esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The experimental section of this study primarily focuses on She ethnic clothing color extraction, clothing classification, and a comparison of classification methods. Firstly, in the color extraction phase, we unearthed the fuzzy relationship between the distribution of She ethnic groups in different regions and the combination of clothing colors. We discussed the characteristics of clothing among different branches of the She ethnic group. Secondly, this study achieved an overall classification accuracy of 98.65% by constructing a neural network discriminative model tailored to the distinctive features of She ethnic clothing. For misclassifications, these were mainly attributed to the small proportion of color features in certain patterns, preventing the formation of effective feature distinctions. Additionally, feature omissions could be present in the process of image collection and reconstruction. Finally, in the comparative experiments of clothing classification methods, the constructed model exhibited significant effectiveness in She ethnic clothing classification. This is because, compared to traditional classification algorithms, this experiment employed receptive fields and weight-sharing techniques, reducing the number of required network weights and burdens during training. Furthermore, through supervised learning, the model's information acquisition capability was further enhanced, effectively improving classification accuracy.  
The study still has certain limitations, such as a scarcity of datasets. Datasets are essential for training models, yet the existing collection of She ethnic clothing images falls short of the required quantity, which necessitates sourcing traditional clothing images from the internet or museums. The collected images also require a series of preprocessing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Training Time/s** & **Average Classification Accuracy/\%** & **Highest Classification Accuracy/\%** \\ \hline CNN & 25.14 & 91.94 & 93.24 \\ Proposed method & 12.15 & 98.38 & 98.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods of classification.  
steps before they can be used. As of now, there is no well-established standardized dataset available.  
In future research, the focus will continue to be on the recognition and classification of ethnic clothing. Firstly, efforts can be directed towards improving classification accuracy. Although the FPA-CNN method proposed in this paper yielded favorable results in classifying She ethnic clothing, there is still room for improvement. Further optimization of feature extraction and classification models, such as introducing additional feature parameters, can enhance classification accuracy. Secondly, there will be an expansion of the variety of ethnic clothing types. In China, there are other precious ethnic clothing cultures that are on the verge of being forgotten, such as Tibetan and Dai ethnic clothing. The restoration and recognition of clothing images from these ethnic groups hold significant practical implications for the preservation of intangible cultural heritage.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 2 PPP Mathematical Model and Satellite Selection Algorithms
### The Elevation Partition Satellite Selection Algorithm  
Inspired by the idea of the distribution of satellite elevations and the fast-rotating partition satellite selection algorithm, we proposed a new satellite selection algorithm which is based on equal distribution of elevation. The principle of this satellite selection method and the fast-rotating partition satellite selection algorithm is similar. It also can greatly reduce the computation time and is almost unaffected by the number of visible satellites selected. The steps of this algorithm and the mathematical function model are given as follows.  
Step 1: According to the elevation mask angle and SV Health, select the healthy satellites.  
Step 2: As shown in Figure 2, divide the elevation angle for \(n\) partitions according to the number (\(n\)) of the visible satellites.  
Step 3: Select the satellite which is closest to the partition midline in each partition. Calculate the absolute value of the elevation angle difference between the satellite and partition midline. Calculate the average of the absolute values of all partitions. The scheme is one of the optional schemes.  
Step 4: Move the midline of each partition at the same time according to a certain angle (\(j\)) to form a new dividing scheme of the elevation. Repeat the steps mentioned above. The scheme with the minimum absolute value is chosen as the final satellite selection scheme. The mathematical equation is as follows:  
\[\begin{array}{l}Sum=\min\sum\limits_{i=1}^{n}\Big{\{}h_{i}-90(\frac{2n-2j+1} {2n})-j\Big{\}},\quad 1\leq j\leq[\frac{90}{n}]\\ h_{i}=\left\{\begin{array}{l}h_{i},\quad h_{i}\leq 90-j\\ h_{i}-90,\quad 90-j<h_{i}\leq 90\end{array}\right.,\end{array} \tag{9}\]  
where \(n\) is the number of visible satellites; \(i\) is the number of partitions; \(h_{i}\) is the elevation mask angle of satellite \(S_{i}\); \(S_{i}\) is the nearest satellite to the centerline in partition \(i\); \(j\) denotes the moving angle.  
