esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mineralogical Results of Laboratory and Pilot Scale Experiments
#### 3.2.1 Laboratory Scale
The addition of quartz sand (sample AC2, Figure 3c) significantly changed the mineral phase proportions by increasing the calcium silicate and spinel formation and repressing the wuestite crystallization. In addition, the mineralogical composition (determined by XRD and, if possible, quantitative EMP analyses (Figure S2 and Tables S2-S21)) changed, because pyroxene formed instead of melilite. According to the FactSageTM calculations, the formation of pyroxene indicates that slightly more SiO\({}_{2}\) was added during the experiments than intended. Quantitative EMP analyses on spinel phases revealed that instead of a Cr/Fe spinel with an Al-rim (original sample, Figure 3a), Fe-rich, or zoned Cr-rich spinels with a Fe-rim were formed (sample AC2, Figure 3c). This is in accordance with [PERSON] et al. (1997) [30], showing that the high FeO concentrations available in the melt were consumed by spinel, olivine, and pyroxene instead of wuestite, resulting in Fe-rich and Mg-poor rims in spinel and Fe-poor and Mg-rich neighboring mineral phases (e.g., olivine) due to intergrain ion exchange, which is induced by temperature changes. Similar to the remelted slag samples, V was only detected in some spinel phases, but with concentrations from 0.06 mass-% up to 0.1 mass-%. The incorporation of V in a lower amount of spinel phases compared to the original samples (Table S2-S21) was consistent with the increased spinel formation, as the V\({}_{2}\)O\({}_{3}\) concentrations in the melt were identical to the original sample A. EMP analyses performed on calcium silicate phases showed that, aside from pyroxene, two types of olivine, rich in Ca or Mg, were formed (Figure 3c). Chromium and V were detected in some measured grains of the Mg-rich olivine, but neither in the Ca-rich olivine (except for sample AC1) nor in pyroxene.
## 2 Method Introduction and Data Processing
### Standardized Processing
#### 2.3.1 Z-Score Standardization  
Z-score standardization can convert dimensional data to dimensionless data. The calculation formulas of this method are shown in Formulas (1) and (2).  
\[\sigma=\sqrt{\frac{1}{\rm m}\sum_{j=1}^{\rm m}\left(\rm x_{j}-\mu\right)^{2}} \tag{1}\]  
\[\rm x_{j}^{\prime}=\frac{\rm x_{j}-\mu}{\sigma} \tag{2}\]  
In these formulas, \(\sigma\) represents the standard deviation of the sample data, \(\mu\) represents the average value of the sample data, \(\rm x_{j}\) for \(\rm j=1,2,\ldots,\rm m\) represents the \(\rm j\)-th sample data, \(\rm x_{j}^{\prime}\) represents the new data of the \(\rm j\)-th sample data after normalization, \(\rm j=1,2,\ldots,\rm m\), and \(\rm m\) represents the amount of sample data.
## 5 Discussion
### Oil and Gas Accumulation Process  
The HZ-A structure's sufficient oil and gas sources, high-quality reservoirs, composite superimposed caprocks, large-scale traps, and efficient transport systems, plus the effective configuration and coupling of these factors during each major oil and gas charging period, laid a solid foundation for the formation of oil and gas reservoirs. During the major oil and gas charging periods, the oil- and gas resources could first migrate vertically through the oil source faults to areas of low potential energy in the target strata in the HZ-A structure pre-Paleogene buried hills and the Paleogene Wenchang and Enping Formations and then migrate laterally through fractures and sedimentary sand bodies to the higher parts of the traps to form the reservoirs. Overall, the hydrocarbon accumulation process experienced three stages, as described below [20; 21].  
From approximately 13.8-10 Ma, the first stage was mainly the charging of low-maturity crude oil, forming small-scale ancient oil reservoirs via the localized oil-displacing-water mode. During this period, the crude oil generated in the low-temperature evolution stage of the source rocks gradually migrated to the double Paleogene field through three-dimensional high-efficiency mesh-like transport systems composed of faults, fractures, sand bodies, and unconformity surfaces, forming small-scale ancient oil reservoirs (Figure 16A).  
