esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 6 Conclusions  
The new method has been tested in various GNSS observation environments, including the open sky, under trees, and urban canyons. All the results have shown a consistent performance and yielded the effectiveness of the proposed CLAMBDA-search method on single-frequency single-epoch ambiguity fixing using low-cost receivers. The searching in the single degree of freedom and the addition of geometric constraints did not significantly increase the algorithm complexity. Instead, the robustness and correctness of the attitude solution are greatly improved, with acceptable computing time consumption. At the same time, the proposed method is less sensitive to the satellite number, satellite geometry, and environmental multipath, and is more suitable for land applications in complex environments.  
[PERSON] designed the study, derived corresponding formulas, drafted the manuscript, prepared the software, performed computations and numerical analysis; [PERSON], [PERSON] and [PERSON] collect the data; [PERSON], [PERSON] and [PERSON] read, commented and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Nos. 41874033 and 41721003), Beijing Key Laboratory of Urban Spatial Information Engineering, No. 2020201, The project was funded by Guangxi Key Laboratory of Spatial Information and Geomatics, No. 19-185-10-15.  
Not applicable.  
The data are not publicly available. You can contact the author by this email ([EMAIL_ADDRESS]) if you need it.  
The authors declare no conflict of interest.
## 4 Test Results and Discussion
### Particle Breakage Characteristics
#### 4.2.4 Stress Level  
The breakage of soil particles in a sample of rockfill under moderate stress will be quite evident [14]. For the purpose of studying the particle breakage characteristics of compacted weathered granite soil under different stress levels, particle size analysis tests were conducted on the samples before and after consolidated drained large-scale triaxial tests, and the relationship between relative breakage and confining pressure (or stress ratio, \(q/p\)) was analyzed.  
Because specimens for the triaxial test must be prepared by the vibrating forming method, particles of the specimens may be partially crushed. In order to improve the accuracy of the experiment, two specimens for the large-scale triaxial test under the same confining pressure were artificially prepared with approximately the same particle size distribution before vibrating compaction. One of the samples was sieved after vibrating compaction and before the triaxial test. The results of the sieving test for this sample were used as the initial gradation of the other sample for the triaxial test.  
From the stress-strain curve of samples in this study, it can be seen that when the axial strain reached 15%, the residual strength of the specimen under different confining pressures reached its constant value. Therefore, when the axial strain reached 15%, the triaxial tests were forced to stop, and then the sieving tests were performed. Figures 22 and 23, respectively, show variations in relative breakage (\(B_{r}\)) with confining pressure (\(\sigma_{3}\)) and stress ratio (\(q/p\)).  
Figure 21: Relationship curve between degree of compaction and \(B_{r}\).  
Figure 20: Relationship curve between blows per layer and \(B_{r}\).  
As indicated in Figure 22, at the end of the triaxial test, the relative breakage of samples increased with the increase of confining pressure, while the increasing amplitude of relative breakage decreased slightly with the increase of confining pressure. It can be considered that the higher confining pressure limited the further compaction and movement of soil particles in soil samples and result in the produce of microcracks and fracture within a single particle. The data in Figure 23 illustrate that at the end of the experiment (\(\varepsilon_{1}=15\%\)), both relative breakage \(B_{\mathrm{r}}\) and the amplitude of \(B_{\mathrm{r}}\) increased with the increase of the stress ratio \(q/p\). These results indicate that under the same axial strain conditions, more particles were broken due to the increased strain confinement caused by higher confining pressure.
