esa-sceva/satcom-chunk-collection huggingface dataset:
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results
### Results of Aberfoyle Forest
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Shape.** & **Band a** & **Ratio b/a** & **Weight** & **Adapt** & **Match** & **Over Segment** & **Under Segment** \\ \hline Sphere1 & 7 & 1 & N & Y & 0.8000 & 0.1111 & 0.0889 \\ Sphere2 & 2 & 1 & N & N & 0.8000 & 0.1556 & **0.0444** \\ Sphere3 & 2 & 1 & Z & N & 0.8000 & 0.1556 & **0.0444** \\ Cylinder1 & 6 & 2 & N & Y & **0.8222** & 0.0444 & 0.1333 \\ Cylinder2 & 7 & 2 & Z & Y & **0.8222** & 0.0444 & 0.1333 \\ Cylinder3 & 2 & 2 & N & N & 0.7333 & 0.0889 & 0.1778 \\ Cylinder4 & 2 & 2 & Z & N & 0.6667 & 0.1111 & 0.2222 \\ Pollock1 & 6 & 2 & N & Y & **0.8222** & 0.0222 & 0.1556 \\ Pollock2 & 7 & 2 & Z & Y & **0.8222** & 0.0222 & 0.1556 \\ Pollock3 & 2 & 2 & N & N & 0.7556 & **0** & 0.2444 \\ Pollock4 & 2 & 2 & Z & N & 0.7111 & 0.0444 & 0.2444 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tree segmentation results of the Aberfoyle forest using mean shift under various settings. Figure 5: Segmentation and tree top detection results of the Aberfoyle forest from three methods (scale in meters): (**a**) Mean shift with Cylinder kernel; (**b**) Mean shift with Pollock kernel; (**c**) Marker controlled watershed method. Red points denote true tree tops, and blue ones are detected tops. smallest RMSE of tree locations (RMSE_xy) was from the Pollock kernel when the crown was centered, which also produced a smaller RMSE of tree height (RMSE_h). The same applied to the Cylinder kernel. Compared to other methods, the lowest match rate, reflected in Figure 5c, were obtained from the watershed method. In addition, the Pollock kernel generates the lowest RMSEs of crown spreads (RMSE_1 and RMSE_1), compared to the Cylinder kernel and the compared methods.
###### Abstract  
Underwater acoustic sensor networks (UASNs) are vital for applications like marine environmental monitoring, disaster prediction, and national defense security. Due to the prolonged exposure of underwater sensor nodes in unattended and potentially hostile environments, the application of UASNs is confronted with numerous security threats. Trust models are an important means to detect anomalous nodes in UASNs and ensure security. However, when confronted with intricate underwater surroundings, the assessment of trust is prone to disruption, and current trust models lack a flexible mechanism for updating trust. Consequently, this study introduces a dynamic evaluation trust model (DRFTM) for underwater acoustic sensor networks that integrate deep reinforcement learning and the random forest algorithm. First, the DRFTM comprehensively considers indicators including communication, data, energy, and environment to provide reliable trust evidence for the next evaluation; second, under the conditions of node mobility and dynamic updating of network topology, we propose a predictive model for assessing the trust status of sensor nodes based on random forest training; last, the utilization of deep reinforcement learning is instrumental in determining the most effective trust update strategy, leading to improved detection accuracy of the trust model. The simulation results demonstrate the effectiveness of the DRFTM in detecting malicious nodes, reducing false positives, and accurately assessing trust, achieving a remarkable 99% accuracy in identifying malicious nodes.  
random forests; deep reinforcement learning; trust evaluation model; trust update mechanism; underwater acoustic sensor networks (UASNs) +
Footnote †: journal: Applied sciences  
0000-0001-9993-3899][PERSON]  
1 School of Control and Mechanical Engineering, Tianjin Chenglian University, Tianjin 300384, China; [EMAIL_ADDRESS]
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 3 Results
### Walking Scenario
The higher the transmit power, the higher the RSSI, and the taller the periodic peak shape, with a greater range between its maximum and minimum values. Figure 16 shows this phenomenon for Subject A, for all the three frequency bands and with the sender node on the wrist location. As can be seen, this is more noticeable for the higher frequencies. Since the walking motion was maintained in a uniform manner for the duration of the experiment, the percentage of time in LOS and NLOS does not change with transmit power. Observing the charts, it can be seen that the average maximum value of the periodic shape increases more significantly with the increase in transmit power than  
Figure 15: RSSI variation with time in the walking scenario for the three subjects on the 868 MHz band for \(-2\) dBm of transmit power and with the sender node located on the right ankle: (**top**) Subject A, (**middle**) Subject B, (**bottom**) Subject C.  
the case of the average minimum. This suggests that an increase in transmit power has a higher impact on the LOS part of the gait cycle, affecting the NLOS transmission in a much weaker way, when body tissue obstructs the signal path.
