esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
###### Abstract  
Future manned and deep space missions require an Electrical Power System (EPS) that can deliver high power while overcoming challenges like weight and volume constraints and the harsh space environment. A variety of DC-DC converters are employed to supply, store, and transmit power to various satellite subsystems. This paper identifies the design specifications of DC-DC converters for a range of satellite applications and offers a state-of-the-art review of non-isolated, isolated, and integrated topologies. Foreseeing the future of electric propulsion, various sources for electric propulsion are compared, and converters for electric propulsion are studied. The topologies are compared regarding practical parameters like reliability, modularity, redundancy, efficiency, and power density. Furthermore, an application-wise comparison of the topologies and the type of satellite they are suitable for is provided. Finally, the research gaps pertaining to various space applications, such as the design of DC-DC converters, electric propulsion, deep space exploration, electronic component selection, and space-based power satellites, are presented.  
DC-DC converters; deep space missions; high-power electric propulsion; satellite power system; space applications +
Footnote †: journal: _Aerospace_
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Asynchronous Duty-Cycled MAC Protocols
#### 5.1.7 PW-MAC  
PW-MAC [34] minimizes the energy consumption of sensor nodes by enabling the sender to predict the receiver's wakeup time. It also introduces an efficient prediction-based retransmission mechanism to achieve high energy efficiency even when wireless collisions occur and packets must be retransmitted. It uses the basic concept of Wise-MAC, by getting the neighbor nodes' wakeup-sleep timing from the ACK. Using a pseudo-random wakeup schedule rather than a fixed schedule, it avoids the possibility of neighboring nodes consistently waking up at the same time. This protocol also addressed the issues of single and multi-hop traffic flows, hidden node scenarios, and scenarios in which nodes have wakeup schedule conflicts, and the experiments showed that this protocol works better than previous protocols. PW-MAC improves protocols such as S-MAC and B-MAC because it uses pseudo-random schedules; thus, not all nodes will wake up and transmit at the same time,  
Figure 10: Packet transmission procedure of DCM-MAC in unicast (**left**) and broadcast (**right**) scenarios [33].  
avoiding collisions. A node that has just woken up sends a short beacon so that other nodes know it is awake. A sender can then transmit a data packet and request more information from the receiver, such as current time and current seed, for the pseudo-random schedule used by the receiver. By using the seed in a linear congruential generator (LCG), the sender in PW-MAC can predict when a receiver wakes up; hence, the sender sleeps until a little bit before the receiver is awake, and it can effectively decrease the waiting time of the sender. PW-MAC reduces the energy consumption of sensor nodes, which is better than RI-MAC, by enabling sensors to predict the receiver's wakeup time.  
Using PW-MAC (Figure 11), at first a sender, A, requests the prediction state of a receiver, B, and wakes up right before B does, after learning the prediction state of B. The prediction-based retransmission mechanism of PW-MAC enables A to detect the transmission failure and efficiently perform packet retransmissions.  
However, there are hardware variations that generate errors in sender prediction. PW-MAC uses a \"sender wakeup advance time\", a compensating value specific to every platform, including clock drift, operating system delay and hardware latency. The value helps to correct errors that each node can address when predicting a receiver wakeup time. The disadvantages of using PW-MAC include overhead created by beacons and idle listening, even if it is small compared to other protocols such as Wise-MAC, RI-MAC and X-MAC. This consumes a large portion of energy storage.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Force Platform** & **My Jump 2-FT** & **My Jump 2-DT** & **Mo-Cap System** \\ \hline ICC (-) & 0.958 & 0.935 & 0.931 & 0.987 \\
95\% CI (-) & 0.912–0.981 & 0.865–0.971 & 0.858–0.970 & 0.973–0.994 \\ SEM (-) & 0.377 & 0.411 & 0.533 & 0.255 \\ CV (-) & 0.008 & 0.013 & 0.012 & 0.006 \\ \hline \hline \end{tabular} Notes: My Jump 2-FT—My Jump 2 app for the flight time of the jumper, My Jump 2-DT—My Jump 2 app for the displacement time of the marker placed on the jumper’s sacrum (new method), mo-cap—motion capture, ICC—intraclass correlation coefficient, SEM—standard error of measurement, CV—coefficient of variation.  