From approximately 10-5.3 Ma, the second stage was mainly the charging of mature crude oil, forming large-scale ancient oil reservoirs via the large-scale oil-displacing-water mode. During this period, mature crude oil generated by the source rocks migrated and accumulated at a large scale in the double Paleogene field. As crude oil charging continued, the previously formed paleo-reservoir continued to expand and transformed into large-scale ancient oil reservoirs (Figure 16B).  
Figure 15: The transportation conditions of the HZ-A structure pre-Paleogene buried hills and the Paleogene.  
From approximately 5.3-0 Ma, the third stage was mainly the charging of high-maturity oil and gas in a sequence of oil first and gas later, forming the current pattern of oil overlying gas via a localized gas-displacing-oil mode. During this period, the source rocks began to generate gas on a large scale. Under the influence of the phase-controlled miscibility mechanism, a large amount of natural gas entered the double Paleogene field following the mature crude oil in a progressive manner, displacing the oil in the ancient reservoirs successively from bottom to top in the pre-Paleogene buried hills and the Paleogene Wenchang Formation and Enping Formation to form natural gas reservoirs. Eventually, the charging stopped in the Paleogene Enping Formation due to insufficient gas supply, resulting in the current pattern of oil overlying gas (Figure 16C).
# Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform
## 8 Conclusions and Perspectives
(iii) On the other hand, adaptive control schemes and Artificial Neural Network (ANN) are also eligible to make and predict the synchronization of this time-delayed chaotic system, particularly in secure image encryption/decryption applications. Conceptualization, M.H.A., G.A.A.-S. and F.R.T. ; methodology, M.H.A., G.A.A.-S. and F.R.T. ; software, M.H.A. and F.R.T. ; validation, M.H.A., G.A.A.-S., F.R.T., S.S.J., K.D. and J.A. ; formal analysis, F.R.T., G.A.A.-S. and M.H.A. ; investigation, G.A.A.-S., F.R.T. and M.H.A. ; resources, M.H.A., G.A.A.-S., F.R.T., A.M.A.A., H.A.A., J.A. and S.S.J. ; writing--original draft preparation, M.H.A., G.A.A.-S. and F.R.T. ; writing--review and editing, G.A.A.-S., M.H.A., A.M.A.A., J.A., K.D. and S.S.J. ; visualization, G.A.A.-S., M.H.A., F.R.T., S.S.J., K.D. and J.A. ; supervision, G.A.A.-S. and F.R.T. ; project administration, G.A.A.-S. and F.R.T. ; funding acquisition, S.S.J., K.D. and J.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. One of the authors, [PERSON], extends his gratitude to the Deanship of Scientific Research at King Khalid University for funding this work through a research groups program under grant number R. G. P. 2/86/43. The authors declare no conflict of interest.
## 3 Results and Discussion
### Deposit Conductivity
However, it is noteworthy that the different chemical composition determines a different mobility of ions for each macroregion (even with a similar mass of the deposit). Figure 10: Conductivity (\(\upmu\)s cm\({}^{-1}\)) measured on glass specimens in AEC for MR1, MR2, and MR3 at the DRH\({}_{\text{s}}\), DRH\({}_{\text{e}}\), 80% RH, 90% RH, CRH\({}_{\text{s}}\), and CRH\({}_{\text{e}}\). The histogram bars represent the median (50 th percentile); the error bars represent the 25 th and 75 th percentiles, respectively.
## 5 Accuracy Verification of Denoising Data
The first and second-order natural frequencies are shown in Figure 10.  
According to the natural frequencies, the first- and second-order modes of the gate show vibrations in the flow direction, with the gate panel divided symmetrically. The first-order natural frequency is 8.27 Hz, and the second-order natural frequency is 18.20 Hz. The numerical simulation frequencies match the peak power points in Figure 6, with an error of no more than 1%. This demonstrates that the method used in this paper effectively preserves the main information of the signal after denoising, as confirmed by numerical results in practical hydraulic engineering projects. Figure 10: Contour plots: (**a**) wet-mode-shape contour plot of the first-order; (**b**) wet-mode-shape contour plot of the second order. Figure 9: Finite element model of gate-structure coupling.
