esa-sceva/satcom-chunk-collection huggingface dataset:
However, despite its attraction, this novel application for vertical air motion retrieval does not work under conditions of no precipitation or light precipitation for W- and Ka-band radars. When small particles, such as liquid droplets and small ice crystals, are present within the cloud radar sampling volume, vertical air velocity can be directly estimated by using the velocity bin of these small targets, and so they are regarded as tracers of clear-air motion in the measured spectra [24; 25; 26; 27]. This methodology may be widely applied to different cloud types, but spectral broadening caused by turbulence and wind shear must be considered as non-negligible uncertainties. In this manuscript, a technique for retrieving vertical air velocity in convective clouds over the TP is introduced. This technique is designed for a Ka-band solid-state cloud radar used in the Third Tibetan Plateau Meteorological Science Experiment (TIPEX-III). In the following section, details of the cloud radar and experiment, data processing of radar recorded Doppler spectra, and an integrated retrieval technique using three-mode Doppler spectra are described. Section 3 analyzes the retrieval results of two typical cases and compares them with another retrieved result from a ground-basedoptical dishorometer. Section 4 discusses the results and the future research directions. Section 5 gives a summary and conclusions of this research.
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
## 6 Results  
The SPL of the prototype 1610_SPK and that of the newly designed 1410_SPK are compared in Figure 20. The average SPL of 1410_SPK at four low frequencies was found to be 79.79 dB. The SPL comparisons between 1610_SPK and 1410_SPK at four frequencies are listed in Table 10.  
Figure 19: Experiment and simulation results of 1410_SPK: (**a**) SPL and (**b**) displacement.  
Figure 20: Comparison of the SPL for 1610_SPK and 1410_SPK.  
From the table, it can be seen that the SPL of 1410_SPK was higher by 1.87 dB at low frequencies than that of the prototype 1610_SPK. In terms of acoustic performance, the 1.87 dB improvement at low frequencies is a considerable improvement. This improvement was achieved via the heavier coil and the increased electromagnetic force, compared with those in 1610_SPK. At around 1000 Hz, the SPL of 1410_SPK was lower than that of 1610_SPK, owing to the damping of the SDP. Except around 1000 Hz, the SPL of 1410_SPK was similar to or better than that of its larger 1610_SPK counterpart. Overall, 1410_SPK yielded an improved SPL at low frequencies, while also featuring reduced speaker dimensions from \(16.0\times 10.0\times 2.11\) to \(14.0\times 10.0\times 2.09\) mm.
## 2 Details and Performance Analysis of QODELFA
### Performance Comparison by Solving Benchmark Functions  
In order to analyze the performance of QODELFA, ten benchmark functions taken from [37], as shown in Table 1, were used as test functions. Besides, the proposed algorithm's performance was compared with several algorithms: PSO, DE, GA, ABC, the sine-cosine algorithm (SCA), and the firefly algorithm (FA). The default parameters of those algorithms were used in this procedure. For each test function and each algorithm, ten independent runs are executed. For the comparison purposes, 40,000 evaluations were considered for all algorithms.  
Table 2 illustrates the results for minimizing the test functions using the mentioned algorithms, including the minimum (Min.), maximum (Max.), mean, and standard deviation (SD) values for each function. The desired optimal value of minimizing all the ten benchmark functions is zero.  
According to the results presented in Table 2, it can be observed that QODELFA gives better values than the other algorithms regarding most of the test functions. Those results verify the robustness of the proposed algorithm.  
The effectiveness of QODELFA was further validated by comparing its convergence characteristics with those of the algorithms listed in Table 2. The functions: Rastrigin, Perm \(0,d,\beta\), Power sum, and Rosenbrock were selected for this test as they are categorized in different groups [37]. A total of 20,000 evaluations were considered for all algorithms in this comparison. Figures 2-5 illustrate the evolution of the mean value of the four functions versus the iteration number of all algorithms. The \"semiology\" command is used in Matlab for plotting, since the logarithmic scale on the \(y\)-axis is needed to clearly depict the comparison. As obviously shown in Figures 2-5, the QODELFA effectively converges to the optimal values faster than the other algorithms.
