esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids
### Artificial Intelligence Methods-Based Energy Management Systems  
In the microgrid, situations that affect power quality such as controller gains, frequency deviation, sudden drops in current, and voltage deviation occur. The difficulty in adjusting many parameters in these complex systems can be overcome with artificial intelligence techniques such as fuzzy logic, artificial neural networks, machine learning, deep learning, and game theory [34]. The fundamental thing about fuzzy logic is that, unlike classical logic systems, it aims to model uncertain modes of reasoning that play a notable role in people's rational decision-making in an environment of uncertainty and ambiguity. This ability refers to performing energy management with the ability to derive an approximate answer to a question that is uncertain, incomplete, or not completely reliable [35].  
To accurately ensure the reliability of artificial intelligence systems used in energy management systems in microgrids, real-world scenarios and real failure modes need to be studied. The quality and variability of data used in artificial intelligence methods directly affect the performance and reliability of energy management systems. As a result, gathering data is necessary to demonstrate the dependability of artificial intelligence, and statistics are crucial in guiding the selection of relevant data. Following the acquisition of artificial intelligence reliability data, reliability predictions, statistical modeling, and analysis offer an overview of anticipated reliability in upcoming scenarios. Additionally, reliability tests and demonstrations can be used to pinpoint the root causes of reliability problems, allowing artificial intelligence system designers to make improvements that will increase reliability. Although it can be difficult, identifying the reasons behind artificial intelligence reliability failures offers opportunities for statistical reliability research.
## 3 Results  
### Precipitation Patterns of Cu-Fe PBA in the Conventional RD Setup for Observing Liesegang Bands  
Figure 2 shows the typical precipitation patterns of Cu-Fe PBA formed in the conventional RD setup (Figure 1a). It was found that the Cu-Fe PBA tended to produce a continuous precipitation band in agarose gels, regardless of preparation conditions such as the initial electrolyte concentration and gel density. Similar trends have also been reported for Co-Fe-based PBA (Co-Fe PBA) [25; 26] and Prussian blue (PB) [23; 25; 27], whereas Mn-Fe PBA tend to generate periodic (Liesegang) bands [28; 29]. The color of the precipitates depends on the oxidation states of Fe: reddish-brown for Cu-Fe\({}^{\text{II}}\) PBA containing [Fe(CN)\({}_{6}\)]\({}^{4-}\) ions (Figure 2a), and ocher for Cu-Fe\({}^{\text{III}}\) PBA containing [Fe(CN)\({}_{6}\)]\({}^{3-}\) ions (Figure 2b).  
As shown in Figure 3, a continuous band formed near the gel boundary immediately after adding the outer electrolyte (Cu\({}^{2+}\)) sol. This band expanded monotonously with time in the inner electrolyte gel (according to the elapsed time indicated at the right of each image) without generating Liesegang bands. Such monotonous development of the precipitation band is, again, similar to that observed in Co-Fe PBA systems [26].
## 3 Results
### Structure Analysis  
Monomers of FdI and FdII were superposed in PyMOL using CE algorithm [57] (cealign function of the program), which resulted in RMSD of 1.75 A over 56 residues. This superposition is shown in Figure S3a. Given their same CATH assignment and almost-identical secondary structure, this is not surprising. Due to REMARK 350, chains of FdII are truly identical, but there is a little bit of difference between the chains of FdI, caused by their outlying termini. Chain A vs. chain B ecalign RMSD is 0.98 A for complete chains but drops to 0.21 A when the two residues at each terminus are ignored.  
When dimers are compared (Figure S3b)--when the other two chains are added back to the superposed monomers--it becomes clear that complex formation happens very much differently between the molecules. In FdI the protein-protein interface (seven residues: G23, D38, E40, G41, A42, S43 and E46) forms an \"extension\" in the helical segment direction, while in FdII, the two chains face each other somewhat with their beta sheets (also seven residues: E3, E23, M24, N25, E26, V32 and D37). The only common member is the G23-E23 pair. P-P interface in FdI is also more compact--relatively small compared to the whole protein but forming a straight \"wall\" with no sidechains protruding significantly into the inter-chain space. In FdII however the interface is assuming a more distributed formation: there are six residues located at one side and a lonely E3 on the other side of a crevice that can be found close to the axis of rotation of the complex.  
