esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Geometry and Mesh Generation
### Expanding the Model--Stability of a Clayey Sand Embankment over a Clayey Soil  
A clayey sand embankment constructed over a clay is considered in the present analysis. The embankment is treated and reinforced with granular columns encased with geogrid. The height of the embankment was considered to be 4.0 m. The angle of the slope of 34\({}^{\circ}\) (3H:2V slope). The height of clay is 4.0 m. Due to the similarity in the geometry, a half embankment was analyzed to save computation time. In this study, a single row of granular columns was analyzed to understand its behavior and effectiveness in supporting the weight of an embankment or other structure as shown in Figure 4. The granular columns were arranged in a square pattern, with a center-to-center spacing of 2.5 times the diameter of the columns. The diameter of the columns was chosen to be 1.0 m. It is important to carefully consider the boundary conditions when analyzing the behavior of a structure, as they can have a significant impact on the results. In this study, the boundary conditions were chosen such that they would not significantly affect the calculated factor of safety (FoS) value. According to [PERSON] [27], the boundary beyond the toe of the embankment should extend at least twice the height of the embankment in order to accurately calculate the FoS. Therefore, in this study, the vertical boundary was placed 10 m beyond the toe of the embankment, ensuring that the boundary condition effects on the FoS value were minimized. To reduce the computational time and make the analysis more efficient, a bracketing solution approach was used. In this approach, if an approximate range for the FoS value is known beforehand, then the number of trials can be reduced by specifying the stable and unstable states at the beginning of the simulation. By specifying a range for the FoS value at the start of the analysis, the number of trials needed to reach a solution can be greatly reduced, making the process more efficient. The following boundary conditions are applied to the numerical models.  
1. The bottom boundaries are fixed in all of the directions (u\({}_{x}\) = 0; u\({}_{y}\) = 0; u\({}_{z}\) = 0);
2. It is assumed that the vertical boundaries are smooth u\({}_{y}\) = 0, same procedure was used by [9; 20; 28]. The water table was assumed to be at ground surface [20].  
The constitutive behavior, or the way in which a material responds to external loads, of the embankment soil, foundation soil, and granular columns was modeled using the Mohr--Coulomb model. This model is particularly well-suited for determining the factor of safety (FoS) of a slopes and embankments using the strength reduction technique. The properties of the materials were given in Table 2, and parametric studies were performed by altering the properties of the geogrid, as indicated in Table 3. To accurately represent  
Figure 4: Details of the problem.  
the granular columns and the surrounding soft soil, both a cylindrical mesh and radially graded meshes were utilized in the modeling process.
## 4 Search Area Path-Planning Model for Mine SAR
### Local Path-Planning Reward Function  
The state space \(s(t)\in S\) of search area path-planning problem consists of the environment and the agent. Meanwhile, the states in the observation space \(\Omega\) are given as  
Figure 1: The workflow of the gray online method.  
\(f_{\text{map}}=f_{\text{information}}\times f_{\text{position}}\) : Environment \(\rightarrow\Omega\) and \(f_{\text{position}}\in N^{2}\) and \(f_{\text{information}}\in R\). Observations \(o(t)\) (5 x 5 FOV in mission) are defined through the tuple  
\[o(t)=\big{(}M(t),V(t),W(t),B(t),p_{\text{target}}(t),\{\varphi(t)\}\big{)} \tag{13}\]  
A security controller is introduced into the system to implement an obstacle avoidance constraint (a). The security controller evaluates the point \(V_{i,j}(t)\) in the map and determines whether it can be reached. If not, it treats this point as \(\emptyset\).  
\[V_{ij}(t)=\left\{\begin{array}{ll}\emptyset,&(x_{i},y_{j})\in W\\ V_{i,j}(t),&\text{otherwise}\end{array}\right. \tag{14}\]  
In addition, a safety controller evaluates the agent's action \(a(t)\) and determines whether the action should be punished. Based on the conclusion, the corresponding safety penalty value \(\beta\) is offered:  
\[\beta(t)=\left\{\begin{array}{ll}-1,&p(t)+a(t)\in B\\ 0,&\text{otherwise}\end{array}\right. \tag{15}\]  
The reward function \(R:S\times A\times S\to R\) of the POMDP is defined as  
\[r(t)=\tau\times\Phi(t)+\beta(t)+\varepsilon \tag{16}\]  
The agent reaches a new state \(s(t+1)\) which may depend on the state \(s(t)\) and action \(a(t)\), and updates value Q.
# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 3 Experiment and Results
### Comparison with State-of-the-Arts Models  
To validate the effectiveness of the proposed method, we conducted comparative experiments against bicubic interpolation and several state-of-the-art (SOTA) models, including EDSR [36], RDN [28], SRFBN [37], HAN [38], SwinIR [23], ELAN [39], CRAFT [40], SMFAN [41], RGT [31], and CDFormer [42]. All baseline models were evaluated using their default configurations.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Reservation and Adaptive Protocols  
#### 5.2.1 Reservation Aloha (R-Aloha)  
This protocol divides the time into \(m\) slots, each with the duration of a packet transmission time. The slots are grouped in frames, and the nodes randomly choose one slot per frame to send a packet. If a node successfully sends a packet, it proceeds to reserve the same slot in future frames. At the end of each frame, the receiver responds with an ACK, also indicating what the available slots for the next cycle are. An example of the operation of reservation Aloha (R-Aloha) is shown in Figure 8. The R-Aloha protocol was proposed for incorporating satellite communications in the ARPA network [57].  
Figure 7: Random frequency time division multiple access (RFTDMA) communication process. Temporal and spectral random access from hundred of nodes.  
The throughput of this protocol depends mainly on the number of packets sent by each node during a reservation. In the worst case, the performance of R-Aloha is similar to the S-Aloha. When the protocol holds reservations for a large number of frames, the normalized throughput \(S\) approaches to 1 [58].  
Although R-Aloha reports having a normalized throughput that tends to 1, it may not meet the requirements of IoT scenarios: the protocol has a good performance when the nodes disputing the channel resemble the number of slots in each frame, and also when the reservations made by each terminal last a large number of frames. However, when the scenario does not comply with these assumptions, R-Aloha turns out to be an unscalable protocol. Moreover, for IoT applications where nodes typically have small and/or infrequent amounts of data to send, the reservation mechanism of R-Aloha may result impractical.
## 4 Physical Audio Features Extraction Techniques
### Cepstral Domain Physical Features  
Cepstral features are compact representations of the spectrum and provide a smooth approximation based on the logarithmic magnitude. They have been largely used for speaker identification and speech recognition but they have also been employed in the context of audio retrieval.  
The main cepstral domain physical features found in the literature are the following:  
* **Complex cepstrum:** is defined as the Inverse Fourier transform of the logarithm (with unwrapped phase) of the Fourier transform of the signal (see [PERSON] and [PERSON] [140]), and has been used for pitch determination of speech signals ([PERSON] [141]) but also for identification of musical instruments (see [PERSON] [142]).
* **Linear Prediction Cepstrum Coefficients:** or LPCC for short. This feature is defined as the inverse Fourier transform of the logarithmic magnitude of the linear prediction spectral complex envelope (Atal [143]), and provide a more robust and compact representation especially useful for automatic speech recognition and speaker identification ([PERSON] and [PERSON] [144]) but also for singer identification ([PERSON] _et al._[145]), music classification ([PERSON] _et al._[146], [PERSON] and [PERSON] [147]) and environmental sound recognition (see [PERSON] _et al._[18], or [PERSON] _et al._[14]).
## 5 Conclusions  
The key figure of merit of any digital electro-optical transceiver is the sensitivity for a given BER. However, traditional mathematical models calculate the signal-to-noise ratio per subchannel without investigating the accuracy of the associated BER and, consequently, the sensitivity. In contrast, the mathematical model presented in [10] attempts to predict the best achievable receiver sensitivities for DD SCM/SSB links based on optical IQ modulators and pre-amplified receivers. Previous works have shown the accuracy of the model in terms of performance difference as a function of parameters like OMI [10]. This paper further extends the accuracy of the model to estimate the best achievable sensitivities as a function of OMI.  
