esa-sceva/satcom-chunk-collection huggingface dataset:
# Present and Future Marine Microwave Satellite Missions in China
## 4 History of Progress in China's Marine Surveillance Satellite Remote Sensing Technology  
The GF-3 satellite is China's marine surveillance satellite and is equipped with 1 m resolution synthetic aperture radar (SAR). It is China's first C-band multi-polarized SAR imaging satellite with that high resolution and plays an increasingly important role in the fields of oceanic theory and applications. For SAR marine remote sensing, the sea surface roughness is the main factor affecting radar backscattering under certain radar parameters and orbital conditions [67; 68; 69]. The sea surface roughness measured by SAR is caused by surface tension waves and short gravity waves ranging from several centimetres to tens of centimetres. The imaging ability of SAR for oceanographic features or phenomena (such as winds, currents, waves, fronts, oil film, vortexes, internal waves, and underwater topography) is dependent on the degree to which those features, or phenomena, change the sea surface roughness in various ways.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 3 Data and Methods
### Genetic Algorithm  
Genetic algorithm (GA) is a search and optimization technique inspired by natural selection. The GA simulates the evolution of a population of chromosomes, each one representing a potential solution to a problem. These chromosomes or individuals are scored according to their fitness, which determines their likelihood of being selected for reproduction. During the reproduction phase, genes from high-performing chromosomes are usually selected, combined, and mutated to produce offspring \"solutions\" potentially better than their progenitors. This iterative selection, reproduction, and mutation process allows the population to evolve towards increasingly optimal solutions over time. Genetic algorithms are powerful methods for solving problems, particularly where no traditional algorithm exists or where traditional optimization methods struggle due to large solution spaces and intricate variable relationships.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 4 Experimentation  
### Dataset  
This experiment is trained and validated on two publicly available datasets, NEU-DET [26] and SSDD (Several Steel Defect Dataset) [27]. The NEU-DET dataset is an open source steel surface defect dataset from Northeastern University, containing 1800 images, each of which is 200 \(\times\) 200 pixels in size. There are six kinds of common surface defects, namely Cr (Crazing), In (Inclusion), Pa (Patches), Ps (Pitted_surface), Rs (Rolled-in_scale), and Sc (Scratches), and there are 300 images for every type of defect, as shown in Figure 7. The SSDD dataset is the Kaggle Steel Defects Competition dataset, a bar defect dataset provided by Schewel Steel. The dataset consists of 6666 images for training and validation, with a size of 800 \(\times\) 128 pixels, including 3082 pits (Class-0), 321 inclusions (Class-1), 14,648 scratches (Class-2), and 1907 patches (Class-3). However, because its light distribution is more complex than other bar defects and the class distribution is not balanced, to strengthen the generalization ability of the model and to reduce the effect of overfitting, the data enhancement of the SSDD dataset is performed in this experiment, which includes up-down flipping, left-right flipping, and up-down left-right flipping. The ratio of the expanded dataset to the raw dataset is 3:1, and the total number of images in the training and validation sets is 19,998, including 9246 Class-0 cards, 963 Class-1 cards, 43,944 Class-2 cards, and 5721 Class-3 cards. Some examples are shown in Figure 8. Both datasets are split 8:1:1 into training, test, and validation sets.  
Figure 6: Concat_BiFPN structure.
# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
###### Abstract  
Machine Learning (ML) requires a certain number of features (i.e., attributes) to train the model. One of the main challenges is to determine the right number and the type of such features out of the given dataset's attributes. It is not uncommon for the ML process to use dataset of available features without computing the predictive value of each. Such an approach makes the process vulnerable to overfit, predictive errors, bias, and poor generalization. Each feature in the dataset has either a unique predictive value, redundant, or irrelevant value. However, the key to better accuracy and fitting for ML is to identify the optimum set (i.e., grouping) of the right feature set with the finest matching of the feature's value. This paper proposes a novel approach to enhance the Feature Engineering and Selection (eFES) Optimization process in ML. eFES is built using a unique scheme to regulate error bounds and parallelize the addition and removal of a feature during training. eFES also invents local gain (LG) and global gain (GG) functions using 3D visualizing techniques to assist the feature grouping function (FGF). FGF scores and optimizes the participating feature, so the ML process can evolve into deciding which features to accept or reject for improved generalization of the model. To support the proposed model, this paper presents mathematical models, illustrations, algorithms, and experimental results. Miscellaneous datasets are used to validate the model building process in Python, C#, and R languages. Results show the promising state of eFES as compared to the traditional feature selection process.  
machine learning; enhanced feature engineering; parallel processing of model; feature optimization; eMLEE; eFES; overfitting; underfitting; optimum fitting +
Footnote †: journal: Applied Soft. 2018, 8, 646; doi:10.3390/app8040646
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 4 Test Protocol  
Figure 3 illustrates the key parameters in the TSR protocol for energy harvesting and information processing at the relay node R and the block diagram of the corresponding receiver. In Figure 3a, \(\alpha\) is the fraction of the total block time \(2N\) in which the relay node harvests energy from the end nodes T\({}_{1}\) and T\({}_{2}\), where \(0\leq\alpha\leq 1\). The remaining block time \(2(1-\alpha)N\) is used for information transmission, such that the first half of that is used for MA and the second half is used for BC. Assuming that the TSR protocol has the same energy constraint as the PSR protocol, the power of the total received signal at  
the relay node (during energy harvesting time and the MA phase) is \(P^{\prime}=P/(1+\alpha)\). In what follows, we describe three multiple-antenna relaying schemes, which correspond to the TSR protocol.
## 3 Results
### Responsivity  
The sample was placed in an Oxford Instrument's TLE200 dilution refrigerator and was measured in the current-bias regime. The asymptotic junction resistances and the zero-bias and subgap resistances, were estimated with a symmetric voltage source with series bias resistances. The bias resistors, which also provide some degree of protection from external interference, range from 200 k\(\Omega\) to 20 G\(\Omega\).  
The responsivity, Sv, is measured in this manner. A modulated signal, generated by internal oscillator of a lock-in amplifier, was biased with a resistor to produce the heating current, which was applied across the outer SIN tunnel junctions. This generates a voltage response across the inner tunnel junctions, measured with the SR830 lock-in amplifier. The dependence of the response across the inner junctions to the bias current was then measured, and then divided by the heating power. The heating power was estimated from the IV characteristics of the outer tunnel junctions. The bias current was converted to bias voltage using the IV characteristics of the response junctions. Figure 2 shows the dependence of the responsivity on the bias voltage for different heating powers of 0.2, 0.8, 3.1 and 16 fW at 35 Hz modulation. For comparison, the theoretical responsivity is also presented in Figure 2. The dependence of the maximum response on varying heating powers plotted against the theoretical responsivity, is shown in Figure 2b. The responsivity has a maximum value of 1.5 \(\times\) 10\({}^{10}\) V/W and decreases with increasing power at a modulation frequency of 35 Hz. The theoretical responsivity, shown as the dashed line in Figure 2b, has a similar trend to the experimental result.  
Figure 2: (**a**) The measured (solid circles) and theoretical (solid lines) responsivities of the CEB for various heating powers at a frequency of 35 Hz. (**b**) The dependence of the responsivity to the heating power. The circles represent the maximum responsivities, while the dashed line represents the prediction by theory.
## 2 Materials and Methods
### Adaptive Retraining Strategy  
To ensure that the predictive model remains accurate and relevant over time, a scheduled batch retraining strategy was incorporated into the pipeline. This approach is particularly suited for operational environments where data accumulates gradually. Instead of relying on continuous updates, the model is periodically re-evaluated with newly acquired data and is only retrained when a significant performance drop is detected.  
