esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simulation Model
### Physical Model  
The physical model used in the simulation is given in Figure 1, which is mainly composed of a needle electrode, a plane electrode, and a solid insulating medium. The simulation area is the red dashed line in the figure. The needle electrode is an anode, and the plane electrode is grounded. The distance between the needle electrode and the plane electrode is 4 mm, and the size of the solid insulating medium is 4 \(\times\) 4 mm.  
Studies have shown that the C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) mixture, with C\({}_{4}\)F\({}_{7}\)N accounting for 7-13%, has the potential to replace SF\({}_{6}\) gas [21]. In this paper, a mixture of 9% C\({}_{4}\)F\({}_{7}\)N and 91% CO\({}_{2}\)  
\begin{table}
\begin{tabular}{c c c} \hline  & **Existing Research** & **This Research** \\ \hline surface discharge & \begin{tabular}{c} Concentrate on SF\({}_{6}\), SF\({}_{6}\)/N\({}_{2}\), air, and other gases [15; 16; 17; 18]. \\ \end{tabular} & \begin{tabular}{c} Concentrated on the mixture \\ of C\({}_{4}\)F\({}_{7}\)N and CO\({}_{2}\). \\ \end{tabular} \\ C\({}_{4}\)F\({}_{7}\)N and its mixture & \
\begin{tabular}{c} For its insulation performance and \\ decomposition product [9; 10; 19; 20]. \\ \end{tabular} &
\begin{tabular}{c} For its surface discharge \\ characteristics. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Comparison between existing research and this research.  
is used; the temperature is 300 K, the pressure is 0.1 MPa, and the initial density distribution of electrons and positive ions is a Gaussian distribution [14]:  
\[n_{e,p}=n_{\text{max}}\times\exp(-\frac{(r-r_{0})^{2}}{2{s_{0}}^{2}}-\frac{(z-z_ {0})^{2}}{2{s_{0}}^{2}}) \tag{1}\]  
where \(n_{\text{max}}=10^{20}\)\((1/\text{m}^{3})\), \((r_{0},z_{0})\) is the coordinate of the needle tip, \(s_{0}\) is the radius of the initial distribution of particles (\(s_{0}=25\)\(\upmu\)m), and the initial density distribution of negative ions is set to 0. The maximum value of the initial electric field is at the junction of the needle tip, the solid dielectric, and the gas, so a large number of particles are clustered around it.
## 3 Results
### Characterization of IFA and FA
Figure 1: X-ray diffraction patterns of LDH-NO\({}_{3}\) (**a**) and of MgAl-O (**b**), MgAl-L (**c**) and MgAl-Ln (**d**) prepared in H\({}_{2}\)O/EtOH as solvent. Figure 2: TGA and DTA curves of the indicated samples prepared in H\({}_{2}\)O/EtOH as a solvent compared with LDH-NO\({}_{3}\). Operative conditions: air, 10 \({}^{\circ}\)C/min. The amount of anion intercalated was obtained by TGA considering the presence of nitrate to balance the positive charge of the lamellae, and the obtained values are reported in Table 1. In MgAl-O and MgAl-L, the percentage of exchanged nitrate ranged between 62% and 68%, whereas in MgAl-Ln, it was about 78%, and it seemed that H\({}_{2}\)O/EtOH solvent slightly favors the intercalation process. The higher amount of nitrate found by TGA in MgAl-O and MgAl-L than in MgAl-Ln is in agreement with the XRPD data. The selectivity of LDH-NO\({}_{3}\) towards the different anions was studied by equilibrating the solid with an equimolar solution of O, L and Ln. The XRPD, the TGA and DTA of MgAl-OLLn are shown in Figure 3. The interlayer distance of 30.6 A is very close to those of the intercalated single anions, and does not give information on the type of anions present in the interlayer region. The DTA profile is different from the previous results, suggesting the contribution of more than one anion. TGA and HRGC-FID data were combined to determine the content of each anion in MgAl-OLLn (IFA), obtaining the following empirical formula: [Mg\({}_{0.63}\)Al\({}_{0.37}\)(OH)\({}_{2}\)](O)\({}_{0.1}\) (L)\({}_{0.11}\)(Ln)\({}_{0.14}\)(NO\({}_{3}\))\({}_{0.02}\)-0.33H\({}_{2}\)O. The LDH showed a higher selectivity towards Ln, as expected from the data of the intercalation of single anions. This slightly preferential intercalation could be ascribed to higher interaction among the Ln anions into the interlayer region than the solution.
