esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Ranking of Flights and Further Research
#### 5.6.3 Flight Duration Analysis  
The variable \"CRSElapsedTime\" represents the scheduled duration of a flight. measured in minutes. This metric helps assess how planned flight lengths correlate with delay patterns, offering insights into whether longer or shorter flights are more prone to delays. Table 17 provides a detailed category report for this variable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Score** & **Condition** & **DR [\%]** & **Share [\%]** & **All** & **Delayed** & **On Time** \\ \hline
310 & MQ & 6.3 & 3.8 & 46,299 & 2912 & 43,387 \\
312 & \textless{OTHES}\textgreater{} & 5.9 & 22.8 & 280,856 & 16,507 & 264,349 \\
313 & AA. OO. UA & 5.7 & 27.3 & 337,117 & 19,227 & 317,890 \\
317 & CO & 4.9 & 4.3 & 52,907 & 2577 & 50,330 \\
323 & DL. NW. US. WN & 3.9 & 41.9 & 516,314 & 19,947 & 496,367 \\  & & & 100.0 & 1,233,493 & 61,170 & 1,172,323 \\ \hline \end{tabular} Source: Authors’ own elaboration.  
\end{table}
Table 16: Variable category report—Reporting_Airline.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Score** & **Condition** & **DR [\%]** & **Share [\%]** & **All** & **Delayed** & **On Time** \\ \hline
310 & \(115.5\leq\textless{}\textless{}\textless{}\textless{}\textless{}\textless{}  
The smallest DR (Figure 10) is for the shortest duration flights, partial score 322. It is observed that almost all lines do not cross except for the partial scores of 310 and 312, which represent the longest duration flights and show an inversion of their DR over time.  
It is notable that an increasing trend is observed for long-duration flights, while a decreasing trend is noted for short-duration flights (Figure 11). An interesting conclusion can be drawn from the variable importance report, as shown in Table 18.  
Figure 11: Share as a function of time for the variable CRSElapsedTime. Source: Authors’ own, elaboration.  
Figure 10: Delayed rate as a function of time for the variable CRSElapsedTime. Source: Authors’ own, elaboration.  
No significant importance for certain variables is observed in the model. The most important variable is identified as the month, which intuitively aligns with cyclic weather patterns. The model's predictive performance is not strong, indicating the need for additional data. However, based on variable importance, areas of interest or priorities can be defined. Weather data should be added first, followed by information on airport traffic and other relevant factors.
## 2 Technology
### Principles of QPI
As the phase delay is a property not always translatable to known biological properties, it can be difficult to compare QPI directly to well-known legacy methods. Instead, QPI images provides a new way of looking at cells with its own distinctive characteristics. The phase delay measured is directly related to cellular dry mass (Equation (1) and is the only physical property measured. However, using image analysis, it is possible to extract further information using the recorded two-dimensional mass distribution within each cell. A number of studies have compared the results of QPI analyses with other methods: QPI for tissue imaging and histological analysis have been investigated using tissue from breast [68] and prostate [76; 79]. The majority of QPI for studies of cell cultures have focused on cell count and/or cellular morphology (Table 1). As dry mass is independent of the intracellular water content, it can be a good indicator for cell growth. The ability to measure cell count was validated for the first time in 2008 by [PERSON] et al. [21], using human breast and prostate cancer cell lines, as well as a mouse fibroblast cell line. Measurement of cell growth has been well documented for both adherent and non-adherent cells [8; 9; 15; 25; 26; 27; 47; 49; 56; 59; 61]. By segmentation of cellular outlines, morphology and motility can also be studied [7; 50; 51; 57; 80]. Figure 1: Example of QPI image: **(a)** Interference pattern from digital holography (DH) recording showing cells in transmission mode as seen by the sensor. An interference pattern between object and reference wave has been disturbed by the refraction of cells in the object light path; **(b)** Reconstructed phase image using a Fresnel approximation, numeric refocusing, and unwrapping. Image intensity is proportional to optical path length through the cells; **(c)** Reconstructed phase image with segmented cell areas. Cell regions (white) have been separated from background (grey) using a simple thresholding. Scale: 200 \(\times\) 175 \(\upmu\)m\({}^{2}\), human prostate DU145 cells in a T25 flask. Furthermore, differences in cell types can be detected if they display distinctly different morphologies or migration patterns in response to cytotoxic drugs [30; 81; 82].
