esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 2 Configuration of the Proposed Grid Impedance Compensator
### Derivations of the Proposed Low-Loss Grid Impedance Compensator  
The circuit in Figure 4 can be simplified into the one as given in Figure 5a, where the output voltage of the compensator equals \(u_{\text{pcc,h}}\). According to [PERSON]'s voltage law (KVL), the circuit in Figure 5a can be written as the following equations:  
\[u_{C}=u_{\text{pcc}}+u_{L_{2}}-u_{\text{pcc,h}} \tag{1}\]  
\[u_{i}+u_{L_{1}}=u_{\text{pcc}}+u_{L_{2}}-u_{\text{pcc,h}} \tag{2}\]  
where \(u_{L_{1}}\) and \(u_{L_{2}}\) represent the voltage across the inverter-side inductors \(L_{1}\) and \(L_{2}\), respectively. Equations (1) and (2) can be rewritten as the following equations:  
\[u_{C}+u_{\text{pcc,h}}=u_{\text{pcc}}+u_{L_{2}} \tag{3}\]  
\[\left(u_{i}+u_{\text{pcc,h}}\right)+u_{L_{1}}=u_{\text{pcc}}+u_{L_{2}} \tag{4}\]  
Figure 4: Circuit illustration of the grid impedance compensator in reference [34].  
Figure 5: Equivalent circuit of the grid impedance compensator. (**a**) Simplified circuit of Figure 4; (**b**) Equivalent transformation of Figure 5a.  
Thus, the added voltage source in series at the inverter output, \(u_{\text{pcc,h}}\), is divided into two equivalent voltage sources as shown in Figure 5b, where the one is in series with the inverter output voltage \(u_{i}\), and the other is in series at the filter capacitor branch. It indicates that the grid impedance cancellation can be realized through two separate PCC voltage feedforward mechanisms, as presented in Figure 5b. The equivalent control block diagram of Figure 5b is presented in Figure 6a, in which the two equivalent voltage sources are inserted through the nodes A and B, respectively. For simplicity, the PCC voltage harmonic extraction at nodes A and B is not revealed in Figure 6a.  
Therefore, the circuit realization of the proposed grid impedance cancellation is drawn in Figure 6b, where \(u_{o,A}=u_{\text{pcc}}\) is feedforwarded to the modulator of the inverter and \(u_{o,B}=u_{\text{pcc,h}}\) is implemented through an active voltage-controlled voltage-source (VCVS) in series at the filter capacitor branch. The power stage of the VCVS is realized through a LV-VSC in series with the filter capacitor as shown in Figure 7. Because the capacitor current through the LV-VSC is small, the proposed LV-VSC is expected to consume much lower power loss than the one as shown in Figure 4.
## 3 Approach
### Global Feature Extraction by CNN and Reclassification  
To generate global features from the image descriptors, we convert image data into a digital feature matrix through a neural network. The VGG19 [33; 34; 35] is a Convolutional Neural Network (CNN) which is a scientific research tool developed by the Oxford University in 2015. The VGG19 is famous because it has strong feature extraction ability. In our system, the neural network used for feature extraction is VGG19.  
As shown in Figure 2, there are a total of 19 layers in VGG19, including 16 layers in the convolution layer and the last three layers in the full connection layer. The convolutional layers in the middle part are connected to a max-pool layer. Finally, the last layer is the soft-max layer. Then, the global features of the image descriptors are extracted at the second full connection layer of the VGG19. Therefore, the feature is a vector of size 1 \(\times\) 4096. In this experiment, we do not need to train the model. We use the VGG19 model directly and the VGG19 uses the pre-trained weights on the dataset ImageNet [36; 37]. The input image size is 224 \(\times\) 224. Therefore, to get a better performance, we set the input image size to 224 \(\times\) 224. At this point, we do not need to input the label information for the training model. Therefore, we only use the scan-context descriptor as VGG19 input, and the feature is output.  
The next step is re-classification based on the extracted global features. If we directly apply the extracted features to the next step, it will cause unnecessary calculations for time and space. Therefore, we used the following method to reduce feature space dimension.  
Figure 2: The structure of VGG19.  