The model has been compared with simulations for a case where the BER threshold is high, in line with modern high-performance transceivers. A good agreement with theory has been obtained and the optimum OMI can be predicted with small errors in sensitivity. The inaccuracies are the result of  
Figure 2: Simulated and theoretical best achievable optical receiver sensitivities for **(a)** the SCM/SSB frequency plan consisting of three 2.7 Gbaud QPSK subchannels and **(b)** the OSCM/SSB frequency plan consisting of three orthogonally overlapping 2.7 Gbaud QPSK subchannels. The values employed in the simulation and the theoretical calculations are: \(N=3\), \(F=5\) dB, \(v=193.4\) THz (1550 nm), \(Q_{F}=2.67\) (BER = \(3.8\times 10^{-3}\)), \(B=2.7\) GHz, and intermodulation product count according to Table 1.  
modelling IMPs and IMD, a deterministic phenomenon, as Gaussian noise. The mathematical model can also be applied to the case of OSCM/SSB, obtaining slightly higher errors. The additional errors occur because the model does not include the impact of IMP on adjacent overlapping subchannels.  
This work was supported by EI CFTD CF/2011/1627 and SFI grants CTVR (10/CE/I1853), IPIC (12/RC/2276), and US-Ireland (15/US-C2C/I3132).  
[PERSON]. [PERSON], [PERSON], and [PERSON] developed the mathematical model. [PERSON] performed the VPI simulations. [PERSON], [PERSON], and [PERSON] analysed the results. [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 2 Materials and Methods
### Data Processing
In each per-channel vector, there are also discontinuities in the GPS satellite tracked. These discontinuities can be detected by the change of the PRN (Pseudo-Random Number) that identifies the tracked GNSS transmitter. Both types of jumps must be avoided in the computation of \(S_{4}\), because they may create false \(S_{4}\) peaks. So, the vector, per each of the 4 channels, is divided into those periods between jumps (PRN or time jumps), and then the computation of the \(S_{4}\) value is done using:  
\[S_{4}=\sqrt{\frac{\langle I^{2}\rangle-\langle I\rangle^{2}}{\langle I\rangle^{ 2}}}, \tag{2}\]  
where \(I\) is intensity in linear units, computed from the \(SNR\) value in dB, and the average is computed using a 12-samples-moving window along the vector. In Figure 5, the \(S_{4}\) values using the SNR data from Figure 4 are plotted. For the sake of clarity in the interpretation, the points where \(S_{4}\) is lower than 0.1 are not shown, and for the rest, their point size and color are proportional to the \(S_{4}\). Additionally, the roughness of the water surface could make the reflection to be diffuse instead of specular. This would happen when there are high waves in the area, as opposed to having a flat ocean surface. In practice, this is making a sort of filter in which the possible scintillation happening over rough sea surfaces, is hidden, keeping only high \(S_{4}\) values over regions with a calm ocean. To check the wind and wave height for day and hour under study, ICON [16; 17] model meteorological online data were used [18]. As an example, continuing with day 21 November 2017, in Figure 6, the colormap of wave-height along the Atlantic ocean is underlaid the \(S_{4}\) values, only showing the ones that are larger than 0.05. It can be check that most of \(S_{4}\) peaks only appear over regions with quite calm ocean. Figure 5: Computed and filtered \(S_{4}\) values over all sea regions from 40\({}^{\circ}\)S to 40\({}^{\circ}\)N for 21 November 2017.
# An Optimized CNN Model for Engagement Recognition in an E-Learning Environment
## 6 Discussion  
### Answering the Research Questions  
**RQ 1:**_Which CNN-based models are effective for recognizing student engagement in an E-learning environment?_  
After our literature review, we decided to use four CNN models: Inception v3, ResNet, basic ShuffleNet v2 (which is a lightweight CNN model designed for mobile platforms), and an optimized ShuffleNet v2 model with an improved activation function and the introduction of an attention mechanism. These models are all able to perform engagement recognition according to our experiments, and ShuffleNet v2 was the main baseline model we used. Inception v3 and ResNet models were used for additional comparison.  
**RQ 2:**_Which methods optimize CNN-based engagement recognition to adapt it to mobile devices?_  
We suggest the use of attention mechanism SE block as a possible optimization method, with more details mentioned above. Further, introducing the activation function also optimized the model. Finally, using Leaky ReLU rather than ReLU achieves better results.  
