esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This study proposes an intelligent reflective surface low-rank matrix reconstruction fingerprint localization (IRS-RFL) algorithm, utilizing the angular-domain power expectation matrix (APEM) as the fingerprint data to leverage the high spatial resolution provided by MIMO technology. In addition, the low-rank matrix fitting (LMaFit) reconstruction algorithm was used to reconstruct the fingerprint database, which solved the problem of the large workload required to build a fingerprint database. Finally, the effectiveness of the proposed positioning scheme was verified through a simulation. The main contributions of this study are as follows:  
1. First, this study addresses the problem of a no-direct-line-of-sight path between the base station and the user to be located by constructing an intelligent reflective surface (IRS) 5G positioning system, which consists of a multi-antenna base station and multiple IRSs for signal reflection, and the signal from the user to be located is reflected by the IRSs and reaches the base station. Therefore, this system overcomes the problem that the localization process cannot be performed due to the lack of line-of-sight (LOS) paths. 2. Secondly, this study addresses the problem of an excessive workload in fingerprint database construction and uses the LMaFit algorithm for fingerprint database reconstruction. In the fingerprint database construction stage, the fingerprint data consist of APEM, and part of the fingerprint reference points are used to construct the local fingerprint database; then, the complete fingerprint database is reconstructed by its combination with the LMaFit algorithm. This method requires fewer fingerprint data to construct the complete fingerprint database using mathematical operations to reduce the workload of fingerprint database construction. 3. In addition, this study increases the gap between fingerprint data by using the maximum ratio combination method, which means that the fingerprint data of each fingerprint reference point are multiplied by their corresponding signal-to-noise ratio (SNR) as the weight, and this facilitates the matching of the data to be compared of the point to be located with the fingerprint data in the fingerprint database in the online matching stage. 4. Finally, the simulation results show that the proposed IRS-RFL algorithm achieves sub-meter localization accuracy in a large NLOS complex environment, which is a significant improvement over the traditional fingerprint localization algorithm, thus confirming the good localization effect of the algorithm.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 2 Evolution and Enablers of Industrial Internet of Things
### Industry 4.0-Building Blocks
#### 2.5.6 Cloud Computing  
Cloud computing is centralized and complex technology that supports high speed, high performance, flexible resource use and dynamic allocation in a network. As IIoT application requirements are low latency, high speed and reliable communication, privacy and security, efficient allocation of resources and energy-efficient communication technology. Some limitations regarding use of cloud computing for IIoT applications are:  
* Confidential data and personal information of an industry should not be shared with outsiders.
* Security and privacy are in high demand by an industry from the cloud service provider.
* Data location on the basis of geographic follows rules and regulations. It also helps in securing the information.
* High load demands high-speed internet connectivity. This processing causes delays in communication.
* Memory and storage capacity may get exhausted because of many applications simultaneously accessing a single cloud server.
* Context awareness is required for speedy processes.
* Different standards cause problems in exchanging data, information, services, and applications among different clouds at different locations.
* Recovery and back-up update are required for industrial processing and decision making, cloud computing will cause delay.
## 2 Individual Methods
### Decomposition Technique  
The data collected in the actual field usually contain noise, and conventional methods cannot accurately extract the main features of the initial data, which has been studied by many previous scholars. [PERSON] established an Empirical Mode Decomposition (EMD) method to decompose the original signal into multiple Intrinsic Mode Functions (IMFs) [30], but the model and the EMD could not be well integrated. To address this issue, [PERSON] proposed an advanced adaptive noise Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) [31], it effectively solves the pattern mixing problem in EMD by adding finite times of adaptive white noise and efficiently eliminates the noise. Additionally, by adding  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Region** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** \\ \hline Mean value & 1.48 & 38.3 & 35,083 & 2.41 & 5.14 & 1.92 & 1695 \\ Max value & 3.36 & 59.4 & 145,877 & 6.77 & 7.69 & 2.97 & 4422 \\ Min value & 0.43 & 20.8 & 29 & 0.5 & 4.18 & 0.79 & 100 \\ Std value & 0.42 & 7.23 & 31,952 & 0.76 & 0.55 & 0.41 & 936 \\ Skewness & 0.51 & 0.54 & 0.77 & 1.29 & 2.42 & \(-\)0.57 & 1.14 \\ Kurtosis & 2.89 & 1.29 & 0.26 & 7.53 & 7.32 & 1.07 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive corrosion data statistics.  
a pair of opposite white noise sources in the EMD process, it overcomes the shortcomings of EEMD due to the poor additive noise completeness and CEEMD due to the increased computational volume and obtains better prediction performance.  
