esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 AOG Downtime Reduction and Implementation Results  
This section outlines the results of analyzing and predicting AOG events using advanced statistical tools in Python, including ARIMA, Holt-Winters, and Logistic Regression. Key variables such as part failure rates, maintenance compliance, and downtime were examined, revealing links between AOG occurrences and factors like maintenance strategies, spare parts availability, and environmental conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Airline** & \begin{tabular}{c} **AOG Incident Rate** \\ **(per 1000 Flight Hours)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Delay Time** \\ **(Minutes)** \\ \end{tabular} &
\begin{tabular}{c} **MTBF (Hours)** \\ \end{tabular} \\ \hline SmartLynx & 11.8 & 28 & 1200 \\ \hline Avion Express & 13.5 & 32 & 1450 \\ \hline Titan Airways & 12.2 & 30 & 1500 \\ \hline Air Belgium & 9.8 & 24 & 1650 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison table.  
Key priorities were identified to improve coordination between maintenance and logistics teams and integrate real-time data into maintenance schedules. Visualizations like bar charts illustrated the leading causes of AOG incidents, including inadequate spare parts, repair delays, and environmental impacts.  
Consequently, the authors identified adopting the most optimal model to improve forecasting accuracy, enhance spare parts availability, and streamline repair processes to reduce downtime and cost. Integrating real-time aircraft data into maintenance planning also allows for proactive interventions within the aviation industry. The chart (Figure 6) details the frequency of AOG events at SmartLynx and their key causes. Inadequate spare part availability stands out as the leading cause, with a frequency of over 30 incidents, followed by delays in repair operations and environmental impacts such as extreme temperatures and humidity. These findings highlight areas where the airline must prioritize improvements to reduce AOG downtime effectively.  
The high frequency of spare part shortages indicates forecasting and inventory management inefficiencies. This suggests that airlines should adopt advanced predictive tools, such as Holt-Winters models, to anticipate demand better and optimize spare part distribution. Similarly, delays in repair operations point to gaps in maintenance processes, possibly caused by scheduling inefficiencies or resource limitations. Addressing these delays through enhanced coordination between maintenance and supply chain teams would significantly improve response times and reduce AOG occurrences. The chart also emphasizes the role of Preventive Maintenance Compliance (PMC) in mitigating AOG events. Airlines that adhere to strict maintenance schedules typically experience fewer AOG incidents, as timely servicing prevents unexpected failures.
# Binary Mixes of Self-Compacting Concrete with Municipal Solid Waste Incinerator Bottom Ash
## 3 Results and Discussion
### Durability-Related Performance
#### 3.5.4 Chloride Ion Migration  
In Figure 10, the evolution of the chloride diffusion coefficient for 28 and 91 days is shown. As expected, there was a reduction of the chloride diffusion coefficient with increasing age. Increasing the content of MIBA naturally led to higher chloride ion migration coefficients; C20 BA, C30 BA and C40 BA specimens showed values  
Figure 9: Carbonation depth (**left**) of samples with MIBA (**right**).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Mix**} & \multirow{3}{*}{
\begin{tabular}{c} **Age** \\ **(Days)** \\ \end{tabular} } & \multicolumn{6}{c}{**Water Absorption (kg/m\({}^{2}\))**} & \multicolumn{6}{c}{**Capillary Absorption**} \\ \cline{3-11}  & & \(\mathbf{t_{3\,h}}\) & & \(\mathbf{t_{6\,h}}\) & & \(\mathbf{t_{24\,h}}\) & & \(\mathbf{t_{72\,h}}\) & **Coefficient (mm/min\({}^{0.5}\))** \\ \cline{3-11}  & & \(\mathbf{Abs_{m}}\) & **S.D.** & \(\mathbf{Abs_{m}}\) & **S.D.** & \(\mathbf{Abs_{m}}\) & **S.D.** & \(\mathbf{Abs_{m}}\) & **S.D.** & \(\mathbf{Coef_{abs}}\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{RC} & 28 & 1.43 & 0.28 & 1.83 & 0.33 & 2.57 & 0.46 & 2.96 & 0.42 & 0.095 & 0.98 \\  & 91 & 0.93 & 0.20 & 1.04 & 0.23 & 1.34 & 0.27 & 1.72 & 0.39 & 0.052 & 0.86 \\  & 28 & 1.48 & 0.11 & 1.87 & 0.10 & 2.75 & 0.24 & 3.45 & 0.26 & 0.095 & 0.96 \\  & 91 & 0.80 & 0.23 & 0.94 & 0.28 & 1.34 & 0.28 & 1.85 & 0.29 & 0.047 & 0.91 \\  & 28 & 1.83 & 0.17 & 2.40 & 0.24 & 3.79 & 0.44 & 4.89 & 0.55 & 0.125 & 0.99 \\  & 91 & 0.97 & 0.04 & 1.15 & 0.07 & 1.68 & 0.09 & 2.34 & 0.23 & 0.058 & 0.92 \\  & 28 & 1.67 & 0.33 & 2.23 & 0.39 & 3.73 & 0.60 & 5.26 & 0.85 & 0.116 & 0.99 \\  & 91 & 0.93 & 0.11 & 1.08 & 0.11 & 1.51 & 0.17 & 2.00 & 0.17 & 0.054 & 0.89 \\  & 28 & 1.84 & 0.03 & 2.45 & 0.09 & 3.96 & 0.20 & 5.21 & 0.23 & 0.126 & 0.99 \\  & 91 & 1.07 & 0.12 & 1.21 & 0.14 & 1.70 & 0.17 & 2.34 & 0.20 & 0.059 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Capillary water absorption results at 28 and 91 days.  