## 5 Thermochemical Conversion of Carbonaceous Feedstocks
### Hydrothermal Liquefaction  
Hydrothermal liquefaction (HTL) is a thermochemical conversion process that transforms wet biomass into liquid biofuel, known as bio-oil. This process takes place at a high temperature and pressure in the presence of water [141]. Figure 11 shows the operating temperature range of the HTL process.  
Hydrothermal processes may be carried out under a wide range of conditions. At low temperatures (75 \({}^{\circ}\)C and 15 bar), a process known as steam explosion occurs, which produces an increment in chemical reactivity and can be seen as a pre-treatment step to separate biomass into its major components for further bioprocessing. As the temperature increases, the macromolecules of the biomass (cellulose and lignin) break up to produce shorter molecules. This leads to hydrothermal carbonization, which occurs at temperatures of 180-250 \({}^{\circ}\)C and under pressures of 20-100 bar. However, some fragments polymerize to produce oily substances, which is known as HTL. At pressures of 50 to 200 bar and at temperatures in the range of 200-374 \({}^{\circ}\)C, this is the dominant pro  
Figure 11: HTL process occurs in a temperature range of 250–400 \({}^{\circ}\)C. After the critical point, the process enters a gasification zone where higher gas yields are achieved (adapted from [142] and data from [143]).  
critical point of water, gaseous products like methane, carbon dioxide, and hydrogen predominate in the SCWG process. If the main interest is to produce hydrogen, temperatures and pressures above 600 \({}^{\circ}\)C and 300 bar, respectively, are needed.  
Due to the high thermal capacity of the biomass-water mixture, hydrothermal processes (HTPs) have higher energy requirements than other transformation processes, like pyrolysis of dry biomass (when the drying heat is not accounted for). Therefore, the possibility of providing this heat through solar energy is very attractive. A few small-scale experimental studies have been carried out with solar reactors for steam explosion, hydrothermal carbonization, HTL, and SCWG. Solar technologies, including PTC, parabolic dishes, and solar furnaces, are utilized to irradiate the high-pressure reactors' walls. However, from the point of view of scalability, theoretical studies have emphasized a different option, indirect heating, where a thermal fluid is first heated in the solar field and then used to heat the reactor. No practical demonstrations of this concept have been reported.  
Among the hydrothermal processes described above, HTL and SCWG are well suited to the operation temperature of solar MSs. In fact, CSP technologies can be used as a heat source of this process, but the use of MSs requires a minimum temperature of 290 \({}^{\circ}\)C.  
Likewise, the cost assessment of the pyrolysis process is not totally clear in the literature. However, it is possible to claim that the minimum fuel selling price of bio-oil is around 2.07 EUR/kg [144].