In our project, we chose random projection to reduce the feature matrix. The dimension reduction matrix is randomly generated to reduce the size of the feature vector from \(1\times 4096\) to \(1\times 256\). From there, we use the k-means algorithm [38; 39; 40] to classify these scan-context descriptors through the dimension-reduced feature vectors. We set category K to 2000, which is the number of clusters obtained by K-means. If K is set very low, this method's advantage will not be obvious. The data of the training dataset is re-distributed to 2000 clusters. Next, we use this result as the input to a neural network for training.
## 3 Indoor Power Line Channel  
Recently, the propagation characteristics of power line channels have been investigated in the context of power line communication [8; 9; 10]. [PERSON] _et al._ estimated the transfer function of indoor power line channels [8]. Figure 6 shows one of the configurations of these channels. The power line is a VVF cable with two wires of \(\phi 1.6\) mm, which is widely used for indoor power lines in Japan. The signal is transmitted and received at points A and D. At points E and F, appliances can be connected. Impedance mismatching causes a signal reflection, which results in a frequency selective fading response in the power line channel. In [8], the practical mathematical model of the transfer function is developed for the VVF cable by adopting measured parameters.  
Here, we consider two configurations for dispatching power packets based on the model obtained in [8]. One is the branched configuration shown in Figure 6. The other is the branch-less configuration based on the above model, _i.e._, a straight VVF cable stretched between points A and D. The transfer function of the branch-less model can be obtained by adopting the parameters of the above model. We assume that a power source and a destination load are set at points A and D, respectively. A mixer is set between the source and the power line, and a router is also set between the power line and the load. Ideally, the mixer and the router are assumed to be included in the source and load, respectively. The impedances are matched among the VVF cable, the switches in the mixer and router, the internal resistance of the source, and the destination load. Thus, there is no signal reflection at the junction points  
Figure 4: Schematic of the router.  
Figure 5: Schematic diagram comparing a general communication system and a power packet dispatching system. Communication and power packet dispatching systems are indicated in blue (upper) and red (lower), respectively.  
among these elements. Here we focus on the power line configurations between the mixer and router. In the branched configuration, we assume open-ended branches with no appliances. Figure 7 shows the voltage gains for the configurations. The sharp drops in voltage gain are caused by resonance.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 7 Discussion on the Review of CNN-Based Approaches for Fruit Image Processing
Besides, when RGB images have bee used, a fairly extensive pre-processing is performed to guarantee the successful extraction of characteristics. Unlike the previous two cases, the main objective of fruit detection tasks is to segment the fruits in the orchard to efficiently perform an automatic harvest. Therefore, the task to be carried out is more complex than the previous two. Then, it is required to design CNN models that can efficiently perform a semantic segmentation in the wild images. For this reason, in the distribution of CNN models shown in Figure 8c, the pre-trained networks prevail as the basis of all applications. The most used CNN architectures are ResNet [51] and VGGNet [39] models. Particularly, the VGG-16 model is considered an excellent Faster Region-based CNN, as well as some of its variants and modifications. Another difference from the previous two cases is that the evaluation of the studies was not based on comparing their results against other ML models, as observed in the column Results of Table 3.  
Figure 8: Distribution of CNN architectures used for: (**a**) fruit classification, (**b**) fruit quality control, and (**c**) fruit detection for automatic harvest. The authors directly evaluate the robotic systems in the orchards because their objective is to put the robotic system into operation in an integral way. Moreover, like the case of quality control, datasets contain different characteristics that are not only provided by individual RGB images. The datasets are then grouped as follows (i) a group that uses images captured with depth sensors (RGB-D), which allow to accurately estimate the distance to the fruit to be able to perform the harvest robotically; (ii) the second group that uses RGB images captured with a multi-vision system (i.e., multi-camera system) to have a wider view and measure distances; and (iii) the group comprised of multi-sensor systems, combining NIR and LiDAR sensors with RGB cameras. We observed that the split of datasets is carried out in two ways, one in training-testing sets (Tr-Ts), and another group in training-validation-testing sets (Tr-V-Ts). The overall distribution of approaches that used each distribution is shown in Figure 9. For the Tr-Ts splits, the most used proportion is 80%-20%, which represents 40% of all works that use this split. The rest of the approaches that adopt Tr-Ts splits vary in proportions, including studies that performed the tests in ranges of 10%-90% of the dataset. For Tr-V-Ts splits, the proportions are very diverse, and they range from a 2/3-1/6-1/6 up to 4/5-1/10-1/10 of the dataset size, respectively. In this regard, the most important conclusion is the datasets splits must adapt to the researcher's decision according to the dimensions of the dataset.