# Impact of Dataset Size on Classification Performance:
## 2 Methodology
### Classification Models  
We used six different widely-used classifiers, which include probabilistic classification using naive Bayes (NB), decision function classification using support vector machine (SVM), neural network (NN), decision tree induction C4.5 (DT), tree ensemble random forest (RF), and ensemble adaptive boosting (AB). Below, we shed light on these classification models:  
* **SVM**: The objective of the SVM algorithm is to find the hyperplane in the data that gives the largest separation margin between data instances and classifies them into two classes. It can be explained based on four basic concepts, the separating hyperplane, the maximum margin hyperplane, the soft margin, and finally the kernel function [25; 26].
* **NB**: It is a supervised learning method based on the Bayesian theorem. Therefore, it is considered as a statistical method for classification. It works by calculating explicit probabilities for hypotheses. NB models use the method of maximum likelihood for parameter estimation. Literature showed that it often performs better in many complex real world applications. One of the features of this method is that it is robust to noise in data, and it can estimate the parameters using a small training set [25; 26; 27].
* **DT**: A Decision Tree is constructed as a binary classification tree, based on the training data. In the tree structure, class labels are represented by leaf nodes, while the internal nodes represent the conjunction of features that assess class. There are several DT algorithms, Notable decision tree algorithms include: ID3 (Iterative Dichotomiser 3), C4.5 (successor of ID3), and CART (Classification And Regression Tree) [25; 26]. In this study, the C4.5 algorithm for DT is selected for deploying the DT classification.
* **NN**: It is one of the most widely-used classification models, as it is a good alternative to several traditional classification methods. One of the main advantages of NN is that it is a data-driven self-adaptive method, in that it is adjustable to the data without the need for explicit specification of the underlying model. Another feature of NN is that it represents a nonlinear model-free method [25; 26; 27].
* **RF**: As the name implies, the RF classifier consists of a number of individual decision trees. Each of the individual decision trees in the forest is used for a majority voting of the output class, the class that has the majority of votes becomes the model's predicted class [25].  
Figure 1: Dataset partitioning algorithm.  
* **AB**: One of the most important \"families\" of ensemble methods is Boosting, and within the boosting algorithms, the adaptive boosting (AB) algorithm is one of the most important. The adaptiveness of AD comes in the form of successive weak learners and fine-tuning them in favor of those instances misclassified by previous classifiers. Some of the properties of AD is that it is sensitive to noisy data and outliers, but, in some cases, it can be less susceptible to the overfitting than other learning algorithms [28].
## 5 Discussion  
Our experiments have illustrated how various machine learning models and their application in the Federated Learning approach can detect DDoS attacks in IoV. The first set used all features of the dataset, which included a huge amount of details about communication between vehicles and infrastructure. Not all of these features were related to DDoS attack detection. In applying the Gini index, we narrow down the features to the top 25. This helped in retaining a few of the most critical bits of information needed for accurate DDoS detection while getting rid of possibly irrelevant or redundant data. The models' performance was slightly better using the selected features compared to using all features. The slight improvement with selected features indicates that they contained the essential information to accurately detect DDoS. Federated Learning (FL) presents challenges such as communication overhead, data heterogeneity, and security risks. Communication overhead can be minimized by reducing the frequency of model updates, while data heterogeneity can be managed through personalized models or weighted averaging. Moreover, FL's security risks, like model poisoning, can be mitigated with differential privacy and secure  
Figure 8: Running time of models under different scenarios.  
Figure 9: Memory usage of models under different scenarios.  
multi-party computation techniques. Among the models tested, Random Forest and XGBoost classifiers showed the highest performance across all setups. XGBoost, known for its efficiency, slightly outperformed Random Forest, especially with the selected features. These models effectively handled the dataset's complexity and interactions. In contrast, simpler models like the K-Neighbors Classifier had lower accuracy but still provided useful insights. Gradient Boosting performed similarly to Random Forest but was slightly behind XGBoost. KNeighborsClassifier, although simpler, had an accuracy of 85%, highlighting the importance of model complexity for capturing intricate patterns in IoV data.  
