esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Comparisons of Computation Costs across Various Methods  
The inference runtimes of the proposed method were compared with those of the baseline approaches, with experiments conducted on a workstation featuring an Intel(R) Core(TM) i7-6850K CPU @ 3.60 GHz, 128 GB of memory, and an NVIDIA GeForce GTX 1080 Ti GPU. This experiment utilized 5000 randomly generated grayscale images, each 256 \(\times\) 256 pixels in resolution. Runtimes were assessed across batch sizes from \(2^{0}\) to \(2^{9}\) for the 5000 data instances.  
Figure 13(a) depicts the average runtime per batch size for each method. The figure reveals that the runtime of the proposed method experiences a moderate increase, akin to that of MagNet. However, at a batch size threshold of 256, the proposed method begins to suffer from out-of-memory errors, a problem not encountered by MagNet. This challenge is linked to the volume of model parameters processed by the GPU, as illustrated in Figure 13(b). Specifically, the proposed method encompasses 704 parameters, in contrast to MagNet's 310. Consequently, as depicted in Figure 13(b), the proposed method fails to operate at batch sizes starting from 256.  
Conversely, each of [PERSON]'s method iterations boasts 31,037,391 parameters, with a notable difference being [PERSON] method's use of discrete Fourier transformation, which is omitted in the Park_spatial method. This significant parameter count causes both versions of [PERSON]'s method to experience a sharper increase in runtime compared to the proposed method and MagNet, rendering them incapable of functioning at batch sizes exceeding 16.  
Both the proposed method and [PERSON]'s approach adopt the same structure for the target segmentation model to detect adversarial attacks. However, a key distinction is that the proposed method from this work relies solely on the first layer. As previously discussed in Section 3.4.1, this strategic focus on the initial layer enables our method to surpass [PERSON]'s in terms of runtime efficiency.
# Green Sweet Pepper Fruit and Peduncle Detection Using Mask R-CNN in Greenhouses
## 2 Materials and Methods
### Mask R-CNN Algorithm  
The state-of-the-art Mask R-CNN method in instance segmentation is selected for its effectiveness and simplicity [32]. The Mask R-CNN method is developed for object instance segmentation (grouping of pixels that belong to the same object) and extends the Faster R-CNN method [33] by adding a function to predict masks in each region of interest (RoI) of parallel form [29]. In general, the Mask R-CNN is a two-step algorithm: (1) it generates proposals (bounding boxes of candidate objects) after reading the image; and (2) predicts the bounding box, the class, and a binary mask (instance segmentation) for each RoI [29]. Now, in structural terms, the Mask R-CNN framework consists of the following stages (Figure 5):  
* In the first stage, the backbone network extracts the initial feature maps from the input images: a first part of the backbone architecture, a ResNet network (residual learning network) [34] used for feature extraction, and a second part of the backbone architecture, a Feature Pyramid Network (FPN) (for more information, please refer to [35]) to improve the representation of objects at multiple scales [36];
* In the second stage, the feature maps obtained from the backbone are sent to the Region Proposal Network (RPN) to generate regions of interest (RoIs);
* In the third stage, the RoIs obtained from the RPN are mapped to obtain the corresponding features in the shared feature maps, and finally sent to a Fully Convolutional Network (FCN) [37] and a Fully Connected layer (FC), respectively, for instance segmentation and target classification: regarding instance segmentation, an FCN with a RoIAlign layer (refinement of the RoI pooling) [29] and bilinear interpolation is used to predict pixel-by-pixel accurate masks;  
Figure 4: Example of instance segmentation sweet pepper dataset with computed bounding boxes (ground truth): (**a**) original image; (**b**) visualization of mask image with computed bounding boxes.  
* Before entering the FC (for target classification), RoIAlign is applied to adjust the dimensions of the RoIs to meet the input requirements of the FC (a fixed-size feature map) [13].  
For a deeper discussion of the Mask R-CNN algorithm please refer to [29].
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 5 Experiment and Evaluation
### Experiment Setup  
Experimental Environment  
**Hardware Environment:** The experiments were conducted on a Linux server equipped with two NVIDIA GeForce RTX 3090 Ti GPU.  
**Software Environment:** The software environment includes Python 3.8 and PyTorch 1.10, along with libraries such as NumPy, Pandas, and Scikit-learn for data processing and machine learning.  
**Parameter Settings:** In our experimental investigation, the TimesFM model was implemented with the following parameter configurations: a context length of 64 units was established to facilitate temporal pattern recognition, while the forecast horizon was set equivalent to the training data duration to maximize predictive capacity. The model architecture incorporated an input patch length of 32, a dimensional parameter of 1280, and 20 transformer layers to ensure comprehensive feature extraction and pattern analysis. Computational efficiency was optimized through a per-core batch size of 4, with the processing backend specifically configured for CPU operation. Additionally, the model was designed with positional embeddings disabled to evaluate performance under pure sequential learning conditions.