Iron-sulfur clusters are bound in pockets located spatially in the same location in each monomer (Figure S3a). Residues interacting with them are also roughly the same in terms of numbers: V6, C11, I12, A13, C14, E15, C17, C54, V56, C58 and I59 in FdI and C8, M9, A10, C11, E12, A13, C14, A31, C50 and I55 in FdII, with the main sequence segments (containing three and two cluster-coordinating cysteines, respectively) being C11-C17 in FdI and C8-C14 in FdII. These segments match each other perfectly in terms of secondary structure once the sequences are aligned (Figure 2). A match is also found in terms of hydrophobicity: five residues belonging to each pocket are identical, while the remaining two pairs (I12-M9 and S16-A13) have similar \(H\) parameter values.  
The other two cysteines in FdII that are not engaged in the interaction with the ligand (C18 and C42) form a disulfide bond instead. They join ends of helices at the other side of the protein, located in opposite direction to the binding pocket.  
Calculation of solvent-accessible surfaces informs that A34 and V47 in FdI and V43 in FdII are the only residues unfeasible for non-bonded contacts with other molecules (no surface vertex within 3.9 A range of any of their atoms).
## 2 Materials and Methods
### MOORA Method
#### 2.3.2 Importance Coefficient Approach  
In the ratio approach, it is assumed that the criteria are of equal importance during the evaluation of alternatives. However, there are instances where the criteria may not hold equal importance. In such cases, different importance values are assigned to the criteria. The initial step in implementing this approach is to construct the decision matrix A, which delineates the values of the alternatives concerning the criteria. Next, each element of matrix A is normalized within the interval [0,1]. Unlike the ratio approach, in this approach, the column values of each criterion of matrix A are multiplied by the importance values of the criterion. In the final step, the values of criteria to be maximized and those to be minimized are summed separately for each alternative. Subsequently, the sum of the maximized values is subtracted from the sum of the minimized values to derive the ranking values, \(y_{j}^{*}\) of each alternative. Finally, ranking is performed based on the obtained \(y_{j}^{*}\) values. The calculation of \(y_{j}^{*}\) values obtained with importance values is illustrated in Equation (27) [21].  
\[y_{j}^{*}=\sum_{i=1}^{g}w_{i}\ x_{ij}^{*}-\sum_{i=g+1}^{m}w_{i}\ x_{ij}^{*} \tag{27}\]  
Here, \(w_{i}\) denotes the importance value of the criterion \(i\).
# Gesture Recognition Based on 3D Human Pose Estimation and Body Part Segmentation for RGB Data Input
## 4 Experimental Results
### Datasets  
* UTD Multimodal Human Action dataset [27]: is a multimodal human action dataset that was collected by using a Microsoft Kinect sensor and a wearable inertial sensor. This data includes 27 different types of actions performed by eight gestures, where each actor repeats each gesture four times. This dataset contains a total of 861 gesture videos after removing corrupted sequences.
* Gaming 3D dataset [28]: is a collection of 600 videos for action recognition in the gaming scenario. This dataset consisted of 20 action classes performed by ten subjects, and each subject repeated each action three times. Besides providing the action class label for each video, this dataset also had the ground truth for the peak frame of each action.
* NTU RGB+D dataset [29]: is a large dataset that contains 56,880 RGB-D videos captured by three Kinect V2 sensors concurrently. This dataset consisted of 60 human activities related to daily actions, mutual actions, and medical conditions. In our work, we only focused on the daily actions category with the RGB video data.
* MSRDailyActivity3D dataset [30]: is a dataset captured for daily human activities in a living room. This collection contained 16 regular activity classes performed by ten different individuals. There are 320 activity videos. This dataset was made with a noisy background with other activities from untargeted people.  
For the MSRDailyActivity3D dataset and Gaming 3D dataset, we stratified a random split of each dataset into 5 folds with a train/valid/test set by ratio 8:1:1, respectively. For the UTD-MHAD dataset, we applied the 8-fold cross-validation method. Seven sets were used for training and the remaining set was used for testing. For the NTU-RGB+D dataset, there are two kinds of benchmarks recommended by the creator of the NTU-RGB+D dataset, which are cross-subject and cross-view benchmarks. We chose the cross-view benchmark, which included 37,462 clips for training and 18,817 clips for testing without validation.
