esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The performance of a MIMO-WCDMA architecture inside a building environment was analyzed. Two transmission modes (diversity combining and spatial multiplexing) were considered along with various amounts of nodes in a three-floor building. For each simulation scenario, a sufficient number of independent MC simulations were carried out for different MIMO orientations. According to the presented results, diversity transmission can improve the overall performance of the proposed approach, considering a multipath environment. In this context, high data rates along with an increased number of mobile nodes can be supported by increasing the order of the MIMO configuration. On the other hand, spatial multiplexing is not an efficient solution, as this type of transmission can be severely affected by multiple access interference. In contrast to wireless cellular networks, the number of nodes  
Figure 7: Mean bit error rate (BER) for various MIMO orientations—diversity combining and spatial multiplexing.  
in indoor BMSs is known a priori. Therefore, for a specific uplink power threshold and transmission rate, an appropriate MIMO deployment can be selected, based on the extracted parametric curves (i.e., power versus nodes, SNR, number of transmit/receive antennas, antenna selection, transmission mode, etc.).  
Ongoing work includes alternate transmission scenarios, such as the use of relay nodes and a cross-layer approach to interconnect the presented architecture with Internet of Things technology.  
**Acknowledgments:** The work in this paper has been funded by the Greek General Secretariat of Research and Technology (GSRT) and the European Commission under the projects ICT-257245 VITRO (Virtualized Distributed Platforms of Smart Objects) and H2020-762013 NRG-5 (Enabling Smart Energy as a Service via 5G Mobile Network advances).  
**Author Contributions:** [PERSON] contributed to the literature review regarding current state of the art in BMS systems as well as in the execution of Monte Carlo simulations in Matlab. [PERSON], [PERSON] and [PERSON] contributed in the formulation of transmission/reception in MIMO-WCDMA systems and in the theoretical analysis of BER in Spatial Multiplexing Systems. All authors also contributed in the writing of the manuscript in the sections related to their aforementioned tasks.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 3 Results and Discussion
### Energy-Dispersive X-ray Spectroscopy (EDX)  
Energy-dispersive X-ray spectroscopy analysis of as-cast pure Mg and Mg/Mg-20 GMB hybrid composite was conducted to quantify the near-surface number of elements present in the composite;  
Figure 4: Scanning electron microscopy of as-cast: (**a**) pure Mg and (**b**) Mg/Mg-20 GMB hybrid composite.  
Figure 5: SEM image of Mg-20 GMB core material under higher magnification.  
the results are shown in Figure 6. The peaks obtained from the EDX results confirm the presence of magnesium and glass microballoon (SiO\({}_{2}\)) elements in the Mg-based hybrid composite.  
EDX analysis reveals the existence of Mg\({}_{2}\)Si as a secondary phase in Mg-20 GMB core syntactic foam. The diffusion of the Si atoms from the filled/fractured GMB particle shell into the Mg matrix may be the reason for the development of Mg\({}_{2}\)Si (see the marked region in Figure 5). Silicon is nearly insoluble in magnesium with the solubility limit \(<\) 30 ppm (\(<\)0.003 wt.%) [31]. A slight diffusion of Si atoms into Mg can result in precipitate formation, as observed in the current study as well. Therefore, the formation of a secondary phase like Mg\({}_{2}\)Si cannot be restricted.
