esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 PPO-Based Optimization of the UAV Trajectory  
After solving the formulated problem \(\mathbf{P}2\) to gain the most optimized pointing error based on leading angles, the constraints of the optimization problem were only based on the trajectory of the UAV. Then, when the optimal phase shift is given, problem \(\mathbf{P}1\) can be rewritten as  
\[\begin{split}(\mathbf{P}3):&\max_{\boldsymbol{G}} \frac{1}{N}\sum_{n=1}^{N}c[n]\\ &\text{s.t. }(2).\end{split} \tag{20}\]  
To solve \(\mathbf{P}3\) as maximizing the average capacity based on the maximum \(h_{p}[n]\) and UAV flight constraints, a PPO algorithm is introduced for planning the operating trajectory of the UAV. As shown in Figure 4, the proposed PPO-based trajectory optimization algorithm is based on the Markov decision process (MDP) and improved by the prioritized experience replay (PER) method, which is introduced in the following.  
MDP formulation  
The deep reinforcement learning (DRL) method is based on the MDP, which includes the state space, action space, and reward design. The MDP of this system considers time steps with the index of time slots \(n\) with the upper limitation \(N\), where \(n\in\{1,2,\ldots,N\}\).  
State space  
The state-space \(S\) includes the UAV's operating zone limitations as the input of the PPO, consisting of all accessible UAV positions in the whole terrain, and also includes the remaining operation time of the UAV.  
The action space \(A\) includes the current direction, velocity, and coordinates of the UAV.  
Reward Design  
The reward function is formulated as the average capacity of the FSO communication. As the UAV can be at any 3D waypoint \(G[n]\), there is a corresponding average capacity \(c[n]\).
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods  
### Datasets  
To evaluate the performance of the network, three mainstream public datasets (LineMOD, YCB-Video, and Occlusion LineMOD) were used for validation.  
The LineMOD dataset is a widely used computer vision dataset for object recognition and pose estimation. It includes RGB-D images and 3D models of 13 objects, featuring both symmetric and asymmetric common objects. Each object has approximately 1000 manually annotated images. These images and models are captured from real-world objects, ensuring high realism and diversity.  
The YCB-Video dataset is based on the YCB dataset and includes 21 objects selected for their high-quality model files and comprehensive depth images. The dataset contains a total of 133,827 frames, all annotated with 6D poses. It includes scenes with clutter and occlusions featuring both symmetric and asymmetric objects, as well as objects with minimal surface features.  
The Occlusion LineMOD dataset is an extended version of the LineMOD dataset, specifically designed to evaluate the performance of models under complex occlusion conditions. It contains many heavily occluded scenes, providing a more challenging benchmark for assessing the robustness of pose estimation models. Compared to the original LineMOD dataset, the LM-O dataset introduces more complex occlusions and object arrangements, allowing for a more comprehensive evaluation of the model's performance when dealing with occluded objects in real-world environments. The LM-O dataset has become an important benchmark for validating the ability of 6D pose estimation algorithms to handle occlusions.
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 2 Generation Algorithm
### Network Topology and Degree Distribution Analysis
Only considering the relay nodes [29] of the fourth layer, we increase communication nodes according to the largest connected subgraph and then increase the number of excessively large relay nodes according to the shortest path strategy, which introduces a new kind of scale-free and small-world network with multiple hubs based on classic networks and presents the trapping problem on them. They show that in the multiple-hub networks, the growth of MFPT with the traps located on the hub or peripheral nodes displays quite a difference from the single-hub networks. Taking the functional of the early warning aircraft as an example, its communication attributes are divided into two parts: internal communication and external communication. The internal communication system establishes voice communication for the operator and the crew; the external communication system consists of several shortwave and ultrashortwave radio stations, which can transmit a large amount of information obtained  
Figure 6: Degree distribution of the communication subnetwork. (**a**) The network topology. (**b**). Histogram of the node degrees of communication subnetwork. (**c**) Log distribution of the node degrees of communication subnetwork. Figure 7: Attack subnetwork degree distribution. (**a**) The network topology. (**b**). Histogram of the node degrees of attack subnetwork. (**c**) Log distribution of the node degrees of attack subnetwork. to the airborne companion aircraft, the sea fleet, or the ground command post. The data transmission can use voice or digital mode. Communication data transmission and cross-linking capabilities are determined by communication capabilities and data transmission capabilities, communication capabilities are determined by air-to-air, air-to-ground, and air-sea communication capabilities, and data transmission capabilities are determined by real time accuracy and reliability [30]. The proposed model not only shows a hierarchically increasing degree of distribution and large clustering coefficients in communities but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, they reconstruct a hierarchical contact network based on the investigation data of a university.
