esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Nano Hydrolcite on the Aging Resistance of a High Binder Content Stone Mastic Asphalt
## Materials and Methods
### Methods
To understand the effects of aging, for both asphalt mixtures--control and modified--a set of specimens was tested under unaged conditions and other set of specimens was tested after accelerated aging. Accelerated aging was conducted in laboratory according to the long-term oven aging (LTOA) method as described in AASHTO R30 [45]. In brief, the LTOA method consists in conditioning the compacted specimens inside a laboratory draft oven at 85 \({}^{\circ}\)C for 120 h. The aged asphalt mixtures were tested for indirect tensile strength, water sensitivity, stiffness, and resistance to fatigue. The water sensitivity of the asphalt mixtures was determined through the indirect tensile strength ratio (ITSR), by testing the cylindrical specimens for indirect tensile strength (ITS). The ITS tests were conducted according to EN 12697-23 [46] and the water conditioning and ITSR determination according to EN 12697-12 [47]. The test temperature, 15 \({}^{\circ}\)C, was selected following the preconized by the Portuguese Infrastructure agency [48]. The stiffness evaluation was conducted according to EN 12697-26--Annex B [49], using the four-point bending (4 PB) test. Under controlled temperature, the prismatic specimens (400 mm \(\times\) 50 mm \(\times\) 50 mm) were subjected to a sinusoidal controlled strain loading under the frequencies of 1, 3, 5, 10, 20, and 30 Hz. The selected strain level was 50 \(\upmu\)m/m. To allow a broader understanding of the stiffness behavior, three test temperatures were selected: 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C, respectively. Resistance to fatigue was evaluated according to EN 12697-24--Annex D [50], using the 4 PB test. The tests were performed at 20 \({}^{\circ}\)C, applying a 10 Hz sinusoidal loading under controlled strain. The adopted failure criterion was 50% reduction of the initial stiffness modulus.
## 2 Materials and Methods
### Sources and Processing of Data  
#### 2.2.1 Data on NPP  
This study's NPP data originated from NASA's MOD17A3 HGF dataset, which covered the years from 2001 to 2021, featuring a spatial resolution of 500 m and a temporal granularity of one year. The datasets were downloaded one by one using software tools ENVI 5.3 and ArcGIS 10.8 Reprojection. Splicing, cropping, and format conversion were performed one by one, using ENVI 5.3 and ArcGIS 10.8 software to multiply the scale factor. Then, the units were converted to gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) and the coordinates were unified to GCS\(\_\)WGS\(\_\)1984. With greater overall accuracy, this dataset simulated NPP values based on the light consumption model and the BIOME-BGC model. Further, it was applied to study NPP changes in regional vegetation.
## 5 Results and Analysis
### The Effect of UAV Density  
The effect of UAV density on a SAR operation is achieved by altering the number of UAVs while keeping their speed constant at 20 m per second (m/s). The density of UAVs was changed between 20, 30, 40, 50, and 60 UAVs to examine the behavior of four distinct routing protocols in low- and high-density networks.  
Figure 10 depicts simulation results indicating that the ML-OLSR obtains the maximum PDR compared to other protocols at varied UAV densities. This is due to the fact that ML-OLSR chooses stable UAVs as MPRs to transfer data packets and control messages. P-OLSR and D-OLSR are marginally more effective than OLSR. Similar to the mobility example, the original OLSR yields the lowest PDR values. As the number of UAVs rises, the PDR metrics for all routing protocols drop significantly. This is because as the number of UAVs increases, the network grows denser, and traffic causes packet loss to increase.  
Figure 8: Energy consumption vs. speed of UAVs.  
Figure 9: Throughput vs. speed of UAVs.  
Figure 11 depicts the latency-based performance comparison of the four routing systems. As shown in Figure 11, the latency measure for all routing protocols increases as UAV speeds increase. As the number of UAVs increases, the number of sent packets inside the UAV ad hoc network also rises, resulting in increased queue length and congestion. ML-OLSR provides a shorter latency since it takes the queue length of the UAV into account when MPR is selected. In addition, ML-OLSR provides the smallest latency at 20 UAVs. The P-OLSR and D-OLSR protocols offer superior latency in comparison to the OLSR protocol. At a network density of 40 UAVs, the four routing protocols cause a significant amount of latency.  
