esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Road traffic crashes constitute a key public health issue globally, resulting in significant property damage and economic loses [1]. Each year, around 1.35 million people are killed and 20-50 million are injured in road traffic crashes around the world, representing the ninth leading cause of death overall, in addition to being the leading cause of death among children and young adults aged 5-29 [2]. The World Health Organization (WHO) has urged public authorities to take immediate actions towards implementing safety strategies that embrace the key principles of the safe systems approach, emphasizing the need to address risky driving behaviors, such as speeding, non-compliance with traffic regulations, illegal overtaking, and lane violations [3]. Risky and aggressive driving behaviors have been linked to various characteristics, such as driving experience, gender, income, and education. Driving experience plays a key role in shaping driving habits [4], with new drivers often facing a higher risk of road crashes due to inexperience.  
This study aims to shed light on the key factors influencing road traffic crashes among young drivers aged 17 to 24. Previous research indicates that young drivers are particularly prone to risky behaviors, making them a vulnerable group in terms of road safety [5,6]. Understanding the factors contributing to the severity of crashes involving young drivers is crucial for developing targeted safety interventions. In this context, this study aims to investigate the severity of crashes involving young drivers in England (from April 2019 to February 2022, a period covering before, during, and slightly after the outbreak of the COVID-19 pandemic), with the key research questions being as follows:  
* How can the severity of crashes involving young drivers (aged 17-24) in England be accurately predicted?
* What are the influential factors affecting crash severity, also considering specific traffic dynamics and behavioral trends observed during the study period?  
To address these questions and gain granular insights into the determinants of crash outcomes involving young drivers, we developed a decision tree prediction model and attempted to enhance its performance by implementing the random undersampling of the majority class (RUMC) method on our decision tree CART model. This integrated approach accounts for data imbalance, which is typically observed in crash severity data and can lead to biased statistical estimates if left unaccounted for. For the quantitative analysis of the study, crash data were drawn from the STATS19 database of the United Kingdom. The outcomes of this study can inform targeted safety measures and strategies for increasing the road safety of young drivers, who constitute a particularly vulnerable population group. In addition, the findings of the analysis can shed light on the predictive capabilities of the RUMC-CART model, especially in the context of crash injury severities, where it has not been extensively employed.
## 3 Materials and Proposed Method
### Proposed System Architecture  
In this study, we used the RandomSearch hyperparameter optimization approach to optimize two CNN transfer learning algorithms for decision support, namely AlexNet and VGG16. These algorithms are employed in MPII human posture analysis to classify human poses. These datasets consist of around 25,000 poses of different activities. When the default hyperparameter settings are used, the accuracy of the algorithms is determined, and then, when each of the HPO techniques is used, it is computed again. There is a comparison between the before and after photos. For enriching the initially limited dataset, we also design an image data augmentation approach. Rotation, translation, zoom, flips, shear, mirror, and color perturbation [48] are examples of picture data augmentation techniques that solve the problem of insufficient training data by including altered original samples in the training set (Figure 2). The classification results with image data augmentation were verified based on AlexNet [49] and VGG16 [5], respectively.  
The proposed system architecture is presented in Figure 5. This represents the classification of human postures using data augmentation at the preprocessing phase after which the models were trained. The MPII dataset was supplied to the system, and the preprocessing phase, which includes normalization, rescaling, and data augmentation, was conducted after which the processed dataset was passed to the models for training. The results were obtained, and the system was evaluated using the training and validation losses and accuracies. Figure 6 shows the proposed system using the hyperparameter-optimization method. The MPII dataset was supplied into the system, and the preprocessing such as normalization and rescaling was conducted. The hyperparameter optimizer was used on the processed data, after which it was passed to the models for training. The system was evaluated using training and validation losses and accuracies.  
The proposed approach relied on image data augmentation, hyperparameters, transfer learning techniques, and classification approaches that were utilized on human posture datasets. The recognition of human postures has become a vital aspect of the scientific area in current centuries.