than those of the RC, whereas those of the C50 BA mix were almost four times higher. In the study of [PERSON] et al. [55], wherein MIBA was used as aggregate, higher chloride ion penetration had also been observed with increasing replacement level. Conventional fly ash presents pozzolanic activity in the presence of the products of hydration of cement, the result of which decreased the penetration of chloride ions [56]. MIBA, on the other hand, apart from presenting little pozzolanicity, which translates into little additional products of hydration, also led to a greatly porous microstructure, due to the oxidization of aluminium, that facilitated the diffusion of chloride ions.
# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 3 Digital Simulation Prediction and Analysis Experiment of Assembly Deviation
### Modeling and Analysis of Assembly-Deviation Prediction Based on Actual Measurement Data  
The assembly of the micro gas turbine core studied in this paper is representative of stacked assembly. Ideally, the axes of different parts are on the same reference axis after assembly, and the combined end face is a plane perpendicular to the cylindrical axis. However, the machined parts are flawed, and there may be various errors in form, thus resulting in a three-dimensional distortion caused to stacked assembly, as shown in Figure 7. To establish the assembly-deviation-prediction and -analysis model based on actual measurement data, it is necessary to extract the dimensional data, form tolerance, axis data and end face data of the machined parts. Then, these measurement data should be inputted into the assembly-deviation-prediction and -analysis model as established in Section 3.1 based on the design tolerance. Meanwhile, the replacement of the design value of the feature with the actual measured value is necessary. This is particularly crucial for the feature that extracts the actual data through point cloud scanning. In this case, the design tolerance must be substituted with the measurement uncertainty of the handheld scanning instrument, which amounts to 0.025 mm. In the validation experiments conducted in this paper, the axial data of the part are extracted through least squares fitting of the cylindrical surface point cloud data related to the assembly, while the three-dimensional point cloud data of the end face of the part are extracted through least squares fitting of the surface and three high points constituting the plane, respectively. Depending on the method of end face data extraction, two simulation models of assembly deviation are established on the basis of measurement data and then compared with the results of assembly to explore the extraction method that can produce the most comparable outcome to the actual results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Probing Method** & **Fitting Method** & **CAD Method** \\ \hline
1 & 170.584 & 170.602 & 170.637 \\
2 & 170.586 & 170.595 & 170.637 \\
3 & 170.568 & 170.603 & 170.637 \\
4 & 170.590 & 170.610 & 170.637 \\
5 & 170.538 & 170.583 & 170.637 \\ Average value & 170.573 & 170.599 & 170.637 \\ Difference & \(-\)0.012 & 0.006 & 0.044 \\ \hline \hline \end
## 4 Discussion of _U(I)_ Curves in the Case of an Oxidised Fixed Contact
### Case of High Peak Intensity
#### 4.2.1 Phenomena Observed during the First Period  
In Figure 10, we have plotted the contact voltage (in arbitrary units) as a function of the instantaneous value of the current intensity (also in a.u.) during the first current period. Several phases can be seen on this \(U(l)\) curve:  
* Phase 1 (in blue): An increase in the contact voltage associated with a high contact resistance. The value of the contact resistance is almost independent of the current value.
