esa-sceva/satcom-chunk-collection huggingface dataset:
# Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval
## 4 Experiment  
### Conditions  
**Dataset**. In the experiments, we used pairs of images and human visual information from a dataset presented by [24] called HAIC. The dataset consists of 4000 images randomly chosen from the MSCOCO [25] and Pascal-505 [54] datasets. Each image has been annotated with a human-generated caption and corresponding gaze trace, indicating the regions of the image that the annotators focused on. With the training on this dataset, the CGT model can accurately learn the deep relationship between gazes, images, and captions to connect image features and the gaze trace using captioning to explicitly express semantic information, such as object relationships.  
In our experiments, we divided the images in the HAIC dataset into training and test sets containing 2000 and 2000 images, respectively. Specifically, the 2000 images in the test set are randomly divided into 1500 candidate images and 500 query images in the test phase. Because each image in the HAIC dataset has a corresponding gaze trace and human-generated caption, we use the paired data of the same image separately in re-ranking during the test phase. Gaze traces of the candidate images are used as feedback information for re-ranking. In contrast, the human-generated caption of the query images is used as the ground truth to retrieve the desired image and perform the evaluation by calculating the metrics, both the initial retrieval results and the re-ranking.  
Considering the balance between the introduction of sufficient gaze information and the calculation time of the network, we set \(a\) = 3 in this experiment, using the gaze trace of the top three images from the initial retrieval results to generate the gaze-dependent image caption as the feedback information for re-ranking. For the \(\lambda*\) in loss function, their values depended on the particular experiment and dataset being used. In this experiment, we set \(\lambda_{1}\) = 0.5, \(\lambda_{2}\) = 0.3, \(\lambda_{3}\) = 0.1, and \(\lambda_{4}\) = 0.1, respectively.  
To assess the effectiveness of our method, we applied recent CMIR and re-ranking approaches in the evaluation. Specifically, we compared our method against other existing techniques, such as [45; 46; 55; 56], to assess its adaptability. The comparative methods were implemented using open-source codes provided by their respective authors. As for the time cost during training and retrieval, it is about 100 h and 70 s, respectively. Thus, it is necessary to consider the reduction of the computation cost for the retrieval phase.
# Impact of Arch Dam Cracking on Monitoring Data
## 2 Methods  
### Numerical Approach  
A campaign of FEM simulations was conducted on a real dam to which different cracks of variable lengths, depths, positions, and orientations were applied. It was determined which records would be obtained by monitoring devices at different points of the dam to analyze whether they were able to detect cracks as a function of the accuracy and position of the device. The Ansys Mechanical Finite Element Analysis Software for Structural Engineering 2022 [45], in combination with 3D designs, was used to perform all these numerical simulations of the dam-soil system contemplated in this work. The model used had the following features: (a) linear materials (stress-strain curve); (b) static models; (c) no separation between the dam and the ground; (d) structural loads; (e) nonlinear contacts (cracks).  
The complexity of nonlinear materials has not been considered in this study to achieve more time-efficient performance of the simulations. Furthermore, the consideration of linear models generally leads to smaller displacements. Given that the objective of the work carried out is to search for the possibility of detecting cracks from the recorded displacements, the choice of linear models to obtain said displacements will offer more conservative results. Additionally, thermal loads have not been included, as the consideration of temperature would introduce an unnecessary element of complexity, since the goal of the study aims to assess the impact on the monitoring of cracks in different locations and severity, regardless of their cause. Therefore, the self-weight of the dam and the hydrostatic pressure of the reservoir on the upstream face of the dam are the only active forces. Hydrostatic pressure inside the crack is considered because it significantly affects the results (as shown in Section 2.2.4); the pressure is included by applying to both inner faces of the crack the same mathematical modeling as to the submerged faces of the upstream face. For this purpose, the hydrostatic pressure is considered to remain inside the crack as a function of elevation.  
The methodology adopted for numerical modeling addresses the fact that the structure of the dam changes due to self-weight before contraction joint sealing and hydrostatic pressure development. The contraction joint grouting transforms the individual cantilevers into a monolithic body. Such a configuration of the dam changes again as soon as a crack opens up.  
The boundary conditions for the simulated models were typical for this type of model and consisted of restricting horizontal movements on the sides of the soil body and restricting any movement at the lower base of the soil. As it was a static analysis, pure displacement constraints were considered. The dimensions of the 3D model on each side of the structure were enough for the boundary conditions applied not to affect the results: vertically, a minimum depth of soil of 150% of the height of the dam was considered, while the lateral expansion was at least 50% of the length of the dam without abutments.
