esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
#### 3.4.1 Overview of CNN Methodology  
CNNs are widely used deep learning models designed for automated feature extraction and the classification of images. Unlike traditional machine learning approaches that require manual feature engineering, CNNs automatically learn spatial patterns from image data through multiple computational layers.  
A CNN typically consists of the following key components:  
* Convolutional layers. These layers apply learnable filters (kernels) to the input image, detecting spatial features such as edges, textures, and object structures. The convolution operation extracts local patterns by computing a weighted sum of pixel intensities;
* Activation functions. A non-linear activation function, such as the Rectified Linear Unit (ReLU), is applied to the feature maps to introduce non-linearity, improving the model's ability to learn complex relationships in the data. ReLU is mathematically defined as \[f(x)=max(0,x)\] where \(x\) represents the input. This function ensures that negative values are set to zero, enhancing the learning efficiency of the network;
* Pooling layers. To reduce dimensionality and retain the most significant information, pooling operations (e.g., max pooling) are applied. The max pooling operation, for example, selects the highest value within a defined window, reducing spatial size while preserving critical features;
* Fully connected layers. After feature extraction, the output from the convolutional and pooling layers is flattened and passed through fully connected layers. These layers aggregate extracted features for classification. The final output is computed using a softmax function, which converts the model's predictions into probabilities for each class, \[\hat{y}_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{C}e^{z_{j}}}\] where \(\hat{y}_{i}\) is the predicted probability for class \(i\), \(z_{i}\) is the output score before activation, and \(C\) is the total number of classes.  
CNNs are trained using backpropagation with gradient descent, which adjusts the model's weights iteratively to minimize classification errors. The weight update follows  
\[W^{(t+1)}=W^{(t)}-\eta\frac{\partial L}{\partial W}\]  
where \(W\) represents the network's parameters, \(\eta\) is the learning rate, and \(\frac{\partial L}{\partial W}\) is the gradient of the loss function \(L\). This optimization process ensures that the network improves its feature representation over multiple iterations.  
Since AlexNet, VGG-19, and ResNet-50 are pre-established CNN architectures widely used for image classification tasks, this study applies them without modification to classify key features in pedestrian crash diagrams. Deep learning research has extensively validated these models, making them suitable for automated crash data extraction. Readersinterested in a more detailed mathematical formulation of CNN operations are referred to foundational deep learning studies, e.g., [30; 36; 47].
## 4 Experiments
### Results and Discussion  
#### 4.3.1 Defect Classification Accuracy  
By completely removing 10 images from each category (60 images) before data pre-processing and training of the models, an objective classification test of unseen input images yielded the results shown in Table 2. Owing to the low-resolution and small number of leather surface images in the training set, relatively low categorical classification accuracies (six-class classification) are to be expected. ViT-02 outperforms all other methods.  
In terms of each vision transformer predicting whether an input image is defective or non-defective (the two classes of defective or not), ViT-01 is outperformed by the Inception-V3 and EfficientNet-B0 models, while ViT-02 outperforms all models. However, when predicting one of the six specific leather categories, the results show that ViT-02 outperforms the EfficientNet-B0 model by two correct predictions. The average approximate inference time per image is calculated by dividing the time taken for all 60 unseen images by 60. From this, we can see that while ViT-01 is slightly faster than the ResNet-50 and Inception-v3 models, the EfficientNet-B0 and ViT-02 are significantly faster than these three. Although the ViT-02 model is slower than the EfficientNet-B0 model by approximately 16.24 milliseconds per image, it is redeemed by being more accurate in both binary and multi-class classification.
## 2 Analysis of Surrounding Rock Control Status in Pinggang Coal Mine  
Pinggang Coal Mine is located in Jixi City, Heilongjiang Province, China, with a mining area of 35.2 km\({}^{2}\). The surface is hilly and mountainous, and the vertical depth of the working face is 650-680 m. The 33# coal seam has a single coal structure or two layered structures. The middle interlayer of the two layered structure is thin, generally 0.30-1.54 m, which is a mixed layer of shale, tuff shale, and coal shale formation. The coal quality is excellent, bright, massive, and low ash (Ag = 22.47%). The specular coal in the coal seam is medium-wide banded, the stripes are dark black, the physical conditions are good, the curves are well reflected, and the characteristics are evident. Most of the mining area is recoverable, and the coal seam dip angle is 15-30\({}^{\circ}\). The dip angle of the right coal seam is relatively stable, basically 20\({}^{\circ}\). The direct roof of the coal seam is silty fine sand rock with a thickness of 6.0 m. The direct base is shale, and the thickness is 6.2 m.  
