esa-sceva/satcom-chunk-collection huggingface dataset:
# Re-Pair in Small Space +
## 1 Introduction
### Our Contribution  
In this article, we propose an algorithm that computes the Re-Pair grammar in \(\mathcal{O}(\min(n^{2},n^{2}\lg\log_{\mathrm{T}}n\lg\lg n/\log_{\mathrm{T}}n))\) time (cf. Theorems 1 and 2) with \(\max((n/c)\lg n,\)\(n\lceil\lg\tau\rceil)+\mathcal{O}(\lg n)\) bits of working space including the text space, where \(c\geq 1\) is a fixed user-defined constant and \(\tau\) is the sum of the alphabet size \(\sigma\) and the number of non-terminals \(\sigma^{\prime}\).  
We can also compute the byte pair encoding [31], which is Re-Pair with the additional restriction that the algorithm terminates before \(\lceil\lg\tau\rceil=\lceil\lg\sigma\rceil\) no longer holds. Hence, we can replace \(\tau\) with \(\sigma\) in the above space and time bounds.  
Given that the characters of the text are drawn from a large integer alphabet with size \(\sigma=\Omega(n)\) the algorithm works in-place. (We consider the alphabet as not effective, i.e., a character does not have to appear in the text, as this is a common setting in Unicode texts such as Japanese text. For instance, \(n^{2}=\Omega(n)\cap n^{\mathcal{O}(1)}\
eq\emptyset\) could be such an alphabet size.) In this setting, we obtain the first non-trivial in-place algorithm, as a trivial approach on a text \(T\) of length \(n\) would compute the most frequent bigram in \(\Theta(n^{2})\) time by computing the frequency of each bigram \(T[i]T[i+1]\) for every integer \(i\) with \(1\leq i\leq n-1\), keeping only the most frequent bigram in memory. This sums up to \(\mathcal{O}(n^{3})\) total time and can be \(\Theta(n^{3})\) for some texts since there can be \(\Theta(n)\) different bigrams considered for replacement by Re-Pair.  
To achieve our goal of \(\mathcal{O}(n^{2})\) total time, we first provide a trade-off algorithm (cf. Lemma 2) finding the \(d\) most frequent bigrams in \(\mathcal{O}(n^{2}\lg d/d)\) time for a trade-off parameter \(d\). We subsequently run this algorithm for increasing values of \(d\) and show that we need to run it \(\mathcal{O}(\lg n)\) times, which gives us \(\mathcal{O}(n^{2})\) time if \(d\) is increasing sufficiently fast. Our major tools are appropriate text partitioning, elementary scans, and sorting steps, which we visualize in Section 2.5 by an example and practically evaluate in Section 2.6. When \(\tau=o(n)\), a different approach using word-packing and bit-parallel techniques becomes attractive, leading to an \(\mathcal{O}(n^{2}\lg\log_{\mathrm{T}}n\lg\lg n/\log_{\mathrm{T}}n)\) time algorithm, which we explain in Section 3. Our algorithm can be parallelized (Section 5), used in external memory (Section 6), or adapted to compute the MR-Re-Pair grammar in small space (Section 4). Finally, in Section 7, we study several heuristics that make the algorithm faster on specific texts.