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 5 Simulation Results  
In this section, we evaluate the complexity and NMSE performance of three categories of algorithms through Monte Carlo simulation with the following parameters: (1) Both transmitter and receiver are equipped with ULAs where \(N_{T}=N_{R}=16\) and \(N_{RF}^{R}=N_{RF}^{T}=N_{RF}=4\). (2) The \(\left\{\sin\theta\right\}\) appearing in the array response vector is uniformly distributed in [\(-\)1,1) [25]. (3) The received SNR is defined as \(SNR_{n}\)=\(10\text{log}_{10}\left(P/\sigma^{2}\right)dB\). In addition, the hardware impairment at transmitter is defined as \(SNR_{e}\)=\(10\text{log}_{10}\left(1/k_{e}\right)dB\), where the ratio coefficient \(k_{e}\) reflects the degree of hardware impairment. Without loss of generality, we set \(k_{e}=0.05\), which is larger than that in the long-term evolution (LTE) standard as the signals in mmWave band are more vulnerable to hardware impairments [14]. In addition, we take the NMSE as the performance metric to evaluate the estimated quality, which is defined as  
\[NMSE\overset{\Delta}{=}E\left\{\left\|\hat{\mathbf{H}}-\mathbf{H}\right\|_{2}^ {2}/\left\|\mathbf{H}\right\|_{2}^{2}\right\} \tag{24}\]  
where \(\hat{\mathbf{H}}\) denotes the estimated channel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Computational Complexity** \\ \hline Convex Relaxation Algorithms [9] & \(O(N_{RF}^{R}N_{RF}^{T}(N_{T}+N_{R})L^{2})\) \\ Greedy Iterative Algorithms [25] & \(O(N_{T}N_{R}L)\) \\ Bayesian Inference Algorithms [30] & \(O(N_{R}log^{2}N_{R})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity comparison of the three categories of algorithms.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 3 System Model for the Navigation System
### Measurement Model of the System
#### 3.2.4 Optical Flow Measurement Equation  
The downward-looking visual system is called the PX4-FLOW sensor, which is an open source embedded metric optical flow CMOS camera designed for indoor and outdoor applications. The raw pixel movement data are used to calculate the horizontal velocity. The observation model of the optic-flow sensor is given by  
\[v_{optic}=kv+\omega_{optic} \tag{7}\]  
where \(v_{optic}\) is the velocity acquired by the optic-flow sensor, \(v\) is the true horizontal velocity, \(\omega_{optic}\) is the white noise of optic-flow observation, and \(k\) represents the coefficient determined by the camera focal length and altitude of the MUAV.
### Motivation and Contributions  
The requirement for innovative spectrum consumption, minimum interference and high security in a 6G wireless communication system motivated us to propose the R-IBFD algorithm. After a thorough study, it has been observed that co-channel interference is the main problem for enabling FD and NOMA in a communication system. A number of algorithms are given by different authors, however, due to the high connectivity of multiple devices, the security problem is not completely resolved. Keeping that in mind, the following are the contributions of this literature:  
* A brief literature review for IBFD, NOMA and their system requirements in 6G technology for developing a background for the reader.
* Propose and discuss a novel Reflective In-Band Full-Duplex (R-IBFD) algorithm.
* Use R-IBFD for interference management and security enhancement.
* Derive secrecy outage probability for R-IBFD, for selection of a relay amongst K relays.
* Show the usefulness of the proposed R-IBFD with ML for the forthcoming 6G system with numerous devices and large data.
* Simulate R-IBFD for N-number of users to show minimal interference and security management as compared to baseline NOMA and HD.  
This paper is an extension of a published paper in a conference [36] where the proposed algorithm is explored for a two-user system and verified the novelty using secrecy outage probability and throughput of the system.
## 5 Simulation Results
### Buffer Compliance Control Performance Simulation in Stable Control Phase  
To show the buffer compliance control performance of the proposed controller, simulations are carried out for stable control phase. The actual parameters of the system are as follows: \(\textbf{{K}}_{2}=\text{diag}(5,5)\), \(\textbf{{\Lambda}}=\text{diag}(5,5,5)\), \(\textbf{{K}}_{z}=\text{diag}(400,400,400)\), \(\varepsilon=0.5\), \(\textbf{{K}}_{a}=\text{diag}(20,20,20)\), \(\textbf{{K}}_{b}=\text{diag}(50,50,50)\), \(\eta=1\), \(\textbf{{K}}_{c}=\text{diag}(10,10,10)\). In pre-impact phase \(\boldsymbol{q}_{\theta}=\left[90^{\circ},45^{\circ},45^{\circ}\right]^{\text{T}}\), assuming that the space robot system capturing a non-cooperative spacecraft at \(t_{0}=0\) s. At this time, the velocity of the spacecraft is \(v_{t}=\left[0.45\text{ m/s},0.45\text{ m/s},0.5\text{ rad/s}\right]^{\text{T}}\), the desired trajectory of post-capture hybrid system is \(\boldsymbol{q}_{\theta d}=\left[100^{\circ},30^{\circ},60^{\circ}\right]^{ \text{T}}\). Assume that when the joint actuators running, the limit of the impact torque it can bear is 90 N\(\cdot\)m. In order to protect the joint actuators, the buffer compliance control strategy of active opening and closing actuators (named switching strategy) is adopted. The shutdown torque threshold is 60 N\(\cdot\)m, and the startup torque threshold is 6 N\(\cdot\)m. The simulation results are shown in Figures 5-10.  
