esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vehicle and Tire Model
### Vehicle Model  
An overly complex vehicle model is not conducive to the real-time performance of the algorithm, so this paper establishes a three-degree-of-freedom (3-DOF) nonlinear vehicle dynamics model under the premise of satisfying the observation accuracy of vehicle dynamics and makes the following assumptions [23].  
1. Assume that the pitch, vertical, and roll motion of the vehicle are ignored, and the influence of the suspension is ignored.
2. Ignore the influence of the camber angle and self-aligning torque of the wheels on the vehicle dynamics performance.  
According to [PERSON] principle, a 3-DOF vehicle dynamics model containing yaw, longitudinal, and lateral motions was established, as shown in Figure 1, and the entire vehicle dynamics equations were established as follows:Longitudinal motion:  
\[\dot{V}_{x}=\sum\limits_{i,j=1}^{2}F_{xij}/m+V_{y}\dot{\psi} \tag{1}\]  
\[\sum\limits_{i,j=1}^{2}F_{xij}=F_{x11}\cos\delta_{11}+F_{x12}\cos\delta_{12}-F_ {y11}\sin\delta_{11}-F_{y12}\sin\delta_{12}+F_{x21}+F_{x22} \tag{2}\]  
Lateral motion:  
\[\dot{V}_{y}=\sum\limits_{i,j=1}^{2}F_{yij}/m+V_{x}\dot{\psi} \tag{3}\]  
\[\sum\limits_{i,j=1}^{2}F_{yij}=F_{x11}\sin\delta_{11}+F_{x12}\sin\delta_{12}+F _{y11}\cos\delta_{11}+F_{y12}\cos\delta_{12}+F_{y21}+F_{y22} \tag{4}\]  
Yaw motion:  
\[\ddot{\psi}=\sum\limits_{i,j=1}^{2}M_{zij}/I_{zz} \tag{5}\]  
\[\begin{array}{c}\sum\limits_{i,j=1}^{2}M_{zij}=a(F_{y11}\cos\delta_{11}+F_{y 12}\cos\delta_{12})+a(F_{x11}\sin\delta_{11}+F_{x12}\sin\delta_{12})-\frac{ \psi}{2}(F_{y11}\sin\delta_{11}-F_{y12}\sin\delta_{12})+\\ \frac{\psi}{2}(F_{x11}\cos\delta_{11}-F_{x12}\cos\delta_{12})-(L-a)(F_{y21}+F_ {y22})+\frac{\psi}{2}(F_{x21}-F_{x22})\end{array} \tag{6}\]  
where \(V_{x}\) is the vehicle longitudinal velocity, \(V_{y}\) is the lateral velocity, \(\dot{\psi}\) is the yaw rate, \(a_{x}\) is the longitudinal acceleration, \(a_{y}\) is the lateral acceleration, \(M_{z}\) is the yaw moment, \(F_{xij}\) and \(F_{yij}\) are longitudinal and lateral forces of the tire (\(ij\) is 11, 12, 21, and 22, which represent the front left, front right, rear left, and rear right wheel, respectively), \(m\) is the vehicle mass, \(a\) is the distance from the center of mass to the front axle, \(b\) is the distance from rear axle to the center of gravity, \(w\) is the wheel track, \(L\) is the wheelbase, \(I_{zz}\) is the yaw moment of inertia, and \(\delta_{11}\) and \(\delta_{12}\) are the steering angles of the left and right front wheels, respectively.
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 2 Measurement Campaign  
The BBWN was implemented using one transmitting and one receiving node attached to the upper arm of two adult males of height 1.80 m and mass 80 kg (Subject A) and 1.85 m and mass 75 kg (Subject B), respectively, using a small strip of Velcro. The body location was chosen as it is commonly used for monitoring devices such as mobile phones during sport activities. The experiments conducted in this study were performed in a 500 m\({}^{\prime}\) outdoor stretch, which is part of a common running and cycling route in Gjevik, Norway. The stretch was bounded by a road on one side and well-spaced industrial buildings on the other. The testbed was a programmable radio transceiver with non-volatile data storage, small enough to be attached on the test subjects (see Figure 1).  
