esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### NMR Spectrum
#### 3.1.2 Natural Mudstone  
As seen from Figure 7, with the increase in the number of freeze-thaw cycles, the \(T_{2}\) patterns of the natural mudstone samples and the confined mudstone samples were similar in shape. They remained single-peaked, with the peak signals corresponding to a significant increase in the chirp time, from 0.345 ms (initial state) to 0.455 ms (1 freeze-thaw cycle), 0.912 ms (5 freeze-thaw cycles), 1.203 ms (10 freeze-thaw cycles), and 1.29 ms (15 freeze-thaw cycles). The peak signals and corresponding relaxation times were quadratically related to the number of freeze-thaw cycles. Under the same number of cycles, the peak NMR signal intensity increased significantly from 1112 (initial state) to 1367 (1 freeze-thaw cycle), 2305 (5 freeze-thaw cycles), 3030 (10 freeze-thaw cycles), and 3191 (15 freeze-thaw cycles), with an increase of 22.93%, 107.28%, 172.48%, and 186.96%, respectively. After freeze-thaw cycles of the natural mudstone samples, the intensity of the NMR signals increased significantly, the new pores became more numerous, and the average pore size increased.  
Figure 6: Peak areas of \(T_{2}\) spectra of sealed mudstone with different numbers of freeze–thaw cycles.  
Figure 7: \(T_{2}\) spectra of natural mudstone under different freeze–thaw cycles.  
The peaks of the \(T_{2}\) spectra were counted for different test cycles to obtain Figure 8. The \(T_{2}\) spectral area of the natural mudstone samples increased with the number of freeze-thaw cycles. The average increase in each freeze-thaw cycle was different, and in general, the increase gradually decreased. According to the test program, the natural mudstone samples were divided into four stages with varying numbers of freeze-thaw cycles, namely, 0-1 freeze-thaw cycle (stage I), 2-5 freeze-thaw cycles (stage II), 6-10 freeze-thaw cycles (stage III), and 11-15 freeze-thaw cycles (stage IV), and the average increase in \(T_{2}\) spectral area of the natural mudstone samples with one freeze-thaw cycle in the four stages was 6266, 4313, 5025, and 525, respectively. For 4313, 5025, and 572, the increased value of porosity in the natural mudstone samples was the largest in the first freeze-thaw cycle; the overall trend gradually decreased; after ten freeze-thaw cycles, the increase in porosity decreased significantly, which was different from the change rule of the porosity of the confined mudstone only under the action of freeze-thaw damage.
## 4 Seismic Response Characteristics of Layered Sites
### Results of Frequency Domain Analysis
#### 4.2.2 Fourier Spectrum Analysis  
Taking Model 2 as an example, two measurement points for each layer were selected and their Fourier spectra are shown in Figure 9b. Figure 9b shows that the predominant frequency of the horizontally layered site is mainly concentrated in the range of 40 Hz, and eight predominant frequencies (\(f_{1}\)-\(f_{8}\)) can be clearly found within 0-40 Hz (Table 3). The PFSA (peak Fourier spectrum amplitude) of \(f_{1}\)-\(f_{8}\) changes with elevation and is shown in Figure 14. Figures 9b and 14 show that in Layers 1 and 2 (hard and soft rock layers), the PFSA of different natural frequency bands of measuring points (B, C, D and F) increases to a certain extent with elevation. This indicates that soft and hard rock mass strata have little influence on the Fourier spectrum characteristics and that the spectral peak value mainly increases to a certain extent. Compared with the hard and soft rock strata, the PFSA of \(f_{1}\)-\(f_{5}\) at measuring points G and H in the third layer (gravel soil layer) increased to a certain extent; in particular, the PFSA of \(f_{6}\)-\(f_{8}\) was rapidly amplified from Layer 2 to 3. This indicates that overlying gravel soil has an obvious amplifying effect on the high-frequency spectrum amplitude, which is consistent with the results of modal analysis (Figure 13), and the high-order natural frequency mainly induces local deformation of the gravel soil stratum. In addition, Table 3 shows that the natural frequencies obtained by Fourier spectrum analysis and modal analysis are consistent, which indicates that the first  
Figure 13: First three vibration modes of the models: (**a**) Model 1, (**b**) Model 2, (**c**) Model 3. Modes 1-3 are shown from left to the right.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fourier Spectrum Analysis** & \multicolumn{3}{c}{**Modal Analysis**} \\ \hline
**Predominant Frequency** & **Value/Hz** & **Order** & **Natural Frequency/Hz** \\ \hline \(f_{1}\) & 7–8 & 1 & 7.05 \\ \(f_{2}\) & 14–15 & 2 & 14.56 \\ \(f_{3}\) & 17–18 & 3 & 18.12 \\ \(f_{4}\) & 21–22 & 4 & 22.16 \\ \(f_{5}\) & 25–26 & 5 & 25.97 \\ \(f_{6}\) & 29–30 & 6 & 29.46 \\ \(f_{7}\) & 33–34 & 7 & 33.45 \\ \(f_{8}\) & 37–38 & 8 & 37.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The predominant frequency and modal analysis results of Model 2.  
