esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Simulation Results
#### 4.2.2 Case 2 Consensus Controller without Leader Considering Geographic Factors  
Under the same operating point change as in case 1, this case is used to test consensus controller considering the geographic factor without leaders. Similarly, all BESSs are working in an isolated model, and the communication topology of all BESSs is shown in Figure 3a. At the same time, a load decrement of \(1.26+j1.26p.u\) and a load increment of \(2.52+j2.52p.u\) at each load node occur at 20 s and 30 s respectively. Unlike case 1, the geographic factor is considered and the improved Laplacian matrix is applied in this case. It can be observed in Figure 6 that all BESSs reach consensus in this case slower than case 1, where BESS 4 is the slowest one. That is because there is a longer distance between BESS 4 and other BESSs, and every BESS reaches consensus at a different time according to the distance from its neighbor. Therefore, the convergence speed is affected by the geographic location.  
Figure 5: Simulation result without a leader regardless of geographic factors (case 1).
## 3 Results and Discussion  
### Scaffolds Fabrication  
The cylindrical scaffold was fabricated using SLS and is displayed in Figure 1, which had well-controlled open pores. The size of the scaffold was about 17.0 mm \(\times\) 6.0 mm (diameter \(\times\) height). The pores size and wall thickness were 1.5 mm and 2.0 mm, respectively. The pore channels were completely interconnected and distributed throughout the whole scaffold, which were necessary for new tissue ingrowth and vascularization construct [26]. Porosity of the cylindrical scaffold can be estimated as 30.8% by Equation (2) [27]:  
\[P=(V_{\mathrm{a}}\ \mathrm{-}V_{\mathrm{t}})/V_{\mathrm{a}} \tag{2}\]  
where the porosity was designated as \(P\) (%), apparent volume was designated as \(V_{\mathrm{a}}\) (mm\({}^{3}\)), and true volume was designated as \(V_{\mathrm{t}}\) (mm\({}^{3}\)).
## 4 Discussion  
Based on the BDS-3 PPP-B2b service, real-time PPP can be used via B2b signals. However, it is still challenging in an urban environment. According to the assessments above and the results summarized in Table 7, the positioning performance of BSSD PPP can be enhanced visibly by the addition of INS and odometer, especially in periods with frequent GNSS outages. The mean position RMS of BSSD PPP is 64.33 cm, 53.47 cm, and 154.11 cm in three components based on PPP-B2b service. By integrating INS, the mean position RMS can be improved by 31.2%, 23.3%, and 27.3%. Such percentages can be furtherly increased by 1.34%, 1.41%, and 1.73% after using an odometer. The test data from 1500 s to 2500 s are adopted to validate the performance in the periods with frequent GNSS outages. The mean position maximum drifts during these periods decreased from 107.96 cm, 59.90 cm, and 78.22 cm of BSSD PPP to 88.52 cm, 54.54 cm, and 62.00 cm of BSSD PPP/INS TCI. After adding an odometer, such values are 66.29 cm, 45.84 cm, and 49.17 cm.  
Figure 23: Attitudes differences between undifferenced PPP/INS/ODO TCI and BSSD PPP/INS/ODO TCI.  
Figure 22: Attitude errors of undifferenced PPP/INS/ODO TCI with different GNSS.  
Anyway, the position accuracy of PPP/INS/ODO TCI based on the BSSD model is slightly higher than the solutions based on the undifferenced model. Compared with undifferenced PPP/INS/ODO TCI, the mean position RMS of BSSD PPP/INS/ODO TCI is improved by 7.71%, 3.09%, and 0.27%. The mean maximum drifts can be reduced from 82.38 cm, 50.66 cm, and 52.30 cm to 66.29 cm, 45.84 cm, and 49.17 cm by utilizing the BSSD model.  
For attitude determination, the mean attitude RMS of PPP/INS TCI is 0.025\({}^{\circ}\), 0.049\({}^{\circ}\), and 0.184\({}^{\circ}\) in three components. The addition of an odometer brings a 25% improvement to the RMS of heading angles and reduces the mean maximum drifts from 0.313\({}^{\circ}\) to 0.174\({}^{\circ}\). The results of the other two components are comparable. Moreover, the accuracy of PPP/INS/ODO TCI with and without the BSSD model is similar to each other.
