esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics
## 2 Problem Definition  
Following [PERSON]'s RDO method, we look for a methodology allowing optimizing a set of Qols, \(\mathbf{J}=\mathbf{J}(\mathbf{x},\mathbf{\xi})\), \(\mathbf{J}\in\mathbb{R}^{m}\) depending on a vector of deterministic design parameters \(\mathbf{x}\in\mathbb{R}^{n_{des}}\) and on a vector of uncertain parameters \(\mathbf{\xi}\in\mathbb{R}^{n_{unc}}\). Note that some of the design parameters may also be uncertain. We formulate the RDO problem by using the expectancy and the variance of \(J\) as measures of robustness, which leads to the solution of the two objective deterministic optimization problem in Equation (1).  
\[\min_{\mathbf{x}}E[\mathbf{J}],\quad\min_{\mathbf{x}}var[\mathbf{J}] \tag{1}\]  
The preceding optimization problem is solved by means of an MOGA. More precisely, following our previous studies [10; 12], we adopt the non-dominated sorting genetic algorithm (NSGA-II) of [PERSON] et al. [58], which provides an approximated Pareto front of optimal solutions corresponding to different trade-offs between average performance and robustness for the various QoIs at hand. For simplicity, in the following, we consider only the case of a single QoI, \(m=1\), but the approach can be extended to multiple QoIs. The required statistics of the QoIs are calculated by means of a non-intrusive UQ method, which, for CFD models, is coupled with a suitable (costly) flow solver. Thus, the first ingredient of the RDO process is an efficient UQ approach, which provides accurate approximations of \(E[\mathbf{I}]\) and \(var[\mathbf{I}]\) based on a set of \(N\) deterministic samples of the solution. The UQ methods investigated in this work are described in Section 3.  
Direct coupling of the MOGA with the UQ solver is overly costly for CFD, due to the high number of function evaluations. For instance, running the MOGA with a population of \(n_{pop}\) individuals over \(n_{gen}\) generations and using \(N\) samples for UQ lead to an overall number of CFD evaluations of the QoI of about \(N\times n_{pop}\times n_{gen}\). The computational cost can be greatly alleviated by running \(N\times n_{pop}\) deterministic runs in parallel at each NSGA generation [8; 25], but: (i) the required number of computational cores may exceed the computational resources available, and (ii) even with a perfect parallel scaling at each generation, the turn-around time of the RDO equals at least the average cost of a CFD run multiplied by \(n_{gen}\). To reduce the computational cost, a second (external) surrogate model is introduced to predict the response of the cost functions to the design parameters, as described in Section 4.
## 2 State of the Art
### Data Collection
#### 2.1.3 Teaching Digital Competencies via Heritage  
Three-dimensional heritage has become subject of teaching digital literacy and skills, e.g., on modelling techniques or VR technologies, while the historical object is used as a training example. This closely relates to the concept of data literacy comprising data collection, exploration, management, analysis, and visualization skills [82] (see also [83]). Moreover, digital 3D reconstruction techniques have been employed in various educational settings, focusing on the process of the reconstruction of cultural heritage objects (e.g., [84; 85]). Despite these activities, there is still no wide consensus on establishing a specific digital visual humanities education paradigm, and larger studies on teaching digital methods in visual humanities are still needed [84].
## 5 Simulation and Results
### Lung Detection Model  
The lung-detection model was trained twice. In terms of the training data of the first model, 100 images were used, with 25 images randomly selected for each class of normal, COVID-19, lung opacity, and viral pneumonia.  
The label information consisted of the position of the left and right lungs in each image. The ratio of training and validation datasets was 80:20, and only one class, i.e., \"lung\", was available. The internal default settings of YOLOv4 were used for data augmentation. Because all CXR images were grayscale images, hue and saturation augmentation were not performed. After learning was completed, all CXR images were evaluated, and the images that did not detect the position of the two lungs were classified as error images. Retraining was performed using 100 cases of the classified error images in addition to the 100 images used in the first training. The training conditions were set to be the same as those in the first training. Table 2 shows the number of bounding boxes in the first and second lung detection models for CXR images. In the first model, 158 false detections occurred; however, in the second model, 35 false detections were indicated.
