esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
#### Paper Contributions  
This study focused on the following requirements regarding dataset and methodology, which were not handled by the previous studies in detail.  
* A database with a hierarchical level of classes which makes it suitable for all DLRI stages,
* A database that consists of a large variety of conditions in terms of geography, weather, and spatial arrangement,
* A method that follows all DLRI stages to present a proper remote sensing solution,* A process that eliminates negative examples (images without ship), which are the largest percentage of images used in the majority of applications, before getting deeper stages,
* A method that forms a suitable base to adopt various deep learning networks and enables end-to-end and repeatable evaluation metrics which exemplify real scenarios.  
This research introduces a unique and rich ship dataset named Very High-Resolution Ships (VHRShips). It proposes a unique deep learning-based Hierarchical Design (HieD) approach for multiscale and multipurpose ship detection and mapping purposes. The VHRShips dataset is specifically generated using Google Earth images obtained for different geographical regions and under various atmospheric conditions. Although we selected some images from offshore locations, most of the images in VHRShips contain complex inshorte parts. Additionally, the dataset has an extensive range of ship types, including various maritime vessels and navy ship types. VHRShips enables the recognition of sixteen parent classes and a customized identification of navy ship classes for the specified eight categories matching the requirements of various maritime applications on different scales.  
The proposed HieD is a multi-stage approach that follows a hierarchy of different stages. The end of each stage is tied to the beginning of the next stage, and all stages have a self-training and self-optimization process. This way, we get the flexibility to use the independent methods for each stage of DLRI, which avoids the loss function optimization for multi-stages. We also compared our proposed HieD approach with the commonly used YOLOv4 model to evaluate the performance of the hierarchical methodology. This research aims to contribute ship detection sub-domain of the maritime re-search field in these aspects: (1) providing a benchmark dataset consisting of large ge-odata, (2) introducing a novel deep learning approach to boost processing and analy-sis of object detection, and (3) automated extraction of 2D geo-information from large satellite image-based geospatial dataset.
## 2 Problem Formulation
### Objective Function  
The main objective of the ED problem is to minimize the total generation costs of a power system by determining the power output of generators that satisfies various constraints, such as the load demands of PD are met within an appropriate period (normally in one hour), the active balance constraint, and the limitation of the generator. The simplified cost function of ED can be written as:  
\[\text{Minimize }F_{cost}=\sum_{i=1}^{g}F_{i}(P_{i}) \tag{1}\]  
\[F_{i}(P_{i})=a_{i}+b_{i}P_{i}+c_{i}P_{i}^{2} \tag{2}\]  
where \(F_{cost}\) is the total generation cost, \(F_{i}\) is the cost function of the \(i\)th generating unit in terms of \(P_{i}\), which is the power output of \(i\)th generating unit. \(a_{i}\), \(b_{i}\) and \(c_{i}\) are the fuel cost coefficients of \(i\)th generating unit. Finally, \(g\) is the total number of generating units.  
Practically, the generators with multiple valve steam turbines exhibit a wide variation with respect to the fuel cost function. This is because the valve-point creates a ripple, whereby the cost function cannot be represented by a quadratic polynomial function as in (2), since it contains nonlinear characteristics. Therefore, the cost function considered in this paper is a combination of quadratic functions and sinusoidal functions, which are represented as follows:  
\[\text{Minimize }F_{cost}=\sum_{i=1}^{g}F_{i}(P_{i})=\sum_{i=1}^{g}\left(a_{i} +b_{i}P_{i}+c_{i}P_{i}^{2}+|e_{i}\sin(f_{i}\times(P_{i,min}-P_{i}))|\right) \tag{3}\]  
where \(e_{i}\) and \(f_{i}\) are the constants of the \(i\)th generating unit reflecting the valve-point effect [30].  