## 3 Related Work and Methodology
### Quadrat Count Analysis (QCA)  
Quadrat Count Analysis is a statistical tool used in the study of spatial patterns. It is particularly useful in seismology for analyzing the distribution of earthquake epicenters. The methodology involves dividing the study area into smaller, non-overlapping subareas of equal size, known as quadrats [19]. The number of seismic events occurring within each quadratic is then tallied, producing a discrete spatial frequency distribution of counts. The variance-to-mean ratio (_VTMR_) or the index of dispersion is used to evaluate the pattern of seismic activity in the Red Sea region. The _VTMR_ is a dimensionless number that serves as a test statistic for the spatial randomness of observed seismic events. It is defined as the variance of the quadrat counts divided by the mean count [19; 37]:  
\[VTMR=\frac{\sigma^{2}}{\mu} \tag{3}\]  
where \(\sigma^{2}\) is the variance of the quadrat counts, and \(\mu\) is their mean. In a completely random spatial process adhering to a Poisson distribution, the _VTMR_ would be approximately equal to one. Deviations from this value indicate non-random patterns. Specifically, a _VTMR_ greater than one suggests clustering, while a value less than one indicates a regular or overdispersed distribution. The significance of the observed _VTMR_ is further evaluated using the \(Z\)-score, which measures the number of standard deviations the observed _VTMR_ is from the expected value under the null hypothesis of spatial randomness [38]. The \(Z\)-score is given by:  
\[Z=\frac{VTMR-1}{\sqrt{\frac{2}{n}}} \tag{4}\]  
where \(n\) is the number of quadrats. A high absolute value of the \(Z\)-score indicates that the null hypothesis can be rejected, implying a substantial departure from spatial randomness. The \(p\)-value associated with the \(Z\)-score quantifies the likelihood of observing a _VTMR_ as extreme as, or more extreme than, the one calculated under the assumption that the null hypothesis holds true. A low \(p\)-value (typically less than 0.05) leads to the rejection of the null hypothesis, affirming the presence of a statistically significant spatial pattern [39].
## 5 Discussion  
### Analysis of Stochastic Uncertainties  
In EES in the real-world, many parameters are expressed as random distribution (i.e., electricity demand, electricity generation, and electricity expansion). In this study, the TSP method was used to tackle the stochastic uncertainties. The decision variables of the electricity generation were divided into two subsets, namely, \(W_{k\text{topt}}^{\pm}\) and \(Y_{k\text{topt}}\), which are respectively the amounts of electricity generated in the first-stage (which must be predetermined) and the second-stage (obtained after the determination of the random variables) [55]. Generally, shortages may occur if the electricity demand-levels are continuously high, and the second-stage electricity generation amount \(Y_{k\text{topt}}\) would be undertaken to avoid insufficient electricity supply.  
In the TSP method, the final optimized electricity generation schemes were equal to \(W_{kt}^{\pm}+Y_{kth}^{\pm}\). In detail, the optimized amount of electricity generated in the first-stage is given by \(W_{kt}^{\pm}=W_{kt}^{-}+rr_{kt}\cdot\Delta W\), where \(rr_{kt}\) denotes the decision variables, \(\Delta W=W_{kt}^{+}-W_{kt}^{-}\), and \(rr_{kt}\in[0,\ 1]\). Take period 1 for example, \(\lambda_{11 opt}\) = 0.55, \(\lambda_{21 opt}\) = 1, \(\lambda_{31 opt}\) = 1.00, and \(\lambda_{41 opt}\) = 1.00, indicating that the optimized electricity generation amounts for coal-based power, natural gas-based power, solar power and wind power are supposed to be (\(221.08\) + 0.52 \(\times\) 58.74) \(\times\) 10\({}^{9}\), (58.41 + 1.00 \(\times\)5.00) \(\times\) 10\({}^{9}\) kWh, (1.78 + 1.00 \(\times\) 0.50) \(\times\) 10\({}^{9}\) kWh, and (49.54 + 1.00 \(\times\) 9.36) \(\times\) 10\({}^{9}\) kWh, respectively. Generally, the variation of \(rr_{kt}\) represents diverse policies of electricity generation under stochastic electricity demands. When \(rr_{kt}\) = 0, the cost would be relatively low, although a higher penalty may have to be paid when the generated electricity does not meet the demand. On the contrary, when \(rr_{it}\) = 1, the cost would be higher, but accompanied by a lower risk of violating the target, and hence of incurring a lower penalty.
## 3 Results  
### The Successful Synthesis of ESO/TETA  
The FT-IR analysis of the ESO, TETA, and their reaction product, ESO/TETA, is presented in Figure 2. The characteristic bands of the ESO, TETA, and ESO/TETA at 2917-2856 cm\({}^{-1}\) were attributed to C-H elongation vibrations (CH\({}_{2}\), CH\({}_{3}\)). The characteristic absorption peak at 1640 cm\({}^{-1}\) corresponded to the -C=N stretching vibration in the TETA, signifying the presence of imine groups. For the ESO, the absorption peaks at 1744 cm\({}^{-1}\) (characteristic of ESO's triglyceride carbonyl group) and 824 cm\({}^{-1}\) were indicative of ester and epoxy groups, respectively, consistent with reports in the literature [25; 26]. A notable decrease in the intensity of the epoxy characteristic peak at 824 cm\({}^{-1}\) in the ESO/TETA sample suggests a reaction between the epoxy group of the ESO and the amine group in the TETA. Additionally, the ESO/TETA spectrum exhibited peaks at 3291 cm\({}^{-1}\), 1560 cm\({}^{-1}\), and 1658 cm\({}^{-1}\), which were attributed to N-H stretching vibrations, N-H bending vibrations, and C=O stretching vibrations, respectively. These peaks are characteristic of secondary amide groups, indicating that the ester groups in the ESO underwent animation with the amine groups in the TETA.  
