esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 2 Problem Statement, Notations, and Assumptions
### Heterogeneous Traffic Flow  
Based on the toll collection methods, the toll station faces a mixed traffic flow composed of CAVs, MTC-HVs, and ETC-HVs. Let the set \(\mathbf{J}=\{1,2,3\}\) represent vehicle categories, indexed by \(j\); \(p_{di}\) denotes the proportion of vehicle type \(j\) in direction \(d\), satisfying the constraints:  
\[\sum_{j\in\mathbf{J}}p_{j}=1, \tag{3}\]  
\[p_{j}\geqslant 0,\quad\forall j\in\mathbf{J}. \tag{4}\]  
ETC-HVs have flexibility, allowing them to use either ETC or MTC lanes. Given the short queuing sections at the toll station, ETC-HV users have full visibility of the toll station's traffic conditions prior to lane selection and can leverage their experience to choose the lane that minimizes their passage time. We assume that all ETC-HVs are fully informed about the traffic conditions in the toll station queuing system and are rational in selecting the lane to minimize their passage time. Let \(\theta_{di}\) represent the proportion of ETC-HVs choosing lane type \(i\) in direction \(d\), with the constraints:  
\[\sum_{i\in\mathbf{I}}\theta_{di}=1,\quad\forall d\in\mathbf{D}, \tag{5}\]  
\[\theta_{di}\geqslant 0,\quad\forall d\in\mathbf{D},i\in\mathbf{I}. \tag{6}\]  
In practice, both CAVs and MTC-HVs aim to reduce travel costs by selecting optimal lanes. However, for CAVs on MTC lanes and MTC-HVs on ETC lanes, the travel cost is considered prohibitive. Consequently, CAVs, which rely on automation, are restricted to ETC lanes, while MTC-HVs without ETC equipment must use traditional manually tolled lanes.  
Let \(\lambda_{d}\) denote the vehicle arrival rate in direction \(d\). For a specific type of lane in a given direction, the serviced vehicles include those restricted to that lane type (such as CAVs or MTC-HVs) and a portion of ETC-HVs choosing that lane type. This relationship can be expressed with the following formula:  
\[\lambda_{di}=(p_{i}+p_{3}\cdot\theta_{di})\lambda_{d},\quad\forall d\in\mathbf{D}, i\in\mathbf{I}. \tag{7}\]  
Additionally, to establish a controlled environment in this study, we assume that traffic demand at each toll station in both directions (including vehicle arrival rates and type proportions) is predetermined and fixed, unaffected by changes in lane allocation strategies.  
To enhance readability, the notations used in this study are listed in Table 2.
# Space Weather Effects on Satellites
###### Abstract  
The study presents a concise overview on the main effects on satellites due to space weather drivers compared to the well-known interplanetary, magnetospheric and ground-based consequences. The solar-activity-driven influences include specific physics-based effects on the spacecraft surface and on-board electronics due to electromagnetic emission and energetic particles as well as complex effects due to geomagnetic storms which may endanger the mission performance and spacecraft longevity. We select as test examples the Starlink satellites in the period 2019-2022 and present the temporal correspondence between their launches and the space weather phenomena. Based on comparative analysis, we discuss whether the occurrence vs. the intensity of solar and interplanetary drivers of space weather can be considered as a cause for orbital stability problems and satellite loss. The results suggest that a sequence of geomagnetic disturbances together with multiple weak space weather events could lead to severe levels of atmospheric drag ending in a service or satellite loss.  
space weather; geomagnetic storms; satellite; orbit stability; satellite sub-system failure; economic losses +
Footnote †: journal: _Astronomy_  
0000-0002-2300-3820][PERSON]
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### BOSS Algorithm Comparison with Traditional Image Segmentation Algorithms
#### 3.4.2 Comparison Results
By a rapid inspection of the figures, it is possible to assess that LTA and LTS show similar segmentation outputs, and the SEUs which cross the detector almost tangentially are segmented as streaks. They are segmented by the LTA and LTS algorithms, while they are removed by BOSS. This is due to the dataset used to train the U-Net algorithm. The NN learned to detect just the the brightest star-like objects, filtering all the less salient other ones. Here, the weak streaks of the SEU are removed, but this does not mean that all the star-like objects which appear in the BOSS prediction of Figure 14 are stars; they can be that part of SEUs with a high impact angle. These examples show that the BOSS algorithm has performances that are slightly lower but acceptable and generalized if compared to the calibrated traditional algorithms. Moreover, it has an intrinsic robustness to SEUs which a traditional algorithm does not normally have.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 5 Simulation Results  
In this Section 5, the proposed scheme is compared with the existing user-serving scheme in the user-centric cell-free massive MIMO model and the original cell-free model. For all simulations, it is assumed that APs serve as many users as the number of installed antennas and the length of the orthogonal pilot sequence is equal to the number of users. Table 2 shows the simulation parameters used in this paper.
