esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Modification Mechanisms and Sample Preparation  
#### 2.2.1 Mechanisms of Modification of Epoxy Bitumen with SBR and SBS  
The modification of epoxy bitumen with SBR and SBS improves its overall performance by enhancing both high and low-temperature properties, elasticity, and durability. The primary modification mechanism involves the interaction between the polymer (SBR/SBS) and epoxy resin, which forms a crosslinked polymer network. The styrene groups in SBR and SBS improve the compatibility with the epoxy resin, while the butadiene segments enhance the flexibility and low-temperature crack resistance of the material. SBS, a block copolymer, further contributes to high-temperature performance by forming a crosslinked structure that increases stiffness and rutting resistance. Additionally, the polymer chains provide a \"toughening\" effect, enhancing elongation at break and low-temperature flexibility. The incorporation of SBR and SBS also reduces the aging rate of the binder, improving its durability and resistance to oxidation. At higher concentrations, phase separation can occur within the modified bitumen, leading to improved mechanical properties by controlling the dispersion of the polymer and asphalt phases. This modification mechanism allows for the tuning of the material's performance, providing a balance between high-temperature stability, low-temperature flexibility, and long-term durability.
# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 3 Localization of Mobile Robot in Ongoing Scene
### Localization Based on Artificial Landmarks in an Ongoing Scene
#### 3.1.2 Localization of Ongoing Scene Based on Reflectors  
Due to factors of sensor noise and differences in numbers of features, the paper adopts the repeated measurement value to calculate the positions of reflectors. Therefore, the nearest distance between the Lidar and the surface of the \(i\)-th reflector column \(\rho_{i}\) is expressed as follows:  
\[\rho_{i}=d_{i}-D_{r}/2,(i=1,2,3) \tag{7}\]  
When the number of reflectors that scanned by the Lidar is greater than or equal to three, the three-sided measurement is used to realize the localization of mobile robot. In addition, [13] concludes that the more artificial landmarks are set in the indoor environment, the localization accuracy of mobile robot will be higher. However, when the number of artificial landmarks is set too much, the difficulty in identifying them increases and the anti-interference ability will be weakened accordingly. Therefore, in this paper, four reflectors are evenly placed in the ongoing scene, and the trilateration method is used to complete the localization of mobile robot. The parameter definition of Figure 4 is\[\left\{\begin{array}{l}\left(x_{r1}-x^{\prime}\right)^{2}+\left(y_{r1}-y^{ \prime}\right)^{2}=\left(\rho_{1}+D_{r}/2\right)^{2}\\ \left(x_{r2}-x^{\prime}\right)^{2}+\left(y_{r2}-y^{\prime}\right)^{2}=\left( \rho_{2}+D_{r}/2\right)^{2}\\ \left(x_{r3}-x^{\prime}\right)^{2}+\left(y_{r3}-y^{\prime}\right)^{2}=\left( \rho_{3}+D_{r}/2\right)^{2}\end{array}\right.. \tag{8}\]  
From above equations, the position of Lidar in the global coordinate system is set as \((x^{\prime},y^{\prime})\). Also, due to the fitting circles formed by the reflector cannot be accurately intersected at one point, the least square method is adopted to reduce the intersection errors of circles. As a result, the optimal calculation result approximates the theoretical value. At this time, the estimated coordinate of Lidar is calculated as  
\[x^{\prime}=\left(\left(\rho_{1}+D_{r}/2\right)^{2}-\left(\rho_{2}+D_{r}/2 \right)^{2}+x_{r2}^{2}\right)/2x_{r2} \tag{9}\]  
\[y^{\prime}=\left(\left(\rho_{1}+D_{r}/2\right)^{2}-\left(\rho_{3}+D_{r}/2 \right)^{2}+x_{r3}^{2}+y_{r3}^{2}-2x^{\prime}x_{r3}\right)/2y_{r3} \tag{10}\]  
\[\mu=\frac{1}{m}\sum\
olimits_{t=1}^{m}arctan(y_{t}{}^{\prime}/x_{t}{}^{ \prime}),t=1,2,\cdots,m. \tag{11}\]  
Finally, the global coordinate of the Lidar in Figure 4 is set in the same direction to the robot. Therefore, the rotation matrix and translation vector are separately as zero and T, and the pose of the global coordinate system for mobile robot is  
\[\left[\begin{array}{c}x_{R}\\ y_{R}\end{array}\right]=\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\end{array}\right]+T. \tag{12}\]  
From above Formula (12), the pose of mobile robot is estimated.
