esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.  
Keywords:brain decoding; visual reconstruction; functional magnetic resonance imaging; Siamese neural network; deep learning; learning to compare +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Importance of Gas Sensor Selection  
Sensor array L exhibited better results than sensor array C as the benchmark array. All 14 components of the target gas were terpenes or similar. The responses of semiconductor sensors can be categorized into gas groups [35]. Therefore, the discrimination of terpene  
Figure 6: Relationship diagram between true and predicted concentrations for target gas No. 12 using (**a**,**b**) 1 min data and (**c**,**d**) 12 min data on sensor arrays (**a**,**c**) C and (**b**,**d**) L. Plot colors are black: single gas, blue: highest concentration component of double gases, green: second highest concentration component of double gas, and red: other double gases.  
Figure 7: Relationship diagram between true and predicted concentrations for target gas No. 6 using (**a**,**b**) 1 min data and (**c**,**d**) 12 min data on sensor arrays (**a**,**c**) C and (**b**,**d**) L. Plot colors are black: single gas, blue: highest concentration component of double gases, green: second highest concentration component of double gas, and red: other double gases.  
gases was expected to be more difficult than that of gases from other groups. Sensor array C included all general n-type semiconductor sensors, whereas sensor array L included not only general semiconductor sensors, such as n-type [33; 36] and p-type [37; 38; 39], but also cerium oxide bulk-response sensors [34]. The resistance change mechanism in a traditional n-type semiconductor sensor involves a change in the electron density at the grain boundaries of n-type semiconductor particles by the oxidation of the target gases on the surface of the grains. In contrast, the resistance change mechanism in a p-type semiconductor sensor involves a change in the hole density at the grain boundaries of n-type semiconductor particles. The resistance change mechanism in the bulk-responsive sensor is that the odor molecules consume the lattice oxygen in the cerium oxide particles to undergo oxidation and generate oxygen vacancies, which diffuse throughout the bulk without accumulating on the particle surface [34]. In addition, sensor array L was not superior in terms of resistance change to number 6 (Figure 4d) compared to number 12 (Figure 4b). However, the relationship between the actual and predicted concentrations in the ANN between numbers 12 (Figure 6b,d) and 6 (Figure 7b,d) were both excellent. Based on these results, the combination of sensors with different response principles results in improved accuracy for the discrimination of effective components. To obtain high accuracy, it is important to determine the differences in the sensor response for each gas condition (not necessarily the magnitude of the sensor response).  
Even with such an excellent sensor array, the results showed large variations when the data acquisition time intervals were different. For future applications, it is desirable to maintain a constant measurement time within the range permitted by operational conditions.
## 1 Introduction
### Machine Learning in Network Security
For example, if we are trying to build a model to recognize images of cats, the data will contain images of cats labeled \"cat\" and different images labeled \"not cat\". * **Data preparation**: This phase involves cleaning, normalizing, and transforming the training data to make it suitable for processing by the machine learning model. This can include eliminating missing data, handling categorical characteristics, and normalizing numeric values. * **Model selection and training**: In this phase, you select the appropriate machine learning model for the problem at hand. The model is then trained on the training data, which consists of making the model learn the patterns and relationships present in the data. During training, the model is iteratively adjusted to minimize the error between its predictions and the corresponding output labels in the training data. * **Model Evaluation**: After training, the model is evaluated using separate test data, which was not used during the training. This allows you to evaluate the effectiveness of the model in generalizing patterns to new data. Several metrics, such as accuracy, precision, and area under the _ROC_ curve, are used to evaluate model performance. * **Model Usage**: Once the model has been trained and evaluated, it can be used to make predictions on new input data. The model applies the relationships learned during training to make predictions about new input instances. In the following sections the main types of ML models and the preliminary data manipulation techniques for the optimal training of the selected algorithm will be described in great detail, as well as a presentation of the results of applying ML algorithms on the most important datasets for validation of ML-based systems in the IDS context.
# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 4 Results and Discussion
### Performance Metrics  
In UWSNs, the four main performance metrics that are usually used in evaluating the performance of the well-known algorithms have been utilized in our algorithms examined below:  
* Energy Consumption: the amount of energy consumed for each sensor.
