esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metrics  
The following matrices represent the performance matrices adopted in this work: [13, 17, 18, 19, 20, 21]  
* Accuracy: The ratio of correctly classified instances to the total instances. \[AC_{U}=\frac{T_{P}+T_{N}}{T_{P}+F_{P}+T_{N}+F_{N}}\] (10) where \(T_{P}\) is a true positive, \(T_{N}\) is a true negative, \(F_{P}\) is a false positive, and \(F_{N}\) is a false negative.
* Precision: The ratio of true-positive predictions to the total positive predictions. \[P_{R}=\frac{T_{P}}{T_{P}+F_{P}}\] (11)
* Recall (Sensitivity): The ratio of true-positive predictions to all actual positive instances. \[S_{EN}=\frac{T_{P}}{T_{P}+F_{N}}\] (12)F1-Score: The harmonic means of precision and recall.
* AUC (area under the curve): The area under the receiver operating characteristic (ROC) curve, indicating discriminative ability.
## 3 Methodology
### SCEHO Algorithm
#### 3.4.1 Process of Clan Updating  
As discussed earlier, all the elephants live together under the leadership of a matriarch elephant. Generally, the positions of the elephants are influenced by a matriarch elephant based on Equation (7). In this scenario, the sine cosine-based clan-updating operator is employed for updating the clans, which is mathematically determined in Equation (8). The use of the sine cosine-based clan-updating operator enhances the optimization algorithm's exploration and exploitation abilities and avoids being trapped in local optima problems.  
\[x_{c_{i,j}}^{new}=x_{c_{i,j}}^{t}+\alpha\times\left(x_{best_{i}}^{t}-x_{c_{i,j }}^{t}\right)\times r1 \tag{7}\]  
\[x_{c_{i,j}}^{new}=\left\{\begin{aligned} x_{c_{i,j}}^{t}+r1\times \sin(r2)\times\left|r3\times x_{best_{i}c_{i}}^{t}-x_{c_{i,j}}^{t}\right|& \;if\;r4<0.5\\ x_{c_{i,j}}^{t}+r1\times\cos(r2)\times\left|r3\times x_{best_{c_{i}}}^{t}-x _{c_{i,j}}^{t}\right|&\;if\;r4\geq 0.5\end{aligned}\right\} \tag{8}\]where \(\alpha\) is represented as a scaling factor, which influences the matriarch elephant on \(x_{c_{i},i}^{t}\); \(x_{c_{i},i}^{new}\) are denoted as old and new positions of the \(j\)th elephant in clan \(c_{i}\); \(x_{best,c_{i}}^{t}\) is stated as a global or best-fitted position of a matriarch elephant in clan \(c_{i}\); \(t\) is indicated as an iteration; and the random numbers \(r1,r2,r3,\text{ and }r4\) perform uniform distribution which ranges between zero and one. On the other hand, the position of a matriarch elephant is updated based on Equation (9).  
\[x_{c_{i},i}^{new}=\beta\times x_{center,c_{i}}^{t} \tag{9}\]  
where the term \(\beta\) influences \(x_{center,c_{i}}^{t}\) on \(x_{c_{i},i}^{new}\), which ranges between zero and one. The center of the clan \(x_{center,c_{i}}^{t}\) in the \(d\)th dimensional space is mathematically determined in Equation (10).  
\[x_{center,c_{i}}^{t}=\frac{1}{n_{c_{i}}}\times\sum_{j=1}^{n_{c_{i}}}x_{c_{i}, j,d} \tag{10}\]  
where \(n_{c_{i}}\) is represented as the number of elephants in clan \(c_{i}\). The architecture of the SCEHO algorithm is mentioned in Figure 2.
