esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Energy-Water Management System
### Energy Management System  
The EMS based on the MPC approach drives the microgrid to minimize the following equation using a predicted horizon of \(N_{y}=48\) h and a sampling time of 10 min:  
\[\min_{\begin{subarray}{c}E_{in},E_{out},\\ B_{p},E_{ch},E_{dch}\end{subarray}}\sum_{j=0}^{N_{y}-1}\left[C_{in}\cdot E_{in} (k+j)-C_{out}\cdot E_{out}(k+j)\right]+\sum_{j=1}^{N_{y}}\sum_{l=1}^{N_{ag}} \left[C_{lst}^{ap}\cdot w_{lst}^{l}(k+j)+C_{ns}^{up}\cdot w_{ns}^{l}(k+j)\right] \tag{12}\]  
The left side of Equation (12) specifies the minimization of costs for energy transactions with the distribution company at a price of \(C_{in}\) in $/kWh for purchases and \(C_{out}\) in $/kWh for sales. The right side of Equation (12) seeks to minimize the water that was not supplied \(w_{ns}^{l}\) at a cost of \(C_{ns}^{up}\) in $/m\({}^{3}\) for \(N_{ag}\) farmers as well as the lost water \(w_{lst}^{l}\) due to excess irrigation at a cost of \(C_{lst}^{up}\) in $/m\({}^{3}\).  
Among the constraints associated with the optimization problem is the energy balance given by:  
\[E_{in}(k+j)+E_{pv}(k+j)+E_{dch}(k+j)=E_{out}(k+j)+E_{ch}(k+j)+E_{p}(k+j)+E_{nc}( k+j)\quad j=1\ldots N_{y} \tag{13}\]  
where \(E_{in}\) is the energy purchased from the distribution company, \(E_{pv}\) denotes photovoltaic generation, \(E_{dch}\) denotes discharge from the battery bank, \(E_{out}\) is the energy sold to the distribution company, \(E_{ch}\) is the charge of the battery bank, \(E_{p}\) denotes energy consumed by the water pumps, and \(E_{nc}\) denotes the non-controllable load, which corresponds to domestic energy consumption by the community. Another relevant constraint is the maximum drop in the hydraulic threshold when pumping from well \(i\) to a height \(r\), which is given by:  
\[\varepsilon\geq s(r,t(n))=\sum_{k=0}^{n}\frac{\left(B_{p}^{i}(k)-B_{p}^{i}(k-1 )\right)\cdot q_{b}^{i}}{4\pi T}\cdot W\left(\frac{Sr^{2}}{4T\left(t(n)-t(k) \right)}\right) \tag{14}\]  
where \(B_{p}^{i}(k)\) is a binary decision variable for pumping into well \(i\) at instant \(k\), \(q_{b}^{i}\) is the nominal flow of pump \(i\) in l/s, and \(r\) is the elevation to be pumped to, in m, considering that the worst case is less than the midpoint between the two closest wells. The control actions \(u(k+j)=\{E_{in},E_{out},B_{p},E_{ch},E_{dch}\}\) are calculated based on the optimization problem defined by the objective function in Equation (12) and the constraints in Equations (13) and (14) at each sampling time, and the action at instant \(k\) is applied to the energy-water system. In the next time step, a new optimal control action is determined (rolling horizon). The 48-h-ahead predictions for electricity consumption and solar radiation, based on auto-regressive integrated moving average (ARIMA)-type models, are used for the EMS based on MPC.  
Figure 2: Integrated EWMS model.  
GUROBI offers advanced methods for solving mixed integer linear programming problems through the YALMIP library [36]. The GUROBI tool solves the MPC optimization of the EMS with a linear programming method that uses a _branch-and-bound algorithm_.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Communication
#### 3.1.1 Technologies  
The technologies involved can be broadly classified into wired, wireless, and hybrid options. Wired solutions encompass Power Line Communication, Fiber Optic Communication, and Ethernet, taking advantage of existing infrastructure to deliver high reliability, speed, security, and ample bandwidth [100].  
