esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 ADMM Formulation and Solution
### Update of \(\overline{\mathbf{s}}_{r}\)  
Similar to the \(\overline{\mathbf{r}}_{r}\) update, in the \(\overline{\mathbf{s}}_{r}\) update we take the gradient of Equation (23b) with respect to \(\overline{\mathbf{s}}_{r}\) and equate the result to \(0\), i.e.,  
\[\
abla_{\overline{\mathbf{s}}_{r}}\mathcal{L}\Big{(}\overline{\mathbf{r}}_{r} ^{m+1},\overline{\mathbf{s}}_{r},\mathbf{u}^{m},\mathbf{v}^{m},\mathbf{w}^{m} \Big{)}=\mathbf{0}. \tag{28}\]  
The solution to Equation (28) is given by  
\[\overline{\mathbf{s}}_{r}^{m+1}=\Xi_{2}^{-1}\xi_{2} \tag{29}\]  
where  
\[\Xi_{2}=\rho_{1}\mathbf{A}_{r}(\Theta)\mathbf{A}_{r}^{T}(\Theta)+\rho_{2} \mathbf{T}^{T}(\overline{\mathbf{r}}_{r})\mathbf{T}(\overline{\mathbf{r}}_{r} )+\rho_{3}\mathbf{I} \tag{30}\]and  
\[\begin{split}\tilde{\xi}_{2}&=\rho_{1}\mathbf{A}_{r}( \Theta)\Big{(}2\mathbf{x}_{r}-\mathbf{u}-\mathbf{A}_{r}^{T}(\Theta)\bar{ \mathbf{r}}_{r}\Big{)}\\ &+\rho_{2}\mathbf{T}^{T}(\bar{\mathbf{r}}_{r})(\mathbf{1}- \mathbf{v})+\rho_{3}(\bar{\mathbf{r}}_{r}+\mathbf{w})\\ &-\mathbf{A}_{r}\Big{(}\widetilde{\Theta}\Big{)}\mathbf{A}_{r}^{ T}\Big{(}\widetilde{\Theta}\Big{)}\bar{\mathbf{r}}_{r}.\end{split} \tag{31}\]
## 4 Results and Discussion
### Optical Microscopy  
The PB and BPAV samples were studied under optical microscopy since they were the softest and hardest samples among the investigated samples. In Figure 5, images of the PB and PAV asphaltenes are presented. The results imply that the BPAV binder is harder than the PB binder due to a higher concentration of asphaltenes.  
Figure 4: Temperature dependence (in the range 25 to 100 \({}^{\circ}\)C) of the elastic modulus for pristine and aged bitumen samples.  
Figure 5: Optical microscopy (20\(\times\)) images of PB (**a–c**) and PAV (**d–f**) asphaltenes at high temperature.  
These images, obtained at 150 (a and d), 160 (b and e), and 165 \({}^{\circ}\)C (c and f), show the materials at different crystallization stages. The high-temperature stage of the experiment also shows the melting point of the crystallized material near 160 \({}^{\circ}\)C for the PB asphaltenes, while the material is partially melted at 165 \({}^{\circ}\)C for the BPAV sample, where the crystals are almost lost.  
These novel results reveal two crucial points: First, the asphaltenes are solid at temperatures lower than 150 \({}^{\circ}\)C. Secondly, the asphaltene phase of virgin bitumen holds a different assembling structure to aged bitumen.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Data Collection Environment and Procedures
#### 2.1.5 Preparation of the Dataset  
Based on the measured data, the training set, verification set, and test set were constructed. The data collection process was as follows:  
(1) Data source: Data were collected from the typical sections of two mountain roads, the X412 County Road and the X001 County Road.  
(2) Data collection time: six consecutive months (March to August 2022).  
(3) Data types:  
Vehicle sensor data: speed, acceleration, steering wheel angle, etc.  
Meteorological environment data: visibility, precipitation, wind speed, etc.  
Driver information: age, driving experience, etc.  
(4) Sampling frequency: vehicle data (10 Hz), environmental data (1 min/time).  
The 47,952 samples were classified into four categories: safety, minor conflict, general conflict, and serious conflict. The datasets were divided as shown in Table 1.