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 4 Results and Discussion
### CasaNova District in Bolzano
#### 4.5.4 Energy Concept  
Before starting the architectural urban design, the energy plan of the neighborhood was developed. It set the compliance with the highest CasaClima certification energy standard (i.e., heating index of 30 to 50 kWh/m\({}^{2}\)y depending on the surface/volume ratio of each building) [62]. The energy concept included passive strategies of bioclimatic planning approach and active systems to produce electricity and DHW (Figure 15).  
The compact shape of the buildings aims to maximize solar exposure and reduce overshadowing effects by placing the higher buildings in the north part of each block with sloped roofs in a unique direction. The PV and ST systems installed on most of the buildings cover almost completely the DHW need for the neighborhood. Space heating during winter and the pre-heating of the DHW is guaranteed by geothermal heat pumps and by the district heating network, which are connected to a cogeneration plant. Space cooling in summer is provided by a controlled ventilation system where the air is pre-cooled by the geothermal pumps. These solutions allow a reduction of 65% of the energy consumption compared to a traditionally built neighborhood.
## 4 Discussion
### Environment Characteristics and Their Impact on Divergence
#### 4.1.1 Low-Divergence Environments: Structured and Convergent  
Environments that foster low divergence and stable performance, such as Freeway, Pong, and Enduro, often share characteristics like a clear, singular objective and a functionally narrow state space that guides agents toward a single dominant strategy.  
* In Freeway, the goal is simple and monotonic: move up. There are no complex subtasks or branching strategic paths. This structure naturally channels all agents toward the same optimal behavior, leading to highly overlapping observation spaces and consistent performance.
* Pong is a purely reactive game where the optimal policy is to mirror the ball's vertical movement. The simplicity and deterministic nature of this strategy mean there is little room for meaningful strategic variation to emerge.
* Enduro, while more complex visually, is also driven by a primary objective of continuous forward progress and overtaking. The core gameplay loop does not contain significant strategic \"bottlenecks\" that could send agents down wildly different learning paths.  
In these games, the path to high rewards is straightforward, causing all agent trajectories and policies to converge. For real-world problems with similar characteristics (e.g., simple process optimization), we can expect DRL training to be relatively stable and reproducible.
# SSDLiteX: Enhancing SSDLite for Small Object Detection
## 4 Experimental Results
### Trade-Off with Complexity  
Table 6 compares the complexity of the configurations. The weight amount of C1 is much smaller than that of C0. This seems to be due to the decrease in the number of additional layers by utilizing more data from the base CNN. The configuration C2 has slightly more weights than C1 because of the channel number increase at the last auxiliary layer. The weight amount increase at C4 is due to the addition of the fc6 layer. The configurations C2 to C5 have more weights than C1 but still less weights than C0. All of the proposed configurations show a lesser weight amount and higher accuracy than SSDLite. If we compare two cases, SSDLite with the original MobileNet V2 and SSDLiteX C5 with the modified MobileNet V2, the accuracy is increased by 8.5%, with a weight amount decrease by 3%. Figure 5 illustrates the two ablation studies of the accuracy and the weight amount. It also compares the combination of MobileNet V3 and SSDLite [27], which has more weights and similar accuracy.  
The amount of computation and the number of MAC operations show a slightly different aspect as shown at the third and fifth column. Basically, 1/8 scale layers have a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**More 1/8 Layers**} \\  & **Weight** & **Computation** & **Weight** & **Computation** \\ \hline C0 & 4.28 M & 0.79 G & 4.28 M & 1.02 G \\ C1 & 3.80 M & 1.17 G & 3.87 M & 1.28 G \\ C2 & 3.85 M & 1.17 G & 3.92 M & 1.28 G \\ C3 & 3.85 M & 1.17 G & 3.92 M & 1.28 G \\ C4 & 4.01 M & 1.19 G & 4.08 M & 1.30 G \\ C5 & 4.09 M & 1.18 G & 4.15 M & 1.29 G \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of complexity.  
Figure 5: Number of weights and MS COCO AP for the configurations.  
larger horizontal and vertical size than 1/16 scale layers, so they require more calculations. Therefore, C1, which extracts and uses 1/8 scale data from the base CNN, increases the amount of computations compared to C0. With the same reason, the modified MobileNet V2 with more 1/8 scale layers requires more computations. C4, in which the fc6 layer was additionally placed, also increases the amount of computations compared to the others. As the amount of computation increases, however, the performance of object detection also improves as shown in Table 4.  
