esa-sceva/satcom-chunk-collection huggingface dataset:
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 2 Swahili Language Structure
### Swahili Syllabic Alphabet  
This section briefly introduces the finite Swahili syllable alphabet, which forms the basis of all Swahili words. Swahili uses the English alphabet letters with the exception of x and q. The vowels a, e,i, o, and u count as syllables and constitute the smallest syllabic unit [35]. Swahili syllables are derived from vowels and consonants of the alphabet, with a syllable normally consisting of a vowel preceded by one to three consonants. In special cases, the syllable is a single vowel or consonant. For example, in the words _mtu_ (person) and _anakula_ (he is eating), the starting letters \"m\" and \"a\" are special syllables. It is imperative that the position of the syllable in a word be preserved to maintain the syntactic and functional information. The following are the rules for Swahili syllabification [35; 39]:  
1. a consonant or a vowel preceded by a vowel is the start of a syllable;
2. a consonant (other than a semi-vowel) preceded by a consonant is the start of a syllable (for n, m and loan words);
3. all syllables end at the beginning of the next syllable or at the end of the word;
4. where a pre-consonantal nasal functions as a syllabic peak, a syllable is formed by a combination of two sounds;
5. a cluster of a consonant and a semi-vowel together with a vowel can also form a syllable; and
6. a segment of clusters of three with a vowel can form a syllable.  
To derive the syllabic alphabet, we apply the above rules, appropriately combine the consonants with the vowels based on the list of letters provided by [PERSON] [40] and add the list of special syllables. It is important to note that new words or loan words can be generated from the alphabet. For example, before a proper Swahili word for television (_running_) was coined, it was and is still referred to as _televisheri_ whose syllables can be found in the alphabet.  
Table 1 outlines the Swahili syllabic alphabet.
## 1 Introduction
Many experts and scholars have also studied the collision avoidance warning system. [PERSON] et al. [14; 31] constructed a collision avoidance system based on the generalized velocity obstacle algorithm, which was applied to manned and unmanned in this study. [PERSON] et al. [11; 32] quantified the liability clauses of stand-on vessels in the COLREGs and divided the severity of conflicts into nine categories based on the ship's intention prediction and conflict evolution. Then, combined with the four stages of ship encounters, they proposed a collision early warning system. based on the perspective of a stand-on vessel. By evaluating the dynamic characteristics of the ship's navigation process, [PERSON] et al. [33] proposed an intelligent decision-making approach based on fuzzy logic for the inland-water TSS. While the above research has contributed to the development of ship collision avoidance, very few studies focus on specific waters, such as the Chengshantou TSS, Gibraltar TSS, Malacca Strait TSS, etc. Some scholars have studied the traffic situation in the above-mentioned area and completed revision studies on the TSS, but few have investigated collision avoidance warning and maneuver decision-making. Therefore, implementing ship collision avoidance early warning and maneuver decision-making TSS waters has both practical and theoretical value for enhancing navigation safety and efficiency, as well as ship autonomization. Regarding the ship collision avoidance warning and maneuver decision-making in unique types of water environments, such as TSS, there are still many issues worthy of in-depth study on the basis of previous research. These issues are listed as follows. 1. The digital model of the traffic environment suitable for special water areas, such as Chengshantou, is established;
2. The collision avoidance mechanism is applicable to special waters, such as TSS;
3. Rule 10 of the COLREGs and good seamanship requirements are incorporated into the decision-making method;  
In this work, we construct a static digital traffic environment based on the Chengshantou TSS's components, taking into account the need for risk identification and collision avoidance decision-making. Secondly, the ship domain and a method for estimating ship position based on ship behavior in TSS are introduced to achieve risk identification. Thirdly, the encounter situation identification model is built, and corresponding avoidance approaches are provided. Fourthly, the collision avoidance mechanism of ships under the constraints of the TSS is studied by analyzing the COLREG rules. Finally, a decision-making method based on time-series rolling is proposed for multi-vessel encounter maneuvers in TSS waters. The rest of the contents of the paper are organized as follows: Section 2 presents the method for constructing a digital traffic environment. Section 3 introduces the methods of risk identification during navigation. The method of collision avoidance decision-making is shown in Section 4, and the simulation results are implemented in Section 5. Finally, conclusions are drawn in Section 6.
