esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence Approach
### Deep Learning
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ML/Deep Learning Method** & **Preprocessing Method** & **Wavelength Selection Method** & **Objective** & **Result Accuracy** & **References** \\ \hline \hline \multirow{4}{*}{MLR} & \multirow{4}{*}{-} & \multirow{4}{*}{SPA} & & RMSECV: 9.5 IU & \multirow{4}{*}{[101]} \\ \cline{3-3} \cline{5-5}  & & & & RMSE: 4.9 JU & \\ \cline{3-3} \cline{5-5}  & & & & REP: 9.879\% & \\ \cline{3-3} \cline{5-5}  & & & & R2 pred: 0.976 & \\ \cline{3-3} \cline{5-5}  & & & & RPDpred: 6.289 & \\ \hline \multirow{4}{*}{PLS-DA} & \multirow{2}{*}{SNV} & \multirow{4}{*}{-} & species discrimination of Brassicas seeds & \multirow{4}{*}{Classification accuracy: 100\%} & \multirow{4}{*}{[103]} \\ \cline{3-3} \cline{5-5}  & & For hardness, springiness, and melability: SG\_Smoothing & & & Hardness: R\({}^{2}\)p = 0.846, RMSEP = 2911.29 & \\ \cline{3-3} \cline{5-5}  & & For adhesiveness and free-oil: & & & Springsiness: R\({}^{2}\)p = 0.85, RMSEP = 0.0939 & \\ \cline{3-3} \cline{5-5}  & & & predicting low-fat & & Melability: R\({}^{2}\)p = 0.728, RMSEP = 22.08 & \\ \cline{3-3} \cline{5-5}  & & For adhesiveness: R\({}^{2}\)p = 0.809, RMSEP = 56.39 & & & Adhesiveness: R\({}^{2}\)p = 0.809, RMSEP = 56.39 & \\ \cline{3-3} \cline{5-5}  & & & Free-oil: R\({}^{2}\)p = 0.992, RMSEP = 0.442 & & \\ \cline{3-3} \cline{5-5}  & & & & & Fore-oil: R\({}^{2}\)p = 0.835, RMSEP = 1446.52 & \\ \cline{3-3} \cline{5-5}  & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & \\ \hline SVM & - & - & Food samples recognition & classification accuracy: 68.74\% & [105] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AI approach in HSI system. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**ML/Deep Learning Method** & **Preprocessing Method** & \begin{tabular}{} \end{tabular}  & **Objective** & **Result Accuracy** & **References** \\ \hline PLS-DA & - & - & Detection of Physical Hazards in a Variety of Fruit Processed Products & Detection accuracy: 97.6\% & [106] \\ \hline Logistic Regression & SG & - & Detection of pesticide residue level in grape & Detection accuracy: 97\% & [107] \\ \hline LDA & MSC & - & detecting adulurans in mixed mates & Detection accuracy: 93\% & [111] \\ \hline SVM & - & PCA & identification of foodborne bacteria & Identification accuracy: 92.9\% & [112] \\ \hline
## 2 Materials and Methods
### Feature Generation  
Feature generation is a key component of the procedure, because the four observed features, namely the T1, T2, T1C, and FLAIR intensities provided by the MRI device, do not contain all the possible discriminative information that can be employed to distinguish tumor pixels from normal ones. A major motivation for feature generation represents the fact that the automated registration algorithm used by the BraTS experts to align the four data channels never performs a perfect job, so for an arbitrary cubic millimeter of brain tissues, represented by the pixel situated at coordinates \((x,y,z)\) in volume T1, the corresponding information in the other volumes is not in the same place, but somewhere in the close neighborhood of \((x,y,z)\). A further motivation consists of the usual property of image data that neighbor pixels correlate with each other.  