On the other hand, wireless technologies, such as Wi-Fi, Zigbee, Wireless Sensor Networks, Bluetooth, Near Field Communication, Cellular, and satellite communication, offer flexibility for both short- and long-range communication needs. They are utilized for  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Ref.** \\ \hline DNMGs & [9; 11; 24; 26; 27; 28; 31; 33; 46; 53; 55; 56; 57; 58; 59; 60] \\ PNMGs & [8; 10; 13; 16; 19; 20; 25; 30; 32; 34; 42; 43; 44; 51; 63; 64] \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of key studies in DNMGs and PNMGs.  
real-time data collection, monitoring and control of power generation, load management, and power supply planning [100].  
Hybrid approaches often blend wired and wireless elements or employ mesh networks, capitalizing on the strengths of both to create resilient and adaptable communication networks. The selection of a specific technology depends on factors like deployment cost, reliability, scalability, and the unique requirements of the microgrid in question.
## 5 Case Studies
### Hysteresis Control with PID Correction for Temperature Control
#### 5.1.2 Results  
To minimize the proposed objective function given by Equation (39), the artificial bee colony optimization algorithm was used. Figure 5 presents the plot obtained for different values of the weighting factor \(\alpha\), while Table 3 presents the numerical indicators.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Static gain of the plant (\(k_{o}\)) & 7.5 \({}^{\circ}\)C/V \\ \hline Time constant of the plant (\(T_{o1}\)) & 150 s \\ \hline Time constant of the plant (\(T_{o2}\)) & 50 s \\ \hline Hysteresis value (\(H\)) & 1 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the heating system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\alpha\) & \(\sum\Delta u\) & **ISE** & \(k_{c}\) & \(T_{c1}\) & \(T_{c2}\) \\ \hline
0.1 & 58 & \(2.54\times 10^{3}\) & 7.12 & 57.0 & 100 \\ \hline
1 & 40 & \(2.55\times 10^{3}\) & 8.96 & 69.5 & 93.6 \\ \hline
8 & 25 & \(2.60  
One can see that the proposed objective function minimizes the oscillations in the quasi-steady state and simultaneously minimizes the control signal's switching frequency. The proposed objective function successfully met the above-discussed assumptions. The balance between these two requirements can be determined by properly selecting the weighting factor \(\alpha\). Unfortunately, in such an objective function, the value of the switching frequency cannot be easily determined. Therefore, the weighting factor \(\alpha\) has to be selected using a trial-and-error approach.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 3 Results
### Effects of PAM on Secretion of VEGF and bFGF  
The secretion of the VEGF and bFGF was detected with ELISA, and the results were shown in Figure 6. The concentrations of VEGF and bFGF secreted by HUVECs in the control group (0 s) were 31.53 \(\pm\) 2.56 pg/mL and 3.23 \(\pm\) 0.85 pg/mL. The concentrations of VEGF and bFGF in 15 s-PAM group were 37.61 \(\pm\) 3.41 pg/mL and 3.63 \(\pm\) 0.90 pg/mL, respectively, which were significantly increased compared with those in the control group (\(p<0.05\)). In 30 s-PAM group, the concentrations of VEGF and bFGF were 27.38 \(\pm\) 1.80 pg/mL and 2.68 \(\pm\) 0.50 pg/mL, with no significant changes (\(p>0.05\)). The concentrations of VEGF and bFGF in 45 s-PAM group were significantly decreased, which were 22.77 \(\pm\) 1.47 pg/mL and 2.43 \(\pm\) 0.42 pg/mL, respectively, and there were no significant changes compared with those in the 0 s group. In 60 s and 75 s-PAM groups, VEGF secretion was significantly decreased, which was 20.46 \(\pm\) 1.82 pg/mL and 18.71 \(\pm\) 2.61 pg/mL, respectively, and bFGF secretion was also significantly decreased, which was 2.15 \(\pm\) 0.34 pg/mL, and 1.66 \(\pm\) 0.24 pg/mL (\(p<0.05\)).
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 4 ML Models Classification for Wind Power
### ML Models for Optimized Wind Power Process  
The results from searching scientific articles on ML models applied in the wind energy production process highlight a notable interest in optimizing and improving this sector through various techniques. This discussion will analyze the results of the ML models used to address specific challenges of the wind energy production process. The results are graphically illustrated in Figure 7.  
The analysis of the application of various ML models in optimizing the wind energy production process suggests diversity in their usage. Each model has different applicability depending on the complexity of the process and the intended purpose.  