# The Role of the Directivity of Various THz Detectors in Multiplexing Systems
## 2 Materials and Methods
### Experimental Setup  
The experiment was conducted in a straightforward optical setup, shown in Figure 4. A tunable frequency multiplier from Virginia Diodes, Inc. was used as a source of radiation. To form the divergent beam from the source into a collimated shape with a quasi-Gaussian distribution, the beam was directed to an \(8^{\prime\prime}\) parabolic mirror placed in such a way that the source was at its focal point \(f_{m}=600\) mm. The reflected beam with the desired geometry was used to illuminate the measured structure (MS) positioned around 700 mm from the mirror. To ensure independence from the lens shape, in all cases, a circular aperture was placed in front of the structure.  
The detection plane is perpendicular to the main optical axis of the system. Its distance from the measured structure can be set according to the focal length of the lens. In our experiments, it was equal to 100 mm (F1) or 700 mm (F2). The detectors were mounted on motorized stages, which allows precise control of their position in the three perpendicular axes, as well as rotation along the vertical axis (\(\theta\)).  
Figure 3: Photographs of the manufactured three-focal-spot lenses: (a) \(f=100\) mm, \(\
u=140\) GHz; (b) \(f=700\) mm, \(\
u=140\) GHz and (c) \(f=100\) mm, \(\
u=260\) GHz.
# Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm
## 3 The Proposed Method
### Design of the Fitness Function  
Since the evaluation index value is a dimensional expression, this paper uses linear normalization to transform it into a scalar expression. Considering the three-machine work of the fully mechanized mining face, the running load of the scraper conveyor (\(p_{m1}\)), the support requirements of the hydraulic support (\(p_{m2}\)) and the efficiency of the shearer chamfering feed (\(p_{m3}\)) are used as evaluation indicators (\(p_{mj}\)). The calculation formulas for the evaluation indicators \(p_{m1}\), \(p_{m2}\) and \(p_{m3}\) are as follows:  
Figure 3: Probability density function curve of Cauchy distribution, t-distribution and Normal distribution.  
\[p_{m1}=W_{(m,d)}+\Delta F_{m} \tag{10}\]  
\[p_{m2}=K \tag{11}\]  
\[p_{m3}=\frac{L_{kp}/t_{cf}}{L/t_{cd}} \tag{12}\]  
where, \(L_{kp}\) is the length of the S-bending area, m [31]; \(t_{cf}\) is the time for the shearer chamfering feed, s; and \(t_{cd}\) is the total time used by the shearer to cut a piece of coal, s.  
The first and second quadrants of the unit circle are used as the scoring system, which is not only easy to calculate, but also has increasing and decreasing trends. The evaluation score formula is calculated as follows:  
\[S_{mj}=100\cdot(1-p_{mj}^{2})^{\frac{1}{2}} \tag{13}\]  
where \(S_{mj}\) is the score of the \(j\)-th index of the known shearer running direction, \(j\) is the index number, \(j=1,2,3\); and \(p_{mj}\) is the evaluation index of the corresponding \(S_{mj}\). The score of the whole working mode can be obtained from each evaluation score, and the total score function \(S_{score}\) is shown as follows:  
\[S_{score}=\sum_{m}^{2}\sum_{j}^{3}a_{mj}S_{mj} \tag{14}\]  
where \(a_{mj}\) is the weight coefficient, and its size is determined by tomographic analysis [32].  
The additional resistance \(\Delta F_{m}\) of the scraper conveyor, the number of the slot (\(K\)) in the S-bending area and the length (\(L_{kp}\)) of the S-bending area are all related to the horizontal bending angle \(\alpha^{\prime}\). Therefore, the fitness function is created with \(\alpha^{\prime}\) as the objective of optimization. The higher is the score of the objective function for \(\alpha^{\prime}\), the better is the working mode of the three-machine collaboration. \(S_{score}\) is used as the fitness function of the MBA to iteratively optimize.  