## 2 Conventional Channel Estimation Methods
### Semi-Blind Channel Estimation Method  
The semi-blind channel estimation method combines the computational simplicity of the pilot-based method with the increased spectrum utilization of the blind method. It utilizes a few pilots and integrates the received signal statistics from the blind channel estimation method, intending to reduce computational complexity and ensure accurate channel estimation. Among them, researchers commonly use the subspace and expectation maximization (EM) algorithms for semi-blind channel estimation.
## 4 Discussion
### Effect of Loading Level  
The extent of the CuCoNiOx deposition time has been investigated for CNTs/GCE, towards the electrocatalytic oxidation of methanol. Figure 9 compares the LSV recorded for methanol oxidation at different deposition times for CuCoNiOx@CNTs/GCE in 1 M NaOH containing 1 M methanol at a scan rate of 100 mV\(\cdot\)s\({}^{-1}\). It can be clearly seen that in the case of CuCoNiOx@CNTs/GCE, as the loading level increases, the current for the methanol oxidation increases as well, and this arises because of an increase of the active sites required for the oxidation process. In addition, there is a noticeable shrinkage in the current difference between the consecutive loading levels, and this may be attributed to some extent of agglomeration that may have occured at the higher loading levels. It is worth mentioning that the agglomeration and stability issues for the Cu alloy catalysts are critical for methanol production in addition to the methanol oxidation [36; 37].
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
###### Abstract  
This article presents a wireless sensor for pest detection, specifically the _Lobesia botrana_ moth or vineyard moth. The wireless sensor consists of an acoustic-based detection of the sound generated by a flying _Lobesia botrana_ moth. Once a _Lobesia botrana_ moth is detected, the information about the time, geographical location of the sensor and the number of detection events is sent to a server that gathers the detection statistics in real-time. To detect the _Lobesia botrana_, its acoustic signal was previously characterized in a controlled environment, obtaining its power spectral density for the acoustic filter design. The sensor is tested in a controlled laboratory environment where the detection of the flying moths is successfully achieved in the presence of all types of environmental noises. Finally, the sensor is installed on a vineyard in a region where the moth has already been detected. The device is able to detect flying _Lobesia botrana_ moths during its flying period, giving results that agree with traditional field traps.  
remote sensing; smart agriculture; pest sensing; acoustic sensor; wireless sensor; real-time sensor +
Footnote †: journal: Medical Imaging  
15 November 2021  
14 December 2021
# Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit
## 3 Multi-Layer Equilibrium System Model
### Battery Pack Equalization Model  
For the ith battery in the battery pack, its SOC can be calculated by Equation (3):  
\[\mathrm{SOC_{i}}(\mathrm{k}+1)=\mathrm{SOC_{i}}(\mathrm{k})+\mathrm{aI_{ex} }(\mathrm{k})+\mathrm{dI_{1,i}}(\mathrm{k}) \tag{3}\]  
\[\mathrm{I_{i}}(\mathrm{k})=\mathrm{I_{ex}}(\mathrm{k})+\mathrm{I_{1,i}}( \mathrm{k}) \tag{4}\]  
where SOC is the ratio of the remaining power of the battery to the nominal capacity; \(\mathrm{a}=\frac{\mathrm{nDT}}{\mathrm{Q}}\), \(\mathrm{d}=\frac{\mathrm{nDT}}{\mathrm{Q}}\),where Q represents the rated capacity of the battery; \(\mathrm{\eta}\) represents the charge/discharge coulomb efficiency of the battery under different conditions; b is the time coefficient, which is numerically equal to the ratio of equilibrium current to maximum equilibrium current; T represents the sampling time; \(\mathrm{I_{ex}}(\mathrm{k})\) is the external current provided by the load or external power supply; \(\mathrm{I_{1,i}}(\mathrm{k})\) is the equalization current flowing through the ith battery, which can be calculated by Equation (2); and \(\mathrm{I_{i}}(\mathrm{k})\) represents the current flowing through the ith lithium battery. During the calculation process, the current is negative when discharging, and the current is positive when charging, which can be calculated; the SOC value of battery can be easily obtained by ampere-hour integration method. It can be seen from Equations (2) and (3) that the multi-layer equalization model of battery pack consisting of \(2^{n}\) batteries can be formulated in the following discrete state form:  