\end{table} 
Table 2: The reliability of the measurement tools for the maximum height in a counter-movement jump. Additionally, this study assessed the MH values obtained as a result of registering the displacement of the marker placed on the jumper's sacrum using the BTS mo-cap system. The ICC value of 0.955 showed a very good absolute agreement between the mo-cap system and the force platform (at a small systematic bias of \(-0.4\) cm), thus demonstrating the high accuracy of this method in the CMJ testing. Moreover, a non-significantly higher MH value (only by 0.9%) for the BTS mo-cap system compared to the force platform was observed. In contrast, [PERSON] et al. [9] reported significantly lower MH values for the CMJ estimated using the BTS mo-cap system than those estimated using the Kistler force platform. However, these authors analyzed the trajectory of the two markers placed on a shoe at the toe and heel. The results of the present study indicated that the correct estimation of the MH during the CMJ using the BTS mo-cap system should be based on an analysis of the displacement of the marker placed on the jumper's sacrum. The results of the MH for the three CMJ trials performed by each participant showed excellent reliability, i.e., an ICC value greater than 0.9 at a low SEM (below 0.6) and CV (below 0.02) for all measurement tools. These values mean that the MH estimation performed using the force platform, My Jump 2 app and mo-cap system in this study had a high internal consistency. Other researchers who examined the MH for the five CMJ trials in each participant also reported the very good reliability of the force platform and My Jump app [2], and the contact platform and My Jump app [3]. Furthermore, excellent reliability was demonstrated by [PERSON] et al. [5] for the Kistler force platform and Optojump system, as well as by [PERSON] et al. [9] for the Kistler force platform, Optojump system and BTS mo-cap system. This study has limitations. The test-retest reliability was not evaluated due to the lack of data for the second measurement session. Some participants' health problems prevented them from repeating the CMJ tests in the scheduled second session.
# Optimal Shape Design of Direct-Drive Permanent Magnet Generator for 1 kW-Class Wind Turbines
## 2 Outer-Rotor Type Generator with AFPM  
### Model Descriptions  
Figure 1a shows the configuration of a vertical-axis wind turbine (VAWT) with three Darrieus-type blades attached to a rotating vertical shaft. The lengths of each blade and reaction arm are 2 m and 1.3 m, respectively, and the weight of the blades including reaction arms is 17.7 kgf. The rated rotation speed of the vertical shaft is 100 rpm at a rated wind speed of 10 m/s, and the sweep area of the blades is 5.2 m\({}^{2}\). The rated power of the VAWT was estimated based on both the operation conditions and the sweep area, using a power coefficient of 0.35 for a general Darrieus-type wind turbine [16]. Eventually, the rated power was designated as 1 kW. To generate electricity at the rated power of 1 kW, an outer-rotor type generator with AFPM was proposed, as shown in Figure 1b. The rotor, which is supported by bearings, has permanent magnets mounted up and down evenly, while the stator, wound by winding coils, is stationary. This feature enables the occurrence of axial-flux in the generator, which can be stacked vertically with two stages in the center of the tower. The requirement of power generation per stage was 500 W, with a torque of 48 Nm applied to the rotating vertical shaft under operation conditions.  
A slotted stator core was laminated radially by 100 stacks of ferromagnetic steel plates with a thickness of 0.8 mm, as shown in Figure 2. This type of core allows for a smaller air gap between the stator core and permanent magnet and can obtain higher magnetic flux density compared to the slotless type stator.  
The design of the winding coils, along with the combination of poles and slots, is one of the important factors determining the performance of the generator. There are two winding  
Figure 1: Configuration of vertical-axis wind turbine (VAWT) and outer-rotor type generator with axial-flux permanent magnet (AFPM): (**a**) VAWT; (**b**) AFPM generator.  
Figure 2: Spiral lamination of slotted stator core.  
methods for the AFPM generator based on the winding shape and coil pitch: concentrated and distributed winding. In this study, the distributed winding method was used, which has been known to provide excellent harmonic disturbance rejection, leakage reactance and thermal dynamic characteristics [17; 18]. Additionally, a two-layer method for winding was considered to contain an even number of coil-sides in two layers. Figure 3 shows the arrangement of stator windings for three phases: A, B and C. To meet the requirements for low rotational speed and a winding factor value of 0.933, the combination of 12-slot and 10-pole were adopted [19].