However, the sequences obtained by CEEMDAN decomposition usually contain high-frequency subsequences, which is another obstacle that restricts the prediction performance. In order to further improve the prediction performance, the two-layer data decomposition algorithm (CEEMDAN-VMD) method is proposed. Compared with other quadratic decomposition algorithms, VMD is able to overcome the endpoint effect and modal component aliasing problems in methods such as EMD and reduce the non-stationarity of time series with high complexity and strong non-linearity, decompose high-frequency subsequences into low-frequency subsequences, obtain smoother sequence components, fully weaken the non-stationarity of the original data, and successfully solve the problem of high-frequency sequences that are difficult to accurately predict [32].
## 3 Results and Discussion
### The Density of Carbon Blocks According to CB Addition  
The density of the prepared carbon blocks was measured using a solid specific gravity meter (bulk density) and a helium measurement method (real density), and the results are shown in Figure 5. The bulk density (Figure 5a) of the non-carbonized C_CB_0 was highest at 2.1 g/cm\({}^{3}\), and as the added CB amount increased, the bulk density decreased. It is considered that the bulk density of the non-carbonized block decreased with an increase of the amount of added CB since CB had a low bulk density of 0.125 to 0.145 g/cm\({}^{3}\). On the other hand, the carbonized C_CB_0 showed the lowest bulk density among the carbonized blocks (1.51 g/cm\({}^{3}\)). It is considered that the bulk density of the  
Figure 4: The carbonization yield of carbon blocks according to the added CB amount.  
Figure 3: TGA spectrum of non-carbonized samples at a heating rate of 10 \({}^{\circ}\)C/min in N\({}_{2}\).  
carbonized block increased as the amount of added CB increased due to the greater carbonization yield by CB. The bulk density of all samples decreased after carbonization regardless of CB addition.  
The real density of the prepared carbon blocks is shown in Figure 5b. According to the increase of the amount of added CB, the real density of the non-carbonized block decreased and the real density of the carbonized block increased. The real density of all samples increased after carbonization regardless of CB addition.
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 4 Heterogeneous WBAN Simulator
### Performance Evaluation Metrics  
To analyze the efficiency of the chosen methods, metrics for assessing the effectiveness of the usage of the available time-frequency resources, including the root mean square (RMS), mean useful rate, \(R_{MEAN}\), and PER, were determined.  
RMS is expressed as follows:  
\[RMS\ =\ \sqrt{\frac{\sum_{i=1}^{n}\left(G_{i}-R_{i}\right)^{2}}{n-1}} \tag{9}\]  
where \(n\) is the number of transmissions, \(G_{i}\) is the bitrate values representing the maximum use (bound) of available time-frequency resources, which is determined based on the knowledge of the radio channel parameters during the next transmission. The value of \(R_{i}\) is the obtained useful rate expressed as follows:  
\[R_{i}=\left\{\begin{array}{cl}R_{TRANS}\,&\ \ \ \textit{where}\ P_{REC}(X)\ =1\\ 0,&\ \ \ \textit{where}\ P_{REC}(X)\ =\ 0\end{array}\right. \tag{10}\]  
where \(R_{TRANS}\) is the transmission rate when the packet is received correctly.  
The mean useful rate, \(R_{MEAN}\), metric is expressed as:  
\[R_{MEAN}\ =\ \frac{\sum_{i=1}^{n}R_{i}}{n} \tag{11}\]  
where \(R_{i}\) is the obtained useful transmission rate in a single time slot and \(n\) is the number of slots.  