The roadway of the first right working face of the 33 # coal is a trapezoid. The width \(\times\) height of the roadway is 4200 mm \(\times\) 2500 mm. The roof is supported by a bolt, a steel belt, and an anchor cable. There are 4 rows of bolts, and the spacing between bolts is 1000 mm \(\times\) 1100 mm. The anchor cable in the middle of the roof, and the support density is 3000 mm \(\times\) 3, as shown in Figure 1.  
According to the field pressure observation, the deformation of the surrounding rock is large, and the metal mesh fracture occurs locally. The amount of floor movement is between 100 mm and 500 mm for 30 days. Through the observation of the roof separation instrument, the amount of roof separation in the first 7 days is generally about 100 mm, and the amount of separation is between 100 mm and 300 mm in 7 days to 30 days.
## 2 Materials and Methods
### Numerical Modeling
#### 2.3.3 Mesh Information  
In the present study, great attention was paid to the mesh grid formation, with several refinements taking place in order to ensure reliable results. This was attended to in both the COMSOL and SolidWorks models.  
Specifically, the mesh along the boundaries between the fluid and the conduit surfaces underwent refinement, and the mesh elements used for the water were carefully adjusted and improved in order to better represent and simulate the thermal exchange involving the fluid and the receiver walls. Additionally, the mesh elements on the mirror and absorbing surfaces were fine-tuned to prevent issues related to absorption of solar irradiation and to enable effective ray tracing. By implementing this enhancement on the reflector, the interactions between the surfaces and the rays of light can be calculated with greater accuracy, providing better representation of the optical behavior of the collector in the simulation.
## 2 Internet of Vehicles (IoV)  
IoV has attracted a great deal of attention as a useful Internet of Things (IoT) scenario for addressing the grand challenges of modern transportation problems. Connected and fully automated vehicles are expected to revolutionize the transportation systems of the future on a global scale, significantly enhancing road safety, alleviating traffic congestion, reducing fuel consumption, and enhancing driving experience and infotainment. IoV evolved from vehicular ad hoc networks (VANET) which mainly focused on providing road safety applications based on communication between vehicles and roadside units, constituting a special case of mobile ad hoc networks (MANET) [1].  
In IoV, vehicles are equipped with computing and storage capabilities, a variety of sensors, including cameras, GPS, RADAR, LIDAR (light detection and ranging), and different communication wireless interfaces, leading to independent autonomous vehicles that can connect and cooperate with each other, with roadside infrastructure, with pedestrians, and with other entities through vehicle-to-everything (V2X) communications.  
Several access technologies have already emerged to support V2X communication, such as wireless local area networks (WLAN), including IEEE 802.11p/ WAVE [21, 22, 23] and its evolution IEEE 802.11 bd [24], wireless wide area networks, cellular technologies, such as LTE/5G [21, 25, 26], as well as LoRa [27, 28, 29, 30] and Bluetooth. These wireless access technologies offer diverse communication options among different smart things. In IoV, each vehicle is envisioned as a smart object having sensing, computing and storage capabilities enabling it to connect with other entities through the vehicle-to-everything (V2X) umbrella, comprising vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle to sensor (V2S), vehicle to roadside unit (V2R), and vehicle to personal device (V2P).
## 4 Experiments and Results
### Baseline Setup  
Firstly, we replicate two other baseline systems based on different frameworks--GMM-HMM [4] and RNN-based CTC-Attention [17]. Since the partition of training/development/test sets is not identical, there is a slight gap between our results and the original, but within the allowable range. A \"big model\" is commonly suggested for the Transformer [19; 20] in monolingual ASR, but we find it unsuitable for CSSR due to insufficient CS data obtained and CSSR output units that are too large. In this work, we chose a \"smaller model\" for the Transformer(\(d^{att}=256\), \(e=6\), \(d=3\)). The input speech is represented as a sequence  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Train Set** & **Dev Set** & **Test Set** & **Total** \\ \hline
**speakers** & 145 & 2 & 8 & 155 \\
**Duration (h)** & 107 & 1.86 & 6.38 & 115.23 \\
**utterances** & 100802 & 1596 & 6276 & 108674 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the SEAME corpus.  
Figure 6: Frameworks for joint LID and ASR decoding.  
of a 40-dim filterbank feature. The filterbank feature is firstly subsampled by a two layer time-axis convolutional neural network with \(ReLU\) activation (stride size is 2, kernel size is 3, the number of channels is 256). The loss weight \(\alpha\) for the CTC joint training is set to 0.3. To prevent training from overfitting to the training set, label smoothing [28] with a penalty of 0.1 is applied. For Mandarin modeling units, a set of 2639 Chinese characters is used, covering the Chinese characters that appear in the training text. For English, a set of 200 subwords is used, which is trained on English segments of the training set using the BPE method [26].  
