esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hybrid Protocols
### Tree Slotted Aloha  
Tree Slotted Aloha (TSA) is a probabilistic protocol created to reduce the number of collisions occurring in FSA [62]. When more tags collide in a slot, FSA attempts to solve this problem in the next frame. However, in the new approach, if more tags collide in a frame, only those tags that are involved in that collision are queried in the next slot.  
TSA uses \(l_{o}\)--estimation for the initial frame size. This protocol provides very good efficiency, despite the fact that this number can be far from the actual number of tags.  
The initial query consists of a request for data by specifying the frame size \(l_{i}\). Then, all tags in the interrogation area will generate a random number in the range \([0,l_{i}]\) and transmit its ID in that randomly selected slot.  
The protocol is organized in a tree structure. The first node in a tree is the first interrogation round. The reader sets the initial frame with the following data: \(l_{0}\), \(N_{i}\) represents the number of transmitting tags in slot \(i\), where \(i\leq l_{0}\), \(N_{i}\geq 0\), and \(\sum_{i}N_{i}\geq n\) must hold. If\(N_{i}\geq 2\), there is a collision in slot \(i\).  
At the end of each interrogation round, if the reader detects a collision, it begins a new frame from each slot where the collision was detected. This is accomplished by adding new nodes to the tree: every new node is a son-frame of the collided slot. In each round, the tags store the generated random number from the previous round and increase their tree level counter by 1 so they will know when they should transmit. Every time the reader detects a collision, it creates a new node in the tree and a new round involving only the tags that have collided in that slot. This procedure is shown for the example in Figure 21.  
TSA is a modified version of the FSA protocol, created to reduce the number of collisions. This protocol behaves better than FSA. TSA achieves an efficiency of between 37% and 41% [62].
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 1 Introduction  
Machine learning (ML) has recently found numerous applications, and it is being leveraged as a powerful tool in various fields: computer science, engineering, telecommunications, chemistry, physics, mathematics, imaging science, materials science, and environmental sciences. The importance of ML is also confirmed by an increasing trend in the volume of work published over the last decade, according to Web of Science data (Figure 1).  
In computer science, ML is used for various tasks, such as natural language processing [1; 2; 3; 4; 5], image recognition [6; 7; 8], or computer vision [9; 10; 11; 12; 13; 14]. In engineering, ML is applied in the optimization and control of complex systems [15; 16; 17; 18], the prediction of equipment failures [19; 20; 21], or the enhancement of manufacturing processes [22; 23; 24; 25]. ML is also extensively used in materials science for materials discovery [26; 27; 28; 29; 30], property prediction [31; 32; 33; 34], and accelerated materials design [35; 36; 37].  
Understanding materials' structure, composition, and properties is essential in experimental materials science. Thus, spectroscopy and microscopy are used to characterize the behavior of materials at various scales. The integration of machine learning methods has brought transformative advancements to the analysis of complex data. Several models have been employed to automate the interpretation of intricate spectroscopic data, facilitating the enhancement of signals, feature extraction, compound classification, and property prediction. Similarly, these methods have enabled automated particle detection, crystallographic analysis, and defect recognition in electron microscopy images, surpassing conventional image processing approaches [38, 39, 40].  
One example is [41], which showed that deep learning has great potential in performing all the steps and emphasized the importance of addressing the estimation of the prediction quality of deep learning models on small datasets with complex covariance structures. Another example is the case of scanning transmission electron microscopy-electron energy loss spectroscopy studies, for which [42] used a principal components  
Figure 1: Number of publications about machine learning, according to Web of Science: (**a**) yearly publication counts; (**b**) classification by research area.  
analysis (PCA) algorithm to analyze the momentum-resolved spectra for SiGe quantum dots and Si-SiGe interfaces. In their case, the low acquisition parameters for mapping datasets required the PCA method to improve the signal quality. The authors state that by using this method instead of traditional Fourier filtering or smoothening techniques, the important features are maintained, and their quality is improved.  
The focus of this paper was to assess the implications of ML in the field of data analysis of X-ray diffraction (XRD), compare the accuracy of the reported models with that of traditional XRD data analysis procedures, and present future development opportunities.
