esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Target Allocation
#### 2.3.2. Based on the Greatest Distance Advantage  
In practical applications, the flight time of the loads affects the timeliness of task allocation to a certain extent, and the distance advantage is also an important factor in the cluster target allocation scenario. Hence, inspired by the concept of the Traveling Salesman Problem, we incorporated the optimization function pertaining to flight time cost as a pivotal evaluation metric for task allocation. Subsequently, the formulation of this time cost  
Figure 2: Integer encoding model.  
optimization function was established, as detailed in Equation (3), offering a quantitative basis for assessing and enhancing operational efficiency.  
\[P_{2}=\sum_{i=1}^{d}\sum_{j=1}^{c}\ \frac{D_{ij}}{\overline{v}} \tag{3}\]  
where \(D_{ij}\) is the distance between the load agent \(M_{i}\) and the target agent \(T_{j}\), and when the decision variable \(x_{ij}\) is 0, it is also set to 0; \(\overline{v}\) is the average flight speed of the load agent \(M_{i}\).
## 6 Experimental Results and Discussion
### Evaluation of the Reconstruction under Different Tampering Rates  
In this section, the results for the evaluation of the reconstruction process are reported. The reconstruction of the test images was evaluated using both alteration schemes. For the first scheme, which is illustrated in Figure 4, the six versions of the reconstruction watermark are utilized for reconstruction. The inpainting method was performed to regenerate the information affected by the tampering coincidence problem.  
Figure 6: Multiple and irregular alterations, (**a**) Kodak-1 ground truth, (**b**) Kodak-3 ground truth, (**c**) Kodak-11 ground truth, (**d**) Kodak-14 ground truth, (**e**) detection for Kodak-1, (**f**) detection for Kodak-3, (**g**) detection for Kodak-11, (**h**) detection for Kodak-14.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **10\%** & **20\%** & **30\%** & **40\  
Figure 7 shows the reconstructed images for Kodak-23. As can be noticed, the reconstruction capability for each alteration rate markedly dropped according to the increment in the alteration rate. Nevertheless, the original content of the image can be clearly distinguished.  
Additionally, it can be observed that a granulated effect is obtained according to the alteration rate. The inpainting process gives this effect due to the replenishment of affected areas by the tampering coincidence problem, with neighboring pixels' intensity values detected as authentic ones.  
Finally, this process was executed on all the test images using the first alteration scheme. Figures 8-10 report the PSNR, SSIM and PSNR-HVS-M results after the reconstruction process, correspondingly. These graphics show an average decrease of 12.72 dB for PSNR and 14.41 dB for PSNR-HSV-M in reconstructing the modification rates between 10% and 90% of the image and an average reduction of 0.3516 for SSIM values. The results can be considered acceptable given the high alteration rate to which the images are subjected. A point worth mentioning is that other works usually test their methods with an upper boundary of 50% of modification since they do not consider the tampering coincidence problem consequences in their entirety.  
Figure 7: Quality measures values (PSNR/SSIM/PSNR-HVS-M) for Kodak-23 reconstruction given different alteration rates: (**a**) 20%, 37.91/0.9762/34.69; (**b**) 30%, 31.99/0.9552/28.82; (**c**) 40%, 31.26/0.9401/27.96; (**d**) 50%, 30.66/0.9250/27.47; (**e**) 60%, 29.96/0.9079/26.68; (**f**) 70%, 28.21/0.8739/24.69; (**g**) 80%, 27.25/0.8418/23.28; (**h**) 90%, 25.21/0.7854/20.47.
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 2 Compression Scheme
### Entropy Encoding  
Entropy coding techniques are used to encode the residual error after quantization in predictive compression. The adaptive arithmetic encoder is adopted in the encoding stage, whose compression ratio is near the theoretical entropy. The quantization residual is represented by 16 bits, meaning that the arithmetic code's codebook needs 65,536 symbols. Based on the probability distribution of the prediction residual, most symbols are not used. Therefore, an adaptive codebook is adopted. The initial codebook contains two symbols: 0 and ESC. When a new symbol needs to be encoded, the encoder will use the probability of ESC to encode it, and the 16-bit symbol will be appended to the code stream. The code book absorbs the new symbol after the symbol has been output to the stream.
