esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
###### Abstract  
Wireless body area networks (WBANs) are a research area that supports patients with healthcare monitoring. In WBAN, the Internet of Things (IoT) is connected with WBAN for a smart/ remote healthcare monitoring system in which various medical diseases are diagnosed. Quality of service (_QoS_), security and energy efficiency achievements are the major issues in the WBAN-IoT environment. Existing schemes for these three issues fail to achieve them since nodes are resource constrained and hence delay and the energy consumption is minimized. In this paper, a blockchain-assisted delay and energy aware healthcare monitoring (B-DEAH) system is presented in the WBAN-IoT environment. Both body sensors and environment sensors are deployed with dual sinks for emergency and periodical packet transmission. Various processes are involved in this paper, and each process is described as follows: Key registration for patients using an extended version of the PRESENT algorithm is proposed. Cluster formation and cluster head selection are implemented using spotted hyena optimizer. Then, cluster-based routing is established using the MOORA algorithm. For data transmission, the patient block agent (PBA) is deployed and authenticated using the four Q curve asymmetric algorithm. In PBA, three entities are used: classifier and queue manager, channel selector and security manager. Each entity is run by a special function, as packets are classified using two stream deep reinforcement learning (TS-DRL) into three classes: emergency, non-emergency and faulty data. Individual packets are put into a separate queue, which is called emergency, periodical and faulty. Each queue is handled using Reyni entropy. Periodical packets are forwarded by a separate channel without any interference using a multi objective based channel selection algorithm. Then, all packets are encrypted and forwarded to the sink nodes. Simulation is conducted using the OMNeT++ network simulator, in which diverse parameters are evaluated and compared with several existing works in terms of network throughput for periodic (41.75 Kbps) and emergency packets (42.5 Kbps); end-to-end delay for periodic (0.036 s) and emergency packets (0.028 s); packet loss rate (1.1%); residual energy in terms of simulation rounds based on periodic (0.039 J) and emergency packets (0.044 J) and in terms of simulation time based on periodic (8.35 J) and emergency packets (8.53 J); success rate for periodic (87.83%) and emergency packets (87.5%); authentication time (3.25 s); and reliability (87.83%).  
wireless body area networks; internet of things; blockchain; secure cluster based routing; quality of service (_QoS_)
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 3 Fatigue Life Assessment for Wind Turbine Blades
### Aerodynamic Load Calculation  
To improve the accuracy of the fatigue load spectrum, precisely calculated load data, as well as sophisticated finite element models, are essentially required. Computational fluid dynamics (CFD) and BEMT are typically used to estimate the aerodynamic loads acting on the wind turbine blade. Since CFD requires complex modeling and considerable computation time, BEMT-based load estimation methods have been widely adopted for practical applications. In this study, aerodynamic loads were calculated using BEMT, and the accuracy of calculation was further increased by applying tip loss correction [38] and turbulent windmill state correction [39]. Detailed procedures for calculating aerodynamic loads using BEMT can be found in the studies by [PERSON] and [PERSON] [15] and [PERSON] et al. [21].
## 4 Results
### Datasets  
We decided to test the performance of the AISAC model for the cancer detection problem. We selected 10 cancer-related datasets, available from international repositories (Table 1). The datasets used were:  
1. Breast Cancer Digital Repository (BCDR) [43]. This is a dataset of real Portuguese patients provided by the Faculty of Medicine of the University of Porto, Portugal;
2. Breast Cancer Wisconsin (Original) Data Set (BCWO) [44,45]. The University of Wisconsin, Madison by Dr. [PERSON], provided this dataset;
3. Breast Cancer Wisconsin (Diagnostic) Data Set (BCWD) [45]. This dataset was created by [PERSON], [PERSON], and [PERSON] from the University of Wisconsin, and was donated to the UCI repository in November 1995;
4. Breast Cancer Wisconsin (Prognostic) Data Set (BCWP) [45]. This is a different version of the former;
5. Breast Cancer SEER (BCSEER) [46]. This dataset was requested to the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program;
6. Mammographic Mass Data Set (MMDS) [47]. [PERSON] donated this dataset to the UCI repository in October 2007. It contains patients' age and attributes collected from digital mammograms of patients between 2003 and 2006 at the Institute of Radiology of the University Erlangen-Nuremberg;
7. Breast Cancer Data Set (BCDS) [45]. This dataset was provided by [PERSON] and [PERSON] and obtained at the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. This dataset contains clinic data of the patients, such as age and menopause status, in addition to data corresponding to the breast tumor and recurrence;
8. Lung Cancer Data Set (LCDS) [45]. This dataset was published in [PERSON] et al.'s work in 1991 and later donated by [PERSON] in May 1992. It describes three types of lung cancers;
9. Haberman's Survival Data Set (HSDS) [45]. [PERSON] donated this dataset in March 1999. It contains cases from a study of patients who had a surgery for breast cancer at the Hospital of the University of Chicago;
10. Thoracic Surgery Data Set (TSDS) [48]. [PERSON], [PERSON], [PERSON], and [PERSON] in Wroclaw, Poland created this dataset, and [PERSON] and [PERSON] donated it in November 2013. It contains data from patients who had lung cancer and eventually a major lung resection in the 2007 to 2011 years at the Wroclaw Thoracic Surgery Centre.
