esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### SoC Performance Under Best PMVO Solution  
Figures 12 and 13 illustrate the hourly state-of-charge (SoC) profiles of the BESS units throughout the 24 h horizon under the optimal dispatch strategies determined by the PMVO algorithm in both operational modes. Each curve corresponds to an optimal solution obtained by minimizing either energy losses or CO2 emissions, although the vertical axis reflects the SoC level (%). The results reveal different charging patterns that align with  
Figure 11: Maximum loadability performance achieved by the PMVO algorithm in a representative daily scenario for the 33-node system in self-powered mode over a 24 h evaluation horizon.  
Figure 10: Maximum loadability performance achieved by the PMVO algorithm in a representative daily scenario for the 33-node system in grid-dependent mode over a 24 h evaluation horizon.  
the underlying optimization goals while still complying with operational constraints and ensuring reliable system performance.  
Regarding the batteries' conditions, in the grid-dependent configuration (Figure 12), all BESS units maintain their SoC within the allowable range (10-90%), with distinct charge and discharge phases clearly observable. A synchronized depletion occurs during the early morning hours (00:00-08:00), followed by a coordinated charging interval during mid-day, peaking around 15:00-17:00. BESS C shows a more aggressive discharge pattern, reaching the lower bound precisely at hour 7, which suggests it is prioritized for higher utilization. BESS B maintains relatively stable behavior, avoiding deep discharges and helping to smooth transitions in SoC levels.  
In the self-powered mode (Figure 13), the storage operation becomes more sensitive due to the absence of external grid support. The early-hour SoC levels are notably lower compared to the grid-dependent case, reflecting the increased autonomy of the system. Again, BESS C reaches the lower SoC threshold, confirming its role in providing fast-acting support. The charging trajectory across all BESS units is slightly delayed but still ensures full recovery within operational boundaries by mid-afternoon.  
Figure 12: SoC profile as the solution achieved by the PMVO for a representative day of operation in the grid-dependent mode over a 24 h evaluation horizon.  
Figure 13: SoC profile as the solution achieved by the PMVO for a representative day of operation in the self-powered mode over a 24 h evaluation horizon.  
The behavior across both modes highlights that the PMVO algorithm enforces constraint satisfaction while achieving adequate storage coordination. There is no evidence of excessive cycling or limit violations, which implies the strategy is compatible with long-term asset reliability.
# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
###### Abstract  
The fast development of connected vehicles with support for various V2X (vehicle-to-everything) applications carries high demand for quality of edge services, which concerns microservice deployment and edge computing. We herein propose an efficient resource scheduling strategy to containerize microservice deployment for better performance. Firstly, we quantify three crucial factors (resource utilization, resource utilization balancing, and microservice dependencies) in resource scheduling. Then, we propose a multi-objective model to achieve equilibrium in these factors and a multiple fitness genetic algorithm (MFGA) for the balance between resource utilization, resource utilization balancing, and calling distance, where a container dynamic migration strategy in the crossover and mutation process of the algorithm is provided. The simulated results from Container-CloudSim showed the effectiveness of our MFGA.  
containerized microservice; resource scheduling; edge computing +
Footnote †: journal: Future Internet
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 2 Materials and Methods
### Microstructural and Compositional Analyses  
Water contents (\(w\)) and particle densities (\(\rho_{s}\)) were determined according to ISO 17892-1 and -3, respectively. The \(\rho_{s}\) was determined using the fluid pycnometer with moist specimens. Two separate determinations were made and an average value was calculated if the difference was <0.03 t/m\({}^{3}\). These analyses were performed at the geotechnical laboratory at the Norwegian Geotechnical Institute (NGI).  
Specific surface area (SFA) determinations were made by the adsorption of gas and calculated according to the Brunauer-Emmett-Teller (BET) theory [72]. The SFA analyses were performed by [PERSON] in Norway.  
Thermogravinetric analyses (TGA) were performed on samples dried at 105 \({}^{\circ}\)C prior to analyses which were then, from room temperature, exposed to a temperature increase rate of 10 \({}^{\circ}\)C/min in a nitrogen atmosphere [73; 74]. The TGA was performed at SINTEF in Norway.  