## 6 Skin Conditions and Evidence of the Beneficial Role of Vitamin D
### Acne Vulgaris and Acne Rosacea  
Acne vulgaris is one of the most common skin disorders affecting many people worldwide. Acne vulgaris' pathogenesis relates to inflammation, mainly enhanced by immune response and specifically targets _Propinibacterium acnes_[89]. Recent study outcomes haveshown that _P. acnes_ is a strong Th17 inducer and that 1\(\alpha\),25(OH)\({}_{2}\)D\({}_{3}\) suppresses Th17 differentiation generated by _P. acne_, which may be useful in controlling acne [44]. Plus, it was demonstrated that sebocytes are 1\(\alpha\),25(OH)\({}_{2}\)D\({}_{3}\)-responsive target cells, suggesting that vitamin D analogs might be useful in acne management, as vitamin D deficiency is pronounced in acne vulgaris patients [44,46]. Vitamin D has pleiotropic effects in preventing acne vulgaris lesions, through directly inhibiting cell division, decreasing sebum secretion and pore blockage, and impeding _Cutibacterium acnes_' growth [206].  
Similarly, another study suggested that in patients with rosacea, a high level of serum vitamin D was traced. Rosacea is a chronic skin disorder that mainly affects an individual's face and is generally recognized by symptoms including flushing or long-term redness, especially in the nose and cheeks. Study findings have suggested that elevated serum 1\(\alpha\),25(OH)\({}_{2}\)D\({}_{3}\) may be correlated with a lower risk of incidence and further development of rosacea [201,207]. However, the link between vitamin D levels and rosacea remains inconclusive, necessitating further research to clarify its therapeutic role [47]. Vitamin D's anti-inflammatory actions may aid in mitigating redness and pustule formation in rosacea, while considering acne vulgaris, vitamin D reduces sebum formation and inhibits _Cutibacterium acnes_-induced inflammation (Table 3) [201,207,208].
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 2 Materials and Methods  
### Method for Collecting Compaction Quality Influence Parameters  
Relevant studies [16] have indicated that material source parameters, compaction parameters, and meteorological parameters significantly affect the compaction quality of earth-rock dams. In this study, material source parameters, compaction parameters, and meteorological parameters serve as input parameters for the prediction model, while compaction dry density is considered as the output parameter. The compaction parameters are collected using the real-time monitoring method for dam compaction quality proposed by [PERSON] et al. [2] and [PERSON] et al. [3], which comprehensively and continuously monitors the compaction parameters (compaction speed, compaction thickness, number of compaction passes, and excitation force). The material source parameters are obtained through on-site test pit experiments (including moisture content tests, density tests, and particle size analysis tests). Meteorological parameters are acquired in real-time through a small meteorological station located at the construction site [19].
## 4 Proposed Soft Parameter Estimation Paradigms
### Elephant Herd Optimization  
The second soft computing paradigm is called the elephant herd optimization algorithm. Its nature emulates the life of elephants that live in social groups with a few clans. Each clan lives under the leadership of female elephants. Besides, male elephants live separately from the clan. Male elephants leave their clans while they are growing up. To emulate this elephant behavior to solve optimization problems, the EHO algorithm applies three main rules [4; 37]:  
* Building the population of elephants: in this rule, a certain number of clans with fixed male and female elephants are considered.
* A mutation process that identifies a fixed percentage of the male elephant will be excluded and live outside their clan.
* Identify a female elephant to be the clan leader. The proposed EHO is described in the following sequence:
* The initial population of elephants is randomly created.
* The population size is divided into a defined number of clans.
* Compute the fitness function of each population element.
