esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Model
### Spectrum Average Mutual Coherence Function of Localized Wave of Bessel-Gaussian Amplitude Envelope
#### 2.2.1 Spectrum Average Coherence Length of Spherical Wave  
Since localized wave is a kind of polychromatic wave, when we analyze the phase structure function, we consider the mean phase perturbation of spectrum \(S(\rho,\theta,z,\widetilde{\Omega})\)[26] caused by seawater turbulence. For the Gaussian spectrum of the frequency of pulse, we have  
\[\begin{split} S(\rho,\theta,z,\omega)&\approx S( \rho,\theta,z,\widetilde{\Omega})\\ &=\frac{\widetilde{\Omega}}{c}\int_{0}^{z}n(\rho,\theta,z)\mathrm{ d}z\end{split} \tag{10}\]  
So we have the spectrum average structure function of the phase fluctuations  
\[\begin{split}\langle\exp\left[\mathrm{i}S(\rho,\theta_{1},z, \omega_{1})-\mathrm{i}S^{*}(\rho,\theta_{2},z,\omega_{2})\right]\rangle_{oc}& \approx\Big{\langle}\exp\left[\mathrm{i}S(\rho,\theta_{1},z, \widetilde{\Omega})-\mathrm{i}S^{*}(\rho,\theta_{2},z,\widetilde{\Omega}) \right]\Big{\rangle}_{oc}\\ &=\exp\left\{\frac{4z\pi\zeta^{-4}}{3T_{0}^{2}c^{2}}\int_{0}^{ \infty}x^{\prime 3}\phi^{\prime}(x^{\prime},\zeta)\mathrm{d}x^{\prime}\right.\\ &\quad\times 2\rho^{2}\left[\cos(\theta_{1}-\theta_{2})-1\right] \Bigg{\}}\\ &=\exp\{-\frac{2\rho^{2}\left[1-\cos(\theta_{1}-\theta_{2}) \right]}{\rho_{oc}^{2}}\}.\end{split} \tag{11}\]  
where \(\rho_{oc}^{2}\) represents the spectrum average coherence length of spherical wave transiting in the seawater turbulence and \(\rho_{oc}^{-2}\) can be written as  
\[\rho_{oc}^{-2}=\frac{4z\pi\zeta^{-4}}{3T_{0}^{2}c^{2}}\int_{0}^{\infty}x^{\prime 3 }\phi(x^{\prime},\zeta)\mathrm{d}x^{\prime}. \tag{12}\]Similar to the discussion [19], we obtain the spectrum average coherence length of spherical wave in the anisotropic oceanic turbulence  
\[\rho_{oc\xi}=\left[\frac{2.2937\times 10^{-7}z(\varepsilon\eta)^{-1/3}\chi_{T}}{T _{0}^{2}c^{2}\zeta^{2}}(0.483-\frac{0.835}{\omega}+\frac{3.380}{\omega^{2}}) \right]^{-\frac{1}{2}}. \tag{13}\]  
Now, we plot the relationship between the spectrum average coherence length \(\rho_{oc\xi}\) of spherical wave and the propagation distance \(z\) under different \"temperature structure\" constant \(C_{m}^{2}\) and different half-modulated pulse width \(T_{0}\) in Figure 1a,b, respectively. There is no doubt that increasing the propagation distance or enhancing the turbulence strength can reduce the spectrum average coherence length [9; 10; 11; 12; 13; 14]. From the expression of the coherence length, the parameter that we can control is the half-modulated pulse width \(T_{0}\), and according to Figure 1b, increasing \(T_{0}\) can effectively reduce the interference of oceanic turbulence on the spectrum average coherence length \(\rho_{oc\xi}\) even in a strong turbulence environment. Therefore, increasing \(T_{0}\) as often as possible is a good choice.