All four MRI data channels equally contribute to the feature generation process. Observed features are treated independently of each other: 20 computed features are extracted from each of them. The inventory of extracted features is presented in Table 2. Minimum, maximum, and average values are extracted in such a way that only the brain pixels are taken into consideration from the neighborhoods indicated in the table. The computation of the gradient features involves the masks presented in Figure 4. The current pixel is part of all masks to avoid division by zero in Equation (3). The 16 gradient features of an arbitrary pixel \(p\) are computed with the formula:  
\[g_{m}^{(c)}(p)=\gamma_{0.5}+k_{g}\left(\frac{\sum\limits_{q\in N_{m}(p)\cap \overline{\Omega}}I_{q}^{(c)}}{|N_{m}(p)\cap\overline{\Omega}|}-\frac{\sum \limits_{q\in N_{m^{\prime}}(p)\cap\overline{\Omega}}I_{q}^{(c)}}{|N_{m^{ \prime}}(p)\cap\overline{\Omega}|}\right)\, \tag{3}\]  
followed by \(g_{m}^{(c)}(p)\leftarrow\min\{\max\{\alpha,g_{m}^{(c)}(p)\},\beta\}\) to stay within the target interval \([\alpha,\beta]\), where \(c\in\{\text{T1,T2,T1C, FLAIR}\}\) is one of the four data channels, \(m\in\{A,B,C,D\}\) is the index of the current gradient mask, \(\overline{\Omega}\) stands for the set of all brain pixels in the volume, \(\gamma_{0.5}\) is the middle of the target interval of intensities, \(k_{g}\) is a globally constant scaling factor, and \(|Q|\) represents the cardinality of any set denoted by \(Q\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Neighborhood** & **AVG** & **MAX** & **MIN** & **GRD** & **GBW** & **Total** \\ \hline \(3\times 3\times 3\) & 4 & 4 & 4 & & 12 \\ \(3\times 3\) & 4 & & & & 4 \\ \(5\times 5\) & 4 & & & & 4 \\ \(7\times 7\) & 4 & & & 16 & 20 \\ \(9\times 9\) & 4 & & & & 4 \\ \(11\times 11\) & 4 & & & 32 & 36 \\ \hline Total & 24 & 4 & 4 & 16 & 32 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inventory of computed features. Observed data channels were equally involved. Together with the four observed features, the total feature count is \(n_{\varphi}=84\).  
Figure 4: Neighborhoods used for the extraction of gradient features, with respect to the current pixel indicated by grey color.
# Voice Activation for Low-Resource Languages
## 3 Experiments
### Neural Network Architectures  
For all our experiments, we use two types of neural network architectures:  
* Feedforward (ff) neural network with three fully-connected layers and ReLU activation.
* Residual convolutional neural networks with different number of layers (res8,res15,res26) and possibly reduced number of feature maps in convolutions (res8-narrow res15-narrow,res26-narrow).  
These models are chosen because they represent a fairly strong baseline for a voice activation system [15] and allow us to directly compare our results with [31] on the Lithuanian data set [24]. We encourage the reader to see works [15; 31] for more detailed description of these architectures; here, we give a short summary.  
ff models accept the audio features, have three fully connected layers with 128, 64 and \(C\) neurons, respectively, where \(C\) is a number of target classes. The first two layers have ReLU activation and the last one has softmax activation. The scheme of the ff neural network is illustrated in Figure 2.  
Residual networks use residual connections to allow a stable gradient path in a neural network with a high number of layers. The basic building block of networks that we use in this work is a residual block. Its scheme is presented in Figure 3. Each residual block has two \(3\times 3\) convolutional layers with ReLU activation. Batch normalization is used for the activation of these layers. The output of such a residual block is equal to the sum of the block's input and the batch normalized activation of the second convolutional layer.  
Figure 3: The scheme of a residual block.  
Figure 2: The scheme of the feedforward neural network used in our experiments. The shape of the resulting tensor is specified after each block in square brackets. FC stands for the fully-connected layer. \(T\) is the number of frames in a sample (98 in all our experiments). \(B\) is the number of channels in the audio features (80 for log-mel filterbanks, 512 for wav2 vec features). \(C\) is the number of classes to discriminate.  
We use the same types of residual neural networks as in [15; 31]. They consist of the following:  
* Bias-free 3 \(\times\) 3 convolutional layer.
* Optional average pooling layer (e.g., 4 \(\times\) 3 layer in res8).
* Several residual blocks (see Figure 3).
* 3 \(\times\) 3 convolutional layer.
* Batch normalization layer.
* Average pooling layer.
* Fully-connected layer of size \(C\), where \(C\) is the number of classes to discriminate.
* Softmax layer.  
All the layers are zero-padded. For some variants, dilated convolutions are applied to increase the receptive field of the model. The scheme of the res8 model is presented in Figure 4.  
We use the same notation and parameters as in [31]. The parameters used for residual neural network variants are specified in Table 1.  
For all our experiments (except for wav2 vec, see Section 3.4.1) log-mel filterbanks with 25 ms frame width, 10 ms frame shift and 80 bins [31] are used.
