esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Original DE Algorithm
### Experiment Results and Analysis
#### 4.3.2 Contrast Experiments for High-Dimensional Segmentation  
In the DE algorithm, the scaling factor can adjust the variation interval range of the population. As in image segmentation, the gray value of an image can only be integers and there are only 256 possibilities to vary, therefore, a larger F is not friendly for individuals to attain suitable mutation results as thresholds are prone to be out of bounds. The experiment shows that when F is set as 0.5, the optimal threshold can be obtained with relatively less iteration compared to other settings and CR is the crossover rate and is set as 0.9; empirical experience shows that high crossover can decrease the time. In this experiment, the Berkeley segmentation dataset image was adopted as the experimental dataset, in which images 1-3 (forest, kangaroo, actinia) are a typical image with relatively concentrated image gray value distribution and small inter-class variance, and their corresponding histograms are shown in Figure 2, while images 4-6 (numbered 41033, 385039, 145086) have a more obvious difference in the image gray value distribution, that is, the inter-class variance is relatively large. For typical images, this paper segments the image through three higher dimensions when D = 8, 9, 10 to test the segmentation effect of the image. The time cost of the algorithm is concerned with the real-time problem, and it is an important standard to evaluate the performance of algorithms. As most practical problems are related to multi-threshold segmentation, which means that multiple parts need to be segmented, the multi-threshold image segmentation problem has high computational complexity and long time consumption. Thus, under the same condition, the time cost is not stable but fluctuated. In our experiment, with the same hardware configuration, the iteration time of DE is set as 1000 to sufficiently evolve, run the same image segmentation algorithm 50 times and take its average running time as an objective evaluation standard. The results of our proposed DE on three typical images with respect to the evaluation criterion including fitness value, PSNR, SSIM, iteration times and running time are given in Tables 1-6. Two of five algorithms that perform better on the evaluation criteria are marked in bold. It can be seen from the tables that every criterion of our method is marked in bold, which means that our method outperforms other competitive methods nearly in overall performance. The comparison of every single indicator is shown in Figures 2-4.
## 3 Numerical Analysis
### Blasting Load Calculation  
In the context of numerical analysis, several empirical equations exist for calculating the blasting load. The blasting load in this study was calculated using the formula presented in [18], which is shown in Equation (1).  
\[P_{D}=0.000424V_{e}^{2}\rho_{e}\Big{(}1-0.543\rho_{e}+0.193\rho_{e}{}^{2}\Big{)} \tag{1}\]  
where, \(P_{D}\): Blasting load (kg cm\({}^{-2}\)); \(V_{e}\): Average explosion velocity (m s\({}^{-1}\)); \(\rho_{e}\): Average density (g cm\({}^{-3}\)).  
The blasting load calculated in [20] was used to obtain the blasting loads for tamping and decoupling using Equations (2) and (3), and the equivalent blasting load (\(P^{\prime}\)) was derived using Equation (4), as proposed by [PERSON] and [PERSON] [21]. It is essential to consider the time history of the vibration generated by blasting before its application as a dynamic load. As shown in Equation (5), the history curve from [21] is commonly used to model the blast load time history.  
\[P_{A}=\frac{2\rho_{a}C_{a}}{\rho_{a}C_{a}+\rho_{e}V_{e}}\times P_{D} \tag{2}\]  
where, \(\rho_{a}\): Dielectric flux density of sand (g cm\({}^{-2}\)); \(C_{a}\): Propagation velocity of shockwaves (sand) (m s\({}^{-1}\)).  
\[P_{B}=\left(\frac{d_{e}}{d_{h}}\right)^{3}\times P_{D} \tag{3}\]  
where, \(d_{e}\): Explosive diameter (mm); \(d_{h}\): Borehole (mm).  
\[P^{\prime}=P_{B}\times W\times 2\pi\times\frac{Diameter\ for\ perforation}{Length\ around\ the\ blast\ hole} \tag{4}\]  
where, \(W\): Charge per delay (kg).  
\[P(t)=4 Pexp\left(\frac{-Bt}{\sqrt{2}}\right)-\exp(-\sqrt{2}\text{Bt}) \tag{5}\]  
where, \(P(t)\): Pressure at time \(t\) (kg cm\({}^{-2}\)); \(B\): Load constant (16,338); \(t\): Arrival time (0.06 us).  
