esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Result
### Qualitative Performance Evaluation
#### 4.2.3 Limitations of the Proposed Method  
In this section, two limitations of the proposed method are introduced. First, the proposed method failed, when the pedestrian head was completely occluded for just a few frames. But, it succeeded when the pedestrian head was partially occluded. Second, it failed also when a pedestrian was located far away about 50 m from the IR camera.  
Full occlusion is most important issues in the pedestrian tracking field. In the Figure 13(a) presents the scenario of the complete pedestrian occlusion. Figure 13(b),c presents the partially occluded scenario. The temperature information helps to track the pedestrian head when the pedestrian head is occluded partially but it fails to track when the head is completely occluded. The conventional mean-shift algorithm is vulnerable to occlusion problem and temperature feature is hardly extract when object is completely occluded. However, if a temperature feature is extracted based on a deep learning network that handles the prior frames, such as a RNN, temperature feature can be much robust in the occlusion issue.  
Remote sensing of the temperature information is another limitation of the proposed method. Figure 15 shows that pedestrian head tracking using the temperature information is somewhat successful at distances from 5 m to 50 m. However, Figure 14(d) shows that the information of the object to distinguish the background are very scarce. This suggests that tracking can fail easily even if small noise occurred. In conclusion, proposed method is difficult to track if it is more than 50 m away.  
Figure 14: Tracking performance of brightness, 14-bit DC, and proposed in occlusion scenario: (**a**) complete occlusion, (**b**) and (**c**) partial occlusion.
# Rapid Characterization Method for SMC Materials for a Preliminary Selection
## 5 Conclusions  
The traditional toroidal magnetic test is the main standard for SMC materials but requires a long time due to the manufacturing and winding process. The single sheet tester turns out to be the faster method compared to toroidal magnetic tests; nonetheless, it always requires calibration through a standard procedure.  
However, many types of insulating layers and variables may affect the SMC materials concerning the research activity. In this context, a preliminary selection and screening based on faster and simpler test methods can be helpful to develop and understand the SMC materials. The proposed preliminary tests measure electrical resistivity and coercivity that may be conducted before and eventually instead of the SST method. In fact, if the coercivity is too high or if the resistivity is too low, the SST measures can be avoided by cataloguing the specimens as non-compliant. In this sense, for SMCs, the constraints for resistivity and coercivity values can be introduced. It would be opportune that electrical resistivity is about or higher than 100 u\(\Omega\)m, and instead, the coercivity should be limited to values under 500 A/m. Furthermore, the SST method was compared with the toroidal magnetic test highlighting a slight overestimation of the specific iron losses and an underestimation of the magnetic permeability.  
This work proved that choosing and guessing more appropriate SMC material development is possible using two simple preliminary tests before conducting SST tests. In this regard, considering the experimental results and the constraints proposed, it is possible to assume that SMCs with 0.2 wt% of phenolic resin, compacted at 800 MPa, and prepared by ball milling 48 h may be a good SMC with the organic layer. On the other hand, the SMC with 0.2 wt% of epoxy resin compacted at 800 MPa has the lowest coercivity value.  
Additionally, the SMC with inorganic layer coated by 0.2 wt% of nano-ferrities shows the best coercivity values specifically for vacuum treatment at 600 \({}^{\circ}\)C. However, the resistivity for proposed SMCs with inorganic layers is unsatisfactory at such a higher thermal treatment.  
Further detailed tests will be conducted to validate the selection of the material. Therefore, further tests will be performed to study other coating processes and parameter effects, and such activities will be followed by microstructural and compositional analysis (SEM-EDS analysis).  
Conceptualization, E.P., L.F. and M.A.G.; methodology, E.P. and F.F.; software, F.F.; validation, E.P., F.F. and L.F.; formal analysis, E.P., F.F. and F.C.; investigation, E.P., F.F. and F.C.; resources, L.F., F.C. and M.A.G.; data curation, E.P. and F.F.; writing--original draft preparation, E.P. and F.F.; writing--review and editing, L.F. and M.A.G.; visualization, F.C.; supervision, L.F., F.C. and M.A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The accuracy of the proposed filter was investigated in detail using the transfer functions of the discrete filter for different displacement sampling rates and target accuracy. The results show that it was difficult to control the influence of low-frequency noise on the integration accuracy. [PERSON] et al. [24] used FEM-FIR filter de-noising to estimate the temporal and spatial derivatives of displacements in an elastic solid and the integral displacement for system identification. [PERSON] et al. [25] proposed a low-frequency cut-off algorithm to reconstruct displacements from measured accelerations and obtained the time-domain waveform of displacements according to the relationship between acceleration and displacement spectra. In the present work, an improved strategy-based SSI method was proposed to identify the structural modal parameters using reconstructed displacements. The proposed strategy consisted of three steps. In the first step, displacements were estimated from measured accelerations. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. In the second step, the moving average method was employed to effectively eliminate random fluctuation interferences in measured data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, the modal parameters of the structure were identified by the proposed SSI method using reconstructed displacements as input. The developed displacement-based SSI method is more stable and accurate for low-frequency-dominated structures.