Linear regression is identified in seven articles, indicating modest usage in optimizing wind energy production processes. Although frequently used in classification problems, logistic regression does not appear in the set of articles in the context of processes, suggesting that classification approaches are not as often used for optimizing wind energy production processes.  
Only three articles mention KNN in the category of classification models, reflecting its relatively limited application in this field. Although potentially useful for data classification and identifying patterns or anomalies in wind turbine performance, this model is less efficient than more sophisticated techniques. Additionally, naive Bayes, which is usually employed for classification tasks, does not appear in the articles on wind energy process optimization, underscoring that more straightforward classification approaches are unsuitable for optimizing the wind energy production process.  
Figure 7: ML models for optimizing wind energy production processes.  
The results regarding SVM and decision tree models suggest that SVM is used in 12 articles, indicating medium applicability. Decision trees are used less frequently, found in only one article, showing that this technique is less commonly used in this specific context.  
Ensemble models, random forest (used in 10 articles) and gradient boosting (used in nine articles), are employed to understand better the variability and complexity of wind energy production processes. XGBoost, found in eight articles, is a popular method for enhancing models through advanced optimization techniques.  
Regarding neural networks, CNNs are encountered in 27 articles, and RNNs in five. The values show that this type of network is used for sequential data processing, but their application in this field is more limited.  
K-Means, found in 10 articles, is a clustering model for grouping data to analyze wind turbine performance patterns. Additionally, dimensionality reduction techniques such as PCA and t-SNE are used in four and two articles, respectively, showing the possibility of applying them for data visualization purposes or identifying relevant features from large datasets.  
This analysis notes that the CNN model is the most extensively analyzed in the specialized literature on optimizing wind energy production processes.
## 2 Research Framework
### Phase 2-Sensitivity Analysis  
To account for the inherent LCA outcomes' uncertainties due to input data, Monte-Carlo (MC) methods are applied to the LCA models for uncertainty propagation [32; 33; 34; 35]. The resulting outputs of the assessments take the form of probability distributions (PDF) of environmental indicators. Sensitivity Analysis (SA) can then be performed to assess the contribution of input assumptions on these output distributions.  
In general, the uncertainty characterization of input parameters entails data collection based on literature and commercial databases (see Section 3.3).  
A quantitative approach, based on parameter estimation techniques and goodness-of-fit tests, is used to fit distributions of the distances between natural/recycled aggregates delivery and the mortar factory. Firstly, companies producing mortars in Italy (with a revenue of over 3 million \(\ell\)) are identified. Then, all the one-way delivery distances between the two factories that produce wastes and the identified mortar factories are measured. Finally, from these data, two normal distributions are obtained, representing the distance between waste composite materials suppliers and production sites in Italy.  
Concerning natural sand transport, a uniform distribution is obtained for the distance between sand suppliers and mortar factories (from 50 km to 500 km).  
The methodology is implemented in R (ver. 4.1.3), an open-source programming language and software environment for statistical computing and graphics [36].  
The distributions of the other LCA input data are retrieved from the ecoinvent database v.3, where the uncertainty of the unitary environmental impact for LCI background data is quantified by using the qualitative assessment of data quality indicators based on the pedigree matrix approach [37].  
For each LCA, MC analysis is then performed using SimaPro software v9.1.0 (10,000 runs) and a data-fitting procedure is followed to identify and characterize the outcomes' PDF.  
Finally, in order to evaluate the effective influence of the uncertainties on materials transports, an SA through a variance-based decomposition technique ([PERSON]'s method) is performed, thus relating outputs' variances to inputs' variances [38]. Two sets of sensitivity indices are calculated for all input data. The \"first-order\" index (Si) represents the main contribution of each input factor to the output's variance. The \"total order\" index (STi) measures the contribution to the output variance due to each input, including all variances caused by its interactions with any other input variables [26]. The higher the value of the sensitivity indices, the more influential are the related parameters' uncertainty on the outcome.
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
## 3 Cartesian Constrained STOMP
### Cost Function  
Due to previously outlined reasons, a definition of the new cost function is needed. Therefore, a Cartesian Distance Cost Function is designed in our research. The goal is to reach the closest approximation to cartesian linear trajectory, which consists of \(n\) waypoints. The change of position and orientation of TCP is linear in each waypoint and is illustrated in Figure 4. The method calculates the distance and rotation angle between waypoints of  
Figure 3: An example of cartesian space around a waypoint. (Blue dots represent waypoints and red dots represent start and goal).  
cartesian linear trajectory and waypoints rollout in each timestep. The count of cartesian linear trajectory waypoints is the same as the count of rollout waypoints for a simpler comparison of both trajectories.  