Figure 3 illustrates the time history curves calculated using Equation (5). The blasting loads applied in this study were 233.68, 350.51, 467.35, and 934.70 kg cm\({}^{-2}\), which were equivalently converted into values during decoupling for powder factors of 0.5, 0.75, 1.0, and 2.0 kg, respectively [18, 22]. In accordance with [23], a duration of 0.001 s, an equivalent frequency of 830 Hz, and a ground damping ratio of 1% were applied.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Title 1** & **Model** & \(\boldsymbol{\gamma}\) **(k N m\({}^{-3}\))** & **E (k N m\({}^{-2}\))** & \(\boldsymbol{\
u}\) & **c (k N m\({}^{-2}\))** & \(\boldsymbol{\Phi}^{\prime}\) **(\({}^{\circ}\))** \\ \hline Weathered & M-C & 20 & 70,000 & 0.3 & 25 & 36 \\ Rock & M-C & 23 & 250,000 & 0.2 & 140 & 32 \\ Soft rock & M-C & 27 & 12,900,000 & 0.2 & 1890 & 40 \\ TBM & Elastic & 78.5 & 20,000,000 & 0.2 & - & - \\ \hline \hline \end{tabular} \(\gamma\): Unit weight of soil; E: Young’s modulus; \(\
u\): Poisson’s ratio; c: Cohesion; \(\Phi^{\prime}\): Angle of shearing resistance.  
\end{table}
Table 6: Physical properties of the ground and TBM [17, 18, 19].
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 4 Case Study
### Experiments  
To evaluate the performance of the proposed method, we performed multiple compressions using the individual algorithms and the proposed combination. The three configurations were PCA configured through principal components and quantization, DWT configured through energy threshold and quantization, and PCA with DWT configured with all three. All configurations were iterated with principal components ranging from 5 to 50 in increments of 5 and quantization and energy threshold ranging from 0 to 10%. We also compared the algorithms with the ZFP compressor [27; 28], which has been used in large dataset compression applications, and the SZ3 compressor [29; 30; 31] for scientific datasets.
# Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph
## 1 Introduction
Some researchers have done great work on the integration of multiple types of sensors for analyzing the human hand motions. [PERSON] et al. employed an artificial skin with both of the multi-axis strains and contact pressure information to analyze the human hand motions [11]. Multimodal tactile sensor (BioTac) was applied to measure overall internal fluid pressure and electrode impedance for haptic perception of the small, finger-sized geometric features [12]. A generalized framework integrating multiple sensors to study and analyze the hand motions that contained multimodal information was proposed in [13]. Based on the vision sensors, [PERSON] et al. proposed a novel hand gesture recognition scheme explicitly targeted at the hand motion analysis [14]. More examples could be found in [15], which presented a comprehensive review of the current state of the biosensing technologies focusing on the hand motion capturing and their applications on hand prostheses. However, most references show that multiple sensory information is integrated through two types of sensors. There are few researchers to analyze the hand motions by using the integration of muscle signals with the finger trajectories and the contact forces. This paper designs a multiple sensor based hand motion capture system, which includes EMG, data glove, and Finger Tactile Pressure Sensing (FingerTPS) for hand motion recognition. The rest of the paper is organized as follows. First, Section 2 describes the current multiclass support vector machines (MSVMs) methods and analyses their advantages and disadvantages, and then the Adaptive Directed Acyclic Graph (ADAG) algorithm is applied to recognise different hand motions based on multiple sensory information. Then, the proposed hand motion capture system and the related preprocessing module are introduced in Section 3. In addition, an improved grid-search approach is used to get the optimal intrinsic parameters. The capability of the proposed hand motion recognition algorithm and the comparative experimental results have been investigated in Section 4. Finally, Section 5 concludes this paper with further discussion.
# Determination of Effective Parameters for Hydropower Plants' Energy Generation: A Case Study
## 2 Materials and Methods  
### Characterization of Studied Area  
The hydropower plant being investigated is the Bagisli Regulator and Hydropower Plant (HPP), referred to hereafter as Bagisli HPP, situated on the Zap River. It operates as a run-of-river (RoR)-type hydropower plant. The length of the river is nearly 200 km (in Turkey) and the basin area of the river and its tributaries spans around 26,325 km\({}^{2}\), with 35% of the catchment being within the territorial boundaries of Turkey, while the other part is situated in Northern Iraq [34]. The Dicle basin, which contains the Zap River, is located in the southeast of Turkey, as shown in Figure 1, and is one of the twenty-six basins. The yearly mean flow rate of Dicle Basin is 21.3 km\({}^{3}\) and with Firat Basin, the two basins share 28.5% of all flows in Turkey [35]. The Zap River feeds the Dicle (Tigris) River with a mean yearly flow volume of 292 m\({}^{3}\)/s.  
