esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
As the channel gains between the satellite and \(N\) elements on RIS \(k\) are highly correlated, we have the followings [24]:  
Figure 1:1 Figure 1:2  
where \(\xi_{k}\) and \(a_{k}\) are the coefficients representing, respectively, the effects of the path loss and beam pattern as follows:  
Figure 1:3 Figure 1:4  
Here, \(d_{k}^{\mathrm{sr}}\) is the distance from the satellite to RIS \(k\), \(G_{\mathrm{max}}\) is the maximum gain of the satellite beam, \(J_{n}(\cdot)\) is the first kind Bessel function of order \(n\), and \(u_{k}=2.07123\frac{\sin\phi_{k}}{\sin\phi_{\mathrm{3 dB}}}\). Also, \(\phi_{k}\) and \(\phi_{\mathrm{3 dB}}\) are the angle between the beam center and RIS \(k\) and the 3 dB angle of the beam, respectively. \(\psi_{k}\) is the phase of the fading channel at RIS \(k\) that is assumed to be distributed uniformly over \([0,2\pi)\) as in [23]. \(|c_{k}|\) is the rain fading channel gain that follows a double-lognormal distribution, such as \(|c_{k}|\sim\mathcal{L}^{2}\mathcal{N}\left(\eta_{k},\sigma_{k}^{2}\right)\), whose probability density function (PDF) and cumulative distribution function (CDF), respectively, follow [25]  
Figure 1:5 Figure 1:6  
where \(0<x<1\) and \(Q(\cdot)\) is a Gaussian Q-function. Rain fading and LoS components in satellite communications are the main channel factors distinguishing them from terrestrial communication systems. The cascaded channel from the satellite to UE \(k\) via RIS \(k\) is given by  
Figure 1:7 Figure 1:8  
Then, the SNR at UE \(k\) and the achievable sum-rate can be obtained, respectively, as follows:  
Figure 1:9 Figure 1.0  
where \(f_{k,n}\) represents the \(n\)-th element of a vector \(f_{k}\). We can observe from (17) that, when \(\theta_{k,n}=-\angle f_{k,n}^{*}\forall n\), \(\gamma_{k}\) attains its maximum value, \(\gamma_{k}\), which is given as follows:Figure 1.1  
In this case, the achievable rate of UE \(k\) is given by \(\tilde{R}_{k}=\log(1+\tilde{\gamma}_{k})\). Note that RISs are generally passive; moreover, they are typically not equipped with RF chains and are installed on UAVs with limited batteries. Thus, it is hard to accurately estimate RIS-related channel information, such as \(\mathbf{f}_{k}\), in the considered SatCom system, and UE \(k\) cannot practically achieve the data rate of \(\tilde{R}_{k}\). Given that it is practically feasible to estimate the partial channel state information (CSI) of RIS-related channels [23], we propose a practical passive beamformer that is solely based on the partial CSI while achieving optimal performance, i.e., \(\tilde{R}_{k}\), asymptotically.
## 2 Materials and Methods
### Dataset and Data Preprocessing
#### 2.1.1 Feature Generation
For our Gram-positive and Gram-negative datasets, 1497 features including amino acid composition (20), dipeptide composition (400), CTD composition (21), CTD transition (21), CTD distribution (105), Moran autocorrelation (240), Geary autocorrelation (240), Moreau-Broto autocorrelation (240), quasi-sequence-order descriptors (100), sequence order coupling number (60), and pseudo amino acid composition (50) are calculated via freely available PyProtein module in PyBioMed python package [72]. These features are also used in other studies for AMP prediction using machine learning [61; 76].