# Sericin for Tissue Engineering
## 3 Sericin and Tissue Engineering
### Sericin for Other Tissues  
In addition to the aforementioned tissue engineering applications, sericin has been used for cartilage or nerve regeneration. [PERSON] et al. reported that a collagen-sericin scaffold improved with hyaluronic acid and chondro sulfate could be used as a temporary physical support for human adipose-derived stem cells for cartilage tissue engineering [111]. Hyaluronic acid and chondro sulfate are the most beneficial prochondrogenic factors used in the design of new biomaterials for cartilage tissue engineering, and adipose-derived stem cells have been demonstrated to exhibit high chondrogenic potential [111]. [PERSON] et al. reported that artificial cartilages were effectively formed 8 weeks after injection of chondrocyte-laden sericin methacryloyl hydrogel in a rat model [112]. This artificial cartilage showed histological, molecular, and mechanical similarities to natural cartilage.  
[PERSON] et al. reported for the first time the use of a pure sericin conduit for peripheral nerve regeneration in a rat nerve injury model [113]. This sericin/silicone nerve guidance conduit can be used as an alternative to autologous nerve grafts to repair damaged peripheral nerves, as it induced functional recovery similar to that of an autologous nerve graft. [PERSON] et al. reported on nerve regeneration using a carbon nanotube/sericin nerve conduit in a rat nerve injury model [114]. This conduit can effectively promote the structural and functional recovery of nerves comparable to an autograft.
## 4 Conclusions  
Based on the obtained experimental data, the equilibrium values of the adsorption capacity for the adsorbent, based on the oxide Al2O3, and the commercial adsorbent (zeolite NaX), the values of the averaged weight by volume of the adsorbents, used in the work, were estimated. When the layer height of the adsorbents under study increases, their dynamic capacity with respect to water vapour and the protective power time increase. At the atmospheric pressure, the adsorption characteristics of the adsorbent NaX are better than those of the aluminium oxide adsorbent, and the protective power time of the layers of different adsorbents is comparable. However, at a pressure higher than 0.3 MPa, things are different-the adsorption capacity and the protective power time of the developed adsorbent Al2O3 increase. Consequently, the use of this aluminium oxide adsorbent, taking into account its higher resistance to moisture, lower cost and lower regeneration temperature, is preferable as compared with other zeolite-based analogues during dewatering under pressure.  
This paper was accomplished based on the collaborative work of the authors. [PERSON] and [PERSON] wrote the article. [PERSON] and [PERSON] performed the experiments, analysed the data and interpreted the experimental results. [PERSON] and [PERSON] supervised the entire research progress. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the Ministry of Science and Higher Education, grant number \"0721-2020-0037\".  
The authors declare no conflict of interest.  
Figure 8: Dependence of the dynamic capacity of the prospective aluminium oxide adsorbent (Al2O3) on the contact time at a water concentration of 15 g/m\({}^{3}\) at the entrance. 1–data of the laboratory unit (air consumption–8.2 L/min); 2–data of the pilot installation (air consumption–25 L/min). Conditions: the saturator temperature is 18 \({}^{\circ}\)C, pressure is 0.1 MPa.
## Appendix A  
### Proof of Identity (7)  
When \(k=1\).  
\(|u|<u_{th}\)  
\[|\Delta q_{\chi}|=|q_{\chi}(|u|)-|u||\leq\delta|u|+b \tag{11}\]  
where \(u\) is input, \(0<\delta<1\), \(b\) is a constant, when \(u=\frac{d}{1+\delta}\), we get \(|\Delta q_{\chi}|=\frac{d}{1+\delta}\).  
\[b=\frac{d(1-\delta)}{1+\delta}\leq d(1-\delta) \tag{12}\]  
The following inequalities are obtained by introducing (12) into (11).  
\[|\Delta q_{\chi}|\leq\delta|u|+d(1-\delta) \tag{13}\]  
\(u_{th}\leq|u|\leq M\)  
\[|\Delta q_{\chi}| =|q_{\chi}(|u|)-|u|| \tag{14}\] \[\leq(\frac{|u|}{\varpi}+\frac{1}{2})\varpi-|u|\] \[=\frac{1}{2}\varpi\]  
When we bring in the dynamic parameter \(k\), the error of the new dynamic quantizer is as follows. \(|u|<ku_{th}\)  
According to (6) and (13), we get  
\[|\Delta q_{\chi}| =|kq_{\chi}(\frac{|u|}{k})-|u|| \tag{15}\] \[=|kq_{\chi}(\frac{|u|}{k})-k\frac{|u|}{k}|\] \[=k|\Delta q_{\chi}(\frac{|u|}{k})|\leq k(\delta\frac{|u|}{k}+d(1- \delta))\]  
\(ku_{th}\leq|u|\leq kM\)  
According to (6) and (14), we get  
\[|\Delta q_{\chi}| =|kq_{\chi}(\frac{|u|}{k})-|u|| \tag{16}\] \[\leq(\frac{|u|}{k\varpi}+\frac{1}{2})k\varpi-|u|\] \[=\frac{1}{2}k\varpi\]
# Citation Oriented AuthorRank for Scientific Publication Ranking
## 3 Methodology
### AuthorRank Combined with PRP  
We have introduced two methods for publication ranking, PRP and AuthorRank. The former, PRP, depends on links in the graph to calculate the authority of each node. The latter, AuthorRank, is decided by author ranking. Ideally, we would recommend publications that have both a high AuthorRank and a high PageRank, meaning that they are really important for the topic. In contrast, papers with a low AuthorRank and a low PageRank have little importance for the topic.  
