esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract
###### Abstract  
Recently, the Ethereum smart contracts have seen a surge in interest from the scientific community and new commercial uses. However, as online trade expands, other fraudulent practices--including phishing, bribery, and money laundering--emerge as significant challenges to trade security. This study is useful for reliably detecting fraudulent transactions; this work developed a deep learning model using a unique metaheuristic optimization strategy. The new optimization method to overcome the challenges, Optimized Genetic Algorithm-Cuckoo Search (GA-CS), is combined with deep learning. In this research, a Genetic Algorithm (GA) is used in the phase of exploration in the Cuckoo Search (CS) technique to address a deficiency in CS. A comprehensive experiment was conducted to appraise the efficiency and performance of the suggested strategies compared with those of various popular techniques, such as k-nearest neighbors (KNN), logistic regression (LR), multi-layer perceptron (MLP), XGBoost, light gradient boosting machine (LGBM), random forest (RF), and support vector classification (SVC), in terms of restricted features and we compared their performance and efficiency metrics to the suggested approach in detecting fraudulent behavior on Ethereum. The suggested technique and SVC models outperform the rest of the models, with the highest accuracy, while deep learning with the proposed optimization strategy outperforms the RF model, with slightly higher performance of 99.71% versus 98.33%.  
Ethereum fraud; logistic regression (LR); k-nearest neighbors (KNN); random forest (RF); support vector classification (SVC)
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### Bit-Flip (X) Mixers
#### a.1.1 Maximum Cut  
**Problem:** Given a graph \(G=(V,E)\), find a subset \(S\subset V\) such that the number of edges between \(S\) and \(V\setminus S\) is the largest.  
**Prior QAOA work:** Numerical [1] and analytical [21] results obtained for \(p=1\).  
**Approximability:** APX-complete [51; 52]. NP-hard to approximate better than \(16/17\)[53]. Semidefinite programming achieves 0.8785 [54], which is optimal under the unique games conjecture [55]. On bounded degree graphs with \(D_{G}\geq 3\), it can be approximated to within \(0.8785+O(D_{G})\)[56], in particular to 0.921 for \(D_{G}=3\), but remains APX-complete [51].  
**Configuration space, Domain, and Encoding:**\(\{0,1\}^{n}\), indicating whether each vertex is in \(S\) or not.  
**Objective:**\(\max|\left\{\{u,v\}\in E:u\in S,v\
otin S\right\}|\).  
**Phase separator:**\(H_{\text{P}}=\sum_{\{u,v\}\in E}Z_{u}Z_{v}\).  
**Resource count for phase separator:**\(m\) gates with depth at most \(D_{G}+1\).  
**Variant:** Directed-MaxCut (Max-Directed-Cut), where we seek to maximize the number of the _directed_ edges leaving \(S\). The phase separator is replaced by \(H_{\text{P}}=\sum_{(u,v)\in E}(Z_{u}-Z_{v}+Z_{u}Z_{v})\).
# Fire Safety Risk Analysis of Conventional Submarines
## 4 Fire Safety Risk Considerations--Technology Review
### Stirling Engine  
Future Submarine Context and Functional Description  
A Stirling engine can be run whilst a submarine is submerged and used to generate electricity at depth, allowing the submarine to stay submerged for extended periods. Stirling engines are very similar to conventional internal combustion engines, the key difference being that fuel is combusted and the resultant heat is applied externally to a working fluid in a closed system [72]. A Stirling engine operates on a closed regenerative thermodynamic cycle which utilises differing temperatures of a gaseous working fluid to achieve cyclic expansion and compression in order to convert heat to work [73].  
There are many types of Stirling engine, though their function normally involves the working fluid moving between a heated and cooled part of the engine. The movement of the gas is used to drive a number of pistons which, in the case of a submarine AIP system, drive an alternator to produce electricity. The typical configuration of a Stirling engine AIP system for a submarine includes LOX, low sulphur diesel fuel and helium gas as the working fluid.  
LOX is evaporated, mixed with the fuel and burnt in the combustion chamber at high pressure to provide a source of heat [74]. The engine cycle operates as follows:  
* Helium gas enclosed within the Stirling engine is passed through tubes in the combustion chamber where it is heated.
