esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Literature Review
Existing research has provided valuable methodologies for optimizing mission planning for a limited number of individual satellites, particularly in the context of Earth imaging and communication objectives. However, there is a relative scarcity of research focused on satellite constellations, aligning with the recent trend in satellite development. Additionally, the current body of work often relies on widely used meta-heuristic algorithms [21; 22; 23; 24; 25; 26] for mission planning optimization. These algorithms, while effective in certain scenarios, tend to fall into local optima and lack consistency in producing identical results in each iteration. Furthermore, the emerging DRL-based algorithms [27; 28; 29; 30; 31], though beneficial for their real-time computation capabilities, encounter inherent limitations in untrained areas, struggling to rectify inappropriate solutions, which poses a challenge for immediate application in required high-robustness ground station mission planning subsystems. This highlights a significant gap in the existing research, particularly in addressing mission planning scenarios involving numerous targets densely distributed within specific regional areas. Recognizing these limitations, our research offers three contributions that aim to bridge these gaps. First, it broadens the scope of mission planning optimization to encompass satellite clusters, with a specific emphasis on South Korea's emerging small SAR satellite constellation that has been relatively underexplored in the realm of satellite mission planning research. Second, we employ a Modified Dynamic Programming (MDP) algorithm, developed in-house [41], that surpasses traditional methods in adaptability to time-varying conditions and ensures the optimal solutions while effectively managing dynamic constraints. Lastly, our work uniquely focuses on the optimization of multi-imaging mission scheduling for high-density target regions with varying levels of significance and urgency, a challenging scenario in satellite mission planning. In summary, our research offers both a theoretical framework and practical applications for optimizing complex SAR satellite constellation operations, delivering actionable insights and robust solutions. The remainder of this paper is organized as follows: Section 2 provides an overarching framework of the imaging mission, elaborating on the mathematical models that encapsulate the problem under study. In Section 3, we develop into the optimization algorithms,with a particular focus on the MDP algorithm developed by our team. For comparative analysis, this section will also introduce the widely utilized greedy algorithm as the heuristic approach employed in this paper. Section 4 delineates the numerical simulation scenarios and presents the resultant findings. Lastly, Section 5 offers concluding remarks and outlines potential avenues for future research.
# A Sea Horse Optimization-Based Approach for PEM Fuel Cell Model Parameter Estimation
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature/Algorithm** & **SHO (Sea Horse Optimization)** & **PSO (Particle Swarm Optimization)** & **GWO (Grey Wolf Optimizer) [13; 42]** & **ABC (Artificial Bee Colony) [11; 43; 44]** \\ \hline
**Biological** & Seahorse swarm behavior & Bird/fish swarm movement & Hunting and social leadership structure of gray wolves & Task-distributed search behavior within a bee colony \\ \hline
**Exploration** & Directional and extensive search with extensive search with Lévy flight and Brownian motion & Extensive search with speed and position updates & Random guidance with alpha, beta, and delta leadership & Random search for new resources with scout bees \\ \hline
**Exploitation** & Local scanning with Brownian motion, directional position optimization & Orientation to the best position & Position updating based on \(\alpha\), \(\beta\), \(\delta\) solves & Concentration around the successful resource \\ \hline
**Local Minima** & High (with Lévy flight and Brownian movement) & Medium (risk of early convergence) & Medium (fixed leader structure can be limiting) & Low–medium (high randomness may cause local stagnation) \\ \hline
**Mathematical** & Position updates via motion, distance motion, distance model with fixed parameters & Speed and position positions and nonlinear distance & Stochastic position updates \\ \hline
**Exploration**, **Exploration and Search Behavior** & Balanced (Lévy and Bownian movement) & Balance depends on user parameters & Exploration/exploitation rate generally fixed & Exploration is dominant, exploitation is limited \\
**Balance** & Medium–high (movement type effective) & Low–medium (parameters fixed) & Low (static leadership hierarchy) & Low (roles unchanged) \\ \hline
**Convergence** & High (controlled convergence) & Medium (rapid but may be unstable) & Medium–high (may converge early) & Low (fluctuating solution quality) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Feature and algorithm comparison for SHO, PSO, GWO, and ABC.