## 3 Results
### Age and Gender
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Task** & & **7 Years** & **8 Years** & **9 Years** & **10 Years** & **11 Years** & **12 Years** & **Adult** \\ \hline \multirow{3}{*}{SOD} & Men & 1.18 (0.42) & 1.08 (0.42) & 0.90 (0.26) & 0.85 (0.22) & 1.02 (0.65) & 0.98 (0.35) & 0.78 (0.28) \\  & Women & 0.92 (0.38) & 0.81 (0.29) & 0.87 (0.23) & 0.86 (0.36) & 0.87 (0.28) & 0.84 (0.20) & 0.68 (0.09) \\ Sig. & & 0.00 & 0.00 & 0.60 & 0.96 & 0.21 & 0.03 & 0.33 \\ T & & 2.76 & 3.09 & 0.52 & 0.05 & 1.25 & 2.17 & 1.03 \\  & Men & 1.32 (0.47) & 1.41 (0.50) & 1.08 (0.53) & 1.09 (0.34) & 1.19 (0.84) & 1.09 (0.40) & 0.73 (0.25) \\ SOS & Women & 1.06 (0.44) & 0.94 (0.32) & 1.02 (0.35) & 1.07 (0.38) & 0.93 (0.32) & 1.04 (0.34) & 0.78 (0.21) \\ Sig. & & 0.02 & 0.00 & 0.58 & 0.80 & 0.09 & 0.59 & 0.62 \\ T & & 2.40 & 4.64 & 0.55 & 0.25 & 1.71 & 0.54 & 0.51 \\  & Men & 1.98 (0.61) & 2.41 (0.89) & 2.07 (1.07) & 2.10 (0.59) & 1.96 (0.79) & 2.01 (071) & 1.67 (1.07) \\ SCD & Women & 1.62 (0.64) & 1.47 (0.53) & 1.82 (0.83) & 1.91 (0.00) & 1.72 (0.60) & 1.77 (0.59) & 1.38 (0.65) \\ Sig. & & 0.02 & 0.00 & 0.28 & 0.37 & 0.15 & 0.14 & 0.48 \\ T & & 2.38 & 5.39 & 1.08 & 0.90 & 1.43 & 1.50 & 0.73 \\ SCS & Men & 2.27 (0.73) & 2.83 (1.18) & 2.33 (1.20) & 2.63 (1.21) & 2.16 (0.71) & 2.29 (2.57) & 1.26 (0.84) \\  & Women & 2.08 (1.03) & 1.82 (0.68) & 1.76 (0.61) & 2.05 (1.04) & 2.07 (0.92) & 2.02 (1.00) & 1.45 (0.45) \\ Sig. & & 0.38 & 0.00 & 0.01 & 0.03 & 0.65 & 0.04 & 0.52 \\ T & & 0.88 & 4.42 & 2.51 & 2.16 & 0.45 & 2.04 & 0.65 \\ \hline \end{tabular} Abbreviations: TOD = feet together and open eyes with dual tasks; TOS = feet together and open eyes without dual tasks; TCD = feet together and closed eye with dual tasks; TCS = feet together and closed eyes without performing dual tasks; SOD = single limb with open eyes with dual tasks; SOS = single limb with open eyes without dual tasks; SCD = a single limb with closed eyes with dual tasks; SCS = single limb with closed eyes without dual tasks.  
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Task** & & **7 Years** & **8 Years** & **9 Years** & **10 Years** & **11 Years** & **12 Years** & **Adult** \\ \hline \multirow{3}{*}{TOD} & Men & 0.88 (0.45) & 0.86 (0.32) & 0.75 (0.40) & 0.84 (0.48) & 0.75 (0.45) & 0.66 (0.27) & 0.50 (0.18) \\  & Women & 0.73 (0.31)
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 4 The 5G Global Tracking System  
The Maritime Platform can enable a number of different logistic applications relying on massive IoT, OneM2M platform as well as satellite networks to collect data also when the vessel is in open sea. Indeed, except for data collected from NB-IoT devices that are different depending on the application (and hence the NB-IoT smart object design has to change accordingly), the other components of the Maritime Platform are agnostic to the application.  
In this paper, we take as an applicative example the case of real-time monitoring of cargo shipping status where the Maritime Platform acts as a container tracking platform, i.e., the 5G Global Tracking System and where the NB-IoT smart object design is composed by the following elements:  
1. Sensors detecting container parameters (for example inside temperature), called container tracking devices (CTDs);
2. A data acquisition board interfaced with sensors and radio transceivers;
3. A NB-IoT transceiver to connect the device to maritime or terrestrial cellular network;
4. A Global Positioning System (GPS) module;
5. An additional Bluetooth 5.0 transceiver to establish a mesh network composed by all devices installed inside the containers. In case a device is not able to attach the cellular network via NB-IoT interface (due to the attenuation introduced by the stack of containers), data are forwarded to a neighbor device by means of the mesh network, enabled by a supplementary radio interface. This second device will try to transmit the received data via a NB-IoT interface. In case of failure, it will forward again the data to its neighbor device (via mesh network) and so on.  