* According to their elephant fitness function, updating each clan individually by applying clan operator using Equation (12). The old positions \(\left(x_{c_{ij}}\right)\) or elephant \(j\) in the clan \(c_{i}\) can be updated to new positions as \(x_{new,c_{i,j}}\) as: \[x_{new,\ c_{ij}}=\ x_{c_{i,j}}+\ \alpha\ \times\left(x_{best,\ c_{i}}-\ x_{c_{i,j}}\right)\ \times\ r\] (12) where \(\alpha\) and \(r\epsilon[0,1]\). \(\alpha\) is defined as the scale factor that finds the influence of matriarch \(c_{i}\) on \(x_{c_{ij},\ x_{new,c_{ij}}}\) represents matriarch \(c_{i}\);
* The following position is influenced by the matriarch \(c_{i}\);
* The updated fittest elephant is obtained from Equation (13) as: \[x_{new,c_{i,j}}=\ \beta\ \times\ x_{center,c_{i}}\] (13) where \(\beta\epsilon[0,1]\) and \(x_{center,c_{i}}\) is the center of clan \(c_{i}\), and for the d-th dimension it can be calculated as: \[x_{center,c_{i}}=\ \frac{1}{n_{c_{i}}}\sum_{j-1}^{n_{c_{i}}}x_{c_{i,j},\ t}\] (14)where \(1\leq d\leq\mathrm{D}\) refers to the dimension at the \(d\)th order, and the total dimension is abbreviated by \(\mathrm{D}\). \(n_{c_{i}}\) refers to the number of elephants in the clan \(c_{i}\). \(x_{c_{i},i,d}\) refers to the \(d\)th individual elephant \(x_{c_{i},i}\);
* Separating operator: this operator emulates the leaving of male elephants in their group and living alone. This separating process can be modeled as: \[x_{worst,c_{i}}=\ x_{min}+(x_{max}-\ x_{min}+1)\times rand\] (15) where \(x_{max}\) and \(x_{min}\) are, respectively, the elephant individual position maximum and minimum boundaries. \(x_{worst,c_{i}}\) refers to the poorest individual elephant in the clan \(c_{i}\). \(rande\{0,1\}\);  
Many points which can affect the results of the EHO algorithm are considered as drawbacks in EHO, such as generating the separating operator in Equation (15) that affects \(x_{worst,\ ci}\) by creating a new member that may change to the worst member value. Then it is possible to say that Equation (15) does not guarantee one will get better values all the time.
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 4 Discussion  
### Isoflavone Removal from Soybeans  
Previous experiments have shown that a 60 min precooking of bounded dehulled soybeans at 90 \({}^{\circ}\)C successfully removed 54.32 \(\pm\) 12.35% of the initial IFs [27]. The present process succeeded in proving that a treatment of only 10 min at 20 \({}^{\circ}\)C was sufficient, after drenching, to reach similar IF removal rates (48.8 \(\pm\) 4.43%) from dehulled soybeans.  
The drenching step of dehulled beans was added at pilot scale for cleaning the seeds before deflavonoidisation, as the dehulling and light grinding favoured the appearance of a superficial layer of fat, which trapped a large part of the dust and flour. The latter two, containing IFs, had to be eliminated before washing in order to improve the contact between water and the matrix surface, helping to achieve a better deflavonoidisation. Indeed, both GEN and DAI are mainly present in a glycosidic conjugated water-soluble form (logP \(<\) 1) in soybean seeds [43]. This finding was confirmed by IFs' assay in water. Drenching helped maintain a good concentration gradient between dehulled beans and water, and improved the effectiveness of IF leaching.  
Surprisingly, IF removal was better at 20 \({}^{\circ}\)C than at higher temperatures. This could be partly explained by the significant decrease in soybean solid content when the temperature rises from 30 to 40 \({}^{\circ}\)C [44]. The hypothesis is that proteins and carbohydrate leakage could prevent IF removal. Another explanation may be that cooking at high temperatures could precipitate the most external proteins of the dehulled beans. Such precipitation could reduce the IFs' transfer to water. This has not been reported in the literature beforehand, thus additional experiments are needed to help understand this result.
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 5 Consumption Forecasts  
Having reliable forecasts allows enterprises to gain a significant advantage in business. Therefore, currently, forecasting has become an extremely important service that can bring measurable benefits to the supplier. The LPG consumption forecast in this work was considered from two different perspectives--short- and medium-term.
## 3 On Orbit Operations and Anomaly Analysis
### ADCS Reboot  
Following spacecraft deployment, the ADCS was initially unable to obtain a valid attitude estimate at the beginning of operations. This was resolved following an autonomous ADCS reboot triggered by FSW at T + 3 days (27 January). Following this reboot, the rate of invalid attitude estimates was greatly reduced, as shown in Figure 25. The reasons that would cause FSW to reboot the ADCS include: (1) Power voltage anomaly; (2) ADCS abnormal shutdown; and (3) ADCS anomaly reported in telemetry. As in the case of the anomalous UHF reboots, the exact reason cannot be identified due to the FSW only being able to maintain 15 min of log information. However, the invalid attitude solutions still continued to occur following the reboot, which means that there were still other factors adversely affecting attitude determination.  
Figure 23: SOC measured on-orbit from beacon data (blue circles) and simulated assuming full panel deployment (red circles) as a function of time.  
Figure 24: The correlation coefficient between observed and simulated SOC assuming different deployment angles in power simulation.  