Additionally, thermal dispatching units are realistically supplied from different fuel sources with several types of fuel. The operation of each dispatching unit should be mathematically represented under multiple piecewise functions to emphasize the effect of multiple fuel sources. Both the valve-point effect and multiple fuel options should be combined to acquire a practical and reliable ELD. Finally, the cost function of the \(i\)th generating unit can be realistically expressed as below:  
\[F_{i}(P_{i})=\begin{cases}a_{i1}+b_{i1}P_{i1}+c_{i1}P_{i2}^{2}+|e_{i1}\sin(f_{ i1}\times(P_{i1,min}-P_{i1}))|&ifP_{i1,min}\leq P_{i}\leq P_{i1}\\ a_{i2}+b_{i2}P_{i2}+c_{i2}P_{i2}^{2}+|e_{i2}\sin(f_{i2}\times(P_{i2,min}-P_{i2} ))|&ifP_{i2,min}\leq P_{i}\leq P_{i2}\\ \cdots\\ a_{ik}+b_{ik}P_{ik}+c_{ik}P_{ik}^{2}+\left|e_{ik}\sin(f_{ik}\times(P_{ik,min}- P_{ik}))\right|&ifP_{ik,min}\leq P_{i}\leq P_{ik}\end{cases} \tag{4}\]
## 2 Materials and Methods
### Measurement Device  
Figure 1A illustrates an athlete wearing our custom measurement system, comprising an RTK GNSS receiver, a head-mounted GNSS antenna, and multiple IMUs. A GNSS receiver (AT-H-35 rev.B, AOBA Technologia LLC, Sendai, Japan) equipped with a Mosaic-X5 module (Septentirio, Leuven, Belgium) provided RTK positioning at up to 100 Hz (Figure 1B). The Mosaic-X5 supports six satellite constellations (GPS, GLONASS, BeiDou, Galileo, QZSS, and NavIC) and operates on multiple bands (L1C/A, L1C, L2, L5, and L6). The housing of the AT-H-35 rev.B has a volume of 52.7 cm\({}^{3}\), weighs 52 g, and contains a 640-mAh lithium-ion polymer rechargeable battery, enabling approximately 60 min of continuous operation at 100-Hz sampling. The RTK positioning accuracy is 0.6 cm + 0.5 ppm horizontally and 1 cm + 1 ppm vertically, as specified by the manufacturer [35]. The receiver was carried in a waist pack and connected to a triple-band helical antenna (AS-ANT3B-HEL-L1256-SMA-00, ArduSimple, Lleida, Spain; 40 mm diameter \(\times\) 82.8 mm length, 35 g) [36] mounted on rugby headgear for satellite signal reception (Figure 1C). The connection was made using an SMA cable, which was secured to the runner's back with masking tape to prevent interference. All participants wore identical headgear and sensor set-ups for consistency. A stationary reference station near the home straight provided real-time corrections to enhance RTK accuracy [18].  
IMUs (IMS-SD, model IMSSD-H-A, Tech Gihan Co., Ltd., Kyoto, Japan; 54 mm \(\times\) 42 mm \(\times\) 14 mm) were used for reference _SF_ validation and synchronization (Figure 1D). Each unit recorded the tri-axial acceleration (\(\pm\)30 G) and angular velocity (\(\pm\)4000\({}^{\circ}\) s\({}^{-1}\)) at 1000 Hz, with data stored internally on microSD cards for post-run analysis. Following the methodology of previous studies [13], each IMU was attached above both ankles, and an additional IMU was mounted near the GNSS antenna on the head to facilitate synchronization.  
Four Basler acA1300 cameras (Basler AG, Ahrensburg, Germany), each with an OnSemi PYTHON 1300 sensor (1280 \(\times\) 1024 pixels), recorded 3D video at 200 fps using StreamPix 8 (NorPix, Montreal, QC, Canada). A GoPro Hero 9 Black (GoPro, Inc., San Mateo, CA, USA) at the start and finish line (120 fps) measured the total sprint time, while another bicycle-mounted GoPro Hero 9 Black followed the runner to verify GNSS-derived step counts.