Our system works efficiently by reducing time complexity and memory usage. The Gini Index-based feature selection cuts down unnecessary data, speeding up processing. On top of that, Federated Learning (FL) trains the model on devices themselves, so instead of transferring large amounts of data, only lightweight updates are shared, saving both time and memory. These improvements make the system more suitable for real-time DDoS detection in IoV systems, as shown visually in Figures 8 and 9, and in the tabular form in Table 7, comparisons are presented.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 3 Trajectory Planning for the MR-SPS Considering Dynamic Constraints
### Description of the Optimization Problem
 \right] \tag{34}\]  
The attitude kinematics equation of the main structure can be expressed as follows:  
\[\dot{\mathbf{Q}}_{a}=\frac{1}{2}\mathbf{G}_{a}\mathbf{\omega}_{a} \tag{35}\]  
where \(\mathbf{G}_{a}=[-\mathbf{q}_{a}\quad(q_{a0}\mathbf{E}_{3}+\mathbf{q}_{a}^{\times})^{T}]^{T}\), and it is easy to find that \(\mathbf{G}_{a}^{T}\mathbf{G}_{a}=\mathbf{E}_{3}\) where \(\mathbf{E}_{3}\) is the identity matrix. So \(\mathbf{\omega}_{a}\) and \(\mathbf{\omega}_{a}\) can be expressed as:  
\[\mathbf{\omega}_{a}=2\mathbf{G}_{a}^{T}\ddot{\mathbf{Q}}_{a} \tag{36}\]  
\[\dot{\mathbf{\omega}}_{a}=2\mathbf{\dot{G}}_{a}^{T}\dot{\mathbf{Q}}_{a}+2\mathbf{G}_{a}^{T} \ddot{\mathbf{Q}}_{a} \tag{37}\]  
Substituting Equations (28)-(34), (36) and (37) into Equations (16) and (17), the compact matrix notation form of \(\mathbf{T}_{c}\) and \(\tau_{scj}\) can be written as \([\mathbf{T}_{c}]_{3m\times 1}\) and \([\tau_{scj}]_{m\times 1}\). Compared with \(\mathbf{T}_{c}\), the value of \(\tau_{scj}\) is much smaller, so only the value of \(\mathbf{T}_{c}\) is constrained. Considering that the maximum torque of \(\mathbf{T}_{c}\) is \(\mathbf{T}_{c\text{max}}=[T_{c1\text{max}}\quad T_{c2\text{max}}\quad T_{c3\text{ max}}]^{T}\) and the minimum torque of \(\mathbf{T}_{c}\) is \(\mathbf{T}_{c\text{min}}=[T_{c1\text{min}}\quad T_{c2\text{min}}\quad T_{c3\text{ min}}]^{T}\), \(\mathbf{\delta}\)\(m\) inequality constraints are obtained as follows:  
\[\mathbf{1}_{m\times 1}\otimes\mathbf{T}_{c\text{minBy calculation at the discretization points, the continuous angular trajectory optimization problem for the MR-SPS can be converted into the following small-scale nonlinear programming problem:  
\[ \begin{array}{ll}\min&J=\sum\limits_{k=1}^{2}\eta_{k}\\ \left[X_{\gamma}\right]_{(n_{\gamma}-3)\times 1}&\left[X_{\theta_{j}}\right]_{(n_{ \theta}-3)\times 1}(j=1,2)\\ \text{s.t.}&\mathbf{1}_{m\times 1}\otimes T_{\text{cmin}}\leq\left[\mathbf{T}_{c} \right]_{3m\times 1}\leq\mathbf{1}_{m\times 1}\otimes T_{\text{cmax}}\end{array}  \tag{40}\]  
\(\left[X_{\gamma}\right]_{(n_{\gamma}-3)\times 1}\) and \(\left[X_{\theta_{j}}\right]_{(n_{\theta}-3)\times 1}(j=1,2)\) are the unknown Bezier coefficients, a total of \(n_{r}+2n_{\theta}-9\) variables need to be optimized.