## 3 Mirror Optimization Design
### Optimization Problem Construction  
In addition to the mirror's surface shape error, the mass of the mirror is also something to be considered. Thus, the mass and RMS value of the surface shape error were selected as the optimization objectives. Considering that the mirror as one part of the optical system was mounted on an aerial platform, the altitude of the mirror changed with the platform altitude and the rotation of the gimbal system during working process; therefore, the average RMS value (\(\overline{\text{RMS}}\)) of the surface shape error in three directions was taken as the optimized objective function. Consequently, the optimized objective functions in this paper were as follows:  
\[\text{min}y=p(\mathbf{X})=(p_{1}(\mathbf{X}),p_{2}(\mathbf{X})),\mathbf{X}\in\mathbf{\Omega} \tag{5}\]  
where \(\mathbf{X}\) is the optimization variable, \(\mathbf{X}=\{D_{1},D_{2},L_{1},L_{2}\}\); and \(\mathbf{\Omega}\) is the sample space.  
\[p_{1}(\mathbf{X})=\text{Mass}(D_{1},D_{2},L_{1},L_{2}) \tag{6}\]  
\[p_{2}(\mathbf{X})=\overline{\text{RMS}} \tag{7}\]  
where  
\[\overline{\text{RMS}}=\frac{1}{3}\big{(}\text{RMS}_{\text{x}}(D_{1},D_{2},L_ {1},L_{2})+\text{RMS}_{\text{y}}(D_{1},D_{2},L_{1},L_{2})+\text{RMS}_{\text{z }}(D_{1},D_{2},L_{1},L_{2})\big{)} \tag{8}\]  
The parameters involved in the whole optimization process were the material properties of the mirror and the optimization parameters in the non-dominated sorting genetic algorithm. Table 1 lists all the parameter settings and the constraints for the optimized structural parameters.  
During the process of finite-element analysis using Abaqus, a tetrahedral mesh type was adopted, and meshes were generated automatically by the software. In order to ensure accuracy of the results, the mesh size needed to be controlled. Here, the global size of mesh was less than 5 mm. In addition, the working condition of concern was to apply 1 G gravity along the Y direction.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Mirror Material Properties**} \\ \hline
**Components** & **Value** \\ \hline Density & 2.7 g/cm\({}^{3}\) \\ \hline Young’s Modulus & 70 Gpa \\ \hline Poisson’s Ratio & 0.27 \\ \hline \multicolumn{2}{c}{**NSGA parameters**} \\ \hline Population size & 20 \\ \hline Mutation probability & 0.15 \\ \hline Cross probability & 0.8 \\ \hline Number of iterations & 35 \\ \hline \multicolumn{2}{c}{**Structural parameter constraints**} \\ \hline \(D_{1}\) & \([60,80]\) \\ \hline \(D_{2}\) & \((0,D_{1}-10]\) \\ \hline \(L_{1}\) & \([0,15]\) \\ \hline \(L_{2}\) & \([17.6,22.6]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization Parameters.
## 2 Components and Methods
### Structure and Parameters of Components
Next, the DP460 NS epoxy resin adhesive is evenly applied to the surface of each branch rod and successively threaded through the copper heat sink, conical sleeve and chuck. The chuck is hydraulically pressed into the cone sleeve to ensure a tight fit. Then, the gap between the support rods is filled with epoxy resin to enhance the firmness of the connection. After application, the components are placed in a small baking box to cure the epoxy resin. After curing, an aluminum film is wrapped around the surface of the rod to reflect thermal radiation. Finally, the rod body is tensioned several times by the tension machine to eliminate the gap between the parts and release the stress to ensure dimensional stability and strong structure. Figures 3 and 4 show the finished product of the tie rod group assembly. Elastic chucks and cone sleeves are reusable. When the rod body is damaged or the length is not appropriate, the chuck is heated to 270-300 \({}^{\circ}\)C, at which moment the epoxy resin will soften and break down [25], the carbon fiber tow can be easily extracted and the chuck can be knocked out of the cone sleeve to achieve the dismantling of the parts. This heating temperature does not affect the structural strength and geometric characteristics of the metal parts. After the residual epoxy is removed, the part can be used again for assembly. The dimensions of the carbon fiber rod body of these four designs are shown in Figure 5 and Table 2. For each of the above four alternatives, samples are made for experiments. For control variables, the CFRP rod body of all tie rod samples was made of T300 carbon fiber composite, as shown in Table 3. Additionally, the low-temperature environment mainly leads to the increase in brittleness of the bar, but does not significantly reduce its axial tensile strength. Figure 4: Details of the components of the type D tie rod group. Figure 5: Dimensions diagram of CFRP rod body of four tie rod types: A, B, C and D.