* Pistons push the helium to through a regenerator which absorbs heat, and a cooler which further cools the gas before entering the lower, cool part of the cylinder.
* Compression of the cooled gas assists the double acting piston to push the cool gas back through the tubes, past the cooler and past the regenerator which transfers the heat absorbed earlier back to the gas.
* Gas moves back through the heater tubes in the combustion chamber and into the hot part of the cylinder where it expands, pushes the piston down and restarts the cycle.  
The cyclic movement of the pistons is used to turn a crankshaft connected to a generator which provides electricity to the submarine. Exhaust gases from the combustion chamber are dissolved in seawater, usually at sufficiently high pressure to enable release overboard without the need for a compressor. Helium and nitrogen are used to purge the  
Figure 13: Photographs Showing Size of a MESMA Module and Its Inside. (**a**) Completed MESMA Module [69], (**b**) Inside of a MESMA module [70].  
combustion chamber when required and seawater is used to provide cooling. A schematic of a typical Stirling engine AIP configuration is shown in Figure 14.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs  
An important part of SC technologies is the huge sets of transmitters, sources of signals, and targets. In this section, we will consider two applications of systems involving wireless energy transfer or propagation. The first, which is related to healthcare, investigates the perturbations due to EMF radiation from wireless communication devices on targets of near or onboard living tissues and medical tools intended for sensing or supporting duties. Actually, in SC environments at clinics, hospitals, homes with remote medical care, or simply in everyday life, we may need wearable or other body-near tools for different supervisions or treatments. The analysis is related to such tools for their sustainable design, permitting their protection against EMF radiation. In the second, which is connected to electric mobility [58], is the analysis of a wireless IPT for battery charging aboard an EV, considering its sustainable design and allowing the reduction of its stray-field BEs in living tissues. In both cases, the involved living tissues are relative to urban SC biodiversity involving humans (adults and children), animals (domestic and wild), plants (ornamental and wild), and all other members of the SC's biodiversity. Moreover, these applications belong to connected remote strategies [59]. In the case of connected SCs, the two applications related to healthcare and mobility could be managed via their connections through the SC network [60].
## 4 Experimental Results and Discussion  
### Gas Flow Volume Change  
Figure 6 shows the measured water flow volume change for soil specimens with a gravimetric water content of 13% at a steady state. The temperatures of 25, 45, and 60 \({}^{\circ}\)C are considered. The abscissa is a period at the steady state while the ordinate is the water volume change in Bottle II during this period. At each temperature, the water volume change increases linearly with time. It indicates that the water change rate has not changed during this 50 min and a steady state has been obtained. According to Equation (4), the slope of each line can calculate the gas flow rate. The gas flow rate through the soil specimen is 139.7, 93.1, and 69.1 mL/min for 25, 45, and 60 \({}^{\circ}\)C, respectively. The gas flow rate at 60 \({}^{\circ}\)C is observed to be 50.5% lower than that at 30 \({}^{\circ}\)C when the gravimetric water content reaches 0.13. The higher the temperature, the smaller the gas flow rate. It is due to the large gas absolute (dynamic) viscosity for high temperature [42; 43; 44; 45].  
Figure 6: Measured water volume changes through soil specimen with the gravimetric water content of 13%.
## 3 Materials and Methods
### Acquisition of EMG Data
#### 3.1.1 Hardware Description  
The data were acquired using an embedded system, Figure 2, based on a 4-channel 24-bit ADC (AD7124-4) connected by serial peripheral interface (SPI) to a microcontroller (MCU) STM32L486 RG microcontroller and running YetiOS [49] as the operating system. The sampling rate was set to 500 Hz with a hardware lowpass filter with a cutoff frequency of 130 Hz. The signals were transmitted via serial to the computer where they were processed using MATLAB [50].
## 3 Dataset  
The dataset used in this work was created by researchers of the Enteric Neural Plasticity Laboratory of the State University of Maringa. For this, male adult rats, of the Wistar (_Rattus norvegicus_) lineage were used. All the proceedings involving the animals were previously approved by the \"Standing Committee on Ethics in Animals Experimentation\" of the university.  
