esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 6 Conclusions
The SMAP dataset employed in this study has been available to the scientific community since 2015. Nowadays it can be downloaded from Earthdata Search engine. The dataset corresponds to the uncalibrated I/Q samples collected by the SMAP radar working in receiver mode only. A calibrated dataset including the one used to develop this manuscript will be publicly available in the future under SMAP NSDIC repository [[[https://nsidc.org/data/smap](https://nsidc.org/data/smap)]([https://nsidc.org/data/smap](https://nsidc.org/data/smap)), accessed on 1 January 2022]. The authors would like to thank the SMAP project team to keep providing and maintaining the collected of the SMAP radar receiver data that makes this project possible. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
presented a machine learning-based approach for chatter prediction in heavy-duty milling operations, validating the potential of predictive analytics to anticipate instability before defects occur [12]. Such frameworks, which integrate empirical modeling, machine learning, and sensor feedback, form a promising foundation for enhancing machining autonomy, robustness, and real-time adaptability in robotic manufacturing systems. Traditional regression models often struggle to accurately capture these nonlinear dependencies, making it difficult to develop reliable predictive models for machining optimization. To address this challenge, machine learning techniques have been extensively explored for predictive modeling in machining. Various algorithms, including support vector machines (SVM), regression models, and ensemble learning approaches, have been employed to model and optimize machining parameters effectively [13]. Among these, gradient boosting algorithms have demonstrated superior performance in machining parameter optimization due to their ability to handle complex, nonlinear relationships. XGBoost (Extreme Gradient Boosting), introduced by [PERSON] and [PERSON] [14], has been widely recognized for its effectiveness in capturing intricate nonlinear dependencies in regression problems. Unlike traditional regression models, XGBoost leverages an ensemble of decision trees, improving predictive accuracy through iterative learning and regularization. Its ability to handle nonlinearities makes it particularly well-suited for machining optimization, where process dynamics are influenced by multiple interacting parameters. In this study, XGBoost is employed to predict the optimal feed rate under varying machining conditions by integrating cutting force dynamics with data-driven modeling. This approach allows for real-time feed rate optimization, minimizing chatter risks while enhancing machining efficiency. Compared to conventional empirical methods, XGBoost offers superior generalization to unseen machining conditions, reducing the reliance on extensive experimental trials. The primary objective of this study is to develop an XGBoost-based approach for feed rate optimization in machining. The methodology involves constructing a GA-calibrated cutting force model, analyzing the impact of feed rate on chatter intensity, and training an XGBeressor model to predict optimal feed rates for given machining conditions. By integrating cutting dynamics with machine learning, this research aims to enhance machining efficiency and stability while reducing the need for time-consuming experimental validations. By generating physics-informed labels through a GA-calibrated force model, our GA-XGBoost framework enhances prediction accuracy while reducing experimental overhead--an advantage over typical hybrid ML methods that rely on empirical chatter classification. An overview of the proposed workflow is illustrated in Figure 1, providing a visual summary of the complete methodology adopted in this study.
## 3 Results and Discussion  
In this study the MAX-DOAS tropospheric NO\({}_{2}\) vertical columns, referred to hereafter as NO\({}_{2}\) VCDs, are calculated by integrating the NO\({}_{2}\) vertical profiles, retrieved by the inversion algorithm (MMF) which is applied to the dSCDs. The NO\({}_{2}\) VCDs can also be derived by applying either the geometrical approximation [7] to the dSCDs measured at 30 or 15\({}^{\circ}\) elevation angles or to a mix of them, or by using proper differential Air Mass Factors (dAMFs), calculated by RTM simulations (e.g., [20]). In any case, the NO\({}_{2}\) vertical columns are a good indicator of the pollution levels around a measurement site; however, when inversion techniques are applied, additional information can be retrieved for the concentration levels near the surface, which is essential especially in the context of air quality monitoring. In this section, the temporal and spatial variability of NO\({}_{2}\) VCDs and surface concentrations using MAX-DOAS observations is discussed. The temporal variability is assessed by calculating seasonal (Section 3.1), diurnal (Section 3.2) and weekly cycles (Section 3.3) for different seasons, which are compared with the in situ data. The spatial NO\({}_{2}\) variations are investigated (Section 3.4) by comparing the concentration levels at the four azimuth viewing directions for two different time periods during the day.
