esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 3 Preliminaries
### Learning to Rank  
Learning to Rank (LTR) is a machine learning technique developed to address ranking problems, with the core objective of learning a ranking model that ensures the ranking results align as closely as possible with the true relevance labels [29]. Specifically, given a set of queries \(Q\) and their corresponding document set \(D\), the goal of LTR is to learn a ranking function \(f(q,d)\), such that for each query \(q\in Q\), the ranking of documents \(d\in D\) reflects their relevance. The relevance labels can be binary (relevant/irrelevant) or multi-level (e.g., grades from 0 to 4).  
LTR algorithms can be broadly categorized into three types: pointwise methods, pairwise methods, and listwise methods. Pointwise methods treat each sample as an independent input and compute the loss function based on individual samples. Typical algorithms include McRank [30] and Prank [31]. Pairwise methods divide the data into sample pairs, such as \((x_{i},x_{j})\), and perform ranking by comparing the relative order of these sample pairs, for example, determining whether \(x_{i}\) should be ranked higher than \(x_{j}\). Classic pairwise methods include RankNet [29] and RankBoost [32]. Listwise methods, on the other hand, take all samples corresponding to a query as a whole input and directly optimize the ranking error of the entire list. Representative methods include ListNet [33]. These approaches generally fall under fully supervised learning and require a large amount of labeled data.  
In this study, our method employs the Random Forest algorithm. The implementation process includes the following steps:  
1. Dataset construction: Historical search data are collected from a selected subset of benchmark problem instances. Feature vectors are extracted for each candidate update behavior, and fitness-based weight values are assigned as labels.
2. Model training: A Random Forest model is trained to learn the mapping from feature vectors to predicted performance scores.
3. Prediction: During the hybrid metaheuristic process, multiple candidate behaviors are generated for each search agent. Their feature vectors are computed, and the trained model predicts a performance score for each. The behavior with the highest predicted score is selected for execution.  
This framework enables real-time ranking and selection of update strategies based on dynamic features, enhancing the adaptability of the algorithm. For full implementation details, please refer to Section 4.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 1 Introduction
### Tracking Target in Aerial Environment  
Radar tracking in an aerial environment is a complex task due to various factors related to the air's physical properties and the airborne object's characteristics, such as size, speed, materials used in manufacturing, altitude, latitude, and shape. Stealth aircraft are more complex to track compared to non-stealth aircraft, while small and fast-moving objects at high altitudes may be more challenging to track than a more significant and slower object moving at a lower altitude [19]. These factors make radar tracking in an aerial environment challenging and highly nonlinear. In Figure 1, the radar system is mounted on a moving vehicle and used to track a single aerial target, and both the target and observer are moving in the direction shown.  
In a scenario shown in Figure 1, a radar sensor is mounted on a moving vehicle traveling at speed vo and tracking an aircraft target moving at a constant speed \(v_{t}\). The sensor will measure the target's range, bearing, and elevation. The combination of the vehicle and target's relative motion and the measurement of these three parameters will allow the radar to accurately track the position and movement of the aircraft target. The parameters mentioned in the above figure are as follows: \(\beta\): Bearing. _tcs_: Target course, _obcrs_: Observer course, r: Range. TBA: Target Bearing Angle, tpi: Target Pitch, obpi: Observer pitch, \(\varepsilon\): Elevation. To simulate a real-life scenario, the specified parameters for the target and observer were initialized and fed into the Python-based simulator. These parameters include observer course, initial range, initial bearing, target velocity, observer velocity, target course, standard deviation in range, standard deviation in bearing, elevation, standard deviation in  
Figure 1: Observer–target movement in an aerial environment. \(\beta\): Bearing. _tcs_: Target course, _obcrs_: Observer course, r: Range. TBA: Target Bearing Angle, tpi: Target Pitch, obpi: Observer pitch, \(\varepsilon\): Elevation.  
deviation in elevation, observer pitch, and target pitch. The study by [PERSON] et al. [20] comprehensively describes the mathematical model used for 3D simulator design.