The animals were randomly separated into a control group (C) and Walker tumor group (TW). Animals from the TW group were inoculated with Walker 256 tumor cells. The dataset is composed of 120 microphotographs taken from samples of rat liver tissue. The images are divided in two classes: control (C), containing 60 microphotographs taken from six healthy rats (10 from each rat) and Walker 256 tumor (TW), containing 60 microphotographs taken from six rats (ten from each rat) with the Walker 256 tumor.  
The liver samples were made in a semi-serialized manner with 5 \(\upmu\)m cuts; they were stained with haematoxylin and eosin. The images were obtained using the camera Moticam\({}^{\text{\textregistered}}\) 2500 5.0 Mega Pixel (Motic China Group Co, Shanghai, China) coupled to the microscope Motic BA 400 (Motic China Group Co., Shanghai, China). The images were collected with magnification of 40\(\times\) and resolution of 1024 \(\times\) 768 pixels, which corresponds to an area of 35,369.85 \(\upmu\)m\({}^{2}\) per image. Figures 1 and 2 show samples from the classes C and TW, respectively. Some details about the images are summarized in Table 1, and additional information about the dataset can be found in [11]. The dataset used in this work was made freely available ([[https://github.com/[PERSON]/Liver_Dataset](https://github.com/[PERSON]/Liver_Dataset)]([https://github.com/[PERSON]/Liver_Dataset](https://github.com/[PERSON]/Liver_Dataset)), accessed on 1 April 2022) for research purposes in such a way that other researchers can benefit from it and properly compare the results obtained using different techniques with those obtained here.  
Figure 1: Liver microphotograph from the control group (C).
## 4 Analysis of Approaches to Modelling Agricultural Systems
### System Dynamics Approach  
The system dynamics (SD) approach is a scientific framework for addressing complex and non-linear feedback systems [82]. This systems thinking approach contrasts with probabilistic and deterministic approaches by their ability to describe non-linearity of the changes in system states responding to external drivers [83] (Figure 2). SD models are usually used for energy policy development, environmental policy analysis, innovation impact evaluation, strategic planning, and public policy evaluation [84; 85; 86]. The SD process is iterative and interactive, and stakeholders can be involved at every stage of the process from question definition, to conceptual/mental model building, formal model development strategy, and scenario testing feedback. SD models are designed to address the \"What if?\" question of a complex problem and are useful for prospective scenario testing. Consequently, they are widely used to develop efficient Decision Support Tools (DST) to help in policy development and decision-making, and are often coupled with other types of models (see Section 4, and Table 1).  
SD approach has been widely used in hydrology and water resources management [87; 88; 89], agricultural land and soil resources [90; 91], or food system resilience [92; 93; 94; 95].  
**Strengths**:  
* At a regional to national scale, the system dynamics approach is well designed for scenario testing and policy prospection.
* SD approach allows multi-disciplinary and multi-method integration. Other approaches can be coupled/linked with SD (participatory, deterministic, probabilistic approaches) to better emphasise the complexity of agricultural and natural resources issues.
* SD approach is suitable to reproduce agricultural systems' organisation and to design new strategies or to experiment management/policy scenarios [96].  
**Weaknesses**:
* The main difficulty in SD approach is validation. Especially when modelling disruptive scenarios, and when internal (model behaviour) and external (outputs) validation possibilities are limited [97]. However, a range of tools can be used to help for validation: the use of local knowledge (participatory-expertise approach) and historical data for calibration (probabilistic approach); running sensitivity analysis of key variables, or analysing scenarios and results in comparison with expert opinions (expert modellers and expert stakeholders).
* There is a risk of formulating erroneous policy by trusting simulations of invalidated models [91].
