esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 2 Materials and Methods
### Methodology
#### 2.2.4 Optimization of Hyperparameters  
In machine learning, hyperparameter optimization is critical for improving model performance by identifying the best set of hyperparameters that minimize model error. This study employs three distinct hyperparameter tuning strategies--grid search (G), random search (R), and Bayesian optimization (B), collectively referred to as GRB in Figure 1--to optimize model performance. Each method adjusts hyperparameters in different ways, and the one that achieves the highest accuracy is selected as the optimal approach. The objective is to identify the most suitable hyperparameter combination while maintaining computational efficiency.  
Grid Search: Grid search is an exhaustive search method that systematically tests all possible combinations of hyperparameters within a predefined grid. Each combination is evaluated across multiple cross-validation folds, and the combination that minimizes the average loss is selected. This method ensures a global optimum is found but can be computationally expensive for large grids.  
Random Search: Random search improves scalability by randomly sampling a fixed number of hyperparameter combinations from the grid. Instead of evaluating all combinations, it tests a subset of possibilities, which significantly reduces computation time. Although not exhaustive, random search can often find near-optimal solutions more efficiently, particularly when dealing with large grids.  
Bayesian Optimization: Bayesian optimization, implemented via BayesSearchCV, is an advanced technique that constructs a probabilistic model of the objective function. This surrogate model is used to iteratively select the most promising hyperparameter combinations for evaluation. Bayesian optimization focuses on the most likely optimal regions of the hyperparameter space, updating the surrogate model with new information at each step. This method balances exploration and exploitation, leading to faster convergence toward the optimal hyperparameters.  
By employing these techniques, this study seeks to maximize model performance while efficiently navigating the trade-off between accuracy and computational complexity.
# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 3 Introduction to the P2E-YOLOv5 Network  
To achieve accurate and efficient detection of small targets in transmission lines, this paper proposes the adoption of the YOLOv5s network model with a simplified architecture. The P2E-YOLOv5 network, as depicted in Figure 2, represents the overall fundamental structure of our proposed approach. The main network structure of the P2E-YOLOv5 network is shown in Figure 3.  
To address the challenges posed by the complex background and vulnerability to environmental factors of small targets in transmission lines, this paper introduces the use of an ECA (Efficient Channel Attention) mechanism. The CBES module is constructed by incorporating an ECA attention module after the BN (Batch Normalization) layer of the CBS module, which is widely distributed in the backbone and neck parts (see Figure 5). This integration encourages the network to focus more on the small target area, reducing interference from complex backgrounds and enhancing the network's ability to extract features relevant to small targets. Considering the characteristics of small targets in transmission lines, such as their small size and low resolution, a P2 detection head is added to the Head part. This head has a high resolution of 160 \(\times\) 160 pixels and facilitates the effective fusion of rich low-level features and deep semantic information. It is generated by low-level and high-resolution feature maps, making it highly sensitive to the detection of small targets. To address the challenge of small targets being indistinct and prone to aggregation, the EIOU_Loss is considered as the loss function of network regression in the prediction part. This choice effectively enhances the regression accuracy of the bounding box, thereby improving the precise positioning and identification of small targets by the network.  
Figure 2: The architectural framework of P2E-YOLOv5 encompasses several crucial components, namely Input, Backbone (comprising the backbone network), Neck (housing the neck network), Head (incorporating the detection head), Prediction, and Output. Notably, this paper introduces certain modifications denoted by a red rectangular dotted line box.
## 3 Results and Discussion
### Characterization of Zn-MOF-8@AgQDs Composite
Figure 3c reveals the strong binding of Ag QDs particles on Zn-MOF-8 surface resulting in a smooth pathway for the collection and transportation of electrons. The elemental composition of Zn-MOF-8 and Zn-MOF-8@Ag was studied by using energy dispersive X-ray analysis (EDXA). The EDX analysis depicted in Figure 4a,b shows the presence of Zn, O, C in Zn-MOF and Zn, Ag, C and O in Zn-MOF@AgQDs, respectively indicating the formation of nanocomposite. This EDX mapping confirmed that the AgQDs are well encapsulated by Zn-MOF-8 and atomic % of Ag within Zn-MOF-8 was determined, see atomic % inset in Figure 4a,b. Figure 3: TEM image of (**a**) Ag QDs particles, (**b**) Zn–MOF-8, (**c**) Zn–MOF-8@AgQDs. Figure 2: TGA curves of (**a**) Zn–MOF-8 and, (**b**) Zn–MOF-8@Ag.
