esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolutionary Design Optimization of an Alkaline Water Electrolysis Cell for Hydrogen Production
## 2 Physics and Economy of the Alkaline Water Electrolysis Cell
### Economy  
The hydrogen cost has two contribution: The _CAPEX_ that represents the cost of the investments and the OPEX that represents the cost during electrolyzer operations (Equation (30)). _CAPEX_ is estimated by using [PERSON]'s law, see Equation (31). The investment cost is divided by the total amount of hydrogen produced during the electrolyzer lifetime. The _OPEX_ depends on the cell voltage and electricity cost.  
\[\mathit{OPEX}\ =\ \frac{U_{cell}\cdot F}{3600}\cdot EC \tag{30}\]  
\[\mathit{CAPEX}\ =\left(\frac{2\cdot F\cdot IC_{S}}{t\cdot M_{H_{2}}}\right) \frac{1}{j} \tag{31}\]  
\[F_{c}=\mathit{CAPEX}\ +\ \mathit{OPEX} \tag{32}\]  
With _OPEX_ and _CAPEX_ in \(\epsilon\) kg\({}^{-1}\), \(U_{cell}\) the cell voltage in V, \(F\) Faraday's constant in A s mol\({}^{-1}\), \(EC\) the energy cost in \(\epsilon\) kWh\({}^{-1}\), \(IC_{S}\) the initial capital cost per unit area in \(\epsilon\) m\({}^{-2}\), \(t\) the electrolyzer lifespan in s, \(M_{H_{2}}\) is the molar mass in kg mol\({}^{-1}\).  
The life of the electrolyzer, the initial capital cost per unit area and the cost of electricity cannot be calculated using a numerical model, but estimates do exist or can be calculated. [PERSON] et al. [18] have indicated that the life of the electrolyzer is approximately 90,000 h. The cost of electricity varies greatly depending on the country or the means of power generation used to produce electricity. In metropolitan France, the cost of electricity is about 0.17 \(\epsilon\) kWh\({}^{-1}\) and in Germany about 0.30 \(\epsilon\) kWh\({}^{-1}\). The effect of these two costs will be studied in the optimization section.  
To estimate the \(IC_{S}\), the cost per kWh given by [PERSON] et al. [18] can be used. They stated that the cost per kW is between 500 and 1400 today and will be between 400 and 850 in 2030 and will reach the interval of 200-700 \(\epsilon\) kW\({}^{-1}\). According to [PERSON] et al. [18], the electrolyzer power density is between 4 and 10 kW m\({}^{-2}\). Thus, \(IC_{S}\) will be explored between 800 and 14,000 \(\epsilon\) m\({}^{-2}\). The Figure 8 presents the _OPEX_, _CAPEX_, and total hydrogen cost for an electrolyzer depending on the current density for a naive design (\(h_{H_{2}}=h_{O_{2}}=1.5\) mm, \(H_{ele}=10\) cm, \(p=1\) bar, \(T=353\) K, \(Y=0.30\), \(b_{an}=0.15\) V dec\({}^{-1}\), \(b_{anth}=0.15\) V dec\({}^{-1}\), \(IC=14,000\)\(\epsilon\) m\({}^{-2}\), and \(EC=0.17\)\(\epsilon\) kWh\({}^{-1}\)) optimum current density is 2923 A m\({}^{-2}\) and the minimum cost is 12.48 \(\epsilon\) kg\({}^{-1}\).
###### Abstract  
There are wireless networks in which typically communications are unsafe. Most terrestrial wireless sensor networks belong to this category of networks. Another example of an unsafe communication network is an underwater acoustic sensor network (UWASN). In UWASNs in particular, communication failures occur frequently and the failure durations can range from seconds up to a few hours, days, or even weeks. These communication failures can cause data losses significant enough to seriously damage human life or property, depending on their application areas. In this paper, we propose a framework to reduce sensor data loss during communication failures and we present a formal approach to the Selection by Minimum Error and Pattern (SMEP) method that plays the most important role for the reduction in sensor data loss under the proposed framework. The SMEP method is compared with other methods to validate its effectiveness through experiments using real-field sensor data sets. Moreover, based on our experimental results and performance comparisons, the SMEP method has been validated to be better than others in terms of the average sensor data value error rate caused by sensor data loss.  