## 4 Machine Learning Methodology
### Model Training and Optimization
#### 4.2.2 Cross-Validation  
A total of 256 data samples were randomly and uniformly divided into four groups, with three groups (75%) allocated for training and one group (25%) reserved for testing. To ensure the predictive model's validity and generalizability and to mitigate overfitting, cross-validation was performed. In each cycle of model development and evaluation, a different group was designated for validation while the remaining groups were used for training. This process was repeated four times, and the final accuracy was determined by averaging the accuracies obtained from each of the four validation rounds.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 2 System Model
### Atmospheric Turbulence Model  
In order to model the intensity fluctuations caused by the atmospheric turbulence, the statistical Gamma-Gamma distribution is here assumed because of its mathematical tractability and accuracy to characterize a wide variety of scenarios ranging from weak to strong turbulence. Thus, and following [21], the probability density function (pdf) of \(h_{a}\) is written as  
\[f_{h_{a}}(h_{a})=\frac{2(\alpha\beta)^{(\alpha+\beta)/2}}{\Gamma(\alpha)\Gamma (\beta)}h_{a}^{(\alpha+\beta)/2-1}K_{\alpha-\beta}\left(2(\alpha\beta h_{a})^ {1/2}\right), \tag{5}\]  
where \(K_{p}(x)\) is the modified Bessel function of the second kind, and \(\Gamma(x)\) is the Gamma function, with \(\alpha\) representing the effective number of large-scale cells of the scattering process and with \(\beta\) denoting the effective number of small-scale cells. Namely, from [39], these latter parameters, \(\alpha\) and \(\beta\), can be obtained as  
\[\alpha=\Big{[}\exp\Big{(}0.49\sigma_{R}^{2}(1+1.11\sigma_{R}^{12/5})^{-7/6} \Big{)}-1\Big{]}^{-1}, \tag{6}\]  
and  
\[\beta=\Big{[}\exp\Big{(}0.51\sigma_{R}^{2}(1+0.69\sigma_{R}^{12/5})^{-5/6} \Big{)}-1\Big{]}^{-1}, \tag{7}\]  
respectively, where \(\sigma_{R}^{2}\) is the Rytov variance which, for uplinks paths, is defined as [39]  
\[\sigma_{R}^{2}=2.25k^{7/6}(Z-z_{0})^{5/6}\int_{z_{0}}^{Z}C_{n}^{2}(z)\bigg{(} \frac{z-z_{0}}{Z-z_{0}}\bigg{)}^{5/6}dz, \tag{8}\]  
with \(C_{n}^{2}(z)\) being the index of refraction structure parameter at altitude \(z\), whereas \(k=2\pi/\lambda\) is the optical wave number and \(Z\) and \(z_{0}\) are the UAV and transmitter heights, respectively.
## 4 Parameter Optimization
### Point Selection
#### 4.1.3 Flat Area for Adjacent Scanning Bands  
Error-checking parameters not only lead to misalignment between buildings generated from different airlines but also cause altitude differences between adjacent scanning bands. The points across two strips have a greater field angle, so their photons are incident on the sensor more marginally. Sometimes, half of them are wasted in the reconstruction process, but the continuously high trend from the middle to sides is still useful for checking the installation error.  
Figure 7: Target flat areas from different air routes; (**a**) the east–west airline; (**b**) the south–north airline; (**c**) the west–east airline.  
The area is not wide enough to accommodate complete buildings on overlapped areas, so we have to select flat targets as in the second selection method. Figure 8 shows the selection positions across scanning bands beneath the east-west airline.  
We select a point that spans both strips and draw a circle horizontally with that point as the center and then average the heights of all the points within the circle. For the \(i\)th circle, we obtain an average value on each of the front and back strips, denoted as \(h_{i}^{f}\) and \(h_{j}^{b}\); the error criterion can be expressed as  
\[\overline{e}_{a}=\frac{1}{9}\sum_{i=1}^{9}\Big{|}h_{i}^{f}-h_{i}^{b}\Big{|}. \tag{17}\]
## 5 Discussion
### Effect of Experimental and Simulation Parameters
#### 5.1.3 Effect of Deep Rolling Offset  
The 0.1 mm offset used in both the experimental setup and FEA simulations ensured that the tool passed over the material in closely spaced intervals, reducing gaps in the treated surface and ensuring consistent deformation. Our results demonstrated that this small offset led to uniform deformation across the material, with deformation peaks reaching a maximum of 2.744 mm, as shown in both the simulation and the experiment. Other studies, such as those by [PERSON], [PERSON] et al. [19], have similarly emphasized the importance of offset in deep rolling processes. They found that smaller offsets lead to more consistent stress profiles, as seen in our results, where minimal gaps between tool passes resulted in evenly distributed compressive residual stresses. However, their research also pointed out that extremely small offsets could extend the processing time significantly, potentially reducing the cost-effectiveness of the operation. In our study, the 0.1 mm offset achieved a balance between uniform deformation and practical efficiency, ensuring that the surface was evenly treated without prolonging the process unnecessarily.