Figure 12 compares the energy consumed by the OLSR, D-OLSR, ML-OLSR, and P-OLSR. It has been shown that ML-OLSR has the highest energy efficiency compared to other protocols. P-OLSR delivers superior energy efficiency over D-OLSR and OLSR. This is because it picks up UAVs with high-quality wireless connectivity to forward data, thereby preventing link disconnection. Compared to other protocols, the OLSR protocol has the highest energy usage. Results indicate that the energy usage of the four routing algorithms increases as the number of UAVs increases. This is because the quantity of data packets and \"hello\" messages conveyed increases.  
Figure 11: Latency vs. number of UAVs.  
Figure 10: PDR vs. number of UAVs.  
Figure 13 illustrates a comparison of the throughput metrics of four routing methods. ML-OLSR has the highest throughput compared to other protocols, according to the findings. This is a result of considering the steady and approachable UAV as MPR. If the number of UAVs rises, so does the throughput since the total number of packets also received increases. Due to a high number of packet losses, the OLSR protocol demonstrates poor throughput performance. The P-OLSR protocol delivers a greater throughput than the D-OLSR protocol.
# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 1 Introduction
Within such flow, the initialized spatial foregroundness and backgroundness are efficiently propagated along the temporal dimension by solving a quadratic programming problem that has an analytic solution. In this manner, primary objects can efficiently pop out, and distractors can be further suppressed. Extensive experiments on three video datasets show that the proposed approach acts efficiently and achieves impressive performances compared with 22 state-of-the-art models. This paper builds upon and extends our previous work in [39] with further discussion of the algorithm, analysis and expanded evaluations. We further formulate the segmentation problem into a new objective function based on the constraint relationship between foreground and background and optimize it using a new complementary deep network. The main contributions of this paper include the following:  
* We formulate the problem of primary object segmentation into a novel objective function based on the relationship between foreground and background and incorporate the objective optimization problem into end-to-end CNNs. By training specific CNNs, two dual tasks of foreground and background segmentation can be simultaneously addressed, and primary video objects can be segmented from complementary cues. * We construct neighborhood reversible flow between superpixels which effectively propagates foreground and background cues along the most reliable inter-frame correspondences and leads to more temporally consistent results. * Based on the proposed method, we achieve impressive performance compared with 22 image-based and video-based existing models, achieving state-of-the-art results. In the rest of this paper, we first conduct a brief review of previous studies on primary/salient object segmentation in Section 2. Then, we present the technical details of the proposed spatial initialization module in Section 3 and temporal refinement module in Section 4. Experimental results are shown in Section 5. At last, we conclude with a discussion in Section 6.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 2 Related Work
### Interaction Styles of Telementoring  
Telementoring systems have employed numerous interaction techniques. Often, these interaction techniques were dominated by devices and displays used (i.e., mobile, wearables, and large-screen displays). [PERSON] et al. identified nine common interactive elements in remote collaboration systems as 3D object manipulation, media sharing, AR annotations, 2D drawing, AR viewport sharing, mid-air drawing in 3D, hand gestures, shared gaze  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Remoteness** & **Mentoring** & **Domain** \\ \hline [PERSON] et al. [33] & No & No & Nursing Education \\ [PERSON] et al. [13] & No & No & Medical \\ [PERSON] et al. [22] & No & No & Nursing Education \\ [PERSON] et al. [25] & No & No & Medical \\ [PERSON] et al. [27] & No & No & Medical \\ [PERSON] et al. [34] & No & No & Medical \\ [PERSON] et al. [14] & Yes & Yes & Medical \\ [PERSON] et al. [23] & Yes & Yes & Medical \\ [PERSON] et al. [35] & Yes & Yes & Medical \\ [PERSON] et al. [26] & Yes & Yes & Medical \\ [PERSON] et al. [32] & Yes & Yes & Medical \\ [PERSON] et al. [15] & Yes & Yes & Medical \\ [PERSON] et al. [36] & Yes & Yes & Medical \\ [PERSON] et al. [37; 38] & Yes & Yes & Medical \\ STAR [16; 17; 18; 19] & Yes & Yes & Medical \\ [PERSON] et al. [20] & Yes & Yes & Medical \\ [PERSON] et al. [39] & Yes & Yes & Medical, Industrial \\ [PERSON] et al. [40] & Yes & Yes & Medical \\ [PERSON] et al. [21] & Yes & Yes & Medical \\ [PERSON] et al. [41] & Yes & Yes & Medical \\ [PERSON] et al. [42] & Yes & Yes & Industrial \\ [PERSON] et al. [31] & Yes & Yes & Medical \\ [PERSON] et al. [7] & Yes & Yes & Industrial \\ [PERSON] et al. [8] & Yes & Yes & Physical Task \\ \hline \hline \end{tabular}
\end{table}
Table 1: Post-2015 Remote Collaboration and Telementoring Studies.  