sensor data loss reduction; wireless sensor networks; underwater acoustic sensor networks; unstable communications; SMEP method; average sensor value error rate +
Footnote †: journal: Sensors  
1
## 2 Related Work: Multi-Channel Multi-Interface Multi-Hop Networks  
Channel assignment (CA) strategies for IEEE802.11 based multi-hop networks, wherein each node equips multi-interface (multi-transceivers), have been widely studied. The main problem is to improve the system throughput by assigning channels to wireless interfaces under the condition where the number of channels is larger than that of interfaces. There are significant proposals for CA protocols and their metrics are mainly classified as interference-aware, traffic-aware, congestion-aware, and delay, etc. [35]. Another viewpoint is from the operating layer: MAC layer alone, coordinated MAC and upper layers (i.e., cross layer), and their integrated approach (defined as common layer) [36]. In any case, each node should acquire arbitrary a priori information to perform CA in a distributed manner. Comprehensive analysis of multi-channel multi-interface multi-hop network based on distributed greedy CA algorithm was conducted in [37]. It proved the system throughput can be maximized even when the number of interfaces is less than that of available channels through extensive simulations using parameters such as the number of channels, wireless interfaces, interfering networks, and node densities. Reference [38] also evaluated the relationship between the number of sinks (gateways), channels, and interfaces. Results also verified the effectiveness of utilizing multi-interface with multi-channel rather than switching multi-channel in a single interface even when multiple sinks are deployed. Reference [39] proposed a distributed dynamic CA in which the node sounds the channel utilization status (considered as interference) of surrounding devices by use of beacon signals and manage them through a table. In addition to that, Reference [40] exploits information about channel usage notified from surrounding nodes and estimates interference amount. Each node then decides channels to use based on the game theory. Its detailed implementation is also disclosed in [41]. Above dynamic CA strategies require frequent channel/interface switching causing processing overhead. Hybrid approaches are also proposed in [42; 43] in which fixed and switchable channels are prepared. Each node dynamically changes its own switchable channel for packet transmission according to the receiver's channel fixedly assigned. Extended research was also done in [44] incorporating network coding technology. Our approach, being different to the above, can be placed as one of static (fixed) CA approach to completely eliminate overheads on interference presumption as well as channel switching. Doubling wireless interface/channel is to fully exploit the advantage of IPT which can maximize the forwarding efficiency in a single frequency channel.
## 2 Materials and Methods
### Power Requirements
#### 2.2.2 Standalone Infrastructure  
Many critical infrastructures are not typically presented as a graph (e.g., factories and healthcare). Instead, as standalone infrastructures, we utilize a weighted mixture of \(m\) context-dependent factors to discover the related importance of infrastructure elements. For instance, the characteristic factors of one healthcare element, such as a hospital, are the capacity of that hospital in terms of power requirements, bed number, and the population inhabited around the hospital.  
Four critical infrastructures have been measured in this paper, as follows:  
Healthcare  
A healthcare infrastructure analysis has shown that hospitals are the most critical components. Therefore, ensuring that hospitals are running is crucial to disaster response and resilience. Accordingly, three parameters were employed to describe hospitals: (1) power consumption, (2) capacity, and (3) population surrounding a hospital area. Hospital size or capacity is typically determined through a specific quantity of beds that can be obtained from publicly available information. Employing the same hospital capacity further pointsto a helpful parameter called energy consumption over applying the formula conducted by Schneider Electric [26], as  
\[P_{H}=BD*\overline{U} \tag{6}\]  
This equation represents the hospital energy use as \(P_{H}\), \(BD\) denotes the number of operated beds in the same hospital, and \(\overline{U}\) signifies the bed's power use in kWh (assumed as 30,000 kWh/year in [26]). The population surrounding the hospital is another parameter assigned to measure the importance of healthcare nodes. It can be assigned using the density data from [27] and a one km radius circular space throughout that node (hospital).  
Water System  
Through natural disasters, ensuring a reserve of drinking water is crucial. Therefore, water treatment plants have been selected to be critical in this model to ensure the supply of such infrastructures. Their size can also be characterized by millions of gallons per day (MGD). Thus, the power consumption \(P_{W}\) was also estimated utilizing the following formula:  
\[P_{W}=G*J \tag{7}\]  
\(G\) in this equation reflects the portion of water processed in the MGD unit, and \(J\) represents the power required to process a million gallons [28], where the average water planet use around 1470 kWh/MG.
## 3 Simulation and Discussion  
In this section, the initial observation data of the hand-eye robot are set in the simulation in order to verify the hand-eye calibration algorithm proposed above. This simulation was conducted on a model of the dual-arm hand-eye robot system, which contains a dual-arm robot SDA5F and two monocular cameras. Moreover, it was important to make the basic assumption that the eye-to-hand robot system in the simulation included one manipulator and one monocular camera typically.  