## 2 Literature Review and Motivation of This Work
### Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Topic** & **HO** & **TO** & **Limits** & **Contribution** \\ \hline
[43] & Ultra-dense network & Yes & Yes & The parameter in optimization function leads into sub-optimum MEC and lower performance & Minimized average delay subjected to communication, computation, and handover under the limited energy budget of users. \\ \hline
[31] & Ultra-dense network & No & Yes & No handover (mentioned as challenge but not considered). & Computation offloading for multi-access MEC in UDN is investigated \\ \hline
[8] & Fog computing & No & Yes & Mobility model has no handover cost (mentioned as challenge but not considered) & Task offloading and migration schemes are studied in fog computing \\ \hline
[33] & Fog computing & Yes & Yes & The energy consumption of UEs is not considered (i.e., dedicated as a future work) & - & Offloading delay and handover cost are considered as performance metrics; The RSU considered the targeted node based on vehicle mobility and dynamic computation resources. \\ \hline
[47] & Deep reinforcement learning & No & Yes & The tasks are completed before handover (i.e., only controls HO) No handover cost & - & DRL is used for offloading scheduling in VEC; The trade-off between task latency and energy consumption is considered by scheduling tasks to wait in the queue. \\ \hline
[48] & Mobility management using reinforcement learning & Yes & Yes & The energy consumption of UEs is not considered. & An online RL was proposed to optimize handover decisions by predicting user movement trajectory and periodic characteristics of the number of users. \\ \hline
[49] & Edge autonomous energy management & No & No & Only energy managed & An RL-based droplet framework is used Droplets learn energy consumption statistics of the devices. \\ \hline Our work & Mobility-aware offloading decision & Yes & Yes & In the offloading process, the BSs are considered to be all ON. & - & Concurrently considering HO delay, UE energy, and TO. Optimized parameters are used to increase overall performance. User-centric approach learns BS-side information without prior knowledge. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._task offloading and wireless power transformation (WPT) are jointly optimized. Moreover, the authors proposed a resource allocation scheme to minimize energy consumption. There are important limitations in the previous summarized literature in that these works do not include HO cost in the optimisation function. Additionally, either energy factor or delay is considered in these methods where the optimum parameter cannot be selected effectively.
# Effect of Wood Densification and GFRP Reinforcement on the Embedment Strength of Poplar CLT
###### Abstract
[PERSON] and [PERSON] investigated the mechanical strength of CLTs manufactured from densified wood [42]. The use of densified timber enhanced the CLT's bending strength and rigidity, with an increase in modulus of elasticity (MOE) and modulus of rupture (MOR). A similar study on the use of densified wood was conducted by [PERSON] et al. [43], who manufactured and tested densified plywood. MOE, MOR, and shear strength increased, as did the bonding quality of the adhesives between panels. There is currently a knowledge gap on evaluating the effects of densification and GFRP reinforcement on the embedment strength of CLT manufactured from fast-growing poplar wood. Thus, this study aims to compare the embedment strength of CLT made of densified timber with that of reinforced with GFRP in two loading directions in order to determine which reinforcing technique may be more advantageous. In addition, different approaches are used to calculate the embedment strength and find the model yielding minimum error compared to the experimental data.