## 3 Experiments and Analysis
### Datasets and Pre-Processing
#### 3.2.2 Data Preprocessing  
In this paper, a data enhancement method [28] was used to expand 15 categories of grape images through rotating, scaling, cropping, and adding Gaussian noise to increase the number of training sets based on a balanced distribution of image data in each category; the effect of some of the images after processing is shown in Figure 11. Figure 11a-c correspond to the damage symptoms of the black spot of the grape, leaf roll of the grape, and felt disease of the grape, with the first image from the left in each category being the original image of the disease and the remaining four images being data enhanced. After the expansion, the number of images in each category was balanced, and the grape disease data set reached 11,056 images, as shown in Figure 12.  
Figure 11: Enhanced grape disease data. (**a**) Grape black spot and its data enhancement effects; (**b**) grape leaf roll disease and its data enhancement effects; and (**c**) grape felt disease and its data enhancement effects.  
Figure 10: Distribution of raw grape disease data.
## 3 Results and Discussion
### Potentialynamic Polarization  
Figure 7 shows the potentiodynamic polarization curves of the specimens tested with different concentrations of SM. The vertical axis represents \(\mathrm{i}_{\mathrm{corr}}\) (current density), which is a measure of the rate of corrosion of a metal or alloy in an electrolyte and expressed in units of amps per square meter (A/cm\({}^{2}\)). The horizontal axis represents the potential of the electrode in an electrolyte when the potential is varied at a selected rate by applying  
Figure 5: (**a**) Macrostructure of welded specimen, and microstructure of (**b**) weld metal (WM), (**c**) heat-affected zone (HAZ), and (**d**) base metal (BM).  
Figure 6: Hardness profile of the specimen weldment. CL means center line of the weld, and black dot is data collection position.  
an electrical source and measured in volts (V) or millivols (mV) relative to a reference electrode. The curve shift is not easily visible for the three zones as they overlap with each other. The corrosion parameters calculated based on Tafel extrapolation are given in Table 3. SM exhibited an inhibitory effect on the WM zone, as the current density decreased with an increasing concentration. At a concentration of 0.4 g/L, the WM zone showed the lowest corrosion rate of 7.21 mm/year with an inhibition efficiency of 59%. However, at the same concentration, the HAZ indicated an efficiency of 4.28%, with a corrosion rate of 6.84 mm/year. The BM zone had the highest inhibition efficiency of 37% at an extract concentration of 0.2 g/L, resulting in a corrosion rate of 2.74 mm/year.
## 5 Discussion  
### Comparing the Performances of Different Machine Learning Algorithms  
The experimental results demonstrate that the UMAP algorithm has significant advantages in dimensionality reduction for multi-source and multi-parameter LWD data. Compared with the linear dimensionality reduction method PCA and the nonlinear method t-SNE, UMAP outperforms in key indicators such as the Davies-Bouldin Index, intra-class compactness, and inter-class separation. The core advantage of UMAP lies in its ability to preserve both the global structure and local details of the data through topological manifold learning. This effectively mitigates the lithological overlap caused by the linear projection of PCA (e.g., the overlap between shale and limestone) and the abnormal dispersion caused by the local overfitting inherent in t-SNE. Moreover, the dimensionality reduction results of UMAP align well with geological sedimentary sequences (such as the energy gradient between silt, fine-grained sand, and coarse-grained sand) and lithological origins (for example, limestone being independently clustered). This consistency further verifies UMAP's high-fidelity characteristics in complex geological environments. On the other hand, t-SNE's engineering applicability is limited by its high memory consumption and lack of global structure, while PCA struggles to capture nonlinear geological responses due to its linear assumptions. In contrast, UMAP offers a more interpretable low-dimensional feature space for identifying strongly heterogeneous strata, making it highly suitable for nonlinear dimensionality reduction and adapting to high-dimensional data.  