## 2 Studied HVDC System  
To test the protection strategy, a four terminal VSC-HVDC system connecting 230 kV, 2000 MVA AC systems each, was implemented in MATLAB-SIMULINK (refer to Figure 1). The converter stations were interconnected through four \(\pm 100\) kV, 100 MVA symmetrical monopolar pi lines to create a meshed system, as shown in Figure 1. The converters were fed from 250 MVA 230/100 kV transformers with an AC side filtering capability of 40 MVAR. The DC side filters only consisted of a third harmonic filter with the smoothing reactor. Under normal operating conditions, VSC-1 and VSC-3 were operated in voltage-control mode, while VSC-2 and VSC-4 were engaged in power-control mode. Eight relaying units were placed at each end of the lines. The purpose was to extract current and voltage waveforms from both positive and negative poles and establish protection decisions based on their features.  
To emulate the accurate behavior of the physical system, the model was simulated at a very high sampling frequency of 135 kHz. However, considering industrial standards and available hardware for data acquisition, the voltage and current waveforms were acquired at a far lower sampling frequency of 3200 Hz or 64 samples per cycle of the AC side voltage. The detailed parameters of the AC grid and converter stations are presented in Table 1.
## 1 Introduction
The transformer network has typically shown better performance in estimating high-dimensional information from low-dimensional information, in comparison to various deep neural networks (i.e., autoencoder, GAN, and LSTM) [25]. The transformer network estimates the output by modeling the information between the input and output through learning the inherent dependencies of each. The attention mechanism in the transformer demonstrates particularly excellent performance in exploring the dependency of the input and output data by modeling the correlation between them [25]. In this work, the transformer network estimates the high-dimensional natural hand poses from the low-dimensional object affordances. Recently, reinforcement learning approaches were introduced to train an anthropomorphic robot hand with the estimated natural hand poses [18; 32]. Furthermore, Demo Augmented Policy Gradient (DAPG) was introduced [33]. Previous works estimated hand poses and used them to initialize finger parameters at the beginning of DRL training through imitation learning. In [18], an object grasping intelligence framework was proposed and trained for three objects (an apple, a potato, and a mustard bottle) for an anthropomorphic robot hand. Grasping of both the apple and potato was successful but grasping of the mustard bottle failed since the thumb and other fingers of the robot hand could not close properly, due to the estimation of an unnatural hand pose. In previous studies it is noted that a DRL methodology with a robust transformer network has not yet been reported. A transformer network incorporated into a DRL policy should improve dexterous object manipulation with an anthropomorphic robot hand. Furthermore, in the policy training phase of DRL, imitation learning and reward function are used to train the robot's grasping intelligence through demonstrations. Imitation learning can help in early-stage learning, and the reward function carries out the learning process until its completion. In the design of the reward function, reward shaping was employed by utilizing various categories of information, such as object location, robot hand location, and hand pose. In this work, we propose a novel methodology of dexterous object manipulation with an anthropomorphic robot hand via a transformer-based DRL with a novel reward shaping-function. The proposed transformer network estimates natural hand poses from object affordance to guide the learning process of DRL. The transformer network estimates high-dimensional information (i.e., 24-DoF hand poses) from low-dimensional information (i.e., 6-DoF object affordance for location and orientation). This reduction provides natural hand poses for the movement of an anthropomorphic robot hand. Then, the DRL algorithm utilizes the estimated natural hand poses in the reward-shaping function and learns dexterous object manipulation.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review  
The implementation of CBM is to be found in various applications, such as vibration modeling, sensor performance, signal processing, noise control, thermodynamic performance monitoring, lubricant oil, corrosion monitoring, non-destructive test, and inspection techniques (e.g., magnetic particle inspection, alternating current potential difference, ultrasonic, eddy current, radiography, acoustic emission) [34; 35; 36; 37]. The selection of an appropriate CBM (and its optimization techniques) is always challenging and highly depends upon the system. We provide description of some of the commonly used such optimization techniques in CBM in the following sub-sections.  
