esa-sceva/satcom-chunk-collection huggingface dataset:
# Attenuation of DC-Link Pulsation of a Four-Wire Inverter during Phase Unbalanced Current Operation
###### Abstract  
This paper proposes a control algorithm for a hybrid power electronic AC/DC converter for prosumer applications operating under deep phase current asymmetry. The proposed system allows independent control of active and reactive power for each phase of the power converter without current pulsation on the DC link connected to an energy store. The system and its algorithm are based on a three-phase converter in four-wire topology (AC/DC 3p-4w) with two dual-active bridge (DC/DC) converters, interfaced with a supercapacitor and an energy storage. The control algorithm tests were carried out in a Hardware in the Loop environment. Obtained results indicate that operation with deep unbalances and powers of opposite signs in individual phases leads to current oscillations in the DC link. This phenomenon significantly limits energy storage utilization due to safety and durability reasons. The proposed algorithm significantly reduces the level of pulsation in the DC link which increases safety and reduces strain on lithium-ion storage technology, enabling their application in four-wire converter applications.  
energy storage; supercapacitor; dual active bridge; three-phase four-wire converter; multi-resonant control +
Footnote †: journal: Applied
## 1 Introduction
AMPs firstly interact with the bacterial cell wall and hence, it is considered that the cell wall composition greatly affects the antimicrobial activity of AMPs [46]. It is also well known that Gram-positive and Gram-negative bacteria have different cell-surface architectures. For example, Gram-negative bacteria have a thin peptidoglycan cell wall, surrounded by an outer membrane mainly containing lipopolysaccharide-charide. Gram-positive bacteria lack an outer membrane but the cell wall contains thicker peptidoglycan layer and teichoic acids. Cell surface envelopes play a crucial role in the penetration and initial interaction of AMPs. Therefore, the prediction of the antimicrobial activity of AMPs need be considered separately for these two different bacterial groups. For this reason, in this paper, we aimed to develop a machine learning approach based on physico-chemical and structural properties of peptides and to predict their activities against Gram-positive and Gram-negative bacteria, separately. For this purpose, two different data sets were created in this study by selecting the peptides that are active against (i) _E. coli, P. aeruginosa, and A. baumannii_ species for Gram-negative bacteria, and (ii) _S. aureus, L. monocytogenes,_ and _B. cereus_ species for Gram-positive bacteria. Different classification models are generated on each dataset and the results are compared using performance evaluation metrics in terms of accuracy, recall, specificity, precision, Area Under Curve (AUC), F1 measure, and balanced accuracy. The rest of this paper is organized as follows. The Materials and Methods section presents our dataset and data preprocessing steps, and the machine learning algorithms that we used to predict AMPs. The Results section highlights our findings and provides an extensive evaluation of our method. The Discussions section discusses the biological relevance of our findings. Finally, the Conclusions section concludes the paper and summarizes avenues for further research.
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 3 Vehicular Communication
### Platooning  
Performing platooning in both ad hoc and cellular networks is a challenging task. A great many small, periodic data packets need to be sent in a reliable and timely manner to satisfy the vehicle layer needs. Very intensive periodic messaging is required to keep the data fresh, but this challenges the capacity of the ad hoc network and is even more challenging for the cellular network's capacity [33]. Multicasts and broadcasts can be a further reinforcement here. Multi- or broadcasts are convenient because the same data packets need to be received by many different vehicles. Unicasts significantly increase the transmission effort.  
In addition to periodic messages, event-triggered messages for platoon management are also involved [20]: Platooned vehicles agree on some structures and react on the input received from selected vehicles. For example, vehicles agree on a leader and adjust their speed according to a predecessor(s)/leader control policy. Neighboring nodes may increase the load even further by transmitting data in the same network and channel. Long platoons may need to consider packet forwarding as not all nodes may be in direct communication range. Typically, inter-vehicle distances are short, meaning that delays need to be kept small.  
Vehicles send cooperative awareness messages (CAMs) [34] and decentralized environmental notification messages (DENMs) [35] to notify their presence and detected events or even use collective perception messages (CPMs) [36] to share information about objects in the surroundings. All vehicles in the communication range receive messages and may process them. Platooned vehicles may react to messages from vehicles of interest, which depend on the selected control policy [37]. For example, a vehicle may adapt its velocity according to the position, velocity and acceleration included in a message from its predecessor. In contrast, the same message from its follower may be ignored. In the event of a packet loss, e.g., due to collisions, crucial information for the stabilization of the platoon is lost, meaning that it may no longer be possible to safely maintain inter-vehicle distances.  
