esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Greedy Scanning Data Collection Strategy
### Network Initialization
#### 3.2.2 The Election of Head Nodes  
It will cause significant energy by broadcasting the mobile sink new location to every node, routing the sensory data to the sink hop by hop. In our GSDCS, the concept \"head nod\" is introduced, it has three tasks: (1) collecting sensory data from the same grid cell nodes; (2) routing the collected data and the packets from neighbor head nodes to the mobile sink; (3) maintaining the \(DN\) value of its current grid cell when the latter is moving. After the mobile sink moves into a new grid, it informs some grid cell head nodes to update their \(DN\) value.  
Two parameters are used to determine which nodes can be chosen as the head nodes: (1) residual energy of each node; (2) distance between each node and center point of corresponding grid cell. The node which has more energy and shorter distance is more suitable to be a head node in the same  
Figure 6: The direction number (\(DN\)) of each grid cell.  
grid cell. Here we have Equation (7). Each node broadcasts its \(K\) value in the current grid cell. The node with a minimum \(K\) is elected as the current grid cell head node.  
\[K=\frac{D_{\text{ToCenter}}}{E_{residual}} \tag{7}\]
# Urban Biorefinery Demonstration: Production of Polyhydroxyalkanoates from a Municipal Solid Waste
## 2 Materials and Methods
### Scl-PHA Extraction  
Once every accumulation cycle was ended, the biomass rich in scl-PHA from the fermenter was centrifugated, thermally treated, and washed with ethanol to prevent PHA content reduction and degradation. Cell debris was separated by batch centrifugation and decantation. The PHA biopolymer was then extracted with dimethyl carbonate (DMC). Finally, a solvent recovery system was used to concentrate scl-PHA in the DMC phase.  
Demo-scale extraction was carried out at the Solvent Extraction Unit. This unit includes two 500 L agitated percolators (Model DP1, UTID, Barcelona, Spain) for scl-PHA extraction and a 160 L rotary extractor (Formeco, model D60 Ax LCD, Noventa Padovana, Italy). The unit also features two 1000 L solvent tanks, a mesh filter, two heat exchangers, four buffer tanks, two condensers, and solvent recovery tanks. The agitated percolators and heating systems ensured efficient mixing and extraction of the biopolymer.  
The scl-PHA extraction process is summarized as follows: First, after each accumulation reaction in the fermenter, the biomass of the fermentation broth was separated from the liquid phase using a continuous disk stack centrifugation system (Alfa Laval CLARA 80, Lund, Sweden) at 8000 rpm with a flow rate of 1.5 m\({}^{3}\)/h in a loop for 4 h. Then, the biomass was heat-treated at 80 \({}^{\circ}\)C for 20 min in an oven (JP Selecta, model 2001253, Barcelona, Spain). Next, the biomass was washed with ethanol 96% vol. (CAS number 64-17-5, VWR) at a biomass/ethanol ratio of 1:3 (\(w\)/\(w\)) at 100 rpm for 4 h to eliminate bacterial debris in the ethanol phase. Subsequently, the biomass was separated from the liquid phase through a 1 um mesh and the PHA was extracted with DMC (CAS number 616-38-6, GERDISA, Guadalajara, Spain) in percolators at a biomass/DMC ratio of 1:10, at 60 \({}^{\circ}\)C for 8 h. After PHA extraction, solid waste biomass was again removed using a 1 um mesh. The biomass was then subjected to a second and third consecutive extraction step with DMC and solid-liquid separation at the same previously described conditions. The DMC containing scl-PHA from the three extraction steps was concentrated (ratio 1:5) in the rotary evaporator (Formeco, model D60 Ax LCD, Noventa Padovana, Italy) and stored at 4 \({}^{\circ}\)C for scl-PHA precipitation. The scl-PHA precipitated was separated by decantation from the supernatant DMC phase. The solvent was recovered through evaporation for the next extraction process. The remaining DMC was removed from the precipitated scl-PHA by evaporation in a lab frame hood system. Finally, scl-PHA was ground into a powder using a Bosch kitchen chopper. No overheating or melting was observed during grinding, and no additional cooling measures were necessary.