Figure 8: Trajectory tracking of base attitude.  
Figure 6: Joints’ impact torque with switching strategy.  
Figure 7: The evaluation signal.  
Figure 5 shows the impact torque acting on the joints when not adopting the switching strategy, where it can be found that the impact torque still exceeds the safety threshold of the joint at this time. Figure 6 shows the impact torque acting on the joints when adopting a switching strategy. By comparing Figures 5 and 6, it can be seen that the impact torque acting on the joints can be limited within a safe range by combining with the buffer compliance control, which ensures the protection of the joint motor during the stable control phase. Figure 7 shows the evaluation factor signal. It can be found that the ACN is optimized through the interaction with the environment and the reward signal is obtained, and finally reaches the stable state.  
To show the effectiveness of the defined reinforcement signal, the tracking accuracy is quantitatively analyzed by comparison of the trajectory tracking error of the proposed RL control scheme, RL with robust controller off and neural network control strategy without reinforcement signal (turn off RL). The mean absolute error \(\text{MAE}=\frac{1}{n}\frac{n}{i=1}|e_{i}|\) was used to evaluate the tracking accuracy, the simulation results are shown in Table 2. It can be seen in Table 2 the mean absolute error of proposed RL is smaller than the other control strategies, which shown that the proposed control method has high tracking accuracy and good tracking performance.  
Figure 10: Trajectory tracking of joint angle 2.  
Figure 9: Trajectory tracking of joint angle 1.
## 3 Results and Discussion
### The Changes in Pore Volume and Surface Area  
The volumes and surface areas of the pores are tabulated in Table 2. To determine the changes in pore volumes and surface area that is due to Sc-CO\({}_{2}\) extraction, \(\Delta V\) and \(\Delta S\) were defined by the following equations, respectively:  
\[\Delta V=V_{\rm A}-V_{\rm B} \tag{2}\]  
\[\Delta S=S_{\rm A}-S_{\rm B} \tag{3}\]  
where \(\Delta V\) is the pore volume change due to Sc-CO\({}_{2}\) extraction, cm\({}^{3}\)/g; \(V_{\rm A}\) is the pore volume of the sample after the Sc-CO\({}_{2}\) extraction, cm\({}^{3}\)/g; \(V_{\rm B}\) is the pore volume of the sample before the Sc-CO\({}_{2}\) extraction, cm\({}^{3}\)/g; \(\Delta S\) is the pore surface area change due to the Sc-CO\({}_{2}\) extraction, m\({}^{2}\)/g; \(S_{\rm A}\) is the pore surface area of the sample after the Sc-CO\({}_{2}\) extraction, m\({}^{2}\)/g; and \(S_{\rm B}\) is the pore surface area of the sample before the Sc-CO\({}_{2}\) extraction, m\({}^{2}\)/g. Table 2 and Figure 4a show that the \(\Delta V_{\rm Ma}\) and \(\Delta V_{\rm Me}\) of the coal are positive, and most of \(\Delta V_{\rm Tr}\) and \(\Delta V_{\rm Mi}\) are positive, indicating that the Sc-CO\({}_{2}\) extraction causes the incremental pore volume to increase, and the incremental macropore volume makes up most of the total pore volume. From Figure 4a, it can be seen that the changes in the incremental macropore and mesopore volumes fluctuate with coal rank before and after the second coalification. The change in the incremental transitional pore volume has an overall increasing trend when coal rank increases, while the change in the incremental micropore volume first increases and then decreases. Table 2 and Figure 4b show that the \(\Delta S_{\rm Ma}\) and \(\Delta S_{\rm Me}\) are positive with small values, the \(\Delta S_{\rm Tr}\) and \(\Delta S_{\rm Mi}\) are positive and negative before and after the second coalification jump; this indicates that the Sc-CO\({}_{2}\) extraction causes the incremental micropore surface area to change significantly, and that the incremental micropore surface area is the mainly contributor to the total pore surface area. Some studies [7, 8] have provided evidence that significant changes in pore structures occur during coalification. At the early stage, aliphatic groups containing oxygen link the multilayered stacks and are not yet dissipated as volatiles in the coalification. Pore structures are compressed as the coalification increases, and thus the aliphatic groups begin to dissipate and fill the pores and cause the size of micropores to be reduced. As maturation advances, oxygen-containing compounds and other volatiles are lost. Therefore, the changes in pore structure are very significant near the second coalification jump by Sc-CO\({}_{2}\) extraction. This indicates that the changes in pore structure due to Sc-CO\({}_{2}\) extraction are controlled by the coalification.
