esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Comparison of Forming Behavior of Steel
#### 3.2.4 Comparison of Forming Response: Punch Force  
The plot in Figure 11 illustrates the comparison of punch force (F) values for different AISI grade steels with varying billet sizes and at specified level combinations of \(a\), \(h\) and \(r\). For all values of \(z\) and for all combinations of \(a\), \(h\), and \(r\), the punch force was much more prominent in the case of AISI 1045 as compared to 1010 and 1018. For all combination settings of \(a\), \(h\), and \(r\), the force tended to increase with an increase in \(z\). For a specified value of \(z\), the force tended to reduce with higher settings of \(a\), \(h\), and \(r\) in all three materials tested.  
Figure 9: Comparison of forming response: effective strain.  
The punch force was obliviously significant for AISI 1045 than AISI 1010 and 1018 because of the increased carbon content in the steel. It is evident from the plots that increased punch angle and land height on the punch increased resistance to metal flow. However, for reduction ratio \(r=0.6\), the punch diameter was 19 mm. hence, it necessitated lower deformation force compared to \(r=0.3\) (25 mm diameter punch). An increase in the billet volume (\(z>0.6\)) for fixed combinational settings of \(a,h,r\) slightly increased the punch forces for AISI 1045 due to lesser yielding, while steel with lower carbon content readily deforms, leading to the major rise in punch force.  
The plots shown in Figures 8-11, derived from the ANN models for forming responses of AISI 1010, 1018, and 1045, revealed the formability nature of the different AISI grade steels. The forming responses were observed to be majorly affected by the reduction ratio, whereas, the marginal influence of the punch angle and land height was witnessed. However, punch angle and land height were major concerns in deciding the service life of the punches in cold forging backward extrusion. The consequence of the billet size ratio was clearly visible in the range of 0.3 to 0.6, and a further increase in the volume merely  
Figure 11: Comparison of forming response: punch force.  
Figure 10: Comparison of forming response: effective strain rate.  
increased the values of the forming responses. The deformation was also primarily affected by the carbon content of the steel, as evidenced by AISI 101, 1018, and 1045 steel.  
From the above response analysis, it is clear that the relationship between various responses and chosen process parameters was highly non-linear and, hence, ANN modeling is appropriate as compared to other modeling approaches, such as response surface methods.
## 4 Application of the Developed PV System Models
### Case 2  
The ELCC and capacity credit of a PV system for different PV topologies are investigated in this work. The LOLE was used in this study in order to evaluate the ELCC for each PV topology. The maximum allowable peak load at adequacy risk of system generation is 32.26 h/year. The amount of load that can be carried by a PV system is estimated by calculating the difference between the two risk indices of LOLE before and after adding PV systems. Figure 15 show the ELCC associated with the addition of 10%-30% of PV systems to SIPS at the three different PV topologies.  
Equation (3) is utilized in this case to evaluate the capacity value of PV systems for different PV topologies. Figure 16 demonstrates the PV capacity credit for the three different topologies.  
Several important observations can be obtained from this analysis:  
* There is evidence of improvement in overall system adequacy when installing more PV systems at all different PV topologies. The relative reliability benefits estimated by capacity value, however, decrease with the addition of PV capacity. Previous studies have also found that the capacity value of PV declines when installing more PV in the electric power system [32; 33]. This is not always the case since the PV topology plays an important role in the contribution of PV capacity value. To illustrate this, when 20% of the PV system is added to SIPS, the solar capacity value increases using both string and a micro-inverter and decreases using a central-inverter as shown in Figure 16.
* The result clarifies that the micro-inverter PV system provides the largest PV capacity contribution. The capacity credit of the PV system increases from 19% to almost 35.5% when the central PV inverter topology is replaced by a micro-inverter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PV Topology** & **PV Capacity (kW)** & **RBECR** \\ \hline Central & 270 & 6.75 \\ String & 180 & 4.50 \\ Micro-inverter & 98 & 2.45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Replacing a conventional generation unit by a PV System.  
Figure 15: Effective load carrying capability for different PV topologies.
# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods
### Data Processing
With advanced technologies, the quality of information has been satisfied, directly providing users with higher-level situation awareness. Finally, no matter how carefully a given system is designed, evaluations of situation awareness are needed to investigate, based on what was happening in the simulation. The comparison of the real and perceived situations provides an objective measure of system situation awareness. The optimization problem (as the simulation methods) might be solved for the entire system, sub-system, or elements. However, all the optimization solutions must be harmonized. This goal can be reached by three types of hierarchy: (i) hierarchy in the system (system structure), (ii) hierarchy in transport (vehicle) classification (e.g., first come first serve, prioritization), and (iii) situation prioritization (disaster response, significant accident). * Solving the optimization problem:
* objective function definition--the total system must be optimized for minimum energy consumption (Equations (2)-(4)) or minimum total cost
* constraints definition--the constraints might be defined for technical, technological operation, economic, societal, environmental impact, safety, and security aspects (like an actual problem, social distance in mass transport)
* optimization problem solution--with linear or non-linear programming or artificial intelligence
* result evaluation--check the applicability of the results  
The objective function/total energy consumption can be calculated with Equation (2). Based on the traffic condition's real information and the active and dynamic optimization of the traffic, the objectives were determined. Therefore, the optimization problem, like Equation (4), can be solved. The results should be evaluated by comparing it with the real traffic condition. * Decision making and actions:
* automated decision making--it might be applied to the small optimization problems like the control of traffic lights at given cross-sections, and it can be determined as a recommended decision for the total traffic management
* decision making for special situations--caused by unwanted events (accident) of planned situations like protests or an essential sports event
* decision making for emergency situations--in case of, e.g., a disaster such as a flood, when lifesaving is the primary objective
* actions--the realization of the decision  
The results of the optimization problem were considered as an input for the decision-making and action process. In this process, several situations were determined based on situation awareness, including optimal energy consumption, special situations, and emergency situations. Then, the users can usually choose the solution that satisfies the real-traffic environment.
# Integrated Digital-Twin-Based Decision Support System for
## 6 Case Study Implementation
### The Current RMS Facility Management System and Issues  
These RMS projects are designed as temporary classrooms that accommodate students during the remodeling of existing schools. After the occupancy phase of each project lifecycle, the modular units from these projects can follow one of two paths. Firstly, they can be disassembled and transported directly to a new location, where they are reassembled in configurations that meet the specific requirements of the next school project. The modular design facilitates straightforward disassembly and reconfiguration, streamlining the transition from one site to another. Alternatively, the units may be returned to storage, where they undergo maintenance or upgrades, remaining on standby until needed for future projects. The relocation strategy for these RMS units is illustrated in Figure 7. Currently, module selection for reassembly is performed manually, ensuring precision and timeliness. However, as the number of projects and individual modules per project increases, this manual selection process becomes increasingly time consuming and labor intensive, and it does not optimize factors such as proximity or transportation routes. Therefore, the development of a decision support system for module allocation planning becomes essential.  
Each RMS project comprises various components, including subunits like roofs, corridors, and foundations, and standard units made up of differing module combinations (Figure 8). In practice, module types are intricately categorized by function (e.g., classrooms, restrooms, and stairs), corridor configuration (e.g., single sided and double sided), and their specific placement within the structure (e.g., rear, middle, left, and right). This leads to a complex assortment of module units and numerous configuration rules for new project combinations. However, for the purposes of this research, the variety of module types was simplified to nine, as shown in Figure 9. Additionally, due to confidentiality and restricted data access, the number of projects and module types were simulated. Consequently, data encompassing 67 RMS projects and two storage sites, detailing locations, number of floors, module types, and usage schedules, were generated, culminating in a database of nearly  
Figure 7: Project module allocation scenarios.  
500 module units. This extensive data collection supports the detailed analysis required to refine facility management practices within the RMS framework.
# Parallel Image Completion with Edge and Color Map
## 3 Materials and Methods
### Loss Function
#### 3.3.2 Color Path  
The color path was designed for solving the constrained optimization problem in Equation (4) to obtain the color prior. The color generator \(G_{2}\) used the masked color map \(\tilde{C}_{gt}=C_{gt}\odot(1-M)\) as input, conditioned with masked RGB images \(\tilde{I}_{gt}=I_{gt}\odot(1-M)\) and the mask \(M\). Hence, the color map generated by \(G_{2}\) was shown as:  
\[C_{pred}=G_{2}\left(\tilde{C}_{gt},\tilde{I}_{gt},M\right). \tag{14}\]  
The composite color map \(C_{comp}\) was obtained by combining the known region of ground truth color map \(C_{gt}\) with the generated color map \(C_{pred}\) in the missing region:  
\[C_{comp}=C_{gt}\odot(1-M)+C_{pred}\odot M. \tag{15}\]The discriminator \(D_{2}\) took \(C_{gt}\) and \(C_{comp}\) as inputs to predict whether or not a patch of these color maps is real. The overall loss consisted of a feature matching loss \(\mathcal{L}_{fm,2}\), a pixel-wise loss \(\mathcal{L}_{pix,2}\) and an adversarial loss \(\mathcal{L}_{adv^{\prime}}^{G_{2}}\); this can be formulated as follows:  
\[\mathcal{L}_{G_{2}}=\lambda_{fm,2}\mathcal{L}_{fm,2}+\lambda_{pix,2}\mathcal{ L}_{pix,2}+\lambda_{adv,2}\mathcal{L}_{adv}^{G_{2}}, \tag{16}\]  
\[\mathcal{L}_{D_{2}}=\mathcal{L}_{adv^{\prime}}^{D_{2}}, \tag{17}\]  
where \(\lambda_{fm,2}\), \(\lambda_{pix,2}\) and \(\lambda_{adv,2}\) are the regularization parameters.  