In this part, we would like to use two methods to combine the results of PRP and AuthorRank to verify whether the performance of publication ranking can be improved. For these methods, we used AuthorRank to enhance the publication prior probability via publication prior probability smoothing.  
Linear Combination:  
\[I_{key_{t}}(p_{i})=\ \alpha*I_{key_{t}}(p_{i}|\text{PR})+(1-\ \alpha)*I_{key_{t}}p_{i}| \text{AR} \tag{13}\]  
where \(\alpha\) is a parameter between 0 and 1, which controls the weight of \(I_{key_{t}}(p_{i}|\text{PR})\) and \(I_{key_{t}}p_{i}|\text{AR}\). \(I_{key_{t}}(p_{i}|\text{PR})\) is the relative importance score for \(paper_{i}\) on \(topic_{t}\) calculated by the PRP algorithm, whereas \(I_{key_{t}}p_{i}|\text{AR}\) is the importance ranking score for \(paper_{i}\) on \(topic_{t}\) generated by the AuthorRank algorithm.  
The linear form assumes that the total score is a linear combination of the two scores. Each score's contribution to the total score is controlled by a parameter, \(\alpha\).  
Cobb-Douglas Form:  
\[I_{key_{t}}(p_{i})=I_{key_{t}}(p_{i}|\text{PR})^{\alpha}*I_{key_{t}}p_{i}| \text{AR}^{(1-\alpha)} \tag{14}\]  
This form assumes that the total score is the product of the two scores, rendering it insensitive to small scores but sensitive to large scores.  
As we already know, one limitation of the topic-based PageRank algorithm is that the importance scores of papers, \(I_{key_{t}}(p_{i}|\text{PR})\), are 0, if the paper is not related to the specific topic (\(key_{t}\)).  
This may limit the performance of the harmonic form. Thus, if \(I_{key_{m}}(p_{i}|\text{PR})\) is 0, the final score of \(I_{key_{m}}(p_{i})\) is infinite. One smoothing method will improve the score of \(I_{key_{m}}(p_{i}|\text{PR})\), as follows:  
\[I_{key_{t}}(p_{i}|\text{PR})^{\prime}=\sigma\cdot I_{key_{t}}(p_{i}|\text{PR}) +(1-\sigma)\cdot\text{P}\Big{(}z_{key_{t}}|Corpus\Big{)} \tag{15}\]  
where \(I_{key_{t}}(p_{i}|\text{PR})^{\prime}\) is the score after smoothing, and \(\text{P}\Big{(}z_{key_{t}}|Corpus\Big{)}\) is always larger than 0, the paper topic score is also always positive. The parameter, \(\sigma\), controls the amount of smoothing. In this research, we used as a tentative value, \(\sigma=0.8\).
## 2 Materials and Methods
### Methodology
#### 2.3.2 Data Preprocessing  
The collected drone data underwent rigorous preprocessing using the Python programming language and the scikit-learn library [16]. The frames from the captured MP4 files were resized to 450 \(\times\) 450 pixels, retaining key visual components, enabling faster processing. Multiple data augmentation techniques were applied, including horizontal flipping, rotations, and brightness adjustments to boost model generalization.  
Model Development: 80% of the data (801 samples) was used to train the RF, SVM, and KNN models, with 20% (200 samples) held out for testing. Hyperparameters like number of RF trees, SVM kernel type, and KNN k-value were tuned through grid search and cross-validation. Model selection was based on the highest recall, to reduce false negatives on the test set.  
Integration and Testing: The chosen model was integrated into our system overlaying the real-time drone camera feed with the pollution detection results. This system underwent field testing for oil spill and trash identification.  
Figure 3: Overview of all data processing steps followed by model training and classification.
# Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
## 2 Data Assimilation Process
However, the statistical approach, though often complex and time-consuming, can provide a richer information structure, i.e., an average and some characteristics of its variability (probability distribution). Assuming the acquisition of the observations in a fixed time steps, the variational approach is named 3 DVar [1; 6], and it consists in computing the minimum of the cost function:  
\[v=\mathcal{H}(u)+\epsilon \tag{2}\]4  
where  
\[v=\mathcal{H}(u)+\epsilon \tag{2}\]5  
Potential problems of a variational method are mainly given by three main points: it heavily relies on correct \(B_{k}\) matrices, it does not take system evolution into account, and it can end up in local minima. Kalman Filter consists in computing the explicit solution of the normal equations:  
\[v=\mathcal{H}(u)+\epsilon \tag{2}\]6  
where the matrix  
\[v=\mathcal{H}(u)+\epsilon \tag{2}\]7  
is called Kalman gain matrix and where \(B_{k}\) is updated at each time step [6]:  
\[v=\mathcal{H}(u)+\epsilon \tag{2}\]8  
A potential problem of Kalman Filter is that \(K_{k}\) is too large to store for large-dimensional problems. A common point of 3 DVar and KF is that the observations that are defined in the window \([0,T]\) (circle points in Figure 2) are assimilated in the temporal point, where the state is defined (grey rhombus in Figure 2 at time \(t_{k}\)). After the assimilation, a new temporal window \([T,2T]\) is considered and new observations are assimilated. Each process starts at the end point of the previous process and the two assimilation processes are defined in almost independent temporal windows. The result of the assimilation process \(u^{\text{DA}}\) (green pentagons in Figure 2), as computed by 3 DVar or KF, is called analysis. The explicit expression for the analysis error covariance matrix, denoted as \(A_{k}\), is [1]:  
\[v=\mathcal{H}(u)+\epsilon \tag{2}\]9  
where \(I\) denotes the identity matrix. Even if the DA process takes information in a temporal window into account, it does not really take the past into account. This is due to the complexity of the real application forecasting models for which a long time window would be too computationally expensive. In next section, we introduce the Deep Data Assimilation (DDA) model, in which we replace the forecasting model \(M\) (black line in Figure 3) with a new Machine Learning model \(\mathcal{M}\) (red line in Figure 3) trained while using DA results and taking the past into account.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 4 Proposed Wind Farm Control Technique in AGC
### Design Procedure of SCA-Based FOPID Controller
#### 4.2.2 Objective Function Formulation and Employed Optimization Algorithm  
In order to damp the frequency deviations and tie-line power oscillations effectively, considering a suitable objective function is essential. The considered objective function should be defined such that the output properties in the time domain, such as peak overshoot, peak time and settling time of the considered variables are minimized. In this paper, the integral of time multiplied squared error (ITSE) performance index is considered as the objective function.  
\[ITSE=\int\limits_{0}^{T_{sim}}t\left[\Delta f_{1}^{2}+\ \Delta f_{2}^{2}+\ \Delta P_{12}^{2}\right]dt, \tag{7}\]  
where \(T_{sim}\) denotes the simulation time. \(\Delta f_{1}\) and \(\Delta f_{2}\) are the frequency deviations of area 1 and area 2, respectively. \(\Delta P_{12}\) is the tie-line power deviation. The ITSE index uses advantages of both ISE and ITAE indices. The ITSE utilizes squared error and time multiplication to diminish large oscillations and decrease long settling time. The SCA optimization algorithm is employed here to minimize the ITSE index and optimize all the adjustable parameters subject to constraints. The constraints for a FOPID controller are as follows:  
\[\begin{array}{l}K_{p}^{\min}\leq K_{P}\leq K_{p}^{\max},K_{I}^{\min}\leq K_{ I}\leq K_{I}^{\max}\\ K_{D}^{\min}\leq K_{D}\leq K_{D}^{\max},0\leq\lambda\leq 1,0\leq\mu\leq 1\end{array}. \tag{8}\]
## 4 Methods for Manipulation Protection and Detection
### MSCL-Net  
The MSCL-Net method [19] is a passive approach for detecting and localizing image manipulations, based on common structures in such algorithms, including an encoder-decoder architecture (for multi-level analysis), feature fusion, and attention mechanisms. The standard approach in these algorithms relies on using color features (RGB) along with additional representations, such as noise features or compression artifacts, to identify manipulations. In this context, the creators of MSCL-Net proposed the Feature Cross Fusion Module (FCFM), which facilitates deeper integration of RGB and Spatial Rich Model (SRM) features through a cross-fusion mechanism, rather than the simple concatenation or addition used in other methods. The Spatial Rich Model (SRM) is characterized by its ability to capture local noise features at the pixel neighborhood level, enabling the detection of noise inconsistencies between altered and untouched regions while minimizing the influence of the image's content. Cross-fusion, on the other hand, involves dynamic interaction between RGB and SRM features, allowing for a more detailed and balanced representation of manipulation information. This approach neutralizes semantic content and amplifies forgery traces, resulting in improved detection and localization of manipulations.  