## 4 Discussion  
In the above experiment, the LEO constellation and seven ground stations are used in order to integrate orbit determination, and so that the centimeter-level orbital accuracy can be obtained. Only the seven sta scheme tracks accuracy at the decimeter or meter level. The LEO satellite can act as a mobile station, significantly enhancing the diversity of the observation data, while increasing the speed of the geometric observation shape changes and improves the accuracy of BDS-3 POD.  
We study the accuracy of BDS-3 orbit determination and time synchronization with simulated observation data of ground stations, LEO satellites, and ISL. According to the simulation experiment results, the LEO constellation enhances the BDS3 precise orbit determination, in the case of a few ground monitoring stations, better than ISL. A LEO constellation can add a large amount of observation information; while LEO satellite flight speed is high, satellite geometry changes faster, and LEO onboard observation is not affected by tropospheric delay error. ISL observation is not affected by atmospheric error, the measurement accuracy is higher, but the corresponding amount of observation is lessened. Both schemes now rely on ground stations to reduce the overall drift of the  
Figure 11: RMS1 of the BDS-3 clock offset using ground stations combined with different LEO constellations.  
Figure 12: RMS2 of the BDS-3 clock offset using ground stations combined with different LEO constellations.  
constellation. With the rapid development of the LEO constellation, the POD accuracy of integrated LEO satellites can be optimized and improved by greater algorithms. If the ISL data and LEO data are used at the same time, the shortcomings of each could be reduced and the POD accuracy of BDS-3 can be improved.  
This study is based on simulated observational data and does not fully reflect the real-world factors that affect measurements, such as the geometry of star-ground tracking, the attitude model of LEO satellites, solar radiation pressure, and antenna calibration. Since the LEO satellites are located at different locations than those of ground-based monitoring stations, they are more susceptible to the effects of the space environment, which can negatively affect the POD. These issues need further research to improve the POD accuracy and time synchronization accuracy of LEO satellites and BDS-3 satellites.
## 2 Materials and Methods
### Data  
This study evaluated the effectiveness of HSF in Henan Province constructed during 2011-2014 mainly based on the MODIS and Landsat data.  
MODIS products. Vegetation indices, such as the Enhanced Vegetation Index (EVI) and Land surface water index (LSWI), were calculated using MODIS 8-day surface reflectance products (MOD9A1) at a 500-m resolution. These two vegetation indices were important inputs for Net Primary Production estimation using the Vegetation Photosynthesis Model (VPM). Considering the averaged 3-year farmland productivity of the project area was used to present the productivity before or after HSF project, the MODIS data during 2008-2016 were used for evaluating the effectiveness of HSF.  
Landsat images. Landsat images from March to June during 2011-2016 in the 1 \(\times\) 1 km around the center point of 630 projects were used in our study for identifying the continuous grain crops project area. Moreover, all the available Landsat images of the growing season in the 8 sampled HSF projects area during 2011-2016 were used for producing NPP with 30 m resolution. These data were accessed from United States Geological Survey (USGS) archives ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/)), accessed on 23 December 2021). The standard false-colorcomposite of these images was applied for the visual interpretation of continuous grain crops in the targeted areas.  
In addition, HSF project distribution, meteorological disasters, grain yield and meteorological data were used in this study. The details of all data used in this study are listed in Table 1. Types and sources of data in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Name** & **Source** & **Time Range** & **Resolution** & **Usage** \\ \hline Location of the central point of HSF & \multicolumn{2}{c}{The national HSF} & & & Used for HSF construction \\ project region & construction effectiveness evaluation project team & 2011–2014 & - & effectiveness evaluation \\ \hline Vector boundary of typical HSF & \multicolumn{2}{c}{Global Positioning System (GPS) in field research} & 2011–2014 & - & Used for sampling survey \\ project region & & & & Used for selecting the \\ \hline Landsat images & \multicolumn{2}{c}{United States Geological Survey (USGS)} & 2011–2016 & 30 m, in crop growth season & \multicolumn{2}{c}{} \\  & \multicolumn{2}{c}{Survey (USGS)} & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types and sources of data in this study.  
Figure 1: Spatial distribution of the MLPF transformation project regions and HSF construction project regions in Henan Province, and general survey projects and sample survey projects in this study. NOTE: For the definition of the general survey projects and the sample survey projects refers to Section 2.3.1.