**RQ 3:**_How does the optimized model perform compared with other selected models in recognizing student engagement in an E-learning environment?_  
We conducted an experiment to evaluate the recognition performance of these deep learning models. After all of the models were implemented and the data were processed, the models were trained on the training dataset. Then, the trained models were tested on the test dataset. The models' accuracy, precision, recall and final F1-scores were calculated. Optimized ShuffleNet v2 had the best performance among the deep learning models that were selected for this study. Furthermore, the accuracy of engagement recognition of our optimized ShuffleNet v2 model was also superior compared to the other published methods and models using the same dataset.
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 2 Raman Spectral Imaging System
### Software  
Interface software of the Raman system was developed on a LabView platform (National Instruments, Austin, TX, USA) for parameter setup and data transfer. The interface software controls system operational parameters, such as initialization, exposure time, spectral acquisition and display, sample movement, and data transfer. The software development kits (SDKs) that were provided by manufacturers of InGaAs detector and positioning table were used to develop the interface software. The acquired hyperspectral data are stored in the band interleaved by pixel (BIP) format, which can be commercially analyzed by available software, such as ENVI (4.5, ITT Visual Information Solutions, Boulder, CO, USA) and Matlab (R2013a, MathWorks, Natick, MA, USA).
## 5 Comparison with Related Studies
However, the utilization of OCS-SCM with its advantages and disadvantages (as in Section 1) limits the number of channels due to the large channel spacing required. A review of this system's architecture leads to a moderate-to-high level of complexity. This complexity is due to the existence of two EDFAs--AWG and long DCF [19]. Dual-arm modulator configurations are proposed in [20] to perform wavelength shifting for the ONUs to mitigate the RB impairment in bidirectional centralized light PONs. Although the proposed configurations reduce the dependence of the system on devices like EDFAs, AWG, and long DCF, the use of a combination of amplitude and phase modulation dramatically limits the US and DS rates [20]. On the other hand, this work, with its low operating rate, can provide perfect integration between a WDM-PON system (as a step toward 5G systems) and other low bit rate commercial 5G systems, such as the single carrier modulation (SCM) visible light communication (VLC) systems, as well as their applications [39; 40]. The limited number of channels and the non-availability of such a modulator at that time are additional limitations [20]. The highest observed US and DS rates are accomplished with a record of 40 Gb/s in [10]. This work utilizes an integrated semiconductor optical amplifier with a reflective electro-absorption modulator (SOA-REAM). The idea of integrating everything into one compact device is currently pioneering and seems promising. Unfortunately, severe inter-symbol interference and strong beat noise are major limitations [10]. A material like a linear/non-linear photonic crystal (Phc) could mitigate these limitations. With its remarkable integration ability, low power consumption, and remarkable performance in variety of applications [41; 42; 43], the functionality of SOA-REAM can be achieved with attractive operational characteristics. In [10], several techniques are proposed to overcome these limitations, but the effects of these limitations dominate, since the optimum allowable BER = \(2\times 10^{-4}\), with 20 Km and one channel.
## 4 Discussion and Conclusions
### Implications for the Rehabilitation of Gait  
The possibility to record ERPs related to human gait in real time offers novel ways for the treatment of people with various gait impairments. The present study suggests that gait-related motor potentials are reliably elicited during treadmill walking and can be analyzed by EEG recordings time-locked to the gait-marker derived from gait recordings via force plates. Regarding the technological solutions, these analyses can be extended to IMU systems in future studies.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 4 Preliminaries and Background
### Time-Series Classification
#### 4.1.3 Dynamic Time Warping (DTW)  
Compared to ED, DTW is more robust in similarity computation. DTW is a very popular tool in temporal data mining, and its distance comparison is less sensitive to signal transformations such as shifting, uniform amplitude scaling, or uniform time scaling [51]. DTW-based time-series similarity measures are less affected by time distortion. It allows elastic transformation of time series to detect similar shapes with different phases. Since DTW is invariant to signal warping, such as scaling in the time axis or the Doppler effect, it is preferred for pattern matching tasks [52].  