* Phase 2 (in red): Above a certain value of the current, the contact resistance (\(U(t)/I(t)\)) decreases and a nonlinear behaviour appears. This can be interpreted as an increase in the contact area due to local softening in connection with Joule-effect heating. A change in the nature of the material within the contact area is also possible. This phase starts during the increase in \(I(t)\) and continues during the decrease in \(I(t)\).
* Phase 3 (in green): Below a certain value of the current, the voltage varies almost linearly with the current. During this phase, the heating of the contact zone is no longer sufficient to continue to modify it.
* Phase 4 (in orange): This phase includes the second half-period of the first alternation (\(I(t)<0\)). The voltage evolutions are quite similar to those observed during the first three phases. When the current intensity increases in absolute value, heating is generated, which modifies the contact zone, always in the way of a decrease in contact resistance. When the current intensity increases again, (\(I(t)\) tends towards 0),  
Figure 9: Qualitative representation of the variation in the contact voltage with the current intensity for a current wave of “low” peak intensity. The figure is valid for the first and subsequent current periods.  
the Joule effect is insufficient to modify the size and nature of the contact zone, and the dependence of the voltage on the current intensity becomes linear again.
* During the first period of the current there is therefore an irreversible evolution of the contact, which tends to decrease the contact resistance.
# Task Assignment of UAV Swarm Based on Wolf Pack Algorithm
## 4 Experiments of Task Assignment for UAV Swarm Using PSO-GA-DWPA
### The Real-Time Analysis of the PSO-GA-DWPA  
In practical application, the situation of the battlefield is rapidly changing; as a result, the time of task assignment is limited. When the optimization problem scale is small, the global optimal solution or sub-optimal solution can be obtained within the specified time. However, if the dimension of the optimization problem is high, it is impossible to get the optimal solution in the specified time. In this case, an algorithm with a faster convergence speed is required in the early stage to obtain a relatively reasonable solution.  
As shown in Figure 9b, to get the convergent solution of the optimization problem with 8 dimensions, the WPA needs 5 s and the PSO-GA-DWPA needs 2 s, which meets the requirement of real time. While as shown in Figures 10b and 11b, the WPA needs 70 s and the PSO-GA-DWPA needs 30 s when the dimension of the problem is 30, even worse, the WPA needs 2500 s and the PSO-GA-DWPA needs 1800 s when the dimension of the problem goes up to 150. Hence in practice, a compromise must be made between the optimal solution and the convergence time.  
In the 150-dimensional scenario, it is required to make the decision of task assignment within a specified time. Fifty experiments with three methods were conducted to compare the adaptability of the three methods. The experimental results are shown in Figure 12. As can be seen from the results in the figure, the PSO-GA-DWPA obtains a better solution within the specified time. If we need to obtain a better but not optimal solution in finite time, the PSO-GA-DWPA is a better choice.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Guidance Law  
Guidance is given in two stages. The first comprises of a constant angle glide trajectory, while the second one is based on a modified proportional law.
