esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Axis-Selective Photoreaction and Thermal Stimulation Using Linearly Polarized 313 nm Light  
The photoinduced molecular reorientations in **P1-P3** films using linearly polarized 365 or 313 nm light show significant thermally stimulated molecular reorientation (\(D>0.7\)) [24; 30]. Similarly, the **P4** film displays a photoinduced molecular reorientation when using linearly polarized 313 nm light (Figure 2a,b). \(A_{\perp}\) (\(A_{\mid\,\mid}\)) initially increases (decreases), but the absorbances of both directions decrease when the exposure energy exceeds 90 J/cm\({}^{2}\). This isdue to the out-of-plane motion and side photoreaction of the mesogenic side groups. The observed photoinduced optical anisotropy is much smaller than that for a **P3** film [30]. Additionally, an increase at 380-420 nm is detected with a small anisotropy when the exposure energy exceeds 20 J/cm\({}^{2}\). The polarized FTIR spectra show decreases in the absorbances at 1725, 1600, 1250, and 1162 cm\({}^{-1}\) (vibration bands of Ph-COO-Ph) (Figure 2c). Changes in the UV-VIS and FTIR spectra are due to the axis-selective photo-Fries rearrangement of the phenyl benzoate moieties. The photoinduced in-plane reorientation initially occurs, but the simultaneous photo-Fries rearrangement of the phenyl benzoate moieties at a high exposure dose restricts the in-plane motion of the mesogenic side groups (Scheme 1).  
Annealing the exposed **P4** films generates a significant in-plane thermal stimulation of the orientation similar to the cases of **P1-P3** films [26; 27; 28; 29; 30]. Figure 3a shows the change in the polarized absorption spectra of a **P4** film after exposure to linearly polarized 313 nm light for 5 J/cm\({}^{2}\) and subsequent annealing at 120 \({}^{\circ}\)C for 10 min. The slight optical anisotropy (\(D_{276}=0.05\), \(D_{325}=0.05\)) after exposure is amplified to \(D_{276}=0.72\) and \(D_{325}=0.76\). However, the thermal amplification is not effective when the exposure energy is 30 J/cm\({}^{2}\) (Figure 3b). A significant thermally stimulated molecular reorientation is attained when the exposure energy is 2-10 J/cm\({}^{2}\) (Figure 3c and Figure S3a,b), as well as subsequent annealing of the exposed films in the LC temperature range of **P4** (Figure 3d). Random motion above the LC temperature of the film vanishes the photoinduced dichroism. This behavior is similar to the thermal stimulation of an exposed **P3** film using linearly polarized 365 nm light. By contrast, the \(D\) value decreases when the exposure energy exceeds 30 J/cm\({}^{2}\), regardless of the annealing temperature (Figure 3c and Figure S3c,d). As the photoreaction of the **P4** film with 313 nm light proceeds, it includes an axis-selective photo-Fries rearrangement of the mesogenic side groups, which may prevent thermal stimulation of the in-plane molecular reorientation.  
Figure 2: (**a**) Change in the polarized absorption spectra of a **P4** film, (**b**) absorbances of a **P4** film as a function of exposure dose, and (**c**) change in the polarized FTIR spectra of a **P4** film, exposed to linearly polarized 313 nm light.
# Deepwater 3D Measurements with a Novel Sensor System
## 2 Materials and Methods
### Data Recording
#### 2.2.4 3D Model Generation  
The resulting 3D model is created in several steps. First, the 3D point cloud is thinned out and reduced to ensure the balanced spatial distribution of the selected points. The 3D point clouds of successive scans are each registered against their predecessor using the Iterative Closest Point (ICP) algorithm. For the ICP registration to converge, there must be a sufficient 3D structure in the point cloud. If this is not the case, only the trajectory estimation of visual odometry is used in the initial registration step.  
Figure 5: Reference bodies’ ball-bar and cylinder (**left**), 3D reconstruction result without (**middle**) and with motion compensation (**right**).  
