esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Objective Optimization (MOO) Algorithm for RRBCS of R-EEV
### Optimization Objective Calculation
#### 3.2.3 Battery Capacity Loss Rate (BCLLR)  
Charging current profiles can be changed and controlled to obtain the optimal charging capacity without affecting the battery service life [25]. The battery is subjected to repeated operations of charging and discharging profiles, and the rate and shape of the charging current undoubtedly affect the BCLR. Battery current \(I(t)\) and life cycles \(N_{cyc}\) are selected as model reference variables in the battery capacity loss model (BCLM) referred to [26; 27]. According to the measurements of battery capacity fading in [25], battery capacity loss rate \(I_{bclr\_cyc}\) and the capacity attenuation coefficient \(\alpha_{1}\) can be calculated as follows:  
\[I_{bclr\_cyc}=\alpha_{1}\times N_{cyc}\times DOD\times Ah_{cell} \tag{13}\]  
\[\alpha_{1}=B_{1}\times\exp(B_{2}C_{n})=B_{1}\times\exp(B_{2}\frac{I(t)}{ Ah_{cell}}) \tag{14}\]  
where \(DOD\) is the depth of discharge; \(Ah_{cell}\) is the cumulative capacity of the battery; \(C_{n}\) is the charging rate; and \(I(t)\) is the battery current.  
\(B_{1}\) and \(B_{2}\) is the fitting parameter, which can be calculated through the least squares method. Under comprehensive driving cycle conditions, the BCLM can be obtained as follows:  
\[I_{bclr\_cyc}=\int_{0}^{T_{cyc}}B_{1}\times B_{2}\times I^{\prime}(t)\times \exp(B_{2}\frac{I(t)}{Ah_{cell}})\times N_{cyc}\times DODdt \tag{15}\]where \(T_{cyc}\) is the total cycle time.  
Considering that the focus of this research is on the application of the BCLM and article length limitation, a reference can be made in [25; 26; 27; 28] for the experimental details of the battery life cycle. By fitting the experimental data, the following can be acquired: \(B_{1}=0.0018\) and \(B_{2}=0.3785\).
## 4 Results and Discussions
### Implementation Challenges  
RQ5 aimed to identify the challenges and limitations of the large-scale adoption of DT, IM, and other interconnected immersive technologies in developing an advanced digital ecosystem and integrating them into OHS practices within manufacturing. Technological advancement is widely regarded as a catalyst for sustainable development, driving innovation and improved solutions [82]. However, advanced digital transformation introduces significant challenges, including high costs and scalability, data privacy and data management complexities, and cultural and organizational resistance. The implementation of IM in manufacturing faces significant challenges, such as cybersecurity, cost, standards compatibility, and computational demands, as metaverse technology continues to evolve and mature [64]. Technological immaturity is a barrier faced by several industries, such as textiles [83] and construction [84]. The challenges and barriers identified through the review of research items addressing RQ5 are discussed in the following section.
## 6 Result and Discuss
### Data Prediction Performance Analysis  
#### 6.2.1 Prediction Accuracy Analysis  
The data of KEO station are collected every hour. The 40-day (960 h) temperature data of four periods are used to predict the data of the next day (24 h) through the ARMA and EP-ARMA algorithms, respectively. The ARMA algorithm adopts the BIC value to automatically select the ARMA parameters. The lowest temperature accuracy of the EP-ARMA algorithm is 0.1 \({}^{\circ}\)C. The prediction performance analysis data are shown in Figure 9.  