To summarize, a network enabling efficient platooning needs to provide high reliability, high availability, short end-to-end delays and data-freshness.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 5 Attitude Maneuvering for Downlink Connection  
This section considers the problem of attitude tracking of a specified location on the Earth surface, for the purpose of establishing a downlink connection. This task must be performed only when the spacecraft is visible from the ground station. Thus, attititude maneuvering is split in two phases:  
* attitude tracking, to point the spacecraft toward the ground station during visible passes, and
* attitude reorientation, aimed at gaining the correct pointing direction while waiting for the succeeding visible pass over the ground station.  
Visibility of the ground station corresponds to an elevation angle \(\varepsilon\) greater than a minimum value \(\varepsilon_{min}\), related to the type of sensors mounted onboard the spacecraft. During attitude tracking, a time-varying attitude and angular velocity are pursued. In contrast, attitude reorientation represents a slewing maneuver, with zero angular rate and specified attitude, identified by the constant matrix \(R_{c}\). This corresponds to aligning a specified body axis of the spacecraft with the direction where the ground station appears, as soon as it becomes visible. It is straightforward to recognize that the commanded attitude and the angular rate are discontinuous across the two trajectory arcs. As a result, the commanded torque is discontinuous at the same point (Equation (9)), as well as \(\underline{\dot{\theta}}_{c}\) (Equation (34), with \(\underline{\dot{\theta}}_{c}\) in place of \(\underline{\dot{\theta}}\)).
## 4 Results and Analysis
### Case of Wheat Futures
#### 4.1.2 Comparison and Analysis
The reasons why the forecasting accuracy of VMD-based hybrid model is superior to other hybrid models lie in the following two causes: (1) the VMD based model searches for a number of modes and their respective center frequencies, such that the band-limited modes reproduce the input signal exactly or in least-squares sense, thus VMD has the ability to separate components of similar frequencies compared with other decomposition methods; (2) VMD is more robust to noisy data such as wind speed, PM\({}_{2.5}\) concentration and agricultural commodity future price. Indeed, since each mode is updated by Wiener filtering in Fourier domain during the optimization process, the updated mode is less affected by noisy disturbances, and therefore the VMD can be more efficiently for capturing signal's short and long variations than other decomposition methods [52].
## 6 Discussion and Future Considerations
### Energy Minimization  
Optimal path planning helps to minimize the aerial mechanical energy needed for navigation, which is dissipated while the UAV flies through obstacle-free zones. Designing accurate models for estimating the aggregate energy consumption due to actions such as flying up or down or horizontal flight [109] is of prime importance in predicting UAV performance and AoI reduction. For instance, mechanical movement takes about 90-95% of drone energy. Particularly, during data gathering, the estimation and modeling of the energy required to collect data should be based on experimental results [109]. In addition to AoI, accurate energy models must be considered for estimating drone energy for throughput maximization and coverage extension. The incorporation of wireless power transfer is a promising form of energy source. However, the aggregate time needed for wireless power transfer and AoI minimization has trade-offs. This should be studied since WPT requires a notable amount of time for energy harvesting. Moreover, timeliness is important, especially in some real-time applications [104].
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 2 Position and Orientation Selection  
### Reachability Map  
The reachability map was generated by voxelizing the space and solving the inverse kinematics for all orientations where the end-effector can approach the respective voxel, adding up the reachability scores only when solutions within the joint limits exist. A pseudo-code for computing a reachability map is shown in Algorithm 1. The reachability map for a manipulator--Open-Manipulator Pro, manufactured by Robotis (shown in Figure 1)--is depicted in Figure 2, where red and blue respectively represent the maximum and the minimum values of the reachability score, with yellow in the middle. The space was voxelized using a grid size of 50 mm, covering 1.4(W) \(\times\) 1.4(D) \(\times\) 0.7(H) in meters, since the manipulator reach is 645 mm. The orientation for approaching the voxels was set to \(\pi/10\) spacing for each axis within the range of \(-\pi\) to \(\pi\).  
