esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
## 4 Discussion  
### Comparisons with Passive Sensors  
Three satellite-borne CO\({}_{2}\) sensors already exist: GOSAT, OCO-2, and TanSat. Additionally, GOSAT-2 is scheduled to be launched in 2018. Consequently, it is necessary to explain why yet another satellite-borne CO\({}_{2}\) sensor is needed. Unlike the existing sensors, China's CO\({}_{2}\)-IPDA is an active sensor for CO\({}_{2}\) measurements. The IPDA has several advantages over passive sensors, such as improved coverage in high-latitude regions, the ability to obtain a greater number of samples over cloudy areas, relatively small systematic errors, and the capability to undertake night measurements. Based on the experiments above, we can now compare the coverage and systematic error of the planned CO\({}_{2}\)-IPDA with OCO-2, which is currently the best CO\({}_{2}\) satellite sensor.  
Figure 13 demonstrates the global map of XCO\({}_{2}\) from OCO-2 in 2016. Referring to Figure 4, the CO\({}_{2}\)-IPDA would provide useful supplementary data that could fill the gaps shown in Figure 13. The lack of precise knowledge on carbon sequestration in boreal terrestrial ecosystems imposes a great challenge on the resolution of the mystery of the missing carbon. With the help of CO\({}_{2}\)-IPDA, XCO\({}_{2}\) could be efficiently mapped in high northern high latitudes.  
Table 4 demonstrates the validation results for OCO-2. The bias was calculated as the absolute difference between OCO-2 retrievals and Total Carbon Column Observing Network (TCCON) measurements. Because there are no real measurements available for CO\({}_{2}\)-IPDA, we compared the performance of the CO\({}_{2}\)-IPDA with that of OCO-2 using theoretical systematic and random errors. The systematic error was compared with the bias while the random error was compared with the Root Mean Square (RMS) error.  
Figure 12: Performance evaluation results. All parameters used to produce this figure are the same as those used in Figure 11 except the orbit height. The orbit height for Figure 11 is 705 km, while for this figure it is 450 km.  
Regardless of the version of XCO\({}_{2}\) products, both random and systematic errors from CO\({}_{2}\)-IPDA products were lower than those from OCO-2. Furthermore, quality filters were applied to the XCO\({}_{2}\) products used in [31; 32] according to [PERSON]'s report [35]. The upper limits for AOD_sulfate and AOD_dust are 0.4 and 0.3, respectively. Referring to Figure 7, with an AOD below 0.3, the predicted random error for CO\({}_{2}\)-IPDA was below 0.3% for nearly all the land areas of China. Consequently, the launch of CO\({}_{2}\)-IPDA would significantly improve the precision of XCO\({}_{2}\) products, especially for areas where the air pollution is too great to obtain useful retrievals using OCO-2.
## 5 Numerical Simulation and Discussion
### Simulation Results and Discussion
#### 5.2.2 Load Step Change  
To further examine the anti-disturbance ability of all the controllers, the step change condition in the current load is employed. The utilized load profile is mathematically presented as  
\[i_{o}=\left\{\begin{array}{ll}1,&\text{if}\quad 0\leq t<0.15\\ 3,&\text{if}\quad 0.15\leq t<0.35\\ 0.5,&\text{if}\quad t\geq 0.35\end{array}\right.\quad(\text{A}) \tag{54}\]  
The output voltages of the studied FIBC under the four control algorithms are depicted in Figure 5. As shown, under the load change condition, the output voltage of the FIBC significantly reduces when the load increases and vice versa. Since the C4 controller lacks the disturbance compensation mechanism in the outer voltage loop, it exhibits the  
Figure 4: **(a)** The inductor currents of the FIBC under the C1 controller. **(b)** The inductor currents of the FIBC under C2 controller. **(c)** The inductor currents of the FIBC under C3 controller. **(d)** The inductor currents of the FIBC under proposed controller C4.  
longest settling time under the presence of the load change condition compared to the three remaining control approaches. In this case, although the C2 controller is capable of reacting faster against the load change, it causes a severe overshoot and consequently a longer settling time in contrast to the C3 and C1 controllers. It is noteworthy that, similar to the above scenario, the recommended control approach possesses the best performance with the smallest settling time and non-overshoot. The simulation results demonstrate the effectiveness and robustness against the load change condition of the suggested control algorithm in comparison with others.