## 4 Results and Discussion  
### Theoretical Analysis of the Interaction between the Shock Wave and Particles  
Energy and momentum are exchanged and depleted in the interaction between the shock wave and solid particles, thus causing a change in the flow field. The variation of flow field is mainly determined by parameters such as shock intensity, particle size, and particle properties. Figure 4 shows the variation of the temperature, pressure, and velocity of the gas and solid phases during the interaction process.  
As shown in Figure 4, a distinct gas-solid nonequilibrium zone emerged near the wave front. The time for shock wave passage through the solid particles is instantaneous; the exchanged momentum and energy between the two phases are very limited. Therefore, it takes a period of time for the solid particles to achieve a new equilibrium state of momentum and energy, which is called the relaxation time [28; 29; 30; 31].  
The time of the shock wave passing through a single particle is denoted as \(t_{p}\), and the relaxation time is expressed as \(t_{s}\). The ratio of \(t_{p}\) and \(t_{s}\) is described as:  
\[\frac{t_{p}}{t_{s}}\approx\frac{D_{p}}{Ut_{s}}\approx\frac{D_{p}}{a_{g}t_{s}} =\frac{18\mu}{D_{p}\rho_{p}\sqrt{\gamma^{RT}}} \tag{11}\]  
where \(D_{p}\) is the diameter of the particle;
# Sentiment Classification Using Convolutional Neural Networks
## 3 The Proposed Method
The pooling layer slides all the values of the matrix, which is the output of the second convolutional layer, with an arbitrary stride, resulting in output vectors. Since max-pooling is the layer that passes to the next layer the largest value among several values, it results in output vectors of a much smaller size. In other words, the convolutional layer looks at the context and extracts the main features, and the pooling layer plays a role in selecting the most prominent features. After passing through the pooling layer, a flattening process is performed to convert the two-dimensional feature map from the output into a one-dimensional format and to delivery it to an \(F\)-dimensional Fully-Connected (FC) layer. Since the FC layer takes a one-dimensional vector as the input, the two-dimensional vector delivered from the pooling layer needs to be flattened. The FC layer connects all input and output neurons. A vector that passes through the FC layer forms an output that is classified as positive or negative. The activation function softmax functions to classify multiple classes in the FC layer. The softmax function outputs, the value, which is the probability value, is generated for each class. Most people might think that with many convolutional layer stacks, it may be better to store local information and to extract contextual information; however, deep networks do not always have higher performance than shallow networks. Because a network's variation (e.g., deep or shallow) might rely  
Figure 1: The graphical representation of the network, where the output dimensions of each layer are represented at the bottom of the corresponding layers. on the length of the data and the number of data and features, in this paper, we argue that data passing through two consecutive convolutional layers and then passing through the pooling layer is successful at storing context information and extracting prominent features. This assertion is demonstrated by experiments and discussed in Section 5.
## 4 Discussion  
The proposed method for SC recognition on the basis of digital differentiating filters was tested using data from INTERMAGNET observatories for the year 2015.  
The probability of correct detection of an SC event is \(\sim\)75% when the probability of detecting a false event is limited to 5%. These results demonstrate the good performance of the proposed method.  
The proposed SC recognition method has large possibilities for improvement, in particular, further optimization of the quantity of the analyzed datasets from magnetic observatories, as well as the temporal interval of the data analyzed in order to improve the probabilistic characteristics. The method can be adapted for solving the problem of short-term forecasting of a geomagnetic storm and has a favorable prospect for its use in applied problems.  
In [4; 5; 6; 7; 8; 9], solutions are given for various formulations of SC recognition problems. The main feature of the solutions presented is that observations from one observatory are used to obtain them. The proposed SC recognition here is based on a whole set of observatories. This approach is potentially more effective. The results supplement the studies of signals of natural origins based on approaches such as nonlinear adaptive filtering [12] or multifractal analysis [13].  