## 3 The Proposed Optimal Power Allocation Strategy
### Problem Formulation
Furthermore, we compare the error performance for the CMIMO model and the traditional WDM model without the consideration of crosstalk problem. Figure 7 illustrates the error performance versus luminous flux under different interference coefficients \(\tau=0.05,0.01\) under \(\text{CCT}=5700\) K. Compared with the traditional WDM model, the CMIMO model always shows better BER performance since we consider the crosstalk problem for the practical multi-color LED based V  
Figure 5: Transmission power comparison between the proposed optimal power allocation strategy and the uniform power allocation method with interference coefficient \(\tau=0.05\) under \(\text{CCT}=5700\) K.  
Figure 6: Total transmission power comparison for the proposed optimal power allocation strategy under different CCT values with interference coefficient \(\tau=0.05\). can see that the BER curves show a down and up tendency with the increase of luminous flux. Unlike radio frequency (RF) system, higher electrical power may cause clipping distortion problem due to the narrow dynamic range of an LED for the VLC system. Thus, proper DC bias value is required to avoid the clipping distortion problem. In conclusion, the proposed optimal power allocation strategy can improve the overall performance for the multi-color LED based VLC system further. Compared with the uniform power allocation method, our proposed scheme behaves more power-efficiently while satisfying the necessary illumination and communication requirements. Moreover, considering the practical crosstalk problem, the CMIMO model can maintain better error performance.
## 6 Simulation Results
Since multi-agent deep RL utilizes centralized training and decentralized execution to reduce the interference of non-stationary environments among agents, the proposed algorithm can increase the transmitted traffic value compared with DDPG and DQN. Furthermore, all the three RL-based algorithms perform better than the greedy methods due to the following two reasons. such as remaining visible time, channel condition, handover latency, and traffic storage capacity. Thus, the RL-based algorithms can improve the total transmitted traffic value of the network from a global perspective, surpassing the performance of Greedy 1 and Greedy 2. * Both Greedy 1 and Greedy 2 rely on static matching algorithms, which fail to account for the randomness of traffic generation at HAP drones. In contrast, the RL-based algorithms can learn the randomness of the traffic generation at HAP drones and make the matching strategy based on this learning. Figure 6 illustrates the total transmitted traffic values of different algorithms with respect to the LEO satellite beam number \(L\). As the number of LEO satellite beams increases, the total transmitted traffic values of all algorithms will also increase. This is because increasing the number of LEO satellite beams can relax the constraint (10c), thereby allowing more HAP drones to transmit traffic to LEO satellites simultaneously, so as to increase the total transmitted traffic value of the space-air-ground integrated network. From Figure 6, we can see that the proposed multi-agent deep reinforcement learning algorithm is the best since it can learn from the experience of the other LEO satellites. Furthermore, all three RL-based algorithms perform better than greedy methods for the same reasons shown in Figure 5.
# Modeling and Control of an Articulated Multibody Aircraft
## 5 Discussion
### Pitch Attitude Control  
The longitudinal control input plots in Figure 5 show which longitudinal control effectors were prioritized for use in pitch-up control. For PC 1, the elevator was used exclusively, indicated by a downward elevator deflection in Figure 5a, and no abdominal deflection in Figure 5b. PC 2 used abdominal pitch deflection exclusively as seen in Figure 5a, and no elevator deflection in Figure 5b. A downward elevator and upward abdominal pitch deflection shown in Figure 5a,b, respectively, show that the elevator and abdomen were simultaneously used for control in PC 3.  
The effects of the control effector choice on longitudinal states over time according to the controller used are shown in Figure 4. The angle of attack \(\alpha\) time history in Figure 4a reveals that exclusive use of a positive elevator deflection (PC 1) caused an initial decrease in angle of attack as expected. Similar results have been obtained in [141; 142] for tailless aircraft. The deflection of the elevator causes a change in the camber of the airfoil of the wing and consequently changes the lift coefficient. A positive elevator deflection for a reflex airfoil, as in the case of the aircraft wing in this study, decreases the wing lift and pitching moment, therefore resulting in a decrease in angle of attack. However, the exclusive deflection of the abdomen (PC 2) shifts the combined _cg_ forward, and consequently causes a change in lift and drag reference offset, as well as gravitational moments, therefore generating control moments. The resulting effect is the initial increase in angle of attack. In the case of PC 3, these effects are combined and reduced. Regarding the time histories of the pitch rate \(q\) (Figure 4b) for the three pitch controllers, an increase in pitch angle resulted in an increase in pitch rate, as expected.  