## 2 Experimental Programs  
### Specim Details  
In order to evaluate the seismic performance of a precast two-column bent connected with both grouted splice sleeves and grouted central tenon, three two-column bents were fabricated. Table 1 lists the specifics of these bent specimens, including a cast-in-place bent (B2-CIP), a precast bent solely employed with \"grouted splice sleeves\" at the joint (B2-PGS), and a precast bent employed with a combination of \"grouted splice sleeves and grouted central tenon\" at the joint (B2-PGT).  
Dimensions and reinforcing details are presented in Figure 1. All three specimens shared identical geometric dimensions and reinforcing details but different connection types. The columns were reinforced with fourteen 16-mm-dia. HRB400 rebar, leading to a reinforcement ratio of 1.02%. Stirrups adopted 8-mm-dia. HPB300 hoops of a 75 mm spacing, resulting in a 1.15% volumetric transverse reinforcement ratio. The footings and cap beams were reinforced with 20-mm-dia. rebar to ensure their structural integrity would be maintained until the destruction of the columns.  
Joint connection details of the two precast bents are shown in Figure 2. In the cap-column joint, grouted splice sleeves were embedded within the cap beam, whereas at the column-footing joint, they were installed at the bottom of the column. Additionally, a 200-mm-long unbonded zone of longitudinal rebar was set below the sleeves to avoid premature failure caused by stress concentration at the joint. For specimen B2-PGT, in addition to using the same grouting splice sleeves' connection scheme as B2-PGS, a grouted central tenon was also utilized in the cap-column and column-footing joint, forming the proposed GSS-GCT connection.  
The grouted central tenon consisted of rebar and a cylindrical steel sleeve. For the column-footing joint, the tenon was reinforced with six 16-mm-dia. HRB400 rebar, whose bonded length was 650 mm inside the footing and 220 mm inside the tenon. A cylindrical steel sleeve was secured at the bottom of the column during the casting process, creating a cylindrical cavity intended for connecting with the rebar embedded in the footing. The tenon sleeve was 180 mm in diameter, 250 mm in height, and 8 mm in thickness, with an inlet at the bottom and an outlet at the top. When the segments were installed in place, no-shrink high-strength mortar was used to grout the sleeves and central tenon.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Bent**} & \multirow{2}{*}{**Joint Connection**} & \multirow{2}{*}{**Concrete**} & \multicolumn{2}{c}{**Rebar**} & \multirow{2}{*}{**Axial Load**} \\ \cline{3-4} \cline{6-6}  & & & **Flexural** & & **Shear** & **Radio** (\(\psi_{c}A_{c}\)) \\ \hline B2-CIP & - & & & & \\ \hline B2-PGS & Grouted splice sleeves & & & & \\ \hline B2-PGT & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Details of tested two-column bents.
## 3 Results and Discussion
### Analysis of \(|E^{\star}|\) and \(|E^{\star}|\)/sin\(\delta\)  
According to [PERSON]'s research, the dynamic modulus ([E*]) and the stiffness parameter (\(|\)E*/sin\(\delta\)) of asphalt mixture have a good correlation with the rutting resistance of asphalt pavement, and the stiffness parameter of asphalt mixture can more precisely reflect its rutting resistance at relative high service temperature [35]. Generally, the higher the stiffness parameter value, the more resistant to rutting (permanent deformation) the asphalt mixture.  
In this study, the dynamic modulus and the stiffness parameter were accessed at the intermediate loading frequency of 10 Hz and high testing temperature of 50 \({}^{\circ}\)C. The test results are shown in Figure 9.  