eight natural frequencies of Model 2 can be determined based on Fourier spectrum analysis and modal analysis.
# UAV Support for Mission Critical Services
## 1 Introduction
According to our knowledge, this is the first approach attempting to use virtualization combined with UAVs in applications for MCS. The UAVs are already used in rescue actions; however, there is no uniform framework for their usage for such applications, which raises many operational problems. As the proposed solution is complex, the paper can be seen as paving the road in showing possibilities and challenges but not as a complete solution. The structure of the paper is following. To better understand the value of the proposed concept, in Section 2 of the paper, we describe the features of the existing MCS solutions, both narrowband and broadband. Section 3 provides an overview of the UAV characteristics, classification and approaches to using UAVs for enhancing communication and service platforms. Section 4 describes different scenarios of the usage of UAVs in MCS. Section 5 describes the VMC concept, developed by the paper's authors, using virtualization technologies up to the end-user terminals to provide MCS functionalities. The role of UAVs in this solution is also described. Finally, Section 6 concludes the paper by proposing future research directions.
###### Abstract  
The spread of the 5G technology in the telecom power applications increased the need to supply high power density with higher efficiency and higher power factor. Thus, in this paper, the performance of the different power factor correction (PFC) topologies implemented to work with high power density telecom power applications are investigated. Two topologies, namely the conventional and the bridge interleaved continues-current-conduction mode (CCM) PFC boost converters are designed. Selection methodology of the switching elements, the manufacturing of the boost inductors, and the optimal design for the voltage and current control circuits based on the proposed small signal stability modeling are presented. The printed circuit board (PCB) for the two different PFC topologies with a power rating of 2 kW were designed. PSIM simulation and the experiments are used to show the supply current total harmonic distortions (THD), voltage ripples, power efficiency, and the power factor for the different topologies with different loading conditions.  
telecom power supply; power factor correction (PFC); small signal stability; conventional CCM PFC; interleaved CCM PFC; total harmonic distortion (THD); UCC28180 and UCC28070 IC controllers +
Applied
## 4 Results
### Experiment Environment and Configuration  
In this research, a server with specific hardware specifications was employed for conducting the experiments. The server was equipped with a 2.3 GHz 16-core Intel(R) Xeon(R) CPU E5-2650 v3 and 128 GB of memory. The operating system utilized was Ubuntu 22.04.2 LTS. Machine learning experiments were conducted using Python version 3.10.6 and Keras version 2.12 as the machine learning library. The presentation of experiment results was facilitated through Jupyter Notebook version 6.5.3. Additionally, this research used Truffle 5.11.5 for smart contracts and blockchain simulators, and a connection to Truffle was established using the web3 library from Python. This research used IPFS Kubo 0.23.0 to create a private IPFS server. Detailed configurations of the DNN hyperparameters used in this research can be found in Tables 3 and 4.  
IPFS enables data to be stored across a network of nodes, ensuring redundancy and accessibility. Smart contracts, running on blockchain platforms like Ethereum, can reference IPFS file addresses, making it feasible to store large data or sensitive information off-chain while retaining a reference to it on the blockchain. The smart contract design of this research can be seen in Algorithm 2.  
This approach mitigates the limitations of blockchains in terms of data storage and costs. Smart contracts can trigger actions based on the content stored on IPFS, automating processes like content delivery, identity verification, or secure data exchange. This combination provides a decentralized and tamper-resistant solution for applications that require secure, auditable, and efficient handling of data while reducing the burden on the blockchain itself.  