## 2 Advanced Instruments in Remote Sensing of EBC
### Constellation of Small Satellites  
A small satellite generally refers to its mass in the range of 1-500 kg and satellite constellation is defined as groups of satellites working in concert [42]. Since 1997, six symposia on small satellites have been organized by the International Academy of Astronautics (IAA) in Berlin, Germany. [PERSON] and [PERSON] reviewed the development of small satellites in remote sensing [43]. With the launch of DMC (Disaster Monitoring Constellation, Table 2), the concept of the Earth-observation constellationof low-cost small satellites has been put into action. It is capable of obtaining multispectral images of any part of the world every day [24]. The DMC was initially proposed in 1996 and led by SSTL (Surrey Satellite Technology Limited), which is a world leader in high performance small satellites [42]. [PERSON] _et al._ briefly introduced the characteristics of DMC imagery and its potential applications in environmental science [44]. Also, HJ-1 (Huan Jing-1, also called Environment-1, operated by China) is another outstanding constellation system. It is designed mainly for environmental protection and disaster monitoring, and the payload instruments onboard consist of a CCD (Charge-Coupled Device) camera, an infrared camera, a hyperspectral imager and an S-band SAR (Synthetic Aperture Radar, [45]).  
Besides the benefits in cost and operation, the constellation of small satellites has two obvious advantages in applications, _i.e._, global surveying and increased revisit frequency [24]. It is relatively easy to obtain observation data across the world in a short time for constellation systems. The increased revisit frequency can not only satisfy the application of detecting rapid surface changes such as crop-growth monitoring and detecting intraseasonal ecosystem disturbance, but also promotes acquisition of good-quality imagery with limited cloud-contamination. [PERSON] _et al._ discussed the issue of clouds and cloud shadows in the environmental remote sensing community, and advised looking for good solutions to the unavoidable problem in optical remote sensing [44]. The development of a constellation of low-cost small satellites is believed to make contributions to this issue at the sensor level. Only a few studies of EBC applied the imagery of small-satellite constellation, though [PERSON] has predicted the bright future of this kind of satellite imagery [11]. [PERSON] _et al._ demonstrated that simulated HJ-1B satellite data performed better on smaller and cooler fires than MODIS or AVHRR data, and believe it will offer a great opportunity for fire detection [46]. The FRIGBP has started testing the applicability of DMC imagery for wildlife large-area habitat mapping in west-central Alberta (Canada) [44].
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
Footnote †: This paper explores the usage of LEO-PNT (Positioning, Navigation, and Timing) for providing navigation integrity to GNSS (Global Navigation Satellite System) constellations. LEO mega-constellations, which are positioned between GNSSs and users, offer closer-to-the user geometry, improving performance, reducing the time to alarm (TTA) and enabling integrity monitoring without complex ground segments of any sort. The aim is to use future LEO mega-constellations as integrity monitors for a forthcoming European Global Navigation Satellite System (EGNSS) specifically focused on automotive users, which has minimal onboard satellite capabilities and no ground involvement. This plan builds on earlier studies, anticipating the performance of the upcoming LEO-PNT In-Orbit Demonstration.