\[function\;\;S_{score}=Fun(\alpha^{\prime}) \tag{15}\]
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 2 Related Work  
The SE block was proposed to improve the performances of the CNNs and to demonstrated its potential in image classification [16]. It consists of two successive operations: squeeze and excitation. In the squeeze operation, the inputted feature maps will be passed to a global-average pooling (GAP) layer to generate channel-wise statistics, where each value is an average of the corresponding feature map. Thereafter, in the excitation operation, the SE block will compute an appropriate weight for each feature map by using the channel-wise statistics and fully connected (FC) layers. The SE block multiplies inputted feature maps by their weights and obtains the channel-weighted feature maps. Due to its success in image classification, the SE block has been adopted in many applications, including HAR. [PERSON] et al. [17] investigated the potential of the SE blocks by adding them to a list of state-of-the-art CNN models (e.g., VGG16, Inception, ReNet18, and PyramidNet18) and comparing the corresponding HAR performances. [PERSON] et al. [18] proposed a DL model called the SEResNet-BiGRU, which is a combination of residual blocks, SE blocks, and bidirectional gate recurrent units (BiGRUs), and applied it for transitional activity recognition. [PERSON] et al. [21] proposed a multi-branch DL architecture where each branch uses a CNN model with an SE block to extract and re-weight feature maps. The above DL models with SE blocks are summarized in Table 3.  
Several DL architectures have been extensively proposed and investigated in HAR using sensor data. In order to improve the HAR performances, instead of using only one branch, we can implement DL architectures with multiple branches such that several different and unique sets of feature maps will be obtained and helpful in classifying the activities. There are two common categories of the multi-branch architectures. In the first category, we consider a scenario wherein there is a set of wearable sensors (e.g., IMUs) attached to parts of the user body (e.g., a wrist, an ankle, the chest). Therefore, different sets of sensor
## 2 GNSS RO Event Simulation and Assessment Approaches
### GNSS and LEO Satellite Orbital Data  
In this study, two-line element (TLE) format satellite orbital data were used for the GNSS RO event simulation. The GNSS satellites' and Spire weather satellites' orbital data were downloaded from the Current TLE (on 20 June 2021) via the link: [[https://www.celestrak.com/](https://www.celestrak.com/)]([https://www.celestrak.com/](https://www.celestrak.com/)) (latest accessed date is 16 December 2021). A multi-GNSS TS system involves GPS Operational (30 satellites), GLONASS Operational (GLO, 27 satellites),GALILEO (GAL, 26 satellites), BDS (50 satellites), satellite-based augmentation system (SBAS, 16 satellites), navy navigation satellite system (NNSS, 18 satellites), and Russian LEO navigation (RLNav, 9 satellites), as well as the Iridium NEXT constellation (IridN, 75 satellites) which has satellite navigation, positioning, and timing functions. Therefore, the selected TS system had 251 satellites in total. Due to the large number of multi-GNSS TS satellites, the satellite ID and major orbital roots of representative TS satellites are given in Table 1.  
In this study, the LEO RS satellites mainly involved 12 designed polar-orbit satellites, a traditional meteorological satellite (MET), and Spire weather satellites. Currently, there are 114 Spire weather satellites in orbit, and about 100 Spire weather satellites have been carrying GNSS RO sounders for atmospheric sounding since 2016; thus, 100 Spire weather satellites were selected to form a RO constellation as the control experimental group. The satellite ID and major orbital roots of the LEO RS satellites are shown in Table 2, in which three representative Spire weather satellites are presented.
## 3 The Proposed Scheme  
As we know, LSTM is more powerful in dealing with the memory sequences' prediction problem since it could handle the long-term dependencies and store the memory parameters, which are related to the channel characteristics. As regards the VLC system, \(y(n)\) experiences the complex optical-to-electrical and electrical-to-optical conversion, and the complicated overall channel nonlinearity involved in \(y(n)\) is very implicit and not very intuitive, which will greatly increase the computational complexity and learning difficulty. Furthermore, it leads to a slow convergence speed and the decrease of equalization performance. In order to improve the learning ability and accelerate the convergence speed, we therefore propose a novel impairment compensation scheme, which utilizes the Volterra structure to construct the spatial features and feed them into the CNN-LSTM network to extract the characteristic of memory nonlinearity. In the following analysis, we assume that the system synchronization has been already achieved at the receiver.
## 4 Results
### Data Pre-Processing
#### 4.1.4 Saccades Smoothing: Moving Median Window  
Four different moving median windows were tested to the gaze-target distance to de-noise it. In comparison to the original data, every sliding window proved to improve and smooth the gaze-target distance (Figure 6). Out of the four tested ones, the 40 s centred moving average window presented less volatility induced by saccadic behaviour and best smoothing in the data.  