## 3 Performance Analysis
### Outage Probability Analysis  
In a dual-hop DF relaying system, an outage occurs when the instantaneous SNR of any hop falls down to a pre-defined value \(\gamma_{th}\). The OP of the considered system is given by [18] (Equation (18)):  
\[P_{out}=F_{\gamma_{t}}(\gamma) =1-P_{r}[\gamma_{1}>\gamma]P_{r}[\gamma_{2}>\gamma]\] \[=1-\int_{\gamma}^{\infty}f_{\gamma_{1}}(\gamma)d\gamma_{\gamma} \int_{\gamma}^{\infty}f_{\gamma_{2}}(\gamma)d\gamma \tag{3}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Fading Distribution** & \(\kappa\) & \(\mu\) & \(m\) \\ \hline Rician shadowed & \(\kappa=\ K\) & \(\mu=\ 1\) & \(m=\ m\) \\ Nakagami-\(\hat{m}\) & \(\kappa\to 0\) & \(\mu=\ \hat{m}\) & \(m\to\infty\) \\ \(\kappa-\mu\) & \(\kappa=\ \kappa\) & \(\mu=\ \mu\) & \(m\to\infty\) \\ Rician & \(\kappa=\ K\) & \(\mu=\ 1\) & \(m\to\infty\) \\ Rayleigh & \(\kappa\to 0\) & \(\mu=\ 1\) & \(m\to\infty\) \\ One-sided Gaussian & \(\kappa\to 0\) & \(\mu=0.5\) & \(m\to\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Special cases obtained from the \(\kappa-\mu\) shadowed distribution [26; 33].  
where \(f_{\gamma_{1}}(\gamma)\) and \(f_{\gamma_{2}}(\gamma)\) are given in Equation (2), \(F_{\gamma_{I}}(\gamma)\) is the CDF of \(\gamma_{I}\), and \(P_{r}[\cdot]\) symbolizes the probability. Using Equation (2), and with the help of [46] (Equation (3.351.2)), the integral terms of Equation (3) become:  
\[\int_{\gamma}^{\infty}f_{\gamma_{I}}(\gamma)d\gamma= \sum_{i=0}^{\infty}\sum_{n=0}^{N_{i}\mu_{I}+i-1}\frac{\xi_{I} \Gamma(N_{\ell}m_{\ell}+i)\Gamma(N_{\ell}\mu_{\ell})\delta_{\ell}^{i}\gamma^{ n}}{i!n!\Gamma(N_{\ell}m_{\ell})}\psi_{\ell}^{-N_{\ell}\mu_{\ell}-i+n}e^{-\psi_{ \ell}\gamma_{I}}d\gamma. \tag{4}\]  
Now, inserting Equation (4) into Equation (3), we obtained the OP as:  
\[P_{out}=F_{\gamma_{I}}(\gamma)= 1-\sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0}^{N_{1}\mu_{I}+ i-1N_{2}\mu_{2}+j-1}\xi_{1}\xi_{2}\Delta\Pi\Psi\gamma^{n+l}e^{-(\psi_{1}+ \psi_{2})\gamma_{I}}d\gamma \tag{5}\]  
where  
\[\Delta=\frac{\Gamma(N_{1}m_{1}+i)\Gamma(N_{1}\mu_{1})\delta_{1}^{i}}{\Gamma(N _{1}m_{1})},\,\Pi=\frac{\Gamma(N_{2}m_{2}+j)\Gamma(N_{2}\mu_{2})\delta_{2}^{j }}{i!j!n!!\Gamma(N_{2}m_{2})},\,\text{and}\,\,\Psi=\psi_{1}^{-N_{1}\mu_{1}-i+ n}\psi_{2}^{-N_{2}\mu_{2}-j+l}.\]  
The exact OP expression in Equation (5) is presented in infinite summations, the summands decay is exponentially due to factor \(\frac{1}{i!j!n!\Gamma}\)[5]. Hence, the summations converge rapidly, with a finite number of terms. The related PDF is obtained by:  
\[f_{\gamma_{I}}(\gamma)= 1-\sum_{i=0}^{\infty}\sum_{j=0}^{N_{1}\mu_{1}+i-1N_{2}\mu_{2}+j-1 }\xi_{1}\xi_{2}\Delta\Pi\Psi\frac{d}{d\gamma}\big{(}\gamma^{n+l}e^{-(\psi_{1}+ \psi_{2})\gamma}\big{)}d\gamma\] \[= \sum_{i=0}^{\infty}\sum_{j=0}^{N_{1}\mu_{1}+i-1N_{2}\mu_{2}+j-1 }\frac{\xi_{1}\xi_{2}\Delta\Pi\Psi}{\exp(\psi_{1}\gamma+\psi_{2}\gamma)}\big{[} (\psi_{1}+\psi_{2})\gamma^{\,n+l}-(n+l)\gamma^{n+l-1}\big{]}d\gamma. \tag{6}\]
## 3 System Architecture
### Frontend Layer  
The React frontend [Figure 4] is structured with multiple components for map, list, and filters, and utilizes global state management to share filter criteria and results among components.  