awareness, and conveying facial expression [5]. In our review of recent telementoring studies, we have similarly identified four main interaction styles as touch-based user interface, voice-based user interface, gesture-based user interface, and telestration user interface.
## 3 Research Design
### Explanation Methods
#### 3.5.9. BayLIME  
BayLIME [37] is a Bayesian adaptation of the well-known LIME (Local Interpretable Model-agnostic Explanations) framework. Instead of employing linear models as local surrogate models, as seen in the original LIME approach, BayLIME utilizes Bayesian regressors. The authors demonstrated that incorporating a Bayesian component enhances the consistency and fidelity of the explanations generated. Additionally, the Bayesian aspect of BayLIME allows for the visualization of uncertainty through the fluctuation of repeated explanations.
# Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning
## 1 Introduction  
While large-scale shopping malls, office buildings, and other multi-functional buildings meet diverse needs, the complexity of buildings has gradually increased. When disasters such as earthquakes and fires occur, the complex structures of buildings hinder evacuation and create a new safety threat. It is also difficult for crowds to identify an optimal escape route, owing to people's ignorance of the building environment, their limited vision, and them panicking. Under the influence of herd behavior, survivors are prone to cause congestion, or even trampling, risking significant additional loss of life [1]. Thus, a method for guiding crowd evacuation using the most effective route is of great significance for protecting lives and reducing personal and property losses during disasters.  
Researchers have developed several crowd evacuation guidance systems based on dynamic guidance signs [1; 2; 3; 4] to assist crowds to evacuate effectively during a disaster. Such systems can model building scenes, collect real-time information such as the disaster location and crowd distribution, use path planning algorithms to determine the optimal escape route, and induce the crowd's movement through dynamic guide signage, thus effectively improving crowd escape efficiency in emergencies. However, existing crowd evacuation guidance systems are inseparable from manual designs based on topological maps or grid forms and the manual input of model parameters according to the scene's characteristics. The manual workload for this process is considerable, and it is easy to introduce human errors, which may interfere with subsequent calculations for steps such as path planning.  
Therefore, this study proposed an end-to-end crowd evacuation guidance method--based on deep reinforcement learning algorithms--in response to this problem. An artificialintelligence agent was trained, which only takes the building layout as the input, automatically explores and learns the scenario model and path planning method while interacting with the environment, then discovers the optimal action strategy, and directly outputs dynamic signage information. For the actualization of this method, a reinforcement-learning agent-simulation interactive environment was designed, which is based on a social force crowd dynamics simulation. To solve the problem of \"dimensional disaster\" that occurs when deep Q network (DQN) [5], a typical method in deep reinforcement learning, is applied to crowd evacuation guidance, this paper proposed a combined action space DQN method (CA-DQN), which reduced the complexity of the network structure and improved the algorithm's practicality in complex architecture scenes.
## 2 Materials and Methods  
### Study Area  
This study was conducted in Harare metropolitan city, the largest and capital city of Zimbabwe (Figure 1). Harare metropolitan province is comprised of Harare Urban, Harare Rural, Chitungwiza, and Epworth areas (see Figure 1). The city is located between 17\({}^{\circ}\)40\({}^{\prime}\) and 1800\({}^{\prime}\) south and between 30\({}^{\circ}\)55\({}^{\prime}\) and 31\({}^{\circ}\)15\({}^{\prime}\) east, lies approximately 1500 m above mean sea level, and occupies approximately 942 km\({}^{2}\)[24]. According to [25], the geology to the north is dominated by gabbro and dolerite, the center by an intrusion of metagreywacke and phyllite, while granites are popular to the east and south. The study area is characterized by a warm, wet season from November to April; a cool, dry season from May to August, and a hot, dry season in October [24]. The dry May-to-August season was chosen for land cover mapping to be used in the study as its generally clear skies would provide the cloudless scenes required for the study. The city of Harare has a radial road network with the central business district (CBD) at its center and most industrial areas are located to the east and south [26]. To the north and northeast are low-density residential areas on plot sizes of about 1000 m\({}^{2}\) or more, while to the extreme east, south, southwest, and west are high-density residential areas on plot sizes of about 300 m\({}^{2}\). Some medium-density residential areas measuring between 800 and 1000 m\({}^{2}\) are found in the southern part of the study area [26].  
