esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, we introduced a method to reconstruct the 3D refractive index distribution by combining TIE and deconvolution. A BF microscope using a camera was constructed, and the proposed technique was applied for a technical sample with well-known properties. The microscope's 3D-PSF, which takes into account optical aberrations, was computed. Its influence on the 3D refractive index recovery, using the TIE method, was demonstrated. Experimental results show that taking the diffraction into account while performing TIE on BF-images can significantly improve the accuracy of the 3D refractive index reconstruction. It was demonstrated that the method is able to infer the size of the sample accurately. Therefore, this approach was used in our laboratory to extract the optical parameters. The proposed technique can be applied, not only to homogeneous specimens, but also to specimens presenting heterogeneous compositions in their structures, such as biological cells. However, they must be pure phase objects, where it is possible to perform linear deconvolution according to Equation (2); otherwise the proposed method cannot be used.  
[PERSON], [PERSON], and [PERSON] conceived the technique presented in this work, and wrote the manuscript. [PERSON] and [PERSON] participated in the imaging acquisitions set-up for the experiment. The authors have all read and approved the submitted version of the manuscript.  
Figure 5: Size’s map for the sample. The intensity of each pixel at a point of the microsphere indicates in \(\upmu\)m the thickness (at this point) of the microsphere. The intensity of the pixel in the middle of the microsphere represents its diameter, as indicated by the arrow.  
Figure 6: Mean values of the refractive index of the microsphere as a function of the z-position along the optical axis. The full width at half maximum (FWHM) indicated by the double arrow corresponds to the diameter of the microsphere.  
**Funding:** This work was financially supported by the International Science Programme (ISP) and The World Academy of Sciences (TWAS).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 6 Development of Individual Models  
The brief architecture of the five deep learning models is given in Section 5. In this section, the training and fine-tuning details of the individual models are provided. First, the training dataset \(S_{tr}=\left\{<Z^{(i)},t^{(i)}>\right\}\)\(\begin{matrix}i=n\\ i=1\end{matrix}\) has been used to train and optimize the parameters of the individual models, where \(Z\) represents the image. The training dataset consists of \(n=5690\) images. It is used to develop the classifiers of ResNet, InceptionV3, ResNetInceptionV2, DenseNet, and VGG-19. To train and fine-tune the ResNet model, global average pooling (GlobalAvgPool2D) is applied to downsample the feature maps so that all the spatial regions may contribute to the output. Moreover, a fully connected layer containing eight neurons with the SoftMax activation function are added to classify eight different classes. The ResNet model is trained with 50 epochs, adaptive moment estimation (Adam) optimizer for the quick optimization of the model, learning rate of 1e-4, and categorical cross-entropy loss function. Inception V3 is fine-tuned by applying GlobalAvgPool2D to downsample the feature maps, adding two dense layers at the end containing 1028 and eight neurons with a rectified linear unit (ReLU) and SoftMax activation functions, respectively. The model is trained using 50 epochs, a learning rate of 0.001, and an RMSprop optimizer, as it uses plain momentum. Additionally, RMSprop maintains a moving average of the gradients and uses that average to estimate the variance.  
DenseNet is fine-tuned by adding a fully connected layer containing eight neurons with SoftMax activation function to classify the eight classes of skin cancer. It is trained using 50 epochs, an Adam Optimizer, and a learning rate of 1e-4. InceptionResNetV2 is fine-tuned by adding two dense layers containing 512 and eight neurons with ReLU and SoftMax activation functions, respectively. GlobalAvgPool2D pooling is applied to downsample the feature map. Moreover, the model is trained with 50 epochs, a stochastic gradient descent (SGD) optimizer, and a learning rate of 0.001 with a batch size of 25. VGG-19 is fine-tuned by applying GlobalAvgPool2D to downsample the feature maps and adding two dense layers containing 512 and eight neurons with ReLU and SoftMax activation functions, respectively. The model is trained with 50 epochs, a learning rate of 1e-4, an SGD optimizer, and a categorical cross-entropy loss function. After training and fine-tuning individual models, the test dataset \(S_{ts}=\left\{<Z^{(i)},t^{(i)}>\right\}\)\(\begin{matrix}i=m\\ i=1\end{matrix}\), (\(m=1797\)) is used to validate the trained component models.
# Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
## 2 Experimental Platform and Vibration Data Sets  
### Experimental Configuration  
The experimental platform PRONOSTIA is a test bed used in the IEEE PHM 2012 prognostic challenge to collect real data from ball bearings under accelerated degradation tests [19]. These tests provide real experimental data sets that characterize the degradation of the complete operational life in only a few hours. Under different operating conditions, vibration data are collected from several ball bearings before their failure. In the experiment, the ball bearings (NSK 6804 DD) are examined throughout the different tests. As shown in Figure 1, the experimental platform PRONOSTIA consists of three main parts: a rotating module, a loading module, and a measurement module. The three modules are summarized below:  
* The rotating module: it is composed of an asynchronous motor, a primary shaft, a speed reducer (gearbox), and a secondary shaft with a speed sensor. The asynchronous motor has a rated power of 250 W and an output speed of 2830 rpm. The gearbox consists of two pulleys connected by a timing belt. The main function of the gearbox is maintaining the speed of the secondary shaft at less than 2000 rpm while delivering a rated torque.
* The loading module: this has a pneumatic jack that generates a force acting on a lever arm. Then, the lever arm amplifies this force and transmits it as a radial force to the ball bearing under test. Adjusting the lever arm can help create different loading conditions on the bearing.
* The measurement module: different types of sensors are used to measure the radial force applied on the bearing, the rotation speed of the secondary shaft holding the bearing, and the torque delivered to the bearing. In addition, two high-frequency accelerometers (DYTRAN 3035B) are mounted perpendicularly to measure the horizontal and the vertical accelerations during the degradation test. In the measurement system, the temperature of the tested bearing is measured via one temperature sensor (PT100).  
The main components in the experimental platform are shown in Figure 2. In the experiment, the ball bearings (NSK 6804 DD) are deep groove, radial, and double rubber sealed. As shown in Figure 2, the dimensions of the bearings are \(20\,\times 32\,\times 7\) mm. In addition, all the technical data of the ball bearing are shown in Table 1.  
Figure 1: Main modules of the experimental platform.
###### Abstract  
This paper explores the challenges and constraints when over the horizon (OTH) maritime surveillance service utilizes an Internet of Things (IoT) as its backbone. The service is based on High Frequency Surface Wave Radars (HFSWRs) and relies on a satellite communication network as its communication infrastructure in harsh environments. The complete IoT OTH maritime surveillance network is currently deployed in the Gulf of Guinea, which due to its tropical climate represents an unfavorable environment for sensors and communications. In this paper, we have examined the service performance under various meteorological conditions specific to the Gulf of Guinea. To the best of our knowledge, this is the first analysis of IoT OTH maritime surveillance service in equatorial environment. Our analysis aims to mathematically describe the impact of harsh weather conditions on the performance of the service in order to mitigate it with careful overall system design and provide constant quality of the service. Analyses presented in the paper show that average service latency is about 90 s, but it can rise to about 120 s, which is used as a key information during the sensor data fusion algorithm design. Validity of the analyses is demonstrated through high quality of service with an outage probability of just 0.1% in the driest months up to the 0.7% in the rainiest months. The work presented here can be used as a guideline for deployment of maritime surveillance service solutions in other equatorial regions. Moreover, the gained experience presented in this paper will significantly facilitate future expansions of the existing maritime surveillance network with more HFSWRs. This will be done in such a way that it will not affect the quality of service of the entire system on a large scale.  
Africa; communication networks; harsh environment; Internet of Things; maritime safety; satellite communication +
Footnote †: journal: Physics Letters A
# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 1 Introduction