## 3 Experiment and Validation of the Proposed Method
### Fault Diagnosis Task Results by Confusion Matrix  
As shown in Figure 7, the result of sensor fault diagnosis shows that the classification accuracy can reach as high as 99.75%. It can be seen from the accuracy confusion matrix that the accuracy of the six modes has reached a very high level, and there is no interaction between the modes. This shows that when the model learns different patterns, the abstract features are fully learned, and the high quality of data is also one of the reasons for the good experimental results.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Training Time (s)** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Testing Time (s)** \\ \hline CNN+RF & 600 & 96\% & 96\% & 96.84\% & 96.45\% & 0.02 \\ Transformer\_En & 600 & 98\% & 98\% & 98.76\% & 98.56\% & 0.02 \\ GLTrans. & 480 & 99.75\% & 99.75\% & 99.99\% & 99.86\% & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diagnosis accuracy based on different methods.  
Figure 7: Fault diagnosis task results by confusion matrix.
## 5 Implementation and Evaluation
### Feature Selection
#### 5.3.2 Cife  
The entropy of cumulant feature CF is denoted by H(CF) to measure the uncertainty and represent total information carried by the CF. Mutual information I (CF; GC) is defined by:  
\[\text{I (CF; GC)}=\text{H(CF)}-\text{H (CF|GC)}, \tag{6}\]  
CIFE organizes an optimal feature subset that maximizes the class-relevant information between the features by reducing the redundancies among the cumulant features [23]. Equation (6) indicates the information delivered from CF to GC will reduce the uncertainty of GC when 'CF' is known. CIFE computes the joint class relevant information for k selected indices (30/50/80) of cumulant features as shown in Equation (2).  
\[\text{I (CF; GC)}\ =\ \sum_{t=1}^{n}\text{I(CF(t); GC)}\ -\ \sum_{u=1}^{t-1}\text{R }_{GC}(\text{CF(u); CF(t)}), \tag{7}\]  
Equation (7) extracts the cumulant features sequentially given that (t - 1) CF are extracted, the tth feature can be extracted by optimizing the conditional informative objective as,  
\[\text{\text
## 3 Results and Discussions
### Results of Isothermal Oxidation Experiments  
To investigate the oxidation kinetics, TG mass gain and gain-rate data were employed. Figure 3a,b shows the results of mass gain and the mass-gain rate in terms of oxygen  
Figure 2: Mass-gain result of the dummy test with alumina tube under 200 mL/min oxygen flow rate condition at 1000 \({}^{\circ}\)C.  
flow rate. It can be identified from Figure 3a that the overall mass-gain trend seemed to follow general oxidation kinetics. The sharp mass gain was measured by the oxygen inflow because oxygen was reacted with pure Zr metal. As an oxide layer grew, the mass gain became slower and followed almost (sub-)parabolic and/or cubic reaction laws. After reaching critical oxide thickness, the reaction rate accelerated and became linear or higher; a phenomenon termed breakaway or reaction transition. However, there are differences between both the extent of the reaction rate at each regime and the breakaway time, as identified in Figure 3b. The reaction rate in the early stages was raised slightly, and then the first minimum point was reached. This might be caused by the early oxygen starvation and this moment. Furthermore, the minimum value was progressively reduced, and this trend almost disappeared at the highest flow rates. The reaction rate increased sharply at the oxygen inflow period, and its degree of escalations became higher as the oxygen flow rate increased. This trend might be influenced by both higher peak temperature [1,4,6] and enhancement of diffusivity [5]. After this abrupt increase in reaction rate, the trend changed adversely, and it decreased. The extent of this decrease increased as the flow rate increased. This might be caused by the formation of a thicker oxide layer, which acted as a diffusion barrier at the early stages. On the other hand, the breakaway is determined by the minimum point after the maximum value in Figure 3b, which was delayed from the 160 mL/min flow rate. The results are presented in Figure 3c, and the actual times are also arranged in Table 1. The average transition time was approximately 1379 s to reach 140 mL/min (Table 1), while the breakaway time was retarded in the higher flow rate region as, shown in Figure 3c. The oxide thickness at the breakaway was calculated from the TG mass-gain data, considering it was dense zirconia, and ignoring the mass gain of the oxygen dissolved in the Zr metal [1]. The thickness increased as the flow rate increased, but it showed an abrupt increase in the 160 to 200 mL/min region (Figure 3d).  