```
foreachiteration in iterationsdo forifrom 1 to 10 do  model_path \(\leftarrow\)./model_federated.h5;  cid \(\leftarrow\) ipfs_api.publish(model_path);  tx_hash \(\leftarrow\) contract.functions.sendHash(cid).transact(\(\{from:\) web3.eth.accounts[0]\});  tx_receipt \(\leftarrow\) web3.eth.wait_for_transaction_receipt(tx_hash);  print(Sent transaction \(+(i+1)+/+(\text{iterations}*10)+\) with hash: \(+\) tx_hash.hex());
```  
**Algorithm 2**Transaction in FL-DNN based on DLT
# 3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
## 2 Related Works
### IR-UWB Based 3D Multi-Hop Self Localization with Bounding-Box and Mobile Tracking Scheme
and utilizing the measurements of the magnetometer, mass-flow sensor and barometer, the predicted INS heading and velocity states are updated in the MSL EKF [14]. The magnetometer data are utilized to compute the magnetometer heading and to build the heading update model in the EKF. Using the mass-flow sensor data, the velocity update in the EKF is processed. For quality control (QC) of the velocity updates, the barometer data are computed to detect height changes. After the heading and velocity updates, the MSL EKF provides the mobile nodes' position, velocity and heading solution. For more details of the MSL EKF-based mobile tracking process, refer to Refs. [14,18,19]. Finally, using the position solutions of the MSL EKF, mobile node \(M\)'s B-box state vector \(\mathbf{b}_{M}\) is updated at every mobile tracking period of interval \(T_{M}\) as follows:  
\[ \begin{split}\mathbf{b}_{M}=[x_{M}^{lub}+x_{m}-e_{p}\quad x_{M}^{ upb}+x_{m}+e_{p}\quad y_{M}^{lub}+y_{m}-e_{p}\quad y_{M}^{upb}+y_{m}+e_{p}\\ z_{M}^{lubb}+z_{m}-e_{p}\quad z_{M}^{upb}+z_{m}+e_{p}]^{T}\end{split}  \tag{2}\]  
Here, \(x_{M}^{lub}\), \(y_{M}^{lub}\), \(z_{M}^{lub}\) are lower bounds of \(\mathbf{b}_{M}\) and \(x_{M}^{upb}\), \(y_{M}^{upb}\), \(z_{M}^{upb}\) are upper bounds of \(\mathbf{b}_{M}\). \(x_{m}\), \(y_{m}\) and \(z_{m}\) are the estimated position displacements between the current interval and the previous interval computed from the MSL EKF position solution. \(e_{p}\) is the maximum statistical position error during one mobile tracking period, \(T_{M}\). In all simulations, \(e_{p}\) is set proportional to 30 cm for 1 s of \(T_{M}\), according to the experimental statistics of position errors presented in [14]. The updated B-box states of mobile nodes improve static nodes' positioning accuracy when UWB ranging messages exchange, further enhancing the network's overall estimated location resolution.
## 3 Methodology and Overall Framework
### Adaptive Process Scheduling
#### 3.4.1 Process Scheduling using a Genetic Algorithm
As the error (i.e., the deviation from a nominal power value) becomes larger, the goal is then to select a lower power value to maximize the fitness value. For instance, if the current requested speed causes much more power consumption than in a nominal case, a chromosome with lower power requirement is selected, which also means a lower speed value. In other words, the system autonomously sacrifices from the operational speed for the sake of protecting the system. As it is clear from Equations (2) and (3), the selection of \(\alpha\) and \(\beta\) values depends on a user's choice of an operation with more risks or a less risky behavior with a slower operational speed. An intermediate crossover is used for the genetic algorithm process. It generates values for an offspring around and between the variable values of its parents. As for the mutation, a random number from a normal distribution is added to the genes using the following formula:  
\[Var_{o}=Var_{i}+\sigma\cdot N(0,1), \tag{5}\]  
where \(\sigma\) is the standard deviation of a normal distribution, which is defined as 0.2 in the current settings. We also use a tournament selection that several candidates compete with each other and the best have the right to breed. In our case, four candidates compete with each other during the selection process. This method guarantees that the worst individual never gets a chance to reproduce. Last but not least, an elitism mechanism is implemented in the algorithm to keep the fittest individual in the next generation.
## 3 Results and Discussions
### XRD Analysis  
The variation in XRD patterns along the cover depth for uncracked samples with minor and moderate corrosion is similar. Therefore, only the XRD patterns of Sample B6 are shown. For the same reason, only the XRD patterns of a cracked sample with severe corrosion, Sample C1, are given.  
The XRD patterns of the samples at different depths are shown in Figure 11. The results indicate that the main phase compositions included quartz, CaCO\({}_{3}\), scolecite, dolomite, ettringite, albite, Ca(OH)\({}_{2}\), CSH gel, C\({}_{3}\)S, and C\({}_{2}\)S.  