Figure 1: The fast-rotating partition satellite selection algorithm.
## 3 Results and Discussion
### Electronic Nasal Analysis  
Flavor is a critical characteristic influencing beverage quality and consumer acceptance. Electronic sensory analysis, such as the electronic nose (E-nose), is widely employed for objective and rapid flavor profiling, offering advantages over traditional sensory evaluation methods due to its simplicity, speed, and objectivity [51]. We utilized E-nose analysis to investigate the olfactory characteristics of rose beverages fermented with different microbial combinations. As shown in Figure 2A, the response patterns of the SFR and LSFR groups (associated with _S. cerevisiae_ fermentation) were similar but exhibited slight differences in intensity across several sensors, including W1S (sensitive to methyl groups), W2S (sensitive to alcohols, aldehydes, and ketones), W1W (sensitive to sulfides), W2W (sensitive to aromatic compounds and organic sulfides), W1C (sensitive to aromatics), and W5S (sensitive to nitrogen oxides). In contrast, the LFR group displayed higher response intensities for W1W and W2W but lower intensities for W1S, W2S, W1C, and W5S, distinguishing its profile from those of SFR and LSFR.  
Figure 2: Discriminant analysis of rose beverages fermented with different microorganisms based on the e\(-\)nose data (\(n\) = 3). (**A**) Radar plot of E\(-\)nose sensor response, significance: ***\(p\) < 0.001; **\(p\) < 0.01; **\(p\) < 0.05. (**B**) Principal component analysis plot of E\(-\)nose results for fermented rose beverage samples, the contribution rates of PC1 and PC2 are 91.7% and 7.4%. UFR: rose beverage without fermentation; LFR: rose beverage fermentation by _L. plantarum_; SFR: rose beverage fermentation by _S. cerevisiae_; LSFR: rose beverage fermentation by _L. plantarum_ and _S. cerevisiae_ collectively.  
Principal component analysis (PCA) was applied to the E-nose response signals to visualize the differences in volatile profiles among the rose beverages fermented with different strains. As shown in Figure 2B, the first two principal components (PC1 and PC2) accounted for 99.4% of the total variance, effectively capturing the key information regarding the volatile compounds. The unfermented samples clustered closely with the samples fermented solely by L. plantarum (LFR), suggesting that lactic acid bacteria fermentation largely preserves the inherent flavor characteristics of the rose beverage. Conversely, the three fermented rose beverage samples (LFR, SFR, LSFR) were distinctly separated in the PCA plot (Figure 2B), indicating that each fermentation condition resulted in a unique flavor profile. This observation is consistent with the distinct patterns observed in the radar plot (Figure 2A). Therefore, the E-nose analysis successfully discriminated the flavor characteristics of the three fermented rose beverages to a certain extent. However, a more in-depth and effective understanding of the influence of different inoculation methods on flavor profiles necessitates the integration of additional analytical techniques.
# Current Status of HbA1c Biosensors
## 2 HbA1c Biosensors
### Affinity Biosensors Based on Boronic Acid Derivatives  
Boronic acid derivatives have a high affinity for the _cis_-diol group of the sugar moiety in glycosylated proteins, which is the fundamental principle behind several affinity-based methods for detection of glycosylated proteins including HbA1c. Here we summarize the methods, which will be elaborated below. One way is to bind Hbs (both HbA and HbA1c) by haptoglobin (Hp) on an electrode surface first, and then ferrocene-boronic acid (FcBA) is added to the system. The FcBA,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Status** & **HbA1c (\%) a** & **HbA1c (mmol/mol) b** \\ \hline Prediabetes & 5.7–6.4 & 39–47 \\ Diabetes & \(\geq\) 6.5 & \(\geq\) 48 \\ \hline \hline \end{tabular}  
* suggested by the Diabetes Control and Complications Trial (DCCT); b suggested by the IFCC.  