## 3 Validation
### Numerical Model
#### 3.2.2 Aerodynamic Load Coefficients of the Train  
[PERSON] et al. [25] have pointed out the significant consistency in the experimental results for the aerodynamic load coefficients between static and moving train models. In a separate study, [PERSON] et al. [26] conducted experiments using static train models to measure the aerodynamic load coefficients of the ICE3 train under different conditions. Considering the similarity in external appearance between the CRH3 train and the ICE3 train, the numerical simulation results are compared with the experiment results conducted by [PERSON] to confirm the accuracy of the aerodynamic load coefficients acquired through simulation in this study. Figure 7 shows a CRH3 train-ground model at a 1:15 scale consistent with [PERSON]'s experiment. The computational domain has a dimension of 8 m in width, 5 m in depth, and 15 m in height, and the train model is positioned in the center. The airflow velocity in the simulation is 78 m/s, and various wind yaw angles (10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), and 60\({}^{\circ}\)) are considered to investigate their influence on the aerodynamic load coefficients of the tail car.  
Figure 8 presents a comparison between the aerodynamic load coefficients of the train, obtained through numerical simulation, and the results from [PERSON]'s experiments. The comparison shows that the variation trend of the aerodynamic load coefficients through numerical simulation aligns with the results obtained from the experiment, with an acceptable margin of error. This indicates that the aerodynamic load coefficients obtained through numerical simulation for the train in this study are accurate and reliable.  
Figure 8: Comparative analysis of aerodynamic load coefficients of tail car: (**a**) lateral force, (**b**) lift force, and (**c**) rolling moment.  
Figure 7: The 1:15 scale model of the tail car: (**a**) ICE3 train [26]; (**b**) CRH3 train.  
In conclusion, the accuracy of the method employed in this paper, along with the established CFD model, has been affirmed by comparing the surface aerodynamic pressure variations and aerodynamic load coefficients of the HST obtained through numerical calculations with the experimental results documented in the existing literature.
## 4 Implementation and Discussion
### Limitation and Future Work  
Although the study covers most of the collaborative medical decision-making situations and factors, there are still a few more factors that will be considered in the future version of this study. A few limitations, drawbacks, and future plans of this study are discussed here.
# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 3 Results
### Stability and Terrain Adaptation  
One of the key challenges in applying Mecanum and omni wheels in assistive mobility devices is maintaining stability on uneven ground and overcoming obstacles. Several studies have proposed mechanical and control solutions to improve wheelchair performance on slopes, curbs, and rough terrain. For instance, [PERSON] et al. [28; 29; 30] designed a six-wheeled wheelchair (MEBot) with an active seat-leveling system that uses pneumatic actuators to keep the seat level during incline navigation, preventing tipping. MEBot successfully climbs curbs and steep ramps, although full holonomic mobility is limited on very uneven terrain. Another solution by [PERSON] and [PERSON] [31] integrated a spring damper suspension into a four-wheeled Mecanum platform. Vibration was reduced by ~75% and minimized tilting (<0.6\({}^{\circ}\)), improving ride comfort. [PERSON] [32] proposed a hybrid 4 \(\times\) 4 platform with regular rear wheels and omni front wheels to improve grip on slippery or uneven ground. In [33], the author introduced a frame tilt mechanism adjusting the center of gravity while climbing curbs, enhancing stability. [PERSON] et al. [34] emphasized the role of independent suspension in Mecanum-based wheelchairs to improve smoothness and reduce vibrations. [PERSON] et al. [35] modified a six-wheeled electric wheelchair frame to climb ~96 mm curbs without increasing wheel size. The data were collected and summarized in Table 1.  
Figure 2: Prisma workflow.
## 4 Results and Discussion
### Model Validation Results  
Based on the actual measurement results in various cases, the airflow speed in the duct varies with \(V=9\) m/s, 12 m/s, 15 m/s, and 18 m/s. The average dust concentration results were measured at a cross-section located 1.0 m, 3.0 m, and 6.0 m from the mirror. Measurements were taken at three points, A(1, 1.5), B(2.25, 1.5), and C(3.5, 1.5), arranged as shown in Figure 1, and the average concentration results were recorded. The measurements were taken at intervals between 9 and 10 min using a Knomax dust concentration meter. The actual and simulated measurement results are shown in Figure 23.  