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 5 Conclusions  
An adaptive and blind audio watermarking algorithm based on chaotic encryption in a hybrid domain is proposed to combat various conventional signal-processing attacks. The watermark picture is encrypted by a chaotic sequence to improve the security of the watermark, and only a user who holds the correct key can extract the watermark in the audio signal. The encrypted binary watermark can be embedded into the high-frequency component of the audio according to the special embedding rules. The embedding depth of each audio segment is controlled by the overall average amplitude, which effectively improves the robustness and imperceptibility of this algorithm. This algorithm does not require the participation of original audio when extracting the watermark, which is very convenient for practical applications. Experimental results confirm the better performance of this proposed algorithm than the related five audio watermarking algorithms. In the process of using audio media, users often perform MP3 compression, resampling and other signal-processing operations on the audio media, just as in several attacks tested in this paper. Sometimes, users also perform some malicious operations to destroy the watermarks concealed in the audio media, such as time scaling or cutting off a piece of audio data, which can seriously damage the watermark and even cause the watermark to be lost. In the future, our research will focus on combating these malicious attacks.  
[PERSON] put forward the idea of this paper and completed the preparation of the paper; [PERSON] instructed [PERSON] to complete the design and construction of the experimental scheme; [PERSON] and [PERSON] completed the submission and revision of this manuscript.  
This research was funded by the National Natural Science Foundation of China for Youth (Grant No. 61602263); Research and Innovation Project for Graduate Students of Jiangsu Province, China (Grant No. KYIX\(\_\)0815).  
Thanks to Nanjing Metro and NJUPT for providing watermark pictures used for experiments.  
The authors declare no conflict of interest.
# From Feed to Fish--Nutrients' Fate in Aquaculture Systems
## 4 Discussion
### Inputs  
Feed and fertilizers represented the most substantial nutrient input (often >70% of the total input) (Figure 5). Management malpractices such as overfeeding and overfertilization can increase the contribution of these budget terms to the total nutrient budgets and even more to financial costs. When feed is the primary source of nutrient supplementation, overfeeding can be mitigated simply through improved record-keeping practices and enhanced farmer training [42].  
Depending on fishpond management, CO\({}_{2}\) uptake due to photosynthetic activity can be an important budget term, particularly in fertilized systems [25]. Uptake of N\({}_{2}\) and CH\({}_{4}\) contributed little to the total N and C input. Even though many studies did not report the water source, studies that did assess it found substantial contributions of inlet water to the total nutrient input. This was particularly the case in Brazilian fishponds that were supplied with nutrient-rich inlet water. This nutrient-rich water was referred to as \"a source of unpaid nutrients\" [21]. Its use could exempt the use of supplemental feed and fertilizers [43] and can, therefore, be seen as contributing to the environmental sustainability of fish production.  
Depending on the hydrology of fishponds, precipitation can be an important component of a pond's water budget. However, it was generally a minor component of the nutrient budget. Although it may be an important source of N in oligotrophic aquatic systems, this is not the case for generally nutrient-rich fishponds [44]. The relevance of precipitation for C and P input to the systems reviewed here was even lower.  
Figure 5: Inputs and outputs of carbon, nitrogen, and phosphorus presented as the percentage of the total input and output, respectively. Data ranges are based on findings presented in the 23 assessed papers. Straight lines represent inputs of nutrients, and dashed lines represent output of nutrients. Wavy blue arrows represent gas influx, and wavy red arrows represent gas efflux.  