\end{table}
Table 1: ADA criteria for the diagnosis of diabetes and prediabetes based on HbA1c.  
which can be probed by electrochemical signals, will interact with only HbA1c, not Hb, (Figure 1A). The second method is to immobilize boronic acid derivatives on the electrode, resulting in a selective binding of HbA1c, by which an appropriate electrical signal can be detected (Figure 1B,C).
## 6 Conclusions  
To deal with the measurement statistical uncertainties and inaccurate battery model, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper for SOC estimation of lithium-ion batteries. First, the variational Bayesian inference is integrated with the extended Kalman filter to jointly estimate the states and the measurement noise covariances. Then, two VB-based extended Kalman filters are alternatively used for online estimation of the battery SOC and model parameters, while simultaneously estimating the measurement noise variances to compensate for the uncertainties in the measurement and battery parameters. Therefore,  
Figure 19: SOC estimation results using VB-ADEKF and DEKF in the case of mistuning in the UDDS test.  
Figure 18: SOC estimation results using VB-ADEKF and DEKF in the case of mistuning in the pulse current discharge test.  
the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. The effectiveness and superiority of the proposed algorithm have been verified by comparing with the dual EKF (DEKF) algorithm through experiments under the constant current discharge test, pulse current discharge test, and UDDS test. The results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF approach in terms of SOC estimation accuracy and convergence rate. Especially, when the quality of measurements changes with the operating conditions, the proposed VB-ADEKF exhibits better robustness than DEKF.  
[PERSON] conceived this paper, designed the experiments, and analyzed the data; [PERSON] and [PERSON] performed the experiments; [PERSON] revised the paper and provided some valuable suggestions.  
This research was funded by Shaanxi Provincial Key Research and Development Programs (No. 2017 ZDXM-GY-06, No. 2017 GY-057), and Xi'an Science and Technology Planning Project-Scientific and Technological Innovation Guidance Project ( No. 201805042 YD20 CG26 (8)).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AEKF Adaptive Extended Kalman Filter  
ANFIS Adaptive Neuro-Fuzzy Inference System  
ALS Autocovariance Least Squares  
ASRUKF Adaptive Square Root Unscented Kalman Filter  
AUKF Adaptive Unscented Kalman Filter  
BMS Battery Management System  
CC Coulomb Counting  
CKF Cubature Kalman Filter  
DEKF Dual Extended Kalman Filter  
EKF Extended Kalman Filter  
EV Electric Vehicle  
FFRLS Forgetting-Factor Recursive Least-Squares  
FL Fuzzy Logic  
KF Kalman Filter  
KL Kullback-Leibler  
MM Multiple Model  
NN Neural Network  
OCV Open Circuit Voltage  
RC Resistor-Capacitor  
SOC State Of Charge  
SVM Support Vector Machine  
UDDS Urban Dynamometer Driving Schedule  
UKF Unscented Kalman Filter  
VB Variational Bayesian
## 2 Materials and Methods
### Finite Difference Method Simulations  
The system of Equation (4) is solved using two approaches, chosen based on the problem's dimensionality and computational efficiency. First, the FDM is applied to simulate wave propagation in a 2-D domain, assuming plane strain conditions in the xz-plane. FDM was selected for its simplicity, robustness, and effectiveness in simulating basic 2-D physical problems. For more complex 3-D scenarios, we employ the PsM, which is computationally efficient for 3-D simulations. This method can handle larger domains with higher accuracy while utilizing fewer grid points. Brief descriptions of the numerical implementations for both methods can be found in Appendix A for the 2-D FDM approach and in Appendix B for the 3-D PsM approach.  
Figure 2: **(a)** Dispersion curves of Lamb modes for a 10 mm steel plate (red line: symmetric modes, blue line: anti-symmetric modes); **(b)** Phase velocity of \(A_{0}\) mode.  