\[\mathrm{SOC(k+1)=SOC(k)+aI_{ex}(k)+dI_{1}(k)} \tag{5}\]  
where \(\mathrm{SOC(k)=[SOC_{1}(k)\), \(SOC_{2}(k)\), \(SOC_{3}(k)\),..., \(SOC_{2^{n-2}}(k)\), \(SOC_{2^{n-1}}(k)\), \(SOC_{2^{n}}(k)]^{\mathrm{T}}\) represents the SOC of each battery in the battery pack; \(\mathrm{I_{1}}(\mathrm{k})=[\mathrm{I_{1,1}}(\mathrm{k})\), \(\mathrm{I_{1,2}}(\mathrm{k})\), \(\mathrm{I_{1,3}}(\mathrm{k})\),..., \(\mathrm{I_{1,2^{n-2}}(k)}\), \(\mathrm{I_{1,2^{n-1}}(k)}\), \(\mathrm{I_{1,2^{n}}(k)}\)]T denoting the equalization current flowing through each battery in the battery pack. Equation (5) only considers the current flowing through the battery without considering the complex chemical reaction inside the battery, which has stronger adaptability and is more conducive to practical use.
## 4 Limitations of Study  
1. Last-mile parcel delivery vehicle vibration measurements in this study were only conducted on delivery routes that served destinations within Seoul due to the predominant parcel delivery volume (58%) in Seoul compared to other cities in Korea. Vibration levels of last-mile delivery routes outside of Seoul, especially rural areas, may be further investigated in the future for better representation of the whole country.
2. Measurements in this study were limited to two major vehicle models for last-mile parcel delivery in Korea. The vehicle models investigated in this study are primarily used for parcel delivery purposes, so the vibration test profiles recommended in this study would be highly beneficial to packaging engineers in Korea. However, it is worth noting that there is a growing trend towards using different types of vehicles for parcel delivery, such as electric trucks and low-profile vehicles. This trend is growing due to the demands for sustainable delivery methods from customers and to accommodate the low height restrictions of underground parking facilities in Korea. Therefore, it may be useful to expand future research to investigate a broader range of courier vehicles.
3. Numerous factors could affect the measured vibration levels in specific countries, such as road conditions, speed, and driving styles. This study provided a wide snapshot of the vibration profile of parcel delivery vehicles in Korea for the first time. Further investigation of the factors mentioned above is recommended for future studies in order to fully characterize the details of last-mile delivery vehicle vibration levels in Korea.
4. It is important to note that the international test standard compared with the recommended test profile from this study has been time compressed by artificially amplifying the vibration levels without providing a time compression factor. Therefore, readers and authors can only compare the Grms values and peak intensity locations of the two profiles.
5. The payload of the investigated vehicles continuously changes (is reduced) since the measurements were conducted during actual parcel delivery service.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 4 Results  
For experimentation, the images from the dataset were first resized to a uniform size of 256 \(\times\) 256 and 512 \(\times\) 512 pixels, in order to facilitate their processing and analysis. Additionally, a range of pre-trained models from the timm library [43] were utilized, such as \"resnet18\", \"resnet34\", \"resnet152\", \"efficientnet_b0\", and \"maxvit_nano_rw_256\". These pre-trained models can be fine-tuned to specific image recognition tasks. The Fastai framework was used to implement the models and to streamline the training process. The results are presented in Table 2.  