Conceptualization, V.G. and A.G.; methodology, V.G.; software, V.G.; validation, A.G.; formal analysis, V.G.; investigation, V.G. and R.S.; resources, A.G.; data curation, R.S.; writing--original draft preparation, V.G.; writing--review and editing, R.S.; visualization, R.S.; supervision, A.G.; project administration, A.G.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
This work was conducted in the framework of budgetary funding of the Geophysical Center of RAS, adopted by the Ministry of Science and Higher Education of the Russian Federation.  
Not applicable.  
The datasets used to obtain the presented results are available free and can be downloaded at [2].  
The results presented in this paper rely on data collected at magnetic observatories. We thank the national institutes that support them and INTERMAGNET for promoting high standards of magnetic observatory practice (www.intermagnet.org (accessed on 16 October 2021)). We also thank the International Service on Rapid Magnetic Variations, Observatori de l'Ebre ([[http://www.obsebre.es/en/rapid](http://www.obsebre.es/en/rapid)]([http://www.obsebre.es/en/rapid](http://www.obsebre.es/en/rapid)) (accessed on 28 October 2021)). The authors would also like to thank three anonymous reviewers for their in-depth and thorough review of our manuscript and  
Figure 3: Estimated probability of correct (blue line with circles) and false (black line without circles) SC recognition. The red dashed line shows the optimal threshold value.  
their comments and remarks, which helped to greatly improve the presentation of the results and the language of the manuscript.  
The authors declare no conflict of interest.
## 6 Evaluation
### Experiments and Results
#### 6.2.1 Use of MEC Servers  
This experiment evaluates behavior when varying the number of MEC servers in the system. Application 1 is used, and the workload was configured according to the description in Table 4. The tested scenario has 500 tasks distributed to 100 IoT devices in two different cases, one with a single MEC server, in Figure 1(a),c and another with two MEC servers, in Figure 1(b),d. Figure 2 depicts the results for the execution of Application 1 and Application 2 in both cases with \(10\times 10^{6}\) CPU cycles. The \(x\)-axis shows the execution time in seconds, and the \(y\)-axis indicates the number of tasks.  
The energy and time coefficients were set, respectively, to \(4/5\) and \(1/5\), that is, a high weight was given to the energy consumed so that it could be minimized. In Figure 1(a) from plot Figure 1(a) to plot Figure 1(b) and from plot Figure 1(c) to plot Figure 1(d), there is an increase in the number of MEC servers, from one to two, which made fewer tasks be to allocated in the CC layer. This positively impacts the total energy consumed because tasks running in the MEC layer demand less energy when the workload is higher. However, when the workload is composed of small tasks with a high transfer rate, the scheduler tends to maintain all tasks nearest from devices due to deadline restrictions.  
Table 5 shows the relationship between the number of MEC servers related to energy consumption. When comparing cases A and B with a third case C with no MEC servers, the reduction in energy consumption for case A was 42.51%, while for case B 44.71%. In case C, tasks are just offloaded to the Cloud, adding too much energy consumption to the system. Thus, the use of MEC servers helps the system to lower the total energy consumed.  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Characteristics** & **Application 1** & **Application 2** \\ \hline Task generation rate (s) & 10 & 0.1 \\ Input Data (MB) & 36.3 & 4 \\ Result data size (bytes) & 1250 & 625 \\ Computational workload (Millions of CPU cycles) & 2000 & 20 \\ Critical tasks (\% from total tasks) & 10 & 50 \\ Deadline for critical tasks (milliseconds) & 500 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of chosen applications.  
With Application 2, which has lower workload compared to Application 1, the allocation profile changed. Most allocations took place on the device itself, regardless of the number of MEC servers. The cause to this phenomenon is due to the low processing workload of Application 2. The hardware of IoT devices presents higher energy consumption per CPU cycle. However, it does not require data transmissions, which add energy cost and elapsed time to the system. Thus, for a small processing workload, IoT devices are the first allocation option.
## 2 Related Work
### Volume Estimation
#### 2.3.2 Learning-Based Multi-View Stereo  
Multi-view stereo (MVS) [28] is a technique that reconstructs three-dimensional structures using multiple images from different perspectives. Traditional MVS methods [28; 29; 30] rely on finding correspondences between pixels in different images and using triangulation to estimate depth information. However, these methods often face difficulties in complex scenes, lighting conditions, and occlusions. Learning-based MVS methods [31; 32] address these challenges using deep learning to predict correspondences and depth maps from images. A notable method is MVSNet [33], which compresses images from different views into a three-dimensional cost volume through homography transformation and decodes this volume into depth maps for each view. This learning-based approach allows the model to generalize to new scenes without extensive fine tuning. It makes it effective in  
Figure 1: CLIP model architecture (adapted from [5]).  
applications such as 3D reconstruction, augmented reality (AR), virtual reality (VR), and robotic vision.
# Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene
## 4 Experiments
### Validation on LFW Dataset
#### 4.1.2 Shuffle-Split Cross-Validation  
Although ten-fold cross-validation is the standard validation method of the LFW dataset, we use shuffle-split cross-validation to explore the influence of different proportions of training sets on the performance of the validation algorithm.  
During the validation of LFW, 6000 pairs are used for face verification, 3000 pairs of which belong to the same tags, and 3000 pairs of which belong to the different target tags. We divided the dataset into the training set and the test set after random shuffling, and the proportion of training set gradually increased from 0.1 to 0.9. There are 9 groups of experiments. We repeated the experiment 10 times in each group and then used the average and median of the results to estimate the accuracy of the algorithm. Table 2 shows the results.  
As shown in Figures 10 and 11, as the proportion of training set increases, the average and median accuracy rise, but the overall change range is not large, which shows good algorithm performance. A small training set can also have good performance, which indicates that the model has great stability. From the perspective of standard deviation, when the training dataset is small, the stability of the model obtained is relatively poor, and the standard deviation is relatively large. In the process of increasing the training dataset, sufficient training data are selected to obtain a stable model. However, as test samples reduce, a few wrong judgments have great impact on the standard deviation of the accuracy. That is the reason the standard deviation goes up after group 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Training Set** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline Average & 99.29\% & 99.32\% & 99.36\% & 99.36\% & 99.35\% & 99.39\% & 99.41\% & 99.43\% & 99.43\% \\ \hline Median & 99.35\% & 99.34\% & 99.38\% & 99.36\% & 99.33\% & 99.40\% & 99.39\% & 99.42\% & 99.42\% \\ \hline Standard & 0.16\% & 0.09\% & 0.05\% & 0.08\% & 0.08\% & 0.13\% & 0.14\% & 0.14\% & 0.20\% \\ deviation & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of different validation methods on the LFW test set.  
Figure 10: The average accuracy of different validation methods on the LFW test set.  
Therefore, we draw the following conclusions:  
1. The higher the proportion of the training data, the higher the accuracy of model verification.
2. In small datasets, a few test samples will increase the standard deviation of accuracy.
3. For the LFW dataset, the ten-fold cross-validation is a good way to estimate generalization performance, but the more appropriate proportion of the training dataset is maybe 0.3-0.8.
###### Abstract  
In this study, we present a dual-hop decode-and-forward relaying-based free-space optical (FSO) communication system. We consider utilizing simultaneous lightwave information and power transfer (SLIPT) with a time-splitting technique at the relay, where the direct current component of the received optical signal is harvested as a transmit power for the relay. It is assumed that the FSO links experience a Malaga turbulence channel with pointing errors. In order to evaluate the performance of the proposed communication system, closed-form expressions for outage probability, ergodic capacity, average bit error rate, and throughput are derived. Additionally, to analyze the physical layer security of the proposed system, closed-form expressions for secrecy outage probability and strictly positive secrecy capacity are obtained. Finally, the accuracy of the derived analytical expressions are validated with Monte Carlo simulations. Results show that our proposed system model outperforms its non-SLIPT counterpart.  
Keywords:dual hop; energy harvesting; FSO systems; performance analysis; physical layer security; SLIPT +
Footnote †: journal:
## 5 Advanced MPPT Techniques
### Smart MPPT Techniques
#### 5.1.2 Artificial Neural Network Algorithm  
The Artificial Neural Network (ANN) MPPT algorithm uses machine learning models to predict and track the MPP of PV systems. ANNs are designed to simulate the way the human brain processes information, learning complex patterns from historical and real-time data to make informed decisions about the optimal operating conditions [59; 60]. A typical ANN architecture is depicted in Figure 10. The input layer processes raw data through hidden layers, which extract and transform features, capture complex patterns, and generate predictions based on this processed information. Input data for neural networks can include characteristics of PV modules, while the output data regulate the DC-DC converter operation. One of the main advantages of the ANN MPPT algorithm is its ability to handle non-linear and dynamic environments with high accuracy, as it can learn from a wide range of data and adapt to changing conditions such as varying irradiance and temperature. However, the effectiveness of ANNs depends heavily on the quality and quantity of the training data, and they may require extensive retraining if system conditions change substantially [61; 62].  
Figure 10: Artificial Neural Network architecture.  
Figure 9: Flowchart of the FLC MPPT.