## 2 Research Methods and Technical Routes
### Research Methods
#### 2.2.1 Data Acquisition  
Before the calculation of regional ship air pollution emissions, it is necessary to analyze and process the obtained AIS data while obtaining the AIS data published on the Internet through the python computer programming language. Firstly, the AIS information is translated and decoded into data that can be read and used intuitively by using Python computer programming language, and the basic information data including ship name, ship type, Captain, ship width, MMSI (Maritime Mobile Communication Service Identification Code), timestamp, longitude and latitude, speed, course, and destination are obtained. The above data are stored in the ship navigation information database. In order to perform the subsequent emission calculation process more intuitively, AIS data were acquired every 3 min in this study. In the acquired AIS data under the same MMSI, if there are duplicate data with the last acquisition, they will not be written into the database. If the subsequently acquired data of the ship is different, they will be written into the database. The time between the two data is acquired by the ship. The interval represents the time in the moored state. The acquired AIS data may contain bit error rate, abnormality, and missing data, and subsequent data elimination and sorting are required. For example, the MMSI and ship position information obviously do not conform to the actual situation, and different ships use the same MMSI and data that the speed obviously does not conform to the actual situation. Therefore, it is necessary to clean the AIS data, automatically identify abnormal data, and supplement the missing data before the subsequent calculation of pollution emissions.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 4 Discussion
### Future Study  
The proposed method for classifying urban green spaces using multiple datasets does not incorporate advanced data fusion techniques; it simply stacks multiple images into single images using the \"add.Bands\" function within the GEE platform. Hence, it is recommended that future research delves into advanced fusion techniques and assesses their outcomes. Such evaluation should not only prioritize accuracy but also factor in computational efficiency.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 3 Results
### Sorption Kinetics  
Adsorption kinetics reflects the rate and efficiency of adsorption of uranium ions by the adsorbent [15; 31; 32]. The uranium adsorption kinetics of TA/GO from an aqueous solution as a function of contact time at pH = 3.6 \(\pm\) 0.05 is displayed in Figure 4. It can be seen from Figure 4 that the percentage of adsorption increases rapidly with time during the first 120 min and then remains constant. The fast adsorption indicates that the main mechanism of adsorption is strong chemisorption or surface complexation [15]. To analyze the adsorption kinetics in detail, the obtained experimental data were fitted by pseudo-first- and pseudo-second-order rate equations [33; 34].  
\[\log\!\left(\mathrm{q_{e}-q_{f}}\right) = \log\!\mathrm{q_{e}-\frac{k_{1}}{2.303}t} \tag{1}\] \[\frac{t}{q_{t}} = \frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{2}\]  
where \(\mathrm{q_{f}}\) (mg\(\cdot\)g\({}^{-1}\)) is the adsorption capacity of uranium ions on TA/GO at time \(t\) (h), \(\mathrm{q_{e}}\) (mg\(\cdot\)g\({}^{-1}\)) is the adsorption capacity at equilibrium, and k\({}_{1}\) (h\({}^{-1}\)) and k\({}_{2}\) (g\(\cdot\)mg\({}^{-1}\)-h\({}^{-1}\)) are the pseudo-first-order rate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Peak** & **Position (eV)** & **FWHM** & **\%** \\ \hline GO & C-C & 283.7 & 2.06 & 2.899753 \\  & C-O & 284 & 1.749 & 43.48051 \\  & C=O & 286 & 1.167 & 51.02671 \\  & O-C=O & 288 & 1 & 2.593018 \\ TA/GO & C-C & 283.91 & 1.862 & 13.47373 \\  & C-O & 284 & 1.854 & 34.69049 \\  & C=O & 285.922 & 1.449 & 44.46788 \\  & O-C=O & 287.8 & 1.429 & 7.367895 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Curve fitting results of XPS C1s spectra of GO and TA/GO composites.  
constant and the pseudo-second-order rate constant, respectively. Table 2 presents the relevant kinetic parameters obtained by fitting. It is clear that the correlation coefficient (R\({}^{2}\)) of the pseudo-secondary-level model was higher than that of the pseudo-first-level model, indicating that the pseudo-secondary model could better describe adsorption kinetics.