Bagisli HPP, which is located in Hakkari Province, was commissioned in 2009. The exact location of the HPP is within 37\({}^{\circ}\)42\({}^{\prime}\)49\({}^{\prime\prime}\)-37\({}^{\circ}\)43\({}^{\prime}\)55\({}^{\prime\prime}\) northern latitudes and 44\({}^{\circ}\)03\({}^{\prime}\)17\({}^{\prime\prime}\)-44\({}^{\circ}\)05\({}^{\prime}\)09\({}^{\prime\prime}\) east longitudes. The rain area is approximately 2889 km\({}^{2}\). The installed capacity of the HPP is 30 MW and its estimated energy generation is 100 GWh/year [36]. The project area is mainly composed of a regulator that stores and regulates the water, transmission tunnels, penstock, and power house. Figure 2 illustrates the locations of the stream gauging station numbered 2621 and the length of the transmission tunnels. While the distance between the gauging station and the regulator is 3 km, the distance between the regulator and the power house is nearly 3.3 km. The stored water in the regulator is transferred via a transmission tunnel along the 3.3 km.  
Figure 1: Location of Dicle (Tigris) Basin, Zap River, and Bagisli HPP.  
The design flow and mean flow, which are given in Table 1 as 31.24 m\({}^{3}\)/s and 15.16 m\({}^{3}\)/s, respectively, are determined by the data obtained from 2621 numbered stream gauging stations. The further technical specifications are summed up in Table 1.
# Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment
## 4 Experiments and Results
### Evaluation of R-Pbft  
To evaluate the performance of BD-FL with R-PBFT, we compared this method to the BD-FL with PBFT that applies the traditional PBFT algorithm to the consensus process of blockchain. In the experiments, BD-FL uses 20 local devices to bind with 4 edge servers according to the stable matching algorithm for both methods. Figure 8 shows the global model training time of these two methods over different numbers of training rounds. It can be seen that as the number of training rounds increases, the training time of the two methods grows, and the BD-FL with R-PBFT consumes significantly less time than the BD-FL with PBFT. This is due to the fact that R-PBFT streamlines the consistency protocol and eliminates the client nodes to optimize the traditional PBFT for the decentralized federated learning system in the edge computing environment. Thus, R-PBFT can effectively reduce the communication overhead of consensus and improve the global model training efficiency compared to the traditional PBFT. Meanwhile, R-PBFT can better guarantee the security of the system by introducing the reputation mechanism. In the experiments, BD-FL with R-PBFT reduces the training time by 12.2% compared with BD-FL with PBFT for 150 rounds of global model training.  
Figure 7: Model training time with different methods.
## 4 Summary and Conclusions  
We confirmed the lower attenuation of large-core PMMA step-index fibers of different numerical apertures at the 520-nm wavelength. In fact, an attenuation coefficient as low as 0.09 dB/m was found at this wavelength for the BH and GH fibers. On the other hand, our results suggest that green light introduces higher modal dispersion attributable to the higher number of modes combined with stronger diffusion that is consistent with the wavelength dependence of the predominant scatterings in PMMA. However, its impact on fiber bandwidth is not large, so transmission performance is not highly compromised. Therefore, the use of green optical sources is particularly interesting for long links where its lower attenuation is crucial, and the bandwidth is practically the same as measured with equivalent red sources. Actually, we found that all tested fibers are able to carry Fast-Ethernet data for 100 m, as required for industrial environments. Our results show that the differences in attenuation and frequency response between red and green light are smaller for MH than for the other fibers, consistent with its lower aperture. In fact, the maximum length achievable with this fiber is limited by attenuation, but on the other hand, its reduced dispersion allows reaching higher bit rates than any of the other fibers below 100 m. Thus, the MH fiber is suitable for high-capacity links demanded for domestic and automotive applications, as it supports Gigabit rates for up to 50 m. The heat-resistant BH fiber showed very good performance at 520 nm, reaching rates above 100 Mb/s at 100 m. In addition, its lower attenuation for green light can help to mitigate the problem of the stringent power budget in avionic networks. Moreover, the introduction of spectrally efficient modulation formats; the application of equalization and FEC techniques; and the use of receivers with higher responsivity for green light could increase these bandwidth-length products.  