## 4 Branch-and-Price-and-Cut Algorithm
### Framework of the Proposed Branch-and-Price-and-Cut Algorithm  
The framework of the proposed algorithm is presented in Figure 4. In the framework of the proposed algorithm, seven main steps are involved, including initializing the RLMP, solving the RLMP, solving the pricing sub-problem, generating constraints related to critical times for empty containers, bounding, cutting, and branching. Specifically, the first step involves constructing the initial set of routes through a greedy heuristic approach to initialize the RLMP (see Section 4.5). Following this, we start the column generation by solving the RLMP via CPLEX (see Section 4.2). Once the RLMP is successfully solved, the dual values obtained are passed to the pricing subproblem, which is then solved by a heuristic and exact version of the label setting algorithm to find columns with negative reduced costs (see Sections 4.3, 4.4 and 4.8.1). The RLMP is augmented with such columns and is re-optimized. The process repeats until new columns can no longer be discovered. Afterwards, empty container constraints are checked for all critical times. Any violated empty container constraint is added to the RLMP, which is then resolved using column generation (see Section 4.6). The iterative process stops when empty container constraints for all critical times are satisfied.  
In cases where a fractional optimal solution is found for the RLMP, cutting planes are introduced to enhance the RLMP (see Section 4.7.1), and a branching procedure is executed to enforce integrality (see Section 4.7.2). Additionally, an upper bound is tentatively generated based on existing routes at each node to efficiently prune nodes (see Section 4.8.2). These iterative steps are applied to the node tree until the entire node tree becomes empty.
# Iterative Interferometric Denoising Filter for Traveltime Picking
The source used here is controlled, such as explosives, vibrioses, or air guns [41], and has a symmetric and regular pattern on its wavelet. Here, we applied pseudo-WDF in both the time and spatial domains directly to capture the dynamic character of wavelets convolved with each trace. Valuable information, including symmetric and regular-source wavelets, can be highlighted; hence, incoherent noises will be suppressed. The suggested approach is post-process auto-correlation in both the spatial and time domains. We should notice that there will be distortion in waveform information, and the frequency of the processed data will increase compared to that of the original seismograms. We can apply this method iteratively until the S/N ratio is acceptable for seismic data with intense incoherent data noises. Still, more iterations will lead to the damage of seismic data quality. This method is referred to as an interferometric denoising filter (IDF). Incoherent data noises will be decreased, so first-arrival traveltime information will be more noticeable and convenient to pick manually or automatically using many algorithms, including AI. In the Methods section, we describe our method by focusing on the physical meaning of the formulas used here. Then, we present a synthetic numerical example and two field data cases to demonstrate the method's potential. These two field datasets were recorded at the Gulf of Aqaba [42] and along the Qademah Fault [43; 44], in Saudi Arabia. In these three numerical cases, the processed data are convenient for refraction traveltime picking and related refraction traveltime tomography applications.
# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 3 Results and Discussion
### Synthetic Images Experiment
The MIND [22] descriptor is tested here. However, in this challenging situation, it suffered from the large search region and did not work out the result of simulating infrared and polarization parameter synthetic image registration. The output of MIND descriptor registration is shown in Figure 4.  