### Study Selection  
Keyword searches of the three databases retrieved 779 articles covering the period from 2010 to July 2020 and an additional 266 articles with the search in 2022 to include journal articles from August 2020 to December 2021. After screening the titles and abstracts, 190 articles remained for detailed full-text analysis. Of these 190 articles, 93 were relevant for inclusion. For quality assurance, each full text was screened twice and data extraction were performed by two authors (once by C.K. and once by W.K., S.K., V.V., H.R., S.M., or S.B.) with an online survey tool (LimeSurvey; LimeSurvey GmbH, Hamburg, Germany) to extract the information systematically. Forty-three articles were excluded because the classification task was not included at all or was not one of the focuses of the article, 28 articles were not related to sports or addressed a different application area, 20 articles did not use IMU data, ten did not clearly describe the analysis, were posters, or were not found, and five were conference papers that were preliminary work for an included journal article that included the same methods or an extension of an existing work without any new classification method. Finally, two papers were excluded because the full text was not in English. Figure 2 provides an overview of the inclusion process. Table 2 lists all articles, including year, discipline, the dependent variable, the classes that are classified, and methods. Figure 2: Flow diagram of the review. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Authors** & **Year** & **Discipline** & **Dependent Variable** & **Classes** & **Methods** \\ \hline \multicolumn{5}{c}{**Classification of a motion primitive**} \\ \hline \multirow{5}{*}{[PERSON] et al. [19]} & \multirow{5}{*}{2017} & \multirow{5}{*}{basketball} & \multirow{5}{*}{basketball exercise type} & forward-backward dribbling, & \multirow{5}{*}{SVM} \\  & & & & left-right dribbling, regular dribbling, & \\  & & & & two hands dribbling, shooting & \\  & & & & and layup & \\ \cline{1-1}  & & & & agilty cuts, walking, printing, & \\ [PERSON] et al. [20] & 2015 & activities during sports training & type of activity & logging, box jumps, and football & \\  & & & & free kicks & \\  & & & & shooting the ball, passing the ball, & \\  & & & & heading the ball, running & \\ \cline{1-1}  & & & & and dribbling & \\ \cline{1-1}  & & & & 18 long punches and & \\ \cline{1-1}  & & & & 24 takewoodb kicks & & \\ \cline{1-1}  & & & & tennis: regional longwend, & &
\begin{tabular}{l} SVM \\ \cline{1-1}  & & & slice, backhand topspin, backhand \\ \cline{1-1}  & & & slice, backhand
# A Two-Port Dual-Band Dual-Circularly-Polarized Dielectric Resonator Antenna
## 3 Experimental Results and Discussion
Meanwhile, with P2 excited, the antenna operates at the UB with \(-\)10 dB IBWs of 5.0-6.0 GHz (18.0%) and 5.14-6.12 GHz (17.4%) in simulation and measurement, respectively. As also observed from Figure 12, the measured port-to-port isolations are more than 15.8 and 18.0 dB within the \(-\)10 dB IBWs for the LB and UB, respectively. Figure 11: Photographs of the (**a**) fabricated prototype and (**b**) radiation pattern measurement setup. Figure 13 depicts a comparison of the measured and simulated results of ARs and realized gains at the boresight direction (\(\theta\) = 0\({}^{\circ}\)). With reference to Figure 13a, when P1 is excited (the LB operation), the simulated 3 dB AR bandwidth is 3.52-4.28 GHz (18.7%) while the measured 3 dB AR bandwidth is 3.58-4.43 GHz (21.2%). When P2 is excited (the UB operation), the 3 dB AR bandwidths of 5.1-5.95 GHz (15.0%) and 5.12-6.03 GHz (16.3%) are achieved in the simulation and measurement, respectively. It is worth mentioning that, at each operation band, the measured 3 dB AR bandwidth is entirely covered by the measured \(-\)10 dB IBW. Within the overlapped 3 dB AR bandwidth/\(-\)10 dB IBW, the measured gains range from 4.89 to 5.64 dBic for the RHCP (with P1 excited) and 5.81 to 8.13 dBic for the LHCP (with P2 excited) (see Figure 13b). It should be noted that higher gain is provided by the higher order mode, which is appropriate due to the greater electrical magnitude produced at higher frequencies of the DRA. Good agreement is obtained between the experiment and simulation results with some slight discrepancies that are mainly attributed to the unavoidable errors in the fabrication process and experiment. Figure 14 shows a comparison of the measured and simulated radiation patterns of the proposed two-port DBDCP DRA in the xz-plane (\(\theta\) = 0\({}^{\circ}\)) and yz-plane (\(\theta\) = 90\({}^{\circ}\)) at 4.0 GHz (P1 excited) and 5.5 GHz (P2 excited). The antenna achieves RHCP characteristic at 4.0 GHz, and the RHCP field is stronger than LHCP field by more than 18.2 dB in the boresight direction, whereas the antenna exhibits LHCP characteristic at 5.5 GHz and the LHCP field is stronger than RHCP field by more than 19.1 dB in the boresight direction. The key performances of the proposed DRA are compared with other DBDCP DRAs available in the literature, and the results are summarized in Table 2. Regarding this table, the proposed DRA has much broader 3 dB AR bandwidths at both operating bands than those of its counterparts. Furthermore, the proposed DRA uses two-port excitation to achieve dual-band performance on separate channels; thus, it has better features compared to previous designs. Figure 12: Measured and simulated \(S\)-parameters characteristic of the proposed two-port DB-DCP DRA. Figure 13: Measured and simulated (**a**) ARs and (**b**) gains of the proposed two-port DBDCP DRA.