The frontend communicates with the backend API using asynchronous calls (AJAX/Fetch). For instance, when the component mounts (i.e., a user opens the page), a call to/recommend might be made to load initial recommendations. When filters change, new calls are issued to  
Figure 4: Application frontend.  
Figure 3: Recommendations based on the RL model.  
update the data. The frontend is decoupled from how the backend generates the recommendations or data; it only receives JSON responses and then updates the view accordingly [6].  
The whole flow of the AI-based recommendation starts, as shown in Figure 5, with a question about the desired university and ends in Figure 5 with a list of actual suitable listings that match the user's desires. In the meantime, two more questions about budget and rooms are asked to ensure user satisfaction.  
The frontend as well as the backend are hosted on the Vercel environment, which is a cloud-based solution for application hosting and deployment. An additional component is the **scraper service**. This is not a user-facing part but runs periodically [9].  
This architecture is not only robust for the current application but also extensible. New features, such as adding a user login system or integrating an additional data source, can be implemented by adding modules or endpoints without overhauling the entire system [10].
## 2 Bridge Damage Identification Methods
### Damage Identification Based on Continuous Wavelet Transform  
#### 2.2.1 Brief on Continuous Wavelet Transform  
The wavelet transform is a versatile signal processing technique extensively applied in bridge health monitoring and damage detection. Applying wavelet transform can decompose a signal into components at different scales and time locations, allowing for a more precise analysis of the signal's local features. Common wavelet transform techniques include the discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Frequently used wavelet basis functions comprise the Haar, Morlet, Mexican hat, and Daubechies wavelets. In this study, the continuous wavelet transform employing the Mexican hat wavelet was utilized to analyze the acceleration response of the vehicle-bridge system, as detailed in [25]:  
\[WT_{f}(a,b)=\frac{1}{\sqrt{a}}\int_{0}^{\infty}f(t)\psi^{*}\left(\frac{t-b}{a }\right)\mathrm{d}t \tag{4}\]  
\[\psi_{a,b}(t)=(1-t^{2})e^{-t^{2}/2} \tag{5}\]  
where \(f\) (\(t\)) represents the signal\(-\)in this study, the acceleration response; \(\psi_{a,b}(t)\) is the Mexican hat wavelet function; \(\psi^{*}(t)\) is the complex conjugate of the mother wavelet; \(t\) is  
Figure 1: Vehicle–bridge interaction element for a two-axle vehicle.  
the time variable; \(a\) is the dilation or scale parameter that controls the wavelet's stretching; and \(b\) is the translation (or shift) parameter that controls the wavelet's position in time.
## 2 Related Work
### Kalman Filter  
Kalman filter is widely used in spatio-temporal dynamic modeling. In recent years, Kriging has elicited considerable attention in describing spatial correlation, and has been widely used to compensate for spatial correlation of Kalman filter, namely Kalman-Kriging filtering model (KKF).  
In KKF, Kriging interpolation is used to construct a spatial field, in order to describe the spatial correlation, and Kalman filtering is used to describe the temporal correlation [20; 21; 22]. Although Kriging [23; 24; 25] has been widely used to consider various fields of spatial correlation, in KKF, the time dimension as an additional dimension, brings non-stationary changes, that greatly increase the computational complexity of the linear equations. Considering the large computation problem, the KKF model is suitable for the interpolation of spatial sparse stations. Therefore, many researchers try to model covariance functions to reduce computational complexity, and several effective methods have been obtained, mainly including sparse matrix algorithm, rank reduction technology, and Gaussian random field. For example, [PERSON] [26] proposed a space-time Kalman filter which has the dimension reduction function and a temporally dynamic and spatially descriptive statistical model. [PERSON] et al. [27; 28; 29] proposed a fixed-rank spatiotemporal Kalman, Spatio-Temporal Random Effects, or Spatio-temporal Mixed-Effects model to calculate massive spatiotemporal data.  