## 3 Experimental Results  
### Failure Modes  
The failure modes that generally occur in push-out tests can be broadly classified into two modes [10; 14; 18; 80]. The first mode of failure is known as channel fracture with cracks in concrete blocks, and the second one is referred to as concrete crushing-splitting.  
In this study, all the observed modes of failure were of the channel fracture type. Two reasons can be mentioned for this mode of failure occurring in all the specimens. The first reason can be related to the high strength of concrete specimens. In high-strength concrete (HSC), stronger molecular structures form between particles, and this in turn reduces the possibility of crack propagation. The second reason can refer to the reinforcement that was applied in concrete blocks. The reinforcement in concrete prevents cracks from propagation, and it closes the growing cracks. Hence, cracks cannot reach a critical length, which subsequently results in concrete crushing-splitting. On the other hand, microcracks, which generally remain after the welding process of channel connectors, are potential locations for crack propagation. Therefore, the mode of channel fracture is more likely to occur in concrete with higher strength or concretes with proper reinforcements.  
Figure 3 shows some of the important observations at the end of the test. Figure 3a,b illustrate channel fracture in the HSC specimens and normal strength concrete, respectively. Figure 3c shows a  
Figure 2: Universal testing machine [79].  
crack in the concrete block of the specimen N-75-50-5-7.5. Although channel fracture occurred in this specimen too, it was seen that some cracks had occurred in the concrete block. This observation also confirmed the mentioned reasons for the mode of failure, as the crack had occurred in the concrete specimen with normal strength, and reinforcement had closed the crack. Figure 3d also presents the concrete block after channel fracture.
### System Planning and Configuration
#### 2.2.2 System Configuration Process and Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Characteristic** & **Applicable Scenario** \\ \hline Single-ended structure & Simple structure, easy to control and protect & DC load concentration areas, such as residential areas, EV charging stations, and other scenarios. \\ Double-ended structure & Moderate structural complexity, relatively simple control, and protection & Places with large capacity and high requirements for power supply reliability, such as industrial parks, and other power supply scenarios. \\ Ring circular structure & Complex structure, complex system flow, difficult control, and protection & Multiple distributed power sources are connected and high-power supply reliability is required. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics and applicable scenarios of typical topological network structures. Figure 5: Typical topology network structures of standard PEDF systems: (**a**) single-ended topology; (**b**) double-ended topology; (**c**) ring topology. system form and module type for the photovoltaic system are identified [9; 31; 34]. Table 2 provides relevant data, showing the percentages of different photovoltaic installation locations, system forms, and component types in PEDF buildings. 3. Guided Configuration. Figure 8 outlines the photovoltaic configuration process, which adapts to different configuration goals and optimizes system performance based on solar radiation data and module parameters [8]. Figure 6: Distribution characteristics of the application type of DC electrical equipment in buildings [8]. Figure 7: Typical daily electricity load power curves for different types of buildings. * Energy storage configuration The energy storage configuration process of the PEDF system is as follows: 1. Select Battery Types. Choose batteries based on their technical performance and economic efficiency for various application scenarios, as detailed in Table 3[8,9], which indicates the distribution of energy storage types in PEDF buildings. 2. Determine rated power. Establish the rated power of the energy storage while considering specific constraints[34,35]. 3. Optimize configuration. Optimize the setup for effective integration with photovoltaic systems. * Critical interactive device configuration The configuration process of critical interactive devices in the PEDF system includes:
# WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds
## 2 Methods
### Close-Range Photogrammetric Methodology  
The photogrammetric pipeline is a process oriented to obtain a dense and three-dimensional point cloud of a weld's surface through the application of different algorithms and phases, which will be detailed in the following subsections.  
Extraction and Matching of Features  
Since image acquisitions were not only limited to vertical but also an oblique point of view, the processing of the images must incorporate last advances and algorithms in computer vision for images matching [23] and images orientation [24; 25]. Before extracting and matching features, images must be pre-processed in order to improve the radiometric contents and ease the successive feature extraction. Image pre-processing has been reported in many papers as a fundamental step, particularly in those cases where the texture quality is non-favourable [26; 27; 28]. Different pre-processing algorithms are available in WELDMAP, including, among others: ACEBSF [29], POHE [30], RSWHE [31], Wallis [32], etc. This step is facultative but highly suggested in order to achieve better results in the feature extraction and matching step.  
