esa-sceva/satcom-chunk-collection huggingface dataset:
# Can Blockchain Strengthen the Energy Internet?
## 7 Grid Intelligence
### Future Directions  
The advancements in big data and computing technologies have facilitated the emergence of DL techniques for pattern recognition from the aggregated information [79]. This would, thereby, enable accurate demand forecasting for optimal load scheduling and load balancing to reduce peak electricity demand, facilitate energy trading and sharing, state estimation of the power grid and perform grid diagnostics for the detection of energy theft. The higher precision achieved in the energy demand speculation would benefit in maximizing the utilization of DER to cater for the demand requirements while maintaining grid stability [80]. Further, fast blockchain-based data-feeding models need to be introduced to facilitate the development of efficient and accurate ML models.  
Initiatives towards collaborative model development approaches using AI and ML techniques have given rise to Federated Learning (FL), in which individual data storage on a decentralized network is encouraged [81, 82, 83]. This facilitates predictive data analysis through training of a shared model using different data sets, offering capabilities of generalized model development with the benefit of privacy preservation of the userdata. Blockchain facilitates trust establishment and prevents data poisoning, improving transparency in training, validation, testing, deployment and storage of training data sets for such shared models. Smart contracts enable the automation of iterative processes, improving the flexibility of model development and data analysis. However, challenges raised through the blockchain architecture relating scalability and smart contract security, need to be addressed further to expand the capabilities of FL techniques [83].  
xAI (explainable AI) is gaining attention in the current context of AI integration with IoT for predictive data analysis. This could improve the transparency of the AI models associated with demand forecasting, evaluation of energy usage patterns, renewable energy modeling and automated grid operation with optimum generation allocation and load scheduling. xAI unravels the black box AI models, thereby improving transparency in predictive data analysis and establishing trust in the decisions arrived through such approaches [76, 84].  
**Lessons:** Grid intelligence would dominate the future autonomous and the challenges arising from AI-integrated smart grids are seldom addressed through the existing literature [70]. Improving transparency to ensure accountability of ML models [15, 85] and trust management in the decisions arrived through the models [21, 85] would be EI grid-specific challenges to be addressed in future research.
## 3 Results and Discussion
### Absorption Spectrum  
Figure 6 shows the absorption spectrum of the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) (red curve) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) (blue curve). The absorption peaks are located at 392 nm (3.16 eV) and 516 nm (2.40 eV), which belong to the typical exciton absorption peaks. Organic inorganic hybrid perovskites have multiple quantum well structures with alternating organic and inorganic layers, therefore the excitons possess larger binding energy, and it is easy to generate exciton absorption due to quantum confinement effects.
### Atomic and Electronic Structure of [M(Salen)] Complexes and Their Polymers
#### 3.2.3 NEXAFS and EXAFS  
The application of X-ray absorption spectroscopy (NEXAFS and EXAFS) methods for the characterization of [M(Salen)] monomers and their polymers has so far also been limited  
Figure 15: Comparison between experimental valence band and DOS spectra that were from DFT calculations for [Ni(Salen)] complex (**a**) and H\({}_{2}\)(Salen) (**b**). Vertical black bars show the energy positions of valence molecular orbitals, which have been broadened by Gaussian lines with a full width at a half maximum of 1 eV. The lowercase letters \(a^{\prime}\), \(a\), \(b\), \(c\), \(d\), \(e\) and \(f\) denote the photoemission signals of individual sub-bands of the valence band [46]. to just a few works [27; 29; 30; 40; 45; 52; 53; 54; 55; 56], in which X-ray absorption spectra of metal atoms were used to test the purity and stoichiometry of the sample (Ni 1\(s\) NEXAFS [27], Ni 2\(p\) NEXAFS [29], Cu 1\(s\) NEXAFS [54], Co 1\(s\) NEXAFS [55; 52]) or characterization of the atomic coordination of a metal atom in a polymer (Ni 1\(s\) EXAFS, Ba 1\(s\) EXAFS [27; 30], Pd 1\(s\) EXAFS [40]). It should also be emphasized that the measured 1\(s\) NEXAFS spectra of Ni, Cu, Co, and Pd were not analyzed to obtain information about the electronic structure of the studied [M(Salen)] monomers and their polymers. In turn, the EXAFS spectra were used primarily to obtain structural information about the first coordination sphere corresponding to the coordination centers [MO\({}_{2}\)N\({}_{2}\)] and approximately the next two coordination spheres covering most of the monomer molecule. All these data are summarized in Table 3. In analyzing these data, one can notice that for all complexes, the square-planar geometry of the coordination center is preserved because the values of the M-N and M-O bond lengths are quite close. At the same time, the minimum values of the M-O, M-N bond lengths in the coordination center is observed in the case of the [Ni(Salen)]-type complex with the complexing nickel atom and the maximum value for the [Pd(Salen)] type with the palladium atom.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 3 Results