The XRD patterns of the uncracked Sample B6 at different depths are shown in Figure 11a. No diffraction peaks are shown in Ca(OH)\({}_{2}\) at depths of 3.5 mm and 10.5 mm. The corresponding pH values are 8.86 and 9.66. This indicates that carbon dioxide diffusion into the concrete had been fully reacted with Ca(OH)\({}_{2}\) to form CaCO\({}_{3}\) in the depth of 0-10.5 mm. It can be found that the full  
Figure 11: XRD patterns of the cover concrete samples at different depths. (**a**) Sample B6; (**b**) Sample C1. 0—quartz; 1—CaCO\({}_{3}\); 2—scolecite; 3—dolomite; 4—ettringite; 5—albite; 6—Ca(OH)\({}_{2}\); 7—CSH gel; 8—C\({}_{3}\)S; 9—C\({}_{2}\)S.  
Figure 10: Variation in carbonation depth with maximum crack width.  
carbonation depth determined by the pH was almost equal to the full carbonation depth (10.95 mm) tested with phenolphthalein. The in-situ test results corresponded well with findings obtained by pH tests and XRD analysis.  
Weakened Ca(OH)\({}_{2}\) peaks began to appear at a depth of 17.5 mm, and the corresponding pH was 11.6. When the depth was greater than 17.5 mm, the diffraction peaks of Ca(OH)\({}_{2}\) became higher, and the pH increased to 12.0 at a depth of 24.5 mm. The carbonation depth 10.95 mm of Sample B6 was much smaller than the cover depth 38.49 mm, as shown in Table 1. However, the in-situ test results indicate that the rebar exhibited minor corrosion, as shown in Figure 5b. This indicates that the rebars had begun to corrode when the carbonation depth failed to reach the surface of the rebar; moreover, a semi-carbonated zone existed between the fully carbonated zone and the rebar.  
The XRD patterns of the cracked Sample C1 at different depths are shown in Figure 11b. The diffraction peaks of Ca(OH)\({}_{2}\) are no longer visible at different depths. The pH at depths of 3.5 mm, 10.5 mm, 17.5 mm, and 24.5 mm are 9.41, 8.94, 9.33, and 9.50, respectively. This indicates that the full carbonation depth had exceeded the cover depth. Thus, the fully carbonated zone determined by the pH was consistent with the in-situ inspection results. The carbonation depth (35.22 mm) of Sample C1 is greater than the cover depth (29.35 mm), as shown in Table 1. The in-situ test results indicate that the rebar exhibits a severe corrosion, as shown in Figure 5d. It could be found that when pH is in a range of 9.0-9.5, the concrete is fully carbonated.
# SoftRec: Multi-Relationship Fused Software Developer Recommendation
## 4 Experiment and Discussion
### Experimental Results and Discussions  
#### 4.2.1 Overall Performance Comparison (RQ1)  
Table 3 compares the overall performance of the five approaches. We have the following observations. First, IR+CN achieves poor performance on both datasets. This indicates that considering only the explicit social relationships does not suffice to capture the potential interactions between developers and tasks. Compared to IR+CN, PR-CF generally achieves better improvements in most cases. This indicates the importance of considering the latent factor similarity between tasks. The reason we chose PR-CF and IR+CN as the comparison algorithm is that both PR-CF and IR+CN are typical developer recommendation approaches and similar to our approach. Where PR-CF utilizes the implicit similarity relationship between tasks to improve the recommendation accuracy, while IR+CN utilizes the social relationship between developers to improve the recommendation accuracy. Second, compared to DMF, NFM achieves a better accuracy in most cases. The reason might be that NFM combines the linearity of a factorization machine in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions, which is more expressive than DMF. Third, among all approaches in comparison, SoftRec achieves the highest accuracy across all different cases. For example, for the GitHub dataset, the precision, recall and ndcgobtained by SoftRec are 0.4673, 0.7633 and 0.4727, respectively. It outperforms other state-of-the-art approaches by an average of 23.26%, 6.69% and 34.56% in precision, recall and ndcg, respectively. For the GitLab dataset, SoftRec's precision, recall and ndcg are 0.4929, 0.7701 and 0.5109, respectively, outperforming other state-of-the-art approache by an average of 29.75%, 5.33% and 23.56%, respectively. The reasons might be that (1) SoftRec can fully explore the explicit and implicit multi-relationships which is helpful to improve recommendation accuracy, unlike NMF and DMF which employ the explicit interactions only; (2) SoftRec can project the feature vectors \(U_{i}\) and \(V_{j}\) into a non-linear space upon the architecture of deep neural network, which helps capture the non-linear and complex feature of real-world data, unlike PR-CF and IR+CN model feature interactions into a linear space only.