The feature matching loss \(\mathcal{L}_{fm,2}\) in the color generator \(G_{2}\) was similar to \(\mathcal{L}_{fm,1}\). Because \(C_{gt}\) and \(C_{comp}\) were not sparse as the edge map, the discriminator \(D_{2}\) did not require additional information except for the color maps. The feature matching loss \(\mathcal{L}_{fm,2}\) was defined as:  
\[\mathcal{L}_{fm,2}=\mathbb{E}\left[\sum_{i=1}^{L}\frac{1}{N_{i}}\left\|D_{2} ^{(i)}(C_{gt})-D_{2}^{(i)}(C_{comp})\right\|_{1}\right], \tag{18}\]  
where \(L\) is the number of layers in the discriminator \(D_{2}\), \(N_{i}\) is the number of elements in the \(i\)th layer and \(D_{2}^{(i)}\) is the activations in the \(i\)th layer.  
Pixel-wise loss measures the \(\ell_{1}\) difference between the synthesized color map \(C_{pred}\) and its corresponding ground truth \(C_{gt}\). It is a straightforward loss and widely used in image completion. It is defined as:  
\[\mathcal{L}_{pix,2}=\frac{1}{F_{mean}(M)}\left\|C_{gt}-C_{pred}\right\|_{1}, \tag{19}\]  
where the function \(F_{mean}(M)\) refers to the mean of the mask \(M\) which ensures that \(L_{pix,2}\) has proper scaling.  
Similar to the edge model, the LSGAN was also used to fit the real color map distribution. The adversarial losses for training the generator \(G_{2}\) and the discriminator \(D_{2}\) are defined as follows, respectively:  
\[\mathcal{L}_{adv}^{G_{2}}=\frac{1}{2}\mathbb{E}\left[\left(D_{2}(C_{comp})-1 \right)^{2}\right], \tag{20}\]  
\[\mathcal{L}_{adv}^{D_{2}}=\frac{1}{2}\mathbb{E}\left[\left(D_{2}(C_{gt})-1 \right)^{2}\right]+\frac{1}{2}\mathbb{E}\left[D_{2}\left(C_{comp}\right)^{2} \right]. \tag{21}\]
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 4 HAR Datasets and Experiment Settings
### HAR Datasets
#### 4.1.3 PHAD Datasets  
Phyphox Human Activity Dataset (PHAD) contains the experimental data from a smartphone's magnetometer, gyroscope, and accelerometer, capturing various motion information. We custom-made this dataset to facilitate better data collection for experimental testing. During the preprocessing process, we distinguished between regular and confused data. A group of 30 volunteers was invited to complete six human activities including walking (WK), sitting (ST), standing (SD), running (RN), walking upstairs (WU) and walking downstairs (WD), as shown in Figure 6.  
Figure 6: Illustration of process of deep learning, data is collected by sensors on typical daily human behaviors, and the OI-EleAttG-GRU network is used for predictive analysis.  
Figure 6 provides a diagram of the six types of human activity and the process of deep learning. The top part of the figure represents a scenario where the human body collects different motion information. The middle part shows the signal data received by the waist and arm sensors, capturing the aforementioned six human behaviors. These time-varying speed data are recorded, processed, and then fed into the deep learning network for model training. The iterative increase in accuracy during training is shown at the bottom of the figure.  
These activities are selected because these are common activities that people regularly perform in their daily routines. Analyzing results of these activities provides the best reflection of the performance of our human activity recognition structure. During the experiment, each volunteer wore a smartphone attached to both their waist and arm to collect various behavioral information for a duration of one minute. The various signal data are collected by the sensor including accelerometer, gyroscope and magnetometer. The detailed class distribution of HAR data and the proportion of mixed data is presented in Table 1. The 200 data items with the highest probability of classification error by conventional methods are marked as confused data.
# A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring
###### Abstract
The applicability of the MHGM is not affected by the different orbit repetition periods, so it is especially suitable for BDS-2. In addition, the use of DD residuals to model multipath error does not require specialized receiver equipment, and the distance between stations is not limited by hardware considerations. Finally, the actual BDS dam deformation monitoring data were used for verification and analysis. We compare MHGM with the SF method and focused on the multipath effects of carrier phase observations for actual and complex applications, such as dam deformation monitoring.