As mentioned above, the KKF model has the advantages of Kriging and can also have high interpolation accuracy for sparse sites or irregular sampling distribution. However, the current calculation process of the KKF model [30] requires a lot of time to try different initial parameters, in order to obtain the optimal accuracy, which limits the usefulness and reliability of the model.  
In addition, the aforementioned method requires sufficient available sensor observations to achieve a balance between the accuracy of the field estimate and the computational burden. This is mainly because the state vector and the observation vector need to be consistent in the time dimension in the KKF process. However, one of the challenges in modeling is that limited and time-varying distributions of sensor observations are available (sampling data at different moments are distributed differently, as shown in the data acquisition in Figure 1).
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 3 Drug Delivery Systems
### Transfersomes/Protransfersomes  
Transfersomes were introduced by [PERSON] and [PERSON] [59] (Figure 1c). These vesicles comprise phospholipids and neutral lipids such as cholesterol and surfactants. A surfactant is a single-chain molecule (containing both hydrophilic and lipophilic moiety) that destabilises the lipid bilayers of vesicles and enhances their deformability, and therefore they are called elastic or deformable vesicles. Examples of surfactants used in formulating transfersomes are Span 60, Span 80, Tween 20, Tween 80 and sodium malate. The selection of the type of surfactant in transfersomes directly affects the physicochemical properties of the vesicles. They offer sustained release and higher entrapment efficiency compared to liposomes [60]. Their disadvantages include high production cost and low drug incorporation due to possible leakage and they might undergo oxidation which would lead to degradation [61]. Protransfersomes refer to the dry formulations of transfersomes [35] and they are prepared similarly to the proliposomes with the addition of a carbohydrate carrier. Paclitaxel has been successfully entrapped within transfersomes intended for lung cancer therapy [35] (Table 1). The intravenous route has been explored; however, they have mostly been studied for transdermal delivery due to their flexibility; enhanced pharmacokinetic parameters, such as AUC and T\({}_{1/2}\), have also been observed for the latter one [62].
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 1 Introduction
Both of them are based on the symmetric encoder-decoder architecture, and there are some recent studies of crack segmentation which also use similar architectures [27, 28]. However, it is demonstrated in [29] that the multi-scale feature information in the encoder cannot be fully utilized by delivering information between the same layers of the encoder and decoder networks. Meanwhile, due to the limitation of the empirical receptive field size [30], the plain convolutional neural networks cannot provide sufficient contextual feature information, which is necessary to adapt to complex scenarios. To address these problems, this paper proposes a multi-scale contextual information enhancement network (MCIE-Net), which redesigns the connection structure between the encoder and the decoder of the U-Net to capture multi-scale feature information and enhance the decoder's ability to restore fine-grained the spatial structure of cracks; meanwhile, a contextual feature enhancement module, which consists of the pyramid pooling network and channel attention mechanism, is designed to enhance the context awareness of the network. Specifically, the contributions of this paper can be summarized as follows:  
1. In order to obtain refined crack segmentation results, a multi-scale skip connection structure is designed to aggregate the multi-level feature information extracted from the decoder and improve the network's ability to capture the spatial features. The multi-scale skip connection also optimizes the feature aggregation method, which reduces the number of network parameters and lowers the computational cost. 2. To accurately distinguish between crack and other interferences in the background, a contextual feature enhancement module is proposed to extract contextual information at multiple scales to improve the context awareness of the network. It consists of a pyramid pooling network and a channel attention mechanism which can re-calibrate the channel weights of feature maps to guide the network to focus on important contextual information. 3. Since the fixed sampling scale of plain convolution is not conducive to extracting the diverse morphological features of cracks, we improve the feature extraction network by introducing the deformable convolution into the deep layers of the encoder, changing the receptive field size of the network through adaptive sampling area adjustment, and enhancing its feature extraction capability.