The 5 GT System aims at the continuous port-to-port (and terminal-to-terminal) tracking of a large amount of goods carried by ships, even during deep sea journeys thanks to the integration between IoT, cellular network and satellite network. According to the containers or cargo ship position, the 5 GT system operation could be split into three scenarios. The first two scenarios (port terminal and near sea) belong to the terrestrial domain, while the third one refers to the maritime domain. For each scenario, the data produced by the NB-IoT smart objects reach the IoT maritime platform for specific application components to be developed on top of the Maritime mIoT service framework. The scenarios are summarized in Table 2.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Global Tracking System** & **Description** \\
**Operating Scenarios** & The NB-IoT smart objects attach the terrestrial cell. The Telco Operator Network authenticates them. \\
1—Containers are stored in the port terminal (terrestrial domain). & 1a—The NB-IoT smart objects send the data to the terrestrial cell; 1b—The terrestrial cell routes the data to the Telco Operator Network; 1c—Data are routed to the IoT maritime platform. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global Tracking System operating scenarios description.
## 4 Discussion
### Program Iteration and User Satisfaction  
In this study, the analysis of iteration loops--the number of times a program was modified before reaching a stable configuration--provided new insights into user behavior that had not been extensively explored in previous research involving cochlear implant recipients. Approximately 57% of the programs created by the participants were deemed satisfactory after their initial setup, requiring no further modifications. This suggests that many users were able to quickly identify settings that met their auditory needs, achieving a high level of satisfaction early in the process. For the remaining 43% of the programs, the participants made modifications at a later time, indicating that some users continued to refine their settings to different acoustic environments or as their preferences evolved. The relatively low number of iteration loops observed, combined with the positive spontaneous evaluations (as seen in Figure 16), indicates that the participants generally found the self-adjustment process effective and user-friendly. This early achievement of satisfactory results reflects the app's potential to empower users by providing them with the tools to fine-tune their auditory experience according to their personal needs. However, while the subjective ratings of sound quality improvements are encouraging, they do not necessarily translate to measurable improvements in speech comprehension or other objective auditory outcomes. Therefore, future investigations should incorporate objective measurement methods such as speech intelligibility tests or determination of the hearing threshold.
## 2 Materials and Methods
### Human Activity Representation
#### 2.2.2 Pose Profile  
Fall-related activities involve different sub-activities that occur over time, such as the sitting pose, fall pose, stand still pose and other sub-activities, as described in Section 2.1. Having the ability to distinguish between these different sub-activities is crucial to enhance the fall detection process. However, different sub-activities may have similar body-postures due to inter- and intra-personal variations, which make the process of identifying these sub-activities challenging. In order to recognize different fall-related poses, we propose to construct a pose profile that can describe the different human postures that are incorporated within the different fall-related sub-activities using two types of geometrical relational-based features, namely the distance-based features \((P_{df})\) and angle-based features \((P_{af})\).  
In order to obtain the distance-based features, we compute the Euclidean distances between the 3D locations of every pair of the skeletal joints as follows:  
\[P_{df}(i,j)=\parallel S_{i}-S_{j}\parallel\,\forall i\
eq j, \tag{6}\]  
where \((i,j)\) represents any pair of the twenty skeletal joints employed in this study for detecting the fall-related activities as described in Section 2.2. \(S_{i}\) and \(S_{j}\) are the 3D positions of the \(i\)-th and \(j\)-th skeletal joints, respectively. Then, for a video frame at index \(t\), the distance-based features \(P_{df}(t)\) are defined as the vector combining all of the distances \(P_{df}(i,j)\) for all \(i\
eq j\).  
The angle-based features is represented as the set of time-varying angles between the different body parts associated with the fall-related sub-activities. Specifically, for each video frame, we utilize the skeletal joint positions to compute the angles listed in Table 2. Then, for a video frame at index \(t\), the angle-based features \(P_{af}(t)\) are defined as a vector, which consists of the values of the nine angles listed in Table 2.  
Figure 3: A schematic diagram that describes the construction of the body-attached coordinate system at the hip center joint along with the three anatomical planes.  
After computing the motion and pose profiles, the modified MPGD of the frame at index \(t\) in a depth-map video sequence is constructed by combining both the motion and pose profiles to form a descriptor vector as follows:  
\[\mathbf{MPGD}(t)=[\mathbf{MP}(t),\mathbf{P}_{df}(t),\mathbf{P}_{af}(t)], \tag{16}\]  
where \(\mathbf{MP}(t)\) represents the motion profile of the video frame at index \(t\). In the next subsection, we describe the proposed classification framework for detecting falls from partially-observed depth-map video sequences.