Figure 9 presents that the dynamic modulus and the stiffness parameter of five groups of asphalt mixtures decrease with the incremental difference of asphalt content in the upper and lower half layers and have the highest values at the optimum asphalt content (5.0% ACU and 5.0% ACL). This suggests that the asphalt migration has weakened the rutting resistance of asphalt mixture, and the more asphalt migrates, the less rutting resistance of the asphalt mixture. It is mainly due to the fact that, on one hand, when the asphalt migration has occurred at high temperature, the free asphalt in the upper half layer increases (as is simulated in this paper), having strengthened the lubrication between asphalt and aggregates (i.e., reduced the cohesion), and finally reduced the rutting resistance. Similarly, on the other hand, when asphalt migration has occurred at high temperature, the structural asphalt in the lower half layer decreases, having reduced the cohesion between asphalt and aggregates, so the rutting resistance of asphalt mixture declines. Therefore, it can be concluded that when asphalt migration occurs amid the asphalt pavement layers in hot climate regions, the rutting resistance of asphalt pavement becomes weaker and the rutting becomes worse.
# Numerical Study on the Route of Flame-Induced Thermoacoustic Instability in a Rijke Burner
## 2 Numerical Model
### Steady-State Solution  
The modeling of thermoacoustic instability proceeded in two stages--the first stage was the simulation of the steady-state reacting flow, and the second stage was the simulation of the transient reacting flow. For the steady-steady reaction simulation, the flow field was initialized with the inlet parameters. The solution of the steady-state reacting flow included the accurate capture of the flame anchoring process shown in Figures 4 and 5, the consumption of reactants and product formation shown in Figure 6, and the steady-state temperature distribution along the combustor shown in Figure 3. Each process/distribution is important for verification of the computational results against the theoretical and experimental results.  
The reaction rate and temperature contour plots downstream of the flame stabilizer are shown in Figures 4 and 5, in which the location of the highest reaction rate and the temperature indicate the flame location. As seen in the figures, the flame sits just on the outlet of the flame stabilizer at a distance of 1-2 mm away from it. The temperature contours show that the incoming flow was preheated to approximately 800 K at the outlet of the honeycomb and then heated to a peak temperature of 1828 K by the reaction. Then, a steep temperature gradient, which was dominated by radiation heat transfer, occurred downstream of the peak temperature point. With decreasing temperature, the influence of radiation weakens, and the temperature gradient becomes increasingly gentle until the flow reaches the outlet.  
Figure 3: Comparison of the grid independence solutions of axial temperature.  
The mass fractions of reactants and products along the centerline in the flame stabilizer area and the flame zone are shown in Figure 6. Due to the preheating effect of the flame stabilizer and flame radiation, the reaction started before the flame stabilizer outlet and rapidly increased to a peak downstream of it. At the flame location, the reaction rate reached its maximum value; the CH\({}_{4}\) fuel was completely consumed, and its mass fraction  
Figure 4: Reaction rate [kmol/m\({}^{3}\)-s] contour plots of the flame stabilizer channels and flame zone.  
Figure 5: Temperature contour plots of the flame stabilizer channels and flame zone.  
Figure 6: Species mass fraction distribution and reaction rate [kmol/m\({}^{3}\)-s] along the centerline.  
became zero. The steady-state species distribution and kinetic reaction rate show the exact location of the flame and demonstrate the effectiveness of the numerical simulation in this work.  
In addition, the average flow velocity in the tube of the cold flow and steady-state reacting flow was obtained. The results show that the average velocity increased from 0.0257 to 0.071 m/s because the heat released from the flame heated and expanded the flow.
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting
### Simulation Calculation and Analysis
#### 4.4.3 Stress Analysis at Typical Locations  
To better analyze the outcomes of the fragmentation as produced by the model when the hole-by-hole blasting method and the three-hole simultaneous blasting method were used, a total of 10 measurement points were placed at typical positions along the model. In addition, five testing points were placed in front of the two holes and on the front side of the blast hole, as shown in Figure 17.  
The data presented in Table 4 was processed using the Origin (8.0) which is professional data processing software. As a result, Figure 18 shows the maximum effective stress histogram at each measurement point when the hole-by-hole blasting method and the three-hole simultaneous blasting method were applied, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement Point**} & \multicolumn{3}{c}{**In Front of the Middle Part of the Two Blast Holes**} & \multicolumn{5}{c}{**Front Side of the Blast Hole**} \\ \cline{2-10}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline Hole-by-hole blasting & 41.9 & 47.0 & 35.4 & 48.5 & 43.5 & 45.2 & 13.1 & 18.1 & 5.35 & 15.5 \\ \hline Three-hole simultaneous blasting & 43.5 & 42.1 & 44.9 & 54.3 & 19.8 & 41.7 & 38.4 & 53.5 & 67.9 & 26.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum effective stress at the typical locations of the model (Unit: GPa).  