Thereafter, we tested the intensity-based algorithm in the synthetic infrared image and the polarized parameter image pairs. Common intensity-based registration algorithms are monomodal. The registered results cannot be obtained by a mere monomodal intensity-based algorithm because the original synthetic image pair does not share the same gray features. Therefore, we compared the algorithms with segmented images. Some of these algorithms did not figure out the results due to their performance limitations. The following figure is the comparison result between the SSR algorithm and Mean Squares, Mattes Mutual Information Phase Correlation, Powell, and PSO. Figure 5 shows the registration result of our SSR and above algorithms, which are not designed for multimodal image registration. Figure 5a shows the initial status of the segmented image pairs. Figure 5(b2) is an intermediate step to Figure 5(b1) in SSR; the SSR algorithm has an absolute advantage in both processing efficiency and registration accuracy in comparative experiments. None of the registration results in group c. The Mean Squares  
Figure 4: The synthetic image pair registered result by MIND. (c1) and Mattes MI (c2) algorithms support the transformation of image size, but cannot find the correct extreme values. Phase correlation (c3) achieves registration on two sides while the algorithm does not support image size transformation. The results of Powell (c4) and PSO (c5) were too misaligned. The cause of their failure varies. Mean Squares directly evaluates the pixel value of the two images. In Figure 5(c1), the targets are mostly overlayed, but the search did not find the right rotation. Mattes MI evaluates the joint histogram of two images, and this kind of method aligns images according to grayscale probability. In Figure 5(c2), the targets are overlayed in a geometrically pleasing but wrong way, which infers that the search was trapped in a local optimal. Phase correlation evaluates the phase difference of images in the frequency domain, which is a shift between two images in the spatial domain. In Figure 5(c3), the search aligned two sides for both targets and got a right rotation angle, but it stopped while one of the targets was completely overlayed. The Powell is easy to be trapped in a local optimal as well. In Figure 5(c4), [PERSON] found the best rotation angle and stop searching. The PSO is good at global searching but poor at local searching, as we can see from Figure 5(c5), while the two targets were largely overlapped, it was difficult for PSO to converge further.
# Clustering Approach for Reliable Wireless Communication
## 4 Results and Discussion
### Scenario 3: Four Antennas at BS/20, 30, and 40 Users/MUSA Code Length of 4  
Compared to the previous results, the simulations presented in Figure 14 account for a higher number of antennas at the base station, equal to 4. While Figure 14c shows the results obtained in a Rice fading channel, Figure 14a,b,d were obtained in a Rayleigh fading channel. When compared to Figure 13, it is expected that all cases involving 20, 30, and 40 users will experience an increase in system performance; however, there is no  
Figure 13: BER vs. SNR for Scenario 2: (**a**) 700 bits transmitted; (**b**) 7000 bits transmitted; (**c**) Rice fading; (**d**) QPSK modulation.  
discernible difference in the BER between the random and clustered allocation methods of codes. The use of clusters results in marginal improvements in a system with 30 users (see Figure 14a). No improvement can be marked for any of the \(K\) values by lengthening the data.  
Similarly, to the previous scenario, a data length of 700 bits was taken into consideration for analysis in Scenario 3, where tests of the QPSK modulation--shown in Figure 14d--and Rice fading--shown in Figure 14c--were conducted. In both cases, using clusters is advantageous for a less loaded system, where \(K\) is equal to 20.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 4 Overall Flow and Discussion of the Experiment
### Experiment Preparation  
In the present study, Causal-Informer was used for power load forecasting. Data were collected from a power plant located in Jiangsu Province, China. The dataset contained a total of 1186 time steps with a minimum time interval of days. The input features and labels of the model are shown in Table 1. Except for the load residuals and date, the remaining input features were also used for the causal effects calculation.
# Nutrition, Flavor, and Microbial Communities of Two Traditional Bacterial Douchi from Gansu, China
## 3 Results and Analysis
### Major Microorganismsin the Two Types of Douchi  
To investigate the variations in the microbial communities in the two types of douchi, we assessed \(\alpha\)-diversity using Chao1, Shannon, and Simpson indices. The bacterial diversity was significantly higher in QY than in LN douchi (\(p<0.05\)) (Figure 1A-C). In contrast, the diversity of fungal communities was significantly higher in LN than in QY douchi (\(p<0.05\)) (Figure 2A-C). The total number of bacterial ASVs in the LN and QY samples was 97 and 131, respectively. Among them, 10 ASVs (4.58%) were shared between them, whereas LN and QY had 87 and 121 unique ASVs, respectively (Figure 1D). As for fungi, the LN and QY samples had 471 and 203 ASVs, respectively. Of these, 22 ASVs (3.37%) were shared between them, whereas 449 were unique to LN, and 181 to QY (Figure 2D).  