By comparing the performances of the SVM, Random Forest (RF), LightGBM, and CatBoost models, it is evident that the combination of CatBoost with the UMAP + K-means method achieves the best performance in the lithological classification task, with a Balanced Accuracy of 0.897 and an F1-Score of 0.872. SVM, but struggles with high-dimensional data and nonlinear relationships, resulting in the weakest performance, with a Balanced Accuracy of only 0.505 under PCA processing. While RF is robust, it has a low upper limit and its ensemble mechanism is insufficient for capturing complex interactive features. Although LightGBM is efficient, it does not perform as well as CatBoost in terms of gradient bias optimization. The advantage of CatBoost lies in its ordered boosting mechanism, which effectively integrates cluster labels through unbiased gradient estimation and enhances the model's ability to capture lithology sub-classes using categorical feature encoding. This enables CatBoost to significantly outperform other models, especially in recognizing scarce classes and thin reservoirs, thereby demonstrating its engineering suitability for heterogeneous reservoir recognition.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Technical Challenges
#### 6.1.2 Privacy Protection and Data Security  
ETC data contains substantial sensitive personal privacy information, including vehicle identity, travel trajectories, temporal habits, and location preferences. The leakage of this information could pose serious threats to user privacy. How to effectively protect user privacy while fully utilizing data value has become a key factor constraining widespread ETC data applications.  
Although the development of data anonymization technologies has alleviated privacy protection pressures to some extent, achieving an optimal balance between anonymization intensity and data usability remains challenging. Traditional anonymization methods, such as data anonymization and generalization processing, often significantly reduce data analytical value, affecting data-based decision-making effectiveness. Therefore, privacy protection technologies require comprehensive breakthroughs in theoretical foundations, algorithm design, and system implementation.  
Differential privacy protection technology provides new approaches for addressing this issue. By incorporating carefully designed noise in data publishing and analysis processes, individual privacy can be protected while maintaining the overall statistical characteristics of data. However, differential privacy technology still faces numerous challenges in ETC data applications, including the reasonable allocation of privacy budgets, the optimized setting of noise parameters, and the collaborative protection of multi-dimensional data. Particularly in application scenarios requiring complex spatiotemporal analysis, maintaining the usability of analysis results while ensuring privacy protection effectiveness requires further theoretical research and technological innovation.  
The improvement of data security transmission and storage technologies remains equally crucial. With the deep integration of ETC systems with other intelligent transportation systems, data flow pathways and storage nodes have become increasingly complex, making traditional encryption technologies insufficient for full-chain security protection requirements. The applications of emerging cryptographic technologies, such as blockchain technology, homomorphic encryption, and secure multi-party computation, provide technical support for constructing more secure and reliable data processing environments. However, these technologies still face challenges in actual deployment, including substantial computational overhead, high system complexity, and low standardization levels, requiring comprehensive trade-offs in technological maturity, deployment feasibility, and cost-effectiveness.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.2 Classifier Training and Testing  
The classifiers were trained on prepared data to learn fault patterns and subsequently tested with separate datasets to evaluate generalization capability.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 4 Technology Trends to Achieve the Requirements in the 5G Network
### Small Cells, Ultra-Dense Networks, and Heterogeneous Networks  
By the use of small cells, it will enhance the system traffic capacity because of increased frequency re-utilisation. The use of small cells in the scenario of ultra-dense network is an another trend for next-generation network 5G. Furthermore, small cells use also maximises the SNR while the transmission power is minimized; it will reduce the communication power, maximise the link capacity, thereby increasing energy-saving. For 5G, a heterogeneous network with small cells, macrocells, picocell, and femtocell is another trend. In this situation, macrocell support control panel to offer mobility and connectivity, the pico and femtocells support data plane to provide data with transport [66; 71; 72]. Figure 7 shows this idea. Nevertheless, it is essential to keep in mind that small cells are not suitable in a WRAN scenario. Super microcells can be considered for the WRAN scenario. For this scenario, millimetres wave communication is also not appropriate. Thus, the combination of low frequencies and millimetres wave communications will be considered for the 5G network.