# A Novel 1-D CCANet for ECG Classification
## 3 Proposed Method
### Hand-Crafted Feature Extraction
#### 3.1.2 Autoregressive Modeling  
Autoregressive (AR) modeling specifies that a time series value depends linearly on its own previous values and a stochastic term, as per Equation (3):  
\[\mathbf{X}_{t}=\sum_{i=2}^{p+1}\varphi_{i}\mathbf{X}_{t-i+1}+\epsilon(t), \tag{3}\]  
where \(\mathbf{X}_{t}\) is the time series, \(\varphi_{i}\) are the AR coefficients computed with [PERSON]'s method and \(p\) is the order of the AR model. Since the choice of the order \(p\) depends highly on the sampling rate, non-normalized ECGs from both databases are resampled to 360 Hz [38]. The order was then chosen by performing best parameter search on the training data for both the MIT-BIH and INCART databases. We chose the order that maximized the average of our performance metrics (accuracy, specificity, sensitivity, ppv) on a validation set. Figure 2 shows that for the INCART data, the best order is 2 while the best order is 3 for the MIT-BIH data. Since the performance for MIT-BIH is quite comparable for orders 2 and 3, we chose an order equal to 2 for both datasets. We preferred a lower order to reduce the computational cost. The vector of AR coefficients obtained for each lead of one heartbeat, \(x_{h}\) (\(h=1,2\)), is denoted as \(x_{h,\text{ar}}\) and is of size 2.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 5 Results
### Dynamic Scenario  
Figure 15 displays the tracking and system performance of the adaptive techniques in a dynamic scenario. SV G17 is challenging to track due to the high LOS jerk dynamics. From Figure 15, it is observed that a high \(q\) and \(N\) are required in the \(C/N_{0}\)-based DSKF to maintain the carrier phase synchronization lock. The \(C/N_{0}\)-based DSKF achieves best tracking performance with \(q=1000\) cycles\({}^{2}/s^{6}\) and a \(N=500\) samples, maintaining the lock until \(45\,\mathrm{dBHz}\). The LBCA-based DSKF keeps the lock until \(38\,\mathrm{dBHz}\). However, this technique presents an error bias compared to the other techniques. This bias exists due to the transitions between the update of \(q\) and the Kalman gains' convergence. In contrast to the latter technique, the LBCA-based LUT-DSKF does not contain any error bias, and it performs almost as well as the LBCA-based PLL, keeping the lock until \(33\,\mathrm{dBHz}\). The LBCA-based PLL has a superior dynamic tracking performance than the other techniques due to the direct relation between loop bandwidth and filter coefficients. This direct relationship leads to a faster reaction of high signal dynamics.  
The results of the dynamic system performance (see Figure 15) are consonant with the dynamic tracking performance. All the adaptive tracking techniques can achieve a PVT solution at high \(C/N_{0}\) levels (\(38\)-\(52\,\mathrm{dBHz}\)). The LBCA-based LUT-DSKF and the LBCA-based PLL have the best system performance results, whereas the \(C/N_{0}\)-based DSKF technique shows poor performance. At low \(C/N_{0}\) levels, the LBCA-based techniques almost achieve a continuous PVT at \(33\,\mathrm{dBHz}\). In contrast to the LBCA-based methods, the \(C/N_{0}\)-based DSKF only achieves a continuous tracking of one SV.  
Figure 14: Static performance of adaptive tracking techniques in third-order Costas phase locked loop (PLL) at different \(C/N_{0}\) levels. (a) Tracking performance of satellite vehicle (SV) G4. (b) System performance.  
Figure 15: Dynamic performance of adaptive tracking techniques in third-order Costas PLL at different \(C/N_{0}\) levels. (a) Tracking performance of SV G17. (b) System performance.
## 6 Conclusions and Future Work  
### Conclusions  
* An analysis of the solar PV potential was carried out, where the community would support system installations between 5 kW and 15 kW, for the 1188 homes. A very important result of this analysis was the finding that approximately 74% of the households have three bedrooms or less; therefore, it is favorable for them to install a 4 kW photovoltaic system.
* In this study, it was found that a PV system with a minimum size of 4 kW can generate cumulative energy that, at the end of the day, supplies the entire cumulative energy demand for a load profile of a three-bedroom house. In practice, this is not enough to maintain the energy balance every moment due to the hours when there is no solar power generation, as the energy consumed cannot be fed and must be supplied by other sources such as batteries or the main power grid.
* A study was carried out to create profiles of houses of between one and five bedrooms to determine energy consumption.
* This study facilitates the steps to find the minimum PV system size for a household using NREL tools.
* This study serves as a basis for determining the minimum PV system size and is a starting point for selecting the capacity of the battery system needed to store excess energy or to supply the energy demanded by a household.