## 2 Materials and Methods
### Transient Wave Model in the Frequency Domain of the Pressure of Tunnel Rock  
Considering a tunnel rock mass with dimensions of 100 m in length, 50 m in width, and 50 m in height. Through simulation, modeling of the rock mass and setting its properties was based on real-world mechanical parameters. For illustration purposes, this model simulated a common rock mass with a density of 2730 kg/m\({}^{3}\), an elastic modulus of 11 GPa, and a Poisson's ratio of 0.21. The simulation was conducted using the Midas GTS NX (2019.v1.2) geotechnical analysis software. A grid system within the tunnel was created, with 50 observation points (\(c_{i}\), where i = \(1,2,\ldots,50\)) evenly distributed along the 100 m length of the rock mass. At one end of the rock mass, dynamic loads in the form of concentrated forces were applied. Table 1 lists some of the simulated data  
Then, we performed experimental simulations, which consisted of two parts:  
Simulating the perfect rock mass under dynamic loads of different angular frequencies to obtain the corresponding data \(Z^{0}(c_{i};\omega)\); simulating the practical rock mass with cracks under dynamic loads of different angular frequencies to obtain the corresponding data \(Z(c_{i};\omega)\).  
During the process of experimental simulation, the model design was based on the geological properties of the tunnel rock mass and the experimental requirements. Figure 1 illustrates the application of dynamic loads at the position and direction indicated by the red arrow in the tunnel, with loads applied at different angular frequencies. Figure 2 demonstrates the introduction of cracks within the rock mass, where a spherical cavity is used to simulate the cracks, representing their location and volume as an indication of their size.  
\begin{table}
\begin{tabular}{c c c} \hline
**Angular Frequency** &
\begin{tabular}{c} **Measuring Point** \\ **Coordinates (m)** \\ \end{tabular} & **Pressure Data (N)** \\ \hline
628.3185 & 1 & 3.262333 \\
628.3185 & 2 & 2.673342 \\
628.3185 & 3 & 2.211276 \\
628.3185 & 4 & 1.845488 \\
628.3185 & 5 & 1.553216 \\
628.3185 & 6 & 1.318036 \\
628.3185 & 7 & 1.127432 \\
628.3185 & 8 & 0.971705 \\
628.3185 & 9 & 0.843691 \\
628.3185 & 10 & 0.737694 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \end{tabular}
\end{table}
Table 1: Position-dependent pressure data at specified angular frequency (200\(\pi\) as an example).  
Figure 1: Dynamic load applied to a perfect rock mass in normal conditions.
## 3 Materials and Methods
### Feature Fusion and Classification  
The fusion and classification stages were designed to optimize maize damage detection accuracy. As shown in Figure 3, the process begins with image enhancement via CLAHE and segmentation via U2 Net, followed by deep and hand-crafted feature extraction from the contrast-enhanced and segmented images.
## Appendix B
\begin{array}{ll}\tilde{\rm H}(0)={\rm c}^{2}(2n-2)!(4\pi{\rm b})^{-(2n-1)} \left\{\frac{1}{2}+\frac{1}{4}{\rm e}^{{\rm j}\varphi}\left[1-{\rm j}\frac{{ \rm f}_{0}}{{\rm b}}\right]^{-(2n-1)}+\frac{1}{4}{\rm e}^{-{\rm j}2\varphi} \left[1+{\rm j}\frac{{\rm f}_{0}}{{\rm b}}\right]^{-(2n-1)}\right\}.\end{array} \] (A27)  
Now, we need to find \({\rm H}({\rm f}_{0})\) to substitute in Equation (A14). The expression \({\rm H}({\rm f}_{0})\) can be obtained by replacing \({\rm f}\) with \({\rm f}_{0}\) in Equation (A10) as:\[ \begin{split}\mathrm{H}(\mathrm{f}_{0})&=\mathrm{c}(n-1)!(2 \pi\mathrm{b})^{-n}\bigg{[}\frac{1}{2}\mathrm{e}^{\mathrm{j}\varphi}\left(1+ \mathrm{j}\frac{\mathrm{f}_{0}-\mathrm{f}_{0}}{\mathrm{b}}\right)^{-n}\bigg{]} +\mathrm{c}(n-1)!(2\pi\mathrm{b})^{-n}\bigg{[}\mathrm{e}^{-\mathrm{j}\varphi} \left(1+\mathrm{j}\frac{\mathrm{f}_{0}+\mathrm{f}_{0}}{\mathrm{b}}\right)^{-n }\bigg{]},\\ &=\mathrm{c}(n-1)!(2\pi\mathrm{b})^{-n}\bigg{[}\frac{1}{2}\mathrm{e }^{\mathrm{j}\varphi}\bigg{]}+\mathrm{c}(n-1)!(2\pi\mathrm{b})^{-n}\bigg{[} \mathrm{e}^{-\mathrm{j}\varphi}\left(1+\mathrm{j}\frac{\mathrm{f}_{0}}{ \mathrm{b}}\right)^{-n}\bigg{]}.\end{split}  \tag{128}\]  
