esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Conversion of XYZ to NEU  
The above model directly obtains the three-dimensional coordinates \((X,\ Y,\ Z)\) of the monitoring points in a specific spatial Cartesian coordinate system. Deformation monitoring is concerned with the change in the displacement of the monitoring point in the horizontal and elevation directions. Therefore, it is necessary to convert the coordinates \((X_{t_{i}},\ Y_{t_{i}},\ Z_{t_{i}})\) at any moment, \(t_{i}\), into coordinates \((N_{t_{i}},E_{t_{i}},U_{t_{i}})\) in the station-centered horizon coordinate system with the initial coordinate point \((X_{0},\ Y_{0},\ Z_{0})\) as the origin. The conversion equation is as follows:  
\[\begin{cases}\Delta X_{t_{i}}=X_{t_{i}}-X_{0}\\ \Delta Y_{t_{i}}=Y_{t_{i}}-Y_{0}\\ \Delta Z_{t_{i}}=Z_{t_{i}}-Z_{0}\end{cases} \tag{14}\]  
\[(X_{t_{0}},Y_{t_{0}},Z_{t_{0}})\rightarrow(B_{t_{0}},L_{t_{0}},H_{t_{0}}) \tag{15}\]  
\[\begin{bmatrix}N_{t_{i}}\\ E_{t_{i}}\\ U_{t_{i}}\end{bmatrix}=\begin{bmatrix}-\sin B_{t_{0}}\cos L_{t_{0}}&-\sin B_{t _{0}}\sin L_{t_{0}}&\cos B_{t_{0}}\\ -\sin L_{t_{0}}&\cos L_{t_{0}}&0\\ \cos B_{t_{0}}\cos L_{t_{0}}&\cos B_{t_{0}}\sin L_{t_{0}}&\sin B_{t_{0}}\end{bmatrix} \tag{16}\]  
Equation (14) is used to find the cumulative displacements \(\Delta X_{t_{i}}\), \(\Delta Y_{t_{i}}\), and \(\Delta Z_{t_{i}}\) of the monitoring point in the spatial three-dimensional direction at moment \(t_{i}\). Equation (15) is the transformation of the spatial three-dimensional coordinates \((X_{t_{0}},\ Y_{t_{0}},\ Z_{t_{0}})\) of the monitoring point at the initial (\(t_{0}\)) moments into geodesic coordinates \((B_{t_{0}},\ L_{t_{0}},\ H_{t_{0}})\). Equation (16) is used to obtain the coordinates of the monitoring station in the station-centered horizon coordinate system at any moment (\(t_{i}\)), where \(N_{t_{i}}\) denotes the cumulative displacement of the monitoring point in the north-south direction relative to the moment (\(t=0\)) and positive to the north; \(E_{t_{i}}\) indicates the cumulative displacement in the east-west direction, positive to the east; and \(U_{t_{i}}\) indicates the accumulated displacement in the elevation direction, where upward is positive. The monitoring point displacement is obtained from the coordinate difference of two consecutive GNSS measurements.
# Utilization of Phosphogypsum and Sediment in Subgrade Material for Pavement Construction
## 2 Materials and Methods  
### Framework of the Experimentation  
To evaluate the efficacy of stabilization and solidification (S/S) techniques in the remediation of contaminated sediments, an empirical investigation utilizing sediment samples from the Begej Canal was conducted. This canal, a crucial waterway linking the Danube River in Vojvodina, Serbia, with the city of Timisoara in Romania's Tamis County, has been heavily impacted by industrial and agricultural activities. Since 1945, the rapid development in these regions has led to significant pollution of both the sediment and water in the canal. In Serbia, the Begej Canal primarily serves as a discharge conduit for untreated municipal and industrial wastewater, exacerbating its pollution issues. It is estimated that around 700,000 m\({}^{3}\) of sediment needs to be dredged from this canal.  
Phosphopypsum is a by-product generated during the production of phosphoric acid at a local facility. As phosphoric acid is extracted from raw phosphate rock, phosphogypsum is formed and requires careful management due to its chemical properties and potential environmental impacts. Phosphogypsum is stored in facilities designed according to national and European standards. The storage floor features a mineral barrier topped with a waterproof membrane, and drainage pipes are installed on the membrane to collect any accumulated water. These meticulous measures reflect the industry's commitment to mitigating the environmental footprint of phosphogypsum, underscoring the importance of sustainable practices in chemical manufacturing. Additionally, the ongoing need for disposal solutions has sparked interest in exploring potential further uses for phosphogypsum. The primary mineral in PG is gypsum, also known as calcium sulfate, which exists in three distinct forms based on the level of hydration: dihydrate (CaSO\({}_{4}\cdot\)2H\({}_{2}\)O), hemihydrate (CaSO\({}_{4}\cdot\)0.5H\({}_{2}\)O), and anhydrite (CaSO\({}_{4}\)) [1].  