## 3 Results and Analysis  
### Effect of Different Treatments on Crystallization Properties of Wood  
#### 3.1.1 Effect of Ultrasonic Pretreatment on the Crystallization Properties of Wood  
The structure change of wood cellulose after ultrasonic pretreatment is shown in Figure 3. It could be seen from the XRD pattern that, after the ultrasonic treatment of the wood veneer, there are still  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Ultrasonic Processing** & **Sputtering** & **Substrate** & **Sputtering** & **Sputtering** & **Film** \\  & **Processing** & **Time**/Min** & **Temperature** & **Power/W** & **Pressure/Pa** & **Thickness/nm** \\ \hline a & 0 & 0 s & Room temperature & - & - & - \\ b & 60 & 50 s & Room temperature & 75 & 5.0 & - \\ c & 60 & 150 s & Room temperature & 75 & 5.0 & 13 \\ d & 60 & 10 min & Room temperature & 75 & 5.0 & 57 \\ e & 60 & 15 min & Room temperature & 75 & 5.0 & 141 \\ f & 60 & 15 min & 200 \({}^{\circ}\)C & 75 & 5.0 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Magnetron sputtering conditions and parameter settings.  
characteristic peaks of three crystal faces (101, 002, and 040) of cellulose in the wood at 20, equal to 17.0\({}^{\circ}\), 22.3\({}^{\circ}\), and 35.0\({}^{\circ}\). The crystal structure of wood cellulose is not damaged, but the ultrasonic treatment of water will cause the diffraction peak intensity of wood cellulose to decrease, while the ultrasonic treatment of alcohol will cause the diffraction peak intensity of wood cellulose to increase. In the process of ultrasonic treatment of wood veneer, the shock wave with huge energy generated by the ultrasonic cavitation effect continuously impacted the amorphous deposits and gum in the pore of the wood veneer. Although the internal extracts and ash of the wood are removed, some cells on the surface of the wood are also destroyed, resulting in a more disordered arrangement of cellulose microfibrils in the non-crystalline area. This is the reason for the decrease in the intensity and crystallinity of the wood diffraction peak caused by water-ultrasonic wood veneer (Figure 3a). Alcohol promotes the surface activity of cellulose microfibrils in the amorphous region, thereby generating hydroxyl groups, which promotes the order of microfibrils in the amorphous region and improves the intensity of the diffraction peak and the crystallinity of cellulose [1; 2; 7]. It could be seen that the combined treatment of alcohol and ultrasound can increase the diffraction peaks and relative wood cellulose crystallinity, which is caused by the dominant treatment of alcohol in the combined treatment (Figure 3b-f).
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 4 Experiments Setup
### State-of-the-Art Method for Comparison  
We first compare the reconstructed free-breathing and ungated images with the fully-sampled breath-hold images. Two medical experts were required for consensus scoring for the image quality of the reconstructed free-breathing images and the fully-sampled breath-hold images based on the scoring criteria scales from 1 to 4 (1--non-diagnosable; 2--diagnosable with average image quality; 3--diagnosable with adequate image quality; 4--excellent image quality). Except for the consensus image quality scoring, we also compare the left ventricular ejection fraction (LVEF) and the right ventricular ejection fraction (RVEF) obtained from the breath-hold images and the free-breathing images. The LVEF and RVEF are defined as  
\[\text{LVEF}=\frac{\text{LVEDV}-\text{LVESV}}{\text{LVEDV}}\times 100\%, \qquad\text{RVEF}=\frac{\text{RVEDV}-\text{RVESV}}{\text{RVEDV}}\times 100\%.\]  
Here EDV and ESV denote the end-diastolic volume and end-systolic volume, respectively. We note that in some cases, the basal slices are missing, and the calculated LVEF and RVEF might not represent the accurate LVEF and RVEF of the subjects.  
For the reconstruction of free-breathing and ungated cardiac MRI, we compare the proposed deep kernel reconstruction with a state-of-the-art kernel reconstruction method \"SToRM\", which was proposed in [18]. The SToRM model exploits the non-linear structure of the dynamic data to estimate the kernel matrix based on a kernel low-rank regularization problem. The kernel matrix is then used to solve the images to be reconstructed. The biggest difference between SToRM and the proposed deep kernel scheme is in the way of calculating the kernel matrix. SToRM relies on a kernel low-rank regularization problem to solve the kernel matrix, while the proposed deep kernel scheme uses CNNs to output the kernel matrix. We note that the SToRM approach yields comparable or improved performance to state-of-the-art self-gated methods.  