As shown in Table 2, the CTC-Transformer baseline had a mixed error rate (MER) of 33.96%, better than that of the RNN-based CTC-Attention baseline (38.38%) and the GMM-HMM baseline (39.6%).
## 3 Proposed Scheme
### Synchronization Scheme
\begin{split} r_{1}(n)&=s_{pss}^{*}(n)\cdot y^{ \prime}_{1}(n)=s_{pss}^{*}(n)[G_{1}s_{pss}(n)e^{j2\pi n\frac{\varphi}{N}}+z_{1} (n)]\\ &=G_{1}\big{|}s_{pss}(n)\big{|}^{2}e^{j2\pi n\frac{\varphi}{N}}+ \psi^{\prime}_{1}(n)\end{split}  \tag{23}\]  
where \(\psi^{\prime}_{1}(n)=s_{pss}^{*}(n)z_{1}(n)\). Divide \(r_{1}(n)\) into two part sequences with the same length. Ignore the influence of the noise, the correlation between the two sequences can be expressed as  
\[ \begin{split} R_{2}&=\sum\limits_{n=0}^{N_{2}/2-1}{ r_{1}}^{*}(n)r_{1}(n+N_{2}/2)\\ &=\sum\limits_{n=0}^{N_{2}/2-1}{G_{1}}^{*}|s_{pss}(n)\big{|}^{2} e^{-j2\pi n\frac{\varphi}{N}}G_{1}\big{|}s_{pss}(n+N_{2}/2)\big{|}^{2}e^{j2\pi(n+N_{2}/2 )\frac{\varphi}{N}}\\ &=e^{j\pi N_{2}\frac{\varphi}{N}}\sum\limits_{n=0}^{N_{2}/2-1}|G_ {1}|^{2}|s_{pss}(n)|^{2}\big{|}s_{pss}(n+N_{2}/2)\big{|}^{2}\end{split}  \tag{24}\]where \(N_{2}\) is the length of \(r_{1}(n)\). The estimated frequency offset is given by  
\[\widehat{\epsilon}_{d}=\frac{angle(R_{2})}{\pi}\frac{N}{N_{2}} \tag{25}\]  
where \(\widehat{\epsilon}_{d}\in(-N/N_{2},N/N_{2})\). As the \(N_{1}>N_{2}\), the residual frequency offset is \(\epsilon_{r}<N/N2\). Therefore, this method can estimate residual frequency offset. To improve the accuracy of the frequency offset estimation, PSS and SSS are used to estimate the frequency offset further. Construct \(r_{pss}\) and \(r_{sss}\) as  
\[ \begin{split} r_{pss}(n)=s_{pss}^{*}(n)\cdot{y^{\prime}}_{1}(n) \\ r_{sss}(n)=s_{sss}^{*}(n)\cdot{y^{\prime}}_{1}(n+L)\end{split}  \tag{26}\]  
where \({y^{\prime}}_{1}(n))\) and \({y^{\prime}}_{1}(n+L)\) represent the extracted PSS and SSS corresponding to the received signal. \(L\) represents the distance between the mapping positions of PSS and SSS. \(s_{pss}\) and \(s_{sss}\) denote the local PSS and SSS, respectively. Correlate \(r_{pss}\) and \(r_{sss}\) to obtain  
\[ \begin{split} R_{3}&=\sum_{n=0}^{N_{2}-1}r_{pss}^{* }(n)r_{sss}(n)\\ &=\sum_{n=0}^{N_{2}-1}G_{1}^{*}\big{|}s_{pss}(n)\big{|}^{2}e^{-j 2\pi n\frac{\
u}{N}}G_{1}\big{|}s_{sss}(n)\big{|}^{2}e^{j2\pi(n+L)\frac{\
u}{N }}\\ &=e^{j2\pi L\frac{\
u}{N}}\sum_{n=0}^{N_{2}-1}|G_{1}|^{2}\big{|}s _{pss}(n)\big{|}^{2}\big{|}s_{sss}(n)\big{|}^{2}\end{split}  \tag{27}\]  
where \(N_{2}\) denotes the length of \(r_{pss}\), which is also the length of \(r_{sss}\). The estimated frequency offset is given by  
\[\widehat{\epsilon}_{d}=\frac{angle(R_{3})}{\pi}\frac{N}{2L} \tag{28}\]  
where the estimated frequency offset range is \(\widehat{\epsilon}_{d}\in(-N/2L,N/2L)\). Therefore, the \(N_{1}\) in (17) could not be less than \(N/2L\). With the two signals streams received from two antennas of the IoT UT, the frequency offset is estimated according to (17)-(28) and compensated, respectively.