* Packet Delivery Ratio: the ratio of the successfully delivered packets to the sink divided by the number of total transmitted packets by each sensor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(R\) & Transmission Range & \(VC\) & Void count sensors in RT \\ C & Candidate set in NIT & \(n_{i}\) & \(i\)-th sensor nodes, \(1\leq i\leq N\) \\ \(BestN\) & Best neighbors & \(RT\) & Routing Table \\ EN & The number of eligible sensors in EVA-DF & \(id\) & Sensor node ID \\ \(DepthDiff\) & Depth differences between sender and receiver & \(OCSet\) & Set of ordinary neighbors in EVA-DF \\ \(depth\) & Depth value & \(RCost\) & The route cost field in the data packet \\ \(iv\) & Void status in the RT & \(Threshold\) & The highest number of the best candidate set \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations in void aware data forwarding.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameter** & **Values** \\ \hline Number of sensor nodes & 50–400 \\ Network topology & Random topology \\ Deployment area & 1250 m\({}^{3}\) \\ Bandwidth & 10 Kbps \\ Communication medium & Acoustic Waves \\ Area of transmission range & 250 m \\ MAC protocol & \(Broadest MAC\) \\ Node movement & 0–3 m/s \\ Hello packet interval & 100 s \\ Data packet size & 64 bytes \\ Initial energy & 100 J \\ Power consumption & 2 w, 0.75 w, and 8 mw \\ Packet generation time & 15 s \\ Number of Runs & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation sitting.  
3. Network Lifetime: the major lifetime that has been measured based on the first die sensor in the network.
4. End-to-End Delay: the average delay caused by each sensor during the forwarding process.
## 4 Discussion
### Comparison of Two 3D Roving Models (BIFC vs. LIAC)  
Presented 3D rowing models that calculated drag and lift forces acting on the oar blade mainly differed in two parameters, i.e., use of angle of attack and the blade area in the water. In comparison to the BIFC model, the LIAC model not only incorporated the effect of angle of attack in horizontal plane(\(\alpha_{xy}\)) but also in the vertical plane (\(\alpha_{yz}\)). In the LIAC model, the resultant drag and lift coefficients in horizontal and vertical planes were continuously adjusted according to the instantaneous angle of attack. On the contrary, a pre-determined value of \(C_{L}^{max}\) was used in the BIFC model for the forces in both planes and the vertical forces were not dependent on the angle of attack due to modeling assumptions. In addition, the BIFC model did not incorporate the effect of the oar-blade area in the water. However, forces from the LIAC model depended on the proportion of the blade area in the water.  
We also investigated the effect of rope tension forces (\(\tau_{rec}^{min}\)) on the rendering of recovery forces. In real rowing, rowers push the oar handle with a minimal force to move the oar from release to catch position during recovery. Thus, to provide a realistic feeling on the simulator during recovery, the rendered forces on the oar blade should be the lowest compared to other sub-phases of the rowing cycle. Therefore, we initially set the \(\tau_{rec}^{min}\) value for the LIAC model to 35 N based on pilot study and compared to the \(\tau_{rec}^{min}\) value of the BIFC model that was kept at 50 N.
## 4 Results and Discussion
### Experiment with Random Test Cases
#### 4.1.1 PCR  
Figure 9 shows the estimated Standard Error of Prediction (SEP) against the different number of principal components (PCs) achieved for PCR model built with different approaches. For the PCR model built using second derivative characteristic features, the SEP was markedly greater than other approaches. In addition, when the numbers of principal  
Figure 8: Two representative PPG signals—(left): relatively prominent PPG signal and (right): a signal with high baseline issues and noises processed through different steps. (**a**) Raw PPG Signal; (**b**) PPG signal preprocessed with ALS and Gaussian filter; (**c**) first derivative of preprocessed signal, and (**d**) extracted features of first derivative signal.  
components were incremented, the SEP increased as well, increasing from 22.95 mg/dL on the second component to 26.54 mg/dL with 11 PCs.  