WELDMAP implements diverse sets of detectors (e.g., SIFT [33], SURF [34], MSER [35], MSD [36], ORB [37], AKAZE [38], BRISK [39], etc.) and descriptors (e.g., BOOST [40], BRIEF [41], DAISY [42], FREAK [43], etc.) to let the user run and test different combinations and assess the results in different conditions. Any kind of combination is allowed in the software. For each detector-descriptor, several advance parameters can be defined by the user.  
Finally, the features extracted are matched robustly using geometric constraints in order to guarantee that they match the same point. To this end, WELDMAP uses projective geometry and more precisely homography, based on epipolar geometry, to refine the matching. Considering this, cameras could be uncalibrated and the possible presence of outliers in the matching, fundamental matrix, and RANSAC [44] are used to verify the matchings from a geometrical point of view.  
Figure 1: WELDMAP: structure and modules organisation.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 1 Introduction
* Introduced a seventh-order post-harmonic suppression network that effectively suppressed harmonics from the second to the fourth order to more than \(-50\) dB, enhancing linearity and PAE. * Achieved a gain of 12 dB, an output power of 42 dBm, a drain efficiency of 80%, and a power-added efficiency of 75.2% in the proposed DPA design. * Validated the simulation results by fabricating and measuring both the power divider and the harmonic suppression network, showing good agreement. * The paper serves as a valuable reference for future studies in the field and paves the way for further enhancements and optimizations in RF PA designs for 5G applications. Overall, the paper offers a comprehensive and innovative approach to achieving high efficiency in GaN DPAs, addressing the specific requirements of 5G applications in the N78 band. Its findings contribute to the ongoing development of advanced RF technologies for enhanced connectivity and performance in the 5G era. The paper is structured as follows: In Section 2, a revision of the Doherty technique to improve efficiency was performed. The basic structure and advantages of the Doherty technique and how to calculate the output power, gain, linearity, and efficiency of that technique are discussed. In Section 3, all parts of the proposed DPA design are presented, including the design of the unequal Wilkinson power divider and the harmonic suppression network. The expected performance of DPA as a function of both frequency and input power is presented and discussed in detail in Section 4, in addition to the review and discussion of simulation results and comparison of this work with the literature. Finally, Section 5 summarizes the conclusions drawn from this research.
## 3 Results and Discussion
### Effect of pH on the Particle Size of AuNPs  
The average particle sizes of AuNPs synthesized by _B. lactis_ at pH 4.5, 5.0, 6.0, and 7.0 were 6.0 \(\pm\) 0.8, 5.6 \(\pm\) 0.3, 38.2 \(\pm\) 1.5, and 32.6 \(\pm\) 1.2 nm, respectively (Figure 2a). The average particle sizes of AuNPs synthesized by _E. coli_ at pH 6.0, 7.0, and 8.0 were 58.3 \(\pm\) 1.0, 6.4 \(\pm\) 0.5, and 4.2 \(\pm\) 0.5 nm, respectively (Figure 2b). A previous study reported that AuNPs with a diameter of \(\leq\)5 nm can be used as an efficient catalyst [4], which indicates that _B. lactis_ and _E. coli_ have strong potential to synthesize commercial AuNPs. Thus, the optimal pH was 5.0 for BLAuNP synthesis and 8.0 for ECAuNP synthesis. Hexagonal BLAuNPs were intracellularly synthesized because they could not be detected without breaking of the cell wall. Conversely, spherical ECAuNPs were produced through extracellular synthesis. Moreover, the stability of BLAuNPs and ECAuNPs was monitored through UV-vis spectroscopy. After storage for 3 months, no change was detected in the band wavelength and intensity, indicating that both AuNPs were highly stable. The particle sizes of BLAuNPs and ECAuNPs were 5.6 \(\pm\) 0.3 nm (at pH 8.0) and 4.2 \(\pm\) 0.5 nm (at pH 5.0), respectively--smaller than the particle size of AuNPs synthesized by most plants, such as _Crescentia cuiete_ L. (32.98 nm), _Virola oleifera_ (15 nm), and _Convolvulus fruticosus_ (35 nm) [12, 19, 20], and some bacteria, such as _Bacillus_ sp. (12-32 nm) and _Streptomyces_ sp. (10-30 nm) [3, 21]. The relatively large size of AuNPs synthesized by plants may be due to the capped protein from plant extract adsorbed onto the AuNPs surface [12]. Additionally, the zeta potential of the BLAuNPs changed from +16.3 \(\pm\) 1.6 mV at pH 4.0 to \(-\)28.2 \(\pm\) 0.9 mV at pH 5.0 and that of the ECAuNPs changed from +23.6 \(\pm\) 1.2 mV at pH 5.5 to \(-\)25.1 \(\pm\) 1.6 mV at pH 8.0, leading to the stability of the nanoparticle. As _B. lactis_ at pH 5.0 and _E. coli_ at pH 8.0 synthesized AuNPs with high yields, small particle sizes, and high stability, BLAuNPs and ECAuNPs were used in the following evaluations.  