## 6 Performance Evaluation  
In order to further evaluate the approach we designed, we use the simulation platform to simulate the above scheme and then analyze and evaluate the performance of the scheme. The experimental environment of simulation is configured as the following: Intel i7-4790 [EMAIL_ADDRESS] ghz (8 CPU cores), memory 4096 MB, system model Dell OptiPlex 9020; the selected network simulation software is _OMNET_ software, and it uses a modular open-source multi-protocol network simulation platform. It supports the functions of a wireless communication network and wired communication network modeling, protocol simulation modeling, queuing network modeling, multi-processor and distributed hardware system modeling, hardware system modeling, and it evaluates the performance of the complex software system. In this paper, the simulation of network topology is built based on the _OMNET_ simulation platform by importing different scenarios and topology types supported by _OMNET_. The experiment simulated six random topologies under inet-flat type, selected 10 groups of topology generation parameters to generate topology, and tested the number of users--100, 200, 300, and 500, respectively. We assume that the transmission delay of the link is 10 ms and the packet loss rate is 0.5% [31]. The average latency of the SNR node is 10 ms [16]. The _OMNET_ presets a random value of response time between 0 and 5 s for each user in the cc-module. The validity of time stamp \(T_{i}\) of the revocation message is set to 10 s. The expiration time of publishing the revocation result is set to 5 s. We conducted the simulation in 10 rounds for each user value to obtain the average revocation delay and number of voters. The number of users at different grades also selects a truncnormal distribution in each test. With the increase in threshold \(th_{cs}\), the total revocation delay and the number of needed votes changed, and the average values of test parameters under different topologies are shown in Sections 6.2 and 6.3.
###### Abstract  
A contribution of this article is to introduce new q-rung Orthopair fuzzy (q-ROF) aggregation operators (AOs) as the consequence of Aczel-Alsina (AA) t-norm (TN) (AATN) and t-conorm (TCN) (AATCN) and their specific advantages in handling real-world problems. In the beginning, we introduce a few new q-ROF numbers (q-ROFNs) operations, including sum, product, scalar product, and power operations based on AATN and AATCN. At that point, we construct a few q-ROF AOs such as q-ROF Aczel-Alsina weighted averaging (q-ROFAAWA) and q-ROF Aczel-Alsina weighted geometric (q-ROFAAWG) operators. It is illustrated that suggested AOs have the features of monotonicity, boundedness, idempotency, and commutativity. Then, to address multi-attribute decision-making (MADM) challenges, we develop new strategies based on these operators. To demonstrate the compatibility and performance of our suggested approach, we offer an example of construction material selection. The outcome demonstrates the new technique's applicability and viability. Finally, we comprehensively compare current procedures with the proposed approach.  
fuzzy sets; t-norm; t-conorm; Aczel-Alsina operations; q-rung Orthopair fuzzy numbers; aggregation operators; multi-attribute decision making +
Footnote †: journal: selected topics  
Article  
Construction Material Selection by Using Multi-Attribute Decision Making Based on q-Rung Orthopair Fuzzy Aczel-Alsina Aggregation Operators
# Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction
## 5 Conclusions  
This paper has proposed a new multi-task framework, Multi-head TrajectoryCNN, for action prediction. In contrast to most prior works, it simultaneously predicts future human motion and action semantics via a unified framework. Specifically, we first proposed two building blocks for our network, including a regression block and a classification block. The regression block is used to extract general spatiotemporal features of the input sequence and build the regression head to predict future human motion. The classification block is improved from the regression head, enabling the model to capture higher semantic features for predictions of action semantics. Experiments on NTU RGB+D have shown the effectiveness of the proposed Multi-head TrajectoryCNN. The results also show that future human motion prediction can greatly help predict action semantics and semantic predictions also help predict future motion to some extent. Although we have achieved great performance, the proposed method suffers a limitation of missing some spatial characteristics of the human body. The natural connections of the human body can be represented by a graph, which can be processed by graph neural networks. We will explore this for efficiency and effectiveness in the future.  