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 4 Experiments  
### Dataset  
This paper evaluates the proposed method on the SIXray dataset proposed in [21]. The SIXray dataset (as shown in Figure 3) contains 1,059,231 X-ray images. There are 8929 images with six classes of prohibited items manually annotated, namely \"gun\", \"knife\", \"wrench\", \"pliers\", \"scissors\", and \"hammer\". Each positive sample image may contain more than one class of prohibited item. Among them, class \"hammer\" is deprecated, as too few samples belong to it.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Output Size** & **Description** \\ \hline input & \(28\times 28\times 128\) & Fused feature \\ conv1 & \(14\times 14\times 256\) & conv: \(3\times 3\), stride \(2\), \(128\)\(\rightarrow\)\(256\); batchnorm, relu \\ conv2 & \(7\times 7\times 512\) & conv: \(3\times 3\), stride \(2\), \(256\)\(\rightarrow\)\(512\); batchnorm, relu \\ pool & \(1\times 1\times 512\) & Global average pooling (GAP) \\ fc & \(5\times 1\) & Fully connected (fc), \(512\)\(\rightarrow\)\(5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Convolutional layer subnet hierarchy (based on the selection of level2/level3/level4).  
Figure 3: Sample images in SIXray dataset.  
SIXray dataset contains three subsets, named SIXray10, SIXray100, and SIXray1000, respectively, according to the ratio of negative samples over positive samples, i.e., 1:10, 1:100, and 1:1000. As positive samples contained in SIXray1000 are different from SIXray10 and SIXray100, we only conducted experiments on SIXray10 and SIXray100 subsets.  
For the partition strategy of the training set and test set, we complied with related settings in [21], splitting 7496 positive samples for training set and 1433 positive samples for test set. Then, negative samples were put in correspondence to positive/negative ratio, respectively. Class distribution of positive samples shows in Table 2.
# Robust Full-Screen Wavelength Calibration Algorithm
## 3 The Proposed Calibration Method
### Full-Screen Spectral Wavelength Calibration Algorithm  
In summary, the relationship between the wavelength \(\lambda_{c}\), the feedback value \(x(x_{c}\) or \(x_{n})\), and the pixel positions \(p_{c}\) and \(p_{n}\) can be formulated as  
\[\lambda_{c}=a\sin(b(x-(e(p_{n}-p_{c})+f))+c)+d, \tag{15}\]  
where \(a,b,c,d,e\) and \(f\) are unknown parameters. Note that \(x\) can serve as the feedback value \(x_{c}\) for the standard wavelength \(\lambda_{c}\) at the central pixel, and \(x_{n}\) for the standard wavelength \(\lambda_{c}\) at the non-central pixel. In other words, Equation (15) can calibrate the wavelengths at central and non-central pixels. It is notable that the difference between Equations (15) and (5) is small when \(p_{n}\) is equal to \(p_{c}\). The difference between the two functions is the initial phase \(-bf\). This may result in a loss of calibration accuracy at the central pixels. However, since the magnitude of \(x\) in our actual experiment was ten thousand, and the calculated parameters \(b\) and \(f\) are single digits, the error caused by the initial phase is negligible after verification; therefore, we still used Equation (15) for the continuity of full-screen calibration (without breaking point).  
The main steps of full-screen spectral wavelength calibration algorithm are listed in Algorithm 3.  
```
0: (\(\lambda_{c}\), \(x_{c}\), \(x_{n}\), \(p_{c}\), \(p_{n}\)\({}^{i}\)), (\(i=1,2,\cdots,N\)), the initial weight \(\theta_{0}=[a_{0},b_{0},c_{0},d_{0}]\), \(\beta_{0}=[c_{0},f_{0}]\), the step size \(1r_{0}\), the hyperparameter \(\eta\), the number of iterations \(E\), the initial value of first-order differential \(D_{0}\).
0: All parameters of Equation (15)
1: According to \(x_{c}\) and the central wavelength \(\lambda_{c}\), \(\theta\) is calculated by Algorithm 1, where \(\theta=[a,b,c,d]\).
2: According to (\(x_{c}\), \(x_{n}\)\({}^{i}\), \(p_{c}\), \(p_{n}\)\({}^{i}\)), \(i=1,2,\cdots,N\), Algorithm 2 is used to calculate \(\beta\), where \(\beta=[e,f]\).
3: Obtain all parameters in Equation (15).