## 4 Performance Results
### Performance Indicator
#### 4.1.7 Mean Squared Error (MSE)  
The Mean Squared Error (\(MSE\)) is the average of squared errors. Error is the difference between the value predicted by the model and the actual correct answer. The better the model guesses the correct answer, the smaller the \(MSE\) value will be. In other words, the smaller the \(MSE\) value, the better the performance of the model. The formula for calculating the \(MSE\) is as follows.  
\[MSE=\frac{1}{2}\Sigma_{i}(y_{i}-\hat{y_{i}})^{2} \tag{15}\]  
The 1/2 in the formula is to eliminate this by multiplying by 1/2 because the exponent that plays a square role when the \(MSE\) is differentiated is multiplied as a constant 2 inthe entire formula. \(y_{i}\), \(g_{i}\) means the correct answer predicted by the model and the actual correct answer, respectively. As the \(MSE\) is a quadratic function, it can be seen that the value of the loss function changes greatly as the error increases. When using \(MSE\) as the loss function, the model adjusts its parameters in the direction that minimizes the error between the given data and the predicted values. By iterating through the aforementioned process, the model seeks to find the optimal parameters.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 5 Conclusions and Future Work  
In this paper, we designed and proposed a device management and data transport scheme for IoT networks based on VLC. For validation and performance analysis, we implemented the proposed VLC-based IoT scheme with VLC-capable LED lights and the open-source oneM2M platform. We also conducted testbed experimentation for _virtual museum_ services. From the experimental results,  
Figure 21: Times taken during VLC data transport.  
Figure 22: Times taken during VR handover.  
we see that the VLC data packets can be exchanged within 590 ms, and the handover between VLC transmitters can be completed within around 210 ms in the testbed network.  
To the best of our knowledge, this paper is the first proposal for VLC-based IoT. In further works, we plan to implement and investigate the proposed scheme with a protocol stack based on CoAP/UDP/6 LoWPAN. We will also consider the bi-directional VLC communication scenario in VLC-based IoT networks. In addition, we will include a comparison of the proposed VLC-based IoT scheme with existing IoT schemes which use other access network technologies, such as Bluetooth and ZigBee, and with other candidate schemes for VLC-based IoT. The testbed experimentation and performance comparisons will be made for a variety of network environments.  
[PERSON] wrote the initial manuscript; [PERSON] revised the manuscript.  
This research was funded by the MSIT (Ministry of Science and ICT), South Korea, under the ICT Standardization program supervised by the IITP (2018-0-00933).  
The authors declare no conflict of interest.
## 3 Problem Statement and Input Data Representation Methods
### Traffic Data and Their Unique Characteristics
By considering these interdependencies, the predictive models can more accurately capture the dynamic nature of traffic patterns, which are influenced by location-specific factors and temporal variations. Ignoring either dimension would undermine the predictive accuracy and reliability of the model.
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 3 Results and Discussion
### Quantitative Assessment
#### 3.1.3 Influence of Signer Representation  
Tables 8 and 9 present the model performance metrics on the validation set, for each of the 16 combinations and each of the four different annotation types. In each table, one line corresponds to a particular combination, i.e., a certain signer representation. For each metric (except accuracy), the best setting is in bold. Not all metrics yield the same conclusion with respect to the best settings: in case of disagreement, we have used the integrated unit-wise metric \(I_{pr}\) as decision rule, which is highlighted in the two tables. For instance, for the binary recognition of Fully Lexical Signs, the best combination--with an \(I_{pr}\) of 0.60--is #15: 3D features, with hand shapes from the Deep Hand model. For Depicting Signs, best performance is reached by 2D features, with both OpenPose and hand shape data. Pointing Signs are better recognized with 3D features and both OpenPose and hand shape data. Last, Fragment Buoys should be recognized with 2D or 3D features, with OpenPose data alone. A few general insights can be drawn from these results:  
* Using preprocessed data instead of raw values is always beneficial to the model performance, whatever the linguistic category. For linguistic annotations with few training instances like PTSs or FBuoys, the model is not even able to converge with raw data. * In the end, it appears that 3D estimates do not always improve the model performance, compared to 2D data. FLSs and FBuoys are better recognized when using 3D, while DSs and PTSs should be predicted using 2D data. However, this surprising result might stem from the limited quality of the 3D estimates that we used. True 3D data (instead of estimates trained on motion capture recordings) might indeed be more reliable thus beneficial in any case. * In terms of hand representation, it appears that the Deep Hand model is beneficial when recognizing FLSs, while OpenPose estimates alone correspond to the best choice--or very close to it--for the other linguistic categories. The fact that Deep Hand alone performs quite well for the recognition of FLSs and not for the other types of units could be explained by the fact that FLSs use a large variety of hand shapes, whereas other units like DSs use few hand shapes, but are rather very determined by the hand orientation, that is not captured by Deep Hand. In other words, it is likely that DSs give a more balanced importance to all hand parameters than FLSs.