Model performance is monitored using the same set of metrics established during the initial evaluation, namely, R\({}^{2}\), MAE, and RMSE. To determine when retraining is warranted, a trigger threshold is defined based on the model's initial performance gain over a baseline predictor (e.g., a mean-based model). After each one-month period of new operational data, the model's performance is reassessed by averaging results over this window. If performance deteriorates to the point where the original improvement is lost, such as RMSE approaching baseline levels, retraining is triggered. This periodic assessment ensures that model updates are evidence-based, timely, and operationally justified, avoiding unnecessary changes while preserving reliability.  
To simulate real-world deployment and evaluate long-term robustness, a one-month batch of data, completely withheld during the model development phase, was used as a final test set. These records, obtained from the historical files described earlier in the data collection phase, serve as a proxy for future operational scenarios. The evaluation of this set determines whether the model requires retraining or whether its prediction error remains acceptable.  
As more operational data becomes available over time, the retraining strategy can evolve not only in frequency but also in modeling complexity. A larger dataset would enable the safe adoption of more sophisticated models, such as ensemble methods or neural networks, while mitigating overfitting risks. Furthermore, the increased data volume would support the inclusion of additional features, potentially reducing prediction variance and enhancing generalization across diverse operational scenarios. This evolution could also extend to the validation strategy: while Leave-One-Out Cross-Validation (LOOCV) was appropriate under data-scarce conditions, it is important to acknowledge its limitations, particularly its high computational cost and tendency to produce high-variance estimates. In later stages, alternative validation methods such as k-fold cross-validation could offer a more efficient and robust solution for larger datasets.  
Although the data used in this case study were manually downloaded, the telematics platform provides an API that enables automated access. The methodology described in this section is designed under the assumption that the API is active, allowing full automation of both the real-time prediction process and the scheduled model evaluations.
# Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
## 2 Context and Related Work  
### From Industry 4.0 to Agriculture 4.0  
One of the main objectives of our approach is to facilitate the use of IT communication and information tools in agriculture. As an analogy to Industry 4.0, digital agriculture, smart agriculture, intelligent agriculture, or Agriculture 4.0 can be defined as a set of modern technologies that today meet the needs of communication, storage, automatism, computing, and security.  
Industries, as well as researchers and decision-makers around the world, have increasingly called for a fourth industrial revolution to enter a new era of digital and connectivity [11].  
According to [PERSON] et al. [12], 10 technology groups can be considered to implement Industry 4.0, such as cloud computing, IoT, CPS, and big data. The use of these technologies allows the transmission of information throughout the entire system and enables better control and monitoring. Practically, they are adapted to the operations in real time according to varying requirements and demand [11].  
From these concepts, we proposed to reuse them in the domain of Agriculture 4.0 (Figure 1) similar to [PERSON]. [13], who proposed a framework to manage traceability in Agriculture 4.0. Agriculture becomes a very important sector to apply the concept of CPS to [14]. In fact, the agriculture sector follows the industrial revolution and moves from traditional systems to the implementation of a full range of modern systems. These systems enable data management of the geographic information, production experiments, climate information, and other data. The beekeeping sector is obviously concerned by this revolution. Figure 1 shows the reuse of 10 technologies from Industry 4.0. The orange circles represent the technologies used in our project.  
In the context of our project and paper, we are only interested in five technologies:  
* Internet of Things, because decisions need data collection done through connected objects like smart hives;
* Cyber-Physical System, a set of primordial principles that allow defining the hierarchy of decision makers and decision cycle;
* Big Data, because data is the most important thing on which decisions are based;
* Cloud computing, which refers to the delivery of resources and services on demand over the Internet, i.e., the access to data from different resources and high computing power via the internet. It is an important computing step that facilitates the deployment of intelligent systems;
* Artificial Intelligence: in fact, nowadays, artificial intelligence is a powerful tool that can be used to help users in the process of making decisions.  