According to the system configuration in Figure 1, the incomplete deployment of the solar panels would affect the field of view of the star tracker, which would affect the determination of the absolute attitude. As mentioned in the previous section, partial solar panel deployment between 60 and 90 degrees cannot be definitively excluded and is a possible factor in invalid attitude estimates.
# Enhanced Efficient EMT-Type Model of the MMCs Based on Arm Equivalence
## 2 General Structure, Control Scheme, and Previous Models of the MMC
### Previous Models  
#### 2.3.1 Type A: Detailed Equivalent Models (DEMs)  
DEMs reduce the size of the admittance matrix (\(\mathbf{Y}\)) and the simulation burden of the MMC significantly, and the individual dynamics of every SM are still depicted [12]. As shown in Figure 3b, with the trapezoidal integration method, each SM capacitor is represented by an equivalent history  
Figure 2: Control scheme of the MMC.  
voltage source in series with a resistance \(R_{\text{C0},x}=\Delta T/(2C_{0})\), where \(\Delta T\) denotes the simulation time-step. Then, based on [PERSON]'s theorem, the \(x\)th SM equivalent circuit in Figure 3c is obtained:  
\[v_{\text{SM},x}(t)=R_{\text{SMEQ},x}i_{\text{SM},x}(t)+V_{\text{SMEQ},x}(t- \Delta T) \tag{1}\]  
where  
\[R_{\text{SMEQ},x}=\frac{R_{\text{l},x}(R_{\text{u},x}+R_{\text{C0},x})}{R_{ \text{u},x}+R_{\text{l},x}+R_{\text{C0},x}} \tag{2}\]  
\[V_{\text{SMEQ},x}(t-\Delta T)=\frac{R_{\text{l},x}}{R_{\text{u},x}+R_{\text{l},x}+R_{\text{C0},x}}V_{\text{C0},x}(t-\Delta T) \tag{3}\]  
where, \(v_{\text{SM},x}(t)\) and \(i_{\text{SM},x}(t)\) denotes the voltage and current of the \(x\)th SM at time \(t\), respectively; \(V_{\text{SMEQ},x}(t-\Delta T)\) denotes the equivalent history output voltage of the \(x\)th SM at earlier time \(t-\Delta T\), and \(V_{\text{C0},x}(t-\Delta T)\) denotes the equivalent history capacitor voltage of the \(x\)th SM capacitor at earlier time \(t-\Delta T\); the values of resistor \(R_{\text{u},x}\) (similarly, \(R_{\text{l},x}\)) are either a low resistance \(R_{\text{on}}\) at ON-state or a large resistance \(R_{\text{off}}\) at OFF state, which depends on the switch state [25]. Then, for the arm in series with \(N\) SMs, the arm equivalent circuit can be constructed as in Figure 3d, where, the arm current \(i_{\text{Arm}}(t)=i_{\text{SM},x}(t)\),  
\[R_{\text{Arm}}=\sum_{x=1}^{N}R_{\text{SMEQ},x} \tag{4}\]  
\[V_{\text{Arm}}(t-\Delta T)=\sum_{x=1}^{N}V_{\text{SMEQ},x}(t-\Delta T) \tag{5}\]  
Here, \(V_{\text{Arm},x}(t-\Delta T)\) denotes the equivalent history arm voltage of \(x\)th SM at earlier time \(t-\Delta T_{i}\)\(v_{\text{Arm}}(t)\) is the arm voltage at time \(t\).
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
## 3 Selection of the Best TEG + DC/DC Converter Combination
### Characterization of TEG #1  
First, the characteristic voltage--current and power--current curves of TEG #1 were obtained by heating the busbar. The hot and cold side temperatures of the TEG were 67.7 \({}^{\circ}\)C and 57.8 \({}^{\circ}\)C, respectively, with an ambient temperature of 21.5 \({}^{\circ}\)C. This corresponds to a temperature gradient \(\Delta T=T_{hot}-T_{cold}\approx\) 10 \({}^{\circ}\)C between the hot and cold sides of the TEG. During the tests, a variable resistor was connected between the terminals of TEG #1, and the voltage \(V\) and current \(I\) flowing through its terminals were measured. These values are shown in Figure 6 together with the \(P-I\) fit, where \(P=V\)-\(I\).  
Figure 4: TEG #1 combined with DC/DC converter #1. (**a**) Ambient, hot and cold side temperatures measured by T-type thermocouples. (**b**) Output power of TEG #1 and DC/DC converter #1 versus the temperature difference between the hot and cold sides of TEG #1. (**c**) Output power of the analyzed TEGs and DC/DC converter versus the temperature difference between the hot and cold sides of the TEG #1.  