The left parts, (a), (c), (e) and (g), of Figure 9 show that the ARMA model based on the automatic optimization of parameters through BIC can indeed accurately use the historical data of the first 960 periods to predict the change trend of the temperature data in the next 24 h, but there are still some errors at each time point, especially at some key points with drastic changes. Although the EP-ARMA (0.1) model only corrects the data whose error between the predicted value and the monitored value exceeds 0.1 \({}^{\circ}\)C based on the prediction results of the ARMA model, it is shown by (b), (d), (f) and (h) on the right side of Figure 9 that the corrected data can accurately reflect the temperature data of the next 24 h in the four periods, and the key data with sharp changes in the period can also be extracted. Even in the face of temperature data in different periods, EP-ARMA can achieve more accurate prediction, especially to ensure that abnormal temperature changes are not omitted. Therefore, EP-ARMA can meet the requirements of data transmission accuracy for underwater monitoring applications, and this prediction ability is robust.
## 4 Simulation Analysis
### Data packet delivery ratio  
The simulation results describe the data packet delivery ratios of the AODV, LAR, ZRP, AODV-DFR schemes and the proposed routing algorithm. In the simulations, 5 m/s and 20 m/s node movements are considered.  
In Figure 7(a), as the node count increases, the packet delivery ratio is decreased. When 50 nodes exist in the network, all protocols show a data packet delivery ratio greater than 75%. They show different success ratios as the number of nodes increases. However, the proposed scheme results in a data packet delivery ratio of 72% even in case of 200 nodes used because each node efficiently stores the node information of its surrounding neighbors. In contrast, the performance degradations in AODV and LAR are significant as the number of nodes increases. This results because there are frequent route changes and reconfigurations in a densely populated environment. When the number of nodes is small, AODV-DFR, LAR and ZRP work well and provide high delivery ratios, while the delivery ratio of AODV quickly falls to less than 70%. The AODV routing protocol is not efficient when numerous nodes exist in a large network.  
The performance levels of AODV, LAR, ZRP, AODV-DFR and the proposed routing protocols are also investigated at high speeds of 20 m/s (72 km/hour) and while varying the number of nodes. The increased speed of node movement stresses the routing protocols and introduces several new problems, as shown in Figure 7(b). As Figure 7(b) shows, every routing protocol experiences large-scale degradation compared to the 5 m/s node movement except for AODV-DFR and the proposed algorithm. At a node movement speed of 20 m/s, the data packet delivery ratios of the AODV and LAR falls to less than 30% with more than 150 nodes. The simulation results prove that AODV and LAR are not appropriate in high-mobility environments.  
In both cases of different node movement speeds, even though the proposed algorithm results in a higher data packet delivery ratio, the AODV-DFR and proposed algorithms show similar delivery ratios. This occurs because AODV-DFR creates more routing control messages than the proposed protocol as the following subsection shows.  
Figure 7: Data packet delivery ratio. (a) 5 m/s node movement; (b) 20 m/s node movement.
# Integrating BIM and GIS for an Existing Infrastructure
## 5 Case Study: Madrid, Calle 30
### Calle 30 Management System  
All the information required for the FM of the infrastructure was stored and organized in the database. This was crucial for the use of the integrated BIM-GIS model. On the one hand, the problem of information loss in the intermediate step when using the IFC standard was solved by keeping all the information in this database and not the model. In addition, all BIM and GIS applications automatically generate internal codes to identify each modelled element, and these codes are always accessible in their respective applications. As many fields must be reserved in the database as there are applications used, so that at any point in the process the information can be accessed from the corresponding ID.  
Figure 4: External database linked for an element by ID.  
Keeping all the documentation associated with each element in an external database has some advantages. First, it lightens the three-dimensional models and makes them easier to operate from any device. Second, all the information is accessible and can be updated, allowing a historical record to be kept. If a parameter within the model needs to be updated, the entire model would have to be updated, which would defeat the purpose of the methodology. For example, if road equipment was replaced after an accident, the same object in the 3D model would represent both the initial element and its replacement, while the database would maintain the information about both elements, such as the date of replacement and the manufacturer of each piece of equipment.  