Advancing the knowledge of fish physiology and growth can contribute to optimizing feed input in aquaculture pond systems. Improvements are also being made through the optimization of the FCR by using species-specific feed formulation. Breeding programs for higher feed efficiency (i.e., lower FCRs) of genetically improved rainbow trout resulted in the reduction of feed costs by 18%, and the low FCR led to 18.3% less nitrogen and phosphorus load into the aquatic environment [45]. A decrease in the FCR by 27% for trout, for instance, may reduce the environmental impact of trout cultivation on eutrophication, acidification potential, and global warming by 16-27% [46].
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 3 Methodology
### Limits Definitions  
Three main detection limits exist in these algorithms. (1) The first if the minimum, which is the minimum value required for each method to detect at least one cycle of the defect signal. (2) Second is the maximum, which is the maximum allowable value for the methods to detect the defect without any outliers. By moving toward the maximum value, the detection length increases; however, the accuracy reduces as the chance of detecting outliers increases. Thus, for comparing the methods together, an average value is defined using the minimum and maximum. This value is the best possible compromise between the achieved defect length and the possibility of detecting false alarms. For method three, the average value is calculated using Equation (11), and minimum and maximum values are achieved by manually replacing the average in Equation (14). For both of the other methods, the average value is calculated manually from the achieved minimum and maximum values in the tests. Also, the \(Safe\)\(zone\) margins are defined as the difference between the minimum and maximum, which illustrate the values where manual inputs will produce a result without any false alarms.
## 3 Results
### Thermal Properties of PS/OMt Nanocomposites
Meanwhile, the presence of a higher content of \(\gamma\)-MPS with the C=C groups acted as the crosslinking agent, connecting the silicate and PS chains, which was covalently bonded on the Mt surface to increase the thermal properties of the nanocomposites [49]. With higher thermal stability, the PS/H-P-Si-Mt nanocomposite particles showed promising applications in the lubricating materials for oil and gas drilling engineering. Figure 7: (a) TGA and (b) DTG curves of pure PS and PS/OMt nanocomposites. The best improvements in the thermal stability of polystyrene nanocomposites, under a nitrogen atmosphere, obtained in this work and in other works from the literature, using this and other types of fillers, are summarized in Table 5. The better improvement of the thermal stability of polystyrene nanocomposites was observed in this work, using very small amounts (3 wt.% loadings) of these fillers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Filler** & **Method** & **Content** & \(\Delta T_{onset}\) & \(\Delta T_{-50\%}\) & \(\Delta T_{max}\) & **Ref.** \\ \hline Organoclay & Bulk polymerization & 5 & & 50 & & [50] \\ \hline Organoclay & Bulk polymerization & 3 & 50 & & & [51] \\ \hline Organoclay & Solvent blending & 5 & & 16 & 24 & [52] \\ \hline Organoclay & Solution polymerization & 5 & 51.1 & & & [53] \\ \hline Organoclay & In situ intercalative polymerization & 10 & 109.5 & & & [54] \\ \hline Fullerene & Melt extrusion & 1 & 7.0 & 4.5 & 2.2 & [47] \\ \hline Layered double hydroxide & Soap-free emulsion polymerization & 1 & 53.3 & & & [55] \\ \hline ZnAl-layered double hydroxide & Solution intercalation & 10 & & 39 & & [56] \\ \hline Cadmium sulfide & Melt extrusion & 10 & & & 95 & [57] \\ \hline Aluminum hypophosphite & Melt extrusion & 30 & -23 & & 6 & [58] \\ \hline Calcium carbonate & Solution casting & 4 & & & 15 & [59] \\ \hline Organoclay & Soap-free emulsion polymerization & 3 & 27 & 31 & 32 & This work \\ \hline \hline \end{tabular}  
\(\Delta\): the corresponding variations in thermal stability of each composite compared with the pure polymer.  