```  
**Algorithm 3** Full-screen spectral wavelength calibration algorithm
## 3 Results and Discussion
### Thermo-Hydrodynamic Characteristics
In the calculations, the sealing temperature ranged from 100 K to 350 K, forming different temperature gradients on the base of a fixed temperature 350 K, at the outlet of the seal. Additionally, the basic film thickness was 3.0 \(\upmu\)m, the rotational speed was 50,000 rpm, and the sealing pressure values at the inlet and outlet of the seal were 5.0 MPa and 0.1 MPa, respectively. Figure 5: Distributions of the pressure, temperature, and film thickness with respect to the sealing pressure. (\(P_{\text{out}}\) = 0.1 MPa, \(T_{\text{in}}\) = 200 K, \(T_{\text{out}}\) = 350 K, \(n\) = 50,000 rpm, \(h_{0}\) = 3 \(\upmu\)m). According to Figure 7, the dynamic pressure effect of the spiral grooves was still obvious with the decrease of the temperature gradient, but there was a mutation around the sealing temperature of 250 K. From Figure 8, the pressure increase of the gas film decreased initially when the sealing temperature increased from 100 K to 240 K, after which the dynamic pressure effect was suddenly enhanced, as the sealing pressure reached 250 K, and later the effect was weakened again with the continuous increase of the sealing temperature. Meanwhile, the temperature of the entire gas film also increased significantly under different temperature-gradient conditions. Similarly with the dynamic pressure effect, a sudden increase of the temperature rise was also found at the sealing temperature of 250 K. The increase of pressure and temperature did not change much, and showed a declining trend as a whole. Therefore, the temperature gradient contributed to obtaining a larger dynamic pressure effect and temperature rise of the sealing medium. Under the collective contributions of the pressure and temperature distributions, the supercritical helium spiral-grooved face seal showed a divergent deformation, according to the film thickness distributions in Figure 7c. According to Figure 8, the degree of the distortions increased with the decrease of the temperature gradient, which further indicated that the ambient-temperature gradient contributed to weakening the film thickness deformation. Figure 6: Variations of the pressure increase, temperature rise, and film thickness deformation with respect to the sealing pressure. (\(P_{\text{out}}\) = 0.1 MPa, \(T_{\text{in}}\) = 200 K, \(T_{\text{out}}\) = 350 K, \(n\) = 50,000 rpm, \(h_{0}\) = 3 \(\upmu\)m). Figure 7: Distributions of the pressure, temperature, and film thickness with respect to the sealing temperature. (\(P_{\rm in}\) = 5.0 MPa, \(P_{\rm out}\) = 0.1 MPa, \(T_{\rm out}\) = 350 K, \(n\) = 50,000 rpm, \(h_{0}\) = 3 \(\upmu\)m).
## 1 Introduction
In our earlier research, we developed an IS approach for evaluating the possibility of packet loss in the MMPP/D/1 queue, with the intention of reducing the processing rate as proposed by [PERSON] (2014) and cited in [PERSON] (2018). The most efficient distribution of IS is achieved by reducing the service rate. The MMPP/D/1 queue system is more intricate compared with the FIFO queue. The traditional approach [4; 5] modified the pace at which events occur in order to produce a greater number of rare events. This necessitates altering user traffic, which is currently impossible within the existing network infrastructure. In this paper, we have more extensive research than our, previous study [7]. The first section, Section 4.1, provides mathematical proof explaining the optimal processing rate formula. The second section, Section 4.3.2, explains the relationship of the MMPP's states in the IS estimation; as a result, these states will be removed to gain more simulation quickly and can be applied in an online (real-time) network. These are two main contributions in this paper. The next sections of this work are structured in the following manner: In Section 3, a review of the fundamental concepts of Monte Carlo simulation and Importance Sampling simulation is provided. Section 4 provides a comprehensive explanation of the modeling of the two-state MMPP/D/1 queue. In Section 5, we assess the effectiveness of our suggested method using simulations. Section 6 presents the conclusions and further research.
## 5 Discussion
[45] have presented that the power spectrum density anomaly on the ELF electric field measured by the CSES satellite happened one month prior to the 2021 Maduo \(M_{\rm S}\) 7.3 earthquake. [PERSON] et al. [43] have reported that the ionospheric and atmospheric disturbances occurred and accelerate 50 days and 15 days before the 2022 China Luding \(M_{\rm S}\) 6.8 earthquake. Theoretically, ionospheric reaction can be expected for an earthquake with a magnitude larger than 4.6 [9]. It is possible that this \(M_{\rm S}\) 5.8 earthquake generated these ionospheric variations beyond 20% even at a distance of 1000 km. Except for that, this strongest aftershock occurred in the Aheqi area instead of in the Wushi area (Figure 3). There is a fact that cannot be ignored: the main damage lies in Aheqi county, even though the epicenter of the Wushi \(M_{\rm S}\) 7.1 earthquake lies in Wushi county. On one hand, Aheqi county is well within 5 km away from the seismic fault, while the distance is about 25 km for Wushi county in the light of the reversion results of the INSAR measurement. On the other hand, the rupture in the southwest of the main shock is closer to the Earth's surface according to the reversion results of the source rupture ([[https://yjgl.luohe.gov.cn/index.php?c=show&id=3957](https://yjgl.luohe.gov.cn/index.php?c=show&id=3957)]([https://yjgl.luohe.gov.cn/index.php?c=show&id=3957](https://yjgl.luohe.gov.cn/index.php?c=show&id=3957)), accessed on 1 August 2024). These processes can also accelerate ionospheric variations prior to this biggest aftershock. However, confirming the right location and the magnitude of the impending event in the light of detected ionospheric perturbations is still a significant challenge during earthquake prediction practice, not to mention that the automatic detection method cannot reveal all ionospheric perturbations associated with seismic activities under these two limitations of \(A>20\%\) amplitude and \(t=20\)-\(120\) s space size. So, on one hand, the temporal-spatial evolution of features of the seismo-ionospheric influence need to be further reconfirmed by employing longer satellite measurements like SWARM and more seismic events in different locations. On the other hand, integrating ground-based observed data analysis with satellite Earth observations is still an effective way to enhance our understanding of the physical mechanism of the seismo-ionospheric influence and lithosphere-atmosphere-ionosphere process. The automatic detection method needs to be improved and more reasonable parameters need to be defined to perform a long-run earthquake prediction practice as well.