# Trends and Technological Challenges of 3D Bioprinting in Cultured Meat: Technological Prospection
## 4 Discussion
### Status of Cultured Meat in in the World
In 2012, the United Nations' Food and Agriculture Organization(FAO) (Rome, Italy) estimated that global demand for meat would reach 455 million tons by 2050 (increasing 76% compared to 2005) [55]. This increase in demand, combined with sustainability policies, has been revolutionizing production processes, especially in food chain sectors, as can be seen in the emergence of cultured meat production [56]. Traditional meat production has some disadvantages when compared to this new technology, such as animal slaughtering, inadequate breeding environments, the transmission of some diseases, and the generation of greenhouse gases (GHG) such as methane (CH\({}_{4}\)) and nitrous oxide (N\({}_{2}\)O) [57, 58].
## 3 Network Scenario
### Flow Prioritization  
The switches handle the radio data bursts, i.e., control, buffer, and transmit them as a whole. The selection of a buffered burst for transmission is based on its priority, according to the strict priority algorithm [8]. Latency-sensitive FH flows have a higher priority than MH flows. We assume Profile A of operation defined in [8], where the transmitted bursts are not preempted by other bursts, even of higher priority.  
The radio data corresponding to different network slices, but associated with the same RU, are encapsulated into Ethernet frames and sent/received as separate FH data flows [53]. Accordingly, the priorities assigned to these flows may be diversified. We analyze the following two cases:  
* SP-FH (same FH priorities), where all the FH flows are assigned the same (high) priority without respect for the network slice they belong to.
* DP-FH (different FH priorities), where the priorities of the FH flows are assigned according to the flow latency requirements. In particular, in this study, the URLLC FH flows have a higher priority than the eMBB FH flows.  
The above cases are illustrated in Figure 2, where three FH bursts (two eMBB and one URLLC) are transmitted at the same switch output link. In SP-FH, the URLLC burst may be selected for transmission as the last one since all bursts have the same priority, which leads to a buffering delay of this burst. The URLLC burst is transmitted without delay if assigned the highest priority in DP-FH.  
Assigning the same priority to the whole FH traffic (SP-FH) is specific to the network slice-unaware mode of operation defined in [7], and is also assumed in [8]. In contrast, the DP-FH approach supports the network slice-aware mode. It can be realized with the QoS-wise FH packetization method discussed in [53] in the studied network scenario, where an RU generates multiple independent FH flows, each related to a different network slice, as mentioned earlier. We evaluate the impact of both policies on network performance in Section 6.4.3.
## 3 Results and Discussions
### Effect of the Leakage Direction  
#### 3.3.1 Stratification  
In order to compare the hydrogen concentration for Model 1-Model 5, the x = 1 plane in the space was selected to study the contour at the time when the envelopes of 4% and 1% mole fraction of hydrogen grew to fill the whole ceiling. As is evident from Figure 10, hydrogen gradually accumulated downward from the ceiling and stratification happened for Model 1-Model 5. The area occupied by the hydrogen concentration below 0.5% for Model4 and Model 5 was larger than that for Model 1, Model 2, and Model 3. The Merton number was used to distinguish the homogeneous and stratification. The definition of the Merton number \(M\) is as follows:  
\[M=\frac{l_{s}}{z}, \tag{12}\]  
\[l_{s}=\frac{M_{0}^{3/4}}{F_{0}^{1/2}}=\frac{0.96\cdot u_{0}\cdot\sqrt{D}}{ \sqrt{\frac{g\cdot(\rho_{\mathrm{air}}-\rho_{H_{2}})}{\rho_{\mathrm{air}}}}}, \tag{13}\]  
where \(u_{0}\) is the exit velocity, m\(\cdot\)s\({}^{-1}\); \(D\) is the diameter of the leak, m\(;g\) is the acceleration of gravity, m\(\cdot\)s\({}^{-2}\); \(\rho_{\mathrm{air}}\) is the density of air, kg.m\({}^{-3}\); \(\rho_{H2}\) is the density of hydrogen, kg.m\({}^{-3}\); and \(z\) is the distance between the leakage position and the ceiling, m.  
Figure 10: _Cont._For \(M<1\), there is a stratification of the concentration of hydrogen, whereas for \(M\gg 1\), homogenization is observed. The Merton number of Model 1, Model 2, and Model 3 was about 0.00236, equally. The number of Model 4 and Model 5 was 0.00282 and 0.013, respectively. The Merton number in all cases was less than 1, so that stratification happened.  