## 2 Exhaled Breath (EB) Analysis
### EB Analysis
#### 2.2.1 Off-line Analysis: Gas-Chromatography (GC)  
GC was the analytical method chosen for the initial studies in breath analysis [77; 78; 79; 80; 18] and even today it is one of the preferred methods when coupled to MS. In fact, almost all of the EB VOCs reported so far have been identified and quantified using MS-based methods, most often GC-MS [75]. GC-MS is possibly the most comprehensive and sensitive approach to characterize EB volatile composition, allowing the selective analysis of one or simultaneous analysis of many compounds that may be in the range from ppb to ppt. The GC-MS involves separation of volatilized samples in chromatographic column based on different parameters, such as polarity of the GC column or the boiling point of the sample components (reviewed in [81]). GC-MS system ionizes the target ions, separate them by mass to-charge (m/z) ratios and uses the resolved fragmentation patterns to quantify the amount of each specific VOC in the sample [81]. There are, however, other detection systems that have been coupled to GC for breath analysis, namely FID and IMS. In the first case, VOCs are burned in the FID, producing ions and electrons that can conduct the electric potential and this information is used for detection and eventually quantification. GC-FID generally exhibits a fairly high sensitivity, large linear response range, and low noise. However, the FID detector is mass sensitive and its response is not altered significantly by changes in mobile-phase flow rate (reviewed in [14]). In turn, in the IMS, ions are separated according to their mobility as they travel through a purified gas, in an electric field at the atmospheric pressure. The IMS detector is also selective, allowing the quantification of VOCs in the EB (reviewed in [14; 74]). Regardless of the detection method used in a GC analysis, this approach presents, however, some drawbacks, the most notable being the requirement for sample pre-treatment (sampling and pre-concentration). Therefore, a GC analysis is only suitable for indirect sampling and not for real-time analysis, being a time-consuming process (a typical GC-MS analysis can take up to an hour). Thus, analytes loss and degradation, particularly of those of reactive or thermally labile metabolites, and possible contaminations, are the important concerns inherent to the sample pre-treatment that need to be carefully addressed to improve the quality of the data obtained in a GC analysis [24; 33; 36; 45; 75].
# AC Microgrids Protection: A Digital Coordinated Adaptive Scheme
## 3 Relays Coordination  
In the previous section, we presented a comprehensive protection scheme not only for PCC, feeders and lines, but also for DGUs. In order to provide reliable protection, all the protection relays must be coordinated properly; otherwise, it leads to unwanted trips of out-of-zone faults. It is worth highlighting that PDs of line and loads are the fastest PDs, which independently operate from the other PDs. On the other side, the rest of protection schemes such as PCC, feeder, and DGUs, as backup protection for loads and lines, are coordinated regarding them.  
The proposed coordination method works based on two different approaches--an offline method and online approach in real-time. According to the first approach, adequate time delay settings for each one of the forward and reverse relays are determined offline according to the structure of the MGs system configuration with the help of load flow and circuit analysis. The generated data are stored. Then, by using these data, the pickup current (lp) for each upstream and downstream relay are calculated by utilizing the load current and fault current considerations. Next, the TMS for each forward and reverse relay are chosen in such a way each relay works as primary protection and provides a backup protection to upstream and downstream relay for forward and the reverse relay. Different settings for the defined network structure of MGs system are stored in each determined time delay unit. In the second approach, an adaptive technique for online calculation method is adopted that can calculate the pickup for each DOCR relay in real-time and the result of both approaches is compared and the best of these or nearest time delay is adopted for fast fault detection isolation. The flow chart of offline and online approaches is shown in Figure 7, respectively. In addition, the time-current characteristics of DOCR relays located in the PCC and feeders are depicted in Figure 8. The following sections briefly describe the proposed offline Dual setting of DOCR and its implementation along with the online adaptive protection approach.  
Figure 6: The proposed comprehensive DGU protection scheme configured by external and local trip commands.  
Figure 7: The proposed algorithm for MG protection: (**a**) offline mode setting, and (**b**) adaptive online mode for setting DOCRs.