It is comprised of a radio transceiver, antenna, microcontroller, microSD memory card and battery. The radio transceiver is CC2500 from Texas Instruments [9]. The nodes use W  
Figure 1: Wearable radio transceiver at 2.4 GHz ISM band. The device is approximately 50 mm \(\times\) 20 mm \(\times\) 20 mm.  
omni-directional chip antenna with a low profile, which is a typical example of antennas to be used in body-mounted transceivers. The device was set to transmit a packet every 4 ms with a constant transmission power of 1 dBm and carrier frequency of 2.425 GHz. This sampling period is way below the estimated coherence time (e.g., 31 ms was reported in [10]) of such channels [10]. At the receiving end, the packet number together with its received signal strength indicator (RSSI) was stored on the MicroSD memory card. The measurement was conducted during two different activities, running at an average speed of 3.33 m/s, and cycling at an average speed of 5 m/s for 500 m. With these conditions, at least 25 kilo-samples for each data set were obtained, which is enough samples for statistical analysis. In each activity, the subjects tried to maintain a separation distance of 1 m between each other. Two different scenarios, which could give an overall representation of the channel dynamics existing in these kind of activities, as shown in Figure 2, were considered:  
* Scenario 1, subject behind the other, Figure 2a.
* Scenario 2, subject beside the other, Figure 2b.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 2 Related Work
### Security Mechanisms for WBAN  
Research on security mechanisms for WBAN can be divided into cryptographic and non-cryptographic authentication mechanisms. Cryptographic authentication mechanisms adapt lightweight traditional cryptographic schemes. Non-cryptographic authentication approaches can be divided into biometric-based, channel-based and proximity-based authentication systems [9].
# Progress of the Computer-Generated Holography Based on Deep Learning
## 1 Introduction  
Computer-generated holography (CGH) can record and reconstruct the whole light field of a 3D scene by mathematical calculation and physical experiment, providing all the depth cues that human eyes can perceive [1; 2; 3]. Thus, it has become a promising technique in various optical engineering directions, such as virtual reality (VR), augmented reality (AR) [4], and 3D display [5]. Many algorithms can be used to generate holograms from either real or virtual objects [6; 7; 8].  
CGH can encode both the amplitude and phase information of the reconstructed scenes. Spatial light modulators (SLMs) are usually used to realize the reconstruction. As the SLMs can only modulate the amplitude or phase of the light field, the complex amplitude holograms are usually converted to amplitude-only holograms or phase-only holograms (POHs) for display. The POH is widely used as it can provide higher diffraction efficiency and avoid the twin image in the reconstruction process [9; 10]. By using the Fourier optics principle [11; 12], the clear image reconstruction can be realized by phase modulation.  
Iterative methods, such as the Gerchberg-Saxton (GS) algorithm [13], have been successfully used for the POH calculation. However, there are still challenges to realizing high-fidelity real-time holographic displays. Various algorithms have been proposed to speed up the calculation [14; 15]. In 2014, a polygon-based method was proposed to realize a CGH calculation six times faster than that of the FFT-based method [16]. Another polygon-based method, called the fast 3D affine transformation method, could achieve the hologram generation for 14,254 polygons in only 720 s [17]. In 2015, [PERSON] et al. proposed an angular-spectrum layer-oriented method to obtain more accurate POHs with less computational load [18]. Furthermore, several techniques such as look-up table (LUT) [19] and foveated hologram [20; 21] have also been proposed.  
In recent years, the rapid development of deep learning networks has affected various fields. For the calculation of CGH, the learning-based CGHs have some innate advantages over the iterative methods, which could realize faster calculation and higher reconstruction quality. The common network structures in CGH include U-Net [10; 22], ResidualNetwork (ResNet) [23; 24], Generative Adversarial Network (GAN) [25; 26; 27], and autoencoder [10; 28]. For example, the multi-depth hologram generation network (MDHGN) can realize multi-depth hologram generation in 0.152 s [29]. The GAN-Holo model can generate 128 \(\times\) 128 holograms with less computational load in 5 ms [27]. The deep-learning computational holography and the state-of-the-art in computer-generated holography have been introduced [30; 31]. In this paper, we would like to present a general literature review on the direction of learning-based CGH in recent years, compare different kinds of deep learning networks, and look forward to their future developments and possible applications.