```
1:Set voxel size and voxelization
2:Initialize Reachability Map (RM)
3:for each voxel \(V_{i}\) position \(\mathbf{P}_{ej}(x_{i},y_{i},z_{i})\)do
4: Initialize reachability score: \(RMs\gets 0\)
5:for each orientation \(\mathbf{R}_{ej}(\theta_{xj},\theta_{yj},\theta_{zj})\)do
6: Calculate inverse kinematics: \(\mathbf{\theta}\leftarrow\mathbf{I}.\mathbf{K}(\mathbf{P}_{ej,i},\mathbf{R}_{ej})\)
7:if\(\mathbf{\theta}\) exists then
8:if\(\mathbf{\theta}\) is within joint limits then
9: Calculate manipulability index: \(\mu\)
10: Update score: \(RMs\gets RMs+\mu\)
11:endif
12:endif
13:endfor
14: Save \((V_{i},\ RMs)\)
15:endfor
16:Normalize entire reachability scores
```  
**Algorithm 1** Reachability Map Construction  
Figure 1: Open\(-\)Manipulator Pro used in this work and its specification.  
Figure 2: Reachability map of Open\(-\)Manipulator Pro. Red and blue respectively represent the maximum and the minimum values of the reachability score, with yellow in the middle.  
A reachability map is a collection of \((\mathbf{P}_{e,i}-\mathbf{P}_{b},\mu_{i})\), \(\mathbf{P}_{e,i}-\mathbf{P}_{b}\in\mathbf{S}\) where \(\mathbf{P}_{e,i}\) and \(\mathbf{P}_{b}\) denote the positions of the end-effector and the manipulator base, respectively. \(\mathbf{S}\) denotes the manipulator workspace and \(\mu\) denotes the manipulability, an indicator on how easily the manipulator moves in all directions. In this work, the one proposed in [31] is used:  
\[\mu_{i}=\sqrt{\frac{\lambda_{min}(\mathbf{J}_{i}\mathbf{J}_{i}^{\intercal})}{\lambda_ {max}(\mathbf{J}_{i}\mathbf{J}_{i}^{\intercal})}} \tag{1}\]  
where \(\lambda_{min}(\cdot)\) and \(\lambda_{max}(\cdot)\) denote the minimum and the maximum eigenvalues and \(\mathbf{J}_{i}=\mathbf{J}(\mathbf{\theta}(\mathbf{P}_{e,i}))\) denotes the manipulator Jacobian at joint angle \(\mathbf{\theta}(\mathbf{P}_{e,i})\), with the inverse kinematics \(\mathbf{\theta}(\mathbf{P}_{e})\).
## 4 Simulation Results
### Load Power Step Up/Down
This behavior is caused by the constancy of the load damping constant and of the inertia of the synchronous generator, which do not depend on the step amplitude. It can be also observed that the time required to stabilize the frequency is practically independent on the step amplitude, since in all the simulations the new steady state condition after the load power step is reached almost at the same time instant. These kind of results are coherent also with the plots for Simulation 4 shown in Figure 14. In this case, such a large loss of load could be associated with the disconnection of an industrial user, and it is evident how large the frequency variation associated with this loss is. The nadir reached in this extreme case is equal to 1.0175 pu (50.875 Hz), which is not compliant with many grid codes, for the \"traditional\" renewables control scenario. In the case of the updated control, on the other hand, the nadir is equal to 1.0106 pu (50.53 Hz), which also exceeds the upper frequency limit but is lower than the previous, highlighting the huge benefit introduced. Figure 14: Simulation 4 frequency trend. Figure 13: Simulation 3 frequency trend. It is very interesting to observe how the different generators contribute to the frequency control by adapting their power, as shown in Figure 15.  
It is clearly visible how the two renewables reduce their power to face a decrease in load. This helps the synchronous generator in containing the frequency increase: it can be seen that the steam turbine power reduction in the case of the _complete FC_ is less intense than that in the case of the frequency control operated just by themselves. The stabilization effect is also emphasized by the reduction in the oscillations after the nadir, that further facilitates the intervention of the synchronous generator. Considering the third metric (nadir-based frequency response), it has been shown with these numerical results that it is practically the same in all the four simulations for what concerns the conventional scenario, while in the case of the _complete FC_ it is a little bit lower in increasing the step magnitude.