In the next step, a second registration is carried out using a continuous-time ICP method to prevent the remaining residual errors from accumulating in the registration and to ensure any existing drift in the visual odometry is completely eliminated. A 3D map is created from the successive scans. This is subsequently reduced in the spatial resolution using an octree-based process and can be visualized on the monitor in the control room on the vessel. The updated overall model can be visualized every 1 to 5 s.
## 3 Proposed Methods
### Short-Term Learning
#### 3.4.1 Fourier Series Descriptor  
Fourier series can be used to represent a given function, say \(f(t)\), in the form of the Fourier polynomial as follows.  
\[f(t)=\frac{1}{2}a_{0}+a_{1}\cos t+b_{1}\sin t+a_{2}\cos 2t+b_{2}\sin 2t+\ldots+a_ {n}\cos nt+b_{n}\sin nt+\ldots \tag{1}\]  
Since the principal information of \(f(t)\) is reserved in lower degree terms, we can approximate \(f(t)\) by the Fourier polynomial of degree \(n\) when \(n\) is sufficiently large. The value of coefficients \(a_{i}\) and \(b_{i}\) leading to the optimal approximation can be determined by finding the orthogonal projection of \(f(t)\) onto the space spanned by the Fourier polynomial of degree \(n\),  
\[\begin{array}{rcl}proj_{Fourier}f(t)&=&\left(f(t),\frac{1}{\sqrt{2\pi}} \right)\frac{1}{\sqrt{2\pi}}\\ &+&\left(f(t),\frac{1}{\sqrt{\pi}}\cos t\right)\frac{1}{\sqrt{\pi}}\cos t+ \left(f(t),\frac{1}{\sqrt{\pi}}\sin t\right)\frac{1}{\sqrt{\pi}}\sin t\\ &+&\ldots\\ &+&\left(f(t),\frac{1}{\sqrt{\pi}}\cos nt\right)\frac{1}{\sqrt{\pi}}\cos nt+ \left(f(t),\frac{1}{\sqrt{\pi}}\sin nt\right)\frac{1}{\sqrt{\pi}}\sin nt\\ \end{array} \tag{2}\]  
To describe the short-term pollution trend, the hourly PM\({}_{2.5}\) data, \(g(t)\), acquired in the immediate past week are converted into the target function in the domain [\(-\pi\), \(\pi\)] as follows.  
\[f(t)=g\bigg{(}\frac{t+\pi}{2\pi}\times 7\times 24\bigg{)},\,t\in[-\pi,\pi], \tag{3}\]  
To Illustrate, Figure 6 shows the PM\({}_{2.5}\) series (during 13 October to 19 October 2019) approximation by using the Fourier polynomial of degree \(n\) equivalent to 50, 60, and 70, respectively. It can be observed that the Fourier polynomials with \(n=50\) and 60 give a better approximation than that with \(n=70\). To determine the best value of \(n\), we have explored several values for the Fourier polynomial degree and found that \(n=60\) provides the best result as will be shown by the experiments in Section 4.2.  
Figure 6: Illustration of PM\({}_{2.5}\) series approximation by using the Fourier polynomial of degree \(n\) equivalent to 50, 60, and 70, respectively.