The PER is defined as:  
\[PER\ =\ \frac{\textit{NP}_{LOST}}{\textit{NP}_{TRS}} \tag{12}\]  
where \(\textit{NP}_{LOST}\) is the number of lost packets, while \(\textit{NP}_{TRS}\) the number of all transmitted packets.
# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
###### Abstract
The edge server aims to continuously provide stable connections to vehicles moving between regions at high speed. The proposed framework seeks to ensure that the edge server will keep track of the vehicle's movement and associate it with the correct/near-edge server by sharing the vehicle's vital information. The proposed framework is implemented using an OpenStack cloud server in front of three edge servers. The evaluation is conducted through three parameters which are throughput, CPU, and latency, for two different scenarios. The main objective of this research is to guarantee and enhance connectivity with an acceptable speed of communication. Aligning with the aforementioned objectives, the paper contributes to (1) designing collaborative edge servers based on regional cluster management; and (2) implement the sector of edge computing servers using OpenStack cloud. We shall begin by presenting the background and related works of edge computing in Section 2. The proposed framework is explained in Section 3. Section 4 shows the implementation part. Then, Section 5 presents the testing methodology. The results are discussed in Section 6. The conclusion is provided in Section 7.
## 5 Structure Equations with Frequency-Dependent Constitutive Equation
### Boundary Value Problem in Terms of \(\{\mathbf{u},p\}\)  
The boundary value problem in terms of \(\{\mathbf{u},p\}\) is written as follows. For all real \(\omega\) and for given \(\mathbf{G}(\omega)\), \(\mathbf{g}(\omega)\), \(p_{\text{given}}|_{\Gamma_{E}}(\omega)\), and \(Q(\omega)\), find \(\mathbf{u}(\omega)\) and \(p(\omega)\), such that  
\[-\omega^{2}\,\rho_{\mathrm{s}}\,\mathbf{u}-\mathrm{div}\,\sigma(\mathbf{u})= \mathbf{g}\quad\text{in}\quad\Omega_{S}\,, \tag{17}\]  
\[\sigma(\mathbf{u})\,\mathbf{n}^{S}=\mathbf{G}-p_{\text{given}}|_{\Gamma_{E}} \,\mathbf{n}^{S}-i\omega\,\mathbf{Z}_{\Gamma_{E}}(\omega)\{\mathbf{u}\cdot \mathbf{n}^{S}\}\,\mathbf{n}^{S}\quad\text{on}\quad\Gamma_{E}\,, \tag{18}\]  
\[\sigma(\mathbf{u})\,\mathbf{n}^{S}=\mathbf{G}+p\,\mathbf{n}\quad\text{on} \quad\Gamma\cup\Gamma_{Z}\,. \tag{19}\]  
\[-\frac{\omega^{2}}{\rho_{0}c_{0}^{2}}\,p-i\omega\frac{\tau}{\rho_{0}}\mathbf{ \
abla}^{2}p-\frac{1}{\rho_{0}}\,\mathbf{\
abla}^{2}p=\frac{1}{\rho_{0}}(i\omega Q -\tau c_{0}^{2}\mathbf{\
abla}^{2}Q)\quad\text{in}\quad\Omega\,. \tag{20}\]  
\[(1+i\omega\,\tau)\,\frac{\partial p}{\partial\mathbf{n}}=\omega^{2}\,\rho_{0} \,\mathbf{u}\cdot\mathbf{n}+\tau\,c_{0}^{2}\,\frac{\partial Q}{\partial \mathbf{n}}\quad\text{on}\quad\Gamma\,. \tag{21}\]  
\[(1+i\omega\,\tau)\,\frac{\partial p}{\partial\mathbf{n}}=\omega^{2}\,\rho_{0} \,\mathbf{u}\cdot\mathbf{n}-i\omega\rho_{0}\frac{p}{Z}+\tau\,c_{0}^{2}\,\frac{ \partial Q}{\partial\mathbf{n}}\quad\text{on}\quad\Gamma_{Z}\,. \tag{22}\]  
In the case of a free surface \(\Gamma_{0}\) in the internal acoustic cavity (see Section 4.3), the boundary condition defined by Equation (10) must be added.  