### Error Rates for the Proposed Methodology  
The displacements for the two reservoir levels used for validation purposes were simulated for each of the four validation cracks and the case with no crack. However, these numerical simulations exclude the measuring errors of actual devices. In the next step, these errors were considered by adding noise under a normal distribution with a mean of zero to the simulated recorded displacements. Systematic errors were excluded as they represent specific scenarios, and sensor drift was assumed to be managed operationally.  
The most common pendulum in dams is the sighting reading pendulum, which has an accuracy of 0.1 mm, while more precise monitoring devices achieve an accuracy of 0.02 mm [23]. Therefore, the displacement values obtained in the simulations were analyzed twice, changing the variance of the noise to 2 or 10 hundredths of a millimeter and rounding the results to the same precision. Noise with a variance greater than the accuracy of the device is not allowed because the noise itself would suppress the accurate representation of the devices.  
Next, under the hypothesis that there is no cracking and that the interpolation equations represent the real displacements, the RMSE was measured for 35 m data for each validation crack. RMSE was calculated as the mean of the comparison of all the displacements for all the cracks simulated, with noise, against the interpolation equation for no-crack. Afterward, the same procedure was carried out using the results of each one of the nine calibration cracks as a hypothesis. All these RMSE values were put together in Figure 12.  
Once all the RMSEs were calculated, length and depth were estimated as those corresponding to the minimum error point. It was estimated by the centroid methodology [17] combined with parabolic interpolation and within the limits of the dimensions for the calibration cracks. Then, these estimated dimensions were compared to the real ones, as shown in Table 5. An analysis of how individual factors affect the error is not carried out because, due to the limited number of cases for each possible value of each factor and the high amount of variables, the sensitivity analysis would not yield reliable conclusions. Next, the same process was carried out for the other validation reservoir levels.  
Figure 11: Effect of temperature on cracks for a constant reservoir level of 82 m. Linear regression is depicted in orange.
## 3 Results
### Surface Treatments of Montmorillonite
#### 3.1.2 XRD Analysis  
The XRD patterns obtained for the MMT and OMMT are presented in Figure 3. The MMT showed a typical XRD reflection at \(2\theta=7.28^{\circ}\), corresponding to a basal spacing of 1.21 nm according to Bragg's law. This value is in agreement with similar hydrated clays reported in the literature [28]. The significant increase in the interlayer space of the MMT was observed after the modification with surfactants. In the case of P-MMT, the characteristic peak shifted from \(7.28^{\circ}\) to \(3.84^{\circ}\), corresponding to an increase in basal space from 1.21 nm to 2.36 nm. This result confirms that the intercalation of phosphonium surfactant was really achieved, which is consistent with the FTIR results. In the XRD spectrum of P-SLS-MMT, the distribution of interlayer space of platelets was characterized by two prominent reflections that appeared at \(2\theta=2.87^{\circ}\) and \(5.79^{\circ}\), corresponding to interlayer spaces of 3.07 nm and 1.52 nm, respectively. The basal space of P-SLS-MMT was 0.71 nm higher than that of P-MMT, which indicated that SLS successfully entered the interlayer space of P-MMT and cation-anion type OMMT was prepared. The anionic surfactants can form ion-pairs with the cationic surfactants to intercalate into the interlayer space of MMT. This result was also observed by other researchers [29; 30]. The appearance of a second peak in the XRD pattern of P-SLS-MMT was attributed to the (002) reflection. The diffraction peak of P-KH570-MMT at \(4.21^{\circ}\) corresponded to an interlayer space of 2.09 nm. Compared with P-MMT, the decrease in the interlayer space of P-KH570-MMT was mainly due to partly intercalated cationic surfactants becoming washed out with the use of hydroalcoholic solution as a solvent in the silylation reaction. This result indicated that the KH570 was mainly grafted onto the broken edges and external surfaces or undergoing ion exchange with the pre-existing surfactants in a very low amount, thus merely had a significant impact on the interlayer structure of P-MMT [31].  