## 2 Materials and Methods
### Automated Hematoma Segmentation Model  
* Training framework and dataset: using the Yale cohort dataset, we applied a 5-fold cross-validation framework [24; 25; 26] to train and optimize the CNN model for automated segmentation of hematomas on non-contrast head CTs. Then, optimized hyperparameters were used to train the model on the whole Yale dataset;
* Preprocessing of head CT: in the preprocessing step, we removed the skull, adjusted CT scan intensities to the brain window/level, and registered all scans to a common space. For the skull stripping process, we first applied a 0-to-200 HU intensity threshold to remove bony structures followed by a mathematical operation based on head morphology [27] to eliminate any remaining soft tissues using dilation-erosion operations--including binary_erosion(), remove_small_objects(), and binary_dilation()--and removing the boundary with the findContours() function, as described previously [28]. Then, we applied the brain window/level (center = 40 HU, and width = 80 HU), which provides optimal contrast for visualization of brain parenchymal pathologies. Finally, we registered all brain scans to a template, homogenizing the voxel spacing and size (128 \(\times\) 128 \(\times\) 128) [28];
* Segmentation model training: we used nnU-Net (no-new-Net) [29] as the backbone for our segmentation model. The nnU-Net (no-new-Net) model is a self-adaptive deep-learning framework designed for medical image segmentation. The model was developed to self-adapt new neural network architecture for each medical image segmentation task, a process that is often time-consuming and requires a grid search strategy. nnU-Net automatically configures itself based on the properties of the provided dataset, adjusting its architecture, pre-processing, and post-processing accordingly. The model has gained considerable popularity in the medical imaging community due to its high performance, ease of use, and proven applicability in a variety of imaging modalities and segmentation tasks. The model was trained in a 5-fold cross-validation process with data augmentation, weight_decay = 3 \(\times\) 10\({}^{-5}\), initial_lr = 1 \(\times\) 10\({}^{-2}\), num_epochs = 100, PolyLRScheduler, optimizer = SGD, and image input size = (128,128,128);Metrics of segmentation performance: we used two metrics to evaluate the hematoma segmentation model performance: the Dice similarity coefficient and the Hausdorff distance. The Dice coefficient [30] measures the volumetric overlap between segmentation results and the ground truth. The Hausdorff distance (HD) [31] measures the surface distance as the maximum distance of a segmentation mask to the nearest point in the ground truth. Details are described in the Supplementary Material.
## 2 Related Work
### Robotics
#### 2.1.2 Agriculture  
In agriculture, Ref. [16] designs a special gripper for picking apples from trees in apple orchards. Ref. [17] designs a smartphone-controlled spraying robot for pepper farms, which can not only help farmers reduce agricultural labor and solve the labor shortage problem, but also reduce people's direct contact with traditional chemicals that are very harmful to their bodies.
## 1 Introduction  
On vault in artistic gymnastics, the final score is calculated as the sum of the difficulty score (D-score) and the execution score (E-score) [1]. Therefore, in addition to a clean execution, gymnasts must perform vaults of sufficient difficulty in order to attain a top ranking in competitions. Higher D-scores are mainly associated with changes in body position (tucked, piked, and layout) or an increased number of rotations around the longitudinal or transverse axis during the second flight phase [2]. These factors generally involve greater flight time and specifically greater translational and/or rotational energy during the second flight phase.  
Although the three most common vault styles, handspring, Tsukahara (handspring with \(\
icefrac{{1}}{{4}}\) or \(\
icefrac{{1}}{{2}}\) turn in the first flight phase), and Yurchenko (round-off entry vaults (with \(\
icefrac{{1}}{{2}}\) turn) in the first flight phase) (Figure 1), have different technical and physical requirements, there is consensus that the run-up is the most important phase in order to generate the necessary energy on vault [3; 4; 5; 6; 7]. A high run up speed is strongly related to physical preconditions (20 m sprint speed, and explosive and reactive strength) [8] and therefore, besides training, genetics may play also a noteworthy role for the performance on vault [9].  
However, this energy needs to be transformed during take-off from the springboard and push-off from the table into optimal amounts of rotational (angular momentum), potential (time of flight), and translational energy needed for the intended salts and twistsduring the second flight phase. These amounts remain elusive. Although the vault is the most researched event of gymnastics [10], only one study reported transformations of energy (i.e., flux of energy) over the course of a vault [11]. Moreover, to the best of our knowledge, differences in energy parameters between simple and difficult vaults have never been investigated. However, such knowledge would help coaches assess the athletes' potential and to ensure their safety as they increase vault difficulty (i.e., number of rotations).  