## 2 Method
### CTC-Attention Based Multi-Task Learning  
Taking advantage of explicitly using the history of the target character without any conditional independence assumptions, the attention-based encoder-decoder framework has often been shown to improve the performance over many sequence-to-sequence tasks. To mitigate misalignment between the speech and labels, a joint CTC-attention model within the multi-task learning framework was put forward in [23].  
The E2E model maps \(T\)-length feature sequence \(X=\{x_{t}\in\mathbb{R}^{D}\mid t=1,2,\ldots,T\}\) to an \(L\)-length label sequence \(\mathcal{C}=\{c_{l}\in\mathcal{U}\mid l=1,2,\ldots,L\}\), where \(D\) is the dimension of feature and \(\mathcal{U}\) is a set of distinct labels. The encoder is typically Bidirectional Long Short-Term Recurrent (BLSTM) layers shared by both attention and CTC networks. The hidden vector \(h_{t}\) at input feature index \(t\) is derived by an encoder when modeling temporal dependencies of the input sequence:  
\[h_{t}=\text{Encoder}(X). \tag{1}\]  
In the attention-based encoder-decoder method, a weighted summation is performed on \(h_{t}\) as shown in Equation (2):  
\[r_{l}=\sum_{t=1}^{T}a_{lt}h_{t}, \tag{2}\]  
where \(a_{lt}\) is obtained from a content-based attention mechanism using previous decoder state \(q_{l-1}\):  
\[a_{lt}=\text{ ContentAttention }(q_{l-1},h_{t}). \tag{3}\]  
An LSTM-based decoder network predicts the next label based on \(r_{l}\) and the previous prediction. The whole architecture is optimized by a multi-task objective function with joint CTC and attention loss as follows:  
\[\mathcal{L}=\lambda\log p_{ctc}(\mathcal{C}\mid X)+(1-\lambda)\log p_{att}^{ \dagger}(\mathcal{C}\mid X), \tag{4}\]  
where \(0\leq\lambda\leq 1\) is a trade-off parameter set in advance. During the decoding phase, the joint model uses a label-synchronous beam search scheme which jointly predicts the next character. Given the input feature sequence \(X\), the most probable label sequence is computed as:  
\[\hat{\mathcal{C}}=\arg\max_{C\in\mathcal{U}^{*}}\{\lambda\log p_{ctc}( \mathcal{C}\mid X)+(1-\lambda)\log p_{att}(\mathcal{C}\mid X).\} \tag{5}\]
# Passive Sampling and Analysis of Naphthalene
## 1 Introduction
### Naphthalene Toxicity  
Naphthalene is a PAH with the simplest 2-ring structure, and is often used as a model and target compound studied in this group. Naphthalene is classified as a \"possible\" human carcinogen [2].  
Acute exposure to naphthalene by inhalation, ingestion and dermal contact leads to a headache, nausea, vomiting, diarrhea, malaise, confusion, anemia, jaundice, convulsions, and coma [2,3]. Chronic exposure to naphthalene by inhalation or skin contact caused cataracts and retinal hemorrhage [2, 3, 4]. Reference Concentration for chronic inhalation exposure (Rfc) of naphthalene is \(3\times 10^{-3}\) mg\(\cdot\)m\({}^{-3}\) (0.57 ppbv) [5,6]. The Rfc is defined by U.S. EPA as a chemical concentration of continuous inhalation exposure that is likely not causing any significant risk of detrimental health effects associated with carcinogenic substances.
## 2 Materials and Methods
### Training Subsets
#### 2.1.2 eXtreme Gradient Boosting, XGBoost  
XGBoost is a versatile ensemble learning algorithm that extends boosting to accommodate any differentiable objective function as defined in Equation (3).  
\[F=\frac{1}{N}\sum_{i=1}^{N}{e_{i}}^{2}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{ i})^{2} \tag{3}\]  
where \(N\) is the total number of samples and \(y_{i}\) and \(\hat{y}_{i}\) represent the observed and predicted scintillation events, respectively. Equation (3) describes the input and output relationship between the parameters. The process starts generating a tree that is being trained on the input data. Then for \(i=1,2,3,\ldots,N\) the gradient can be computed as  
\[-d_{in}=-\bigg{[}\frac{\partial F}{\partial G\left(x_{i}\right)}\bigg{]}G=G_{m-1} \tag{4}\]  
The negative gradient represents the residual between the actual and predicted scintillation events. Shrinkage is a regularization technique used by the improved gradient-boosting algorithm XGBoost to prevent overfitting. This is achieved by applying a scale factor to each tree given by  
\[G_{m}=F_{m-1}(x_{i})+V\cdot\sum_{y=1}^{j_{m}}\beta_{jm}\big{(}x_{i}\ \in\ R_{jm}\big{)} \tag{5}\]  
where \(V\) is the learning rate.  
