esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Opengdc Software Architecture  
OpenGDC is an open-source and cross-platform software, written in Java programming language; (The corresponding code is openly available on the GitHub repository: [[https://github.com/DEIB-GECO/OpenGDC](https://github.com/DEIB-GECO/OpenGDC)]([https://github.com/DEIB-GECO/OpenGDC](https://github.com/DEIB-GECO/OpenGDC))) it allows the extraction, extension and standardization of publicly available data from the GDC. The software is provided as a standalone desktop application with a friendly user interface and supports the BED, CTF, CSV, JSON and XML standard formats as output. Its architecture has been implemented following the Model-View-Controller (MVC) design pattern, as shown by the flowchart in Figure 2. The software is composed of two main pipelines: (i) the GDC data download procedure and (ii) the data conversion one.  
The whole system can be summarized by three main software components:  
* _Controller_: it redirects the user instructions to the correct software module and initializes an instance of the software able to download and/or convert the GDC data;
* _Data Download_: it manages the process of search and retrieval of the public GDC data, taking advantage of the GDC APIs;
* _Data Standardization_: it allows to easily convert and standardize data according to a specific data type. The process is facilitated by the ad-hoc class _BioParser_, which provides an abstract representation for all GDC data types; this class can be extended to support new data types in case of future extensions of the GDC repository.  
OpenGDC benefits from the public GDC APIs during the data download procedure to retrieve the original genomic, clinical and biospecimen data. It also makes use of the GDC APIs during the conversion procedure of the Clinical and Biospecimen Supplements to extract additional information such as the size of the downloaded files, their MD5 checksum, as well as the last creation and update timestamps, which are then added in the metadata files.  
Data conversion uses a different parser depending on the type of converted data. Additionally, the process retrieves complementary information from a set of external data sources, such as NCBI  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**GDC Naming** & **OpenGDC Flattened** & **OpenGDC Renamed** \\ \hline cases\_diagnoses\_age\_at\_diagnosis & gdc\_cases\_diagnoses\_age\_at\_diagnosis & gdc\_diagnoses\_age\_at\_diagnosis \\ analysis\_input\_files\_data\_category & gdc\_analysis\_input\_files\_data\_category & gdc\_input\_files\_data\_category \\ cases\_project\_program\_name & gdc\_cases\_project\_program\_name & gdc\_program\_name \\ \hline \hline \end{tabular} Column 1: attribute names as they are specified in GDC APIs parameters; Column 2: OpenGDC naming convention; Column 3: results of the renaming phase applied to the attributes in Column 2.  
\end{table}
Table 2: Examples of metadata attribute renaming rules and their results.  
Genome and Gene databases, GENCODE, HGNC and miRBase, to extract the genomic coordinates, Entrez Gene ID and gene symbols starting from the information already existing in the original data.
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 5 Results
### Results of the Maritime Jamming Campaign
#### 5.2.3 Comparison of Different Weighting Models  
The UKF results as presented above were all processed using a constant noise variance model for the GPS measurements. Here we will compare the positioning results of the tightly coupled solutions using different pseudorange variance models. For this purpose, we have processed the same  
Figure 11: An example of the performance of the hybrid IMU/GNNS/DVL system during the time when fewer than four satellites are available: segment overview (**left**); number of available satellites (**top right**); and the HPE (**bottom right**).  
Figure 12: The performance of the hybrid IMU/GNNS/DVL system during the complete measurement scenario using the midship GPS antenna: overview (**left**); number of available satellites (**top right**); and the HPE (**bottom right**).  