## 3 Optical Error Analysis
### Optical Errors of FTIS
#### 3.2.2 Beam Splitter Spectral Error  
In an ideal FTIS beam splitter, the reflectivity and transmissivity would maintain constant 50% values across all wavenumbers. However, in practical implementations, the reflectivity exhibits wavenumber-dependent variations, which can be modeled as \(R_{FTIS}(\
u)=0.5+\delta R_{FTIS}(\
u)\), with the corresponding transmissivity given by \(T_{FTIS}(\
u)=1-R_{FTIS}(\
u)\), under the assumption of negligible thin-film absorption losses. By incorporating Equation (2) and neglecting the DC component, the interferogram \(I_{1}(\Delta)\) can be expressed as follows:  
\[I_{1-BS}(\Delta) =\int\left[1-4\delta R_{FTIS}(\
u)^{2}\right]B(\
u)\cos(2\pi\
u \Delta)d\
u\] \[=FT\Big{\{}\Big{[}1-4\delta R_{FTIS}(\
u)^{2}\Big{]}B(\
u)\Big{\}} =I_{0-BS}(\Delta) \tag{24}\]  
A spectral reflectivity deviation in the beam splitter effectively modifies the incident spectrum to \(B(\
u)[1-4\delta R_{FTIS}(\
u)^{2}]\). This perturbation introduces a convolutional distortion in \(I_{1-BS}\) compared to the theoretical interferogram \(I_{0}\), resulting in a beam-splitter-degraded signal denoted as \(I_{0-BS}\).  
The interferogram \(I_{2}\) is  
\[I_{2-BS}(\Delta) =\int\left[1-4\delta R_{FTIS}(\
u)^{2}\right]B(\
u)T_{FPI}(\
u) \cos(2\pi\
u\Delta)d\
u \tag{25}\] \[=I_{0-BS}(\Delta)*\left[a_{0}\delta(\Delta)+\sum\limits_{m=1}^{ \infty}a_{m}\delta(\Delta\pm m\cdot\Delta_{FPI})\right]\.\]  
The linear relationships between \(I_{1-BS}\), \(I_{2-BS}\), and \(I_{0-BS}\) remain fundamentally unchanged, ensuring the inversion computation remains unaffected by these perturbations. The final calculated spectrum corresponds to the modulated spectral signal \(B(\
u)[1-4\delta R_{FTIS}(\
u)^{2}]\).
# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 3 Materials and Methods
### Yolo Architecture
* Yolov3 benefits from a powerful feature extractor network named DarkNet-53 with 53 convolutional layers and residual blocks. In addition to the DarkNet-53, 53 more layers have been added for the detection task, thus making a 106 layered fully convolutional architecture. Figure 9 illustrates DarkNet-53 architecture. * Yolov3 employs strided convolution for down sampling instead of max-pooling. * Yolov3 is made up several convolutional layers in addition to the base feature extractor layers, enhancing its capability of multiscale predictions at three different sizes. This allows the system to make more accurate detections on small objects in the image. * The last improvement in Yolov3 is cross-layer connections between the prediction layers. The feature maps obtained from the up-sampling operation have been combined with the feature maps of with the previous layers by using concatenation operation. This combination provided more accurate detection performance on small objects.
# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 3 Results
### Evaluation Metrics
#### 3.3.2 Metric for Al-La Alloy  
The segmentation target of the Al-La alloy is to extract the dendritic region from the microscopic image. In contrast to the austenite image, the foreground and background proportions are approximately equal in the Al-La alloy, and each foreground target is interspersed with various background areas, which facilitates the algorithm to separate each area. In this work, we conduct experiments using intersection over union (IoU), a widely used evaluation metric in the field based on intraclass overlap [66]. The bigger the IoU, the better segmentation performance, as a value of 1 indicates that the set of regions from the two samples is identical. The idea behind IoU is to calculate the ratio of intersection and union of the ground truth and predicted results. The IoU is calculated as shown in Equation (2) below.  