Lastly, access to this database was made possible thanks to the implementation of a web management application. This application also allowed the visualization of the 3D scene or just the BIM models, as well as the visualization of the data in an intuitive way. This makes it easier for the technical staff responsible for O&M to use these models, even if they do not have knowledge of the BIM or GIS applications used, from any connected device. In addition, the database can grow and host new information. This information can come from IoT devices that link real-time data to road management and vice versa, from the model to the real environment.  
The BIM-GIS integration provided a detailed model of Calle 30 in its urban environment. This complex infrastructure has an annual budget of more than 630 M for maintenance, and its complexity causes difficult and time-consuming implementation. Therefore, the evaluation of quantitative metrics comparing possibilities with the existing management system, such as cost and time savings, is critical. The developments presented in this paper are limited to the integration of the BIM model in a GIS environment using standards. However, this is one of the current lines of research.
## 4 LEACH Protocol Improvement Based on the Differential Evolution Algorithm
### Partitioning Initial Clusters  
In this phase, the traditional LEACH routing algorithm is used for partitioning initial clusters for WSNs. The selection process of cluster head assignment by the LEACH protocol works as follows: each node creates a random number between 0-1, and if the number is less than the threshold _T_(_n_), it sends a message to others that it is the cluster head. In each round of a cycle, if one node was a cluster head before, then _T_(_n_) is set to 0, so that node will not be elected again as a cluster head in this round. Non-elected nodes will be elected as cluster heads with probability _T_(_n_). The number of elected nodes increases, the threshold _T_(_n_) to elect the nodes from the remaining nodes increases, and the probability of the random number created by nodes less than _T_(_n_) increases, and the probability of nodes to become cluster heads increases correspondingly. When there is only one node that is not elected, _T_(_n_) = 1, which means the node must be elected as the cluster head. _T_(_n_) is given as:  
\[T(n)=\begin{cases}\frac{P}{1-P\times\{r\bmod(1/P)\}},&\text{$n\in G$}\\ 0,&\text{$\alpha\,\text{{her}}$}\end{cases} \tag{2}\]  
where \(P\) = _k_/_N_ is the percentage of cluster head accounted for all nodes, r is the number of election rounds, r mod (1/_P_) refers to the number of nodes elected in the previous r-1 round of cycle, and G is a set of non-elected nodes in the previous r-1 round. It can be seen that the selection of cluster heads is determined by the elected times of nodes.  
Nodes will broadcast ADV messages via the CSMA mechanism to all other ones to inform them that they became cluster heads when they compete successfully. The messages contain information such as IDs of cluster heads, _etc._ Non-cluster head nodes receive them, analyze and compare, find out the one with the strongest signal as its head cluster, and send a join-request message back to that cluster head, which contains the information of its own ID and that cluster head's ID. When receiving it, that cluster head starts to create a TDMA schedule, uses the TDMA mechanism to allocate slots for each member in the cluster, and informs all nodes inside it. By now, the LEACH protocol has completed partitioning clusters. After that phase, cluster heads and clusters are basically determined, but blind nodes tend to appear in the clusters. Cluster heads in this phase is defined as auxiliary cluster heads.