Figure 3: Results of the isothermal oxidation experiments under various oxygen flow rate conditions at 1000 \({}^{\circ}\)C: (**a**) TG mass gain (log–log diagram), (**b**) TG mass-gain rate (log–log diagram), (**c**) breakaway time, (**d**) oxide thickness at the breakaway.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 3 Materials and Methods
### OLSR Improvement Selection
#### Enhanced Node Willingness Algorithm  
In this section, we present the algorithms for the improved OLSR algorithm in multihop networks. Figure 2 shows the flowchart for the node willingness based on the node count of each node. All nodes in the network will go through this additional process when sending HELLO messages. Figure 3 is the flowchart for the indexing of the HELLO messages received. Each node will index the HELLO message received and process the IP address of the node. Then, using this unique index, all the one-hop neighbour nodes are counted which will be used for the node willingness.  
Both algorithms (Algorithm 1 and Algorithm 2) modify the MPR selection algorithm. The rest of the OLSR algorithm remains the same.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node \(i\)** & **Node \(j\)** & \\ \hline \multirow{3}{*}{_willingness\_always_} & _willingness\_always_ & _willingness\_never_ \\  & G-c & G-c \\  & G & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Utility function of the \(i\)th and \(j\)th node.  
Figure 3: Indexing the HELLO messages and counting the corresponding node.  
Figure 2: Node willingness based on the node count.  
```
1:Proced OLSR algorithm considering node willingness and node count.
2:Let \(t_{1}\): Lower bound time; \(t_{2}\): Upper bound time; \(T\) is the threshold of node density. sim-time[\(x\)]: Arrival time of hello packet [\(x\)]; sim-time [0]: Initial time of hello packet is sent.
3:Define Pass time = sim-time[\(x\)]-sim_time[0].
4:if (pass time \(<t_{1}\))
5:then increase the sim-time[a]
6:else count the nodes (terminal_count)
7:if ((sim_time[a]> \(t_{2}\)) && (terminal_count\(<=T\)))
8:then:
9: node_willingness=WILL_NEVER;
10:else node willingness = WILL_ALWAYS.
```  
**Algorithm 1**Node willingness of nodes to become MPR
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Exergy Analysis  
The exergy of a system is the maximum valuable work possible during a process, bringing the system into equilibrium with a heat reservoir. The system boundary, as shown in the Figure 3 was used in the calculation of exergy efficiency. In conformity with the work by [PERSON] et al. [22; 23], the conventional exergy analysis was performed by applying Equation (6) to each component of the investigated system. Equation (6) represents the exergy rate balance within the selected control volume (i.e., the selected component) at steady state conditions, allowing for the assessment of the source of its thermodynamic inefficiencies by calculating its exergy destruction rate (\(\dot{E}_{D}\)). Equation (6) is presented as follows:  
\[\sum_{j}\left(1-\frac{T_{0}}{T_{j}}\right)\cdot\dot{Q}_{j}-\dot{W}+\sum_{in} \dot{m}_{in}\cdot e_{in}-\sum_{out}\dot{m}_{out}\cdot e_{out}-\dot{E}_{D}=0 \tag{6}\]  
in which the term \(\left(1-T_{0}/T_{j}\right)\cdot\dot{Q}_{j}\) indicates the exergy transfer rate related to heat transfer at the rate \(\dot{Q}_{j}\) occurring at the location on the boundary characterized by a temperature \(T_{j}\), while the term \(\dot{W}\) accounts for power. The specific exergy of the selected component was calculated as follows:  
\[\mathrm{e}=h-h_{0}-T_{0}(s-s_{0}) \tag{7}\]  
in which \(h_{0}\) (kJ\(\cdot\)kg\({}^{-1}\)) and \(s_{0}\) (kJ\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\))) are the enthalpy and entropy of working medium calculated at the dead state, i.e., \(T_{0}=308.15\) K, \(311.15\) K and \(313.15\) K (ambient temperature of system operation) and \(p_{0}=0.1013\) MPa. It is important to highlight that the kinetic, chemical and potential exergy variations are negligible for all vapor compression systems.  