# Analyzing Demand with Respect to Offer of Mobility
## 4 Model and Analyzer
### Modelling Public Transport Service Offer  
The modelling of _public transportation services_ from _offered_ side by a (set of) mobility operator(s) should lead to evaluate the people number moving from a location to another. In turn the movement of people can be re-produced in the form of an offered Origin-Destination Matrix (ODM) for various time slots. The goal is to verify if people demand is satisfied by means of the service planning of the public transport. Therefore, we have considered the time set \(T=\{mug\), \(aft\}\), for the two main time slots in working days (morning (\(mug\)) and afternoon (\(aft\))). In Table 2 the used notations in the DORAM model are shown. The morning offer \(\mathrm{ODM}\ O_{|L|\times|L|}(mug)\) is defined by means of the matrix having in the cell \(O_{i,j}(mug)\left(O_{i,j}(aft)\right)\) the total number of people moving from locality \(l_{i}\) to \(l_{j}\), in the morning time (afternoon time). Formally, the cells \(O_{i,j}\left(t\in T\right)\) are calculated as in (2). \[O_{i,j}(t)=\sum_{m=1}^{|M|}\sum_{\mathbf{o}=1}^{|\mathrm{O}|}\mathds{t}_{i,j}( t,\ m,\ \mathbf{o}).\ c(m,\ \mathbf{o}) \tag{1}\]  
where \(\mathds{t}_{i,j}(t,\ m,\ \mathbf{o})\) denote the number of vehicle trips with mobility modality \(m\in M\) (e.g., bus, tram) and mobility operator \(\mathbf{o}\in\mathbb{O}\) (e.g., ATAF, Busitalia) from locality \(l_{i}\) to locality \(l_{j}\), in \(T=\{mug,aft\}\). Moreover, \(c(m,\mathbf{o})\) denotes the vehicle capacity with modality \(m\) and operator \(\mathbf{o}\). The operator \".\" denotes the multiplication between scalars. Figure 1: The general DORAM architecture.
# Laser Finishing of Ti6 Al4V Additive Manufactured Parts by Electron Beam Melting
###### Abstract  
In this work, the feasibility of laser surface finishing of parts obtained by additive manufacturing (AM) was investigated. To this end, a 450 W fiber laser (operating in continuous wave, CW) was adopted to treat the surface of Ti-6 Al-4V samples obtained via electron beam melting (EBM). During the tests, different laser energy densities and scanning speeds were used. In order to assess the quality of the treatment, either the as-built or the treated samples were analyzed by means of a three-dimensional (3D) profilometer, digital microscopy, and scanning electron microscopy. Analysis of variance (ANOVA) was performed to check which and how process parameters affected the finishing. The results show that, in the best conditions, the laser treatment reduced surface roughness by about 80%.  
EBM; surface roughness; microhardness; fiber laser Article
# Modeling of Transmission Compliance and Hysteresis Considering Degradation in a Harmonic Drive
###### Abstract  
Transmission compliance and hysteresis behaviors are associated with harmonic drives. The experimental observation of a harmonic drive showed that these behaviors are significantly degraded with increasing service time, and their accurate modeling is expected to improve the performance of the control system of harmonic drive-based devices such as robot manipulators. In this paper, a new model is proposed to capture transmission compliance and hysteresis and their degradation. The phenomenon is represented through a combination of the nonlinear stiffness component and the micro-sliding friction in the tooth engagement area. The proposed model considered the multi-tooth meshing and the interference effects of the reducer. The parameters of the model were identified using optimization techniques. Numerical simulations and experimental data using a specialized harmonic drive test device were compared to demonstrate the proposed model.  
transmission compliance; hysteresis degradation; interference; multi-tooth meshing +
Footnote †: journal: Applied sciences
# Research on Key Technologies of 260 \({}^{\circ}\)C/210 MPa Ultra-Deep Logging Equipment
## 3 Research on Key Technologies of Ultra-High-Temperature and High-Pressure Logging
### 260 \({}^{\circ}\)C/210 MPa Low Power Consumption High-Integration Circuit System
#### 3.2.2 Low Power Consumption Design of Acquisition and Processing Circuits  
When designing the downhole logging instrument system, priority is given to reusing acquisition and processing circuit systems. For instance, common instruments such as four parameters, continuous inclination, caliper, dual laterolog, and digital acoustic instruments are designed using a single circuit board, while spectral, neutron, and density instruments use a different circuit board. The basic functions such as analog detection and signal sourcing are implemented through hardware virtualization, and low-power-consumption components rated for 175 \({}^{\circ}\)C are selected to meet the requirements for acquisition control and weak signal processing for different instruments, as shown in Figure 10.  
The acquisition and processing circuit system achieves high-precision downhole signal acquisition across a broad frequency range, meeting the measurement needs of various instruments. This is accomplished through technology streamlining, chip selection, design simulation, and functional verification. Based on the circuit design and simulation, and according to the specific design functions of different instruments, combined with the requirements for acquisition accuracy, control, computing power, and communication, the system ensures high-precision signal measurement for different types of instruments.
# The Experimental Study of Innovative Methods Regarding the Removal of Sm(III)
## 1 Introduction  
Russia has no less than 20% of world's REE (rare earth elements) reserves but the volume of the extraction of raw materials is only 2% of the world's extraction volume [1, 2]. The production of products with REM in Russia is less than 1% of world's production volume [3, 4, 5].  