## 2 Materials and Methods  
### Study Area  
The present study area encompasses the entire Churu district in the Indian state of Rajasthan. It covers a total area of 13,835 km\({}^{2}\) and stretches from 27\({}^{\circ}\)24\({}^{\prime}\) North to 29\({}^{\circ}\)00\({}^{\prime}\) North in latitude and from 73\({}^{\circ}\)40\({}^{\prime}\) East to 75\({}^{\circ}\)41\({}^{\prime}\) East in longitude [34]. Except for the northeast, the study area is surrounded by different districts of Rajasthan. In the west and north, it shares a district boundary with Bikaner and Hanumangarh, while in the southeast, south, and southwest, it is bordered by the Jhunjhunu, Sikar, and Nagaur districts, respectively (Figure 1). In the northeast, it shares the state boundary with Haryana [34]. In terms of its topography, this region may be divided into three distinct sections: the Taal area in the south, the Plains of Matiyars in the north, and the Desert area in the central portion [34]. Agro-climatically the region is categorized as an arid western plain zone of Rajasthan, India [35]. The climate of the arid western plain is characterized by a large seasonal temperature swing, with temperatures reaching as high as 49 degrees Celsius in the summer and as low as \(-1\) degrees Celsius in the winter and typical annual rainfall reaching as low as 350 millimeters [34].  
The overall methodology of the present study is illustrated in Figure 2.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 2 Hardware Accelerated Crypto-Algorithms
### Contributions  
The main contribution of this work is providing an embedded low power microcontroller to be used in sensor network applications with high security demands. The designed 16-bit microcontroller, compatible with the TI MSP430x family, is capable of power efficient execution of complex cryptographic algorithms with superior performance. The microcontroller implements hardware acceleration for well-established crypto algorithms (ECC, AES and SHA-1) as well as a communication accelerator improving connectivity in noisy environments. The hardware implementation of symmetric and asymmetric cryptography provides several orders of magnitude improvement in power efficiency over compatible software implementations on commonly used sensor node platforms based on the same type of microcontroller. The performance, when measured in execution time, is also improved dramatically in favour of the proposed hardware implementation. Additionally, the security level provided by a full scale hardware implementation of the cryptographic features overcomes the security level provided by a lightweight software implementation of crypto algorithms, which performance is fairly limited due to the lack of hardware resources in the node.
## 3 Materials and Methods
### TAC, TPC, and Protein Content Determinations  
Considering the paper of [PERSON] et al. [32], and our previous paper on spirulina samples [28], hydrophilic extraction was chosen to determine TPC and TAC of the present honey samples and performed as follows: samples were extracted overnight in the dark in a ratio 1:25 (\(w/v\)) with phosphate-buffered saline (PBS). Then, the samples were centrifuged at 5000\(\times\) g (ALC PK121R centrifuge; Bodanchimica s.r.l., Cagliari, Italy) for 10 min at 4 \({}^{\circ}\)C. The supernatants were collected and stored at \(-\)80 \({}^{\circ}\)C until further processing.  
The TPC was determined using the Folin-Ciocalteu standard method as modified by [PERSON] et al. [33] and adapted for 96-well plates and an automatic reader (Infinite 2000, Tecan, Salzburg, Austria). Briefly, 30 \(\upmu\)L of deionized water, was added to 10 \(\upmu\)L of ethanolic extract, 10 \(\upmu\)L of Folin-Ciocalteau reagent, and 200 \(\upmu\)L of 30% Na\({}_{2}\)CO\({}_{3}\). The absorbance of the mixture was measured at 725 nm on a plate reader (Infinite F200, TECAN, Mannedorf, Switzerland). A gallic acid standard curve was prepared and the results were expressed as mg of gallic acid equivalents (GAE)/g of edible portion (EP) of the sample.  