###### Abstract  
This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current harmonics and radial electromagnetic forces introduced by DPWM are characterized and identified. Then, the principle and implementation method of three-state Markov chain random number generation are proposed, and particle swarm optimization (PSO) algorithm is chosen to quickly find the key parameters of transition probability and random gain. A Simulink and JMAG multi-physics field co-simulation model is built to simulate and predict the suppression effect of the MRCFM method on the sideband vibro-acoustic response. Finally, a 12-slot-10-pole PMSM test platform is built for experimental testing. The results show that the sideband current harmonics and vibro-acoustic response are effectively suppressed after the optimization of Markov chain algorithm. The constructed multi-physics field co-simulation model can accurately predict the amplitude characteristics of the sideband current harmonics and vibro-acoustic response.  
premment magnet synchronous motor; Markov chain random carrier frequency modulation; multi-physics field co-simulation model; sideband current harmonic; vibro-acoustics control; discontinuous pulse-width modulation 2024144808. [[https://doi.org/10.3390/app1414808](https://doi.org/10.3390/app1414808)]([https://doi.org/10.3390/app1414808](https://doi.org/10.3390/app1414808))
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Physical Crosslinking
#### 3.2.1 Hydrogen Bonding  
Hydrogen bonding is a special type of intermolecular interaction that occurs between molecules with highly polarized bonds, including hydroxyl or amine groups [69; 151]. Hydrogen bonds are formed between a hydrogen atom bound to an electronegative atom and another electronegative atom, like nitrogen, oxygen, or fluorine. These bonds are reversible and weaker than covalent bonds, allowing the gel constructed via hydrogen bonding to be highly responsive to environmental conditions like pH and temperature [19; 76]. For example, lowering the pH of the aqueous solution protonates chemical moieties, promoting hydrogen bonding. PVA and gelatin-based hydrogel systems are common examples, but the hydrogel solely relies on hydrogen bonding and typically exhibits weaker mechanical stability [42; 146; 152].
## 4 Implementation and Discussion of Results
### LSTM Deep Learning (Setup and Training)  
To build a forecasting model, a two-layer LSTM neural network structure has been employed. In different aspects, the presented LSTM algorithm varies from other predictive algorithms. For instance, while choosing hyperparameters, the presented LSTM reflects actual-time datasets without making any assumptions. We use the currently available COVID-19 Saudi Arabia dataset to train and test the LSTM proposed model in this analysis. From the original data, the LSTM chooses dependent input variables that affect the training process. The lag observations of the previous 14 days, as well as the mean for the previous 14 days, have been selected as features of the COVID-19 dataset of confirmed cases, death cases, and recovered cases. The purpose of this step is to train the models because the period of infection with COVID-19 in an infected person lasts less than 14 days. It is worth noting that in training the models, each dataset of the three output variables includes: confirmed cases, death cases, and recovered cases independent of the others. The outcome is an output vector containing the predicted output values of confirmed cases, death cases, and recovered cases. The features are translated into a machine-readable form, and the Keras package is used to manage variable input shapes. The original dataset is turned into data-wise total numbers of the COVID-19 confirmed, death, and recovered cases across Saudi Arabia. For further assessment, all lists and arrays should be joined with the same shape, and Python lists require more memory than NumPy arrays.
# Design of Time-Synchronized Switch Based on Zynq
## 4 Clock Synchronization Module Design
### RT Subsystem Module  
The internal structure of the RT Subsystem module is shown in Figure 13.  
The function of the SoftPLL module is to synchronize the local 62.5 MHz reference clock with the RX clock recovered from the data stream. It consists of two phase-locked loops: the auxiliary phase-locked loop and the main phase-locked loop. The structure of the SoftPLL is shown in Figure 14.  