_Remarks_
* Equation (17) corresponds to the structure equation (see Equations (11)-(13)), in which \(\{\operatorname{div}\sigma(\mathbf{u})\}_{i}=\ \sigma_{ij,j}(\mathbf{u})\).
* Equations (18) and (19) are the boundary conditions for the structure (see Equations (15) and (16)).
* Equation (20) corresponds to the internal dissipative acoustic fluid equation (see Equation (5)).
* Equations (21) and (22) are the boundary conditions for the acoustic cavity (see Equations (7) and (9)).
* It is important to note that the external acoustic pressure field \(p_{E}\) has been eliminated as a function of \(\mathbf{u}\) using the acoustic impedance boundary operator \(\mathbf{Z}_{\Gamma_{E}}(\omega)\) (see Equation (4) of Section 3 and Appendix B), while the internal acoustic pressure field \(p\) is kept.
# Biomimetic Origami: Planar Single-Vertex Multi-Crease Mechanism Design and Optimization
## 5 Conclusions  
This research explored the design and optimization of single-vertex multi-crease origami (SVMCO) patterns, resulting in a deployable structure with improved efficiency.  
The analysis examined how the number of units in a single-ring array impacts the folding ratio, revealing that an array with six units achieves the optimal balance between compactness and structural efficiency. A mathematical model linked facet sector angles to folding performance, identifying that the optimal folding ratio occurs when the minimum facet sector angle is half the maximum.  
This model also accounted for panel thickness, inner diameter, and outer diameter to determine the best design for given parameters. Inspired by bionic principles, the study introduced a rib support system mimicking natural structures, enhancing stability and usability. A prototype with a 1.5 m radius validated this design, showcasing a theoretical folding ratio of 0.51 and an actual folding ratio of 0.54.  
The structure demonstrated high deployability, reduced support requirements, and a smooth reflective surface. These findings contribute to deployable structures with practical applications and inspire further research into bionic-inspired designs and advanced engineering implementations.  
Future work will focus on extending this planar structure to curved surfaces, which is critical for applications such as deployable antennas. Key challenges include the design of specialised hinges to accommodate curvature and the development of thick-plate origami techniques to address material thickness limitations. Advancing work in these areas will improve the structural integrity and functionality of deployable systems in aerospace and communications applications.  
Figure 18: Sequence of the prototype’s deployment process: (**a**) folded configuration, (**b**) 25% deployed configuration, (**c**) 50% deployed configuration, and (**d**) deployed configuration.  
**Author Contributions:** Conceptualization, Y.W. and B.H.; methodology, Y.W.; investigation, Y.W., B.H., M.H. and J.D.; writing--original draft preparation, Y.W.; writing--review and editing, Y.Z., B.H., M.H. and J.Y.; validation, J.D.; formal analysis, J.D.; supervision, Y.Z., B.H. and J.Y.; funding acquisition, Y.Z. and B.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China [Nos. 52105035 and 62203094], the Special Central Funds for Guiding Local Scientific and Technological Development [No. 236Z1801G], the Higher Education Youth Top Talent Project of Hebei Province of China [No. BJK2024042], the Natural Science Foundation of Hebei Province of China [Nos. E2021203109 and F2023501021] and the Graduate Student Innovation Capability Training and Support Project of Hebei Province [No. CXZZBS2024053].  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Validation of the Redesigned Axial-Flow Fan
### Experimental Validation  
To validate the performance of the optimized axial-flow fan, the flow and noise experiments are conducted using the manufactured prototype of the fan. Table 4 shows the experimental results, which include critical performance factors such as the volume flow rate, power consumption, and overall sound pressure level. The optimized fan shows an increase in the volume flow rate of 1%, demonstrating greater air transport capabilities. Notably, the power consumption is lowered by approximately 8%, which is significant. Power consumption is inversely proportional to fan efficiency. Therefore, a considerable reduction in power consumption indicates a significant gain in efficiency. Finally, it is discovered that the overall sound pressure level of the optimized fan dropped by 1.5 dBA, indicating that the noise propagated from the optimized fan was reduced. These experimental results confirm and validate the anticipated improvements from the prior three-dimensional numerical study. The consistency between the numerical and experimental results underlines the applicability of the currently proposed design strategy in improving fan performance aspects such as the aerodynamic and aeroacoustic performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Volume Flow Rate** & & \\  & **[CMM]** & **OSPL [dBA]** & **Power Consumption [W]** \\ \hline Original model & A & B & W \\ Optimized model & \(1.01\) A & B–1.5 & 0.92 W \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the volume flow fan, overall sound pressure level, and power consumption measured for the two fan models.  