## 4 Experimental Results  
In this section, we present the findings from our proposed system for DME grading based on retinal fundus images. The results were analyzed in terms of accuracy, precision, recall, and other relevant metrics. The following subsections detail the performance of each module, demonstrating the effectiveness and robustness of our approach. The results highlight the segmentation accuracy of HaEx lesions, the precision of OD detection and macula localization, and the reliability of the DME grading module. Visual examples and quantitative metrics are provided to support our findings. These results are crucial for validating the efficacy of our system and its potential application in assisting ophthalmologists with DME diagnosis.
## 4 Discussion
### Research Features and Enlightenment  
We used the equivalent factor method to link the ESV with different landform types and terrain gradients to compare and highlight the spatial and temporal variation characteristics of the ESV in each karst-landform-type area and its terrain gradients. First, this paper can reflect the great value that different karst landform ecosystems bring to humans and arouse public attention toward karst landform ecosystems. Second, this paper also reflects the shift in human understanding of the services provided by karst ecosystems from \"unpaid\" to \"paid\" [58]. Finally, this paper quantifies the effect of regional ecological conservation. However, the study still has some shortcomings. First, the ESV coefficient of paddy fields and drylands was not calculated separately in the estimation of the ESV, which may be slightly low. Second, this study only carried out the study on the ESV of different karst landform areas at the macro-level, and did not take into account the ecological process and ecological demand of specific areas. Future studies should be based on the naturalenvironment, social economy, and other perspectives of each karst geomorphic area to explore the ESV of different karst geomorphic areas from the supply-demand balance level. This will promote the rational allocation of land resources and ecological restoration in karst areas. In general, from 1985 to 2020, the ESV in Guizhou Province increased by USD 385 million, and its ecological environment showed a tendency to improve, mainly due to the increase in forest areas, indicating that the policies in Guizhou Province such as the Grain for Green Policy and hillclosing afforestation are effective in improving the ecological environment. However, from the perspective of different karst landform types, the ESV in the pure-karst areas decreased by USD 122 million, the semi-karst areas increased by USD 367 million, and the non-karst areas increased by USD 140 million. Some local ecological environments in Guizhou Province are still not well-preserved.  
In the future, Guizhou Province should pay attention to the following:  
1. Development plans should be formulated in strict accordance with the principle of ecological priority and green development, and green GDP should be incorporated into the performance assessment system of government officials.
2. The ecological environment construction and protection of pure-karst landscape areas should be strengthened.
3. The ecological land area and layout of the pure-karst and semi-karst areas should be reasonably increased within an altitude less than or equal to 500 m.
4. In areas with slopes greater than 25\({}^{\circ}\), all three geomorphic areas should strictly implement the Grain for Green policy.
# Monitoring of Recovery Process at Yeongildae Beach, South Korea, Using a Video System
## 3 Results
### General Pattern of Shoreline Recovery
This indicates that a continuation of low-powered wave conditions is necessary for a recovery process to occur consistently. In contrast, the erosion process occurred in relatively shorter periods, when high-powered waves attacked the site. Specifically, it is noted that the gradual increase of \(\left\langle y\right\rangle\) stopped in T3, when other high-powered waves with \(P_{w}\) of \(\sim\)50 kWatt/m (Figure 6b) attacked the site, which indicates that the high-powered waves not only caused the rapid erosion but also could change the accretion trend and bring it to a halt. Following the time variation of the total mean beach width \(\left\langle y\right\rangle\) in Figure 7b, c displays the time variations of the beach widths in three different parts of the shoreline. In the figure, the solid blue line shows the time variation of averaged beach width for the baselines #2-#5, which is symboled as \(\left\langle y\right\rangle_{s}\) as it represents the pattern in the southern end area. Similarly, the orange line, \(\left\langle y\right\rangle_{m}\), represents the variation in the middle of the beach as averaged for the baselines #15-#17. The yellow line, \(\left\langle y\right\rangle_{m}\), represents the variation in the northern end as averaged for the baselines #30-#33. The variations of each group of beach widths show different changes from time to time. However, all the time variations of each group of beach widths follow the trend of the total mean beach width \(\left\langle y\right\rangle\) in T1, T2, and T3. That is, \(\left\langle y\right\rangle_{s}\), \(\left\langle y\right\rangle_{m}\), and \(\left\langle y\right\rangle_{n}\) started to decline rapidly at T1 and formed troughs around T2, and then they all gradually increased until around T3.