A significant reduction in SEP could be observed in the other approaches. Using a model built with the DeIT features obtained from the signal preprocessed through Gaussian and ALS filtering, the SEP was observed to be 21.3 mg/dL with the first two PCs, whereas in the models that included first derivative and second derivative features, the lowest SEP was observed with five components at 18.33 mg/dL. The SEP reduced even more (17.09 mg/dL) for the PCR model built with first derivative characteristic points extracted from the preprocessed PPG using 11 PCs. The preprocessing and feature extraction approach significantly improved the prediction ability of the PCR model. Here, it is essential to note that systolic and diastolic features alone did not improve the accuracy; on the other hand, the performance with the second derivative features was not noteworthy. The comparison of performances among different PCR models in terms of the lowest SEP achieved and minimum no. of PCs required has been shown in Table 2.
## 5 MGTO for Solving Engineering-Optimization Problems
### Compression/Tension-Spring Design  
Compression/tension-spring-design problems minimize the weight of the spring (shown in Figure 8) [60]. The three constraints impacting frequency, shear stress and deflection should be satisfied in the optimization design. There are three variables displayed in Figure 8, namely the wire diameter (\(d\)), mean coil diameter (\(D\)) and the number of active coils (\(N\)).  
The mathematical model is as follows:  
Consider:  
\[\overset{\rightarrow}{x}=[x_{1}\ x_{2}\ x_{3}\ x_{4}]=[d\ \mathrm{D}\ \mathrm{N}] \tag{64}\]  
Minimize:  
\[f(\overset{\rightarrow}{x})=(x_{3}+2)x_{2}x_{1}^{2} \tag{65}\]  
Constraints:  
\[g_{1}(\overset{\rightarrow}{x})=1-\frac{x_{2}^{3}x_{3}}{71785x_{1}^{4}}\leq 0 \tag{66}\]  
\[g_{2}(\overset{\rightarrow}{x})=\frac{4x_{2}^{2}-x_{1}x_{2}}{12566(x_{2}x_{1 }^{3}-x_{1}^{4})}+\frac{1}{5108x_{1}^{2}}\leq 0 \tag{67}\]  
\[g_{3}(\overset{\rightarrow}{x})=1-\frac{140.45x_{1}}{x_{2}^{2}x_{3}}\leq 0 \tag{68}\]  
\[g_{4}(\overset{\rightarrow}{x})=\frac{x_{1}+x_{2}}{1.5}-1\leq 0 \tag{69}\]  
Variable range:  
\[\left\{\begin{aligned} 0.05\leq x_{1}\leq 2.00\\ 0.25\leq x_{2}\leq 1.30\\ 2.00\leq x_{3}\leq 15.00\end{aligned}\right. \tag{70}\]  
As Table 10 shows, the optimal solution calculated by MGTO is 0.0099, when variable \(x\) is equal to 0.05000, 0.3744 and 8.5465. According to the results, it is clear that the MGTO is capable of obtaining the best solution in comparison with other optimization algorithms.  
Figure 8: The compression/tension-spring-design problem.
## 2 Trajectory Generation
### Global Trajectory Generation
All derivatives of the start point and endpoint are fixed to 0. \[\mathbf{M}\mathbf{d}= \begin{bmatrix}\mathbf{d_{F}}\\ \mathbf{d_{P}}\end{bmatrix}  \tag{8}\]  
where \(\mathbf{M}(\in\mathbb{R}^{n\times n})\) is the mapping matrix used to convert the trajectory vector \(d\) into a vector with fixed derivatives (\(\mathbf{d_{F}}\)) and free variables (\(\mathbf{d_{P}}\)). \(\mathbf{M}\) and \(\mathbf{d_{F}}\), \(\mathbf{d_{P}}\) vectors are not fixed because the number of waypoints decides its size. However, the principle is the same. Combining Equations (4) and (8) yields the next equation. \[\mathbf{p}=\mathbf{A}^{-\intercal}\mathbf{M}^{-\intercal} \begin{bmatrix}\mathbf{ d_{F}}\\ \mathbf{d_{P}}\end{bmatrix}  \tag{9}\]  