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 3 A Condition with Which to Compare Risk Evaluation Formulas
### Comparing Two Risk Evaluation Formulas
By assuming that the faulty statement is at the middle of \(S_{F}^{R}\), we can then calculate the ranking of the faulty statement. We now formally define our condition for comparing two risk evaluation formulas. For two risk evaluation formulas \(R_{1}\) and \(R_{2}\), we said that \(R_{1}\) performs better than \(R_{2}\) if the expected faulty location of \(R_{1}\) is in front of that of \(R_{2}\); that is, \(EFL(R_{1})<EFL(R_{2})\). We have the following proposition. Proposition 1: _For any two risk evaluation formulas \(R_{1}\) and \(R_{2}\), if \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{2}}\), then \(R_{1}\) performs better than \(R_{2}\)._  
Proof: For any two risk evaluation formulas \(R_{1}\) and \(R_{2}\), the expected faulty locations of \(R_{1}\) and \(R_{2}\) are \(EFL(R_{1})=\frac{1}{2}(n+n_{B}^{R_{1}}-n_{A}^{R_{1}})\) and \(EFL(R_{2})=\frac{1}{2}(n+n_{B}^{R_{2}}-n_{A}^{R_{2}})\) respectively. Since \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{2}}\), the expected faulty location for \(R_{1}\) is in front of that for \(R_{2}\); that is, \(EFL(R_{1})<EFL(R_{2})\). Hence, \(R_{1}\) performs better than \(R_{2}\).
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
## 3 Results
### Effects of Temperature, pH, Metal Ions, and Chemical Agents on DgoKerA Activity  
A 1% soluble keratin was used to measure the enzyme activity. As shown in Figure 4, the optimal temperature of DgoKerA was 60 \({}^{\circ}\)C in 50 mmol/L Tris-HCl buffer (pH 7.0). The thermostability of DgoKerA was characterized. The residual activity of DgoKerA was approximately 78% after 30 min incubation at 60 \({}^{\circ}\)C and approximately 40% after 60 min incubation. DgoKerA showed maximum activity at around pH 6-8. DgoKerA was sensitive in the range of pH 3-4, and was stable in range of 5-9. The residual activity of DgoKerA was approximately 88% when incubated at pH 6 for 60 min. Based on Figure 4, the keratinase BliKerA activity was lost more rapidly under 60 \({}^{\circ}\)C treatment, with less than 20% residual activity after 60 min incubation. The date appears to suggest that DgoKerA has the potential of industry application, due to the outstanding thermostability.  
The effects of various metal irons on keratinocyte activity were shown in Figure 5A. K+, Li+, Mg2+ and Co2+ increased the enzyme activity of DgoKerA. Other tested metal ions slightly detected activity of DgoKerA, except Cr3+, which markedly decreased the activity of DgoKerA. All of the surfactants and organic solvent employed negatively impacted on the stability of DgoKerA, with more drastic effect obtained in the presence of \(\beta\)-mercaptoethanolhanol. The good tolerance of DgoKerA to organic solvent DMSO and surfactant (Tween-80 and Tween-20) was discovered (Figure 5B).  
Figure 1: Phylogenetic analysis of amino acid sequence of keratinases. The name and GenBank accession No. for each protease are in front of the Latin name of each strain. The phylogenetic tree was constructed by MEGA 6.0 with neighbor-joining (NJ) method. The percentages of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches.  
Figure 2: Alignment of the amino acid sequences of DgoKerA. The keratinases used were MtaKerA from _M. taiuunensis_ WR-220 and BliKerA from _B. licheniformis_ PWD-1. Sequence alignment showing structural elements of DgoKerA were generated with ESPript3.0 ([[https://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi](https://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi)]([https://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi](https://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi)) (accessed on 7 November 2021)). The identical residues of all aligned proteins are shaded red. \(\alpha\), \(\beta\), \(\eta\), TT above the sequences represent \(\alpha\) helix, \(\beta\) sheet, 310 -helices and \(\beta\)-turns, respectively. Positions of the starting residues of the mature protease (mature) was marked by black arrows at the bottom of the sequences.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 2 Problem Formalization  
### System Model  
The proposed V2G network communication architecture contains cloud, fog, and user layers, as shown in Figure 1.  
1. TA: The TA is responsible for the registration of entities such as the EV, CP, and FN and tracking the real identities of EV users. The TA is a comple  
Figure 1: The communication architecture in the V2G network.  
2. KGC: The KGC is responsible for generating public and private keys for entities such as the CP, FN, and CSO. The KGC is a partially trusted entity.
3. CSO: The CSO, located in the cloud, is responsible for batch verification and decryption of the regional charging and discharging data reports uploaded by the FN, as well as processing the charging and discharging data. If the CSO detects abnormal charging and discharging data for the EV, it can request the TA to track the real identity of the EV user.
4. FN: The FN is deployed at the level of the charging stations, with certain computing, communication, and storage capabilities. The FN is responsible for aggregating and verifying charging and discharging data reports, generating local regional charging and discharging data reports, and uploading them to the CSO, thereby avoiding the computational and communication overhead caused by direct data exchange between the CSO and each CP.
5. CP: The CP is responsible for encrypting and signing the charging and discharging data of the EVs, generating charging and discharging data reports, and uploading them to the local FN. It is also responsible for providing power connections to the EV and charging or discharging the EV based on charging and discharging control commands issued by the CSO.