Conceptualization, X.L. and J.Y.; methodology, X.L.; software, X.L.; validation, J.Y. and X.L.; formal analysis, X.L.; investigation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L. and J.Y.; visualization, X.L.; supervision, J.Y.; funding acquisition, J.Y. and X.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **Average Error** \\ \hline w/o CB & 69.44\% & **70.64** \\ w/o RH & 66.11\% & - \\ w/o CH & - & 71.15 \\ Multi-head TrajectoryCNN & **74.55\%** & 70.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablative results, where the bold denotes the best results.  
**Funding:** This work was supported partly by the National Natural Science Foundation of China (Grant No. 62173045, 61673192), partly by the Fundamental Research Funds for the Central Universities (Grant No. 2020 XD-A04-2), and partly by the BUPT Excellent Ph.D. Students Foundation (CX2021314).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The research in this paper used the NTU RGB+D action recognition dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 3 A Practical Non-Stationary of 3-D Massive MIMO Channel Model
### Generation of the Cluster/Channel in System Level
#### 3.1.1 Generating the Clusters at the Transmitter  
Let us denote the \(qt=\{1,...,Qt\}\), where \(Qt\) is equal to Cluster\({}_{t}=Qt(t+\Delta t)\) as total clusters, in which \(i_{1\,th}\) path from \(lk_{th}\) transmit antenna element is add up with time \((t+\Delta t)\). In other words, the first path from \((0,0)_{th}\) antenna element with time \(t\) is add up to the nearest cluster. Additionally, the rest of the paths from \(lk_{th}\) adjacent element calculating with distance \(d^{BS}\) are add up with succeeding time instantaneously \((\Delta t)\) to the same cluster.
## 3 Results and Discussion
### Brightfield Images and Videos  
Time-lapse movies were constructed from the stabilized time-series images. In the microgravity simulation movie, migrating cells, nuclei, nucleoli, and cellular structures are visible in a field of view of approximately 560 um \(\times\) 400 um (Figure 3 and Supplementary Materials Video S1). The particles outside of the cells in the surrounding media are also visible passing through the field of view but have a negligible effect on the image quality and post-acquisition process. The movement of cell lamellipodia, the emergence of cell protrusions, and the dynamic cell-to-cell interactions can also be observed. From these acquired real-time images in SMG, we demonstrate here that multiple measurable endpoints can be quantified and analyzed to understand various cellular functions.  
Figure 3: Brightfield images of AG01522 cells captured using the live-imaging module while experiencing simulated microgravity (SMG) on the RPM. (**A**) The images show a dividing cell over a period of 60 min. At T-30 min, the cell is spread out and we can see the nucleus intact. Starting from time T,  
which is the breakdown of the nuclear envelope, all the phases of cell division can be visualized clearly: the alignment of the chromosomes in the center of the cell, the sister chromatids being pulled in opposite directions, the cleavage furrow separating the daughter cells, and the formation of the daughter cells. (**B**) Images of lamellipodial formation in a cell. We can trace the leading edge of the cell, which is ruffling (outlined with a dotted black line) due to dense actin polymerization. The overlay of the white dotted line representing the lamellipodial boundary at T = 0 s and black dotted line at T = 12 min clearly show the extension of the lamellipodium. (**C**) Brightfield images of AG01522 cells at 0, 4, 8, and 10 h after wounding are shown. The edges of the wound are highlighted in black. (**D**) Images show the translation of the nucleus in a cell over time. The shapes and position of the nucleus could be traced, indicated in dashed outlines, and they are overlayed to track the path of the nucleus. (**E**) Captured images show nuclear rotation in the cell, as seen in the top row with the nuclear boundary marked with dashed black lines. The bottom row marks the two nucleoli with dashed black lines; their position can be used to accurately quantify the angle by which the nucleus has rotated over time.