## 2 Materials and Methods
### Post-Operation Morphological Parameter Measurement
#### 2.3.3 Reference Frame Orientation and Positional Deviations  
The reconstructed and healthy reference frames were imported (Figure 5), and the function used conformed with the established convention [65]. This quantified the orientation difference between the two. The origins' positions were compared, yielding angular and positional deviations, recorded on each axis of the healthy pelvis reference frame.  
Figure 4: Simulated healthy pelvis (white) obtained by mirroring the healthy pelvis along the symmetry plane and the reconstructed pelvis (blue).  
Figure 5: A superimposition of both reference frames based once on the reconstructed pelvis and once on the simulated healthy pelvis.  
2.3.4. Centers of Rotation and Deviations  
The reconstructed and healthy femoral head centers, representing rotation centers, alongside the healthy pelvis reference frame axes, were imported into MATLAB. The vector from each center to the reference frame origin was calculated by subtracting the anatomical reference frame origin vector from the hip center of the rotation vector. The resultant vector was then decomposed into each of the three axes of the pelvis reference frame. This was repeated for both the healthy and reconstructed hip center of rotations, and then the deviations were calculated.  
2.3.5. Greater Trochanter Offset Deviations  
Healthy pelvis reference frame axes were imported into MATLAB, along with both the reconstructed and healthy femoral head centers, as well as their corresponding greater trochanters. The vector for each hip center of rotation, as well as each greater trochanter apex, was obtained by comparing their positions with the anatomical reference frame origin. Then, these vectors were decomposed into each of the reference frame axes to obtain their local coordinates. Offset magnitudes of the reconstructed and healthy contra-lateral sides were calculated, as well as their deviations.  
2.3.6. Acetabular Inclination Angle Analysis  
A script by [66] was used to calculate the normal vector of the best-fit plane on the acetabular ridge (Figure 6). This vector was projected onto the pelvis reference frame's frontal plane, and the acetabular inclination angle was measured.  
First, the reconstructed inclination angle deviation was calculated by comparing it with the contra-lateral healthy side. Then, absolute reconstructed acetabular inclination angles were compared with the safe zones proposed in the literature [67; 68], which have since been used as a convention, the former ranging from 30 to 50 degrees and the latter ranging from 30 to 45 degrees.
## 3 Experimental Results
### Contrast Experiment  
This study conducted comparative experiments with the NISQA model, the P.563 model, and the Quality-Net model. The P.563 model is the non-intrusive objective speech quality assessment standard established by the ITU, while the NISQA and Quality-Net models are non-intrusive speech quality prediction models proposed in recent years. They are all classic models in the field of non-intrusive speech quality assessment, and many speech quality assessment models have been compared with them. Therefore, this paper also compares with the NISQA model, the P.563 model, and the Quality-Net model.  
Figure 9 shows the scatter plots of the subjective and objective evaluations for the four models of the air traffic control speech test set. The vertical axis represents the predicted MOS values, while the horizontal axis represents the subjective MOS values. By comparing the distribution of the scatter plots, we can evaluate the prediction consistency and accuracy of the proposed model compared to the NISQA model, the P.563 model, and the Quality-Net model.  
According to Figure 9, it can be observed that the scatter distributions in (a), (b), and (d) are closer to the diagonal line compared to (c), indicating that the predicted results of the proposed model, the NISQA model, and the Quality-Net model are more consistent with human evaluations. The difference between (a), (b), and (d) is not significant. To visually compare the performance of the four methods, we plotted third-order polynomial fitting curves and calculated their PCC and RMSE. The specific results can be found in Figure 10 and Table 6. These figures and tables provide reliable quantitative indicators for comparing the performance of different models and further support the differences between the proposed model, the NISQA model, and the Quality-Net model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PCC** & **RMSE** \\ \hline ResNet–BiLSTM (Ours) & 0.87 & 0.68 \\ NISQA & 0.85 & 0.95 \\ P.563 & 0.81 & 1.33 \\ Quality-Net & 0.82 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of four methods.  
is closer to y = x. This indicates that the proposed model has a more accurate prediction of the subjective MOS.  