###### Abstract  
In order to better assess the seismic hazard in the northern region of Algeria, the shear-wave velocity structure in the Middle-Chelf Basin is estimated using ambient vibration single-station and array measurements. The Middle-Chelf Basin is located in the central part of the Chelif Basin, the largest of the Neogene sedimentary basins in northern Algeria. This basin hosts the El-Asnam fault, one of the most important active faults in the Mediterranean area. In this seismically active region, most towns and villages are built on large unconsolidated sedimentary covers. Application of the horizontal-to-vertical spectral ratio (HVSR) technique at 164 sites, and frequency-wavenumber (F-K) analysis at 7 other sites, allowed for the estimation of the ground resonance frequencies, shear-wave velocity profiles, and sedimentary cover thicknesses. The electrical resistivity tomography method was used at some sites to further constrain the thickness of the superficial sedimentary layers. The soil resonance frequencies range from 0.75 Hz to 12 Hz and the maximum frequency peak amplitude is 6.2. The structure of the estimated shear-wave velocities is presented in some places as 2D profiles to help interpret the existing faults. The ambient vibration data allowed us to estimate the maximum depth in the Middle-Chelf Basin, which is 760 m near the city of El-Abadia.  
Middle-Chelf sedimentary basin; HVSR; array measurement; frequency-wavenumber (F-K) method
## 2 Impedance-Based Model of Multi-Converter Grid-Tied System
### Equivalent Model of Multi-Converter Grid-Tied System  
Figure 3 shows a typical system with multi-paralleled grid-tied VSCs. According to Equation (11), the VSC is equivalent to the Norton circuit. The transmission line is represented with the equivalent grid inductance \(L_{\text{g}}\). In the multi-converter grid-tied system, the converter section and the power grid section are considered separate subsystems [19]. The converter subsystem includes all converters, while the power grid subsystem includes transmission lines and the ideal power grid.  
Furthermore, the converter subsystem can be equivalent to the form of multiple current sources and admittance in parallel as shown in Figure 3. Based on [PERSON]'s current law, its impedance model can be derived as  
\[\mathbf{Y}(s)=\sum_{x=1}^{x=n}\mathbf{Y}_{\text{vscx}}(s) \tag{12}\]  
The power grid subsystem only contains inductance and an ideal grid and its impedance matrix model can be accessed as  
\[\mathbf{Z}_{\text{g}}(s)=\begin{bmatrix}sL_{\text{g}}&-\omega_{1}L_{\text{g}}\\ \omega_{1}L_{\text{g}}&sL_{\text{g}}\end{bmatrix} \tag{13}\]  
According to the relationship between the voltage and current of PCC, the return ratio matrix of the multi-converter grid-tied system can be obtained as [20].  
\[\mathbf{L}(s)=\mathbf{Y}(s)\mathbf{Z}_{\text{g}}(s)=\begin{bmatrix}L_{11}&L_{12}\\ L_{21}&L_{22}\end{bmatrix} \tag{14}\]  
where \(L_{11}\), \(L_{12}\), \(L_{21}\), \(L_{22}\) are the elements in the return ratio matrix \(\mathbf{L}(s)\).  
The small-signal stability of the multi-converter grid-tied system can be determined by the \(\mathbf{L}(s)\)[20].
## 1 Introduction
The multi-agent PFL is further enhanced using an attention mechanism, which considers each vehicle and the UAV to determine the transmission, scheduling and queuing strategy. Based on an adaptive asynchronous optimal policy, the vehicles maximize a global objective function [18]. The asynchronous FL-based task offloading schemes minimize delay and power consumption. Transformer personalization methods have also been explored to model the dependency, priority, and associations between tasks [19]. For trajectory prediction, hidden Markov models (HMMs) and graph theory-based approaches have been applied and explored in existing works such as [20; 21; 22]. By considering state transitions between an agent's trajectories as Markovian dynamics, a likelihood matrix is generated to predict the trajectory. Memoryless Markov models, however, assume that an agent's present state separates its future trajectory from its past locations [23]. In  
Figure 1: An illustrative scenario where continuous and uninterrupted coverage is provided to vehicles using a UAV as local model hyper-parameters are updated and aggregated. (**a**) Handoff delay accumulation is minimal when coverage zones are continuous. However, the signal strength is low when the vehicles are distant from the base station. (**b**) In 6G communication, handoff delay accumulation can be reduced to minimal even in the presence of some no-coverage zones by deploying a UAV as a mobile base station. UAV-assisted communications, the UAV's state transitions may not only depend on the current state, but also on the past trajectories, covering different vehicles [24]. Consequently, HMM-based models do not explain the heterogeneity of UAVs' trajectories, resulting from their different past locations and random trajectories. The attention mechanism determines the degree of dependence of future UAV trajectories and vehicle states on a UAV's available battery power [25]. Using model aggregation based on PFL, we predict UAV trajectory coordinates [26]. To maximize learning efficiency, our method allows local model training with subsequent parameter transmission to the UAV [27]. The simulations indicate that our proposed approach significantly improves the computation rate and maintains queue stability compared to several baseline methods.
# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient
Some previous studies have proved that considering the influence of the geomagnetic gradient can improve the accuracy of estimated compensation coefficients. [PERSON] et al. [19] applied the World Magnetic Model 2010 to a generate geomagnetic field, which was then used to solve the compensation coefficients. The simulation results showed that the compensation performance can be improved. [PERSON] et al. [28] proposed a relatively simple geomagnetic model to solve the compensation coefficients. The flight test results showed that the compensation performance of the two flights was improved by 27% and 22%, respectively. [PERSON] et al. [29] analyzed the influence of the geomagnetic gradient on the towed bird system and established the towed bird interference model; the feasibility of this compensation method was verified by actual flight tests. To eliminate the residual interference field caused by the geomagnetic gradient as far as possible, an improved aeromagnetic compensation method robust to geomagnetic gradient is proposed in this paper. The application steps are as follows: first, the high-precision flight profiles are obtained by the Differential Global Positioning System (DGPS); secondly, the geomagnetic field information is obtained according to the IGRF model; finally, the background field, including the geomagnetic gradient, is substituted into the compensation model to obtain the compensation coefficients. Flight tests were carried out, and the results show that the residual noise of the proposed method was lower than that of the traditional method, which demonstrates that the geomagnetic gradient was effectively compensated; meanwhile, a higher detection ability was obtained for the aeromagnetic survey system. The detailed study results are presented in the following four sections: Section 2 summarizes the principle of the traditional Tolles-Lawson model and the calibration flight. In Section 3, a new aeromagnetic compensation model robust to the geomagnetic gradient is extended by introducing the IGRF model, which was subsequently used to solve the compensation coefficients. An aeromagnetic survey system with low noise instruments and its data analysis are presented in Section 4. Section 5 presents the conclusion of this paper.
## 4 Results
### Taxonomy of the Skill Sets by Competency Areas  
In this phase of the analysis, the knowledge domains and skill sets were categorized and presented in a more systematic way. The knowledge domains and skills denoted by the 32 topics were allocated into six competency areas, and a systematic competency map was developed for big data skills. The percentages of the skills according to the competency areas are presented in Figure 2. As shown in Figure 2, the first competency area, \"big data knowledge\" (36.30%), contains ten knowledge and skill items consisting of \"big data processing\", \"big data tools\", \"big data architecture\", \"information security\", \"scalable systems\", \"database\", \"distributed systems\", \"data warehousing\", \"Hadoop ecosystem\", and \"data streaming\". The second, \"developer skills\" (20.25%), has six items, namely \"remote development\", \"programming languages\", \"Agile development\", \"business applications\", \"application development\", and \"testing\". The third, \"big data analytics\" (14.88%), contains four items, specifically \"data analytics\", \"analytical skills\", \"business analytics\", \"machine learning\", \"data visualization\", and \"decision-making\". The fourth, \"cloud services\" (10.51%), has four items: \"Amazon EMR\", \"Google Cloud\", \"AWS Data Services\", and \"Azure Cloud\". The fifth, \"soft skills\" (10.16%), consists of three items: \"communication skills\", \"project management\", and \"team working\". The sixth, \"technical background\" (7.90%), has three items comprising \"technical knowledge\", \"bachelor degree\", and \"troubleshooting\".  
This taxonomy systematically reveals the most in-demand competency areas within the big data ecosystem. The fact that \"big data knowledge\" ranks first, with a high proportion of 36.30%, highlights the central role of technically grounded expertise in the industry. The substantial shares of \"developer skills\" and \"big data analytics\" further emphasize the importance of practical software development and analytical capabilities. In contrast, the lower percentages associated with \"soft skills\" and \"technical background\" suggest that while technical competencies are prioritized, complementary skills are also necessary. In summary, this distribution reflects the industry's strong demand for professionals who possess both domain-specific technical knowledge and applied problem-solving abilities.