In particular, the influence of a generalized multi-step skewing on the performance of SynRM, considering a torque ripple harmonic content approach and not the traditional one-slot-pitch skewing technique, has not been examined. This study aims to serve as an input of post-optimization processes in the design stage of SynRMs. This article presents a comprehensive analytical expression for multi-step discrete skewing in SynRM. The method calculates a specific skew angle to eliminate a specific harmonic component and a mitigation factor to showcase the impact of skewing on each harmonic component. The use of this methodology ensures a suitable selection of the harmonic to be mitigated, and reduce the number of rotor steps required to effectively decrease the presence of dominant harmonics in the electromagnetic torque, in this manner improving the machine's performance. The article validates the proposed approach by analyzing two SynRM designs through FEA and includes three-dimensional (3D) FEA to consider the border effect, which is not present in two-dimensional (2D) FEA. The optimal step skew angle predicted by the analytical formula closely matches both 2D and 3D FEA results. The proposed equations are evaluated for two-step, three-step, and four-step skewing, showing promising results in mitigating the selected undesired harmonics and reducing other harmonic content collaterally. In addition, an optimized triple-barrier SynRM is post-optimized by means of the proposed method. The paper is organized as follows: Section 2 presents the selected machines, describing their dimensions and main data. Section 3 provides the details of the proposed analytical expressions to select the step angle of N-step skewing, generalized for machines with any pole count. These expressions are assessed and validated in Section 4, which summarizes the results and discussion of several case studies evaluated by means of 2D and 3D FEA. Section 5 shows a study case of the proposed approach as a post-optimization process, reducing the torque ripple of an optimized triple-barrier SynRM. Conclusions are drawn at the end of the paper summarizing the benefits of the proposed approach and its applicability to the design of SynRM.
# Set-Point Control of a Spatially Distributed Buck Converter
## 3 Open-Loop Simulation  
### Simulation Setup  
For the distributed converter we used the same setup as in [11]. In particular, the transmission line consists of \(l=6\,\mathrm{m}\) coaxial cable RG 58 C/U [31]. Figure 3 shows the cross-section of the coaxial cable. The inner and outer conductors are made of copper. Since copper is a very good conductor, we assume the lossless case with \(R^{\prime}=0\,\Omega/\mathrm{m}\). For the insulation (dielectric), polyethylene is used. Assuming perfect insulation we set \(G^{\prime}=0\,\mathrm{S/m}\). Based on the geometry, capacitance and inductance per length can be computed by  
\[C^{\prime}=2\pi\varepsilon\frac{1}{\ln\frac{D}{d}}\quad\text{and}\quad L^{ \prime}=\frac{\mu}{2\pi}\ln\frac{D}{d},\]  
where \(\varepsilon\) is the permittivity and \(\mu\) is the permeability. Note that polyethylene has a relative permittivity \(\varepsilon_{r}\approx 2.3\) and a relative permeability \(\mu_{r}\approx 1\). The numerical values of the relevant parameters of the coaxial cable are listed in Table 1.  
To simulate the distributed buck converter, we used the method of lines (MOL) to discretize Equation (4a,b) w.r.t. the spatial direction [32; 33; 34]. The line between \(z=0\,\mathrm{m}\) and \(z=6\,\mathrm{m}\) was divided into 100 equidistant points. This discretization of the spatial voltage and the current distribution results in a 200-dimensional state-space system. The numerical simulation was carried out with the open source tool GNU Octave [35] using the function ode45. This ODE solver employs the explicit Dormand-Prince method, which is a special Runge-Kutta method of order \((4,5)\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & & **Value** \\ \hline length & \(l\) & 6 m \\ diameter of the inner conductor & \(d\) & \(0.9\pm 0.01\,\mathrm{mm}\) \\ diameter of the outer conductor & \(D\) & \(2.95\pm 0.05\,\mathrm{mm}\) \\ capacitance per length & \(C^{\prime}\) & \(100\,\mathrm{pF/m}\) \\ inductance per length & \(L^{\prime}\) & \(241\,\mathrm{nH/m}\) \\ characteristic impedance & \(Z_{0}\) & \(50\,\Omega\) \\ propagation delay & \(\tau\) & \(5\,\mathrm{ns/m}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the coaxial cable RG 58 C/U [11; 31].  