Thus, the first aim of this study was to describe and compare the flux of energy (translational, rotational, potential, and total energy) of different Tsukahara and Yurchenko style vaults. The second aim was to compare energy flux between simple and difficult Yurchenko and Tsukahara style vaults.  
Therefore, we first hypothesized that for Yurchenko vaults, a substantial amount of total energy is generated with the preliminary round-off and second that the necessary amount of additional angular kinetic energy in order to perform a more difficult vault (+180\({}^{\circ}\) of rotation around longitudinal axis) is (almost) negligible.
## 2 Project Overview
### Engineering Geological Conditions
#### 2.4.2 Geological Conditions  
The bridge site lies in a region of relatively stable tectonic activity. Geophysical surveys indicate no Holocene active faults affecting bridge construction, confirming favorable regional stability. Comprehensive analysis suggests that major nearby faults will not directly impact the project's stability.  
According to exploration data, the exposed strata consist entirely of Quaternary unconsolidated deposits. The shallow strata comprise Holocene alluvial silty clay, silt, and fine sand, underlain by Late Pleistocene medium sand and coarse sand. Based on borehole data from critical strata and pumping tests, the physical and mechanical parameters of the soil layers are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Soil Layer** & **Thickness** & **Unit Weight** & **Cohesion** & **Friction** & **Elastic Modulus** \\  & **(m)** & **(kN/m3)** & **(kPa)** & **Angle (°)** & **(MPa)** \\ \hline
1 & Silty Clay & 2.19 & 18.40 & 12.8 & 7.9 & 12.72 \\
2 & Silt & 3.10 & 19.00 & 3.1 & 27.9 & 32.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of soil layers.
## 3 An Indirect Measurement Method for Gas Consumption
### Key Technologies
#### 3.2.3 Fire State Changing Detection  
When the user adjusts the fire knob of the gas stove, the gas flow velocity in the gas pipeline will jump and the gas pressure will fluctuate steeply. Figure 7 shows the fire state changing detection results for four different situations. The fluctuation range of the pressure signal caused by the user adjusting the gas fire power is usually 50 Pa\(\sim\)300 Pa, so the identification of the user's adjusting fire action can be realized by detecting the jump of pressure output value of the DPS310 sensor. The test results show that the pressure of the outlet of the gas meter fluctuates about 40 Pa when the user adjusts the fire minimally. However, the peak and valley values of the periodic signal waveforms are between 10 Pa and 50 Pa, so it is necessary to set the characteristic limit conditions to distinguish the jump of the fire conversion from the jump of periodic signal. The flow chart of the fire conversion point recognition algorithm is shown in Figure 8.  
Figure 7: The detecting result of the fire conversion points. (**a**) The first situation with 6 large degree fire conversions; (**b**) The second situation with 5 large degree fire conversions; (**c**) The third situation with 4 small degree fire conversions; (**d**) The fourth situation with 2 small degree fire conversions.  
As depicted in Figure 8, if the absolute value of the difference between the pressure value of the current sampling point and the previous point is greater than 25 Pa, it will temporarily be regarded as a fire conversion point. However, in order to avoid the judgment error due to the noise signal, this method judges the difference between the mean value of the first 5 points to the first 3 points of the above conversion point and the mean value of the last 3 points to the last 5 points of that conversion point.