# Replacement of Cobalt in Lithium-Rich Layered Oxides by n-Doping: A DFT Study
## 3 Results
### Electronic Structure of the LRLOs
The representation of selected CoO6, NiO6, and MnO6 octahedra is shown in Figure 5, where the JT distortions are highlighted. Co3+ ions are not JT ions, and therefore all CoO6 bond distortions are induced by the different chemical environment of TM-layers. Ni3+ and Mn3+ are in an octahedral coordination, and therefore the \(\text{e}_{\text{g}}\) orbitals emerge as a combination between \(\text{d}_{\text{2/d}}\text{d}_{\text{2-y2}}\) and oxygen p orbitals. In both (Ni3+)O6 and (Mn3+)O6 octahedra, the partial occupancy of the \(\text{e}_{\text{g}}\) orbitals leads to alterations in the oxygen-metal bond lengths: usually, the axial bond length increases, whereas the four equatorial bond lengths decrease [63]. Our calculations correctly highlight the JT-distortions in the LNMC12, LNMC08, LNMC04, and LNM structures. Figure 5: JT distorted and undistorted octahedra in LNMC12, LNMC08, LNMC04, and LNM supercells. It is remarkable to observe the four times larger JT distortion concentration in the Co-free LNM lattice, compared to the LNMC12 one (Table S5 of the SI). Apparently, cobalt ions act as electronic disorder \"modulators\", and their substitution induces an increase in local octahedral distortions due to JT defects. According to literature, the increase of JT distortions hinders the lattice stability during the lithiation and de-lithiation process, as they can facilitate phase segregations and structural transitions [64; 65; 66]. In this respect, the n-doping strategy here proposed is unable to fully suppress the JT disorder induced by the Co removal. However, as far as we know, this is the first ever reported quantitative evaluation of the JT distortions occurring upon Co substitution in LRLO lattices, and is a benchmark for further studies.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Select Operation
#### 5.3.3 Select Using Two Joins  
A select operation that uses two joins is presented in Table 10. In this example, we conducted a selection to obtain all the clients of a business.  
In the case of relational MySQL, it takes two joins to obtain the clients, and for each join, the condition of _deleted_ is _null_ or _false_, while in document-based MySQL, the field _deleted_ is applied per document, with only one condition being necessary. The difference from the previous selection is the fact that, now, the client name must be different from null, meaning it must exist.  
Filtering customers of a business involves the use of two joins in this case, one to find the users of the business, and one to find the customers of each user. The response times increase automatically with each join added and, in our case, with each \"where\" condition. In document-based MySQL, the difference is not very big compared to the selection of  
\begin{table}
\begin{tabular}{c} \hline \hline \multicolumn{1}{c}{**Relational MySQL: Select Using Two Joins**} \\ \hline @Query(value = “select c.* from business b inner join user u on b.id = u.business\_id inner join \\ client c on u.id = c.user\_id where b.name = :businessName and (b.deleted is null \\ or b.deleted is false) and (u.deleted is null or u.deleted = false) and (c.deleted is null \\ or c.deleted = false)”, nativeQuery = true) \\ List+Client\textgreater{} findByBusinessName(@Param(“businessName”) String businessName); \\ \hline \multicolumn{1}{c}{**Document-based MySQL: Select using two joins**} \\ \hline DocResult res = collection.find(“name = ‘Beauty by Ali” and \\ (deleted is null or deleted = false) and user.client.name is not null “).execute(); \\ \hline \hline \end{tabular}
\end{table}
Table 10: Select operation using two joins.  
Figure 9: Performance time (ms) for the select operation with a single join.  
users, changing the search condition a little; therefore, there is not a big difference between the response times for finding clients or users, scanning the same elements, and checking the same fields.  
For a small number of elements, the optimization methods do not have a significant impact, as can be seen in Figure 10, and the response times are very close to each other, regardless of whether or not optimizations were conducted, in the case of both databases.  
The differences start to appear with the increase in the number of elements. In the case of the selection without optimizations, the response times increase significantly with the number of elements, especially in the case of relational MySQL, because indexes do not exist in the fields involved in the \"where\" condition.  
The optimization methods improved the performance for both databases, but as can be seen from Figure 10, they have a much greater impact on relational MySQL. Document-based MySQL has a better performance both before and after optimizations compared to relational MySQL in the case of the select operation using two joins.