Figure 21: The maximum dust concentration at a position 6.0 m from the face corresponds to the location of the air duct.  
Figure 22: The maximum dust concentration at a position 1.5 m from the floor corresponds to the location of the air duct.  
From the data in Figure 23, it can be seen that the simulated dust concentration results are consistent with the actual measured results, with a relative error ranging from 4.89% to 14.7% and an average of 6.03%. Therefore, the numerical simulation results can accurately reflect the actual conditions in the Mong Duong coal mine.
## Appendix A The Derivation Process of the Frequency-Domain Transfer Matrix in Section 3
Substituting the above vectors into Equation (16) gives  
\[\widetilde{\mathbf{\eta}}(z,s)=\mathbf{E}(z,s)\widetilde{\mathbf{\eta}}_{0}(s) \tag{20}\]  
Then combined with Equation (10) yield  
\[\widetilde{\mathbf{\Phi}}(z,s)=\mathbf{S}(s)\mathbf{E}(z,s)\widetilde{ \mathbf{\eta}}_{0}(s) \tag{21}\]  
When \(z=0\), \(\mathbf{E}(0,s)=\mathbf{I}\)  
\[\widetilde{\mathbf{\Phi}}(0,s)=\mathbf{S}(s)\widetilde{\mathbf{\eta}}_{0}(s) \tag{22}\]By substituting Equation (A16) to Equation (A15) gives  
\[\widetilde{\Phi}(z,s)=\mathbf{M}(z,s)\widetilde{\Phi}(0,s)\] (A18)  
where \(M(z,s)\) is the field transfer matrix of the pipeline, which can be expressed as  
\[\mathbf{M}(z,s)=\mathbf{S}(s)\mathbf{E}(z,s)\mathbf{S}^{-1}(s)\] (A19)  
In order to obtain the initial end state variables of pipeline, boundary conditions must be set at both ends of the pipeline. Assuming the pipeline length is \(L\), there are seven boundary conditions at each end of the pipeline (\(z=0\) and \(z=L\)) as follows  
\[\left[\mathbf{D}_{0}(s)\right]_{\gamma\times 14}\!\!\left[\widetilde{\Phi}(0,s)\right]_{14\times 1}=\left[\mathbf{Q}_{0}(s)\right]_{\gamma\times 1}\] (A20)  
\[\left[\mathbf{D}_{L}(s)\right]_{\gamma\times 14}\!\!\left[\widetilde{\Phi}(L,s )\right]_{14\times 1}=\left[\mathbf{Q}_{L}(s)\right]_{\gamma\times 1}\] (A21)  
where \(\mathbf{D}_{0}(s)\) and \(\mathbf{D}_{L}(s)\) are the boundary matrices with dimensions of \(7\times 14\), \(\mathbf{Q}_{0}(s)\) and \(\mathbf{Q}_{L}(s)\) are the excitation matrices with dimensions of \(7\times 1\) at each end of the pipeline. According to Equation (A18), the transfer relationship between the state vectors at both ends of the pipeline is  
\[\widetilde{\Phi}(L,s)=\mathbf{M}(L,s)\widetilde{\Phi}(0,s)\] (A22)  
The initial end state vectors of the pipeline are as follows  
\[\widetilde{\Phi}(0,s)=\mathbf{D}\,\star^{-1}(s)\mathbf{Q}(s)\] (A23)  
where  
\[\mathbf{D}^{\star}(s)=\left( \begin{array}{c}\mathbf{D}_{0}(s)\\ \mathbf{D}_{L}(s)\mathbf{M}(L,s)\end{array} \right)\] (A24)  
\[\mathbf{Q}(s)=\left( \begin{array}{c}\mathbf{Q}_{0}(s)\\ \mathbf{Q}_{L}(s)\end{array} \right)\] (A25)  
When solving the FSI 14-eqation model of the pipeline, the overall field transfer matrix needs to be established. \[\mathbf{M}_{\text{global}}(L,s)=\mathbf{M}_{N}(L_{N},s)\cdots\mathbf{M}_{i}(L_ {i},s)\cdots\mathbf{M}_{1}(L_{1},s)\] (A26)  
where \(L_{i}\) is the length of each pipeline section, \(\mathbf{M}_{i}\) is the field transfer matrix of each pipeline section. According to Equation (A23), Equation (A26) and Equation (A18), the state variables at any position of the pipeline can be obtained.