Multiple sets of dual quaternions that represented the transformations between the robot base coordinate and the camera coordinate were set. The simulation data included the information of translation vectors and rotation angles of the camera coordinate with respect to the robot coordinate. Furthermore, the dual quaternions that contained the information on the measurement data of the corresponding hand-eye equation were set as random quantities at the same time. For the comparison, the method proposed by [PERSON] in reference [21] was used to process the sets of the same data in this simulation. Furthermore, the norm values of the position were set in the range (50, 500) mm, and random noise was added in the angle data which was in the range (\(-\)0.01, 0.01). In general, the errors between the sets of the nominal pose data and the sets of the pose results that were obtained by the identification were greatly affected by interference. MATLAB was used for algorithm simulation and discussion using the Windows operating system in this part.  
As a result, the quadratic norm of its position vector error was changed according to the quadratic norm of the nominal position vector in the simulation. In addition, the orientation error changed with the random characteristic to some extent.  
Figure 2 shows the root mean square (RMS) relative errors in translation between the simulation results and the nominal values without noise interference, and the RMS errors in translation are less than 0.33 mm. Figure 3 shows the RMS errors in rotation between the simulation results and the nominal values without noise interference, and the RMS errors in rotation are less than 0.02 rad.  
Figure 2: The RMS errors in translation without noise interference.
###### Abstract
For remote village communities, the DC microgrid is the most preferred power supplying utility because the main utility grid may not electrify these village communities because of transmission issues. Therefore, the residential, lighting and commercial loads will be met by the DC microgrid with the help of a battery [8; 9; 10; 11]. The battery energy storage system will be important in the DC microgrid because the batteries are charged during non-peak load condition and discharged during peak load condition with some fluctuations in the power generated by the renewable energy sources [11; 12; 13; 14]. There are several research areas on the subject of the microgrid, of which the demand side management (DSM) is the main focus area. It will adjust the demand with respect to the consumers' requirements with respect to time (kW/h). Therefore, the utility can reduce the peak load and reduce congestion in the system. This system will decrease the difference between the peak load and total generation at peak time periods [15; 16; 17]. The transition from a minimum load to maximum load approachingthe compress load curve is a great advantage of demand-side management in microgrid applications. The energy storage has different ramp rates, which are used to reduce the deviation in the voltage and improve stability [15]. Biomass is one of the non-conventional energy sources that generate power in small capacity and that is also integrated into the DC microgrid to supply power to remote communities [16]. However, by integrating the various sources into the grid, protection has to be considered for the DC microgrid, which is discussed in [17; 18]. The depth penetration of renewable energy sources into the microgrid and EMS when considering the intermittent of photovoltaic systems is proposed in [19; 20]. The main control function of EMS in any DC microgrid structure is to handle the load-power balancing. It should be reliable and cost-effective based on the power obtained from the distributed generation [18; 19; 20]. A conventional EMS has some ability to meet the above-mentioned problems for the DC microgrid structure. A rural community microgrid design and a review of the various architectures in some countries are discussed in [21; 22]. Power quality is one of the issues in hybrid and AC microgrids [21; 22; 23; 24; 25; 26]. This research work will focus on the EMS for a DC microgrid to supply remote village communities. The proposed EMS overcomes the drawbacks of the conventional system by load-power balancing between each source (renewable and storage) in a DC microgrid for dynamic load variation and reduces the consumption of non-conventional energy sources (such as a diesel generator). The general concept of the microgrid, the load profile of the village communities, and the functional block diagram of the proposed DC microgrid are discussed in Section 2. In Section 3, the mathematical modeling of different renewable energy sources is discussed. In Section 4, the proposed EMS is discussed. The simulation studies and experimental results are discussed in Sections 5 and 6. Finally, the DC microgrid system is concluded in Section 7.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam
### Analysis of Parameters Affecting the Fire Resistance of Prestressed Concrete T-Beams
#### 3.3.3 Effect of Different Flange Plate Thicknesses on Fire Resistance  
The fire resistance of prestressed concrete T-beams at different flange plate thicknesses is shown in Figure 12. As the height of the flange plate increases, the fire resistance of the prestressed concrete T-beam gradually increases, but the rate of increase in the fire resistance will gradually become slower with the increase in the height of the flange plate. When the thickness of the flange plate increases from 90 mm to 120 mm, the fire resistance increases by about 15%. When the height of the flange is greater than 120 mm, the growth of the fire resistance gradually tends to equalize. This is because increasing the height of the flange plate will affect the temperature field of the prestressed concrete T-beam at high temperatures, as well as changing the temperature of the steel in the structure, and so will increase the fire resistance. However, the prestressed concrete T-beam flange plate mainly has the role of pressure resistance, so when the height of the flange plate increases to a certain extent, the prestressed concrete structure fire resistance will gradually tend to balance.