Figure 17: Typical measurement points of the model.  
It was noted from the results presented in Figure 18 that when the hole-by-hole blasting method was used, the effective stress at the measurement points B, C, and F was relatively high, reaching 47 GPa, 48.5 GPa, and 45.2 GPa, respectively. On the other hand, when the three-hole simultaneous blasting method was adopted, the effective stress at the measurement points D and I was found to be relatively high, attaining 54.3 GPa and 67.9 GPa, respectively. Overall, the effective stress was highlighted as being relatively high at the measurement point deployed in the front side of the blast hole when the three-hole simultaneous blasting method was employed.
## 2 Materials and Methods
### Research Method
#### 2.3.1 Machine Learning Models  
To compare and analyze the differences between different machine learning algorithms concerning the accuracy of albacore fishing ground forecasting, five fishing ground forecasting models, namely, KNN, SVM, XGBoost, GP and RF, were constructed in this paper based on the feature dataset of marine environmental factors. Different types of machine learning represent different classification algorithms, and each has its own advantages. The KNN algorithm has high classification accuracy and is insensitive to abnormal samples [41, 42]; SVM has the advantage of being able to handle small data samples, as well as having global optimality and a strong generalization ability [43]; RF uses the average of many decision trees in the classification process to minimize overfitting and produce good regression results, thus improving the classification accuracy [44]; GP is widely used in time-series prediction tasks because of its ability to effectively exploit correlations between features [45]; XGBoost can adjust hyperparameters to maximize model performance and prevent model overfitting, while using a gradient descent optimization algorithm to integrate decision trees sequentially to minimize model error [46, 47].
# A Source Code Similarity Based on Siamese Neural Network
## 3 WICE-SNN Framework
### Details of WICE-SNN
#### 3.2.2 SNN Layer  
In previous layer, we got the word embedding matrix \(U\). One simple representation for code snippet can be modeled as a vector by a weigthed sum aggregated result of \(U\). But, different words have different contribution to code snippet. Moreover, this representation will lost their program structure information. A CNN model is proposed to learn a representation for an input source code by integrating its semantic and structural materials. As shown in Figure 2, the CNN model contains five layers, i.e., input layer, convolution layer, pooling layer, connection layer and output layer. In the following, we will describe one by one.  
Figure 2: WICE-SNN Framework.  
_Input Layer_: We take a pair of pre-trained word embedding matrix \(U^{A},\ U^{B}\) in previous part as input for WICE-SNN model, \(U^{A}\in R^{N_{1}\times d},\ U^{B}\in R^{N_{2}\times d}\), here \(N_{1},N_{2}\) is the number of words respectively in source code A and B, \(d\) is the vector dimension. We pad the two code snippets with 0 to have the same length \(N=max[N_{1},N_{2}]\). After filling with 0, the initialized marix \(U\in R^{N\times d}\).  
_Convolution Layer_: Each kernal \(K\in R^{N\times d}\) does convolution operation in the word sequence \(\{v_{1},v_{2},\ldots,v_{N}\}\).  
\[p_{i}=U_{i}*K \tag{2}\]  
Here, \(*\) is convolution operator, \(U_{i}=[u_{i_{1}}u_{i+1,\ldots},u_{i+s-1}]\), that is the embedding matrix of word sequence \(\{v_{i},v_{i+1},\ldots,v_{i+s-1}\}\), \(1\leq i\leq N-S+1\). \(p_{i}\) is a real number, because the dimension of kernal and word vector are same. \(P=[p_{1},\ldots p_{i},\ldots p_{N-s+1}]^{\prime}\in R^{d_{1}}\), where \(d_{1}=N-s+1\).  
_Pooling Layer_: Pooling (including min, max, average pooling) is commonly used to extract robust features from convolution, to reduce its dimension. A convolution layer transforms an input feature map \(U\) with \(d\) columns into a new feature map \(P\) with one column. We get the maximum after max-pooling over each vector P, which can be expressed as  
\[x_{i}=\max\{p_{i}\},i=1,2,\ldots M \tag{3}\]  
Here, M is the filter number we set in convolution layer.  
_Connection Layer_: In connection layer, we concatenate each \(x_{i}\), which get from pooling layer, into a vector for source code A and B.  
\[X=x_{1}\oplus x_{2}\ldots\oplus x_{M} \tag{4}\]  
where \(\oplus\) is the operation that merges two vectors into a long vector, \(X\) is a new feature map for source code.