\end{table} 
Table 5: Parameters of enhanced thermal properties. Figure 8: TEM images obtained for (**A**) PS/Si-P-Mt, (**B**)PS/P-Si-Mt, (**C**) PS/H-Si-P-Mt and (**D**) PS/H-P-Si-M nanocomposites.
## 2 Methodology
### Comprehensive Evaluation
This part involves the initial calculation of two crucial parameters, among which contrast intensity can be expressed as a standard deviation by Equation (9), and the correlation coefficient represents the other, which is calculated by Equation (10):  
\[S_{j}=\sqrt{\left(\sum_{i=1}^{n}\left(x^{\prime}_{ij}-(1/(n\sum_{i=1}^{n}x^{ \prime}_{ij}))\right)^{2}\right)/(n-1)} \tag{9}\]  
\[R_{j}=\sum_{j^{\prime}=1}^{m}\left(1-r_{j,j^{\prime}}\right) \tag{10}\]  
where \(S_{j}\) and \(R_{j}\) denote the standard deviation and correlation coefficient of the \(j\)th indicator, respectively, \(n\) and \(m\) denote number of design and indicators, respectively, and \(r_{j,j^{\prime}}\) denotes the correlation coefficient of the \(j\)th and \(j^{\prime}\)th indicators. After that, the volume of information can be determined using Equation (11). \[C_{j}=S_{j}\times\sum_{j^{\prime}=1}^{m}\left(1-r_{j,j^{\prime}}\right)=S_{j} \times R_{j} \tag{11}\]  
4. Weight assignment and scoring  
A higher value of \(C_{j}\) implies a more significant effect and corresponds to a higher weight assigned to the objective weight of the \(j\)th indicator in \(k\)th case, as expressed in Equation (12). Furthermore, the score of the \(i\)th design in \(k\)th case can be obtained using Equation (13). \[w_{k,j}=C_{j}/\sum_{j=1}^{m}C_{j} \tag{12}\]  
\[z_{i}=\sum_{j=1}^{m}\left(w_{k,j}\times\left(x^{\prime}{}_{ij}/\sum_{i=1}^{n}x^{ \prime}{}_{ij}\right)\right) \tag{13}\]
# Laser Irradiation on Limestone and Cracking: An Experimental Approach
###### Abstract
[27; 28] found that the speed of rock-breaking by laser irradiation could reach about 105-115 m/h, which was ten times higher than that of traditional mechanical drilling. [PERSON] et al. [29] studied that increasing the number of irradiated holes at high power is beneficial for crack extension under the condition of constant laser energy. [PERSON] et al. [10] studied the effect of laser irradiation on the properties of rocks and showed that the fundamental factor leading to changes in the physical properties of rocks was the drastic change in the temperature field. Heat transfer in laser irradiation can be described as a non-stationary heat transfer process of heat conduction, convection, and radiation [30]. [PERSON] et al. [31] combined an improved level-set method with a two-dimensional model simulating the laser punching process of a millisecond pulsed laser on an aluminium plate, using an equivalent specific heat capacity and a gas kinetic source term to model the phase transition. [PERSON] et al. [32; 33] obtained the fluid configuration and associated velocity field for a given timestep using the VOF method of tracking free surfaces and solved the energy equation as an advection diffusion equation using the control volume finite difference method. [PERSON] et al. [34] employed the linear Stefan problem to model the phase transformation of limestone and sandstone after short-pulse laser irradiation. [PERSON] et al. [35] used COMSOL multiphysics to study the heat transfer process during phase change in laser irradiation. [PERSON] et al. [36]  
Figure 1: Schematic diagram for (**a**) drilling principle [18], and (**b**) laser-assist principle. simulated the generation and expansion of cracks under laser irradiation using PFC2D. [PERSON] et al. (2017); [PERSON] et al. (2018) used the finite element method to develop a numerical model of laser scattering in non-homogeneous granites to explain the presence of microfractures within the rock. Rock fragmentation is becoming increasingly challenging in obtaining energy resources stored in deep and hard rock layers. Laser irradiation is a new drilling method with an appreciable prospect. The effects of laser parameters and rock properties on rock fragmentation were frequently heated in early studies; however, limestone thermal cracking by laser irradiation under immersion conditions has been studied in a few studies. In this study, the effects of immersion conditions on rock-breaking by laser irradiation on the temperature, hole size, rock-breaking efficiency, macro-fracture after laser irradiation, and fracture length were investigated. In addition, the changes in rock weight loss were determined by a temperature thermogravimetric analyzer. The results can be used to make a reference for the efficiency of rock crushing under laser-beam assistance and provide the experimental basis for the practical application of laser.