The authors also introduced the Adaptive Self-Attention Module (ASAM) [60], which analyses dependencies in both spatial and channel dimensions, unlike traditional attention mechanisms that operate in a single dimension. This enables the model to identify better global and local differences between altered and untouched regions, enhancing the precision of manipulation detection. Additionally, the method incorporates a Supervised Contrastive Learning Module (SCLM), which employs multi-scale contrastive learning. This approach maximizes the differences between the features of altered and untouched regions at various representation levels, improving accuracy and reducing false positives. The loss function in MSCL-Net goes beyond standard approaches, which typically focus on classification and segmentation losses. The authors introduced an additional component in the form of contrastive losses calculated at four different scales. This allows for a more comprehensive analysis of differences, further improving the model's ability to detect and localize manipulations.  
Using ConvNeXt-T [61] as the encoder, instead of the popular ResNets, allows for a larger receptive field and greater sensitivity to local traces of manipulation. This is achieved by using larger convolutional kernels, which cover more extensive areas of the image in a single step, and replacing traditional max-pooling operations with downsampling convolutions. As a result, the network is more sensitive to local traces of manipulation, as it better preserves critical details related to differences between altered and untouched regions.
# Raman Spectroscopy to Monitor the Delivery of a Nano-Formulation of Vismodegib in the Skin
###### Abstract  
Raman spectroscopy was used to detect low quantities of Vismodegib in the skin after its topical application via transfersomes. Vismodegib is a novel antineoplastic drug approved for oral administration for treatment of basal cell carcinoma. Transfersomes loaded with Vismodegib were prepared by thin film resuspension and extrusion, and were characterized physicochemically. Transfersomes were applied to human and pig skin specimens using the Saarbrucken penetration model. The skin was then sectioned by tape stripping, followed by penetration assessment by UV-Vis spectroscopy and Raman spectroscopy in a confocal Raman microscope. Raman signals from Vismodegib and transfersomes were recovered from skin sections, showing a similar distribution in the _stratum convenum_ obtained by the other techniques. On the other hand, pig and human skin showed differences in their penetration profiles, proving their lack of equivalence for assessing the performance of these transfersomes. Raman spectroscopy appears as a potential non-invasive, direct tool for monitoring hard-to-detect molecules in a complex environment such as the skin.  
Raman spectroscopy; skin penetration; Vismodegib Article
# Controllable Height Hopping of a Parallel Legged Robot
## 5 Simulation and Experiment
### Single Landing-Jumping Experiment  
The fundamental purpose of this section is to verify the experimental effects of the actuator mechanism and the VMC method and to gather actual data during hopping to rectify the control function. According to VMC as (12), the leg performed as a virtual polar-coordinate spring-damper system. An external force acting on the foot end can cause it to be displaced; after the force is removed, the leg recovers to its initial posture. The leg generally realized good active compliance on its toe and this played an important role in impact mitigation.  
In the landing-jumping experiment, the robot fell to the floor and then rebounded with higher stiffness. As noted above, the leg in the flight phase is controlled using constant stiffness and damping. In this experiment, the stiffness \(k_{vl}\) is 1000 N/m, the damping \(c_{vl}\) is 30 Ns, and the original length is 0.5 m. In the takeoff phase, \(k_{vl}\) is 1700 N/m, \(c_{vl}\) is 0, and the original length is 0.6 m. In the experiment, the robot jumped to approximately 0.64 m (from ground to toe).  
Figure 8 shows the measured data recorded during the experiments. Except for the ground-contact moment, the leg length curve shows ideal continuous and smooth characteristics, thus proving the effectiveness of the VMC method. Circular marks in the figure indicate the sudden changes in the curves. Obviously, current can be used to detect the external impact more quickly. However, the subsequent current curve is not continuous because a little chattering occurs during impact. In the other two graphs, the velocity fluctuates dramatically during the touchdown process but remains relatively smooth during takeoff and flight, while the acceleration is noisier throughout the movement.