## 5 Experimental Results and Discussion
### Response Surface Modelling  
According to the experimental results, the stroke error \(\Delta S\), the RMS values of the input force \(F_{F(\text{rms})}\) and \(F_{B(\text{rms})}\), and the input force at the toggle position \(F_{TP}\) can be considered as quantitative indices for evaluating the operating performance of the constructed mold/die clamping mechanism. To this end, statistical regression models for estimating the four quantitative indices, \(\Delta S\), \(F_{F(\text{rms})}\), \(F_{B(\text{rms})}\), and \(F_{TP}\), caused by the two independent variables (\(\Delta\)\(\pi_{8}\), \(v\)) must be established. By using the well-known response surface methodology (RSM) [31; 32; 33; 34; 35; 36] with a modified cubic model [36] and the mean values of the four terms listed in Tables 3-8, four approximate functions with respect to \(\Delta\)\(\pi_{8}\) and \(v\) can be obtained as  
\[\Delta S(\Delta\)\(\pi_{8},v) \approx 0.3723+0.0046v-0.1098\Delta\)\(\pi_{8}+0.0257v\Delta\)\(\pi_{8}-0.0000794v^{2}\) (44) \[+0.1225\Delta\)\(\pi_{8}^{2}-0.000664v^{2}\Delta\)\(\pi_{8}-0.0028v\Delta\)\(\pi_{8}^{2}\) (45) \[F_{F(\text{rms})}(\Delta\)\(\pi_{8},v) \approx 27.1115-0.6196v-138.5438\Delta\)\(\pi_{8}+3.3977v\Delta\)\(\pi_{8}+0.0225v^{2}\) (46) \[+335.6090\Delta\)\(\pi_{8}^{2}-0.0269v^{2}\Delta\)\(\pi_{8}-1.8854v\Delta\)\(\pi_{8}^{2}\) (47) \[F_{B(\text{rms})}(\Delta\)\(\pi_{8},v) \approx 81.7199-4.6574v-311.1964\Delta\)\(\pi_{8}+13.5623v\Delta\)\(\pi_{8}+0.1032v^{2}\) (48) \[+429.60\Delta\)\(\pi_{8}^{2}-0.1994v^{2}\Delta\)\(\pi_{8}-5.0464v\Delta\)\(\pi_{8}^{2}\)\[F_{TP}(\Delta r_{8},v) \approx 7.6677-1.0773v-1.2923\Delta r_{8}+3.5459v\Delta r_{8}+0.0240v^{2} \tag{47}\] \[+361.5857\Delta r_{8}^{2}-0.0587v^{2}\Delta r_{8}+2.5694v\Delta r_{8} ^{2}\]  
The norms of residuals of the four approximate models are reasonably small. Subsequently, according to Equation (36), the output error of the upper sub-mechanism (as shown in Figure 5) induced only by \(\Delta r_{8}\) is  
\[\Delta L_{8}=\Delta r_{8}\sec\theta_{8} \tag{48}\]  
When \(\Delta r_{8}\geq 0\) and \(-90^{\circ}<\theta_{8}<+90^{\circ}\) are considered, and the mechanical errors of other link members are ignored, then from Equations (38)-(41), \((\Delta L_{\rm wor})_{L}=(\Delta L_{\rm rss})_{L}=0\) and \((\Delta L_{\rm wor})_{U}=(\Delta L_{\rm rss})_{U}=\Delta L_{8}\) may be obtained. Thus, the evaluation indices shown in Equations (42) and (43) are  
\[\delta L_{\rm wor}=\delta L_{\rm rss}=\Delta L_{8} \tag{49}\]  
If only the toggle position is considered, a simplified evaluation index termed \(\delta L_{TP}\) can be defined as  
\[\delta L_{TP}=\Delta L_{8}(x_{TP})=\Delta r_{8}\sec\theta_{8}(x_{TP}) \tag{50}\]  
in which, \(x_{TP}\) is the position of the input link when the mold/die clamping mechanism is in the toggle position. For the presented case study and the constructed prototype, \(x_{TP}\) is 51.566 mm and \(\theta_{8}(x_{TP})\) is \(-2.729^{\circ}\).