## 2 Background
### IoT Service  
IoT services can be classified into two categories: edge IoT service and cloud IoT service. The edge IoT services are located in the constrained networks and provided by the smart devices. Users interact with the smart devices using the edge services. Smart devices also exchange information with them using these services. The edge services may also be used for device maintenance purposes, such as updating or upgrading embedded software.  
Cloud IoT services are located on the Internet and publicly accessible. These services enable users to interact with smart devices remotely. The cloud IoT services communicate to the edge services to receive sensor information and then process the information for making decisions. The cloud IoT services also handle the tasks of device registration, device management, sensor data management, and process automation.
## 3 Results  
### Global Morphology of Geomagnetic Storms  
The global morphology of this geomagnetic storm was demonstrated in Figure 2. The solar wind component started increasing drastically at 20:00 UT 22 June 2015 and reached its maximum value of 742 m/s, 8 h later. The solar wind speed remained at a high level until 27 June 2015 and had another peak at 07:00 UT on 27 June. It then decreased gradually to be less than 400 km/s. The IMF Bz turned southward just a few minutes after the burst of solar wind and reached a minimum value of \(-26.3\) nT at 20:00 UT. The IMF Bz oscillated with variations from the 22 to 23 June due to the rapid Alfven fluctuations. The minimum AL value was \(-1535\) nT on the 23 rd at 04:39 UT and the maximum AU value was 1503 nT on the 23 rd at 01:46 UT. The Dst value dropped down to \(-204\) nT during the second sub-phase of the storm on the 23 rd at 05:00 UT. The perturbation of AE/AU/AL had several sub-patterns during the storm. The first perturbation sub-pattern started at 05:00 UT and lasted for 6 h, the AE peak value got to 1262 at 07:20 UT, and the minimum AL value dropped to \(-676\). The second perturbation sub-pattern occurred at the commencement of the storm, the maximum AE reached 2083 nT around 20:20 UT, with the AL falling to \(-1058\) nT. The AU demonstrated strong variations during this sub-pattern. The third perturbation sub-pattern was related to the second sub-phase of the storm, starting at midnight on the 23 rd and lasting over 7.5 h. The AU reached a peak value of 1469 nT after 02:00 UT.  
Several magnetic observatories (MAW, Mawson (67.604\({}^{\circ}\)S, 62.879\({}^{\circ}\)E); MEA, Meanook (54.616\({}^{\circ}\) N, 113.347\({}^{\circ}\) W); YAK, Yakutsk (61.96\({}^{\circ}\) N, 129.66\({}^{\circ}\) E); KIR, Kiruna (67.8428\({}^{\circ}\) N, 20.4201\({}^{\circ}\) E)) as shown in Figure 1 were selected at high latitudes, as the variation of \(D_{iono}\) shown in Figure 3. It showed that the geomagnetic fields at high latitudes oscillated drastically during the storm due to the influence of high-speed solar winds. This is in good consistency with previous discoveries of magnetic responses to high-speed solar winds at high latitudes regions [1; 39], leading to ionosphere TEC variations. The asymmetry feature was also found between the northern hemisphere and southern hemisphere. In the southern hemisphere, only the MAW observatory had strong magnetic perturbations. The rest  
Figure 2: The geomagnetic morphology of the storm (listing the interplanetary magnetic field (IMF) southward value (Bz), solar wind, Dst/AE/AU/AL indices).  
of the responses of the southern hemisphere were comparatively light. It is also demonstrated that the main phase of the geomagnetic perturbation was from 20:00 UT on 22 nd to 15:00 UT on 23 rd, similar to the behavior of AE/AU/AL in Figure 2, due to the ionosphere dynamo in the storm.