## 2 GNSS Signal Characteristics
### Duty Cycle  
The existence of duty cycle affects the continuity of GNSS carrier phase measurements of smartphones. According to the GNSS carrier phase measurements of the four smartphones of Huawei, it is found that the carrier phase measurements of early smartphones, such as the Huawei Mate9, are severely affected by duty cycle. The carrier phase measurements are missing in the logging data of Huawei Mate9 at the beginning after a time period of 3-5 min, but the other measurements such as pseudorange, doppler, and signal strength are still continuously logged. However, with the analysis of the carrier phase measurements of the other three smartphones, our view is consistent with that of [3; 9], which hold that some recent smartphones such as the Huawei Mate30 and Huawei P30 are little or not affected by duty cycle even in the standard static positioning mode.
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 4 Conclusions and Future Work  
This paper proposed a method for evaluating the preferences for walkability environments based on road images. The proposed method aimed to understand the correlation between people's preferences for walkability environments and the components of road images. To assess people's preferences for walkability environments, several regression models were trained using the semantic segmentation results of the road images, and feature selection was conducted based on the analysis of feature importance from the trained results. Furthermore, a retraining process using only selected features was performed to improve the accuracy of walkability preference evaluations. The proposed method is an automated walkability evaluation method that can replace traditional methods using various GIS data manually.  
Figure 6: Performance results of XGBoost according to the number of features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline road & sidewalk & tree & sky & signboard & building \\ car & plant & earth & pole & fence & person \\ traffic light & pot & minibike & wall & streetlight & grass \\ truck & mountain & field & base & railing & box \\ van & bus & trade name & ashcan & dirt track & bag \\ stairs & bridge & refrigerator & palm & grandstand & apparel \\ flag & sand & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Importance of the 38 features in the optimized XGBoost model.  
Figure 7: Performance of regression models using the feature importance presented in Table 8.  
The proposed study needs many improvements: Firstly, due to the pedestrian preference evaluations being obtained from an unspecified number of individuals through crowdsourcing, the walkability preference dataset is too diverse to be captured by generalized deep learning models. Therefore, there is a need for the development of deep learning models capable of representing the diversity of pedestrian preferences. Secondly, when people evaluate walkability preferences, they tend to make judgments based on quickly observable holistic features rather than detailed specifics. Therefore, it is necessary to identify the correlations between more specified semantic objects during the feature analysis process.  
Conceptualization, software, validation, investigation, data curation, writing--original draft preparation, visualization, J.H.; methodology, writing--review and editing, C.L.; supervision, project administration, K.N. and C.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143336).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Collision Frequency and Energy Transfer Rate in e-He Scattering
## 2 Methods
### Effective Collision Frequency and Energy Transfer Rate  
The effective collision frequency \(\overline{v}_{\text{eff}}\) for an electron interacting with a neutral atom in a Maxwellian plasma is derived from the momentum transfer cross section \(Q_{T}^{ea}\) as follows [22]:  
\[\overline{v}_{\text{eff}}=\frac{8}{3\sqrt{\pi}}n\bigg{(}\frac{m_{e}}{2k_{B}T _{e}}\bigg{)}^{5/2}\int_{0}^{\infty}v^{5}Q_{T}^{ea}(v)\exp\bigg{(}-\frac{m_{e} v^{2}}{2k_{B}T_{e}}\bigg{)}dv, \tag{16}\]  
where \(v\) is the electron velocity, \(k_{B}\) is the Boltzmann constant, \(n\) is the density of the atoms, and \(T_{e}\) is the electron temperature.  
In this equation, the integral captures the contributions of various electron velocities to the effective collision frequency, emphasizing the weight of higher velocities through the \(v^{5}\) term. The exponential term accounts for the thermal distribution of velocities in the plasma, ensuring that lower velocities have a significantly reduced impact on the effective collision frequency.  
Using the effective collision frequency \(\overline{v}_{\text{eff}}\) from the previous equation, the energy transfer rate can be determined as follows:  
\[\frac{dU_{e}}{dt}=-3n_{e}\frac{m_{e}}{m_{He}}k_{B}(T_{e}-T_{He})\overline{v}_ {\text{eff}}. \tag{17}\]  
where \(T_{He}\) and \(m_{He}\) are the temperature and mass of the helium atoms.