# Xanthone Derivatives and Their Potential Usage in the Treatment of Telangiectasia and Rosacea
## 2 Xanthone Derivatives and Their Vascular Activity
### Anti-Inflammatory Mechanisms of Xanthone Derivatives  
The anti-inflammatory effects of xanthone derivatives are attributed to their ability to modulate key molecular targets involved in the inflammatory process. These targets include pro-inflammatory enzymes such as cyclooxygenase (COX) and lipoxygenase (LOX), as well as inflammatory mediators like cytokines (e.g., interleukins IL and tumor necrosis factor-alpha TNF-\(\alpha\)), reactive oxygen species (ROS), and NO. The anti-inflammatory effects were shown based on a study where a few xanthone derivatives were isolated from the stem bark of _Garcinia delpyana_. The following compounds were isolated: two new ones, delpyxanthone A and delpyxanthone B, together with four known ones, gerontoxanthone I, \(\alpha\)-mangostin, cowanin, and cowanol (Figure 7). Additionally, xanthone derivatives have been shown to influence signaling pathways such as nuclear factor-kappa B (NF-\(\kappa\)B) and MAPKs, which play pivotal roles in orchestrating inflammatory responses. Furthermore, the antioxidant properties of xanthone derivatives contribute to their anti-inflammatory effects by neutralizing ROS and mitigating oxidative stress, a common feature of inflammatory conditions. Additionally, xanthone derivatives may modulate immune responses by regulating the activity of immune cells involved in inflammation, further enhancing their anti-inflammatory actions. Xanthones have been shown to influence the production and activity of cytokines such as interleukins (ILs) and tumor necrosis factor alpha (TNF-\(\alpha\)). By modulating cytokine production, xanthones can affect the balance between pro-inflammatory and anti-inflammatory responses, thereby regulating immune function. These compounds can also affect the activities of macro macrophages, T cells, and dendritic cells, leading to pausing the activation of pro-inflammatory immune cells or enhancing the function of regulatory immune cells and thereby exerting anti-inflammatory effects. [40].  
Figure 7: Structures of xanthone derivatives isolated from _Garcinia delpyana_.  
Investigations on the potential of _G. mangostana_ L. pericarps ethanol extracts for acne treatment revealed its anti-inflammatory effects, suppressing the production of the pro-inflammatory cytokine TNF-\(\alpha\). Some research suggests that xanthone derivatives possess antibacterial properties that could be beneficial for treating conditions like acne. Specifically, studies have shown that xanthone derivatives exhibit antibacterial activity against bacteria such as _Propionibacterium acnes_ and _Staphylococcus epidermidis_, which are commonly associated with acne [41]. The formation of acne is also associated with one of the stages of rosacea.
## 3 Preliminaries  
### Gaussian Blur  
The Gaussian blur is a mathematical operation involving the convolution of an image with a Gaussian function to reduce image noise and ensure privacy protection by obscuring image details [16]. Gaussian filters can be formulated using the Gaussian function, where the variables \(i\) and \(j\) represent the pixel location's rows and columns, respectively. The parameter \(\sigma\) represents the standard deviation, determining the breadth of the Gaussian distribution. The normalization factor \(2\Pi\sigma^{2}\) ensures a constant area under the Gaussian curve:  
\[G(i,j)=\frac{1}{2\Pi\sigma^{2}}e^{-}\frac{\left(i^{2}+j^{2}\right)}{2\sigma^{2}} \tag{1}\]  
The specific degree of smoothing provided by a Gaussian filter is determined by the choice of filter size, denoted by the parameter \(\sigma^{2}\). The relationship between the degree of smoothing and the value of \(\sigma^{2}\) is straightforward: a higher \(\sigma^{2}\) corresponds to a more extensive Gaussian filter, resulting in a higher level of filtration. Large Gaussian filters can enhance privacy preservation without incurring significant computational costs, thanks to the separability of Gaussian functions. A possible approach to achieving two-dimensional Gaussian convolution involves applying a one-dimensional Gaussian filter to an image and convolving the resulting image with another one-dimensional filter orthogonally to the first. This technique reduces the computational complexity of a 2D Gaussian filter, resulting in a linear relationship with the size of the filter mask rather than a quadratic relationship [33]:  
\[g(i,j)=\frac{1}{2\Pi\sigma^{2}}\sum_{m=1}^{K}\sum_{n=1}^{K}e^{-\frac{n^{2}+p^{ 2}}{2\sigma^{2}}}f(i-m,j-n) \tag{2}\]  
\(g(i,j)\) is the output; \(m\) and \(n\) are the width and height of the kernel, respectively; and \(f(i-m,j-n)\) is the input that convolves with the Gaussian Kernel. As seen in the equation, we can split the Gaussian kernel into two exponents, thus verifying the separability:  
\[g(i,j)=\frac{1}{2\Pi\sigma^{2}}\sum_{m=1}^{K}e^{-\frac{m^{2}}{2\sigma^{2}}} \sum_{n=1}^{K}e^{-\frac{n^{2}}{2\sigma^{2}}}f(i-m,j-n) \tag{3}\]