\[\text{IoU}=\frac{\left|P\cap T\right|}{\left|P\cup T\right|} \tag{2}\]
# Rotor Position Alignment of FSTPI Based PMSM Drive Using Low Frequency Signal Injection
## 3 Mathematical Analysis of the Proposed Zero Angle Initial Rotor Position Alignment Scheme  
For proper zero angle rotor position alignment in FSTP inverter based PMSM drive, open-loop voltage control with \(d\)-axis low-frequency (\(f_{LF}=50\) Hz) reference signal \(V_{d}^{*}=V_{LF}sin(\omega_{LF}t)\) is injected along with \(V_{q}^{*}=0\) to the drive scheme, as shown in Figure 3. By injecting the fluctuated voltage signal to the \(d\)-axis, constant \(V_{DC}/2\) voltage with fluctuated AC components are generated on the upper and lower capacitor voltages given by  
\[\begin{split} V_{C_{1}}&=V_{DC}/2-kV_{LF}sin( \omega_{LF}t)\\ V_{C_{2}}&=V_{DC}/2+kV_{LF}sin(\omega_{LF}t)\end{split} \tag{1}\]  
where \(k\) is the resultant AC voltage amplitude constant \(k=\frac{1}{2\mathbb{C}|Z_{EQ}|\omega_{LF}}\) in the DC-link capacitors due to the motor parameters, capacitor values, and the injected signal frequency. \(|\ Z_{EQ}\ |\) is the equivalent impedance when phase \(a\) is energized and phase \(b\) and \(c\) are shorted under voltage signal injection. As a result, current flows in phase \(a\) and therefore alignment to zero position is achieved. The magnitude of \(V_{LF}\) and injected signal frequency \(f_{LF}\) determine the time elapse to lock the rotor to the desired zero angle position. Using Equation (1), the four active voltage vectors under low-frequency \(d\)-axis signal injection case are tabulated in Tables 1 and 2.  
Figure 2: Non-zero voltage vectors of the FSTP VSI under low-frequency signal injection (LFSI) applied to the \(d\)-axis synchronous reference frame.
## 2 Design of a Comb-Shaped Half-Mode SIW (HMSIW) with Comb Slots  
Figure 1 shows the basic HMSIW structure with feed designed on an FR4 substrate with \(\epsilon_{\text{r}}=4.4\), tan \(\delta=0.02\), and a thickness of 1.6 mm. Further, comb slots are embedded on the upper plane of the HMSIW. The basic SIW acts as a high-pass filter. The advantage of the chosen design method is that it is simple to design and economical. It has the disadvantage that the SIW needs via, and hence, more precision is needed. Using the standard SIW [16] and HMSIW [17] design equations for cutoff frequency f\({}_{\text{c}}\) = 8 GHz, the various parameters are found to be as follows: HMSIW length L\({}_{\text{HM}}\) = 12 mm, microstrip line length L\({}_{\text{M}}\) = 3.5 mm, microstrip line width W\({}_{\text{M}}\) = 0.65 mm, width of HMSIW W\({}_{\text{HM}}\) = 5.2 mm, via diameter D = 0.75 mm, pitch P = 1.0 mm, and X = 1.4 mm. The dimensions of the comb slot for optimum performance are found as follows: thickness T = 0.25 mm, interspacing between consecutive combs B = 1.25 mm, and height A = 4.75 mm.  
According to the Babinet principle, a slot is reciprocal to its metallic structure and can be represented by a corresponding model of inductance and capacitance [18]. The resonant circuit's L and C create a passband, which causes this slotted structure to produce a rejection band at the desired frequency.  
The spacing between two comb-shaped slots is found using parametric analysis as shown in Figure 2. It is evident that as the distance (S) between slots increases, the selectivity of the filter degrades, although there is a slight improvement in the loss, so the optimum result (for acceptable insertion loss and roll-off rate) is found for S = 1.2 mm.  
Figure 3 elucidates the simulated S21 response of the multimode resonator filter for weak coupling, from which it is evident that this filter creates two resonant modes to form the passband. Moreover, from the mode chart shown in Figure 4, it can be realized that by varying the stub length (L), transmission zeros' (TZs') and resonant modes' locations change. The TZ frequency swings down with an upsurge in the stub length. TZ2 is more affected by this than TZ1. This effect is more perceptible for TZ2 than TZ1. Also, two distinct resonant modes (5.8 and 6.65 GHz) form the passband for the optimal value of slot length (L) = 3.4 mm.  
Figure 1: Half-mode substrate-integrated waveguide with a comb slot.  
Figure 3: Simulated S21 response of a comb-slotted MMR filter under weak coupling.  