## 2 Materials and Methods
### Growth with Crude Oil as a Sole Source of Carbon  
Fungal conidia were cultured using crude oil as a sole carbon source to evaluate if oil metabolization occurred. Fungal strains were grown on PDA plates and incubated for five days at 30 \({}^{\circ}\)C to obtain conidia. Even though the strains could grow at room temperature, at 30 \({}^{\circ}\)C, an accelerated growth was observed. Conidia were recovered from the PDA plates using distilled sterile water, separated from mycelium through filtration, and washed twice with sterile distilled water. Conidia were counted using a Neubauer Chamber. Flasks containing 50 mL of modified Czapek minimal medium without a carbon source (4-g NaNO\({}_{3}\), 2-g K\({}_{2}\)HPO\({}_{4}\), 1-g MgSO\({}_{4}\)\(\bullet\)7H\({}_{2}\)O, 1-g KCl, 0.02-g FeO\({}_{4}\)\(\bullet\)7H\({}_{2}\)O, 32-g sea salt, and 0.5 mL of trace mineral solution) [40] were inoculated with 2 \(\times\) 10\({}^{7}\) conidia. The flasks were supplemented with 0.5% of three different types of petroleum according to the American Petroleum Institute (API) classification: light crude oil (40\({}^{\circ}\) API from the well Xux in Tabasco, Mexico), heavy (16-20\({}^{\circ}\) API from the field Bacal in Tabasco), or extra-heavy (7-10\({}^{\circ}\) API from the well Ayatsil-Telek in Campeche, Mexico) crude oil as a sole source of carbon. The cultures were incubated at 30 \({}^{\circ}\)C under 150 rpm up to a month with weekly revisions.
## 3 Results Analysis and Discussion
### Cooling Effectiveness
However, Case 4 (four-lobed) has the best lateral expansion of cooling air and can provide good protection in the spanwise direction when used in a cooling structure with multiple tilted holes and high film stacking capability. Therefore, it has great potential for use in liner cooling applications in combustors. To further analyze the effect of the formed holes on the lateral coverage of the film coolant, Figure 8 shows temperature contour maps along the flow direction at X/D = 1. Comparing Case 1 and Case 2, it can be seen that in Case 2 the high-temperature gas enters the interior of the coolant near the wall surface, while in Case 1 the coolant still expands laterally toward the side surfaces near the wall surface. Based on Cases 3 and 4, it can be seen that except for Case 2, the other formed film holes have more lateral expansion and less expansion of the normal distance, which stands for better adhesion of the coolant to the wall and closer proximity to the wall surface by the cooling airflow; this is also the reason why they have a better cooling effect. In addition, Case 1 has a higher minimum temperature for the coolant in the range X/D = 1 compared to the other structures. Figure 8: Comparison of cross-sectional average temperature distribution contour maps. Figure 7: Distributions of laterally averaged cooling effectiveness profiles.
## 2 Materials and Methods
### Equipment  
Brain activity was recorded with electroencephalography (EEG). A 32-electrode system actiCap (Brain Products GmbH, Germany) was employed to record EEG signals. The 27 channels selected for acquisition were: F3, FZ, FC1, FCZ, C1, CZ, CP1, CPZ, FC5, FC3, C5, C3, CP5, CP3, P2, F4, FC2, FC4, FC6, C2, C4, CP2, CP4, C6, CP6, P4. They were placed following the 10-10 international system on an actiCAP (Brain Products GmbH, Germany). Four electrodes were located next to the eyes to record electrooculography(EOG) and ground and reference electrodes were located on the right and left ear lobes, respectively. Each channel signal was amplified with BrainVision BrainAmp amplifier (Brain Products GmbH, Germany). Finally, signals were transmitted wirelessly to the BrainVision recorder software (Brain Products GmbH, Germany).  
H3 exoskeleton (Technaid, Madrid, Spain) was employed to assist the movement and participants used crutches as support. Control start/stop gait commands were sent via Bluetooth. The experimental setup can be seen in Figure 1.