## 5 Experimental Results and Discussion
### Effect of Incident Power on Stray Radiation
According to the definition of \(R_{rel}\), the variation of \(R_{rel}\) for the ceramic coating with laser incident power was calculated, as shown in Figure 10.  
From Figure 10, it can be seen that, when the laser incident power was no more than 0.4 W, the ceramic coating had the ability to weaken the stray radiation. In this case, the stray radiation intensity was reduced by 70% compared with the case without coating. When the incident laser power exceeded 0.4 W, the ceramic coating could no longer absorb more stray radiation due to the absorption saturation effect. Increasing the ceramic coating could increase the stray radiation; hence, the relative stray radiation suppression ratio \(R_{rel}\) decreased. When the laser power reached 0.8 W, the relative stray radiation suppression ratio \(R_{rel}\) of the ceramic coating was the lowest at \(-214.3\%\).
## 3 Methods and Materials  
### Hardware Setup  
The conceptual architecture described in Section 2 was validated through a proof of concept prototype, whose hardware setup is described in this section. Indeed, some of the subsystems shown  
Figure 1: The Conceptual architecture. HMI, Human–Machine Interface.  
in Figure 1 are realized through hardware components, which are interfaced by the system software over dedicated structures of software.  
As shown in Figure 2, the _Robotic Arm_ was realized through a five-DOFs manipulator. The arm links are connected to each other only by means of rotary joints, whose position is established through _HS-785 HB DC Servo Motors_.  
The end-effector of the arm is realized to perform only simple tasks, i.e., pressing a panel lift button or touching an object. Nevertheless, thanks to the modularity of the system software, it is possible to increase the functionalities by substituting the existent modules.  
In addition to the manipulator, the other hardware components used are:  
* **Raspberry PI 3B board**: This is the platform that runs the system software with the sole exception of the AI algorithm, as explained in Section 3.2. It interfaces all the other hardware components directly or through dedicated boards (e.g., the _Phydget board_, as successively explained).
* **Phydget v1.1 board**: It is a commercial motors driver board. Its use is necessary since the _Raspberry PI 3B board_ is not able to provide the sufficient power to the five motors. A dedicated driver motor board allows choosing a platform for the sub-system independently by its ability to drive the motors.
* **5 MP Raspberry Camera module**: The hardware part of the conceptual subsystem _Camera_ is a low cost monocular camera that provides images from which some information is extracted for the control of motion in the space. The acquired images are 640 \(\times\) 480 with 8 bits for each color (RGB).
* **HCSR04 proximity sensor**: The hardware component of _Depth Sensor_ was realized through a low cost ultra-sound proximity sensor. As described in Section 2, it is necessary to estimate the distance along the axis of the camera frame between the camera and the interest object (_depth_). Due to the low cost nature of the sensor, the accuracy of the measurement is low, but it is compensated by the IBVS approach, as explained in Section 3.4.3.
* **Force sensor**: The force sensor is used to avoid breaking the end-effector or damaging the object while the manipulator grabs them.  
Figure 2: The prototype.  
* **Raspberry 7\" touch screen display**: As described in Section 2, the _HMI_ is necessary to show the user the selectable objects to interact with. In addition, it might also be exploited by users with a limited level of manual skills. Thus, a touch screen display has been used.  
It is clear by knowing the used hardware the low cost nature of the manipulator. This prototype, in fact, costs less than EUR 1500 to demonstrate that it is also possible to build a low-cost robotic arm for assistive technologies.