## 2 Glycerol-Based Biomanufacturing Using _Yarrowia_
### Bioconversion of Glycerol to Lipids
lipolytica_, unlike some other oleaginous microorganisms, does not possess cytosolic MAE [46]; instead, the PPP generates the required cofactor NADPH by an up-regulation of glucose-6-phosphate dehydrogenase (G6 PD) and 6-phosphogluconolactone (6 PGL) [47]. Apparently, lipid biosynthesis is a complex phenomenon initiated by the activity of three enzymes, i.e., AMPD, ACL, and ACC, which direct carbon flux from the central carbon metabolism towards the formation of the FA precursors, acetyl-CoA and malonyl-CoA. However, there are many more enzymes involved, which act in many different cellular compartments and undergo multiple levels of transcriptional, post-translational, and feedback inhibition [47]. In addition, lipogenesis requires energy (ATP), reducing equivalents (NADPH), and glycerol head groups. It is reported that acetyl-CoA, ATP, and NADPH are required at an approximate 1:1:2 ratio [48]. Apart from the aforementioned critical biochemical parameters, the biotechnological production of lipids by oleaginous microorganisms has to overcome many challenges. These are:  
1. The allosteric inhibition of the lipid biosynthetic pathway by saturated FA [49];
2. The maximum lipid accumulation capacity;
3. The level of dissolved oxygen which has to sustain specific growth rate and cell density at a stable productive state but also prevent the downregulation of lipid biosynthesis [44];
4. The operational factors which impact both lipid content and FA composition, such as type of carbon source, medium composition, pH, temperature, agitation, and aeration [23; 50; 51]. In general, the ratio of carbon to nitrogen (C/N), pH, and oxygen saturation in the medium appear to be the key factors which influence lipid accumulation [14].
## 5 Applications and Discussions
### Waste Heat Utilizing System Modeling
#### 5.3.3 Comparison and Discussion of System Performances  
The parameters of the identified model and its performance index are shown in Table 7. It can be known from the modeling curve and the performance index that the agent based online multi-model method can model the nonlinear waste heat utilizing process efficiently. In order to test the performance of the mentioned approach, an recursive least square (RLS) method simulation result is shown in Figure 6. It is clear that all the performances of the ABFT-SMM system are better (have the smaller criteria values) than those of the RLS system. These superiorities benefited from the multi-model based structure and the parameter adjust characteristics of the agents. The application result shows an accurate and stable performance, it provides a good foundation for detection and control of the system.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{4}}\) & \(\mathbf{a_{5}}\) & \(\mathbf{a_{6}}\) & **Centers** & **RMSE** \\ \hline \(-0.4641\) & \(-0.2905\) & \(-0.2148\) & \(5.299\times 10^{-5}\) & \(3.954\times 10^{-5}\) & \(2.440\times 10^{-5}\) & 8 & \(1.783\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 7: Parameters and the indexes of the identification model.  
Figure 5: Comparison of the ABFT-SMM outputs with the actual system outputs. (**a**) is the tracking curve of the identification model; (**b**) is the tracking error.  
Figure 6: Comparison of the RLS outputs with the actual system outputs.
# Study of the Direct Metal Deposition of AA2024 by ElectroSpark for Coating and Reparation Scopes
## 3 Results and Discussion
### Mechanical Characterization  
The effect of mixed refined microstructure on the hardness values in the deposit cross-section was evaluated by indentations performed both within some layers and at the interfaces between the layers (Figure 10a-e). The indentations on the layer-layer interfaces, as well as on the inner of different layers exhibited different values of micro-hardness as a function of the solidification microstructure. The layer interfaces in Figure 10a,b exhibit values of micro-hardness respectively of 156.4 HV and 132.5 HV. Figure 10c-e shows the value of micro-hardness as equal to 145, 140, 132 HV, respectively. In some cases, the micro-hardness values can also be influenced by micro defects present within the deposit. So, the hardness within the deposit is not constant but strongly variable. Nevertheless, the average hardness values evaluate along lines of indentation parallel to the substrate/deposit interface do not significantly change both with the spark pulse-energy (_Es_) and/or with frequency at fixed _Es_ (Figure 11a-c). Three different average micro-hardness values can be read for each distance as a function of the increasing frequency, except for the deposit fabricated at the lowest _Es_ and frequency (Figure 11a), where just one line of indentation has been produced due to the reduced thickness of the deposit. Our result, therefore, indicated that the three set values of _Es_ and frequency do not significantly affect the mechanical behavior of the deposits. Figure 12 shows the average deposit hardness as a function of _Es_ for F1, F2 and F3. In close accordance with the above results (Figure 11), the average deposits of micro-hardness does not significantly change, indicating that all the ESD parameters sets produce similar microstructures. The average micro-hardness values of the analyzed deposits are in fact distributed in a small range (100-120 HV). Of course, the average hardness values are characterized by high standard deviations due to the great variability of the microstructure. Moreover, the micro-hardness of the substrate close to interface do not exhibit any change, which confirms the absence of a heat affected zone (HAZ).  