To further verify whether the amine group and the epoxy group and ester group participated in the reaction, we tested the \({}^{1}\)H-NMR spectroscopy of the ESO, TETA, and ESO/TETA, as shown in Figure 3. The \({}^{1}\)H-NMR spectra facilitated the assignment of the synthetic ESO/TETA constituents based on chemical shifts, peak morphologies, and established coupling constants. By comparing the ESO, TETA, and ESO/TETA, the chemical shift range of 6.58-6.21 ppm was attributed to protons attached to nitrogen atoms, indicating the presence of amine functionalities. The chemical shift range of 2.69-2.61 ppm corresponded to protons on the hydroxyl groups formed via the ring-opening reaction of the epoxy groups, underscoring the occurrence of this reaction. Additionally, the absorption peaks within the 2.13-2.03 ppm range were associated with protons on the carbon atoms adjacent to the secondary amine group in the TETA, further confirming the modification of the amine group. These spectral findings substantiate the successful synthesis of the desired bio-oil-based modifier, demonstrating the reaction between the epoxy group of the ESO and the amine group of the TETA, as well as the involvement of ester groups in the process.  
Figure 2: FT-IR spectra of ESO, TETA, and ESO/TETA.
## 3 Results and Discussion
### Plant Height, Number of Tillers and Chlorophyll SPAD Value
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Genotypes**} & \multicolumn{5}{c}{**Salinity Level (dS m\({}^{-1}\))**} & \multicolumn{2}{c}{**LSD**} & \multirow{2}{*}{**CV**} \\ \cline{3-3} \cline{5-8}  & & **0** & **5** & **10** & **15** & **20** & \\ \hline \multirow{2}{*}{**Plant height**} & CV-massaba & 101.3 & 100.3 & 97.0 & 76.3 & 63.8 & & \\  & ILRI-6633 & 120.8 & 117.8 & 103.6 & 78.3 & 68.9 & 7.3 & 9.0 \\  & ILRI-7384 & 93.0 & 92.5 & 81.4 & 60.3 & 52.0 & & \\ \hline \multirow{2}{*}{**Total tillers**} & CV-massaba & 6.9 & 6.8 & 6.3 & 5.2 & 5.5 & & \\  & ILRI-6633 & 5.4 & 5.3 & 5.0 & 4.7 & 3.1 & 1.1 & 17.6 \\ \multirow{2}{*}{**(\#)**} & ILRI-7384 & 9.4 & 9.3 & 8.4 & 8.0 & 5.9 & & \\ \hline \end{tabular} (LSD = Least Significant Difference; CV = Coefficient of Variation).  
\end{table} 
Table 1: Effect of different salinity levels on plant height and the number of tillers of three selected _Chloris gayana_ genotypes. Figure 3: Effect of different salinity levels on chlorophyll content (SPAD value) of three selected _Chloris gayana_ genotypes.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### Results of the Performance Analysis for the Proposed DM Acquisition Method
#### 3.3.1 The Correlation Function of DM and Conventional Method  
The decision variable \(d_{conv,P}(\tau)\) in Figure 7, which is the CCF of the received signal \(r(t+\tau)\) and the generated signal \(g(t)=\ g_{conv,P}(t)\) for the conventional acquisition scheme is plotted with respect to the code phase \(\tau\) in Figure 8.  
The correlation peak is shown in Figure 8a and shown again in detail around the peak within \(\pm 1\) chip \(\left(T_{0}=\frac{1}{1.023\times 10^{6}}\ \mathrm{s}\right)\) in Figure 8b, which shows severe multiple peaks around the maximum peak.  