Figure 3: XRD patterns of MMT, P-MMT, P-SLS-MMT, and P-KH570-MMT.  
Figure 2: Fourier-transform infrared (FTIR) spectra of (**A**) sodium montmorillonite (MMT), (**B**) P-MMT, (**C**) P-SLS-MMT, and (**D**) P-KH570-MMT.
# Studying the Process of Enzyme Treatment on Beef Meat-Bone Paste Quality
## 3 Results and Discussion
### Mineral Composition of Meat-Bone Paste  
Bone collagen, due to the specific structure and function of bone tissue, can facilitate the deposition of crystalline mineral substances, such as calcium phosphate. This process, known as mineralization, is critical for bone strength and rigidity. The mineral crystals that accumulate within the collagen fibers make bone collagen more stable and durable compared to other collagen types, which do not undergo such mineral deposition. This enhanced stability is essential for the mechanical strength of bones [56].  
This study highlights the exceptional richness of MBP in calcium and phosphorus, with significantly higher levels compared to beef meat. The primary minerals found in this paste were calcium (Ca) and phosphorus (P), with concentrations of 5318.13 mg and 2342.78 mg per 100 g, respectively (Table 3). These levels are significantly higher than the recommended daily intake for adults, making this paste a valuable supplement for bone health and energy metabolism [57]. The paste also contains high levels of sodium (Na), sulfur (S), chlorine (Cl), and potassium (K), with concentrations of 1279.63 mg, 1085.93 mg, 847.49 mg, and 377.92 mg per 100 g, respectively. These minerals play crucial roles in various physiological processes, such as fluid balance, nerve function, and muscle contraction [58,59].  
The MBP tested also contained high levels of magnesium (207.62 mg/100 g), crucial for bone health and enzyme function. Other trace elements like zinc (7.20 mg/100 g), copper (4.35 mg/100 g) and manganese (0.41 mg/100 g) were also present. These minerals are essential for various biological and metabolic processes, such as enzyme activation, oxygen transport, and antioxidant defense [60,61].  
The daily recommended intake of the following minerals is: calcium, 1000 mg; phosphorus, 1500 mg; and magnesium, 500 mg) [62]. This is particularly relevant for populations prone to deficiencies in these vital minerals. According to the USA's National Institute of Health, the average person does not obtain up to 500 mg of calcium in their daily diet [63]. Calcium contained in food products is a hard-to-digest element, so it is extremely difficult to ensure optimal intake of calcium into the human body through traditional food products [64]. Thus, incorporating MBP into meat products could significantly enhance mineral consumption.  
The mineral composition of MBP, especially its high calcium and phosphorus content, makes it a promising source of essential minerals for dietary supplementation, with potential applications in food product development.  
The analysis of the mineral composition of cattle meat-bone paste revealed a high calcium content--5318 mg/100 g--which is significantly higher in the offal of cattle (liver--5.00 mg/100 g; heart--8.00 mg/100 g; kidneys--13.00 mg/100 g; tongue--6.39 mg/100 g, brain--43.00 mg/100 g) and poultry (chicken liver--15 mg/100 g).