Figure 1: Effects of pH on the AuNPs synthesized by (**a**) _B. lactis_ and (**b**) _E. coli_ after 24, 48, and 72 h of incubation.
# Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
### Contributions  
In this work, to deal with Noise Not At Random (NNAR), we propose a new method derived from the filter framework with the expert knowledge being used in the label noise detection part. By incorporating the advantages of the second and third approaches, not only expert knowledge about the training dataset is exploited, but the method can also be combined with other algorithms for label noise processing part to maximize the effect of removing label noise. In detail, our method leverages expert knowledge about the training dataset to detect label noise samples in the label-noise-detection part of the filter framework. Unlike the previous works, the proposed method is applicable for every learning algorithm, and does not require any change in the main model structure, thereby shortening the project implementation time and cost. For evaluating our algorithm, conventional training algorithm, and two other related works are used in the image classification problem on Manga109 [24; 25] and CIFAR-10 [26] dataset. The experimental results show that, when level of label noise is high, our method achieves higher accuracy than the related works and conventional learning.  
Our contributions are summarized as follows:  
* We have established a new method that leverages the experts' knowledge for enhancing the learning model in the presence of label noise without changing the model structure. The proposed method is simple, intuitive, explainable, applicable for every learning algorithm, and can combine with other algorithms.
* The proposed method is realized as two algorithms: \"Rule-Weight\" algorithm weights the data sample differently to partially alleviate the bad effect in the sample; \"Rule-Remove\" algorithm excludes the bad sample from the training model. The performance comparison with the algorithms in [27] show that the Rule-Weight is the best even when the level of label noise in the dataset is as high as 50%.
* The experiments on image processing problem confirm that the label noise can be detected by simple pre-defined rules created from the expert knowledge.
## 3 Analysis of the Selected Case Studies
### Inductive Analysis of Urban Fabric Elements and Composition
#### 3.3.1 Urban Design in Hot Climates in West Asian Cities  
The overall planning of old cities was based fundamentally on the local climate features and protection strategies required to face their enemies during wars. As for hot-dry areas, the urban fabric is characterized by features that help it to reduce the impact of intense solar radiation, high temperatures, and various other harsh climatic conditions (e.g., dust storms), with different, highly compact components of the urban fabric [20; 34]. The density of the urban fabric is a critical factor in the planning of residential neighborhoods in traditional cities of West Asia for climate response [34]. We also noticed similar charac  
Figure 6: Structural unity, components, shape, and layout of the traditional urban fabric for study cities in West Asia.  
teristics in all traditional cities of West Asia, in that they contain vital components such as narrow winding alleys that infiltrate the contiguous blocks of houses and buildings, as well as contiguous and harmonious urban facades, which contain frequent shading elements. The gradation of urban squares and central courtyards inside buildings containing trees, water pools, fountains, thick walls, and building materials with high heat capacity was also observed [21]. Yazd, Kashan (Iran), Baghdad (Iraq), Damascus (Syria), Shebam Hadhramaut, Sana'a (Yemen), and others are formed from a very dense and compact organic urban fabric with narrow and irregular alleys. In Yazd, houses are contiguous with attention to orientation and have a functional distribution based on the direction of the sun and the movement of the winds. The general layout of the houses is characterized by a spatial gradation, with private inner courtyards surrounded by one- or two-story buildings. The local building materials are mud bricks, stones, and wood, depending on availability. Most of the ancient urban fabric of Yazd, Baghdad, Damascus, and Sana'a is similar to that of other desert cities in Western Asia.  