In general, a generic CBM system consists of three basic components: (i) data acquisition, (ii) data processing, and (iii) decision-making process [27; 38]. Data are usually acquired by means of various sensors (e.g., electrical, electronic, mechanical, electro-mechanical). The acquired data are then processed in order to determine the health of the system under investigation. Numerous techniques are available, including the use of wavelet, neural network, feature recognition, statistical approaches, signal processing, and artificial intelligence [39]. Upon processing the data, it is then used in the decision-making process, such as in determining/predicting (i) remaining useful life, (ii) the confidence level, (iii) failure analysis, (iv) proactive maintenance steps, (v) cost/benefit analysis, (vi) downtime reduction, (vii) performance improvement strategy, (viii) maintenance scheduling, and (ix) health condition [40; 41]. In some of the instances, a CBM system may not be effective, e.g., (i) unavailability of the data required in assessing the state of the system, and (ii) in some cases, the acquired data may be qualitative rather than quantitative in nature, which makes the assessment more complex.
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 4 Evaluation Results and Discussion
### The Evaluation Results
#### 4.1.5 Artificial Intelligent Models  
The artificial intelligent models are built using the neural network toolbox provided by MATLAB. A feedforward neural network with 12 inputs, 1 output and 10 neurons in the hidden layer is first built. The best performance appears when the training function is set to be 'trainlm'. Compared with the other proposed models, the artificial intelligent model performs worst. Only 10% of all the airports can be predicted by the neural network. The evaluating results of the artificial intelligent model on different airport levels are shown in Table 17.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Criterion 1**} & \multicolumn{2}{c}{**Criterion 2**} & \multicolumn{2}{c}{**Criterion 3**} & \multicolumn{2}{c}{**Criterion 4**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Good**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Number of**} \\  & \multicolumn{2}{c}{**Good**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} \\ \hline Summation & 46 & 23\% & 67 & 33\% & 88 & 43\% & 71 & 35\% \\ of all levels & 46 & 23\% & 67 & 33\% & 88 & 43\% & 71 & 35\% \\ \hline APT & 18 & 14\% & 26 & 20\% & 37 & 28\% & 37 & 28\% \\ \hline APT & 18 & 14\% & 26 & 20\% & 37 & 28\% & 37 & 28\% \\ between & 4 & 12\% & 10 & 29\% & 17 & 50\% & 10 & 29\% \\
2 million and & 10 million & & & & & & \\ APT more than & 24 & 62\% & 31 & 79\% & 34 & 87\% & 24 & 62\% \\
10 million & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: The applicability of analogy-based method on different levels of airports.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Airport** & **Province** & **Area** \\ \hline OHE & Heilongjiang & Northeast China \\ CHG & Liaoning & \\ CNI & Liaoning & \\ \hline LCX & Fujian & East China \\ JDZ & Jiangci & \\ \hline NZH & Inner Mongolia & North China \\ WUA & Inner Mongolia & \\ BAV & Inner Mongolia & \\ \hline IQN & Gansu & Northwest China \\ JGN & Gansu & \\ HZG & Shaanxi & \\ \hline PZI & Szechwan & Southwest China \\ DIG & Yunnan & \\ \hline KRY & Xinjiang & \\ KJI & Xinjiang & Xinjiang \\ \hline \hline \end{tabular}
\end{table}
Table 15: The airports that can be predicted by the time series models but cannot be predicted by the market share methods.
# Solar Radio Emissions and Ultralight Dark Matter
## 4 Propagation of the Converted Photons
Therefore, Equation (26) represents a simplified and conservative scenario. More realistically, the converted photons will be scattered in different directions with angular distribution. As pointed out in [73], the directions of solar radio emissions will be quickly randomized by the scattering effect (mainly due to electron density fluctuations) near the resonant region where the plasma frequency is equal to the emission frequency, while the large-scale refraction generally tends to shift the propagation path towards the radial direction. Solving exactly the evolution dominated by repeated scatterings and inverse bremsstrahlung processes is difficult, and this problem is usually transformed into the framework of Fokker-Planck Equation (e.g., [73; 75; 76; 77]). Essentially, the Fokker-Planck equation can describe the evolution of the phase-space distribution \(N(\mathbf{r},\mathbf{k},t)\) of photons,where the refraction effect represented by the gradually varying \(n_{e}(r)\), the absorption effect represented by a collision absorption coefficient, and the scattering effect represented by a diffusion tensor, are included [73; 75]. Based on the Fokker-Planck equation, ref. [73] developed a numerical Monte Carlo ray-tracing method to study the three-dimensional stochastic propagation process of radio emissions where the anisotropic \(n_{e}\) fluctuation has been considered. Applying the ray-tracing numerical code [73], ref. [78] studied the propagation of converted photons in the plasma. The survival probability \(P_{\rm sur}\) defined in the numerical study is only related to absorption regardless of whether scattered or not, which is different from that in Equation (26). The scattering effect against the refraction effect is represented by the angular distribution function \(g(\theta)\) where \(\theta\) is the angle between the final propagation direction and the radial direction on the last scattering surface (\(\sim\)6\(R_{\odot}\), beyond which the interactions of photons with the plasma are negligible). The numerical results show that \(P_{\rm sur}\simeq\) 0.5-0.07 and \(g(\theta)\) distributes mostly within the range \(\theta\)\(\sim\)0.15 rad, in the radio frequency range 30-80 MHz, and both the two effects are stronger for higher frequencies.