The total antioxidant capacity was assessed by two different methods: through ferric reducing antioxidant power (FRAP), and 2,2\({}^{\prime}\)-azino-bis (3-ethyl-benzothazoline-6-sulfonic acid) (ABTS\({}^{\star+}\)) radical scavenging activity assays as described as follows.  
FRAP assay was performed using the method described by [PERSON] [34], which was adapted for 96-well plates and an automatic reader (Infinite 2000, Tecan, Salzburg, Austria). Briefly, 160 \(\upmu\)L of FRAP assay solution (consisting of 20 mM ferric chloride solution, 10 mM TPTZ solution, and 0.3 M acetate buffer at pH 3.6) was prepared daily, mixed with 10 \(\upmu\)L of the sample, standard, or blank, and dispensed into each well of a 96-well plate. The absorbance was measured at 595 nm at 37 \({}^{\circ}\)C after 30 min of incubation. The results were expressed as \(\upmu\)mol Fe\({}^{2+}\) equivalents/g EP.  
The ABTS\({}^{\star+}\) radical scavenging activity was evaluated by the OxiSelectTM Trolox Equivalent Antioxidant Capacity (TEAC) Assay Kit (ABTS) (Cell Biolabs Inc., San Diego, CA, USA) following the manufacturer's instructions. The absorbance was recorded at 405 nm in an automatic reader (Infinite 2000, Tecan, Salzburg, Austria). A standard curve for Trolox was prepared and the antioxidant capacity was expressed as \(\upmu\)mol of Trolox equivalents (TE)/g EP.  
Crude protein content (conversion factor, 6.25) was estimated using the Kjeldahl standard method AOAC 2001.11 [35].
# Structural Design, Simulation and Experiment of Quadruped Robot
## 3 Forward and Inverse Kinematic Solving
### Establishment of the DH Coordinate System  
According to the establishment of the DH coordinate system, the coordinate system \(O_{b}\) is defined as the global coordinate system, which is fixed to the ground. The body coordinate system \(O_{b}\) is located at the geometric center of the torso section of the robot, where \(x_{b}\) is the forward direction of the robot and \(z_{b}\) is the inverse direction of gravitational acceleration. The coordinate systems \(O_{k1}\), \(O_{k2}\), \(O_{k3}\) and \(O_{k4}\) (\(k=1\), \(2\), \(3\), \(4\), respectively) are located at the three rotational joints of each of the four legs and at the ends of the feet. To simplify subsequent calculations, the coordinate system orientations are defined identically for all four legs, with only the positive and negative signs of individual parameters differing. The specific coordinate system orientations are shown in Figure 10.  
In the DH method, each linkage can be described by four linkage parameters. When the linkage coordinate system \(\{i\}\) is shown in Figure 4, each linkage parameter is found by the following definition:  
\(a_{i}=\) the distance from \(Z_{i}\) to \(Z_{i+1}\) along the \(X_{i}\) axis;  
\(\alpha_{i}=\) the angle of rotation around the \(X_{i}\) axis, from \(Z_{i}\) to \(Z_{i+1}\);  
\(d_{i}=\) the distance along the \(Z_{i}\) axis, from \(X_{i-1}\) to \(X_{i}\);  
\(\theta_{i}=\) the angle of rotation around the \(Z_{i}\) axis, from \(X_{i-1}\) to \(X_{i}\).  