X-ray fluorescence (XRF) analyses of major elements were performed using a PANalytical Axios 4 kW equipped with a rhodium X-ray tube on fused glass beads. The material was mixed with a lithium borate flux and heated to 1000 \({}^{\circ}\)C. X-ray diffraction (XRD) analyses were made on whole rock, i.e., not sieved, and were hand-mortared prior to the analyses. The analyses were made with a BRUKER D8 Advance using CuK\(\alpha\) radiation (40 kV/40 mA). Scans were acquired on a rotating disk using 2.5\({}^{\circ}\) Soller slits and a fixed divergence slit (0.6 mm) in the range of 3 to 75\({}^{\circ}\) 2\(\Theta\) with a step size of 0.02\({}^{\circ}\) 2\(\Theta\) and a 1 s count time. Mineral identifications were performed using the BRUKER Diffrac.EVA ver. 5.2 software, Crystallographic Open Database (COD) and International Centre for Diffraction Data (ICDD) databases. Mineral quantification was conducted using Rietveld modelling with the TOPAS 5.0 software. The XRF and XRD analyses were performed by X-ray Mineral Services, UK.  
Figure 1: Photographs of selected biochars and IBPs: (**a**) biochar (BC3), (**b**) ladle slag (LS1), (**c**) ladle slag (LS2), (**d**) paper sludge ash (PSA), (**e**) bioash (BA) and (**f**) bioash (FA).
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 5 Fundamentals of Nonlinear Optimization
### Karush-Kuhn-Tucker (KKT) Conditions  
The KKT conditions are widely known conditions that are necessarily satisfied at any feasible solution \(\mathbf{\hat{x}}\) for Equation (6) that is also a critical point in the objective function (i.e., \(\
abla f\left(\mathbf{x}\right)=\mathbf{0}\)),  
Figure 11: An example of convex and nonconvex sets. _Left_. The hexagon, which includes its boundary (shown darker), is convex. _Right_. The kidney shaped set is not convex, since the line segment between the two points in the set shown as dots is not contained in the set.  
Figure 10: Graph of a convex function. The chord (i.e., line segment) between any two points on the graph lies above the graph.  
given certain _constraint qualifications_ are satisfied. Constraint qualifications can be any of _Slater's conditions, linear independence_ of the gradients of active and equality constraints, _Karush-Tucker_ constraint qualifications and _weak reverse convex_ constraint qualification [30]. If the feasible region is a convex set, and the objective function we are trying to minimize is convex (or maximize a concave function), then the KKT conditions are sufficient conditions for **global** optimality. It is only for very simple problems, usually in an impractical sense, that we could use KKT conditions to solve NLPs. However, devising solution algorithms for solving large NLPs, rely on the insights that are provided by KKT conditions. The following set of equations and inequalities are the KKT conditions:  
\[\
abla f\left(\bar{\mathbf{x}}\right)+\boldsymbol{\lambda}^{T}\
abla\mathbf{h }\left(\bar{\mathbf{x}}\right)+\boldsymbol{\mu}^{T}\
abla\mathbf{g}\left(\bar{ \mathbf{x}}\right)=\mathbf{0} \tag{8a}\] \[\mathbf{h}\left(\bar{\mathbf{x}}\right)=\mathbf{0}\] (8b) \[\mathbf{g}\left(\bar{\mathbf{x}}\right)\leq\mathbf{0}\] (8c) \[\boldsymbol{\mu}^{T}\mathbf{g}\left(\bar{\mathbf{x}}\right)=0\] (8d) \[\boldsymbol{\mu}\geq\mathbf{0} \tag{8e}\]  
The conditions in Equation (8) become sufficient for **local** optimality if convexity is satisfied for the objective functions and constraints within a ball of radius \(\epsilon\) around \(\bar{\mathbf{x}}\). In other words, a sufficient condition for local optimality would be as follows: if for every non-zero vector \(\mathbf{u}^{T}\
abla h_{i}\left(\bar{\mathbf{x}}\right)=0\ \ \forall\ i\) and \(\mathbf{u}^{T}\
abla g_{j}\left(\bar{\mathbf{x}}\right)=0\ \forall\ j\in J\equiv\left\{j:g_{j}\left(\bar{ \mathbf{x}}\right)\ =\ 0\ \right\}\), then,  
\[\mathbf{u}^{T}\
abla^{2}\mathcal{L}\left(\bar{\mathbf{x}},\bar{\boldsymbol{ \lambda}},\bar{\boldsymbol{\mu}}\right)\mathbf{u}\geq 0\]  
guarantees a local optimum solution to be \(\bar{\mathbf{x}}\).