## 2 Methodology
### GNSS/INS Tightly Coupled Positioning Model
The difference between the distance from the satellite to the ground station predicted by the INS and the GNSS double-differenced pseudorange is solved, which is used as the EKF measurement to achieve high-precision positioning for GNSS/INS tightly coupled system. The measurement equation is written in matrix form:  
\[\mathbf{Z}_{k}=\mathbf{H}_{k}\mathbf{X}_{k}+\left[e_{\rho},e_{ins}\right]^{\mathrm{T}} \tag{13}\]  
where \(\mathbf{Z}_{k}\) represents the measurement vector at time epoch \(k\), \(\mathbf{Z}_{k}=\Delta\
abla\rho^{*}-\Delta\
abla\rho_{ins}\), and \(\Delta\
abla\rho_{ins}\) indicates the satellite-to-ground distance predicted by INS; \"*\" represents different satellite systems including uses GPS and BDS in this paper; \(\mathbf{H}_{k}\) is the measurement model coefficient matrix; \(e_{\rho}\) and \(e_{ins}\) are the pseudorange observation noise and INS observation noise, respectively. The final GNSS/INS tightly coupled positioning results can then be solved based on the following EKF procedures. Prediction stage:  
\[\mathbf{\hat{X}}_{k/k-1}=\mathbf{\Phi}_{k,k-1}\mathbf{\hat{X}}_{k-1} \tag{14}\]  
\[\mathbf{P}_{k/k-1}=\mathbf{\Phi}_{k,k-1}\mathbf{P}_{k-1}\mathbf{\Phi}^{\mathrm{T}}_{k,k-1}+Q _{k-1} \tag{15}\]  
Update stage:  
\[\mathbf{K}_{k}=\mathbf{P}_{k/k-1}\mathbf{H}^{\mathrm{T}}_{k}\left(\mathbf{H}_{k}\mathbf{P}_{k/k-1} \mathbf{H}^{\mathrm{T}}_{k}+\mathbf{R}_{k}\right)^{-1} \tag{16}\]  
\[\mathbf{\hat{X}}_{k}=\mathbf{\hat{X}}_{k/k-1}+\mathbf{K}_{k}\left(\mathbf{gain matrix at time epoch \(k\); In addition, \(\blacksquare_{k,k-1}\) represents matrix/vector \(\blacksquare\) propagation from time epoch \(k{-}1\) to \(k\).
## 6 Discussion
### Effects of Different Features on Identifying Results  
Related studies have demonstrated that reducing irrelevant features can reduce model complexity, and reducing data dimensionality through feature selection can improve the model's classification performance and enhance the model's generalization performance [41]. This is consistent with the results of this study. In this study, we investigate the model performance of feature approximation using backward elimination based on importance metrics. The RFECV process allows us to systematically assess the importance of each feature by recursively removing features and evaluating the model's performance using cross-validation. This approach was chosen because it effectively identifies the features that contribute the most to the identification of the enclosure grades while also considering the interactions between the excavation parameters and their joint impact on the identification performance. By selecting these features, the Optuna-XGBoost model can achieve superior performance by focusing on the most informative variables, which reduces the risk of overfitting and improves the generalization of new data.  
Through detailed experimental analyses, we demonstrate that the selected features significantly impact model performance. For example, we demonstrate that the model's accuracy significantly improves after applying feature selection. In contrast, the model's complexity is reduced, further validating the effectiveness of our feature selection strategy. As seen from Figure 4a, RFECV-RF extracts features with the highest accuracy among all of the methods on both the training and test sets. From Figure 4b, it can be seen that, by adaptively approximating the input features to an optimal subset of features containing only six metrics, the method achieves an accuracy of 0.9217. The results show that the optimal feature subset obtained by ranking the features in terms of their importance includes T, N, F, SE, TPI, and PR, which is consistent with the preliminary metrics derived from the preliminary statistical analysis. T and N contribute the most to the model among the six input features.