We also want to compare the behavior of the distributed converter with the classical buck converter. There, we also assumed the lossless case, i.e., \(R=0\,\Omega\) and \(G=0\,\mathrm{S}\). For the reactive lumped elements we used the overall capacitance \(C=l\cdot C^{\prime}=600\,\mathrm{pF}\) and the inductance \(L=l\cdot L^{\prime}=1446\,\mathrm{nH}\). For the simulation, the state-space system (2) was also solved with the function ode45.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases
### Guanine Nucleotide Exchange Factors (GEFs)
#### 3.2.2 Structural and Functional Characteristics of the DH domain  
The DH domain is the signature of DBL family proteins. The catalytic guanine nucleotide exchange activity of DBL family proteins is realized entirely within the DH domain, which is not only sufficient for catalytic activity but also critical for substrate specificity [47; 92]. The catalytic DH domain consists of approximately 200 residues, and as determined by x-ray and NMR analyses of the DH domain in several DBL proteins, it is composed of a unique extended bundle of 10-15 alpha helices [93]. This helical fold is mainly composed of three conserved regions, CR1, CR2, and CR3, each of which is 10-30 residues long and forms separate alpha helices that are packed together [45; 53]. The CR1 and CR3 regions are solvent exposed until complexed with RHO proteins [47]. Except for these three conserved regions (CR1, CR2, and CR3) in DH domains, DBL family members share little homology with each other [87].
## 4 Discussion
### Comparative Analysis of Binding Affinities and Inhibition Constants  
For both COX-1 and COX-2, AA consistently exhibits the lowest binding energies (\(-\)7.53 kcal/mol for COX-1 and \(-\)7.07 kcal/mol for COX-2) and inhibition constants (3.02 \(\upmu\)M for COX-1 and 6.55 \(\upmu\)M for COX-2), as summarized in Tables 2 and 3. These results confirm AA's strong and optimal positioning within the catalytic sites of both enzymes, aligning with its physiological role as the natural substrate.  
Among the investigated terpenes, menthol and camphor consistently demonstrate the most favorable binding parameters for COX-1 and COX-2. For COX-1, menthol shows the lowest binding energy (\(-\)6.56 kcal/mol) and Ki (15.50 \(\upmu\)M) among the terpenes, followed closely by camphor (\(-\)6.32 kcal/mol and 23.37 \(\upmu\)M, respectively), according to Table 1. Similarly, for COX-2, camphor exhibits the lowest binding energy (\(-\)6.48 kcal/mol) and Ki (17.84 \(\upmu\)M) among the terpenes, followed by menthol (\(-\)6.42 kcal/mol and 19.80 \(\upmu\)M), as presented in Table 2. While these terpenes did not achieve the full interaction profile characteristic of AA, their relatively low binding energies and Ki values suggest a promising inhibitory potential against both cyclooxygenaseases.  
A distinct binding pattern emerges for 5-LOX and PLA\({}_{2}\). In the case of 5-LOX, limonene (\(-\)5.57 kcal/mol) and menthol (\(-\)5.45 kcal/mol) exhibit binding energies comparable to that of AA (\(-\)5.45 kcal/mol). Furthermore, limonene (82.28 \(\upmu\)M) shows an even lower Ki value than AA (101.13 \(\upmu\)M) for 5-LOX, with menthol (102.00 \(\upmu\)M) having a very similar Ki to AA, as reported in Table 3. This suggests that limonene and menthol can interact effectively with 5-LOX, potentially exerting a competitive inhibitory effect.  
Similarly, for PLA\({}_{2}\), limonene (\(-\)6.58 kcal/mol) and menthol (\(-\)6.57 kcal/mol) display even lower binding energies and significantly lower Ki values (14.99 \(\upmu\)M and 15.22 \(\upmu\)M, respectively) compared to AA (\(-\)6.23 kcal/mol and 27.27 \(\upmu\)M), as shown in Table 4. These findings strongly support their candidacy as potential inhibitors of 5-LOX and PLA\({}_{2}\), enzymes critical in the initial steps of the inflammatory cascade.  
In contrast, linalool consistently shows weaker interactions across all enzymes, particularly with 5-LOX (Ki value of 564.63 \(\upmu\)M; see Table 3), indicating lower inhibitory potential.