# Mn(II) Complexes of Enlarged Scorpiand-Type Azamacrocycles as Mimetics of MnSOD Enzyme
## 3 Results
### Basicity of the Ligands and Mn(II) Coordination
With these values, the corresponding species distribution diagrams were built (Figure 3). We can estimate the overall stability of the complexes at pH 7.4 by calculating the percentage of free Mn\({}^{2+}\), in relation to the total amount of metal (1.0 mM), at this pH value. The results obtained are 6.1% and 6.8% for L1 and L2, respectively. This should be taken into consideration, since manganese salts might show, on their own, a certain SOD activity [30]. By looking at the distribution diagrams, we can observe a striking difference between the behaviour of these two molecules when interacting with Mn\({}^{2+}\). In the case of L1, the prevalent species in solution at pH 7.4 is [MnL]\({}^{2+}\), accounting for 75%, while for L2, under the same conditions, the main species is [HMnL]\({}^{3+}\), with a prevalence of more than 90% over the other species. This is related to the differences in basicity described before, and will impact the SOD mimetic activity of each system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reaction (a)** & **L1** & **L2** & **L3** \\ \hline L + Mn \(\rightleftarrows\) MnL (b) & 9.67 (3) & 8.42 (3) & N.D. \\ MnL + H \(\rightleftarrows\) MnHL & 6.80 (4) & 8.97 (3) & N.D. \\ MnL + H\({}_{2}\)O \(\rightleftarrows\) MnL(OH) + H & - & \(-\)11.1 (1) & N.D. \\ \hline \hline \end{tabular} \({}^{(a)}\) Logarithms of the stability constants. Values were determined at 298 K in 0.15 M NaCl. Numbers in parentheses are standard deviations in the last significant figure. (b) Charges have been omitted.  
\end{table} 
Table 2: Stepwise stability and protonation constants for Mn\({}^{2+}\) complexes of L1–L3 at 298.1 \(\pm\) 0.1 K and NaCl 0.15 M.  
Figure 3: Distribution diagrams for the systems L1/Mn\({}^{2+}\) (**A**) and L2/Mn\({}^{2+}\) (**B**) in a 1:1 ratio ([L] = [Mn\({}^{2+}\)] = 1.0 mM) as a function of pH. The dashed line represents the molar fraction of free Mn\({}^{2+}\).
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 5 Conclusions  
This paper explored the sharing of 5G infrastructure through the development of a flexible and dynamic architecture that allows for the efficient provision of mobile network services to various types of clients, regardless of whether they are operators or not, while ensuring an efficient provision of the services and the isolation of each client's traffic.  
The complexity of the proposed architecture, coupled with limitations of the available open-source software tools and computational resources, led to some restrictions in the architecture implemented for validation of the proposed solution.  
Nevertheless, the tests performed in this work using the implemented neutral host architecture showed promising results, with successful validation of the different types of authentication and traffic isolation requirements, and a significantly better performance (higher throughput and lower PLR per user) of the proposed architecture based on network slicing and roaming when compared to the baseline test, i.e., a single client using all the available network resources.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PLR (\%)**} \\  & \multicolumn{2}{c}{**VM2**} & \multicolumn{2}{c}{**VM3**} & \multicolumn{2}{c}{**VM4**} & \multicolumn{2}{c}{**VM5**} \\ \hline One client/slice & 1.11 \(\pm\) 0.37 & 1.12 \(\pm\) 0.38 & 1.10 \(\pm\) 0.39 & 1.13 \(\pm\) 0.41 & 1.06 \(\pm\) 0.45 & 1.05 \(\pm\) 0.44 & 1.19 \(\pm\) 0.41 & 1.15 \(\pm\) 0.39 \\ \hline Four clients/slices & 0.05 \(\pm\) 0.05 & 0.04 \(\pm\) 0.03 & 0.03 \(\pm\) 0.03 & 0.03 \(\pm\) 0.02 & 0.04 \(\pm\) 0.04 & 0.04 \(\pm\) 0.04 & 0.03 \(\pm\) 0.02 & 0.03 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PLR and maximum feasible throughput per user with eight users distributed by four VMs for both configurations.  
**Author Contributions:** Conceptualization, J.P.F., V.C.F., S.L.N., J.M.F. and J.A.A.; methodology, J.P.F., V.C.F. and J.A.A.; software, J.P.F.; validation, J.P.F., V.C.F. and J.A.A.; investigation, J.P.F., V.C.F. and J.A.A.; writing--original draft preparation, J.P.F., V.C.F. and J.A.A.; writing--review and editing, S.L.N. and J.M.F.; supervision, V.C.F., S.L.N. and J.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by FCT national funds under the national support to R&D units grant through the reference project UIDB/04436/2020 and UIDP/04436/2020.  
**Instructional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] was employed by the company dstelecom, S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.