## 4 Results and Discussions
### Laboratory Testing Results
#### 4.1.2 Tensile Strength Results  
Figure 4 shows the variation in tensile strength across different concrete samples with various fibre types and compositions. Error bars indicate the standard deviation in strength between the samples. The addition of fibres led to an increase in tensile strength, with KFT reaching 3.58 MPa at 28 days, compared to the control, which measured 3.41 MPa at the same age. MKT also showed similar results, achieving 3.38 MPa at 28 days. The chemical composition of metakaolin (MK) in the concrete mix appears to influence tensile strength through several factors, such as pozzolanic reactivity, microstructural features, and the synergy between fibres. MK contains amorphous silica and alumina, both highly reactive within cementitious materials. This improved bonding capacity, which allows for better stress transfer between the two materials, reduces microcrack formation [67]. MK also helps improve particle packing by reducing the pore structure. However, it is worth noting that a 5% cement reduction with MK does not completely prevent the formation of calcium hydroxide that can degrade fibres within the matrix. This is shown in the lower tensile strength of MKT at 28 days.  
SFKF5 recorded the lowest tensile strength among the samples, which can be attributed to fibre degradation within the matrix. Natural fibres, in comparison to synthetic, are more  
Figure 4: Tensile strength results after 28 days.  
Figure 3: Compressive strength results after 28 days.  
susceptible to degradation in cementitious materials. These fibres absorb moisture from the matrix, leading to dimensional changes and weakening over time. This absorption and release of moisture contribute to physical degradation, reduced strength, and loss of structural integrity. Additionally, natural fibres are more susceptible to alkali attacks in a cementitious matrix, which further weakens the fibre structure and causes degradation. However, pairing SFKFs with textile fibres led to a notable strength increase. The fine surface area of textile fibres helped create a denser matrix by reducing the composite's porosity. When both types of fibres are used together, the load transfer within the matrix can shift between fibre types, creating a stronger composite by distributing forces more evenly and minimizing strain on any one fibre type. The optimal cement replacement in this mix design is 5%, as demonstrated in KFT, but only when textile fibres are integrated into the mix.  
The laboratory testing results are essential, as they will ensure the product is suitable for industry implementation and can be certified prior to benchmarking for economic or environmental performance.
# Novel Drug Combinations in Lung Cancer: New Potential Synergies Between 5-FU and Repurposed Drugs
## 3 Results
### The Effect of Tezosentan as a Single Agent and in Various Combinations with 5-FU  
Treatment of A549 cells with Tezosentan did not exhibit any significant anticancer effects at any concentration, according to the MTT assay results (Figure 4). The combination of Tezosentan with 5-FU also failed to show notable cytotoxicity compared to 5-FU alone. However, the combination did result in significant reductions in cell viability across all tested concentrations, with decreases ranging from 26% to 28% compared to Tezosentan alone. Furthermore, morphological evaluations revealed that 5-FU was the sole active agent in this cell line, as Tezosentan did not induce any changes in cell phenotype. Observable morphological alterations were only seen in the combinations that included 5-FU, implying that these changes were attributed to the effects of the antineoplastic drug 5-FU (Figure 5).  
Figure 4: Cell viability of A549 cells following treatment with Tezosentan alone and in combination with 5-FU. Data are presented as percentages relative to the control group and represent the mean \(\pm\) SD. Each experiment was performed independently three times (\(n=3\)); * statistically significant vs. drug alone at \(p<0.05\); *** statistically significant vs. control at \(p<0.0001\). The green, red, black and gray bars are used solely to differentiate between data groups and do not represent specific experimental variables.  
Figure 5: Morphological assessment of A549 cells treated with 5-FU, Tezosentan alone, and their combination. Cells were exposed to the vehicle (0.1% DMSO) as a control. The results are representative of three separate experiments. Scale bar: 50 \(\upmu\)m.