## 2 Materials and Methods
### Protein Expression and Purification  
For protein expression, the respective plasmids were introduced into _E. coli_ BL21(DE3)-competent cells. Transformed cells were inoculated in LB medium with 50 ug/mL ampicillin for expression of Rep mutants and with 20 ug/mL kanamycin for expression of the fusion proteins VpNRH and VtPNRH. Cells were then cultured at 37 \({}^{\circ}\)C until the OD\({}_{660}\) reached 0.6-0.8, followed by addition of 1 mM isopropyl-\(\beta\)-D(-)-thiogalactopyranoside (IPTG) for induction of protein expression. Cells were cultured overnight at 16 \({}^{\circ}\)C for expression of fusion proteins and for 6 h at 25 \({}^{\circ}\)C for expression of Rep mutants. Cells were then harvested by centrifugation. The collected cells were suspended in phosphate-buffered saline (PBS: 150 mM NaCl, 16 mM Na\({}_{2}\)HPO\({}_{4}\), 4 mM NaH\({}_{2}\)PO\({}_{4}\), pH 7.4) and disrupted by sonication, followed by centrifugation to obtain the soluble fraction. The supernatant was added to ProfinityTM IMAC Ni-Charged Resin (Bio-Rad) equilibrated with PBS followed by rotation at 4 \({}^{\circ}\)C for 30 min. After rotation, the samples were washed with Ni-NTA buffer (0.5 M NaCl, 20 mM phosphate buffer, pH 8.0) 5 times, and then washed twice with Ni-NTA buffer containing 10 mM imidazole. Proteins were eluted with Ni-NTA buffer containing 10, 100, 150 mM imidazole. The eluted samples were dialyzed against PBS 3 times using dialysis tubing. The concentrations of purified proteins were evaluated using a BCA assay kit (Pierce).
## 2 System Overview  
As shown in Figure 1, the closed-loop system of the hydraulic wind turbine with hydraulic accumulator is totally different from that we commonly used. The displacements of the pump and the motor in this hydraulic wind turbine are both variable. But in the most frequently used closed-loop system, there is only one variable component between the pump and motor. The variable pump placed in the nacelle is coupled to the rotor of the wind turbine, through which the hydraulic oil in the low-pressure lines is sucked and pressured into the high-pressure lines. Passing through the check valve, the high pressure oil from the pump flows into the hydraulic accumulator and the variable motor located on the ground. The synchronous generator is connected with the variable motor through the shaft couplings. The boost pump is added to supplement the lost hydraulic oil of the closed-loop system and also complete the heat exchange of the system. There are two relief valves in this system and their function is different. The relief valve connected with the check valve is used to limit the maximum operating pressure of the entire system and ensure the system security. The other relief valve keeps the  
Figure 1: Schematic diagram of the closed hydraulic wind turbine with energy storage system.  
pressure of the low-pressure lines constant, which will protect the variable pump from damage because of the cavitation. The rotary joint is indispensable to the hydraulic wind turbine, which can help to transmit freely the hydraulic oil from the nacelle to the variable displacement motor without damaging the hydraulic lines when the yaw system is operating. Figure 2 is a diagram that demonstrates the energy conversion and transfer process of the hydraulic wind turbine with the energy storage system. Adding the hydraulic accumulator to the high-pressure lines, the outlet pressure of the pump and the inlet pressure of the motor are determined by that of the hydraulic accumulator. The difference between wind energy captured by wind turbine and the electrical energy generated by the generator decides that the high pressure oil flows into or discharges from the hydraulic accumulator.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Inhomogenous Plasmas
Herefrom, it can be concluded that the electron density distribution is represented well by \(\hat{n}_{\text{e}}\) for \(\
u_{\text{m,}i}\ll 2\pi f_{\text{res}}\). For higher collision frequencies, the outcome becomes less predictable. \(\hat{n}_{\text{e}}\) is an overestimation--even to the point that \(\hat{n}_{\text{e}}\) could be higher than all local values--when in one region the collision frequency is the highest while the electron density is lowest. An underestimation is observed when the electron density and collision frequency are lowest in the same region. Note that these anomalies only occur when the collision frequency is not uniform over the plasma volume. Concluding, \(\hat{v}_{\text{m}}\) and \(\hat{n}_{\text{e}}\) might misrepresent the actual profile of the collision frequency and the distribution of the electron density for plasmas with a spatial dependency. It is shown that using Equations (15) and (16) potentially results in large errors when not carefully considered in non-uniform plasmas. This challenge can be circumvented by describing the plasma with the complex permittivity obtained by single- or even multi-mode MCRS. Moreover, by combining the information obtained by monitoring multiple resonant modes, a more accurate separation of plasma parameters can be realized. For users of the diagnostic method that do not want to abandon the usage of \(n_{\text{e}}\) and \(\
u_{\text{m}}\), it is highly recommended to ensure that there is no spatial profile in the collision frequency or in the electron density and for the usage of \(n_{\text{e}}\) that \(\