# An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
###### Abstract  
This work proposes a power control strategy based on the linear quadratic regulator with optimal reference tracking (LQR-ORT) for a three-phase inverter-based generator (IBG) using an LCL filter. The use of an LQR-ORT controller increases robustness margins and reduces the quadratic value of the power error and control inputs during transient response. A model in a synchronous reference frame that integrates power sharing and voltage-current (V-I) dynamics is also proposed. This model allows for analyzing closed-loop eigenvalue location and robustness margins. The proposed controller was compared against a classical droop approach using proportional-resonant controllers for the inner loops. Mathematical analysis and hardware-in-the-loop (HIL) experiments under variations in the LCL filter components demonstrate fulfillment of robustness and performance bounds of the LQR-ORT controller. Experimental results demonstrate accuracy of the proposed model and the effectiveness of the LQR-ORT controller in improving transient response, robustness, and power decoupling.  
Keywords:grid-following control; hardware-in-the-loop experiment; LQR controller; microgrids; optimal control; power control; three-phase inverters +
Footnote †: journal: Applied sciences
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 4 Discussion
### La Miera Landslide Characterization
In Asturias, [4] pointed out that most of the landslide activity recorded (1232 events of 1455 between 1980 and 2015) was registered during periods of high monthly rainfall. Moreover, studies by [3, 7, 62] established meteorological patterns and rainfall episodes as the main landslide-triggering factors in Asturias, especially in the Central Coal Basin [6]. As shown in Figure 6, the displacement measured by the EGMS is directly related to rainfall periods (Table 2), while stabilization and recovery periods of displacement match with periods of scarce rainfall. Deformation periods analyzed between rainfall and displacements present correlation coefficients between 0.45 and 0.91 (Table 2). This demonstrates a quantitative spatial and temporal direct correlation between rainfall and the EGMS. Furthermore, the proximity of the meteorological station to the La Miera landslide (only about 1.5 km away), ensures sufficient accuracy to perform an adequate and reliable analysis of the effects of rainfall on the La Miera Landslide.
# Evaluation of a New-Concept Secure File Server Solution
## 3 Research Methodology
### Methodology of the Performance Tests
#### 3.2.5 Methodology for Evaluating the Results  
While processing measurement results, the measured values were analyzed using statistical methods. In repeated measurements, transmission rate values showing a transient state were both detected and excluded; from the remaining analyzable data, the measurements were then evaluated by treating them as normally distributed probability variables, and in this way, the characteristic values for each architecture were determined. Based on the differences between the measurements, characteristic deviation values for the statistical distribution were determined, allowing for the results to be compared.  
In the case of different hardware and software configurations, the average transfer times are unique characteristics; they are not suitable for comparing the effectiveness of the systems. However, it is important to see the performance values compared to an arbitrary, selected reference system. To solve this problem, the following metric was calculated:  
Let \(T_{a}\) be the transfer time of the current (examined) environment, and \(T_{r}\) the transfer time of the reference environment. The reference-based performance indicator (\(PR_{ar}\)) can be calculated as follows:  
\[PR_{ar}=\frac{T_{a}}{T_{r}} \tag{1}\]  
If \(PR_{ar}>1\), then the current (tested) environment's performance is better than the performance of the reference environment; if \(PR_{ar}=1\), then the performance of the \(T_{r}\) and \(T_{a}\) environments is almost on the same level, and if \(PR_{ar}<1\), then the performance of the reference environment is better than the currently tested environment.
## 1 Introduction
This can free up radiologists and other medical professionals to focus on more complex tasks and cases, reducing the workload and improving the efficiency. DL algorithms can also help reduce costs by reducing the need for expensive and invasive diagnostic tests and procedures. In this study, a DL method is proposed for CAD diagnosis. The input of the proposed system is the four associated Polar Maps that aggregate the information of the MPI SPECT scan. Though MPI SPECT is not a definite diagnostic test, it is a reliable non-invasive method. The study's methodology lies in an innovative attention-based network that performs hierarchical and non-hierarchical feature extraction and classification. The contributions of the present study can be clarified as follows:  
* A novel attention-based modification of the VGG19 network is proposed that improves the identification of essential areas of the Polar Map and performs feature-fusion via concatenation. * The network is evaluated with reference to the invasive coronary angiography (ICA) test results and shows high classification accuracy, sensitivity, and specificity. * The network agrees with the medical experts, who visually inspected the Polar Maps and delivered their diagnostic yields. * The post hoc explainability algorithm reveals the crucial areas of the Polar Map, which the medical experts assessed to verify the correctness of the model. The rest of the paper is organized as follows. The literature review is presented in Section 1.1. The materials and methods of the study are introduced in Section 2. More specifically, Section 2.1 describes the DL method of the study, i.e., the Attention-based Feature-Fusion VGG19 network, Section 2.2 discusses about the explainability-enhancing method, Section 2.3 presents the dataset of the study, Section 2.4 the image preprocessing methodology, and Section 2.5 presents an overview of the experiments. The results are presented in Section 3, where the model is assessed based on its agreement with the human expert (Section 3.1) and its diagnostic efficiency (Section 3.2). Various comparisons are held in Sections 3.3-3.5. A discussion is held in Section 4, with the major remarks in Section 5.