In the 2-D FDM, a one-dimensional grid is used, while a 2-D grid is employed in the PsM studies. Each grid node in the 2-D FDM has two degrees of freedom, whereas nodes in the PsM have three degrees of freedom. A convergence analysis was performed through iterative grid refinement, establishing an element size of 2 mm (corresponding to 18 grid nodes per wavelength of the \(A_{0}\) mode at 50 kHz) for the 2-D FDM grid, and 4 mm (equivalent to 9 grid nodes per wavelength) for the PsM grid. Based on the size of the domain, a simulation time of 400 us was set, using a step increment of 0.5 us, which is determined to be sufficient according to the Courant-Friedrichs-Lewy criterion [37]. The representative time step was verified by reducing the time step size until the maximum amplitudes of the received signals differed by less than 1%. To minimize reflections from the edges of the plate and maintain a compact model, we incorporated absorbing regions. Sponge layer damping is utilized, applying a viscous damping coefficient that increases as one approaches the edges, which effectively absorbs outgoing waves. This damping mechanism is included in the constitutive equations of the Mindlin model as a first-order time derivative, efficiently reducing wave amplitudes in the boundary regions. In both models, excitation was applied using a five-cycle tone burst with a center frequency of 50 kHz, modulated by a Hanning window. Numerically stable excitation of the \(A_{0}\) mode was achieved by applying a unit force to the main grid node, with adjacent nodes receiving 75% of the amplitude and diagonal neighbors receiving 50% (in the PsM method). This configuration was specifically designed for the studied frequency range to ensure that nearly pure \(A_{0}\) mode generation occurs when applying the normal force at this frequency [38].
# Analysis of Feed Inlet and Optimal Feeding Amount of Waste Ground Film Impurity Removal Equipment
## 5 Conclusions  
1. In this study, we addressed the problem that a large amount of residual film-impurity mixture is not efficiently depolymerized during the operation of a residual film-impurity mixture separator. Based on the principle of residual film-impurity mixture depolymerization and the flow-solid coupling simulation method, the maximum collision force between the residual film-impurity mixture and the inside of the device was determined as the key factor affecting the mixture depolymerization.
2. Analysis of the whole feeding process revealed that when the residual film-impurity mixture separation device was in stable operation, the factors influencing the magnitude of the collision force between the residual film-impurity mixture and the device interior originated from the feed inlet position. The optimal conditions were a square inlet port and a feeding rate of 202 kg/h.
3. The above-mentioned inlet structure and feeding rate were used as standards, and machine tests were conducted. The test results showed that the average value of the ratio of impurities in the residual film was 6.966%, which was 5.004% lower than the value of 11.97% before optimization. Based on all statistical data, the coefficient of variation was calculated to be 7.38% with a variance of 0.36453. The dispersion of the statistical results was small, and the ratio of impurities in the residual film remained unchanged during the continuous operation of the film-impurity wind separator.  
Conceptualization, C.X. and J.K.; methodology, C.X.; software, C.X.; validation, C.X., J.K. and Q.P.; formal analysis, C.X. and J.K.; investigation, C.X., J.K., Q.P., N.W., X.W. and Y.Z.; resources, J.K. and Q.P.; data curation, C.X., J.K., Q.P., N.W. and Y.Z.; writing--original draft preparation, C.X. and Q.P.; writing--review and editing, C.X., Q.P. and J.K.; visualization, C.X.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Projects (grant number 52175238) and the Natural Science Foundation of Shandong Province Key Projects (grant number ZR2020 KE045).  
The data presented in this study are available upon request from the first author at ([EMAIL_ADDRESS]).  
The authors would like to thank the instructor for his help during the writing of the manuscript, as well as all reviewers who participated in the review of the manuscript.  
The authors declare no conflict of interest.
## 5 Discussion
### Comparison with Other Author's Research  
[PERSON] presented a horizontal urban land dynamic of Beijing City between 1984-2013 based on image classification and change detection [20]. [PERSON] derived the horizontal urban expansion in Beijing-Tianjin-Tangshan metropolitan region from 1990 to 2015, based on image classification [22]. Both of them have certain reference significance, but they involve horizontal dynamic monitoring, and do not involve vertical dynamic changes.  