u_{\text{m}}(\mathbf{r})\ll 2\pi f_{\text{res}}\). Furthermore, a new definition for the means of the plasma parameters and a novel method to separate the contributors \(n_{\text{e}}\) and \(\
u_{\text{m}}\) should be considered. Alternative definitions could potentially even be used to verify the assumption of a uniform plasma volume.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.11. Random or Messy Infill  
A broken or missing internal infill may affect the structural integrity of the model. The most common reasons are due to incorrect infill density, semi-clogged nozzle, and/or the type of filament used. Solutions include the following:  
1. Changing the infill density. A density value of 40% is usually sufficient for most models to achieve good mechanical strength; 50% will generate a tighter filled model using more material; 20% is usually the minimum required to support flat ceilings [74], but this can change depending on the material being used.
2. Remove any foreign debris that is preventing proper extrusion (refer to 3.1.4: Clogged nozzle).
3. Flexible filaments are vulnerable to imperfect infills. The grid infill pattern is the best option when using flexible materials.
###### Abstract  
Demand-side energy storage is an important foundation for enhancing load flexibility to accommodate renewable energy. With the widespread adoption of renewable energy, demand-side energy storage planning, and its incentive mechanism have also attracted the attention of a large number of scholars. However, there are still few studies on incentives from a carbon perspective. To fill the gap, a dynamic carbon incentive mechanism is proposed in this study. In addition, a hierarchical two-stage robust planning model for demand-side energy storage that incorporates the proposed carbon incentive mechanism is developed. At the first level, the economic dispatch is performed, and bus carbon intensities are calculated based on the carbon emission flow theory. The second level is a two-stage robust planning model to obtain the optimal capacities of demand-side energy storage, which is solved based on the nested column and constraint generation algorithm. The proposed model is implemented and evaluated on the MATLAB/YALMIP platform using IEEE 24-bus power systems. The results validate the efficacy of the model in promoting carbon-oriented demand-side energy storage planning, leading to a substantial reduction of carbon emissions by 8.44%. Notably, when compared to existing incentive mechanisms, the proposed carbon incentive mechanism exhibits distinct advantages in achieving carbon reduction with less both subsidy costs and fixed investments.  
carbon emission flow theory; demand-side energy storage; dynamic carbon incentive mechanism; nested column and constraint generation algorithm; two-stage robust planning
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 2 Related Works
We define the input parameters as node residual energy, node degree and neighbor nodes' residual energies. Fuzzy logic is appropriate for making real-time decisions with multi-causal factors in the absence of complete environment information for sensor nodes in the network. We introduce neighbor nodes' residual energies as the additional input value which can optimize the load balance of relay nodes, avoid the hotspots problem caused by multi-hop communication and it will be more helpful to the optimization of energy consumption and extend the lifetime of the network system.
## 4 Experiments and Analysis
### Model Structure and Parameter Settings  
This experiment uses a 5-layer model with 4 convolutional layers and 1 linear classification layer. Model parameters are shown in Figure 8. The graph is constructed using the method proposed in this paper for constructing residential graph data and extracting node attributes. The constructed graph with labels is input into the model in batches, the weight parameters are initialized, the model parameters are learned, and the weight parameters are updated by back propagation, with a maximum of 350 iterations. After experimentation, the model with a learning rate of 0.005 and a training batch of 10 is selected. To prevent overfitting, training is stopped when the number of successive iterations is 100 and the accuracy of the validation set is no longer increasing.  
Figure 8: Parameters of the areal settlement classification model.  
In the first graph input layer, the input feature dimension is 5 and the embedding vector dimension is 64. After the graph convolution operation, the output value of the first layer is passed to the next layer and the ReLU function is used as the activation function. Layers 2 to 4 of the graph convolutional layers are used as the hidden layers of the graph convolutional neural network. The input and output vectors of the model have a dimension of 64, and the activation function is a ReLU function. The fifth layer is a linear classification layer, which uses a fully connected neural network and a softmax function as the classification layer to classify the output of the hidden layer through the classification layer. The output is the predicted shape classes of the areal settlement, corresponding to the shape classes in the dataset.  
