a smaller margin; the cases are clearly over the chance rate, but may be heard less than third of the population within the limits of the confidence intervals. Comparison between optimized setups did not cross \(\mathrm{P_{d}}\), thus reaching significant similarity. In other words, less than one third of the population can ever discriminate the setups from each other.  
On the contrary, none of the concert hall comparisons were significantly recognizable. Comparison of uniform setups appeared to be the most recognizable out of these setups, but further experiments are needed to conclude how recognizable the case is in the end. The rest of the comparisons and their CIs did not cross \(\mathrm{P_{d}}\), again reaching significant similarity. In particular, comparison between O16 and uniform setups could not be recognized, probably because the direct sound was reproduced similarly in all of them.  
Figure 5: The discrimination rates of different listening conditions and their one-sided 95% confidence intervals (\(\mathrm{P_{d}}\) set discrimination level, \(\mathrm{P_{0}}\) chance rate).
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 1 Introduction
### Factors That Raise Marine Traffic Engineering Awareness
#### 1.2.2 Accidents and Hazards  
The Aegean Sea has the highest accident rate in whole the Mediterranean region. According to a recent survey conducted by \"Archipelagos\", (a Greek institute of marine conservation) in three key vessel passages of the Aegean Sea, it was observed that about 65% of the moving vessels were cargo ships, 21%, were tankers and only 5% were passenger ships. Furthermore, it was observed that a large number of vessels sailing under \"flags of convenience\", about 16%, increased chances of an accident.  
The lack of regulated waterways allows vessels to moving at will, without any control and safe shipping lane policy, as illustrated in Figure 1. The enforcement of designated shipping lanes through the most suitable routes has been proven as a factor of reducing the number of accidents in other seas globally, therefore reducing the chance of ecological and socio-economic disaster.