## 3 Finite Element Analysis
### Analytical Results
#### 3.2.2 Crack Tip Opening and Sliding Displacements  
The crack tip displacements are important since they could influence the crack growth rate and path direction. Therefore, the FEA results were used to obtain the crack tip opening displacement (CTOD), the crack tip sliding displacement (CTSD), and the residual crack tip opening displacement, which corresponded to the CTOD when the 50 th loading was ended, of RP and RF. Figure 16 compares the resulting ranges of CTOD (\(\Delta\)CTOD), CTSD (\(\Delta\)CTSD), and residual CTOD of the two different materials (such as the comparison of ARP3 and ARF1). All these values were smaller for the RF case.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 5 Implementation
### Schema Design
#### 5.2.1 Basic Indexing  
For the PostgreSQL and JanusGraph database setup classical R-trees [25] are used to speed up multidimensional searches. In particular, we used _PostGIS_ ([[https://postgis.net/](https://postgis.net/)]([https://postgis.net/](https://postgis.net/))) as a spatial extension in the case of PostgreSQL and _ElasticSearch_ for our JanusGraph configuration. In contrast, for TigerGraph data access was optimized using the _geograph_, a grid-based indexing technique. Here two-dimensional coordinates are mapped to a given grid ID, where the grid is represented as graph vertices; any vertex associated to some ID are linked by an edge. Such a grid can be of any size but setting this size may be dependent on the distribution of points in the dataset, which we solved by manually configuring best matching grid sizes.
## 2 Literature Review and Hypotheses
### Information Technology Capabilities, Entrepreneurial Orientation, and Firm Performance
Existing literature has focused on the relationship of EO with other constructs that imply technological capabilities commonly present in developed economies, such as big data analytics capabilities [9]. A knowledge gap is identified concerning the role of ITC in the EO-FP relationship in emerging economies [20]. In the literature, ITCs have also been recognized as a key mediating factor in various organizational dynamics. It has been shown that ITC mediates the relationship between dynamic capabilities and organizational agility [50] as well as between alliance management capabilities and business resilience [51]. In the technological domain, [PERSON] and [PERSON] [16] demonstrated that the adoption of ITC acts as a mediator between artificial intelligence and competitive advantage. These findings underscore the strategic importance of ITC as an element that amplifies other organizational capabilities, significantly contributing to business success. However, this topic remains relatively unexplored, indicating a promising avenue for research focused on examining the mediating role of ITC. We propose that EO fosters the development of ITC, and in turn, ITC enables a company to analyze and synthesize various types of information, as well as identify and exploit opportunities in their competitive environments. In essence, ITCs act as amplifiers of the EO-FP relationship. Consequently, the following hypothesis is formulated:  
**Hypothesis 4 (H4). **_ITC mediates the relationship between EO and FP._  
The proposed model and its hypotheses are shown in Figure 1.
# MIMO Over-the-Air Computation for Distributed Estimation
## 3 The Proposed MIMO-AirComp Framework
### Optimization  
In Equation (4), the transmit and receive beamforming attempts to reduce the distortion of the received vector with respect to the desired vector \(\mathbf{d}\) by compensating the fading MIMO channel with additive noise. Note that the receive beamforming is applied to both the aggregated received \(K\) symbol vectors and the noise. The mean square error (MSE) is commonly used as the performance metric to evaluate the distortion of the received vector compared to the desired one. Thus, the joint beamforming optimization problem of the MIMO-AirComp has the objective of minimizing the MSE, subject to the constraints in the transmit and receive beamforming matrix; namely,  
\[\min_{\mathbf{\mu},\mathbf{\
u}} \parallel\mathbf{d}-\hat{\mathbf{d}}\parallel_{2}^{2} \tag{8}\] \[\mathbf{\mu}\in\tilde{\mathbf{\mu}},\mathbf{\
u}\in\tilde{\mathbf{\
u}}, \tag{9}\]  
where \(\tilde{\mathbf{\mu}}\) and \(\tilde{\mathbf{\
u}}\) are the feasible sets of the transmit and receive beamforming matrices, respectively. Several recent works have considered the similar optimization problem of the transmit and receive beamforming gains [15; 17; 18; 19].