measurement data three times using the GPS measurements from the midship antenna. The results of the constant noise error model are the ones from Figure 12. In Table 1, the positioning performance of the tightly coupled UKF using the three different noise models is compared. Only small differences between the models are found, as all the three models perform reasonable well. The elevation depended noise model shows the best results, especially in the maximum HPE. The results of the constant noise model and the CN0 weighting model are almost identical. These results support the results from the lab experiment of Section 5.1. The rather small differences in the positioning results of the three models are surprising. This might result from the fact that, due to the usage of a FOG IMU combined with the DVL, the filter is remarkably robust and therefore is less sensitive to the GPS pseudorange measurements.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 1 Introduction
### Computational Domain Discretization
#### 1.2.1 Hexahedral Mesh  
In fluid dynamics applications, it is the most desirable to divide the domain into hexahedral (HEX) control volumes (Figure 1--left) as the resulting mesh is characterized by low numerical diffusion. Unfortunately the numerical diffusion increases in the case of the flow not being perpendicular to the faces of control volumes and it is not always possible to construct a structured HEX mesh for complex geometries. Moreover, the generation of such a mesh usually requires high engineering skills and is a time-consuming task. Therefore this mesh type is usually ineffective in industrial applications due to the high cost of engineering time.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Parallel Acceleration of Computational Layers in CNN
#### 2.1.2 Parallel Acceleration of Pooling Layer and Fully Connected Layer  
Compared to the convolutional layer, the pooling layer does not need to be loaded with parameters. The computational effort and bandwidth of the accessed data are significantly reduced, so only a reasonable parallel computation needs to be planned. The TinyYolo model used in this paper contains only MaxPooling of size 2 \(\times\) 2, so the parallel loop is designed as follows:  
1. The mapping strategy of the input data remains unchanged compared to that of the convolutional layer, using input channel parallelism plus pixel parallelism so that the pooling layer computational kernel accesses eight input channels and four pixels at the same time.
2. The pooling layer differs from the convolutional layer in that the convolutional layer needs to iterate through the length and width of the convolutional kernel on a pixel. In contrast, the pooling layer must only iterate through each pixel once. Therefore, after reading 4 pixels of data in this paper, we change rows and read 4 pixels in the second row. After two reads of 2 \(\times\) 4 pixels, the maximum value in the channel is taken, and the data of the two output pixels is obtained. The specific pooling layer parallelism method is shown in Figure 5.  
The input data of the fully connected layer is 1 \(\times\) 1 in length and width. However, the number of input channels is generally more significant. The number of parameters needed for computation is multiplied by the number of output channels, making the number of parameters much larger than that of the convolutional layer. In this paper, we adopt a similar approach to the partial loading of convolutional layers by partially loading the parameters of the fully connected layer and loading only one or a few output channels at a time to reduce the need for on-chip BRAM. The computational scheduling kernel transfers the input data and weights to the external computation matrix in parallel. The FPGA's on-chip DSP performs the multiplication operation between them and then returns them to the computational scheduling kernel for accumulation.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 7 Conclusions  
This paper reviewed the C-V2X system security risks and the development of C-V2X security technology. It pointed out a low-cost and efficient authentication and encrypted communication scheme is needed in small- and medium-sized C-V2X systems. A lightweight V2V identity authentication and key agreement scheme is designed based on identity-based cryptograph technology.  
The main contributions and innovations of this paper are:  
Figure 5: Authentication Process Simulation with Software.  
(1) The scheme innovatively uses identity-based cryptograph technology in V2V communication for identity authentication with no need to establish a CA, does not need certificate management, and is lightweight and easy to implement.  
(2) The C-V2X equipment interactive secret data is encrypted by a public key, verifying the response secret data, and confirms whether the opposite equipment is registered equipment, does not need to share secrets in advance between the equipment, and is convenient for deployment and application.  
(3) The working key was negotiated synchronously in the identity authentication process. The scheme is convenient, efficient and has less impact on the communication delay, and is suitable for the low-delay and high-reliability requirements of the C-V2X system.  
(4) The working key is used to encrypt the communication data to achieve the data confidentiality and integrity. The working key is time effective and can be changed regularly to prevent attacks on the working key.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 3 Preliminaries
### Energy Consumption Model  
The energy consumption is based on the first-order radio dissipation model [5]. The transmitter consumes energy \(E_{\mathrm{TX}}\) in running the radio electronics and transmission amplifier circuitry. The receiver consumes energy \(E_{\mathrm{RX}}\) in radio electronics. Depending on the transmission distance \(d\), the free space \(\epsilon_{\mathrm{fs}}\) and multipath fading \(\epsilon_{\mathrm{mp}}\) channel models are used. The energy consumption of transmitting and receiving \(l\)-bit data over the distance \(d\) is according to Equations (1) and (2), respectively. A cluster head node consumes \(E_{\mathrm{da}}\) (nJ/bit/signal) amount of energy in data aggregation and consumes \(E_{\mathrm{com}}\) (nJ/bit/signal) in the processing of additional information of ranks and residual energy of associate nodes. We assume the sensed information by the nodes is highly correlated.  