## 3 Phase Modulation Effects of the Parameters and Parameter Selection
### Parameter Selection for Phase Modulation  
We demonstrate three intuitive phase modulation manners and analyze the phase modulation effects of the parameters, including the locked phase \(\delta_{k}\), fringe density \(f_{k}\), and step size \(S_{k}\). The results show that the desired phase distribution can be acquired by varying the parameter combinations as follows: (a) \(f_{k}\) and \(\delta_{k}\); (b) \(f_{k}\) and \(S_{k}\); (c) \(f_{k}\), \(S_{k}\), and \(\delta_{k}\). These parameters can be selected for phase modulation.  
In an actual SBIL system, it is difficult to modulate the exposure phase by only varying \(S_{k}\) because of the stage positioning error. The stage positioning error can be corrected with \(\delta_{k}\) according to the phase compensation relationship. Under this condition, an actual stage can be considered an ideal stage without a positioning error, similar to that in an EP-SBIL system. Therefore, parameter combinations (a) and (c) are applicable to practical applications. Comparing the groove density errors in Tables 1 and 2, the parameter combination (a) has a better performance, and \(x_{comb}=(X_{k}+X_{k-1})/2\) is the optimal coordinate for phase compensation.  
In addition to the grating example in Section 3.1, other VLS gratings, including another grating in a monochromator (\(n_{g0}=400\) L/mm, \(n_{g1}=-0.2594\) L/mm and \(W_{g}=80\) mm) [20] and a grating in a diagnostic spectrometer (\(n_{g0}=600\) L/mm, \(n_{g1}=-0.7869\) L/mm and \(W_{g}=30\) mm) [24], are designed for further verification. The results are consistent with the above results. Compared with the existing fabrication methods of VLS gratings, the relative errors of the groove density can satisfy the technical requirements [1; 2; 3; 8; 20; 24], and the exposure contrast can satisfy the demands for VLS grating fabrication.
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 1 Introduction
However, few studies have considered the lung as a porous medium and investigated whether differences in lung tumors have an impact on the results of microwave ablation. There is a lack of basic reference data for microwave ablation of lung tumors, and the selection of treatment parameters (ablation time and power) that are only based on experience is prone to inadequate or excessive ablation. Therefore, it is necessary to establish a model which is closer to the actual situation of lung tissue, and the results of this model can provide help for obtaining more accurate ablation parameters. In this study, microwave ablation of a spherical tumor surrounded by healthy lung tissue was simulated, and heat transfer in porous media was modeled, based on the local thermal equilibrium equation. The maximum temperature obtained by Pennes heat transfer equation and the porous medium heat transfer equation are compared. At the same time, the effects of different tumor diameters, different tumor porosities, and different microwave ablation powers on the therapeutic effect of microwave ablation of lung tumors were analyzed from three aspects: the highest temperature of the tissue, the volume of ablation, and the size of the ablation area. The final ablation area and volume can be predicted more accurately by using the porous heat transfer model, and thus the ablation parameters can be determined, which is beneficial to the accurate treatment of microwave ablation of lung tumors.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 5 Diagnosis Simulation  
### Generation of Car-Body Vibration Data  
We performed a vehicle travel simulation using SIMPACK and calculated the car-body vibration when the vehicle was traveling on tracks with different irregularities in some sections. The obtained data were used to detect track faults using the developed algorithm.  
The track geometry used for the simulation differed from that shown in Figure 8. The track faults were modelled by increasing the reference track irregularities by factors of 1.5 and 2.0 times and added to the reference track geometry. Figure 9 shows the modelled track faults. The track geometry used in the simulation was generated by adding the track fault in Figure 8 to the reference track geometry. The total length of the track geometry was 5000 m. Furthermore, 100 faults of longitudinal level, alignment, and cross level irregularities were randomly provided in different places.  
Figure 8: Track fault.  
Figure 9: \(Cont\).  
Figure 10 shows the calculation result of the car-body vibrations when a vehicle travels the track geometry shown in Figure 9. As shown in Figure 10, only the vertical or lateral acceleration increases when the longitudinal level irregularity or the alignment irregularity increase. As shown, both the lateral acceleration and roll rate increase when the cross level irregularity increases.