Inserting Equation (9) into Equation (6), the following equation is yielded. Now the optimization argument is not the coefficient \(\mathbf{p}\) but the free derivatives \(\mathbf{d_{P}}\). The number of optimization parameters is also reduced. \[\mathbf{J}= \begin{bmatrix}\mathbf{d_{F}}\\ \mathbf{d_{P}}\end{bmatrix} ^{\intercal}\underbrace{\mathbf{M}^{-\intercal} \mathbf{A}^{-\intercal}\mathbf{Q}\mathbf{A}^{-\intercal}\mathbf{M}^{- \intercal}}_{\mathbf{R}} \begin{bmatrix}\mathbf{d_{F}}\\ \mathbf{d_{P}}\end{bmatrix} = \begin{bmatrix}\mathbf{d_{F}}\\ \mathbf{d_{P}}\end{bmatrix} ^{\intercal} \begin{bmatrix}\mathbf{R_{FF}}& \mathbf{R_{FP}}\\ \mathbf{R_{FP}}&\mathbf{R_{PP}}\end{bmatrix}\begin{bmatrix}\mathbf{d_{F}}\\ \mathbf{d_{P}}\end{bmatrix}  \tag{10}\]where \(\mathbf{J}\) is the cost function. \[\mathbf{J}=\mathbf{d}_{\mathbf{F}}^{\intercal}\mathbf{R}_{\mathbf{F}}\mathbf{d}_ {\mathbf{F}}+\mathbf{d}_{\mathbf{F}}^{\intercal}\mathbf{R}_{\mathbf{F}}\mathbf{d }_{\mathbf{F}}+\mathbf{d}_{\mathbf{P}}^{\intercal}\mathbf{R}_{\mathbf{F}} \mathbf{d}_{\mathbf{P}}+\mathbf{d}_{\mathbf{P}}^{\intercal}\mathbf{R}_{ \mathbf{F}}\mathbf{d}_{\mathbf{P}} \tag{11}\]  
Differentiating \(\mathbf{J}\) by free derivatives yields the following equation. \[\frac{\partial\mathbf{J}}{\partial\mathbf{d}_{\mathbf{P}}}=2\mathbf{d}_{ \mathbf{F}}^{\intercal}\mathbf{R}_{\mathbf{F}}+2\mathbf{d}_{\mathbf{P}}^{ \intercal}\mathbf{R}_{\mathbf{F}} \tag{12}\]  
After equating Equation (12) to zero, the optimal vector \(\mathbf{d}_{\mathbf{P}}^{*}\) is obtained. \[\mathbf{d}_{\mathbf{P}}^{*}=-\mathbf{R}_{\mathbf{P}}^{-1}\mathbf{R}_{\mathbf{F }}^{\intercal}\mathbf{d}_{\mathbf{F}} \tag{13}\]  
By substituting the optimal vector \(\mathbf{d}_{\mathbf{P}}^{*}\) into Equation (9), the optimal coefficients of the vector are obtained, and finally, the trajectory generation is complete. We allocate the segment time along the distance ratio to keep as constant a velocity as possible because we want a safe and stable quadrotor flight. This means that matrices \(\mathbf{A}\) and \(\mathbf{Q}\) are computed before flight once the waypoints are determined. After the global trajectory is computed, the quadrotor will fly while following the global trajectory.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 5 Simulation Results
### Effect of Compression on Target Detection  
Figures 19 and 20 show the target detection results based on the original image and the compressed image, respectively. The original image shows the Shanghai Pudong International Airport. We can see that most of the aircraft in the original image can be detected correctly. The detection results with a compressed image with a compression ratio of 12 were nearly the same as those for the original image, except for few false detections and missed detections. We marked the false detections and missing detections in Figure 20. Therefore, it was better to compress the original image before target detection, as this could reduce the detection complexity and achieve a similar detection performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compression Rate Set in ADV212** & **2:1** & **4:1** & **10:1** \\ \hline Size after compression & 82 KB & 82 KB & 59 KB \\ \hline MSE & 38,508.815755208336 & 38,508.815755208336 & 91,708.72526041667 \\ \hline RMSE & 196.2366320420536 & 196.2366320420536 & 302.8344849260346 \\ \hline PSNR & 50.473864443709296 & 50.473864443709296 & 46.70535950680697 \\ \hline SSIM & 0.9966228304307309 & 0.9966228304307309 & 0.9934589782137567 \\ \hline UQI & 0.9999321996609332 & 0.9999321996609332 & 0.9998540667907824 \\ \hline MSSSIM & (0.9993673349222898 + 0j) & (0.999367334922898 + 0j) & (0.9986344999440212 + 0j) \\ \hline ERGAS & 291.1348285608546 & 291.1348285608546 & 426.4397995898409 \\ \hline SCC & 0.7612884162903569 & 0.7612884162903569 & 0.6717694464656159 \\ \hline RASE & 72.78370714021365 & 72.78370714021365 & 106.60994989746023 \\ \hline VIFP & 0.3912624911023905 & 0.3912624911023905 & 0.3430493507692442 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The compression indices’ comparison with different compression rates.  