Figure 1: Location of the study area. District boundaries for Harare (Urban and Rural), Chitungwiza, and Epworth are overlaid on the Landsat 8 image in bands 4, 3, and 2 (R, G, and B) acquired on 20 June 2018.  
The population in Harare Urban District has been increasing at a fast rate since independence in 1980, increasing from 642,191 in 1982 to 1,485,231 in 2012 [27]. During the same period, the population in Harare Rural District increased from 16,173 to 113,599. The 2012 Zimbabwe population census put the population of Chitungwiza at 356,840 and that of Epworth at 167,462 [27]. Harare is the business capital of Zimbabwe owing to its wide range of commercial activities, although unemployment is high in the city. The city has several conditions favorable to urban food production, which include a relatively wet climate, large residential plot sizes, and large open spaces within the city boundaries [5]. Maize is the main crop produced during the wet season, while vegetables, fruits, and other horticultural products are produced throughout the year for consumption and sale. Due to the ever-rising cost of living and high unemployment rate in the city, produce from urban agriculture plays an increasingly important role, especially for the low-income strata.
## 4 Sensitivity Analysis for Single Factor
### The Effect of Ventilation Volume  
The primary factor influencing the effects of cooling and ventilation is the velocity of ventilation. Without changing the size of the ventilation duct, increasing the air volume represents an increase in the air velocity. In this section, ventilation velocities of 7.5 m/s, 8.5 m/s, 9.5 m/s, and 10.5 m/s were tested, while the other test parameters remained the same as in the previous section (the air outlet is 0.5 m away from the WF, the height of the ventilation duct is 0.025 m, the insulation layer length is 0.6 m, and the thickness is 0.01 m). Figure 6a depicts the change in the ATWA at different ventilation rates.  
With the gradual increase in the ventilation volume, the average temperature in the tunnel drops faster and eventually reaches a lower temperature in thermal equilibrium. The increase in the air velocity will bring about greater heat exchange, and the ATWA decreases from 29.5 \({}^{\circ}\)C to 27.6 \({}^{\circ}\)C as the ventilation velocity rises from 7.5 m/s to 10.5 m/s. However, the large ventilation volume will also carry more cold volume for heat exchange. The temperature difference between the curves in Figure 6a decreases with the increasing ventilation volume, and the reduction in ATWA is only 0.2 \({}^{\circ}\)C when the velocity changes from 9.5 m/s to 10.5 m/s, respectively. There is still a limit to improving the thermal environment by altering the ventilation volume in mine tunnels. The temperature distribution at 0.1 m from the floor is shown in Figure 6b. It can be found that the area with a high temperature decreases significantly when the ventilation volume rises in the range of 7.5 m/s to 9.5 m/s. As the air velocity increases, the temperature in the region with a faster airflow falls. Furthermore, a large amount of air is pushed into the tunnel, which drives the vortex area back and raises the temperature in the back area of the working field. Therefore, blindly increasing the air volume will not necessarily bring a better cooling effect, but it will multiply the ventilation load and have an impact on the dust removal situation at the WF.
## 3 Influence of Boundary Distance in the Integral Response Deformation Method
### Influence of the Bottom Artificial Boundary Distance  
For underground structures located far above bedrock, positioning the bottom boundary at the bedrock surface can lead to an excessively large computational domain, substantially increasing computational complexity and resource demands. To balance efficiency and accuracy, this study examines the influence of the bottom boundary distance on the computational precision of the IRDM. A dimensionless bottom boundary distance ratio, \(R_{\text{B}}=H_{\text{B}}/H\), is introduced for parametric analyses with \(R_{\text{B}}\) set to 0.5, 1.0, 2.0, 3.0, 4.0 and 5.0. Moreover, the lateral boundaries are fixed at three times the structural width, following the findings from Section 3.1.
# Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection
## 5 Conclusions  
The demand for using more precise and effective IDS has grown more critical due to the quick increase in network traffic and the development of intrusions. Therefore, a deep learning-based network intrusion detection was implemented in this research. The outcomes demonstrated the performance of the proposed approach in terms of recognizing and categorizing cyber-security threats. Different performance metrics, including accuracy, F-score, recall (sensitivity), and precision (detection rate) were used in the evaluation process to analyze the usefulness of the suggested models on the three benchmark datasets. In contrast to previous attack detection techniques, the proposed framework achieved superior results with 99.38%, 99.26%, and 99.43% accuracy for Bot-IoT, CICIDS2017, and UNSW_NB15 datasets, respectively. This outcome was attained by the ABC-based feature selection method, which improved the data quality. Based on the findings of this study, it is determined that the recommended model will help create a successful intrusion detection system with a high detection rate. In the forthcoming endeavors, there will be a focus on refining the suggested IDS to detect different categories of attacks. Furthermore, the suggested approach has the potential to be adapted and utilized in a robust security application.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**With Feature Selection**} \\ \hline Dataset & Precision & Recall & F1 & Accuracy \\ \hline Bot-IoT & 99.46 & 99.61 & 99.49 & 99.38 \\ \hline CICIDS2017 & 99.19 & 99.15 & 99.17 & 99.26 \\ \hline UNSW-NB15 & 99.39 & 99.41 & 99.38 & 99.43 \\ \hline \multicolumn{4}{c}{**Without Feature Selection**} \\ \hline Bot-IoT & 99.37 & 99.53 & 99.31 & 99.29 \\ \hline CICIDS2017 & 99.06 & 99.03 & 99.04 & 99.17 \\ \hline UNSW-NB15 & 99.28 & 99.30 & 99.26 & 99.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with and without feature selection.  
**Author Contributions:** Conceptualization, M.B. and R.N.J.; formal analysis, A.A., M.B. and F.S.; funding acquisition, Y.I.C.; investigation, A.A. and R.N.J.; methodology, M.B.; project administration, Y.I.C.; software, M.B.; supervision, A.A.; validation, R.N. and F.S.; writing--original draft, M.B., A.A. and F.S.; writing--review and editing, A.A., Y.I.C. and M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by Korea Agency for Technology and Standards in 2022, project numbers are K_G012002236201, K_G012002234001 and by the Gachon University research fund of 2022 (GCU- 202300770001).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments:** The authors would like to thank the editor and anonymous referees for their constructive comments toward improving the contents and presentation of this paper.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 3 System Model and Problem Formulation
### Problem Formulation  
According to (7), it is clear that the total data arriving at the network affects the throughput of the satellite network. Thus, optimizing the data acquisition rate can enhance the network transmission capacity while avoiding congestion and achieving system stability. On this basis, we give the objective function, which maximizes the time-average system utility (MTASU), as  
\[\text{MTASU}:\max_{\Theta(t)}\lim_{\text{T}\rightarrow\infty} \frac{1}{\text{T}}\sum_{t=0}^{\text{T}}\mathbb{E}[\mathcal{H}(t)]\] \[s.t.\text{ }feasible\text{ }contact\text{ }constraints:\text{ (\ref{eq:1})--(\ref{eq:2})};\] \[\text{ }data\text{ }transmission\text{ }constraints:\text{ (\ref{eq:3})--(\ref{eq:7})};\] \[\text{ }energy\text{ }management\text{ }constraints:\text{ (\ref{eq:8})--(\ref{eq:11})}.\]  
Wherein \(\mathcal{H}(t)=U[\varphi_{i}(t)]=\sum_{i=1}^{\text{M}}\log\,(1+\varphi_{i}(t))\) is the system utility. \(\Theta(t)\) is defined as the set of vectors in slot \(t\), including ISL contact state, data acquisition rate, inter-satellite transmission rate, energy consumption, and energy harvesting, which can be written as \(\Theta(t)=(\delta_{ij}(t),\varphi_{i}(t),\gamma_{i}(t),E_{i}^{c}(t),E_{i}^{h}(t))\).