## 3 Simulation Results
### Analysis of Braking Torque Performance  
The well-designed magnetic field distribution determines the magnitude of the produced field torque. The variance in field strength throughout the MRB is caused by the homogeneity of yield stress on the surface, which influences field torque. However, the higher the input currents, the stronger the field strength generated, which simultaneously increases the braking torque. First, the current is steadily increased from 0 A to 2.0 A at 0.2 Amp intervals. The change in field strength is modest, with an input current of 0.4 A, as illustrated in Figure 12. On the surface of MR, a minor quantity of flux flow may be visible. The fluid, yellow area depicts the low field that occurs in the core region. As the applied current increases, so does the braking torque in an almost linear fashion. The applied current is 1.2 Amp, the flux flow on the MRF surface is modest, and the orange-colored core area has a somewhat greater field strength, as illustrated in the figure. When the current is changed from 1.2 to 1.6 A, the growing rate is pretty fast, as shown in Figure 12b, and the change pattern is clear. However, when the current is increased within 1.6-2.0 A, the output torque tends to be flat because the internal magnetic field of the brake is practically saturated, making the applied current less effective. Observations from the simulation show that a slight difference in magnetic flux density has the edge effects of a magnetic field. The torque in the proposed multipole MRB is linear for input current and may be precisely adjusted. We conclude that the multipole MRB can provide a high braking torque while maintaining a compact design with a low input current.  
Figure 11: Optimization results.  
The pole head structure of the MRB is symmetrical along the central axis, and the dispersion of magnetic flux lines is rather dense and progressively rises with the applied current. Figure 13 shows the average increase in braking torque with increasing coil current. When the current approaches 2 A, the braking performance rarely improves because the shear stress of the MRF practically hits saturation, such that the observed maximum braking torque is estimated to be 18.64 N-m. This finding demonstrates that the sector pole head improves the torque of the MR brake.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 4 Results and Discussion
### Measurement of Oxygen Separation Energy Consumption after Process Optimization  
The aim of the measurements was to determine the energy consumption of the oxygen separation process with optimal generator settings. Three measurements were made for each adsorption pressure from 2.5 to 6.0 bar(g), every 0.5 bar (24 in total). For each adsorption pressure, the optimal transfer time and the oxygen flux determined in the third stage were set. The measurement results are shown in two figures--depending on the adsorption pressure (Figure 7a) and the oxygen flux (Figure 7b).  
It can be observed that the energy consumption of the oxygen separation process at optimal generator settings is within the range of 1330-1540 kWh/t. The average value over the entire range is 1460 kWh/t.  
Figure 7a shows a clear minimum energy consumption of the process at an adsorption pressure of 4.5 bar. The uncertainty of measuring the energy consumption for individual pressures is \(\pm\)50 kWh/t (approx. 3.5%). As the adsorption pressure increases, the compression work in the compressor increases, but the adsorption capacity of the bed also increases (Figure 3). For low pressures, the increase in adsorption capacity is large and causes a significant increase in the generator's capacity. This reduces the energy consumption of the process up to a pressure of 4.5 bar. Above this value, the increase in adsorption capacity becomes smaller, and the increase in compression work causes an increase in energy consumption of the process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**adsorption Pressure, bar(g)** & **2.5** & **3.0** & **3.5** & **4.0** & **4.5** & **5.0** & **5.5** & **6.0** \\ \hline transfer time, s & 13 & 14 & 14 & 15 & 15 & 15 & 15 & 15 \\ oxygen flux, m\({}^{3}\)/h (\(\pm\)0.05) & 0.67 & 0.73 & 0.87 & 0.87 & 1.01 & 1.04 & 1.08 & 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimum transfer times and corresponding average oxygen flux depending on the adsorption pressure.  
Figure 6: Optimum transfer time and maximum oxygen separation capacity depending on the adsorption pressure.  
The results of the energy consumption measurements shown in Figure 7b show that the oxygen separation process in the adsorption generator can be carried out with optimized energy consumption within fairly wide capacity limits from almost 0.7 m\({}^{3}\)/h to 1.1 m\({}^{3}\)/h. There is also a visible minimum energy consumption for a capacity of approx. 1 m\({}^{3}\)/h, which corresponds to an adsorption pressure of 4.5 bar.