## 3 System Model
### PPI Interleaver Mechanism
For example, the interleaving pattern of \(\mathrm{K_{3}}\equiv\mathrm{K_{1}}(\mathrm{K_{2}})\) and interleaving pattern of \(\mathrm{K_{4}}\equiv\mathrm{K_{2}}(\mathrm{K_{3}})\). The generation of the next user pattern depends on the patterns of the previous two users and requires only a single cycle. For instance, the interleaving pattern of \(\mathrm{K_{3}}\) is derived as \(\mathrm{K_{1}}(\mathrm{K_{2}})\), and the interleaving pattern of \(\mathrm{K_{4}}\) is derived as \(\mathrm{K_{2}}\) (\(\mathrm{K_{3}}\)). Using the progressive pattern interleaving method reduces the memory requirements at each stage of the communication system compared to random interleaving (RI) and other interleavers discussed in the literature, with the added benefit of enhanced security. Overall, PPI surpasses other interleavers suggested in the literature in terms of bandwidth requirements and implementation complexity. Following the interleaver assignment to each user, orthogonal sub-carrier mapping is implemented. Orthogonal sub-carriers can be mapped [40; 41] in a localized manner or in a distributed manner. Distributed sub-carrier mapping distributes modulated symbols across the whole channel for a single user, whereas localized sub-carrier mapping assigns symbols in a contiguous manner for a single user. The corresponding multiple access schemes for SC-FDMA-IDMA system are termed as distributed frequency-division multiple access (DFDMA) and localized frequency-division multiple access (LFDMA). An appropriate sub-carrier mapping results in a frequency domain data vector as follows [19; 20]:  
\[\mathrm{X_{k}}=[\mathrm{X}(0),\ \mathrm{X}(1)\ldots\mathrm{X}(\mathrm{N}-1)]^{ \mathrm{T}} \tag{3}\]  
Here, for a particular user (N-S) orthogonal sub-carriers out of total N orthogonal sub-carriers have been assigned zero value, zero padding is applied. Following this, frequency domain signal is transformed to time-domain signal through N point IFFT operation after implementing appropriate sub-carrier mapping. Further, the resulting time-domain signal is transmitted through a multipath channel.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 6 Numerical Result: Storage Overhead  
In this section, we show that the storage overhead for metadata servers to manage those Kinetic Drives is affordable.  
For example, we assume there are 32 regions worldwide. In each region, there are 2048 Kinetic Drives including drives for _original data_ and additional backup copies for other regions. In that case, the first five bits in a _key_ are used to decide the region, and the additional 11 bits are used to locate drives within a region.  
We have 32 metadata servers in total. For each metadata server, there are 2048 rows and four columns (_key_ range plus three IPs) in its mapping table, as shown in Table 2, assuming all drives are used. The _key_ range takes 5 bits + 11 bits = 16 bits = 2 bytes to store. Furthermore, to support the drive splitting mentioned in Section 5.5, the metadata server maintains the smallest and largest _keys_ for each drive. Each _key_ is up to 4 KB, so that each entry in the first column for each row takes 4 KB * 2 + 2 B \(\approx\) 8 KB. Assuming IPv6 is used for the drives, each of the second to fourth columns takes 128 bits = 16 B. Hence, each row in the mapping table takes fewer than 9 KB (if a shorter _key_ is used, it can be further reduced). The total storage overhead for each metadata server is less than 9 KB * 2048 = 18,432 KB = 18 MB, which is affordable (To be precise, we also need to include the access frequencies of drives accessed by all regions mentioned in Section 5.4. However, the storage overhead of these numbers is relatively very small compared to the current result, so that it can be negligible and does not affect our conclusion.), We can see that the dominating storage overhead for each row in the mapping table is the cost to store the smallest and greatest _keys_ for drive splitting, and others can be negligible.  
Based on the above calculation, we can find that the storage overhead for each metadata server is approximately up to 9\(M\) KB (9 KB * M), where \(M\) is the number of Kinetic Drives for the region. Table 3 summarizes the total storage capacity of a region and the storage overhead of its metadata server for different numbers of drives. For example, only 72 MB of storage overhead are needed for a cluster of 8192 Kinetic Drives to provide a 32,768 TB storage capacity.  