###### Abstract  
The inability to tune the frequency of MEMS vibration energy-harvesting devices is considered to be a major challenge which is limiting the use of these devices in real world applications. Previous attempts are either not compatible with microfabrication, have large footprints, or use complex tuning methods which consume power. This paper reports on a novel passive method of tuning the frequency by embedding solid microparticle masses into a stationary proof mass with an array of cavities. Altering the location, density, and volume of embedded solid filler will affect the resonant frequency, resulting in tuning capabilities. The experimental and computational validation of changing and tuning the frequency are demonstrated. The change in frequency is caused by varying the location of the particle filler in the proof mass to alter the center of gravity. The goal of this study was to experimentally and numerically validate the concept using macro-scale piezoelectric energy-harvesting devices, and to determine key parameters that affect the resolution and range of the frequency-tuning capabilities. The experimental results demonstrated that the range of the frequency tuning for the particular piezoelectric cantilever that was used was between 20.3 Hz and 49.1 Hz. Computational simulations gave similar results of 23.7 Hz to 49.4 Hz. However, the tuning range could be increased by altering the proof mass and cantilever design, which resulted in a tuning range from 144.6 Hz to 30.2 Hz. The resolution of tuning the frequency was <0.1 Hz.  
energy harvester; tuning; frequency; piezoelectric; embedded mass Article
## 4 Materials and Methods
### Statistical Analysis  
For the metabolite measurements, significant differences between the two varieties were calculated using the independent samples \(t\)-tests, following testing for normality by the [PERSON]'s test ofhomogeneity of variance. Values are expressed as the mean \(\pm\) standard error of the mean (SE). For the gene expression analysis, statistical significance of the Relative Quantification (RQ) of gene expression between the two varieties was assessed evaluating whether the average \(2^{-\Delta\text{CI}}\) value of the test genotype was significantly different from that of the calibrator genotype (Student's \(t\)-test). Analyses were performed with the SPSS 20 statistical package (IBM). For the integration of metabolic and gene expression data, we collectively used the measurements of the two varieties. For each fruit stage, gene expression data were normalized against the beta-tubulin expression level, averaged among the three replicates, and \(\log_{2}\) transformed. tAsA and dAsA concentrations in replicated samples were first checked for consistency of abundance (i.e., coefficient of variation \(<\) 0.3) and then averaged. Integration of data followed a linear modelling approach (Pearson product-moment correlation coefficient). Clustering among genes was performed using the Average Linkage method (UPGMA) based on Euclidean distances. Calculations and graphical representation of specific gene-metabolite associations were carried out in R.  
The following are available online at [[http://www.mdpi.com/2223-7747/8/7/206/s1](http://www.mdpi.com/2223-7747/8/7/206/s1)]([http://www.mdpi.com/2223-7747/8/7/206/s1](http://www.mdpi.com/2223-7747/8/7/206/s1)), Figure S1: An example of the phenotype of the pepper fruits at the different harvesting times. **A**: PEP1 (immature green); **B**: PEP1 (mature green); **C**: PEP1 (red); **D**: PEP10 (immature green); **E**: PEP10 (mature green); and **F**: PEP10 (red). Scale bar: 3 cm. Figure S2: Ratio, expressed as percentage, between reduced ascorbic acid (dAsA) and total ascorbic acid (tAsA) concentration during fruit development. Data are mean \(\pm\) s.d. of three biological replicates. IG: immature green; MG: mature green; R: red.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Evaluation of Impact Resistance
As is clear from Figure 11, the specimen exhibited a brittle fracture pattern at the same time as the occurrence of abrupt micro-cracks in the vertical direction in the case of the PM specimen, whereas the MFRCM and HyFRCM specimens containing SF and CF showed cracking in the vertical or diagonal direction. It is analyzed here that the impact resistance increases as the fibers in the cement matrix exert a tensile force against the impact transmitted to the outside. As a consequence, it seems that after the first crack appearance, CF could not bridge on macro-cracks due to their short length. On the other hand, SF bridged on macro-cracks and arrested them. Figure 11: Failure surface of specimens under impact resistance; (**a**) PM, (**b**) MFRCM (SF), (**c**) MFRCM (CF), and (**d**) HyFRCM (SF and CF). Figure 10: Number of blows for specimens; (**a**) first crack, and (**b**) final failure.