Conceptualization, M.A.L.; methodology and data acquisition, M.M., M.A.L., A.L. and C.M.; writing--original draft preparation, M.A.L. and M.M; writing--review and editing, A.L. and J.M.; supervision, M.A.L. and J.M.; project administration and funding acquisition, M.A.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Spanish Ministry of Science and Innovation (MICINN/FEDER), grant number RTI2018-094669-B-C33; by the Aragon Government (DGA), grant number T20-20R; and by Altran Innovacion S.L.U. under OTRI grant 2019/0626.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** [PERSON] and [PERSON] acknowledge the Mobility City--University of Zaragoza Chair for the scholarships received.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Existing Commercial Software and Services  
Apart from the various accomplished case studies within the research sphere, there are various companies that offer proprietary software or services in order to assist various businesses in their efforts to modernize and improve. A selected and indicative list of such companies is given below:  
**Descartes Lab** offers a plethora of business intelligence services through the use of satellite imagery and their fusion with their clients' integral data. Their clients range from privately owned businesses to governments, and they have solutions for mining, agriculture, and other areas. An interesting case study of Descartes Labs is their use of the Google Cloud Platform to provide accurate predictions for global food supplies and detect early warnings of famine ([[https://cloud.google.com/customers/descartes-labs](https://cloud.google.com/customers/descartes-labs)]([https://cloud.google.com/customers/descartes-labs](https://cloud.google.com/customers/descartes-labs)), accessed on 1 September 2023).  
**LiveEO** offers satellite monitoring solutions for various industries, including the monitoring of various infrastructures. They can detect changes in very high-resolution images and create risk-monitoring models.  
**Blacksshark.ai** is a geospatial platform that combines satellite imagery and machine learning to provide insights into infrastructure at a global scale. Their AI-enriched methods have the ability to complement missing image attributes. They offer various enterprise solutions such as visualization, simulation, and mapping, updated in real time. An interesting application of Blacksshark.ai is the display of the entire planet Earth in Microsoft's Flight Simulator game.  
**Iceye** is a small and agile radar satellite constellation that provides effective change detection of any location on Earth multiple times during the day and night, independent of weather conditions. Iceye has been used by the insurance industry, and their SAR ecosystem may well support government agencies, environmental groups, emergency response units, and companies in general.  
**AgroApps** is a Greek company that provides crop and weather monitoring and forecasting services using ML and satellite imagery.  
**Agrotech** is similar to AgroApps; it is a Greek company that uses satellite imagery to monitor crop growth cycles for optimal fertilizer use.
## 2 The Proposed Model and Strategies
### Fractional Lyapunov Function Strategy  
In this section, some fundamentals regarding the Lyapunov function technique in discrete fractional calculus will be given.  
Consider a class \(V_{0}\) of all positive definite and decreasing functions \(V:\mathbb{N}_{0}\times\mathbb{R}^{n}\to\mathbb{R}\) such that \(V(\eta_{I},0)=0,l\in\mathbb{N}_{1}\).  
**Lemma 3** ([46]).: _If, for a function \(V\in V_{0}\), there exists a constant \(\mathcal{L}\), such that \(0<\mathcal{L}<1\) and_  
_(i) \(C\
abla_{0}^{a}V(\eta,\mathcal{U}(\eta))\leq-\mathcal{L}V(\eta,\mathcal{U}( \eta))\), \(\eta\
eq\eta_{I}\), \(\eta\in\mathbb{N}_{0}\), \(l\in\mathbb{N}_{1}\), \(0<\alpha<1\);_  
_(ii) \(V(\eta_{I},\mathcal{U}(\eta_{I}))\leq V(\eta_{I}-1,\mathcal{U}(\eta_{I}-1))\), \(l\in\mathbb{N}_{1}\),_  
_then_  
\[V(\eta,\mathcal{U}(\eta))\leq V(0,\mathcal{U}^{0})\mathbb{E}_{\overline{\alpha }}(-\mathcal{L},\eta),\ \eta\in\mathbb{N}_{0}.\]  
**Remark 9**.: _Lemma 3 is an impulsive generalization ofCorollary 3 in [26]and of Lemma 6 in [28]. For impulse-free models, similar results are reported in [25; 65]._  
The following generalization of Lemma 3 will also be useful.  