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
###### Abstract  
Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (\(C_{c}\)), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (\(w\)), initial void ratio (\(v_{0}\)), and specific gravity (\(G_{s}\)), plays a vital role in understanding soil behavior. This study employs machine learning algorithms--the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)--to predict the \(C_{c}\) values based on a dataset comprising 915 samples. The dataset includes LL, PL, \(W\), PI, \(G_{s}\), and \(e_{0}\) as the inputs, with \(C_{c}\) as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R\({}^{2}\) of 0.925 and a testing R\({}^{2}\) of 0.930 with the input combination [\(w\), PL, LL, PI, \(e_{0}\), \(G_{s}\)]. The RFR model follows closely, with a training R\({}^{2}\) of 0.970 and a testing R\({}^{2}\) of 0.926 using the same input combination. The ABR model records a training R\({}^{2}\) of 0.847 and a testing R\({}^{2}\) of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the \(C_{c}\) value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing.  
compression index; machine learning; random forest; gradient boosting; AdaBoost +
Footnote †: journal: Applied Sciences
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Orbital Angular Momentum Antennas
#### 3.4.5 OAM System Feeding  
New antennas (that are in the vast majority of arrays) require new and more complex feeding mechanisms (ref. [81; 82; 84; 85]). To be more specific, such mechanisms must be capable of producing the desired phase difference on each array element. In such a way, we have an abundance of elements with the correct phase difference that radiate the desired OAM mode. Also, one can use multiple simultaneous excitations to radiate more than one OAM mode from only one antenna array, which subsequently can be funneled in the same channel by multiplexing (ref. [85]).
## 2 Background
### Distributed Coherence and MIMO Extensions  
Distributed coherence refers to the synchronization and simultaneous operation of a distributed network of systems. CHP2 enables the distributed coherence implicitly by synchronizing each user and estimating the relative position of every node in the network. One of the original time synchronization algorithms is known as the NTP [72; 73]. Numerous time synchronization algorithms have been deployed in modern sensor networks [74; 75]. Several other studies have also discussed algorithms for maintaining distributed coherence in the context of distributed transmit beamforming [16; 76; 77]. In this study, we use an extended version of NTP that jointly estimates clock parameters and ToF [15]. Similar ranging and synchronization systems have been considered previously [17] but have not yet been applied to modern hardware platforms.  
Multi-antenna, distributed coherent systems also enable powerful applications such as advanced relay networks [78], energy-efficient multi-beamforming [79], multiple access networks [80], and IoT connectivity [81]. The CHP2 prototype presented in this work is a multi-antenna system intended to support this class of applications, but we have not yet demonstrated these capabilities on our early hardware demonstration platform.
## 2 Geometrical Layout of Steps  
[PERSON] et al. [11] performed both numerical modelling and laboratory tests, and documented the hydraulic properties of flows in a stepped spillway with both V-shaped and conventional steps. It is the latter that is adopted in this study. By conventional steps, we mean steps of rectangular shape (Figure 1a,b).  
In their studies, a sufficiently large water tank conveys water to the test rig. On one side of the tank, an overflow weir is installed to guarantee constant water stage at a given flow rate. From the tank, the water flows, via a short conduit, into the spillway chute, with a width \(B=0.40\) m. The conduit is rectangular in cross-section, converging vertically and covering the whole flume width. The downstream cross-section of the conduit, where the flow transits from pressurized to free surface, is at a 0.80 m distance from the first step and 0.12 m in height (measured perpendicular to the flume bottom). The spillway chute has a slope of \(\varphi=26.6^{\circ}\) and 56 steps. It features two types of steps, first with 28 transitional steps of a linearly enlarged step size and then with 28 major steps of a constant step dimension. Let \(h\) and \(l\) denote step height and length. From the twenty-ninth step and downward, \(h=0.06\) m and \(l=0.12\) m. This longitudinal step layout is maintained in the study.  
Let \(\theta\) be the inclination angle (in degree) of step surfaces with the horizontal. Five geometrical layouts are devised through changes in the step angling, i.e., \(\theta=-20^{\circ}\), \(-10^{\circ}\), \(0^{\circ}\), \(10^{\circ}\) and \(20^{\circ}\). A positive  
Figure 1: Sketch of the spillway (**a**) stepped spillway, (**b**) conventional layout \(\theta=0\), (**c**) upward step \(\theta>0\), and (**d**) downward step \(\theta<0\).  
and negative \(\theta\) value denotes an upwards and downwards sloping surface, respectively (Figure 1c,d). For the conventional steps, \(\theta\) = 0\({}^{\circ}\). The equivalent step height is defined as \(H\) = \(h\) + \(l\)\(\times\) tan\(\theta\); the equivalent step surface length is \(L\) = \(l\)/cos\(\theta\). The range of \(H\) = 0.016-0.104 m and \(L\) = 0.120-0.108 m. The pseudo-bottom refers to the straight line running through the step vertices (Figure 1). An \(x\)-\(y\) coordinate system is also set up. The \(x\)-axis coincides with the pseudo-bottom; the perpendicular \(y\)-axis has its origin on it. The layouts are simulated to look into the effects of \(\theta\) on hydraulic behaviors expressed in terms of flow velocity, flow pressure and energy dissipation.