## 3 Results and Discussion
### Measurement Results
Based on the results presented in Tables 2 and 3, it is evident that at 0.9 GHz, the coverage radius (maximum communication distance) for sensors placed near the ground (\(\mathrm{h_{Tx}=\lambda/3\ m}\)) was less affected by antenna polarization compared to sensors positioned 0.5 m above the ground. Additionally, at 2.4 GHz, antenna polarization had minimal impact on the coverage area.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 4 Smart EMS Control
### Dynamic Energy Management System Strategy
#### 4.1.2 Peak Hours (Summer 16:00-22:00/Non-Summer 15:00-21:00)  
The primary objective of the control strategy is to maximize the use of the ESS and minimize reliance on grid electricity. When solar energy generation exceeds the station's power needs, it first meets these needs, with any excess energy directed to charge the ESS. Once the ESS reaches its full capacity, surplus solar power is then exported to the grid. Conversely, when solar output is insufficient to meet the station's power requirements, the deficit is primarily covered by the ESS. If the ESS cannot fully compensate for the shortfall, additional power is sourced from the grid. However, if the additional power required from the grid exceeds the contracted capacity, the system will distribute the available power according to predefined priorities, ensuring that the station operates within its contractual limits. For a detailed illustration of this energy management process, please refer to Figure 4.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Storage Phase
#### 5.3.4 Data Channel  
The data channel is primarily used to record the medical data index and the usage records of EMR that are produced following patient diagnosis and treatment.  
The medical data index includes the digest of the medical data, \(MD_{EMR}\), the hash address CID generated from storing the original medical data in the IPFS, the doctor's signature on the medical data, \(DocSignEMR\), and hospital information \(H\). Together, these data form the medical data index \(TreatmentData=\{N_{EMR_{hash}},CID,MD_{EMR},DocSign_{EMR},H,t\}\).  
To ensure that the DU cannot obtain the \(N_{EMR}\) through reverse engineering, this scheme adopts the hash value generated from the EMR's number, \(N_{EMR_{hash}}\), as the key for the medical data index \(TreatmentData\). Differing from the encrypted EMR's number employed in the attribution channel, using \(N_{EMR_{hash}}\) in the data channel. Because the inverse hash function \(hash^{-1}\) either does not exist or is extremely difficult to solve, the DUare unable to obtain the plaintext of the EMR's number. This effectively protects the privacy of medical data attribution relationships.  
When patients or other authorized DUs access medical data, the system generates corresponding data usage records, \(DataUsageRecord\), and uploads these records to the blockchain for persistent storage. This mechanism enables patients and the HMC to effectively track and trace the usage of EMR data.
###### Abstract  
Recent advancements in space exploration have significantly increased the volume of astronomical data, heightening the demand for efficient analytical methods. Concurrently, the considerable energy consumption of machine learning (ML) has fostered the emergence of Green AI, emphasizing sustainable, energy-efficient computational practices. We introduce the first large-scale Green AI benchmark for galaxy morphology classification, evaluating over 30 machine learning architectures (classical, ensemble, deep, and hybrid) on CPU and GPU platforms using a balanced subset of the Galaxy Zoo dataset. Beyond traditional metrics (precision, recall, and F1-score), we quantify inference latency, energy consumption, and carbon-equivalent emissions to derive an integrated _EcoScore_ that captures the trade-off between predictive performance and environmental impact. Our results reveal that a GPU-optimized multilayer perceptron achieves state-of-the-art accuracy of 98% while emitting \(20\times\) less CO\({}_{2}\) than ensemble forests, which--despite comparable accuracy--incur substantially higher energy costs. We demonstrate that hardware--algorithm co-design, model sparsification, and careful hyperparameter tuning can reduce carbon footprints by over 90% with negligible loss in classification quality. These findings provide actionable guidelines for deploying energy-efficient, high-fidelity models in both ground-based data centers and onboard space observatories, paving the way for truly sustainable, large-scale astronomical data analysis.  
computer vision; astronomical image analysis; green AI; energy-efficient machine learning; benchmarking performance; carbon footprint in AI; EcoScore; sustainable computing 2025183999