## 4 Conclusions  
Pull-out experiments were performed to compare and analyze the bond properties of conventional deformed steel bars, epoxy-coated steel bars, and GFRP hybrid rebars in an effort to design RC structures capable of withstanding highly corrosive environments. The following conclusions were drawn:  
1. Bond failure modes are generally determined by the radial stress distribution of the rebar. When the tensile strength of the matrix is smaller than the generated stress, splitting failure occurs. When it is larger, splitting failure or tensile failure caused by yielding of the rebar occurs.
2. The experiment on the bond performance of deformed steel bars revealed that there was no significant difference in bond strength based on the diameter of the deformed steel bar. Pull-out failure and tensile failure occurred in rebars with small diameters. In contrast, as the rebar diameter increased, the specimens exhibited splitting failure due to reduced structural tensile strength.
3. The bond properties of GFRP hybrid rebars with concrete were analyzed through experiments. The epoxy-coated steel bar exhibited bond strengths similar to a deformed steel bar with the same diameter. As this result was obtained using a rebar diameter of D13, which is too small to be used in the field as flexural reinforcement, future work is necessary to identify more accurate bond properties with concrete by comparing and analyzing cases with relatively larger rebar diameters.
4. The bond properties of GFRP hybrid rebars were lower compared to those of the deformed steel bars. However, the bond properties improved with increased GFRP thickness on the outer surface of the rebar.
5. Therefore, an external geometry that exhibits improved bond properties must be developed to apply these GFRP hybrid rebars to RC structures for high durability. Relative bond strength properties must also be further interrogated by calculating bond strengths based on the precise analysis of failure modes. These will be topics for further study by our group.  
Conceptualization, S.K., J.K. and C.P.; methodology, S.K., J.K. and C.P.; formal analysis, S.K., J.K. and C.P.; investigation, S.K., J.K. and C.P.; writing--original draft preparation, S.K., J.K. and C.P.; writing--review and editing, S.K., J.K. and C.P.; visualization, S.K., J.K. and C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20215810100020).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
## 1 Introduction
### Presented Research Area in the Literature Review
The results showed higher prediction accuracy and robustness of the model than SVR. Another data-mining temperature model for BOF endpoint prediction was proposed in [28; 32; 33; 34]. The use of machine learning methods and computer models in managing and monitoring steel production is closely related to optimizing production procedures using the most modern technological knowledge and information technologies to increase production. In short, it is a process known as Industry 4.0 that refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry [35]. Reindustrialization in the European Union proves that the manufacturing industry around the world is developing in three directions of digitization, networking, and intelligence through information technologies [36]. One goal of the Industry 4.0 era is to apply data-driven approaches to optimize industrial processes. Steelmaking takes a long time, sometimes months, to produce the final product. A steel manufacturer usually produces steel of different grades for different uses. For the improvement of individual steel production processes and their analysis, engineers have a large amount of data from the production chain at their disposal. The lack of process data reduces the possibilities for critical optimizations in steel production. Through the intelligent management and control of the integrated systems of the production process, the efficiency of steel production can be effectively improved, and the energy consumption in production can be significantly reduced, which is of great importance for the improvement of production. Industry 5.0 also constitutes the reconciliation between humans and machines where artificial intelligence (AI), big datasets, cloud computing, machine learning, and digital transformation are the most often used [35]. Machine models intended for predicting process variables are also used in intelligent factories based on cyber-physical systems (CPS), which are the essence of Industry 4.0. The reason is that these systems connect virtual space with physical reality and operate reliably and efficiently. Among the capabilities of CPS belongs, e.g., self-monitoring and self-diagnosis, implemented by some producers [37]. An example is machine learning-based models that can improve the production monitoring process implemented on human-machine-interface (HMI) devices. The aspect of asset tracking is core in Industry 4.0. The use of AI together with RFID technology to search and track the real-time geo-position of monitored assets can be found, for example, in [38]. Although the steel industry invests significantly in research and automation, there is still much to do in the way of transformation to Industry 4.0. Nowadays, apart from the human factor, there are research gaps in sustainability, responsibility, safety, and others in the Industry 4.0 concept. The critical elements are also high investments and the pace of implementing technological changes [39].