Figure 15: Comparison of the acoustic performance between the original fan model and optimized fan model: power spectral density spectrum of the predicted sound pressure level.
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 3 Results
### Cloud Occurrence Frequency
These seasonal COF changes were generally shown on all products from SEVIRI, CPR, and CALIOP, although some systematic differences were noted in the frequency values. As mentioned in relation to Figure 1, SEVIRI detected significantly more clouds than CPR over the ocean regardless of the season, except for the constant cloud-free region. CALIOP estimated the cloudy region more widely near the ITCZ, whereas SEVIRI showed a lower frequency in this area. This tendency was more clearly observed over ocean than over land. In addition, SEVIRI caught more clouds where the satellite zenith angle of Meteosat-10 became larger, which was more evident in the winter hemisphere compared with the CPR COFs.
## 1 Introduction  
The activity of military personnel is associated with exposure to numerous harmful and stressful stimuli, such as noise, shock waves, physical injuries, and traumatic events. One of the most important problems in this group is the occurrence of traumatic brain injury (TBI), as experienced by 19% of veterans returning from Afghanistan and Iraq [1], which may further contribute to chronic traumatic encephalopathy. According to the report of the United States Armed Force Health Surveillance Center, there were 430,720 U.S. service members who sustained TBI between 2000 and the third quarter of 2020; the most frequent were mild (around 82%) [2]. Approximately 84% of TBIs are related to blast exposure and 63% are connected with loss of consciousness [3]. Regrettably, these statistics remain underestimated due to a large number of negligible subconcussive traumatic brain events, which are not reported to medical services, but their cumulative effect may also lead to chronic traumatic encephalopathy.  
The development of various imaging techniques has enabled the notable broadening of knowledge regarding traumatic brain injury in military personnel. Of these techniques, the most effective are magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), magnetic resonance spectroscopy (MRS), and susceptibility-weighted imaging (SWI) as well as positron emission tomography [4; 5; 6; 7]. MRS, in particular its most common type--proton magnetic resonance spectroscopy (\({}^{1}\)HMRS), allows for a non-invasive examination of metabolites in brain tissue in vivo, namely N-acetyl aspartate (NAA), choline (Cho), creatine (Cr), myoinositol (mI), lipids (Lip), and lactate (Lac). The detected metabolites are markers of various cells and processes present in the brain tissue--N-acetyl aspartate is a marker of neuronal health, choline of cell membrane turnover, creatine of energy metabolism, and myoinositol of glial cells. These metabolites are found in the healthy brain. In states of pathology additional metabolites appear, such as lipids, which are markers of cell membranes and lactates that increase in such conditions as hypoxic or ischemic injury. Several studies focusing on the \({}^{1}\)HMRS of soldiers' brains [1; 8; 9; 10; 11; 12; 13; 14] report contradictory results and different methods of measurement (as absolute units, a ratio to creatine, choline, or water). Thus, further research in this field is vital in order to investigate the potential health consequences of long-term military service, especially in terms of the presence of the first signs of chronic traumatic encephalopathy. The awareness of such sequelae will enable the provision of the highest standard of medical care for soldiers, tailored to their actual needs.  
The aim of this study was to assess the most frequent anomalies in the MRI examination of the heads of soldiers. The second aim was to compare the relative concentrations of \({}^{1}\)HMRS metabolites in the brain of soldiers and healthy age-matched controls.