Figure 5 elucidates the frequency response of the BPF. The BPF possesses a resonance frequency of 6.45 GHz with an insertion loss of 0.45 dB and a maximum return loss of 18.5 dB. The bandwidth is evaluated to be 0.78 GHz (6.12 GHz-6.90 GHz).
# Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning
## 3 Results and Discussions
### Performance of the M1 Model with Hybrid Structural and Elemental Features  
First, we compared the performances of our GBR and other ML models such as RFR, Lasso, and SVR using the hybrid structural and elemental descriptors as raw features. In order to obtain stable results, each algorithm was evaluated using 10-fold cross-validations ten times. Figure 6 shows the fitting accuracy of all models using the same number of samples. It can be clearly seen that GBR has the best prediction performance, followed by RFR, and the worst is SVR. In terms of RMSE, MAE, and R\({}^{2}\) evaluation criteria, the GBR model scores are 0.28, 0.20, and 0.91, respectively. As shown in Table 3, the scores of these three evaluation measures of GBR are the best among all these ML models. In addition to the above three machine learning models, we also tried other machine learning models (such as linear regression models, K-neighbor regression models, etc.) for comparisons. However, their predictions are extremely poor and are therefore not listed here.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Descriptors** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Ong\_Descriptors & 0.15 & 0.29 & 0.78 \\ Magpie & 0.11 & 0.25 & 0.83 \\ Hybrid\_descriptors & 0.08 & 0.20 & 0.88 \\ \hline \hline \end{tabular} RMSE: root mean square error; MAE, mean absolute error.  
\end{table}
Table 2: Comparison of three feature sets for formation energy prediction.  
Figure 5: Feature importance from random forest (RF) for the formation energy of perovskite.  
The high accuracy of our GBR prediction model can be attributed to the following reasons. One reason is the nonlinear nature of the GBR algorithm; another reason may be the structural similarity of the materials considered in the data set. In a given data set, the crystal structure of all materials is perovskite. Given the same structure, materials with similar chemical compositions may have similar properties, making it more feasible to interpolate properties in predictive models. Discussing structural similarities are beyond the scope of our research, but it has become an important topic in ML research in materials science.
# Exploring a New Adaptive Routing Based on the Dijkstra Algorithm in Optical Networks-on-Chip
## 2 Internal Insertion Loss Analysis of ONoCs
### Five-Port Non-Blocking Router Model  
The optical router plays an important role in ONoCs. The five-port non-blocking router [24] has fewer microring resonators (MRRs) and crossings, which means less transmission loss to some extent. The five-port non-blocking router used in this paper is shown in Figure 3; \(l_{i}^{j}=l_{i}^{0}\) is the input port of the \(i\)th port and \(l_{i}^{j}=l_{i}^{1}\) is the output port of the \(i\)th port. Meanwhile, when \(i=0\), \(1\), \(2\), \(3\), and \(4\), they represent ejection, north, east, south, and west ports.  
When an optical signal is transmitted from the \(i\)th port to the \(j\)th port in the router, the transmission loss can be defined as \(L_{i,j}(x,y)\) in (13). The propagation loss is integrated at the network level into our general optical router model [25]. The CSE, PSE, waveguide crossings, and propagation loss \(L_{p}\) will cause switching loss \(S_{L_{i,j}}(x,y)\). \(W_{l_{i,j}}(x,y)\) represents the waveguide length from the \(i\)th input port to the \(j\)th output port inside the router. When \(j=0\), the output port is ejection. When \(j\
eq 0\), the calculation of \(L_{i,j}(x,y)\) also needs to take the waveguide transmission loss between the current router and the next router into account. In (14), hop length \(D\) can be attained based on chip size \(S\) and network size \(M\times N\). If the input optical power \(P_{i}^{0}(x,y)\) is determined, then the output power \(P_{i,j}(x,y)\) from the \(i\)th port to the \(j\)th port of the router can be calculated as the formula shown in (15).  