Figure 9 presents the validation accuracy and loss of the training and validation sets in this experiment. From the results, it can be concluded that the loss value decreased to 0.069 at the end of the training and the accuracy value of the validation set was 95.3%, while the model was able to converge well after 250 iterations. From the accuracy value of 95.3%, it can be concluded that the model has good generalization ability, has good sensitivity to the skeleton line of the face areal settlement, and can effectively classify the shape of the areal settlement.
## 3 Onboard Train Localization Algorithm  
The onboard train localization scheme based on SINS/OD aided by digital track map matching is principally divided into three phases: SINS/OD dead reckoning, map matching based on SINS/OD position, and data fusion, as depicted in Figure 3. In the SINS/OD dead-reckoning phase, the onboard SINS/OD system generates three-dimensional measurements for the acceleration, angular rate, and one-dimensional velocity parameters for the train. Compared to SINS, SINS/OD dead reckoning utilizes the velocity information from odometers and can further improve accuracy through closed-loop feedback to compensate the gyro and accelerometer biases. However, due to the cumulative errors associated with odometers, for the long-duration SINS/OD dead-reckoning system, the navigation accuracy can still diverge.  
During the map-matching phrase, the ellipsoidal coordinates need to be projected to the plane coordinates using the Gaussian Kruger projection, and then the position outputted from the combined navigation system of SINS/OD is projected to the digital track map to constrain the cross-track error [25]. Afterward, the train's position is calculated using the speed of the odometer and the direction of the digital track map of the track; finally, the two positions are weighted as the train position.  
In the data fusion phase, a dual-loop Kalman filtering framework is employed. In the absence of map matching, the SINS/OD dead-reckoning system within the inner loop for high-frequency updates is employed. During the measurement update, the observations consist of the position difference between the DR and the position obtained from the SINS [26]. The combination position will be matched with the digital track map in this paper to perform matching every 10 m on average based on the digital track map we used. The distance depends on the density of the digital track map. If not matched, the DR result is outputted. If the position matching is successful, the matched position replaces the DR-derived position for measurement updates. Because the map data are defined in the Earth-Centered Earth-Fixed (ECEF) absolute coordinate system, obtaining accurately matched position information is equivalent to obtaining accurate absolute GNSS positioning information.  
The reference coordinate frames involved in this paper are defined as follows:  
1. e frame: ECEF orthogonal reference frame.
2.  
Figure 3: Data processing scheme of digital track map-assisted SINS/OD fusion algorithm for onboard train localization.  
2. b frame: orthogonal reference frame aligned with Right-Forth-Up (RFU) axes.
3. n frame: orthogonal reference frame aligned with actual East-North-Up [27].
4. m frame: the odometer measurement coordinate system, abbreviated as \(om\), with \(oy_{m}\) axes in the ground plane in contact with the wheels of the vehicle and pointing straight ahead of the vehicle, \(oz_{m}\) axes perpendicular to the ground plane and pointing upward, and \(ox_{m}\) axes pointing to the right; the odometer coordinate system is a right-handed right-angled \"right-front-up\" coordinate system solidly attached to the body of the vehicle.
## 3 Results
### Description of Developed Software Tool: \"PetriNet editor + PetriNet engine\"
#### 3.1.3 Transition Firing Strategy  
In the main cycle of the program, the PetriNet engine goes through all the transitions and searches for those that are executable, and then, according to the chosen strategy, selects the next transition to be executed. Four strategies are implemented:  
1. **In the order the individual transitions were created in the editor--**Each crawl of executable transitions starts from the first transition. As a result, for example, if the first transition is still executable, it will also be executed and no other transition will ever be selected to start.
2. **By priority--**Each transition carries information about the priority. The priority is any number in the range 0-32,767. The range is limited by the supported size of the int data type on Arduino boards with ATMega microcontroller. A transition with a higher number has a higher priority. In case of equality of priorities, the first strategy is taken into account.
3. **In the order that takes into account the last running transition--**Internally, the last running transition is stored and the transition with the following index is selected from the set of executable transitions. The difference from the first rule is that all executable transitions have the ability to be run.
4. **Random--**The next transition is selected randomly from the set of currently executable transitions.