Substituting \(\tilde{\mathrm{H}}(0)\) and \(|\mathrm{H}(\mathrm{f}_{0})|\) in Equation (117), we can find the final expression of the \(\mathrm{H}_{\mathrm{ERB}}\) as:  
\[\mathrm{H}_{\mathrm{ERB}}=\frac{\mathrm{c}^{2}(2n-2)!(4\pi\mathrm{b})^{-(2n-1) }\bigg{\{}\frac{1}{2}+\frac{1}{4}\mathrm{e}^{2\varphi}\left[1-\mathrm{j}\frac {\mathrm{f}_{0}}{\mathrm{b}}\right]^{-(2n-1)}+\frac{1}{4}\mathrm{e}^{-\mathrm{ j}\varphi}\left[1+\mathrm{j}\frac{\mathrm{f}_{0}}{\mathrm{b}}\right]^{-(2n-1)} \bigg{\}}}{2\bigg{|}\mathrm{c}(n-1)!(2\pi\mathrm{b})^{-n}\bigg{[}\frac{1}{2} \mathrm{e}^{\mathrm{j}\varphi}\bigg{]}+\mathrm{c}(n-1)!(2\pi\mathrm{b})^{-n} \bigg{[}\mathrm{e}^{-\mathrm{j}\varphi}\left(1+\mathrm{j}\frac{\mathrm{f}_{0} }{\mathrm{b}}\right)^{-n}\bigg{]}\bigg{|}^{2}}. \tag{129}\]  
The plot for the \(\mathrm{H}_{\mathrm{ERB}}\) with varying \(\mathrm{f}_{0}/\mathrm{b}\) is shown in Figure 11. This figure shows that the \(\mathrm{H}_{\mathrm{ERB}}\) varies with the \(\mathrm{f}_{0}/\mathrm{b}\) for \(1<\mathrm{f}_{0}/\mathrm{b}<3\). However, the \(\mathrm{H}_{\mathrm{ERB}}\) becomes independent of \(\mathrm{f}_{0}/\mathrm{b}\) when it becomes greater than 3.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 3 Results
### Classification Results  
To validate the performance of our proposed approach, we have developed five evaluation scenarios based on the utilized dataset to train and test the SVM model. In particular,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Set of Features**} & \multicolumn{2}{c}{**Number of Features**} & \multicolumn{2}{c}{**Number of Features**} \\  & **Used for the** & **Used for the** & **Used When Both** \\  & **Office Environment** & **Hallway Environment** & **Environments Are Mixed** \\ \hline Time series features & 17 & 13 & 26 \\ SNR & 1 & 1 & 1 \\ Frequency features & 12 & 11 & 14 \\ Statistical Tests & 26 & 20 & 33 \\ Correlation features & 31 & 19 & 45 \\ Causation test & 2 & 1 & 2 \\ Residual Heteroscedasticity & 0 & 0 & 0 \\ Cointegration features & 3 & 2 & 3 \\ Instantaneous frequency & 17 & 9 & 23 \\ Spectral Entropy & 18 & 10 & 23 \\ Audio features & 123 & 64 & 240 \\ Total Number of features & 250 & 150 & 410 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selected features.  
Figure 17: The effect of the number of features on the overall system’s accuracy.  
the first evaluation scenario utilizes the dataset recorded in the office environment to train and test the SVM model, the second evaluation scenario utilizes the dataset recorded in the hallway environment to train and test the SVM model, the third evaluation scenario utilizes the datasets recorded in the office and hallway environments to train and test the SVM model, the fourth evaluation scenario utilizes the dataset recorded in the office environment to train the SVM model and the dataset recorded in the hallway to test the trained SVM model, and the fifth evaluation scenario utilizes the dataset recorded in the hallway environment to train the SVM model and the dataset recorded in the office environment to test the trained SVM model.  
Moreover, for each of the first three evaluation scenarios, a leave-one-subject-out cross-validation (LOSO-CV) procedure is employed to train and test the constructed SVM classifiers. Specifically, the SVM classifier is trained using the feature vectors extracted from all subjects except one subject that is used to test the SVM classifier. This procedure is repeated \(n\) times, where \(n\) represents the number of subjects in the dataset used in each of the three evaluation scenarios, and the average performance is computed over the repetitions.  