In terms of the bacterial community at the phylum level, Firmicutes was the most abundant taxon, accounting for \(>\)69% (Figure 1E). In samples of the LN douchi, four genera (_Bacillus_, _Ignatzschineria_, _Proteus_, and _Providencia_) were dominant, with relative abundances of 62.76, 8.65, 8.21, and 6.43%, respectively. Three dominant genera (_Carnobacterium_, _Ignatzschineria_, and _Bacillus_) were found in the QY douchi, with relative abundances of 51.14, 27.79, and 10.17%, respectively (Figure 1F). Ascomycota and Basidiomycota were the predominant phyla in the fungal community, accounting for more than 90% of the total abundance (Figure 2E). In samples of the LN douchi, three fungal genera (_Pichia_, _Candidida_, and _Rhodosporidium_) were dominant, with relative abundances of 64.53, 16.93, and 9.31%, respectively. _Pichia_, the only dominant fungal genus, was observed in the QY douchi, with a relative abundance of 93.83% (Figure 2F).  
Figure 1: Alpha diversity assessed with Chao1 index (**A**), Shannon index (**B**), and Simpson index (**C**) of bacterial taxon in different groups. Venn plot of bacterial ASV number from different groups (**D**). Relative abundance of bacterial taxon at phylum (**E**) and genus (**F**) levels in different groups.
## 3 Algorithm Principles
### Introduction to RIME  
RIME is an optimization algorithm inspired by the growth behavior of frost ice [38]. It operates through five stages:  
(1) Initialization: Generate a population \(R=\{R_{1},R_{2},\ldots,R_{N}\}\) of \(N\) solutions (each a \(d\)-dimensional vector). Subsequently, evaluate fitness \(f(R_{i})\) and identify the best solution \(R_{best}\).  
(2) Soft Frost Search (Exploration): Mimics gentle frost movement using adaptive parameters:  
\[\begin{split}& R_{ij}^{new}=R_{best,j}+r_{1}\cdot\cos(\theta) \cdot\beta\cdot\left(h\cdot(U_{bij}-L_{bij})+L_{bij}\right),\ r_{2}<E\\ &\theta=\pi\cdot\frac{t}{10\cdot T},\beta=1-\frac{w\cdot t}{T}/w,E =\sqrt{\frac{t}{T}}\end{split} \tag{6}\]  
where \(\theta\) denotes the iteration ratio; \(\beta\), \(h\) represent control parameters; and \(r_{1}\), \(r_{2}\) are random values. Adhesion coefficient \(E\) increases over iterations to balance exploration.  
(3) Hard Frost Perforation (Exploitation): Simulates aggressive frost growth under strong winds, and fitness-guided updates prioritize high-quality solutions:  
\[R_{ij}^{new}=R_{best,j},\ r_{2}<\text{normalized}_{\text{rime rates}}(i) \tag{7}\]  
where \(\text{normalized}_{\text{rimerates}}(i)\) is the normalized fitness value of the \(i\)-th individual, controlling the probability of hard frost perforation.  
(4) Forward Greedy Selection: Retain better solutions:  
\[\text{if}\ f(R_{ij}^{new})<f(R_{ij}),\ R_{ij}=R_{ij}^{new} \tag{8}\]  
where \(f\left(R_{ij}^{new}\right)\) is the fitness value of the new individual, while \(f\left(R_{ij}\right)\) is the fitness value of the original individual.  
(5) Boundary Handling: Absorb out-of-bounds solutions back into the search space.