## 2 Study Area and Data
### Experimental Datasets  
The input dataset for this experiment comprises time-series imagery from Sentinel-1 and Sentinel-2, from which [PERSON] et al.'s CLCD dataset [30] was used to identify cropland pixels. Based on these cropland masks, a pixel-level semantic segmentation dataset was created, containing four classes: cropland (background), rapeseed, tea, and cotton. Model training and classification were restricted to cropland regions by applying the cropland mask; pixels outside this mask were excluded from both training and inference to prevent non-cropland areas from biasing the classifier. The number of manually labeled pixels per class is summarized in Table 1. All annotation was performed in ArcGIS Pro (version 3.0) using the sample data described in Section 2. Both SAR and optical time series consist of 12 monthly time steps. To accommodate the spatial extent of the study area, the dataset was tiled into non-overlapping 512 \(\times\) 512 pixel patches, uniformly distributed across the region of interest. All data products were exported in TIFF format.
## 3 Results
The lower-addition multifocal IOLs induce primary vertical coma of +0.11 microns for a 3-mm optical zone and +0.24 microns for a 5-mm optical zone. The higher-addition multifocal IOLs induce primary vertical coma of +0.21 microns for a 3-mm optical zone and +0.51 microns for a 5-mm optical zone. Primary vertical coma induced by the multifocal models do not seem to depend on the base power of the IOL, but just on the near addition. Secondary vertical coma (Figure 6) shows an opposite pattern as the primary vertical coma, with a similar different behavior between monofocal and multifocal models as the primary vertical coma. Figures 5 and 6 have been depicted with the same axis scale to better appreciate the highest contribution of primary vertical coma. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Wavefront Error Variance**} & \multicolumn{3}{c}{**Contribution Wavefront Error Variance**} \\ \hline \multirow{2}{*}{**Zone (mm)**} & **IOL (D)** & **HOA (\(\mu^{2}\))** & **SA** & **COMA** & **\%** & **\%** \\  & **(D)** & **HOA (\(\mu^{2}\))** & **(\(\mu^{2}\))** & **(\(\mu^{2}\))** & **SA** & **COMA** \\ \hline
3 & AN6 & 0.00351 & 0.00336 & \(3.5\times 10^{-6}\) & 96 & 0 \\ \hline
3 & AN6V & 0.08884 & 0.00066 & 0.03723 & 1 & 42 \\ \hline
3 & AN6 VM & 0.34315 & 0.00194 & 0.14969 & 1 & 44 \\ \hline
4,5 & AN6 & 0.06988 & 0.06912 & \(2.7\times 10^{-5}\) & 99 & 0 \\ \hline
4,5 & AN6V & 0.48104 & 0.00712 & 0.13796 & 1 & 29 \\ \hline
4,5 & AN6 VM & 1.84837 & 0.00334 & 0.59934 & 0 & 32 \\ \hline \hline mm: millimeters; D: diopter; \(\mu\): microns; \%: percentage. & & & & & \\ \end{tabular}
\end{table} 
Table 1: Wavefront error variance and contribution for an optical zone diameter of 3.00 and 4.50 mm for the three IOL models (AN6, AN6V, AN6 VM) for the nominal power of +20.00 D.  
Figure 5: Third- to fifth-order Zernike coefficients, including second- and eighth-order spherical aberrations, were measured from 0.75 mm from the center to an optical zone diameter of 5.00 mm for the three IOL models (AN6, AN6V, AN6 VM) and for the three nominal powers (+10.00 D, +20.00 D, +30.00 D).
## 2 Materials and Methods
### Data
#### 2.1.3 PROBA-V Collection 2 Level 3 S10-TOC  
Designed as a successor for the SPOT VGT, the PROBA-V mission provides continuity products to SPOT VGT, although products at a higher spatial resolution (300 m and 100 m) are also disseminated. Detailed descriptions of the PROBA-V mission and processing chains are provided in [4; 25]. Also, the PROBA-V archive was reprocessed after the end of its operational lifetime, aiming at improving the time series and harmonizing its content. The resulting Collection 2 (PV-C2) was released in 2023 [8]. In this study, we use PV-C2 Level-3 S10-TOC surface reflectance and the NDVI for the period of January 2014-June 2020.  
It should be noted that at the time of the switch between SPOT VGT and PROBA-V, there was an important difference in the equator local overpass times between SPOT5 and PROBA-V. Since PROBA-V had no onboard propulsion, the satellite experienced a constantly varying overpass time.  
PV-C2 data products are available through the Terrascope platform ([[https://www.terrascope.be](https://www.terrascope.be)]([https://www.terrascope.be](https://www.terrascope.be))).  