## 2 Protective Tunnel
### Overpressure Mitigation Inside the Main Tunnel  
Two methods to mitigate blast overpressure inside the main tunnel were considered: the slope angle of the external tunnel entrance and blast pocket. Figure 2 shows the blast wave propagation when slope angles are \(\alpha=90^{\circ}\), \(\alpha>90^{\circ}\), and \(\alpha<90^{\circ}\) to the bottom of the entrance. The distance from the lower end of the tunnel entrance to the center of detonation does not vary with the slope angle. The surrounding land was not considered, as this study is in the stage of fundamental research, not in the stage of practical application. Consider \(\alpha=90^{\circ}\), which is the standard external tunnel entrance. The blast waves start to propagate radially from the center of the detonation. Some of the blast waves enter the tunnel, and the remaining ones propagate out of it. For instance, Shock C enters the tunnel and is reflected from the top of the tunnel, increasing the internal overpressures, \(P_{1}\), whereas Shocks A and B do not affect \(P_{1}\), as shown in Figure 2a. As shown in Figure 2b, when \(\alpha>90^{\circ}\), Shocks B and C enter the tunnel, and only Shock A propagates outside it. This indicates that the internal overpressure increases with increasing slope angle \(\alpha\). For \(\alpha<90^{\circ}\), all the blast waves A, B, and C propagate out of the tunnel as shown in Figure 2c. Thus, it is expected that the maximum overpressure, \(P_{3}\), would be the smallest compared with the others, \(P_{1}\) and \(P_{2}\). The effect of the slope angle of the external tunnel entrance on the internal overpressures was numerically simulated, and the optimal slope angle for overpressure mitigation was proposed.  
The second method to reduce blast overpressures inside the tunnel involves the use of a blast pocket at the corners where the direction of the blast waves changes. Figure 3 shows the blast wave propagations at the corners with and without a blast pocket. The blast waves initially propagate from region 1, are refracted, and then propagate into region 2 owing to the effect of shock-wave diffraction at the corner, as shown in Figure 3a. As shown in Figure 3b, when a blast pocket is applied to the corner, some of the initially propagated blast waves from region 1 (Shock 1) are refracted and propagated into region 2, whereas some (Shock 2) propagate after a while into region 2 via the blast pocket. Since Shock 2 propagates later, after Shock 1, the maximum overpressures at the front of Shock 1 can be mitigated compared to the case without the blast pocket. The effects of the blast pocket on the overpressure mitigation, along with the effects of the slope angle, were studied through numerical analysis.  
Figure 1: Protective tunnel.  
Figure 3: Blast wave propagations at corners with and without blast pocket. (**a**) No blast pocket; (**b**) With blast pocket.  
Figure 2: Blast wave propagation varying angle of the entrance slope. (**a**) \(\alpha\) = 90\({}^{\circ}\) (original shape); (**b**) \(\alpha\) > 90\({}^{\circ}\); (**c**) \(\alpha\) < 90\({}^{\circ}\).
## 4 Results and Discussion
### PH Value
#### 4.4.2 Effect of Alkali Equivalent on pH Value  
Figure 15 shows the pH values of RPSG with different alkali equivalents. As shown in Figure 15, the pH value of specimen RP-S30-M1.3N6 was the smallest (pH \(\approx\) 9.4), and the pH value of specimen RP-S30-M1.3N15 was the largest (pH \(\approx\) 11.4) at the 2 h immersion time. The results were validated against each other with the visual efflorescence results in Figure 12. After 24 h of immersion, all specimens reached a pH value above 10.0 and gradually increased in the later stages. Meanwhile, as can be observed in Figure 15, the pH value of the specimens increased with increasing alkali equival  
Figure 13: Visual efflorescence of RPSG with different moduli.  
efflorescence potential of the specimens becomes higher with increasing alkali equivalents. However, this does not indicate the actual degree of efflorescence in relation to the alkali equivalents. According to the results in Figures 9 and 12, the efflorescence of the specimen is also influenced by the denseness of the matrix.