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 2 Preliminary and Related Work
### Specifications of IEEE 802.15.6  
#### 2.1.1 MAC Layer of WBAN  
Sitting above the PHY Layer, the MAC layer of IEEE 802.15.6 is designed to control communication access. To do this, the MAC layer divides the entire communication into a chain of superframes through the hub (coordinator). At the boundary of these superframes, the hub chooses beacon periods of equal length to bound each superframe. If needed, one could shift the offset of the beacon period through the hub. The superframes will be normally sent in each beacon period [5], unless there is a restriction by regulations in the Medical Device Radiocommunications Service (MICS) band or the superframes are inactive. Figure 4 provides an overview of the structure of superframes in the standard. The superframe can be divided into three different components, the MAC header, the MAC frame body, and the Frame Check Sequence (FCS). With a length of seven bytes, the MAC header can be further divided into recipient ID, sender ID, WBAN ID, and frame control, which contains information such as protocol version, ack policy, and so on. The MAC frame body has a variable length; it contains low-order security sequence number, frame payload with selected types, and MIC. The last two bytes of a MAC frame are the FCS to detect possible errors in transmission. The standard specifies the CRC-16-CCITT sequence to be used in error detection. The Cyclic Redundancy Check (CRC) polynomial is shown in Equation (1), where \(a_{15}\) is the Least Significant Bit (LSB) of the field, \(a_{0}\) is the Most Significant Bit (MSB), and \(a_{15}\), \(a_{14}\),..., \(a_{0}\) are the binary coefficients.  
\[G(x)=x^{16}+x^{12}+x^{5}+1 \tag{1}\]
## 4 IoT Systems in the Pre-Disaster Stage
### Sensors
#### 4.1.1 Earthquake  
We commonly use accelerometers to detect earthquakes. Accelerometers include triaxial accelerometers and dual-axis accelerometers. Regarding triaxial accelerometers, we can choose the accelerometer ADXL362 [39] for low-power use, while we can choose the accelerometer EPSON M\(-\)A351 AU [39] and the accelerometer LSM9 DSO [16] for high-precision use. Regarding dual-axis accelerometers, we can choose the accelerometer ADXL203, which is low-power and high-precision [52]. Also, we can choose triaxial accelerometers, such as L1S3 DSH sensors (manufactured by STMicroelectronics) and EpiSensors [53]. The L1S3 DSH sensor is ultra-low-power and high-performance [53]. The accelerometers are placed on the object being detected (e.g., buildings or bridges). The single computer boards include Raspberry [38], CC2420 DBK [52], and Sparrow v4 [16]. The microprocessors include the ATmega128L [52], ATmega128 RFA1 [16], and ARM processor [53]. Sensors should be equipped with antennas to enable data transmission over long distances [39]. The sensors should be energy-saving [39]. Regarding batteries, we can choose d-cell batteries [39] and CR2032-3V lithium-ion batteries [16]. The sensors should sleep when they do not need to collect data [39]. In addition, the sensors should be low-cost [39]. For example, each sensor in [PERSON] et al.'s [39] earthquake detection system costs 2300 USD.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Number of** \\  & **Publications** \\ \hline Sensors & 5 \\
2019 5 th IEEE International Smart Cities Conference (IEEE ISC2 2019) & 2 \\ Applied Sciences & 2 \\ Earth Science Informatics & 2 \\ IEEE Access & 2 \\ Materials Today-Proceedings & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top six distribution of publication by sources.  
In addition, some scholars may use other sensors for earthquake detection. For example, [PERSON] et al. [54] also combined triaxial velocimeters and ultrasonic measurements to build the earthquake early warning system. [PERSON] et al. [16] combined an LSM9 DSO-16 bit high-resolution triaxial accelerometer with an SI7020 humidity and temperature sensor, a three-axis gyroscope, a triaxial magnetometer with embedded FIFO, an SI1145- infrared proximity detection, a high-precision altimeter, a UV and ambient light sensor, and a MPL3115A2-pressure and temperature sensor.
## 3 Micro-Settlement Control Techniques
### Method for Determining the Appropriate Support System  
(1) Main forms of support systems  
The stiffness of the support system must be sufficiently high to control surface micro-settlement during the construction of large cross-section tunnels. The main forms of high-stiffness support systems include high-stiffness primary support, primary support combined with temporary support, and integrated support structure. As the tunnel span increases, the stiffness of the primary support alone often fails to meet micro-settlement  
Figure 5: Relationship curve between load-to-pipe roof stiffness ratio and surface settlement.  