# Optimization of Buoy Shape for Wave Energy Converter Based on Particle Swarm Algorithm
## 2 Theory and Methodology
### Validation  
Due to the limitations of the AQWA software for time domain calculations of multi-degree-of-freedom models, this study performed hydrodynamic calculations of multi-degree-of-freedom models by combining the AQWA frequency domain and WEC-Sim time domain calculations numerically. In order to verify the correctness of the above method, the results computed by the combined AQWA/WEC-Sim were compared with those of [PERSON] et al [43]. [PERSON] et al used WAMIT to obtain the frequency domain hydrodynamic data and used WEC-Sim and Orca-flex software to perform time domain calculations for RM3 in a multi-degree-of-freedom model. In this study, the same models and settings were used for the comparison. The simulation model is presented in Figure 7.  
The two models used the same wave conditions, and a regular wave with a wave height of 2.5 m and a period of 8 s was selected. As shown in Figure 8, the validated data from the literature are shown as blue lines and the data calculated by AQWA/WEC-Sim are shown as red lines. By comparing the pitch response curves of the RM3 device, it can be seen that the results of the method used in this paper are similar to those of the literature. The deviation of 0.02 deg was within the acceptable error range.  
Figure 6: Geometry definition.  
Figure 7: RM3 model: (**a**) geometric model from [PERSON] et al.; (**b**) grid model for AQWA frequency domain calculations in this paper.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 7 Discussions on Three Applications
### Parametrizations in Three Applications  
For _FILS15.4_, all the possible parameters are parameterized to optimize the values except for the number and locations of receivers that should be allocated in the field. Currently, these parameters can be optimized by manually inserting, moving, or removing receivers. They can be optimized if the accurate model of the signal propagation is available for the field, which will be in future works.  
For _Face Model_, currently, only two simple functions, _half circle_ and _quadratic curve_, are considered. Then, there is a gap in approximating the jaw part of the face contour. The _half circle_ can be too fat, whereas the _quadratic curve_ can be too thin. Therefore, other functions will be necessary to continuously approximate it. Thus, the optimization of the approximate function should be more generalized and parameterized to further improve the accuracy, which will be in future works.  
For _CFD_, currently, only _heat flux_ is optimized. To improve the calculation accuracy of _CFD_, basically, finer meshes and more physical parameters should be considered. However, they will further increase the CPU time. Since _heat flux_ often differs from the value described in the specifications of the wall materials due to construction conditions, it is optimized in this paper. Other parameters, such as the thermal conductivity, the wall thickness, the number of meshes, and the time step, can be optimized to further improve the accuracy, which will be in future works with the speedup of _CFD_.
# Structure of All-Digital Frequency Synthesiser for IoT and IoV Applications
###### Abstract  
In recent years number of Internet of Things (IoT) services and devices is growing and Internet of Vehicles (IoV) technologies are emerging. Multiband transceiver with high performance frequency synthesisers should be used to support a multitude of existing and developing wireless standards. In this paper noise sources of an all-digital frequency synthesiser are discussed through \(s\)-domain model of frequency synthesisers, and the impact of noise induced by main blocks of synthesisers to the overall phase noise of frequency synthesisers is analysed. Requirements for time to digital converter (TDC), digitally controlled oscillator (DCO) and digital filter suitable for all-digital frequency synthesiser for IoT and IoV applications are defined. The structure of frequency synthesisers, which allows us to meet defined requirements, is presented. Its main parts are 2D Vernier TDC based on gated ring oscillators, which can achieve resolution close to 1 ps; multi core LC-tank DCO, whose tuning range is 4.3-5.4 GHz when two cores are used and phase noise is \(-116.4\) dBc/Hz at 1 MHz offset from 5.44 GHz carrier; digital filter made of proportional and integral gain stages and additional infinite impulse response filter stages. Such a structure allows us to achieve a synthesiser's in-band phase noise lower than \(-100\) dBc/Hz, out-of-band phase noise equal to \(-134.0\) dBc/Hz and allows us to set a synthesiser to type-I or type-II and change its order from first to sixth.  
CMOS; integrated circuit; frequency synthesiser; time to digital converter; digitally controlled oscillator +
Footnote †: journal: Electronics Letters