Figure 13: The internal structure of the RT Subsystem module.  
The SoftPLL module uses two externally tuned VCXOs controlled by DACs. The first DAC is responsible for generating a 62.5 MHz main reference clock to synchronize with the RX clock by adjusting the DDMTD offset frequency to bring it closer to the frequency of the RX clock recovered from the data stream. The second DAC outputs an auxiliary clock for the DDMTD. Both DACs are controlled via a Wishbone bus interface. The period measurement module is a linear frequency detector used to correctly set the DDMTD offset frequency based on the RX clock recovered from the data stream. Its implementation is based on a set of counters that periodically output the frequency error to the Wishbone bus interface. When the DDMTD offset clock is slower than the RX recovered clock, the generated value is positive; otherwise, when the DDMTD clock is faster than the RX recovered clock, the value is negative.  
The 1-PPS module is responsible for generating and inputting the 1-PPS signal. It contains two time counters: cntr_utc and cntr_nsec. The cntr_utc is used to record the whole seconds of the WR time, while the cntr_nsec is used to record the fractional part of each second. The cntr_nsec is clocked with a 62.5 MHz reference clock, which provides a resolution of 16 ns. At the beginning of each second, the value of cntr_nsec is 0.  
The Wishbone registers of the 1-PPS allow modifications to the cntr_utc and cntr_nsec counters. This can be accomplished in two ways: first, by setting the time, which directly stores a new value to the counters upon request; second, by adjusting the time, which means adding new values to the current state of both counters at the beginning of a new second.
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 3 Methodology
### Anomaly Trajectory Recognition Based on Trajectory Rules
#### 3.3.5 Definition of Abnormal Trajectory Rule
Identification dangerous vehicle lane changes: In the context of highway scenarios, dangerous lane changes are highly hazardous driving behaviors that often result in severe traffic accidents. The occurrence of such behavior typically leads to serious consequences. Rule-based recognition of dangerous lane change behavior is primarily concerned with the vehicle's steering angle during travel. It mainly detects actions such as U-turns and excessively large steering angles during lane changes that are highly likely to lead to dangerous lane change situations. A threshold value for the vehicle trajectory path slope, denoted as \"u\" is established. When the path slope exceeds this threshold, it is recognized that the vehicle's travel direction has deviated significantly from the defined coordinate vertical axis, indicating the presence of a dangerous lane change behavior. The maximum steering angle during a vehicle lane change generally depends on the vehicle's speed and type.
# Electron Impact Cross Sections and Transport Studies of C\({}_{3}\)F\({}_{6}\)[PERSON]
## 3 Results and Discussion
### Transport Parameters  
The drift velocity of SF\({}_{6}\) and C\({}_{3}\)F\({}_{6}\)O is compared in Figure 3. It was computed as the product of mobility and the applied electric field. A smaller drift velocity directly indicates a better gas insulating behavior. Further, electrons interacted strongly with C\({}_{3}\)F\({}_{6}\)O as compared to SF\({}_{6}\) since the former had larger dipole polarizability. Our quantum chemistry work determined a dipole polarizability of 6.996 A\({}^{2}\) for C\({}_{3}\)F\({}_{6}\)O and the theoretical value for SF\({}_{6}\) was 4.546 A\({}^{2}\). Hence, we found that the drift velocity of C\({}_{3}\)F\({}_{6}\)O was twice as small as that of SF\({}_{6}\) and it certainly functions as an alternative for insulating applications.  
The gas density normalized diffusion coefficient (ND) for SF\({}_{6}\) and PMVE is shown in Figure 4. The coefficient slowly increased with increasing reduced electric field. As desired, the ND value for PMVE was smaller than that of SF\({}_{6}\) for the entire investigated electric field range. The smaller value of the diffusion coefficient for PMVE stems from the high attachment cross section when compared to that of SF\({}_{6}\).  
Figure 3: Drift velocity comparison of SF\({}_{6}\) and PMVE.  