The quantitative comparisons between the proposed deep kernel method and SToRM are made using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). We normalize the values for the images for the SNR and CNR calculation. The definitions of SNR and CNR are as follows:  
* The SNR is calculated as \[\text{SNR}=20\log(\frac{\mu_{s}}{\sigma_{n}}),\] where \(\mu_{s}\) is the mean intensity value of the user-defined region of interest, and \(\sigma_{n}\) is the standard deviation of the intensity value of a chosen noise region. A higher SNR usually means better image quality. The unit of SNR is dB.
* The CNR is calculated as \[\text{CNR}=20\log(\frac{|\mu_{A}-\mu_{B}|}{\sigma_{n}}),\] where \(\mu_{A}\) and \(\mu_{B}\) are the mean intensity value of two regions within the region of interest and \(\sigma_{n}\) is the standard deviation of the intensity value of a chosen noise region. A higher CNR usually means better image quality. The unit of CNR is dB.
## 4 Experiments
### Comparison with State-of-the-Art Models  
In order to illustrate the effectiveness of the model for the sonar image synthesis problem under specific category conditions for different categories (square, columnar), positions, and noise value of sonar images, this method is compared with the most advanced image generation models (DCGAN [15], WGAN [16], SAGAN [25]). These methods are based on unsupervised learning. In order to verify the effectiveness of the conditional information fusion module, this paper fused the category condition information on the basis of these models, calculated and compared the FID value of the sonar images generated by these methods; the smaller the FID value, the better the quality of the sonar image generated under the condition of this category. The sonar images generated under the different models are shown in Figure 4, and the calculated FID values are shown in Table 1.  
Figure 4: Comparison of experimental results of different types of conditions under different models. First line represents the real image, second line represents images generated by DCGAN, third line represents images generated by WGAN, fourth line represents images generated by SAGAN, fifth line represents images generated by With-CAM, sixth line represents images generated by CDBA-GAN.  
Comparing the sonar images and their FID values under different category attributes, the qualitative results are shown in Figure 4. Our method generates image details and an overall image quality that are more realistic, and has strong anti-interference ability under a background with a low signal-to-noise ratio. The sonar images generated by the DCGAN method are not clear enough and greatly affected by noise. WGAN adds the EM distance evaluation criterion on the basis of DCGAN, which can better measure the difference between the real and fake distribution, and the clarity of the generated images are better than DCGAN, but the ability in processing image details is not strong. SAGAN enhances the correlation between pixels by introducing a self-attention mechanism, but with the increase in noise intensity, the training ability of the model is limited by the noise interference problem, and some details are missing. As shown in Table 1, the FID values of images generated by our method under the label0, label1, and label2 category labels are the lowest, achieving a reduction of more than 50 percent compared to the other four methods. In label3 with Gaussian noise, the FID value of our method is tied for the lowest with that of SAGAN, and it is more than 40 percent lower than the other three comparison methods. However, we observe that the target and background details of the SAGAN method are more blurred than our method, and there are greater differences compared to the real image, which shows the image quality of our method is best. The With-CAM method obtains the weight matrix between channels by calculating the channel-level self-attention, which strengthens the connection between channels, and improves the quality of the generated image compared with WGAN, and there is also the problem of poor model training under noise interference.
# IDEINFO: An Improved Vector-Weighted Optimization Algorithm
## 2 Materials and Methods
### Improved INFO Algorithm Design
#### 2.6.3 \(t\)-Distribution Strategy with the Number of Iterations as a Parameter  
The \(T(n)\) distribution (i.e., student distribution) contains parametric degrees of freedom, \(n\), which determine its curve shape. The smaller the value, the flatter the curve and lower the middle [38]. The \(t-\) distribution algorithm perturbs the positions of the vectors to achieve population variation, as follows:  
\[x_{i}^{t}=x_{i}+x_{i}\cdot t(\ iter\ ), \tag{39}\]  
where \(x_{i}^{t}\) is the new position of the \(i\)th vector in the population after mutation, \(x_{i}\) is the position of the individual before mutation, and \(t(iter)\) is the value of the \(t\)-distribution using the number of iterations as the degrees of freedom.  
In the early iterations, the value of \(iter\) is small, and the results generated by the \(t\)-distribution are similar to the Coasean variant in economics, which has a strong global search capability. In later periods, the value of \(iter\) grows, becoming more similar to the Gaussian variant, which has a strong local search capability. Thus, INFO's algorithmic accuracy is improved.  