The decision variable \(d_{DM,PP}(\tau)\) in Figure 5 is the CCF of the received DM signal \(s_{DM,\sigma_{0}}(t+\tau)\) and the generated DM signal \(g(t)=g_{DM,PP}(t)\) for the DM acquisition scheme and is plotted with respect to the code phase \(\tau\) in Figure 9. The correlation peak is shown in Figure 9a and shown again in detail around the peak within \(\pm 1\) chip in Figure 8b, where we can see that the multiple peaks around the maximum peak are completely removed.  
Figure 8: The CCF for the conventional method; (a) Correlation plot for 10,230 chips (600,000 samples); (b) Correlation plot for \(\pm 1\) chip (59 samples) around the correlation peak.
## 3 Results
### Simulations, Case Studies, and Real-World Applications  
#### 3.3.1 Main Trends  
The validation of energy management strategies through simulations and case studies has become a cornerstone in the transition from theoretical frameworks to practical, scalable solutions. Hierarchical control frameworks, for instance, have been extensively studied and validated in various contexts. A prominent example is the scheduling of TCLs aggregated into a virtual battery model. This hierarchical system coordinates energy trading among aggregators while preserving user privacy. By addressing the scalability challenges of centralized systems, this approach has demonstrated its ability to optimize distributed energy trading with significant efficiency improvements in simulated scenarios [17]. Networked microgrids have also been a focus of simulation studies, particularly in isolated and constrained environments. A hierarchical energy management system (EMS) for islanded microgrids incorporates blockchain for secure energy transactions and synchronization algorithms for microgrid interconnection. The EMS also includes a real-time controller for energy storage systems, addressing the unique challenges posed by floating photovoltaic platforms. Hardware-in-the-Loop (HIL) simulations confirm the efficacy of the EMS in maintaining stability and coordinating energy flows under dynamic conditions, underscoring its practicality in real-world applications [52].  
The rise of P2P energy trading frameworks has introduced innovative mechanisms for decentralized energy management. A scheduling strategy for interconnected microgrids, incorporating robust optimization (RO) and Nash bargaining principles, ensures fair profit sharing while handling the uncertainties of wind power. Decentralized solutions using ADMM algorithms have shown through simulations that such systems can achieve cost reductions and maintain participant privacy, validating their application in DES [53]. Similarly, Decentralized Autonomous Organizations (DAO) for smart buildings utilize day-ahead scheduling mechanisms combined with P2P energy sharing. These frameworks enhance the operational efficiency, reducing costs by 33.7% while maintaining resilience against communication failures [54]. Further advancing energy management, studies on commercial prosumers and neighborhood-level flexibility systems have highlighted their potential for achieving net-zero energy communities. For instance, a hierarchical system in subtropical Hong Kong integrates demand-side flexibility with P2P trading, reducing operational costs by 24.6% and CO2 emissions by 7.1%. Such studies underscore the economic and environmental viability of coordinated energy sharing in diverse urban contexts [55].
## 4 Conclusions  
In the present paper, we investigated the quantum confinement effect and photoenhancement of colloidal PbS QDs and PbS/MnS core shell QDs with various shell thicknesses after they were irradiated with continuous laser light for 40 min. At the beginning of the PL measurement, it was found that the PL peak energy was gradually increased with shell thickness due to the presence of MnS shells which confined charge carriers inside the core of PbS. As the thickness of the MnS shell increased from 0.3 to 1.5 ML, localization of charge carriers was also increased which translated to strong carrier confinement, and this was proven by a blueshift of PL peak energies up to 6 meV. After 40 min of continuous irradiation, the blueshifts of PL peak energies of 0.3 ML-1.5 ML PbS/MnS core shell samples were maintained as compared with PbS QDs. However, the enhancement of luminescence intensities was observed in PbS QDs and PbS/MnS core shell QDs. Oxygen present in the aqueous solvent accelerates PL activation as it induces a slow photocorrosion, thus increasing the PL intensity. Large broadening of the FWHM of PbS/MnS CS 1.5 with the value of \(\sim\)50 meV indicates wider size distribution. In comparison, the FWHMs of PbS/MnS CS 1.2, CS 0.9, CS 0.6, CS 0.3 and PbS QDs show a small linewidth shift with the values of 27, 21, 20, 22 and 15 meV, respectively. The effect of laser light on the photoluminescence of QDs can be considered as a useful standard for selection of appropriate nanocrystals for certain applications.  