The most distinctive and noticeable feature of traditional cities in West Asia is the relationship between blocks and voids. The dominance of the masses over urban fabric spaces and roads is remarkable. The solid-to-hollow ratio in house plans is also similar: 77:23% [34; 38]. At the city scale, voids include public open spaces, roads, alleys, and central squares of homes. In general, the urban fabric of ancient cities in Western Asia is formed from significant components such as markets, mosques, palaces, squares, public baths, religious buildings, and courtyard houses [14; 21; 23; 36].
## 2 Synthetic Aperture Radar
These baselines can be implemented in multiple passes, on which the interferogram is constructed with data of points of view obtained after several days when there is a repetition cycle on the ground track, or along a single pass when the multi-static SAR is composed of neighboring satellites flying in formation. The latter may improve the SAR product in several ways, as the interferogram can be obtained in almost real-time. Figure 4: A simplified SAR classification. Solid lines are definitive classification and dashed line are the possible combination. In SFF, there are several topologies to implement the relation between the satellites, for instance, the typical leader-follower approach. In this case, a Deputy satellite (also called follower or secondary) can choose from among the several formation geometries described previously to follow the chief, which can be reconfigured on-board [31]. The chief may also have an autonomous control objective to maintain the absolute orbit (for instance, drag-free), which is therefore followed by the Deputy. Beyond the topology, there are objectives to follow the absolute orbit (for example, to guarantee a constellation repeat cycle for a desired coverage) and other objectives to follow a specific relative orbit geometry within the formation. A more general approach will be proposed to deal with these two types of objectives, to be presented as a _Constellation of Formations_. The type of formation useful for the MDA application is first taken into account, followed by simulation results using low-thrust continuous control for formation maintenance and reconfiguration.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 3 Problem Solution
### Digital and Analog Precoder Design [21]  
Based on the optimal all-digital precoding matrix \(\mathbf{w}^{\text{opt}}\) obtained in Section 3.1, we let the Euclidean distance between the hybrid precoding matrix and \(\mathbf{w}^{\text{opt}}\) be the shortest and analyze the constant-mode constraint on the subarray structure. Since the joint optimization of two unknown variables is complicated, the alternating minimization (Alt-min) algorithm is used to solve \(\mathbf{A}\) or \(\mathbf{f}\) alternately, while fixing the other. Based on problem (6) and (7), the hybrid precoding matrix computational problem can be written as follows\[\min_{\mathbf{A},\mathbf{f}}\|\mathbf{w}^{\mathrm{opt}}-\mathbf{Af}\|^{2}\] (17) such that \[|\mathbf{a}_{b}|=1. \tag{18}\]  
Since the subarray structure deploys fewer phase shifters, and each RF chain is connected to \(N_{\mathrm{t}}/N_{\mathrm{RF}}\) antennas, the analog precoder \(\mathbf{A}\) belongs to a set of block matrices \(\Phi_{s}\), where each matrix block is a \(N_{\mathrm{t}}/N_{\mathrm{RF}}\) constant module dimension matrix, and the structure of \(\mathbf{A}\) can be expressed as  
\[\mathbf{A}=\left[\begin{array}{cccc}\mathbf{s}_{1}&0&\dots&0\\ 0&\mathbf{s}_{2}&&0\\ \vdots&&\ddots&\vdots\\ 0&0&\dots&\mathbf{s}_{N_{\mathrm{RF}}}\end{array}\right],\]  
where \(\mathbf{s}_{i}=[\exp(j\theta_{(i-1)\frac{N_{\mathrm{t}}}{N_{\mathrm{RF}}}+1}),\dots,\exp(j\theta_{i}\frac{N_{\mathrm{t}}}{N_{\mathrm{RF}}})]\), and \(\theta_{i}\) represents the phase of the \(i\)-th phase shifter. The optimization objective can be rewritten as  
\[\min_{\mathbf{A},\mathbf{f}} \|\mathbf{w}^{\mathrm{opt}}-\mathbf{Af}\|^{2}\] (19) such that \[\mathbf{A}\in\Phi_{s}. \tag{20}\]