6. EV: A vehicle with energy storage capacity, capable of bidirectional data communication and power transmission, is charged and discharged through the CP under the control of the FN, regulating the load on the power grid.
# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 2 Literature Review
### Contribution of the Study
#### 2.1.3 Approach  
The study approaches the research based on several critical research questions to deepen the reader's understanding. RQ 1 explores how integrating CINFU AHP and CO-DAS methods can enhance decision-making under conditions of uncertainty and ambiguity compared to traditional methods. This question focuses on the value added by combining fuzzy multi-criteria decision-making techniques, specifically their ability to accommodate vague or imprecise data in expert evaluations and to ensure consistency in ranking alternatives. RQ 2 investigates the most critical criteria influencing machine selection for inventory tracking within lean manufacturing systems. This question identifies and prioritizes operational and technological factors, such as speed, setup time, ease of operation, energy cost, and maintenance, which are essential for selecting machines that align with efficiency and sustainability objectives. Finally, RQ 3 examines how modifying the significance of criteria through sensitivity analysis affects the ranking of machine alternatives. This question assesses the framework's robustness under varying weighting scenarios, demonstrating its applicability in dynamic decision-making environments where priorities may shift. By validating the framework through a real-world case study, this research effectively bridges the gap between theoretical models and industrial practice, offering a replicable approach for tackling similar challenges in other manufacturing contexts.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
###### Abstract  
Free-floating and submerged wave energy converters (SWECs) are regarded as promising technologies for renewable energy production. These converters rely on a heave-motion buoy to capture the kinetic energy of ocean waves and convert it into electrical energy through power conversion systems. To better understand the impact of various factors on power generation and efficiency, the effects of different buoy shapes (rectangular, circular cylinder, and trapezoidal fin), submergence depths (0, 0.1, and 0.2 m), wave heights (0.04, 0.06, and 0.1 m), and spring stiffness (50 and 100 N/m) were investigated. A 2D numerical wave tank with a buoy was simulated, and the results were validated against experimental data. Information on vorticity, vertical displacement, power absorption, and efficiency are provided. The findings indicate that the buoy shape and wave height significantly affect power absorption and efficiency. Additionally, this study reveals that increasing submergence leads to higher power absorption and lower conversion efficiency.  
wave energy converter; computational fluid dynamics; wave-structure interaction; power absorption; efficiency +
Footnote †: journal: Applied sciences  
0000-0002-319-7880][PERSON]  
1 Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India; [EMAIL_ADDRESS]
## 5 Experiments and Results
### Low-Rank Structures and Correlation in Multi-Attribute Sensing Data  
Through SVD [13], the low-rank data and attribute interrelations of the data matrix can be confirmed. t-SVD [22] is a potent resource for investigating the interplay among multiple attributes. The low-rank characteristics of both datasets are shown in Figure 7 and were obtained by evaluating the singular values of the block diagonal matrix. In t-SVD, the correlation among numerous attribute surfaces is determined by assessing the core tensor's block-diagonal matrices' singular values. The initial singular values primarily influence the energy of the tensor data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **NDBC-TAO** & **U.S. Climate Normal** \\ \hline No. of nodes & 8 & 89 \\ Period of observation & 1 October 2020 00:00:00 & 1 December 2022 00:00:00 \\ Measured attribute & -3 October 2020 23:50:00 & -15 December 2022 23:00:00 \\ Sampling period & Temperature, density, and salinity & Dew, temperature, and wind \\ Tensor dimension & 10 min & 1 h \\ Tensor dimension & \(8\times 3\times 432\) & \(89\times 3\times 360\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Real-world datasets for WNS.  
In Figure 7, it can be seen that each singular value has a different importance value, and the larger singular values contain more main data information; therefore, it is necessary to shrink different singular values using different weights rather than a fixed weight.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 3 Problem Description and Formulation
### Constraints
#### 3.3.6 Fault Tolerance Constraint  
Considering that individual satellites or their resources (e.g., computation, storage, or payload) may fail in a multi-satellite system, once a failure or partial malfunction is detected in a satellite's resources, the system will trigger a dynamic task reallocation process. Based on the existing resource constraints (16) and load balancing constraints (17), the task allocation matrix \(\mathbf{X}\) is adjusted to promptly reassign the affected tasks, thereby ensuring the continuity and stability of the overall task scheduling.
# Evaluation of Noise-Reduction Techniques for Gas-Turbine Test Stands: A Preliminary Analysis
## 6 Results
### Pressure Loss and Self-Noise Assessment
These fluctuations are transmitted through the air as sound waves, the latter being perceived as a disturbing noise--the more intense the turbulence, the higher the generated noise level. Square-edged deflectors promote flow separation and the formation of strong turbulence, leading to significantly higher noise. When using the flow fields, the broadband noise was computed. The acoustic field inside each analyzed configuration is shown in Figure 16.  