## 4 Experiments  
### Experimental Settings  
**Experimental Dataset:** We assess the performance of our network using three widely recognized datasets: CASIA [28], Nist Nimble 2016 (NIST16) [29], and Coverage [30]. Table 1 provides an overview of the forgery types present in each dataset, along with the number of images utilized in the evaluation of our network.  
Experimental Metrics: In the experiment, selecting appropriate evaluation metrics is crucial for effectively assessing the model's performance. When it comes to image tampering detection, accurately identifying the manipulation regions at a pixel level is of utmost importance. In this study, we adopt evaluation metrics that have been commonly used in previous related works [22]. The F1 score combines recall and precision to provide a comprehensive measure of network performance. Meanwhile, the AUC score represents the area under the Receiver Operating Characteristic (ROC) curve. ROC curves are widely used to evaluate the classification effectiveness of a given classifier. In the context of manipulaiton localization, we classify each pixel in images as either tampered or un-tampered. Therefore, we utilize the AUC score as the experimental metric to assess model performance.  
Compared Methods: To evaluate the performance of the proposed method and ensure the reliability of the experiment, we select a set of well-regarded manipulation detection  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Number**} & \multicolumn{2}{c}{**Type**} \\ \cline{2-6}
**Dataset** & **Train** & **Test** & **Splicing** & **Copy** & **Removal** \\ \hline Coverage & 80 & 20 & ✗ & ✓ & ✗ \\ CASIA & 5078 & 532 & ✓ & ✓ & ✗ \\ NIST & 457 & 107 & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of test datasets for our network (number denotes the number of images used for training and testing, ✓ and ✗ represent whether or not the manipulation type is involved).  
and localization approaches as comparative methods. These approaches include RGB-N (detailed in [16]), SPAN ([19]), RRU-Net ([7]), PSCCNet ([20]), and ObjectFormer ([22]).  
Implementation Details: The implementation of our method is executed on a server equipped with an NVIDIA GeForce RTX 3090 GPU. We utilize PyTorch 1.8.2 to implement both RRU-Net, PSCC-Net, and GP-Net.
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 4 Experiments and Computational Results  
### Network Operation Data  
To evaluate the effectiveness of the proposed method, a case study was conducted on a network composed of the Beijing-Zhangjiakou, Zhangjiakou-Hohhot, and Zhangjiakou-Datong high-speed railways. The network includes major stations such as Beijing North, Qinghe, Yanqing, Taizicheng, Zhangjiakou, Ulanqab, Datong South, Hohhot East, and Baotou. Among these, Yanqing, Taizicheng, and Ulanqab stations are not equipped with overnight stabling facilities. The network structure is shown in Figure 8.  
Three depots are connected to Qinghe, Datong South, and Hohhot East stations, respectively, which provide multi-point Level I maintenance services for EMUs. According to the operational timetable, the rolling stock required includes two EMU types: CRH3A and CR400 BF. Based on national maintenance standards, the Level I inspection threshold for CR400 BF (350 km/h) is 7700 km or 48 h, and for CRH3A (250 km/h), it is 6600 km or 72 h.  
Train connection rules stipulated a minimum of 15 min between consecutive services, and 20 min was required for coupling or decoupling operations. All computations were performed using a machine with an Intel Core i7-11800H CPU at 2.3 GHz and 32 GB of RAM. Using the proposed approach, EMU routing plans for a six-day operation period were obtained within 30 min, demonstrating the computational efficiency and practical viability of the method.