The proposed SSDLiteX configurations improve the detection capability with a lesser number of weights and an increased amount of computation. In an environment with a CNN accelerator, the convolutional layer processing is bound by the external DRAM communication rather than the computation capacity [28]. In such environments, the small weight amount of the proposed SSDLiteX will suggest a good trade-off between the complexity and the object detection performance.
## 1 Introduction
The cluster can be used to run jobs in parallel, taking advantage of its multiple compute core. It uses Open Multi-Processing (OpenMP) to run memory-intensive jobs and shared memory programs. The work provides two different implementations of the microwave-based image reconstruction algorithm. The sequential implementation is based on the general Python using NumPy array, while the parallel implementation is based on PySpark. The goal is to assess the effectiveness of using big data technologies for medical image reconstruction that involves multiple iterations on a massive amount of data points. The main contributions of this paper are as follows:  
* The parallelism of the microwave image reconstruction (MIR) algorithm is first identified. Then, the computational model of Apache Spark is studied, and the parallel version of the algorithm is presented. * A novel distributed approach, which adopts both data and algorithm in parallel, for efficient image reconstruction of head imaging is proposed. The imaging algorithm is optimized using PySpark on HPC clusters to improve the processing speed and make it real-time. The imaging system retrieves input data generated through radio frequency (RF) sensors and store in Eddie. An integrated imaging algorithm optimized through PySpark creates and saves images back to Eddie Storage. The proposed parallel microwave image reconstruction (PMIR) algorithm is implemented on the PySpark version of Apache Spark. Experimental results indicate that the proposed PMIR algorithm fully inherits the parallelism of the MIR algorithm, resulting in the fast reconstruction of images from RF sensor's data. The proposed parallel algorithm performs an average of 28.56 times faster than sequential Python implementation. The remaining part of the paper proceeds as follows: In Section 2, related literature on image processing on cloud and/or HPC is presented. Section 3 briefly describes image reconstruction, the microwave imaging reconstruction algorithm, Hadoop, and Spark, along with the Map-reduce programming model. In Section 4, data acquisition through RF sensors and the implementation of the PMIR algorithm are presented in detail. Section 5 describes the experimental results. Finally, conclusions and future work are presented in Section 6.
# Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
## 3 Modeling Method
### Damage Modeling  
Using linear elastic fracture mechanics, an edge crack is simulated by a non-dimensional compliance, \(\phi\). The non-dimensional compliance was used to model the local flexibility in the form of a torsional spring. It is defined as:  
\[\phi=\frac{EI}{K_{t}L} \tag{12}\]  
In Equation (12), \(K_{t}\) is the torsional stiffness of an opening crack under a bending load. Since the ratio of the beam length to the lateral length is large, it can be assumed that the contribution of mode-II to the total strain energy is negligible as compared to the contribution of mode-I. Thus, only the opening mode is considered for analysis. The local flexibility is captured by calculating the change in strain energy near the crack tip [30] and can expressed as:  
Figure 2: Configuration of a simply supported beam.  
\[\frac{1}{K_{t}}=\frac{6\pi h}{EI}\int\limits_{0}^{\alpha}\alpha q(\alpha)^{2}d\alpha \tag{13}\]  
In Equation (10), \(q(\alpha)\) is the non-dimensional geometrical factor, and \(\alpha\) is the relative crack size, defined as the ratio between the crack size, \(a\), and the beam height, \(h\). The non-dimensional factor for an opening mode crack is determined empirically [31] as follows:  
\[q(\alpha)=1.122-1.4\alpha+7.33\alpha^{2}-13.08\alpha^{3}+14.0\alpha^{4} \tag{14}\]  
It may be noted the FRF of an undamaged beam can be obtained by setting the non-dimensional compliance to zero.