\[L_{i,j}(x,y)=\begin{cases}1-S_{L_{i,j}}(x,y)L_{p}^{W_{l_{i,j}}(x,y)}&j=0\\ 1-S_{L_{i,j}}(x,y)L_{p}^{W_{l_{i,j}}(x,y)+D}&j\
eq 0\end{cases} \tag{13}\]  
\[D\approx\sqrt{\frac{S}{M\times N}} \tag{14}\]  
\[P_{i,j}(x,y)=P_{i}^{0}(x,y)(1-L_{i,j}(x,y)) \tag{15}\]  
Figure 3: Five-port non-blocking router model.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## 1 Introduction
Since shales mainly consist of clay minerals, shale hydration becomes another important factor causing borehole instability that cannot be ignored [38; 39; 40]. Amphiphilic polymer has been reported to be able to inhibit shale hydration [41]. The combination of amphiphilic polymer and silica nanoparticles may have the advantages of both physical plugging and chemical inhibition. In this study, a novel shale stabilizer poly(St-MMA-AM)/nano-SiO\({}_{2}\) composite (SMA/SiO\({}_{2}\)) was synthesized. Poly(St-MMA-AM) was used as an amphiphilic polymer where St and MMA provided the hydrophobic segments and AM provided the hydrophilic segments. First, materials and methods were presented. Then, the novel shale stabilizer SMA/SiO\({}_{2}\) was prepared and characterized. To investigate the plugging ability of SMA/SiO\({}_{2}\), the pressure transmission experiment and BET analysis were conducted. To evaluate the shale hydration inhibition capacity of SMA/SiO\({}_{2}\), the rolling dispersion experiment and contact angle test were carried out. Finally, the compatible test was conducted to evaluate the effect of SMA/SiO\({}_{2}\) on the rheological and filtration control properties of the WBMs.
## 1 Introduction
Polluting emissions from road infrastructure can originate in any of the five stages of its life cycle: materials production, construction, operation, maintenance, and end of life [16]. This research will analyze the emissions generated in the operation stage of a highway. According to the literature, various methodological approaches exist for the identification and quantification of pollutant emissions. However, these approaches often involve a model with various input variables, and in developing countries, the application of such tools is challenging due to the lack of inputs and data required for their proper implementation. Therefore, the objective of this study is to adapt a methodology by incorporating calculation tools using available or easily obtainable information to quantitatively estimate pollutant emissions produced by vehicular transportation on roads. It is worth mentioning that emissions on roads are important to analyze because even though there might not typically be immediate populations near road sections, the emissions disperse into the atmosphere, negatively impacting the environment. To validate the results, the Centinela-La Rumorosa highway (Figure 1) is used as a case study, located in the northwest of Mexico, specifically in the state of Baja California between the municipalities of Mexicali and Tecate. This road section is significant for the country, as it serves as the only land communication route connecting the states of Baja California and Baja California Sur with the rest of the country. The traffic on this highway averages around 7000 vehicles daily, with a vehicle composition of 70% cars and 30% freight trucks. The highway consists of separated carriageways with varying alignments and different topographic conditions throughout its stretch. Both uphill and downhill sections span 64 km in total length, comprising 40 km on level terrain and 20 km through mountainous terrain. It is important to highlight that this highway experiences an average of 200 accidents annually due to its complex system of curves and slopes, as well as the environmental conditions of the area. These conditions include minimum temperatures dropping below \(-7\) degrees Celsius and maximum temperatures exceeding 54.3 degrees Celsius, coupled with strong winds, rain, and snow. Additionally, the highway is located in a seismic zone [17].
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization  
In this section, we characterize the fading and interference distribution required to compute the average number of RR considering different antenna configuration using Single-User and Multiuser (MU-MIMO) communications.
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 3 Variance Characteristics of Surface Albedo for Essential Land Types and Scales
### Albedo Variances for Mixed Land Types  
Natural surfaces are always composed of mixtures of multiple land types rather than a single type. Surface albedos of the 16 International Geosphere-Biosphere Program (IGBP) ecosystem classes accompanied by snow have been investigated [70], especially for forests [148; 149], and an obvious increase in surface albedo has been observed in snow-covered areas due to their high albedo magnitude. Mixed forest albedo varies with stand age, canopy height, tree species composition and FVC [148; 149]. Moss and biocrusts growing on soil significantly decrease overall soil albedo due to increased darkness [88], and a similar result is found in Greenland ice sheet supraglacial streams due to microbial dark sediments [125]. In addition, urban areas always contain soil-vegetation plots and various artificial elements (e.g., blocks, street canyons, and pavement), which present complex albedo variances [25; 140; 146].