In order to compare the difference between horizontal expansion and vertical expansion in Beijing Tianjin Hebei region, the construction areas in the two phases were extracted through image classification. Then, the horizontal construction expansion volume of each county was calculated through regional building area statistics, as shown in Figure 11. It is significantly different from the vertical expansion statistical result in this paper (Figure 10). The areas with the largest expansion are Beichen, Ninghe, Dongli, Bazhou, Wuqing, etc. One possible reason may be related to the stage of regional development. Beichen, Ninghe, Dongli, Bazhou, Wuqing, and others belong to the fourth-tier counties, with relatively slow growth. Their undeveloped areas are vast, and their expansion is mainly horizontal. On the contrary, there is no space for horizontal expansion in the central urban area, but primarily vertical expansion. Obviously, the real three-dimension expansion speed is difficult to reveal only through the horizontal expansion statistics interpreted by the images.  
Figure 11: Comparison of building horizontal expansion between counties. Note: The average horizontal expansion of buildings in Figure 11 expresses the expansion ratio of construction land per unit area, which is dimensionless.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
###### Abstract  
Device-to-device (D2D) communication is becoming an increasingly important technology in future networks with the climbing demand for local services. For instance, resource sharing in the D2D network features ubiquitous availability, flexibility, low latency and low cost. However, these features also bring along challenges when building a satisfactory resource sharing system in the D2D network. Specifically, user mobility is one of the top concerns for designing a cooperative D2D computational resource sharing system since mutual communication may not be stably available due to user mobility. A previous endeavour has demonstrated and proven how connectivity can be incorporated into cooperative task scheduling among users in the D2D network to effectively lower average task execution time. There are doubts about whether this type of task scheduling scheme, though effective, presents fairness among users. In other words, it can be unfair for users who contribute many computational resources while receiving little when in need. In this paper, we propose a novel blockchain-based credit system that can be incorporated into the connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network. Users' computational task cooperation will be recorded on the public blockchain ledger in the system as transactions, and each user's credit balance can be easily accessible from the ledger. A supernode at the base station is responsible for scheduling cooperative computational tasks based on user mobility and user credit balance. We investigated the performance of the credit system, and simulation results showed that with a minor sacrifice of average task execution time, the level of fairness can obtain a major enhancement.  
D2D communication; blockchain; fairness; connectivity-aware +
Footnote †: 9, 85; doi:10.3390/f9040085
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 4 Architectures
### Coordinated Operations  
This control architecture is designed for persistent connectivity scenarios since it relies on SDN-enabled networks and NFV. This network softwarization is designed for highly connected terrestrial networks and adapted to NTN. The framework designed to manage and orchestrate these VNF is the MANO. The coordinated operations architecture assumes current state-of-the-art MANO, currently not adapted to NTN. The scenario used to describe the coordinated adaptive operations architectures is depicted in Figure 8, featuring a LEO satellite mega-constellation with regenerative payload that offers direct-to-handset connectivity.  
Figure 9 presents the coordinated operations control architecture for a persistent connectivity use cases like the one presented. Both SatOps and NetOps are coordinated by the CMS. They are executed not only using SatOps interfaces and agents, but also using the NetOps interfaces and MANO from the network operator. The control flow using this architecture starts by the MNO providing the NetOps intent rules to the CMS using the standard OSS interface. Then, the CMS generates a task plan required for the MNO intent and aligns with the SatOps criteria of the SNO. The plan is then converted to: (1) satellite OM plans, (2) ground control commands, and (3) MANO inputs. The satellite OM plan and ground control commands will prepare the system for the MANO to execute its actions based on the adapted input from the CMS. This CMS-MANO interface is the same as the standard OSS. The MANO then interacts directly with the SDN controller and VIM onboard the satellite.  
Figure 7: Control diagram using integrated operations for a persistent connectivity constellation. The CMS manages both LDA for operational control and HDA for service data transmission. Platform control coordinates satellite OM for communications subsystem management, while ground control manages multiple GS types for different data/control planes. The architecture separates operational control (using LDA) from high-throughput data services (using HDA), enabling continuous service provision. Control flows maintain traditional satellite operational interfaces while supporting network service requirements.