Additionally, based on the data in Table 6, it can be observed that the proposed model exhibits higher correlation coefficients and lower root-mean-square errors compared to the other models, thus validating the effectiveness of the proposed approach. These results further support the academic contribution of this research and indicate that the proposed model has an advantage in predicting the MOS.
## 3 Results and Discussion
### Performance Evaluation of the Coatings  
#### 3.2.1 NaCl Immersion Test  
The deposited coatings were immersed in a 5 wt.% NaCl solution to determine their corrosion performance before and after immersion. The digital images of the coating surfaces before and after 28 days of immersion are shown in Figure 6. The Cu coating exhibited delamination (Figure 6a) due to the instability of Cu\({}_{2}\)O and Cu in seawater [42], even though the concrete surface was still visible after immersion. After immersion in the NaCl solution, the color of the coating surface became light brown. This coating dissolved in the NaCl solution, and the color of the solution became blue-green (the figure is not shown) after 28 days of immersion owing to the dissolution of Cu and Cu\({}_{2}\)O and the formation of copper chloride, that is, CuCl\({}_{2}\). By comparison, slight fading was observed in the Ti coating, as shown in Figure 6b. This result suggests that the NaCl solution did not have a significant effect on the deterioration of the Ti coating because of the formation of Ti\({}_{3}\)O and TiO as oxide films, which resisted the ingress of the solution owing to their stability. The 85 Zn-15 Al coating exhibited the deposition of white rust on the surface, as shown in Figure 6c. The deposition of a white rust layer could be attributed to the formation of ZnO/Zn(OH)\({}_{2}\)/Zn\({}_{5}\)(OH)\({}_{8}\)Cl\({}_{2}\cdot\)H\({}_{2}\)O. The nature of the oxide film was confirmed with the XRD analysis used for the characterization of the corrosion products, as indicated in Section 3.3.2.  
Figure 5: Bond adhesion measurement.  
Figure 6: Digital images of (**a**) Cu, (**b**) Ti, and (**c**) 85 Zn-15 Al in 5% NaCl solution after 28 days of immersion.
## 4 Experiment and Results
### Result on MICC-SRI Dataset
The images illustrating the supervised learning results are shown in Figure 8, which reveals similar results for the small area because of the stabilized supervised learning process. However, even with supervised learning, it is difficult to fill large regions in the corner of the map, as observed in the first picture. The first image implies that our method learns the best structural information, such as for the sidewalk. Figure 7: Detailed results of the purposed semantic segmentation inpainting network (SSIN) on Media Integration and Communication Center Semantic Road Inpainting (MICC-SRI) data.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 3 Development of the MINLP Model Formulation for Truss Superstructures
### The MINLP Model Formulation for Truss Superstructures
#### 3.2.2 Variables  
The MINLP-TS model formulation includes continuous variables as well as discrete binary variables. In general, continuous variables are partitioned into independent (design) variables and into dependent (non-design) variables. In the specific case of truss synthesis, the design variables are sizing variables (i.e., dimensions of the cross-sections of bars, \(d_{i,j}\)) and shape variables (i.e., nodal coordinates \({}^{\mathrm{c}}x_{i}\) and \({}^{\mathrm{c}}y_{i}\)). These variables define the structural design. Non-design performance variables are directly dependent on the design variables and represent the cross-section properties, internal forces, deformations, resistances of bars, etc.  