[PERSON] carried out the experiments and acquisition of data, analyzed and interpreted the data and prepared and revised the manuscript. [PERSON] helped to draft and revise the manuscript. [PERSON] contributed in chemistry knowledge, participated in the design of the synthesis method and manuscript revision. [PERSON] contributed to the photoluminescence setup and manuscript revision. [PERSON] participated in designing the study, analyzing and interpreting the data and drafting a manuscript. All authors read and approved the final manuscript.  
This work was supported by the Ministry of Higher Education Malaysia (FRGS/I/2018/STG07/UPM/02/14), (AKU254: HiCoE Phase 2), and Universiti Putra Malaysia (vot number: 9567500, 9664000).  
The authors declare no conflict of interest.
## 5 Future Research
### Current Market and Commercial VI Service  
Currently, the recognition of VI as an electrical ancillary service is still in an early stage. It is considered as an inherent property instead of an ancillary service in most modern power systems. Hence, to design VI as a commercialized service, the inertial requirements including RoCoF, frequency nadir, response time, and other relevant parameters must be technically specified. European VSYNC research group has demonstrated the effectiveness of VSG through real-time simulations only [99]. VI can be commercialized as a standardized service to regulate frequency and voltage amplitude. The microgrid without VI that affects the power grid should be penalized. The QoS metrics should be deployed to continuously access the power quality. The electricity utility providers should emphasize the significance of VI and implement the requirement of VI as standard. For commercialization of VI, a suitable trading unit for VI is inertia in kg\(\cdot\)m\({}^{2}\)[100]. This SI unit represents the behavior of present SM and suitable for the application of VI. The cost of inertia can be defined as cost per kg m\({}^{2}\) to indicate how much the cost will it be for every single unit of kg m\({}^{2}\) inertia. VI is also crucial for any power which requires high reliability and stability. This includes modern data centers, hospitals, server centers, and power grids which require an uninterruptible and stable power supply. Any commercial inverter can be improved by emulating VI to become VI-based converter to be power grid ready. The EV requires bidirectional converter (DC to AC) and VI is potential to stabilize the power transfer.
# Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images
## 2 Related Work  
The earliest source of knowledge in knowledge distillation is response-based. [PERSON] et al. [10] involves applying Softmax to the classification outputs of the student and teacher models to generate soft logits. Using the soft logits to calculate the KL-divergence loss for knowledge transfer. [PERSON] et al. [16] introduced a decoupling and analysis approach to KD. It separates logits into target and non-target classes, assigning greater weight to the more informative non-target classes. This method can make more effective use of the logits information of the teacher and further improve the performance of knowledge distillation based on response.  
Non-response-based knowledge distillation focuses on relationships between samples or feature layers. [PERSON] et al. [11] extended the knowledge distillation proposed by [PERSON] and designed FitNets. It begins by using intermediate layer features of the teacher model as the knowledge to be distilled. [PERSON] et al. [17] proposed the improvement of network performance through attention transfer (AT). [PERSON] et al. [18] argued that distillation supervision should not only be based on neuron activation values but should also consider the neuron activation boundary (AB). [PERSON] et al. [19] found that similar semantic input will produce similar activation in a trained network and proposed similarity-preserving (SP) knowledge distillation. [PERSON] et al. [20] utilized the inner product between feature layers to obtain the FSP matrix for knowledge distillation. [PERSON] et al. [21] introduced factors as an interpretable form of intermediate layer features to implement factor transfer (FT). [PERSON] et al. [12] proposed contrastive representation distillation (CRD) to bring the student closer to the teacher on outputs of the same class while pushing them further apart on outputs of different classes. [PERSON] et al. [22] proposed self-supervised knowledge distillation (SSKD) which designed a self-supervised module to identify hidden knowledge. [PERSON] et al. [13] combined the model's penultimate layer outputs with a locality-sensitive hashing algorithm for knowledge distillation, achieving excellent results on the majority of distillation model groups.  
The distillation knowledge used in KD [10] comes from the output of the model classification layer after Softmax. FitNet [11], AT [17], and AB [18] use distillation knowledge from intermediate layers of the model. We observed that there has been limited research focused on the model's penultimate layer features. Our work is related to the LSH [13] algorithm, which allows students to fit the feature output of the teacher's penultimate layer by designing a knowledge distillation algorithm based on location-sensitive hashing. However, it did not further explore the potential feature learning ability of the model's penultimate layer. We improve distillation performance by mapping image classification and feature output distributions to ground-truth distribution of images.