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 4 Results  
The proposed analytical model was utilized to examine the relationships between the dimensions of the array, the focal length and the number of elements, as well as the behaviour of the transmit array in response to these variations. After defining all the parameters, the proposed configuration was simulated using Ansys HFSS[25]. The effect of the package has been included in the final model. The system comprises an 8 \(\times\) 8 element transmit array with a spacing of \(\lambda\)/3 between elements. Each TA element has been dimensioned based on the results shown in Figure 6a to compensate for the phase differences of the signals coming from the feeding patch. Figure 10 presents the simulated radiation pattern of the antenna feed both with and without the transmit array.  
A prototype of the proposed transmit array configuration has been fabricated (Figure 11). To measure the antenna with WR10 waveguide equipment, a printed circuit board (PCB) launch connector (BN 533411, Spinner GmbH, Munich, Germany) has been used.  
Figure 10: Simulated gain patterns (H plane) at 77 Hz for the EBG patch antenna with (black line) and without (gray line) a transmit array.  
Figure 9: x8 array: calculated directivity of the unit cell as a function of inter-element spacing.  
Figure 11: Fabricated prototype of the transmit array. (**a**) A 12 \(\times\) 12 mm\({}^{2}\) package. (**b**) The feed patch with the EBG (**c**) measurement setup.  
Figure 12 shows the simulated and measured matching of the fabricated prototype, while the radiation H-plane patterns are compared in Figure 13. the Simulated side lobes are lower than \(-20\) dB. This is a value perfectly in line with those typically obtained from a high-performance transmit array. The E-plane patterns were not measured because of the limitations of the measurement setup. Minor discrepancies are visible in the measured beam, as well as a slightly higher side-lobe level. This can be attributed to several reasons like the PCB fabrication tolerances of the feeding patch or the Tas and the alignment of the lower patch to the upper TA and the transmit array to the feeding horn used in the high-frequency measurement setup (Figure 11c). However, the measured results agree quite well with the simulations. The measured gain is 14.2 dB at 77 GHz, with a 68% radiation efficiency and a First Null Beamwidth of 52.5\({}^{\circ}\).  
Figure 12: Simulated (continuous line) and measured (dots) reflection coefficients.  
Figure 13: Transmit array radiation pattern: comparison between full-wave simulations and measurements. The measured gain is 14.2 dBi.
## 6 Discussion
### Effect of the Compression Force on the Measurement  
The previous studies [33; 39; 40] show that the contact force between the pipette tip border and the sample surface has an impact on the measurement, while the effect of the pipette tip wall compression force and friction on the sample surface behaviour has not been studied yet. In this present study, the measurement and the simulation were done in the two boundary approaches, connected pipette and pipette with gap conditions. The results revealed that the friction or the tangential displacement constraint and the force at the sample-pipette contact region can separately affect the present linear analysis significantly. Comparing the graphs in Figure 8 clearly shows that the measured pressures in the 10 mN, 50 mN and 100 mN are identical while the generated displacements are lower when the pipette applies more compression force on the silicone surface. This is because, when the surface is under compression, it became harder; therefore, with identical excitation pressure, the lower displacement is generated when the force is higher.  
On the other hand, the problem of using a small gap to prevent the pipette tip wall compression force on the surface is that the excitation pressure lessens and therefore the generated displacement amplitude declines. The generated displacement and the excitation pressure have a nonlinear relation when the pipette is placed on the surface with gap. By comparing the model Young's modulus, which is considered as the true value of the sample modulus of elasticity, with the calculated modulus of elasticity in Figure 11, it can be concluded that the calculated modulus of elasticity declines unrealistically in the pipette with gap boundary conditions. We add a new correction factor to the Equation (1) to take into account the nonlinear relation in the calculation to make a suitable analytic approximation for pipette with gap boundary condition as the Equation (13) for the present study.  
Using a gap can cancel the impact of the pipette tip wall compression force and friction, which are the major causes of uncertainty, but, if a connection between the pipette and the surface is unpreventable, the contact compression force should be restricted to absolutely zero. As depicted by the connected pipette index in Figure 8, the compression force even in a range of tens of (mN) can interfere in the measurement. Furthermore, the force scale is based on the mixture stiffness; in the softer mixtures, the force in hundreds of (\(\upmu\)N) range can act on the behaviour of the surface. If the pipette tip compression force is unavoidable, the Aoki et al. equation will need further modification by adding a term considering the influence of the force.