Hyperparameter tuning is a crucial aspect of ensemble model optimization, as the computational time and model accuracy depend heavily on the choice of hyperparameters. In this study, we employed a grid search strategy to systematically explore a predefined range of hyperparameter values, including the learning rate, maximum depth, and the number of estimators. After conducting multiple rounds of training and testing, the optimal combination of hyperparameters was selected based on performance metrics such as prediction accuracy and computational efficiency, ensuring the best possible model generalization and performance. The optimal hyperparameters utilized in this study are detailed in Table 1.
## 3 Numerical Results
### Power Performance and Load Simulation
It can be seen that \(C_{p}\) is similar in all the four scenarios whereas introducing the shear and yaw slightly reduces \(C_{p}\) especially when \(C_{p}\) becoming more stationary, i.e., after 110 s. The reduction of the mean \(C_{p}\) is mainly due to the yaw misalignment, where the axial wind speed is smaller. It also should be noted that the scenarios with a yaw angle, \(C_{p}\) has more periodical fluctuations due to the yaw angle. A similar trend like \(C_{p}\) can be found in the thrust coefficient \(C_{T}\), shown in Figure 10b, where the reduction of \(C_{T}\) is realized by the shear and yaw. The most significant difference happened in the scenario with both yaw and wind shear. Moreover, it can be observed that the difference between the yaw versus the yaw plus shear scenarios is relatively small. The reduction of \(C_{T}\) in Case 2, Case 3 and Case 4 is due to the reduction of the axial wind speed caused by the yaw. By implementing the pitch adjustment, more fluctuations in Figure 11 are observed in the \(C_{p}\) and \(C_{T}\) signals while the mean \(C_{p}\) and \(C_{T}\) almost remain the same. By introducing the control methods, the fluctuation magnitudes of both control strategies increase, especially in control technique I. Some jumps of \(C_{p}\) and \(C_{T}\) values occur in the transient period as well. Moreover, the transient periods when performing both control techniques are longer, as compared to no control. For control technique I, the unexpected large fluctuations in \(C_{T}\) and \(C_{p}\) might be explained by the large pitch changes at each time step. For control technique II, similar results are observed when the fluctuation magnitudes are much smaller. The possible explanation is that there are fewer input pitch values and the corresponding interpolation enables better convergence of the elastic code. Figure 10: \(C_{p}\) and \(C_{T}\) without control strategy: (**a**) power coefficient; (**b**) thrust coefficient. Figure 9: Pitch in a cross-section of 35.24 m (90%R) with and without control strategy: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4.
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Mangostin Isomers and Derivatives
#### Apoptosis Phenomenon
The compound showed cytotoxic activity: decreased cell viability and proliferation, induction of apoptosis, and reduction in adhesion at concentrations lower than 10 \(\upmu\)M (the **IC\({}_{\text{50}}\)** values were **6.43**, **9.59**, and **8.97 \(\upmu\)M** for **SCC-15**, **U-118 MG,** and **BJ**, respectively) [45]. Moreover, our research showed that \(\alpha\)-Mangostin strongly upregulated the expression of pro-apoptotic Bax in comparison to antiapoptotic Bcl-2 [4]. Figure 2: Prenylated xanthone derivatives from _Garcinia mangostana_ pericarp. \(\alpha\)-Mangostin significantly suppresses cell proliferation, induces cell cycle arrest, and triggers apoptosis in hepatocellular carcinoma HCC cell lines such as **HepG2**, **SK-Hep-1**, **Huh7**, and **SMMC-7721**. It was also found that the compound inhibits tumor growth in nude mice bearing HepG2 or SK-Hep-1 xenografts. The mechanism of a potent \(\alpha\)-Mangostin anti-HCC effect is in the blocking of the STAT3 signaling pathway by means of suppression of the degradation of SHP1 induced by the ubiquitin-proteasome pathway [46]. \(\gamma\)-Mangostin