# Parametric Analysis of a Polygeneration System with CO\({}_{2}\) Working Fluid
###### Abstract  
The objective of the present work is the investigation of a novel polygeneration system for power, refrigeration and heating production at two temperature levels. The present system uses CO\({}_{2}\) as the working fluid, which is an environmentally friendly fluid. The total configuration is a combination of a transcritical refrigeration cycle coupled to a Brayton cycle with recompression, which is fed by a biomass boiler. The examined system, at nominal operating conditions, produces refrigeration at 5 \({}^{\circ}\)C, and heating at 45 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. Additionally, the system can be converted into a trigeneration system where the two heating outputs are produced at the same temperature level. The system was studied parametrically by changing the following seven critical parameters: turbine inlet temperature, high pressure, medium pressure, heat exchanger effectiveness, refrigeration temperature, heat rejection temperature and high heating temperature. In nominal operating conditions, the system energy and exergy efficiencies were 78.07% and 26.29%, respectively. For a heat input of 100 kW, the net power production was 24.50 kW, the refrigeration production was 30.73 kW, while the low and high heating production was 9.24 kW and 13.60 kW, respectively. The analysis was conducted with a developed model in Engineering Equation Solver.  
polygeneration; CO\({}_{2}\) working fluid; R744; parametric study; multigeneration Article
## 4 Compression Ignition Engine Simulations
### Simulation Results
As for the online versus CPV simulation results, both simulations resulted to be in close agreement with each other across the whole range of simulated data. The tabulated chemistry solver predicted a combustion phasing within less than 0.5 CAD difference with the detailed online chemistry solver at the mid and high loads. For the low load points (HD04 and HD05), a slightly more noticeable difference (\(\approx\)2.0 CAD) between online and tabulated chemistry solutions can be seen when comparing the predicted start of combustion (See Figures A4 and A5 in Appendix A). At low loads, combustion initiates while the mixture is the NTC region, which, as discussed in Section 3.2, is the most challenging regime for progress-variable-based models. It is therefore likely to happen that under these conditions the interpolation error starts to play a visible role. Nevertheless, a 2 CAD discrepancy in start of combustion is well within a range typically considered acceptable for engine performance studies and considering accuracy of the sensors used during the experimental campaigns. Figure 8: Experimental and simulated engine-out emissions as well as performance parameters (CA50 and PCP\({}_{\text{CAD}}\) location) for all operating conditions of the heavy-duty engine simulation campaign. Data have been normalized with respect to experimental values. With respect to engine-out emissions, both solvers showed good agreement with experimental data for CO\({}_{2}\), uHC and NO. Differences in CO\({}_{2}\) are explained by differences in the C/H ratio of the real fuel blend and the surrogated fuel blend. These differences also influence the CO emission calculation. With respect to NO, the differences between tabulated and detailed chemistry are lower than differences between the SRM predictions and the engine measurements. For these species, it can be stated that the accuracy of the tabulated chemistry solver is not influencing the accuracy of the tool chain. This statement is also true for the prediction of CA50 and PCP. For carbon monoxide emissions, the online chemistry solver showed a noticeably closer match (less than 100 ppm difference) with experimental data. While the CO predictions from the CPV solver lie within a more than acceptable range from the engineering point of view, it is important to note that correct tabulation of CO during the expansion phase is another challenging area when progress variable models are concerned. Unlike methods proposed in [23; 24], the present method does not account for a time-scale dependent retrieval of the CO emissions from the table. This means that the accuracy of the final CO yield depends on how the close to (or far from) equilibrium the value stored in the table at progress 1 is. In the present study, the presented level of accuracy between online and tabulated chemistry solver-based CO values (\(\pm\) 200 ppm) was considered to be acceptable. In future studies, however, a time-scale dependent CO retrieval strategy will be considered.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Natural Biomaterials
#### 4.2.6 Alginate  
Alginate, a natural biomaterial derived from brown seaweed, originates from the cell walls of algae, primarily composed of alginic acid. It forms a gel-like structure when combined with divalent cations, creating a three-dimensional matrix for cell encapsulation. Alginate can be easily processed into various forms such as hydrogels and microbeads, allowing for customization in scaffold fabrication [246].  
Alginate provides a nurturing microenvironment for cells, facilitating tissue regeneration. Alginate can also be modified to incorporate bioactive molecules, promoting specific cellular responses [247].  
In cardiac tissue engineering alginate can be used to develop injectable hydrogels, cell encapsulation systems, and bioink for 3D bioprinting [248].