## Materials and Methods
### General Methodology
#### 2.3.4 Inversion Stategies  
This study follows a LUT-based approach consisting in finding the DART model parameters minimizing a cost function comparing measured \(y\) to computed \(\hat{y}\) reflectances. Since the inversion problem is ill-posed, multiple sets of parameters can yield similar reflectances. A common estimation method is to consider the mean values from the \(q\) best sets of parameters as the final solution [53; 54]. Various cost functions were tested: Root mean square error (RMSE, Equation (4)), spectral angle (SAM, Equation (5)) and vegetation index (VI) differences (\(D_{VI}\), Equation (6)).  
\[RMSE(y,\hat{y})=\sqrt{\frac{1}{N}\Sigma_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
\[SAM(y,\hat{y})=cos^{-1}\left(\frac{\Sigma_{i=0}^{N}y_{i}\hat{y}_{i}}{\sqrt{ \Sigma_{i=1}^{N}\hat{y}_{i}\hat{y}_{i}}\sqrt{\Sigma_{i=1}^{N}y_{i}y_{i}}}\right) \tag{5}\]  
\[D_{VI}(y,\hat{y})=abs\left(VI(y)-VI(\hat{y})\right) \tag{6}\]  
RMSE and SAM were computed using variable-specific spectral intervals. The near infrared (NIR) and SWIR were used for LAI (INT LAI, 0.8-2.45 um), while parts of the visible range were used for C\({}_{ab}\) and Car (INT CAB, 0.5-0.75 and INT CAR, 0.5-0.55 um respectively). \(D_{VI}\) was computed using various indices. Concerning LAI, the NDVI was selected, as it is commonly used to estimate vegetation cover, as well as MSAVI2, which is a soil-adjusted index; for C\({}_{ab}\), Maccioni, GM_94b, gNDVI, MCARI2, and TCARI/OSAVI were considered, since they are soil-adjusted indices specifically designed for low-LAI environments, and they were successfully used in a sparse forest context [47; 55; 56; 57]; concerning Car, CRI and R515/R570 were chosen, as they were also successfully used in previous studies [30; 57]. Table 5 summarizes the selected methods for all investigated variables as well as the number of spectral bands used.  
The number \(q\) of best-matching cases to use for the inversion was selected on a per-variable basis. The objective was to find an optimal value that would minimize the RMSE between field data and estimations when performing the LUT-based inversion. Therefore, inversions were done with increasing \(q\) values, from 1 to 400 (0.9% of the LUT). If an optimal value existed across all validation dates, it would determine the number of solutions to keep for the inversion validation step (Section 2.3.5) and then the inversion of all remaining AVIRIS-C dates available.
## 4 Numerical Examples
### Performance Indicators  
#### 4.2.1 Standardized Inverse Generation Distance (IGD_s)  
The inverse generation distance (\(IGD\)) [34] is an indicator of MOOPs that was used to estimate the diversity and convergence ability of algorithms. To calculate \(IGD\), a reference solution set should be defined in advance. Then, minimal distance between each point in the reference solution set to all particles in the solution set will be calculated and averaged. For RMOPs, a standardized version of \(IGD\) was proposed as defined in Equation (20):  
\[IGD\_S(O,P)=\text{std}(d(v,O))_{v\in P} \tag{20}\]  
where \(P\) is the reference point set that evenly distributed on the optimal region; \(O\) denotes the acquired point set and \(v\) is a point in \(P\); \(d(v\),\(O)\) denotes the minimum distance between \(v\) and all point in \(O\); \(std()\) is a standard deviation operator.  
A small \(IGD\_S\) suggest that the resulting particle set can evenly cover the optimal region.