\[E_{TX}(l,d)=\begin{cases}l\times E_{\mathit{elec}}+l\times\varepsilon_{f_{S}} \times d^{2}&\text{if }d\leq d_{0}\\ l\times E_{\mathit{elec}}+l\times\varepsilon_{\mathit{mp}}\times d^{4}&\text{ otherwise}\end{cases} \tag{1}\]  
\[E_{RX}(l,d)=l\times E_{\mathit{elec}} \tag{2}\]  
where \(E_{\mathit{elec}}\) is the power consumption for transmitting and receiving circuitry, a signal is amplified that depends on the distance \(d\), and the reference distance \(d_{0}=\surd(\epsilon_{f_{S}}/\varepsilon_{\mathit{mp}})=87\) m.
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 3 Experiments and Results
### Evaluation Metric  
Due to an imbalance problem in the EmoDB database (Table 1 and Figure 4), we adopt two different evaluation metrics to compare speech emotion recognition results: unweighted accuracy (UA) and weighted accuracy (WA) [8; 33; 34].  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Speaker ID** & _ang_ & _bor_ & _fea_ & _hap_ & _neu_ & _sad_ & _dis_ & **Sum** \\ \hline
03 & 14 & 5 & 4 & 7 & 11 & 7 & 1 & 49 \\
08 & 12 & 10 & 6 & 11 & 10 & 9 & 0 & 58 \\
09 & 13 & 4 & 1 & 4 & 9 & 4 & 8 & 43 \\
10 & 10 & 8 & 8 & 4 & 4 & 3 & 1 & 38 \\
11 & 11 & 8 & 10 & 8 & 9 & 7 & 2 & 55 \\
12 & 12 & 5 & 6 & 2 & 4 & 4 & 2 & 35 \\
13 & 12 & 10 & 7 & 10 & 9 & 5 & 8 & 61 \\
14 & 16 & 8 & 12 & 8 & 7 & 10 & 8 & 69 \\
15 & 13 & 9 & 8 & 6 & 11 & 4 & 5 & 56 \\
16 & 14 & 14 & 7 & 11 & 5 & 9 & 11 & 71 \\ Total & 127 & 81 & 69 & 71 & 79 & 62 & 46 & 535 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of recordings for individual speakers and the emotional states.  
Figure 4: The distribution of recordings in EmoDB database.  
In the case of the RAVDESS database, where the distribution of recordings for individual emotions is equal, we presented the results only in weighted accuracy. The weighted accuracy is calculated as:  
\[WA=\frac{\sum_{c=1}^{C}TP_{c}}{\sum_{c=1}^{C}(TP_{c}+FN_{c})}, \tag{6}\]  
whereas unweighted accuracy is calculated using the following equation:  
\[UA=\frac{1}{C}\sum_{c=1}^{C}\frac{TP_{c}}{TP_{c}+FN_{c}}, \tag{7}\]  
where \(TP\) (True Positives) represents the number of samples correctly predicted as positive, \(FP\) (False Positives) is the number of samples wrongly predicted as positive, \(FN\) (False Negatives) is the number of samples wrongly predicted as negative, and \(C\) is the number of emotion classes.
## 5 Experimental Results  
### Open-Loop Calibration of VP Tiltmeter  
To investigate the response of the VP to tilt, the VP tiltmeter works in an open-loop mode without feedback from the piezoelectric actuator. The sensitivity of the VP tiltmeter is calibrated experimentally with a cylindrical piezoelectric transducer of the 20 VS12 type, and with a displacement resolution of about \(6.3\times 10^{-4}\) um. The PZT is placed under the base plate, and the screw supporter is adjusted to make the VP close to horizontal. A voltage controller of the XE501D type drives the PZTs to produce three displacement steps of \(1\) um. Figure 6 shows the output curve of the device with a baseline of \(165.2\) mm during the calibration, and the parameters \(\Delta\mathrm{V}_{1}=(0.97536\pm 0.00632)\) V, \(\Delta\mathrm{V}_{2}=(0.96172\pm 0.08689)\) V, and \(\Delta\mathrm{V}_{3}=(1.02244\pm 0.09860)\) V are the three increments of output voltage, respectively. We chose the average of three calibrations as the tilt sensitivity of the VP tiltmeter, so the sensitivity was about \(S_{\theta}=(1.63\pm 0.11)\times 10^{5}\) V/rad.  