Figure 1: Timeline of Sentinel-3 SYN VGT product updates. Three 12-month periods are identified for further analysis.
## 2 Physics of Hall Thruster Erosion
### Discharge Chamber Wall Material  
Hall thrusters require ceramic discharge chamber walls to protect the solenoids from plasma exposure. Selection of wall material can impact thruster performance and operating conditions. The material must have low electrical conductivity at high temperatures, high mechanical strength, good thermal shock resistance, low outgassing rate, low secondary electron emission rate, and low sputtering yield [12; 26; 27]. Boron nitride-silica (BN-SiO\({}_{2}\)) composite is the most commonly reported flight thruster wall material, but alumina (Al\({}_{2}\)O\({}_{3}\)), silicon carbide (SiC), and various grades of BN featuring different binder materials have been used in laboratory thrusters [28].  
Figure 2: Front and side cross-section schematics of a Hall thruster [3; 7]. Electrons (yellow) trapped in a Hall drift by crossed electric (green) and magnetic (orange) fields ionize neutral (grey) propellant. The resulting ions (blue) are accelerated into the thruster plume by the electric field.  
BN-SiO\({}_{2}\) composite is formed by hot-pressing hexagonal BN platelets in amorphous SiO\({}_{2}\) binder. The resulting composite is orthotropic because properties along directions parallel to the platelet plane differ from properties along the platelet normal [29; 30; 31]. Figure 4 is a scanning electron microscope (SEM) image of grade M26 BN-SiO\({}_{2}\) composite taken without the aid of conductive surface coating. The fibrous appearance of the BN grains is due to the on-edge view of the BN platelets.
## 1 Introduction
[PERSON], [PERSON], and [PERSON] [32] showed that such mechanical stimulation of the cutaneous mechanoreceptors in the sole of rat feet prevents the decrease in muscle weight and the cross-sectional area of the soleus muscle as well as prevents the reduction in strength. In contrast to vibratory and pressure devices that can be expensive and difficult to use in daily life, other simple and inexpensive interventions are available that could potentially enhance somatosensory feedback and counteract the lack of balance for those who have difficulty maintaining balance such as elders, individuals with the disease, and individuals with a previous injury. [PERSON] et al. [33] exhibited that, by adding a textured surface to stimulate the plantar sole by way of the skin's sensory receptors, this simple mechanical deformation works to improve balance. This same finding has been demonstrated using a variety of people with differing conditions, including persons with Parkinson's disease, multiple sclerosis, chronic ankle instability and other instability issues, the elderly, and the young, using different balance tests (i.e., dynamic balance, proprioception, gait analysis, and static balance tests) [33]. This was further researched by [PERSON] et al. [34] who found that textured material could improve the balance of elderly persons even on unstable surfaces. Textured material can consequently be said to enhance the somatosensory in the skin of the plantar sole. Therefore, this study suggests that insoles that are textured help increase somatosensory feedback and, thus, help maintain balance by stimulating receptors that are otherwise not being stimulated and improve balance functionality. However, little work has been conducted to test the efficacy of enhanced somatosensory input on balance in the individuals with knee OA. Furthermore, it is not certain if individuals with knee OA may benefit from enhanced somatosensory information stimulated by textured insoles compared to other populations that have previously been studied. It is imperative to investigate the effects of textured insoles on this population, though, OA compromises the knee joint, which causes these individuals to have an increased risk of falling [11; 35]. Due to OA effects on the somatosensory system, it was expected that textured insole attenuation effects on balance were likely to be greater in the knee OA group, compared to healthy knee group. If it can be substantiated that balance in individuals with knee OA can be improved by adding textured insoles, simple and inexpensive interventions can be developed for use in their daily activities to improve somatosensoryfeedback and benefit balance to prevent falls. Therefore, the purpose of this study was to firstly compare the balance between individuals with knee OA and their matched healthy individuals, then, determine if the enhanced somatosensory input provided by a textured insole would benefit not only healthy individuals but would enhance to a greater degree the balance of corresponding individuals with knee OA.