# A Size-Dependent Cost Function to Solve the Inverse Elasticity Problem
## Methods
### Inverse Problem  
The inverse problem is solved by an optimization-based algorithm [7], where a cost function is minimized. Typically, a L2 norm correlation term, that is, the absolute error of the measured and computed displacement fields, is used to define the cost function:  
\[F=\frac{1}{2}\parallel\mathbf{u}^{c}(\mu)-\mathbf{u}^{meas}\parallel_{0}^{2} +\frac{1}{2}\alpha\sqrt{\mu^{2}+c^{2}} \tag{3}\]  
where nodally-defined \(\mathbf{u}^{meas}\) and \(\mathbf{u}^{c}\) are the measured and computed displacement fields, respectively. The computed displacement field is acquired by solving the forward problem using the finite element method at the current estimated shear modulus distribution of the domain of interest. The second term \(\mathbf{n}\) Equation (3) is the total variation diminishing (TVD) regularization term to regularize the inverse problem. In addition, \(\alpha\) is the regularization factor determining the significance of regularization term and chosen by smoothness criteria [10], while \(c=10^{-2}\) is a small constant that resolves the singularity issue when computing the gradients of the cost function with respect to nodal shear moduli. The inverse problem is solved by a quasi-Newton approach referred to as the L-BFGS method [11; 12]. The gradients of the cost function with respect to the nodal shear moduli are evaluated efficiently by the adjoint method [7].  
In this paper, we propose a novel approach to address the issue that the solution to the inverse problem in elasticity is dependent on the size of the inclusions (see Figure 3b,e). The approach is divided into the following steps. First, we adopt the inverse approach with the standard cost function (Equation (3)) to detect the location of different heterogeneous regions. Second, we delimitate and compute the area of each inclusion from the reconstructed shear modulus distribution. Finally,we solve the inverse problem again by optimization-based inverse algorithms, with the following size-dependent cost function:  
\[\pi=\frac{1}{2}\parallel\frac{(\mathbf{u}^{c}(\mu)-\mathbf{u}^{meas})}{\overline{ A}^{i}}\parallel_{0}^{2}+\frac{1}{2}\alpha\sqrt{\mu^{2}+c^{2}} \tag{4}\]  
\(\overline{A}^{i}\) is a weight function depending on the normalized area of the \(i\)-th inclusion. That is, for the nodes within the \(i\)-th inclusion, \(\overline{A}^{i}=A^{i}/A_{\text{min}}\) is the area ratio between the \(i\)-th inclusion and the smallest inclusion (the areas of the \(i\)-th inclusion and the smallest inclusion are denoted by \(A^{i}\) and \(A_{\text{min}}\), respectively). For the background of the domain of interest, \(A^{i}=1\). In the next section, we will employ the proposed approach to solve the inverse problem.
## 1 Introduction  
Today's growing global demand for energy requires greener and more sustainable storage technologies. In the field of energy conversion and storage, lithium-ion batteries have successfully fulfilled a great proportion of global storage demand, but these devices present important safety and environmental drawbacks [1]. The search for new low-cost and environmentally friendly multivalent systems that offer high performance is thus a current challenge. In this context, zinc-ion batteries (ZIBs) meet the requirements to be a true alternative for various devices: high theoretical specific capacity, the use of aqueous electrolytes, the natural abundance and safety of zinc [2]. In addition, ZIBs can be easily fabricated in an open-air environment, which significantly reduces their overall production cost [3]. Nevertheless, it is still necessary to overcome several fundamental and practical problems for the extensive use of ZIBs to become the norm [4, 5, 6]. Aqueous ZIBs are secondary Zn-based batteries, in which the storage and conversion of energy is realized through the transfer of Zinc cations between the cathode and the anode. Thus, one of the challenges is related to the increase in electrostatic interactions that inhibit the diffusion of Zn\({}^{2+}\) within the electrode structure, and hence, there is a shortage of electrode materials capable of fast, reversible (de)intercalation of Zinc ions [7]. Moreover, intercalation of multivalent ions can also be impeded by hydrated cations which have to be desolvated at the electrode-electrolyte interface. Another issue that has received a great attention from the scientific community is the significant challenge of H\({}^{+}\)/Zn\({}^{2+}\) co-intercalation in aqueous media.  
In this sense, our work focuses on various ZIBs built with non-expensive manganese oxides with birmesite-type structure as cathodes, aqueous and innocuous electrolyte, and Zn as anode. The electrolyte employed was a solution of 0.1 M Mn(II) nitrate and 2 M Zn(II) nitrate. Thus, K\({}_{\mathrm{x}}\)MnO\({}_{2}\) materials employed as cathodes are prepared by straightforward, low cost and green methods in which tuned morphologies are obtained. Surface modification of the manganese oxide electrode is considered a viable strategy to improve electrochemical properties in aqueous zinc-ion batteries. Therefore, the paper aims to analyse the influence of structural features and particle morphology in the electrochemical performance of different birmesite-type manganese oxides.