6.4 Improving the INFO Algorithm with Refined Two-Stage Backward Learning DE and \(t\)-Distribution Strategies  
The steps for improving the INFO algorithm based on two-stage backward learning DE and \(t\)-distribution strategies are presented as a flowchart in Figure 1. The stepwise descriptions are as follows:  
Step 1: Initialize the algorithm parameters, including the population size, maximum number of iterations \(T\), and variable dimensionality.  
Step 2: The two-stage backward learning strategy initializes the vector positions and calculates the fitness of the individuals.  
Step 3: Start iterations (\(t<T\)).  
Step 4: Update the vector positions using the mean-value rule, calculate the average position of the population, and update the positions.  
Step 5: Vector merging and position updating.  
Step 6: Local searching and position updating.  
Step 7: Calculate the current vector fitness and compare it to obtain the current optimal individual.  
Step 8: Generate probability \(p\) and perturb the position of the vector using the DE strategy if \(p>0.5\), and vice versa, perturbing the position of the vector according to the \(t\)-distribution strategy.  
Step 9: Calculate the vector fitness values after perturbation and compare and update the positions of the optimal vectors.  
Step 10: The individual positions and fitness values of the best vectors are recorded.  
Step 11: Repeat Steps 4-10, and when the maximum number of iterations is reached, the optimal vector position and fitness are output.  
Figure 1: Steps of improving the INFO algorithm based on two-stage backward learning DE and \(t\)-distribution strategies.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Title** & **Patent Application or Patent Number** \\ \hline Akkuuser Ltd. & Battery recycling method & US 8 979 006 B2 \\ [27] & & \\ \hline \multirow{3}{*}{Duesenfeld GmBH} & Recycling method for treating used & \\  & batteries, particularly rechargeable & \\  & batteries, and battery processing & \\  & installation & \\ \hline \multirow{3}{*}{Recupyl} & Method for the recycling of mixed batteries & \\  & and cells comprising lithium-based anodes & EP 1733451 B1 \\ \hline \multirow{3}{*}{Retriev Technology [30; 31]} & (1) Recovery of lithium-ion batteries & \\  & (2) Process for recovering and regenerating & (1) 8 616 475 \\ \cline{1-1}  & \multirow{2}{*}{Taxco} & lithium cathode material from & (2) 8 882 007 \\ \cline{1-1}  & & lithium-ion batteries & (3) 5 888 463 \\ \cline{1-1}  & \multirow{2}{*}{(3) Li reclamation process} & \\ \hline \multirow{3}{*}{Umicore NV SA [33; 34]} & (1) Process for recycling li-ion batteries & (1) WO 2011/035915 A1 \\ \cline{1-1}  & (2) Process for the recovery of lithium & (2) WO 2018/184876 A1 \\ \hline \multirow{3}{*}{Worcester Polytechnic [35]} & Method and apparatus for recycling & \\  & & lithium-ion batteries & US10522884B2 \\ \cline{1-1}  & & lithium-ion batteries & \\ \hline \multirow{3}{*}{Commissariat a I’Energie Atomique et aux Energies Alternatives [36]} & Method for recycling the electrolyte of a & \\ \cline{1-1}  & Li-ion battery and method for recycling & US1049/7993B2 \\ \cline{1-1}  & Li-ion batteries & \\ \hline \multirow{3}{*}{Li-Cycle Corp. [37]} & Process, apparatus and system for & \\ \cline{1-1}  & recovering materials from batteries & \\ \hline \multirow{3}{*}{Lithium Inc. [38]} & Lithium-ion battery recycling process & CA3076688C \\ \cline{1-1}  & SOLVAY SA & A process for manufacturing & \\ \cline{1-1}  & \multirow{2}{*}{WO 2021/105365 A1} & \\ \cline{1-1}  & & nickel sulphate & \\ \hline \multirow{3}{*}{Urban Mining PYT LTD [40]} & Process for the recovery of cobalt, lithium & \\ \cline{1-1}  & and other metals from spent lithium-based & US2021079495A1 \\ \cline{1-1}  & batteries and other feeds & \\ \hline \multirow{3}{*}{Smith W.N., Swoffer S.D. [41]} & Process for recycling cobalt and nickel from & \\ \cline{1-1}  & lithium-ion batteries & 113116208 \\ \cline{1-1} \cline{1-1}  & & lithium-ion batteries & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of patent applications and patents related to the processing and/or recycling of used and/or waste lithium-ion cells by pyrometallurgical, hydrometallurgical and electromechanical methods.