During training, we used a batch size of 64. The training process was repeated for three epochs, and a learning rate of \(1\times 10^{-4}\) was used to control how quickly the model updates its parameters during training. The performance of the models was evaluated using accuracy, AUC, precision, recall, and the F1 score. Accuracy measures the proportion of correct predictions made by the model, while AUC measures the model's ability to distinguish between positive and negative examples; the F1 score is a measure of the model's precision and recall. An important aspect is that the training process was only performed with a small number of epochs because it was found that models started to overfit very quickly. This can be seen in Figure 4, which shows the evolution metrics used for training. It should also be noted that although the accuracy obtained in the third stage was slightly weaker than that achieved in the second stage, the values of the AUC metric and the F1 metric were significantly higher. This indicates that the models were able to better distinguish between positive and negative examples, which is crucial for many computer vision applications. This can be better seen in Figure 5 where the confusion matrices for the ResNet18 model experiments are represented. Figure 4: ResNet18 training metrics evolution. Figure 3: ResNet 18 Architecture. The performance of relatively simple CNN models, such as ResNet18, was almost as good as larger models such as ResNet152 or EfficientNet B0. Furthermore, the performance of the CNN models was found to be comparable to that of the model based on visual transformer architecture. However, what made the most significant difference was the pre-processing of the dataset, specifically the augmentation of the dataset using synthetically generated images. This pre-processing technique helped to improve the overall performance of the models, resulting in better evaluation metrics.
## 3 Result and Discussion
### Parametric Study
#### 3.2.1 Effects of the Component Performance
The results demonstrate that the exergy destructions in compressor and turbine can be decreased mainly by promoting the same component itself. In a word, larger turbomachinery efficiency favors reducing the exergy destruction. Figure 4 depicts the influence of pinch temperature in CS (\(\Delta\tau_{min,CS}\)) on exergy analysis results. It is observed in Figure 4a that an increase of \(\Delta\tau_{min,CS}\) results in a relatively large rise of the CS exergy destruction \(\dot{E}_{\text{D,}CS}\) and the exergy destruction in overall system \(\dot{E}_{\text{D,}tot}\), and therefore a decline of total exergy efficiency \(\varepsilon_{\text{tot}}\). A 1 \({}^{\circ}\)C degree increment of \(\Delta\tau_{\text{min,CS}}\) will increase \(\dot{E}_{\text{D,}CS}\) and \(\dot{E}_{\text{D,}tot}\) by 58.99 kW and 74.49 kW respectively, leading to a quite large potential for improvement. As can be seen in Figure 4b, one can notice that tendencies depicted for the split parts of exergy destruction \(\dot{E}_{\text{D,}CS}^{\text{EN}}\), \(\dot{E}_{\text{D,}CS}^{\text{AV}}\), \(\dot{E}_{\text{D,}CS}^{\text{AV,EN}}\) and \(\dot{E}_{\text{D,}CS}^{\text{AV,EX}}\) are similar, all increasing monotonically with \(\Delta\tau_{\text{min,CS}}\). Moreover, the \(\dot{E}_{\text{D,}CS}^{\text{AV,EN}}\) is larger than \(\dot{E}_{\text{D,}CS}^{\text{AV,EX}}\), and the difference becomes larger with the increase of \(\Delta\tau_{min,CS}\) since the increasing trend of the former part is sharper than the latter one. This make it clear that a reduction of \(\dot{E}_{\text{D,}CS}\) is of more dependence on improving the performance of CS itself. In short, a smaller \(\Delta\tau_{min,CS}\) favors the system performance for decreasing the exergy destruction and enhancing system exergy efficiency. Figure 5 shows the variation of exergy destructions and \(\varepsilon_{tot}\) by changing the pinch temperature in HE2 and HE3. The results of exergy analyses for HE1 and HE4 are not given in this section since the values of their exergy destruction rate are rather small. It is pointed out that analyzing the component  
Figure 3: Effects of turbomachinery efficiency: (**a**) conventional and (**b**) advanced exergy analysis results versus compressor 1 efficiency; (**c**) conventional and (**d**) advanced exergy analysis results versus turbine 1 efficiency. with high exergy destruction is more meaningful and advisable [35]. It is seen in Figure 5a,c that an increase in HE pinch temperature \(\Delta\tau_{min,HE}\) shifts up \(\dot{E}_{\text{D},HE}\) and \(\dot{E}_{\text{D},tot}\) with a sensitivity of 13.40 kW and 7.60 kW for HE2 and 13.48 kW and 27.78 kW for HE3 respectively per Celsius degree. However, the avoidable part \(\dot{E}_{\text{D},HE}^{\text{AV}}\) is so small, as shown in Figure 5b,d, that results in a rather low potential of improvement.