Figure 3: The boundary in the exergy analysis.  
The specific exergy of air can be calculated by Equation (8) [24] as follows:  
\[\begin{array}{ll}\mathbf{e}_{a}&=(c_{p,a}+\omega_{a}c_{p,v})(T_{a}-T_{0})\\ &-T_{0}\Big{[}(c_{p,a}+\omega_{a}c_{p,v})ln\left(\frac{T_{a}}{T_{0}}\right)-(R_ {a}+\omega_{a}R_{v})ln\left(\frac{P_{a}}{P_{0}}\right)\Big{]}\\ &+T_{0}\Big{[}(R_{a}+\omega_{a}R_{v})ln\left(\frac{1+1.6078\omega_{0}}{1+1.6078 \omega_{a}}\right)\\ &+1.6078\omega_{a}R_{a}ln\left(\frac{\omega_{a}}{R_{v0}}\right)\Big{]}\end{array} \tag{8}\]  
for which \(\omega_{a}\) is the absolute humidity of air (\(kg_{water}\)/\(kg_{dry\,air}\)); \(R_{a}\) and \(R_{v}\) are the air and vapor constant (k)-(kg-1.K-1); and \(c_{p,a}\) and \(c_{p,v}\) are the specific heat of air and vapor at constant pressure (k)-(kg-1.\({}^{\circ}\)C-1).  
The total product exergy rate of the investigated air conditioning unit (\(\dot{E}_{P}\)) was:  
\[\dot{E}_{p,tot}=\dot{W}_{tot}-\dot{E}_{D,tot}-\dot{E}_{loss,tot} \tag{9}\]  
in which the exergy loss rate \(\dot{E}_{loss,tot}\) (kW) refers to the exergy rate of the air flowing through the gas cooler. In this paper, \(\dot{E}_{loss,tot}\) was neglected.  
The exergy efficiency (\(\eta\)) of the transcritical CO2 cycle was calculated as  
\[\eta=1-\frac{\dot{E}_{D,tot}}{\dot{W}_{tot}} \tag{10}\]
## 4 Discussion
Ester bonds are certainly hydrolyzed by both esterases and lipases, but these enzymes differ in the reactions they perform. \"True\" esterases (EC 3.1.1.1) hydrolyze primarily short-chain esters, and, unlike lipases, the interfacial activation phenomenon is not observed in them. Furthermore, only lipases and cutinases are active in low water/high organic solvent environments [28; 29]. Finally, _Y. lipolytica_ KKP 379, the strain that showed the most interesting properties in terms of the production of both extra- and intracellular lipases as well as their specificity, and the strain to which the most space was devoted in the current study, can be used as a biocatalyst in esterification reactions, confirming the findings of [PERSON] et al. [30]. In their study, freeze-dried biomass of this yeast species was used in the synthesis of butyl esters of 3-phenylpropanoic acid and its derivatives. Reactions proceeded with 75-98% conversion, but most interestingly, these values were higher compared to the commercial preparation, which was lipase B from _C. antarctica_ (CALB). Both enzymes exhibited different substrate specificities, which were also observed for the esterification of phenylacetic acids, and the _Y. lipolytica_ biomass, unlike CALB, was not able to proceed with these reactions [30]. In the future, further research should investigate, e.g., the stability of the enzyme, its reusability, or the impact of solvents on immobilized biocatalysts. [PERSON] et al. investigated CALB-immobilized catalyst aggregation when using short-chain alcohols as solvents. The conformations were only partially prevented by immobilization, and alcohols modified the texture of the solid support, promoting the enzyme release [31]. Notably, enzymatic productivity measures were provided to investigate substrate disappearance over time, at a prescribed temperature under specified reaction conditions. According to [PERSON] et al. [32] and [PERSON] et al. [33], this is the only measure that reliably summarizes the durability and reaction yield (a measure of substrate conversion) of an enzymatic process. Kinetic productivity analysis was employed to assess the catalytic capacity of the immobilization carrier for affecting the productivity of the crude extracellular enzyme preparation. Our results also revealed that for commercial use, the stability of enzymatic activity should be improved.