Figure 10: Micro-hardness measurement at a layer-layer interface (**a,b**); inside some layers with different microstructure (**c–e**).
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 4 In Situ Temperature-Dependent Structural Characterization of Amorphous Metals  
### Possibilities of In Situ Structural Analysis  
Although temperature-dependent structural analysis using X-ray and spectroscopic methods may not be conventional, it is valuable to explore the insights these techniques can provide. This exploration can help better understand the aspects and challenges involved in the development of amorphous metals and bulk metallic glasses [78]. These materials must  
Figure 4: (**a**) The change in total stress as a function of heating and (**b**) varying moduli as a function of annealing temperature for deposited tungsten oxide coatings. The blue line, \(G\)/K represents a shear to bulk modulus ratio, which is representative of the ductility of the material. It can be seen that the stiffness increases during crystallization and grain growth, followed by an increase in density. (reprinted with permission via CC Attribution 4.0 License; multiple Figures were merged for a better overview of the authors’ work) [77].  
possess an amorphous structure, characterized by short-range order within the system. Therefore, diffraction techniques such as electron and neutron diffraction, applied to both bulk and thin film configurations, are typically used initially to confirm the absence of long-range order before any scientific investigation begins [79]. [PERSON] et al. combined nanocalorimetry with synchrotron-grade X-ray diffraction to analyze the supercooled quenched metallic glasses, where they managed to vaguely correlate crystallization kinetics with glass-forming ability [80]. It is also possible to study application properties via X-ray diffraction, given that a good enough source is provided. [PERSON] et al. studied the in situ formation of nanocrystalline Fe metallic glassy alloys, and they tracked the shift in magnetic behavior with respect to the temperature at a constant heating rate. They also derived pair distribution functions (PDFs) to describe the crystallization kinetics [81]. Moreover, local atomic structure can be investigated by spectroscopic techniques such as FTIR and Mossbauer spectroscopy [82]. However, this short-range order can nowadays be determined accurately enough to show differences in structure during even subtle structural changes, such as relaxation and rejuvenation processes.
## 4 Discussion and Conclusions
### Influence of Fault Deformation  
When pressurized water is injected directly into the fault, the diffusion of water is affected not only by the rock permeability but also by the contact state and deformation of the fault plane. The actual contact area of the fault plane is controlled by the contact area of asperities, which is related to the mineral composition of the rock [43]. The pressurized water can only enter the non-contact area of asperities.  
The granite in this experiment is composed of high-strength minerals such as quartz and feldspar, having more asperities and a large actual contact area under particular normal stress. With this, the volume of the water entering the fault is relatively small, and the actual pore pressure may be much less than the injection pressure. The sandstone is composed of quartz and feldspar, having an asperity distribution similar to that of granite. However, the high permeability facilitates fluid diffusion, and the actual pore pressure is close to the injection pressure. Limestone is composed of calcite, having a few asperities and small actual contact areas under the same normal stress. Thus, pressurized water can diffuse into most parts of the fault plane. Also, because of the plastic blunting effect of calcite [44], the pressurized water is easily sealed on the fault plane to form local high pore pressure zones, making the actual pore pressure much higher than the injection pressure.  