1.5 Colour Change Recognition Sub-Task and Scotoma Radius as Cutoff for the Maximum Positive and Negative Predictive Values  
The test revealed that indeed there was a big variability when comparing positive and negative predictive values across different scotoma radii, starting already at 5.5\({}^{\circ}\) indicating that the actual radius of the coverage area was smaller than 6\({}^{\circ}\). The probability of positive and negative predictive values had less considerable variability when reaching the 5\({}^{\circ}\) radius of scotoma coverage and started to reach a plateau (Figure 7). This indicated that the ball could have been perceived when the distance was \(\leq\)5\({}^{\circ}\), and not 6\({}^{\circ}\) from the target.  
On the other hand, it was observed that some subjects performed the task correctly across all conditions, while others did not. The probability of positive and negative predictive values did not show the same trend as the majority of subjects, with no plateau reached (Figure 8); those subjects were excluded from the analysis, as a poor performance in the test was suspected. A total of 5 subjects had to be excluded due to this criterion. For the analysis, a total of 8 subjects were included, four from each group.  
Figure 6: Original gaze samples (blue) and different moving window medians applied to the original data smoothing the saccades (red).
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Comparison with Similar Existing Antennas  
The existing antennas described in the literature are compared in Table 7 with the proposed antenna regarding the resultant parameters. It is observed that the antenna's FSS size and the materials used are the same for both the FSS and antenna; the FSS size may even be higher in comparison with the main radiator antenna's size [49; 51]. It is also noticed that the FSS layer exists either above (superstrate) or below (reflector) the antenna. Some antennas' FSS layers are fabricated on certain substrates, while the patch is developed on another substrate [49; 51]. The different substrates are used to create a hybrid effective cumulative material permittivity effect to control the gain and bandwidth. The air gap in the FSS results in frequency tuning, while the hybrid materials increase the overall cost of the antenna. Therefore, it is essential to ensure that the antenna is economical using low-cost materials, and the size of the FSS must be smaller as compared to the radiator. The FSS must be simple and less complicated. The proposed antenna takes all of these advantages into account.  
Figure 17: Current density distribution of the proposed fractal antenna: (**a**) current density without FSS at 3.5 GHz, (**b**) current density with FSS at 3.5 GHz, (**c**) current density with FSS at 3.62 GHz, (**d**) current density with FSS at 7.82 GHz, and (**e**) current density with FSS at 10.58 GHz.
# Optimization of Coverage and Capacity Using Smart Antennae
## 3 Methodology  
### Modeling  
Assuming the entire network system consists of \(n\) base stations, denoted as, \(S_{i}\) for \(i=1\), \(2\), \(\ldots\), \(n\), each base station has \(j\) antenna directions. Let \(X_{j}^{i}\) represent the \(j\)-th antenna direction for the \(i\)-th base station. If \(X_{j}^{i}=1\), the antenna in that direction is activated, and if \(X_{j}^{i}=0\), the antenna is deactivated. Each base station has a maximum power of P\({}_{\text{max}}\) and a minimum power of P\({}_{\text{min}}\). Additionally, each \(S_{i}\) can provide a bandwidth of up to B and can simultaneously serve a maximum of \(t_{S_{i}}\) terminal devices.  
In the entire system, there are K terminal devices, denoted as \(u_{1}\), \(u_{2}\), \(\ldots\), \(u_{k}\), with \(B_{k}\) representing the bandwidth that \(u_{k}\) can be allocated. Pi represents the power of \(S_{i}\), PL denotes the path loss signal attenuation, \(\gamma\) represents the path loss exponent (where \(\gamma\geq 2\)), and \(d_{k,S_{i}}\) indicates the distance between \(u_{k}\) and base station \(S_{i}\). A detailed explanation of the system model parameters is shown in Table 1.