To find the distance between two time series \(X=(x_{1},\ldots,x_{i},\ldots,x_{n})\) of length \(n\) and \(Y=(y_{1},\ldots,y_{j},\ldots,y_{m})\) of length \(m\), DTW is computed by first finding the best alignment between them. An \(n\)-by-\(m\) matrix is constructed, in which its (\(i\)th, \(j\)th) element is equal to \(\left(x_{i}-y_{j}\right)^{2}\), representing the cost to align the point \(x_{i}\) of time series \(X\) with point \(y_{j}\) of time series \(Y\). An alignment between the two time series is represented by a warping path, \(W=w_{1},w_{2},\ldots,w_{K}\), in the matrix. The path must be contiguous, monotonic, start from the bottom-left corner, and end at the top-right corner of the matrix. The warping path has a length \(K\), which is  
\[max(n,m)\leq K<(|n|+|m|). \tag{4}\]  
The \(k\)th element of the warping path, \(W\), is \(w_{k}=(i,j)\), where \(i\) and \(j\) are corresponding indices from time series \(X\) and \(Y\), respectively [32]. The best alignment is given by a warping path through the matrix that minimizes the total cost of aligning its points. The minimum total cost is the \(DTW\) distance, as follows [43; 53]:  
\[DTW(X,Y)=argmin_{(W)}=\sqrt{\sum_{k=1,\alpha_{k}=(i,j)}^{K}(x_{i}-y_{i})^{2}}. \tag{5}\]  
To compute the minimum cost alignment, dynamic programming (DP) is used, which increases the complexity of the computation. More critically, DP is a sequential process, making DTW non-parallelizable. Some techniques have been proposed to parallelize DP [52].
## 4 Experimental Results
### Performance Metrics  
First, we should note that all of the implementations have been tested on common data sets of varying sizes. Here, there is no influence of NMP run-time performance on either data quality or the data inputs. To measure the algorithm's performance, we compared NMP with the STAMP and STOMP algorithms. Based on the testing results, the following metrics were considered:  
* Time required for the similarity search;
* Accuracy;
* Detection efficiency.
# FEM Modelling Approaches of Bolt Connections for the Dynamic Analyses of an Automotive Engine
## 7 Conclusions  
Three FEM modelling approaches for the numerous bolts interconnecting the components of a petrol engine were compared in terms of computational burden, preprocessing time, and modelling accuracy. FRF analyses were selected to characterize the vibration response of a reduced model of the engine, when different approaches were adopted for modelling bolts.  
As a matter of fact, the 3D FEM modelling presented relevant drawbacks in terms of memory requirements (30 to 60 GB of memory occupation) and runtimes (up to two days' calculus to obtain the needed FRFs) for a full engine dynamic analysis.  
At relatively low frequencies (<2000 Hz), gluing the surfaces turned out to provide a slight accuracy improvement but at expenses of runtimes and preprocessing times. Using the \"1D model\" was more advantageous for users since it is standardized company procedure, consequently less prone to error and involving a lower user effort.  
At higher frequency and when high pretension loads are needed, \"Glue\" provided a slight accuracy improvement in comparison with \"1D model\", whereas the reverse happened for low pretension load.  
Compared to \"1D model\", the \"Glue\" approach increased computational time since additional equations had to be solved to take into account the interactions between surfaces. Such an increase could be estimated at +20%.  
\"1D model\" did not increase preprocessing time significantly, since creation of 1D bolts was automated and allowed for completing a modal analysis up to 4000 Hz with 6 h of runtime.  
Figure 8: FRFs in the frequency range 2000–4000 Hz, for the four hammered positions with highlight of eigenmodes related to the highest resonance amplitudes.  
In conclusion, the \"1D model\" approach could be envisaged as the recommended one for a simplified modelling of the bolt connections, thanks to its substantial advantages in terms of preprocessing times and, at the same time, providing a comparable accuracy with \"Glue\" approach.  
Conceptualization E.A. and R.C.; methodology, V.G. and R.S.; software, V.G.; formal analysis, V.G. and R.S.; investigation, V.G.; writing--original draft preparation, R.S. and R.C.; writing--review and editing, R.S., R.C., and E.A.; supervision, E.A. and R.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.