Figure 5: A block diagram of the PID feedback controller with hysteresis model compensation.  
Figure 6: Calibration curve of the device.
## 3 Methodology
### Rain Streak Extraction Network (RSENet)
#### 3.3.1 Channel Attention Mechanism  
The channel attention mechanism (CAM) [22; 23] assigns varying weights to different channels predicated on the importance of the details in each channel. This makes it possible for the network to concentrate more on the details in important channels while suppressing irrelevant information in less important channels, as shown in Equation (4).  
\[M_{c}=\sigma(W_{2}*(ReLU(W_{1}*GAP(F_{CAM}^{i})+y_{1}))+y_{2}) \tag{4}\]  
Figure 4: Structure of CSAB.  
In this equation, \(F_{CAM}^{i}\) represents the feature values of the feedback to the channel function for awareness, \(\sigma\) and ReLU symbolize the sigmoid activation role and the ReLU activation function, correspondingly. _GAPs_ refers to global average pooling, and \(*\) denotes convolution. \(W_{i}\) and \(y_{i}\) indicate the convolution matrices and the prejudice vector, respectively.  
In the channel attention mechanism, the input feature information is first passed through pooling of the global average to capture the overall spatial features. The input feature information with dimensions \(C\times H\times W\) is then converted to \(C\times 1\times 1\), where \(C\), \(H\), and \(W\) stand for the input feature map's width, height, and channel, respectively. Subsequently, the channel attention map \(M_{c}\) is obtained through two convolutions layers followed by the ReLU activation function.  
\[F_{CAM}^{0}=F_{CAM}^{i}\otimes M_{c} \tag{5}\]  
After acquiring the channel attention map \(M_{c}\), the last output \(F_{CAM}^{0}\) is obtained via carrying out a duplication of elements within \(F_{CAM}^{i}\) and \(M_{c}\) as shown in Equation (5). In this context, \(\otimes\) represents the element-wise multiplication operation.  
The final output image from the rain streak extraction network (RSENet) proposed in this paper is identical in size to the source picture. However, after wavelet transform, the first version image's dimensions is reduced to half of its size. Therefore, a convolution functioning is required to process the output image from RSENet.
## 4 Model Development
### Error Measurement Using the Loss Index  
In the model development, the term error was utilized to express the loss for measuring the fitness of the datasets to the neural network. In the developed model, the mean squared error (MSE) was used for developing the approximation based NN model. In the case of an MSE equal to one, the developed NN is predicting the data \"on the mean,\" and if the MSE has a value of zero, it indicates an excellent data prediction. Figure 6 depicts the performance of the initial built NN.  
Figure 5: Architecture of the initial built NN.  
Mean squared error (MSE) is calculated using the following equation:  
\[\text{MSE}=\frac{1}{q}\sum_{i=1}^{q}{(y_{i}-\ t)}^{2}, \tag{1}\]  
where \(q\): the number of datasets, \(y_{i}\): value of the output of the NN model for instance number \(i\), and \(t\): actual value of the instance number \(i\).
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 2 Study Area and Data Source
### Sentinel-1 SAR Data
It is composed of Sentinel-1A, launched on 3 April 2014, and Sentinel-1B, on 22 April 2016. Both satellites have a useful life of 7 years and a 12-day orbital repetition cycle (6 days between them). These temporal limitations of image capture mean that we did not have images of the day of the highest level of the Ebro at the study points. The available image that was closest to the moment of maximum flood was from 13 April 2018 and it was the one used for the development of the study. Table 3 shows the characteristics of the two images used in the study. Usually, depending on the methodology used, the pre-event image serves as a reference and record of permanently flooded areas, while the post-event image serves to establish the differences with the initial image [33]. Each image has a coverage range of 250 km with a \(5\times 20\) m resolution. The images are Level-1 Ground Range Detected (GRD) products; this means that they were detected, multi-looked, and projected to the ground range using an Earth ellipsoid model [34]. The acquisition mode is Interferometric Wide Swath, and the images have a high resolution; the sensor pass is ascending, with incidence angles between \(29^{\circ}\) and \(46^{\circ}\), and dual polarization, VV (Vertical-Vertical) and VH (Vertical-Horizontal). The study area is covered by VV and VH only according to the observation scenarios shown in Earth Observation Data Center for Water Resources Monitoring (EODC). In the free modes of IW (Interferometric Wide) Sentinel-1, only VV + VH polarization over land is available [35].