Discrete variables are represented by a vector of binary variables \(\boldsymbol{y}\) = {\(y^{\mathrm{top}}\), \(y^{\mathrm{gt}}\)}, where sub-vectors \(\boldsymbol{y}^{\mathrm{top}}\) and \(\boldsymbol{y}^{\mathrm{gt}}\) stand for the topological and discrete/standard dimension binary variables, respectively. In the first sub-vector, topological binary variables \(y_{i,j}^{\mathrm{top}}\) are defined for all alternative structural elements involved in the superstructure. Topological binary variables are subsequently partitioned into those defining nodes (\(y_{i,j}^{\mathrm{top}}\), \(I\) = \(j\), further on in the text \(y_{i,j}^{\mathrm{top}}\)) and into those defining bars (\(y_{i,j}^{\mathrm{top}}\), \(\forall\ (i,j)\ \ |\ \ el_{i,j}\) = 1). Each topological binary variable represents the existence or selection (\(y_{i,j}^{\mathrm{top}}\) = 1) or non-existence or rejection (\(y_{i,j}^{\mathrm{top}}\) = 0) of its associated structural element.  
The second sub-vector of binary variables contains binary variables attributed to the discrete/standard dimensions. Let \(L\) be a set of discrete/standard alternatives of a particular cross-section dimension. A binary variable \(y_{i,j,l}^{\text{st}}\) is then defined for each discrete cross-section alternative \(l\), \(l\)\(\in\)\(L\), for each bar element \(i\)\(\rightarrow\)\(j\).
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 3 Modular Software Architecture Definition
### Class Structures
#### 3.2.1. Topside Level Classes  
Topside level classes are instantiated in the topside computer, implemented in NI LabVIEW, aided by its OOP tools. Derived classes in this level implement functionality to GPS and USBL instruments. An example of the derived methods and specific implementation can be illustrated with the GPS class; Init method establishes connection with a marine GPS through serial port; Stop closes the port and flushes buffers; Read receives data from the instrument and parses NMEA 0183 frames; Compose writes the type-defined data structure that holds the topside data, making the acquired data available to the whole application at every level. Additional instruments can be easily incorporated to the system at this level by inheriting Instrument class, writing the code needed by the instrument, and instantiating the new derived class in the DAQ.vi actor.
# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
## 4 Computer Simulation
### Adaptive Modulation  
The results produced by the simulation procedure described in Section 4 are shown in Figure 5, in which the left-hand and right-hand figures show the relationship between the applicable modulation method and amplitude correlation and received power, respectively. Even though amplitude correlation increases with the modulation order, it is not appropriate to select a modulation method using only amplitude correlation because the standard deviation of the results is large. Therefore, the received power relationship shown in the right-hand figure was used to develop an indicator for determining the modulation method (Table 2). In the table, \(\rho\) and \(P\) represent amplitude correlation and received power, respectively.  
\begin{table}
\begin{tabular}{c c} \hline Number of receiving antennas & 64 \\ \hline Number of UTs & 20 \\ \hline Number of demodulated UTs & 8 \\ \hline SNR per UT, before array gain acquisition & 20 [dB] \\ \hline Modulation scheme & 4 QAM (QPSK) – 1024 QAM \\ \hline Trial number & 3000 \\ \hline Data length & 3000 \\ \hline Data smoothing size & 3000 \\ \hline Number of iterations & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation conditions.  
Figure 5: Relationships between applicable modulation scheme and amplitude correlation (**left**) and received power (**right**).
## 3 Methodology
### The Proposed CNN-ResBiGRUI Model  
The proposed approach entails creating a hybrid deep learning model comprising convolution, residual BiGRU blocks, and a hybrid architecture. This end-to-end model's overall structure is depicted in Figure 5.  
In our proposed framework, the first component, the convolution block, is responsible for extracting spatial features from the pre-processed input data. To reduce the time-series length, we adjust the step size of the convolution kernel, thus speeding up the recognition process. Following this, we employ the BiGRU network to extract temporal characteristics from the data processed by the convolution block. This enhances the model's ability to capture long-term dependencies in the time-series data by leveraging the benefits of a BiGRU. Integrating these components makes the model better equipped to understand complex temporal patterns, thereby improving recognition accuracy. The behavior data are then classified using a fully connected layer and the SoftMax algorithm. The output of this classification process serves as the recognition result, predicting the specific activity being performed. We delve deeper into each component in subsequent sections, outlining their roles and contributions within our proposed framework.