## 1 Introduction
### Notation  
The following notations are used in this paper: \(\mathbf{x}\in\mathbb{C}^{\mathrm{M}\times 1}\) represents an \(\mathrm{M}\times 1\) complex vector, \(\mathbf{X}\in\mathbb{C}^{\mathrm{M}\times\mathrm{N}}\) is an \(\mathrm{M}\times\mathrm{N}\) complex matrix. \(\mathbf{X}^{\mathrm{H}}\) is the Hermitian transpose of \(\mathbf{X}\) matrix. \(||\cdot||_{\mathrm{F}}\) is the Forbenius norm. \(N\sim(m,\sigma^{2})\) describes a complex Gaussian random variable with mean \(m\) and variance \(\sigma^{2}\).
## 5 Aircraft Performance Regulations
### Summary  
International regulation of aircraft performance does not include any specific instruction or recommendations for the assessment of landing distance. Landing distance, however, should be determined for a particular aircraft based on the landing performance data and aircraft operations manual by the manufacturer. Hence, approximate data are provided to the pilot for consideration before landing. Elsewhere, the ICAO Aircraft Performance Manual contains recommendations based on the European and American aircraft certifications standards. These recommendations include landing distance and friction coefficient assessment, based on the RWYCC. These methods, however, do not take the airplane characteristics and CFME results into consideration. Part 2 of the Airport Service Manual, however, provides one method for the assessment of approximate landing distance based on the airplane type and friction measurement results. However, this approach is outdated and is no longer used in practice.  
According to the results of the aircraft performance standards analysis, the following conclusions were obtained:  
* Currently, there is no reliable method for the assessment of aircraft landing distance based on friction measurements and aircraft parameters;
* International standards do not provide a unified landing distance assessment methodology;
* It is important to upgrade the current landing distance assessment methodology to avoid risks of runway excursion.  
It is also important to relate aircraft performance to the friction assessment and unify the friction assessment methodology. This highlights the importance of developing best practice surface characteristic assessment methods and consistent stochastic data analysis to determine a characteristic value.  
Figure 17: Aircraft wet/dry braked stopping distance ratio versus average MU-EFF [34].
# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
###### Abstract
Even in computer vision, detecting and classifying drones and birds under different operational conditions is a very challenging task. They are also not easy to be detected at large distances as most object detection models are designed for medium-sized object detection. Convolutional Neural Networks (CNNs) tend to look for meaningful features that can help to classify the images or, in the case of object detection, to draw the boundary boxes enclosing the target of interest. Unfortunately, only limited annotated datasets were available for this purpose. A Drone vs. Bird Detection Challenge has been launched ( [[https://wosdetc2020.wo](https://wosdetc2020.wo)]([https://wosdetc2020.wo](https://wosdetc2020.wo)) rdpress.com/Dronevs.Bird-detection-challenge/, accessed on: 14 April 2021) in 2017, during the first edition of the International Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques (WOSDETC) [16] organized in Lecce, Italy, as part of the 14 th edition of the IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS). A second edition of the challenge has been organized in 2019, again as part of the WOSDETC and held at the 16 th edition of AVSS in Taipei, Taiwan. The aim of this challenge is to address the technical issues of discriminating between drones and birds [16; 17]. Given their characteristics, in fact, drones can be easily confused with birds, particularly at long distances, which makes the surveillance task even more challenging. The use of video analytics can solve the issue, but effective algorithms are needed that can operate under unfavorable conditions, namely weak contrast, long range, low visibility, etc. The present paper discusses and compares the most recent advances in this respect, originated from the 3 rd edition of the grand challenge held in 2020. The dataset of provided video sequences has been expanding over the years, as it will be described in the following section, and represents today one of the few freely available datasets for research in video-based drone detection. Over the three editions of the challenge, more than one hundred research groups from all over the world have obtained the dataset. The rest of this paper is organized as follows. Section 2 describes the Drone vs. Bird Detection Challenge 2020, including details on the dataset, evaluation protocol, and participating teams. Section 3 provides a review of the state of the art on drone detection, with emphasis on deep learning approaches, also reviewing the other datasets available in the literature. Sections 4-6 are individually devoted to the three best teams that excelled in the 2020 edition of the Drone vs. Bird Detection Challenge, named \"Gradiant Team\", \"Eagledrone Team\", and \"Alexis Team\": the proposed approach, processing, and evaluation results are illustrated for each team. Section 7 reports the performance comparison among the three methods, and finally Section 8 concludes the paper.
## 2 Theoretical Foundation
### Visual Variables
#### 2.1.4 Orientation  
According to [17], the orientation visual variable has its structure formed by the change in alignment of the elements that compose it. Some studies reported in the literature [16; 20; 23; 24] use a group of lines as the base element to form the orientation visual variable where their angles are modified to represent different values.  