Figure 5: Combined performance of TEGs #1 and #2 combined with DC/DC converters #1 and #2. (**a**) Power generated by the analyzed TEG modules versus the temperature difference between the busbar and the ambient during the heating and cooling cycles. (**b**) Output power of the different combinations of TEG + DC/DC converter versus the temperature difference between the busbar and the ambient during the heating cycle.  
Figure 6: Characteristic TEG curves, \(V-I\) and \(P-I\). The quadratic fit obtained is \(P\) (mW) = \(-\)0.008014\(\cdot I^{2}\) + 0.7995\(\cdot I\) - 0.1176. The parameters related to goodness of fit are \(R^{2}\) = 0.9992, SSE = 0.676, and RMSE = 0.2373.  
The results presented in Figure 6 and Table 4 show that the power that can be drawn from the TEG is highly dependent on the resistance of the load, so the use of a DC/DC converter that includes a maximum power point tracker (MPPT) could be beneficial.
## 4 Analyses and Discussion
### XRD and BET Analyses  
The XRD results of fresh oxygen carrier and oxygen carriers after the last oxidation process in test 1 to test 3 are shown as Figure 11.  
Under different conditions, the XRD analyses of these oxygen carriers were similar, which meant that the injection time of CO could not change the internal phase and components. The Fe-containing composition was Fe\({}_{2}\)O\({}_{3}\), except Fe\({}_{2}\)SiO\({}_{4}\). The Fe\({}_{2}\)SiO\({}_{4}\) could be found in the oxygen carriers after different tests. This was due to the reaction between Fe\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) under the local high temperature condition over multi cycles. The property of the Fe\({}_{2}\)SiO\({}_{4}\) was stable, which caused the decrease of Fe\({}_{2}\)O\({}_{3}\) and the decline of the reactivity of oxygen carriers. This phenomenon also has been found in previous work [46; 47]. Meanwhile, the melting point of Fe\({}_{2}\)SiO\({}_{4}\) is relatively low [46], and the local high temperature from exothermic reaction could lead to the melting of Fe\({}_{2}\)SiO\({}_{4}\) and the sintering of oxygen carriers.  
The BET results of these oxygen carriers are shown as Figure 12.  
The pore diameter, volume and specific area of the oxygen carriers after 100 cycles were larger than those of the fresh oxygen carrier. These parameters increased with the injection time of CO.  
Figure 11: XRD analyses of different oxygen carriers: (**a**) fresh oxygen carrier; (**b**) test 1; (**c**) test 2; (**d**) test 3.  
The specific areas and pore volumes of oxygen carriers after test 2 and test 3 were almost same. That might be the fact that the reaction did not diffuse into the inner of particles. The SEM results demonstrated that the surface of OC after test 1 was compact, however, the pore volume of OC after test 1 was the biggest. It demonstrated that the compact surface was caused by the sintering, and the inner porous structure still maintained. Large cycle number was beneficial to form the porous structure in the oxygen carrier and increase the specific surface area, which could promote the reaction between the oxygen carrier and CO.
## 3 Method
### Squeeze-and-Excitation  
We use SE blocks [7] to consider interdependencies between channels. In the SE block, global spatial information is squeezed into a channel descriptor using global average pooling. A channel descriptor \(z\in R^{C}\) is extracted by averaging the input feature map \(U\in R^{H\times W\times C}\) through its spatial dimensions \(H\times W\). To utilize the information aggregated in the squeeze operation, a simple gating mechanism is employed. The channel descriptor \(z\) is transformed into a set of channel weights \(s\in R^{C}\), which is given by  
\[s=\sigma(W_{2}\delta(W_{1}z)) \tag{4}\]  
where \(\sigma\) and \(\delta\), respectively, refer to sigmoid and rectified linear functions. To reduce model complexity and aid generalization, a bottleneck is formed by \(W_{1}\in R^{\frac{C}{7}\times C}\) and \(W_{2}\in R^{C\times\frac{C}{7}}\). We set the dimensionality-reduction ratio \(r\) to 4 in our system. The final output of the SE block is obtained by scaling \(U\) with channel weights \(s\) for each channel. In this manner, channels possessing more important information can be emphasized.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 2 Imaging Modalities of Electrical Impedance Tomography (EIT)
### Time-Difference  
The time-difference imaging technique works on the principle that it estimates the differences between the measured voltages at time \(t\) to a reference calculation attained at time \(t_{0}\) and states the difference in voltages by a variation in conductivity between these two points.