Rollouts with a calculated distance or rotation angle outside of user-defined tolerance are invalid. The optimization goal of this cost function is to minimize the distance and rotation angle in each STOMP iteration. Figure 5 shows the generated rollout (green curve with blue dots representing timesteps) and optimal linear trajectory (yellow line). The grey arrow illustrates the distance that should be minimized. Figure 5 displays the ideal linear trajectory and one generated rollout where it is possible to infer distances between individual tool-point poses. These are used in the cost function which is considered by the cartesian constraints.  
The first step of the cost function is to convert rollout timesteps from joint space to cartesian space of robot TCP. The transformation matrices between each waypoint of cartesian linear trajectory and rollout are calculated by the formula:  
\[Diff_{i}={P_{Li}}^{-1}*P_{Ri} \tag{2}\]  
where \(P_{Li}\) is i-waypoint of cartesian linear trajectory and \(P_{Ri}\) is the \(i\)-timestep of rollout. The evaluation strategy is split into translation and rotation errors. The translation error is  
Figure 4: An example of cartesian linear trajectory.  
Figure 5: Ideal linear trajectory (yellow) and one rollout (green) with each tool-point pose visualized with a frame of reference.  
denoted as \(e_{ti}\), which is the Euclidean norm of the translation vector of matrix \(Diff_{i}\). The variable \(e_{ri}\) represents rotation error and is calculated as the angle of the rotation vector, which is created from the rotation part of matrix \(Diff_{i}\)[24]. The rollout is invalid when the translation or rotation error exceeds tolerances \(t_{t}\) and \(t_{r}\). The final cost is given by the formula:  
\[C=\sum_{i=0}^{n}w_{t}\frac{e_{ti}}{t_{t}}+w_{r}\frac{e_{ri}}{t_{r}} \tag{3}\]  
where \(n\) is the number of timesteps (waypoints), with \(e_{ti}\) and \(e_{ri}\) obtained from the transformation matrix between the i-waypoint of cartesian linear trajectory and rollout. Parameters \(t_{t}\) and \(t_{r}\) work in this function as normalizers and parameters \(w_{t}\) and \(w_{r}\) represent cost weights. These tolerances and cost weights are parameterizable (recapitulated in Table 1).
## 3 Proposed Scheme
### The Side-Match Evaluation  
For convenience, we apply a small codebook of length 16 in all illustrative examples, although typical lengths are 256, 512, or 1024 in most real applications. In addition, the head and rear codewords, i.e., CW0 and CW15, are reserved as explained. The reserved codewords are determined after codebook training. After obtaining the codebook, the principal component analysis (PCA) is used to determine the projection values of the fourteen codewords along the first component as shown in Figure 5. The codebook is denoted by \(C_{V}=\left\{V^{1},V^{2},\ldots,V^{14}\right\}\), where \(V^{j}\) represents a codeword of 16 pixel-values in the codebook of size 14. The first principal component \(W\) of the codewords in the codebook is defined by  
\[W=\operatorname*{arg\,max}_{\left\|W\right\|=1}\sum_{j}\left(W\cdot V^{j} \right)^{2} \tag{1}\]  
The projections of codewords to this component constitute a data set with maximum variance. Then, the codewords are sorted according to the ascending order of projection value. If the codebook shown in Figure 6 is a PCA-sorted one, we further divide the codewords into two clusters. The upper half is cluster \(1\), while the lower half is cluster \(0\). Besides, a one-to-one mapping in their corresponding order within the clusters is defined as illustrated in the figure.  
Figure 5: Illustration of the codebook sorting.  
Figure 6: An illustration of codewords clustering.  