In this research, a group of arrows was used because the arrow's direction would provide additional information to identify different visual encoding values during the tasks and expand how the set of arrows could be arranged.  
Compared to the study presented by [15], the different visual encoding values for the orientation visual variable were based on straight lines where the elements had their inclination angles changed to represent these visual values, which resulted in many errors (accuracy) and difficulties in the identification tasks.
## 3 Results
### Novel Mathematical Modelling  
Once the correlation and the tendency of the obtained fx and fy values were analyzed, where relevant results were obtained, in the present sub-section, different software tools were employed to obtain novel mathematical models. In this sense, a design of experiments (DOE) resource and the response surface technique of Minitab version 18 were selected. To proceed with the data obtained from the simulations performed, different datasheets were employed. As multiple variables were playing in the simulations for calculating the fx and fy values, making use of the ANOVA resource, the correlation between the most relevant variables was classified. It was obtained that these independent variables were the CS values (from 100 kN to 400 kN), and the vertical and longitudinal stowage positions of the cargo item. Firstly, a polynomial curve fitting was developed for independently calculating the fx and fy. In this approach, the vertical stowage position (VSP) was defined as a categorical factor, and the other two independent variables (the CS and the longitudinal stowage position, LSP) were defined as continuous factors. The maximum and minimum given for these factors were the same as used in the simulations. The categorical factor was codified as is shown in Table 5:  
In the calculation of the fy, the response surface based on the indicated variables showed a very high determination factor of the model in R-square of 0.9738 for all regression models, shown in the following Equations:  
\[\text{Codel}:\] \[\text{fy}=1.9829-0.3475\cdot\text{LSP}-0.007516\cdot\text{CS}+0.4401 \cdot\text{LSP}^{2}+0.000009\cdot\text{CS}^{2}-0.000204\cdot\text{LSP}\cdot \text{CS} \tag{13}\]  
\[\text{Code}2:\] \[\text{fy}=1.8079-0.3538\cdot\text{LSP}-0.007134\cdot\text{CS}+0.4401 \cdot\text{LSP}^{2}+0.000009\cdot\text{CS}^{2}-0.000204\cdot\text{LSP}\cdot \text{CS} \tag{14}\]  
\[\text{Code}3:\] \[\text{fy}=0.9921-0.3424\cdot\text{LSP}-0.005396\cdot\text{CS}+0.4401 \cdot\text{LSP}^{2}+0.000009\cdot\text{CS}^{2}-0.000204\cdot\text{LSP}\cdot \text{CS} \tag{15}\]  
\[\text{Code}4:\] \[\text{fy}=0.8840-0.3371\cdot\text{LSP}-0.005170\cdot\text{CS}+0.4401 \cdot\text{LSP}^{2}+0.000009\cdot\text{CS}^{2}-0.000204\cdot\text{LSP}\cdot \text{CS} \tag{16}\]  
where CS is the capacity securing of the hashing lines and LSP is the longitudinal stowage position of the cargo item. Figure 15 represents, in 3D maps, the obtained fy models for the continuous variables of CS and LSP, where the high deck and the lower hold were selected as the VSP categorical factors. As can be noted, when the cargo item is stowed on a high deck, as the CS of the hashing line increases, a linear decrement of fy is registered. Contrastingly, a convex shape in the fy tendency is registered in the middle of the range of CS. \begin{table}
\begin{tabular}{c c} \hline
**Vertical Stowage Position** & **Code** \\ \hline High deck & 1 \\ \hline Main deck & 2 \\ \hline Tween deck & 3 \\ \hline Lower hold & 4 \\ \hline \end{tabular}
\end{table} 
Table 5: Code of stowage vertical position.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 4 Results and Comparisons
### Timing Performance  
In the process of implementing our algorithm and MLAA in FPGA, we let the system clock of the algorithm and the read & write clock of each Block RAM equal 200 Mhz. We compared the time required for the two algorithms to process images of different sizes and obtain the results shown in Table 1.  
It can be seen in Table 1 that our algorithm consumes much less time than MLAA [22, 25]. This is because our algorithm performs the detection of the image edge, the classification of the aliasing patterns, the acquisition of the blend weight coefficients, and the calculation of the anti-aliased color for the three channels of the image simultaneously.  
In the process of classifying the aliasing patterns and obtaining the blend weight coefficients, our algorithm can also be performed from the horizontal and vertical directions of each channel of the image simultaneously.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Size of the Image** & **Ours** & **MLAA** \\ \hline Height = 100 & 3216.3 us & 11,951.2 us \\ Width = 100 & & \\ \hline Height = 150 & 7214.8 us & 26,817.2 us \\ Width = 150 & & \\ \hline Height = 200 & 12,813.3 us & 47,633.3 us \\ Width = 200 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Timing performance of the two algorithms.  