REE is used in various industry fields (Figure 1) such as in: magnet production (samarium, neodymium, and dysprosium); lumininophores (yttrium, europium, and terbium); capacitors (lanthanum); optical glass (lanthanum and cerium); ceramics (yttrium), etc.  
Current Russian and foreign REM production techniques are rated for small-scale production. A special feature of available Russian raw material sources is the REM low content and complex chemicomineralological composition. On the achieved technological level, processing of this type of raw materials is unprofitable [6, 7, 8]. The complex systematic study of the interaction of structural components in nitrate systems of lanthanides of the cerium family and the representatives of the Li, Na, and K chemical group established the formation of the representative class of Ln alkali coordination nitrates [9, 10, 11]. The findings form the basis for revealing and controlling the phase state of processing facilities in preparatory stages when forming construction material coating layers, as well as the determination of process and functional dependences for changing the composite material properties [12, 13, 14]. In addition, the studies conducted at Kola Peninsula, one of the mostdeveloped Arctic regions in Russia, are of interest. Ores mined at the existing deposits may be used for the production of up to 30 different concentrates. Materials produced from local mineral deposits include metallic nickel; cobalt; copper; titanium; niobium; rare earth metals; steel; sealants; electrode coating components and welding fluxes; sorbents for burial of nuclear wastes and wastewaters containing heavy non-ferrous metals and other pollutants; highly porous heat-insulating and fire-resistant materials; cement; and concrete with improved frost resistance [15; 16].  
The three methods of lanthanide cations removal are studied, namely ion flotation, solvent sublimation, and extraction.  
The work objective is an experimental and thermodynamic study of the possibility of selective REE removal (by the example of Sm+3) by adsorptive bubble methods.
# HAIPO: Hybrid AI Algorithm-Based Post-Fabrication Optimization for Modern 3D NAND Flash Memory
## 4 HAIPO: Hybrid AI Algorithm-Based Post-Fabrication Optimization
### Additional Optimization Modules: Process-Aware and Multi-Objective Optimization  
To further improve the entire optimization process, we devise two modules and integrate them with our HAIPO: (_i_) process-aware optimization and (_ii_) multi-objective optimization.  
**Process-Aware Optimization.** As explained in Section 3, strong process variability originating from the 3D manufacturing process results in different characteristics of NAND flash chips even in the same eFuse configuration, thus increasing the complexity of post-fabrication optimization. Due to process variability, even NAND flash chips within the same wafer can exhibit different performance and reliability. (NAND flash memory is fabricated on the wafer, and more than hundreds of NAND flash chips are produced in a single wafer.) Moreover, blocks within a single NAND flash chip and WLs in a block also represent different characteristics. Therefore, achieving the global optimal eFuse configuration for different NAND flash chips becomes more challenging. To address such challenges, we introduce process-aware optimization that efficiently compensates for a noise factor from process variability. Figure 11a illustrates the basic concept of process-aware optimization technique. For individual NAND flash chips, the EA calculates the achievement score and ranks the score of each eFuse configuration. The EA selects two eFuse configurations from different NAND flash chips and generates a new population through the crossover and  
\begin{table}
\begin{tabular}{c c} \hline \hline Model & Sklearn GPR (Gaussian Process Regressor) \\ \hline Kernel & Radial Basis Function Kernel + Constant Kernel \\ \hline Normalization & Standard Scaling \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of Bayesian simulation.  
Figure 10: Decision of final populations with the Bayesian model.  
mutation. The EA evaluates the achievement score of a new population in different NAND flash chips and repeats this procedure for all generations to obtain the effect of circulating eFuse configurations on all NAND flash chips. By doing so, our HAIPO efficiently achieves global optimization for all target NAND flash chips with different characteristics.  
**Multi-objective Optimization.** Various objectives (i.e., requirements) should be satisfied in our optimization process. However, as the number of objectives increases, the total time for optimization becomes longer. To efficiently optimize multiple objectives, HAIPO introduces an adaptive fitness function that performs optimization by flexibly changing the target objectives instead of optimizing all objectives simultaneously. As shown in Figure 10(b), HAIPO selects reliability objectives in the initial step. It gradually expands the target objectives by adding performance in the mid-step and robustness in the final step. This approach makes our HAIPO reduce the optimization TAT efficiently (e.g., by up to 20%).