The primary goal of our study was to evaluate the feasibility of repurposing sediment and phosphogypsum treated through S/S methods as potential subgrade material. A thorough examination of contamination levels in the canal's sediment was undertaken, with a specific focus on metal concentrations and associated toxicity. Given the extent of metal pollution, implementing effective remediation strategies is imperative to mitigate ongoing environmental damage [22].
## 3 Results and Discussion  
### Structural Stability and Total Energy  
In general, the EQH compounds have LiMgPdSn-type structures [15]. Three types of crystal structures and their distinct atomic positions of the ScFeRhP EQH compound are shown in Figure 2 and Table 1, respectively. Following the atom-occupation rule, Rh tends to occupy the site D (3/4, 3/4, 3/4), P and Sc tend to occupy the A (0, 0, 0) and C (1/2, 1/2, 1/2) sites, respectively, and Fe tends to occupy the B (1/4, 1/4, 1/4) site. Therefore, for the ScFeRhP compound, type I (see Figure 2) is the most probable configuration owing to its lowest energy.  
Figure 1: Schematics of the densities of states of a (**a**) half-metal (HM), (**b**) spin-gapless semiconductor (SGS), and (**c**) magnetic semiconductor (MS).  
For geometric optimization of the ScFeRhP EQH compound, the crystal cell energy is minimized as a function of the lattice constant. The three possible crystal structures are shown in Figure 2. Each of them has ferromagnetic (FM) and non-magnetic (NM) states. We calculated the energies by the CASTEP software. Figure 3a shows total energies in FM states for the type 1, 2, and 3 structures, respectively, and Figure 3b shows the energies of both FM state and NM state for type 1 structure. Among the considered cases the calculated result that the type 1 structure at the FM state exhibits the minimum energy. More details about the equilibrium lattice constants and the total energies of these three type structures can be seen in Table 2. This indicates that the most stable of these structures is the type 1 structure at the FM state. According to the calculations, the equilibrium lattice constant of the ScFeRhP EQH compound is 5.97 A.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **P** & **Fe** & **Sc** & **Rh** \\ \hline Type 1 & A (0, 0, 0) & B (1/4, 1/4, 1/4) & C (1/2, 1/2, 1/2) & D (3/4, 3/4, 3/4) \\ Type 2 & A (0, 0, 0) & C (1/2, 1/2, 1/2) & B (1/4, 1/4, 1/4) & D (3/4, 3/4, 3/4) \\ Type 3 & B (1/4, 1/4, 1/4) & A (0, 0, 0) & C (1/2, 1/2, 1/2) & D (3/4, 3/4, 3/4) \\ \hline \end{tabular}
\end{table}
Table 1: Three crystal-structure atomic positions of the ScFeRhP equiatomic quaternary Heusler (EQH) compound.  
Figure 3: Total-energy-lattice-constant curves of the ScFeRhP compound in ferromagnetic (FM) states for the type (**a**) 1, 2, and 3 structures, and (**b**) FM and non-magnetic (NM) states for type 1.  
Figure 2: Three possible crystal structures (**a**) type 1, (**b**) type 2, (**c**) type 3 for ScFeRhP compound.
## 5 Discussion  
This study addressed two critical challenges in low-altitude UAV operations: the absence of performance-based classification standards for heterogeneous UAVs and the lack of universally applicable safety interval criteria. We proposed a UAV clustering model based on the K-Means++ algorithm, which demonstrated significant improvements in classification accuracy compared to the initial clustering results. The optimized silhouette coefficient showed a 53.9% improvement, the sum of squared errors was reduced by 55.6%, and the Davies-Bouldin index decreased by 46.6%. These metrics indicated enhanced intra-cluster cohesion and inter-cluster separation. Such advancements lay a robust foundation for categorizing UAVs based on performance metrics, thereby addressing gaps in existing  
Figure 12: Comparison of conflict probability in multiple scenarios.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Category 1** & **Category 2** & **Category 3** & **Category 4** \\ \hline Drone safety intervals & 10 m & 15 m & 25 m & 33 m \\ \hline \end{tabular}
\end{table}
Table 3: UAV safety interval calibration results.  
classification systems that primarily rely on weight or application scenarios rather than safety-critical flight characteristics.  