Among the concepts of the agriculture 4.0 presented in this section, the main concept used in this paper and our system is IoT. The next section presents this concept.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 3 Model of a Drone-Assisted Mobile Edge Computing System
### Blockchain Resource Transaction Framework
As shown in pseudocode Algorithm 1, this is the process of trading star blocks between the server and the drone in this model. ```
1: Initialize blockchain network:
2: Generate public/private keys for each ECS and UAV node
3: Set initial blockchain parameters (e.g., consensus mechanism, difficulty)
4: While (system is running):
5: For each user request:
6: User sends resource request to UAV
7: UAV collects user demands and forwards request to ECS
8: For each UAV request:
9: ECS records transaction (timestamp, requester, demand, price)
10: Encrypt transaction data with public/private keys
11: If (transaction pool size > = threshold or time interval reached):
12: Create new block
13: Add encrypted transactions to block
14: While (block not validated):
15: Generate PoW puzzle
16: Solve PoW puzzle by finding valid hash
17: Broadcast solution to other nodes
18: If (majority of nodes validate solution):
19: Add block to blockchain
20: Update local ledger
21: Reward ECS node with cryptocurrency
22: Broadcast new transactions to all nodes
23: Receive confirmation from nodes
24: Deploy and execute smart contracts for resource allocation
25: Optimize algorithms and system performance as needed
26: Expand system by adding more ECS nodes or enabling cross-chain interoperability
27: End While
```  
**Algorithm 1**Blockchain Resource Transaction Process
# Predicting the Effect of Processing Parameters on Caliber-Rolled Mg Alloys through Machine Learning
## 2 Materials and Methods  
### Experimental Procedures  
Commercially extruded AZ31 Mg alloy was prepared for the present research. The chemical composition of the alloy was 3.6 wt.% Al, 1.0 wt.% Zn, 0.5 wt.% Mn, and balance Mg. This alloy exhibits a tensile yield strength of 248 MPa, compressive yield strength of 109 MPa, ultimate tensile strength of 339 MPa, and elongation to failure of 20.2% [27]. Eight cylindrical samples were machined from the single extruded alloy which were 15 mm in diameter and 175 mm in length and aligned with the extrusion direction. These samples were homogenized at 673 K for 1 h and then subjected to the MPCR process in an ambient atmosphere. The samples were soaked in a furnace at 673 K for 1 h before being inserted to the first pass of MPCR. For each deformation pass, the samples were heated for 1 min at the sample temperature, rotated 90\({}^{\circ}\) with the axis of extrusion, and then inserted into the next caliber. The eight samples were deformed up to the first to the eighth MPCR passes, respectively, to induce various microhardness levels and distributions.  
Variations in Vickers microhardness values were obtained per MPCR deformation pass (HM-220, Mitutoyo, Kawasaki, Japan). The cross-sectional areas were mechanically polished with #1200 and #2400 emery paper to increase the data reliability. The microhardness values were measured along two directions of horizontal and vertical axes with a step size of approximately 500 \(\upmu\)m.  
The experiments yielded 1610 instances with five features: microhardness, MPCR deformation pass, and the position of measurement in three-dimensional Cartesian coordinates. Before the machine learning, all data (\(x\)) were standardized with an average (\(\mu\)) and standard deviation (\(\sigma\)) to minimize the scale effect of each feature:  
\[z=\frac{x-\mu}{\sigma} \tag{1}\]  
Seventy-one instances after eight-pass MPCR deformation were designated as the test dataset. It is noted that such a designation requires extrapolation, which increases the difficulty in prediction on purpose. The remaining 1539 instances were arbitrarily divided into the train and validation datasets at a ratio of 8:2 (i.e., 1231 and 308 instances, respectively).
# Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based Visual-Inertial Odometry
## 2 Model of Visual-Inertial Odometry
### Coordinate Systems  
Two moving coordinate systems connected with the sensors were used in this paper: (1) The IMU coordinate system (ICS) with its origin at the center of the IMU, where the linear accelerations and angular rates are measured; and (2) The camera coordinate system (CCS) with its origin at the optical center of the camera and the z-axis aligned with the optical axis of the lens.  