**Lemma 4**.: _If, in Lemma 3, condition (i) is replaced by_  
\((i^{*})\,^{C}\
abla_{0}^{a}V(\eta,\mathcal{U}(\eta))\leq-\mathcal{L}V(\eta, \mathcal{U}(\eta))+\sigma\)_, \(\eta\
eq\eta_{I}\), \(\eta\in\mathbb{N}_{0}\), \(l\in\mathbb{N}_{1}\), \(\sigma>0\), then_  
\[V(\eta,\mathcal{U}(\eta))\leq\Big{(}V(0,\mathcal{U}^{0})-\frac{\sigma}{ \mathcal{L}}\Big{)}\mathbb{E}_{\overline{\alpha}}(-\mathcal{L},\eta)+\frac{ \sigma}{\mathcal{L}},\ \eta\in\mathbb{N}_{0}.\]  
Proof.: Consider the function  
\[\mathcal{E}(\eta,\mathcal{U}(\eta))=V(\eta,\mathcal{U}(\eta))-\frac{\sigma}{ \mathcal{L}},\ \eta\in\mathbb{N}_{0}.\]  
Obviously, for the above function, condition (ii) of Lemma 3 is true, i.e.,  
\[\mathcal{E}(\eta_{I},\mathcal{U}(\eta_{I}))\leq\mathcal{E}(\eta_{I}-1, \mathcal{U}(\eta_{I}-1)),\ l\in\mathbb{N}_{1}. \tag{7}\]  
For \(\eta\
eq\eta_{I}\), \(l\in\mathbb{N}_{1}\), condition \((i^{*})\) implies  
\[{}^{C}\
abla_{0}^{a}\mathcal{E}(\eta,\mathcal{U}(\eta))\leq-\mathcal{L}V(\eta,\mathcal{U}(\eta))+\sigma=-\mathcal{L}\mathcal{E}(\eta,\mathcal{U}(\eta)). \tag{8}\]  
Thus, from (7) and (8), and applying Lemma 3, it follows that  
\[V(\eta,\mathcal{U}(\eta))\leq\Big{(}V(0,\mathcal{U}^{0})-\frac{\sigma}{ \mathcal{L}}\Big{)}\mathbb{E}_{\overline{\alpha}}(-\mathcal{L},\eta)+\frac{ \sigma}{\mathcal{L}},\ \eta\in\mathbb{N}_{0}.\]  
**Remark 10**.: _Lemma 4 is an impulsive generalization of Lemma 6 in [26]. Lemma 3 is a particular case of Lemma 4 for \(\sigma=0\)._  
**Remark 11**.: _The Lyapunov function strategy is a universal method for the study of the stability of solutions of numerous dynamical systems, including neural networks. The Lyapunov function candidate is applied to estimate the distances between the trajectories of the optimal model solution (optimal design solution in product form design) and all other state trajectories that satisfy specific initial conditions [49; 64]._
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### How Can ChatGPT Assist throughout Each Stage of the Nutrition Care Process?
#### 3.2.3 Nutrition Intervention  
After the nutrition diagnoses have been established, dietitians and nutrition professionals proceed to create a comprehensive plan aimed at addressing the identified issues. This plan encompasses specific nutrition interventions, goals, and strategies that are tailored to meet the unique needs of the individual. The interventions employed may involve various approaches, such as dietary modifications, personalized meal planning, nutrition education, and counseling. In some cases, collaboration with other healthcare professionals (e.g., physicians and other specialists) is necessary to ensure comprehensive care. Collaborative practice in nutrition and dietetics plays a pivotal role in promoting the achievement of a healthier and more fiscally responsible healthcare system because it fosters interdisciplinary cooperation, enhances the quality of patient care, and optimizes resource utilization [52]. Some examples of how ChatGPT can assist in this phase are as follows.  
* Offer general dietary recommendations for various health conditions or goals.
* Recommend personalized meal plans based on specific dietary needs or preferences.
* Provide tips for modifying recipes to improve nutritional content.
* Suggest behavior change techniques that can support adherence to interventions.
* Give guidance on portion control and meal frequency for weight management.
* Suggest resources for finding nutritionally balanced recipes and meal ideas.