Furthermore, our safety interval calibration model, which incorporates random heading dynamics, introduces a novel approach to conflict probability assessment. By transforming collision risk analysis into a geometric problem involving intersecting spherical safety zones, this model effectively quantifies conflicts in 3D airspace. The experimental results revealed a calibrated safety interval of 10 m for Category 1, 15 m for Category 2, 25 m for Category 3, and 33 m for Category 4. These intervals strike a balance between safety requirements and airspace utilization efficiency. Notably, the conflict probability curves for multi-category UAV operations converged as the safety intervals increased, validating the applicability of single-category calibration results in mixed airspace environments. This finding aligns with prior studies emphasizing dynamic risk assessment in shared airspace, but extends their scope by integrating performance-based classification.  
The implications of this work are twofold. Firstly, it supports the standardization of low-altitude airspace management by providing data-driven UAV classification and safety interval criteria. Secondly, it offers a scalable framework for incorporating emerging UAV technologies into existing air traffic management systems. However, limitations exist, including the assumption of a uniform heading distribution and the simplification of spherical safety zones, which may not fully account for complex UAV maneuvers or environmental factors. Future research should aim to explore adaptive safety intervals for dynamic environments and validate the model in real-world scenarios involving heterogeneous UAV fleets.
## 4 Respiratory Motion Correction Strategies
### Translational Correction  
Translational correction, in conjunction with ARA gating, is the most commonly used respiratory motion compensation approach, as respiratory motion can be well approximated by translational motion in the end-expiratory phase. The translational motion \(\Delta a_{x}\), along encoding direction \(x\), creates a phase shift \(\theta_{x}\) inversely proportional to the image field-of-view, \(FOV_{x}\), and can be calculated as:  
\[\theta_{x}=\frac{2\pi\cdot\Delta a_{x}}{FOV_{x}} \tag{1}\]  
For prospective translational correction of in-plane motion, the spatial encoding gradients can be appropriately adjusted, and if translational correction is performed in the through-plane direction (slice encoding) then the phase of the RF pulse and A/D sampler will be modulated as well, resulting in a corresponding change in position of the excited imaging plane. For obliquely aligned, small slab CMRA such as targeted scans, through-plane respiratory motion may be substantial which requires prospective correction [84]. Whole-heart CMRA [85] with an axial orientation experiences the dominant respiratory motion component (FH motion) along the slice encoding direction which may introduce significant through-plane motion unless prospective correction is used. Nevertheless, the absolute respiratory induced displacement of the heart is typically on the order of magnitude smaller than the 3D FOV of whole-heart CMRA, particularly for coronal or sagittal orientations, which mitigates the effects of through plane motion and enables the use of retrospective translational correction. Typically, retrospective translational motion is performed in _k_-space, where the phase shift \(\theta_{x}\) can be applied to the raw data sample \(\mathbf{k}_{j}\) at _k_-space index \(j\) acquired along encoding direction \(x\) as:  
\[\mathbf{k}^{\prime}_{j}=\mathbf{k}_{j}\cdot\mathbf{e}^{-i\cdot\theta_{x}\cdot j} \tag{2}\]  
where \(\mathbf{k}^{\prime}_{j}\) is the motion corrected data.
## 2 Literature Review  
### Research on Missing Value Prediction  
Researchers have proposed methods to predict missing data [7]. Effective missing value prediction methods can make data more complete, which is helpful in subsequent data analysis. Statistical and machine learning methods are primarily used for missing-value predictions.  
Based on statistical methods, [PERSON] used the maximum likelihood method and expectation maximization algorithm to predict the parameters for complete and right-censored data [8]. [PERSON] et al. proposed a vector autoregression model that integrated minimizing expectation and prediction error to predict missing data. However, this method did not fully consider the category of the data, leading to large differences between the predicted and actual values [9]. [PERSON] constructed a super-complete deep denoising autoencoder for multiple missing data imputations; it required that the proportion of missing data was not too high. Better prediction results can only be achieved with complete data [10].  