## 2 Proposed Method  
The proposed realization of a third-order quadrature oscillator is shown in Figure 1, and consists of a current-mode lossy integrator and two current-mode lossless integrators. The system can be analyzed as:  
\[\frac{I_{2}}{I_{1}}=\frac{-a}{s+a^{\prime}},\frac{I_{3}}{I_{2}}=\frac{b}{s}, \frac{I_{1}}{I_{4}}=\frac{c}{s} \tag{1}\]  
\[I_{4}=I_{2}+I_{3}=(1+\frac{b}{s})I_{2} \tag{2}\]  
Equation (1) shows the functions of one current-mode inverting lossy integrator and two current-mode non-inverting lossless integrators in the functional block diagram. Substitute Equation (1) into Equation (2), and obtain the relationship between \(I_{1}\) and \(I_{4}\) currents as:  
\[\frac{I_{1}}{I_{4}}=\frac{I_{1}}{(1+\frac{b}{s})I_{2}}=\frac{c}{s} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**No. of Components Used**} & \multicolumn{2}{c}{**No. of Output Signals Used**} \\ \cline{2-5}  & **Active Elements** & _CIR/Resistorless Structure_ & **Voltage** & **Current** \\ \hline
[22] Figure 7 & 3 OTAs & 3/0/yes & 1 & 0 \\
[23] & 4 MO-CCCIIs & 3/0/yes & 0 & 4 \\
[24] Figure 1 & 3 CCIs & 3/5/no & 2 & 0 \\
[25] & 2 MO-CCCCAs & 3/0/yes & 0 & 2 \\
[26] Figure 2 & 3 MO-DVCCs & 3/3/no & 5 & 2 \\
[26] Figure 3 & 3 VC-DVCCs & 3/0/yes & 3 & 2 \\
[27] & 3 MO-CCCIIs & 3/0/yes & 2 & 4 \\
[28] & 3 MO-CDTAs & 3/0/yes & 2 & 2 \\
[29] & 2 MO-CCIs & 3/3/no & 2 & 2 \\
[30] & 3 MO-DVCCs & 3/3/no & 2 & 4 \\
[31] Figure 3 & 3 OTRAs & 3/5/no & 2 & 0 \\
[32] Figure 4a & 2 MO-DVCCTAs & 3/1/no & 3 & 3 \\
[32] Figure 4b & 2 MO-DVCCTAs & 3/2/no & 3 & 3 \\ This work & 2 MO-CCCCTAs & 3/0/yes & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of component and number of output signal comparisons with previous third-order oscillators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Electronic Tuning FO**} & **Independent Control** & **Controllable Amplitude** \\  & **without Disturbing CO** & **of CO and FO** & **of AM/ASK** \\ \hline
[22] Figure 7 & no & no & no \\
[23] & yes & yes & no \\
[24] Figure 1 & no & no & no \\
[25] & yes & yes & no \\
[26] Figure 2 & no & yes & no \\
[26] Figure 3 & yes & yes & no \\
[27] & yes & yes & no \\
[28] & yes & yes & no \\
[29] & no & yes & no \\
[30] & no & no & no \\
[31] Figure 3 & no & yes & no \\
[32] Figure 4a & no & yes & no \\
[32] Figure 4b & no & no & no \\ This work & yes & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic comparisons with previous third-order oscillators.  
Rearrange Equation (3), and yield the transfer function between \(I_{1}\) and \(I_{2}\) currents as follows:  
\[\frac{I_{1}}{I_{2}}=(1+\frac{b}{s})(\frac{c}{s})=(\frac{s+b}{s})(\frac{c}{s}) \tag{4}\]  
Then, the characteristic equation of the functional block diagram in Figure 1 can be expressed as:  
\[s^{3}+as^{2}+acs+abc=0 \tag{5}\]  
From Equation (5), the CO and the FO are obtained as follows:  
\[\text{CO:}\;a=b \tag{6}\]  
\[\text{FO}:\;\omega_{o}=\sqrt{bc} \tag{7}\]