The simulation results presented in Figures 4 and 5 and Table 6 showed that the PC 3 controller had better tracking performance for pitch angle than the PC 1 and PC 2 controllers, with a settling time of 2.96 s and the least overshoot of 3.81%. The PC 1 controller was the poorest performer of the three controllers with the highest overshoot of 3.84% and the longest settling time of 3.08 s. All three controllers had zero steady-state error and fall within the set performance requirements. The corresponding control efforts for elevator deflection \(\delta_{e}\) and abdominal pitch angle are well within the physical bounds with maximum absolute deflections being 0.37\({}^{\circ}\) and, 0.04\({}^{\circ}\) respectively. The states also fall within the simulation constraints set for \(\alpha\) and \(q\), with the maximum absolute values of 5.00\({}^{\circ}\) and 27.14\({}^{\circ}\)/s, respectively. For parallel comparison, the results indicate that a dragonfly using its abdomen in conjunction with its wings would obtain improved controllability and control performance compared to just using the wings.
## 5 Analysis of Deformation Performance of CFRP-Strengthened Corroded RC Beams under Fatigue Loading
### Effect of Fatigue Loading on Flexural Stiffness of CFRP-Strengthened Corroded RC Beams  
The stiffness of CFRP-strengthened corroded RC beams under fatigue load will further degrade. To address this issue, a modification factor \(\lambda(N)\) related to the number of fatigue loading cycles is introduced to modify Equation (19), as shown in Equation (20):  
\[B_{\rm s}(N)=\lambda(N)\cdot B_{\rm s} \tag{20}\]  
where \(B_{\rm s}(N)\) is the flexural stiffness of the corroded strengthened beam after \(N\) cycles of fatigue loading. \(B_{\rm s}\) is the initial flexural stiffness of the corroded strengthened beam.  
From the formula for calculating beam deflection in material mechanics, the mid-span deflection of the corroded strengthened beam can be obtained as:  
\[f=s\frac{Ml_{0}^{2}}{B_{\rm s}} \tag{21}\]  
where \(s\) is the deflection coefficient related to the form of the load and the support conditions. \(B_{\rm s}\) is the flexural stiffness of the corroded strengthened beam. \(M\) is the mid-span bending moment value. \(l_{0}\) is the calculated span.  
Due to the lack of fatigue experimental data on CFRP-strengthened corroded RC beams, based on the verification of the correctness of the model in Section 4, Beam No. 8 was used as the reference beam, and the other parameters were kept unchanged but with varying levels of corrosion. Finite element modeling analysis was used to obtain the mid-span deflection data of the strengthened beam under different numbers of fatigue loading cycles, named FB-1, FB-2 and FB-3 for convenience of description, as shown in Table 4. Multiple nonlinear regression analysis was performed using MATLAB, and after several attempts, the function of \(\lambda(N)\) was chosen in the following form, as shown in Equation (22):  
\[\lambda(N)=a+b\ln N+c(\ln N)^{2} \tag{22}\]  
The undetermined coefficients were calculated to be \(a=1\), \(b=-0.00594\) and \(c=-0.005378\). In summary, the formula for calculating the section flexural stiffness of CFRP-strengthened corroded RC beams under fatigue loading is obtained as follows:  
\[B_{\rm s}(N)=[1-0.00594\ln N-0.005378(\ln N)^{2}]\cdot\frac{E_{\rm s}A_{\rm s} (\eta_{\rm s})h_{0}^{2}+E_{\rm cf}A_{\rm cf}h^{2}}{1.15k(\eta_{\rm s})\varphi+6.28(\alpha_{\rm E}\rho_{\rm s}+\alpha_{\rm F}\rho_{\rm cf})+0.27}\ \ \ \eta_{\rm s}\leq 17\% \tag{23}\]