## 3 Results
Germacrene D, bicyclogermacrene, \(\delta\)-adinene, \(\alpha\)-bisabolone A oxide, and \(\alpha\)-bisabolol A oxide are compounds mentioned in the literature as components of chamomile oil and occur in the following amounts: 3%, 0.64%, 12.81%, 2-60%, and 0.4-12%, respectively [17; 18; 20; 32]. The content of \(\alpha\)-caryophyllene (0.33%) was confirmed in the extract from live flowers obtained using the headspace method [17]. No information was found in the literature of the occurrence of 7-epi-dehydrosequicineol and \(\alpha\)-amorphene in chamomile essential oil. After 7 days of storing the inserts, \(\beta\)-farnesene, \(\alpha\)-bisabolone oxide A, and artemisia ketone were detected in the gas phase of the matrices. However, they were not identified on the day of the inserts' preparation. The presence of germacrene D (absent in OE:CM 1:3) and \(\alpha\)-bisabolol (absent in EO:C 1:1 and EO:CM 1:3) was also found above most of the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Chemical Compounds**} & \multicolumn{4}{c}{**EO:C**} & \multicolumn{4}{c}{**EO:MC**} \\ \cline{3-8}  & & **1:1** & **1:2** & **1:3** & **1:1** & **1:2** & **1:3** \\ \hline
9 & Bicyclogermacrene & 2.26 & 3.06 & NA & ND & 2.20 & ND \\
10 & \(\delta\)-Kadinen & 3.15 & 2.52 & 1.93 & ND & 2.53 & ND \\
11 & \(\alpha\)-Bisabolololol & ND & 2.48 & 1.76 & 2.46 & 1.51 & ND \\
12 & \(\alpha\)-Bisabolone oxide A & 1.32 & 1.91 & 1.44 & 4.18 & 1.57 & 6.44 \\ \hline \hline \multicolumn{8}{c}{After 14 days} \\ \hline
1 & Artemisia ketone & ND & ND & 3.06 & ND & 2.01 & ND \\
2 & \(\beta\)-Farnesene & ND & 100 & 82.05 & 83.10 & 92.42 & 68.68 \\
3 & \(\alpha\)-Farnesene & ND & ND & 5.04 & 3.84 & ND & ND \\
4 & \(\beta\)-Kubeben & ND & ND & 5.04 & ND & ND & ND \\
5 & Germacrene D & ND & ND & ND & 3.84 & ND & ND \\
6 & \(\beta\)-Caryophyllene & ND & ND & ND & 1.94 & ND & ND \\
7 & Bicyclogermacrene & ND & ND & ND & 1.60 & ND & ND \\
8 & \(\delta\)-Kadinen & ND & ND & ND & 1.82 & ND & ND \\
9 & \(\alpha\)-Bisabololol & ND & ND & 2.14 & 1.51 & 2.10 & 19.12 \\
10 & \(\alpha\)-Bisabolone oxide A & ND & ND & 2.67 & 2.35 & 3.46 & 12.19 \\ \hline \hline \multicolumn{8}{c}{After 28 days} \\ \hline
1 & \(\beta\)-Farnesene & ND & 69.83 & 88.57 & 70.15 & 65.46 & ND \\
2 & Germacrene D & ND & ND & 4.65 & ND & ND & ND \\
3 & \(\alpha\)-Bisabololol & ND & ND & 2.46 & ND & ND & ND \\
4 & \(\alpha\)-Bisabolone oxide A & ND & 30.17 & 4.31 & 29.85 & 34.54 & 100 \\ \hline \hline \multicolumn{8}{c}{After 56 days} \\ \hline
1 & \(\beta\)-Farnesene & ND & 53.06 & 38.54 & 45.80 & ND & ND \\
2 & \(\alpha\)-Bisabololol oxide B & ND & ND & 11.62 & ND & ND & ND \\
3 & \(\alpha\)-Bisabolol & ND & ND & 16.04 & ND & 47.41 & ND \\
4 & \(\alpha\)-Bisabolone oxide A & ND & 46.94 & 33.80 & 54.20 & 52.59 & 100 \\ \hline \hline \end{tabular}  
* ND--not detected.  
\end{table} 
Table 6: _Cont._matrices. After the other storage periods (14, 28, and 56 days), \(\beta\)-farnesene and \(\alpha\)-bisabolone oxide were the compounds most frequently identified in the gas phase.