## 2 Results
### Regulation of ABA and NAA on the Biosynthesis and Accumulation of Norisoprenoids
#### 2.2.3 An Integrated Gene Co-Expressed and Regulatory Network Regulating Norisoprenoid Biosynthesis  
Our results indicate that ABA and NAA treatments can significantly influence the expression of norisoprenoid-related genes, previously identified switch genes, and the genes involved in ABA and auxin biosynthesis and signaling nearly at the same time. Therefore, we hypothesized that there is a crosstalk between norisoprenoid-related genes and the other genes. The potential link between these genes is supported by the study demonstrating norisoprenoid-related gene VviCCD4b (VIT_202s0087g00930) is a berry switch gene [16]. Furthermore, the ABA and norisoprenoid share the common substrate of carotenoids, and hence are under the regulation of the same upstream genes. Therefore, we integrated the results of WGCNA and DAP-seq analysis to build a gene co-expressed and regulatory network, the objective of which was to improve our understanding of the regulation of norisoprenoid accumulation.  
In the norisoprenoid-related WGCNA modules of turquoise, cyan, and black, we observed some switch genes and hormone-related genes exhibited similar expression patterns with the genes involved in norisoprenoid biosynthesis (Table S5). A high edge weight threshold of 0.4 was chosen to select the candidate genes to construct the network within these modules. After removing the edges according to the threshold, the gene interactions observed in the network were all come from the module of turquoise. The norisoprenoid-related genes of VviPSY1, VviZDS (VIT_214s0030g01740), and VviABA-Hase (VIT_204s0079g00680), which were the target genes of VviGATA26 identified by DAP-seq, were also included in the network. It was found that VviCCD4a co-expressed with VviGATA26, and two switch genes encoding eukaryotic peptide chain release factor subunit 1-3 (eRF1-3, VIT_218s0072g01010) and CBL-interacting protein kinase 25 (CIPK25, VIT_204s0008g05770) (Figure 7A). In addition to these three genes, the VviPSY3 interacted with the other three switch genes encoding myb RADIALIS (VIT_207s0005g02730), phosphatidylinositol synthetase 2 (PTDSS2, VIT_201s0011g04370), and MAD-box (VIT_213s0158g00 100). Additionally, the heatmap clearly exhibited that VviGATA26 expression was up-regulated at the E-L 36 stage of NAA-treated grape berries. A relationship between VviZEP and VviGATA26 was also observed in the network. The expression pattern of these genes in all samples suggested VviGATA26 could negatively regulate the expression of the DAP-seq identified genes of VviPSY1, VviZDS, and VviABA-Hase in cluster 1, while positive correlations were found between pairs of genes in cluster 2 (Figure 7B).
## 1 Introduction
Then, the joint space solution could be obtained through inverse kinematics. The inverse kinematic model from the joint space to the wire-length space was derived using a modified curvature model. Finally, the two inverse kinematics were connected by substituting the calculated values, which could effectively generate an accurate actuation path corresponding to each wire-driving length in real-time. In this project, tungsten wire was considered to be a rigid structure, and the friction and elongation of the wire were negligible. The robotic arm could be operated only by position control. This paper proposes two algorithms to solve the inverse kinematics from the forward kinematic model. One of them is a numerical method with sequential quadratic programming (SQP), and the other uses differential kinematics with a control algorithm. In the numerical method, the constraint logic for the mechanical limits of the robot arm works well in real-time, even when a human operator mistakenly manipulates the master device to an excessive value. Compared to another method, the numerical method can easily respond to changes in robot structure, since only the forward kinematic model needs to be replaced without Jacobian or gain tuning. However, calculation speeds due to iteration should always be considered and designed. The differential kinematic method is much faster and much more accurate than the numerical method. However, it cannot restrict the actuation value to the mechanical limits. The remainder of this paper is organized as follows. Section 2 presents a derivation of the forward kinematic model of the joint structure using frame transformation and compares the results to the curvature model. Section 3 describes the inverse kinematic model from the joint space to the wire-length space with the modified curvature model. The advantages of this model in preventing slack and solving the coupling problem are discussed. Section 4 introduces the two path generation algorithms. The strengths and weaknesses of each algorithm are discussed with simulation results. Section 5 presents experimental results with a surgical robot prototype to verify the feasibility of the path generators. The last section gives a conclusion and discusses future work.