## 3 Four-Port MIMO Antenna
### Results and Discussion  
#### 3.2.1 S-parameters  
Figure 11 depicts the simulated and measured reflection and transmission coefficient of the proposed four-port ultra-wideband MIMO antenna. It can be seen that the antenna offers a broadband of 26.5-41.7 GHz with resonance frequencies of 30 GHz, 33 GHz, and 38 GHz. It can also be observed from the figures that each element of the MIMO antenna shows good correlation with each other, as well as with measured results. On other hand, the figure also shows the transmission the coefficient of the proposed antenna. According to the figures, the antenna offers minimum isolation around \(-\)55 dB and maximum isolation around \(-\)35 dB for nearby element and minimum isolation of \(-\)52 dB and maximum isolation of \(-\)32 dB with adjacent element. The results in the form of transmission and reflection coefficient and strong agreement between simulated and measured results, making the proposed MIMO antenna a good applicant for future 5G devices operating over broadband applications.  
The minor difference between measured and simulated results may be due to:  
* The fabrication tolerance of apparatus used for fabrication of antenna;
* Measurement setup tolerance due to usage of old wires;
* Connectors used in measurements as mismatching occur due to connectors at higher frequency due to increase in losses on connectors.
###### Abstract  
Forecasting the output power of solar PV systems is required for the good operation of the power grid and the optimal management of energy fluxes occurring in the solar system. Before forecasting the solar system's output, it is essential to focus on the prediction of solar irradiance. In this paper, the solar radiation data collected for two years in a certain place in Jiangsu in China are investigated. The objective of this paper is to improve the ability of short-term solar radiation prediction. Firstly, missing data are recovered through the means of matrix completion. Then the completed data are denoised via robust principal component analysis. To reduce the influence of weather types on solar radiation, spectral clustering is adopted by fusing sparse subspace representation and k-nearest-neighbor to partition the data into three clusters. Next, for each cluster, four neural networks are established to predict the short-term solar radiation. The experimental results show that the proposed method can enhance the solar radiation accuracy.  
solar radiation; matrix completion; robust principal component analysis; sparse subspace representation; k-nearest-neighbor; artificial neural network +
Footnote †: journal: Applied Sciences
## 3 Facility and Experimental Section  
### Experiment Facility  
The thickness of a single scrap could be determined precisely by using laser triangulation, which has already been widely applied for decades for volume stream measurement. The laser triangulation system could be installed at a specific distance ahead of the scrap volume stream and was able to determine the height of a flake object within 45 ms.  
The experimental facility was established as in Figure 2.  
The plastic scraps of specific particle size were individually fed onto a conveyor belt through a vibration feeder and then at the end of the belt they were dropped into the impact chamber in which the impact processes were implemented. Thicknesses of scraps were measured by laser beam and a camera installed above the conveyor belt and then the results stored in a computer. The AE pickup was installed inside the chamber. An optical trigger was fitted at the entrance of the chamber, by which the pickup could acquire the impact AE of the falling scraps sequentially according to the corresponding thickness data scraps. At the exit of the chamber there was also an optical trigger, which controlled a compressed air nozzle array together with a computer; the nozzle array had the same width as the exit of the chamber, which was able to blow every recognized scrap out of the main material stream and resulted in sorting tasks. The compressed air nozzle array is mainly used as an additional force field for different kinds of sensor aided sorting mechanisms. Reference [26] mentioned that the nozzle array separation frequency could achieve theoretically 40 pieces per second, but here in this research, in order to insure the sorting purity and efficiency, we tested the samples by using a feeding velocity of about 1-2 pieces/s. Under this velocity a sorting purity and efficiency of more than 99% could be achieved. Of course, sorting of flakes with extreme high purity and efficiency has already been implemented, for example, company TORMA has developed a facility with a sorting yield purity of up to 99% and an efficiency up to 97%, achieved under a feeding stream of 30 t/h.  
All impact AE signals were acquired by an acoustic pickup, _i.e._, an industrial microphone as analog signal input and then transferred to a soundcard where the analog signals were converted to digital signals. The A/D and D/A capacity of the soundcards are already adequate for industrial measurements and the prices of soundcards are much lower than professional devices. Normally a precision of 16 bit soundcards is better than the 12 bit professional data acquisition (DAQ) cards. Soundcards transfer data by Direct Memory Access (DMA) technology results in a massive reduction of the CPU occupation, which could realize high speed data communication. The Peripheral Component Interconnect (PCI) bus  
Figure 2: Experimental impact AE sorting facility.  
technology allows high speed data communication between soundcard and system and makes online analysis and real time manipulation using virtual instruments possible.