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Mapping Methods  
BR is a common technique used in mineral exploration. It involves calculating the ratio between a band that reflects the target signal and a band that absorbs that signal [14]. The difference between the reflecting band and the absorbing band highlights specific characteristics of the target [14]. Many works use Fe\({}^{3+}\) as a key to map Hematite, and it can also be applied to map Goethite [26; 38; 22].  
To analyze the potential of PS satellites in detecting iron oxides, we developed and tested a new spectral ratio for PS while applying recognized methods to other satellites (Table 2). We then assessed the similarity of the results to determine whether PS can achieve comparable outcomes to satellites with proven effectiveness in identifying these minerals. We focused on mapping the primary iron hydroxides associated with Au-Sb mineralization in Freixeda, specifically Hematite and Goethite. Threshold values were applied to mask spectral outliers, following the approach of [PERSON] et al. [16], to exclude anomalous high-reflectance pixels unrelated to iron oxide signatures. Since we are working with different sensors, it is not possible to define a single threshold value for all of them, as the minimum and maximum band ratio (BR) values vary depending on the satellite.
# Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems
## 4 Experiment Preparation and Implementation
### Evaluation Metrics  
This section presents the evaluation metrics used to evaluate the proposed approach to determine the impact of the dynamic analysis in improving the detection accuracy of Android ransomware apps.  
**Detection accuracy:** The accuracy of correctly detecting the ransomware sample and identifying it as ransomware. To estimate the accuracy of detecting a ransomware sample, we calculated the cases of the dynamic detection, which were tested using the MobSF tool to ensure that the app sample was ransomware, and compared it to the accuracy of statically detecting it using VirusTotal. The detection accuracy based on static analysis is calculated in Equation (2):  
\[DetectionAccuracy=\frac{Total\ \#\ of\ Engines\ that\ detected\ malware}{Total\ \#\ of\ Engines} \tag{2}\]  
**Classes and methods frequencies (\(ClsMth\)):** The frequency was calculated by counting the number of times the class or method appeared when the app was dynamically analyzed. Equation (3) shows that for each class and method that belongs to a ransomware application, the frequency of \(ClsMth\) is updated.  
\[ClsMth\ Frequency=\forall\ (Class\ \&\ Method)\in\{RansApps\}\to ClsMth+1 \tag{3}\]  
**Permissions frequency (Per):** The permissions' frequency was calculated based on the extracted results from statically analyzing the selected 22 ransomware samples by using the Static Analyzer of MobSF tool and VirusTotal. As Equation (4) elaborates, the output results of VirusTotal and MobSF were summed up to find the total frequency of the permissions of the application.  
\[Per\ Frequency=Total\ \#\ of\ Pers\ extracted\ by\ (VirusTotal+MobSF) \tag{4}\]
## 3 Results
### Comparison of Marginal Bone Loss between the Long and Short Implants  
Marginal bone loss after the implant insertion was evaluated for 46 implants using periapical radiographs taken at 12 weeks and 48 weeks after surgery (Table 3 and Figures 4 and 5). The average marginal bone loss from the fixture platform top for the control and experimental groups was \(-\)0.07 \(\pm\) 0.78 mm and 0.03 \(\pm\) 0.63 mm after 12 weeks and 0.06 \(\pm\) 0.82 mm and 0.05 \(\pm\) 0.77 mm after 48 weeks, respectively. Afte  a 12-week healing period, the distal surface exhibited slightly greater bone loss than the mesial side, but by the end of the trial, no differences in marginal bone loss between the two implant groups gained statistical significance (\(p\)-value > 0.05).  