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Influence of Adsorption Conditions  
#### 3.3.1 Effect of Composite Material Dosage  
Figure 9 shows the effect of different dosages on the removal of methylene blue. It can be seen that the removal effect of methylene blue gradually increased with the increase in the dosage of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) from 0.5 to 5.5 g/L. This may be due to the fact that more loading provided more surface area and pore structure, thus increasing the available active sites [39].  
However, when the dosage of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) was from 5 to 8.5 g/L, even when the dosage increased, methylene blue removal effect did not change. This can be attributed to the fact that the increase in AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) provided sufficient adsorption sites for the rapid removal of methylene blue solution [40].
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 1 Introduction
This relies on a symmetric key to share safety messages within the mix-zone, which ensures that all vehicles can use the same key to avoid linkability within the mix-zone, whereby the _RSU_ provides the symmetric key to all vehicles within the mix-zone [8]. [PERSON] et al. later considered the density and suggested a CMIX based on Chaff, believing that it would provide location privacy and irresistible security [14]. However, the filter used in [PERSON] et al. 's scheme to differentiate between real and fake messages is weak, and an internal adversary may expose the hash table to malicious injections. Additionally, the filter performance disrupts the Chaff messages' concept, which may break the system and make it vulnerable to linking attacks. We believe that the authentication of the transmitted messages and senders' identities must be improved significantly for safety applications to address these problems. Therefore, in our scheme, entities might prove the possession of some secret information preloaded in the OBU of the vehicle.
## 3 Model Problems and Numerical Results
### Model Problem 1
#### 3.1.2 Finite Element Method  
The Finite Element method will be used to compute the displacement of the beam assuming plane stress. An equidistant, regular rectangular mesh consisting of Lagrange quadrilateral elements is employed. The underlying equations and solution methods are reviewed hereunder.  
The system equation is of the form  
\[\mathbf{K}\underline{\mathbf{u}}=\underline{\mathbf{f}}, \tag{30}\]  
with \(\mathbf{K}\) the global stiffness matrix, \(\underline{\mathbf{f}}\) the global nodal force vector and \(\underline{\mathbf{u}}\) the displacement. The global stiffness matrix and nodal force vector are obtained from the element stiffness matrices \(\mathbf{K^{e}}\) and the element force vectors \(\mathbf{f^{e}}\). These are computed numerically by evaluation of the following integrals by means of Gauss quadrature:  
\[\mathbf{K^{e}}=\int_{\Omega}\mathbf{B}^{T}\mathbf{D}\mathbf{B}d\Omega\quad \text{and}\ \ \mathbf{f^{e}}=\int_{\Gamma_{t}}\mathbf{N}^{T}\overline{\mathbf{t}}_{n}d\Gamma _{t}. \tag{31}\]  
The element nodal force vector \(\mathbf{f^{e}}\) is modeled as a Neumann boundary condition, where \(\overline{\mathbf{t}}_{n}\) stands for the surface traction, specified as a force per unit area, and \(\mathbf{N}\) is the element shape function matrix, integrated over the free element boundary \(\Gamma_{t}\). The element stiffness matrix \(\mathbf{K^{e}}\) is obtained  
Figure 11: Cantilever beam clamped at both ends loaded in the middle.  
by integrating the matrix \(\mathbf{B^{T}}\mathbf{D}\mathbf{B}\) over the element's surface \(\Omega\). Matrix \(\mathbf{B}\) is defined as \(\mathbf{LN}\) with \(\mathbf{L}\) the derivative matrix specified below. \(\mathbf{D}\) is the elastic constitutive matrix for plane stress, containing the element-wise material parameters,  
\[\mathbf{L}=\begin{bmatrix}\frac{\partial}{\partial x}&0\\ 0&\frac{\partial}{\partial y}\\ \frac{\partial}{\partial y}&\frac{\partial}{\partial x}\end{bmatrix}\quad \text{and}\quad\mathbf{D}=\frac{E}{1-\
u^{2}}\begin{bmatrix}1&\
u&0\\ \
u&1&0\\ 0&0&\frac{1-\
u}{2}\end{bmatrix}\,. \tag{32}\]