Figure 18: The original image of buildings.
## 3 Data and Methods
### DEM Generation with the Ames Stereo Pipeline
#### 3.2.1 Bundle Adjustment and Map-Projection  
Prior to image correlation, camera or sensor parameters and positions need to be corrected and adjusted. This step is referred to as bundle adjustment and relies on correlating features in 2D image space and 3D object location: the least squares bundle adjustment minimizes the error between the 3D location of image features. This results in parameters stored in matrices containing the new camera orientations and positions, which are later used to improve the correlation and triangulation results. Self-consistent cameras also reduce blunders during stereo triangulation of point clouds.  
In a second step, the left and right image of a given image pair are roughly aligned (i.e., rectified) to each other before correlation begins. In the classic computer vision approaches relying on images taken with optical cameras, this is often achieved by translating and rotating the images against each other to form a homography that generates epipolar lines across the images. This means that after the rotation and translation, points (pixels) on one row of a warped left image correspond exactly to the points along the same row in the warped right image. Finding matching pixel patterns only along the same row greatly increases computational efficiency. In the case of satellite or aerial imagery of a planetary surface, another option exists for initial left and right image rectification: map-projection. This is preferable for line-based scanners, such as most satellite images.  
Map-projection takes each individual image (e.g., the SPOT6 A scene) and uses the results of the bundle adjustment step along with the associated RPC metadata from the scene to project the image onto a provided, often coarser (30 m), DEM. Rather than intersecting the SPOT6 images with each other for rectification in the classic computer vision method, map-projection intersects each image with the smooth DEM; both images are independently orthorectified resulting in a pair of similar images that differ only in small perspective differences, which are the features detected during stereo correlation. This is somewhat comparable to differential interferometric synthetic aperture radar approaches, where an interferogram is formed by removing the topographic phase from both radar scenes to end up with a cleaner deformation signal without the topographic component. A map-projection of the satellite or aerial images is strongly recommended, and an initial smooth DEM is required for the study area.
## Nomenclature  
\begin{tabular}{l l} \(D\)(s) & time-varying delay \\ \(D\)(s) & denominator \\ \(F\)[ ] & Fourier transform \\ \(Jn\) & Bessel function of order \(n\) \\ \(L\) & length of filter \\ \(N\)(s) & polynomials of numerator \\ \(P_{c}\)(z) & secondary path dynamics \\ \(T\)(s) & arbitrary plant \\ \(Q\) & internal model controller \\ \(a\) & proportional gain \\ \(d_{k}\) & desired value \\ \(e_{k}\) & measured error \\ \(k\) & sampling instant \\ \(p\)(s) & process model \\ \(q\)(s) & internal model controller \\ \(r\) & relative order \\ \(u_{k}\) & reference input \\ \(w_{k}\) & weight vector of filter \\ \(z^{-\Delta}\) & time delay block \\ \(x_{a}\)(t) & amplitude-modulated \\ \(x_{b}\)(t) & frequency-modulated \\ \(y_{k}\) & adaptive filter output \\ \(y_{s}\) & set point \\ \(\delta\) & Dirac delta function \\ \(\xi\) & damping ratio \\ \(\lambda\) & filter time constant \\ \(\mu\) & convergence factor \\ \(\tau\) & time delay \\ \(\omega_{n}\) & natural frequency \\ \(\cdot\) & parameters of FX LMS \\ \(\cdot\) & after secondary path \\ \end{tabular}