Evaluation of the Peri-Implant Soft Tissue Parameters and Success Rates of the Long and Short Implants  
All of the mean values of the soft tissue parameters were clinically healthy within normal limits throughout the clinical trial (Table 4). There were no statistically significant differences in the soft-tissue parameters between the two implant groups (\(p\)-value > 0.05). The success criteria described by [PERSON] et al. [24] were applied to evaluate implant success. The implants that did not fulfill the success criteria were considered failures. At the end of the 48-week follow-up period, all 46 implants fulfilled the strict success criteria. Consequently, the overall success rates were 100%.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Experimental**} \\  & **Neobiotech CMI IS-III** & **Neobiotech CMI IS-III** & \\  & **Active\({}^{\text{\textregistered}}\) Long Implant** & **Active\({}^{\text{\textregistered}}\) Short Implant** & \\ \hline \multicolumn{2}{c}{Participant number} & 19 & 27 & \\ \hline Duration & Area & Mean \(\pm\) SD (mm) & Mean \(\pm\) SD (mm) & \(p\)-value * \\ \hline
12-week follow up & Mesial & \(-\)0.22 \(\pm\) 0.98 & -0.15 \(\pm\) 0.79 & 0.893 \\  & Distal & 0.08 \(\pm\) 0.81 & 0.20 \(\pm\) 0.78 & 0.728 \\  & Avg. & \(-\)0.07 \(\pm\) 0.78 & 0.03 \(\pm\) 0.63 & 0.885 \\
48-week follow up & Mesial & \(-\)0.15 \(\pm\) 0.94 & -0.13 \(\pm\) 0.82 & 0.719 \\  & Distal & 0.27 \(\pm\) 0.80 & 0.23 \(\pm\) 0.92 & 0.573 \\  & Avg. & 0.06 \(\pm\) 0.82 & 0.05 \(\pm\) 0.77 & 0.655 \\ \hline \end{tabular}  
* The \(p\)-values were calculated using the Mann–Whitney test.  
Normality test was failed (Shapiro-Wilk, \(p\) < 0.05).  
Area, the radiographic measurement area for calculation of marginal bone loss; Avg., the average value of mesial and distal bone loss; SD, standard deviation.  
\end{table}
Table 3: Comparison of marginal bone loss between the long and short implants.
# Nexus between Building Information Modeling and Internet of Things in the Construction Industries
## 3 Results and Discussion
### An Overview of the Role of IoTs in Construction Industries
\begin{table}
\begin{tabular}{c c c c c} \hline
**Index** & **Area of Application** & **Methodology** & **Findings** & **Reference** \\ \hline \multirow{4}{*}{Scopus} & IoT enabled & \multirow{4}{*}{Experimental Design and Fabrication} & A multi-dimensional IoT-enabled & \multirow{4}{*}{[55]} \\  & Prefabricated & & Building Information Modeling (BIM) & \\  & construction & & and Fabrication & platform (MITBIMP) for achieving & \\  & & construction & & real-time tracking in prefabricated & \\  & & & building was achieved & \\ \hline \multirow{4}{*}{Scopus} & IoTs in Nigeria & \multirow{4}{*}{Questionnaire Survey} & The results indicate that power & \multirow{4}{*}{[56]} \\  & Construction Industries & & availability, cost, limited customer & \\ \cline{1-1}  & & & demand, and issues with online security & \\ \cline{1-1}  & & & and data volume are the most significant & \\ \cline{1-1}  & & & barriers to the adoption of the Internet of & \\ \cline{1-1}  & & & Things in the construction business. & \\ \hline \end{tabular}
\end{table} 
Table 2: Cont. study also investigated their knowledge of IoT and whether or not it can be implemented and expanded in such projects. [PERSON] et al. [17] analyzed the trend on IoTs in construction industry. Based on the analysis, the primary implications of IoT adoption in the construction sector have been highlighted as high-speed reporting, total process control, data explosion resulting in deep data analytics, and severe ethical and regulatory requirements. The following were identified as key drivers of IoT adoption: interoperability; data privacy and security; adaptable governance frameworks; and adequate business planning and modelling. IoT enables prefabricated construction has been investigated by [55]. The authors offered a multi-dimensional IoT-BIM platform for achieving real-time tracking in prefabricated building construction. For the purpose of designing the IoT-BIM platform, design considerations for an RFID Gateway Operating System, visibility and traceability tools, data source interoperability services, and decision support services were provided.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Orbit Generator  
In IoST networks, satellite orbits describe the mobility of the nodes. Network topology is directly related to node mobility and parameters such as minimum link duration [30; 31].  