Therefore, using the right front leg of the quadruped robot as an example, the parameters of each linkage are derived as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Linkage**} & \multicolumn{2}{c}{**Linkage Length \(a_{1-1}\)**} & \multirow{2}{*}{**Linkage Torsion Angle \(\mathbf{\kappa}_{i-1}\)**} & \multirow{2}{*}{**Linkage Offset \(\mathbf{d}_{i}\)**} & \multicolumn{2}{c}{**Joint Angle \(\mathbf{\theta}_{i}\)**} \\ \cline{2-2} \cline{5-7}  & \(\mathbf{a}_{1-1}\) & & **Value** & & & \(\mathbf{\theta}_{i}\) & **Value** \\ \hline
1 & 0 & 0 & 0 & 0 & \(\theta_{1}\) & 0 \\ \hline
2 & 0 & 0 & 0 & \(a_{1}\) & \(\theta_{2}\) & \(90^{\circ}\) \\ \hline
3 & \(a_{2}\) & \(110\) & \(-90^{\circ}\) & 0 & \(\theta_{3}\) & \(-90^{\circ}\) \\ \hline
4 & \(a_{3}\) & \(113\) & 0 & 0 & 0 & 0 & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Single-leg joint parameters for quadruped robots.  
Figure 10: Diagram of the DH coordinate system.
## 5 Results and Discussion
### Residual Energy  
The technology's advanced CH selection method leads to better performance and effectively achieves higher average residual energy. This leads to minimized premature node failures and an extended network lifetime. The EPOA-CHS algorithm demonstrates a higher average residual energy, indicating enhanced network survivability. Additionally, the smaller energy deviation observed in EPOA-CHS signifies its ability to effectively reduce energy imbalances, preventing premature failures caused by excessive energy consumption.  
As a result, the energy consumption among nodes using EPOA-CHS is more evenly distributed, leading to a more balanced network operation. Combined with the additional energy savings achieved by the algorithm, this results in an increased network lifetime compared to the other four algorithms. Figure 2a-d illustrate the network's total residual energy. It is obvious that EPOA-CHS exhibits higher total residual energy, indicating an extended network lifespan.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Network Field & (100,100) \\ Number of nodes & 100 \\ \(E_{0}\) & 0.5 J \\ Packet Size & 4000 Bits \\ \(E_{dec}\) & 50 nJ/bit \\ \(E_{fs}\) & 10 nJ/bit/m\({}^{2}\) \\ \(E_{amp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \(E_{DA}\) & 5 nJ/bit/signal \\ \(d_{0}\) & 70 m \\ \(P_{opt}\) & 0.1 \\ Fraction of the & m = 0, m = 0.1, \\ advanced nodes & m = 0.2, m = 0.3 \\ Times more energy & \(\alpha=0,\alpha=2\), \\ than normal nodes & \(\alpha=3,\alpha=1.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation correlation parameter.
# Hyperspectral Imaging Techniques for the Study, Conservation and Management of Rock Art
## 2 Materials and Methods
### Pigment Analysis
#### 2.5.1 Minimum Noise Fraction Transformation  
The modified minimum noise fraction (MNF) transformation was implemented as described by [PERSON] [22]. It is a technique that transforms the data hypercube into a set of canals with increasing noise levels by means of noise whitening and the subsequent application of main components. The MNF transformation is also used to eliminate noise from data by performing a direct transformation, determining which bands contain coherent images (examining images and auto-values), and then executing an inverse transformation using a spectral subset that includes only good bands, or smoothing the noisier bands before executing the inverse.
## Materials and Methods
### Module Description
#### 2.3.6 Tree Masking  
At the spatial resolution at which we are investigating images, trees appear as individual objects and can thus be identified and spatially isolated. This allows us to eliminate their spectral contribution from that of the farm fields and to determine pure soil or crop spectra. The tree mask is taken from the image coregistration step, as described in Section 2.3.4. We apply a lowered NDVI threshold to eliminate false negatives. A tree mask based on the blobs detected is built on a master image and then adapted for other images, using the image's nadir angle to account for sensor-target geometry.  
Our method provides a detailed tree crown in the case of DG data. However, for RapidEye data, due to coarser resolution, the degree of tree crown detection is lower. This sets a case for using a single high resolution image in the season combined with a time series of lower resolution images. Our method works well for isolated tree crowns but not sufficiently well for tree rows or (small) forest patches. This presented problems in the Bangladesh study area, where isolated trees are scarce and trees commonly align the field edges.