The stratification was found to happen in a large velocity range, and tended to occur in the space with the leakage taking place near the ceiling [42]. As is evident from Figure 10, the stratification of both 4% and 1% mole fraction of hydrogen for Model 5 was more obvious than the other models due to the leakage position closer to the ceiling. [PERSON] et al. [26] also found stratification in a prismatic cavity.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 3 Implementation Using the Difference Signals
### Dispersion Curves  
The dispersion curves were drawn for Lamb wave propagation in aluminum plate, which had both symmetric (S) and anti-symmetric (A) modes propagating simultaneously, as shown in Figure 5.  
In the low-frequency range, only S\({}_{0}\) and A\({}_{0}\) modes propagated, with the group velocity of S\({}_{0}\) mode significantly higher than that of the A\({}_{0}\) mode. If a modified signal with narrow frequency band is excited, both the modes can be identified separately, with the peak of S\({}_{0}\) arriving first. In case of mode conversion after scattering from damage, S\({}_{0}\) will still reflect a faster  
Figure 4: Square arrangement of piezoelectric (lead zirconate titanate, PZT) transducers on a damaged plate.  
Figure 5: Dispersion curves for Lamb wave propagation in aluminum plate.  
a slower anti-symmetric mode A\({}_{0}\). Therefore, the scattered S\({}_{0}\) mode is also easy to identify as one propagating faster, and it was exclusively chosen in the method proposed here.  
The frequency-thickness product (\(f\times d\)) was calculated as 574.5 Hz-m for the excitation frequency of 383 kHz and plate thickness of 1.5 mm. This point existed in the low-frequency range of the dispersion curves, where the S\({}_{0}\) mode could be easily separated from the A\({}_{0}\) mode, as shown with the vertical red dashed line in Figure 5. The simulation model was validated first by calculating the group velocity, \(V\), in Equation (1). The group velocity calculated through simulations was 5.31 \(\times\) 103 m/s, which was almost the same as obtained from the dispersion curves. It indicates that the simulation model had good accuracy to calculate the Lamb wave propagation.
# Investigation of THz Absorptive Signatures in Opioids
## 2 Materials and Methods
### Sample Preparation  
Oxycodone was the focus of our experimental studies. Three generic oxycodone tablets (labeled as O#1, O#2, O#3) were studied. Each tablet contained about 4.48 mg oxycodone, 325 mg acetaminophen and roughly 195 mg lactose [19]. The tablets were ground into a fine powder, which was squeezed into steel washers with a thickness of 1 mm and a diameter of 1 cm using a 12-ton hydraulic press. By doing this, the background scattering from loose particles of SUT powder could be greatly reduced.  
For every sample, three measurements were conducted: background without sample (B), sample (S), and noise floor with the THz beam blocked (N). The transmittance for each sample was calculated with the ratio of (S-N)/(B-N) [4].  
In addition to the opioid samples, we prepared two control ones (labeled as L#1, L#2) containing pure lactose monohydrate and performed scans over the same frequency range as the opioid samples.
# Single-Shot Temporal Contrast Enhancement Measurement of a Plasma Mirror by a Chirped Pulse
## 1 Introduction
In recent years, pulse chirp has been widely applied to the spatiotemporal measurement of various ultrashort phenomena. The group velocity dispersion (GVD) of a chirped pulse leads to the continuous queuing of wavelengths in the time domain. Benefiting from it, it is very suitable for the measurement of pulse or plasma with ultrafast response, such as the spatiotemporal measurement of ultrashort laser pulse [25], laser-generated fast electron measurement [26], etc. In the previous research, [PERSON] et al. proposed the explanation of the measurement of the temporal chirped pulse profile by its spectrum [27]. Using a 1.1-ps chirped pulse, the spectral phase and temporal reflectivity after the PM were obtained within a single shot. Indeed, the potential of a chirped pulse could be further exploited for the temporal contrast enhancement measurement of PMs. In this work, we focus on the detailed research of a single-shot method for measuring the temporal contrast enhancement of a PM. It relies on a single-shot measurement of the reflective spectrum of a spatiotemporally overlapped chirped pulse (SOCP), with the igniting pulse producing a PM. We analyzed and estimated the temporal contrast enhancement measurement capability by SOCP, and verified the feasibility by experiments. The experimental results show that the temporal contrast enhancement of a PM in a hundred picosecond scale can be obtained conveniently and accurately.