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 5 Metrical Description  
Tracking a large range of metrical levels enables a detailed description of the dynamic evolution of the metrical structure. For instance in Figure 7, the meter is initially and for the most part ternary. However between 40 and 50 s. (corresponding to bars 77 to 92), a little before 80 s. as well as between 120 and 130 s., we see that the ternary rhythm is actually perceived as a binary rhythm, as shown by the metrical level 0.5. Conversely in Figure 8, the meter is initially binary, but turns ternary after 80 s.  
What is particularly interesting in those examples is also that the metrical structure changes, but the tempo remains somewhat constant. This shows that tempo is not a sufficient information for the description of metrical structure.  
To give an indication of metrical activity that would not reduce solely on tempo but takes into consideration the activity on the various metrical levels, we introduce a new measure, called _dynamic metrical centroid_, which assesses metrical activity based on the computation of the centroid of the periods of a range of selected metrical levels, using their autocorrelation score as weight. The metrical centroid values are expressed in BPM, so that they can be compared with the tempo values also in BPM. High values for the metrical centroid indicate that more elementary metrical levels (i.e., very fast levels corresponding to very fast rhythmical values) predominate. Low values indicate on the contrary that higher metrical levels (i.e., slow pulsations corresponding to whole notes, bars, etc.) predominate. If one particular level is dominant, the value of the metrical centroid naturally approaches the corresponding tempo value on that particular level.  
Figure 11 shows the dynamic metrical centroid curve related to the _Allegro con fuoco_ of [PERSON]'s _New World Symphony_ as shown in Figure 8. The temporal evolution of the dynamic metrical centroid clearly reflects the change of rhythmical activity between the different metrical levels, and the transition between binary and ternary rhythm, which increases the overall perceived rhythmical speed.
## 2 Theoretical Background  
### Mode Synthesis Method  
In structural modal analysis, a motion equation containing mass, stiffness and damping matrices should be solved. In order to reduce the computational requirements, the motion equation can be replaced by a low-order equation through finding the presumed mode sets based on the Rayleigh-Ritz method [26]. The mode synthesis method can find reasonable presumed mode sets by substructures to achieve structural modal analysis. The procedures of the mode synthesis method for analyzing structural modal characteristics are illustrated as follows. First, the original complex structure is divided into simple substructures based on construction features. The modal analysis is performed independently for each substructure by use of the finite element method. Second, enough modes in modal coordinates are extracted to represent the modal characteristics of the substructure. The transformation from physical coordinates to modal coordinates is conducted to obtain the modal coordinates of the coupling system. Third, the dependent modal coordinates are eliminated based on compatible conditions between substructures to transform the modal coordinates of substructure into generalized ones. The generalized motion equation is established by only independent generalized coordinates. Fourth, the motion equation under free vibration after mode synthesis is solved to obtain the modal parameters according to the generalized coordinates. Fifth, the eigenvector matrix of the system is transformed from generalized coordinates to physical ones in order to obtain the results of each substructure under physical coordinates.  
In the process of modal coordinate transformation, a few low-order modes are selected to represent a substructure, which makes the number of independent generalized coordinates for the whole system less than the number of degrees of freedom in the finite element model of the structure. This is the main reason for obtaining the reduced-order motion equation.