was reported to strongly inhibit **dioblastoma multiforme** cell proliferation. It is suspected to mediate cytotoxicity via apoptosis in **U87 MG** and **GBM 8401** cell lines [47]. \(\gamma\)-Mangostin exhibits **IC50** values **74.14** and **64.67 \(\upmu\)M** in **U87 MG** and **GBM 8401** cell lines, respectively. These values are significantly lower than for the clinical chemotherapeutic agent Carmustine (BCNU), with **IC50** values **632.1** and **346.6 \(\upmu\)M** in **U87 MG** and **GBM 8401** cell lines, respectively. Considering the mechanism of action in **U87 MG** and **GBM 8401**, cells treated with \(\gamma\)-Mangostin were observed: condensed cells and hypodiploid cells that carry on several intracellular reactive oxygen species (ROS) production and mitochondrial dysfunction [47]. There are also results from in vivo studies available. **Panaxanthone (\(\alpha\)-Mangostin, 80-90%; y-Mangostin, 10-20%)** was administrated orally to mice every day by gavage with doses of 20 and 40 mg/kg for 30 days. The NK (natural killer) cell activity was determined by measuring LDH after the incubation of **YAC-1 cells** (target cells) and splenocytes (effector cells) at a ratio of 1:50. The activities of both groups were significantly elevated (control group 0 mg/kg). A significant increase in the NK cell activity by Panaxanthone was also observed in the human pilot study on healthy people at a dose of 150 mg/day per person for 7 days [39].
## 4 Experiments and Results
### Comparison with the Existing Correction Model
This could be because of the higher side SWV observations estimated from the HFM in Figure 14, which results in the tomography solutions being closer to the radiosonde measurements. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**STD**} & \multicolumn{3}{c}{**Bias**} \\ \cline{2-10}  & **Max.** & **Min.** & **Mean** & **Max.** & **Min.** & **Mean** & **Max.** & **Min.** & **Mean** \\ \hline Scheme I & 3.36 & 0.50 & 1.59 & 3.37 & 0.45 & 1.60 & 0.55 & \(-\)1.26 & \(-\)0.26 \\ Scheme II & 2.42 & 0.40 & 1.28 & 2.30 & 0.41 & 1.25 & 0.66 & \(-\)1.33 & \(-\)0.14 \\ Scheme III & 1.98 & 0.28 & 1.08 & 2.00 & 0.29 & 1.07 & 0.87 & \(-\)0.55 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics (RMSE, STD, and bias) of the tomography results for the three schemes (unit: g/m\({}^{3}\)). Figure 17: The mean differences between the tomographic results and radiosonde data along the radiosonde vertical columns from DOY 213 to DOY 243, 2017. (**a**) Scheme I; (**b**) Scheme II; and (**c**) Scheme III.
###### Abstract  
The purpose of this study was to analyze the incidence and risk factors of possible inferior alveolar nerve (IAN) injury after extraction of the mandibular third molars. A total of 6182 patients were examined for 10,310 mandibular third molar teeth. Panoramic radiography and patients' medical records were used to analyze age, gender, and impaction pattern of the mandibular third molar. Cone beam computed tomography (CBCT) was used to investigate the detailed pathway of the inferior alveolar nerve and evaluated the presence of symptoms of nerve damage after tooth extraction. In CBCT, 6283 cases (61%) of the inferior alveolar nerve were actually in contact with the root of the mandibular third molar. The correlation with the panoramic signs of root darkening (\(p<0.001\)), root deflection (\(p<0.001\)), interruption of the IAN (\(p<0.001\)), diversion of the IAN (\(p<0.001\)), and narrowing of the IAN (\(p<0.001\)) had statistical significance. Of the 4708 patients who underwent surgical extraction, 31 (0.658%) complained of nerve damage. Among them, 30 patients (0.637%) complained of symptoms of inferior alveolar nerve damage, and 1 patient (0.02%) complained of symptoms of lingual nerve damage. There was a significant correlation with IAN injury in cases where the roots became dark at the IAN area (\(p=0.018\)) and there was diversion of the IAN at the root area (\(p=0.041\)). When the narrowing of the IAN and the lingual driving pathway of the inferior alveolar nerve appeared simultaneously in CBCT, the risk of IAN injury was high.  