Figure 6: Relationship curve between pipe roof stiffness and surface settlement.  
control requirements. This necessitates the use of temporary supports to enhance stiffness, as illustrated in Figure 7. The number of the blocks represents the order of excavation.  
In highly sensitive environments with stricter settlement control requirements, where pipe roofs combined with primary and temporary supports fail to meet the requirements, an integrated support system can be adopted. This system integrates the pipe roof, primary support, and secondary lining into a unified high-stiffness structure. This approach involves circumferentially cutting the pipe roof to create interconnected spaces, followed by constructing a reinforced concrete structure to effectively control surface settlement caused by tunnel excavation.  
(2) Determining the appropriate stiffness of the support system  
The stiffness of the support system is one of the critical factors affecting surface settlement caused by tunnel excavation. To better understand settlement patterns, a relationship curve between the load-to-support system stiffness ratio (\(\beta\)) and the transverse surface settlement (u1) is established based on Equation (4), as presented in Figure 8. Based on specific settlement control standards and load conditions, the stiffness of the support system is preliminarily determined, and Figure 9 illustrates the relationship curve between surface settlement and support system stiffness. The curve AB represents the sharp descent segment, indicating that an increase in support system stiffness significantly reduces the surface settlement. The curve BC represents the transition segment between a sharp descent to a gentle slope, where the support system stiffness is both effective in controlling settlement and cost-efficient. Therefore, the stiffness of the support system should be designed to fall within this range. The curve CD represents the gentle slope segment, indicating that an increase in support system stiffness has minimal effect on surface settlement control. If the preliminarily determined support system stiffness falls within the BC range, it is deemed reasonable; otherwise, adjustments should be made to bring it into this range.  
Figure 8: Relationship curve between load-to-support system stiffness ratio and surface settlement.  
Figure 7: Schematic diagram of primary support combined with temporary support.
# PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
## 1 Introduction
With the development of artificial intelligence, there has been an increasing number of studies applying deep reinforcement learning techniques to computer network optimization research. [PERSON] et al. [9; 10] utilized the DQN framework to enhance the performance of MPTCP by analyzing path information and using the transmission results as parameters for the reward function. However, these deep reinforcement learning methods overlook the spatiotemporal correlation during the MPTCP transmission process. The link contains valuable channel data information at each moment, and the channel data exhibit temporal correlation along the time axis. From a vertical perspective, there are differences between the subflow states at each moment. We can abstract each subflow as a node on the graph and extract the spatial correlation between subflows based on their abstract relationships. For example, when a data packet arrives at a certain moment, the relationship between subflows can serve as the basis for selecting the link for the packet. The correlation between links can optimize the network environment as a whole and improve overall transmission efficiency. Graph neural networks are well-suited for this task requirement. Therefore, within the scope of this research article, we introduce a novel multipath scheduling algorithm, namely PDASTSGAT (path diversity-aware spatial-temporal graph attention network), which leverages spatial-temporal synchronous graph convolutional networks [11]. It addresses the network scenario where packets of different sizes traverse the links by using a graph convolutional neural network to analyze the current state of the links in real time and allocate different-sized packets. This improves the utilization of link resources and enhances the throughput and stability of MPTCP links. The design objectives of PDASTSGAT are as follows: (1) Utilize graph attention neural networks to analyze packet sizes and link states and allocate transmission resources to the corresponding links, thereby enhancing link throughput. (2) Address fairness issues in link performance by distributing transmission resources appropriately and balancing the throughput of each link. We evaluated the proposed PDASTSGAT algorithm through simulations. The results demonstrate that when there are significant performance differences among links, PDASTSGAT outperforms scheduling algorithms such as round-robin, Average-RTT, Fast-RTT, and PDAA3C [12] in terms of transmission performance. It achieves throughput improvements ranging from 8% to 14% compared to PDAA3C. One of the characteristics of STSGAT is its ability to ensure subflow fairness by selecting the optimal subflow based on the current subflow quality and the learned experiences of the model when data packets need to be transmitted.