## 3 The Vectorized Earth Observation Retrieval Classifier
### Derivation of Look-Up Vectors (LUV)  
The simplified derivation of LUV for the 2D case, with binary labels and two artificial features, is presented in Figures 1 and 2. Red points indicate label 1, and blue points label 0. The gray lines denote percentiles of feature values ranging from 0 to 100% with 10% intervals forming an irregular grid, where the probability \(Px\) for a cell \(x\) (Equation (1)) is computed as a ratio between the number of samples labeled with 1 (red points) and the total number of samples \(N\) within cell \(x\) (red + blue points).  
\[P_{x}=\frac{\sum_{i=1}^{N}c_{i}}{N} \tag{1}\]  
where:  
\(P_{x}\)--classification probability within cell \(x\);  
\(N\)--number of samples within cell \(x\);  
\(c_{i}\)--binary label of a sample within cell \(x\).  
However, if there is a significant imbalance between the number of red and blue points, the computed probability is biased towards the more frequent class. Therefore, the initial probability is recomputed after class balancing in which every class is oversampled relative to the total number of samples; this effectively doubles the initial number of samples. The oversampling is performed while randomly assuming the initial probability distribution of each class within each cell of the percentile grid. Incorporation of percentiles as the grid coordinates instead of varying magnitudes of features (corresponding to different physical units) suppresses the need for feature scaling. Within the standard VEOR implementation, the grid is composed of 254 percentiles ranging from 0 to 100, which normalize values for each feature to \(2^{8}\) (8-bit) integers. Further, this allows for the computation of a unique ID for each cell consisting of 8-bit values stacked together (by means of bitwise_or and bitwise_left_shift operators for little-endian machines) on consecutive bit ranges within a 64-bit value. Consequently, the VEOR can analyze up to eight features with 256 degrees of freedom each. The percentile grid significantly reducesthe number of samples relative to the number of cells, which gives great flexibility in terms of the size of the training sample and the incorporation of new samples. This feature of the VEOR algorithm is especially important for frequently updated training datasets that require frequent adjustment of LUV with classification probabilities.
# Multi-Layer Problems and Solutions in VANETs: A Review
## 2 Multi-Layer Challenges in VANETs
### Proposed Solutions  
#### 2.2.1 PHY Layer  
* Single versus Dual Radio: Single Radio can be used for both the safety and non-safety exchange of data. It has the benefit of being low cost and simple but suffers from poor channel utilization compared to Dual or Multi radio. With dual radio, we can use one radio for safety and other for non-safety data. The dual antenna has the advantage of better spectrum utilization, fast, and reliable delivery of real time safety data [25]. The obvious drawback of using a dual antenna is higher complexity and the cost of deployment.
* Channel Estimation Techniques: The author in [26] proposed an inter-vehicle cooperative channel estimation method. It presents an interpolation assisted channel estimation technique to obtain the accurate Channel State Information (CSI) and mitigate the effects of imperfect CSI. The performance of channel estimation can be improved by set of different time, frequency and spatial observations. Some research in this domain is required. In [27], a Fuzzy-Based Channel Selection for Location Oriented Services in Multichannel is presented. Interference and delay are taken into consideration in order to select transmission channel in order to reduces traffic imbalance problem among the service channels. The said scheme increases throughput and offers a lower end-to-end delay compared to older techniques.
* Diversity Techniques: The use of antenna diversity techniques such as the Multiple Input Multiple Output (MIMO) can increase the range of communication through beam forming, spatial diversity and high throughput. However, there are certain challenges of applying the MIMO technology to the vehicular environment. [PERSON], et al. [28] mention the impact of cost and unsuitability of 802.11n for dynamic wireless channel encountered in V2V scenarios. The MIMO in V2V scenario will need channel modeling, estimation, and cross layer optimization. The benefits of diversity techniques include range extension, increased data rate, security, and reliability.
* Software Defined Cognitive Radios: Several research in recent past has focused on using the software defined Cognitive Radios (CR) for the VANETs. The most prominent feature of CR includes their spectrum sensing ability which can enhance the efficient utilization of the spectrum. Spectrum scarcity is a major shortcoming in VANETs. However, deploying the CR in VANETs comes with its own challenges due to the dynamic nature of network, with high node mobility and unpredictable nature. A comprehensive survey of using the CR in VANETs is provided in [29]. This paper discusses the challenges of both the CR and VANETs individually and then the collective challenges when both technologies are merged. Furthermore, the paper also mentions the spectrum sensing, MAC protocols, security, and routing perspectives of the CR based VANETs.  
In Table 1 a list of Physical layer challenges is presented with some solutions along with references.