third molar; inferior alveolar nerve; third molar extraction; impacted mandibular third molar; CBCT +
Footnote †: journal: Applied Sciences
## 4 Experiment
### Effect of Response Delay and Emotional Expression
\begin{table}
\begin{tabular}{l l l l} \hline
**Item** & **Response Delay** & **Emotional Expression** & **Interaction** \\ \hline Animation & 0.917 & 0.298 & 0.093 \\ Conversation skill & 0.794 & 0.192 & 0.270 \\ Information accuracy & 0.529 & 0.027 * & 0.581 \\ Interactivity & 0.119 & 0.237 & 0.287 \\ Interface & 0.771 & 0.649 & 0.062 \\ Naturalness & 0.336 & 0.474 & 0.683 \\ Responsiveness & 0.026 * & 0.069 & 0.038 * \\ Satisfaction & 0.938 & 0.027 * & 0.485 \\ Usability & 0.781 & 0.383 & 0.122 \\ Visual aspect & 0.834 & 0.273 & 0.258 \\ \hline \end{tabular}
\end{table} 
Table 11: Results (\(p\)-values) of the statistical test (ANOVA) for the evaluation of conversation. We used the response delay (500, 1000, 1500, and 2000 ms) and the emotional facial expression (yes or no) as factors. An asterisk (*) means 5% significance. Figure 7: The evaluation result of “information accuracy” with and without the agent’s emotional expression. The error bars indicate the standard error. The effect of emotional expression was statistically significant (\(p=0.027\)) but that of response delay was not (\(p=0.581\)). [PERSON] et al. reported that inserting a filler in parallel with the response generation process improved the users' impression of the system's responsiveness [54]. In their experiment, the physical response delay was actually shorter than the system without filler insertion; on the other hand, the response delay time of our system was constant regardless of the conditions. The reason the perception of the response delay changed is still unclear. One possible reason is that the users paid more attention to the agent's facial expression, which made the perception of the response delay less sensitive.
## 3 Results and Discussion
### Grid Voltage Values Recorded at Each PV Installation during the Monitoring Period
The distribution of \(V_{AC}\) values at each PCC was around 240 V in all the installations, except in the case of installation 3, where the values were lower, at around 220 V. The outliners that appear in Figure 14 (any values outside of the whiskers), show on the one hand, voltage values higher than the upper horizontal black lines, which correspond to the higher values recorded in each installation; and on the other hand, voltage values below the lower horizontal black lines are shown, which are point data that basically correspond to instants of time in which, after a fault, the inverter returns to operation but is not yet injecting energy into the grid. Therefore, these are not real grid voltage data but a consequence of how the inverter measures during reconnection after a fault period. If the grid voltage is outside the allowed range, the inverters have mechanisms to disconnect from the grid. The highest grid voltage values were recorded in installation 2, which, as will be seen later, is where the greatest increase in voltage values occurs with the production of the PV plant; and despite the presence of this general increase in grid voltage values when there is power injection into the grid from the PV installations, only a couple of values recorded during the three years of operation in the different plants analysed are outside the ranges permitted by the regulations for this parameter.
## 4 Experiments and Results
### Results
#### 4.4.3 Comparative Experiments between Different Attentional Mechanisms  
By giving distinct weights to different aspects, the attention mechanism can effectively increase the accuracy of medical picture categorization. This allows the model to focus more on those features. By analyzing the current state-of-the-art attention mechanisms, this research sought to determine the best scheme for the model to apply to the interclass and picture learning modules. This paper compared methods for the image learning module, such as CBAM, self-attention mechanism [25], spatial attention with channel attention removed [26], and SGE. For the interclass learning module, this paper compared ECA-Net [27] and existing improved methods by [PERSON] et al. [28] based on SE-Net. Table 5 displays the results of the experiment. Various attention mechanisms were found to increase the accuracy of the model. The most successful method was the combination of the CBAM and the SE-Net-improved method, which maximized the consideration of the features on the graph space and more effectively captured the important information in the image, thereby improving the model's performance.
## 2 QTL Mapping Problem Description
### Genetic Algorithms QTL Solution Methods  
Genetic Algorithms (GAs) are a subset of evolutionary algorithms that have been proved effective in solving QTL mapping problem. [PERSON] _et al_. [21] proposed a GA-based approach for searching the QTL space and demonstrated that for smaller problem sizes their method's accuracy was comparable to an exhaustive search for locating QTL. [PERSON] and [PERSON] [22] suggested a hybrid genetic algorithm to optimize a fitness function relating genetic markers to quantitative traits in F2 mice. [PERSON] _et al_. [23] used GA for haplotype reconstruction in locating QTL where they demonstrated an improvement in efficiency and reliability compared to SimWalk 2 [24].