Because 1 MPa is far below the normal stress, larger than 50 MPa, the difference in actual pore pressure caused by injection was very small, resulting in a slight change in fault slip. In contrast, the 15 MPa pressurized water injection significantly reduced the effective normal stress of the fault and induced fault sliding. Moreover, three faults showed apparent differences in the permeability, fault contact state, and deformation property, causing apparent differences in the actual pore pressure, so noticeable differences were observed in several parameters, such as stress drop, sliding duration, and displacement.  
The experimental results suggest that it is difficult for low-pressure injection to affect the fault stress significantly, but the higher fluid pressure may reduce the inherent strength of the fault, thereby inducing fault sliding. When fault reactivation is induced, rock permeability and fault deformation properties control the fault sliding rate, duration, and stress drop. High permeability and strong, brittle rock are conducive to the rapid sliding of a fault.
# A Complex Network Important Node Identification Based on the KPDN Method
## 5 Results and Analysis
### Result of Time and Space Complexity of Different Methods  
We have organized the time complexity and space complexity of the various methods mentioned in this article. For detailed proof, please refer to Supplementary File S2. The results are shown in Table 3:  
At the same time, it can be seen that although our time complexity and space complexity are not the smallest among all methods, they are not excessively large like methods such as BC, HC, CI, and CC. As we have stated, the complexity of the method proposed in this paper is not particularly high. Therefore, from the perspective of complexity analysis, the method proposed in this paper can be attempted on large-scale networks during actual use.
## 3 Methodology
### Evaluation  
Since aerial images during the LiDAR data acquisition were not available and the study area is a coastal area, the elevation was used to label the LiDAR points as references. In coastal areas, the water part usually has the largest area with the lowest elevations in the scene. Therefore, an elevation histogram was first constructed with a bin size of 1 m, where the entire range of elevations was divided into a series of equal intervals (i.e., bins), as shown in Figure 4a-c. Then, the highest peak was detected and considered the average water surface elevation. The elevation of the first peak represents the average water level for C1 or C2. The second peak represents the average water level for C3, whereas the first peak is for returns from the benthic layer. A threshold of 0.5 m (half bin size) was added to the average water surface elevation, and all points with elevation less than or equal to that elevation (i.e., 39.5 m) were labeled as water points (Figure 4d-f).  
To evaluate the success of using the different features and the region growing algorithm in labeling water points, the confusion matrix was constructed as shown in Table 3. The completeness, correctness, and overall accuracy were then calculated for the evaluation of the results between the extracted water points from different LiDAR point features or seeded region growing algorithm and the labeled water points based on elevation.  
Figure 4: Elevation histogram of **(a)** C1; **(b)** C2; and **(c)** C3; and labeled LiDAR points based on elevation using **(d)** C1; **(e)** C2; and **(f)** C3.  
where  
_TP_: the point was labeled as water in both classification process and reference data  
_TN_: the point was labeled as land in both classification process and reference data  
_FP_: the point was labeled as water in classification process and as land in reference data  
_FN_: the point was labeled as land in classification process and as water in reference data.  
The completeness (or recall) indicates how complete were the extracted water points, while the correctness (or precision) indicates how correct were the extracted water points. The overall accuracy indicates how successful was the classification process. The three quality measures are defined by [49] as follows:  
\[Completeness\ (\%)\ =\ \frac{TP}{TP+FN}\ \times\ 100 \tag{13}\]  
\[Correctness\ (\%)\ =\ \frac{TP}{TP+FP}\ \times\ 100 \tag{14}\]  
\[Overall\ Accuracy\ (\%)\ =\ \frac{TP+TN}{TP+FP+TN+FN}\ \times\ 100 \tag{15}\]  
Since the land/water interface is very important to monitor, the land/water boundary was delineated and compared with the digitized boundary from the reference points. Ten transects were then drawn perpendicular to the land/water boundary. The 2D distances between the reference boundary and the delineated boundary were measured and the root mean square error (RMSE) was calculated.