Next, the side-match evaluation is defined as illustrated in Figure 7. For a block in the VQ image, for example 'CW1', the pixel values along the border with upper and left blocks are collected to form two vectors, one is constituted by inside pixels and the other is constituted by the outside pixels. For the illustrated case, \(v_{in}=[125,130,133,130,129,126,140]\) and \(v_{out}=[(132+128)/2,\,135,\,133,\,133,\,132,\,133,\,138]\). Compute the distance between the two vectors by \(d_{t}=[(130-125)^{2}+(135-130)^{2}+(133-133)^{2}+(133-130)^{2}+(132-129)^{2}+(1 33-126)^{2}+(138-140)^{2}]^{1/2}=11\). The corner pixel is compared with the average value of its top and left neighbors. By replacing the codeword with its counterpart 'CW8' according to the one-to-one mapping, another distance \(d_{f}\) can be obtained. Normally, \(d_{t}\) is much smaller than \(d_{f}\), since the neighboring image blocks are usually like each other. Based on this property, a switching between counterparts can be easily detected and therefore can be exploited to embed secret data.
## 3 Results and Discussion
### Hyperspectral Imaging
#### 3.1.3 Fat Profile Prediction and Visualization  
The chemical images (PLS images) of the samples were generated by applying the accepted beta coefficients to the masked 3D data HSI images (Figure 5, row 1). This operation produced a classification image of the samples, which can visualize the fat content map of the samples. Previous research also used similar methodology to visualize corn viability [22] and almond adulteration detection [36]. Figure 5, row 2 shows the chemical images of the three samples from the three different classes. The difference among the three groups is significantly evident in the fat part of the samples, as predicted by the beta coefficient plot. For clear observation of the fat mapping in the samples, a binary classification model of the samples was also generated. The binary images (Figure 5, row 3) represented the fat part as pixel value 1 (white) and the lean and background as pixel value 0 (black). Fat content of the samples was evaluated considering the white part of the images and this information was used to classify the three grades of the beef samples. From the PLS image, the difference among the three groups is significantly evident in the fat content regions of the samples, as predicted by the peaks in the beta coefficients plot  
Figure 4: (**a**) Classification results of the 3 beef grade samples based on the thresholds of \(<\)1.5 = Akaushi, 1.5 to \(<\)2.5 = USDA prime, and 2.5 and greater = USDA choice; red = Akaushi, green = USDA prime, blue = USDA choice. (**b**) Beta coefficient curve derived from the PLS-DA model using Savitzky–Golay second derivatives pre-processing methodology. Significant wavelengths for distinguishing different beef grades are marked with violet circles.  
(Figure 5, row 3). Hence, the developed PLS-DA model-based image analysis performed satisfactorily to classify the beef steaks based on the fat profile.
# Damage Analysis and Evaluation of Light Steel Structures Exposed to Wind Hazards
## 1 Introduction  
Compared to hot-rolled steel structures, cold-formed light steel structures are especially susceptible to extreme winds because of the light weight of the building and its components. It is observed many damage phenomenon in typhoons \"Yun Na\" [1], \"Hgupit\" [2,3], Cyclone Yasi, Cyclone Larry [4,5] and \"Caihong\" [6]. Many modern cold-formed steel structures had sustained significant structural damage ranging from loss of cladding and the buckling of the components to the complete collapse in recent cyclones.  
As can be seen in Figure 1, the cladding-fastener connections were broken by strong wind. Moreover, the corrugated cladding profiles were torn by the wind after the cladding broken.  
As the second member to transfer the load from cladding to the main resisting frame, the C/Z cold formed sections purlins were also vulnerable to strong wind. As can be seen in Figure 2, the purlins were buckled under wind uplift loading and the sheeting was also torn by the wind force in some cases.  
Figure 1: Failure of cladding-fastener connections.  
The Figure 3 shows the main resisting frame collapsed during Typhoon \"Caihong\" [6] in Southeast of China. The beam of the frame buckled while most of its connection or purlins stayed safe during the hurricane event.  
To solve these problems and provide practical solutions to avoid these types of damage, research has been conducted around the world for several decades, mainly concentrating on the performance of claddings under static and fatigue loadings, as well as buckling behavior of purlins under wind uplift loadings to the resisting capacity of the original resisting frame under wind uplift or lateral loads. However, from the aspect of wind hazard investigation on the light steel structures, detailed and careful work, such as the damage level of different components under different wind load, should be studied. Previous studies seem too general to make an accurate damage evaluation on light steel structures. Design and safety checks are performed for individual members, components, or connections based on simple assumptions of structure concepts. Therefore, it is hard to make a precise prediction of the real light steel structure systems under extreme wind loads.