Figure 13: Anti-aliasing performance achieved by our algorithm and other algorithms.  
We can see in Table 1 that, for the same image, the processing time of MLAA is about 3.7 times that of our algorithm. Thus, compared with MLAA, our algorithm achieves a better timing performance.
## 3 System Architecture and Methodology
### Data Flow and the Realisation of Real-Time Transmission
The checkable button \"Load Flight Data\" then follows. When \"Load Flight Data\" is checked, another thread is activated to run the sub-process of retrieving the flight parameters exported by the data request application. Multithreading is utilised to tackle the problem of having two simultaneous processes being executed by the same thread. The main worker remains responsible for executing BlueSky, while the sub-worker is deployed to import the flight parameters. The retrieval rate of the flight parameters is identical to the writing interval. Whenever the flight parameters are read successfully into BlueSky, the sub-process emits said data into the main process. The main process continues to set the corresponding command according to the syntax previously described. The constant radar map update in BlueSky then includes the command entered automatically when flight parameters are received. Eventually, an intelligent and automated data transfer and visualisation process is realised to support real-time displays of the situation in the flight simulator on BlueSky.
# Parallel Complex Quadrature Spatial Modulation
## 7 Conclusions  
In this paper, we proposed the PCQSM MIMO technique to achieve a high spectral efficiency and reduction in the number of required transmit antennas of the conventional SM techniques. To achieve these advantages, the proposed PCQSM splits the available set of transmit antennas into subgroups of equal size. Two signal symbols are modulated and encoded using the conventional CQSM in each of the subgroups. By doing so, the proposed technique requires a small fraction of the number of transmitted antennas needed by the conventional systems to achieve a certain spectral efficiency. This is achieved without any sacrifice in the error performance or increase in the hardware requirements, as compared to CQSM. We provided the analytical performance of the proposed technique and validate it via simulation results. The simulation results show that the proposed PCQSM performs close to several conventional techniques and outperforms others, while maintaining low hardware requirements and number of required transmit antennas.  
Conceptualization, S.M., M.M., and K.-S.C.; methodology, S.M., T.H., N.M., M.M., and K.-S.C.; software, S.M., T.H., and N.M.; validation, S.M., T.H., N.M., M.M., and K.-S.C.; formal analysis, M.M. and S.M.; investigation, S.M., T.H., N.M., M.M., and K.-S.C.; resources, K.-S.C. and M.M.; data curation, S.M., T.H., and N.M.; writing--original draft preparation, S.M., M.M., and K.-S.C.; writing--review and editing, S.M., T.H., N.M., M.M., and K.-S.C.; visualization, S.M., T.H., and N.M.; supervision, K.-S.C. and M.M.; project administration, M.M.; funding acquisition, K.-S.C. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Performance comparison of SM schemes with transmission rate of 24 bits/s/Hz with \(N_{r}=4\) and modulation order, \(M=16\)-QAM.  
**Funding:** This work was supported by the BK21 plus program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experiment and Evaluation
### Time Complexity Analysis  
In this section, the time complexity of the cross-layer routing protocol is taken into consideration. The neural network has three fully connected layers in the simulation experiment. The number of neurons in the input and output layer is equal to the dimension of the state \(n_{feature}\) and the size of action space \(n_{action}\), respectively. The neuron quantities in the first and second layer are \(n_{1}\) and \(n_{2}\), respectively. The input layer is not included in the total number of layers in the neural network because it only transmits data into the network and does not participate in the calculation. The feed-forward calculation for one sample contains two matrix operations, which needs \(n_{1}\times n_{2}\) and \(n_{2}\times n_{action}\) times of computation [20]. Since the size of the action space \(n_{action}\) is a constant, the time complexity for one sample in the feed-forward calculation can be calculated as \(o(n_{1}\times n_{2}+n_{2}\times n_{action})=o(n_{1}\times n_{2})\). The complexity of the back propagation is the same as that of the feed-forward calculation, such that the time complexity of training the neural network for one sample is \(o(n_{1}\times n_{2})\). We assume that the algorithm converges after \(T\) episodes, and the average hops along one path are \(H\) (it can be seen that \(T\) is 800 and \(H\) approximates to 5 from the simulation results in Figure 10). Thus, the total number of samples in the simulation process is \(T\times H\), and the time complexity of the apprenticeship learning-based scheme is \(o(T\times H\times n_{1}\times n_{2})\). Since the values of both \(T\) and \(H\) are less than those in other algorithms, the proposed routing protocol has a lower time complexity than the self-learning schemes.