## 2 Materials and Methods
### Deep Learning Approach
#### 2.4.2 Convolutional Neural Networks CNN
\[f(x)=x.\mathit{sigmoid}(x)=\frac{x}{1+e^{-x}} \tag{35}\]  
**-Leaky ReLU**: It was introduced to address the issue of \"dead neurons\" in the rectified linear unit (ReLU) by adding a small negative slope to keep weight updates active throughout the entire propagation process [43]. The parameter \(\alpha\) was then introduced to ensure that gradients never reach zero during training. LReLU calculates the gradient with a low fixed value for the negative gradient within a range of 0.01, so the LReLU function is calculated as below. \[f(x)=\alpha x+x= \begin{cases}x&\quad\text{if}\quad\quad x>0\\ \alpha x&\quad\text{if}\quad\quad x\ \leq 0\end{cases}  \tag{36}\]  
**-Exponential Linear Units (ELUs)**: ELU is a variant of the rectified linear unit (ReLU) proposed by [PERSON] et al. [44]. It is used to speed up deep neural network (DNN) training and has the advantage of overcoming the vanishing gradient problem by using the identity function for positive values and enhancing learning features. ELUs also have negative values that push the average unit activation closer to zero, reducing computational complexity and improving training speed. ELUs are a good alternative to ReLUs because they can reduce bias shifts by driving the average activation toward zero during training. The ELU is expressed as:  
\[f(x)= \begin{pmatrix}x,&\quad\text{if}\ x>0\\ \alpha\text{exp}(x)-1,&\quad\text{if}\ x\leq 0\end{pmatrix}  \tag{37}\]  
The gradient or derivative of the ELU equation is provided as follows:  
\[f^{\prime}= \begin{pmatrix}1,&\quad\text{if}\ x>0\\ f(x)+\alpha,&\quad\text{if}\ x\ \leq 0\end{pmatrix}  \tag{38}\]  
where hyperparameter \(\alpha\) = ELU monitors the point of saturation for the negative net inputs and is typically set to 1.0. There is a well-defined saturation plateau in the negative mode of the ELU, which allows learning robust representations and provides faster learning and higher generalization than LReLU and ReLU with a particular network structure, especially over five layers, and ensures state-of-the-art results regarding the ReLU variants. However, a critical restriction of the ELU is it fails to center the values at zero as well as the parametric ELU was suggested to solve this problem [45]. Table 4 provides a detailed comparison of the different activation functions used in this study, including their advantages and disadvantages.
## 5 Simulation Results and Discussion
### Evaluation in Two-Area Four-Machine System  
The system diagram presented in Figure 5 is implemented in the simulation. The 2-area 4-machine system with wind farm is used to verify the control effect of the damping controller designed. Area 1 and area 2 have two synchronous generators each, and each generator produces 835 MW rated power. The transmission power between the two areas is 408 MW. Only generator 4 is equipped with a PSS to increase the damping of its local oscillation. The parameters of the synchronous generators are shown in Table A1 of Appendix A. Wind farm based on DFIG is connected to the grid in area one and represented by one aggregated DFIG. The rated exporting level of the wind farm is 200 MW. The DFIGs operate in the maximum-power tracking active-control mode, and the reactive power adopts the unit power factor control mode. The specific parameters of the DFIG are shown in Table A2 of Appendix B.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 5 Summary of Research Findings and Practical Implications
### Real-World Implications  
The findings of this study have significant implications for real-world applications, particularly in scenarios where heterogeneous networks are essential. For instance, DYMO's superior performance in handling mixed-device networks makes it a strong candidate for disaster recovery operations, where communication between devices with varying capabilities (e.g., drones, smartphones, and laptops) is critical. Similarly, in military field operations, DYMO's ability to maintain low latency and high throughput under dynamic conditions could enhance mission-critical communication. Furthermore, the protocol's adaptability to varying node mobility and transmission rates makes it suitable for IoT deployments in smart cities, where devices with diverse energy and computational capabilities must interact seamlessly. These real-world applications underscore the practical relevance of our findings and highlight the potential for DYMO to address communication challenges in heterogeneous environments.