To quantify the results obtained for each activity, the metrics shown in Table 5 are used, where \(\beta\) in the F1 score metric refers to the importance of the precision metric over the TPR metric. In our work, we fixed the value of \(\beta\) to be equal to 1 to indicate that precision and TPR are equally important.
## 2 Materials and Methods
### Transfer Learning Based Snapshot Ensemble Method (TLSE)  
In this paper, a snapshot ensemble combined with transfer learning approach (TLSE) is proposed for the 7-classes cervical cells classification task. Different from the traditional ensemble methods that combined several CNN models together to get the ensemble benefits, the proposed method can obtain a comparable ensemble result within a single model training procedure. Furthermore, in order to address the small sample issue as well as explore the full capacity of deep CNN model, transfer learning is conducted ahead of snapshot ensemble. This is a new attempt to get ensemble benefits from deep convolutional neural network in medical application areas. This method will expend the application field of snapshot ensemble in cervical cells classification, as well as bring some new exploration on transfer learning by integrate it with an ensemble method. Furthermore, a new training strategy is proposed in order to guarantee the integration of these two methods. To sum up, the proposed approach integrates the transfer learning with snapshot ensemble based on deep CNN for the fine-grained cervical cells classification task.  
This section describes the details of the transfer learning based snapshot ensemble (TLSE) method. An overview is reported in Figure 3.  
Figure 1: The original cervical cell images on Herlev dataset.  
Figure 2: Cervical cell images by cropping and rotation (**left**: abnormal; **right**: normal).  
The transfer learning phase is conducted to fine-tune the pre-trained model towards the target dataset, while the snapshot ensemble phase is designed for getting several snapshot models within one single model training process. During the testing process, these saved snapshot models are combined together by averaging their soft-max outputs.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 2 Related Work
### Data Fusion  
Data fusion can produce more consistent and accurate results than any single positioning technique. [PERSON] et al. (2013) compared the fusion results of KF and particle filter (PF) in WiFi positioning tests [29]. By applying fingerprinting with all available WiFi APs, they concluded there is no clear difference between KF and PF trajectories. However, in the case of fewer APs (three), the performance of PF is better than KF.  
[PERSON] et al. (2013) proposed a complementary extended Kalman filter (CEKF) to fuse WiFi RSS and foot-mounted PDR data and designed a PF to fuse PDR data with map information [6]. Although the PF generates user trajectories with the higher accuracy, the PF algorithm is quite time-consuming. The CEKF only needs 0.2 s for computation, while the PF needs more than 100 s in 10 m \(\times\) 10 m area. Thus, PF may not be the first choice for smartphone applications.  
KF has high computational efficiency and it is suitable for real-time application. Recently, [PERSON] et al. (2017) developed an application in tablet to provide a test in a small region on the fusion of iBeacon and PDR with KF. The result of fusion positioning is accurate (around 1 m) [14]. However, the robustness of this positioning approach is not clear since the test route is simple. Another study fuses iBeacon ranging data with PDR computation [13]. Based on the start location derived by iBeacon and WiFi, the ranging result of iBeacon RSS is fused in an extended Kalman filter to calibrate PDR results.  
In this paper, we focus on the fusion of iBeacon and PDR data on the basis of the accuracy improvement of iBeacon positioning. In our designed KF, the accurate coordinates (i.e., 'baseline') of RSS-based positioning are fused with the heading and stride length of PDR. The accuracy of fusion is comparable to other WiFi/iBeacon localization, while the computation load is limited.
## 4 Validation of Proposed Method
### Comparison of Iron Loss  
The copper loss of the winding during the preliminary design process can be directly obtained by multiplying the square of the current by the resistance. For both the 2D and 3D methods, the copper loss of the motor is a theoretical estimate and is completely equal. Therefore, the core loss on the stator side is the main comparison object for verifying the feasibility of the model. The iron core loss is an essential component in the loss evaluation of axial flux permanent magnet synchronous motors. The values of iron core loss calculated by 2D and 3D methods are 31.18793 W and 31.69523 W, respectively, with a deviation of only 1.60% between the two methods. The calculation results of the two methods are very close.  
Unfortunately, based on the calculation principle of the eddy current losses in the permanent magnet, it is currently not suitable to simulate and calculate eddy current losses in permanent magnets with fan-shaped shapes using a two-dimensional linear motor equivalent model, which is one of our future research goals.