## 6 Conclusions  
In this paper, we have derived the MGF and its approximated bound for SWIPT-enabled AF relaying systems operating without an external power supply at the relay. Based on these MGFs, closed-form BER expressions over quasi-static Rayleigh fading channels were obtained. Additionally, an asymptotic BER expression was derived to provide analytical insight into how the SNR relationship between the SR and RD links impacts the system performance. Using the derived asymptotic BER, we proposed a method to optimize the PS factor \(\rho\), thereby enhancing the performance of the SWIPT relay system. Furthermore, we introduced an analytical approach that interprets the SWIPT relay as a generalized AF relay system, leading to an alternative approximated BER expression. The accuracy of the proposed BER expressions and the optimization framework was validated through simulations. The analytical expressions not only match the simulation results closely but also serve as simplified performance bounds, explicitly revealing the role of the SR and RD link SNRs in determining system performance.  
Moreover, the analytical framework was successfully extended to the multiple SWIPT AF relaying system. For the multiple relay scenario, approximated performance metrics including BER, outage probability, and channel capacity were derived and verified. The results demonstrated that increasing the number of relays \(R\) improves the BER and outage performance, while the channel capacity tends to decrease with \(R\). The derived asymptotic and approximate expressions provide useful bounds and accurately capture the system behavior across a range of SNRs and network configurations. This extension confirms the generality and robustness of the proposed analytical methods for practical multi-relay SWIPT systems.  
Future work may extend this research to alternative SWIPT relaying protocols such as TS, multi-hop relaying, and WPCN configurations. Additionally, the development of energy-aware routing and power allocation algorithms for wireless-powered multi-hop networks remains a promising direction to further improve system efficiency and reliability.  
Conceptualization, K.K. and C.S.; methodology, C.S.; software, K.K.; validation, K.K. and C.S.; formal analysis, K.K.; investigation, K.K. and C.S.; resources, K.K.; data curation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, C.S.; visualization, K.K.; supervision, C.S.; project administration, K.K. and C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A2C2012558).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 2 Related Works
Despite that one of the five use cases mentioned in [45] is slicing and multi-tenancy, there is no description of how to implement and manage both mMTC and URLLC network slices and no definition for the format of packets carrying INT data in an SDN environment with P4 switches. In a word, these studies on SDNs for implementing 5G NS either have not fully considered the heterogeneous service quality requirements of mMTC and URLLC or have not completely defined the operation of the P4 switches.
# Molecular Mechanisms of Topography Sensing by Osteoblasts: An Update
## 2 Influence of Topography on Cells
### Scale Effect  
All scales of topography are capable of inducing cell alterations, such as cell morphology, adhesion, migration, differentiation and gene expression [26; 58]. However, different scales can influence the osteogenic behavior of cells in different ways, as a consequence of the cellular interaction with the surrounding environment. Nanoscale topographies mostly regulate cell behavior through spatial restrictions on the place and size of cell-substrate adhesion complexes [59]. On the other hand, cell-substrate interaction with micrometric scale topographies generate effects by modifying the entire cellular morphology [60; 61; 62].  
Regarding the nanoscale, the literature suggests that this type of surface can modulate cell adhesion to adsorbed proteins through integrin linkage, and consequently, induce intracellular expression of specific genes [50]. Nanotopography in glass wafers has been shown to induce an increase in differentiation of hMSCs, as demonstrated by [PERSON] et al. [63]. Similarly, bioinspired nanostructures on titanium showed an increase in the osteogenic differentiation of hMSCs, as well as a bactericidal effect in vitro, proving their multibio-functional properties and their potential as next-generation biomaterials for orthopedic implant [37]. Implants with nanogrooves also induce better osseointegration in rats, with an increase in trabecular and cortical bone formation in the bone face of the implant [44].  
Microtopographies usually present cell-scale geometrical pattern and can induce cell deformation due to cytoskeletal remodeling, with cells adapting to substrate shape [64]. In a mouse calvaria osteoblastic cell line (MC3T3-E1) cultivated on a surface with micrometric pillars, there was an increase in osteogenic differentiation and cell proliferation compared to cells grown on a flat surface [40]. Similarly, [PERSON] et al. observed that the osteoblasts preferentially adhere to peaks on sand-blasted/acid-etched (SLA) titanium discs, whereas the concave regions presented gaps, demonstrating the importance of new studies that deal with the understanding of the cellular events involved in these cell-surface interactions [65]. Microscale is also known to improve the osseointegration of implants [38; 48].