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
###### Abstract
This method has the advantages of high frequency resolution, high noise immunity, and high robustness. It can be used to solve the frequency overlapping problem. In addition, it is applicable in the case of a small amount of data and is simple and easy to implement. For this reason, the IAA method has been widely used in array signal processing, underwater communication, image processing, and other fields [16; 17; 18]. However, the resolution of IAA is proportional to the calculation amount. In the rolling production process, the rolling speed varies widely and the frequency of the roll eccentricity signals changes in a wide range. In this case, the use of IAA for a highly accurate detection of eccentricity signals will lead to a very high calculation amount and cannot meet the requirements for real-time performance. Considering that the roll eccentricity signal is a typical sparse signal, this paper used SFFT to investigate the frequency distribution of each harmonic of roll eccentricity signals and then set several small frequency bands according to the frequency distribution. In each small frequency band, IAA was used to accurately detect the signals to construct an accurate roll eccentricity model. The simulation results and the engineering experiments indicated that the proposed method can guarantee the accuracy of eccentricity signal estimation, meet the requirements for real-time performance, and show a good compensation effect, which has a high application value in engineering fields. The major contributions of this paper are as follows:  
1. On the problem of roll eccentricity signal detection, the IAA algorithm is improved by using SFFT, and SFFT-IAA is proposed. 2. The similar frequency components in the roll eccentricity signal can be detected. A high precision roll eccentricity compensation model is established. 3. The application method of roll eccentricity signal detection in engineering is discussed. The remainder of this paper is organized as follows: Section 2 discusses the related problems of roll eccentricity of a cold rolling mill and gives a mathematical model of the eccentricity signal. Section 3 is a fine description of SFFT-IAA. Section 4 is a simulation study to verify the performance of SFFT-IAA. Section 5 introduces the application and verification of the proposed method in the actual single-stand reversible cold rolling mill. Section 6 contains some conclusions.
# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 2 Architectures for Coordinated Control of DDEV Chassis  
In the implementation process of coordinated chassis control for DDEVs, the design and selection of the control architecture constitutes a core challenge. The control architecture plays a dual role. It not only defines the system's internal mechanics--such as information flow paths, decision-making authority, and command transmission modes--but also directly dictates the system's ultimate outcomes. These outcomes include the performance of coordinated control, the complexity of implementation, operational reliability, and future functional scalability. Currently, architectures applied to vehicle chassis' integrated control can mainly be categorized into decentralized architectures, centralized architectures, and hierarchical architectures [22, 23].
## 2 Materials and Methods
### Summary  
A primary objective of this study was the assessment of the heading estimates produced by the Applanix APX-18 UAV with and without the use of GAMS. As a commercially available sensor designed for use on multiple UAS platforms, the results of this APX-18 analysis are intended to serve as a representative performance of typical single-antenna and dual-antenna GNSS/INS MEMS-based sensors used within UAS-lidar mapping systems. The methods used to achieve this objective included both theoretical and practical analyses. First, an investigation was conducted into the theoretical concepts underlying the different heading determination techniques employed by GNSS/INS sensors including analytical formulas for the expected precisions. Then, a theoretical analysis was performed examining the impact of heading precision on the positional precision of a UAS-lidar point cloud when utilizing direct georeferencing. Lastly, a field experiment was conducted that collected multiple UAS-lidar mapping datasets over a single project area. Each dataset was collected at a different horizontal speed to ensure the APX-18 experienced a variety of sensor dynamics. The trajectories and point clouds from each dataset were analyzed, compared to expected values and trends, and used to support the conclusions of this study.