Another method was based on machine learning. [PERSON] et al. used several machine learning models, including two support vector machines (SVM), six regression models, and artificial neural networks (ANN) to predict various water quality parameters of the Langat River in Malaysia. Based on the model performance metrics, the ANN model is superior to other models, while the SVM model exhibits overfitting characteristics [11]. Researchers have also combined denoising autoencoders with generative adversarial networks to process industrial IoT data with high missing rates; however, such models cannot produce missing values with high accuracy [12].  
The problem of missing heavy-metal data in soil is serious; statistical methods are commonly used for missing-value imputation. Although statistical methods are relatively simple, there are shortcomings and limitations to using statistical methods for missing-value imputation. Although current machine learning-based missing-value prediction methods can accurately predict missing values, feature extraction between the data is insufficient, and the prediction accuracy can be improved. Predicting missing values in soil heavy-metal data can make the data more complete, improve data utilization, and provide technical support for subsequent data analysis. Thus, an efficient and accurate method for producing the missing values in soil pollution data is necessary.
## 3 Results
### Quantitation of Tumor Volumes upon Treatments  
As shown in Figure 7, at day 22, upon sacrifice of mice, PRKCA siRNA+NPs-treated mouse group showed significant reduction in tumour volume compared to the NPs control group (\(\sim\)245 mm\({}^{3}\) vs. 532 mm\({}^{3}\), \(p\) < 0.05). In addition, PTEN+NPs and PRKCA siRNA+PTEN+NPs treated mice had smaller tumour volumes compared to the control (\(\sim\)162 mm\({}^{3}\) vs. 532 mm\({}^{3}\), \(p\) < 0.05) and (\(\sim\)261 mm\({}^{3}\) vs. 532 mm\({}^{3}\), \(p\) < 0.05), respectively. With  
Figure 5: (**a**) Following subcutaneous injection, tumour growth appeared on mouse mammary pad and reached an approximate volume of \(\sim\)25 mm\({}^{3}\) within day 8. (**b**) Tumor images captured following sacrifice of mice. At the end of the study, at day 22, mice were humanely sacrificed and tumours were excised.  
regards to the control, all three treatment groups had reduced tumour volumes, implying the crucial roles of PRKCA suppression and restoration of PTEN tumour suppressor's function in tumour volume reduction.  
Figure 6: Tumor regression study following intravenous delivery of NPs, PRKCA siRNA+NPs, PTEN+NPs and PRKCA siRNA+PTEN+NPs in MCF-7-induced xenograft breast tumour model. MCF-7 cells were inoculated subcutaneously on the mammary pad of mice. On day 8, as the tumour reached an approximate volume of \(\sim\)25 mm\({}^{3}\), mice were treated intravenously through tail-vein injection with 100 \(\upmu\)L of NP (8 M Ca\({}^{2+}\)), PRKCA siRNA+NP (100 nM siRNA), PTEN+NP (40 \(\upmu\)g of plasmid) and PRKCA siRNA+PTEN+NP (100 nM siRNA and 40 \(\upmu\)g of plasmid); followed by the 2 nd dose at day 10. The tumour outgrowth was monitored until day 22. Four mice were used per group and data were represented as mean \(\pm\) SD. Compared to NP control group, statistical analysis was significant, * when \(p<0.05\).  
Figure 7: Quantitation of tumour volumes in the mouse groups treated with NPs (control), PRKCA siRNA+NPs, PTEN+NPs and PRKCA siRNA+PTEN+NPs. Statistical analysis was found significant compared to NP control group, * when \(p<0.05\).
## 4 Results and Discussion  
The main purpose of drift detection methods is to timely identify the drift in and trigger the ML model which is employed as the base classifier to adjust itself accordingly so that the classification performance degradation can be avoided. Keeping this in view, the predictive performance of the classifier has assessed when combined with each of the 10 drift detection methods. It is hypothesized in the current study that the performance of the base classifier will be improved using drift detection methods. The experiments were performed on data streams with sudden drift, gradual drift, and incremental drift. We discuss the results for each drift type separately in the following sub-sections.
## 4 General Discussion
### Recommendations and Plans for Future Research  
In Section 3.6, the effect of a partly brine-saturated pore space (saturation equalled 25 \(\pm\) 2%) on the seismic velocity was investigated for post-failure calcschist sample TD12-CS4 (Figure 11). As mentioned previously, an effort was made to replicate the velocities, corresponding to the 25 \(\pm\) 2% saturation case, using [PERSON]'s fluid substitution framework [40]. Related results are shown in Figure 17.  