Another two reference coordinate systems were defined correspondingly: The static IMU coordinate system (SICS) with the same origin and axes as the ICS at the time of constructing the current keyframe; and the static camera coordinate system (SCCS) with the same origin and axes as the CCS at the time of constructing the current keyframe. The SICS and SCCS were static relative to the inertial space in the egomotion estimation.  
In this paper, the superscript at the top-left corner of a vector represents the coordinate system the vector is expressed in. Specifically, \({}^{\text{I}}\mathbf{p}\) represents the vector \(\mathbf{p}\) in the ICS, \({}^{\text{C}}\mathbf{p}\) represents the vector \(\mathbf{p}\) in the CCS, \({}^{\text{SI}}\mathbf{p}\) represents the vector \(\mathbf{p}\) in the SICS, and \({}^{\text{SC}}\mathbf{p}\) represents the vector \(\mathbf{p}\) in the SCCS.
# Impact of Environmental Factors on Passenger Ride Comfort
## 4 Methodology
### Acoustic Comfort  
Both the intensity and nature of noise can vary depending on the type of vehicle and the operating conditions. Sources of noise can be the power units, the contact of types with the road surface or wheels with the rails, induced by the air flow around the body, or related to announcements made. The perception and annoyance of noise are influenced by the level of noise intensity, its variability, and its frequency. As with other factors, the perception of acoustic comfort also depends on the exposure time. In addition, the characteristics of the human ear cause a significant increase in the perception of loudness with even a small increase in sound intensity [35].  
The most relevant quantity describing noise is the A-curve-corrected equivalent sound level LAeq, expressed in decibels (dB), which reflects the perception of noise by the human ear. The UIC charter 660-2002 [36] distinguishes between permissible noise levels due to the speed of the rail vehicle, the position in the vehicle, and the operating conditions (tunnel, open space). The noise level, according to this standard, should not exceed 65 dB(A) for journeys outside the tunnel and 73 dB(A) in the tunnel. It should be noted that this document refers to limit values, but these are not explicitly defined as comfortable. The World Health Organization (WHO), on the other hand, recommends not exceeding, for reasons of hearing health, a value of 70 dB(A) for daily exposure and a 5 dB(A) higher value for eight-hour exposure [37]. In terms of sound annoyance, this organisation identifies LAeq levels below 55 dB(A) as significantly annoying for only a small group of people and defines moderate annoyance at 50 dB(A). Table 3 summarizes noise recommendations provided in standards and institutions.  
Figure 4 shows the ranges of noise levels for the five proposed acoustic comfort assessment levels, C2. Given that the majority of passenger journeys are journeys of a few tens of minutes, the authors set acoustic comfort level 5 at a value not exceeding 65 dB(A), meaning \"comfortable\" travel conditions. This value corresponds to the permissible value set out in UIC 660-2002. Comfort level 4 on the proposed scale is 65-75 dB(A), at which, as the WHO points out, humans are at risk of adverse health effects in case of prolonged exposure, and with shorter exposure, sounds at this level can become at least annoying. At level 4, the ride is described as \"slightly uncomfortable\".  
Figure 3: Proposed scale of vibrational comfort index C1 with five levels (1 to 5) defined in Table 1.  
Due to the high sensitivity of the human ear to even a small increase in sound intensity, subsequent levels 2 and 3 on the comfort scale correspond to smaller ranges of sound level LAeq. Thus, level 3 on the acoustic comfort scale is assigned to results in the range of 70-80 dB(A) and indicates \"quite uncomfortable\" conditions. At level 2, the range is 80-85 dB(A), described as \"uncomfortable,\" and level 1 applies to values exceeding 85 dB(A), indicating \"very uncomfortable\" conditions.