Figure 16 illustrates the acoustic field and noise levels for different deflector configurations. The figure above triggers the following observations: The sharp-edged design causes flow separation at the deflector inlet, leading to recirculation areas and turbulence downstream, both generating noise. For the linear attenuator versions (Conf. I, Conf. II, and Conf. III), the sound power level of the acoustic sources does not show significant increases, with the primary noise sources being located near the leading edge of the baffle. In the case of the staggered attenuator variants (Conf. IV and Conf. V), the interaction of the upstream channel airflow with the leading edge of the downstream baffle amplifies the phenomenon downstream. The results of self-noise obtained with FEM simulation are presented in Table 2, showing that the triple-baffled staggered configuration is the noisiest option, despite this configuration obtaining the best results as far as noise attenuation is concerned. According to [PERSON] [19] (Equation (3)), the resulting self-noise is 79.7 dB for single, double, and triple baffles because, in this formula, the length of the baffle is not considered, while taking into account the noise generated by the impact of air on the leading edge of the baffle, rather than the noise produced along the length of the blade or its trailing edge. In contrast, the FEM results are similar to the analytic ones and more reliable to be used in practice. This study primarily aimed to evaluate various topologies of lamellar attenuators to be used in gas-turbine test stands, mainly from an acoustic perspective and under static conditions. Although some flow conditions were introduced in the evaluation, the study did not actually include airflow testing. Further research should focus on conducting acoustic simulations under flow conditions. Additionally, optimizing the geometry of the attenuators by incorporating rounded leading and trailing edges on baffles could further enhance their flow performance, although important acoustic improvements are not expected to be obtained (acoustic attenuation is less dependent on geometric optimizations).
## 4 Aging Model Establishment  
### Introduction of the Aging Model  
Since the aging of a battery is a nonlinear problem, it is difficult to apply the traditional mathematical programming method. This research proposes the use of the rain flow algorithm, which has been widely used in the technical and economic analysis of BESS [2; 14; 15] to solve this nonlinear problem. It extracts the aging features from SOC curves. For details, please refer to Section 4.2.  
The aging model of lithium-ion batteries in this study can be roughly divided into two parts: the extraction of aging features and the superposition of aging quantity.  
In cycle aging, the feature values of each cycle, such as cycle depth, cycle average SOC, and cycle times, were extracted and recorded as cycle aging events. Meanwhile, in calendar aging, the continuous SOC and its duration were recorded as calendar aging events.  
The behavior of battery aging has a lot to do with the current aging state of the batteries; thus, even if the operating conditions remain the same, different aging results will be obtained corresponding to the state of aging. Therefore, it is vital to arrange the aging event sequence in the correct order.  
After the extraction, the aging features should be quantified and superpositioned. Seeing that the result from each aging event can not be superpositioned directly because the aging curves are not linear, this study proposed a method named the mapping superposition method to solve the problem.  
Figure 6: Cycling aging curves.
## 2 Materials and Methods
### Physicochemical Characterization  
The particle size is a crucial parameter for MoS2 since its properties change with the number of layers. The particle size distribution (PSD) of the obtained particles was measured using an LS 13 320 Particle Size Analyzer from Beckman Coulter, which enables high-resolution measurements using polarization intensity differential scattering (PIDS) technology. The PSDs by number, surface, and volume were carried out for the product suspension after synthesis from Sections 2.2-2.5 and after each stage of the preparation procedure (Section 2.6). The specific sizes L10 (arithmetic mean), L32 (surface-weighted mean size), and L43 (volume-weighted mean size) were determined based on the method of moments (Equations (7) and (8)).  
\[L_{ij}=\frac{m_{i}}{m_{j}} \tag{7}\]  
\[m_{k}=\int\limits_{0}^{\infty}L^{k}f(L)dl\approx\sum\limits_{l=1}^{N}L_{l}^{k} f(L_{l}) \tag{8}\]  
where \(m\) is the moment of distributions; \(k\) is the moment rank; and _f(L)_ is the number PSD function.  
Raman analysis of the final products synthesized using both citric and formic acids was performed. Raman spectra made it possible to determine the difference in the purity of the synthetic materials. The analysis was performed using an inVia Raman microscope from the Renishaw system with a 633 nm laser (at 5% laser power and 1% as lower power was used); each measurement consisted of 10 counts for 10 s. The presented Raman spectra are shown as received. No normalization of the spectra was performed.  
The scanning electron microscopy (SEM) images were presented to check the particle morphology and to compare the size of the obtained materials. For this purpose, a Hitachi S5500 microscope with a resolution of 0.4 nm was used. The STEM images were obtained in the secondary electrons (SE) mode and the bright-field scanning transmission electron microscopy (BF-STEM).