Commonly the two-line element sets (TLEs) contain encoded orbital elements upon which orbits for actual satellites can be calculated [63]. The Orbit Generator module in Figure 1 downloads TLEs from the Celestrack database [64]. These data are then formatted to feed the IoST simulator mobility module. Using the TLE data and the SGP4 model described in [65], orbits are generated for each satellite. Sets of satellites following orbits with particular characteristics can be defined using SGP4 equations to program different scenarios in the Orbit Generator (e.g., a simulation can include a predefined number of satellites in low, medium, or geostationary orbits).  
The \"IoST Configuration files\" provided by the Base Experiment module are the input of the Orbit Generator. The module downloads all TLEs that comply with the simulation constraints. Afterward, a selection of satellites participating in the IoST network is made following predefined criteria or randomly. The outputs of this module are I = G \(\times\) N \(\times\) M files appropriately formatted to be used in the IoST Simulator module.
## 3 Results and Discussion
### NLCs Preparation and Characterization
Then, the SLM-NLCs were freeze-dried in the presence of a cryoprotectant, both to prolong its physical and chemical stability over time, and to reduce microbial contamination. From the literature, different cryoprotectants, are reported, such as glucose, maltose, trehalose, sucrose, mannitol and lactose, from 5 to 20% \(w/v\)[38]. Glucose (10% \(w/v\)) and mannitol (10% \(w/v\)) were tested for the NLCs (Table 3). A sample of SLM-NLCs was lyophilized without cryoprotectant, while two other samples in the presence of glucose and mannitol, respectively. The dispersions were physically characterized with DLS after resuspension with water. The results (Table 3) highlighted a significant increase in size and PDI values in the absence of cryoprotectant. On the contrary, the lyophilization with glucose allowed preserving the stability of the system providing nanoparticles with almost identical dimensions and homogeneity those detected before the freeze-drying process. Finally, the nanoparticles lyophilized with mannitol revealed a certain increase in terms of average diameter and inhomogeneity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **Particle Size** \\ **(nm)** \\ \end{tabular} & \begin{tabular}{c} **Polydispersity Index** \\ **(PDI)** \\ \end{tabular} & \begin{tabular}{c} \(\zeta\)**-Potential** \\ **(mV)** \\ \end{tabular} & \
\begin{tabular}{c} **EE (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Recovery (\%)** \\ \end{tabular} \\ \hline NLCs & \(194.5\pm 3.7\) & \(0.24\pm 0.02\) & \(-36.0\pm 0.9\) & - & - \\ SLM-NLCs & \(213.6\pm 16.0\) & \(0.17\pm 0.04\) & \(-31.6\pm 0.5\) & \(92.4\pm 4.1\) & \(94.8\pm 1.8\) \\ FITC-NLCs & \(215.0\pm 0.3\) & \(0.24\pm 0.01\) & \(-37.4\pm 0.9\) & \(95.7\pm 1.2\) & \(98.5\pm 0.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physical and chemical properties of empty NLCs, SLM-NLCs and Fluorescein isothicarate-NLCs (FITC-NLCs). Results are expressed as means \(\pm\) standard deviation, \(n=3\). Figure 1: Transmission Electron Microscope (TEM) images of Silymarin (SLM)-NLCs. The results confirm the advantage of cryoprotectants in preventing the aggregation of nanoparticles during the lyophilization process. In particular, glucose allows obtaining a freeze-dried product easily re-dispersible, soft and voluminous. In fact, the cryoprotectants interact with the polar portions of surfactants, forming a kind of protective coating. They also reduce the crystallization processes, favoring the amorphous state of the frozen sample [38].