The critical field strength study is discussed in this subsection. Figure 5 illustrates the Townsend coefficients of C3F6O and SF6. For SF6 we used the cross section from the Siglo database provided on the LXCat website [20]. The BOLSIG+ calculation was performed for the spatial growth case, which corresponds to the steady-state Townsend experiments. The ionization channel opens above the threshold energy, whereas the attachment reaction decreases dramatically as the energy decreases. The critical field is determined at the point where ionization and attachment rates balance each other. For C3F6O, the value was found to be 475 Td and for SF6 it was 355 Td. In terms the dielectric strength alone, C3F6O showed a good insulation performance.
# Robust Deep Speaker Recognition: Learning Latent Representation with Joint Angular Margin Loss
## 7 Discussion  
In this paper, three different methods, namely, AF-SincNet, Ensemble-SincNet, and ALL-SincNet, were proposed. The proposed methods were compared with previous state-of-the-art methods SincNet [16] and AM-SincNet [21]. Tables 2 and 3 show the proposed method's performance on the TIMIT and LibriSpeech datasets. Tables 2 and 3 show that the performance of AF-SincNet fell within a single phase, which was CER in LibriSpeech test data. Figure 2 and 3 shows the comparison of FER over training epochs for both the datasets. Figure 2 shows that only the SincNet curve stopped converging at its optimal point. All curves were still converging towards the optimal point. So, the proposed methods can still be improved by training more epochs (we trained 1600 epochs). Table 4 shows that ALL-SincNet outperformed all other configurations, whereas Tables 2 and 3 show that it did not perform well, which implies that our proposed ALL-SincNet is more robust than the traditional SincNet and other settings are, and this has actual implications in open-set biometric-authentication tasks, e.g., speaker or facial recognition. Table 5 (pretrained model that trained with the TIMIT dataset and was tested on the Bengali ASR dataset) shows that ALL-SincNet also performed better than with other settings, which implies that our proposed ALL-SincNet is robust and helps to generalize the SincNet architecture towards interlanguage speaker-recognition tasks.  
During our training, the original SincNet with softmax loss suffered from overfitting problems in small datasets, e.g., TIMIT. Our proposed model also mitigated that issue. Figure 4 shows that our proposed angular-margin-loss-based methods were less prone to overfitting than the original SincNet architecture.  
Figure 4 shows that our proposed margin-loss-based models were less prone to overfitting than the original SincNet architecture. This also supports the findings of the interdataset evaluations in Table 4.  
Figure 2: FER (%) comparison over training epochs on LibriSpeech dataset.
## 5 Implementation Details of Curve Arithmetic
### Scalar Decomposition
However, this odd-only scalar recoding method requires that the first element \(k_{0}\) of the muli-scalars is always odd. For constant-time execution and odd-only recoding, they found two offset vectors \(\mathbf{c_{1}}\) and \(\mathbf{c_{2}}\) such that \((k_{0},k_{1},k_{2},k_{3})+\mathbf{c_{1}}\) and \((k_{0},k_{1},k_{2},k_{3})+\mathbf{c_{2}}\) are valid decompositions of the scalar \(k\) and one of the two multi-scalars had a first element that was odd. To utilize these methods for curve Ted127-glv4, we carefully chose two offset vectors \(\mathbf{c_{1}}=2b_{0}+b_{1}-3b_{2}-4b_{3}\) and \(\mathbf{c_{2}}=3b_{0}+2b_{1}-3b_{2}-2b_{3}\). The multi-scalars \((k_{0},k_{1},k_{2},k_{3})+\mathbf{c_{1}}\) and \((k_{0},k_{1},k_{2},k_{3})+\mathbf{c_{2}}\) are valid decompositions of the scalar \(k\). Finally, all four coordinates of the two decompositions are positive and less than \(2^{65}\), and \(k_{0}\) in one of them is odd. Because all coordinates of multi-scalars are less than \(2^{65}\), scalar decomposition and recoding require more computational cost compared to Four\(\mathbb{Q}\)-based implementation, which has coordinates of multi-scalars less than \(2^{64}\). However, this additional cost is an extremely small portion of the scalar multiplication.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 2 Related Work
Therefore, the integration of real-world measurements into simulations and the thorough examination of relevant parameters can enhance the relevance and applicability of the findings to practical scenarios.