It is evident from Figure 17 that [PERSON]'s concept of fluid substitution [40] significantly underestimates the P-wave velocity for the specific saturated calcschist case. Even with an assumed brine saturation of 100%, the measured velocities could not be approached by the theoretical framework. The reasons for this are most probably the assumptions related to [PERSON]'s theory. As the concept is derived for, and frequently applied for, fluid estimation, and velocity calculation, from velocities of sandstones (gas versus water bearing), it assumes a fully connected pore space as well as a constant shear modulus. Several researchers observed significant underestimation of the velocity, for carbonate rocks, according to [PERSON]'s theory [69; 70]. For future studies, it is advised to investigate alternative theoretical frameworks for modelling seismic velocities, as a function of pore fluid, for rock formations that do not obey [PERSON]'s assumptions. An example of an adapted version of [PERSON]'s theory, derived for carbonates, is presented in [71].  
All experimental data presented in this manuscript, with the exception of one experiment discussed in Section 3.6, are related to dry pore space conditions. Though it reflects the baseline seismic-response characterisation (Figure 8), one might speculate whether it is representative of real field conditions, as in reality the fractured reservoir rocks in Kizldere will be (partly) saturated with brine and/or CO\({}_{2}\) due to (re-)injection. Future plans involve large-scale (sample dimensions of 40 \(\times\) 60 cm; 10 times larger than samples discussed here) acoustic-assisted CCS experiments where the seismic response of altered reservoir rocks, with CO\({}_{2}\)/water-saturated flow, will be studied. These large-scale experiments will shed light on the effect of the pore fluid on the resulting velocities; can one, based on the obtained seismic signal, quantify and/or qualify the behaviour of the injected CO\({}_{2}\) in the subsurface?  
Besides studying the stress-dependence of the seismic velocity on dry samples, the temperature-dependence, of P- and S-wave velocities, was investigated on dry core samples as well (Section 3.7 and Figure 12), in order to get an initial idea about the temperature effect. In line with previous paragraphs, dry reservoir rocks are not representative of the hydrothermal subsurface conditions at Kizldere. For future research, it is planned to include a pore fluid for studying the temperature-velocity dependency, at varying stress levels.
## 1 Introduction
HFS methods are a combination of filter and wrapper methods and therefore, reduce the deficiencies of both methods [30; 31]. Although comprehensive studies have identified CLP factors, few works have focused on applying different FS methods to CLP factors to reduce the risk of overfitting. In other words, a research gap exists regarding the development of HFS methods as an essential data cleaning process prior to CLP modeling. Despite the wide application of predictive CLP models for project planning and control, a predictive model as a sole application cannot offer construction companies the optimum value of influencing factors for improving CLP [7]. [PERSON] and [PERSON] [32] developed a new discrete-event simulation for estimating the duration of activities under uncertainty, optimizing resource allocation in building construction projects, and improving productivity. However, they only considered resource allocation and activity duration as factors that influence productivity. Concretely, no study has presented a hybrid model for finding the maximum value of CLP and optimum value of each influential factor, using optimization techniques. This study aims to fill the gap in the literature by developing a hybrid model that can identify the factors that most influence CLP as well as predict and optimize CLP and the factors influencing it. The proposed hybrid model will help project managers have more confidence in predicted CLP and be able to plan for improving each CLP factor. The major contribution of this paper is developing a model for both predicting and optimizing CLP, using a combination of FS, AI, and evolutionary optimization techniques. To achieve this goal, this study had the following objectives: (1) identify factors that are the most predictive of CLP, using a combination of filter and wrapper methods as a hybrid feature selection (HFS) method; (2) predict CLP by developing and comparing four different predictive models, using the factors that most influence CLP; and (3) develop a novel hybrid evolutionary optimization model for finding the maximum CLP value and the optimum value of each selected factor. The remainder of this paper is organized as follows. Section 2 presents a brief review of past research on modeling CLP. In Section 3, the proposed methodology for predicting and optimizing CLP is presented. Section 4 provides the experimental results from using the proposed methodology to predict and optimize CLP, using a data set. Finally, Section 5 presents conclusions and recommendations for future work.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 4 Simulation Results  
In the last section, we proposed a new design that could significantly reduce the sampling rate of the receiver. We did not expect this advantage to come for free, but we hoped that the performance loss would be acceptable. In this section, we use simulations to verify this.