We conclude that the storage overhead for each metadata server is very affordable and roughly linearly proportional to the number of drives in its region. Hence, a good scalability is achieved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of** & **Total Storage Capacity** & **Storage Overhead** \\
**Kinetic Drives** & **(in TB)** & **up to (in MB)** \\ \hline
512 & 2048 & 4.5 \\
1024 & 4096 & 9 \\
2048 & 8192 & 18 \\
4096 & 16,384 & 36 \\
8192 & 32,768 & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Storage Capacity and Overhead of a Region (4 TB per drive).
## 3 Results and Discussion
### Removal Efficiency of Copper
When 0.1 m HAc is used as an electrolyte (T2 and T6) for EK remediation, the H\({}^{+}\) ions in HAc can promote the dissolution of copper and form Cu\({}_{x}\)(AC)\({}_{y}\) in the form of various complexes and chelates and can also adjust the pH. Thus, the copper removal efficiency is improved significantly. The average removal efficiency for T2 and T6 are 39% and 54%, respectively. When CA is used as an electrolyte, the removal efficiency of Cu from soil can be significantly improved. The average removal rates of T3 and T7 are 51% and 62%, respectively. This is because the H\({}^{+}\) ions produced by CA can acidify the contaminated soil, and the acidic conditions are conducive to the migration of Cu from the soil. Moreover, CA has one -OH group and three -COOH groups that can form soluble copper chelates in soil, which can promote the desorption of Cu in the soil and remove it [28]. Figure 6: Copper removal effect before and after electric repair. (a) EK Remediation; (b) EK\(-\)PRB Remediation. Similar to CA, PASP has multiple -COOH groups that can form soluble, negatively charged copper chelates. They migrate toward the anode under the action of an electric field. However, the pH value of 1% PASP is 9. The alkaline conditions hinder the removal of Cu, so its removal rate is lower than that of the CA electrolyte. After 10 days of electric repair, the removal rates of T4 and T8 are 40% and 55%, respectively, with some accumulation in the S2 section.
# An LSM-Tree Index for Spatial Data
## 2 Related Works
### LSM-Tree Structure  
The basic structure of an LSM-tree [17] is shown in Figure 1. When a writing operation arrives, it first caches data in the memory MemTable. When the memory is complete, it turns the MemTable into an immutable MemTable and then immediately flushes the immutable MemTable to the disk through a minor compaction operation to achieve sequential I/O processes and avoids the significant system overhead caused by random I/O. Moreover, to improve the read performance, the LSM-tree performs a major compaction operation according to the storage situation of SSTable in the disk to reduce the redundancy of the data stored there and to retain the data of an SSTable in the same layer in order by the key. The key range does not overlap (except the level 0 layer).
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.2 Validation of RCFBS Using Numerical FEA
Although the frequency-based substructuring (FBS) method employs consistent generalized coordinates and degrees of freedom across substructures, further discretization through finite element analysis (FEA) can enhance the accuracy of the results.
## 6 Simulation Results
### Robustness to Geometric Attacks
#### 6.4.2 Robustness against Signal Amplitude Scaling  
The multiplication of a constant value \(\delta\) by the amplitude of a signal function \(f(t)\) enables generation of a scaled signal \(f^{*}(t)=\delta f(t)\), while the values on the time axis remain unchanged. In this test, we examined the effectiveness of the proposed method in resisting scaling attacks of signal amplitude. For conducting the present test, an ECG signal called \"Record_232\" was used, which was distorted by scaling attacks of factors \(\delta=0.5\) and \(\delta=1.5\). Then, the copyrights of the attacked signals were checked via our security scheme. The results of the actual test, presented in Figure 22, showed that the BER values were equal to zero. This indicates that our scheme can effectively resist the amplitude scaling attack. This finding is because, in the feature vector calculation stage, the signal amplitude must first be normalized between 0 and 1, which allows for avoidance of amplitude scaling attacks.  
Figure 21: Original EEG signal and its circularly shifted versions with the corresponding BER values.  
Figure 22: Original ECG signal and its amplitude scaled versions with the corresponding BER values.