# Broadcast Ephemeris with Centimetric Accuracy: Test Results for GPS, Galileo, Beidou and Glonass
## 2 Mathematical Model and Results for GPS, Galileo, BeiDou
### Results for Beidou C12  
The SV C12 belongs to the part of the Beidou GNSS which is in Medium Earth Orbit (MEO), like GPS. Therefore, it is meaningful to use Equation (1) and model Helmert transformations on a full day arc, and arc parameters (mean anomaly, cosine and sine amplitudes of the periodic perturbations along track, cross track and radial) on two-hour arcs.  
Figures 8 and 9 summarize our results for C12. The broadcast ephemeris shows, as for C07, a discrepancy relative to the precise orbit of the order of the meter, and there is a constant bias in the clock corrections (Figure 8). After the fit we observe that the post-fit residuals for both coordinates and clock agree very closely with the corresponding precise ephemeris, suggesting that the correction to the 12 sets of seven parameters each (mean anomaly at epoch Toe; amplitudes and phases of the second zonal components) plus the seven Helmert parameters have successfully accounted for the modeling imperfections of the broadcast message.  
The overall improvement for Beidou's C12 in doing the fit described by Equation (1) is summarized as follows: the mean and rms of the pre-fit residuals is \(-\)5.263 \(\pm\) 9.351 m, and 0.000 \(\pm\) 0.046 m for the post-fit residuals. This statistic includes the spatial and temporal coordinates. The large bias in the pre-fit residuals is, similarly to C07, primarily due to the clock offset of the broadcast message relative to the SP3 values.  
Figure 8: _Cont._  
Figure 8: Pre-fit (dots, different colours for different ephemeris blocks) and post fit (diamonds) residuals of (**a**) X, (**b**) Y, (**c**) Z, and (**d**) T for C12. Post-fits of the time offset T (diamonds) are plotted on the right y-axis.  
Figure 9: _Cont._  
Table 4 summarizes the seven Helmert parameters estimated for C12 on a daily basis. There is a clear shift of the origin in the z direction of ca. 0.7 m, and a smaller one in the x direction. Again, this can be interpreted as a good indication that our optimization process does require an adjustment of the reference frame parameters, if centimetric rms of the post-fit residuals relative to a precise ephemeris is the goal.
## 1 Introduction  
Blockchain technology has transformed industries by enhancing data security and management [1, 2, 3]. Rapidly adopted in sectors such as finance, healthcare, education, and logistics, this technology enhances transparency, security, and efficiency in data storage and sharing processes [4, 5, 6, 7]. The immutability feature of blockchain ensures the secure storage and traceability of sensitive information, while its decentralized architecture preserves data integrity and strengthens access control mechanisms [8, 9].  
Blockchain-based data storage methods provide innovative solutions to the challenges faced by various industries concerning security, scalability, and cost efficiency [10, 11, 12]. Issues such as security vulnerabilities in centralized systems, high transaction costs, and data privacy concerns can be mitigated through blockchain's distributed and secure framework [13]. Additionally, emerging blockchain security solutions such as Zero-KnowledgeProofs (ZKP) and Fully Homomorphic Encryption (FHE) are being widely studied to ensure both privacy and computational efficiency [14]. However, a critical trade-off between performance and security arises when choosing between on-chain and off-chain storage methods [15; 16]. Therefore, understanding the advantages and limitations of different blockchain-based storage solutions has become a critical necessity for system designers. To address the scalability limitations of blockchain, Layer-2 solutions have become increasingly essential. These advanced frameworks improve network efficiency by optimizing data flow and enhancing overall performance [17; 18; 19; 20].  
Recent research has explored data compression and encryption methods for enhancing blockchain storage security. For example, the use of Discrete Cosine Transform (DCT) compression and nonlinear dynamics provides high-quality reconstruction mechanisms, ensuring data privacy while minimizing storage overhead [7].  
However, blockchain-based data storage systems face challenges not only in terms of scalability but also in terms of security and cost. In this context, systematically reviewing different blockchain-based storage approaches is crucial for understanding the future direction of this field.  
This study is a comprehensive review of blockchain-based data storage methods and provides an in-depth examination and classification of existing approaches, highlights their advantages and limitations, and identifies potential research gaps. Blockchain-based data storage methods present significant opportunities and challenges in terms of security, scalability, and cost. However, balancing the trade-offs between different approaches remains an open research question. By synthesizing blockchain storage approaches, this work contributes to a deeper understanding of the existing literature and highlights key areas that require further research.