## 2 Materials and Methods
The spectral resolution was ~2 cm\({}^{-1}\) in each case. The RRUFF ([[http://rruff.info/](http://rruff.info/)]([http://rruff.info/](http://rruff.info/)); [7]) international Raman spectroscopy database was used for evaluation of spectra. Typical chemical compositions of 70 red mud samples, as well as the mineral concentrates, were investigated using ICP-MS for a wide spectrum of elements. For modified aqua regia digestion 0.5 and 15 g samples of material were used and analysed following the protocol of the Acme Labs' AQ-251-EXT+REE and AQ-250-EXT+REE, respectively. For further details and detection limits of the analyses applied, the reader is referred to the website of the Acme Lab (www.acmelab.com). Separation of not only grain size fractions, but also \"pure\", i.e., selectively enriched mineral fractions was carried out using the wet grinding, magnetic separation, and sieving approach. For this experiment, a single red mud sample of representative mineral and chemical composition was chosen. Magnetic grains were separated from water suspended coarse grain (>20 microns) mixture using Nd-magnet. As some well-cemented aggregates were extremely hard or even impossible to disaggregate even after an hours-long ultrasonic disruption. After 6 h long treatment only mineral concentrates were received instead of pure separates. The mineral concentrates were studied using the same analytical methods as detailed above. For statistical analysis of the geochemical database, the SPSS 24.0 software (IBM Corp., Armonk, NY, USA) was used. Following a unimodal evaluation of the distributions of all elements, principal component analysis with Varimax rotation algorithm helped to define the correlating sets of elements and so to identify the basic accumulation processes. To find the natural groups of the red mud samples, a hierarchical cluster analysis algorithm was followed, using principal components as variables. With this choice, each sample can be classified in the space of the main accumulation processes that is, based on the intensity of the background geological processes. As principal components are orthogonal to each other, Euclidean measure with furthest neighbour method was applied. To be able to define the difference between the natural sample groups we applied the discriminant function analysis algorithm with principal components as variables.
# Discovering Trends in the Digitalization of Shipping:
## 4 Analysis
### Supervised Learning
#### 4.6.2 Grouping  
Another interpretation is to group those topics and see what the evolution is of those groups. The grouping can be seen in Table 14.  
Key insights of the grouped topics:  
Technology and Connectivity:  
The topics in this category have shown varying levels of importance over the years. \"Global Xpress Satellite Network\" has declined in importance since 2016, while \"Web sourcing\" has remained consistently important. \"Broadband offshore communication\" and \"VSAT Capabilities\" have shown a sudden increase in importance in 2020, while \"Mini VSAT Broadband\" and \"KVH antennas\" have remained relatively stable.  
Cybersecurity and Data Management:  
The topics in this category have shown consistent importance over the years, with \"Cybersecurity\" and \"Data leaks and contamination\" being consistently important. \"Electronic learning\" and \"Information recorded in Planned Maintenance System databases\" have shown a decline in importance over the years.  
Environmental Sustainability:  
The topics in this category have shown a steady increase in importance over the years, with \"Greenhouse gas\" and \"Fuel efficiency\" being consistently low. \"Emissions reduction\" and \"Energy Efficiency Design Index (EEDI) to be calculated\" have shown a sudden increase in importance in 2017 and 2018, respectively.  
Maritime Operations and Management:  
The topics in this category have shown varying levels of importance over the years. \"Overcoming software complexity\" has declined in importance since 2018, while \"ECDIS (Electronic Chart Display and Information System)\" and \"Ship Energy Efficiency Management Plan\" have remained relatively stable. \"Voyage data recording\" and \"Performance KPI Standardization\" have shown a sudden increase in importance in 2020 and 2021, respectively.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Topic** & **Slope** \\ \hline Ship Energy Efficiency Management Plan & 0.0065 \\ ECDIS (Electronic Chart Display and IS) & 0.0047 \\ Crew welfare & 0.0037 \\ Greenhouse gas & 0.0034 \\ Voyage data recording & 0.0021 \\ Web sourcing & 0.0004 \\ Overcoming software complexity & \(-\)0.0025 \\ Global Xpress Satellite Network & \(-\)0.0042 \\ Cyber security & \(-\)0.0099 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The slopes of the top topics.  
Learning and Development:  
The topics in this category have shown a decline in importance over the years, with \"E-learning\" and \"Computer-based learning onboard\" being consistently low.  
Regulatory Compliance and Safety:  
The topics in this category have shown varying levels of importance over the years. \"Energy Efficiency Design Index (EEDI) to be calculated\" and \"Future of navigation\" have remained relatively stable, while \"IMO e-navigation regulation\" has shown a sudden increase in importance in 2016 and 2017.